CLOUD-BASED COMPUTING AND HUMAN RESOURCE MANAGEMENT PERFORMANCE A DELPHI STUDY

CLOUD-BASED COMPUTING AND HUMAN RESOURCE MANAGEMENT

PERFORMANCE: A DELPHI STUDY

Chapter 1 Introduction

Human resource (HR) leaders are the catalysts for ensuring efficiency in HR

practices and influencing organizations to implement technology-based solutions

(Indranil, 2011; Polen, 2009). The implementation of Cloud-Based Computing is slowly

progressing (Willcocks, Venters & Whitely, 2013) for HR processes, for example,

solutions such as online personal profiles and social networking sites are quickly

abolishing the résumé (Bohnert & Ross, 2010; Dickson & Nusair, 2010; Rickborn, 2012;

SHRM, 2011; Sprague, 2011; The Resume, 2011; Weiss, 2011). However, most of the

HR industry failed to implement new technologies or standardize the use of Cloud-Based

Computing for the HR function in areas such as interviewing, screening, and performance

management, although social media and online recruitment is prevalent (Bohnert & Ross,

2010; SHRM, 2011; Sprague, 2011; Weiss, 2011). Cloud-Based Computing is any third

party, web-based software, platform, infrastructure, or business process as a service. This

study focused on the use of Cloud-Based Computing in the HR field. Businesses aware

of which services are suitable for Cloud-Based Computing can use the technology as a

competitive edge ultimately increasing business value (Aljabre, 2012).

Leadership, retention, training, talent acquisition, and engagement are the most

urgent issues in HR (Bersin, 2014). HR leaders must have the right tools and technology

in place to manage multiple talent processes (Lombardi, 2014) by leveraging cloud

technology to transform and reinvent (Bersin, 2014) HR as a strong strategic partner.

Additionally, the human resource leader’s function shifted to that of a business partner

(Ulrich, 1997). As a value-adding business partner, the human resource leader yields a

list of characteristics including commitment to employee development, assisting the

business through change management, and efficient design and implementation of HR

systems adding value by aligning to organizational strategies and outcomes (Maslak,

2008; Ulrich, 1997) and aligning with characteristics of a transformational leader.

Cloud computing enables a range of information and communication technology

services ubiquitously available through network access from a service provider (Athale,

Barde, Kamble, Mirajkar, & Singh, 2012; Ross, 2011). Increased broadband and

improvements in the technology allow Cloud technology service providers the ability to

exploit new markets (Willcocks et al., 2013) and transform traditional HR business

practices (Deloitte, 2011; Ross, 2011). Cloud-Based Computing is replacing traditional

software installations (Athale et al., 2012) changing traditional business processes, and

increasing business value (Aljabre, 2012; Indranil, 2011; Rickborn, 2012).

However, HR professionals are reluctant to standardize implementation of

CloudBased Computing in traditional HR practices despite the overwhelming agreement

of key challenges remedied by Cloud solutions (Deloitte, 2010, 2011; Indranil, 2011;

Starner; 2011) that may influence Human Resource Management (HRM) and overall

organizational performance. Chapter 1 includes the background and evolution of HRM,

electronic Human Resource Management (e-HRM), Human Resource Information

Systems (HRIS), and the introduction of Cloud-Based Computing to HR with a review of

transformational leadership in HR with respect to technology adoption. The definition of

the problem and purpose for the study is in alignment with the significance of the study to

HRM, leadership theory, and academia. Finally, Chapter 1 includes a review of the

research questions, scope, assumptions, and delimitations for the study.

Definition of Terms

The following is a list of terms and definitions used in the research proposal that

can have unique connotations for this study.

Cloud-Based Computing. Cloud-Based Computing is a term generated by the

researcher to refer to any third party, web-based software, platform, infrastructure, or

business process as a service. This study focused on Cloud-Based Computing in the HR

field.

Cloud Technology. Leveraging third party, web-based technology and services to

complete business tasks, use applications, and maintain data (Qaisar & Khawaja, 2011).

Curriculum Vitae. Curriculum Vitae and résumé are interchangeable; however,

key differentiators are the length and use of the CV to expand on a candidate’s

professional and scholarly identity (Miron, Osoian, & Zaharie, 2011).

Delphi (Delphi Method, Delphi Technique, modified Delphi). The RAND

Corporation developed the Delphi method to obtain opinions from a small group of

participants over multiple questioning rounds for the purpose of consensus and

forecasting (Dalkey & Helmer, 1963). The classic Delphi (Skulmoski et al., 2007) has

since been modified in multiple ways in many research studies (Linstone & Turoff, 1975)

and the terms Delphi (Dalkey & Helmer, 1963), Delphi Method (Linstone & Turoff,

1975), and modified Delphi (Adler & Ziglio, 1996) are often used interchangeably to

refer to a Delphi research design. Changes to this modified Delphi study consisted of

recruitment of informed participants with 5 years HR experience, not necessarily experts;

and the gathering of prioritized responses with ranking, however not necessarily for

consensus (Beakley, 2015; Hall, 2009; Hall & Jordan, 2013).

Diffusion of Innovations (DOI). A theory developed by E. M. Rogers in 1962

explaining how and why innovative ideas, products, or behaviors are adopted and

diffused throughout a population or social system (McGrath & Zell, 2001; Rogers, 2003).

Human Resource Information System (HRIS). The technology tools that collect,

store, maintain, and retrieve an organization’s employee and HR-related data

(Karimidizboni, 2013; SHRM, 2014) and assist organizations in managing talent

(Karimidizboni, 2013; Spaulding, 2011).

Human Resource Management (HRM). HRM is the “formal structure within an

organization responsible for all the decisions, strategies, factors, principles, operations,

practices, functions, activities and methods related to the management of people”

(SHRM, 2014).

Human Resource Management Systems (HRMS). A software application

combining HR duties including payroll, benefits, performance management, recruiting,

screening, and training (SHRM, 2014).

Human Resource (HR) strategic business partner. A strategic business partner

that aligns business objectives with human resource practices and serves as a consultant

to management and executive leadership on Human Resource related issues (SHRM,

2012).

Infrastructure as a Service (IaaS). Infrastructure as a Service is a service of

virtual machines including raw storage, firewalls, and networks (Athale et al., 2012;

Skiba, 2011).

Legacy Technologies. Legacy Technologies refer to any onsite or customized

HRIS or HRMS not accessible via Cloud.

Platform as a Service (PaaS). Platform as a Service is Cloud computing module,

which provides platforms such as operating systems and application development

platforms via the internet (Athale et al., 2012; Skiba, 2011).

Software as a Service (SaaS). Software as a Service is a Cloud-computing model

that deploys software behind a firewall on a local area network and arrayed over the

internet (Howarth, 2009; Skiba, 2011).

Strategic Human Resource Management. Strategic Human Resource

Management is an approach to managing human resources that align with organizational

performance.

Society of Human Resource Management (SHRM). Originating in 1948, SHRM is

the largest human resource-focused organization in the world dedicated to the practice

and evolution of human resource management (SHRM, 2014).

Technology-Organization-Environment (TOE). A conceptual framework using

three contextual groups of technology, organization, and environment to explain the

process by which an organization adopts and implements technology innovations

(Tornatzky & Fleischer, 1990).

Background

Human Resource professionals are an essential part of strategic and organizational

leadership adding value by contributing to organizational strategy (Timmons, 2008). The

focus of human resource leaders is to identify cost-effective methods for hiring,

managing, and developing talent. Minimizing costs, optimizing talent, and positively

influencing business outcomes means the HR leader has the opportunity to align with the

business as a valuable business partner unlike historical perceptions as a compliance

officer (Chiu & Selmer, 2011). The importance of human resources integration into the

business strategy sphere continues to increase since the 1980s (Chiu & Selmer, 2011).

Scholars agreed HR, as an important business partner, are becoming increasingly critical

to maintain a competitive edge (Kapoor & Sherif, 2012).

Organizational leaders understand that hiring top talent, and retaining talent, is

crucial to maintaining a competitive advantage (Aladwan, Bhanugopan, & D’Netto,

2015). The processes of HRM, such as employee recruitment, selection, development,

performance, and rewards, are emphasized within a technology-based business

environment (Aladwan et al., 2015). The contribution of the Human Resource leader as a

value-add business partner includes activities in recruiting and staffing, employee

development, performance management, compensation management, and regulatory

compliance (Armstrong & Pitrowski, 2006; Eisner, 2010) with influence to organizational

performance.

Chiu and Selmer (2011) posited companies that find and retain the best talent have

a competitive edge. Staffing a business with top talent is dependent on recruiting,

screening, and hiring the right candidate for a specific position against the skills,

knowledge, and competencies required for success. The cost to hire and retain top talent

is a business goal HR leaders can leverage with new technology (Dickson & Nusair,

2010). While electronic web-based employment practices are widely used, many

companies rely on standard HR processes such as recruitment, selection, screening, and

performance management (Armstrong & Pitrowski, 2006; Eisner, 2010). Human

resource professionals are constantly introduced to technology-based strategies and trends

requiring organizational awareness of potential challenges (Morris & Revels, 2012;

Shilpa & Gopal, 2011). Indecisiveness in managing business operations is an

organizational recruiting challenge (Morris & Revels, 2012). Organizations rely on HR

leaders to implement solid processes and strategies with integrated technology to

maintain an efficient workforce (Morris & Revels, 2012) and department. The objective

of HRM is to transform organizational strategy into effective HR strategies that create a

competitive edge (Convertino, 2008; Tyson, 1995). In the role of a business partner, the

HR leader must add value by providing direction, leadership, and solutions to

successfully implement change (Nair, 2011), and achieve business objectives.

Information, technology, and systems have become a critical component in most

HR departments for hiring, payroll, training, and other responsibilities (Corsello, 2012).

Cloud-Based Computing is expected to intensify the need to change technologies and

enable HR to improve performance (Corsello, 2012). Employers use résumés to

determine if candidates possess the necessary background, skills, knowledge, and other

competencies to warrant investment in more costly selection measures, such as interviews

(Cole, Feild, Giles, & Harris, 2008). Digitized portfolios, or e-Portfolios, are tools for

students compiling work and identifying how curricular and work activities apply

professionally (Eisner, 2010) and are highly encouraged, if not required, by higher

learning institutions (Dubinsky, 2003) but not standardized as a tool for talent selection in

HR. At the time of this study, some employers use Cloud-Based Computing with social

networking and popular websites to screen candidates (Bohnert & Ross, 2010; Marx,

2011; SHRM, 2011; Sprague, 2011; Weiss, 2011); however, most use on-site legacy

systems.

Recruitment and screening are important processes for organizational success and

are transforming slowly through Software-as-a-Service (Deloitte, 2011; Starner, 2011).

Video interviewing is becoming a popular Cloud-based method for conducting interviews

in the hiring process (Abardeen, 2012; Banham, 2011; Crenshaw, 2006). Key drivers for

implementing video interviewing in the Cloud influence the whole organization including

costs, resources, reach, and quality of hire (Abardeen, 2012; Latham, & Luman, 2009),

are linked to business outcomes, and driven by the HR leader as a business partner.

Employment recruitment and screening are ubiquitous processes of talent acquisition for

human resource professionals (Collins, Domagalski, & Wright, 2011); thereby affecting

HR leaders.

Software as a Service (SaaS) will potentially replace traditional software in many

departments including human resources (Balbaa, 2011). In all HR leader competency

models, the ability to adapt and manage change is a critical success factor (Madu, 2009).

According to a survey by Deloitte (2011), standard HR practices in the Cloud are

inevitable; however, HR leaders are resistant to implementing Cloud-Based Computing

despite their role as an innovative leader, change agent, and business partner, and the

potential of using Cloud-Based Computing as a tool to achieve business goals.

The role of a business partner requires competence in diagnosing organizations,

reengineering processes, listening and responding to employees, and managing cultural

transformation (Loshali & Krishnan, 2013). The HR business partner adds value to a

firm through strategy execution, administrative efficiency (Sternberger, 2002), employee

commitment, and cultural change (Ulrich, 1997). HR leaders could implement

CloudBased Computing for HR practices and be willing to influence organizations to use

Cloud technologies as an alignment to business strategy (Indranil, 2011).

The HR professional’s perceptions change with the introduction of new or

advanced technologies (Polen, 2009) with Cloud-Based Computing being the latest

evolution to affect HR professionals. Numerous Cloud-Based Computing applications

are available to Human Resource professionals, and more recently to employees and

candidates, as technology becomes increasingly sophisticated and accessible (Aljabare,

2012; Indranil, 2011). Organizations are competing for access to a global talent pool, to

retain top talent, and require a cost effective platform for conducting traditional talent

acquisition processes (Dickson & Nusair, 2010; Kapoor & Sherif, 2012). Existing

challenges for large and small businesses substantially invested in fixed behaviors and

practices (Gibson, 2012) hinder adoption of new technologies and processes. However,

HR leaders need information supporting the decision to incorporate Cloud-Based

Computing solutions into effective standard HR processes such as validation from other

HR leaders based on experience.

Human resource leaders, as business partners, consider long-term business goals

to ensure talent acquisition and development processes align with business strategies

resulting in better talent choices (Collins, Domagalski, & Wright, 2011) potentially

increasing competitive advantage and organizational performance. Considerations for the

study include key drivers, processes, and concerns for implementing Cloud-Based

Computing for HR functions, characteristics of an HR leader having successfully

implemented Cloud-Based Computing, and the potential effect on HRM and

organizational performance. As more companies compete for the finest in the global

talent pool, and maintaining a competitive advantage (Kapoor & Sherif, 2012),

organizations and HR leaders must offer the best opportunity for success when

implementing standard HR processes to achieve extraordinary results (Loshali &

Krishnan, 2013).

Change initiatives start with leadership; therefore, leaders require adequate

preparation for successfully deciding upon, and implementing, changes. Increasing

competition for top talent requires organizations to implement effective strategies for

hiring from a global talent pool to stay competitive. Human resource leaders are

responsible for influencing organizational leadership when implementing HR technology

solutions and must have solid support to affect the decision positively (Polen, 2009; Yeh,

2012). The effects of Cloud-Based Computing are slowly evolving despite prevalent

interest in its benefits (Willcocks, Venters, & Whitely, 2013). Additionally, a need exists

to evaluate the performance of HRM to organizational performance (Fitz-enz &

Davidson, 2002; Ulrich, 2010). This study contributes to scholarship, HRM, and

leadership by providing information regarding the HR leader’s perspective and insight to

their respective experiences implementing Cloud-Based Computing and the potential

influence on organizational performance.

The most recent findings include identifying the benefits of Cloud-Based

Computing, current HR processes conducted via Cloud solutions, and reasons for slow

implementation by HR leaders. Findings include key success factors for technology

implementation from industry experts in organizations such as KPMG International,

General Electric, LinkedIn, Deloitte, and Sierra-Cedar. Cited throughout this study are

foundational theorists in the profession of HR leadership such as Ashbaugh, Rowan,

Ulrich, Gibson, and Fitz-enz, and thought leaders in Cloud technology such as Indranil,

Luman, Bohnert, Ikhlap, Khan, Mujtaba, Sadiq, and Ross.

Problem Statement

The general problem is the slow adoption of Cloud-Based Computing for

efficiency of the HR function (Bersin, 2014; Chen, Low, & Wu, 2011; Deloitte, 2011;

Deloitte 2012; Laurano, 2014; Sierra-Cedar, 2015). Bersin (2014) reports a significant

gap between organizations considering HR technology an urgent issue and organizational

readiness for technology change. Companies all over the world are competing for talent

and maintaining a competitive advantage, and must implement strategies and technology

for the ability to enhance HR performance and achieve organizational goals (Kaur & Rin

Yahya, 2010; Polen, 2009). Consequently, organizations are cautiously implementing

new methods for cutting costs and improving efficiency in standard HR processes such as

talent acquisition (Toldi, 2010). Traditional hiring practices are extensive and may

incorporate different phases, modalities, standards, or strategies, potentially requiring

different technologies. Human resource leaders experience resistance to implementing

technology-based solutions during the hiring process (Polen, 2009), and in standardized

processes, such as the traditional screening interview (Crenshaw, 2006; Evuleocha, 2002)

or online résumé. Globalizing and standardizing processes and technologies are

emerging issues in HR (Keebler & Watson, 2014).

Human resource professionals influence organizational leadership when making

the decision to integrate technology resources and the factors influencing the

organization’s adoption of technology (Polen, 2009; Yeh, 2012). HR technology is a

minimum requirement for HR professionals (Madu, 2009) and the adoption of

CloudBased Computing becomes a competitive advantage in the HR profession (Deloitte,

2011; Yeh, 2012). The specific problem is that the literature does not provide clarity how

a more rapid adoption of Cloud-Based Computing will affect HR processes. Human

resource leaders should understand the advantages, disadvantages, and risks associated

with moving business applications and processes to the Cloud (Wright, 2011), and

redefine HR strategies with an understanding of how Cloud-Based Computing can

contribute to operational efficiency, revenue growth, and strategic value (Deloitte, 2011).

Qualitative studies regarding the factors influencing decisions in HR to adopt technology

should be conducted (Bahli, Borgman, Heier, & Schewski, 2013). This study involved

using a qualitative method with a Delphi research design. The intention of this study was

to investigate the reasons HR leaders are slow to implement Cloud-Based Computing,

how Cloud-Based Computing influences human resource management and HR

effectiveness, and the overall performance of the organization.

Purpose Statement

The purpose of this qualitative study with a Delphi research design was to

examine how the adoption of Cloud-Based Computing affects HRM and organizational

performance. Business executives and HR leaders acknowledge the effect of technology

on business processes and strategies, and their influence on technology implementation

and adoption (Deloitte, 2011, 2012; Indranil, 2011; Polen, 2009; Starner; 2011).

Additionally, executives, HR leaders, and professionals acknowledge the need for

aligning HR and organizational strategies when implementing new technologies to

increase business value (Aljabre, 2012; Chiu & Selmer, 2011; Starner, 2011). Balbaa

(2011) stated Software-as-a-Service (SaaS) would potentially replace traditional software

in many departments including Human Resources. According to a survey by Deloitte

(2011), standard HR practices in the Cloud are inevitable.

Significance of the Study

Aberdeen (2013) reports 48% of organizations still manually manage and

integrate data. Organizations further along the maturity curve are more likely to adopt

new technology with 62% of organizations indicating technology will be delivered via

cloud within the next two years (IBM, 2015). The adoption of Cloud-Based Computing

is slow despite the benefits of its implementation and use (Whitley et al., 2013). The

study may be relevant to academia in fields of leadership and HR, the cloud technology

industry serving HR, HR leaders as transformational leaders, and leaders of change,

HRM, and organizational performance, and in identifying areas for further research,

contributing to the HR body of knowledge. Human resource professionals reported to

have little knowledge of information systems and HR technology used to facilitate HR

activities although HR technology is a minimum requirement for HR professionals

(Madu, 2009). An increased understanding and knowledge of HR leaders’ perspectives

on implementing Cloud-Based Computing for the HR function could influence HRM and

the role of the HR leader as a value-added business partner.

Significance of the Study to Leadership

Transformational leaders strive toward the mutual pursuit of enhanced individual

and organizational performance. Transformational leadership contributes to enhancing

organizational talent and achieving competitive advantage through people (Birasnav &

Dalpati, 2010). Human Resources leads require the technology and tools to manage HR

processes effectively (Lombardi, 2014).

The findings from this study contribute to leadership and HR leaders, as

transformational leaders, general leaders, and human resource managers, by establishing

an understanding of HR professionals’ experience (Maslak, 2008; Nair, 2011)

implementing Cloud-Based Computing. Transformational leaders inspire others toward a

common vision, encourage innovation (Birasnav, Rangnekar, & Dalpati, 2011; Chen, Lin,

Lin, & McDonough, 2012), assist in developing skill sets, led by example, and establish

expectations for performance (Bass & Avolio, 1993; Mueller, 2009). The results of this

study benefit transformational leaders in implementing Cloud-Based

Computing as an innovative strategy and tool for HRM and the HR function.

The HR leader should be able to identify suitable technology solutions based on

organizational priorities and workforce needs (Polen, 2009; Ulrich, 1997; Yeh, 2012).

The results of this study benefit HR strategic business partners and professionals,

considering a similar strategy, by identifying Cloud-Based Computing applied, concerns,

and key success factors of implementing Cloud-Based Computing for HR processes

(Deloitte, 2010; Indranil, 2011). A better understanding of HR professionals’ perceptions

enhances the potential of the workforce and organization, enriches the organizational

leader’s ability to enhance business operations, expands understanding within the HR

profession, and assists future research (Polen, 2009).

The study findings contribute to the body of knowledge of leadership in HRM and

leadership in general by understanding the perceived effect of Cloud-Based Computing

on HRM and HR performance. The results of this study may influence the perspective of

HR professionals understanding the characteristics of HR leaders with successful

implementation of Cloud-Based Computing, building a business case for moving forward

with Cloud-Based Computing, and providing a model for implementing Cloud-Based

Computing.

Research Questions

More companies are progressively using Cloud technologies for standard Human

Resource processes (Deloitte, 2011). Human Resource leaders are business partners and

asked to suggest strategies for streamlining standard HR practices. However, Human

Resource leaders may be reluctant to move toward Cloud-Based Computing and look to

the experience of peers to understand how technologies are implemented and the results

of implementation prior to making a technology change. To study the topic of HR

leader’s perspective on implementing Cloud-Based Computing, the following research

questions guided the study:

RQ1: What are the Cloud-Based Computing tools that appear to be most effective

for Human Resources?

RQ2: What are the impediments to adoption of Cloud-Based Computing in

Human Resources?

RQ3: How does successful Cloud-Based Computing affect the performance of

HRM?

RQ4: What leadership traits of successful Cloud-Based Computing adopters also

contribute to HR performance?

RQ5: How does HR contribute to the overall performance of an organization?

RQ6: What are the implications of best practices in Cloud-Based Computing

adoption within HR and to general leadership theory?

Nature of the Study

The conduction of this study involved a qualitative research methodology with a

modified Delphi research design modified to leverage the strengths of qualitative and

quantitative research allowing for insight to subjective and objective issues and determine

the HR leader’s perception of the topic (Skulmoski, Hartman, & Krahn, 2007). The

Delphi method is an exploratory technique of subjectivity (Hall, 2009; Skulmoski et al.,

2007) as an iterative process to collect the subjective opinions and feedback of study

participants through a series of surveys using small sample sizes (Skulmoski et al., 2007).

While studies existed depicting the decision-making process for adopting cloud

technologies, benefits of cloud technologies, and the effect of technology in HR, most

studies are conducted in countries outside of the United States, and very little research

explains how Cloud-Based Computing potentially affects HRM performance or the

reasons behind the slow implementation.

The objective for researchers using a Delphi research design is to sample a range

of diverse views and perspectives (Okoli & Pawlowski, 2004). Modifications to the

Delphi can occur between studies, however the following definition captures the essence

of the Delphi method: “The Delphi Technique is a method for the systematic solicitation

and collection of judgements on a particular topic through a set of carefully designed

sequential questionnaires interspersed with summarized information and feedback of

opinions derived from earlier responses” (Delbecq, Van de Ven, & Gustafson, 1975, p.

10). For the purposes of this study, the use of the terms Delphi, Delphi Method, and

modified Delphi are interchangeable. The research methods considered for the study

included quantitative and qualitative. Quantitative research methods require more data

for an effective study on the topic and thus inappropriate for this study given the lack of

data available. A review and the consideration of qualitative research designs including

Grounded Theory, Phenomenological, Hermeneutics, and Case Study revealed these

methods as inappropriate for this study. A modified Delphi research design was most

appropriate for this study to gather data of subjective judgments, is effective with small

samples, and to identify, compare and contrast the success factors from the perspective of

the HR leader. Researchers using a Delphi research design are interested in

understanding the unbiased expressed views of participants to improve understanding of

problems, solutions, or opportunities when knowledge about a problem or phenomenon is

incomplete (Skulmoski et al., 2007). Modifications to the study included the

identification of informed participants opposed to experts, as consensus is not required

(Hall & Jordan, 2013). Hall (2009) argued that the level of quantitative analysis possible

in later rounds determines if the study should be characterized as purely qualitative, or

mixed method; qualitative analysis followed by quantitation, if later rounds are

conducted. Some research studies, referred to as modified Delphi studies (Hall, 2009),

changed from the approach originally developed by RAND Corporation in the 1960s.

A convenience sampling of 14 informed participants with a minimum of 5 years

of HR experience was recruited to participate in the study. The study consisted of a

tworound pilot study followed by two rounds of a complete Delphi study with the full

panel of participants distributed through SurveyMonkey®. The first round of the full

Delphi consisted of demographic, informative, and open-ended questions and concluded

with Likert-type scale questions for ranking in the final round. The questionnaire design

of each round of the study was built upon the information collected from the previous

round until the sufficient information was uncovered (Skulmoski et al., 2007).

NVivo10®, Excel®, and SPSS® software were used to discover subtle connections

among data providing additional insight and ideas for answering research questions and

justifying findings. Significant results were obtained through correlational analysis

during Round 2 of this study in an attempt to further understand and triangulate the data.

The results of the analysis resulted in additional recommendations for future research

provided in Chapter 5. However, given the small sample size and ordinal data obtained,

this study did not justify reclassification to quantitative or mixed methods and remains a

qualitative study.

The information in this study directly challenges HR leaders with demonstrating

discovered characteristics demonstrated by participants of those HR leaders successfully

implementing HR while aligning with transformational leadership. The results of this

study distinguish leadership characteristics of technology early adopters in HR, and

identify issues hindering adoption and that may assist HR leaders in designing effective

change management strategies for adopting and implementing Cloud-Based Computing,

and may provide the foundation of a model for implementing Cloud-Based Computing or

a model for transformational leaders adopting technology.

Population Sample and Criteria

For the purpose of this study, 14 participants were recruited to participate in this

study. A requirement of 5 years minimum of HR experience ensured all participants were

knowledgeable about HR, not necessarily experts (Hall, 2009). Informed participants

included HR leaders, administrators, executives, generalists, and specialists using and not

using, Cloud-Based Computing. A selected subset of the population represented the

population under study. Participants were recruited from HR-focused LinkedIn forums

and Arizona-based HR organizations. In Round 1, 14 participants completed survey with

12 participants completing the Round 2 survey. A convenience sampling technique was

appropriate given the number of LinkedIn forums and Arizonabased HR groups

considered for research and incorporating a snowball sampling technique to reach other

participants through referrals from participants who initially met the study criteria for

participation (Christensen, Johnson, & Turner, 2011).

Data Collection

A minimum of 12 participants were targeted with a convenience sampling and a

snowball sampling technique was useful as the desired population was challenging to

obtain (Christensen et al., 2011) and expectation of participation was low. Twelve

participants completed both rounds of the study. Study participants received an e-mail

containing an informed consent form with details about the research process, withdrawal

procedures, personal risks, and primary intent of the study. Once participants submitted a

signed informed consent, each received a link to the Round 1 questionnaire via e-mail.

The data collection process used online surveys housed with SurveyMonkey® for

all rounds of the modified Delphi research design. Each round of the study was built

upon the information collected from the previous round until the researcher uncovered

sufficient information (Skulmoski et al., 2007). Participants were able to withdraw from

the study at any time before, during, and after data collection. Data analysis did not

include information collected from participants choosing to withdraw from the study

without their permission for use.

Data Analysis

Data analysis attempted to find commonalities between leader’s perceptions about

the implementation of Cloud-Based Computing, HR practices using Cloud-Based

Computing, characteristics of HR leaders skilled at adopting Cloud-Based Computing,

and the potential influence on HR effectiveness and overall productivity of an

organization using NVivo10® and Microsoft Excel® software in Round 1. NVivo10® is

a secure, software program specifically for analyzing qualitative and mixed methods

research data. The NVivo10® software allows the researcher to import, code, organize,

and query data, discover themes, and reflect on outcomes for rigorous support of the

research findings. The software is a powerful tool for uncovering subtle connections

among data for answering research questions and justifying findings. The use of the

NVivo10® software was for analysis of the qualitative data for Round 1 and served to

create the questionnaire instrument for the subsequent iteration. Excel® and SPSS® are

software packages for descriptive statistical analysis and used for analyzing data from

Round 2. Chapter 3 includes specifics regarding sample size and criteria, methodology,

research design, instrumentation, and data analysis.

Theoretical and Conceptual Framework

Theoretical and conceptual frameworks present an overview of the foundational

theories and concepts related to the study (Leshem & Trafford, 2007). The basis for the

theoretical and conceptual framework supporting the study included HRM, leadership,

and technology adoption theories and concepts with respect to innovation, performance,

behavior, and change management. This study focused on the experiences and

perceptions of HR leaders and their influence with implementing Cloud-Based

Computing for HRM and the perceived effect to HRM and organizational performance.

Figure 1 provides a visual of the conceptual framework for the study depicting Human

Resources as the social system by which innovations are communicated and technology

as factors perceived to influence HRM and organizational performance. Figure 2 shows

the relationship between the dependent and independent variables for the study.

Figure 1. Conceptual Framework

Figure 2. Relationship Between Variables

Technology is an organizational factor for maintaining competitive advantage

(Oliveria & Martins, 2011). Numerous theories existed explaining the adoption, or lack

thereof, of new technologies from an individual perspective; however, few were from an

organizational perspective and fewer specific to HR and cloud technology. The study

uses Rogers’ (2003) theory of Diffusion of Innovations (DOI) and the Technology,

Organization, and Environment (TOE) conceptual framework by Tornatzky and Fleischer

(1990).

The TOE framework categorizes three contextual groups: technological,

organizational, and environmental. The technological context refers to the internal and

external factors to an organization such as perceived barriers, mobility, relative

advantage, and accessibility to data and applications (Kraemer, Xu, Zhu, 2006). The

organizational context describes factors of the firm including size, scope, structure,

leadership support, firm culture, and organizational readiness (Bahli et al., 2013). The

environmental context refers to the firms industry, competition, and government policy

(Chen et al., 2011; Oliveria & Martins, 2011; Wang et al., 2010).

Diffusion of Innovations theory attempts to explain “how, why, and at what rate

new ideas of technology spread through cultures operating at the individual and firm

level” (Oliveria & Martins, 2011, p. 111). Innovation is communicated through certain

channels over time among members of a social system (Rogers, 1995). Organizations

adopt innovation that suggests various degrees of resistance effectively visualized as a

bell curve with 2.5% considered innovators, 13.5% as early adopters, 34% as early

majority, 34% as late majority, and 16% as laggards. Independent variables related to

organizational innovation adoption include individual leadership characteristics, internal

characteristics of organizational structure, and external characteristics of the organization

(Rogers, 1995). The diffusion of innovation consists of five characteristics that influence

innovation adoption:

•Relative advantage – the degree to which innovation can bring benefits to an

organization;

•Compatibility – the degree to which an innovation is consistent with existing

business;

•Complexity – the degree to which innovation is difficult to use;

•Observability – the degree to which the results of an innovation are visible to

others and;

•Trialability – the degree to which an innovation may be experimented. (Rogers,

2013, p. 15)

The TOE framework and DOI theory align with principles of change management

theory and strategies for implementing change in HR (Ruta, 2005) and for HR technology

(Benvenuti, 2011; Ruta, 2005). Oliveria and Martins (2011) conducted a thorough review

of research studies using DOI and TOE from an empirical perspective (Kraemer et al.,

2006; Wang, Wang, & Yang, 2010). Wang, Wang, and Yang (2010) and Chen, Low, and

Wu (2011) conducted separate studies on Radio Frequency Identification (RFID) and

Cloud technology adoption, respectively, using the TOE framework and DOI theory.

Human resource management focuses on theory development, actionable

managerial principles, historical origins, and development of management scholarship

and practice (Kaufman, 2012). The theory explains how human resource management

practices align with organizational performance (Huselid, 2011; Kaufman, 2012; Marler,

2012; Wright, Gardner, Moynihan, & Allen; 2005). Thought leaders, in the human

resource management field, agree the HR professional must be a change agent,

technology proponent, and innovation champion to create value toward organizational

performance (Fitz-enz & Davidson, 2002; Ulrich, 1997, 2012).

Transformational leadership is marked by a leader’s ability to influence others

(Bass & Avolio, 1993), embrace innovation (Wren, 1995), and guide others toward

accomplishing goals and improving organizational performance using the

transformational characteristics and behaviors the leader possesses (Birasnav et al., 2011).

Transformational leader characteristics in alignment with those required of HRM are

professionals as champions of innovation and change, particularly when adopting new

technologies (Yost et al., 2011). Previous studies using the DOI theory and TOE

framework have an empirical approach to determinants of technology adoption without

reference to leadership style and characteristics, not within the HR profession, and limited

association to organizational performance. Additionally, the study took a qualitative

approach for better understanding of the problem to answer the research questions.

Assumptions

The study involved five assumptions regarding bias, sample size, and participants.

Impartiality throughout the study is critical for maximizing understanding (Christensen et

al., 2011). One assumption was that the researcher remains unbiased (Linstone & Turoff,

1975; Murray & Hammon, 1995a). Delphi is an optimal methodology for small sample

sizes (Skulmoski et al., 2007). Given the newness of Cloud-Based Computing in HR,

and the resistance to implement these technologies, 17 HR informed participants were

petitioned for the study resulting in 14 participants completing Round 1, and 12

participants completing Round 2. Another assumption was the selected method of

recruitment through LinkedIn groups and Arizona HR organizations were viable locations

for recruiting study participants. Delphi rounds may be considered a timeconsuming

activity. Participants were assumed to have time availability for engaging in the study

and commit to completing two rounds of questions. A final assumption was that study

participants responded honestly and candidly, and had the information needed to answer

the questionnaire (Salkind, 2003). Participants received assurance of confidentiality and

remained informed of the procedures for the study and privacy measures. This study

assumed the responses of HR professionals as panelists in the study would mirror those

of HR professionals across all industries through the recruitment of participants from

various professional backgrounds.

Scope, Limitations and Delimitations

Scope

The scope of this qualitative study with modified Delphi research design was an

exploration of the perceptions of human resource leaders regarding the reasons for, and

against implementation of Cloud-Based Computing. Additionally, the scope included

exploring which of the many HR processes used Cloud-Based Computing, what

characteristics were common for human resource leaders skilled at implementation of

Cloud-Based Computing, and the potential influence of Cloud-Based Computing on

HRM and organizational performance. This study included a convenience sampling of

14 HR informed participants recruited through LinkedIn forums and with expanded reach

via snowball chain method. All participants were confirmed, as required for this study, in

having a minimum of 5 years of experience in HR. The full panel of 14 participants

responded to two rounds of surveys including open-ended questions, demographic and

informative questions, and Likert-type scale ranking questions. The problem is the

resistance in adopting and standardizing Cloud-Based Computing for HR. Cloud

technologies are vast and services span a variety of technical options including platform,

infrastructure, and business processes.

Limitations

Limitations are the boundaries or weaknesses potentially experienced during the

study (Leedy & Ormrod, 2010). The validity of the research for a population is a

limitation because the design of Delphi studies uses small sample sizes (Linstone &

Turoff, 1975). Avoiding the urge to oversimplify the complexity of the problem is

necessary as the perceptions of participants enhance the outcomes, as consensus is

pursued (Linstone & Turoff, 1975). A pilot study occurred to ensure accuracy and

reliability of the study results. Suggested revisions to the questionnaires were

implemented prior to distribution to the full-panel Delphi study participants. Additional

limitations to the final study are expounded in Chapter 5.

Delimitations

Delimitations narrow the focus of the study by stating what is not included in the

study (Leedy & Ormrod, 2010). The first control was to limit the population to HR

informed participants with 5 or more years of HR experience. The second delimitation

was to begin recruiting study participants within Arizona-based HR groups and

HRfocused LinkedIn forums given the researcher’s location. The implementation of

CloudBased Computing occurs globally and clients could reside in any location despite

their online group affiliation. A snowball sampling effect extended outside of Arizona;

however, the sample was limited to organizations primarily based in the United States.

Additional delimitations to the final study are expounded in Chapter 5.

Summary

Human resource processes are slowly transforming through the SaaS process

(Deloitte, 2011; Starner, 2011). However, HR professionals are slow or reluctant to

standardize the implementation of Cloud-based technologies despite the overwhelming

agreement of key challenges remedied by Cloud-Based Computing (Deloitte, 2010, 2011;

Indranil, 2011; Starner; 2011). The general problem is the slow adoption of Cloud-Based

Computing for efficiency of the HR function (Chen et al., 2011; Deloitte, 2011; Deloitte,

2012). The specific problem is the literature is unclear of how effective the HR function

may be with the more rapid adoption of Cloud-Based Computing. Human resource

leaders must understand the advantages, disadvantages, and risks associated with moving

business applications and processes to the Cloud (Wright, 2011).

The theoretical and conceptual framework of the study includes the Technology,

Organization, and Environment framework, HRM, transformational leadership, and

Diffusion of Innovation theories. Definitions provided for these concepts and theories are

helpful, in addition to the common terms for this research project. Arguments existed

that HRM theory is piecemeal (Ferris, Hall, Royal, & Martochhio, 2004); however, the

study creates another connection between HR theory, HRM performance, and

organizational performance.

This modified Delphi study served to provide insight to the perceived effect of

Cloud-Based Computing in HR and slow adoption of the technology. Chapter 1

contained information regarding the nature of the study, a review of research questions,

scope, assumptions, limitations, and delimitations. Chapter 2 involves a detailed review

of the literature applicable to this study, a historical overview, current findings, germinal

research, and gaps in the literature.

Chapter 2 Review of the Literature

The purpose of the qualitative study with modified Delphi research design was to

understand the reasons human resource leaders are slow to implement Cloud-Based

Computing, the common characteristics between human resource leaders and human

resource leaders with successful Cloud-Based Computing implementation, and how the

implementation relates to HRM performance and organizational performance. Little

research exists about the influence of Cloud-Based Computing to human resource (HR)

performance and less on organizational performance. Much research exists on the

adoption of technology; however the limited information on the adoption of Cloud-Based

Computing in HR and information available is often specific to other industries or

countries (Chen, Low, & Wu, 2011; Obeidat & Turgay, 2013).

People are an organization’s largest resource and the key to maintaining a

competitive advantage (Karimidizboni, 2013; Kaur & Rin Yahya, 2010; Theriou &

Chatzoglou, 2008). The HR function is responsible for all factors related to people within

the organization and influential in the adoption of new technologies. William Gibson

(2012) posited adoption of Cloud-based technologies is a competitive advantage for HR

performance. Extensive studies addressed innovation from transformational leaders and

HR leaders as change agents; however, a gap exists between the leadership style and

competencies for technology adoption and the effect on performance. Companies are

competing for obtaining and maintaining talent on a global level, and technology is at the

forefront of HR (Abardeen, 2012; Deloitte 2012; Kapoor & Sherif,

2012; SHRM, 2011). This chapter is a review of literature on Human Resource

Management (HRM), the evolution of technology in HR, Cloud-Based Computing, the

adoption of new technology in HR with respect to change management, and the related

organizational effect of technology in HR. The literature review presents theories in

support of HRM, transformational leadership, technology adoption, and leadership in

general. The writings of HRM and leadership theorists provide an understanding of

current thought on the effect of technology, specifically Cloud-Based Computing on

HRM.

Title Searches, Articles, Research Documents, and Journals

The review of literature contains peer-reviewed journal articles and dissertations

obtained through University of Phoenix Library including EBSCOhost and ProQuest

databases, related books and textbooks by industry, academia recognized thought leaders,

and industry studies from notable organizations related to technology within the HR

profession. The review of the literature focuses on providing a review of HRM, the

introduction, use, and evolution of technology in HR, and the use of Cloud-Based

Computing in HR. The review includes identified gaps in the literature regarding the

effect of Cloud-Based Computing on HRM, leadership, and organizational performance

and serves as a preliminary step in bridging the gap between the current knowledge of

HRM and technology, and the potential effect on leadership practice, HRM, and

organizational performance.

Literature Review

The objective of the literature review is to provide historical background of HRM,

technology evolution within the HR function, HRM leadership challenges with

implementing new technologies, and review Cloud-Based Computing available for HRM.

Notable HR thought leaders and theorists expressed the necessity for technology within

the HR function and consistent improvement (Fitz-enz & Davidson, 2002; Ulrich; 1997,

2012). Many notable organizations predict a timeframe for updating to new Cloud-based

solutions; however, the adoption of HR technology remains historically slow. Although

many studies existed about the adoption of technology and the effect to performance in

general, few studies explored this from the HR perspective, and fewer explored

CloudBased Computing within the HR function. The lack of quantitative research and

need for establishing qualitative information is the basis for the naturalistic approach to

this literature review (Price & Walker, 2009).

The Framework of Research Literature

A broad representation of a discerning review of topics surrounding the

phenomena presented for this study was through the identification of common themes

used to link sources together found within the literature. The literature review includes

information from 168 resources with the majority of resources dated from 2008 to 2014,

with the exception of some historical sections or pertinent resources. The literature

review spans the depth and breadth of relevant topics intended to identify a potential gap

that the study will address. Literature sources consisted 223 sources including 14

dissertations, 40 books, 116 peer-reviewed journal articles, 22 white papers, and 31 other

resources. Literature maturity includes the majority of sources dated 2009 through 2015,

61 sources dated 2000 through 2008, and 24 sources dated 1999 or earlier.

A variety of keywords associated with this study was used find and identify

significant scholarly research contributing to the literature review. The keywords were

used singularly and in combination with other terms including: (a) human resources, (b)

cloud, (c) cloud computing, (d) HRIS, (e) human resource technology, (f) technology

adoption, (g) eHR, (h) human resource management, (i) eHRM, (j) organizational

performance, and (k) leadership characteristics. The exploration of foundational topics

within the available relevant literature include the adoption of technology in HR,

transformational leadership in HR, the role of HR as change agent, Cloud-Based

Computing, and the effect of technology on HRM and organizational performance,

respectively. The review contains a presentation of content-rich, pertinent literature

supported by theory, peer-review, and germane thought leaders. A clear gap exists

because of the lack of available literature, framework, and standards for adopting Cloud-

Based Computing in HRM.

Evolution of Human Resource Management (HRM)

Human Resource Management evolved through time with name changes and

similar practices. Beginning in 1870 Industrial Relations in Great Britain sparked the

growth of unions, government labor relations, policy, and law (Zeitlin, 1987). Frederick

Wilson Taylor founded Scientific Management in the late 1880s by focusing on personnel

managers, synthesizing and standardizing manufacturing processes to reduce employee

input and effort, and maximizing productivity (Wren, 2005). The late 1920s brought

about organized labor and personnel relations; however, the focus remained upon

industry. In the 1930s, the role of personnel managers expanded to fuse relations with

labor unions and additional duties of negotiation, regulatory compliance, hiring, record

keeping, and compensation (Arthur, 2004; Fitz-enz & Davidson, 2002). Additionally, the

profession of psychology emerged in personnel management with psychological

employment testing and the significance of the Hawthorne Studies on employee

motivation (Doyle, Florman, & Pignatelli, 1985).

After World War II, the complexity of personnel management expanded further to

include benefits administration, performance appraisal, employer/employee relations

programs, employee selection testing, interviewing, and training; thus garnering a change

to employee relations, a more human-centric focus in HR by attracting more skilled

workers (Arthur, 2004; Fitz-enz & Davidson, 2009). This was the beginning of a more

human-centric focus in HR. The balance of power remained with personnel managers

throughout the 1950s and 1960s, until legislation brought about uniform policies and

regulatory acts requiring compliance and adherence by personnel, and supplemented by

employee handbooks, policies, and procedures. Arthur (2004) coined the term human

resources in the 1980s when referring to the personnel function or department. The

development and implementation of required global standards in HR occurred through

the American Society of Personnel Administration (ASPA), founded in 1948, and since

1989 changed the name of the organization to the Society for Human Resource

Management (Fitz-enz & Davidson, 2002; HRCI, 2014; SHRM, 2014).

The Society for Human Resource Management (2014) defines HRM as the

configuration within an organization responsible for all things related to managing people

including policies, operations, procedures, activities, and decisions. The reference to

HRM today is human capital management or talent management (Arthur, 2004; Fitz-enz

& Davidson, 2002; Nadler & Nadler, 1990).

Human resource management. The foundation of HRM began during the

Industrial Revolution of the late 1800s and rooted in the emergence of personnel

management after World War II. The traditional function of personnel management was

the oversight of hiring and firing employees, compensation, and training. Some argument

exists that the functional overlap of personnel management and HRM was an academic

façade (Strauss, 2001) of progress lacking practical and applicable theory (Kaufman,

2012; Storey, 1995) while other researchers argued a gap between HRM practice and

theory is ongoing (Prabu, 2011). Numerous scholars and thought leaders provide a

historical review of HRM in theory and practice over the past 2 centuries describing the

changing roles, practices, environmental influences, and technologies created and

adopted for HRM (Lee, 2008).

Nadler and Nadler (1990) provided a detailed review of a nine-period historical

framework on the development of HRM in combination with Sherman, Bohlander &

Snell (1996), Fletcher (2005), and Ulrich (1997), briefly presented in this literature

review. The conceptualization of HRM occurred before 1800 by larger organizations

with numerous resources such as the Catholic Church, military, and government. The

evolution of HRM continued during the Industrial Revolution Era (1880 – 1990) with the

development of group learning in employee training, legislative developments, scientific

management achievements, and Frederick Taylor’s experiments to maximize productivity.

The beginning of personnel management (Fitz-enz & Davidson, 2002; Sherman,

Bohlander & Snell, 1996), reintroduction of military veterans to the workforce, and the

first academic course in personnel administration offered at Dartmouth College (Sherman

et al., 1996) occurred during World War I. During this time, HR academicians

acknowledged notable experiments in HRM development with the Hawthorne Studies

from 1920 through 1940, bringing a more human focus to personnel administration.

During World War II, a shift of the workforce to women and older men occurred.

Additionally, support of work rights, and large companies, such as GE, pioneered the

adoption of technology for HRM processes (Fletcher, 2005). The foundation of the

American Society for Personnel Administration (ASPA) in 1948 was to combat the view

of HR as a non-essential area within the organization and thus the development of human

relations occurred in tandem. From 1960 through 1973, the introduction of legislature for

employment equality and worker safety began along with the emergence of other

industrial nations (Fletcher, 2005). Rapid development of technology began to affect HR

and the introduction of behavioral science, later known as organizational development

(Nadler & Nadler, 1990) was the next evolution of HR.

Significant changes in HR occurred with the operation of the Organization of

Petroleum Exporting Countries (OPEC) (Nadler & Nadler, 1990) and heavy competition

from Japan in the international auto industry, accompanied by increased mergers from

1973 through 1990 (Fletcher, 2005). During this time the HR profession was introduced

to certifications from the Human Resources Certification Institute (HRCI) (Lee, 2008;

HRCI, 2014), GE developed technology for HR payroll, and HR technology continued to

evolve by incorporating non-value adding HR processes (Fletcher, 2005).

From 1980 through 1990, socioeconomic issues gave rise to HRM focusing on

young workers as an advantage (Nadler & Nadler, 1990), and catapulted a shift of HRM

to a strategic role including adoption of technology to streamline administrative

responsibilities and free up HR resources for strategic, consultative responsibilities

(Fletcher, 2005; Ulrich, 1997). During the decade prior to the turn of the millennium, the

Internet, and emerging technologies brought globalization to the forefront (Lee, 2008).

Educating line managers came to the forefront of HR issues and supported as an added

HRM value (Sherman et al., 1996). The focus of HR as a strategic partner, administrative

expert, employee champion, and change agent (Brandl & Pohler, 2010; Ulrich, 1997) was

accepted. Today, technology increasingly plays a critical role in HRM

(Crenshaw, 2006; Fitz-enz & Davidson, 2002; Lee, 2008).

HRM and Organizational Performance

Human Resource Management continues to evolve respective roles and

information technology (IT) continues to play an ongoing critical piece in HRM functions

(Fitz-enz & Davidson, 2002; Huselid, 2011, Lee, 2008). Today HR is an internal formal

structure responsible for all aspects related to managing people (SHRM, 2014).

Functions of HR in the value chain include staffing; compensation and benefits;

employee relations; labor relations; employee training; and organizational development

(Fitz-enz & Davidson, 2002). The categorization of each HR function is under different

HR roles. Ulrich (1997) described the roles of HR as strategic partner, administrative

expert, employee champion, and change agent. Fitz-enz and Davidson (2002) described

the roles of HR as strategic business partner, manager of human talent, change manager,

and supporter of organizational goals. The commonality between Ulrich, Fitz-enz, and

Davidson (2002) is the idea of the HR function and HR leaders as value-adding business

partners to the organization.

A thorough examination by researchers occurs regarding each of the functions and

roles linking HRM practices to organizational performance outcomes and the creation of

competitive advantage (Becker, Huselid & Ulrich, 2001; Huselid, Jackson & Schuler,

1997; Jackson & Schuler, 1995; Theriou & Chatzoglou, 2008; Ulrich, 1997).

Outcomes include dependent variables such as cost efficiency, time efficiency, individual

performance, employee retention, return on investment (Fitz-enz & Davidson, 2002;

Lake, 2006; Ulrich, 1997), and individual productivity (Saniewski, 2011; Theriou &

Chatzoglou, 2008; Ullah, 2010). Theriou and Chatzoglou (2008) shared their theoretical

framework of linking HRM best practices of knowledge management and organizational

learning to enhancing organizational performance through the alignment of business and

HRM strategies developed by leaders (Loshali & Krishnan, 2013; Timmins, 2008)

because Kumar and Pandya (2012) presented strategies for leveraging HR technology to

improve organizational performance and improve leader’s decision-making. Zakaria

(2013) used five factors from Argawala’s (2003) HRM innovative practice categories

contributing to organizational performance and competitive advantage including training

and development, reward systems, performance appraisal, staffing, and communication

and information sharing. Zakaria (2013) posits small to medium-sized organizations

must engage in innovative HRM practices as part of corporate culture for the organization

to remain relevant and long-standing.

Leadership in HRM

Numerous theories existed regarding leadership or the role of a leader such as

great man theory, situational leadership, transactional leadership, servant leadership, and

others (Avolio & Yammarino, 2002; Nahavandi, 2011; Wren, 1995; Yukl, 2006). The

focus of this review is transformational leadership as it relates to HRM and the

implementation of technology. Human resource leaders have a multifaceted role as

administrative expert, employee champion, strategic partner, and change agent (Fitz-enz

& Davidson, 2002; Ulrich, 1997). Accomplishments and completed goals should be the

focus of leadership. Transformational leaders are concerned with inspiring and

developing others to achieve common goals and are innovative thinkers capable of

identifying new ways to solving old problems (Wren, 1995). Formal leaders are not only

responsible for change; middle managers are also mediating variables between HRM and

performance (Savaneviciene & Stankeviciute, 2012).

Organizational leaders demonstrate significant change when implementing

technology (Lavie, 2006; Mueller, 2009). Leadership support and an understanding of

technology complexity and employee reaction toward the implementation of the

respective technology, determines success or failure (Mueller, 2009; Polen, 2009).

Innovative measures in technology for HRM are often brought to focus by

transformational leaders (Mueller, 2009) making leaders core influencers for the

formulation and implementation of innovative technology (Michel, 2007; Mueller, 2009;

Pollen, 2009; Vonk, Geertman & Schot, 2007). This is essential for creating and

maintaining a competitive advantage (Linying, Heshan, & Yulin, 2007; Theriou &

Chatzoglou, 2008). The model of transformational leadership by Bass and Avolio (1993)

focused more on a leader’s ability to make task and mission outcomes attractive to

followers. Although, transformational leadership is more strongly correlated to higher

employee satisfaction, productivity, and retention (Hater & Bass, 1988) by inspiring

employees to enhance performance and reach organizational goals (Wren, 1995).

The link between people and technology is foundational to individual and

organizational effectiveness (Linying, Heshan, & Yulin, 2007) along with the courage of

leaders toward value-driven management (Kridel, 2006). Leadership is not limited to a

person with a specific title within an organization making individuals capable of

effectively influencing others (Cyrus, 2010). Organizations should have people with the

appropriate placement in defining tasks and managing relationships (Cyrus, 2010), but

even more important, organizations should have leaders capable of bringing organizations

into futures not yet realized (Timmins, 2008).

No longer is the perception of HR as the dumping ground for personnel related

tasks and poor performing managers (Arthur, 2004). The perception of the HR manager

is increasing as a business partner involved in executive decision-making directly related

to organizational growth and productivity (Arthur, 2004; Mueller; 2009). HRM is a

complex, multifaceted, multidimensional function (Chiu & Selmer, 2011; Fitz-enz &

Davidson, 2002). HR roles include change agent, administrator, employee relations, and

strategic business partner (Chiu & Selmer 2011; Welch & Welch, 2012). Change

management for HR staff and employees is critical for acceptance of new technology

(Morgan, 2000; Yusliza & Ramayah, 2011). Advances in technology allow HR to

become a value-added business partner within the organization (Morgan, 2000) by

providing consulting to employees and management, talent development, and strategic

planning.

Strategic HR is the ability for HR leaders to align HR initiatives with business

strategy. Loshali and Krishnan (2013) reported a significant positive correlation between

transformational leadership, HR strategy, and organizational performance. This

alignment can occur by developing strategic talent strategy and curriculum for driving

employee training and development (Loshali & Krishnan 2013).

The present concept of leadership shifted because of societal, technological, and

organizational changes in values, awareness of stress on employees, and the need to

maintain competitive advantage (Timmins, 2008). Thus, leadership qualifications are

adapted toward more transformational, ethical, and relationship-building styles.

Transformational leaders desire to motivate followers “by appealing to moral values and

ideals” while stimulating creativity and innovation (Islam & Muhammad, 2011, p. 236).

In a study by Islam and Muhammad (2011) of the perceived leadership styles of HRM

executives in public sector universities, the findings revealed that few teachers viewed

HRM leaders as demonstrating transformational leadership characteristics. Chen, Lin,

Lin, and Mcdonough (2012) reported that transformational leadership positively

influences technological innovation and indicated a strong innovative business culture

can effectively substitute for transformational leadership.

The HR function must consistently add value to the organization through effective

policies, practices, and strategies (Fitz-enz & Davidson, 2002; Ulrich, 1997; Ulrich &

Brockbank, 2005). The human capital of any organization is its greatest asset

(Karimidizboni, 2013; Subramanian, 2012) through learning, experience, knowledge,

creativity, and innovation. As a department, human resources likely have more data than

any other area of business with respect to talent management (Ingham, 2013). The

management of human capital is where HR holds value to the organization (Timmins,

2008). Organizational performance serves as the outcome of effective HRM, thus

business leaders, managers, and researchers have continued concerns for factors

influencing performance (Ferguson, 2006). Technology removed the barriers to

accountability and performance measures in HR allowing for quantitative measurement

systems related to organizational performance factors (Fitz-enz & Davidson, 2002).

The change agent may have more influence on organizational performance and

development (Timmins, 2008). In a study conducted by William Gibson (2012), the

findings indicated that adoption of Cloud-Based Computing in the HR space is a

competitive advantage for efficient performance, streamlined processes, enhanced

recruiting, advanced analytics, and excellent training. However, challenges exist for large

and small businesses substantially invested in fixed behaviors and practices (Gibson,

2012), thereby requiring effective HR change agents. Deloitte (2012) reported most

participants to a study of HR professionals hold a modest and traditional view of HR,

which is a contributing factor to the slow adoption of technology, transformation of

archaic processes, and a lack of alignment to business strategy. Bersin (2014) reports

two-thirds of global survey participants stated cloud technologies are an important to

urgent issue for respective organizations however, 56% reported their companies had no

plans for updating their current systems.

The Society for Human Resource Management (2014) defines leadership in

human resources as a process where an individual identifies direction, influences others,

and guides a group toward common goals. Change management relates closely to

leadership, defined as applying knowledge, resources, and tools for dealing with change

through a systematic approach. Change management means adopting corporate

strategies, procedures, structures, and technologies to handle change externally and

within the business environment (SHRM, 2014). The Society for Human Resource

Management describes change agents as a group or individual who directly or ultimately

instigate behavioral change or change within a group or culture. Human resource leaders

must be agents of change to assist leading and guiding the organization and to attain goals

through HR policies, technology, and assessment (Fitz-enz & Davidson, 2002; Ulrich,

1997).

Transformational leadership correlates strongly with lower attrition and higher

retention, productivity, and employee satisfaction (Hater & Bass, 1988) while

transformational leaders are perceived as more competent, innovative, charismatic, and

inspirational. Transformational leaders often encourage the use of technology to realize a

vision and accomplish organizational goals (Birasnav, Rangnekar & Dalpati, 2011).

Schepers, Wetzels, and Ruyter (2005) reported a correlation between acceptance and

usage of new technology by employees and transformational leadership. However, a lack

of information existed regarding specific characteristics of effective transformational

leaders experienced in successful implementation of Cloud-Based Computing.

Transformational leaders in organizations are individuals demonstrating

flexibility, innovation, ability to handle change, and lead organizations to unrealized

futures (Timmins, 2008). Transformational leaders can positively influence

organizational performance through their effect on organizational strategies, systems, and

climates (Liao & Chuang, 2007). Purvis, Sambamurthy, and Zmud (2001) used

institutional theory as the foundation for describing how and why managers influence an

end user’s beliefs as to the benefits of new technologies to work activities. Users form

individual cognitive interpretations of new technology based on the norms and values

established by managers via legitimization and domination via established policies

(Linying, Heshan, & Yulin, 2007; Purvis et al., 2001) further supporting the leader’s

influence on technology adoption (Polen, 2009). Although studies exist regarding new

skills required by HR professionals (Yeh, 2012), little exists regarding leadership and

technology adoption in HR.

Human Resource Information Technology and e-HRM

The expanding role of HR and development of technology introduced the Human

Resource Information System (HRIS) (Karimidizboni, 2013; Arthur, 2004) and

Electronic Human Resource Management (e-HRM) (Dhamija, 2012; Obeidat, 2012).

Human Resource Information Systems are the technology tools that collect, store,

maintain, and retrieve an organization’s employee data (Karimidizboni, 2013) and can

assist organizations in managing talent (Karimidizboni, 2013; Spaulding, 2011).

Electronic Human Resource Management, as described by Dhamija (2012), is using the

Internet and other technologies for conducting business transactions (Lee, 2008; Shilpa &

Gopal, 2011). Dhamija (2012) stated, “e-HRM is a way of implementing HRM

strategies, policies, and practices in an organization through a directed support of web

technology based channels” (p. 33). Considerable confusion exists between the terms

HRIS, e-HRM, and Electronic Human Resources (e-HR), as a significant portion of

literature refers to these interchangeably. However, each of the terms describes the digital

means of managing human capital.

A review of technology in HR begins with the HR function focus on records

management in the 1930s with transaction-based retention storing résumé and employee

files (Arthur, 2004). The first HR systems came to market in the early 1970s as

recordkeeping software (Fitz-enz & Davidson, 2002) advancing toward automating more

HR functions. The HR department became one of most dependent users of technology

(Ikhlap, Khan, Mujtaba, & Sadiq, 2012) with increased computer use in the workplace,

new technology, and added responsibilities required of the HR function (Shilpa & Gopal,

2011). In the 1990s, HR technologies incorporated automated interviewing,

computerbased training, performance evaluation software, electronic forms, and

advanced e-mail applications (Fields, 1995). Human resource executives look to

technology and corresponding information to drive decision-making for organizational

success (Wilcox, 1997). Expansion of client-server networks allowed employees within

domestic organizations limited access to information, however unavailable overseas until

KPMG’s technical staff leveraged a web-enabled front-end system that provided access to

employees overseas (Wilcox, 1997).

The rationale for implementation of HRIS varies between organizations and

industries to include reduced costs, time efficiency, improved HR performance (Stanley

& Pope, 2000), and increased strategic contribution (Ikhlap et al., 2012). However,

organizations require the use of information systems to manage the base of all HR

activities (Karimidizboni, 2013) yet, employee acceptance of new technology varies

between organizations based on attitude (Yusliza & Ramayah, 2011), organizational

culture, demographics, and change management effectiveness (Morgan, 2000). One

challenge for the HR function is the ongoing evolution of technology and the effect to the

profession and function (Ashbaugh & Rowan, 2002). HRM-related breakthroughs in

technology typically occur because of the effects of HRM by environmental

discontinuities, regulatory, or legislative modifications (Fitz-enz & Davidson, 2002). The

future of the global business environment requires the HR function to fill a strategic role

(Shilpa & Gopal, 2011) and add more value to the organization by further automating

administrative responsibilities thus reducing time, cost, and people resources, and the

need for additional on-site technology.

Globalization increases because of the shortage of talent in many developed

countries, accessibility to low-cost labor, increasing consumer numbers in developing

countries, technology, and diversity. HR departments of large-scale organizations must

have global databases of employee information, including information regarding attrition,

hiring, compensation, benefits, demographic, and national data. Large companies must

modify their HR practices to attract, develop, maintain, and retain high performing talent

for sustainability and success in a global market, including having a globally accessible

HRIS (Kapoor & Sherif, 2012). Electronic human resource management integrates the

administrative responsibilities of HR and related outcomes to the technology required for

creating value across the organization (Sareen & Subramanian, 2012). Technology is a

tool allowing HR professionals the ability to streamline functional responsibilities and

delivery of services to focus on more strategic, consultative, and management roles

(Fitzenz & Davidson, 2002; Ulrich, 1997, 2010). The explosive evolution of technology

requires the transformation of HRM (Saleem, 2012). As such, HR leaders must influence

the adoption of technology within respective organizations (Bassellier, Benbasat, &

Reich, 2003; Polen, 2009; Toldi, 2010; Ulrich, 1997) and, as transformational leaders, are

the innovative thinkers to implement new technologies (Mueller, 2009).

Ashbaugh and Rowan (2002) identified a general list of benefits to an

organization when leveraging an HRIS, including aligning to organizational performance

issues (Muenstermann, Alexander, Laumer, & Eckhardt, 2010), improving core business

processes, developing human capital inventory, planning and budgeting,

labormanagement relations, employee-manager self-service, and business intelligence.

The literature includes HRIS related studies conducted in developing nations with small

to large firms resulting in improved technology acceptance with access to information,

ease of use, and training (Morgan, 2000). Extensive literature existed regarding the

adoption, usage, and implementation of technology in general; however, little research

existed about the successful use of information technology (IT) in HRM practice (Mishra

& Akman, 2010). Mishra and Akman (2010) indicated the industry in which the use of

technology increases for HRM practice is significant in terms of early adoption. Skiba

(2011) reported adoption efforts in healthcare and education are gradually increasing

while pharmaceutical and life science industries are emerging (Subramanian, 2012).

Kinanga (2012) reported a positive correlation between accessible IT infrastructure to IT

adoption and HR performance in Kenyan universities; however, information is lacking

referencing IT or Cloud-Based Computing adoption and HR performance in the United

States.

Human Resource leaders, theorists, and thought leaders agreed that people are an

organization’s most important resource (Deloitte, 2011; Fitz-enz & Davidson, 2002;

Karimidizboni, 2013; Kaur & Rin Yahya, 2010; Theriou & Chatzoglou, 2008; Ulrich,

1997). Organizational leaders must rely on HR and IT to increase effectiveness,

efficiency, and productivity of the organization (SHRM, 2014). Gainey and Klaas (2008)

report e-HR is often associated with improved delivery of HR services resulting in

reduced costs, higher employee engagement, and greater strategic involvement by the HR

function. Electronic human resource services include the use of social media, HRIS, and

digital means for recruitment, selection, interviewing, performance management,

compensation, benefits, and payroll (Gainey & Klaas, 2008). The significant

differentiator between e-HR and e-HRM is employee access to key functionality within

the HRIS (Shilpa & Gopal, 2011). Most organizations provide some form of online

access to employees (Overman, 2002); however, the extent to which the HR function uses

technology varies. Sareen and Subramanian (2012) stated, “e-HRM helps to create

competitive advantage and align the function more closely with business/corporate

strategy and the longer-term branding and reputational objectives of organizations” (p.

123). In a study conducted by Gainey and Klaas (2008), 18% of surveyed organizations

did not use electronic means for any HR activities. However, Ankrah and Sokro (2012)

indicated that improved technology would cause efficiencies in the HR function resulting

in better HRM.

Cloud Computing and Human Resources

Organizations are facing technological obsolescence with globalization and high

competition (Birasnav, Rangnekar, & Dalpati, 2011). Technology is evolving faster than

business organizations can keep up, including the HR function, requiring collaboration

between HR and IT to improve HR practices (Stryve Advisors & The RBL Group, 2011).

Technology substantially and strategically affects business and HR capabilities as does

those responsible for talent management (Ingham, 2013). The roles and functions of

HRM continue to evolve in addition to the use of technology for HR. Human resource

leaders are responsible for implementing plans for reducing costs, time efficiency,

decreasing attrition, and increasing performance (Gainey & Klaas, 2008; Jordan, 2002).

Cloud-Based Computing is a new paradigm shift for distributed applications, platforms,

and infrastructures globally accessible through any mobile device with Internet access

(Chen et al., 2011) providing HR leaders the opportunity to complete these tasks.

Studies conducted on the adoption of Cloud-Based Computing include small to

large high tech organizations in the United States and other countries (Cegielski, Hazen,

Jones-Farmer, & Wu, 2012; Indranil, 2011; Obeidat & Turgay, 2013) and industries

(DeFelice, 2010), with few directed toward HR (Indranil, 2011; Yeh, 2012). Chen, Low,

and Wu (2011) conducted a study in several Taiwanese high-tech organizations

identifying relative advantage, firm size, top management support, competitive pressure,

and trading partner pressure as determinants with significant effect on the adoption of

Cloud-Based Computing. HR must transform and reinvent itself by leveraging cloud

technology (Deloitte, 2014). A survey by Deloitte (2014) found 68% of participants

consider HR technology an important to urgent trend. Yet, results from the HR Systems

Survey by CedarCrestone (2012) indicate 79% of surveyed domestic, and 84% of global,

organizations have core HR systems located on-site because SaaS solutions consisted of

only 11%, and Laurano (2014) reports 48% of organizations still manually handle data

(Aberdeen, 2013).

Reasons for slow adoption of Cloud-Based Computing in HR include a lack of

leadership support (Deloitte, 2012), substantial investment in traditional HR practices,

poor change management (Gibson, 2012), security (Carcary, Conway, & Doherty, 2014;

Khawaja & Qaisar, 2012; Laurano, 2014; Sierra-Cedar, 2015) and departmental or

organizational silos (Jones, 2012). Time, cost, and effective change management are

consistent risk factors for public and private sector organizations implementing new

technology (Ashbaugh & Rowan, 2002). Cloud-Based Computing services can be used

to reduce time and capacity constraints, lower costs by changing fixed costs to variable

usage-based costs, provide anytime-anyplace connectivity, and scalability without

responsibility for maintenance or updates (Rader, 2012).

Significant cost reduction is possible with Cloud-Based Computing (Aljabre,

2012; Carcary, Conway, & Doherty, 2014; Geczy et al., 2012). Gibson (2012) reported

large companies must focus on overcoming long-standing HR practices to move toward

Cloud-Based Computing while small to medium-sized businesses can take advantage of

low capital expenditure. Obeidat and Turgay, (2013) used the technology trade theory

(Triple-T) model for identifying a multi-perspective approach for evaluating technology

adoption. The model used 16 advantages and disadvantages for technology adoption and

found IT executives agreed to a balance of benefits and suggest organizations adopting

technology must highlight the benefits, that may include cost savings, time and space

efficiency, flexibility, scalability, and improved output to improve acceptance of

technology within the organization (Obeidat & Turgay, 2013).

Studies using the Technology-Organizational-Environment framework found

relative advantage, compatibility, complexity, organizational readiness, top management

support, and training are critical variables to successful cloud computing adoption (Bahli,

Borgman, Heier, & Schewski, 2013; Gangwar, Date, & Ramaswamy, 2014). Willcocks,

Venters, & Whitely (2013) identify three antecedents to implementing Cloud-Based

Computing including collaboration, innovation implementation process, and attributes to

the technology. Attributes to the technology include the relative advantage, risk level,

ease of use, complexity, and Trialability (Rogers, 1995). Innovation implementation

process includes the receptiveness to change, user attributes, organizational readiness,

and the quality of the organization’s implementation plan (Willcocks et al., 2013).

Innovations exist on a variety of levels once cloud computing is adopted including

operational innovations, business process innovations, and market innovations (Willcocks

et al., 2013). HR leaders must be prepared to link top and bottom line results when

deciding upon new technology (Lombardi, 2014) however, cost savings may emerge

more slowly than organizations anticipate (Willcocks et al., 2013). Additional advantages

of Cloud-Based Computing include efficient use of resources, instant scalability, no

hardware or software maintenance, anti-virus security, data recovery, and upcoming

standardized data security legislation (Aleem & Sprott, 2013). The benefits of adopting

technology within the HR function are apparent; however, potential negative outcomes

exist including the cost for wiring the workforce and successful change management

methods for ensuring user acceptance (Sareen & Subramanian, 2012). Risks for

transferring information through Cloud-Based Computing include privacy of data (Shilpa

& Gopal, 2011), inconsistent data protection legislation, remigration challenges when

changing service providers, lack of disaster recovery by the user organization, inadequate

employee training, unauthorized access (Card & Sivak, 2015; Dutta, Peng, & Choudhary,

2013; Géczy, Izumi, & Hasida, 2012; Sierra-Cedar, 2015), and lack of control over

availability (Aleem & Sprott, 2013).

In a study by Deloitte (2012) of 334 senior business leaders and HR executives’ at

large businesses, the findings reported 67% of businesses would significantly increase

technology as part of their talent strategies. In a study conducted by SHRM (2011), 48%

of participants stated rapid growth toward mobile technologies strategically affected

business and that Cloud-Based Computing, via SaaS and mobile technologies, are

increasingly becoming the preferred solution for HR technology while other HR

professionals indicated system integration to Cloud-Based Computing is a top priority

(Jones, 2012). While Cloud-Based Computing is slowly realized, mobile technologies

are fast becoming an HR issue (Card & Sivak, 2015; Keebler & Watson, 2014) with only

10% of organizations providing mobile applications for employees (Keebler & Watson,

2014).

Conclusion

Wang and Shyu (2007) reported effectiveness of HRM practices and

organizational performance improved when HRM and business strategies align (Loshali

& Krishnan, 2013; Timmins, 2008). Theriou and Chatzoglou (2008) found HR best

practices in knowledge management and organizational learning is two of the greatest

factors affecting organizational performance. Knowledge management and

organizational learning are more effective when using Cloud-Based Computing (Theriou

& Chatzoglou, 2008). Human resource management performance may increase with the

use of Cloud-Based Computing technologies and directly effecting organizational

performance.

Although prior study results revealed that HRIS, e-HR, and Cloud-Based

Computing assisted with achieving HR and organizational goals, little research existed

concerning the cause for the slow adoption of Cloud-Based Computing and the related

effect of Cloud-Based Computing on HR performance, and overall influence on

organizational performance. Information technology experts believed legal and technical

complexity hinders the adoption of Cloud-Based Computing (Aleem & Sprott, 2013;

Dutta, Peng, & Choudhary, 2013). Studies regarding the understanding and

implementation of Cloud-Based Computing in various industries provided insight to the

advantages and disadvantages of adoption. A lack of information existed regarding the

direct influence of Cloud-Based Computing use on organizational performance in

nontechnology related industries. Bassellier et al. (2003) stated, “To achieve successful

IT planning and IT implementation, essential for business managers to take a leadership

position” (p. 317). Results from the study may assist with HRM and talent management

as globalization becomes more apparent with small- to mid-sized businesses (Xiaoya,

Marler, & Zhiyu, 2012) and may support that the implementation of Cloud-Based

Computing globally could improve HRM and change management on an international

scale.

Summary

This chapter was a review of the pertinent literature germane to this study. The

HR function is viewed increasingly more as a critical business partner within

organizations. The demands on HR require additional tools for managing the

administrative responsibilities of the function for focus on strategic roles. This requires

leaders to influence respective organizations and end users to adopt new technologies,

such as Cloud-Based Computing, in an effort to improve the performance of HRM.

Human resource managers with strategic competence and management skills must

consider an IT-based workplace a top priority (Mishra & Akman, 2010). However, the

HR function is historically slow to adopt new technologies, and Cloud-Based Computing

is no different. The information provided in the literature review identified the

importance of innovative leaders, as characteristically seen in a transformational

leadership style, examine the evolution of HRIS and e-HRM to Cloud-Based Computing,

and consider the possibility of improved HRM and overall organizational performance.

Extensive research existed regarding the adoption of technology, implementation of

Cloud-based technology, adoption of technology in HR, and the effect of technology on

organizational performance. However, a gap exists for literature specific to the adoption

of Cloud-Based Computing in HR and on the implications of Cloud-Based Computing on

HRM and organizational performance. Chapter 3 will present the methodology for use

with this specific study of informed HR professionals.

Chapter 3

Research Method

Companies are competing for talent on a global level and technology is at the

forefront of human resource HR change (Abardeen, 2012; Deloitte 2012; SHRM, 2011).

An extensive literature search suggested that no definition existed for the characteristics

of successful leaders implementing Cloud-Based Computing related to the effect on

human resources management (HRM) performance through the adoption of Cloud-Based

Computing, and limited information available regarding the reasons for slow adoption in

HR. The purpose of this qualitative study with a modified Delphi research design was to

understand the reasons HR leaders are slow to implement Cloud-Based Computing, the

common characteristics of effective HR leaders and leaders with successful

implementation experience, and how the implementation of Cloud-Based Computing

relates to HRM and organizational performance. The problem is the slow adoption to

Cloud-Based Computing for efficiency of the HR function. The information in this

chapter includes a detailed review of the research method and design appropriateness,

population, sampling, instrument, data collection, and data analysis techniques.

Study Method and Design Appropriateness

Rationale for Selecting a Qualitative Method

Quantitative research is a numerical research approach involving one or more

variables, as opposed to a qualitative research approach that involves reviewing

characteristics and complexities of a given phenomenon (Leedy & Ormrod, 2010). An

extensive literature search led to the identification of a gap in the body of knowledge

regarding reasons behind the slow adoption of Cloud-Based Computing, describing the

characteristics of successful leaders implementing Cloud-Based Computing, and the

potential effect to HRM performance through the adoption of Cloud-Based Computing.

Little research is available on the effect of Cloud-Based Computing to HRM (Indranil,

2011; Yeh, 2012). Quantitative research is used for validating existing theories (Creswell,

2009); however, the prior research was incomplete regarding information about

technology adoption in HRM. Qualitative information is required to provide key topics

for research and move forward to quantitative research (Neuman, 2009). The focus of the

study was to understand the perceived effect of Cloud-Based Computing to HRM and

research factors documented making a qualitative approach appropriate for examining the

given phenomenon.

According to Christensen, Johnson, and Turner (2011) qualitative research relies

on subjective perspectives from participants. Creswell (2009) stated a qualitative

research method is appropriate for exploring undefined phenomenon experienced by

participants. Peshkin (1993) stated qualitative studies could serve one or more purposes

of description, interpretation, verification, or evaluation (Leedy & Ormrod, 2010). This

study focused on investigating the experiences and perspectives of informed participants

on the HR practices transitioned to Cloud-Based Computing, interpret the impediments of

adoption, characteristics of leaders successfully transitioning to Cloud-Based Computing,

and evaluate the perceived effect of Cloud-Based Computing to HRM performance.

Salkind (2003) described qualitative research as a rigorous method for exploring

academic questions needing additional context and explanation of phenomenon. Bahli,

Borgman, Heier, & Schewski (2013) recommended further qualitative study regarding the

factors influencing leader’s decision to adopt cloud computing. The goal of this study

was to obtain an understanding of the phenomenon associated with Cloud-Based

Computing adoption and HRM performance; thereby, a qualitative method was

appropriate to address the complexity of the research problems.

Rationale for the Research Design

After identifying the proper research method, an evaluation of the appropriate

research design is necessary. Considerations included the type of data required to answer

the research questions, data collection and analysis processes, interpretation, and

researcher skill (Leedy & Ormrod, 2010). The review and consideration of several design

approaches included grounded theory, hermeneutics, phenomenological, case study, and

Delphi.

Grounded theory. The grounded theory approach is useful for developing theory

from existing data and is least likely to begin with a theoretical framework (Leedy &

Ormrod, 2010). Creswell (2008) described grounded theory as an appropriate design

when available theories are insufficient or nonexistent for a particular phenomenon and

requires the discovery of a new theory or the creation of a new theoretical model

(Creswell, 2009). The literature review was inconclusive in identifying an applicable

theoretical model suggesting the need for further study of the phenomenon.

Hermeneutics. Smith, Flowers, and Larkin (2009) defined hermeneutics as “the

theory of interpretation” (p. 21). The researcher interprets the meaning of the

participant’s lived experience in found themes (Creswell, 2013). Hermeneutics, used

often in the study of humanities, helps researchers to interpret the essence of lived

experience by examining text in the form of writings, pictures, or conversations

(Neuman, 2009). Hermeneutics requires comprehensive investigation of the phenomena

from personal observations (Neuman, 2006). Extensive knowledge is gained from

analysis of field notes and additional observations or data obtained regarding the

phenomena (Neumann, 2006). For the purposes of this study, hermeneutics represents

interpretation of the lived experiences of participants’ involvement in adoption of

CloudBased Computing and perceived effect to HRM performance.

Phenomenology. Edmund Husserl developed the phenomenological research

design to provide a fundamental understanding regarding how a person experiences an

occurrence by conducting interviews of study participants (Christensen et al., 2011). The

design of phenomenological studies is to gain a better understanding of other’s

perspectives or perceptions based on experience (Creswell, 2009; Leedy & Ormrod,

2010). Christensen, Johnson, and Turner (2011) stated phenomenological studies are

appropriate when the researcher attempts to understand the essence of an individuals’

lived experience of a phenomenon (Creswell, 2009).

Case study. Christensen, Johnson, and Turner (2011) described case study “as

the intensive and detailed description and analysis of one or more cases” (p. 374). Social

science researchers are familiar with case studies across multiple industries including law,

medicine, psychology, and political science as it presents a thorough understanding of a

case (Creswell, 2013; Neuman, 2009). Studies existed for organizations in various

industries adopting different technologies and technological organizations adopting

Cloud-Based Computing at the time of the research; however, none addressed Cloud-

Based Computing within HR specifically.

Delphi method. The Delphi method, developed by Helmer and Dalkey (1963) in

the 1950s at the RAND Corporation, gathered consensus of participants on a specific

topic through the individual, and anonymous, questioning of study participants via a

series of questionnaires. The use of the Delphi method can be a judgment, decision, or

forecasting tool (Linstone & Turoff, 1975; Rowe & Wright, 1999). Skulmoski, Hartman,

and Krahn (2007) described the Delphi method as "an iterative process to collect and

distill the anonymous judgments of experts using a series of data collection and analysis

techniques with feedback” (p. 1). Classical Delphi is useful for helping to describe the

research method that adheres to the original Delphi method established by Helmer and

Dalkey (1963). The method is well suited when information about a phenomenon is

incomplete or nonexistent and “well suited to rigorously capture qualitative data”

(Skulmoski et al., 2007, p. 9). Delphi was the appropriate research design for this study

given the search for agreement regarding applicable phenomena (Adler & Ziglio, 1996)

and factors not established by prior research (Hall, 2009; Linstone & Turoff, 1975).

Additionally, the anonymity of participants eliminates exposure to bias from others

participants. The flexibility for modification of the Delphi model (Linstone & Turoff,

1975; Skulmoski et al., 2007) and lack of studies similar in scope or design, and the

likelihood of answering the research questions (Hsu & Sandford, 2007) contributed to the

appropriateness of modified Delphi for this study.

Overview of Research Methodology

The selection of the modified Delphi research design for this study was

appropriate for several reasons. The purpose of this study was to explore the experiences,

perceptions, and perspectives of informed participants in HR on the adoption of

CloudBased Computing, leadership characteristics of those successfully implementing

CloudBased Computing, and the perceived effect to HRM and organizational

performance. "Researchers may want to look forward to see what will be the key

information systems issues in a wireless world, the ethical dilemmas in social network

analysis, and the lessons of early adopters" (Skulmoski et al., 2007, p. 2) making the

modified Delphi a reasonable research design for answering the research questions of this

study.

Key characteristics of Delphi include minimizing the effects of bias during group

interaction using electronic means of communication (Hsu & Sandford, 2007), providing

controlled feedback to study participants, and maintaining participant anonymity (Adler

& Ziglio, 1996; Dalkey, 1972). Primary considerations include the lack of literature

available regarding the phenomenon (Skulmoski et al., 2007) and established standards,

the flexibility for modification of the method for identifying subjective themes, the

request for informed participants over experts, and the ability for the researcher to request

further insight to participant ranked items through subsequent iterations (Hsu & Sandford,

2007). The modified Delphi approach aligns with and benefits the research by assisting

with the exploration of the experiences and opinions of informed participants through

sequential, iterative, anonymous collaborative contribution (Skulmoski et al., 2007). The

results of the study benefit the existing body of knowledge on the phenomena of

leadership, adoption of Cloud-Based Computing, HRM, organizational performance, and

may advance future research through quantitative methods.

Delphi Process

The Delphi technique is the methodical gathering of perspectives on a topic

through sequential surveys created from the participant responses of earlier iterations

(Agarwal & Balasubramanian, 2012). Hall (2009) expressed the process for Delphi

research as illustrated in Figure 3. A structured approach in qualitative study contributes

to validity and reliability (Shank, 2006). The number of rounds varies based on the

purpose of the research (Skulmoski et al., 2007). When the goal is to understand nuances,

reach consensus, or reveal sufficient information the research process may require three

rounds or fewer (Skulmoski et al., 2007). Various modes of interaction are acceptable for

Delphi studies, including paper-based and electronic e-mail questionnaires, and online

surveys (Skulmoski et al., 2007). This study used SurveyMonkey® for online

distribution of the questionnaire instrument to answer the research questions.

Figure 3. Process for Delphi Research (Hall, 2009). Note. Adapted from “The Delphi

primer: Doing real-world or academic research using a mixed-method approach.” by

Elmer Hall, in Cheryl Lentz, The refractive thinker: Vol. 2: Research Methodology, (p.

Research Questions

The following research questions drove the study regarding the topic of HR

leader’s perspective on the phenomenon:

RQ1: What are the Cloud-based Computing tools that appear to be most effective

for Human Resources?

RQ2: What are the impediments to adoption of Cloud-Based Computing in

Human Resources?

RQ3: How does successful Cloud-Based Computing affect the performance of

HRM?

RQ4: What leadership traits of successful Cloud-Based Computing adopters also

contribute to HR performance?

RQ5: How does HR contribute to the overall performance of an organization?

RQ6: What are the implications of best practices in Cloud-Based Computing

adoption within HR and to general leadership theory?

The research questions were addressed with open-ended questions in free-form format in

Round 1, and Likert-type scale questions for ranking in Round 2. Qualitative analysis of

the data occurred to identify themes and connections using NVivo10® and Excel®, and

statistical analysis using Excel® and SPSS® occurred after Round 2. The data from

Round 1 guided the creation of ranking questions for the Round 2 survey while

addressing the research questions with free form questions in Round 1. The data analysis

began by using NVivo10® to identify themes, patterns, and connections. The resulting

qualitative data were useful for creating additional questions in Round 2 as outcomes

revealed the need for further insight. The research questions were also part of the data

collection process in Round 2 using ranking and percentage questions based on common

answers obtained in Round 1. The results of the study for RQ5 included findings

associated with the other research questions of this study and integrated with available

literature to identify themes and patterns within existing leadership theory. Statistical

analysis of the outcomes from all research questions of Round 2 was conducted using

Excel® and SPSS®.

Population and Geographic Location

Identifying the population for a Delphi study is critical for ensuring involvement

of appropriate participants (Helmer, 1967) and typically dependent on the area of

experience (Hsu & Sandford, 2007). The population for this qualitative study consisted

of HR informed participants with 5 or more years of experience in HR with a targeted

number of 12 participants associated with HR focused LinkedIn forums and HR groups in

Arizona. Round 1 of the study secured 14 participants. The accessibility of LinkedIn is

global and not limited to any specific geographic area that provided a larger population

sample of HR informed participants.

Participant Selection Criteria

Classic Delphi requires the identification of experts on a study participant panel

(Helmer, 1967; Helmer & Dalkey, 1963). A modification to the Delphi allows for

flexibility in identification of study participants. For the purpose of this study, the target

sample relates to the identified subsets of the total population for this study, including

participants with 5 or more years of HR experience, knowledge of Cloud-Based

Computing in HR, members of select HR LinkedIn groups, and Arizona-based HR

groups. Figure 4 provides a visual representation of the target population.

Figure 4. Target Sampling Frame

In Delphi research, a large panel is unnecessary (Delbecq, Van de Ven, &

Gustafson, 1975; Hall, 2009; Skulmoski et al., 2007) because a smaller informed group

can more easily obtain consensus (Linstone & Turoff, 1975). The recommendation from

the literature was 10 to 18 participants on a Delphi panel, as size does not depend on

statistical power (Agarwal & Balasubramanian, 2012). Previous Delphi studies included

a similar number of participants. The expected convenience sample was a targeted group

of 12 HR informed participants with 14 recruited from various professional organizations

and social media groups to participate in the study. Participation was low upon initially

posting a request for study participants in the LinkedIn forums. A purposive snowball

sampling technique was then used to recruit participants.

Why use social networking groups and local chapters. LinkedIn is a top

professional social networking site with over 250 million individual profiles. LinkedIn

groups provide a forum for like-minded professionals to connect with others within a

respective industry or similar interest. The groups provide an immediate filter for

recruiting volunteer participants within the target population. The HR groups where the

recruitment of participants occurred for this study required administrative approval and

proof of the requesting member’s affiliation within the respective industry or area of

interest. Most social media groups are open groups once members are approved allowing

members to post content, which promotes the progress or welfare of the group industry or

interest. Select HR groups received a request to solicit (see Appendix A) study

participants and responded affirmatively for recruiting study participants (see Appendixes

B, C, & D).

Miller and Salkind (2002) stated that, “when practical considerations preclude the

use of probability sampling” (p. 53) a subgroup might be selected for group

representation. Identification of potential participants occurred through qualifying

questions in the demographic portion of the questionnaire. Participants included HR

professionals with varying levels of HR, managerial, and executive experience. All

participant organizations were based in the United States and all participants were

familiar with Cloud-Based Computing in HR. The combined experience, leadership

level, industry background, and technology experience provided informative feedback.

Participant attrition commonly occurs with the completion of each round making the goal

of each round to obtain information saturation in the event a participant withdrew prior to

completion.

Informed Consent

A fundamental ethical principle of research is participation in research must be

voluntary and potential participants must know their rights to make an informed decision

(Leedy & Ormrod, 2010; Neuman, 2009). The informed consent statement explains all

aspects of a study requesting the potential participants’ voluntary agreement prior to the

start of the study (Neuman, 2009). Leedy and Ormrod (2010) stated no data collected

should refer back to the participant and personal data must remain confidential.

All prospective participants received an e-mail containing a consent form to

obtain informed consent from each participant prior to involvement in the study (see

Appendix E). Upon successful submission of an electronically signed consent form, the

participant received an introduction e-mail with a link to the survey housed on

SurveyMonkey®. The potential participant received access to the Round 1 questionnaire

after providing a signed informed consent via e-mail or fax. The informed consent form

explained the process, withdrawal procedures, risks, and primary intent of the study.

Participants were able to withdraw at any point before, during, and after data collection.

Participants who choose not to participate were directed to submit a written withdrawal

via e-mail to the researcher. Had participants withdrawn, the participant’s data would

have been removed from data gathered, deleted, and destroyed. Forms are stored and

saved for a minimum of 3 years.

Confidentiality

Maintaining the privacy, anonymity, and confidentiality of research participants

(Neuman, 2009) is paramount and involves physically and electronically securing data

(Stiles & Petrilla, 2011). The assignment of unique and individual alphanumeric codes

ensured the confidentiality of all participants and organizations discussed remained

anonymous. During each phase of the study, the participants’ unique data codes helped to

identify specific phases, themes, and participant. The code issued to the first participant

in the pilot phase of the study is TCPS001 and TCFDS001 for the first participant in the

full Delphi study. Using a coding system provides further protection of participant

confidentiality and aids in the organization of the data. Creswell (2012) recommends

using a coding system to enhance readability of data while maintaining confidentiality of

the participant and respective data.

Data code keys remain separate from the research data to which the researcher

holds exclusive access. Data will remain exclusive to the researcher, stored on a digitally

encrypted, password protected external hard drive or housed in a fireproof safe for a

minimum of three years. The hard drives and folders containing data code keys,

participant codes, and research data will be erased, formatted, and physically destroyed

and all documentation will be shredded after the 3-year period.

Reliability and Validity

Qualitative research requires objectivity and as such, researchers must emphasize

rigor and trustworthiness to ensure validity and reliability (Lincoln & Guba, 1985;

Neuman, 2009). The establishment of reliability must occur prior to validity (Lincoln &

Guba, 1985) and refers to the dependability of the outcomes (Christensen, Johnson, &

Turner, 2011) while validity refers to how effectively the phenomenon observed matches

with the constructs used to understand the phenomenon (Neuman, 2009). Rigor relates to

the logical and systematic manner of planning and implementing a research design with

accuracy. The Delphi methodology serves to obtain agreement between the participants

thus, by its very nature provides validity because of expert consensus (Bourgeois,

Pugmire, Stevenson, Swanson, & Swanson, n.d.; Linstone & Turoff, 1975) while the

processes of identifying and selecting experts, ranking, and consensus are rigorous (Okoli

& Pawlowski, 2004). Linstone and Turoff (1975) explained that creating a valid and

reliable population sample on a Delphi panel of experienced participants uses the

researcher’s established selection criteria. Design considerations such as sample size,

sample composition, methodology, number of rounds, and mode of interaction contribute

to rigor of the method, a successful Delphi and deeper understanding of the topic

(Skulmoski et al., 2007). To maintain validity and reliability in a Delphi study 5 to 10

carefully selected participants are needed, according to Loo (2002), 10 to 50 participants

according to Turoff (1970), and 10 to 15 according to Meijering, Kampen, and Tobi

(2013), and Skulmoski (2007). This study obtained the opinions of 12 qualified fullpanel

participants completing both survey rounds.

The Delphi method works well when the objective of a study is obtaining a

consensus, developing forecasts, or obtaining a deeper understanding of issues, problems,

or solutions (Skulmoski et al., 2007). Adler and Ziglio (1996) determined the reliability

of the Delphi research method was significantly comparable to traditional interviews and

group discussions. The iterative process of the Delphi method incorporates the

knowledge and experience of participants providing the researcher with controlled

feedback and data critical for analysis that is valid and reliable (Dalkey, 1967). Lincoln

and Guba (1985) stated establishing trustworthiness is necessary in research and defined

credibility, confirmability, dependability, and transferability as key factors to traditionally

defined validity and reliability in qualitative study. All efforts were made to mitigate

potential issues and maintain the validity and reliability of this study.

Credibility and Triangulation

Internal validity is the degree to which the observation of a phenomenon is causal

(Christensen et al., 2011; Leedy & Ormrod, 2010). Credibility refers to the internal

validity of a study and suggests corroboration of study participants as a strategy for

addressing credibility (Lincoln & Guba, 1985). Credibility requires establishment

through triangulation and is useful as an equivalent or extension to, validity and reliability

in qualitative research (Willis, 2007). The objective of triangulation is ensuring multiple

perspectives of an observed phenomenon (Willis, 2007; Lincoln & Guba, 1985). Four

types of triangulation exist including measures, methods, theory, and observers (Neuman,

2009). This study used data triangulation and theoretical triangulation. Data

triangulation provided various perspectives regarding best practices for Cloud-Based

Computing implementation, HR leader characteristics, and factors championing and

challenging cloud adoption. Data triangulation was accomplished by obtaining data from

multiple study participants (Barusch, Gringeri, & George, 2011; Christensen et al., 2011)

in different settings (Christensen et al., 2011) where participants had the opportunity to

corroborate findings in Round 2, enhancing the credibility, or internal validity, of the

study.

Triangulation is viewing data through multiple means. Denzin and Lincoln

(2000) posit the use of multiple data collection procedures enhances validity and

improves understanding, duplication, and comprehension of research questions

(Christensen et al., 2011; Denzin, 1978). Data sources triangulation is used by collecting

data at two different points in time (Patton, 2002) through the Delphi process using

openended questions in Round 1 and Likert-type scale questions in Round 2.

Triangulation occurred by using open-ended questions during Round 1 of the study and

categorizing responses according to how many years the participants indicated the target

company was in business and the years of managerial experience for each participant,

respectively, sliced the data. Theoretical triangulation occurs when multiple theoretical

perspectives are used during data analysis to better understand and interpret the data

(Christensen et al., 2011; Neuman, 2009). In this study, theoretical triangulation was

accomplished by comparing previous study results discussed in Chapter 2 with outcomes

from this study and by using concepts of the DOI and TOE theory and conceptual

frameworks (Neuman,

2009).

Spearman rank correlation is a bivariate, nonparametric test used in statistical

analysis (Hollander & Wolfe, 1973). Robust parametric statistical tests are not reliable

given small sample sizes, therefore, descriptive statistics and non-parametric tests were

used to explore and analyze the data (Bahli, Borgman, Heier, & Schweski, 2013) testing

the reliability of the range of data and mitigating limitations in ranking Delphi methods

(Patton, 2002). Although correlational analysis was conducted in this study, the small

sample size, and lack of statistical power did not justify the need to reclassify this study

as mixed-methods, rather this study remains qualitative.

Confirmability

Reliance on researcher interpretation increased confirmability of qualitative

studies, often considered subjective in academia (Leedy & Ormrod, 2010). Lincoln and

Guba (1985) referred to confirmability as the neutrality of the researcher when

interpreting data. “Researchers strive for objectivity” (Leedy & Ormrod, 2010, p. 21);

however, objectivity may not be possible or desirable in qualitative studies (Creswell,

2009; Leedy & Ormrod, 2010). According to Lincoln and Guba (1985), the researcher

establishes confirmability through triangulation or by providing auditable proof of

corroborated results including raw data, methodological decision, analysis notes, personal

interpretations, and other pertinent information to ensure objectivity. The study used data

triangulation and maintained accurate, descriptive documentation for auditing. The pilot

study conducted allowed pilot participants to review the surveys for bias and ensure

objectivity. Feedback from pilot participants was incorporated into the final creation of

each survey with subsequent iterations designed based on replies and feedback from pilot

participants.

Dependability

In qualitative research dependability, “that the results will be subject to change”

(Creswell, 2013, p. 246), corresponds to reliability in quantitative research (Lincoln &

Guba, 1985). Dependability refers to the reliability, or consistency (Neuman, 2009) of a

measurement. Researchers can ensure dependability by providing a transparent account

of the research process including an audit trail of data, findings, analysis, and

interpretations (Lincoln & Guba, 1985). Participant responses were gathered, examined,

and complied into Excel® spreadsheets prior to analysis. The data were then manually

analyzed and cross-referenced to data analysis from NVivo10® for each survey round,

and supportive literature to preserve dependability.

Transferability and Limited Generalizability

Transferability corresponds to the external validity of a qualitative research study

(Lincoln & Guba, 1985). External validity in qualitative research is the degree to which

one can apply the study results to another or similar population (Christensen et al., 2011;

Willis, 2007). Kirchner (2014) describes transferability as “descriptive adequacy” (p. 82)

conducted by providing detailed descriptions of data collection and analysis processes

(Lincoln & Guba, 1985). Krefting (1991) argues strategies for establishing

trustworthiness of transferability include nominated sample, time sample, and dense

description. Lincoln and Guba (1995) describe transferability as the original researcher

providing adequate detail about a phenomenon where implications are applicable to other

settings, times, situations, and people, and argue proof of transferability, after sufficient

detail is provided, lies with the researcher seeking application elsewhere. This study

attempts to provide a dense description of the research process, participant backgrounds,

and study outcomes to increase the possibility of transferability. The study findings from

this research may be transferable and applicable, in larger scale, across industries in HR,

or Cloud-Based Computing.

Generalizability is a determination on the applicability of study outcomes to a

larger population and a major criterion in quantitative research (Polit & Beck, 2010).

Generalizability may be limited because of the challenges in exact replication of a study,

the nature of the Delphi methodology, and small sample size; however, generalizability is

attainable when the sample closely represents the population (Salkind, 2003). The study

appears to be consistent with a representative sample (Leedy & Ormrod, 2010) of HR

informed participants who met the requirements of consideration, despite the small

number of respondents, generalizability is possible however, applying the results to the

broader population of HR managers should be judiciously conducted. Correlational

analysis was conducted to understand the data and potential contribution to triangulation

better. Several statistically significant relationships were produced implying the strength

of relationships would increase if the population were larger and fully representative of

all HR manager’s responses. However, small sample sizes may not provide robust,

reliable statistical results (Bahli et al., 2013) thereby limiting generalizability of the study

results.

Data Collection

Data collection began by recruiting volunteer participants via LinkedIn HR

specialized forums and Arizona-based HR groups. The administrators for three Arizona

HR forums on LinkedIn received a written request (see Appendix A) and returned emails

of written permission from each of the administrators (see Appendixes B, C, and

D). The study included a modified Delphi technique with two rounds of questionnaires

(Skulmoski et al., 2007). Study participants had five or more years of HR experience to

ensure the participants were knowledgeable on the topic. Participants received a letter of

recruitment (see Appendix H) with details regarding the purpose of the study and

informed consent form detailing privacy measures. All participants were required to

physically sign and return the informed consent form (see Appendix E). After receiving a

signed informed consent from each participant, the researcher provided an introductory

letter with the link to the Round 1 questionnaire (see Appendix F) via e-mail. The

expectation was to use the same participants in Round 1 and in Round 2 in the study.

Each round of the study built upon the information collected from the previous

round until reaching information saturation among participants (Skulmoski, 2007). The

objective of the first round questionnaire was to obtain information from participants

using open-ended questions and free form answers. The answers from Round 1 were then

used to develop the questionnaire for data collection in Round 2 (see Appendix G).

Questionnaires were distributed via the Internet using SurveyMonkey®. Online

surveys and electronic e-mail questionnaires are acceptable modes of interaction for

Delphi studies (Skulmoski et al., 2007). SurveyMonkey® provides multilayered security

and encryption features ensuring the protection of data transmission, storage, and access

ensuring the anonymity and privacy of participants and data integrity.

Instrumentation

The Delphi method is a process requiring a series of data collection rounds using

participant feedback to modify the instrument used for data collection in subsequent

iterations (Skulmoski et al., 2007) including the modified Delphi. The online

questionnaire was a combination of free-form answers, Likert-type scale, and ranking

questions. The study used an online questionnaire instrument for each round of the full

Delphi study Round 1 (see Appendix F), Round 2 (see Appendix G), distributed via

SurveyMonkey®. Expediency of electronic communication and questionnaire

distribution assists with maintaining enthusiasm and retention of study participants

(Skulmoski et al., 2007). Online distribution of the questionnaire instrument preserves

anonymity of participants and data integrity by avoiding bias, group think (Straus, Parker,

Bruce, & Dembosky, 2009), dominant perspectives (Hall & Jordan, 2013), or influence

from other study participants (Linstone & Turoff, 1975).

Modification of the Round 1 questionnaire occurred based on results from the

pilot study. Round 1 of the full Delphi began after the pilot study was complete and

results analyzed. All study participants received an e-mail letter of recruitment

explaining the purpose of the study (see Appendix I), background of the researcher,

detailed explanation of the Delphi process and an attached informed consent form (see

Appendix J) with instructions for form submission. After receiving, coding, and storing

signed informed consent forms, the participants received an email link to the online

questionnaire with instructions for completion. The Round 1 questionnaire consisted of

demographic, informational, and open-ended questions. Round 1 data were analyzed and

participant feedback aided in creating the Round 2 survey.

Creating the Round 2 questionnaire occurred after obtaining and analyzing the

Round 1 data from participants. The Round 2 questionnaire (see Appendix G) required

revisions from the initial survey based on data collected from Round 1 and considering

feedback from pilot participants. The Round 2 questionnaire consisted of Likert-type

scale questions for ranking purposes. Data collected were reviewed, examined, and

analyzed to produce the results of this study. All electronic written communication,

informed consent forms, and storage of the data are housed on encrypted, secure external

drives in a fireproof safe with physical documentation accessible only to the researcher.

Pilot Study

A pilot Delphi is a small-scale version of the full Delphi study and is “sometimes

conducted with the goals of testing the Delphi questionnaire to improve comprehension,

and to work out any procedural problems” (Skulmoski et al., 2007, p. 4). A pilot test of

the survey instrument ensures participants can complete the questionnaire (Creswell,

2009), improves reliability (Neuman, 2009), and increases rigor of the study (Lincoln &

Guba, 1985). Five individuals assisted with conducting a pilot for the study. Prior to the

Round 1 pilot study start, pilot participants were sent the Informed Consent form (see

Appendix J). An introduction letter to the pilot study was sent to all pilot participants

upon receipt of a signed consent form (see Appendix I). The introduction letter

emphasized the need for their trusted opinion based on content, structure, phrasing,

understanding, and ease of use.

The pilot study participants were not invited to participate in the full Delphi;

although the pilot results were reviewed as part of the data triangulation process in

Chapter 4. Feedback received from the pilot included slight verbiage changes, the

deletion of one question, and the addition of three clarifying questions. All participants in

Round 1 of the full Delphi study received the revised questionnaire instrument after the

pilot testing and incorporation of feedback from pilot participants.

Data Analysis

The data analysis phase of the Delphi study included results from the pilot study

and full Delphi rounds. Analysis techniques for the Delphi relate to the questions

presented to participants (Skulmoski et al., 2007). Electronic collection of raw data can

benefit expediency of collection and minimize transcription during analysis (Skulmoski et

al., 2007). Analyzing qualitative data requires organizing data, themes, coding,

interpretation, and presentation (Creswell, 2009). NVivo10® is a secure, online software

program supportive of qualitative and mixed methods research. NVivo10® functionality

allows the researcher to import, code, organize, and query data, discover themes, and

reflect on outcomes to support research findings rigorously. NVivo10® is a powerful tool

for uncovering subtle connections among data providing the researcher with additional

insight and ideas for answering research questions and justifying findings. The software

was useful for analyzing the data from Round 1. SurveyMonkey® and NVivo10® work

seamlessly and securely together preserving confidentiality and ensuring data integrity.

Data from Round 1 was initially collected into Excel®, organized and manually coded.

The data were then imported into, and analyzed in, NVivo10® to identify themes and

cross-referenced with key themes identified in NVivo10® and analysis from Excel®.

The NVivo10® software is capable of managing unstructured, text-based data with

theorizing and indexing ability (Creswell, 2012.

Visual graphic representations are a feature within NVivo10®.

The Round 2 questionnaire consisted of one identification question for the

participant code, seven Likert-type scale questions for ranking, and one open-ended

question. Likert-type scale presents qualitative data with a quantitative value (Neuman,

2009). SPSS® is a statistics software package for managing and analyzing data. Excel®

and SPSS® are programs located on a password-secured computer owned by and

accessible only to the researcher. Given the small sample size, robust parametric

statistical tests are not reliable, therefore, SPSS® software was used to explore and

analyze the data using descriptive statistics and non-parametric tests (Bahli, Borgman,

Heier, & Schweski, 2013).

A total of 12 participants completed both survey rounds, more than sufficient for a

Delphi study. Correlational analysis was conducted using Spearman’s Rho resulting in

noteworthy information, although a sample size of 30 or greater is recommended for

quantitative analysis (Neil & Rasmussen, 2007),. The data collected from the small

sample size used for this study provides valuable information yet less than reliable

results. This study was not reclassified as a quantitative or mixed-method study, although

correlational analysis was conducted and statistically significant results were found

(Beakley, 2015; Kirchner, 2014). The outcomes of this study are presented in Chapter 4

and discussed further in Chapter 5, along with recommendations for future research.

Summary

The purpose of this qualitative study with a modified Delphi research design was

to understand the reasons HR leaders are slow to implement Cloud-Based Computing,

common characteristics between effective HR leaders, and HR leaders experienced with

successful implementation of Cloud-Based Computing, and discover how the

implementation of Cloud-Based Computing may potentially affect HRM and

organizational performance. Chapter 3 provided an explanation and justification of the

appropriateness for selecting the methodology and research design for the study. This

chapter contained an explanation regarding the conduction of the study, including the

selection of the study participants and the data collection and data analysis processes.

Chapter 3 also provided a description of the instrumentation, validity, reliability, and

ethical considerations for ensuring privacy and confidentiality of study participants and

data.

The gaps found during the literature review assisted with the formulation of the

research questions for this study to address these gaps. Quantitative approaches require

more data than currently available; therefore, a qualitative approach is appropriate. After

reviewing various qualitative methods, the modified Delphi approach is the most

appropriate method to answer the research questions. A modified Delphi research design

was appropriate to gather data of subjective judgments, highly effective with small

samples, flexible for modifications (Skulmoski et al., 2007), and suitable to identify,

compare and contrast the success factors from the perspective of the HR leader. The

modified Delphi was used to acquire input from informed participants by organizing

feedback obtained through questionnaires delivered through multiple iterations and

revised based on the input from previous iterations.

SurveyMonkey® is a secure website where the collected data from the two Delphi

rounds remained until the data analysis process begins. The data analysis process

includes using Excel® and NVivo10® software to identify themes and subtle patterns,

and using SPSS® for descriptive statistics and non-parametric tests. Chapter 4 will

include the results of the data analysis obtained from the Delphi study.

Chapter 4

Results

The objective of this modified Delphi study was to obtain a deeper understanding

of how a more rapid adoption of Cloud-Based Computing will potentially affect HR

processes and outcomes. Chapter 1 included an examination of background information

and the necessity for this study, a presentation of the research questions that guided this

study, and overview of the conceptual framework. Chapter 2 included an examination of

relevant literature about current research on HRM, Cloud-Based Computing in HR, and

leadership. Chapter 3 detailed the appropriateness of the research method with

participant criteria and overview of the methodology for this study. Chapter 4 includes

details regarding the pilot study, participant recruitment, qualifying criteria, research

design, and presentation of the data. This chapter provides the results from the two

rounds of respondent surveys.

Review of the Purpose Statement and Research Questions

The purpose of this study was to examine how the adoption of Cloud-Based

Computing affects HRM and organizational performance. This study served to gain a

deeper understanding of the slow adoption of Cloud-Based Computing for efficiency of

the HR function and how a more rapid adoption might affect HR processes and outcomes.

This modified Delphi study focused on attaining the opinions of informed HR

participants via two rounds of surveys. The study was conducted to obtain data pertinent

to answering the research questions that guided this study. Six research questions are

analyzed. The pilot assisted with validating the study questionnaires and ensuring the

information captured aligned with answering the research questions.

Pilot Study

Pilot studies are a tool to determine the general effectiveness and validity of

research instruments, and collection and analysis procedures. A pilot study was

conducted with five panelists testing the effectiveness of the questionnaires. The data

analysis results from the pilot study served as the foundation for creating the final

questionnaires of the full-panel study. Five pilot participants met the selection criteria

and were emailed Informed Consent forms 1 week prior to the Round 1 pilot study start

date. An introduction letter to the pilot study was sent to all pilot participants upon

receipt of a signed consent form. The introduction letter emphasized the need for their

trusted feedback based on content, structure, phrasing, understanding, and ease of use.

The Round 1 questionnaire consisted of demographics questions, open-ended

questions with free form answers, and multi-selection questions. Participants of the pilot

study were afforded the opportunity to provide additional feedback at the end of each

questionnaire via open format text box. Round 1 of the pilot study began on November

14, 2014 and concluded November 20, 2014. Due to a heavy holiday schedule at the end

of November and throughout December, Round 2 of the pilot commenced January 7,

2015 and concluded on January 14, 2015. One pilot participant did not complete Round

2 and did not respond to email requests for completion.

Pilot Participant Demographics

Five participants were selected for the pilot. All participants expressed

understanding the definition of Cloud-Based Computing. However, forty percent of pilot

participants indicated having no experience with Cloud-Based Computing in HR. Three

participants were classified as individual contributors while two participants were

classified at manager or executive level. The participant who dropped out of the Round 2

pilot survey was a female at the individual contributor level. Twenty percent of pilot

participants expressed having six to nine years of HR experience and 80% with 10 years

or more of HR experience. All pilot participants were based in the United States and

worked for domestic organizations.

Pilot Study Findings

Feedback from the pilot resulted in minor verbiage changes, the deletion of one

question, and vetting of three additional questions in the Round 1 questionnaire. Results

from Round 2 of the pilot resulted in verbiage changes for better clarity of the questions.

Two participants provided additional statements in the open comment field at the end of

the Round 1 pilot survey. Pilot participant TCPS001 stated, “Security is a wild and big

issue for everyone. That may be the biggest reason that companies hold back on

implementing cloud.” Pilot participant TCPS003 stated, “Cloud-computing can have a

strong impact on overall organizational performance and in creating greater productivity

and efficiencies.” The consensus among pilot participants was the research questions

were relevant and the study evoked ideas significant to the research topic and questions

resulting in a successful pilot. Pilot participants did not receive a request to participate in

the full-panel Delphi study. No major changes were required based on feedback from the

pilot study. Since four panelists in the pilot completed both rounds of the Delphi study,

the findings from the pilot are useful in corroborating the results of the full study; this is

done in the triangulation section pertaining to data triangulation.

Sample Collection

The goal of the study was to obtain input from a panel of informed participants

with a minimum of 5 years’ experience in HR. Participants were recruited via

topicspecific HR LinkedIn forums and Arizona-based HR groups. LinkedIn forums

provide a community platform for individuals with common interests, professions, or

affiliations.

Permission was requested from each forum administrator (see Appendix B, Appendix C,

& Appendix D). Each forum administrator provided permission to solicit study

participants from within the forums. A digital post was submitted to each forum one time

per week for three weeks prior to the start of Round 1 of the full-panel Delphi. A low

response rate resulted in frequent postings, which kept the request at the top of the forum

feed (see Table 1).

Table 1

First Responses Influenced by Signed Informed Consent Forms

Responses to the requests for participants 17 n/a Returned signed

Informed Consent form 14 100

Participated in Round 1 of full-panel Delphi 14 100

Participated in Round 2 of full-panel Delphi 12 0.86

Note: Eighteen percent of initial participants elected not to proceed with participating

after reading the letter of informed consent.

The additional visibility prompted requests for participation and a snowball affect

extended reach to additional participants. Each participant request was vetted to ensure

participants met selection criteria and participants were then emailed an Informed

Consent form. Seventeen individual requests were received with 14 participants

completing the Informed Consent form resulting in an 82% initial response rate.

Full-Panel Delphi Demographics

This section focuses on the demographic characteristics of the full-panel

participants. All informed participants met the requirement of 5 years minimum HR

experience. Fourteen individuals completed Round 1 of the full-panel Delphi with 12

Response Type %

participants completing Round 2 resulting in a 14% attrition rate. Participants who did

not complete Round 2 provided permission for use of their data from the previous round.

The study contained a high concentration of women participants, with 93% females and

62% of all participants with 10 or more years of HR experience. Table 2 illustrates the

demographics of participant’s experience and gender.

Table 2

Participant’s Experience and Gender

Years of Experience Gender n %

5 Years Female 2 14

Male 0 0

6 to 9 Years Female 3 21

Male 0 0

10 to 14 Years Female 2 14

Male 1 7

15+ Years Female 6 44

Male 0 0

Totals 14 100

Note: Only one male responded to, and participated in, the study.

All participants indicated having experience with electronic HR systems and were

employed in a multitude of industries with 50% of participants in organizations

established for 30 years or more. Twenty-one percent of participants worked for

organizations with 10,000 or more employees. Industries of full-panel participants are

included in Table 3.

Table 3

Industries of Full-Panel Participants

Education 3 21.44

Industry Type %

Manufacturing 2 14.30 Construction 1 7.14

Consulting 1 7.14

Financial Services 1 7.14

Government 1 7.14

Healthcare 1 7.14

HR Recruiting & Screening 1 7.14

Social Services 1 7.14

Technology 1 7.14

Transportation 1 7.14 Totals 14 100.00

Data Collection

The study used a modified Delphi research design to assist with exploring the

experiences and opinions of informed participants through iterative rounds of Likert-type

scale survey information and open-ended, free-form text questions. SurveyMonkey®

served as the channel for distributing study questionnaires and collecting data. The data

collection process included two rounds of questionnaires in both the pilot and full-panel

study. The pilot served to identify ambiguities in the questionnaire, solidify the data

collection and analysis processes, and validate the questionnaire. Areas of focus for the

study included HR leadership characteristics, leadership characteristics of HR leader’s

successfully implementing technology, technology implementation best practices in HR,

and HR organizational performance factors with technology.

A total of 14 participants completed Round 1 of the study and 12 participants

completed Round 2 that was above the minimum participants needed for this type of

modified Delphi study. All initial participants received an Informed Consent form via

email (see Appendix E) and introduction letter (see Appendix H) upon completion of the

pilot study. All informed participants met the criteria of 5 years minimum HR

experience. Participants returned signed informed consent forms by fax and email. Each

participant was sent an introduction letter with detailed information regarding the nature

and purpose of the study, expected time commitment, the need for their honest opinion,

and thanking them for participating.

Round 1 included 14 participants and the questionnaire consisted of 33 total

questions in a combination of open-ended free form text answers and select closed-format

multiple choice (see Appendix F). Fifteen questions related to demographic information

about the participant and the target company being referenced by the participant to

answer study questions. The remaining 18 questions related to identifying HR leadership

characteristics, technology implementation best practices in HR, characteristics of HR

leaders having successfully implemented technology, and the effect of technology to HR

and organizational performance.

The data derived from the first iteration guided the formulation of the

questionnaire used in the second iteration. Round 2 consisted of four questions on a

7point Likert-type rating scale of probability and two questions on a 5-point Likert-type

rating scale of importance (see Appendix G). Panelists ranked factors affecting

implementation of Cloud-Based Computing, leadership characteristics, best practices for

implementation, and factors affected by the implementation of Cloud-Based Computing.

Ranking across all areas of study related to the research questions was complete;

therefore, the second iteration was the final round. A third iteration would have

commenced if additional questions arose or ranking was unachievable in the second

round.

Data Analysis

Data from Round 1 of the full-panel Delphi study were analyzed using NVivo10®

and Excel®. The results were used to create the questionnaire format and questions, and

determine ranking scales, for Round 2. The entire process took approximately two

months longer than originally anticipated. Recruiting through LinkedIn forums required

multiple posts at various points throughout the day in order to maintain visibility to the

group. Delays occurred with holidays, obtaining signed Informed Consent forms and in

providing gentle reminders to participants to complete questionnaires. Attrition is

expected in any Delphi study and was experienced in both rounds (Davidson, 2013;

Donohoe, 2011).

Fourteen participants responded to the Round 1 questionnaire. The same 14

participants received an email inviting them to participate in Round 2 of which 12

responded by completing the Round 2 questionnaire resulting in a 14% attrition rate.

Rigorous qualitative research is suitable for discovering high-level concepts applicable to

participants or settings (Polit & Beck, 2010). Thorough inductive analysis, combined

with confirmatory strategies that require credibility of conclusions, assist qualitative

researchers in building insightful, inductive conclusions (Polit & Beck, 2010).

Analysis of the data was rigorous. Rigorous qualitative research depends upon

inclusion of credibility, confirmability, dependability, and transferability (Hasson &

Keeney, 2011; Lincoln & Guba, 1985). Credibility refers to the internal validity of a

study through triangulation (Neuman, 2009; Willis, 2007) by ensuring multiple

perspectives of an observed phenomenon (Willis, 2007; Lincoln & Guba, 1985). This

study included participants of various HR experience levels, industry, function, and

leadership. Confirmability refers to researcher objectivity and neutrality (Leedy &

Ormrod, 2010) through data triangulation to ensure accurate and descriptive

documentation. Survey questions within the study were strategically aligned with other

questions to provide a slightly different perspective and confirm the participant’s intent.

Dependability refers to the stability of data and transparency in collection, analysis, and

interpretations without ambiguity (Hasson & Keeney, 2011; Lincoln & Guba, 1985).

Transferability refers to the applicability of study results (Christensen et al., 2011)

attained when the sample closely represents the population (Salkind, 2003).

The thematic analysis involved progressively coding the data to uncover and

verify themes while carefully preserving the integrity of the participant’s opinions.

Themes revealed during the data analysis included information directly related to the

research questions for leadership characteristics, factors challenging and supporting

Cloud-Based Computing, and best practices for implementing Cloud-Based Computing in

HR. Interpretation of the data from Round 1 of the full-panel Delphi was important

because meaning was derived as participants chose from options presented and provided

open comments to elaborate on selections. Each of the participant’s unique industry, level

of HR experience and experience with technology in HR influenced the answers.

Round 1 analysis of comments included open coding to identify preliminary

analytic categories (Neuman, 2006). Axial coding revealed category clusters with links to

preliminary analytic categories (Neuman, 2006). Round 1 data were manually analyzed

using Microsoft Excel® to find preliminary categories and themes and then analyzed

using NVivo10® to identify and verify additional themes, patterns, and word frequencies.

The Round 2 survey consisted of Likert-type scale questions requiring participants

to rank responses based on likelihood or importance, with one question requesting

percentage. The second round provided participants an opportunity to provide additional

information or change responses leading to consensus and data saturation. A consensus

was considered reached when a convergence of topics and themes was identified and new

information was exhausted. Data were then analyzed using SPSS® to obtain descriptive

statistics including minimum, maximum, mean, and standard deviation scores. This

study obtained qualitative data including ranking of open-ended responses from Round 1

in Round 2. The minimum, maximum, and standard deviation obtained from Round 2

data provided agreement among participants and was further analyzed using Spearman’s

Rho. Correlational analysis resulted in statistically significant results implying the

strength of relationships would increase with a larger sample size more representative of

all HR leader’s responses. The small sample size did not justify reclassification of the

study to quantitative or mixed-methods and remains a qualitative study. Appendices Y, Z,

AA, and AB display Spearman’s Rho and statistical criteria of specified correlations.

SPSS® was used to conduct correlational analysis and calculate descriptive statistics.

Appendices K through X display data triangulation based on the two categories

described. Although statistically significant results were found, limitations exist with use

of the data requiring care when interpreting results due to confines of the study and small

sample size.

Full-Panel Delphi Findings

The focus of this study was to examine how the adoption of Cloud-Based

Computing potentially affects HRM and organizational performance. Two rounds of

surveys were distributed. Round 1 consisted of open-ended questions to obtain data that

were further refined in Round 2. The survey for Round 2 required participants to rank

analyzed data obtained in Round 1 and the opportunity to expand on their previous

responses until consensus was reached or data exhausted.

Round 1 Findings

The first round survey questions addressed each research question. Research

Question 1 addressed in survey Questions 9, 17, 18, 19, and 20. Research Question 2 is

addressed in survey Questions 24 and 27. Research Question 3 is addressed in survey

Questions 25, 26, and 29. Research Question 4 is addressed in survey Questions 21, 22,

and 23 with additional supportive information gathered from survey Questions 24

through 28. Research Question 5 is addressed in survey Questions 28 and 29. Research

Question 6 is addressed in survey Question 28 with additional supportive information

gathered in survey Questions 21 through 27, 30, 31, and 32.

The first section of the survey consisted of qualifying and demographic questions

about the participant and the target company with which the participant had prolonged

experience. Fourteen qualified participants responded to the Round 1 survey of which

93% were female and 62% had 10 years or more of HR experience and 100% had HR

management experience and 50% with executive experience. All participants indicated

having worked with electronic HR systems. Participants described electronic HR systems

by process or system brand name where Table 4 displays the top five processes most used

in HR by function. Brand names mentioned included ADP, Kronos,

PeopleSoft, AS400, Ascentis, Ceridian, FileMaker Pro, HireSystems, InfiniSource,

Oracle, Paychex, Taleo, and Timeforce. Additional electronic processes included

attendance, coaching, customer relationship management (CRM), employee relations,

learning management systems (LMS), performance reviews, and screening.

Table 4

HR Processes Using Electronic Systems

HR Function f%

Recruiting 8 57

Payroll 7 50

Benefits 6 40

HRIS 6 40

Interviewing 5 36

The National Institute of Standards & Technology (NIST) defines Cloud Computing as,

“a model for enabling ubiquitous, convenient, on-demand network access to a shared pool

of configurable computing resources (e.g., networks, servers, storage, applications, and

services), that can be rapidly provisioned and released with minimal management effort

or service provider” (Grance & Mell, 2011). Three service models include Software-as-

a-Service SaaS), Infrastructure-as-a-Service (IaaS), and Platform-as-aService (PaaS).

Participants in Round 1 indicated understanding the definition of Cloud

Computing as defined by NIST. When asked with which Cloud-Based Computing

systems participants had experience, items were listed by function and brand name

including ADP, BLR Employer Resources, Cisco, SaaS, IaaS, PaaS, Paychex, PayCom,

SAP, SuccessFactors, Ultimate Software, UltiPro, WorkDay, and Workforce Logic.

Participants were asked which HR processes use on-site technology in question 17 and

which currently use Cloud-Based Computing in question 18. Table 5 reflects analysis

results of participant responses to questions 17 and 18. On-site technology was used

most often for Benefits, Hiring, Performance Management, and Training & Development,

for 50% of participants while 57% used on-site technology for Hiring. Cloud-Based

Computing for other, or undefined, HR processes for 50% of participants. The

participants indicated Compensation/Payroll (43%), Time and Attendance (36%) were the

following two HR process most used with Cloud-Based Computing.

Table 5

HR Processes Using Various Technologies

Using On- % Using On-

Using % Using

HR Process Site Site

CBC CBC

Technology Technology

Benefits 7 50 3 21

Compensation/Payroll 6 43 6 43

Diversity & Inclusion 4 29 0 0

Employee Labor Relations 5 36 0 0

Hiring 7 50 2 14

Onboarding 6 43 2 14

Performance Management 7 50 2 14

Performance Analytics 3 21 0 0

Recruiting 8 57 3 21

Screening/Interviewing 6 43 2 14

Time & Attendance 5 36 5 36

Training & Development 7 50 2 14

Workforce & Succession Planning 5 36 2 14

Other 2 14 7 50

Note. Items are bolded noting a majority of participants using a variety of technologies

for HR processes.

Understanding the slow adoption of Cloud-Based Computing required insight to

the current and planned use of Cloud-Based Computing for standard HR processes for the

referred organizations to identify possible innovators, early adopters, and early majority.

Similar to results in Table 6, 43% of participants indicated Compensation/Payroll and

Time & Attendance were the top two HR processes currently accessed using CloudBased

Computing. Within one year, 36% of participants indicated Onboarding efforts would

move to Cloud-Based Computing while 29% of participants indicated Benefits and

Training & Development would use Cloud-Based Computing technologies within one

year.

Table 6

HR Processes Moving to Cloud-Based Computing

HR Process

Currently Within 1 Within 5

No Plans

Using Year Years

Unknown n

Benefits 3 4 0 3 4 14

Compensation/Payroll 6303214

Diversity & Inclusion 0 0 0 0 0 14

Employee Labor Relations 1 0 1 4 8 14

Hiring 2 2 1 5 4 14

Onboarding 1 5 1 4 3 14

Performance Management 2 2 1 5 4 14

Performance Analytics 1 1 1 4 7 14

Recruiting 2 3 0 5 4 14

Screening/Interviewing 2 3 0 5 4 14

Time & Attendance 6203314

Training & Development 1 4 3 3 3 14

Workforce & Succession Planning 1 2 2 4 5 14

Note. Items are bolded noting a majority of participants using or plan to be using

CloudBased Computing.

Although Compensation/Payroll, Benefits, and Time & Attendance were the top HR

processes currently using Cloud-Based Computing, or within one year, 50% of

participants stated plans to use Cloud-Based Computing for Benefits unknown or none,

38% stated unknown or no plans for Compensation/Payroll and 43% indicated unknown

or no plans for Time & Attendance. Performance Analytics, Diversity & Inclusion, and

Employee Labor Relations are the top three HR processes participants indicated were the

least likely to use Cloud-Based Computing.

The topic of the next question focused on HR Processes currently in use via

mobile devices (see Table 7). Benefits, Compensation/Payroll, and Recruiting were

currently accessible via mobile by 14% of participants, while 7% of participants indicated

Screening/Interviewing, Performance Management, Time & Attendance, and Training as

currently used, mobile-accessible, HR processes.

Table 7

Currently Mobile Accessible HR Processes

HR Process f% Using

Benefits 2 14

Payroll 2 14

Recruiting 2 14

Screening/Interviewing 1 7

Performance Management 1 7

Time & Attendance 1 7

Training 1 7

The focus on leadership characteristics was a key consideration in identifying a

difference between successful HR leaders and those HR leaders successfully

implementing Cloud-Based Computing. For Question 21 participants listed 20

characteristics of successful HR leaders (see Table 8). Knowledge, including education

and certification, was considered a top characteristic for 71% of participants. The ability

to coach, motivate, and empower employees was a top characteristic for 64% of

participants while 43% agreed Communication, the ability to be Strategic, and having

Business Acumen were key characteristics of successful HR leaders. Remaining

characteristics had 36% or less agreement among participants.

Table 8

HR Leadership Characteristics

Characteristic f%

Agreed

Knowledgeable (Education/Certification) 10 71

Coaching/Motivating/Empowering 9 64

Communication 6 43

Strategic 6 43

Business Acumen 6 43

Integrity/Credibility 5 36

Open-Minded/Approachable 4 29

People Savvy/Interpersonal Skills 4 29

Analytical/Detailed 3 21

Initiative 3 21

Managing Vision & Purpose 3 21

Technology Savvy 3 21

Innovative/Resourceful 2 14

Project Management 2 14

Authenticity 1 7

Change Management 1 7

Confident 1 7

Dependable 1 7

Relationship Building 1 7

Time Management 1 7

Note. Items bolded reflect leading responses from participants.

For Question 22, participants were asked to list the top three characteristics that

they believe contribute most to being a successful HR leader adopting Cloud-Based

Computing using the list each participant created in question 21. For Question 23,

participants were asked if there were any characteristics to add to the list. Participants

shifted focus on top characteristics when considering successful adoption of Cloud-Based

Computing and expanded the list when asked if additional characteristics should be added

generating a list of 21 total characteristics. Table 9 is a combination of characteristics

generated by participant data from Questions 22 and 23 that displays the list of

characteristics with 57% stating Technology Savvy as the top characteristic. Forty-three

percent of participants rated the ability to be Strategic as a leading characteristic with

29% agreeing to Business Acumen and Communication. Remaining responses had 21%

agreement or less.

Table 9

Characteristics of an HR Leader Successfully Implementing CBC

Technology Savvy 8 57 Strategic 6 43

Business Acumen 4 29

Communication 4 29

Analytical 3 21

Foresight/Innovative 3 21

People Savvy 3 21

Project & Change Management 3 21

Confident 2 14

Knowledgeable 2 14

Managerial Courage 2 14

Open-Minded 2 14

Operations Focused 2 14

Influencing 1 7

Problem Solver 1 7

Purpose Driven 1 7

Resourceful 1 7

Time Management 1 7

Training 1 7

Trustworthy 1 7

Dependable 1 7

Note. Items bolded reflect leading responses from participants.

Characteristic % Agreed

For Question 24, participants listed reasons for the target company not

implementing Cloud-Based Computing in HR to which participants generated a list of 13

reasons contributing to not implementing. Seventy-one percent of participants agreed

Cost/Budget as the leading factor while 36% agreed aversion to change and lack of

technology awareness as top contributing factors for not implementing Cloud-Based

Computing (Table 10). Security was a concern for 29% of participants and 21% agreed

executive fear or unfamiliarity, lack of user skills, and sufficient legacy systems as

contributing factors for non-implementation.

Table 10

Reasons for Not Implementing CBC at Target Company

Cost/budget 10 71 Aversion to change 5 36 Lack of technology

awareness 5 36

Security 4 29

Executive fear or unfamiliarity 3 21

Lack of user skill 3 21

Legacy systems function sufficiently 3 21

Limited staff & resources 2 14

Company culture 1 7

HR concerned for people not technology 1 7

Other higher priorities 1 7

Small company size 1 7

Time constraints 1 7

Question 25 of the Round 1 survey asked participants to list the top reasons

Cloud-Based Computing was implemented at the target company. Accessibility and

availability to the application and data were identified as the top reasons for supporting

implementation. Although Budget/Cost was identified as the top reason the participant’s

target company had not implemented Cloud-Based Computing, 21% indicate

Cost/Budget as a leading reason for implementation along with mobility of the

Reasons f% Agreed

application and data (Table 11). Remaining answers had 14% or less agreement among

participants.

Table 11

Reasons Why CBC Was Implemented at Target Company

Accessibility & availability to application an

Budget/cost

Mobility of application and data

Business priority

Efficiency in productivity

Global expansion (business strategy)

Ability to implement a phased approach

Consistency of information

Encourage technology awareness and ability

Improved customer experience

Prioritizing specific HR processes per leadership

Security

Streamlining plants in multiple sites

Technical agility

Note. Items bolded reflect leading responses from participants.

In response to Question 26, 57% of participants agreed having change

management and implementation plans contributed most to successful implementation of

Cloud-Based Computing. Solid executive and technology support along with training

were listed as success factors by 36% of participants. Additional factors included

communication and vendor support (29%, security and understanding of the technology

available (21%), followed by ease of use and having a solid business case (14%) as seen

in Table 12.

Table 12

Factors Contributing to Successful CBC Implementation in HR

Reason % Agreed

Change management plan 8 57 Implementation plan 8 57

IT support 5 36

Senior leadership support 5 36

Training 5 36

Communication 4 29

Vendor support 4 29

Security 3 21

Understanding of technology available 3 21

Ease of use 2 14

Solid business case 2 14

Cost effective 1 7

Metrics for success 1 7

Pressure from leadership 1 7

Scalability 1 7

Note. Items bolded reflect leading responses from participants.

Table 13 illustrates the results of Round 1, Question 27 with the factors identified

by participants as challenging or limiting successful Cloud-Based Computing

implementation. Fifty percent of participants indicate a lack of planning or scoping a

Cloud-Based Computing project as the leading factor challenging implementation. The

participants indicated lack of subject knowledge (43%), fear or close-mindedness (29%),

ineffective training (29%), and lack of executive leadership support (29%) as leading

factors hindering Cloud-Based Computing implementation in HR.

Table 13

Factors Challenging

CBC Implementation

in HR

Success Factor % Agreed

Challenge Factors

Lack of planning/scoping

Lack of subject knowledge

Fear/close-minded

Ineffective training

Lack of executive leadership support

Resistant to change

Budget

Lack of vendor/support

Low employee adoption

Security

Company culture

Disaster recovery

Hidden fees/costs

Lack of internal IT support

Vendor selection

% Agreed

Note. Items bolded reflect leading responses from participants.

In Question 28 of Round 1, participants were asked to indicate best practices for

implementing Cloud-Based Computing in HR. Table 14 presents the frequency and

percentages of the collected best practices. Efficient training for end users was identified

by 71% of participants followed by 57% stating that creating and understanding a clear

implementation plan was a best practice and addressing security needs/concerns and

conducting a detailed needs analysis with 29% of participants in agreement, respectively.

Table 14

Best Practices for Adopting CBC in HR

Best Practice f%

Agreed

Efficient training for end users 10 71

Creating and understanding a clear implementation plan 8 57

Addressing security needs and concerns 4 29

Detailed needs analysis 4 29

Communication 2 14

Global scalability 2 14

Accessibility 1 7

All-in-one integration 1 7

Ample testing time 1 7

Buy-in from executive leadership 1 7

Commitment to long-term goal 1 7

Cost efficiency 1 7

Data accuracy 1 7

Feedback loop from end users 1 7

Hire a professional consultant 1 7

Identify super users 1 7

Innovation awareness 1 7

Involve business operations leaders 1 7

Project management streams 1 7

Service level agreements with vendor 1 7

Teaching executive leadership CBC basics 1 7

Utilization metrics 1 7

Note. Items bolded reflect leading responses from participants.

In Round 1, Question 29 participants were asked to indicate the effect of

CloudBased Computing on the specified factors. Table 15 shows how participants rated

each factor based on a five-point scale from no effect to critical effect. Seventy-four

percent of participants agreed Cloud-Based Computing has a low to no effect on

Absenteeism. A moderate effect by Cloud-Based Computing in HR has an effect on

employee retention

(57%) and general employee performance (50%). However, participants agreed Cloud-

Based Computing in HR has high to critical effect on cost (71%) and time efficiency

(71%), the HR individual’s performance (57%), overall HR department performance

(71%), and overall organizational performance (71%).

Table 15

The Effect of CBC on the Following Factors:

Factor None Low Moderate High Critical

Absenteeism 4 6 4 0 0

Employee retention 2 3 8 1 0

Cost efficiency 00455

Time efficiency 00455

General employee performance 1 3 7 3 0

HR individual’s performance 11453

Overall HR department’s performance 01355

Overall organizational performance 01373

Note. Bold indicates a majority selecting high or critical effect on select factors.

Table 16 displays descriptive statistics of the participant’s responses. The average

response for cost and time efficiency was 4.07 with a small standard deviation (SD) of .80

with 29% of participants on the lower end of the scale and 71% expressing that Cloud-

Based Computing had high to critical impact. The average response for effect on the

overall HR department’s performance was 4.00 with .93 standard deviation. The effect of

Cloud-Based Computing on overall organizational performance was found high to critical

with a mean of 3.86 and standard deviation of .83. Seven percent of participants

disagreed while 21% found CBC to have a moderate effect on organizational

performance.

Table 16

Descriptive Statistics for the Effect of CBC on the Following Factors:

Factor nMin. Max M SD

Absenteeism

Employee retention

Cost efficiency

Time efficiency

General employee performance

HR individual’s performance

Overall HR department’s performance

Overall organizational performance

2.00

2.57

4.07

2.86

3.57

4.00

3.86

0.76

0.82

0.80

0.83

1.12

0.93

0.83

Note. Bold indicates a majority selecting high or critical effect on select factors.

Round 1, Questions 30, 31, and 32 attempted to gather information from

participants for better understanding of customized and successfully implemented Human

Resource Technology Systems (HRTS). Question 30 asked participants what percentages

of off-the-shelf HRTS were customized, in their experience. The range of responses

spanned 85 points with a mean of 37.73 for minimally customized HRTS. Moderately

customized HRTS responses ranged 70 points with a mean of 29.93 and HRTS with

significant modifications ranged 80 points with a mean of 41.36. Based on the means, a

higher percentage of HRTS with significant modifications were experienced

(see Table 17).

Table 17

HRTS Customization Factor

Very little customization (10% or less is customized)

Some modification (25% or less is customized)

Significant modification (more than 25% is customized)

Range

Min

Max

37.73

29.23

41.36

Descriptive Statistics for the percentage of customized off-the-shelf HRTS:

Question 31 asked participants to indicate the percentage of customized HRTS

successfully implemented. The range of responses spanned 85 points with a mean of

38.18 for minimally customized HRTS. Moderately customized HRTS responses ranged

70 points with a mean of 32.27 and HRTS with significant modifications ranged 80 points

with a mean of 48.33 (see Table 18). Based on the means, a higher percentage of

HRTS with significant modifications were successfully implemented.

Table 18

HRTS Implementation Factor

Very little customization (10% or less is customized)

Some modification (25% or less is customized)

Significant modification (more than 25% is customized)

Range

Min

Max

38.18

32.27

48.33

Descriptive Statistics for the percentage of customized HRTS successfully implemented:

Question 32 asked participants to indicate what percentage of Cloud-Based

Computing systems are successfully implemented at the respective target company.

Fifty-seven percent of participants had experience with implementation of Cloud-Based

Computing at the target company with a mean of 62.5 successfully implemented (see

Table 19). Figure 5 represents the individual participant responses displaying the span

between percentages of successfully implemented Cloud-Based Computing Systems.

Table 19

The percentage of Cloud-Based Computing systems successfully implemented:

nMin Max

Cloud-Based Computing HRTS successfully implemented 8 90 20 90 62.50

Participants were provided a free form comment section at the end of the Round 1

survey where two participants offered additional information. Participant TCFDS007

stated, “There is a lack of technological understanding regarding using cloud based HR

HRTS Factor Range M

systems within the HR world.” Participant TCFDS010 stated, “We are in a four year

phased project of moving HRIS to cloud based in partnership with Oracle People Soft.”

Figure 5. Histogram Representation of Individual Participant Responses. Responses

included from eight respondents with four non-responses.

Round 2 Findings

Round 2 of the Delphi study began shortly after data from Round 1 was analyzed

using NVivo10® and Microsoft Excel®. The results from Round 1 were used to create

the survey questions and determine the 7-point and 5-point Likert-type rating scales for

Round 2. The same 14 participants received an introduction letter with as an invitation

with instructions to complete Round 2. Of the initial 14 participants, 12 completed the

Round 2 survey. Each Round 2 survey question was built to answer the guiding research

questions. Research Question 1 is addressed in survey Question 9. Research Question 2

is addressed in survey Questions 2 and 5. Research Question 3 is addressed in survey

Question 9 with supportive information from Question 3. Research Question 4 is

addressed in survey Questions 7 and 8 with supportive information gather from survey

Question 9. Research Question 5 is addressed in survey Question 9. Research Question

6 is addressed in survey Questions 6, 7, 8, and 9 with supportive information gathered

from survey Questions 3 and 4.

The purpose of survey Question 1 of the Round 2 survey was to collect the

participant’s individual code. Data analysis from Round 1 generated a list of 14 reasons

for non-implementation of Cloud-Based Computing in HR. Table 20 illustrates the

results from Question 2, responses rating the probability of the accompanying reasons for

not implementing Cloud-Based Computing at the participant’s respective target company,

frequency from data analysis in Round 1 (n), and analyzed using mean, minimum,

maximum, and standard deviation. Appendix K (Table 28) and Appendix L (Table 29)

display a comparison of means broken down by years the target company has been in

business and years of management experience for each participant, respectively. Outlier’s

two standard deviations from the mean occurred with participants in a target company of

21 to 25 years in business and with participants 10 to 14 years of management experience

and over 15 years of management experience.

Table 20

Reasons for Not Implementing Cloud-Based Computing in HR

Q2a Cost/budget

Q2k Limited staff & resources

Q2l Other priorities rank higher

Q2b Lack of technology awareness

Q2e Executive fear/unfamiliarity

Q2d Aversion to change

Q2m Outsourcing HR processes

Q2i Company culture

Q2f Legacy systems function sufficiently

Q2n Small company size

Q2c Security

Q2h HR processes not defined

5.50

4.58

4.42

4.17

4.08

3.92

3.67

3.58

3.42

3.33

1.09

1.68

1.73

1.27

1.24

1.62

1.97

1.17

1.78

2.07

1.38

0.99

Reasons Min. Max. M SD

Q2j HR concerned about people and not technology

Q2g Lack of user skill

3.08

1.17

0.79

Note. Items are bolded noting a majority of participant agreement. f = frequency of the

item from Round 1.

A list of 14 reasons for implementing Cloud-Based Computing in HR at the

respective target organizations was generated during analysis in Round 1. Table 21

illustrates the results of Round 2, Question 3 where participants rated the probability of

the reasons for implementing Cloud-Based Computing, frequency from Round 1, and

analyzed using mean, minimum, maximum, and standard deviation. Appendix M (Table

30) and Appendix N (Table 31) display a comparison of means broken down by years the

target company has been in business and years of management experience for each

participant, respectively. Outlier’s two standard deviations from the mean occurred with

participants in a target company of 21 to 25 years in business and with participants 10 to

14 years of management experience and over 15 years of management experience.

Table 21

Reasons for Implementing Cloud-Based Computing in HR

Q3a Accessibility/availability to application and data

Q3e Efficiency in productivity

Prioritizing specific HR processes as dictated by

Q3k company and leadership

Q3m Streamlining multiple company sites

Q3i Improved customer experience

Q3c Mobility of application and data

Q3g Consistency of information

Q3n Technical agility

Q3b Budget/cost

Q3j Ability to implement a phased approach

Q3l Security

Q3d Business priority

Q3h Encourage technology awareness & ability

Q3f Global expansion as a business strategy

5.17

4.92

4.83

4.75

4.67

4.50

4.42

4.25

4.17

4.08

3.92

3.83

3.50

0.94

1.00

1.27

1.82

0.79

1.07

1.31

0.90

1.22

1.12

1.31

1.17

1.19

1.62

Note. Items are bolded noting a majority of participant agreement. f = frequency of the

item from Round 1.

Reasons Min. Max. M SD

In Question 4, participants were allowed to indicate which best practices

contributed to the successful implementation of Cloud-Based Computing in HR. Data

analysis of Round 1 responses generated a list of best practices. Participants rated the

probability of these factors contributing to successful implementation of Cloud-Based

Computing. Table 22 illustrates the frequency of each factor from Round 1, and analyzed

using mean, minimum, maximum, and standard deviation. Appendix O (Table 32) and

Appendix P (Table 33) display a comparison of means broken down by years the target

company has been in business and years of management experience for each participant,

respectively. Outliers, two standard deviations from the mean, occurred with participants

in a target company of 11 to 15 years in business.

Table 22

Likelihood of Factors in Contributing to Successful Implementation of Cloud-Based

Computing:

Q4e Communication

Q4d Training

Q4f IT support

Q4b Implementation plan

Q4c Senior leadership support

Q4l Cost effective

Q4j Ease of use

Q4a Change management plan

Q4m Metrics for success

Q4g Security

Q4i Vendor Support

Q4k Solid business case

Q4h Understanding of technology available

Q4o Scalability

Q4n Pressure from leadership

6.50

6.42

6.33

6.08

6.00

5.92

5.83

5.75

5.67

5.50

5.42

5.00

4.75

0.91

0.90

0.89

1.08

1.13

1.17

1.19

1.42

1.44

1.07

1.16

1.31

1.00

1.35

1.49

Note. Items are bolded noting a majority of participant agreement. f = frequency of the

item from Round 1.

Success Factors Min. Max. M SD

Question 5 of Round 2 allowed participants to rate the probability of factors

challenging the implementation of Cloud-Based Computing in HR. Round 1 data

analysis generated a list of factors. Table 23 illustrates the frequency results from Round

1, and analyzed using mean, minimum, maximum, and standard deviation. Appendix Q

(Table 34) and Appendix R (Table 35) display a comparison of means broken down by

years the target company has been in business and years of management experience for

each participant, respectively. No significant outliers from the mean occurred.

Table 23

Challenging Factors

Q5a Lack of planning

Q5f Budget

Q5d Ineffective training

Q5b Lack of subject knowledge

Q5m Hidden fees/costs

Q5e Lack of executive leadership support

Q5n Lack of internal IT support

Q5g Lack of vendor support

Q5o Vendor selection

Q5c Fear/close-minded

Q5l Disaster recovery

Q5h Low employee adoption

Q5j Security

Q5i Resistant to change

Q5k Company culture

Min.

Max.

5.67

5.33

5.25

5.17

5.08

5.00

4.83

4.67

4.50

4.42

4.17

4.08

1.58

1.30

1.49

1.47

1.88

1.21

1.19

1.16

1.57

1.56

1.24

1.40

1.31

Likelihood of Factors Challenging Successful Implementation of Cloud-Based

Computing:

Note. Items are bolded noting a majority of participant agreement. f = frequency of the

item from Round 1.

Round 1 data analysis generated a list of best practices for successfully

implementing Cloud-Based Computing in HR. Question 6 in Round 2 allowed

participants to rate the importance of each best practice. Table 24 exhibits the frequency

of each best practice from Round 1, and analyzed using mean, minimum, maximum, and

standard deviation. Appendix S (Table 36) and Appendix T (Table 37) display a

comparison of means broken down by years the target company has been in business and

years of management experience for each participant, respectively. Outlier’s two

standard deviations from the mean occurred with participants in a target company of less

than 5 years in business.

Table 24

Importance of Best Practices for Successfully Implementing Cloud-Based Computing:

Best Practices f Min. Max. M SD

Q6a Create & follow a comprehensive

implementation plan

Q6f Create a communication plan

Q6bTraining plan for all end users

Q6k Implement feedback loop for ongoing

improvements

Q6e Leadership buy-in & support

Q6c Address data security (access/accuracy/backup)

Q6d Conduct detailed needs analysis

Q6i Cost efficiency

Q6g Ensure scalability

Q6l Solution should fit company culture and needs

Q6h All-in-one integration

Q6j Hire a professional consultant

4.50

4.17

4.08

3.83

3.50

2.58

0.80

0.67

0.72

1.03

1.00

1.03

0.94

1.47

1.38

1.51

Note. Items are bolded noting a majority of participant agreement. f = frequency of the

item from Round 1.

In Round 1, participants provided a list of leadership characteristics indicative of a

successful HR leader and an HR leader successfully implementing Cloud-Based

Computing. Data analysis from Round 1 generated lists where participants rated the

importance of each characteristic in Questions 7 and 8, Round 2. Tables 25 and 26

display the frequency of each characteristic from Round 1, and analyzed using minimum,

maximum, mean, and standard deviation. Appendix U (Table 38), Appendix V (Table

39), Appendix W (Table 40), and Appendix X (Table 41) display a comparison of means

broken down by years the target company has been in business and years of management

experience for each participant, respectively. No significant outliers from the mean

occurred.

Table 25

Importance of Characteristics Indicative of a Successful HR Leader:

Q7d Communication

Q7e Integrity/credibility

Q7a Knowledgeable (education/certification)

Q7b Coaching/empowering/influencing

Q7f Adaptability/flexibility Q7c Strategic

Q7h Open-minded/approachable

Q7i People savvy/interpersonal skills

Q7r Relationship building

Q7g Business acumen

Q7q Confident

Q7o Initiative

Q7j Analytical/detail-oriented

Q7s Time management

Q7k Compassionate

Q7l Managing vision & purpose

Q7m Project/change management

Q7p Innovative/foresight

Q7n Technology Savvy

4.83 4.83

4.67 4.58

4.50 4.50

4.42 4.42

4.33 4.17

4.17 4.00

3.92 3.83

3.75 3.75

3.67 3.50

3.25

0.39

0.65

0.52

1.00

0.52

0.67

0.79

0.72

0.94

0.74

0.69

0.84

1.22

0.97

0.89

0.91

0.75

Note. Items are bolded noting a majority of participant agreement. f = frequency of the

item from Round 1.

Table 26

Importance of Characteristics Indicative of a Successful HR Leader Successfully

Implementing Cloud-Based Computing:

Characteristics Min. Max. M SD

Q8g Communication

Q8o Integrity/credibility

Q8r Time management

Q8q Problem solver

Q8i Project/change management

Q8f Analytical/detail-oriented

Q8k Confident

Q8e Innovative/foresight

Q8c Strategic

Q8d Adaptability/flexibility

Q8h People savvy

Q8j Coaching/empowering/influencing

Q8m Managerial courage

Q8b Business acumen

Q8l Knowledgeable (education/certification)

Q8n Open-minded/approachable

Q8a Tech Savvy

Q8p Managing vision and purpose

4.58

4.25

4.17

4.08

4.00

3.92

3.83

3.75

3.67

3.58

0.52

0.97

0.58

0.52

1.00

0.74

1.05

0.74

0.52

1.08

0.84

1.03

0.75

0.97

1.22

0.65

1.08

Note. Items are bolded noting a majority of participant agreement. f = frequency of the

item from Round 1.

Question 9 of Round 2 asked participants to identify a percentage Cloud-Based

Computing contributes to improving a list of select outcomes. Table 27 displays the list

of outcomes analyzed with minimum, maximum, and mean.

Table 27

Percentage Cloud-Based Computing Contributes to the Following Outcomes:

Responding

to the

Research

Questions

This study

was guided by six research questions answered through a series of two rounds of surveys

and distributed to 14 participants. The general problem is the slow adoption of Cloud-

Based Computing for efficiency in HR. The specific problem is that the literature does

not provide with clarity how a more rapid adoption of Cloud-Based Computing will affect

HR processes. The purpose of this study was to examine how the adoption of Cloud-

Characteristics Min. Max. M SD

Outcome Min. M

Absenteeism

Cost efficiency

Time efficiency

Employee performance

Individual HR employee performance

Overall HR department performance

Overall organizational performance

Max.

22.50

55.83

53.33

42.50

54.25

61.83

56.00

Based Computing potentially affects HRM and organizational performance. The research

questions were created to address key factors of leadership in HR, best practices for

Cloud-Based Computing adoption, and the perceived effect of HR on organizational

performance as it relates to the adoption and use of Cloud-Based

Computing. The research questions guiding the study are:

RQ1: What are the Cloud-Based Computing tools that appear to be most effective

for Human Resources?

RQ2: What are the impediments to adoption of Cloud-Based Computing in

Human Resources?

RQ3: How does successful Cloud-Based Computing affect the performance of

HRM?

RQ4: What leadership traits of successful Cloud-Based Computing adopters also

contribute to HR performance?

RQ5: How does HR contribute to the overall performance of an organization?

RQ6: What are the implications of best practices in Cloud-Based Computing

adoption within HR and to general leadership theory?

This study produced results that answered the research questions. Each research question

is addressed with those results in the following sections.

Research Question 1 Analysis and Data Triangulation

The first research question asked, “What are the Cloud-Based Computing tools

that appear to be most effective for Human Resources?” Participants indicated having

experience using electronic systems for recruiting (57%), payroll (50%), benefits (40%),

HRIS (40%), and interviewing (36%). On-site technology is currently used for recruiting

(57%), benefits, hiring, performance management, and training and development (50%),

and compensation/payroll, onboarding, and screening/interviewing (43%). Cloud-Based

Computing is currently used for 50% of participants for undefined HR processes with

compensation/payroll (43%), and time and attendance (36%) as the following two HR

process most used with Cloud-Based Computing. Participants indicated respective target

companies had plans to move to Cloud-Based Computing in the next year for onboarding

(36%), training and development, and benefits (29%). Recruiting,

screening/interviewing, and compensation (21%) were listed as the next top priorities for

change to Cloud-Based Computing within the next year.

Pilot participants indicated having experience using electronic systems for HRIS

(80%) and recruiting (40%). Additionally, pilot participants indicated using on-site

technology for recruiting, screening, and hiring while Cloud-Based Computing was most

used for compensation/payroll, screening/interviewing, and training and development.

Research Question 2 Analysis and Data Triangulation

Research Question 2 asked, “What are the impediments to adoption of Cloud-

Based Computing in Human Resources?” Questions 24 and 27 in Round 1 addressed this

research question through open-ended questions. Responses generated a list of factors

impeding adoption of Cloud-Based Computing at the respective target companies. Table

10 displays the list generated of reasons for not implementing Cloud-Based Computing in

HR. The top 50% of responses include cost/budget, aversion to change, lack of

technology awareness, security, executive fear or unfamiliarity, lack of user skill, and

legacy systems function sufficiently. Table 13 displays a list of factors challenging the

implementation of Cloud-Based Computing in HR at the respective companies.

Responses with the most frequencies include lack of planning/scoping, lack of subject

knowledge, fear/close-minded, ineffective training, lack of executive leadership support,

and resistant to change.

Table 20 displays Round 2 responses where participants collectively identified

cost/budget can impede the adoption of Cloud-Based Computing with a mean (M) of 5.50

on a 7-point Likert-type scale and standard deviation (SD) of 1.09. The next highest

scored items include limited staff and resources with a mean of 4.58 and standard

deviation of 1.68, and other undefined organizational priorities ranking higher with a

mean of 4.42 and standard deviation of 1.73. Lack of technology awareness (M = 4.17)

and executive fear/unfamiliarity (M = 4.08) were also prominently listed as impediments.

The participants listed lack of planning (M = 5.67), budget (M = 5.33), ineffective

training (M = 5.25), lack of subject knowledge (M = 5.36), hidden fees/costs (M = 5.17),

lack of executive leadership support (M = 5.08), and lack of internal IT support (M =

5.00) as factors challenging the successful implementation of Cloud-Based Computing.

Individual outliers occurred with participants within organizations in business between 21

to 25 years and participants with 10 or more years of management experience with

standard deviation 1.25+/- than the overall standard deviation (see Appendices K & L).

The high mean scores for cost/budget, limited staff and resources, and having other

organizational priorities indicate the likely impediments for adoption of Cloud-Based

Computing. Interestingly, lack of planning, budget, and ineffective training are identified

as factors challenging successful implementation, two of which support the top three key

impediments.

Appendix Y (Table 42) displays the correlation between reasons for not

implementing Cloud-Based Computing and factors challenging implementation of Cloud-

Based Computing. Spearman’s Rho is presented where rs = Spearman’s Rho, and p =

statistical criteria. Statistical significance was found between other priorities ranking

higher and fear/close-minded (rs = .888, p = <.01), and between limited staff and

resources, and lack of vendor support (rs = .865, p = <.01), lack of subject knowledge (rs

= .864, p = <.01), fear/close-minded (rs = .844, p = <.01), and resistance to change (rs =

.840, p = <.01). Significant correlations were found between cost/budget and lack of

vendor support (rs = .860, p = <.01), and between lack of technical awareness and

fear/close-minded (rs = .823, p = <.01). Additional significant correlations are reported

between reasons for not implementing Cloud-Based Computing and characteristics of a

successful HR leader (see Appendix Z, Table 43).

Research Question 3 Analysis and Data Triangulation

Research Question 3 asked, “How does successful Cloud-Based Computing affect

the performance of HRM?” Questions 25, 26 and 29 in Round 1 addressed this research

question through open-ended questions. Responses generated a list of reasons why

Cloud-Based Computing was implemented at the respective target companies. Table 11

displays the list with the leading responses including accessibility and availability to

application and data, budget/cost, and mobility of application and data. Table 12 displays

a list of factors contributing to the successful implementation of Cloud-Based Computing

in HR with the leading factors consisting of change management plan, implementation

plan, IT support, senior leadership support, and training. Tables 15 and 16 display the

effect of Cloud-Based Computing with high to critical effect on cost efficiency, time

efficiency, HR individual’s performance, overall HR department performance, and

organizational performance.

In Round 2, Table 30 displays responses where participants indicated CloudBased

Computing contributed to increasing time and cost efficiency with a mean of 4.07 and

standard deviation of .80, respectively, by an average of 52.73%. The next highest rated

areas of contribution by HRM with Cloud-Based Computing include overall HR

department performance with a mean of 4.0 (SD = .93), overall organizational

performance with a mean of 3.86 (SD = .83), and HR individual performance with a mean

of 3.57 (SD = 1.12). The reasons for implementing Cloud-Based Computing for the

increased performance in HRM (Table 21) include accessibility/availability to

applications and data (M = 5.17, SD = .94), efficiency in productivity (M = 4.92, SD =

1. 00), prioritizing specific HR processes as dictated by company and leadership (M

4.83, SD = 1.27), and streamlining multiple company sites (M = 4.75, SD = 4.75).

Improved customer experience (SD = .79) and mobility of application and data (SD =

1.07) had a mean of 4.67, respectively.

Research Question 4 Analysis and Data Triangulation

Research Question 4 asked, “What leadership traits of successful Cloud-Based

Computing adopters also contribute to HR performance?” Table 8 displays a list of 19

characteristics indicative of a successful HR leader generated during Round 1 analysis.

The leading characteristics include knowledgeable (education/certification),

coaching/motivating/empowering, communication, strategic, business acumen,

integrity/credibility, open-minded/approachable, and people savvy/interpersonal skills.

Table 9 displays a second list of 18 characteristics of an HR Leader successfully

implementing Cloud-Based Computing also generated during Round 1 analysis. The

leading characteristics include technology savvy, strategic, business acumen,

communication, analytical, foresight/innovative, people savvy, and project and change

management.

In Round 2, participants used a 5-point Likert-type scale to rate the importance of

characteristics indicative of a successful HR leader (Table 25) and of an HR leader

successfully implementing Cloud-Based Computing (Table 26). The leading five

characteristics of a successful HR leader include communication, integrity/credibility,

knowledgeable (education/certification), coaching/empowering/influencing, and

adaptability/flexibility. The leading five characteristics of an HR leader successfully

implementing Cloud-Based Computing include communication, integrity/credibility, time

management, problem solver, and project/change management. Appendix AC displays a

full comparison of participants’ answers between the two lists of characteristics. The top

five common characteristics between the two include communication (M = 4.58, SD

=.52), integrity/credibility (M = 4.25, SD = .97), time management (M = 4.17, SD = .58),

problem solver (M = 4.08, SD = .52), and project/change management (M = 4.08, SD =

1.00). The low standard deviation indicates a high level of agreement between

participants.

Research Question 5 Analysis and Data Triangulation

Research Question 5 asks, “How does HR contribute to the overall performance

of the organization?” Questions 28 and 29 in Round 1 addressed this research question

through open-ended questions. Responses from Question 28 generated a list of best

practices for adopting Cloud-Based Computing in HR. Table 14 displays the list of best

practices generated during Round 1 analysis with the leading practices including efficient

training for end users, creating and understanding a clear implementation plan, addressing

security needs and concerns, and conducting a detailed needs analysis. Tables 18 and 19

display the effect of Cloud-Based Computing on select HR and organizational factors

indicating high to critical effect on cost efficiency, time efficiency, HR individual’s

performance, overall HR department performance, and overall organizational

performance.

In Round 2 participants were asked to rate the effect on select factors using a

5point Likert-type scale. Participants identified Cloud-Based Computing in HR affected

organizational performance (M = 3.86) through cost efficiency (M = 4.07), time

efficiency (M = 4.07), overall HR department performance (M = 4.00), general employee

performance (M = 2.86), and employee retention (M = 2.57). Participants rated reasons

for implementing Cloud-based Computing in HR on a 7-point Likert-type scale of

importance as increased accessibility and availability to applications and data (M = 5.18),

efficiency of productivity (M = 4.91), mobility of application and data (M = 4.64), and

budget/cost (M = 4.18).

Research Question 6 Analysis and Data Triangulation

Research Question 6 asked, “What are the implications of best practices in Cloud-

Based Computing adoption within HR and general leadership theory?” Traditional

Transformational leaders inspire followers towards realizing common goals (Wren,

1995), however Chen, Lin, Lin, and McDonough (2012) found transformational

leadership positively influences technological innovation. Research Question 6 sought to

identify possible links between characteristics between transformational leaders in HR as

drivers of best practices for implementing Cloud-Based Computing. Question 28 (Table

17) regarding best practices for adopting Cloud-Based Computing in HR. Questions 21

(Table 8) and 22 (Table 9) address the leadership component of RQ6.

In Round 2 participants were asked to rate the importance of best practices for

successfully implementing Cloud-Based Computing (Table 24). Descriptive statistics

results show best practices for successfully implementing Cloud-Based Computing

include creating and following a comprehensive implementation plan (M = 4.50, SD

= .80), creating a communication plan (M = 4.50, SD = .67), training plan for all end

users

(M = 4.50, SD = .67), implementing a feedback loop for ongoing improvements (M =

4.17, SD = .72), leadership buy-in and support (M = 4.17, SD = 1.03), addressing data

security, and conducting detailed needs analysis (M = 4.08, SD = 1.00). Participants

identified important HR leadership characteristics in Table 25 and HR leadership

characteristics for successfully implementing Cloud-Based Computing are displayed in

Table 26 as communication (M = 4.58, SD = .52), integrity/credibility (M = 4.25, SD

= .97), time management (M = 4.17, SD = .58), problem solver (M = 4.08, SD = .52), and

project and change management (M = 4.08, SD = 1.00).

Appendix AA (Table 44) displays the correlation between best practices for

implementing Cloud-Based Computing and characteristics of an HR leader successfully

implementing Cloud-Based Computing. Statistical significance was found between

having a training plan for all end users and innovative/foresight (rs = .740, p = <.01), and

between all-in-one integration and open-minded/approachable (rs = .844, p = <.01).

Additional significant correlations are reported between factors contributing to successful

implementation of Cloud-Based Computing and best practices for successfully

implementing Cloud-Based Computing in HR (see Appendix AB, Table 45) and between

reasons for not implementing Cloud-Based Computing and characteristics of a successful

HR leader (see Appendix Z, Table 43).

Chapter Summary

Chapter 4 presented the data comprised from two rounds of this Delphi study. A

pilot study was conducted with five participants to test SurveyMonkey® as the

distribution instrument, validate the survey, and to ensure accuracy, reliability, and

anonymity of participants. Fourteen participants shared their informed opinions through

Round 1 of the full-panel Delphi via open-ended and Likert-type scale questions. The

results from Round 1 were analyzed and used to create the survey for Round 2. Round 2

consisted of Likert-type scale questions and attrition of three participants was

experienced resulting in 12 active participants. Two rounds of surveys were needed to

reveal sufficient information before repetitive data were obtained.

The objective of this Delphi study was to obtain a deeper understanding on how

the adoption of Cloud-Based Computing affects HRM and organizational performance by

addressing the six research questions guiding this study. The results revealed data

necessary to answer the Research Questions. Participant’s insights included identification

of HR processes most effective with Cloud-Based Computing, key leadership traits of HR

leaders implementing Cloud-Based Computing, impediments to successful

implementation of, and best practices for, successfully implementing CloudBased

Computing.

Chapter 5 includes theoretical triangulation, interpretation of findings, limitations

of the study, and expands the results into conclusions and recommendations based on

major themes of the study aligned to the Research Questions. The implications of this

study’s findings provide an understanding of the issues surrounding the effect of

CloudBased Computing on Human Resource Management, human resource leadership

characteristics and overall organizational performance. Chapter 5 includes a review of

the study’s methods as the foundation of a model for implementing Cloud-Based

Computing and leadership profile for HR leaders effectively implementing technology.

These conclusions provide suggestions to human resource management leaders and for

future research.

Chapter 5

Introduction

The purpose of this qualitative Delphi study was to explore the reasons HR

leaders are slow to implement Cloud-Based Computing, characteristics between

successful HR leaders and those successfully implementing Cloud-Based Computing, and

how the implementation of Cloud-Based Computing affects HRM and organizational

performance. Chapter 5 reiterates the research questions, interprets the results from the

study, and compares the findings to literature reviewed in Chapter 2. This comparative

analysis between literatures is intended to cross-examine the outcomes for validity.

Discussions for implications to leadership, Human Resource Management, organizational

performance, and the implementation of Cloud-Based Computing in HR are presented in

this chapter. Finally, the chapter includes delimitations, limitations, recommendations for

future research, and research summary.

Interpretation of Findings

This Delphi study consisted of two iterative survey rounds to a panel of 14

informed participants in HR with a minimum of 5 years of experience in HR. The

questions led to discovery of factors challenging, and contributing to, successful

implementation of Cloud-Based Computing, a comparative list of HR leadership

characteristics, best practices for implementing Cloud-Based Computing, and potential

affects to organizational and HRM performance through use of the technology. Findings

derived from the study provide new information, and support existing information, to the

HR field, leadership, Cloud-Based Computing adoption, and academia. The following is

a discussion of each research question, findings, and related literature.

Research Question 1

The first research question asked, “What are the Cloud-Based Computing tools

that appear to be most effective for HR?” According to Jones (2012), an integrated HR

solution (Card & Sivak, 2015) including recruiting workforce planning, compensation,

learning and development, career management, and succession management as a

complete performance management system is top-of-mind with HR professionals (Shilpa

& Gopal, 2011; Sienna-Cedar, 2015). Deloitte (2012b) indicates a cloud-based service

model with “recruitment, screening, predictive analytics, performance management,

payroll, and time and attendance” (p. 3), can help HR work more effectively. All study

participants agreed to either having experience with electronic HR processes with on-site

or cloud technologies with recruiting, payroll, benefits, HRIS, and interviewing rating the

highest. The majority of participants were not innovators or early adopters and may be

considered early majority in using Cloud-Based Computing for compensation/payroll,

and time and attendance. Participants identified the potential Cloud-Based Computing

tools that appeared to be immediately most effective for HR as compensation/payroll and,

time and attendance.

Ikhlap, Khan, Mujtaba, and Sadiq (2012) focus on HRIS as the technology

solution for HR. The processes reviewed and evaluated by study participants are

available via Cloud-Based Computing solutions including HRIS as a Software-as-

aService platform. Study participants agreed HRIS was an effective tool for HR and

align with Indranil (2011) that Cloud-Based Computing solutions for payroll,

compensation, recruitment, and performance management contributed to the efficiency of

the HR department. Kumar and Pandya (2012) found higher technology in specialized

HR software and HRIS could lead to efficiency, effectiveness, and productivity in HR.

Gainey and Klass (2008) stated the tools that appear to be most effective for

Human Resources are those that increase cost and time efficiency, overall HR department

performance, and overall organizational performance to which participants also agree

Cloud-Based Computing has a significant effect. Indicators would lead to the adopting

Cloud-Based Computing tools that are currently used for some participants, and those

that are planning to move to Cloud-Based Computing within the next year to 5 years,

including compensation, time and attendance, benefits, screening and interviewing,

recruiting, and onboarding.

Research Question 2

The second research question asked, “What are the impediments to adoption of

Cloud-Based Computing in HR?” Deloitte (2012) reported HR professionals hold a

traditional view of HR contributing to the slow adoption of technology, transformation of

archaic processes, and a lack of alignment to business strategy. Five factors within the

DOI framework influence the adoption of innovation including relative advantage,

compatibility, complexity, observability, and trialability. It may be suggested that

compatibility (Chen, Low, Wu, 2011; Wang et al., 2010), or the degree to which

CloudBased Computing is consistent with existing business processes, values, and

practices is an agreed point of impediment to adoption according to Deloitte (2012).

Willcocks,

Venters, and Whitely (2013) posit a large education gap exists about the value of

CloudBased Computing. Participants in this study indicated lack of technology

awareness and subject knowledge as leading factors, in alignment with Deloitte (2012)

and Whitley et al. (2013).

Dai, Yang, and Xing (2015) state security issues, network-transmission problems,

and a lack of knowledgeable developers contribute to impediments of Cloud-Based

Computing, to which Willcocks et al. (2013) also note. Data privacy is an ongoing

concern and impediment to adoption (Shilpa & Gopal, 2011) of Cloud-Based Computing.

Data concerns include privacy, data integrity and accessibility, internal employee access,

lack of disaster recovery measures (Card & Sivak, 2014; Dutta et al., 2013; Geczy, Izumi,

& Hasida, 2012) and availability (Aleem & Sprott, 2013).

Carcary, Conway, and Doherty (2015) found the leading drivers for moving

processes to the cloud included cost reduction and mobility of data and devices (Staley &

Gandi, 2014). Study participants identified the same drivers as reasons for implementing

Cloud-Based Computing in HR. Additionally, Carcary et al. (2015) listed Internet

connectivity (Chen, Low, & Wu, 2011) and trust in the service provider as the leading

barriers to cloud adoption that were neither in agreement with this study, nor listed as

contributing challenges for Cloud-Based Computing in U.S. based companies.

According to Lee Sears in the Human Resource Management International Digest

(2012), “HR leaders in the future need to develop greater insight from employees,

develop the means for bringing the insight and savviness into play within the

organization, and find a voice to share the meaningful messages from all the insight” (p.

24). Organizational factors may contribute to impeding technology adoption including

perceived financial cost, organizational culture, perceived resource availability,

leadership behavior, and informal communication. Sareen and Subramanian (2012)

indicate a lack of planning as a limiting factor with implementing an effective change

management strategy that considers user acceptance. Study participant’s responses

aligned in including limited staff and resources, lack of planning, budget, and lack of

executive leadership support as contributing organizational factors.

Size of the organization was implied as a hindrance to technology adoption by

study participants supporting that organizations further along in the maturity curve, and

being a quantified organization, are higher in Cloud-Based Computing adoption than are

smaller, less mature organizations (Bahli, Borgman, Heier, & Schweski, 2013; IBM,

2015; Sierra-Cedar, 2015). Bahli et al. (2013) found firm size to be inconclusive among

Cloud-Based Computing adopters and non-adopters. Study participants indicate

cost/budget as a leading barrier to adoption. This aligns with Whitley et al. (2013) who

suggest the economic benefit could take significantly longer than anticipated by most

organizations. However, IBM (2015) reports cost reduction and efficiency are concerns

and adoption hindrances for organizations lower on the maturity curve. According to

IBM (2015), organizations higher in the maturity curve are more focused on business

growth as a contributing factor to realized Cloud-Based Computing.

Research Question 3

The third research question asked, “How does successful Cloud-Based Computing

affect the performance of HRM?” Gibson (2012) posited adoption of Cloud-Based

Computing is a competitive advantage for HR performance for organizations of all sizes

and revenue. The ability to attract and attain top talent is a competitive advantage to

which Cloud-Based Computing can be the means by which to achieve (Gibson, 2012)

potentially improving HRM performance and of the overall organizational workforce.

Saleem (2012) posits transformation of HRM is required with the explosive evolution of

technology. Compelling advantages to Cloud-Based Computing include reduction in

expenses via minimal initial investment, monthly service plans, and decreased

maintenance, and via increased operational agility through easy of accessibility,

scalability and deployment (Gibson, 2012). Cloud-Based Computing may offer a unique

opportunity to build competitive advantage in the HR function by providing accessibility

to, and use of, big data for increased efficiency in HR performance (Gibson, 2012).

HRIS is a tool deliverable by means of Cloud-Based Computing and is found to

have a significant impact on HRM. Ikhlap, Khan, Mujtaba, and Sadiq (2012) conducted a

study of HRIS in Pakistan-based banks finding the HR function most dependent on

technology. While Ankrah and Sokro (2012) found HRIS positively correlated to cost

and timesaving, Ikhlap et al. (2012) found HRIS improved HR processes and resulted in

timesaving however, did not find a link to cost savings, communication, or effective

recruitment.

Gibson (2012) states Cloud-Based Computing frees HR from administrative tasks

leaving more time to devote to decision-making and strategic endeavors (Ikhlap et al.,

2012). Ankrah and Sokro (2012) posit improved decision-making with greater use of

HRIS and indicate improved technology may positively affect efficiency of the HR

function resulting in better HRM. However, Ikhlap et al. (2012) acknowledge HRIS is

underutilized for HR strategic decision-making. Karimidizboni (2013) acknowledged the

challenges of implementing technology and states the HR function must implement more

advanced instruments and technology for enhanced HRM performance.

Implementing technology allowing the HR function to use data for better

decision-making improves HRM through increased competiveness, facilitated

information processing, enhanced reporting, and global accessibility (Karimidizboni,

2013), to which participants agree accessibility, and availability to applications and data

is crucial for implementing Cloud-Based Computing.

Participant identification of efficiency in productivity, improved customer

experience, mobility, and global organizational expansion, as reasons for implementing

Cloud-Based Computing, supports Kapoor and Sherif’s (2012) position of using a

globally accessible HRIS for HR efficiency. Participants agree the cost and time

efficiency afforded by implementing Cloud-Based Computing improves performance of

the HR employee, thus of the overall HR department, and provides value to the

organization (Ankrah & Sokro, 2012; Gibson, 2012; Staley & Gandi, 2014). This

sustains the position of Saleem (2012), and Kapoor and Sherif (2012) that HRIS

significantly supports HRM. HRIS via Cloud-Based Computing may provide a globally

accessible platform supporting and potentially improving HRM in organizations.

Research Question 4

The fourth research question asked, “What leadership traits of successful Cloud-

Based Computing adopters also contribute to HR performance?” According to the TOE

framework (Tornatzky & Fleisher, 1990), leadership style can be considered an

organizational and environmental factor and compatibility characteristic that influences

the adoption of innovation (Rogers, 2003). Loshali & Krishnan (2013) support Bass’

definition of transformational leadership and aligned characteristics including

charismatic, inspirational, individualized consideration, and intellectual stimulation. The

researchers also posit the characteristics support employee retention, productivity, and

cost variables for improved organizational performance (2013). Leadership style is

viewed as an independent variable in relation to the effect on technology adoption in HR

and related outcomes.

Mueller (2007) and Polen (2009) indicate the success or failure of technology

hinges on leadership support and an understanding of the technology complexity (Bahli et

al., 2013) along with employee reaction to the implemented technology. Participants

found leadership support a factor contributing to successful implementation and a lack of

subject knowledge a challenge to implementing Cloud-Based Computing. Kumar and

Pandya (2012) state technology is a critical driver of HR serving a strategic role and

“changes the competencies required for success of HR professionals” (p. 112, para. 4).

Study responses support the conclusion as participants found characteristics of a

successful HR leader differed from those of an HR leader successfully implementing

technology.

Study participants identified communication as a key characteristic for HR leaders

implementing Cloud-Based Computing, additionally identifying effective communication

and training plans as best practices. This supports Polen’s (2009) findings that the use,

knowledge, and communication of e-technologies by HR professionals may contribute to

success in HR. Gangwa, Hema, and Ramaswamy (2014) posit managers must train

employees for Cloud-Based Computing for effective implementation and improved

performance to accomplish organizational objectives.

Kraemer, Xu, and Zhu (2006) posit HR and IT develop the infrastructure for

technology change and influence technology readiness and assimilation within the

organization (Mueller, 2009) making HR leaders core influencers of technology adoption

(Michel, 2007; Vonk, Geertman, & Schot, 2007) and attitude of intention to use the new

technology (Yusliza & Ramayah, 2011). Transformational leaders often encourage the

use of technology to realize organizational vision and goals however, must consider the

employee’s perception of need and ease of use (Birasnav, Rangnekar & Dalpati, 2011;

Purvis, Sambamurthy, & Zmud, 2001; Timmons, 2008).

A positive correlation between Transformational Leadership and strategic HRM

exists, as transformational leaders are likely to create processes aligned with

organizational goals through strategic practices of staffing, performance management,

training, compensation, and rewards (Loshali & Krishnan, 2013). Chen, Lin, Lin, and

McDonough (2012) found transformational leadership had a positive effect on

technological innovation, however also reported an organization with a strong innovative

culture fostered technological innovation, and may substitute for transformational

leadership while maintaining a positive impact on technological innovation. Study

participants recognize the effect of characteristics aligned with those of a successful

transformational leader on implementation of Cloud-Based Computing and the resulting

performance of HRM. This study may offer better insight to the perceived competencies

required by successful HR leaders implementing Cloud-Based Computing.

Research Question 5

The fifth research question asked, “How does HR contribute to the overall

performance of the organization?” Ferguson (2006) suggests the strategic alignment of

HR to organizational goals through training, development, and employee engagement

may positively affect organizational performance. A transformational leader emphasizes

development, growth, and an alignment of HR with organizational strategies required for

effect on organizational performance and found a positive correlation between

transformational leadership, strategic HR, and organizational performance (Loshali &

Krishnan, 2013). One suggestion could be the increased operational agility of

CloudBased Computing on HRM may also contribute to organizational performance.

Human Resource Management practices are expected to improve organizational

performance (Huselid, 1995; Theriou & Chatzoglou, 2008) by using unique

characteristics in HR specifically through knowledge management and organizational

learning capability (2008). A majority of participants found accessibility and availability

to applications and data a critical reason for implementing Cloud-Based Computing. The

use of Cloud-Based Computing as a platform for knowledge management and

organizational learning may enhance and contribute to the efficiency of HRM and overall

organizational performance. Gainey and Klass (2008) report e-HR is often connected to

improved delivery of HR services resulting in reduced costs, improved employee

engagement, and greater strategic contribution by the HR function.

Sierra-Cedar (2015) confirmed the Value Chain Analysis model in linking HR

technology adoption to talent and business outcomes finding organizations that adopt HR

technologies experience improved financial performance from a Return on Equity than

late majority and laggard organizations. Although study participants found Cloud-Based

Computing to have a high to critical effect on overall organizational performance, a

distinct link was not made as to specifically how Cloud-Based Computing influences this

outcome.

Research Question 6

The sixth research question asked, “What are the implications of best practices in

Cloud-Based Computing adoption within HR and general leadership theory?” Study

findings derived from participant responses identified the Cloud-Based Computing

solution should fit the organizations needs and culture. This aligns with Harper and Utley

(2001) and Mueller (2009) who posit cultural attributes contribute to effective technology

implementation by fostering behaviors such as trust, flexibility, and information sharing.

Additionally, best practices identified by study participants included the creation of a

communication plan and training for end users that may foster the previously listed

behaviors. According to study participants, leadership buy-in and support,

comprehensive implementation plan, communication, and adequate training are best

practices for successfully implementing Cloud-Based Computing. This aligns with

research presented by Shilpa and Gopal (2011) who stated prerequisites for implementing

e-HRM including a commitment to change management (Sierra-Cedar, 2015),

stakeholder buy-in, communication of value to end users, and adequate training.

Participants identified best practices for successfully implementing Cloud-Based

Computing and challenges to implementation. The results align with Shilpa and Gopal’s

(2011) list of challenges to implementing e-HRM including cost, aligning e-HRM to

business requirements, security, data management, traditional mindset, customization

required, training, return on investment, and monitoring feedback. Morgan (2000) and

Yusliza and Ramayah (2011) indicated change management as a critical factor in new

technology acceptance.

Study participants stated, fear, close-mindedness, and lack of executive leadership

support, challenged the implementation of Cloud-Based Computing. This aligns with

Deloitte (2012), Wang, Wang, and Yang (2010), and Bahli, Borgman, Heier, and

Schweski’s (2013) findings that lack of leadership support is a critical cause for the slow

adoption rate (Babcock, 2010). Reasons for slow adoption of Cloud- Based Computing

included traditional mindset and HR processes (Deloitte, 2012; Gibson, 2012).

A transformational leader influences the perceived usefulness and acceptance of

new technology of the user (Schepers, Wetzels, & Ruyter, 2005). Obtaining executive

and general leadership support while following a comprehensive implementation plan

with transparent communication are suggested best practices from study participants that

may increase the adoption rate of Cloud-Based Computing in HR. Bersin (2014) reports

the four key factors for organizations successfully moving to Cloud-Based Computing

solutions include integration, ease of use, enterprise strength, and support, and

emphasizes key leadership support with the use of a comprehensive implementation plan,

also supported by study participant responses.

Limitations

A pilot study was conducted to ensure accuracy and reliability of the results with

suggested revisions included in the final surveys distributed to study participants. The

modified Delphi study required two survey iterations to achieve validity. The validity for

the population is a limitation because of the small sample sizes with a Delphi research

design (Linstone & Turoff, 1975). While membership within LinkedIn forums was high

and an open request for study participants submitted on multiple occasions, the response

rate was low, and may not be fully representative of the HR community. However, the

study received responses from the amount of participants targeted and all organizations

discussed were based in the United States.

The scope of this study was focused on informed participants in HR although

leadership experience and level varied among participants. Limitations to

generalizability and transferability may occur because of the small sample size of the

Delphi research design and qualitative nature of the study (Lincoln & Guba, 1985). Items

outside the control of the study included the timeframe of the study, holidays, participant

availability, and participant’s willingness to respond to requests to complete the surveys.

Finally, researcher experience, interpretation, and bias could be limitations based on, bias,

previous professional experience, or ideas.

Delimitations

The study was delimited to informed participants with a minimum of 5 years of

experience in HR. The focus of this study was HR professionals and may be limited in

generalizability to other industries. Further research may be required focusing on a

specific sector, industry, or organizational department. The electronic distribution of

surveys through SurveyMonkey® allowed for unrestricted access and disclosure from

participants while ensuring minimal bias among participants.

Implications and Significance of the Findings

Implications and Significance for Implementing Cloud-Based Computing in HR

The study findings indicated leading consultancies, researchers and field

practitioners agree Cloud-Based Computing is the predominant method of deployment for

HR systems and services with mobile fast becoming the focus of design (Card &

Sivak, 2015; Deloitte, 2012; Deloitte, 2011; Sierra-Cedar, 2015). Sierra-Cedar (2015)

expects the adoption of Cloud-Based Computing, subscription-based services to increase

58% by the end of 2016. According to study participants customized HR systems are

more in-demand with low implementation success rates, while Cloud-Based Computing

solutions success rates are higher, perhaps attributable to the onus of maintenance and

customization on the service provider. Technology continues to play a critical role in

HRM and despite reports from numerous researchers and consultancies, adoption of

Cloud-Computing (Deloitte, 2012); continues to increase in forecast but lacks realization

for most organizations (Géczy, Izumi, & Hasida, 2012; IBM, 2015; Sierra-Cedar, 2015).

Data security is among the top challenges for cloud adoption (Babcock, 2010;

Farah, 2010; Géczy, Izumi, & Hasida, 2012; IBM, 2015; Indranil, 2012; Sierra-Cedar,

2015). Attempting to mitigate challenges of adoption, the informed participants of this

study emphasized following a comprehensive implementation plan, communication, and

training as leading factors in successful implementation of Cloud-Based Computing. End

user support and training may increase the likelihood of better technology adoption

(Ruyter et al., 2005). This may be accomplished through following a conceptual model

for implementation and ensuring a strong, simplified communication to all stakeholders

(Babcock, 2010). Based on innovation diffusion (Rogers, 1995) and

technologyorganization-environment (TOE) frameworks (Tornatzky & Fleischer, 1990),

Kraemer et al. (2006) developed a model addressing seven factors for e-business

assimilation. Results from this study identified factors aligning with each component of

the model suggesting the model may be an effective method for cloud technology

adoption.

According to this study and those of other researchers and consultancies,

implementation of Cloud-Based Computing propels efficiency, effectiveness,

performance, and engagement (IBM, 2015; Sierra-Cedar, 2015). Organizational

maturation challenges the organizational readiness for technology adoption (Bahli et al.,

2013; IBM, 2015; Sierra-Cedar, 2015). In this study, most participants aligned with

factors challenging adoption indicative of organizations slowly progressing through the

maturity curve. A suggestion to HR leaders is to consider the technological readiness of

the organization with consideration to maturation and organizational goals.

Implications and Significance for Leadership and Human Resource Management

Study participants identified characteristics indicative of successful HR leaders

and of HR leaders successfully implementing Cloud-Based Computing. HR leaders hold

a multidimensional role as employee advocate, strategic leader, administrative expert, and

change agent (Fitz-enz, 2002; Ulrich, 1997) and must lead their social systems to new

technological realizations (Rogers, 1995). The study results indicate the characteristics

between a successful HR leader and one implementing new technology are the same

however rated and emphasized differently. This study may contribute to the foundation

of a new leadership model in consideration and inclusive of rapid technology growth in

organizations.

Cultural resistance is a leading factor challenging technology adoption (IBM,

2015) to which HR leaders can navigate to invigorate productivity, effectiveness,

flexibility, performance, and innovation (Florea, Goldbach, & Goldbach, 2011).

Strategic organizational change requires effective decision-making by leaders (Bass,

2007). Human resource management theory includes various leadership styles, such as

transformational leadership, identified in this study as preferable to implementing

innovative technologies (Yukl, 2012). Encouraging innovating thinking and diverse

perspective, as provided by transformational leaders, from employees may encourage

higher levels of technology acceptance (Ruyter et al., 2005). Kramer et al. (2006)

suggested managers create an infrastructure to support new technology reinforced by

study participant’s responses that leaders must be knowledgeable of technology available

and its alignment with departmental and organizational needs.

Recommendations for Future Research

This research study focused on identifying factors contributing to the slow

adoption of Cloud-Based Computing, the potential effect on HRM processes, and

contributes to clarifying literature. Additional research in the study of HR leadership and

technology adoption, and contributing factors of Cloud-Computing on HRM and

organizational performance is necessary. Further research may be conducted based on the

information presented within this study. The current study consisted of 14 participants. A

mixed-methods or quantitative study, with a larger sample fully representative of HR

managers, could provide further confirmation of study outcomes and additional insight to

the problem. Focusing the scope of the study on HR leadership characteristics during

technology adoption, specifying the administrative or strategic HR factors affected by

cloud technology, or targeting the dependent organizational performance variables may

further validate, and increase generalizability and transferability of study results.

The transferability of the study findings could be further investigated and

strengthened by surveying a larger sample of the population or specifying HR

professionals in specific industries. Additionally, targeting a larger population of HR

professionals with specific leadership or executive experience may provide insight to

perspectives and challenges faced when deciding upon or implementing Cloud-Based

Computing. Triangulating the data in this way could identify themes, perceptions, or

create the constructs of a new technology implementation model for HR leaders that may

translate to other fields.

Isolating how communication of technological innovation such as Cloud-Based

Computing is communicated, accepted, and adopted in the HR field may assist with

mitigating challenges for future innovators and adopters. Another opportunity for

research may include a quantitative study of the impediments to adoption in HR, and best

practices or potential solutions for implementation and adoption of Cloud-Based

Computing. Study participants identified extensive customizations to off-the-shelf

HRMS solutions often contributed to poor or failed implementation. A quantitative study

may include the types of HRMS, percentage of customization, extent of successful

implementations and identification of impediments.

Mobility is fast becoming a primary reason for organizations to implement new

technologies. In some cases, organizations are choosing incorporating mobile

applications over replacing legacy systems. Future study efforts could include a

mixedmethods study on mobility of HR systems, the benefit of mobility to the workforce

and resulting organizational performance outcomes.

Conclusion

The objective of this modified Delphi study was to identify the perceived effect of

Cloud-Based Computing in HRM performance and factors contributing to its slow

adoption from a panel of HR informed participants. Through two rounds of surveys, the

study attempted to provide insight to the effect of Cloud-Based Computing on HRM, an

underdeveloped area of research in the United States using data and theory triangulation.

Some research has been conducted on the effect of Information Technology to HRM

(Kinanga, 2012; Mishra & Akman, 2010); however, most is conducted with organizations

or specific sectors in countries outside of the United States. In accordance with the

Delphi method, a deeper understanding of the problem was gained and can be explored

further in future research.

The study findings confirms, and extends, research conducted on the slow

adoption of cloud technologies in HR (Bahli et al., 2013; Chen, Low, & Wu, 2011;

Deloitte, 2011; Deloitte, 2012, Deloitte 2012b; IBM, 2015; Indranil, 2012; Sierra-Cedar,

2015; Wright, 2011). Results from this study included factors contributing to, and

challenging, successful adoption of Cloud-Based Computing and cloud technology tools

most effective for HR. Additionally, participants identified best practices for successful

implementation and defined characteristics of HR leaders effectively adopting and

implementing Cloud-Based Computing. Human resource leaders practicing

transformational leadership may be most aligned with the characteristics of successful

early technology adopters, and more likely to succeed leading employees and

organizations to do the same. The study is significant because a gap in literature exists

regarding Cloud-Based Technology adoption in the HR field and the results of this study

may serve to assist academia, HR leaders implementing Cloud-Based Computing, and

leadership in general. This study may be used to establish an HR technology leadership

competency model or model of best practices for implementing Cloud-Based Computing

in HR and potentially other fields.

Globalization requires standardization. The HR field is experiencing emerging

issues of globalizing and standardizing processes and technologies. Organizations are at

risk of technological inefficiency with globalization and high competition (Birasnav,

Rangnekar, & Dalpati, 2011). Technological evolution is occurring faster than

organizations can maintain, including the HR function, and requiring collaboration

between HR and IT to improve HR practices (Stryve Advisors & The RBL Group, 2011).

Researchers and participants in this study identified outdated processes, fear, and

close-mindedness as leading challenges to adopting new technologies in the HR field.

The rate of adoption in the field diverges leaving the HR field with arbitrary practices,

responsibilities, and limited strategic influence. As organizations continue to access

global talent pools and expand customer reach, technology may alleviate these challenges

increasing outcomes of organizational and strategic importance including performance,

engagement, productivity, effectiveness, efficiency, and sustainability of the organization.

Academicians and HR leaders may benefit from, and use the findings of, this study to

extend future research and provide a more targeted perspective to HRM and

organizational performance.

CLOUD-BASED COMPUTING AND HUMAN RESOURCE MANAGEMENT PERFORMANCE A DELPHI STUDY

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