Who is able to complete this assignment by tomorrow evening?

Chapter 1, 11, 12 & 13

Cited:

Cascio, W. F., & Aguinis, H. (2019).  Applied psychology in talent management  (8th ed.). Retrieved from https://www.vitalsource.com

Chapter 1

1 ORGANIZATIONS, WORK, AND APPLIED PSYCHOLOGY

Wayne F. Cascio, Herman Aguinis

Learning Goals

By the end of this chapter, you will be able to do the following:

· 1.1 Describe what an organization is and how applied psychology can help organizations make the wisest use of the people who staff them

· 1.2 Define the terms applied psychology, talent management, human resource management, and personnel psychology and understand how they differ

· 1.3 Explain how demographic changes and diversity will affect recruitment and staffing

· 1.4 Understand the managerial implications of generational diversity

· 1.5 Illustrate how technology and globalization are changing work and organizations

· 1.6 Describe the difference between job security and employment security, as well as the implications of each one for individuals and organizations

· 1.7 Explain the changing roles of managers and workers as the structure and design of organizations continue to evolve

· 1.8 Describe how the digital revolution will affect the workplace of the future, and identify emerging research needs in that area

The Pervasiveness of Organizations

Throughout our lives, each of us is deeply touched by organizations of one form or another. In the normal course of events, a child will be exposed to a school organization, a church or a religious organization, and perhaps a Little League or a Boy or Girl Scouts organization, as well as the social organization of the local community. After leaving the school organization, the young person may choose to join a military, business, or government organization, and as his or her career unfolds, the person probably will move across several different organizations. The point is simply that our everyday lives are inseparably intertwined with organizational memberships of one form or another.

What common characteristics unite these various activities under the collective label organization? The question is not an easy one to answer. Many different definitions of the term have been suggested, and each definition reflects the background and theoretical point of view of its author with respect to what is relevant or important. Yet certain fundamental elements recur in these definitions.

In general, an organization is a collection of people working together in a division of labor to achieve a common purpose (Hitt, Miller, & Collela, 2014). Another useful concept views an organization as a system of inputs, throughputs, and outputs. Inputs (raw materials) are imported from the outside environment, transformed or modified (e.g., every day tons of steel are molded into automobile bodies), and finally exported or sold back into the environment as outputs (finished products). Although there are many inputs to organizations (energy, raw materials, information, etc.), people are the basic ingredients of all organizations, and social relationships are the cohesive bonds that tie them together (see  Figure 1.1 ).

Figure 1.1 Inputs to Organizations

This book is about people as members and resources of organizations and about what applied psychology can contribute toward helping organizations make the wisest, most humane use of human resources. At the outset, let’s be clear about the definition of some important terms. Applied psychology, as used in this book, is a branch of psychology that seeks to apply psychological principles to practical problems in organizations. Talent managementis the process through which organizations anticipate and meet their needs for talent in strategic jobs (Cappelli & Keller, 2017). Talent management is part of the broader field of human resource management (HRM)—an overall approach to management that comprises staffing, retention, development, adjustment, and managing change (Cascio, 2018). Personnel psychology, a subfield of industrial and organizational (I/O) psychology, is concerned with individual differences in behavior and job performance and with methods for measuring and predicting such differences. We consider some of the sources of these differences in the sections that follow.

Differences in Jobs

In examining the world of work, one is immediately awed by the vast array of goods and services that have been and are being produced as a result of organized effort. This great variety ranges from the manufacture of tangible products—such as food, automobiles, plastics, paper, textiles, and glassware—to the provision of less tangible services—such as legal counsel, health care, police and fire protection, and education. Thousands of jobs are part of our work-a-day world, and the variety of tasks and human requirements necessary to carry out this work is staggering. Faced with such variability in jobs and their requirements on the one hand, and with people and their individual patterns of values, aspirations, interests, and abilities on the other, programs for the efficient use of human resources are essential.

Differences in Performance

People represent substantial investments by firms—as is immediately evident when one stops to consider the costs of recruiting, selecting, placing, and training as many people as there are organizational roles to fill. But psychology’s first law is that people are different. People differ in size, weight, and other physical dimensions, as well as in aptitudes, abilities, personality, interests, and myriad other psychological dimensions. People also differ greatly in the extent to which they are willing and able to commit their energies and resources to the attainment of organizational objectives.

If we observe a group of individuals doing the same kind of work, it will soon be evident that some are more effective workers than others. For example, if we observe a group of carpenters building cabinets, we will notice that some work faster than others, make fewer mistakes than others, and seem to enjoy their work more than others. These observations pose a question of psychological interest: Why? That is, what “people differences” cause these “work differences”? Perhaps these variations in effectiveness are due to differences in abilities. Some of the carpenters may be stronger, have keener eyesight, and have more finely developed motor coordination than others. Perhaps another reason for the observed differences in behavior is motivation. At any given point in time, the strength of forces impelling an individual to put forth effort on a given task, or to reach a certain goal, may vary dramatically. In other words, differences in individual performance on any task, or on any job, could be due to differences in ability, or to differences in motivation, or to both. This has clear implications for the optimal use of individual talents in our society.

A Utopian Ideal

In an idealized existence, our goal would be to assess each individual’s aptitudes, abilities, personality, and interests; to profile these characteristics; and then to place all individuals in jobs perfectly suited to them and to society. Each individual would make the best and wisest possible use of his or her talents, while in the aggregate, society would be making maximal use of its most precious resource.

Alas, this ideal falls far short in practice. The many, and often gross, mismatches between individual capabilities and organizational roles are glaringly obvious even to the most casual observer—history Ph.D.s driving taxicabs for lack of professional work, and young people full of enthusiasm, drive, and intelligence placed in monotonous, routine, dead-end jobs.

Point of View

In any presentation of issues, it is useful to make explicit underlying assumptions. The following assumptions have influenced the presentation of this book:

1. In a free society, every individual, regardless of race, age, gender, disability, religion, national origin, or other characteristics, has a fundamental and inalienable right to compete for any job for which he or she is qualified.

2. Society can and should do a better job of making the wisest and most humane use of its human resources.

3. Individuals working in the field of human resources and managers responsible for making employment decisions must be as technically competent and well informed as possible, since their decisions will materially affect the course of individual livelihoods and lives. Personnel psychology holds considerable potential for improving the caliber of HRM in organizations. Several recent developments have combined to stimulate this growing awareness. After first describing what personnel psychology is, we will consider the nature of some of these developments.

Personnel Psychology and Talent Management in Perspective

People have always been subjects of inquiry by psychologists, and the behavior of people at work has been the particular subject matter of industrial and organizational (I/O) psychology. Yet sciences and subdisciplines within sciences are distinguished not so much by the subject matter they study as by the questions they ask. Thus, both the social psychologist and the engineering psychologist are concerned with studying people. The engineering psychologist is concerned with the human aspects of the design of tools, machines, work spaces, information systems, and aspects of the work environment. The social psychologist studies power and influence, attitude change, communication in groups, and individual and group social behavior.

As noted earlier, personnel psychology is a subfield within I/O psychology. Some of the major areas of interest to personnel psychologists include job analysis and job evaluation; recruitment, screening, and selection; training and development; and performance management.

Personnel psychology and talent management overlap both psychology and the broader field of HRM. Both exclude, for example, such topics as labor and compensation law, organization theory, industrial medicine, collective bargaining, and employee benefits. Psychologists have already made substantial contributions to the field of HRM; in fact, most of the empirical knowledge available in such areas as motivation, leadership, and staffing is due to their work. Over the past decade, dramatic changes in markets, technology, demographics, organizational designs, the “psychological contract,” and the respective roles of managers and workers have inspired great emphasis on and interest in personnel psychology and talent management (Cascio, 2010; Cascio & Boudreau, 2016). The following sections consider each of these topics in more detail. Figure 1.2 illustrates them graphically.

Figure 1.2 The Changing Nature of Work and Organizations

Globalization of Product and Service Markets

Globalization—the ability of any individual or company to compete, connect, exchange, or collaborate globally—is exploding. The ability to digitize so many things, to send them anywhere and to pull them in from everywhere via our mobile phones and the Internet, has unleashed a torrent of global flows of information and knowledge. Global flows of commerce, finance, credit, social networks, and more are interlacing markets, media, central banks, companies, schools, communities, and individuals more tightly together than ever before (Cascio, 2018). That same connectivity is also making individuals and institutions more interdependent. As author Tom Friedman notes, “Everyone everywhere is now more vulnerable to the actions of anyone anywhere” (Friedman, 2016, p. 27). Product and service markets have truly become globalized.

Global labor markets are another feature of globalization, created by cheap labor and plentiful resources, combined with ease of travel and communication. This is fueling mobility as more companies expand abroad and people consider foreign postings as a natural part of their professional development. Beyond the positive effects that such circulation of talent brings to both developed and developing countries, it enables employment opportunities well beyond the borders of one’s home country (Dulebohn & Hoch, 2017). This means that competition for talent will come not only from the company down the street but also from the employer on the other side of the world (Economist Intelligence Unit, 2014).

Consider three other emerging trends spawned by globalization (Cascio, 2018). The first is increasing workforce flux as more roles are automated or outsourced and more workers are contract based, are mobile, or work flexible hours. This may allow companies to leverage global resources more efficiently, but it also will increase the complexity of management’s role. Second, expect more diversity as workers come from a greater range of backgrounds. Those with local knowledge of an emerging market, a global outlook, and an intuitive sense of the corporate culture will be particularly valued. Not surprisingly, talented young people will more frequently choose their employers based, at least in part, on opportunities to gain international experience. Finally, technical skills, although mandatory, will be less defining of the successful manager than the ability to work across cultures and to build relationships with many different constituents (Lublin, 2011; McGovern, 2017).

Why then, is there sometimes a backlash against globalization? It stems largely from a fear on the part of many people that globalization benefits big companies instead of average citizens, as stagnating wages and growing job insecurity in developed countries create rising disenchantment. In theory, less-developed countries win from globalization because they get jobs making low-cost products for rich countries. Rich countries win because, in addition to being able to buy inexpensive imports, they also can sell more sophisticated products, like financial services, to emerging economies. The problem, according to many experts, is that workers in the West are not equipped for today’s pace of change, in which jobs come and go and skills can quickly become redundant (Brynjolfsson & McAfee, 2014; Friedman, 2016).

Despite these concerns, economic interdependence among the world’s countries will continue. Global corporations will continue to be created through mergers and acquisitions of unparalleled scope. These mega-corporations will achieve immense economies of scale and compete for goods, capital, and labor on a global basis. As a result, prices will drop, and consumers will have more options than ever (Bhagwati, 2007; Ghemawat, 2017).

It takes more than trade agreements, technology, capital investment, and infrastructure, however, to deliver world-class products and services. It also takes the skills, ingenuity, and creativity of a competent, well-trained workforce. Workers with the most advanced skills create higher value products and services and reap the biggest rewards. Attracting, developing, and retaining talent in a culture that supports and nurtures ongoing learning is a continuing challenge for all organizations. Human resource professionals are at the epicenter of that effort.

Impact on Jobs and the Psychological Contract

The job churning that characterized the labor market in the 1990s and early twenty-first century has not let up. If anything, its pace accelerated during and after the Great Recession (Farber, 2011; Schwartz, 2009). Both white- and blue-collar jobs aren’t being lost temporarily because of a recession; rather, they are being wiped out permanently as a result of new technology, improved machinery, and new ways of organizing work (Friedman, 2016; Hamlin & Roberts, 2017). These changes have had, and will continue to have, dramatic effects on organizations and their people.

Corporate downsizing has become entrenched in American culture since the 1980s, but it was not always so. It was not until the final 20 years of the 20th century that such downsizing and the loss of the perceived “psychological contract” of lifelong employment with a single employer in the public and private sectors of the economy came to characterize many corporate cultures and the American workforce (Cascio, 1993b, 2002a, 2002b). The psychological contract refers to an unwritten agreement in which the employee and employer develop expectations about their mutual relationship (Payne, Culbertson, & Boswell, 2008; Rousseau, 1995). For example, absent just cause, the employee expects not to be terminated involuntarily, and the employer expects the employee to perform to the best of his or her ability.

Stability and predictability characterized the old psychological contract. In the 1970s, for example, workers held an average of three to four jobs during their working lives. Change and uncertainty, however, are hallmarks of the new psychological contract. Soon workers will hold 7–10 jobs during their working lives. Job-hopping no longer carries the stigma it once did. Indeed, the massive downsizing of employees has made job mobility the norm rather than the exception. This has led workers operating under the new psychological contract to expect more temporary employment relationships. Paternalism on the part of companies has given way to self-reliance on the part of employees, and also to a decrease in satisfaction, commitment, intentions to stay, and perceptions of an organization’s trustworthiness, honesty, and concern for its employees (Lester, Kickul, Bergmann, & De Meuse, 2003; Llopis, 2013). Indeed, our views of hard work, loyalty, and managing as a career will probably never be the same.

Effects of Technology on Organizations and People

We live in a global world where technology, especially information and communication technology, is changing the manner in which businesses create and capture value, how and where we work, and how we interact and communicate. Consider five technologies that are transforming the very foundations of global business and the organizations that drive it: cloud and mobile computing, big data and machine learning, sensors and intelligent manufacturing, advanced robotics and drones, and clean-energy technologies. These technologies are not just helping people to do things better and faster but also enabling profound changes in the ways that work is done in organizations (Cascio & Montealegre, 2016).

The new wave of technological innovation features the emerging general paradigm known as “ubiquitous computing,” or an environment where computational technology permeates almost everything, enabling new ways of connecting people, computers, and objects. The ubiquitous computing infrastructure also enables the collection of enormous amounts of structured and unstructured data, requiring the adjective big to distinguish this new paradigm of development. Ubiquitous computing also blurs the boundaries between industries, nations, companies, providers, partners, competitors, employees, freelancers, outsourcers, volunteers, and customers. These blurred boundaries yield opportunities to unify the physical space and the electronic space, which has implications for privacy and security, as well as how companies are organized and manage talent (Montealegre & Cascio, 2017).

As with other new developments, there are negatives as well as positives associated with new technology, and they need to be acknowledged. Workers may be bombarded with mass junk e-mail (spam), company computer networks may be attacked by hackers who can wreak havoc on an organization’s ability to function, and employees’ privacy may be compromised. A comprehensive review of literature in this area revealed three lessons about the effects of ubiquitous computing. One, the effects of ubiquitous computing on jobs is a process of creative destruction. Ubiquitous computing is not the first technology to affect jobs. From steam engines to robotic welders to ATMs, technology has long displaced humans, often creating new and higher skilled jobs in its wake. Two, ubiquitous computing can be used to enable or to constrain people at work. As an example, consider electronic monitoring systems. Evidence indicates that attitudes in general, and attitudes toward monitoring in particular, will be more positive when organizations monitor their employees within supportive organizational cultures (Alge & Hansen, 2014). Supportive cultures welcome employee input into the monitoring system’s design, focus on groups of employees rather than singling out individuals, and focus on performance-relevant activities. Three, ubiquitous computing is changing the nature of competition, work, and employment in ways that are profound and that need to be managed actively.

A caveat is in order here, however. It relates to the common assumption that since production and service processes have become more sophisticated, high tech can substitute for skill in managing a workforce. Beware of such a “logic trap.” When it comes to engaging and inspiring people to move in the same direction, empathizing with customers, and developing talent, humans will continue to enjoy a strong comparative advantage over machines. No computer will ever manage by walking around, but inspirational leadership will always be in demand (Cascio & Montealegre, 2016). At a broader level, to succeed and prosper in a world where nothing is constant except the increasingly rapid pace of change, companies need motivated, technically literate workers who are willing to retrain continually. However, organizations of the future will look very different from organizations of the past, as the next section illustrates.

Changes in the Structure and Design of Organizations

Many factors are driving change, but none is more important than the rise of Internet technologies. Like the steam engine or the assembly line, the Web has already become an advance with revolutionary consequences, most of which we have only begun to feel. The Web gives everyone in the organization, from the lowliest clerk to the chairperson of the board, the ability to access a mind-boggling array of information—instantaneously from anywhere. Instead of seeping out over months or years, ideas can be zapped around the globe in the blink of an eye. Organizations are adapting to management via the Web: premised on constant change, not stability; organized around networks, not rigid hierarchies; built on shifting partnerships and alliances, not self-sufficiency; and constructed on technological advantages, not bricks and mortar (Cascio, 2018; Friedman, 2016). Twenty-first-century organizations are global in orientation, and all about speed. They are characterized by terms such as virtual, boundaryless, and flexible, with no guarantees to workers or managers.

This approach to organizing is no short-term fad. The fact is that organizations are becoming leaner and leaner, with better and better trained “multi-specialists”—those who have in-depth knowledge about a number of different aspects of the business. Eschewing narrow specialists or broad generalists, organizations of the future will come to rely on cross-trained multi-specialists in order to get things done. One such group whose role is changing dramatically is that of managers.

Changing Roles of Managers and Workers

In the traditional hierarchy that once made up most bureaucratic organizations, rules were simple. Managers ruled by command from the top (essentially one-way communication), used rigid controls to ensure that fragmented tasks (grouped into clearly defined jobs) could be coordinated effectively, and partitioned information into neat compartments—departments, units, and functions. Information was (and is) power, and, at least in some cases, managers clung to power by hoarding information. This approach to organizing—that is, 3-C logic—was geared to achieve three objectives: stability, predictability, and efficiency.

In today’s unpredictable, hypercompetitive work environment, the autocratic, top-down command-and-control approach is out of step with the competitive realities that many organizations face. To survive, organizations have to be able to respond quickly to shifting market conditions. In this kind of an environment, a key task for all managers, especially top managers, is to articulate a vision of what their organizations stand for, what they are trying to accomplish, and how they compete for business in the marketplace. Managers need to be able to explain and communicate how their organizations create value. The next step is to translate that value-creation story into everything that is done, including the implications for employee knowledge and behavior, and to use it as a benchmark to assess progress over time.

Leadership in the digital age is not about control, but comfort with uncertainty. Companies need agility, that is, collaborative innovation to solve unstructured problems. In an attempt to derive a culture that employees wanted to see, GE crowdsourced input from employees and managers. The result was a renewed emphasis on acceleration, agility, and customer focus. IBM embraced a similar approach, known as agile management: a set of values and principles that emphasizes iterative, collaborative interactions among members of small teams working in a series of short cycles under conditions of full transparency. From the start, teams incorporate feedback and customer perspectives to deliver solutions that result from experimenting and learning from failure (Knowledge@Wharton, 2017).

The kinds of teams we are describing—intact, identifiable social systems (even if small or temporary) whose members have the authority to manage their own task and interpersonal processes as they carry out their work—go by a variety of names—autonomous work groups, process teams, self-managing work teams, and so on. The kinds of skills needed to succeed in this environment simply weren’t needed in organizations designed and structured under 3-C logic. Indeed, lack of management support, organizational resistance to change, and company cultures at odds with the values of agile management limit its innovative potential.

Does this imply that we are moving toward a universal model of organizational and leadership effectiveness? Hardly. Contingency theories of leadership such as path-goal theory (House & Mitchell, 1974), normative decision theory (Vroom & Yetton, 1973), and LPC contingency theory (Fiedler, 1967) suggest that an autocratic style is appropriate in some situations. More often, however, today’s networked, interdependent, culturally diverse organizations require transformational leadership (Bass & Riggio, 2006; Lord, Day, Zaccaro, Avolio, & Eagly, 2017). Leaders who transform followers to bring out their creativity, imagination, and best efforts require well-developed interpersonal skills, founded on an understanding of human behavior in organizations. Such strategic leadership is particularly effective under unstable or uncertain conditions (Colbert, Kristof-Brown, Bradley, & Barrick, 2008; Waldman, Ramirez, House, & Puranam, 2001). I/O psychologists and HR professionals are well positioned to help managers develop those kinds of skills.

An alternative approach is to engage talent as needed, thereby lowering overhead costs and improving response time. This is a talent-on-demand model and it is a central feature of the “gig” economy (Boudreau, Jesuthasan, & Creelman, 2015; Cascio & Boudreau, 2017; McGovern, 2017). More and more workers are operating outside the traditional confines of regular, full-time employment. They may be “free agents” or “e-lancers” (i.e., freelancers in the digital world) who work for themselves, or they may be employees of an organization a firm is allied with, employees of an outsourcing or temporary-help firm, or even volunteers. Two factors combine to make nonstandard work more feasible for organizations and workers. The first is technology. Internet-based communication tools, including collaborative workspaces and the opportunity for remote monitoring by companies, makes nonstandard work attractive to individuals as well as organizations (Cascio & Montealegre, 2016). Second, creativity and problem-solving skills play critically important roles in production and value creation in today’s knowledge-based economy, and those can originate either inside or outside organizational boundaries. For certain specialized skills, the best way to obtain and keep them current is through a freelance or nonstandard work ecosystem (Boudreau et al., 2015; Meyer, Somaya, & Williamson, 2012).

In this kind of an environment, the managerial roles of “controllers,” “planners,” and “inspectors” are being replaced by “coaches,” “facilitators,” and “mentors” (Lund, Ramaswamy, & Manyika, 2012; Srivastava, Bartol, & Locke, 2006). This doesn’t just happen—it requires well-developed interpersonal skills, continuous learning, and an organizational culture that supports and encourages both. Demographic diversity will characterize almost all organizations, as our next section illustrates.

Changing Demographics

Demographically, today’s organizations are more diverse than ever before. They comprise more women at all levels; more multiethnic, multicultural workers; older workers; younger workers; more workers with disabilities; robots; and contingent workers. Consider some of the contours of these changes.

Around the globe, the number as well as the mix of people available to work are changing rapidly. The U.S. labor force is aging, as the proportion of the labor force composed of people aged 55 and older rises from 19% in 2010 to 24% in 2050. As Figure 1.3 shows, by 2040 the non-Hispanic white population is projected to drop below 50%, with Hispanics making up more than a quarter of the population, and Asians, African Americans, and other ethnic groups constituting the rest. Immigration is projected to account for 88% of U.S. population growth over the next 50 years, such that by 2055 there will be no majority racial or ethnic group. Globally, the United Nations estimates that by 2060, for every 100 people of working age, there will be 30 people who are 65 and older. That is more than double the ratio of old to young people today. Because of low birth rates, the age wave is more acute in developed countries, increasing the cost of social programs and limiting economic growth. Younger migrants may ease that pain, however (“The first world is aging,” 2015; Jordan, 2015).

Figure 1.3 U.S. Population by Race, 2000 to 2050

Source: National Association of Corporate Directors. (2014, January 16). The US demographic tsunami: What directors need to know, p. 6.

In developed economies, many employers are unable to find people with the skill sets they need. By 2020, that talent gap could reach 1.5 million people in the United States and as many as 23 million in China (Lund et al., 2012; Qi, 2017). These trends have two key implications: (1) The reduced supply of workers (at least in some fields) will make finding and keeping employees a top priority, and (2) the task of managing a diverse workforce, of harnessing the motivation and efforts of a wide variety of workers, will present a continuing challenge.

Earlier we noted that more women than ever are found at all levels of organizations. Women constitute 47% of the U.S. workforce, and they hold 52% of all managerial and professional positions. So much for the myth that women don’t hold high-level business jobs because they supposedly don’t aim high enough (Catalyst, 2016; U.S. Bureau of Labor Statistics, 2015). Age diversity is even more pronounced. At present, five generations comprise the U.S. workforce: The silent generation (born 1930–1945); the baby boom generation (born 1946–1964); Generation X(born 1965–1980); Generation Y, also known as millennials (born 1981–1995); and Generation Z (born 1996–2010).

Evidence from time-lag and cross-sectional studies suggests that, despite a number of similarities, the generations in today’s workplace differ in aspects of their personalities, work values and attitudes, leadership and teamwork preferences, leader behaviors, and career experiences (Lyons & Kuron, 2014; Twenge, 2010). Meta-analytic results, however, indicate that the relationships between generational membership and work-related outcomes (job satisfaction, organizational commitment, and intent to quit) are moderate to small, essentially zero in many cases. Differences that appear to exist are likely attributable to factors other than generational membership (Costanza, Badger, Fraser, Severt, & Gade, 2012). An overarching theme across studies, however, is that individualism characterizes all generations (Twenge, 2012). An open question is the extent to which observed differences will remain stable or shift over time as the generations move through their respective life courses and career stages.

Age-based stereotypes are common (Posthuma & Campion, 2009), particularly among older workers, but this is just as true for middle-aged and younger workers (Finkelstein, Ryan, & King, 2013). As those authors noted, supervisors can serve as powerful ambassadors of positive age-diverse interactions, both by embodying and facilitating positive views of outgroup members and by promoting open communication and treating people as individuals. To support an aging workforce, Truxillo, Cadiz, and Hammer (2015) outlined 11 possible interventions, from work redesign to optimizing total worker health.

What are the implications for leaders? First, individual differences are always bigger than generational differences (Schumpeter, 2015). Generational differences are manifestations of broader trends in society and work that continue to evolve as the generations move through their respective life courses. Leaders cannot simply assume that past management practices will work in the modern context and that today’s practices will work in the future (Lyons & Kuron, 2014). They should focus on finding qualified employees who best fit the organization’s values and HR practices rather than attempting to craft strategies to attract the average member of a generation. For example, an organization that emphasizes high commitment might emphasize work–life fit and flexible schedules, while looking for workers who are enthusiastic and hardworking, and who have the requisite skills and experience the organization needs.

It should be clear by now that we are in the midst of a revolution—a revolution at work. Twenty-first-century organizations, both large and small, differ dramatically in structure, design, and demographics from those of even a decade ago. Paternalism is out; self-reliance is in. There is constant pressure to do more with less and a steady emphasis on empowerment, cross-training, personal flexibility, self-managed work teams, and continuous learning. Workers today have to be able to adapt to changing circumstances and to be prepared for multiple careers. Job security (the belief that one will retain employment with the same organization until retirement) has become less important to workers than employment security (having the kinds of skills that employers in the labor market are willing to pay for). In our next section we consider some organizational responses to these new realities.

Implications for Organizations and Their People

In a world where virtually every factor that affects the production of goods or the delivery of services—capital, equipment, technology, and information—is available to every player in the global economy, the one factor that doesn’t routinely move across national borders is a nation’s workforce. Today the quality of a nation’s workforce is a crucial determinant of its ability to compete and win in world markets.

Human resources can be sources of sustained competitive advantage as long as they meet three basic requirements: (1) They add positive economic benefits to the process of producing goods or delivering services; (2) the skills of the workforce are distinguishable from those of competitors (e.g., through education and workplace learning); and (3) such skills are not easily duplicated (Barney, 1991). A human resource system (the set of interrelated processes designed to attract, develop, and maintain human resources) can either enhance or destroy this potential competitive advantage (Lado & Wilson, 1994).

Perhaps a quote attributed to Albert Einstein, the famous physicist, best captures the position of this book. After the first atomic reaction in 1942, Einstein remarked: “Everything has changed, except our way of thinking” (Workplace of the Future, 1993, p. 2). As I/O psychology in general, and talent management in particular, move deeper into the 21st century, our greatest challenge will be to change the way we think about organizations and their people. As just one example, consider how the digital revolution will affect the workplace of the future, and some emerging research needs in that area (Colbert, Yee, & George, 2016).

There is no doubt that the increasing prevalence of technology influences the way people approach work. We are in near-constant communication with one another, and our lives are chronicled for friends and followers in real time on social media. At the same time, people vary in their proficiency and comfort in achieving desired outcomes at work using technology, often referred to as “digital fluency” (Briggs & Makice, 2012). Clearly, research is needed to fully understand how digital fluency may influence job performance and career progression across a range of professions, as well as how it affects conflict and collaboration in diverse groups (Colbert et al., 2016).

Digitally fluent or not, the effects of technology at work may be both positive and negative. On the positive side, technology has facilitated leaps in productivity, collaboration, and connectivity with others that were unimaginable a few decades ago. At the same time, however, the ubiquitous presence of technology in our lives may limit opportunities to develop deep levels of self-awareness and to behave authentically, especially among those who spend lots of time in online worlds and working with avatars. Managers and organizations need to consider how to address the possibility of reduced self-awareness and authenticity among members of the digital workforce while also remaining aware of the ways that technology might be used to promote healthy identity development (Colbert et al., 2016). To be sure, the prevalence of technology in our daily lives may affect the quality of our interactions with others and may lead to a decline in our level of empathy (a cognitive understanding of another’s perspective and an affective response to another’s experiences). Meta-analysis revealed that dispositional empathy levels decreased between 1979 and 2009 among college students in the United States (Konrath, O’ Brien, & Hsing, 2011). A possible reason for this finding is that the kinds of fully present, face-to-face interactions that foster empathy have become less common in a world of digital communication. More research is needed to fully understand how digitally mediated communication may influence communication, relationship quality, and empathy, especially in the workplace (Colbert et al., 2016).

In our “always-on” society, technology has blurred boundaries between work and nonwork, sometimes to our detriment. Thus, in a study of the daily intrusions of e-mail in nonworking hours, Butts, Becker, and Boswell (2015) found that time required to respond to e-mail outside of work was associated with higher levels of anger, which in turn led to increased work–family conflict. Research is just beginning to provide guidance on how organizations can most effectively manage the digital workforce and leverage technology while avoiding potential downsides.

To be sure, the future world of work will not be a place for the timid, the insecure, or the low skilled. For those who thrive on challenge, responsibility, and risk taking, security will come from seizing opportunities to adapt and to develop new competencies (Gunz & Peiperl, 2007; Hall & Mirvis, 1995). The need for competent HR professionals with broad training in a variety of areas has never been greater.

Chapter 11

11 RECRUITMENT

Wayne F. Cascio, Herman Aguinis

Learning Goals

By the end of this chapter, you will be able to do the following:

· 11.1 Describe the recruitment process as a talent supply chain

· 11.2 Explain the three sequential stages of recruitment and key activities that affect each one

· 11.3 Identify fundamental questions to address when planning for recruitment

· 11.4 Discuss the pros and cons of hiring internally versus externally

· 11.5 Explain why a positive organizational image and employer brand help attract candidates

· 11.6 Know the fundamental questions about internal recruitment that all organizations need to address

· 11.7 Craft a strategy for increasing the diversity of an organization’s workforce

· 11.8 Identify situations in which realistic job previews will and will not work well

Whenever human resources must be expanded or replenished, a recruiting system of some kind must be established. Advances in technology, coupled with the growing intensity of competition in domestic and international markets, have made recruitment a top priority as organizations struggle continually to gain competitive advantage through people. Recruitment is a business, and it is big business (Bersin, quoted in Deloitte, 2015; Overman, 2008). It demands serious attention from management because any business strategy will falter without the talent to execute it. According to former Apple CEO Steve Jobs, “Recruiting is hard. It’s finding the needles in the haystack. I’ve participated in the hiring of maybe 5,000-plus people in my life. I take it very seriously” (Jobs, 2008).

This statement echoes the claims of many recruiters that it is difficult to find good workers and that talent acquisition is becoming more rather than less difficult (Kandefer, 2017; Maurer, 2017b; Ryan & Delaney, 2017). As an example, consider how the Internet has revolutionized the practice of recruitment. For the nearly 20% of the world’s workforce who change jobs each year, there are more than 50,000 job-recruitment sites globally, as well as the ability to research employers and to network (Maurer, 2016a).

About a third of LinkedIn’s revenue comes from corporate customers who buy rights for their recruiters to use LinkedIn’s software as a service. This rapid evolution is expected to continue, with dynamic, customized job postings that use cookie-based targeting to communicate job advertisements to relevant individuals based on their online behaviors and the incorporation of mobile technology to access Internet-based job information (Dineen & Allen, 2014; Maurer, 2016c). The result? A “leveling of the information playing field” brought about by Web technology.

Organizations recruit periodically in order to add to, maintain, or readjust their total workforces in accordance with HR requirements. Building on the ideas shown in  Figure 3.2 , the external staffing supply chain,  Figure 11.1 illustrates the recruitment process as a talent supply chain.

In  Figure 11.1 , attract describes the overall process of generating and inducing interest among suitable applicants for potential employment opportunities in the organization. Source is the process of generating a pool of applicants. Assess is the evaluation of knowledge, skills, and abilities, and other characteristics in order to perform a job. Employ is the process of moving the desired candidate into employment. Note, however, that some of the activities associated with each function overlap other functions.

The logic of recruitment calls for sound strategic workforce planning systems (talent inventories, forecasts of workforce supply and demand, action plans, and control and evaluation procedures) to serve as a base from which to launch recruiting efforts. This will be evident as we begin to examine the operational aspects of the recruitment function.

In this chapter, our objective is to describe how organizations search for prospective employees and influence them to apply for available jobs. Accordingly, we consider recruitment planning, operations, and evaluation, together with relevant findings from recruitment research, and we include organizational examples to illustrate current practices.  Figure 11.2 , from Dineen and Soltis (2011), serves as an overarching framework for the processes described in this chapter. It integrates earlier views of recruitment in terms of sequential stages (Barber, 1998; Breaugh, Macan, & Grambow, 2008), while also integrating contextual/environmental and “key-process” issues (Rynes & Cable, 2003). As shown in  Figure 11.2 , two key decision points (application and job choice) separate these primary recruitment stages. Within each stage, the framework also identifies important subcategories.

Figure 11.1 The Recruitment Process as a Talent Supply Chain

Source: Based on © ISO. This material is reproducted from ISO 30405: 2016 with permission of the American National Standards Institute (ANSI) on behalf of the International Organization for Standardization. All rights reserved. Based on Cascio, W. F., & Boudreau, J. W. Investing in People: Financial Impact of Human Resource Initiatives, 2nd ed. © 2011.

Figure 11.2 An Integrated Model of the Recruitment Process

Source: Dineen, B. R., & Soltis, S. N. (2011). Recruitment: A review of research and emerging directions. In S. Zedeck (Ed.), Handbook of industrial and organizational psychology (Vol. 2, pp. 43–66). Washington, DC: American Psychological Association.

Three contextual/environmental features affect all recruitment efforts, namely, characteristics of the firm (the value of its “brand” and its “personality”; Cascio & Graham, 2016; Lievens & Slaughter, 2016); characteristics of the vacancy itself (is it mission critical?); and characteristics of the labor markets in which an organization recruits. Likewise, three sequential stages characterize recruitment efforts: generating a pool of viable candidates, maintaining the status (or interest) of viable candidates, and “getting to yes” after making a job offer (postoffer closure).

Figure 11.1  also identifies key activities that affect each of these three stages. These include strategies for targeting potential candidates and for communicating information to them (“messaging strategies”); issues related to screening viable candidates and interactions with organizational agents (recruiters, managers, and employees encountered during site visits); and issues related to actual job offers (e.g., timing, “exploding” offers that disappear after specified periods of time). Finally,  Figure 11.2  identifies some key processes that affect the outcomes of each stage of the recruitment process, for example, social networking and information processing (seen through the lens of the elaboration likelihood model; Jones, Shultz, & Chapman, 2006) at the candidate-generation stage; communication, rapport building, and signaling to maintain viable candidates; and negotiation, decision making, and competitive intelligence at the postoffer stage. Space constraints do not permit us to discuss each of the issues, activities, and processes shown in  Figure 11.2 , but we present it here because it is rich in implications for advancing both the theory and practice of recruitment. Let’s begin by considering recruitment planning.

Recruitment Planning

The process of recruitment planning begins with a clear specification of HR needs (numbers, skills mix, levels) and the time frame within which such requirements must be met. This is particularly relevant to the setting of workforce diversity goals and timetables. Labor-force availability and internal workforce representation of women and minorities are critical factors in this process. In the United States, the Census Bureau provides such information based on national census data for specific geographic areas.

Beyond these issues, two other important questions need to be addressed, namely, whom to recruit and where to recruit (Breaugh, 2008; Ployhart & Kim, 2014; Rynes, Reeves, & Darnold, 2014). Answers to both questions are essential to determining recruitment objectives. For example, a prehire objective might be to attract a certain number of applications for pivotal or mission-critical jobs from passive job candidates—those who are not currently looking for a job. Objectives are also critical to recruitment evaluation, namely, if an employer wishes to compare what it hoped to accomplish with recruitment outcomes.

Having established recruitment objectives, an organization should be able to develop a coherent strategy for filling open positions. Among the questions an employer might address in establishing a recruitment strategy are (a) when to begin recruiting, (b) what message to communicate to potential job applicants, and (c) whom to use as recruiters. As Breaugh (2012) has noted, answers to these questions should be consistent with the recruitment objectives previously established. In terms of messages, consider the finding that satisfaction with coworkers enhances older-worker engagement (Avery, McKay, & Wilson, 2007). Messages to recruit older workers might therefore be geared toward enhancing perceptions of fit with immediate coworkers (person–group fit). Such messages might also build on the findings of a study by Rau and Adams (2005) that targeted EEO statements, the opportunity to transfer knowledge, and flexible schedules, all of which positively influenced attraction of older workers.

Internal Recruitment

Primed with a comprehensive workforce plan for the various segments of the workforce (e.g., entry level, managerial, professional, and technical), recruitment planning may begin and internal candidates should be considered first. There are four key advantages to recruiting internally (Breaugh, 2014; Maurer, 2016b). First, there is less transition time moving into new jobs. Current employees are already familiar with an employer’s products, people, and operating procedures. Second, there is a greater likelihood of filling a position successfully. In contrast to external candidates, an employer has considerably more information about internal candidates (e.g., past performance, temperament, work ethic). Third, it is generally cheaper to fill a higher level position internally than it is to fill it from outside. Fourth, assuming that those promoted from within are seen as deserving, there is a positive impact on the motivation levels of other employees.

At the same time, however, organizations must confront a common problem: the reluctance of managers to grant permission for their subordinates to be interviewed for potential transfer or promotion. In a recent survey, fully half of 665 firms reported talent hoarding as a serious problem (Lublin, 2017). To overcome this aversion, promotion-from-within policies must receive strong top-management support, coupled with a company philosophy that permits employees to consider available opportunities within the organization and incentives for managers to release them. At EY (formerly Ernst & Young), Johnson & Johnson, and PepsiCo, pay is now determined, in part, by how well a manager does at nurturing people. Technology consulting firm Avanade shifts leaders to new roles every few years to ensure that high-potential employees get noticed (Church, 2017; Lublin, 2017).

With respect to timing, the effective use of “in-house” talent should come first. If an organization undertakes external recruitment efforts without considering the desires, capabilities, and potential of present employees, it may incur both short- and long-term costs. In the short term, morale may degenerate. Over the longer term, an organization with a reputation for consistent neglect of in-house talent may find it difficult to attract new employees or to retain experienced ones. In light of those concerns, organizations are making wider use of job postings (via company intranets, on internal social media, or in company newsletters), employee referrals (one way to attract “passive” candidates; Ryan, 2017), and temporary worker pools (Kauflin, 2016).

External Recruitment

Today, firms are hiring externally to fill jobs at all levels (Cappelli & Keller, 2017). To do that well, begin by estimating three key parameters: the time, the money, and the staff necessary to achieve a given hiring rate (Hawk, 1967). The basic statistic needed to estimate these parameters is the number of leads needed to generate a given number of hires in a given time. Certainly, the easiest way to derive this figure is on the basis of prior recruitment experience. If accurate records were maintained regarding yield ratios and time-lapse data, no problem exists, since trends may be determined and reliable predictions generated (assuming labor-market conditions are comparable). Yield ratios are the ratios of leads to invites, invites to interviews, interviews (and other selection instruments) to offers, and offers to hires obtained over some specified time period (e.g., six months or a year). Time-lapse data provide the average intervals between events, such as between the extension of an offer to a candidate and acceptance or between acceptance and addition to the payroll.

If no experience data exist, then it is necessary to use “best guesses” or hypotheses and then to monitor performance as the operational recruitment program unfolds. For the moment, however, suppose ABC Engineering Consultants is contemplating opening two new offices and needs 100 additional engineers in the next six months. Fortunately, ABC has expanded in the past, and, on that basis, it is able to make predictions like this:

With technical candidates, we must extend offers to 2 candidates to gain 1 acceptance, or an offer-to-acceptance ratio of 2:1. If we need 100 engineers, we’ll have to extend 200 offers. Further, if the interview-to-offer ratio has been 3:2, then we need to conduct 300 interviews, and, since the invites-to-interview ratio is 4:3, then we must invite as many as 400 candidates. Finally, if contacts or leads required to find suitable candidates to invite are in a 6:1 proportion, then we need to make 2,400 contacts.

Figure 11.3 Recruiting Yield Pyramid—Engineering Candidates, ABC Engineering Consultants

Source: Adapted from Roger H. Hawk, The Recruitment Function. Copyright © 1967 by the American Management Association, Inc.

A recruiting yield pyramid for these data is presented in Figure 11.3.

Increasingly, companies are using artificial intelligence to screen résumés. Here are some typical yield ratios (Weber, 2012): Of 1,000 people who notice a job posting online, approximately 200 will begin the application process. Of those, approximately 100 people complete the application process, and of those, a hiring manager reviews approximately 25 résumés. Roughly four to six candidates will be invited to interview for the vacancy, one to three finalists will complete the final steps of the qualification process, and one person will be offered the position. He or she accepts 80% of the time.

Figure 11.4 Time-Lapse Data for Recruitment of Engineers

Additional information, critical to effective recruitment planning, can be derived from time-lapse data. For ABC Engineering Consultants, experience may show that the interval from receipt of a résumé to invitation averages four days. If the candidate is still available, he or she will be interviewed five days later. Offers are extended, on average, three days after interviews, and, within a week after that, the candidate either accepts or rejects the offer. If the candidate accepts, he or she reports to work, on average, three weeks from the date of acceptance. Therefore, if ABC begins today, the best estimate is that it will be 40 days before the first new employee is added to the payroll. With this information, the “length” of the recruitment pipeline can be described and recruiting plans fitted to it. A simple time-lapse chart for these data is presented in Figure 11.4. Data on 350,000 cases from Glassdoor indicate that companies take an average of 23 days to screen and hire new employees (excluding the time from acceptance to report for work) (Shellenbarger, 2016). Time to fill averages 42 days (Society for Human Resource Management, 2016c).

All of this assumes that intervals between events in the pipeline proceed as planned. In fact, longitudinal research indicates that delays in the timing of recruitment events are perceived negatively by candidates, especially high-quality ones, and often cost job acceptances (Boswell, Roehling, LePine, & Moynihan, 2003; Bretz & Judge, 1998; Chapman, Uggerslev, Carroll, Piasentin, & Jones, 2005; Rynes & Cable, 2003; Wright, 2017). In addition, time to fill can be misleading, especially if measures of the quality of new hires are ignored (Maurer, 2015b). Here is a simple example: It is one thing to know that a firm’s sales openings average 55 days to fill. It’s another thing to know that the difference between filling them in 55 versus 30 days costs the firm $30 million revenue, or that a 20% improvement in quality of hire will result in an $18 million productivity improvement.

Note, however, that these yield ratios and time-lapse data are appropriate only for ABC’s engineers. Other segments of the workforce may respond differently, and widespread use of the Internet by job seekers in all areas may change both yield ratios and time-lapse data. From the perspective of job seekers, it takes people who apply online an average of three more weeks to get a job than if they contacted the employer directly and six more weeks compared to a personal referral (Maurer, 2016a). Of course, the time period also depends on labor-market conditions. As we noted on Chapter 10, a labor market is a geographic area within which the forces of supply (people looking for work) interact with the forces of demand (employers looking for people) and thereby determine the price of labor. However, since the geographic areas over which employers extend their recruiting efforts depend partly on the type of job being filled, it is impossible to define the boundaries of a local labor market in any clear-cut manner (Newman, Gerhart, & Milkovich, 2016). If the supply of suitable workers in a particular labor market is high relative to available jobs, then the price of labor generally will be cheaper. By contrast, if the supply is limited (e.g., suppose ABC needs certain types of engineering specialists who are unavailable locally), then the search must be widened and additional labor markets investigated in order to realize required yield ratios.

Table 11.1 Advantages and Disadvantages of Internal Versus External Recruitment

	Advantages	Disadvantages
Internal recruitment	Faster, cheaper Motivates other employees More successful placements	May encourage hoarding of top talent May perpetuate the status quo
External recruitment	New ideas Facilitates expansion Fills jobs where internal talent is not available	Longer trajectory to full productivity Slower, more expensive

In traditional internal labor markets, employees are brought into organizations through a small number of entry-level jobs and then are promoted up through a hierarchy of increasingly responsible and lucrative positions. In recent years, however, internal labor markets have weakened, such that high-level jobs have not been restricted to internal candidates, and, as noted earlier, new employees have been hired from the outside at virtually all levels (Bidwell, 2017; Cappelli & Keller, 2017). This has had the predictable effect of weakening employee loyalty and trust in management, and it puts employers at a disadvantage when labor markets tighten. Table 11.1 presents some of the advantages and disadvantages of internal versus external recruitment.

Staffing Requirements and Cost Analyses

Experienced professional and technical recruiters can be expected to produce about 50 new hires per year, but that number varies by company size. It could be 85 hires per year for a large company, and as few as 20–30 for a small one (Glenn, 2009). Let’s assume four full-time recruiters will be required to meet ABC’s staffing requirements for 100 engineers in the next six months.

So far, we have been able to estimate ABC’s recruiting time and staffing requirements on the basis of its previous recruiting experience. Several other parameters must be considered before the planning process is complete. The most important of these, as might logically be expected, is cost. Before making cost estimates, however, let’s assume that an organization has no prior recruiting experience (or that the necessary data were not recorded). The development of working hypotheses about yield ratios is considerably more complex under these conditions, though far from impossible.

It is important to analyze the external labor market by source, along with analyses of demand for similar types of people by competitors. It is also important to evaluate the entire organizational environment in order to do a “company advantage study.” Numerous items must be appraised, including geographic factors (climate, recreation), location of the firm, cost of living, availability of housing, proximity to shopping centers, quality of local public and parochial schools, and so forth.

Capitalizing on special factors that are likely to attract candidates, such as organizational image, reputation, and future opportunities, is important as well (Collins, 2007; Collins & Han, 2004; Feffer, 2016; Weber, 2016). Image is a strong predictor (ρ = .48) of organizational attraction (Allen, Mahto, & Otondo, 2007; Lievens & Slaughter, 2016). Such information will prove useful when developing a future recruiting strategy and when gathering baseline data for estimates of recruiting yield.

Here are three reasons why a positive organizational image or reputation might influence prospective candidates to apply (Lievens & Slaughter, 2016; Rynes & Cable, 2003): (1) People seek to associate themselves with organizations that enhance their self-esteem. Job seekers might pursue high-reputation companies to bask in such organizations’ reflected glory or to avoid negative outcomes associated with working for an employer with a poor image. (2) A positive reputation may signal that an organization is likely to provide other desirable attributes, such as high pay and strong opportunities for career growth and development. (3) A positive reputation may make applicants more receptive to whatever information an organization provides.

Yield ratios and time-lapse data are valuable for estimating recruiting staff and time requirements. Recruitment planning is not complete, however, until the costs of alternative recruitment strategies have been estimated. Expenditures by source must be analyzed carefully in advance in order to avoid any subsequent “surprises.” In short, analysis of costs is one of the most important considerations in determining where, when, and how to approach the recruiting marketplace.

The International Organization for Standardization (2017) has issued a cost-per-hire (CPH) international standard. The CPH metric is designed to measure the costs associated with the sourcing, recruiting, and staffing activities borne by an employer to fill an open position in the organization. CPH is a ratio of the total dollars expended (in both external and internal costs) to the total number of hires in a specified time period (see Equation 11.1).

CPH = ∑ (External Costs) + ∑ (Internal Costs)/Total Number of Hires in a Time Period    (11.1)

External costs include all sources of spending outside the organization on recruiting efforts during the time period in question. Examples of external costs include third-party agency fees, advertising costs, job-fair costs, and travel costs for recruiting.

Internal costs include all sources of internal resources and costs used for staffing efforts during the time period in question. Examples of internal costs include the fully loaded salary and benefits of the recruiting team and fixed costs, such as physical infrastructure (e.g., talent-acquisition system costs).

Total number of hires refers to the total added during the time period in question. Regardless of the hires’ staffing type (e.g., regular full time, regular part time, temporary) and total number, we assume that the fully loaded costs to staff the included positions have been calculated in external costs and internal costs.

Source Analysis

Analysis of recruiting sources facilitates effective planning. Three types of analyses are typical: cost per hire, time lapse from candidate identification to hire, and source yield. In terms of cost per hire, the most expensive sources generally are private employment agencies and executive search firms, since their fees may constitute as much as 35% of an individual’s first-year salary (Stewart, 2015). The next most expensive sources are field trips, for both advertising expenses and recruiters’ travel and living expenses are incurred. Less expensive are advertising responses, Internet responses, write-ins, and internal transfers and promotions. Employee referrals, direct applications (mail or Web based), and walk-ins are the cheapest sources of candidates.

Time-lapse studies of recruiting sources are especially useful for planning purposes, since the time from initial contact to report onboard varies across sources. In the case of college recruiting, for example, a steady flow of new employees is impossible, since reporting dates typically coincide closely with graduation, regardless of when the initial contact was made.

For those sources capable of producing a steady flow, however, employee referrals and direct applications usually show the shortest delay from initial contact to report (Maurer, 2016a). By contrast, when an organization has an efficient recruiting infrastructure in place, it may be difficult to beat the Internet. Today’s cloud-based applicant tracking systems, such as IBM Kenexa BrassRing, Greenhouse, or SmartRecruiters, have intuitive interfaces and tools that allow recruiters to cast a wider net for candidates. They also include robust analytics with “dashboards” that illustrate key recruitment metrics (Maurer, 2017a; Zielinski, 2015). Fully 75% of hiring and talent acquisition managers use applicant tracking or recruiting software to improve the hiring process (Kandefer, 2017).

From an applicant’s perspective, it takes an average of about 15 applications to different employers to get a job through an online job site, versus 10 for those who apply directly to a company, and six for those who are referred by current employees (Maurer, 2016a). Competition for top candidates is intense; the organization whose recruiting section functions smoothly and is capable of responding swiftly has the greatest likelihood of landing high-potential people.

The third index of source performance is source yield (i.e., the ratio of the number of candidates generated from a particular source to hires from that source). Although no ranking of source yields would have validity across all types of organizations and all labor markets, Breaugh, Greising, Taggart, and Chen (2003) examined the relationship between five recruitment methods (i.e., employee referrals, direct applicants, college placement offices, job fairs, and newspaper ads) and prehire outcomes for applicants for information-technology jobs. They found no difference for level of education or interview score. Not surprisingly, those recruited from college placement offices had less experience than applicants in the other groups. In terms of a job offer, employee referrals and direct applicants were more likely to receive one than were those in the other groups. This pattern also held for those who were hired. Thus, although employee referrals and direct applicants did not differ from those in the other groups on two measures of applicant quality, they still were viewed as being more deserving of job offers.

A more recent study of employee referrals used longitudinal data from 386 referrer–referral hire pairs at the same job level in a U.S. call center over a two-year period (Pieper, 2015). Referral hires from high-performing referrers performed better but had higher turnover propensities than did those from lower-performing referrers.

The most commonly used sources are changing, however (Ryan & Delaney, 2017). Fully 87% of recruiters now use LinkedIn and 55% use Facebook (many use both sources) (Kandefer, 2017). At the same time, informal contacts are used widely and effectively at all occupational levels. In fact, word-of-mouse (informal, Web-based conversations about companies) are viewed as more credible and associated with higher organizational attractiveness than are Web-based testimonials (Farrell, 2012; Van Hoye & Lievens, 2007).

Having examined source yield, we are almost ready to begin recruiting operations at this point. Recruiting efficiency can be heightened considerably, however, once employment requirements are defined thoroughly in advance. This is an essential step for both technical and nontechnical jobs. Recruiters must be familiar with the job descriptions of available jobs; they must understand (and, if possible, have direct experience with) the work to be performed. Research has shown clearly that characteristics of organizations and jobs (e.g., location, pay, opportunity to learn, challenging and interesting work) have a greater influence on the likelihood of job acceptance by candidates than do characteristics of the recruiter (Barber & Roehling, 1993; Rynes, 1991; Taylor & Bergmann, 1987).

Nevertheless, at the first stage of recruitment, characteristics of recruiters (personable, trustworthy, informative, competent) do affect the perceptions of candidates (Chapman et al., 2005), particularly with respect to the procedural justice of the process (Chapman & Webster, 2006), but not their intentions to accept job offers (Stevens, 1998). Neither the job function (HR versus line management) nor the gender of the recruiter seems to make much difference to candidates (Chapman et al., 2005). At the same time, there are at least three reasons why 

recruiters might matter (Breaugh et al., 2008). Different types of recruiters may be important because (a) they vary in the amount of job-related information they possess (and therefore can share), (b) they differ in terms of their credibility in the eyes of recruits, and (c) they signal different things to job candidates. In online social networks, recruiters who secure a central network position as a connector and who brand themselves well (in addition to employer branding) are most successful in attracting quality candidates (Ollington, Gibb, & Harcourt, 2013). Still, it is likely that applicants rely less on recruiter signals as more information about job and organizational characteristics becomes salient (Dineen & Soltis, 2011).

Planning is now complete. Strategic workforce plans have been established; time, cost, and staff requirements have been specified; sources have been analyzed; and job requirements and employment standards have been determined and validated. Now we are ready to begin recruiting operations.

Operations

Operationally, all organizations need to address five important questions about internal recruitment. To illustrate each one, consider the role of technical professionals and their potential moves to managerial positions (Cascio, 2018):

· How does the organization create a talent pool? For example, how does it prepare technical professionals for future management positions?

· How does the organization attract candidates for promotion? Do the most suitable technical professionals want to advance to management, or do they prefer to pursue technical work?

· How does the system choose candidates? Are promotion candidates chosen as a reward for good technical performance or for their leadership abilities?

· How does the organization make offers to land candidates? How successful is the organization in convincing technical professionals to move into leadership positions?

· How does the organization bring new employees on board? How much support and training are technical professionals given after they assume their new positions to help them become effective leaders?

Internal mobility may be beneficial from the standpoint of creating social network ties to new areas of the business (Somaya, Williamson, & Lorinkova, 2008). In many cases, however, organizations turn to external sources to fill entry-level or higher level jobs, jobs created by expansion, and jobs whose specifications cannot be met by present employees. To that topic we now turn.

External Sources for Recruiting Applicants

A variety of external recruiting sources are available, with the choice of source(s) contingent on specific hiring requirements and source-analysis results. Available sources include the following:

· Advertising—Internet-based job boards or talent platforms, newspapers (physical or online), social media, technical and professional journals, television, radio, and (in some cases) outdoor advertising

· Employment agencies—federal and state agencies, private agencies, executive search firms, management consulting firms, and agencies specializing in temporary help

· Educational institutions—technical and trade schools, colleges and universities, co-op work/study programs, and alumni placement offices

· Professional organizations—technical society meetings and conventions (regional and national) and society placement services

· Military—out-processing centers and regional and national retired officer associations’ placement services

· Labor unions

· Career fairs—physical or virtual

· Outplacement firms

· Direct application (walk-ins, write-ins, online applicants)

· Intracompany transfers and company retirees

· Employee referrals

To illustrate how companies try to gain a competitive advantage over their rivals in university recruiting for top talent, consider the example in Box 11.1.

The sources listed here may be classified as formal (institutionalized search methods such as employment agencies, advertising, search firms) or informal (e.g., walk-ins, write-ins, employee referrals). In terms of the most popular sources used by employers, evidence indicates the following (Breaugh, 2012; Dineen & Soltis, 2011; Griffeth, Tenbrink, & Robinson, 2014):

· Use of public employment services declines as required skills levels increase.

· The internal market is a major recruitment source except for entry-level, unskilled, and semiskilled workers.

· Larger firms are the most frequent users of walk-ins, write-ins, and the internal market. They clearly have a hiring advantage (Knowledge@Wharton, 2014).

· There is no consistent relationship between recruitment sources and person–job fit.

· Use of multiple recruitment sources together with informal sources provides the most realistic and accurate information.

Box 11.1 How Goldman Sachs Uses Video to Screen University Students for Summer Jobs

Goldman Sachs is changing its focus from on-campus interviews at elite schools to find students who are passionate about and committed to careers in finance. To do that, it is changing the way it interviews and assesses candidates for summer analyst roles, typically the first-rung jobs for a career in banking. To cast a wider net for candidates, especially those from non-elite schools, it is experimenting with video interviews, in which candidates answer prompts from a software program (Bazaaz, 2016). Videos are reviewed by Goldman’s recruiting team and the bank invites those who make the cut to one of its offices for a day of in-person interviews, called “super days.” The new procedure allows recruiters to conduct significantly more first-round interviews than in the past.

In addition, the firm is deploying more résumé-screening technology, and it is piloting the use of personality measures to better identify students with dispositions that reflect the firm’s core values, such as “grit,” “judgment,” and “problem solving.” Goldman recruiters still visit a few dozen campuses to get to know students, host gatherings, and answer questions about life at the firm. They also still conduct on-campus interviews for MBA students (Gellman, 2016).

In practice, most applicants use more than one recruitment source to learn about jobs. However, the accumulated evidence on the relationship among recruitment sources, turnover, and job performance suggests that such relationships are quite weak (Griffeth et al., 2014). What may be more important than the source per se is how much support and information a source provides, or the extent to which a source embeds prescreening on desired applicant characteristics (Maurer, 2017b; Rynes et al., 2014).

With respect to job advertisements, those that contained more information resulted in a job opening being viewed as more attractive (e.g., Allen et al., 2007) and as more credible (Allen, Van Scotter, & Otondo, 2004) than those that contained less information. Research also has shown that advertisements that contain more specific information about a position increase applicant interest in the position and may result in better person–organization fit (Roberson, Collins, & Oreg, 2005). Meta-analysis shows that fit, in turn, is the best predictor of applicant attraction (Uggerslev, Fasina, & Kraichy, 2012). Box 11.2 highlights the special issues associated with recruiting for diversity.

Employee referrals have been and remain extremely popular. They are a major source of new hires at many levels, including professionals. The logic behind employee referral is that “it takes one to know one.” Interestingly, the rate of employee participation seems to remain unaffected by such efforts as higher cash bonuses, cars, or expense-paid trips (Breaugh, 2012). This suggests that good employees will not refer potentially undesirable candidates, even if the rewards are outstanding. Finally, considerable research evidence indicates that referrals result in consistently lower quit rates than other sources (Griffeth et al., 2014).

Research shows that employee referrals work for three major reasons. First, assuming that current employees value their reputations, they prescreen referrals. Second, referrals are more likely to have accurate expectations about jobs, assuming they have discussed them with current employees. Third, newly hired employee referrals have someone they can go to for coaching (Breaugh, 2014, Griffeth, Hom, Fink, & Cohen, 1997). Employee referrals clearly have advantages, but from an EEO perspective, employee referrals are fine as long as the workforce is diverse in gender, race, and ethnicity to begin with. A potential disadvantage, at least for some firms, is that employee referrals tend to perpetuate the perspective, the belief systems, and, in some cases, the lack of diversity of the current workforce. This may not be the best way to go for organizations that are trying to change those things.

Managing Recruiting Operations

Administratively, recruitment is one of the easiest activities to foul up—with potentially long-term negative publicity for the firm. Recent evidence indicates that this area needs serious work, as almost 60% of job seekers report having a poor candidate experience (see Box 11.3). Of those, 72% proceeded to share information about their poor experiences online on an employer-review site, such as Glassdoor, on a social networking site, or directly with a colleague or friend (Kandefer, 2017). Among other things, organizations are trying to eliminate operational problems by using an applicant tracking system (ATS).  Figure 11.5  shows the flow of activities in a typical ATS.

Box 11.2 Recruiting for Diversity

For organizations that wish to increase the diversity of their workforces, the first (and most difficult) step is to determine their needs, goals, and target populations. Once you know what you want your diversity program to accomplish, you can take steps such as the following (Babcock, 2017; Dineen & Soltis, 2011; Kravitz & Klineberg, 2000; Volpone, Thomas, Sinisterra, & Johnson, 2014):

· Emphasize the ready availability of training and career development programs, the presence of a diverse upper management, and the presence of a diverse workforce (Avery, 2003).

· Make initial contacts and gather information from community-support and other external recruitment and training organizations.

· Develop one or more results-oriented programs. What actions will be taken, who will be involved, and how and when will actions be accomplished?

· Invite program representatives to tour your organization, and recognize that they will pay attention to three aspects: the number of minorities at the site, the level of jobs held by minorities, and the types of interactions observed between minority- and majority-group members (McKay & Avery, 2006).

· Select a diversity of organizational contacts and recruiters for outreach and support, including employees outside the HR department.

· Get top-management approval and support. Train managers to value diversity in the workplace.

· Develop procedures for monitoring and follow-up; make revisions as needed to accomplish objectives.

· Think carefully about the messages your organization wishes to transmit concerning its diversity programs; do not leave interpretation to the applicant’s imagination. For example, Cropanzano, Slaughter, and Bachiochi (2005) found that preferential-treatment plans are generally unappealing to prospective minority candidates, who want to ensure that they will be perceived as having been treated fairly and not as receiving preferential treatment.

The overall goal is to create a consistent corporate image that will support recruiting efforts across the board. After all, it’s not just hiring. People want to feel like they belong (Parsi, 2017; Wells, 2017).

Box 11.3 Improving the Completion Rate of Online Job Applications

More than 80% of active candidates now use smartphones to search and apply for jobs, yet as many as 60% of them quit in the middle of filling out online job applications because of their length or complexity. The cost to organizations is the loss of top talent, poor word-of-mouth from candidates frustrated with the process, and higher costs to firms that use cost-per-click recruiting models. What can organizations do? A study by recruitment company Appcast of 500,000 job seekers looking at online applications across diverse platforms and more than 30,000 completed applications revealed that completion rates drop by almost 50% when an application asks 50 or more questions vs. 25 or fewer questions. Other barriers include double logins (one account to log on to a careers site and another to apply through an ATS) and job descriptions that are too brief. Those between 250 and 2,000 words produce conversion rates five times higher than job descriptions of 170—250 words (Zielinski, 2016a).

The process begins with a requisition from a hiring manager to authorize the filling of one or more positions. Once a job posting is created, it is published in a variety of potential hiring channels (e.g., company website, job boards, social media). As candidates apply, they receive acknowledgments, and the documents they submit proceed through rough screening on a pass/fail basis. Hiring managers then interview the most promising candidates and select the one or more who receive the highest ratings.

Figure 11.5 Flow of Work in an Applicant Tracking System

Source: Alliance Investigative. (2016, July 26). Retrieved October 23, 2017, from  www.allianceinvestigative.com/applicant-tracking-system/ .

Through cloud- or computer-based ATS applications, firms have reengineered the entire recruitment process (see Box 11.4). ATS applications have evolved from résumé databases to well-rounded recruitment-optimization tools—cloud-based models that incorporate intuitive interfaces and tools that allow recruiters to cast a wider net for candidates (Maurer, 2017b; Zielinski, 2015, 2017). For example, HiringSolved is using artificial intelligence and machine learning to develop a conversational interface to a robust talent analytics platform—basically “Siri” for recruiting—to leverage massive data analysis and learning capabilities known as RAI (recruiting artificial intelligence) (Maurer, 2017a). Many ATS applications can easily be integrated with “add-on” recruiting technologies from outside vendors, such as video interviewing, prehire assessments, and the sourcing of passive candidates. The last are not actively looking for jobs. To attract them, LinkedIn has a feature called “Open Candidates” that lets users announce to recruiters their openness to changing jobs while keeping that decision hidden from their current employers (Maurer, 2016f).

Box 11.4 Analytics and Artificial Intelligence Help Improve the Recruitment Process

Every year more ATS applications incorporate robust analytics. Companies want to know how long it takes candidates to move through interview stages, how that influences drop-out rates, and where recruiters spend the most time. IBM Watson helps recruiters measure the degree of effort required to fill certain job openings, helps prioritize job requisitions, accurately predicts the likelihood of candidates being successful, and performs social-media “listening” to create insights that help recruiters improve their messages to candidates. Greenhouse’s ATS automatically sends a recruiter a pop-up e-mail form when candidates are rejected, reminding him or her to communicate with that candidate. More broadly, the ability to communicate automatically with candidates as their applications move from one stage to another is a game changer for many companies, helping to improve the talent-acquisition process (Wright, 2017; Zielinski, 2017).

Measurement, Evaluation, and Control

If advance recruitment planning has been thorough, later evaluation of the recruitment effort is simplified considerably. Early hypotheses regarding cost and quality can be measured against actual operating results. Critical trade-offs can be made intelligently on the basis of empirical data, not haphazardly on the basis of hunch or intuition. Any number of cost and quality analyses might be performed, but it is critical to choose those that are strategically most relevant to a given organization (Cascio & Boudreau, 2011b; Feffer, 2017; Maurer, 2016d).

Another consideration is to choose measures of recruiting success that are most relevant to various stages in the recruitment process (see  Figure 11.2 ). An important metric in the first stage, whose objective is to generate viable candidates, is total résumés received. In the second stage, maintaining the status of viable applicants, analysis of postvisit and rejection questionnaires is particularly relevant. In the final stage, postoffer closure, the acceptance-to-offer ratio, combined with an analysis of reasons for acceptance and rejection of job offers, are appropriate metrics. Ultimately, however, the success of recruitment efforts depends on the number of successful placements made.

To assess the quality of hires, it is necessary to calculate metrics both pre- and posthire. Quality measures that focus on recruitment (e.g., time-to-fill, candidate assessment scores) are distinct from those that focus on posthire performance quality (e.g., measures of performance, productivity, and cultural fit) (Maurer, 2015b). Here are some other possible metrics, including those noted above:

· Cost of operations

· Cost per hire

· Cost per hire by source

· Total résumés received

· Résumés by source

· Quality of résumé by source

· Source yield and source efficiency

· Time lapse between recruiting stages by source

· Time lapse between recruiting stages by acceptance versus rejection

· Workflow conversion rates (time spent in each of the workflow steps of an ATS)

· Geographical sources of candidates

· Individual recruiter activity

· Individual recruiter efficiency

· Acceptance-to-offer ratio

· Offer-to-interview ratio

· Interview-to-invitation ratio

· Invitation-to-résumé input ratio

· Biographical data analyses against acceptance/rejection data

· Analysis of postvisit and rejection questionnaires

· Analysis of reasons for acceptance and rejection of job offers

· Analysis of post–reporting date follow-up interviews

· Placement-test scores of hires versus rejections

· Placement-test scores versus observed performance

· Salary offered—acceptances versus rejections

· Salary versus age, year of first degree, and total work experience

Results of these analyses should be presented graphically (e.g., via “dashboards”) for ease of interpretation and communication. Software makes that easy to do. With this information, each recruiter can analyze his or her own performance, and senior managers can track cost and hiring trends. In addition, future needs and strategies can be determined.

Formal procedures for translating recruitment-related differences in sources and costs into monetary payoffs from recruitment and selection activities are also available (Boudreau & Rynes, 1985; Cascio & Boudreau, 2011a; De Corte, 1999; Law & Myors, 1993; Martin & Raju, 1992). Operating executives want to see the financial return on their investments in recruiting, and monetary payoffs can provide this additional perspective.

Job Search From the Applicant’s Perspective

How do individuals identify, investigate, and decide among job opportunities? Research has found that many job applicants (a) have an incomplete and/or inaccurate understanding of what a job opening involves; (b) are not sure what they want from a position; (c) do not have self-insight with regard to their knowledge, skills, and abilities; and (d) cannot accurately predict how they will react to the demands of a new position (Breaugh, 2008, 2012; Breaugh et al., 2008; Rynes & Cable, 2003). At the same time, the Internet has been a game changer in recruitment. Dineen and Allen (2014) noted how it has increased the richness of information, especially early in the process; increased the customization of information; changed from pushing information to job seekers to candidates pulling information; and decentralized the recruitment function in organizations.

Applicants should exploit the vast capabilities of the Internet, since 92% of companies use social media for recruitment, and 45% of Fortune 500 firms include links to social media on their career-page sections (Staff.com, 2013). Beyond that, company websites such as Accenture, Goldman Sachs, Ikea, and L’Oréal offer interactive tools to help candidates assess person–organization fit (Ryan & Delaney, 2017). Online networking sites—such as LinkedIn, Facebook, Google+, and Twitter—have become increasingly important to job seekers. Geared toward professional relationships, networking websites allow their members to build a web of social and business associates and to interact person-to-person with new contacts. As an example, consider SilkRoad Technology’s OpenHire. Once a candidate submits a résumé to a job site using OpenHire, he or she can view potential connections between the organization and his or her existing professional network (Career Profiles, n.d.).

Networking is crucially important (Rosato, 2009; Sundheim, 2014; Wanberg, Kanfer, & Banas, 2000; Yang, 2009), because it’s often casual contacts who point people to their next jobs. Social networking sites such as LinkedIn, Twitter, or Facebook can facilitate virtual networks, but experts caution that a solid network of 50 people is better than 1,000 acquaintances (Rosato, 2009).

How do organizational characteristics influence applicants’ attraction to firms? This is an important question, since many applicants are at least as concerned about picking the right organization as with choosing the right job. Chapman et al.’s (2005) meta-analytic evidence revealed that work environment (ρ = .60) and organizational image (ρ = .48) are both strong predictors of organizational attraction. Employer image is part of the broader multidimensional construct of organizational image (e.g., financial image, image as a responsible corporate performer, and image as a provider of goods and services) (Lievens & Slaughter, 2016). Employer image is an amalgamation of transient mental representations of specific aspects of a company as an employer, as held by individual constituents (Highhouse, Broadfoot, Yugo, & Devendorf, 2009). Lievens and Slaughter’s (2016) review confirmed earlier findings that organizations with a positive image are able to attract more and better applicants. They also emphasized that external employer branding is a synonym for employer image management.

The most feasible way to improve an employer’s recruitment image is to provide more information, not only recruitment information, but also product and service advertisements (Cable, Aiman-Smith, Mulvey, & Edwards, 2000). Being a good community citizen is another strategy to enhance the employment brand. Employers can sponsor community events and provide opportunities for employees to donate their work time in community projects. Finally, be sure to emphasize information about the company’s culture, values, collaboration, and teamwork (Society for Human Resource Management, 2016a). Show how those elements may affect a new employee on a day-to-day basis. What about information customized to job seekers? Dineen and Noe (2009) found that the effects of customization tend to encourage poor-fitting job seekers to self-select out, rather than encouraging well-fitting job seekers to self-select in.

In today’s global markets it is crucial to invest in effective employer branding (Cascio, 2014; Cascio & Graham, 2016; Graham & Cascio, in press). Since employer branding is targeted at current and prospective employees, it is important to understand target employees cross-culturally and to develop branding strategies that fit those differences. Thus, the employer-brand message of global accounting giant EY reflects key features of the work environment—diversity, challenge, variety, flexibility, and teamwork—in all countries and in all cultures where it operates (Society for Human Resource Management Foundation, 2012). Likewise, KFC Asia Pacific operates thousands of restaurants in the Asia-Pacific region, and millennials staff many, if not most, of them. To attract and retain talent, KFC emphasizes three key features of its work environment in its employer-brand message: fun, family, and flexibility. That is, working at a KFC restaurant is fun, crew members at each restaurant treat each other like family, and employees have a lot of flexibility to set a work schedule that fits the constraints of their lifestyles (Society for Human Resource Management Foundation, 2015).

Finally, what about the effects of site visits on candidates? They clearly affect the eventual choices of candidates (Ryan & Delaney, 2017), but Slaughter, Cable, and Turban (2014) provided a more nuanced interpretation. They found that when recruits had little confidence in their initial views, they were much more likely to be affected by the site visit than those who already had strong image perceptions (positive or negative). The latter group’s views were less likely to change.

Realistic Job Previews

A final line of research deserves mention. Given that many employers try to make themselves appear to be a good place to work (Buss, 2017), applicant expectations generally are inflated. If hired, individuals possessing inflated job expectations are thought to be more likely to become dissatisfied with their positions and more likely to quit than applicants who have more accurate expectations. Nearly all (90%) of the 1,817 executives polled in a recent survey by Futurestep said that new-hire retention is an issue for their organizations. More than half said that up to 25% of newly hired workers leave within the first six months (Maurer, 2017d). One way to counter these tendencies is to provide realistic information to job applicants.

Numerous investigations have studied the effect of a realistic job preview (RJP) on withdrawal from the recruitment process, job acceptance, job satisfaction, performance, and turnover. In general, when the naive expectations of job applicants are lowered to match organizational reality, job acceptance rates may be lower but job performance, job satisfaction, and survival are higher for those who receive an RJP (Breaugh, 2008; Hom, 2011; Landis, Earnest, & Allen, 2014; Phillips, 1998; Premack & Wanous, 1985; Wanous, 1977; Weller, Michalik, & Muhlbauer, 2014). Over many studies, meta-analysis shows that RJPs improve retention rates, on average, by 9%, but smaller improvements can be expected in low-complexity jobs than in high-complexity jobs (McEvoy & Cascio, 1985).

Despite their potential advantages, RJPs are not appropriate for all types of jobs. They seem to work best (a) when few applicants are hired (i.e., the selection ratio is low); (b) when used with entry-level positions (because those coming from outside to inside the organization tend to have more inflated expectations than those who make changes internally); and (c) when unemployment is low (because job candidates are more likely to have alternative jobs to choose from) (Breaugh, 1983, 1992; Wanous, 1980, 2017).

Longitudinal research shows that RJPs should be balanced in their orientation. That is, they should be conducted to enhance overly pessimistic expectations and to reduce overly optimistic expectations. Doing so helps to bolster the applicant’s perceptions of the organization as caring, trustworthy, and honest (Meglino, DeNisi, Youngblood, & Williams, 1988). Applicants’ overall evaluations of organizations, however, are influenced more by the averageintensity of their immediate emotional reactions to an RJP, rather than by the relative balance of positive and negative information in the message. Thus, one extremely positive aspect may offset multiple mildly negative aspects (Reeve, Highhouse, & Brooks, 2006).

Intrinsic rather than extrinsic job factors seem most in need of an RJP. Recruiters find it much easier to communicate factual material than to articulate subtle, intrinsic aspects of organizational culture. Yet intrinsic factors are typically more potent contributors to overall job satisfaction than are extrinsic factors (Kacmar & Ferris, 1989). Those responsible for recruitment training and operations would do well to heed these results.

Thus far, we have been discussing RJPs in the context of entry-level hiring, but they can also be quite useful for internal recruitment. For example, a study by Caligiuri and Phillips (2003) described how an employer successfully used an RJP to help current employees make decisions concerning overseas assignments. Similarly, Templer, Tay, and Chandrasekar (2006) documented the effectiveness of an RJP in facilitating cross-cultural adjustment for employees transferred to non-U.S. assignments. RJPs are sound management practices from the employer’s perspective. Box 11.5 presents advice from experts to job candidates about what not to do when trying to find a new job.

For ease of exposition, we have treated recruitment as if it exists separately, but as  Figure 11.1  shows, it is part of a talent supply chain. All components of the recruitment–selection process are interrelated, for they are dynamic, open systems. Keep this in mind as you read subsequent chapters in this book.

Box 11.5 How Not to Find a New Job

Consider the following scenario, which has happened all too frequently in recent decades (as a result of mergers, restructurings, and downsizings) and is expected to occur often in the future as economic conditions change. You are a mid-level executive, well regarded, well paid, and seemingly well established in your chosen field. Then—whammo!—a change in business strategy or a change in economic conditions results in your layoff from the firm you hoped to retire from. What do you do? How do you go about finding another job? According to management consultants and executive recruiters, the following are some of the key things not to do (Boswell, 2017; Nishi, 2010; Shellenbarger, 2010):

· Don’t panic.  A search takes time, even for well-qualified middle- and upper-level managers. Seven months to a year is not unusual. Be prepared to wait it out.

· Don’t be bitter.  Bitterness makes it harder to begin to search; it also turns off potential employers.

· Don’t be ashamed.  Some executives are embarrassed and don’t tell their families or friends what’s going on. A better approach, experts say, is to get the word out that you are looking for work, whether it’s by phone, e-mail, or an online social network.

· Don’t drift.  Develop a plan, target companies, and go after them relentlessly. Realize that your job is to find a new job.

· Don’t kid yourself.  Do a thorough self-appraisal of your strengths and weaknesses, your likes and dislikes about jobs and organizations. To land an in-person meeting, don’t just ask for a job. Offer something in return, like information about the competition. After all, organizations may not have jobs, but they always have problems. If you can help to solve one or more, they are more likely to offer you a job.

· Don’t be lazy.  Remember, the heart of a good job search is research. Use the Internet or personal contacts to develop a list of target companies. If negotiations get serious, talk to a range of insiders and knowledgeable outsiders to learn about politics and practices. You don’t want to wind up in a worse fix than the one you left.

· Don’t be shy or overeager.  Since personal contacts are the most effective means to land a job, you simply must make and maintain them. Find out who will be attending events, show up early, and then mix and mingle. Networking is a gradual process of building trust with people. At the same time, resist the temptation to accept the first job that comes along. Unless it’s absolutely right for you, the chances of making a mistake are quite high.

· Don’t ignore your family.  Some executives don’t tell their families what happened because they’re embarrassed. A better approach is to bring the family into the process and deal with issues honestly.

· Don’t lie.  Experts are unanimous on this point. Don’t lie and don’t stretch a point—either in résumés or in interviews. Be willing to address failures as well as strengths. More important, use the interview to show what you will deliver in the first 30, 60, and 90 days. In today’s economic environment, companies don’t have time for you to grow into a job.

· Don’t jump the gun on salary.  Always let the potential employer bring up this subject first, but, once it surfaces, thoroughly explore all aspects of your future compensation and benefits package. If possible, have a candid conversation earlier with an outside recruiter about your present and desired salary (Ryan, 2016).

Those who have been through the trauma of job loss and the challenge of finding a job often describe the entire process as a wrenching, stressful one. Avoiding the mistakes described here can ensure that finding a new job doesn’t take any longer than necessary.

Evidence-Based Implications for Practice

· Recruitment is not a “one-shot” activity. It is important to recognize three contextual/environmental features that affect all recruitment efforts: (a) characteristics of the firm—the value of its “brand” and its “personality” (make the effort to learn how customers and the public perceive it); (b) characteristics of the vacancy itself (is it mission critical?)—these affect not only the resources expended on the search but also the labor markets from which to recruit; and (c) characteristics of the labor markets in which an organization recruits (tight versus loose).

· Three sequential stages characterize recruitment efforts: generating a pool of viable candidates, maintaining the status (or interest) of viable candidates, and “getting to yes” after making a job offer (postoffer closure). Devote special attention to each one.

· Recognize that the Internet is where the action is in recruiting. Nearly 60% of all Internet hires come from a company’s own website, and the best ones make it simple for candidates to apply for jobs. They provide a wealth of information about the company and leave candidates with a favorable impression.

· Finally, provide a realistic job preview to candidates. Ensure that it enhances overly pessimistic expectations and reduces overly optimistic expectations about the work. Convey realistic information about an organization’s culture.

Chapter 12

12 SELECTION METHODS

Wayne F. Cascio, Herman Aguinis

Learning Goals

By the end of this chapter, you will be able to do the following:

· 12.1 Gather personal history data from job applicants in a manner that minimizes distortions and embellishments

· 12.2 Assess letters of recommendation and reference checks in terms of factors that affect their validity (e.g., degree of writer familiarity with the candidate and job in question)

· 12.3 Choose an appropriate honesty test (e.g., overt vs. personality oriented)

· 12.4 Use valid and reliable measures of past training and experience

· 12.5 Implement drug screening and polygraph testing using appropriate legal guidelines

· 12.6 Design and implement employment interviews taking into account possible response distortion and considering social/interpersonal, cognitive, and individual differences that affect the process and outcomes of interviews

· 12.7 Administer structured employment interviews that maximize validity and reliability

· 12.8 Use caution in relying on social media and other big data and technological advancements (e.g., mobile and Web-based technology, computer scoring of text, remote interviewing, and virtual reality technology) for selection purposes

Personal History Data

Selection and placement decisions often begin with an examination of personal history data (i.e., biodata) typically found in application forms, biographical inventories, and résumés. Undoubtedly one of the most widely used selection procedures is the application form. Like tests, application forms can be used to sample past or present behavior briefly but reliably. Studies of the application forms used by 200 organizations indicated that questions generally focused on information that was job related and necessary for the employment decision (Lowell & DeLoach, 1982; Miller, 1980). However, over 95% of the applications included one or more legally indefensible questions. To avoid potential problems, consider omitting any question that

· Might lead to an adverse impact on members of protected groups,

· Does not appear job related or related to a bona fide occupational qualification, or

· Might constitute an invasion of privacy (Miller, 1980).

What can applicants do when confronted by a question that they believe is irrelevant or an invasion of privacy? Some may choose not to respond. However, research indicates that employers tend to view such a nonresponse as an attempt to conceal facts that would reflect poorly on an applicant. Hence, applicants (especially those who have nothing to hide) are ill advised not to respond (Stone & Stone, 1987).

Psychometric principles can be used to quantify responses or observations, and the resulting numbers can be subjected to reliability and validity analyses in the same manner as scores collected using other types of measures. Statistical analyses of such group data are extremely useful in specifying the personal characteristics indicative of later job success.

Opinions vary regarding exactly what items should be classified as biographical, since such items may vary along a number of dimensions—for example, verifiable–unverifiable; historical–futuristic; actual behavior–hypothetical behavior; firsthand–secondhand; external– internal; specific–general; and invasive–noninvasive (see  Table 12.1 ). This is further complicated by the fact that “contemporary biodata questions are now often indistinguishable from personality items in content, response format, and scoring” (Schmitt & Kunce, 2002, p. 570). Nevertheless, the core attribute of biodata items is that they pertain to historical events that may have shaped a person’s behavior and identity (Mael, 1991).

Some observers have advocated that only historical and verifiable experiences, events, or situations be classified as biographical items. Using this approach, most items on an application form would be considered biographical (e.g., rank in high school graduating class, work history). By contrast, if only historical, verifiable items are included, then questions such as the following would not be asked: “Did you ever build a model airplane that flew?” Cureton (see Henry, 1965, p. 113) commented that this single item, although it cannot easily be verified for an individual, was almost as good a predictor of success in flight training during World War II as the entire Air Force Battery.

Weighted Application Blanks

A priori one might suspect that certain aspects of an individual’s total background (e.g., years of education, previous experience) should be related to later job success in a specific position. The weighted application blank (WAB) technique provides a means of identifying which of these aspects reliably distinguish groups of effective and ineffective employees. Weights are assigned in accordance with the predictive power of each item, so that a total score can be derived for each individual. A cutoff score then can be established, which, if used in selection, will eliminate the maximum number of potentially unsuccessful candidates. Hence, one use of the WAB technique is as a rapid screening device, but it may also be used in combination with other data to improve selection and placement decisions. The technique is appropriate in any organization having a relatively large number of employees doing similar kinds of work and for whom adequate records are available. It is particularly valuable for use with positions requiring long and costly training, with positions where turnover is abnormally high, or in employment situations where large numbers of applicants are seeking a few positions (England, 1971).

Weighting procedures are simple and straightforward (Owens, 1976), but, once weights have been developed in this manner, it is essential that they be cross-validated. Since WAB procedures represent raw empiricism in the extreme, many of the observed differences in weights may reflect not true differences, but only chance fluctuations.

Table 12.1 A Taxonomy of Biographical Items

Historical How old were you when you got your first paying job?	Future or hypothetical What position do you think you will be holding in 10 years? What would you do if another person screamed at you in public?
External Did you ever get fired from a job?	Internal What is your attitude toward friends who smoke marijuana?
Objective How many hours did you study for your real-estate license test?	Subjective Would you describe yourself as shy? How adventurous are you compared to your coworkers?
Firsthand How punctual are you about coming to work?	Secondhand How would your teachers describe your punctuality?
Discrete At what age did you get your driver’s license?	Summative How many hours do you study during an average week?
Verifiable What was your grade point average in college? Were you ever suspended from your Little League team?	Nonverifiable How many servings of fresh vegetables do you eat every day?
Controllable How many tries did it take you to pass the CPA exam?	Noncontrollable How many brothers and sisters do you have?
Equal access Were you ever class president?	Nonequal access Were you captain of the football team?
Job relevant How many units of cereal did you sell during the last calendar year?	Not job relevant Are you proficient at crossword puzzles?
Noninvasive Were you on the tennis team in college?	Invasive How many young children do you have at home?

Source: Republished with permission of John Wiley and Sons Inc., from Mael F. A. (1991). Conceptual rationale for the domain and attributes of biodata items. Personnel Psychology, 44, 773; permission conveyed through Copyright Clearance Center, Inc.

Biographical Information Blanks

The biographical information blank (BIB) technique is closely related to the WAB technique. Like WABs, BIBs involve a self-report instrument; although items are exclusively in a multiple-choice format, typically a larger sample of items is included, and frequently items are included that are not normally covered in a WAB. Glennon, Albright, and Owens (1966) and Mitchell (1994) have published comprehensive catalogs of life history items covering various aspects of the applicant’s past (e.g., early life experiences, hobbies, health, social relations), as well as present values, attitudes, interests, opinions, and preferences. Although primary emphasis is on past behavior as a predictor of future behavior, BIBs frequently rely also on present behavior to predict future behavior. Usually BIBs are developed specifically to predict success in a particular type of work. One of the reasons they are so successful is that often they contain all the elements of consequence to the criterion (Asher, 1972). The mechanics of BIB development and item weighting are essentially the same as those used for WABs (Mumford & Owens, 1987; Mumford & Stokes, 1992).

Résumés

Résumés are a source of personal history data in most employee selection situations. Although résumés are now usually submitted electronically, as far back as 1975, the estimate was that about 1 billion paper résumés were screened each year (Brown & Campion, 1994). When examiners extract personal history data from a résumé, they are particularly prone to cognitive biases and heuristics because information is often limited to one or two pages. Specifically, applicants are likely to be placed into stereotype-based categories in a rather automatic fashion, and then attributes believed to be typical of the group are assigned to individual applicants—even if those beliefs are factually incorrect. Many so-called “paper people” or “vignette” studies (Aguinis & Bradley, 2014) have been conducted in which résumés of hypothetical applicants are presented to judges, who have to provide ratings regarding each applicant’s job suitability (Derous, Ryan, & Serlie, 2015).

Social categorization can take place on more than one category. For example, Derous et al. (2015) conducted an experiment in which 60 Dutch recruiters rated the job suitability of applicants whose résumés included information on ethnicity (Dutch, Arab) and gender (female, male). Results showed that ratings were influenced by applicants’ ethnicity (i.e., Arabs were rated more negatively) and gender (i.e., men were rated more negatively), raters’ prejudice (i.e., those with more negative attitudes toward a particular group rated members of those groups more negatively), and job characteristics (i.e., results were more pronounced when jobs included more client contact).

A recent innovation is the use of video résumés, which are recorded video and audio messages in which job applicants can present themselves to potential employers. Video résumés allow applicants to express themselves in a way that is not possible using the more traditional paper format. It is also possible to create multimedia résumés, in which job applicants also include animations and text (Hiemstra, Derous, Serlie, & Born, 2012). Not much research is yet available on video résumés; however, Hiemstra et al. (2012) conducted a study involving 445 unemployed job seekers who had received a two-day job-application training in the Netherlands and found that they perceived video résumés to be more fair compared to traditional paper résumés regardless of applicant ethnicity (i.e., Dutch, Turkish, Moroccan, Surinamese/Antillean, other non-Westerners, and other Western applicants).

Overall, given the many factors that influence raters’ evaluation of personal history data based on résumé screening, it is important to (a) train raters to make sure they focus on job-related factors and (b) assess interrater reliability (Brown & Campion, 1994). Another important concern is the extent to which applicants may distort the information they provide, hoping to increase their chances of receiving a job offer. We discuss this topic later in the chapter.

Credit History

The big data movement has provided organizations with personal history data that were unthinkable just a few years ago. For example, a survey of members of the Society for Human Resources Management revealed that about 50% of employers conduct credit background checks on at least some applicants (Bernerth, 2012). One type of personal history data, credit scores, seem to be an objective indicator of a job applicant’s conscientiousness and even integrity—two clearly desirable KSAs for many jobs. If an applicant fails to keep a promise to his or her financial institution, this may be an indicator that he or she will similarly fail to keep a promise at work. Also, perhaps individuals who are under financial duress may be more prone to engaging in counterproductive behaviors at work (e.g., theft) (Bernerth, 2012).

Using credit background checks for employment purposes is legally permissible in the United States under the Fair Credit Reporting Act if applicants provide written authorization (Bernerth, 2012). However, some states, including California, Colorado, Connecticut, Delaware, Hawaii, Illinois, Maryland, Nevada, Oregon, Vermont, and Washington, as well as Washington, D.C., have restricted the use of credit histories of applicants and employees. For example, Colorado’s Employment Opportunity Act (SB13-018) prohibits an employer’s use of consumer credit information for employment purposes if the information is unrelated to the job. Moreover, it requires an employer to disclose to an employee or applicant if the employer uses consumer credit information to take adverse action against the employee or applicant and the particular credit information upon which the employer relied. It also authorizes an aggrieved employee to sue for an injunction, damages, or both.

The regulations in these jurisdictions seem justified, given evidence that credit scores are related to several demographic variables that in many cases are unrelated to job performance. For example, Bernerth (2012) collected Fair Isaac Corporation (FICO) scores for 112 university employees and alumni and conducted a regression analysis using credit scores as the criterion and the following demographic variables as predictors: minority status (nonminority, minority); gender (male, female); marital status (never been divorced, divorced); educational attainment (high school degree/GED, some college, 2-year college degree, 4-year college degree, some graduate or professional education, graduate degree); and age. The five predictors combined accounted for 34% of variance in credit scores and the predictors (a) minority status (minority status associated with lower scores), (b) educational attainment (less education associated with lower scores), and (c) age (younger applicants received lower scores) had the strongest effects. Although educational attainment may be a job-related factor for some occupations and positions, the strong relation between ethnicity and credit scores guarantees that the use of this particular type of personal history data will result in adverse impact. In addition to legal issues, the use of credit scores has ethical connotations. Specifically, “critics of credit scores contend that using such information to make hiring decisions unfairly disadvantages individuals with low scores and traps them in a ‘vicious downward spiral’ where unemployment damages personal credit which, in turn, can hurt their job prospects” (Bernerth, 2012, p. 245). As is the case for all types of predictors, validity information is required—and this is particularly important in the presence of adverse impact.

Response Distortion in Personal History Data

Can job applicants intentionally distort personal history data? The answer is yes. For example, the “sweetening” of résumés is not uncommon, and one study reported that 20—25% of all résumés and job applications include at least one major fabrication (LoPresto, Mitcham, & Ripley, 1986). The extent of self-reported distortion was found to be even higher when data were collected using the randomized-response technique, which absolutely guarantees response anonymity and thereby allows for more honest self-reports (Donovan, Dwight, & Hurtz, 2003).

A study in which participants were instructed to “answer questions in such a way as to make you look as good an applicant as possible” and to “answer questions as honestly as possible” resulted in scores almost two standard deviations higher for the “fake good” condition (McFarland & Ryan, 2000). In fact, the difference between the “fake good” and the “honest” experimental conditions was larger for a biodata inventory than for other measures including personality traits such as extraversion, openness to experience, and agreeableness. In addition, individuals differed in the extent to which they were able to fake (as measured by the difference between individuals’ scores in the “fake good” and “honest” conditions). So, if they want to, individuals can distort their responses, but some people are more able than others to do so.

Fortunately, there are situational characteristics that an examiner can influence, which may make it less likely that job applicants will distort personal history information. The first such characteristic is the extent to which information can be verified. More objective and verifiable items are less amenable to distortion (Kluger & Colella, 1993). The concern with being caught seems to be an effective deterrent to faking. Second, option-keyed items are less amenable to distortion (Kluger, Reilly, & Russell, 1991). With this strategy, each item-response option (alternative) is analyzed separately and contributes to the score only if it correlates significantly with the criterion. Third, distortion is less likely if applicants are warned of the presence of a lie scale (Kluger & Colella, 1993) and if biodata are used in a non-evaluative, classification context (Fleishman, 1988). A fourth approach involves asking job applicants to elaborate on their answers. These elaborations require job applicants to describe more fully the manner in which their responses are true or to describe incidents to illustrate and support their answers (Schmitt & Kunce, 2002). For example, for the question “How many work groups have you led in the past 5 years?” the elaboration request can be “Briefly describe the work groups and projects you led” (Schmitt & Kunce, 2002, p. 586). The rationale for this approach is that requiring elaboration forces the applicant to remember more accurately and to minimize managing a favorable impression. The use of the elaboration approach led to a reduction in scores of about .6 standard deviation units in a study including 311 examinees taking a pilot form of a selection instrument for a federal civil service job (Schmitt & Kunce, 2002). Similarly, a study including more than 600 undergraduate students showed that those in the elaboration condition provided responses much lower than those in the non-elaboration condition (Schmitt, Oswald, Kim, Gillespie, & Ramsay, 2003).

Validity of Personal History Data

Properly cross-validated biodata have been developed for many occupations, including life insurance agents; law enforcement officers; service station managers; sales clerks; unskilled, clerical, office, production, and management employees; engineers; architects; research scientists; and Army officers. Criteria include turnover (by far the most common), absenteeism, rate of salary increase, performance ratings, number of publications, success in training, creativity ratings, sales volume, and employee theft.

Evidence indicates that the validity of personal history data as a predictor of future work behavior is quite good. For example, Reilly and Chao (1982) reviewed 58 studies that used biographical information as a predictor. Over all criteria and over all occupations, the average validity was .35. A subsequent meta-analysis of 44 such studies revealed an average validity of .37 (Hunter & Hunter, 1984). A later meta-analysis that included results from eight studies of salespeople’s performance that used supervisory ratings as the criterion found a mean validity coefficient (corrected for criterion unreliability) of .33 (Vinchur, Schippmann, Switzer, & Roth, 1998).

As a specific illustration of the predictive power of these types of data, consider a study that used a concurrent validity design including more than 300 employees in a clerical job. A rationally selected, empirically keyed, and cross-validated biodata inventory accounted for incremental variance in the criteria over that accounted for by measures of personality and general cognitive abilities (Mount, Witt, & Barrick, 2000). Specifically, biodata accounted for about 6% of incremental variance for quantity and quality of work, about 7% for interpersonal relationships, and about 9% for retention. As a result, we now have empirical support for the following statement by Owens (1976) from more than four decades ago:

Personal history data also broaden our understanding of what does and does not contribute to effective job performance. An examination of discriminating item responses can tell a great deal about what kinds of employees remain on a job and what kinds do not, what kinds sell much insurance and what kinds sell little, or what kinds are promoted slowly and what kinds are promoted rapidly. Insights obtained in this fashion may serve anyone from the initial interviewer to the manager who formulates employment policy. (p. 612)

A caution is in order, however. Commonly, biodata keys are developed on samples of job incumbents, and it is assumed that the results generalize to applicants. However, a large-scale field study that used more than 2,200 incumbents and 2,700 applicants found that 20% or fewer of the items that were valid in the incumbent sample were also valid in the applicant sample. Clearly motivation and job experience differ in the two samples. The implication: Match incumbent and applicant samples as closely as possible, and do not assume that predictive and concurrent validities are similar for the derivation and validation of BIB scoring keys (Stokes, Hogan, & Snell, 1993).

Bias and Adverse Impact

Since the passage of Title VII of the 1964 Civil Rights Act, personal history items have come under intense legal scrutiny. While not unfairly discriminatory per se, such items legitimately may be included in the selection process only if it can be shown that (a) they are job related and (b) they do not unfairly discriminate against either minority or nonminority subgroups.

In one study, Cascio (1976b) reported cross-validated validity coefficients of .58 (minorities) and .56 (nonminorities) for female clerical employees against a tenure criterion. When separate expectancy charts were constructed for the two groups, no significant differences in WAB scores for minorities and nonminorities on either predictor or criterion measures were found. Hence, the same scoring key could be used for both groups.

Results from several studies have concluded that biodata inventories are relatively free of adverse impact, particularly when items do not reflect cognitive abilities (Breaugh, 2009). However, a meta-analysis by Bobko and Roth (2013) emphasized that most results are based on concurrent validity designs using incumbent samples, which likely decrease observed ethnicity-based differences. They estimated that the black—white mean standardized difference is d = .31, which was based on biodata that included a large number of KSAs.

Unfortunately, other than the degree of cognitive abilities saturation, when differences exist, we often do not know why. This reinforces the idea of using a rational (as opposed to an entirely empirical) approach to developing biodata inventories, because it has the greatest potential for allowing us to understand the underlying constructs, how they relate to criteria of interest, and how to minimize between-group score differences. As noted by Stokes and Searcy (1999):

With increasing evidence that one does not necessarily sacrifice validity to use more rational procedures in development and scoring biodata forms, and with concerns for legal issues on the rise, the push for rational methods of developing and scoring biodata forms is likely to become more pronounced. (p. 84)

What Do Biodata Mean?

Criterion-related validity is not the only consideration in establishing job relatedness. Items that bear no rational relationship to the job in question (e.g., “applicant does not wear eyeglasses” as a predictor of theft) are unlikely to be acceptable to courts or regulatory agencies, especially if total scores produce adverse impact on a protected group. Nevertheless, external or empirical keying is the most popular scoring procedure and consists of focusing on the prediction of an external criterion using keying procedures at either the item or the item-option level (Stokes & Searcy, 1999). As defined by Mael (1991), “[T]he core attribute of biodata items is that the items pertain to historical events that may have shaped the person’s behavior and identity” (p. 763). Accordingly, as shown in Table 12.1, items measure behavioral intentions, self-descriptions of personality traits, and personal interests, among other constructs. Note, however, that biodata inventories resulting from a purely empirical approach do not help us understand what constructs are measured.

More prudent and reasonable is the rational approach, including job analysis information to deduce hypotheses concerning success on the job under study and to seek from existing, previously researched sources either items or factors that address these hypotheses (Stokes & Cooper, 2001). Essentially, we are asking the following questions: “What do biodata mean?” “Why do past behaviors and performance or life events predict non-identical future behaviors and performance?” (Breaugh, 2009; Dean & Russell, 2005). Thus, in a study of recruiters’ interpretations of biodata items from résumés and application forms, Brown and Campion (1994) found that recruiters deduced language and math abilities from education-related items, physical ability from sports-related items, and leadership and interpersonal attributes from items that reflected previous experience in positions of authority and participation in activities of a social nature. Nearly all items were thought to tell something about a candidate’s motivation. The next step is to identify hypotheses about the relationship of such abilities or attributes to success on the job in question. This rational approach has the advantage of enhancing both the utility of selection procedures and our understanding of how and why they work (cf. Mael & Ashforth, 1995). Moreover, it is probably the only legally defensible approach for the use of personal history data in employment selection.

The rational approach to developing biodata inventories has proven fruitful beyond employment testing contexts. For example, Douthitt, Eby, and Simon (1999) used this approach to develop a biodata inventory to assess people’s degree of receptiveness to dissimilar others (i.e., general openness to dissimilar others). As an illustration, for the item “How extensively have you traveled?” the rationale is that travel provides for direct exposure to dissimilar others and those who have traveled to more distant areas have been exposed to more differences than those who have not. Other items include “How racially (ethnically) integrated was your high school?” and “As a child, how often did your parent(s) (guardian(s)) encourage you to explore new situations or discover new experiences for yourself?” Results of a study including undergraduate students indicated that the rational approach paid off because there was strong preliminary evidence in support of the scale’s reliability and validity. However, even if the rational approach is used, the validity of biodata items can be affected by the life stage in which the item is anchored (Dean & Russell, 2005). In other words, framing an item around a specific, hypothesized developmental time (i.e., childhood versus past few years) is likely to help applicants provide more accurate responses by giving them a specific context to which to relate their response.

Recommendations and Reference Checks

Another source of personal history data is information provided by others in the form of recommendations and reference checks. Many prospective users ask a practical question: “Are recommendations and reference checks worth the amount of time and money it costs to process and consider them?” In general, four kinds of information are obtainable: (1) employment and educational history (including confirmation of degree and class standing or grade point average); (2) evaluation of the applicant’s character, personality, and interpersonal competence; (3) evaluation of the applicant’s job performance ability; and (4) willingness to rehire.

For a recommendation to make a meaningful contribution to the screening and selection process, however, certain preconditions must be satisfied. The recommender must have had an adequate opportunity to observe the applicant in job-relevant situations, he or she must be competent to make such evaluations, he or she must be willing to be open and candid, and the evaluations must be expressed so that the potential employer can interpret them in the manner intended (McCormick & Ilgen, 1985). Although the value of recommendations can be impaired by deficiencies in any one or more of the four preconditions, unwillingness to be candid is probably the most serious. However, to the extent that the truth of any unfavorable information cannot be demonstrated and it harms the reputation of the individual in question, providers of references may be guilty of defamation in their written (libel) or oral (slander) communications (Ryan & Lasek, 1991).

Written recommendations are considered by some to be of little value. For example, consider the opinions based on a survey of about 600 HR professionals with titles such as recruiting manager, employment lawyer, personnel consultant, and human resources specialist (Nicklin & Roch, 2009). About 80% of respondents agreed with the statement that “letter inflation is a problem that will never be entirely alleviated.” To a large extent, this opinion is justified, since the available evidence indicates that the average validity of recommendations is .14 (Reilly & Chao, 1982). A meta-analysis focused exclusively on academic performance found similar results: the average observed correlation with GPA in medical school was .13 (N = 916) and the correlation with clinical and internship performance was .12 (N = 1,120). The average correlation with GPA in college seems higher, r = .28 (N = 5,155) (Kuncel, Kochevar, & Ones, 2014). But, meta-regression analysis (Gonzalez-Mulé & Aguinis, in press) showed that letters of recommendation contributed only .003 additional proportion of variance to the prediction of grade point average in graduate school and only .011 to the prediction of faculty ratings of performance above and beyond undergraduate GPA and verbal and quantitative GRE exam scores. Results were slightly more encouraging regarding the proportion of additional variance explained in the prediction of degree attainment: .024.

One of the biggest problems, and possibly the main reason for their overall lack of value-added predictive power, is that such recommendations rarely include unfavorable information and, therefore, do not discriminate among candidates. In addition, the affective disposition of letter writers has an impact on letter length, which, in turn, has an impact on the favorability of the letter (Judge & Higgins, 1998). In many cases, therefore, the letter may be providing more information about the person who wrote it than about the person described in the letter.

The fact is that decisions are made on the basis of letters of recommendation, particularly in academic settings (Nicklin & Roch, 2009). If such letters are to be meaningful, they should contain the following information (Knouse, 1987):

· Degree of writer familiarity with the candidate: This should include time known and time observed per week.

· Degree of writer familiarity with the job in question: To help the writer make this judgment, the person soliciting the recommendation should supply to the writer a description of the job in question.

· Specific examples of performance: This should cover such aspects as goals, task difficulty, work environment, and extent of cooperation from coworkers.

· Individuals or groups to whom the candidate is compared.

Unfortunately, many employers believe that reference checks are not permissible under the law. This is not true (Hedricks, Robie, & Oswald, 2013). In fact, employers may do the following: seek information about applicants, interpret and use that information during selection, and share the results of reference checking with another employer (Sewell, 1981). In fact, employers may be found guilty of negligent hiring if they should have known at the time of hire about the unfitness of an applicant (e.g., prior job-related convictions, propensity for violence) that subsequently causes harm to an individual (Gregory, 1988; Ryan & Lasek, 1991). In other words, failure to check closely enough could lead to legal liability for an employer.

Reference checking is a valuable screening tool (see Box 12.1). An average validity of .26 was found in a meta-analysis of reference-checking studies (Hunter & Hunter, 1984). To be most useful, however, reference checks should be

· Consistent: If an item is grounds for denial of a job to one person, it should be the same for any other person who applies.

· Relevant: Employers should stick to items of information that really distinguish effective from ineffective employees.

· Written: Employers should keep written records of the information obtained to support the ultimate hiring decision made.

· Based on public records: Such records include court records, workers’ compensation, and bankruptcy proceedings. (Ryan & Lasek, 1991; Sewell, 1981)

Reference checking can also be done via telephone interviews (Taylor, Pajo, Cheung, & Stringfield, 2004). Implementing a procedure labeled structured telephone reference check (STRC), a total of 448 telephone reference checks were conducted on 244 applicants for customer-contact jobs (about two referees per applicant) (Taylor et al., 2004). STRCs took place over an eight-month period; they were conducted by recruiters at one of six recruitment consulting firms, and they lasted on average 13 minutes. Questions focused on measuring three constructs: conscientiousness, agreeableness, and customer focus. Recruiters asked each referee to rate the applicant compared to others they have known in similar positions, using the following scale: 1 = below average, 2 = average, 3 = somewhat above average, 4 = well above average, and 5 = outstanding. Note that the scale used is a relative, versus absolute, rating scale so as to minimize leniency in ratings. As an additional way to minimize leniency, referees were asked to elaborate on their responses. As a result of the selection process, 191 of the 244 applicants were hired, and data were available regarding the performance of 109 of these employees (i.e., those who were still employed at the end of the first performance appraisal cycle). A multiple-regression model predicting supervisory ratings of overall performance based on the three dimensions assessed by the STRC resulted in R2 = .28, but customer focus was the only one of the three dimensions that predicted supervisory ratings (i.e., standardized regression coefficient of .28).

Box 12.1 How to Get Useful Information From a Reference Check

In today’s environment of caution, many supervisors are hesitant to provide information about a former employee, especially over the telephone or via e-mail. To encourage them, consider doing the following:

· Take the supervisor out of the judgmental past and into the role of an evaluator of a candidate’s abilities.

· Remove the perception of potential liability for judging a former subordinate’s performance by asking for advice on how best to manage the person to bring out his or her abilities.

Questions such as the following might be helpful (Falcone, 1995):

· We’re a mortgage banking firm in an intense growth mode. The phones don’t stop ringing, the paperwork is endless, and we’re considering Mary for a position in our customer service unit dealing with our most demanding customers. Is that an environment in which she would excel?

· Some people constantly look for ways to reinvent their jobs and assume responsibilities beyond the basic job description. Others adhere strictly to their job duties and “don’t do windows,” so to speak. Can you tell me where Ed fits on that continuum?

In closing, few organizations are willing to abandon altogether the practice of recommendation and reference checking, despite all the shortcomings. One need only listen to a grateful manager thanking the HR department for the good reference checking that “saved” him or her from making a bad offer to understand why. Also, from a practical standpoint, a key issue to consider is the extent to which the constructs assessed by recommendations and reference checks provide unique information above and beyond other data collection methods that we describe later in this chapter (e.g., employment interview).

Polygraph Tests

Polygraph instruments are intended to detect deception and are based on the measurement of physiological processes (e.g., heart rate) and changes in those processes. An examiner infers whether a person is telling the truth or lying based on charts of physiological measures in response to the questions posed and observations during the polygraph examination. Although they are often used for event-specific investigations (e.g., after a crime), they are also used (on a limited basis) for both employment and preemployment screening.

The use of polygraph tests has been severely restricted by a federal law passed in 1988. This law, the Employee Polygraph Protection Act, prohibits private employers (except firms providing security services and those manufacturing controlled substances) from requiring or requesting preemployment polygraph exams. Polygraph exams of current employees are permitted only under very restricted circumstances. Nevertheless, many agencies (e.g., U.S. Department of Energy) are using polygraph tests, given the security threats imposed by international terrorism.

Although much of the public debate over the polygraph as a lie detector focuses on ethical problems (Aguinis & Handelsman, 1997a, 1997b), at the heart of the controversy is validity—the relatively simple question of whether physiological measures can assess truthfulness and deception (Saxe, Dougherty, & Cross, 1985). An analysis of the scientific evidence on this issue is contained in a report by the National Research Council, which operates under a charter granted by the U.S. Congress. Its Committee to Review the Scientific Evidence on the Polygraph (2003) conducted a quantitative analysis of 57 independent studies investigating the accuracy of the polygraph and concluded the following:

· Polygraph accuracy for screening purposes is almost certainly lower than what can be achieved by specific-incident polygraph tests.

· The physiological indicators measured by the polygraph can be altered by conscious efforts through cognitive or physical means.

· Using the polygraph for security screening yields an unacceptable choice between too many loyal employees falsely judged deceptive and too many major security threats left undetected.

In sum, as concluded by the committee, the polygraph’s “accuracy in distinguishing actual or potential security violators from innocent test takers is insufficient to justify reliance on its use in employee security screening in federal agencies” (p. 6). These conclusions are consistent with the views of scholars in relevant disciplines. Responses to a survey completed by members of the Society for Psychophysiological Research and Fellows of the American Psychological Association’s Division 1 (General Psychology) indicated that the use of polygraph testing is not theoretically sound, claims of high validity for these procedures cannot be sustained, and polygraph tests can be beaten by countermeasures (Iacono & Lykken, 1997).

In spite of the overall conclusion that polygraph testing is not very accurate, potential alternatives to the polygraph, such as measuring brain activity through electrical and imaging studies have not yet been shown to outperform the polygraph (Committee to Review the Scientific Evidence on the Polygraph, 2003). Such alternative techniques do not show any promise of supplanting the polygraph for screening purposes in the near future. Thus, although imperfect, it is likely that the polygraph will continue to be used for employee security screening until other alternatives become available.

Honesty Tests

Honesty testing is a multimillion-dollar industry, especially since the use of polygraphs in employment settings has been severely curtailed and “ban-the-box” laws in some states restrict employers from asking candidates about prior criminal convictions until later in the hiring process. Written honesty tests (also known as integrity tests) fall into two major categories: overt integrity tests and personality-based measures. Overt integrity tests typically include two types of questions. One assesses attitudes toward theft and other forms of dishonesty (e.g., endorsement of common rationalizations of theft and other forms of dishonesty, beliefs about the frequency and extent of employee theft, punitiveness toward theft, perceived ease of theft). The other deals with admissions of theft and other illegal activities (e.g., dollar amount stolen in the last year, drug use, gambling). Personality-based measures are not designed as measures of honesty per se, but rather as predictors of a wide variety of counterproductive behaviors, such as substance abuse, insubordination, absenteeism, bogus workers’ compensation claims, and various forms of passive aggression. Overall, personality-based measures assess broader dispositional traits such as socialization and conscientiousness. (Conscientiousness is one of the Big Five personality traits; we discuss these in more detail in  Chapter 13 .) In fact, despite the clear differences in content, both overt and personality-based tests seem to have a common latent structure reflecting conscientiousness, agreeableness, and emotional stability (Berry, Sackett, & Wiemann, 2007).

Do honesty tests work? Overall, the answer is yes, as several reviews have documented (Ones, Viswesvaran, & Schmidt, 1993; Van Iddekinge, Roth, Raymark, & Odle-Dusseau, 2012a). However, the precise extent to which such tests predict performance—and what specific facets of performance they predict—is less clear. Ones et al. (1993) conducted a meta-analysis of 665 validity coefficients that were based on 576,460 test takers. The average validity of the tests, when used to predict supervisory ratings of performance, was .41. Results for overt and personality-based tests were similar. However, the average validity of overt tests for predicting theft per se was much lower: .13. Van Iddekinge et al. (2012a) conducted a subsequent meta-analysis that relied on fewer studies (i.e., 104 studies representing 134 independent samples) because of “concerns centered around the perceived lack of methodological rigor within this literature and a heavy reliance on unpublished data from firms that publish the integrity tests (e.g., 90% of the studies in Ones et al.’s 1993 meta-analysis)” (Van Iddekinge, Roth, Raymark, & Odle-Dusseau, 2012b, p. 543). Van Iddekinge et al.’s (2012a) updated meta-analytic results revealed the following mean observed and corrected (for unreliability in the criterion) validity coefficients: .12 and .15 for job performance, .13 and .16 for training performance, .26 and .32 for counterproductive work behaviors, and .07 and .09 for turnover.

The Van Iddekinge et al. (2012a) results were controversial and led to a forceful reaction on the part of test vendors (Harris et al., 2012), who concluded, “In light of Van Iddekinge et al.’s substantially smaller sample of studies, and arguable methodological decisions, we are inclined to accord more weight to Ones et al.’s findings when there is a difference in conclusion” (p. 535). In their defense, regarding the number of studies included in their meta-analyses, Van Iddekinge et al. (2012b) wrote, “After several months of correspondence … we were informed that it was no longer possible to provide us access to the additional studies. Moreover, Jones informed us that Vangent’s corporate attorneys wanted us to know that we did not have permission to use several technical reports we had obtained from another researcher because the reports had not been released into the public domain (yet apparently were provided to Ones et al., 1993, and other researchers).” Clearly, this not the end of the discussion regarding the relative validity of honesty tests. At this point, we do not know what factors caused the different results reported by Ones et al. (1993) compared to Van Iddekinge et al. (2012a), but it seems that different study-inclusion criteria, corrections for artifacts, and second-order sampling error are not the culprits and more details on meta-analytic procedures, including coding, are necessary to address this issue (Sackett & Schmitt, 2012).

Although honesty tests are overall good predictors of certain performance facets, at least four key issues have yet to be resolved. First, as in the case of biodata inventories, there is a need for a greater understanding of the construct validity of integrity tests given that integrity tests are not interchangeable (i.e., scores for the same individuals on different types of integrity tests are not necessarily similar). Some investigations have sought evidence regarding the relationship between integrity tests and some broad personality traits. But there is a need to understand the relationship between integrity tests and individual characteristics more directly related to integrity tests such as object beliefs, negative life themes, and power motives (Mumford, Connelly, Helton, Strange, & Osburn, 2001). Second, women tend to score approximately .16 standard deviation unit higher than men, and job applicants aged 40 years and older tend to score .08 standard deviation unit higher than applicants younger than 40 (Ones & Viswesvaran, 1998). At this point, we do not have a clear reason for these findings. Third, many writers in the field apply the same language and logic to integrity testing as to ability testing. Yet there is an important difference: While it is possible for an individual with poor moral behavior to “go straight,” it is certainly less likely that an individual who has demonstrated a lack of intelligence will “go smart.” If they are honest about their past, therefore, reformed individuals with a criminal past may be “locked into” low scores on integrity tests (and, therefore, be subject to classification error) (Lilienfeld, Alliger, & Mitchell, 1995). Thus, the broad validation evidence that is often acceptable for cognitive ability tests may not hold up in the public policy domain for integrity tests. Fourth, there is the real threat of intentional distortion (Alliger, Lilienfeld, & Mitchell, 1996). It is quite ironic that job applicants are likely to be dishonest in completing an honesty test. For example, as mentioned earlier, McFarland and Ryan (2000) found that, when study participants who were to complete an honesty test were instructed to “answer questions in such a way as to make you look as good an applicant as possible,” scores were 1.78 standard deviation units higher than when they were instructed to “answer questions as honestly as possible.” Finally, test publishers have an undeniable conflict of interest regarding research addressing the validity of their own tests, much like we described in  Chapter 8  regarding the assessment of test fairness (i.e., differential prediction). At the same time, they have legitimate concerns that “[i]t would be helpful to publishers, as well as the field, if there were mechanisms to protect the interest of testing clients in the same manner as human subjects, and more opportunities to publish or distribute the many strong validity studies in publishers’ files” (Harris et al., 2012, p. 532).

Given the challenges and unresolved issues, researchers are exploring alternative ways to assess integrity and other personality-based constructs (e.g., Van Iddekinge, Raymark, & Roth, 2005). One promising approach is conditional reasoning testing (Frost, Chia-Huei, & James, 2007; James et al., 2005), which focuses on how people solve what appear to be traditional inductive-reasoning problems. However, the true intent of the scenarios presented is to determine respondents’ solutions based on their implicit biases and preferences. These underlying biases usually operate below the surface of consciousness and are revealed based on the respondents’ responses. Another promising approach is to assess integrity as part of a situational judgment test (discussed in detail in  Chapter 13 ), in which applicants are given a scenario and are asked to choose a response that is most closely aligned with what they would do (Becker, 2005). Consider the following example of an item developed by Becker (2005):

Your work team is in a meeting discussing how to sell a new product. Everyone seems to agree that the product should be offered to customers within the month. Your boss is all for this, and you know he does not like public disagreements. However, you have concerns because a recent report from the research department points to several potential safety problems with the product. Which of the following do you think you would most likely do?

Possible answers:

· A. Try to understand why everyone else wants to offer the product to customers this month. Maybe your concerns are misplaced. [–1]

· B. Voice your concerns with the product and explain why you believe the safety issues need to be addressed. [1]

· C. Go along with what others want to do so that everyone feels good about the team. [–1]

· D. Afterwards, talk with several other members of the team to see if they share your concerns. [0]

The scoring for the above item is –1 for answers A and C (i.e., worst-possible score), 0 for answer D (i.e., neutral score), and +1 for item B (i.e., best-possible score). One advantage of using scenario-based integrity tests is that they are intended to capture specific values rather than general integrity-related traits. Thus, these types of tests may be more defensible both scientifically and legally because they are based on a more precise definition of integrity, including specific types of behaviors. A study based on samples of fast-service employees (n = 81), production workers (n = 124), and engineers (n = 56) found that validity coefficients for the integrity test (corrected for criterion unreliability) were .26 for career potential, .18 for leadership, and .24 for in-role performance (all as assessed by managers’ ratings) (Becker, 2005).

Evaluation of Training and Experience

Judgmental evaluations of the previous work experience and training of job applicants, as presented on résumés and job applications, is a common part of initial screening. Sometimes evaluation is purely subjective and informal, and sometimes it is accomplished in a formal manner according to a standardized method. Evaluating job experience is not as easy as one may think because experience includes both qualitative and quantitative components that interact and accrue over time (Aguinis, O’Boyle, Gonzalez-Mulé, & Joo, 2016); hence, work experience is multidimensional and temporally dynamic (Tesluk & Jacobs, 1998). However, using experience as a predictor of future performance can pay off. Specifically, a study including more than 800 U.S. Air Force enlisted personnel indicated that ability and experience seem to have linear and noninteractive effects (Lance & Bennett, 2000). Another study that also used military personnel showed that work experience items predict performance above and beyond cognitive abilities and personality (Jerry & Borman, 2002). These findings explain why the results of a survey of more than 200 staffing professionals of the National Association of Colleges and Employers revealed that experienced hires were evaluated more highly than new graduates on most characteristics (Rynes, Orlitzky, & Bretz, 1997).

An empirical comparison of four methods for evaluating work experience indicated that the “behavioral consistency” method showed the highest mean validity, at .45 (McDaniel, Schmidt, & Hunter, 1988). This method requires applicants to describe their major achievements in several job-related areas. These areas are behavioral dimensions rated by supervisors as showing maximal differences between superior and minimally acceptable performers. The applicants’ achievement statements are then evaluated using anchored rating scales. The anchors are achievement descriptors whose values along a behavioral dimension have been determined reliably by subject matter experts.

A similar approach to the evaluation of training and experience, one most appropriate for selecting professionals, is the accomplishment record (AR) method (Hough, 1984). A comment frequently heard from professionals is “My record speaks for itself.” The AR is an objective method for evaluating those records. It is a type of biodata/maximum performance/self-report instrument that appears to tap a component of an individual’s history that is not measured by typical biographical inventories. It correlates essentially zero with aptitude test scores, honors, grades, and prior activities and interests.

Development of the AR begins with the collection of critical incidents to identify important dimensions of job performance. Then rating principles and scales are developed for rating an individual’s set of job-relevant achievements. The method yields (a) complete definitions of the important dimensions of the job, (b) summary principles that highlight key characteristics to look for when determining the level of achievement demonstrated by an accomplishment, (c) examples of accomplishments that job experts agree represent various levels of achievement, and (d) numerical equivalents that allow the accomplishments to be translated into quantitative indexes of achievement. When the AR was applied in a sample of 329 attorneys, the reliability of the overall performance ratings was a respectable .82, and the AR demonstrated a validity of .25. Moreover, the method appears to be fair for females, minorities, and white males.

What about academic qualifications? They tend not to affect managers’ hiring recommendations, as compared to work experience, and they could even have a negative effect. For candidates with poor work experience, having higher academic qualifications seems to reduce their chances of being hired (Singer & Bruhns, 1991). These findings were supported by a national survey of 3,000 employers by the U.S. Census Bureau. The most important characteristics employers said they considered in hiring were attitude, communication skills, and previous work experience. The least important were academic performance (grades), school reputation, and teacher recommendations (Applebome, 1995). Moreover, when grades are used, they tend to have adverse impact on ethnic minority applicants (Roth & Bobko, 2000).

Drug Screening

Drug screening tests began in the military, spread to the sports world, and now are becoming common in employment (Aguinis & Henle, 2005). In fact, about 50% of employers use some type of drug screening for all of their job applicants in the United States (Lieberman, 2017). Critics charge that such screening violates an individual’s right to privacy and that the tests are frequently inaccurate (Morgan, 1989), for example, as a result of cheating (see Box 12.2). These critics do concede, however, that employees in jobs where public safety is crucial—such as nuclear power plant operators and commercial jet pilots—should be screened for drug use. In fact, perceptions of the extent to which different jobs might involve danger to the worker, to coworkers, or to the public are strongly related to the acceptability of drug testing (Murphy, Thornton, & Prue, 1991).

Do the results of such tests forecast certain aspects of later job performance? In perhaps the largest reported study of its kind, the U.S. Postal Service took urine samples from 5,465 job applicants. It never used the results to make hiring decisions and did not tell local managers of the findings. When the data were examined six months to a year later, workers who had tested positively prior to employment were absent 41% more often and were fired 38% more often. There were no differences in voluntary turnover between those who tested positively and those who did not. These results held up even after adjustment for factors such as age, gender, and race. As a result, the Postal Service implemented preemployment drug testing nationwide (Wessel, 1989).

Is such drug screening legal? In two rulings in 1989, the Supreme Court upheld (a) the constitutionality of the government regulations that require railroad crews involved in accidents to submit to prompt urinalysis and blood tests and (b) urine tests for U.S. Customs Service (now U.S. Customs and Border Protection) employees seeking drug-enforcement posts. Overall, an employer has a legal right to ensure that employees perform their jobs competently and that no employee endangers the safety of other workers. So, if illegal drug use, on or off the job, may reduce job performance and endanger coworkers, the employer has adequate legal grounds for conducting drug tests.

To avoid legal challenge, consider instituting the following procedures:

· Inform all employees and job applicants, in writing, of the company’s policy regarding drug use.

· Include the drug policy and the possibility of testing in all employment contracts.

· Present the program in a medical and safety context—namely, that drug screening will help to improve the health of employees and also help to ensure a safer workplace.

If drug screening will be used with employees as well as job applicants, tell employees in advance that drug testing will be a routine part of their employment (Angarola, 1985).

To enhance perceptions of fairness, employers should provide advance notice of drug tests, preserve the right to appeal, emphasize that drug testing is a means to enhance workplace safety, attempt to minimize invasiveness, and train supervisors (Konovsky & Cropanzano, 1991; Tepper, 1994). In addition, employers must understand that perceptions of drug testing fairness are affected not only by the program’s characteristics but also by employee characteristics. For example, employees who have friends who have failed a drug test are less likely to have positive views of drug testing (Aguinis & Henle, 2005).

Box 12.2 Practical Application: Cheating on Drug Tests

Employers are increasingly concerned about job applicants and employees cheating on drug tests. The Internet is now a repository of products people can purchase at reasonable prices with the specific goal of cheating on drug tests. Consider the Whizzinator, an easy-to-conceal and easy-to-use urinating device for men that includes synthetic urine and an adjustable belt. The price? Just under $150.

Hundreds of similar products, particularly targeting urine tests, are offered on the Internet. Leo Kadehjian, a Palo Alto–based consultant, noted that “by far the most preferred resource is dilution” (Cadrain, 2003, p. 42). However, a very large number of highly sophisticated products are offered, including the following (Cadrain, 2003):

· Oxidizing agents that alter or destroy drugs and/or their metabolites

· Nonoxidizing adulterants that change the pH of a urine sample or the ionic strength of the sample

· Surfactants, or soaps, which, when added directly to a urine sample, can form microscopic droplets with fatty interiors that trap fatty marijuana metabolites

Computer-Based Screening

The rapid development of computer technology over the past few years has resulted in faster microprocessors and more flexible and powerful software that can incorporate graphics and sound. These technological advances now allow organizations to conduct computer-based screening (CBS). Using the Internet, companies can conduct CBS and administer job-application forms, structured interviews (discussed later in this chapter), and other types of tests globally, 24 hours a day, 7 days a week (Jones & Dages, 2003).

CBS can be used simply to convert a screening tool from paper to an electronic format that is called an electronic page turner. These types of CBS are low on interactivity and do not take full advantage of technology (Olson-Buchanan, 2002). By contrast, Nike uses interactive voice-response technology to screen applicants over the telephone, the U.S. Air Force uses computer-adaptive testing (CAT) on a regular basis (Ree & Carretta, 1998), and other organizations such as Home Depot and JCPenney use a variety of technologies for screening (Chapman & Webster, 2003; Overton, Harms, Taylor, & Zickar, 1997). CAT presents all applicants with a set of items of average difficulty and, if responses are correct, items with higher levels of difficulty. If responses are incorrect, items with lower levels of difficulty are presented. CAT uses item response theory (see  Chapter 6 ) to estimate an applicant’s level of the underlying trait based on the relative difficulty of the items answered correctly and incorrectly. The potential value added by using computers as screening devices is obvious when one considers that implementation of CAT would be nearly impossible using traditional paper-and-pencil instruments (Olson-Buchanan, 2002).

There are several potential advantages of using CBS (Kantrowitz et al., 2011; Olson-Buchanan, 2002). First, administration may be easier. For example, standardization is maximized because there are no human proctors who may give different instructions to different applicants (i.e., computers give instructions consistently to all applicants). Also, responses are recorded and stored automatically, which is a practical advantage, but can also help minimize data-entry errors. Second, applicants can access the test from remote locations, thereby increasing the applicant pool. Third, computers can accommodate applicants with disabilities in a number of ways, particularly since tests can be completed from their own (possibly modified) computers. A modified computer can caption audio-based items for applicants with hearing disabilities, or it can allow applicants with limited hand movement to complete a test. Finally, preliminary evidence suggests that Web-based assessment does not exacerbate adverse impact.

Despite the increasing availability and potential benefits of CBS, concerns about implementation include cost and potential cheating. Moreover, some testing experts believe that high-stakes tests, such as those used to make employment decisions, cannot be administered in unproctored Internet settings (Tippins et al., 2006). CAT is able to address some of these concerns because, in contrast to static testing (i.e., all applicants receive the same items), with CAT each applicant is administered a test including potentially different items, which addresses the cheating concern. Additional challenges in implementing CBS include the relative lack of access of low-income individuals to the Internet, or what is called the digital divide (Stanton & Rogelberg, 2001).

Olson-Buchanan (2002) concluded that innovations in CBS have not kept pace with the progress in computer technology. This disparity was attributed to three major factors: (1) costs associated with CBS development, (2) lag in scientific guidance for addressing reliability and validity issues raised by CBS, and (3) the concern that investment in CBS may not result in tangible payoffs.

Fortunately, many of the concerns are being addressed by ongoing research on the use, accuracy, equivalence, and efficiency of CBS. For example, Ployhart, Weekley, Holtz, and Kemp (2003) found that proctored, Web-based testing has several benefits compared to the more traditional paper-and-pencil administration. Their study included nearly 5,000 applicants for telephone-service-representative positions who completed, among other measures, a biodata instrument. Results indicated that scores resulting from the Web-based administration had similar or better psychometric characteristics, including distributional properties, lower means, more variance, and higher internal-consistency reliabilities. Another study examined reactions to CAT and found that applicants’ reactions are positively related to their perceived performance on the test (Tonidandel, Quiñones, & Adams, 2002). Thus, changes in the item-selection algorithm that result in a larger number of items answered correctly have the potential to improve applicants’ perceptions of CAT.

In sum, HR specialists now have the opportunity to implement CBS in their organizations. If implemented well, CBS carries numerous advantages. In fact, the use of computers and the Internet is making testing cheaper and faster, and it may serve as a catalyst for even more widespread use of tests for employment purposes (Tippins et al., 2006). However, the degree of success in implementing CBS will depend not only on the features of the test itself  but also on organizational-level variables such as the culture and climate for technological innovation (Anderson, 2003).

Employment Interviews

Use of the interview in selection is almost universal today (Moscoso, 2000). Perhaps this is so because in the employment context the interview serves as much more than just a selection device. The interview is a communication process, whereby the applicant learns more about the job and the organization and begins to develop some realistic expectations about both.

When an applicant is accepted, terms of employment typically are negotiated during an interview. If the applicant is rejected, the interviewer performs an important public relations function, for it is essential that the rejected applicant leave with a favorable impression of the organization and its employees. For example, several studies found that perceptions of the interview process and the interpersonal skills of the interviewer, as well as his or her skills in listening, recruiting, and conveying information about the company and the job the applicant would hold, affected the applicant’s evaluations of the interviewer and the company (Kohn & Dipboye, 1998; Schmitt & Coyle, 1979). However, the likelihood of accepting a job, should one be offered, was still mostly unaffected by the interviewer’s behavior (Powell, 1991).

As a selection device, the interview performs two vital functions: It can fill information gaps in other selection devices (e.g., regarding incomplete or questionable application blank responses; Tucker & Rowe, 1977), and it can be used to assess factors that can be measured only via face-to-face interaction (e.g., appearance, speech, poise, and interpersonal competence). Is the applicant likely to “fit in” and share values with other organizational members (Cable & Judge, 1997)? Is the applicant likely to get along with others in the organization or be a source of conflict? Where can his or her talents be used most effectively? Interview impressions and perceptions can help to answer these kinds of questions. In fact, well-designed interviews can be helpful because they allow examiners to gather information on constructs not typically assessed via other means such as empathy (Cliffordson, 2002) and personal initiative (Fay & Frese, 2001). For example, a review of 388 characteristics rated in 47 actual interview studies revealed that personality traits (e.g., responsibility, dependability, and persistence, which are all related to conscientiousness) and applied social skills (e.g., interpersonal relations, social skills, team focus, ability to work with people) are rated more often in employment interviews than any other type of construct (Huffcutt, Conway, Roth, & Stone, 2001). In addition, interviews can contribute to the prediction of job performance over and above cognitive abilities and conscientiousness (Cortina, Goldstein, Payne, Davison, & Gilliland, 2000), as well as experience (Day & Carroll, 2002).

Since few employers are willing to hire applicants they have never seen, it is imperative that we do all we can to make the interview as effective a selection technique as possible. Next, we consider some of the research on interviewing and offer suggestions for improving the process and outcome.

Response Distortion in the Interview

Distortion of interview information is probable (Weiss & Dawis, 1960), the general tendency being to upgrade rather than downgrade prior work experience. That is, interviewees tend to be affected by social desirability bias, which is a tendency to answer questions in a more socially desirable direction (i.e., to attempt to look good in the eyes of the interviewer). In addition to distorting information, applicants tend to engage in influence tactics to create a positive impression, and they typically do so by displaying self-promotion behaviors (Stevens & Kristof, 1995).

But will social desirability distortion be reduced if the interviewer is a computer? According to Martin and Nagao (1989), candidates tend to report their grade point averages and scholastic aptitude test scores more accurately to computers than in face-to-face interviews. Perhaps this is due to the “big brother” effect. That is, because responses are on a computer rather than on paper, they may seem more subject to instant checking and verification through other computer databases. To avoid potential embarrassment, applicants may be more likely to provide truthful responses. However, Martin and Nagao’s study also placed an important boundary condition on computer interviews: There was much greater resentment by individuals competing for high-status positions than for low-status positions when they had to respond to a computer rather than a live interviewer.

A more comprehensive study was conducted by Richman, Kiesler, Weisband, and Drasgow (1999). They conducted a meta-analysis synthesizing 61 studies (673 effect sizes), comparing response distortion in computer questionnaires with traditional paper-and-pencil questionnaires and face-to-face interviews. Results revealed that computer-based interviews decreased social-desirability distortion compared to face-to-face interviews, particularly when the interviews addressed highly sensitive personal behavior (e.g., use of illegal drugs). Perhaps this is so because a computer-based interview is more impersonal than the observation of an interviewer and from social cues that can arouse an interviewee’s evaluation apprehension.

A more subtle way to distort the interview is to engage in impression-management behaviors (Lievens & Peeters, 2008; Roulin, Bangerter, & Levashina, 2015). For example, applicants who are pleasant and compliment the interviewer are more likely to receive more positive evaluations. Two specific types of impression management, ingratiation and self-promotion, seem to be most effective in influencing interviewers’ rating favorably (Higgins & Judge, 2004). A research program involving five different experiments using real-time video coding showed that interviewers are not able to detect impression management—although they were better at detecting honest impression management (i.e., truthfully describing actual job-related abilities, accomplishments, and experiences) than deceptive impression management (i.e., embellishing job-related credentials or creating credentials that fit with the job requirements) (Roulin et al., 2015). Moreover, experienced interviewers were no better than novices. Training can help improve this situation. Specifically, such training should emphasize that deception detection improves when the interviewer focuses on story-related cues (e.g., vagueness, contradictions) instead of nonverbal cues (e.g., gaze aversions, posture change, fidgeting) (Roulin et al., 2015).

Reliability and Validity

An early meta-analysis of only 10 validity coefficients that were not corrected for range restriction yielded a validity of .14 when the interview was used to predict supervisory ratings (Hunter & Hunter, 1984). Subsequent meta-analyses that did correct for range restriction and used larger samples of studies reported more encouraging results. Wiersner and Cronshaw (1988) found a mean corrected validity of .47 across 150 interview validity studies involving all types of criteria. McDaniel, Whetzel, Schmidt, and Maurer (1994) analyzed 245 coefficients derived from 86,311 individuals and found a mean corrected validity of .37 for job performance criteria. However, validities were higher when criteria were collected for research purposes (.47) than for administrative decision making (.36). Marchese and Muchinsky (1993) reported a mean corrected validity of .38 across 31 studies. A fourth review (Huffcutt & Arthur, 1994) analyzed 114 interview validity coefficients from 84 published and unpublished references, exclusively involving entry-level jobs and supervisory rating criteria. When corrected for criterion unreliability and range restriction, the mean validity across all 114 studies was .37. Finally, Schmidt and Rader (1999) meta-analyzed 40 studies of structured telephone interviews and obtained a corrected validity coefficient of .40 using performance ratings as a criterion. The results of these studies agree quite closely.

Regarding reliability, a meta-analysis of 125 interrater reliability coefficients with a total sample size of 32,428 derived from employment interviews revealed an overall mean of .68 (Huffcutt, Culbertson, & Weyhrauch, 2013). However, the 80% credibility interval, meaning that 80% of the true population coefficients fall within this interval, ranged from .42 to .94. In fact, an analysis of the level of structure of the interviews revealed that reliability was lowest when structure was low (i.e., .36), and highest when structure was high (.76). Given our discussion in Chapter 7 regarding the relation between reliability and validity, the best way to improve validity is to improve the degree of structure of the interview (discussed later in this chapter).

As Hakel (1989) noted, interviewing is a difficult cognitive and social task. Managing a smooth social exchange while simultaneously processing information about an applicant makes interviewing uniquely difficult among all managerial tasks. Research continues to focus on cognitive factors (e.g., preinterview impressions) and social factors (e.g., interviewer–interviewee similarity). As a result, we now know a great deal more about what goes on in the interview and about how to improve the process. At the very least, we should expect interviewers to be able to form opinions only about traits and characteristics that are overtly manifest in the interview (or that can be inferred from the applicant’s behavior), and not about traits and characteristics that typically would become manifest only over a period of time—traits such as creativity, dependability, and honesty. In the following subsections, we examine what is known about the interview process and about ways to enhance the effectiveness and utility of the selection interview.

Factors Affecting the Decision-Making Process

A large body of literature attests to the fact that the decision-making process involved in the interview is affected by several factors. Specifically, 278 studies have examined numerous aspects of the interview (Posthuma, Morgeson, & Campion, 2002). Posthuma et al. (2002) provided a useful framework to summarize and describe this large body of research. We follow this taxonomy in part and consider factors affecting the interview decision-making process in each of the following areas: (a) social/interpersonal factors (e.g., interviewer–applicant similarity), (b) cognitive factors (e.g., preinterview impressions), (c) individual differences (e.g., applicant appearance, interviewer training and experience), and (d) structure (i.e., degree of standardization of the interview process and discretion an interviewer is allowed in conducting the interview).

Social/Interpersonal Factors

As noted earlier, the interview is fundamentally a social and interpersonal process. As such, it is subject to influences such as interviewer–applicant similarity and verbal and nonverbal cues. We describe each of these factors next.

Interviewer–Applicant Similarity.

Similarity leads to attraction, attraction leads to positive affect, and positive affect can lead to higher interview ratings (Schmitt, Pulakos, Nason, & Whitney, 1996). Moreover, similarity leads to greater expectations about future performance (García, Posthuma, & Colella, 2008). Does similarity between the interviewer and the interviewee regarding race, age, and attitudes affect the interview? Lin, Dobbins, and Farh (1992) reported that ratings of African American and Latino interviewees, but not white interviewees, were higher when the interviewer was the same race as the applicant. However, Lin et al. (1992) found that the inclusion of at least one different-race interviewer in a panel eliminated the effect, and no effect was found for age similarity. Further, when an interviewer feels that an interviewee shares his or her attitudes, ratings of competence and affect are increased (Howard & Ferris, 1996). The similarity effects are not large, however, and they can be reduced or eliminated by using a structured interview and a diverse set of interviewers.

Verbal and Nonverbal Cues.

In terms of verbal cues, Anderson (1960) found that the applicant was more likely to be hired in interviews where the interviewer did a lot more of the talking and there was less silence. Other research has shown that the length of the interview depends much more on the quality of the applicant (interviewers take more time to decide when dealing with a high-quality applicant) and on the expected length of the interview. The longer the expected length of the interview, the longer it takes to reach a decision (Tullar, Mullins, & Caldwell, 1979).

Several studies have also examined the impact of nonverbal cues on impression formation and decision making in the interview. Nonverbal cues have been shown to have an impact, albeit small, on interviewer judgments (DeGroot & Motowidlo, 1999). For example, Imada and Hakel (1977) found that positive nonverbal cues (e.g., smiling, attentive posture, smaller interpersonal distance) produced consistently favorable ratings. Most important, however, nonverbal behaviors interact with other variables such as gender. Aguinis, Simonsen, and Pierce (1998) found that a man displaying direct eye contact during an interview is rated as more credible than another one not making direct eye contact. However, a follow-up replication using exactly the same experimental conditions revealed that a woman displaying identical direct eye contact behavior was seen as coercive (Aguinis & Henle, 2001a).

Overall, the ability of a candidate to respond concisely, to answer questions fully, to state personal opinions when relevant, and to keep to the subject at hand appears to be more crucial in obtaining a favorable employment decision (Parsons & Liden, 1984; Rasmussen, 1984). High levels of nonverbal behavior tend to have more positive effects than low levels only when the verbal content of the interview is good. When verbal content is poor, high levels of nonverbal behavior may result in lower ratings.

Cognitive Factors

The interviewer’s task is not easy because humans are limited information processors and have biases in evaluating others (Kraiger & Aguinis, 2001). However, we have a good understanding of the impact of factors such as preinterview impressions and confirmatory bias, first impressions, stereotypes, contrast effect, and information recall. Let’s review major findings regarding the way in which each of these factors affects the interview.

Preinterview Impressions and Confirmatory Bias.

Dipboye (1982, 1992) specified a model of self-fulfilling prophecy to explain the impact of first preinterview impressions. Both cognitive and behavioral biases mediate the effects of preinterview impressions (based on letters of reference or applications) on the evaluations of applicants. Behavioral biases occur when interviewers behave in ways that confirm their preinterview impressions of applicants (e.g., showing positive or negative regard for applicants). Cognitive biases occur if interviewers distort information to support preinterview impressions or use selective attention and recall of information. This sequence of behavioral and cognitive biases produces a self-fulfilling prophecy.

Consider how one applicant was described by an interviewer given positive information:

Alert, enthusiastic, responsible, well-educated, intelligent, can express himself well, organized, well-rounded, can converse well, hard worker, reliable, fairly experienced, and generally capable of handling himself well.

On the basis of negative preinterview information, the same applicant was described as follows:

Nervous, quick to object to the interviewer’s assumptions, and doesn’t have enough self-confidence. (Dipboye, Stramler, & Fontanelle, 1984, p. 567)

Content coding of employment interviews found that favorable first impressions were followed by the use of confirmatory behavior—such as indicating positive regard for the applicant, “selling” the company, and providing job information to applicants—while gathering less information from them. For their part, applicants behaved more confidently and effectively and developed better rapport with interviewers (Dougherty, Turban, & Callender, 1994). These findings support the existence of the confirmatory bias produced by first impressions.

Another aspect of expectancies concerns test score or biodata score information available prior to the interview. A study of 577 candidates for the position of life insurance sales agent found that interview ratings predicted the hiring decision and survival on the job best for applicants with low passing scores on the biodata test and poorest for applicants with high passing scores (Dalessio & Silverhart, 1994). Apparently, interviewers had such faith in the validity of the test scores that, if an applicant scored well, they gave little weight to the interview. When the applicant scored poorly, however, they gave more weight to performance in the interview and made better distinctions among candidates.

First Impressions.

An early series of studies conducted at McGill University over a 10-year period (Webster, 1964, 1982) found that early interview impressions play a dominant role in final decisions (select/reject). These early impressions establish a bias in the interviewer (not usually reversed) that colors all subsequent interviewer–applicant interaction. Early impressions were crystallized after a mean interviewing time of only four minutes!

In addition, the interview is primarily a search for negative information. For example, just one unfavorable impression was followed by a reject decision 90% of the time. Positive information was given much less weight in the final decision (Bolster & Springbett, 1961).

Consider the effect of how the applicant shakes the interviewer’s hand (Stewart, Dustin, Barrick, & Darnold, 2008). A study using 98 undergraduate students found that quality of handshake was related to the interviewer’s hiring recommendation. It seems that quality of handshake conveys the positive impression that the applicant is extraverted, even when the candidate’s physical appearance and dress are held constant. Also, in this particular study women received lower ratings for the handshake compared with men, but they did not, on average, receive lower assessments of employment suitability.

Prototypes and Stereotypes.

Returning to the McGill studies, perhaps the most important finding was that interviewers tend to develop their own prototype of a good applicant and proceed to accept those who match their prototype (Rowe, 1963; Webster, 1964). Later research has supported these findings. To the extent that the interviewers hold negative stereotypes of a group of applicants, and these stereotypes deviate from the perception of what is needed for the job or translate into different expectations or standards of evaluation for minorities, stereotypes may have the effect of lowering interviewers’ evaluations, even when candidates are equally qualified for the job (Arvey, 1979).

Similar considerations apply to gender-based stereotypes. The social psychology literature on gender-based stereotypes indicates that the traits and attributes necessary for managerial success resemble the characteristics, attitudes, and temperaments of the masculine gender role more than the feminine gender role (Aguinis & Adams, 1998). The operation of such stereotypes may explain the conclusion by Arvey and Campion (1982) that female applicants receive lower scores than male applicants.

Contrast Effects.

Several studies have found that, if an interviewer evaluates a candidate who is just average after evaluating three or four very unfavorable candidates in a row, the average candidate tends to be evaluated favorably. When interviewers evaluate more than one candidate at a time, they tend to use other candidates as a standard. Whether they rate a candidate favorably, then, is determined partly by others against whom the candidate is compared (Hakel, Ohnesorge, & Dunnette, 1970; Heneman, Schwab, Huett, & Ford, 1975; Landy & Bates, 1973).

These effects are remarkably tenacious. Wexley, Sanders, and Yukl (1973) found that, despite attempts to reduce contrast effects by means of a warning (lecture) and/or an anchoring procedure (comparison of applicants to a preset standard), subjects continued to make this error. Only an intensive workshop (which combined practical observation and rating experience with immediate feedback) led to a significant behavior change. Similar results were reported in a later study by Latham, Wexley, and Pursell (1975). In contrast to subjects in group discussion or control groups, only those who participated in the intensive workshop did not commit contrast, halo, similarity, or first impression errors six months after training.

Information Recall.

A practical question concerns the ability of interviewers to recall what an applicant said during an interview. Here is how this question was examined in one study (Carlson, Thayer, Mayfield, & Peterson, 1971).

Prior to viewing a 20-minute videotaped selection interview, 40 managers were given an interview guide, pencils, and paper and were told to perform as if they were conducting the interview. Following the interview, the managers were given a 20-question test, based on factual information. Some managers missed none, while others missed as many as 15 out of 20 items. The average number was 10 wrong.

After this short interview, half the managers could not report accurately on the information produced during the interview! By contrast, managers who had been following the interview guide and taking notes were quite accurate on the test. Those who were least accurate in their recollections assumed the interview was generally favorable and rated the candidate higher in all areas and with less variability. They adopted a halo strategy. Those managers who knew the facts rated the candidate lower and recognized intraindividual differences. Hence, the more accurate interviewers used an individual-differences strategy.

None of the managers in this study was given an opportunity to preview an application form prior to the interview. Would that have made a difference? Other research indicates that the answer is no (Dipboye, Fontanelle, & Garner, 1984). When it comes to recalling information after the interview, there seems to be no substitute for note taking during the interview. However, the act of note taking alone does not necessarily improve the validity of the interview; interviewers need to be trained on how to take notes regarding relevant behaviors (Burnett, Fan, Motowidlo, & DeGroot, 1998). Note taking helps information recall, but it does not in itself improve the judgments based on such information (Middendorf & Macan, 2002). In addition to note taking, other memory aids include mentally reconstructing the context of the interview and retrieving information from different starting points (Mantwill, Kohnken, & Aschermann, 1995).

Individual Differences

A number of individual-difference variables play a role in the interview process. These refer to characteristics of both the applicant and the interviewer. Let’s review applicant characteristics first, followed by interviewer characteristics.

Applicant Appearance and Other Personal Characteristics.

Findings regarding physical attractiveness indicate that attractiveness is only an advantage in jobs where attractiveness per se is relevant. However, being unattractive appears never to be an advantage (Beehr & Gilmore, 1982). One study found that being perceived as being obese can have a small, although statistically significant, negative effect (Finkelstein, Frautschy Demuth, & Sweeney, 2007). However, another study found that overweight applicants were no more likely to be hired for a position involving minimal public contact than they were for a job requiring extensive public contact (Pingitore, Dugoni, Tindale, & Spring, 1994).

Some of the available evidence indicates that ethnicity may not be a source of bias (Arvey, 1979; McDonald & Hakel, 1985). As noted earlier, there is a small effect for race, but it is related to interviewer–applicant race similarity rather than applicant race. However, a study examining the effects of accent and name as ethnic cues found that these two factors interacted in affecting interviewers’ evaluations (Segrest Purkiss, Perrewé, Gillespie, Mayes, & Ferris, 2006). Specifically, applicants with an ethnic name who spoke with an accent were perceived less positively compared to ethnic-named applicants without an accent and nonethnic-named applicants with and without an accent. These results point to the need to investigate interactions between an interviewee’s ethnicity and other variables. In fact, a study involving more than 1,334 police officers found a three-way interaction among interviewer ethnicity, interviewee ethnicity, and panel composition, such that African American interviewers evaluated African American interviewees more favorably than white applicants only when they were on a predominately African American panel (McFarland, Ryan, Sacco, & Kriska, 2004). Further research is certainly needed regarding these issues, given the demographic and societal trends discussed in Chapters 1 and 2.

Evidence available from studies regarding the impact of disability status is mixed. Some studies show no relationship (Rose & Brief, 1979), whereas others indicate that applicants with disabilities receive more negative ratings (Arvey & Campion, 1982), and yet a third group of studies suggests that applicants with disabilities receive more positive ratings (Hayes & Macan, 1997). The discrepant findings are likely due to the need to include additional variables in the design beyond disability status. For example, rater empathy can affect whether applicants with a disability receive a higher or lower rating than applicants without a disability (Cesare, Tannenbaum, & Dalessio, 1990).

Applicant personality seems to be related to interview performance. For example, consider a study including a sample of 85 graduating college seniors who completed a personality inventory. At a later time, these graduates reported the strategies they used in the job search and whether these strategies had generated interviews and job offers (Caldwell & Burger, 1998). Results revealed correlations of .38 and .27 for invitations for a follow-up interview and conscientiousness and extraversion, respectively. And correlations of .34, .27, .23, and −.21 were obtained for relationships between receiving a job offer and extraversion, agreeableness, openness to experience, and neuroticism, respectively. In other words, being more conscientious and extraverted enhances the chances of receiving follow-up interviews; being more extraverted, more agreeable, more open to experience, and less neurotic is related to receiving a job offer. Follow-up analyses revealed that, when self-reports of preparation and all personality variables were included in the equation, conscientiousness was the only trait related to number of interview invitations received, and extraversion and neuroticism (negative) were the only traits related to number of job offers. A second study found that applicants’ trait negative affectivity had an impact on interview success via the mediating role of job-search self-efficacy and job-search intensity (Crossley & Stanton, 2005). Yet another study found that individuals differ greatly regarding their experienced anxiety during the interview and that levels of interview anxiety are related to interview performance (McCarthy & Goffin, 2004). Taken together, the evidence gathered thus far suggests that an applicant’s personality has an effect during and after the interview, and it also affects how applicants prepare before the interview.

Another issue regarding personal characteristics is the possible impact of pleasant artificial scents (perfume or cologne) on ratings in an employment interview. Research conducted in a controlled setting found that women assigned higher ratings to applicants when they used artificial scents than when they did not, whereas the opposite was true for men. These results may be due to differences in the ability of men and women to “filter out” irrelevant aspects of applicants’ grooming or appearance (Baron, 1983).

Applicant Participation in a Coaching Program.

Coaching can include a variety of techniques, including modeling, behavioral rehearsal, role playing, and lecture, among others (Maurer & Solamon, 2007; Tross & Maurer, 2008). Is there a difference in interview performance between applicants who receive coaching on interviewing techniques and those who do not? Two studies (Maurer, Solamon, Andrews, & Troxtel, 2001; Maurer, Solamon, & Troxtel, 1998) suggest so. They included police officers and firefighters involved in promotional procedures that required an interview. The coaching program in the Maurer et al. (1998) study included several elements that included (a) introduction to the interview, including a general description of the process; (b) description of interview-day logistics; (c) description of types of interviews (i.e., structured versus unstructured) and advantages of structured interviews; (d) review of knowledge, abilities, and skills needed for a successful interview; (e) participation in and observation of interview role plays; and (f) interview tips. Participants in the coaching program received higher interview scores than nonparticipants for four different types of jobs (i.e., police sergeant, police lieutenant, fire lieutenant, and fire captain). Differences were found for three of the four jobs when controlling for the effects of applicant precoaching knowledge and motivation to do well on the promotional procedures. In a follow-up study, Maurer et al. (2001) found similar results.

Now let’s discuss interviewer characteristics and their effects on the interview.

Interviewer Experience.

Although it has been hypothesized that interviewers with the same amount of experience will evaluate an applicant similarly (Rowe, 1960), empirical results do not support this hypothesis. Carlson (1967) found that, when interviewers with the same experience evaluated the same recruits, they agreed with each other to no greater extent than did interviewers with differing experiences. Apparently, interviewers benefit very little from day-to-day interviewing experience, since the conditions necessary for learning (i.e., training and feedback) are not present in the interviewer’s everyday job situation. Experienced interviewers who never learn how to conduct good interviews will simply perpetuate their poor skills over time (Jacobs & Baratta, 1989). By contrast, a positive relationship may exist between experience and improved decision making when experience is accompanied by higher levels of cognitive complexity (Dipboye & Jackson, 1999). In that case, experience is just a proxy for another variable (i.e., complexity) and not the factor improving decision making per se.

Interviewer Cognitive Complexity and Mood.

Some laboratory studies, mainly using undergraduate students watching videotaped mock interviews, have investigated whether cognitive complexity (i.e., ability to deal with complex social situations) and mood affect the interview. Although the evidence is limited, a study by Ferguson and Fletcher (1989) found that cognitive complexity was associated with greater accuracy for female raters, but not for male raters. However, more research is needed before we can conclude that cognitive complexity has a direct effect on interviewer accuracy.

Regarding the effect of mood, Baron (1993) induced 92 undergraduate students to experience positive affect, negative affect, or no shift in current affect. Then students conducted a simulated job interview with an applicant whose qualifications were described as high, ambiguous, or low. This experiment led to the following three findings. First, when the applicant’s qualifications were ambiguous, participants in the positive affect condition rated this person higher on several dimensions than did students in the negative affect condition. Second, interviewers’ mood had no effect on ratings when the applicant appeared to be highly qualified for the job. Third, interviewers’ moods significantly influenced ratings of the applicant when this person appeared to be unqualified for the job, such that participants in the positive affect condition rated the applicant lower than those induced to experience negative affect. In sum, interviewer mood seems to interact with applicant qualifications such that mood plays a role only when applicants are unqualified or when qualifications are ambiguous.

Effects of Structure

Another major category of factors that affect interview decision making refers to the degree of structure in the interview (Levashina, Hartwell, Morgeson, & Campion, 2014). Structure is a matter of degree, and there are four dimensions one can consider: (1) questioning consistency, (2) evaluation standardization, (3) question sophistication, and (4) rapport building (Chapman & Zweig, 2005). Overall, structure can be enhanced by basing questions on results of a job analysis, asking the same questions of each candidate, limiting prompting follow-up questioning and elaboration on questions, using better types of questions (e.g., situational questions, which are discussed shortly), using longer interviews and a larger number of questions, controlling ancillary information (i.e., application forms, résumés, test scores, recommendations), not allowing the applicant to ask questions until after the interview, rating each answer on multiple scales, using detailed anchored rating scales, taking detailed notes, using multiple interviewers, using the same interviewer(s) across all applicants, providing extensive interviewing training, and using statistical rather than clinical prediction (discussed in detail in Chapter 13) (Campion, Palmer, & Campion, 1997).

The impact of structure on several desirable outcomes is clear-cut. First, a review of several meta-analyses reported that structured interviews are more valid (Campion et al., 1997). Specifically, the corrected validities for structured interviews ranged from .35 to .62, whereas those for unstructured interviews ranged from .14 to .33. Second, structure decreases differences between racial groups. A meta-analysis found a mean standardized difference (d) between white and African American applicants of .32 based on 10 studies with low-structure interviews and d = .23 based on 21 studies with high-structure interviews (Huffcutt & Roth, 1998). Note, however, that these differences are larger for both types of interviews if one considers the impact of range restriction (Roth, Van Iddekinge, Huffcutt, Eidson, & Bobko, 2002). Third, structured interviews are less likely than unstructured interviews to be challenged in court based on illegal discrimination (Williamson, Campion, Malos, Roehling, & Campion, 1997).

A review of 158 U.S. federal court cases involving hiring discrimination from 1978 to 1997 revealed that unstructured interviews were challenged in court more often than any other type of selection device, including structured interviews (Terpstra, Mohamed, & Kethley, 1999). Specifically, 57% of cases involved charges against the use of unstructured interviews, whereas only 6% of cases involved charges against the use of structured interviews. Even more important is an examination of the outcomes of such legal challenges. Unstructured interviews were found not to be discriminatory in 59% of cases, whereas structured interviews were found not to be discriminatory in 100% of cases. Taken together, these findings make a compelling case for the use of the structured interview in spite of HR managers’ reluctance to adopt such procedures (van der Zee, Bakker, & Bakker, 2002).

Why are structured interviews qualitatively better than unstructured interviews? Higher reliability alone does not seem to be a sufficient explanation (Schmidt & Zimmerman, 2004). Most likely the answer is that unstructured interviews (i.e., the interviewer has no set procedure but merely follows the applicant’s lead) and structured interviews (i.e., the interviewer follows a set procedure) do not measure the same constructs (Huffcutt, Conway et al., 2001). Typically, structured interviews are the result of a job analysis and assess job knowledge and skills, organizational fit, interpersonal and social skills, and applied mental skills (e.g., problem solving). Therefore, constructs assessed in structured interviews tend to have a greater degree of job relatedness as compared to the constructs measured in unstructured interviews. When interviews are structured, interviewers know what to ask for (providing a more consistent sample of behavior across applicants) and what to do with the information they receive (helping them to provide better ratings).

Structured interviews vary based on whether the questions are about past experiences or hypothetical situations. Questions in an experience-based interview are past oriented; they ask applicants to relate what they did in past jobs or life situations that are relevant to the job in question (Janz, 1982; Motowidlo et al., 1992). The underlying assumption is that the best predictor of future performance is past performance in similar situations. Experience-based questions are of the “Can you tell me about a time when … ?” variety.

By contrast, situational questions (Latham, Saari, Pursell, & Campion, 1980; Maurer, 2002) ask job applicants to imagine a set of circumstances and then indicate how they would respond in that situation. Hence, the questions are future oriented. Situational interview questions are of the “What would you do if … ?” variety. Situational interviews have been found to be highly valid and resistant to contrast error and to race or gender bias (Maurer, 2002). Why do they work? Apparently the most influential factor is the use of behaviorally anchored rating scales. Maurer (2002) reached this conclusion based on a study of raters who watched and provided ratings of six situational interview videos for the job of campus police officer. Even without any training, a group of 48 business students showed more accuracy and agreement than job experts (i.e., 48 municipal and campus police officers) who used a structured interview format that did not include situational questions. Subsequent comparison of situational versus nonsituational interview ratings provided by the job experts showed higher levels of agreement and accuracy for the situational type.

Both experience-based and situational questions are based on a job analysis that uses the critical-incidents method (as described in Chapter 9). The incidents then are turned into interview questions. Each answer is rated independently by two or more interviewers on a five-point Likert-type scale. To facilitate objective scoring, job experts develop behavioral statements that are used to illustrate 1, 3, and 5 answers. Table 12.2 illustrates the difference between these two types of questions.

Taylor and Small (2002) conducted a meta-analysis comparing the relative effectiveness of these two approaches. They were able to locate 30 validities derived from situational interviews and 19 validities for experience-based interviews, resulting in mean corrected validities of .45 for situational interviews and .56 for experience-based interviews. However, a comparison of the studies that used behaviorally anchored rating scales yielded mean validities of .47 for situational interviews (29 validity coefficients) and .63 for experience-based interviews (11 validity coefficients). In addition, mean interrater reliabilities were .79 for situational interviews and .77 for experience-based interviews. Finally, although some studies have found that the situational interview may be less valid for higher level positions (Pulakos & Schmitt, 1995) or more complex jobs (Huffcutt, Weekley, Wiesner, DeGroot, & Jones, 2001), the meta-analytic results found no differential validity based on job complexity for either type of interview.

Table 12.2 Examples of Experience-Based and Situational Interview Items Designed to Assess Conflict Resolution and Collaborative Problem-Solving Skills

Situational item: Suppose you had an idea for a change in work procedure to enhance quality, but there was a problem in that some members of your work team were against any type of change. What would you do in this situation? (5) Excellent answer (top third of candidates)—Explain the change and try to show the benefits. Discuss it openly in a meeting. (3) Good answer (middle third)—Ask them why they are against change. Try to convince them. (1) Marginal answer (bottom third)—Tell the supervisor.

Experience-based item: What is the biggest difference of opinion you ever had with a coworker? How did it get resolved? (5) Excellent answer (top third of candidates)—We looked into the situation, found the problem, and resolved the difference. Had an honest conversation with the person. (3) Good answer (middle third)—Compromised. Resolved the problem by taking turns, or I explained the problem (my side) carefully. (1) Marginal answer (bottom third)—I got mad and told the coworker off, or we got the supervisor to resolve the problem, or I never have differences with anyone.

Source: Campion, M. A., Campion, J. E., & Hudson, J. P., Jr. (1994). Structured interviewing: A note on incremental validity and alternative question types. Journal of Applied Psychology, 79, 999.

Summary of Evidence-Based Suggestions for Improving the Interview Process and Outcome

Emphasis on employment interview research within a person-perception framework should continue. Also, this research must consider the social and interpersonal dynamics of the interview, including affective reactions on the part of both the applicant and the interviewer. The interviewer’s job is to develop accurate perceptions of applicants and to evaluate those perceptions in light of job requirements. Learning more about how those perceptions are formed, what affects their development, and what psychological processes best explain their development are important questions that deserve increased attention. Also, we need to determine whether any of these process variables affect the validity, and ultimately the utility, of the interview (Zedeck & Cascio, 1984). We should begin by building on our present knowledge to make improvements in selection-interview technology. Here are seven research-based suggestions for improving the interview process and outcome:

1. Link interview questions tightly to job analysis results, and ensure that behaviors and skills observed in the interview are similar to those required on the job. A variety of types of questions may be used, including situational questions, questions on job knowledge that is important to job performance, job sample or simulation questions, and questions regarding background (e.g., experience, education) and “willingness” (e.g., shift work, travel).

2. Ask the same questions of each candidate because standardizing interview questions has a dramatic effect on the psychometric properties of interview ratings. Consider using the following six steps when conducting a structured interview: (1) Open the interview,  explaining its purpose and structure (i.e., that you will be asking a set of questions that pertain to the applicant’s past job behavior and what he or she would do in a number of job-relevant situations), and encourage the candidate to ask questions; (2) preview the job; (3) ask questions about minimum qualifications (e.g., for an airline, willingness to work nights and holidays); (4) ask experience-based questions (“Can you tell me about a time when … ?”); (5) ask situational questions (“What would you do if … ?”); (6) close the interview by giving the applicant an opportunity to ask questions or volunteer information he or she thinks is important, and explain what happens next (and when) in the selection process.

3. Anchor the rating scales for scoring answers with examples and illustrations. Doing so helps to enhance consistency across interviews and objectivity in judging candidates.

4. Whether structured or unstructured, interview panels are no more valid than are individual interviews (McDaniel et al., 1994). In fact, some panel members may see the interview as a political arena and attempt to use the interview and its outcome as a way to advance the agenda of the political network in which they belong (Bozionelos, 2005). As we have seen, however, mixed-race panels may help to reduce the similar-to-me bias that individual interviewers might introduce. Moreover, if a panel is used, letting panel members know that they will engage in a group discussion to achieve rating consensus improves behavioral accuracy (i.e., a rating of whether a particular type of behavior was present or absent) (Roch, 2006).

5. Provide a well-designed and properly evaluated training program to communicate this information to interviewers, along with techniques for structuring the interview (e.g., a structured interview guide, standardized rating forms) to minimize the amount of irrelevant information. As part of their training, give interviewers the opportunity to practice interviewing with minorities or persons with disabilities. This may increase interviewers’ ability to relate to these populations.

6. Document the job analysis and interview-development procedures, candidate responses and scores, evidence of content- or criterion-related validity, and adverse impact analyses in accordance with testing guidelines.

7. Institute a planned system of feedback to interviewers to let them know who succeeds and who fails and to keep them up-to-date on changing job requirements and success patterns.

There are no shortcuts to reliable and valid measurement. Careful attention to detail and careful “mapping” of the interview situation to the job situation are necessary, both legally and ethically, if the interview is to continue to be used for selection purposes.

The Future Is Now: Technology and Big Data

In previous sections, we described the use of computers, video résumés, the Internet, and credit scores. As technology progresses and HR specialists can take advantage of new technology as well as big data, we anticipate important changes in how and what type of information can be gathered to make selection decisions. Consider the following innovations.

Social Media

Social media permeates the lives of billions of individuals worldwide. As of 2016, the number of users of Facebook surpassed 1.9 billion people; Twitter, 330 million; and LinkedIn, 500 million (www.statista.com). Social media is an example of a source of big data (Harlow & Oswald, 2016), and it is changing the way in which people communicate, create relationships, and do business with each other (McFarland & Ployhart, 2015). Users of these sites share information on their personal history, attitudes, preferences, life choices, and behaviors. It seems natural, then, for employers to investigate applicants’ accounts before making a job offer. In fact, a senior manager for the EEOC noted that approximately 75% of recruiters are required to do online research on applicants, and 70% of recruiters surveyed reported rejecting individuals as a result (Roth, Bobko, Van Iddekinge, & Thatcher, 2016).

Can information available on social media sites be used in a valid and fair manner for selecting employees? Unfortunately, despite its current use, little evidence is available to answer this question. Kluemper, Rosen, and Mossholder (2012) asked two undergraduate students and a faculty member to assess the suitability for hire of 56 employed students based on the content of their Facebook pages and correlated those scores with ratings of job performance provided by supervisors. The resulting validity coefficient was .28. This is clearly an important study because it is one of a kind. But, in addition to the small sample size, suitability ratings were based on a hypothetical job of manager in a service industry, whereas the performance measure was from the students’ current (not necessarily supervisory) position.

In a more recent study, 86 recruiters rated the Facebook pages of graduate and undergraduate students who were near graduation and were looking for a job. Results suggested much less promising—and even troubling—results (Van Iddekinge, Lanivich, Roth, & Junco, 2016). Recruiters rated the students using questions such as, “I can see how this person would be an attractive applicant to an organization,” “I would consider this person further for employment if they had the skills to fill an open position,” and “I would be hesitant to pursue this person as an applicant after viewing their Facebook profile” (reverse scored). About 14 months after students were hired, the authors collected performance information for 142 of them from their supervisors. Performance was measured with items such as “The employee performs tasks they are asked to complete,” “The employee goes out of their way to help other employees,” and “Overall, I am happy with this employee’s performance.” Students whose performance was rated by their supervisors also completed surveys including measures of turnover and turnover intentions. Recruiter ratings of applicants’ Facebook information were unrelated to supervisor ratings of job performance (rs = −.13 to –.04), turnover intentions (rs = −.05 to .00), and actual turnover (rs = −.01 to .01). Facebook ratings did not predict performance above and beyond general cognitive abilities, the Big Five personality traits, core self-evaluation, self-efficacy, and grade point average.

The fact that social media data are available, and in vast quantities, does not mean they are useful. The popular press, in particular, frequently shares anecdotal reports about the use of social media for employment decisions. These include claims discussing the merits, but also warning about risks, of using social media for this purpose. Unfortunately, there is not much empirical evidence to substantiate those claims (McFarland & Ployhart, 2015). Clearly, social media provides access to many applicants on a global scale. So, using it for recruitment purposes may be beneficial in terms of increasing the quantity and quality of the applicant pool. From the perspective of applicants, social networking sites, such as LinkedIn, allow them to connect directly with members of targeted organizations to receive more realistic previews about their potential employers. In addition, sites such as Glassdoor allow employees to rate their organizations regardless of what senior leaders want the employees to say (McFarland & Ployhart, 2015). So, social media increases the flow of information substantially—both for  job applicants and for employers. As is the case with big data in general, however, there is a need to understand when and how such information can be used in a valid and fair manner to predict performance and other important criteria. Until such evidence becomes available, the jury is still out.

Mobile and Web-Based Selection

The use of computers and the Internet for administering tests is now pervasive. Moreover, the use of mobile devices allows applicants to upload application materials and complete forms and assessments anywhere and anytime. Technology for staffing is developing quickly, and it involves all aspects of the process, beginning with recruitment. In this section, we offer a brief overview of major issues, based on a review by Tippins (2015).

In terms of the delivery of assessments, the use of computers allows for more complex formats in addition to the traditional multiple-choice format. For example, an assessment may include audio or video content containing real people or equipment, avatars, or other forms of animation. Another technological innovation is the availability of an assessment portal, which is a single link from which applicants can access all assessments. Other possibilities include novel response-option formats, such as drag-and-drop, as well as thermometer response scales that allow for finer distinctions. These innovations may make it easier for applicants to respond more precisely, avoiding the scale-coarseness problem of traditional scales described in Chapter 6.

Mobile and Web-based selection also allows for innovations regarding scoring. For example, many vendors have large electronic databases of test takers, which allows them to create norms based on various types of jobs, occupations, industries, and even regions of the world. The availability of large electronic databases provides another advantage: the possibility of creating detailed reports to be shared with applicants. This is particularly useful for promotion decisions because employers can store large amounts of performance-related data over the span of employees’ tenure at the company.

Although there are advantages to using mobile and Web-based selection, there are also potential pitfalls. For example, the percentage of people with access to a fast Internet connection is quite large in the United States (73%), Canada (82%), South Korea (94%), Japan (86%), Switzerland (91%), and the Netherlands (88%), but this is not the case in countries such as Panama (12%), India (4.9%), and the Philippines (4.2%). Also, having access to the Internet does not guarantee equal opportunity regarding Web-based testing. For example, KSAs that are necessary in this particular testing context may not be necessary in the traditional paper-and-pencil context (i.e., familiarity with a computer, typing speed). If those KSAs are not job related, then they can have a negative effect on the validity of the assessment. Other challenges involve distractions present during test administration that are outside the test administrator’s control and possible cheating. The latter can be addressed by using proctors at test-administration sites and webcams for individual assessments. Finally, applicant reactions, especially about privacy, are particularly relevant in mobile and Web-based selection. Employers should be sure to explain to applicants the safeguards that are in place regarding the storage and use of data.

Computer Scoring of Text

Campion, Campion, Campion, and Reider (2016) provided an illustration of what is usually referred to as automated essay scoring (AES) or computer-automated scoring (CAS). Essentially, this is a technological advancement that is part of a family of techniques called computer-aided text analysis (CATA, as discussed in Chapter 6) to score the narrative responses of job applicants. A traditional challenge in using this technology is what is called the information retrieval problem, which is the difficulty in making a precise lexical match between words in a user’s query and words used within the document analyzed (e.g., essays, résumés). For example, we may be interested in learning about applicants’ leadership skills, but the candidate may not have used the terms leader or leadership. Instead, the narrative may include the term manager (Campion et al., 2016).

Campion et al. (2016) created a program using the SPSS-IBM Premium Modeler package to measure six competencies: communication skill, critical thinking, people skill, leadership skill, managerial skill, and factual knowledge. The program identifies key terms within text and also constructs models on the relations among them to infer higher order characteristics or constructs in a candidate (such as the six competencies).

Campion et al.’s results demonstrated the potential of computer scoring of text because they showed that it is possible to program a computer to emulate a human rater when scoring narrative data. The computer-based scores were as reliable as those produced by human raters, and there was evidence of construct validity. From a practical perspective, computer scoring resulted in substantial savings compared to using human raters.

Remote Interviewing

The use of videoconferencing (e.g., using Skype) allows employers to interview distant applicants remotely and inexpensively (Chapman & Rowe, 2002). Telephone interviewing is quite common (Schmidt & Rader, 1999). However, some key differences between face-to-face interviews and interviews using technologies such as the telephone and videoconferencing may affect the process and outcome of the interview (Chapman & Rowe, 2002). In the case of the telephone, an obvious difference is the absence of visual cues (Silvester & Anderson, 2003). By contrast, the absence of visual cues may reduce some interviewer biases based on nonverbal behaviors that were discussed earlier in this chapter. Regarding videoconferencing, the lack of a duplex system that allows for both parties to talk simultaneously may change the dynamics of the interview.

A hybrid way to conduct the interview is to do it face to face, record both audio and video, and then ask additional raters, who were not present in the face-to-face interview, to provide an evaluation (Van Iddekinge, Raymark, Roth, & Payne, 2006). However, a simulation that included 113 undergraduate and graduate students provided initial evidence that ratings may not be equivalent. Specifically, face-to-face ratings were significantly higher than those provided based on the videotaped interviews. Further research is needed to establish conditions under which ratings provided in face-to-face and videotaped interviews may be equivalent.

One study compared the equivalence of telephone and face-to-face interviews using a sample of 70 applicants for a job in a large multinational oil corporation (Silvester, Anderson, Haddleton, Cunningham-Snell, & Gibb, 2000). Applicants were randomly assigned to two groups. Group A received a face-to-face interview followed by a telephone interview, and group B received a telephone interview followed by a face-to-face interview. Results revealed that telephone ratings (mean = 4.30) were lower than face-to-face ratings (mean = 5.52), regardless of the interview order. Silvester et al. (2000) provided several possible reasons for this. During telephone interviews, interviewers may be more focused on content rather than extraneous cues (e.g., nonverbal behavior), in which case the telephone interview may be more valid than the face-to-face interview. Alternatively, applicants may have considered the telephone interview as less important and could have been less motivated to perform well, or applicants may have had less experience with telephone interviews, which could also explain their lower performance. Another experimental study compared face-to-face interviews with videoconferencing interviews using a sample of undergraduate students being interviewed for actual jobs (Chapman & Rowe, 2002). Applicants in the face-to-face condition were more satisfied with the interviewer’s performance and with their own performance during the interview, as compared to applicants in the videoconferencing condition (Chapman & Rowe, 2002).

Virtual Reality Technology

Virtual reality technology (VRT) is a technological advance that has the potential to alter the way screening is done (Aguinis, Henle, & Beaty, 2001). Imagine applicants for truck-driver positions stepping into a simulator of a truck to demonstrate their competence. Or imagine applicants for lab-technician positions entering a simulated laboratory to demonstrate their ability to handle various chemical substances. VRT has several advantages because it has the potential to create such job-related environments without using real trucks or real chemicals. Thus, users can practice hazardous tasks or simulate rare occurrences in a realistic environment without compromising their safety. VRT also allows examiners to gather valuable information regarding future on-the-job performance. As noted by Aguinis, Henle, and Beaty (2001), “Just a few years ago, this would have only been possible in science fiction movies, but today virtual reality technology makes this feasible” (p. 70).

The implementation of VRT presents some challenges, however. For example, VRT environments can lead to sopite syndrome (i.e., eyestrain, blurred vision, headache, balance disturbances, drowsiness; Pierce & Aguinis, 1997). A second potential problem in implementing VRT testing is its cost and lack of commercial availability. However, VRT systems are becoming increasingly affordable. In fact, a Google Daydream View VRT headset costs less than $100. A final challenge faced by those contemplating the use of VRT is its technical limitations. In virtual environments, there is a noticeable lag between the user’s movement and the change of scenery, and some of the graphics, including the virtual representation of the user, may appear cartoonlike. However, given the frantic pace of technological advances, we should expect that some of the present limitations will soon be overcome.

In closing, new technology and the availability of big data are making possible the use of innovative selection methods. Some of these methods may be passing fads, whereas others may become popular. Regardless of their attractiveness and availability, understanding the constructs being measured and their job relatedness will continue to be essential for determining the appropriateness of these methods. It will also be important to understand important hidden costs, such as negative applicant reactions and scores that are not as valid as those resulting from more traditional and well-established methods.

Chapter 13

13 MANAGERIAL SELECTION METHODS

Wayne F. Cascio, Herman Aguinis

Learning Goals

By the end of this chapter, you will be able to do the following:

· 13.1 Design managerial selection systems that predict objective and subjective criteria

· 13.2 Use cognitive ability tests to predict managerial performance, considering challenges associated with this type of tool

· 13.3 Use objective personality inventories to measure several types of traits

· 13.4 Minimize distortion (faking) in personality assessment

· 13.5 Predict managerial and leadership success using alternative predictors, such as leadership ability tests, motivation to manage, personal history data, and peer assessments

· 13.6 Use work samples of managerial performance (i.e., leaderless group discussion, in-basket test, business games, and situational judgment tests) to predict future performance

· 13.7 Predict future managerial performance using the assessment center method

· 13.8 Consider implementing selection systems that include different combinations of predictors

Managerial selection is a topic that deserves separate treatment because of the unique problems associated with describing “managerial effectiveness” and developing behaviorally based predictor measures to forecast managerial effectiveness accurately. A wide assortment of data collection techniques is currently available—cognitive ability tests, objective personality inventories, leadership ability tests, personal history data, work samples, situational judgment tests, and assessment centers—each demonstrating varying degrees of predictive success in particular situations. These are flexible techniques that can be used to predict job success for a variety of occupations and organizational levels. This chapter addresses each of them, emphasizing their use in the context of managerial selection but also recognizing that they can be used for some nonmanagerial positions as well.

We first consider the criterion problem for managers. As just noted, although the emphasis of this chapter is managerial selection, many of the instruments of prediction described (most notably cognitive ability tests and personality inventories) are also useful for selecting employees at lower organizational levels. Thus, when appropriate, our discussion describes the use of these instruments for positions other than managerial positions.

Criteria of Managerial Success

Both objective and subjective indicators frequently are used to measure managerial effectiveness. Conceptually, effective management can be defined in terms of organizational outcomes. In particular, Campbell, Dunnette, Lawler, and Weick (1970) view the effective manager as an optimizer who uses both internal and external resources (human, informational, material, and financial) in order to sustain, over the long term, the unit for which the manager bears some degree of responsibility.

The primary emphasis in this definition is on managerial actions or behaviors judged relevant and important for optimizing resources. This judgment can be rendered only on rational grounds; therefore, informed, expert opinion is needed to specify the full range of managerial behaviors relevant to the conceptual criterion. The process begins with a careful specification of the total domain of the manager’s job responsibilities, along with statements of critical behaviors believed necessary for the best use of available resources. The criterion measure itself must encompass a series of observations of the manager’s actual job behavior by individuals capable of judging the manager’s effectiveness in accomplishing all the things judged necessary, sufficient, and important for doing his or her job (Campbell et al., 1970). The overall aim is to determine psychologically meaningful dimensions of effective executive performance. It is only by knowing these dimensions that we can achieve a fuller understanding of the complex web of interrelationships existing between various types of job behaviors and organizational performance or outcome measures (e.g., promotion rates, productivity indexes, accounting-based and market-based measures of firm performance, such as return on assets and Tobin’s q).

Global Criterion Measures

Many managerial prediction studies have used objective, global, or administrative criteria (e.g., Hurley & Sonnenfeld, 1998; Ritchie & Moses, 1983). For example, Hurley and Sonnenfeld (1998) used the criterion “career attainment” operationalized as whether a manager had been selected for a top management position or whether he or she had remained in a middle-level management position. Because of the widespread use of such global criterion measures, let’s pause to examine them critically. First, the good news. Global measures such as supervisory rankings of total managerial effectiveness, salary, and organizational level (statistically corrected for age or length of time in the organization) have several advantages. In the case of ranking, because each supervisor usually ranks no more than about 10 subordinate managers, test–retest and interrater reliabilities tend to be high. In addition, such rankings probably encompass a broad sampling of behaviors over time, and managers themselves probably are being judged rather than organizational factors beyond their control. Finally, managers are compared directly to their peers; this standard of comparison is appropriate, because all probably are responsible for optimizing similar amounts of resources.

At the same time, overall measures or ratings of success include multiple factors (Dunnette, 1963a; Hanser, Arabian, & Wise, 1985). Hence, such measures often serve to obscure more than they reveal about the behavioral bases for managerial success. We cannot know with certainty what portion of a global rating or administrative criterion (such as level changes or salary) is based on actual job behaviors and what portion is due to other factors such as luck, education, “having a guardian angel at the top,” differential opportunities, political savvy, and so forth. Such measures suffer from both deficiency and contamination—that is, they measure only a small portion of the variance due to individual managerial behavior, and variations in these measures depend on many job-irrelevant factors that are not under the manager’s direct control.

Such global measures may also be contaminated by biases against members of certain groups (e.g., women). For example, a large body of literature shows that, due to gender-based stereotypes, women are often perceived as not “having what it takes” to become top managers (Lyness & Heilman, 2006). Specifically, women are usually expected to behave in a more indirect and unassertive manner as compared to men, which is detrimental to women because directness and assertiveness are traits that people associate with successful managers (Aguinis & Adams, 1998). The incongruence between stereotypes of women’s behavior and perceptions of traits of successful managers may explain why women occupy fewer than 5% of the most coveted top-management positions in large, publicly traded corporations.

In short, global or administrative criteria tell us where a manager is on the “success” continuum, but almost nothing about how he or she got there. Because behaviors relevant to managerial success change over time (Korman, 1968), as well as by purpose or function in relationship to the survival of the whole organization (Carroll & Gillen, 1987), the need is great to develop psychologically meaningful dimensions of managerial effectiveness in order to discover the linkages between managerial behavior patterns and managerial success.

What is required, of course, is a behaviorally based performance measure that will permit a systematic recording of observations across the entire domain of desired managerial job behaviors (Campbell et al., 1970). Yet, in practice, these requirements are honored more in the breach than in the observance. Potential sources of error and contamination are rampant (Tsui & Ohlott, 1988). These include inadequate sampling of the job behavior domain, lack of knowledge or lack of cooperation by raters, differing expectations and perceptions of raters (peers, subordinates, and superiors), changes in the job or job environment, and changes in the manager’s behavior (cf.  Chapter 5 ). Fortunately, we now have available the scale-development methods and training methodology to eliminate many of these sources of error, but the translation of such knowledge into everyday organizational practice is a slow, painstaking process.

In summarizing the managerial criterion problem, we hasten to point out that global estimates of managerial success certainly have proven useful in many validation studies (Meyer, 1987). However, they contribute little to our understanding of the wide varieties of job behaviors indicative of managerial effectiveness. We are not advocating the abandonment of global criteria, but employers need to consider supplementing them with systematic observations and recordings of behavior so that a richer, fuller understanding of the multiple paths to managerial success might emerge. Also, from the individual manager’s perspective, the variables that lead to objective career success (e.g., pay, number of promotions) often are quite different from those that lead to subjective career success (job and career satisfaction). Although ambition and quality and quantity of education predict objective career success, accomplishments and organization success predict subjective career success (Judge, Cable, Boudreau, & Bretz, 1995).

The Importance of Context

Management selection decisions take place in the context of both organizational conditions (e.g., culture, technology, financial health, availability of resources) and environmental conditions (e.g., internal and external labor markets, competition, legal requirements). These factors may explain in part why predictors of initial performance (e.g., resource problem-solving skills) are not necessarily as good for predicting subsequent performance as other predictors (e.g., people-oriented skills) (Russell, 2001). Such contextual factors also explain differences in HR practices across organizations (Schuler & Jackson, 1989), and especially with respect to the selection of general managers (Guthrie & Olian, 1991). Thus, under unstable industry conditions, knowledge and skills acquired over time in a single organization may be viewed as less relevant than diverse experience outside the organization. Conversely, a cost-leadership strategic orientation is associated with a tendency to recruit insiders who know the business and the organization. For example, consider an organization particularly interested in organizational responsibility, defined as “context-specific organizational actions and policies that take into account stakeholders’ expectations and the triple bottom line of economic, social, and environmental performance” (Aguinis, 2011). For this organization, criteria of success involve economic-performance indicators such as the maximization of short-term and long-term profit, social-performance indicators such as respecting social customs and cultural heritage, and environmental-performance indicators such as the consumption of fewer natural resources. The criteria for managerial success in an organization that emphasizes the triple bottom line are obviously different from those in an organization that emphasizes only one of these three organizational performance dimensions.

The lesson? A model of executive selection and performance must consider the person as well as situational characteristics (Russell, 2001). There needs to be a fit among the kinds of attributes decision makers pay attention to in selection, the business strategy of the organization, and the environmental conditions in which it operates. Keep this in mind as you read about the many instruments of prediction described in the next sections.

Instruments of Prediction

Cognitive Ability Tests

After reviewing hundreds of studies conducted between 1919 and 1972, Ghiselli (1966, 1973) reported that managerial success has been forecast most accurately by tests of general intellectual ability and general perceptual ability, with validity coefficients ranging between .25 and .30. However, when these correlations were corrected statistically for criterion unreliability and range restriction, the validity of tests of general intellectual ability increased to .53 and those for general perceptual ability increased to .43 (Hunter & Hunter, 1984).

The fact is that general cognitive ability is a powerful predictor of job performance (Ree & Carretta, 2002; Sackett, Borneman, & Connelly, 2008; Schmidt, 2002). It has a strong effect on job knowledge, and it contributes to individuals being given the opportunity to acquire supervisory experience (Borman, Hanson, Oppler, Pulakos, & White, 1993). General cognitive ability is also a good predictor for jobs with primarily inconsistent tasks (Farrell & McDaniel, 2001) and unforeseen changes (LePine, 2003)—often the case with managerial jobs. In general, most factor-structure studies show that the majority of variance in cognitive ability tests can be attributed to a general factor (Carretta & Ree, 2000). In sum, there is substantial agreement among researchers regarding the validity of cognitive ability tests. For example, results of a survey of 703 members of the Society for Industrial and Organizational Psychology showed that 85% of respondents agreed with the statement that “general cognitive ability is measured reasonably well by standardized tests” (Murphy, Cronin, & Tam, 2003). Also, results of a survey including 255 HR professionals indicated that cognitive ability tests were seen as one of the three most valid types of assessments (Furnham, 2008).

Grimsley and Jarrett (1973, 1975) used a matched-group, concurrent-validity design to determine the extent to which cognitive ability test scores and self-description inventory scores obtained during preemployment assessment distinguished top from middle managers. A matched-group design was used to control for the effects of age and education, which were presumed to be related both to test performance and to managerial achievement. Hence, each of 50 top managers was paired with one of 50 middle managers, matched by age and field of undergraduate college education. Classification as a top or middle manager (the success criterion) was based on the level of managerial responsibility attained in any company by which the subject had been employed prior to assessment. This design also has another advantage: Contrary to the usual concurrent validity study, these data were gathered not under research conditions, but rather under employment conditions and from motivated job applicants.

Of the 10 mental-ability measures used (those comprising the Employee Aptitude Survey), eight significantly distinguished the top from the middle manager group: verbal comprehension (r = .18), numerical ability (r = .42), visual speed and accuracy (r = .41), space visualization (r = .31), numerical reasoning (r = .41), verbal reasoning (r = .48), word fluency (r = .37), and symbolic reasoning (r = .31). In fact, a battery composed of just the verbal reasoning and numerical ability tests yielded a multiple R (statistically corrected for shrinkage) of .52. In comparison to male college students, for example, top and middle managers scored in the 98th and 95th percentiles, respectively, on verbal comprehension and in the 85th and 59th percentiles, respectively, on numerical ability. In sum, these results support Ghiselli’s (1963, 1973) earlier conclusion that differences in intellectual competence are related to the degree of managerial success at high levels of management.

Controversial Issues in the Use of Cognitive Ability Tests

Tests of general mental ability (usually referred to as g) are not without criticism. Although g seems to be the best single predictor of job performance (Murphy, 2002), it is also most likely to lead to adverse impact (e.g., differential selection rates for various demographic groups, cf.  Chapter 8 ). The overall standardized difference (d) between whites and African Americans is about .72 for moderate-complexity jobs and .86 for low-complexity jobs (Bobko & Roth, 2013; no information was provided for high-complexity jobs). As we noted in  Chapter 8 , there are numerous reasons that may explain such between-group differences (Cottrell et al., 2015), including physiological factors (e.g., prenatal and postnatal influences such as differential exposure to pollutants and iron deficiency), economic and socioeconomic factors (e.g., differences in health care, criminal justice, education, finances, employment, and housing), psychological factors (e.g., the impact of stereotypes), and societal factors (e.g., differences in time spent watching television).

Regardless of the specific magnitude of d and the relative merits of the various explanations for between-group differences, the presence of adverse impact has led to a polarization between those who endorse the unique status and paramount importance of g as a predictor of performance and those who do not (Murphy et al., 2003). The position that g should be given a primary role in the selection process has policy implications that may be unpalatable to many people (Schmidt, 2002) because the unique or primary reliance on g could degenerate into a “high-tech and more lavish version of the Indian reservation for the substantial minority of the nation’s population, while the rest of America tries to go about its business” (Herrnstein & Murray, 1994, p. 526). Such societal consequences can be seen at a closer and more personal level as well: LePine and Van Dyne (2001) hypothesized that low performers perceived as possessing less general cognitive ability are expected to receive different responses from coworkers and different levels of help. Thus, perceptions of a coworker as having low cognitive ability can become a reinforcer for low performance.

Another criticism is that g represents a limited conceptualization of intelligence because it does not include tacit knowledge (i.e., knowledge gained from everyday experience that has an implicit and unarticulated quality, often referred to as “learning by doing” or “professional intuition”) and practical intelligence (i.e., ability to find an optimal fit between oneself and the demands of the environment, often referred to as being “street smart” or having “common sense”) (Sternberg, 1997; Sternberg & Hedlund, 2002). Also, using a global g factor masks the predictive power of more specific (i.e., “second-stratum”) cognitive abilities: verbal aptitude, numerical aptitude, spatial aptitude, form perception, and clerical perception (Wee, Newman, & Joseph, 2014). For example, individuals with higher scores on verbal aptitude would be expected to demonstrate better performance in occupations that require this second-stratum ability (e.g., senior partner in a law firm) compared to, for example, numerical aptitude (e.g., a senior partner in an engineering firm).

Related to the issue of predicting different types of performance is the finding that cognitive ability tests are better at predicting maximum as compared to typical performance (Marcus, Goffin, Johnston, & Rothstein, 2007). Moreover, scores on g-loaded tests can improve after retaking the same test several times, as we described in  Chapter 7  (e.g., Van Iddekinge & Arnold, 2017). In other words, the factor underlying retest scores is less saturated with g and more associated with memory than the latent factor underlying initial test scores (Lievens, Reeve, & Heggestad, 2007), and a meta-analysis of 107 samples and 134,436 test takers revealed that the effects are larger when identical forms of the test are used and individuals receive coaching between test administrations (Hausknecht, Halpert, Di Patio, & Moriarty Gerrard, 2007). Finally, others have argued that gshould be viewed as a starting point rather than an ending point, meaning that an overemphasis or sole reliance on g in selecting managers and employees is a basis for a flawed selection model (Goldstein, Zedeck, & Goldstein, 2002).

A Recommendation to Address the Controversy

Based on the evidence available thus far, cognitive ability is a good predictor of performance across situations. The observed correlation between job performance and measured cognitive ability is usually in the .20s and this correlation is .40 or higher when corrected for methodological and statistical artifacts, such as measurement error and range restriction (Schmitt, 2014). In light of the criticisms of g, it has been suggested (Outtz, 2002) that tests of general mental ability be combined with other instruments such as structured interviews, biodata (discussed in  Chapter 12 ), and objective personality inventories. In particular, personality assessment has received recent attention because correlations between cognitive ability measures and various personality traits are usually .10 or smaller (Schmitt, 2014).

Objective Personality Inventories

Until recently, reviews of results obtained with personality measures in forecasting employee and managerial effectiveness have been mixed at best. However, also until a few years ago, no well-accepted taxonomy existed for classifying personality traits. Today researchers generally agree that there are five robust factors of personality (the “Big Five”), which can serve as a meaningful taxonomy for classifying personality attributes (Barrick, Mount, & Judge, 2001; Woods & Anderson, 2016):

· Extroversion—being sociable, gregarious, assertive, talkative, and active (the opposite end of the extroversion dimension is labeled introversion)

· Neuroticism—being anxious, depressed, angry, embarrassed, emotional, worried, and insecure (the opposite pole of neuroticism is labeled emotional stability)

· Agreeableness—being curious, flexible, trusting, good-natured, cooperative, forgiving, softhearted, and tolerant

· Conscientiousness—being dependable (i.e., being careful, thorough, responsible, organized, and planful), as well as hardworking, achievement oriented, and persevering

· Openness to experience—being imaginative, cultured, curious, original, broad minded, intelligent, and artistically sensitive

Such a taxonomy makes it possible to determine if there exist consistent, meaningful relationships between particular personality constructs and job-performance measures for different occupations. The widespread use of the five-factor model (FFM) of personality is evident, given that Barrick and Mount (2003) reported that at least 16 meta-analytic reviews have been published using this framework since 1990. There is no other area in talent management research in which such a large number of meta-analytic reviews have been published in such a short period of time.

Results averaged across meta-analyses revealed the following average corrected correlations for each of the five dimensions (Barrick & Mount, 2003): extroversion (.12), emotional stability (the opposite pole of neuroticism) (.12), agreeableness (.07), conscientiousness (.22), and openness to experience (.05). Therefore, conscientiousness is the best predictor of job performance across types of jobs. In addition, personality inventories seem to predict performance above and beyond other frequently used predictors such as general cognitive ability. For example, agreeableness and conscientiousness predicted peer ratings of team-member performance above and beyond job-specific skills and general cognitive ability in a sample of over 300 full-time HR representatives at local stores of a wholesale department store organization (Neuman & Wright, 1999).

Barrick et al. (2001) summarized reviews of three meta-analyses that examined the relationship between the FFM of personality and managerial performance specifically. The combination of these three meta-analyses included a total of 67 studies and 12,602 individuals. Average corrected correlations across these three meta-analyses were the following: extroversion (.21), emotional stability (.09), agreeableness (.10), conscientiousness (.25), and openness to experience (.10). Thus, conscientiousness and extroversion seem to be the best two predictors of performance for managers.

Judge, Bono, Ilies, and Gerhardt (2002) conducted a related meta-analysis that examined the relationship between the FFM of personality and leadership, a key variable for managerial success. Results indicated the following corrected correlations: extroversion (.31), emotional stability (.24), agreeableness (.08), conscientiousness (.28), and openness to experience (.24). The combination of these meta-analytic results firmly supports the use of personality scales in managerial selection.

Why and When Does Personality Predict Performance?

Given the encouraging results regarding the predictability of performance using personality traits, there is now a need to understand why certain components of the FFM of personality are good predictors of managerial and non-managerial performance and its various facets (Murphy & Dzieweczynski, 2005). Some research is starting to shed light on this issue. Barrick, Stewart, and Piotrowski (2002) studied a sample of 164 telemarketing and sales representatives and found that status striving (exerting effort to perform at a higher level than others) and accomplishment striving (exerting effort to complete work assignments) serve as mediators between personality (conscientiousness and extroversion) and job performance. In other words, conscientiousness leads to a motivation to strive for accomplishments, which, in turn, leads to higher levels of performance. Extroversion leads to a motivation for status striving, which, in turn, leads to higher levels of performance. A related meta-analysis found that emotional stability (average validity = .31) and conscientiousness (average validity = .24) were the personality traits most highly correlated with performance motivation (Judge & Ilies, 2002). These results suggest that further research is needed to better understand the relationships among personality, motivation, and performance.

Another theoretical perspective that has potential to explain why and under which conditions personality predicts performance is socioanalytic theory (Hogan & Holland, 2003). Socioanalytic theory suggests two broad individual motive patterns that translate into behaviors: (1) a “getting along” orientation that underlies such constructs as expressive role, providing consideration, and contextual performance and (2) a “getting ahead” orientation that underlies such constructs as instrumental role, initiating structure, and task performance. Hogan and Holland (2003) defined getting ahead as “behavior that produces results and advances an individual within the group and the group within its competition” (p. 103) and getting along as “behavior that gains the approval of others, enhances cooperation, and serves to build and maintain relationships” (p. 103). Then they conducted a meta-analysis of 43 studies that used the Hogan Personality Inventory (HPI), which is based on the FFM. Prior to analyzing the data, however, subject matter experts (SMEs) with extensive experience in validation research and use of the HPI classified the criteria used in each primary-level study as belonging in the getting-ahead or getting-along category. Subsequently, SMEs were asked to identify the personality trait most closely associated with each performance criterion. Thus, in contrast to previous meta-analyses of the relationship between personality and performance, this study used socioanalytic theory to align specific personality traits with specific job-performance criteria. Then specific predictions were made based on the correspondence between predictors and criteria. When only criteria deemed directly relevant were used, correlations for each of the Big Five traits were the following: extroversion (.35), emotional stability (.43), agreeableness (.34), conscientiousness (.36), and openness to experience (.34). These correlations, based on congruent predictor–criterion combinations based on socioanalytic theory, are substantially larger than correlations obtained in previous meta-analytic reviews. Thus, this meta-analysis demonstrated the potential of socioanalytic theory to explain why certain personality traits are related to certain types of criteria. This finding reinforces the idea that choosing work-related personality measures on the basis of thorough job and organizational analyses is a fundamental element in the managerial selection process.

To try to understand why and which personality trait is useful in predicting performance and managerial performance in particular, further theory-driven research is needed on how to improve the validity of personality inventories (Mayer, 2005; Schneider, 2007; Tett & Christiansen, 2007). One promising avenue is to move beyond the Big Five model and focus on compound traits that are broader than the Big Five traits (Viswesvaran, Deller, & Ones, 2007) and also on narrower traits (e.g., components of the Big Five traits) (Dudley, Orvis, Lebiecki, & Cortina, 2006). For example, consider the case of the construct core self-evaluations (Judge & Hurst, 2008). Core self-evaluation is a broad, higher order latent construct indicated by self-esteem (i.e., the overall value one places on oneself as a person), generalized self-efficacy (i.e., one’s evaluation regarding how well one can perform across a variety of situations), neuroticism (i.e., one of the Big Five traits, as described earlier), and locus of control (i.e., one’s beliefs about the causes of events in one’s life, and locus is internal when one believes that events are mainly caused by oneself as opposed to external causes) (Johnson, Rosen, & Levy, 2008). Across the four traits that indicate core self-evaluations, the average correlation with performance is .23 (Judge & Bono, 2001). This high correlation, which is comparable to the mean meta-analytically derived corrected correlation between conscientiousness and performance, is presumably due to the effect of core self-evaluations on motivation: Those higher on core self-evaluations have more positive self-views and are more likely to undertake difficult tasks (Bono & Judge, 2003).

Some have argued that at least 30 traits not included in the Big Five may also have potential in terms of predicting performance (Hough, Oswald, & Ock, 2015). Examples of these traits that may be particularly useful for managerial selection are tolerance for contradiction, fairness, consideration, ease in decision making, and enterprising. Hough et al. (2015) grouped these specific traits into the following clusters: honesty, interpersonal, intrapersonal, values, self-evaluation, interests, and other. Some of these specific traits have also been clustered into what has been labeled the dark triad of personality (Spain, Harms, & LeBreton, 2014):

· Machiavellism—lack of empathy, low affect, willingness to manipulate

· Narcissism—grandiosity, entitlement, dominance, and superiority

· Psychopathy—impulsivity and thrill-seeking combined with low empathy and anxiety

The dark triad traits relate to the Big Five such that Machiavellism and psychopathy are negatively related to conscientiousness and narcissism, whereas psychopathy is positively related to openness to experience and extroversion. Moreover, contrary to what its label may suggest, dark personality does not always relate to negative work outcomes. For example, although there is a clear and positive relation between the dark triad and counterproductive work behaviors, it is less clear for other types of criteria. Overall, the relation between the dark triad and leadership is not yet well understood. For example, leader narcissism can be a predictor of leader failures and successes. Regarding failures, dark personality traits have been found to predict managerial derailment. Specifically, problems regarding interpersonal relationships can be explained by high scores on the dark triad and associated behaviors such as authoritarianism, eccentricity, and others (Spain et al., 2014).

Another conceptual perspective that allows us to understand why and when personality predicts performance is what has been called the “too-much-of-a-good-thing” effect (Pierce & Aguinis, 2013). Specifically, this principle “accounts for an apparent paradox in organizational life: ordinarily beneficial antecedents causing harm when taken too far” (p. 314). In other words, relations that are linear become flat (i.e., no relation) or even negative (i.e., curvilinear or even inverted U-shaped relation). In the particular domain of personality, Le et al. (2011) hypothesized that individuals scoring very high on conscientiousness can be considered “rigid, inflexible, and compulsive perfectionists” (p. 114). So, too much conscientiousness, a good thing, is not necessarily good. Le et al. conducted a study in which they collected self-reports of personality and supervisory ratings of performance for more than 600 employees. They found a negative, curvilinear relationship between conscientiousness and (a) task performance and (b) organizational citizenship behaviors. In other words, the relations are initially positive but then become negative. Le et al. found similar results for another personality trait: emotional stability. In fact, for low-complexity jobs, the relation between the personality traits and the various types of performance became negative for the high end of the distributions of personality scores. The lesson? Don’t forget the “too-much-of-a-good-thing” effect when trying to predict future managerial performance using personality traits as predictors.

Response Distortion in Personality Inventories

In  Chapter 12 , we described evidence regarding the extent to which job applicants can intentionally distort their scores on personal history data and honesty tests and how to minimize such distortion. Similar concerns exist regarding personality inventories (Fell & König, 2016). Specifically, Morgeson et al. (2007a, 2007b) 

· concluded that response distortion (i.e., faking) on self-report personality tests is virtually impossible to avoid. More important, they concluded that a perhaps even more critical issue is that validity coefficients in terms of performance prediction are not very impressive and have not changed much over time, particularly if one examines observed (i.e., uncorrected for statistical artifact) coefficients. Thus, they issued a call for finding alternatives to self-report personality measures. Ones, Dilchert, Viswesvaran, and Judge (2007) and Tett and Christiansen (2007) provided counterarguments in defense of personality testing. These include that personality testing is particularly useful when validation is based on confirmatory research using job analysis and that, taking into account the bidirectionality of trait–performance linkages, the relationship between conscientiousness and performance generalizes across settings and types of jobs. Moreover, they argued that personality adds incremental validity to the prediction of job performance above and beyond cognitive ability tests.

· Based on this research, HR specialists interested in using personality inventories must consider whether intentional response distortion (i.e., faking) affects the validity of such instruments and whether faking affects the quality of decision making (Mueller-Hanson, Heggestad, & Thornton, 2003). Although the preponderance of the evidence shows that criterion-related validity coefficients do not seem to be affected substantially by faking (Barrick & Mount, 1996; Hogan, Barrett, & Hogan, 2007), faking may still change the rank order of some individuals in the upper portion of the predictor score distribution, which would obviously affect decision making (Komar, Brown, Komar, & Robie, 2008; Mueller-Hanson et al., 2003). Unless selection ratios are large, decision making is likely to be adversely affected, and organizations are likely to realize lower levels of performance than expected, possibly also resulting in inflated utility estimates.

· Strategies to Mitigate Response Distortion

· Fortunately, specific strategies can be used to mitigate distortion. Those strategies described in Chapter 12 to minimize faking in other types of instruments (e.g., biodata, interviews, honesty tests) also apply to the administration of personality inventories. In addition, other strategies that are available specifically to mitigate distortion in personality inventories could also be used for other types of tests. These involve using forced-choice personality test items and warning against faking (Converse et al., 2008). The use of forced-choice items improved validity in both warning and no-warning conditions. However, the use of warnings against faking did not produce an improvement in the resulting validity coefficient. Note that each of these methods may produce negative reactions on the part of test takers.

· Four additional methods were developed specifically to address response distortion in personality tests (Hough, 1998; Kuncel & Borneman, 2007). Two are based on the Unlikely Virtues (UV) scale of Patrick, Curtin, and Tellegen’s (2002) Multidimensional Personality Questionnaire to detect intentional distortion. The UV scale consists of nine items using “Yes,” “Not sure,” and “No” response options. An example of a question that is similar to a question in the UV scale is “Have you ever been grouchy with someone?” (Hough, 1998).

· First, one can correct an applicant’s score based on that person’s score on the UV scale. Specifically, applicants whose scores are inordinately high are “penalized” by a reduction in their scores based on the amount of “overly virtuous” responding on the UV scale. For example, if an applicant’s score is three or more standard deviation units (SDs) above the incumbent UV scale mean, Hough (1998) recommended that his or her score on the personality scale be reduced by 2 SDs (based on incumbent scores). This strategy is different from statistically removing variance due to a social desirability scale because, when a residual score is created on a personality measure using that strategy, substantive variance may also be removed (Ellingson, Sackett, & Hough, 1999).

· Second, the UV scale can be used as a selection instrument in itself: Applicants scoring above a specific cut score can be disqualified automatically. Hough (1998) recommended removing applicants whose scores fall within the top 5% of the distribution of UV scores.

· Hough (1998) illustrated the benefits of the two UV scale–based strategies using samples of job applicants in three different contexts: a telecommunications company, a metropolitan police department, and a state law enforcement agency. The conclusion was that both strategies reduced the effects of intentional distortion without having a detrimental effect on criterion-related validity. However, some caveats are in order (Hough, 1998). First, these strategies can be implemented only in large organizations. Second, these strategies should not be used if UV scores correlate with performance scores. Third, if the personality scale in question is not correlated with the UV scale, then the strategies should not be implemented. Finally, specific contextual circumstances should be taken into account to assess whether the use of UV scale–based corrections would be appropriate in specific settings and for specific job applicants. The importance of taking these caveats into account and the vulnerability of using UV scale–based corrections were confirmed by a study by Hurtz and Alliger (2002), who found that individuals who were coached to “fake good” were able to fake a good impression and also avoid endorsing UV scale items.

· The third method proposed for specific use in personality testing is based on idiosyncratic item response patterns (Kuncel & Borneman, 2007). This approach is based on scoring items that yield dramatically different response patterns under honest and faking conditions that are not merely an upward shift in scores. An initial study including 215 undergraduates from a large university in the Midwestern United States yielded promising results: Researchers were able to successfully classify between 20% and 37% of faked personality measures with a false-positive rate of only 1% in a sample comprising 56% honest responses.

· The fourth method relies on avoiding self-reports altogether. What if personality traits are not rated by applicants themselves, but by others such as former and current coworkers, supervisors, and subordinates? Results based on a meta-analysis of 16 primary-level studies involving 18 independent samples provided evidence that observer ratings of personality predict performance quite well (Oh, Wang, & Mount, 2011). In fact, when the average number of observers was about 1.7, the mean observed correlations between the Big Five personality traits and performance ranged from .21 for emotional stability to a high of .37 for conscientiousness. When a single observer provided personality scores, validities ranged from .18 to .32. An additional and very promising result was that observer ratings of personality predicted performance above and beyond self-rated personality—even when a single observer provided the ratings. The average incremental validity of observer ratings across the five personality traits was r = .13. An important implication of these results is the usefulness of gathering personality scores from observers rather than job applicants. This could be done as part of the reference-check process—either online or via telephone.

· Clearly, like all selection methods, personality inventories have advantages and disadvantages. Fortunately, personality inventories are rarely the sole instrument used in selecting managers. So, the effects of faking are mitigated somewhat. Next, we turn to one such additional type of selection instrument: leadership ability tests.

· Leadership Ability Tests

· Logically, one might expect measures of “leadership ability” to be highly predictive of managerial success, because such measures should be directly relevant to managerial job requirements. Scales designed to measure two major constructs underlying managerial behavior, providing consideration (one type of “getting along” construct) and initiating structure (one type of “getting ahead” construct), have been developed and used in many situations (Fleishman, 1973).

· Providing consideration involves managerial acts oriented toward developing mutual trust, which reflect respect for subordinates’ ideas and consideration of their feelings. High scores on providing consideration denote attitudes and opinions indicating good rapport and good two-way communication, whereas low scores indicate a more impersonal approach to interpersonal relations with group members (Fleishman & Peters, 1962).

· Initiating structure reflects the extent to which an individual is likely to define and structure his or her own role and those of subordinates to focus on goal attainment. High scores on initiating structure denote attitudes and opinions indicating highly active direction of group activities, group planning, communication of information, scheduling, willingness to try out new ideas, and so forth.

· Instruments designed to measure initiating structure and providing consideration (the Leadership Opinion Questionnaire, the Leader Behavior Description Questionnaire, and the Supervisory Behavior Description Questionnaire) have been in use for many years. However, evidence of their predictive validity has been mixed, so Judge, Piccolo, and Ilies (2004) conducted a meta-analysis of the available literature. These authors were able to synthesize 163 correlations linking providing consideration with leadership outcomes and 159 correlations linking initiating structure with leadership outcomes. Each of the leadership dimensions was related to six different leadership criteria (i.e., follower job satisfaction, follower satisfaction with the leader, follower motivation, leader job performance, group/organization performance, and leader effectiveness). Overall, the corrected correlation between providing consideration and all criteria combined was .48, whereas the overall corrected correlation between initiating structure and all criteria was .29. In addition, providing consideration was more strongly related to follower job satisfaction, follower motivation, and leader effectiveness, whereas initiating structure was slightly more strongly related to leader job performance and group/organization performance. In spite of these encouraging overall results, substantial variability was found for the correlations even after corrections for sampling error and measurement error were applied. In short, the ability of these two dimensions to predict leadership success varies across studies in noticeable ways.

· Overall, the key challenge is to be able to identify “high-potential” leaders—usually labeled “HiPos.” However, a relevant question is, “High potential for what?” Our previous discussion of cognitive abilities and personality suggests that these are good predictors of general performance. To identify HiPos, however, use of these predictors should be only the first step in the process (Silzer, Church, Rotolo, & Scott, 2016). It is also important to measure other predictors of leadership success, such as learning skills (e.g., adaptability, learning interest and orientation, and openness to feedback) and motivation skills (e.g., drive, energy, and initiative; career ambition and commitment; and results and achievement orientation). In addition, it is also important to measure leadership skills (e.g., managing people; motivating, influencing, and inspiring others; and developing others) as well as functional and technical capabilities. Based on these predictors, Silzer et al. (2016) implemented a high-potential assessment program at PepsiCo that they called Leadership Assessment and Development (LeAD). A unique aspect of this program was the emphasis on specific job and leadership requirements in the future. The program seemed to work at PepsiCo (Silzer et al., 2016), but evidence needs to be gathered on its generalizability, implementation details, and specific results—for example, what proportion of individuals identified as HiPos became successful leaders and how was success defined, specifically?

· Our ability to predict successful managerial behaviors will likely improve if we measure more specific predictors and more specific criteria rather than general abilities as predictors and overall performance as a criterion. For example, a study including 347 managers and supervisors from six different organizational contexts, including a telecommunications company, a university, a printing company, and a hospital, found that conflict resolution skills, as 

· measured using an interactive video-assessment instrument, predicted ratings of on-the-job performance in managing conflict (Olson-Buchanan et al., 1998). Specific skills (e.g., conflict resolution) predicted specific criteria that were hypothesized to be linked directly to the predictor (e.g., ratings of on-the-job conflict resolution performance). This is point-to-point correspondence.

· Motivation to Manage

· We mentioned earlier that one’s motivation, and one’s ability to motivate others, have good potential in terms of predicting future managerial success. One measure that has been used frequently is the Miner Sentence Completion Scale (MSCS), a measure of motivation to manage.

· The MSCS consists of 40 items, 35 of which are scored. The items form seven subscales (authority figures, competitive games, competitive situations, assertive role, imposing wishes, standing out from the group, and routine administrative functions). Definitions of these subscales are shown in Table 13.1. The central hypothesis is that a positive relation exists between positive affect toward these areas and managerial success. Median MSCS subscale intercorrelations range from .11 to .15, and reliabilities in the .90s have been obtained repeatedly with experienced scorers (Miner, 1978a).

· Validity coefficients for the MSCS have ranged as high as .69, and significant results have been reported in over 25 different studies (Miner, 1978a, 1978b; Miner & Smith, 1982). By any criterion used—promotion rates, grade level, choice of managerial career—more successful managers have tended to obtain higher scores, and managerial groups have scored higher than nonmanagerial groups on the MSCS (Miner & Crane, 1981). Longitudinal data indicate that those with higher initial MSCS scores subsequently are promoted more rapidly and that those with the highest scores (especially on the subscales related to power, such as competing for resources, imposing wishes on others, and respecting authority) are likely to reach top-executive levels (Berman & Miner, 1985). In another study, 59 entrepreneurs completed the MSCS as they launched new business ventures. Five and a half years later, MSCS total scores predicted the performance of their firms (growth in number of employees, dollar volume of sales, and entrepreneurs’ yearly income) with validities in the high .40s (Miner, Smith, & Bracker, 1994). The consistency of these results is impressive, and, because measures of intelligence are unrelated to scores on the MSCS, the MSCS can be a useful addition to a battery of management selection measures. Further, because the causal arrow seems to point from motivation to success, companies might be advised to include “motivation to manage” in their definitions of managerial success.

· Table 13.1 Subscales of the Miner Sentence Completion Scale and Their Interpretation

Subscale	Interpretation of Positive Responses
Authority figures	A desire to meet managerial role requirements in terms of positive relationships with superiors
Competitive games	A desire to engage in competition with peers involving games or sports and thus to meet managerial role requirements in this regard
Competitive situations	A desire to engage in competition with peers involving occupational or work-related activities and thus to meet managerial role requirements in this regard
Assertive role	A desire to behave in an active and assertive manner involving activities that in this society are often viewed as predominantly masculine and thus to meet managerial role requirements
Imposing wishes	A desire to tell others what to do and to use sanctions in influencing others, thus indicating a capacity to fulfill managerial role requirements in relationships with subordinates
Standing out from group	A desire to assume a distinctive position of a unique and highly visible nature in a manner that is role-congruent for the managerial job
Routine administrative functions	A desire to meet managerial role requirements regarding activities often associated with managerial work, which are of a day-to-day administrative nature

· Source: Miner, J. B., & Smith, N. R. (1982). Decline and stabilization of managerial motivation over a 20-year period. Journal of Applied Psychology, 67, 298.

· Why does motivation predict performance? McClelland and Burnham (1976) found that a distinctive motive pattern, termed the leadership motive pattern (LMP)—namely, moderate-to-high need for power (nPow—power either to control other people or to achieve higher goals), low need for affiliation (nAff—concern over establishing, maintaining, or restoring a positive affective relationship with other people), and high activity inhibition (a stable tendency to restrain or inhibit motivational impulses)—was related to success in management. The theoretical explanation for the LMP is as follows. High nPow is important because it means the person is interested in the “influence game,” in having an impact on others. Lower nAff is important because it enables a manager to make difficult decisions without worrying unduly about being disliked; and high self-control is important because it means the person is likely to be concerned with maintaining organizational systems and following orderly procedures (McClelland, 1975).

· Whereas high need for achievement (nAch) seems not to be related to managerial success in corporate environments, it is strongly related to success as an entrepreneur (Boyatzis, 1982). As for technical managers, the LMP did not predict who was more or less likely to be promoted to higher levels of management in the company, but verbal fluency clearly did. These individuals were probably promoted for their technical competencies, among which was the ability to explain what they know. When these findings are considered, along with those for the MSCS, one conclusion is that both the need for power and the willingness to exert power may be important for managerial success only in situations where technical expertise is not critical (Cornelius & Lane, 1984).

· Another approach to assessing motivation to manage has been proposed by Chan and Drasgow (2001). These researchers defined motivation to lead (MTL) as an individual differences construct that “affects a leader’s or leader-to-be’s decisions to assume leadership training, roles, and responsibility and that affects his intensity of effort at leading and persistence as a leader” (p. 482). The scale developed to assess MTL includes three components: (1) affective-identity MTL (example item: “I am the type of person who likes to be in charge of others”), (2) noncalculative MTL (example item: “If I agree to lead a group, I would never expect any advantages or special benefits”), and (3) social-normative MTL (example item: “I agree to lead whenever I am asked or nominated by the other members”).

· Using the MTL in a sample of over 1,300 military recruits in Singapore demonstrated that affective-identity MTL scores (r = .39) and noncalculative MTL scores (r = .20) were reasonable predictors of multisource behavioral-leadership potential ratings. MTL scores also provided additional explained variance in the criterion (i.e., leadership potential ratings) above and beyond other predictors including general cognitive ability, military attitude, and the Big Five personality factors. These promising results provide HR specialists with an additional tool to predict leadership success.

· Personal History Data

· Biographical information has been used widely in managerial selection—capitalizing on the simple fact that one of the best predictors of future behavior is past behavior. Unfortunately, the approach has been characterized more by raw empiricism than by theoretical formulation and rigorous testing of hypotheses. On the positive side, however, the items are usually nonthreatening and, therefore, are probably not as subject to distortion as are typical personality inventories (Cascio, 1975).

· One review found that, across seven studies (total N = 2,284) where personal history data were used to forecast success in management, the average validity was .38. When personal history data were used to predict sales success, it was .50, and, when used to predict success in science/engineering, it was .41 (Reilly & Chao, 1982). Another study examined the relationship between college experiences and later managerial performance at AT&T (Howard, 1986). The choice of major (humanities, social science, business versus engineering) and extracurricular activities both validly forecast the interpersonal skills that are so critical to managerial behavior.

· In conducting a literature review on managerial success, Campbell et al. (1970) concluded:

· What is impressive is that indicators of past successes and accomplishments can be utilized in an objective way to identify persons with differing odds of being successful over the long term in their management career. People who are already intelligent, mature, ambitious, energetic and responsible and who have a record of prior achievement when they enter an organization are in excellent positions to profit from training opportunities and from challenging organizational environments. (p. 196)

· Can biodata instruments developed to predict managerial success (e.g., rate of promotional progress) in one organization be similarly valid in other organizations, including organizations in different industries? The answer is yes, but this answer also needs to be qualified by the types of procedures used in developing the instrument. Four factors are believed to influence the generalizability of biodata instruments (Carlson, Scullen, Schmidt, Rothstein, & Erwin, 1999). First, the role of theory is crucial. Specifically, there should be clear reasons why the instrument would generalize to other populations and situations. In the absence of such clear expectations, some predictive relationships may not be observed in the new setting. Second, the criterion measure used for key development should be valid and reliable. When criterion measures are not adequate, there will be little accuracy in identifying meaningful relationships with the biodata items. Third, the validity of each item in the inventory should be determined. Doing so reduces the sample dependence of the instrument. Sample dependence increases when items are developed using an empirical as opposed to a theory-based approach (see Chapter 12). Finally, if large samples are used to develop the instrument, results are less likely to be affected as adversely by sampling error, and the chances of generalization increase. Next, we discuss another type of predictor of managerial success: peer assessment.

· Peer Assessment

In the typical peer-assessment paradigm, raters are asked to predict how well a peer will do if placed in a leadership or managerial role. Such information can be enlightening, for peers typically draw on a different sample of behavioral interactions (i.e., those of an equal, non–supervisor–subordinate nature) in predicting future managerial success. Peer assessment is a general term for three more basic methods used by members of a well-defined group in judging each other’s performance. Peer nomination requires each group member to designate a certain number of group members (excluding himself or herself) as being highest (lowest) on a particular dimension of performance (e.g., handling customers’ problems). Peer rating requires each group member to rate every other group member on several performance dimensions using, for example, some type of graphic rating scale. A final method, peer ranking, requires each group member to rank all the others from best to worst on one or more factors.

Reviews of over 50 studies relevant to all three methods of peer assessment (Kane & Lawler, 1978, 1980; Mumford, 1983; Schmitt, Gooding, Noe, & Kirsch, 1984) found that all the methods showed adequate reliability, validity (average r = .43), and freedom from bias. However, the three methods appear to “fit” somewhat different assessment needs. Peer nominations are most effective in discriminating persons with extreme (high or low) levels of knowledge, skills, or abilities from the other members of their groups. For example, peer nomination for top-management responsibility correlated .32 with job advancement 5 to 10 years later (Shore, Shore, & Thornton, 1992). Peer rating is most effective in providing feedback, while peer ranking is probably best for discriminating throughout the entire performance range from highest to lowest on each dimension.

The reviews noted three other important issues in peer assessment:

· The influence of friendship: It appears from the extensive research evidence available that effective performance probably causes friendship rather than the independent influence of friendship biasing judgments of performance. These results hold up even when peers know that their assessments will affect pay and promotion decisions.

· The need for cooperation in planning and design: Peer assessments implicitly require people to consider privileged information about their peers in making their assessments. Thus, they easily can infringe on areas that will either raise havoc with the group or cause resistance to making the assessments. To minimize any such adverse consequences, it is imperative that groups be intimately involved in the planning and design of the peer-assessment method to be used.

· The required length of peer interaction: It appears that the validity of peer nominations for predicting leadership performance develops very early in the life of a group and reaches a plateau after no more than three weeks for intensive groups. Useful validity develops in only a matter of days. Thus, peer nominations possibly could be used to identify managerial talent if the competitive atmosphere of such a context does not induce excessive bias. We hasten to add, however, that in situations where peers do not interact intensively on a daily basis (e.g., life insurance agents), peer ratings are unlikely to be effective predictors for individuals with less than six months’ experience (Mayfield, 1970, 1972).

In summary, peer assessments have considerable potential as effective predictors of managerial success, and Mumford (1983) has provided integrative models for future research. To be sure, as Kraut (1975) noted, the use of peer ratings among managers may merely formalize a process in which managers already engage informally.

Work Samples of Managerial Performance

Up to this point, we have discussed tests as signs or indicators of predispositions to behave in certain ways, rather than as samples of the characteristic behavior of individuals. Wernimont and Campbell (1968) have argued persuasively, however, that prediction efforts are likely to be more fruitful if we focus on meaningful samples of behavior rather than on signs or predispositions. Because selection measures are really surrogates or substitutes for criteria, we should be trying to obtain measures that are as similar to criteria as possible. Criteria also must be measures of behavior. Hence, it makes little sense to use a behavior sample to predict an administrative criterion (e.g., promotion, salary level), since the individual frequently does not exercise a great deal of control over such organizational outcome variables. To understand more fully individual behavior in organizations, work-sample measures must be related to observable job-behavior measures. Only then will we understand exactly how, and to what extent, an individual has influenced his or her success. This argument is not new (cf. Campbell et al., 1970; Dunnette, 1963b; Smith & Kendall, 1963), but it deserves reemphasis.

Particularly with managers, effectiveness is likely to result from an interaction of individual and situational or context variables, for, as we noted earlier, the effective manager is an optimizer of all the available resources. It follows, then, that a work sample whose objective is to assess the ability to do rather than the ability to know should be a more representative measure of the real-life complexity of managerial jobs. In work samples (Flanagan, 1954b):

Situations are selected to be typical of those in which the individual’s performance is to be predicted… . [Each] situation is made sufficiently complex that it is very difficult for the persons tested to know which of their reactions are being scored and for what variables. There seems to be much informal evidence (face validity) that the person tested behaves spontaneously and naturally in these situations… . It is hoped that the naturalness of the situations results in more valid and typical responses than are obtained from other approaches. (p. 462)

These ideas have been put into theoretical form by Asher (1972), who hypothesized that the greater the degree of point-to-point correspondence between predictor elements and criterion elements, the higher the validity. By this rationale, work-sample tests that are miniature replicas of specific criterion behavior should have point-to-point relations with the criterion. This hypothesis received strong support in a meta-analytic review of the validity of work-sample tests (Schmitt et al., 1984). In fact, when work samples are used as a basis for promotion, their average validity is .54 (Hunter & Hunter, 1984). A more recent meta-analysis found an average correlation (corrected for measurement error) of .33 with supervisory ratings of job performance (Roth, Bobko, & McFarland, 2005). High validity and cost-effectiveness (Cascio & Phillips, 1979), high face validity and acceptance (Steiner & Gilliland, 1996), lack of bias based on race and gender (Lance, Johnson, Douthitt, Bennett, & Harville, 2000), and apparently substantially reduced adverse impact (Brugnoli, Campion, & Basen, 1979; Schmidt, Greenthal, Hunter, Berner, & Seaton, 1977) make work sampling an especially attractive approach to managerial selection. In fact, studies conducted in the Netherlands (Anderson & Witvliet, 2008) and in Greece (Nikolaou & Judge, 2007) concluded that, similar to results of past studies conducted in the United States, France, Spain, Portugal, and Singapore, work samples are among the three most accepted selection methods among applicants (Hausknecht, Day, & Thomas, 2004). Although the development of “good” work samples is time consuming and can be quite difficult (cf. Plumlee, 1980), monetary and social payoffs from their use may well justify the effort.

Note, however, that further research is needed regarding the conclusion that work samples reduce adverse impact substantially given that a study using incumbent, rather than applicant, samples revealed that range restriction may have caused an underestimation of the degree of adverse impact in past research (Bobko, Roth, & Buster, 2005). In fact, a more recent meta-analysis including samples of job applicants found that the mean score for African Americans is .80 standard deviations lower than the mean score for whites for work-sample test ratings of cognitive and job knowledge skills. This difference was much lower, in the .21 to .27 range, but still favoring white applicants, for ratings of various social skills (Roth, Bobko, McFarland, & Buster, 2008).

In the context of managerial selection, two types of work samples are used. In group exercises, participants are placed in a situation in which the successful completion of a task requires interaction among the participants. In individual exercises, participants complete a task independently. Both individual and group exercises can be specified further along several continua (Callinan & Robertson, 2000): (a) bandwidth (the extent to which the entire job domain is part of the work sample), (b) fidelity (the extent to which the work sample mirrors actual job conditions), (c) task specificity (the extent to which tasks are specific to the job in question or more general in nature), (d) necessary experience (the extent to which previous knowledge of the position is needed), (e) task types (e.g., psychomotor, verbal, social), and (f) mode of delivery and response (e.g., behavioral, verbal, or written). Based on these categories, it should be apparent that there are numerous choices regarding the design and implementation of work samples. Next, we discuss four of the most popular types of work samples: the leaderless group discussion, the in-basket test, the business game, and the situational judgment test.

Leaderless Group Discussion

The leaderless group discussion (LGD) is a disarmingly simple technique. A group of participants simply is asked to carry on a discussion about some topic for a period of time (Bass, 1954). Of course, face validity is enhanced if the discussion is about a job-related topic. No one is appointed leader. Raters do not participate in the discussion but remain free to observe and rate the performance of each participant. For example, IBM used an LGD in which each participant is required to make a five-minute oral presentation of a candidate for promotion and then subsequently defend his or her candidate in a group discussion with five other participants. All roles are well defined and structured. Seven characteristics are rated, each on a five-point scale of effectiveness: aggressiveness, persuasiveness or selling ability, oral communications, self-confidence, resistance to stress, energy level, and interpersonal contact (Wollowick & McNamara, 1969).

Reliability

Interrater reliabilities of the LGD generally are reasonable, averaging .83 (Bass, 1954; Tziner & Dolan, 1982). Test–retest reliabilities of .72 (median of seven studies; Bass, 1954) and .62 (Petty, 1974) have been reported. Reliabilities are likely to be enhanced, however, to the extent that LGD behaviors simply are described rather than evaluated in terms of presumed underlying personality characteristics (Bass, 1954; Flanagan, 1954b).

Validity

In terms of job performance, Bass (1954) reported a median correlation of .38 between LGD ratings and performance ratings of student leaders, shipyard foremen, administrative trainees, foreign-service administrators, civil-service administrators, and oil-refinery supervisors. In terms of training performance, Tziner and Dolan (1982) reported an LGD validity of .24 for female officer candidates; in terms of ratings of five-year and career potential, Turnage and Muchinsky (1984) found LGD validities in the low .20s; and, in terms of changes in position level three years following the LGD, Wollowick and McNamara (1969) reported a predictive validity of .25. Finally, since peer ratings in the LGD correlate close to .90 or higher with observers’ ratings (Kaess, Witryol, & Nolan, 1961), it is possible to administer the LGD to a large group of candidates, divide them into small groups, and have them rate each other. Gleason (1957) used such a peer rating procedure with military trainees and found that reliability and validity held up as well as when independent observers were used.

Effects of Training and Experience

Petty (1974) showed that, although LGD experience did not significantly affect performance ratings, previous training did. Individuals who received a 15-minute briefing on the history, development, rating instruments, and research relative to the LGD were rated significantly higher than untrained individuals. Kurecka, Austin, Johnson, and Mendoza (1982) found similar results and showed that the training effect accounted for as much as 25% of criterion variance. To control for this, either all individuals trained in the LGD can be put into the same group(s), or the effects of training can be held constant statistically. One or both of these strategies are called for to interpret results meaningfully and fairly.

The In-Basket Test

The in-basket test is an individual work sample designed to simulate important aspects of the manager’s position. Given the decreased use of paper and increased use of electronic and digital media, the name of this predictor should probably be changed to “Inbox” (referring to e-mails) or “Unread” (referring to smartphone texts) test. Different types of in-basket tests may be designed, corresponding to the requirements of various levels of managerial jobs. The first step in in-basket development is to determine what aspects of the managerial job to measure. For example, in assessing candidates for middle-manager positions, IBM determined that the following characteristics are important for middle-management success and should be rated in the in-basket simulation: oral communications, planning and organizing, self-confidence, written communications, decision making, risk taking, and administrative ability (Wollowick & McNamara, 1969). On the basis of this information, problems then are created that encompass the kinds of issues the candidate is likely to face, should he or she be accepted for the job.

In general, an in-basket simulation takes the following form (Fredericksen, 1962):

It consists of the letters, memoranda, notes of incoming telephone calls, and other materials which have supposedly collected in the in-basket of an administrative officer. The subject who takes the test is given appropriate background information concerning the school, business, military unit, or whatever institution is involved. He is told that he is the new incumbent of the administrative position, and that he is to deal with the material in the in-basket. The background information is sufficiently detailed that the subject can reasonably be expected to take action on many of the problems presented by the in-basket documents. The subject is instructed that he is not to play a role, he is not to pretend to be someone else. He is to bring to the new job his own background of knowledge and experience, his own personality, and he is to deal with the problems as though he were really the incumbent of the administrative position. He is not to say what he would do; he is actually to write letters and memoranda, prepare agendas for meetings, make notes and reminders for himself, as though he were actually on the job. (p. 1)

Although the situation is relatively unstructured for the candidate, each candidate faces exactly the same complex set of problem situations. At the conclusion of the in-basket test, each candidate leaves behind a packet full of notes, memos, letters, and so forth, which constitute the record of his or her behavior. The test then is scored (by describing, not evaluating, what the candidate did) in terms of the job-relevant characteristics enumerated at the outset. This is the major asset of the in-basket: It permits direct observation of individual behavior within the context of a highly job-relevant, yet standardized, problem situation.

In addition to high face validity, the in-basket also discriminates well. For example, in a middle-management training program, AT&T compared the responses of management trainees to those of experienced managers (Lopez, 1966). In contrast to experienced managers, the trainees were wordier; they were less likely to take action on the basis of the importance of the problem; they saw fewer implications for the organization as a whole in the problems; they tended to make final (as opposed to investigatory) decisions and actions more frequently; they tended to resort to complete delegation, whereas experienced executives delegated with some element of control; and they were far less considerate of others than the executives were. The managers’ approaches to dealing with in-basket materials later served as the basis for discussing the “appropriate” ways of dealing with such problems.

In-basket performance does predict success in training, with correlations ranging from .18 to .36 (Borman, 1982; Borman, Eaton, Bryan, & Rosse, 1983; Tziner & Dolan, 1982). A crucial question, of course, is that of predictive validity. Does behavior during the in-basket simulation reflect actual job behavior? Meta-analytic results relying on 31 primary-level studies (N = 3,958) indicated that the mean observed validity coefficient was .16, whereas the mean operational validity coefficient (i.e., corrected for criterion reliability and range restriction) was .42 (Whetzel, Rotenberry, & McDaniel, 2014). Also, the correlation between in-basket scores and general mental abilities was .26, which suggests that the in-basket test is somewhat g-loaded.

The Business Game

The business game is a “live” case. For example, in the assessment of candidates for jobs as Army recruiters, two exercises required participants to make phone calls to assessors who role played two different prospective recruits and then to meet for follow-up interviews with these role-playing assessors. One of the cold-call/interview exercises was with a prospective recruit unwilling to consider Army enlistment, and the other was with a prospect more willing to consider joining. These two exercises predicted success in recruiter training with validities of .25 and .26 (Borman et al., 1983). A desirable feature of the business game is that intelligence, as measured by cognitive ability tests, seems to have no effect on players’ success (Dill, 1972).

A variation of the business game focuses on the effects of measuring “cognitive complexity” on managerial performance. Cognitive complexity is concerned with “how” persons think and behave. It is independent of the content of executive thought and action, and it reflects a style that is difficult to assess with paper-and-pencil instruments (Streufert, Pogash, & Piasecki, 1988). Using computer-based simulations, participants assume a managerial role (e.g., county disaster control coordinator, temporary governor of a developing country) for six task periods of one hour each. The simulations present a managerial task environment that is best dealt with via a number of diverse managerial activities, including preventive action, use of strategy, planning, use and timeliness of responsive action, information search, and use of opportunism. Streufert et al. (1988) reported validities as high as .50 to .67 between objective performance measures (computer-scored simulation results) and self-reported indicators of success (a corrected measure of income at age, job level at age, number of persons supervised, and number of promotions during the past 10 years). Although the self-reports may have been subject to some self-enhancing bias, these results are sufficiently promising to warrant further investigation. Because such simulations focus on the structural style of thought and action rather than on content and interpersonal functioning, as in assessment centers (discussed later in this chapter), the two methods in combination may account for more variance in managerial performance than is currently the case.