Business Research

Industrial Marketing Management 40 (2011) 128–138

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Industrial Marketing Management

Motivating the industrial sales force in the sales forecasting process

Teresa M. McCarthy Byrne a,⁎, Mark A. Moon b,1, John T. Mentzer b,1

a Bryant University, Department of Marketing, 1150 Douglas Pike, Smithfield, RI 02917, USA b University of Tennessee, Department of Marketing and Logistics, 324 Stokely Management Center, Knoxville, TN 37996, USA

⁎ Corresponding author. Tel.: +1 401 232 6801. E-mail addresses: [email protected] (T.M. McCar

(M.A. Moon). 1 Tel.: +1 865 974 8062.

0019-8501/$ – see front matter © 2010 Elsevier Inc. Al doi:10.1016/j.indmarman.2010.06.003

a b s t r a c t

a r t i c l e i n f o

Article history: Received 14 July 2008 Received in revised form 2 June 2009 Accepted 10 May 2010 Available online 10 July 2010

Keywords: Sales forecasting Motivation Job satisfaction Job seriousness Forecasting training

Previous research has recognized the value of the industrial salesperson's role in the sales forecasting process, and offered normative descriptions of what that role should be. However, no studies have been conducted to determine the variables that motivate industrial sales force involvement in and contribution to the sales forecasting process. This study employed depth interviews and survey research to develop and test a conceptual model of industrial sales force forecasting motivation. The research identifies five environmental signals that can be employed by managers to impact an industrial salesperson's level of satisfaction with, effort directed towards, and seriousness placed in the sales forecasting process.

thy Byrne), [email protected]

l rights reserved.

© 2010 Elsevier Inc. All rights reserved.

1. Introduction

The integral role of sales forecasting in the corporate planning process and as a contributor to corporate success is widely recognized (Dalrymple, 1975, December, Fildes, and Beard, 1992; Makridakis, and Wheelwright, 1977, October; Mentzer, and Moon, 2005; Reid, 1985). The critical role of an organization's sales force in developing the forecast has also been well established (Dalrymple, 1975, Decem- ber;1987; McCarthy, Davis, Golicic, and Mentzer, 2006, August; Mentzer, and Kahn, 1995a; Moon, Mentzer, and Thomas, 2000). For example, a company's salespeople can contribute valuable information tothe sales forecastingprocess byacquiringmarket intelligence through direct conversations with their customers. Indeed, research has shown that sales force composite continues to be a popular and frequently used forecasting technique (Dalrymple, 1987; McCarthy et al., 2006, August; Mentzer, and Kahn, 1995a). Furthermore,the trendtoward partnerships within the supply chain has fueled the practice of collaborative forecasting (McCarthy, and Golicic, 2002; Sriram, Krapfel, and Spekman, 1992, December), and the sales force fulfills this boundary-spanning forecasting function between the organization and its downstream supply chain partners.

Despite the important role of the industrial sales force in sales forecasting, research illustrates that many salespeople are unmotivated to perform their forecasting responsibilities, arising from a belief that

time spent on forecasting usurps time that should be spent developing customer relationships and selling products (Moon, and Mentzer, 1999). Research on sales force forecasting is largely devoted to empirical studies examining the salesperson's impact on forecast accuracy related to the use of a specific technique or practice, such as sales force composite (Peterson, 1993), the assortment forecasting method (Småros, and Hellström, 2004), survey of buyer intention (Peterson, 1988), and involvement in quota setting (Wotruba, and Thurlow, 1976). Inaddition,severalapplied cases describethe natureof thesalesperson's involvement in a particular company's forecasting process (e.g., Barash, and Mitchell, 1998; Reese, 2000; Riehm, 2001; Småros, and Hellström, 2004). Cox (1989) and Moon, and Mentzer (1999) descriptive studies recommend strategies for improving sales force forecasting perfor- mance. Notwithstanding the contribution of these studies to increasing our knowledge of the role of salespeople in sales forecasting, there is a dearth of research focused on identifying the specific factors that motivate and direct industrial sales force behavior in the sales forecasting process. Our study fills this gap in the literature by identifying and testing variables that impact sales force motivation in the sales forecasting process. The purpose of this paper is to develop a theory of industrial sales force forecasting that will inform managers of the controllable variables that can motivate sales force involvement in their forecasting responsibilities.

The following section reviews the theory building approach and methodologies applied in this paper. In the next section we employ qualitative interviews and literature review to identify the variables and develop a conceptual model relevant to motivating the industrial sales force in the sales forecasting process. Subsequently, we develop an exploratory measurement instrument to empirically test the model using survey methodology. Results of the exploratory survey are

Fig. 1. Conceptual model of industrial sales force sales forecasting motivation.

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presented and discussed, followed by implications and suggestions for future research.

2. Theory building

Our research follows the Reynolds (1971) three-step approach to theory building: exploratory, descriptive, and explanatory research. Among the data-gathering methods recommended for theory devel- opment are observation, unstructured and structured interviews, surveys, documents, and experiments (Schendel, and Hofer, 1979; Reynolds, 1971). Mentzer, and Kahn (1995b) recommend that researchers begin the idea generation process with observation and literature review. As such, in this multi-method study, we employ observation from depth interviews, literature review, and a survey questionnaire to build theory.

Specifically, for the exploratory stage of theory building, our study employs observation and analysis of the sales forecasting manage- ment practices at 24 industrial marketing companies to “identify the phenomena of interest and describe its key characteristics” (Schendel, and Hofer, 1979, p. 385.), as well as a review of the literature related to sales forecasting and industrial sales force motivation. Our descriptive stage involves developing the theoretical model and hypotheses explaining the relationships between variables identified in the first stage. The explanatory stage empirically tests the relationships hypothesized in the second stage using exploratory survey method- ology. Following the description and findings of each stage of the research presented below, discussion, implications, and recommen- dations for industrial marketing managers and for future research are presented.

2.1. Exploration and description

Depth interviews were conducted following conventional protocols developed by Churchill (1979, February) and McCracken (1988) at 24 industrial marketing companies toobserve sales forecasting management practices. Informed by persistent themes emerging from the interviews, we reviewed the extant literature on industrial sales force motivation and sales forecasting practices to provide a theoretical foundation for the interview themes. This dual approach to exploration allows for triangulation whereby the researcher is constantly tacking back and forth among the data sources – including the interview data and relevant literature – to discern the dimensions associated with sales force forecasting, and to facilitate development and refinement of constructs and measures for the descriptive stage of theory building (Morgan, Anderson, and Mittal, 2005, July). Specifically, in this section, we conduct exploration by reviewing the qualitative interview data and the sales force motivation and sales force forecasting literature to identify relevant situational and motivational variables, and description by discerning patterns and making empirical generalizations in the form of identifying relationships among the variables. We explore the situational variables that lead to increased motivation in industrial sales force forecasting, and then examine the specific motivational dimensions related to sales forecasting productivity. The variables that emerged from the depth interviews and the sales force motivation and forecasting literature review, and the relationships between those variables, are shown in Fig. 1.

The companies that participated in the interviews span a variety of industries,2 and are representative of all levels in the industrial

2 Interviews were conducted at the following industrial marketing companies: AET Films, AlliedSignal, Applied Micro Circuits Corporation, Avery Dennison, ConAgra, Continental Tire, Cooper Tire, Corning, Cummins Filtration, DuPont, Eastman Chemical, Exxon-Mobil, Federal Express, John Deere, Lucent Technologies, Maxtor, Michelin, Motorola, Orbit, Peerless Pump, ProSource, Smith & Nephew, Sysco, and Union Pacific Railroad.

marketing supply chain: raw material suppliers, manufacturers, and distributors. At these companies, over 1000 individuals from across multiple functional areas were interviewed, including sales people, sales managers, and sales executives. The roles these individuals play in the forecasting process were explored in depth, as were issues surrounding forecasting systems utilized, performance measurement, customer participation in sales forecasting, feedback, training, and how sales forecasts are used in the firm. The following findings from the interviews and literature suggest that the presence of certain situational variables increases industrial sales force motivation in the sales forecasting process.

2.1.1. Situational variables Bagozzi (1978, November) describes situational variables as “a

bundle of physical characteristics that are in some way coercive, facilitative, or constraining on the individual or his or her perfor- mance, or some other physical events related to the individual” (1978, p. 521). We adapt Bagozzi's term situational variables in this study to indicate the environmental signals that coerce, facilitate, constrain or motivate industrial sales force forecasting. Environmental signals are comprised of managerial and financial resources provided by the organization to assist the industrial sales force forecasting process. Following is a discussion of nine environmental signals (see Fig. 1) that emerged from the interview data and literature review as impacting industrial sales force motivation in the sales forecasting process. The discussion for each variable begins with presentation of the depth interview findings followed by a review of the relevant literature in industrial sales force motivation and sales force forecasting.

2.1.1.1. Compensation and performance evaluation. One theme that persistently emerged from interviews within every company was related to perceptions of the impact of compensation and perfor- mance evaluation in motivating sales force forecasting. Consistently, individuals from sales expressed frustration that they were asked to spend considerable time and energy forecasting, although their efforts were neither measured nor rewarded for this activity. The impact of the absence of forecasting compensation and performance evaluation was manifest in the salespeople's resistance towards forecasting responsibilities, which were perceived as an unnecessary complexity beyond the scope of their sales function. Furthermore, individuals within the firm who are downstream users of the forecasts developed by the sales organization frequently expressed a lack of confidence in the forecasts, often because they perceived a lack of accountability.

Research has shown that salespeople often resent having to perform their forecasting responsibilities because they consider sales forecasting as ancillary to and incompatible with their primary function, which they

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believe is to sell (Harris, and Pike, 1996; Mentzer, and Moon, 2005). In an environment in which the role of the sales force is characterized as increasingly complex (Jones, Brown, Zoltners, and Weitz, 2005), inclusion of forecasting responsibilities to the role expectations of salespeople can result in feelings of role incongruity and incompatibility (Behrman, and Perreault, 1984, p. 12). Incongruity and incompatibility of expectations often stem from a failure of incentive structures such as compensation and performance evaluation to encompass and thereby motivate the expected performance (Brown, Evans, Mantrala, and Challagalla, 2005). In these situations, the importance of the salesperson in the forecasting process and the resultant organizational performance implications must be made evident to mitigate feelings of role incongruity. Thus, developing compensation schemes and performance evaluations incorporating incentives for forecast accuracy is one approach to underscoring the importance of sales force forecasting accountability (Mantrala, and Raman, 1990). Based on the interview data and literature review, compensation for and performance evaluation of forecasting responsibilities are environmental signals that motivate industrial sales force forecasting.

2.1.1.2. Use of environmental conditions. Interview informants per- ceived importance in having access to information related to internal and external environmental factors that impact demand. Information regarding endogenous factors that were noted as important included the timing of new product introductions, and changes in organiza- tional marketing expenditures and pricing policies. Among the exogenous conditions identified by informants were industry trends, competitive trends, and economic forecasts. The perceived impor- tance of access to environmental conditions was emphatically expressed by the interview informants, particularly when the information salespeople desired was neither accurate nor timely. One forecast analyst described the reaction of salespeople when asked to submit a 12 month forecast without sufficient information; “They hate it…. It's like pulling teeth.… They aren't given enough information regarding pricing and new products to create an accurate 12 month forecast.” As described by one salesperson recognizing the inadequacy of the information supplied, “the forecast becomes a joke.” In situations where salespeople were not given accurate and timely information, they clearly expressed dissatisfaction with the process, took the process less seriously, and spent less time and effort developing their forecasts.

Research indicates that a determinant of salesperson performance relates to the level and quality of information and knowledge needed to perform the role adequately (Agarwal, 1999, Brown, and Peterson, 1993, February; Churchill, Ford, Hartley, and Walker, 1985, May; Johlke, Duhan, Howell, and Wilkes, 2000). The sales literature (Baldauf, and Cravens, 2002; Matsuo, and Kusumi, 2002) supports the concept advanced by the knowledge-based view of the firm suggesting that “knowledge is the critical characteristic which enables salespeople to cope effectively with their dynamic, competitive environments” (Rapp, Ahearne, Mathieu, and Schillewaert, 2006, p. 281). Knowledge of endogenous and exogenous environmental conditions are resources that can be used by salespeople to inform the forecasting process (Moon, and Mentzer, 1999).

2.1.1.3. Use of judgmental input. Most salespeople in the interviews recognized the value they could add to the forecast stemming from information collected during formal and informal conversations with customers. Examples of judgmental information that informants used as input to the forecast include perceived probability of opening new or closing existing accounts, conversations with customers about orders placed with competitors due to the focal firm's inability to meet the customer's demand, changes in the customers' selling efforts, changes in competitors' marketing activity, and probabilities of securing large orders.

Several studies have recognized the importance to the forecasting process of the salespersons' subjective or judgmental input stemming from conversations with customers and exposure to other external environments (Dalrymple, 1987; McCarthy, and Golicic, 2002; Mentzer, and Kahn, 1995b; Sanders, and Ritzman, 2004). The exposure salespeople have to customers and other externalities often results in aggregation of a tacit and disperse knowledge base that can be used as a valuable source of input to the forecast, such as those identified above during the interviews. This type of tacit information has been shown to add value to the forecast under certain conditions, particularly in changing environments and when little historical data exist (Webby, and O'Connor, 1996).

Although salesperson judgment was noted as critical to effective sales forecasting in both the interviews and literature, a key issue with judgmental input that emerged from the interviews was the “game- playing” that often takes place when salespeople contribute to judgment based forecasts. The interviews identified two categories of game-playing in which salespeople often engage. First, if salespeo- ple perceive that their judgment-based forecasts will affect their quotas or targets, they will underestimate future demand so that those targets are more easily reached. Second, if salespeople perceive that product shortages are likely, they will overestimate future demand so that when allocations are made, their customers will receive the actual quantity desired. In either case, the credibility of the judgmental input developed by the sales force is compromised by such game-playing. Therefore, although the literature suggests that the use of judgmental input is an environmental signal motivating sales force forecasting, the interview results were equivocal.

2.1.1.4. Forecasting training. Another situational variable that emerged from the interviews is related to forecasting training, or more precisely, the lack thereof. In most companies, both salespeople and downstream users of the forecast expressed frustration with the absence of forecasting training for salespeople, which they believed would improve sales forecasting efforts. Very few of the 24 companies provided any formal sales forecasting training for their salespeople. In most cases, forecasting was an afterthought, and any training that did take place was to informally show salespeople how to input their numbers. In fact, in many of the companies participating in the interviews, salespeople were unaware of what happened to their forecasts after they were submitted. Thus, not only are salespeople frequently not trained on how to forecast, they are frequently not trained on why they forecast.

Ingram, LaForge, Avila, Schwepker, and Williams (2001) discuss the importance of sales force training to achieve role clarification; that is, a clear understanding of what tasks are to be performed, how they are to be performed, and how such efforts lead to improved outcomes. Jantan, Honeycutt, Thellen and Ashraf (2004) suggest that a strong commitment to sales training improves role perceptions and salesperson performance. Ramlall (2004) asserts that organizations cannot afford to passively assume that sources of knowledge are being accessed and utilized throughout an organization, such as the use of judgmental information obtained by salespeople during conversa- tions with customers. Sales managers that train the sales force in application of the various sources of knowledge in the sales forecasting process work toward reducing role ambiguity. Training in knowledge management practices, including coaching, mentoring, and workshops for knowledge sharing, is considered essential for a company to translate tacit knowledge into more objective and codified explicit knowledge (Demers, 2003; Nonaka, and Takeuchi, 1995). For example, McCarthy, and Golicic (2002) found that judgmental input improved sales forecast accuracy when firms actively commit resources to train salespeople in intelligence gathering and forecasting. Therefore, we include forecasting training as an environmental signal that motivates industrial sales force forecasting.

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2.1.1.5. Feedback on forecasting performance. Another environmental signal that was evidenced as a pain point due to its absence is feedback from managers on forecasting performance. Very few of the companies provided individual salespeople with concrete forecasting performance data, although many informants indicated that they desired the information and expressed frustration with the absence of feedback. Among the companies that did measure and communicate forecast accuracy, salespeople were readily able to recall their level of accuracy and often discussed targeted improvement goals. In many cases, lack of feedback resulted from lack of systems capability to measure forecast accuracy at the individual salesperson level. The interviews revealed that if individual measurement took place, it often required manipula- tion of data outside the formal forecasting system, and it was the rare company that went to this length to provide such feedback.

Locke, and Latham (1990) identify managerial feedback as an important motivator of performance. Clear and frank supervisory feedback provides clarification of performance expectations leading to increased job satisfaction, effort, and performance (Brown, and Peterson, 1993, February; Chakrabarty, Oubre, and Brown, 2008, Humphreys, and Einstein, 2004, Teas, and Horrell, 1981, February). A formal process providing salespeople with feedback about sales forecast accuracy or the reasons for inaccuracy is a tool that can be used by salespeople to adjust their performance, and by management to emphasize accountability for forecast accuracy performance.

2.1.1.6. Knowledge of how the forecast is used throughout the organization. In some companies involved in the interviews, processes were established to ensure that salespeople understood the impact of their forecasting efforts on corporate performance. These companies discussed having regularly scheduled sales and operations planning (S&OP) meetings attended by salespeople, resulting in a consensus understanding of the forecast and how it was used to inform resource allocation decisions throughout the company. In other companies, the interview data revealed a common lack of understanding among the salespeople concerning the impact of their forecast on the supply chain. In these companies we observed a cultural disconnect between the demand and supply sides of the enterprise, and salespeople showed little understanding of the effects their forecasts had on production planning, procurement, logistics planning, and other aspects of supply chain management. In one example, salespeople were no longer invited to attend S&OP meetings because their input was “disruptive.” As a result, one salesperson commented, “I don't care what happens after I send my forecast to [the forecast analyst],” resulting in the prevalent attitude characterized as “it's our job to sell, and their job to supply.” These interview results indicate that a lack of understanding regarding how the salespeople's forecasts are used throughout the company results in a lack of motivation in the forecasting process.

In his research on motivation in the workplace, Pinder (1997) asserts that overall effectiveness of employees is significantly related to the perceived importance of performing a task. Specifically, employees who believe that their contribution in performing a task impacts the entire organization will be motivated to perform the task more effectively. Salespeople's perceptions of the importance of the forecast explain the dichotomy in levels of engagement with the forecasting process between those who understand the impact of their forecasting efforts on corporate performance and those who do not. Therefore, managers are enjoined to educate salespeople about the use of their forecast within the company and the impact of the forecast throughout the organization.

2.1.1.7. Access to a forecasting computer program. Thus far, the interview data have identified multiple sources of information that can be used to reduce role conflict and ambiguity and, correspond- ingly, to motivate salespeople to add value to the forecasting process. However, it became clear in the interviews that without the proper

tools and technology, effectively organizing, storing, retrieving, and analyzing the abundant sources of data can become a daunting task, resulting in underutilization of the data. Among the participating organizations, a wide variety of tools were provided (or not provided) to salespeople to support their forecasting efforts. At one company, salespeople were given access to the formal forecasting system, presented with the system-generated statistical forecast for their “A” level customers and their “A” level products, and provided with a space to enter their adjustments to the system-generated forecast as well as a space to note their reasons for making adjustments. At this company, the tools greatly supported the process. More commonly, many companies reported reliance on spreadsheets to create and share forecasts for which a common procedure and data source were not utilized. The abundance of personal spreadsheet forecasts was characterized by one firm as “spreadsheet mania.” At other compa- nies, salespeople were simply asked to send an e-mail to the forecasting department if they knew of anything occurring that might affect future demand. Clearly, at these companies, system tools were not provided to enhance the salespeople's forecasting perfor- mance. One salesperson who had experience with both spreadsheet forecasting and computer programs described her efforts with the computer program as more “substantive” and “rewarding” than the effort associated with the manual data input necessary with spreadsheet forecasting.

Brown et al. (2005) suggest that technology facilitates the storage, organization, and flow of information, thereby improving the quality of communications within and across firms. Forecasting computer programs provide a common procedure and data source for forecasting, and reduce the need for manual data input and potential for error associated with personal spreadsheet forecasts. Thus, organizations providing salespeople access to forecasting computer programs can help them manage and optimize use of the abundant information available in the forecasting process.

2.1.1.8. Level of others' seriousness placed in the salesperson's forecast. Insights from the interview database were at times discouraging concerning the level of seriousness placed upon the salesperson's forecasts by others in the organization. Frequently, substantial disconnects were observed between the users of salesperson- generated forecasts and the sales organization. As described previ- ously, forecast users often perceive that significant “game-playing” is taking place, rendering the forecasts generated by salespeople less than useful. In some firms, forecast users perceive that salespeople are “sandbagging,” or understating the potential demand in anticipation that these understated forecasts will result in lower sales quotas. In other firms, users perceive salespeople to be unrealistically optimistic. In yet others, users perceive that salespeople “pad” their forecasts to try to assure adequate product supply. In all these cases, there is a perception by forecast users that salespeople are not accountable for the accuracy of their forecasts and therefore the forecasts are not taken seriously.

Other observations emerging from the interviews illustrating a perceived lack of seriousness relate to the “executive override.” Salespeople expressed frustration when the forecasts they developed were altered by the executive team, thereby undermining the salespersons' efforts. As stated by one salesperson, “[Management] flushes all my analytics for 1 h of qualitative judgment.” Executive changes to the forecast were often not communicated back to the salespeople, compounding the perception that their forecasting efforts were not taken seriously. As described by one salesperson, “That loop doesn't get closed.” Managers suggested their incentive for overrides may stem from conflicting objectives, such as pressure to inflate the forecast to achieve favorable earnings projections. In total, these insights from the interviews paint a picture of salespeople not being taken seriously by users of the forecast. The lack of seriousness is often perceived by salespeople, and when they believe their

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forecasts are not being taken seriously, they feel intense reluctance to spend time working on their forecasting tasks. As one salesperson commented, “they don't believe what I tell them anyway, so why should I bother?”

The literature supports the notion that salespeople whose forecasting efforts are not taken seriously will perceive a disconnect with their forecasting responsibilities. When individuals perceive that their work is taken seriously by others, they are more motivated to expend attention and effort toward the task (Nissenbaum, 1998, Stevens, 1996) and take it seriously themselves. A rich body of literature on motivational theories underscores the importance of considering the expectations of significant others in predicting effective task performance (e.g., Hackman, and Oldham, 1980; Mitchell, and Nebeker, 1973; Ramlall, 2004). Thus, in order to motivate sales force behavior in the forecasting process, the sales force's role in developing the forecast needs to be taken seriously by others in the organization.

In sum, analysis of the interview data as supported by previous research on sales forecasting and sales force motivation suggests several environmental signals that managers can utilize as resources to motivate sales force forecasting: compensation and performance evaluations tied to forecasting performance; ability to use environ- mental conditions and judgment; forecasting training; feedback on forecasting performance; knowledge of how the forecast is used throughout the organization; access to a forecasting computer program; and the level of seriousness others in the organization place in the salesperson's forecast. These environmental signals represent resources and direction provided by the organization to assist and motivate the industrial sales force sales forecasting process. In the following section, we further explore the literature on sales force motivation to describe the motivational dimensions that are impacted by the environmental signals present for the sales force.

2.1.2. Motivational dimensions Motivational research in the sales literature generally deals with

efforts to increase sales force performance and productivity toward organizational goals (Brown et al., 2005). Ramlall (2004) suggests four motivational theories that are generally applicable to motivating sales people: (1) need theory, (2) equity theory, (3) expectancy theory, and (4) job design (see Ramlall, 2004 for a comprehensive review of each of these theories). Need theory identifies internal factors that stimulate behavior. Equity theory is concerned with the level of reward received for behavior as well as the level of reward relative to what others receive. Expectancy theory focuses on anticipatory end states or goals, thereby modeling motivation as a function of the expectation that certain performance will result from a given action. Job design theory suggests that the combination of tasks comprising a job impacts employee motivation to perform.

2.1.2.1. Satisfaction with the forecasting process. In Ramlall (2004) summary of the core dimensions and outcomes of these four theories, one of the dimensions most frequently associated with the theories is satisfaction. For example, expectancy theory posits that salespeople's affective orientation toward their job is related to the level of satisfaction they expect to receive from performing the job (Ramlall, 2004; Vroom, 1964). In discussing need theories, Steers, and Porter (1983, p. 32) suggest, “Managers have the responsibility to create a proper climate in which employees can develop to their fullest potential. Failure to provide such a climate would theoretically… result in lower job satisfaction.” Champagne, and McAfee (1989)) offer several approaches to satisfying employees that include providing praise and economic rewards, clearly explaining job function expectations, encouraging creativity, and providing training. Among the factors that lead to job satisfaction in the job character- istics model of motivation are recognition, responsibility, feedback, knowledge of the results of one's efforts, and learning (Hackman, and

Oldham, 1980; Ramlall, 2004). In the context of sales force forecasting, managers enabling the environmental signals identified above create an environment leading to satisfaction with the forecasting process to motivate performance.

2.1.2.2. Forecasting effort. Motivational theories also consistently recognize level of effort as an outcome associated with various job- related situational factors (Igalens, and Roussel, 1999). Need theory, expectancy theory, equity theory, and job characteristics theory each perceives effort as a function of managerial practices that motivate behavior (Ramlall, 2004). For example, Porter, and Lawler (1968) perceive effort as a function of perceived value of a reward (e.g., recognition or compensation), and that employees exhibit higher levels of effort with higher levels of reward. Ultimately, as seen in Fig. 1, motivational theories advance the belief that the right combination of variables impacts a salesperson's level of satisfaction and level of effort. Introducing environmental signals to reduce feelings of incongruity and incompatibility with job expectations, and that provide salespeople with the tools needed to perform the expected role will result in increased satisfaction with the forecasting process, (Behrman, and Perreault, 1984; Brown, and Peterson, 1993, February) and increased effort expended on forecasting responsibilities.

2.1.2.3. Seriousness placed in the forecasting process. In addition to satisfaction and effort as dimensions that are impacted by environ- mental signals motivating sales force forecasting behavior, process models of behavior suggest that seriousness is related to responsive- ness to a task (Schwartz, 1977, Shiarella, 1998 (1998, January). When assessing a task, individuals who perceive higher levels of seriousness are more likely to direct attention toward the task than those perceiving lower levels of seriousness. Describing the job character- istics model of motivation, Pinder (1997) advocates designing work environments to generate experiences that are meaningful and, thus, taken seriously. Meaningfulness results from allowing the salesperson to exercise a certain amount of autonomy, and from understanding how one's actions contribute to the organization (Pinder, 1997). In the context of sales forecasting, salespeople who are allowed to use their own judgment as input into the forecast, and know the impact of their forecasting efforts on the organization, take their responsibilities more seriously. In addition, Hackman, and Oldham (1980, p. 78) suggest “the degree to which a job requires a variety of different activities in carrying out the work, involving the use of a number of different skills and talents of the person” results in job meaningful- ness. Thus, in order to achieve consistent improvements in forecasting performance within companies, the sales force must find meaning- fulness in their forecasting responsibilities and take those responsi- bilities seriously.

In summary, the interview data supported by the review of literature on sales forecasting and sales force motivation reveals that having the right combination of environmental signals leads to satisfaction with, effort directed towards, and seriousness placed in the forecasting process. The conceptual framework presented in Fig. 1 reflects the variables and relationships among the variables observed above in the exploration and description stages of theory develop- ment. In the next section, we move to the explanation stage of theory development and empirically test the relationship between each situational variable and the motivational dimensions associated with sales force forecasting.

2.2. Explanation

In the third stage of theory development, explicit theory development serves to explain the empirical generalizations observed repeatedly in the earlier stages of the research. We therefore empirically test the relationships between the environmental signals

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and the motivational dimensions of satisfaction, seriousness, and effort (see Fig. 1). The environmental signals identified in the interview data and the literature that were measured and tested include: (1) compensation based on forecasting performance, (2) performance evaluation based on forecasting performance, (3) access to environmental conditions provided by the company, (4) sales- person's use of their own judgment as input into the forecast, (5) forecasting training, (6) feedback on forecast accuracy, (7) knowledge of how the forecast impacts the organization, (8) access to forecasting computer programs, and (9) others' level of seriousness.

2.2.1. Methodology To test the relationships between the environmental signals and

the motivational dimensions, a survey of salespeople was conducted across a variety of industries and sales settings, as recommended by Schendel, and Hofer (1979). The sample of 1024 was taken from a commercially purchased mailing list of salespeople who indicated their involvement in selling products and/or services to companies (in other words, industrial marketing, not retail sales or sales to individuals). From this mailing, 382 returned a postcard indicating that they were appropriate respondents and willing to complete the survey. The survey was mailed to these individuals and 262 completed surveys were returned from business-to-business sales- people (68.6% response rate). The survey was designed to capture data that would allow us to test for patterns in relationships between variables identified in the exploratory and descriptive stages of research that motivate sales force participation in the forecasting process, leading to a prescriptive theory of industrial sales force sales forecasting. As such, the survey was developed to collect information regarding the salesperson's role in sales forecasting, the level of satisfaction, effort, and seriousness placed on the forecasting process, and the presence of environmental signals that motivate sales force forecasting. In addition, demographic data were collected to further explore possible patterns among variables. The items and frequen- cies used to measure the variables are presented in Appendix A.

3. Data analysis and results

Results indicate that, among the 262 respondents, 18.3 percent (n=48) do not have any forecasting responsibilities. The remaining results reported in this paper pertain only to those 214 respondents who are engaged in the forecasting process. The respondents' characteristics indicated that they were well qualified to address the issues under consideration. In addition to indicating that they were responsible for forecasting in their territory, the average respondent had “10 to 15 years” in sales. They also worked in a variety of industries, including manufacturing (55.3%), service providers (22.2%), wholesaling (14.0%), publishing (3.7%), health care (2.8%), and financial institutions (2.0%).

Table 1 Survey results.

Variable Variable name Seriousness R2=.598,α=.000

β t-stat

V1 Compensation .025 .2 V2 Performance evaluation .147 1.5 V3 Use of environmental conditions .153 1.7 V4 Use of judgment −.228 −1.8 V5 Training .301 2.4 V6 Feedback −.131 −1.4 V7 Knowledge .286 5.8 V8 Forecasting computer program −.023 −.2 V9 Others' level of seriousness .488 9.7

a=significant at pb.05. b=significant at p≤ .001.

The explanatory power of the relationships between the environ- mental signals and motivational variables (Fig. 1) is investigated by the following multiple regression equations:

Equation 1: Y1 = β0 + β1V1 + β2V2 + … + β9V9 + E1

Equation 2: Y2 = β0 + β1V1 + β2V2 + … + β9V9 + E2

Equation 3: Y3 = β0 + β1V1 + β2V2 + … + β9V9 + E3

where Y1, Y2, and Y3 denote the dependent variables, seriousness placed in the forecasting process (Seriousness), satisfaction with forecasting process (Satisfaction), and forecasting effort (Effort), respectively; β0 represents the intercept terms; β1, β2, … β9 denote the regression coefficients; V1, V2, … V9 denote the predictor variables described in the Appendix; and E1, E2, and E3 denote the error terms associated with each equation. Although R2 can be inflated with an increased quantity of independent variables, the number of variables in the model falls within the accepted rule of thumb (i.e., no more than one independent for each 10 cases in the sample) (SAS Institute Inc. 2002). Results from collinearity diagnos- tics indicate model fit is not affected by multicollinearity. Tolerance statistics range from .683 to .955, all above the .20 threshold (Hair, Black, Babin, Anderson & Tatham, 2005); VIF ranges from 1.05 to 1.46 — within acceptable criteria; and proportion of variation parameters reveals none of the variables contribute strongly (0.5 or above) with two or more variables (Belsley, Kuh & Welsch, 1980). As shown in Table 1, in each equation the dependent variable is well fitted by the set of independent variables as reflected by the significant R2 coefficients of determination. The set of independent variables explains the largest percent of variability in the dependent variable Seriousness (R2=.598), followed by Effort (R2=.198) and Satisfaction (R2=.155) (see1).

Among the environmental signals in the model predicting Serious- ness, the coefficients for training (β=.301,α=.030), knowledge of how the forecast is used (β=.286,α=.000), and others' level of seriousness (β=.488,α=.000), were positive and significant (see Table 1). Results indicated Satisfaction with the forecasting process is associated with higher levels of training (β= .949,α=.021) and feedback (β=.844,α=.001). Three environmental signals were sig- nificantly associated with level of Effort. Knowledge of how the forecast is used (β=.366,α=.049) and access to forecasting computer program (β=1.382,α=.003) had positive significant relationships, and feed- back revealed a negative relationship (β=−.877,α=.013).

We also tested for interaction between variables. It is intuitive that receiving forecasting training may have an interaction with other variables, specifically, using a forecasting computer program and knowledge of how the forecast is used. When added to the regression equations, the interaction variables were not significant.

Satisfaction R2=.155,α=.000

Effort R2=.198,α=.001

istic β t-statistic β t-statistic

27 .005 .019 −.478 −1.122 32 −.234 −.986 .625 1.670 59 −.368 −1.706 .612 1.789 33 .066 .214 −.284 −.582 66a .949 3.145a .429 .897 59 .844 3.767b −.877 −2.498a 11b .107 .880 .366 2.008a

01 −.212 −.739 1.382 3.035a 43b −.123 .984 .018 .090

134 T.M. McCarthy Byrne et al. / Industrial Marketing Management 40 (2011) 128–138

4. Discussion

4.1. Seriousness in the forecasting process

Results from the multiple regression analysis indicate that the environmental signals account for 59.8% of the variability in the level of seriousness placed in the forecasting process, with three variables – training, feedback, and knowledge of how the forecast is used – demonstrating a significant relationship. Survey results were consis- tent with the findings from the interview data indicating managers striving to create a serious forecasting environment train their salespeople in forecasting processes and techniques. The interview data suggest that very few companies provide training, and survey results were consistent, with only 14% of respondents receiving forecasting training from their companies. Managers that dedicate resources for forecasting training establish a commitment to the process and inculcate performance expectations among salespeople.

Multiple regression results also revealed that salespeople take forecasting responsibilities more seriously when they are knowledge- able about how their forecast is used to inform other decisions throughout the company. Results indicate a wide range of knowledge among respondents regarding how their forecasts are used, with 52% indicating they have “a good deal of knowledge” to “a great deal of knowledge” about what is done with their forecasts after they are submitted. As evidenced in the interview data, formally involving salespeople in sales and operations planning (S&OP) meetings is a practice that has been successfully used to expose salespeople to the various resource allocation decisions for which their forecasts are used, thereby elevating the perceived importance of the task and reducing role ambiguity.

The finding that salespeople are more likely to take their forecasting responsibilities seriously when others in the organization also regard those forecasting efforts seriously confirmed patterns observed in the interview data. During the interviews, salespeople identified two groups of people they often perceived were not taking their forecasts seriously — management and downstream users of the forecast such as production and purchasing. Management's unilateral changes to forecasts developed by the sales force undermined the salespersons' efforts and discouraged them from taking the process seriously. Downstream users of the forecast often believed that salespeople developed forecasts that were intentionally high or low in an effort to achieve some other objective – such as assuring adequate product availability or exceeding quota – and thus adjusted the forecasts to align with their beliefs. To create a climate in which salespeople perceive others are taking their forecasting efforts seriously, managers can align sales forecasting processes to mitigate lack of seriousness in the forecast by users and managers by establishing an S&OP process in which a shared interpretation and consensus agreement on the forecast is established.

4.2. Satisfaction with the forecasting process

Quantitative results indicate that the environmental signals explain 15.5% of the variability in salespersons' satisfaction with the forecasting process, with two variables – training and feedback – showing a significant relationship. Salespeople who receive forecast- ing training are significantly more likely to be satisfied with the forecasting process. Training provides direction for the salesperson's forecasting input and thus reduces role ambiguity, thereby increasing satisfaction with the process. Training was the only variable significantly related to both Satisfaction and Seriousness and, as such, is an important factor motivating sales force forecasting. The complexity of processes, techniques, and technology can render forecasting a daunting challenge for those not trained adequately. Salespeople receiving training are more able to identify valuable

sources of demand information and use the forecast as a tool with which to disseminate the information.

In addition, salespeople receiving feedback on the accuracy of their forecasts were found to be more satisfied with the process. Without feedback on forecast accuracy, salespeople are unaware if their efforts are worthwhile or if they need to be improved. Salespeople in the interviews expressing frustration with absence of feedback perceived their forecasting efforts to be “busy work” that added little value and detracted from their selling responsibilities. Conversely, one of the managers in the interviews that provided feedback used forecast accuracy as a tool to motivate salespeople by relating accuracy to customer service levels. Performance feedback can provide a sense of justification for the salesperson's efforts in the forecasting process, thus reducing role ambiguity and increasing satisfaction.

4.3. Forecasting effort

Results from the multiple regression analysis indicate that the environmental signals account for 19.8% of variability in the level of seriousness placed in the forecasting process, with three variables – feedback, knowledge of how the forecast is used, and forecasting computer program – demonstrating a significant relationship. The negative relationship between receiving adequate feedback and forecasting effort is counterintuitive. It is possible that the same level of feedback may be perceived as adequate by salespeople spending the most time on the forecast but inadequate by those spending a lesser amount of effort on the forecast. For example, if those spending less time on the forecast experience lower levels of forecast accuracy than those spending more time, they may perceive the need for more feedback and instruction on what can be done to improve their performance. Based on the positive relationship between feedback and satisfaction and the negative relationship between feedback and effort, it is important for managers to establish a mechanism to determine if the salesperson perceives the level of feedback adequate.

Salespeople who are knowledgeable about how their forecasts are used throughout the company spend significantly more time forecasting than those who are less knowledgeable. These regression analysis results support the qualitative data suggesting that when employees believe their efforts have a direct impact on the company's performance, they are more motivated to allocate time and effort to the task. As discussed in the literature review, motivational theories suggest that employee's efforts toward a task are conditioned by the ability to achieve desirable goals through those efforts (Ramlall, 2004). Managers who create an environment in which salespeople understand the impact of their forecasting efforts on the company – for example encouraging involvement in S&OP meetings – create an expectation of attainment of goals such as improved organizational performance resulting in increased financial rewards. Furthermore, personal achievements such as improved self esteem can result from the knowledge that the salesperson's forecasting efforts are a critical aspect impacting the performance of several processes throughout the organization.

The third environmental signal to exhibit a significant relationship with forecasting effort was access to a forecasting computer program. The fact that salespeople who have access to a forecasting computer program spend more time and effort developing their forecasts than those without a computer program may seem counterintuitive because the system should allow them to work more efficiently. However, the system provides immediate access to abundant information that the salesperson might not be able to consider using a simple forecasting spreadsheet. In addition, as salespeople acquire new demand information from external sources, they may be more likely to utilize the computer system to analyze the impact on demand as they acquire the information rather than waiting to communicate the information until the next period in which a

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forecast update is due. As described in the qualitative research, the salespersons' time and efforts are more substantive and rewarding when making forecasting decisions with the computer program. The significant finding between the environmental signal forecasting computer program and the motivational dimension Effort illustrates that motivational theories can be applied in a sales forecasting context, suggesting that work effort is motivated by the perceived value of the reward.

To summarize, among the 9 environmental signals in the model, 5 significantly impact the motivational dimensions of sales force forecasting: training, feedback, knowledge of how the forecast is used, forecasting computer program, and others' level of seriousness. Three of the environmental signals demonstrated significant relation- ships with two of the motivational dimensions: Training had a significant positive relationship with seriousness and satisfaction; feedback was positively associated with satisfaction but negatively associated with effort; and knowledge of how the forecast is used was positively related to seriousness and effort.

5. Managerial implications

Successfully managing the sales forecasting process involves much more than deciding what techniques and computer system to adopt within an organization. This research provides a model for managers interested in motivating the industrial sales force in their sales forecasting efforts. Recognition of the important role of the sales force in improving the quality of sales forecasts can provide a source of competitive advantage for firms that rely on the forecast to inform other organizational processes such as purchasing, manufacturing, warehousing, capacity planning, and logistics scheduling, to name a few. The survey research revealed five environmental signals that motivate sales force forecasting. Managers investing in these resources can alter the prevailing attitude among salespeople that forecasting is a non-value added activity which constrains the time they can devote to selling.

5.1. Forecasting training

Providing forecasting training for the sales force increases both satisfaction with and seriousness placed on the process. Forecasting training provides structure and direction for the sales force to identify sources of information that impact demand and how that information is most effectively used as an input to the forecast. Training programs can be designed to include those aspects of forecasting that are most relevant to the sales force, such as identifying, gathering, and interpreting qualitative intelligence from customers and other external sources that can impact demand. Salespeople can also be trained to understand how the qualitative customer data can complement a statistically generated forecast. Moon, and Mentzer (1999) found that salespeople's forecasting efforts are most effective when they are asked to alter a statistical forecast rather than create a new forecast. However, to be most effective, salespeople should be trained with a basic understanding of the variables that have been considered in the statistical forecast so any changes they make are adding value rather than duplicating the impact of variables already included.

5.2. Knowledge of how the forecast is used throughout the organization

Educating the sales force on how their forecasting efforts are used throughout the firm increases seriousness placed in and effort devoted to the process. Managers can establish this environmental signal by involving the sales people in cross-functional meetings such as S&OP to illustrate the systemic effect of their forecast on other organizational process decisions. S&OP is the process by which an organization develops a consensus understanding of demand result-

ing in a single-number forecast that drives the operational plans to match supply and demand. Managers are encouraged to involve salespeople in the S&OP process to demonstrate how their sales force forecasting efforts directly impact production and order fulfillment rates. Salespeople who understand that the quality of their forecasting efforts ultimately impacts their order fulfillment and customer service levels will take the process more seriously and devote more effort to producing quality forecasts.

5.3. Feedback on forecasting performance

Providing forecasting feedback increases satisfaction with the process. Without regular reports of forecast accuracy, salespeople have little incentive to strive for improved accuracy. Forecasting management systems have been developed that track the accuracy of forecasting input for each individual that contributes to the forecast. Managers can utilize these system tools to monitor and provide feedback on the accuracy of forecasting efforts among the sales force. Existing forecast accuracy performance can also be used as a benchmark for setting goals for improvement for each salesperson.

The negative relationship found between feedback and forecasting effort indicates that managers must gauge the appropriate amount and content of the feedback based on the salespersons' needs and level of forecast accuracy. If those spending less time on the forecast experience lower levels of accuracy than those spending more time, they may perceive the need for more feedback. Forecasting manage- ment systems can allow managers to customize automated accuracy reports at varying levels and varying frequencies based on the feedback needs of the individual salesperson, and eliminate the burdensome responsibility associated with manual manipulation of the numbers.

5.4. Access to a forecasting computer program

Fourth, managers are also encouraged to make the investment in a forecasting computer program that can assist the sales force in effectively organizing, analyzing, and communicating information that can impact demand. Providing forecasting software tools that are interfaced with the company's central database enables salespeople to complete their forecasting responsibilities efficiently. For example, forecasting software packages eliminate the need for manual input of historical data thereby reducing potential for error, and improve the ability for salespeople to view and analyze multiple dimensions of the forecast, such as by product category, customer, channel, time horizon, or geographic region, to name a few. As described in the interview, the forecasting efforts become more “substantive” and “rewarding” with a computer program as more effort is dedicated to understanding the customer and the business rather than trying to determine how to manage complex sets of data. However, sales managers are cautioned against assuming a forecasting software package is a panacea for all forecasting problems. The technology becomes useful when it is used as a tool to enhance an existing forecasting process (Mentzer, and Moon, 2005).

5.5. Level of others' seriousness placed in the salesperson's forecast

Finally, if managers want their salespeople to take the forecasting process more seriously, it is important to ensure that others in the organization also regard the salespeople's forecasting efforts serious- ly. Once again, the S&OP meeting is a forum in which the content and quality of the sales force's forecasts can be shared with a cross- functional group to increase awareness of and seriousness placed in the forecast. Sales force involvement in S&OP may reduce the incidence of game-playing and thus reduce the skepticism with which many users of the forecast view the salespersons' efforts, and thereby increase the level of seriousness conferred upon the forecast.

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In addition, communicating the rationale behind “executive over- rides” that alter a salesperson's forecast will “close the loop” and reduce the perception that the salesperson's forecasting efforts are not taken seriously.

To our knowledge, this is the first study to identify variables that specifically motivate sales force behavior related to sales forecasting. The large majority of research on sales force forecasting has focused on the salesperson's use of a specific technique or practice rather than an overall assessment of how managers can motivate the industrial sales force to improve their forecasting efforts. The lack of research related to sales forecasting motivation may explain why, in spite of the significant findings from our quantitative study, many companies are not employing these environmental signals in practice. An implication is that, although not addressed in this study, training sales managers in state-of-the-art forecasting and coaching techni- ques may be necessary before implementation of the five environ- mental signals can be effective. Ultimately, our research suggests that organizations that devote managerial and financial resources to provide the environmental signals identified in this study foster an atmosphere in which salespeople are more satisfied with and spend more effort on the forecasting process and take the process more seriously.

6. Theoretical implications

Our paper developed a theory of industrial sales force forecasting. The theory suggests that salespeople are more motivated to perform their forecasting responsibilities when the necessary environmental signals are provided. Given that studies over the last thirty years have shown no real improvement in sales forecasting accuracy (McCarthy et al., 2006), and given the fact that a preponderance of sales forecasting research during that time period has concentrated on improved techniques and systems, perhaps less research attention should be paid to the techniques and systems of sales forecasting, and more to the management of the function. This is the major theoretical implication of this study. Industrial salespeople have considerable insight to provide to the sales forecasting process, if their input is properly managed, rewarded, and supported. This fact deserves considerable future theory development and testing.

The finding that compensation and performance evaluation were not significantly related to the motivational dimensions of industrial sales force forecasting was surprising given the frequency with which they were mentioned in the interviews. In addition, abundant research on sales force productivity focuses on how judicious design of compensation plans align salespeople's goals with organizational objectives and, thus, motivate behavior to achieve desired outcomes (Brown et al., 2005). However, an explanation for the lack of significance for compensation as a motivator of forecasting behavior may be found in some of the other environmental signals that were found to be significant. For example, salespeople who, (a) are properly trained to gather and incorporate customer data into the forecast, (b) receive appropriate levels of feedback in the form of forecast accuracy measures on the effectiveness of their efforts, and (c) understand the impact of their forecasting efforts on resource allocations throughout the organization, may be more likely to understand that the forecasting process can be used as a tool to learn more about and consequently develop a better relationship with their customers, resulting in increased sales. That is, the forecasting process becomes a tool to align supply with demand, increase sales and, in turn, increase commissions and bonuses within existing compensation structures and negates the need for additional motivation in terms of alternative compensation structures specific to forecasting.

We found that training, feedback, knowledge of how the forecast is used, forecasting computer program, and others' level of seriousness have considerable impact on salesperson seriousness, satisfaction, and effort in sales forecasting. This suggests further theory development

addressing what other factors are antecedent to seriousness, satisfaction, and effort, and what are the forecasting and firm consequences of increasing seriousness, satisfaction, and effort. This theoretical development (through additional qualitative methods) and testing of the resultant theories (through quantitative methods) should provide practitioners with additional insights on how to manage the industrial marketing salesperson input to sales forecast- ing, and what benefits these improvements have for firm and supply chain performance. This, in turn, should lead to a wealth of future research.

7. Directions for future research

Our research employed a theory-building approach to identify factors that motivate sales force forecasting. Extant literature on the general topic of sales force motivation was placed in a sales forecasting context through the use of depth interviews, and a survey was developed and administered based on the findings in the literature and interviews. The interviews revealed that many variables believed to motivate behavior are not employed in a forecasting context, and the survey results produced some insignificant findings that have been found significant in other contexts. Specifically, several researchers have found compensation and performance evaluation to be strong motivating factors for salespeople (Brown et al., 2005; Mantrala, and Raman, 1990; Mentzer, and Moon, 2005; Ramlall, 2004), but this research found no significant relationship. Future research should explore in more detail the nature of the compensa- tion and performance evaluation used in the sales force forecasting process to determine if certain approaches are more successful than others in motivating forecasting behavior.

Findings from our depth interviews revealed that many salespeo- ple are unaware of the level of accuracy of their forecasts. Capturing the impact of environmental signals and motivational dimensions on forecast accuracy is another important approach to understanding and improving sales force forecasting. Structural equation modeling can be used to examine the strength of relationships among the environmental signals, motivational dimensions, and forecast accu- racy performance. Furthermore, a longitudinal study capturing accuracy levels before and after implementation of environmental signals could confirm the direction of the relationships among the variables, revealing important insights for motivational researchers and for managers attempting to motivate sales force forecasting performance.

It is also important that future research into sales force forecasting work to more fully understand the impact that enhanced salesperson performance has on important corporate outcomes. Ultimately, there needs to be a clear link between the resources that are expended to improve the quality of salesperson contributions to forecasting performance and enhanced customer service levels, reduced costs, and profitability. Both managers and academics will benefit from empirical insight into the relationship between salesperson forecast- ing performance and these broader corporate performance metrics.

Finally, additional work is needed to develop and refine reliable and valid measures of some of the constructs introduced here. The survey instrument developed for this study was designed to be exploratory in nature, and given this objective, each of the nine environmental signals, as well as the three motivational dimensions identified in Fig. 1 are measured using single-item measures. The use of single-item dichotomous variables is a limitation of the current research. More robust, multi-item measures that tap into all dimensions of the constructs can only lead to more precise tests of the relationships explored, and may generate significant relationships between the environmental signals and motivational dimensions that were insignificant in our study. Once this stream of research moves into a theory testing phase, more robust measures of these constructs should be developed.

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Appendix A. Measures

Environmental signals

Frequencies

V1 Compensation: Is any part of your compensation tied in any way to the quality of your forecast? [0=no, 1=yes]

No=162, Yes=52

V2 Performance Evaluation: Is your performance evaluation based in any way on the quality of your forecast? [0=no, 1=yes]

No=120, Yes=94

V3 Use of Environmental Conditions: Has your company provided you with environmental information (i.e., pricing policies, industry trends, economic forecasts, etc.) to help you do your forecasting? [0=no, 1=yes]

No=128, Yes=87

V4 Use of Judgment: Do you use your own judgment to help you forecast? [0=no, 1=yes]

No=28, Yes=187

V5 Training: Has your company provided you with forecasting training to help you do your forecasting? [0=no, 1=yes]

No=185, Yes=30

V6 Feedback: Do you think you receive adequate feedback on the accuracy of your forecasts? [0=no, 1=yes]

No=87, Yes=126

V7 Knowledge: How much knowledge do you have about what is done with your forecasts after you submit them? [response option: (1) no knowledge to (5) a great deal of knowledge]

1=5, 2=22, 3=76, 4=60, 5=51

V8 Forecasting Computer Program: Has your company provided you with specific forecasting computer programs to help you do your forecasting? [0=no, 1=yes]

No=181, Yes=34

V9 Others' Level of Seriousness: How seriously are the forecasts that you provide taken by others in your organization? [response option: (1) not at all seriously to (5) extremely seriously]

1=3, 2=17, 3=73, 4=74, 5=41

Motivational dimensions

Y1 Seriousness: How seriously do you take your forecasting responsibilities? [response options – (1) not at all seriously to (5) extremely seriously]

Range=1 to 5, Mean=3.60, SD = .904

Y2 Satisfaction: Overall, how satisfied are you with the forecasting process that you participate in? [response options – (1) extremely dissatisfied to (5) extremely satisfied]

Range=1 to 5, Mean=3.09, SD = .857

Y3 Effort: Approximately how much time do you spend forecasting in an average month [open-ended response option: number of hours per month]

Range=0.5 to 100, Mean=5.26, SD=9.44

References

Agarwal, S. (1999). Impact of job formalization and administrative controls on attitudes of industrial salespeople. Industrial Marketing Management, 28, 359−368.

Bagozzi, R. P. (1978, Novemberr). Sales force performance and satisfaction as a function of individual difference, interpersonal, and situational factors. Journal of Marketing Research, 15, 517−531.

Baldauf, A., & Cravens, D. W. (2002). The effect of moderators on the salesperson behavior performance and salesperson outcome performance and sales organiza- tion effectiveness relationships. European Journal of Marketing, 36(11/12), 1367−1388.

Barash, M., & Mitchell, D. H. (1998). Account based forecasting at Nabisco Biscuit Company. Journal of Business Forecasting, 17(2), 3−6.

Behrman, D. N., & Perreault, W. D. (1984). A role stress model of the performance and satisfaction of industrial salespersons.Journal of Marketing, 48, 9−12 Fall.

Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: John Wiley and Sons.

Brown, S. P., Evans, K. R., Mantrala, M. K., & Challagalla, G. (2005). Adapting motivation, control, and compensation research to a new environment. Journal of Personal Selling and Sales Management, 25(2), 155−167.

Brown, S. P., & Peterson, R. A. (1993, Februaryy). Antecedents and consequences of salesperson job satisfaction: meta-analysis and assessment of causal effects. Journal of Marketing Research, 30, 63−77.

Chakrabarty, S., Oubre, D. T., & Brown, G. (2008). The impact of supervisory adaptive selling and supervisory feedback on salesperson performance. Industrial Marketing Management, 37, 447−454.

Champagne, P. J., & McAfee, R. B. (1989). Motivating strategies for performance and productivity: A guide to human resource development. New York: Quorum Books.

Churchill, G. A. (1979, Februaryy). A paradigm for developing better measures of marketing construct. Journal of Marketing Research, 16, 64−73.

Churchill, G. A., Ford, N. M., Hartley, S. W., & Walker, O. C. (1985, Mayy). The determinants of salesperson performance: a meta-analysis. Journal of Marketing Research, 22, 103−118.

Cox, J. E. (1989). Approaches for improving salespersons' forecasts. Industrial Marketing Management, 18, 307−311.

Dalrymple, D. J. (1975, Decemberr). Sales forecasting methods and accuracy. Business Horizons, 18, 69−73.

Dalrymple, D. J. (1987). Sales forecasting practices. International Journal of Forecasting, 3 (3&4), 379−391.

Demers, J. (2003). Networked knowledge. CMA Management, 76(10), 43−46. Fildes, R., & Beard, C. (1992). Forecasting systems for production and inventory control.

International Journal of Operations and Production Management, 12(5), 4−27. Hackman, J. R., & Oldham, G. R. (1980). Work redesign. Reading, Massachusetts: Addison

Wesley. Hair, J. F., Black, B., Babin, B., Anderson, R. E., & Tatham, R. L. (2005). Multivariate data

analysis (6th Edition). Englewood, NJ: Prentice Hall. Harris, K., & Pike, J. (1996). Issues concerning adoption and use of sales force automation in

the agricultural input supply sector. Agribusiness, 12(4), 317−326. Humphreys, J. H., & Einstein, W. O. (2004). Leadership and temperament congruence:

extending the expectancy model of work motivation. Journal of Leadership and Organizational Studies, 10(4), 58−79.

Igalens, J., & Roussel, P. (1999). A study of the relationships between compensation package, work motivation and job satisfaction. Journal of Organizational Behavior, 20 (7), 1003−1025.

Ingram, T. N., LaForge, R. W., Avila, R. A., Schwepker, C. H., Jr., & Williams, M. R. (2001). Sales management: Analysis and decision making, 4th ed.

Jantan, M. A., Honeycutt, E. D., Thellen, S. T., & Ashraf, M. A. (2004). Managerial perceptions of sales training and performance. Industrial Marketing Management, 33, 667−673.

Jones, E., Brown, S. P., Zoltners, A. A., & Weitz, B. A. (2005). The changing environment of selling and sales management. Journal of Personal Selling and Sales Management, 25(2), 105−111.

Johlke, M. C., Duhan, D. F., Howell, R. D., & Wilkes, R. W. (2000). An integrated model of sales managers' communication practices. Journal of the Academy of Marketing Science, 28(2), 263−277.

Locke, E. A., & Latham, G. P. (1990). In U. Kleinbeck (Ed.), Work motivation: the high performance cycle, in Work Motivation. Hillsdale, NJ: L. Erlbaum.

Makridakis, S., & Wheelwright, S. C. (1977, Octoberr). Forecasting: issues & challenges for marketing management: a framework for relating the available techniques to specific situations. Journal of Marketing, 24−38.

Mantrala, M. K., & Raman, K. (1990). Analysis of a sales force incentive plan for accurate sales forecasting and performance,. International Journal of Research in Marketing, 7, 189−202.

Matsuo, M., & Kusumi, T. (2002). Salesperson's procedural knowledge, experience and performance: an empirical study in Japan. European Journal of Marketing, 36(7/8), 327−330.

McCarthy, T. M., & Golicic, S. L. (2002). Implementing collaborative forecasting to improve supply chain performance. International Journal of Physical Distribution and Logistics Management, 32(6), 431−454.

McCarthy, T. M., Davis, D. F., Golicic, S. L., & Mentzer, J. T. (2006, Augustt). The evolution of sales forecasting management: a 20-year longitudinal study of forecasting practices. Journal of Forecasting, 25, 303−324.

McCracken, G. (1988). The Long Interview. Beverly Hills, CA: Sage Publications, Inc.. Mentzer, J. T., & Kahn, K. B. (1995a). A framework of logistics research. Journal of Business

Logistics, 16(1), 231−249. Mentzer, J. T., & Kahn, K. B. (1995b). Forecasting technique familiarity, satisfaction, usage,

and application. Journal of Forecasting, 14, 465−476. Mentzer, J. T., & Moon, M. A. (2005). Sales forecasting management: A demand management

approach. Thousand Oaks, CA: Sage Publications. Mitchell, T. R., & Nebeker, D. M. (1973). Expectancy theory predictions of academic effort

and performance. Journal of Applied Psychology, 57, 61−67. Moon, M. A., & Mentzer, J. T. (1999). Improving sales force forecasting. Journal of Business

Forecasting Methods & Systems, 18(No. 2), 7−12. Moon, M.A., Mentzer, J.T., & Thomas, D.E., Jr. (2000). CustomerdemandplanningatLucent

Technologies. Industrial Marketing Management, 29, 19−26. Morgan, N. A., Anderson, E. W., & Mittal, V. (2005, Julyy). Understanding firm's customer

satisfaction information usage. Journal of Marketing, 69, 131−151. Nissenbaum, H. (1998). A grounded approach to social and ethical concerns about

technology and education. Journal of Educational Computing Research, 19(4), 411−432. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. New York: Oxford

University Press. Peterson, R. T. (1993). Improving sales force composite: forecasting by using scripts.

Journal of Business Forecasting, 12(3), 10−14. Peterson, R. T. (1988). How efficient are salespeople in surveys of buyer intentions.

Journal of Business Forecasting, 7(1), 11−12. Pinder, C. C. (1997). Work motivation in organizational behavior. Upper Saddle River, NJ:

Prentice-Hall. Porter, L. W., & Lawler, E. E. (1968). Managerial attitudes and performance. Homewood,

IL: Richard D. Irwin, Inc. Ramlall, S. (2004). A review of employee motivation theories and their implications for

employee retention within organizations. Journal of the Academy of Business, 5(1/2), 52−63.

Rapp, A., Ahearne, M., Mathieu, J., & Schillewaert, N. (2006). The impact of knowledge and empowerment on working smart and working hard; the moderating role of experience. International Journal of Research in Marketing, 23, 279−293.

138 T.M. McCarthy Byrne et al. / Industrial Marketing Management 40 (2011) 128–138

Reese, S. (2000). The human aspects of collaborative forecasting. Journal of Business Forecasting, 19(4), 3−9.

Reid, R. A. (1985). How to set up a forecasting process. Journal of Business Forecasting, 4 (4), 9−10.

Reynolds, P. D. (1971). A primer in theory construction. New York: Macmillan. Riehm, S. R. (2001). The Tao of forecasting with promotional events. Journal of Business

Forecasting, 20(1), 21−23. Sanders, N. R., & Ritzman, L. P. (2004). Integrating judgmental and quantitative

forecasts: methodologies for pooling marketing and operations information. Journal of Operations and Production Management, 24(5/6), 514−529.

SAS Institute Inc., SAS for Windows, Cary, NC., Version 9.1, 2002. Schendel, D. E., & Hofer, C. W. (1979). Strategic management: A new view of business

policy and planning.Brown and Company, Boston: Little 1979. Schwartz, S. H. (1977). In L. Berkowitz (Ed.), Normative influences on altruism, in

Advances in Experimental Social Psychology. New York: Academic Press. Shiarella, A. H. (January, 1998), Scale of Community Service Attitudes for College Students.

Paper presented at the Annual Meeting of the Southwest Educational Research Association, Houston, TX. Academic Press.

Småros, J., & Hellström, M. (2004). Using the assortment forecasting method to enable sales force involvement in forecasting. International Journal of Physical Distribution and Logistics Management, 34(1/2), 140−157.

Sriram, V., Krapfel, R., & Spekman, R. (1992, Decemberr). Antecedents for buyer-seller collaboration: an analysis from the buyer's perspective. Journal of Business Research, 25, 303−320.

Steers, R. M., & Porter, L. W. (1983). Motivation & Work Behavior, Third ed. . New York: McGraw-Hill.

Stevens, S. (1996). Serious work: students learning from students. Journal of Composition Theory, 16(2), 313−324.

Teas, K. R., & Horrell, J. F. (1981, Februaryy). Salespeople satisfaction and performance feedback. Industrial Marketing Management, 10, 49−57.

Webby, R., & O'Connor, M. (1996). Judgmental versus statistical time series forecasting: a review of the literature. International Journal of Forecasting, 12, 91−118.

Wotruba, T. R., & Thurlow, M. L. (1976). Sales force participation in quota setting and sales forecasting. Journal of Marketing, 40(2), 11−16.

Vroom, V. H. (1964). Work and motivation. New York: Wiley.

Teresa M. McCarthy Byrne is an Associate Professor of Marketing at Bryant University. Her current research projects explore the degree to which firms integrate activities with other firms in their supply chain, such as collaborative forecasting, demand management, and demand planning. Her research has been published in The Journal of Forecasting, International Journal of Physical Distribution and Logistics Management, Transportation Journal, Foresight, Journal of Marketing Education, and Journal of Business Forecasting. Prior to earning her Ph.D. from the University of Tennessee, Knoxville, Dr. McCarthy Byrne worked in the retail industry for 14 years in forecasting and inventory planning and control.

Mark A. Moon is an Associate Professor of Marketing and Director of the Sales Forecasting Management Forum at the University of Tennessee, Knoxville. Prior to joining the UT faculty in 1993, Dr. Moon earned his Ph.D. from the University of North Carolina at Chapel Hill. He also holds MBA and BA degrees from the University of Michigan in Ann Arbor. He has published in the International Journal of Forecasting, Supply Chain Management Review, Foresight, Journal of Personal Selling and Sales Management, Journal of Business Forecasting, Journal of Marketing Education, Marketing Education Review, Business Horizons, Industrial Marketing Management, Journal of Marketing Theory and Practice, and several international conference proceedings. Dr. Moon is also the author, along with Dr. John T. (Tom) Mentzer of Sales Forecasting Management: A Demand Management Approach.

John T. Mentzer (1951-2010) was a Chancellor's Professor and the Harry J. and Vivienne R. Bruce Excellence Chair of Business in the Department of Marketing and Logistics at the University of Tennessee. He published 9 books and more than 200 articles and papers in the Harvard Business Review, Industrial Marketing Management, Journal of Business Logistics, Journal of Marketing, International Journal of Physical Distribution and Logistics Management, Journal of Business Research, Transportation and Logistics Review, Transportation Journal, Journal of the Academy of Marketing Science, Columbia Journal of World Business, Research in Marketing, Business Horizons, and other journals. He was the past president of the Academy of Marketing Science and the Council of Supply Chain Management Professionals (then the Council of Logistics Management). He was the 2004 recipient of the Council of Logistics Management Distinguished Service Award, the 2007 recipient of the International Society of Logistics Armitage Medal, and the 2008 recipient of the Academy of Management Science Distinguished Service Award.