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This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information
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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, USAb University of Tennessee, Department of Marketing and Logistics, 324 Stokely Management Center, Knoxville, TN 37996, USA
a b s t r a c ta r t i c l e i n f o
Article history:Received 14 July 2008Received in revised form 2 June 2009Accepted 10 May 2010Available online 10 July 2010
Keywords:Sales forecastingMotivationJob satisfactionJob seriousnessForecasting training
Previous researchhas recognized thevalue of the industrial salesperson's role in the sales forecastingprocess, andoffered normative descriptions of what that role should be. However, no studies have been conducted todetermine the variables that motivate industrial sales force involvement in and contribution to the salesforecasting process. This study employed depth interviews and survey research to develop and test a conceptualmodel of industrial sales force forecastingmotivation. The research identifies five environmental signals that canbe employed bymanagers to impact an industrial salesperson's level of satisfactionwith, effort directed towards,and seriousness placed in the sales forecasting process.
The integral role of sales forecasting in the corporate planningprocess and as a contributor to corporate success is widely recognized(Dalrymple, 1975, December, Fildes, and Beard, 1992; Makridakis, andWheelwright, 1977, October; Mentzer, and Moon, 2005; Reid, 1985).The critical role of an organization's sales force in developing theforecast 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). Forexample, a company's salespeople can contribute valuable informationto the sales forecastingprocessbyacquiringmarket intelligence throughdirect conversations with their customers. Indeed, research has shownthat sales force composite continues to be a popular and frequently usedforecasting technique (Dalrymple, 1987; McCarthy et al., 2006, August;Mentzer, andKahn, 1995a). Furthermore, the trend towardpartnershipswithin the supply chain has fueled the practice of collaborativeforecasting (McCarthy, andGolicic, 2002; Sriram,Krapfel, and Spekman,1992, December), and the sales force fulfills this boundary-spanningforecasting function between the organization and its downstreamsupply chain partners.
Despite the important role of the industrial sales force in salesforecasting, research illustrates that many salespeople are unmotivatedto perform their forecasting responsibilities, arising from a belief that
time spent on forecasting usurps time that should be spent developingcustomer relationships and selling products (Moon, and Mentzer,1999). Researchon sales force forecasting is largely devoted to empiricalstudies examining the salesperson's impact on forecast accuracy relatedto the use of a specific technique or practice, such as sales forcecomposite (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, several applied cases describe thenatureof the salesperson'sinvolvement 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 studiesrecommend strategies for improving sales force forecasting perfor-mance. Notwithstanding the contribution of these studies to increasingour knowledge of the role of salespeople in sales forecasting, there is adearth of research focused on identifying the specific factors thatmotivate and direct industrial sales force behavior in the salesforecasting process. Our study fills this gap in the literature byidentifying and testing variables that impact sales force motivation inthe sales forecasting process. The purpose of this paper is to develop atheory of industrial sales force forecasting that will informmanagers ofthe controllable variables that can motivate sales force involvement intheir forecasting responsibilities.
The following section reviews the theory building approach andmethodologies applied in this paper. In the next section we employqualitative interviews and literature review to identify the variablesand develop a conceptual model relevant to motivating the industrialsales force in the sales forecasting process. Subsequently, we developan exploratorymeasurement instrument to empirically test themodelusing survey methodology. Results of the exploratory survey are
presented and discussed, followed by implications and suggestions forfuture research.
2. Theory building
Our research follows the Reynolds (1971) three-step approach totheory 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 thatresearchers begin the idea generation process with observation andliterature review. As such, in this multi-method study, we employobservation from depth interviews, literature review, and a surveyquestionnaire to build theory.
Specifically, for the exploratory stage of theory building, our studyemploys observation and analysis of the sales forecasting manage-ment practices at 24 industrial marketing companies to “identify thephenomena of interest and describe its key characteristics” (Schendel,and Hofer, 1979, p. 385.), as well as a review of the literature related tosales forecasting and industrial sales force motivation. Our descriptivestage involves developing the theoretical model and hypothesesexplaining the relationships between variables identified in the firststage. The explanatory stage empirically tests the relationshipshypothesized in the second stage using exploratory survey method-ology. Following the description and findings of each stage of theresearch presented below, discussion, implications, and recommen-dations for industrial marketing managers and for future research arepresented.
2.1. Exploration and description
Depth interviews were conducted following conventional protocolsdeveloped by Churchill (1979, February) and McCracken (1988) at 24industrialmarketing companies toobserve sales forecastingmanagementpractices. Informed by persistent themes emerging from the interviews,we reviewed the extant literature on industrial sales forcemotivation andsales forecasting practices to provide a theoretical foundation for theinterview themes. This dual approach to exploration allows fortriangulation whereby the researcher is constantly tacking back andforth among the data sources – including the interview data and relevantliterature – to discern the dimensions associated with sales forceforecasting, and to facilitate development and refinement of constructsand measures for the descriptive stage of theory building (Morgan,Anderson, andMittal, 2005, July). Specifically, in this section, we conductexploration by reviewing the qualitative interviewdata and the sales forcemotivation and sales force forecasting literature to identify relevantsituational and motivational variables, and description by discerningpatterns and making empirical generalizations in the form of identifyingrelationships among the variables. We explore the situational variablesthat lead to increasedmotivation in industrial sales force forecasting, andthen examine the specific motivational dimensions related to salesforecasting productivity. The variables that emerged from the depthinterviews and the sales force motivation and forecasting literaturereview, and the relationships between those variables, are shown in Fig. 1.
The companies that participated in the interviews span a variety ofindustries,2 and are representative of all levels in the industrial
marketing supply chain: raw material suppliers, manufacturers, anddistributors. At these companies, over 1000 individuals from acrossmultiple functional areas were interviewed, including sales people,sales managers, and sales executives. The roles these individuals playin the forecasting process were explored in depth, as were issuessurrounding forecasting systems utilized, performance measurement,customer participation in sales forecasting, feedback, training, andhow sales forecasts are used in the firm. The following findings fromthe interviews and literature suggest that the presence of certainsituational variables increases industrial sales force motivation in thesales forecasting process.
2.1.1. Situational variablesBagozzi (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 toindicate the environmental signals that coerce, facilitate, constrain ormotivate industrial sales force forecasting. Environmental signals arecomprised of managerial and financial resources provided by theorganization 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 asimpacting industrial sales force motivation in the sales forecastingprocess. The discussion for each variable begins with presentationof the depth interview findings followed by a review of therelevant literature in industrial sales force motivation and salesforce forecasting.
2.1.1.1. Compensation and performance evaluation. One theme thatpersistently emerged from interviews within every company wasrelated 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 tospend considerable time and energy forecasting, although theirefforts were neither measured nor rewarded for this activity. Theimpact of the absence of forecasting compensation and performanceevaluation was manifest in the salespeople's resistance towardsforecasting responsibilities, which were perceived as an unnecessarycomplexity beyond the scope of their sales function. Furthermore,individuals within the firm who are downstream users of theforecasts developed by the sales organization frequently expresseda lack of confidence in the forecasts, often because they perceived alack of accountability.
Research has shown that salespeople often resent having to performtheir forecasting responsibilities because they consider sales forecastingas ancillary to and incompatiblewith their primary function,which they
Fig. 1. Conceptual model of industrial sales force sales forecasting motivation.
2 Interviews were conducted at the following industrial marketing companies: AETFilms, 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 PacificRailroad.
believe is to sell (Harris, and Pike, 1996; Mentzer, and Moon, 2005). Inan environment in which the role of the sales force is characterized asincreasingly complex (Jones, Brown, Zoltners, and Weitz, 2005),inclusion of forecasting responsibilities to the role expectations ofsalespeople can result in feelings of role incongruity and incompatibility(Behrman, and Perreault, 1984, p. 12). Incongruity and incompatibilityof expectations often stem from a failure of incentive structures such ascompensation and performance evaluation to encompass and therebymotivate the expected performance (Brown, Evans, Mantrala, andChallagalla, 2005). In these situations, the importance of the salespersonin the forecasting process and the resultant organizational performanceimplications must be made evident to mitigate feelings of roleincongruity. Thus, developing compensation schemes and performanceevaluations incorporating incentives for forecast accuracy is oneapproach to underscoring the importance of sales force forecastingaccountability (Mantrala, and Raman, 1990). Based on the interviewdata and literature review, compensation for and performanceevaluation of forecasting responsibilities are environmental signalsthat 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 internaland external environmental factors that impact demand. Informationregarding endogenous factors that were noted as important includedthe timing of new product introductions, and changes in organiza-tional marketing expenditures and pricing policies. Among theexogenous conditions identified by informants were industry trends,competitive trends, and economic forecasts. The perceived impor-tance of access to environmental conditions was emphaticallyexpressed by the interview informants, particularly when theinformation salespeople desired was neither accurate nor timely.One forecast analyst described the reaction of salespeople when askedto submit a 12 month forecast without sufficient information; “Theyhate it…. It's like pulling teeth.… They aren't given enoughinformation regarding pricing and new products to create an accurate12 month forecast.” As described by one salesperson recognizing theinadequacy of the information supplied, “the forecast becomes ajoke.” In situations where salespeople were not given accurate andtimely information, they clearly expressed dissatisfaction with theprocess, took the process less seriously, and spent less time and effortdeveloping their forecasts.
Research indicates that a determinant of salesperson performancerelates to the level and quality of information and knowledge neededto 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) supportsthe concept advanced by the knowledge-based view of the firmsuggesting that “knowledge is the critical characteristic which enablessalespeople to cope effectively with their dynamic, competitiveenvironments” (Rapp, Ahearne, Mathieu, and Schillewaert, 2006, p.281). Knowledge of endogenous and exogenous environmentalconditions are resources that can be used by salespeople to informthe forecasting process (Moon, and Mentzer, 1999).
2.1.1.3. Use of judgmental input. Most salespeople in the interviewsrecognized the value they could add to the forecast stemming frominformation collected during formal and informal conversations withcustomers. Examples of judgmental information that informants usedas input to the forecast include perceived probability of opening newor closing existing accounts, conversations with customers aboutorders placed with competitors due to the focal firm's inability tomeet the customer's demand, changes in the customers' sellingefforts, changes in competitors' marketing activity, and probabilitiesof securing large orders.
Several studies have recognized the importance to the forecastingprocess of the salespersons' subjective or judgmental input stemmingfrom conversations with customers and exposure to other externalenvironments (Dalrymple, 1987; McCarthy, and Golicic, 2002;Mentzer, and Kahn, 1995b; Sanders, and Ritzman, 2004). Theexposure salespeople have to customers and other externalitiesoften results in aggregation of a tacit and disperse knowledge basethat can be used as a valuable source of input to the forecast, such asthose identified above during the interviews. This type of tacitinformation has been shown to add value to the forecast under certainconditions, particularly in changing environments and when littlehistorical data exist (Webby, and O'Connor, 1996).
Although salesperson judgment was noted as critical to effectivesales forecasting in both the interviews and literature, a key issue withjudgmental input that emerged from the interviews was the “game-playing” that often takes place when salespeople contribute tojudgment based forecasts. The interviews identified two categoriesof game-playing in which salespeople often engage. First, if salespeo-ple perceive that their judgment-based forecasts will affect theirquotas or targets, they will underestimate future demand so thatthose targets are more easily reached. Second, if salespeople perceivethat product shortages are likely, they will overestimate futuredemand so that when allocations are made, their customers willreceive the actual quantity desired. In either case, the credibility of thejudgmental input developed by the sales force is compromised bysuch game-playing. Therefore, although the literature suggests thatthe use of judgmental input is an environmental signal motivatingsales force forecasting, the interview results were equivocal.
2.1.1.4. Forecasting training. Another situational variable that emergedfrom the interviews is related to forecasting training, or moreprecisely, the lack thereof. In most companies, both salespeople anddownstream users of the forecast expressed frustration with theabsence of forecasting training for salespeople, which they believedwould improve sales forecasting efforts. Very few of the 24 companiesprovided any formal sales forecasting training for their salespeople. Inmost cases, forecasting was an afterthought, and any training that didtake place was to informally show salespeople how to input theirnumbers. In fact, in many of the companies participating in theinterviews, salespeople were unaware of what happened to theirforecasts after they were submitted. Thus, not only are salespeoplefrequently not trained on how to forecast, they are frequently nottrained on why they forecast.
Ingram, LaForge, Avila, Schwepker, andWilliams (2001) discuss theimportance of sales force training to achieve role clarification; that is,a clear understanding of what tasks are to be performed, how they areto be performed, and how such efforts lead to improved outcomes.Jantan, Honeycutt, Thellen and Ashraf (2004) suggest that a strongcommitment to sales training improves role perceptions andsalesperson performance. Ramlall (2004) asserts that organizationscannot afford to passively assume that sources of knowledge are beingaccessed and utilized throughout an organization, such as the use ofjudgmental information obtained by salespeople during conversa-tions with customers. Sales managers that train the sales force inapplication of the various sources of knowledge in the salesforecasting process work toward reducing role ambiguity. Trainingin knowledge management practices, including coaching, mentoring,and workshops for knowledge sharing, is considered essential for acompany to translate tacit knowledge into more objective andcodified explicit knowledge (Demers, 2003; Nonaka, and Takeuchi,1995). For example, McCarthy, and Golicic (2002) found thatjudgmental input improved sales forecast accuracy when firmsactively commit resources to train salespeople in intelligencegathering and forecasting. Therefore, we include forecasting trainingas an environmental signal that motivates industrial sales forceforecasting.
2.1.1.5. Feedback on forecasting performance. Another environmentalsignal that was evidenced as a pain point due to its absence is feedbackfrommanagers on forecasting performance. Very few of the companiesprovided individual salespeople with concrete forecasting performancedata, although many informants indicated that they desired theinformation and expressed frustration with the absence of feedback.Among the companies that did measure and communicate forecastaccuracy, salespeople were readily able to recall their level of accuracyand often discussed targeted improvement goals. In many cases, lack offeedback resulted from lack of systems capability to measure forecastaccuracy at the individual salesperson level. The interviews revealedthat if individual measurement took place, it often required manipula-tion of data outside the formal forecasting system, and it was the rarecompany that went to this length to provide such feedback.
Locke, and Latham (1990) identify managerial feedback as animportant motivator of performance. Clear and frank supervisoryfeedback provides clarification of performance expectations leadingto increased job satisfaction, effort, and performance (Brown, andPeterson, 1993, February; Chakrabarty, Oubre, and Brown, 2008,Humphreys, and Einstein, 2004, Teas, and Horrell, 1981, February). Aformal process providing salespeople with feedback about salesforecast accuracy or the reasons for inaccuracy is a tool that can beused by salespeople to adjust their performance, and by managementto emphasize accountability for forecast accuracy performance.
2.1.1.6. Knowledge of how the forecast is used throughout theorganization. In some companies involved in the interviews, processeswere established to ensure that salespeople understood the impact oftheir forecasting efforts on corporate performance. These companiesdiscussed having regularly scheduled sales and operations planning(S&OP) meetings attended by salespeople, resulting in a consensusunderstanding of the forecast and how it was used to inform resourceallocation decisions throughout the company. In other companies, theinterview data revealed a common lack of understanding among thesalespeople concerning the impact of their forecast on the supplychain. In these companies we observed a cultural disconnect betweenthe demand and supply sides of the enterprise, and salespeopleshowed little understanding of the effects their forecasts had onproduction planning, procurement, logistics planning, and otheraspects of supply chain management. In one example, salespeoplewere no longer invited to attend S&OP meetings because their inputwas “disruptive.” As a result, one salesperson commented, “I don'tcare 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 ofunderstanding regarding how the salespeople's forecasts are usedthroughout the company results in a lack of motivation in theforecasting process.
In his research on motivation in the workplace, Pinder (1997)asserts that overall effectiveness of employees is significantly relatedto the perceived importance of performing a task. Specifically,employees who believe that their contribution in performing a taskimpacts the entire organization will be motivated to perform the taskmore effectively. Salespeople's perceptions of the importance of theforecast explain the dichotomy in levels of engagement with theforecasting process between those who understand the impact oftheir forecasting efforts on corporate performance and those who donot. Therefore, managers are enjoined to educate salespeople aboutthe use of their forecast within the company and the impact of theforecast throughout the organization.
2.1.1.7. Access to a forecasting computer program. Thus far, theinterview data have identified multiple sources of information thatcan 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, andanalyzing the abundant sources of data can become a daunting task,resulting in underutilization of the data. Among the participatingorganizations, 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 aspace to enter their adjustments to the system-generated forecast aswell as a space to note their reasons for making adjustments. At thiscompany, the tools greatly supported the process. More commonly,many companies reported reliance on spreadsheets to create andshare forecasts for which a common procedure and data source werenot utilized. The abundance of personal spreadsheet forecasts wascharacterized by one firm as “spreadsheet mania.” At other compa-nies, salespeople were simply asked to send an e-mail to theforecasting department if they knew of anything occurring thatmight affect future demand. Clearly, at these companies, system toolswere not provided to enhance the salespeople's forecasting perfor-mance. One salesperson who had experience with both spreadsheetforecasting and computer programs described her efforts with thecomputer program as more “substantive” and “rewarding” than theeffort associated with the manual data input necessary withspreadsheet forecasting.
Brown et al. (2005) suggest that technology facilitates the storage,organization, and flow of information, thereby improving the qualityof communications within and across firms. Forecasting computerprograms provide a common procedure and data source forforecasting, and reduce the need for manual data input and potentialfor error associated with personal spreadsheet forecasts. Thus,organizations providing salespeople access to forecasting computerprograms can help them manage and optimize use of the abundantinformation 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 discouragingconcerning the level of seriousness placed upon the salesperson'sforecasts by others in the organization. Frequently, substantialdisconnects 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” istaking place, rendering the forecasts generated by salespeople lessthan useful. In some firms, forecast users perceive that salespeople are“sandbagging,” or understating the potential demand in anticipationthat these understated forecasts will result in lower sales quotas. Inother firms, users perceive salespeople to be unrealistically optimistic.In yet others, users perceive that salespeople “pad” their forecasts totry to assure adequate product supply. In all these cases, there is aperception by forecast users that salespeople are not accountable forthe accuracy of their forecasts and therefore the forecasts are nottaken seriously.
Other observations emerging from the interviews illustrating aperceived lack of seriousness relate to the “executive override.”Salespeople expressed frustration when the forecasts they developedwere altered by the executive team, thereby undermining thesalespersons' efforts. As stated by one salesperson, “[Management]flushes all my analytics for 1 h of qualitative judgment.” Executivechanges to the forecast were often not communicated back to thesalespeople, compounding the perception that their forecastingefforts were not taken seriously. As described by one salesperson,“That loop doesn't get closed.”Managers suggested their incentive foroverrides may stem from conflicting objectives, such as pressure toinflate the forecast to achieve favorable earnings projections. In total,these insights from the interviews paint a picture of salespeople notbeing taken seriously by users of the forecast. The lack of seriousnessis often perceived by salespeople, and when they believe their
forecasts are not being taken seriously, they feel intense reluctance tospend time working on their forecasting tasks. As one salespersoncommented, “they don't believe what I tell them anyway, so whyshould I bother?”
The literature supports the notion that salespeople whoseforecasting efforts are not taken seriously will perceive a disconnectwith their forecasting responsibilities. When individuals perceive thattheir work is taken seriously by others, they are more motivated toexpend attention and effort toward the task (Nissenbaum, 1998,Stevens, 1996) and take it seriously themselves. A rich body ofliterature on motivational theories underscores the importance ofconsidering the expectations of significant others in predictingeffective task performance (e.g., Hackman, and Oldham, 1980;Mitchell, and Nebeker, 1973; Ramlall, 2004). Thus, in order tomotivate sales force behavior in the forecasting process, the salesforce's role in developing the forecast needs to be taken seriously byothers in the organization.
In sum, analysis of the interview data as supported by previousresearch on sales forecasting and sales force motivation suggestsseveral environmental signals that managers can utilize as resourcesto motivate sales force forecasting: compensation and performanceevaluations tied to forecasting performance; ability to use environ-mental conditions and judgment; forecasting training; feedback onforecasting performance; knowledge of how the forecast is usedthroughout the organization; access to a forecasting computerprogram; and the level of seriousness others in the organizationplace in the salesperson's forecast. These environmental signalsrepresent resources and direction provided by the organization toassist and motivate the industrial sales force sales forecasting process.In the following section, we further explore the literature on salesforce motivation to describe the motivational dimensions that areimpacted by the environmental signals present for the sales force.
2.1.2. Motivational dimensionsMotivational research in the sales literature generally deals with
efforts to increase sales force performance and productivity towardorganizational goals (Brown et al., 2005). Ramlall (2004) suggestsfour motivational theories that are generally applicable to motivatingsales people: (1) need theory, (2) equity theory, (3) expectancytheory, and (4) job design (see Ramlall, 2004 for a comprehensivereview of each of these theories). Need theory identifies internalfactors that stimulate behavior. Equity theory is concerned with thelevel of reward received for behavior as well as the level of rewardrelative to what others receive. Expectancy theory focuses onanticipatory end states or goals, thereby modeling motivation as afunction of the expectation that certain performancewill result from agiven action. Job design theory suggests that the combination of taskscomprising 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 issatisfaction. For example, expectancy theory posits that salespeople'saffective orientation toward their job is related to the level ofsatisfaction 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 aproper climate in which employees can develop to their fullestpotential. Failure to provide such a climate would theoretically…result in lower job satisfaction.” Champagne, and McAfee (1989))offer several approaches to satisfying employees that includeproviding praise and economic rewards, clearly explaining jobfunction 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 createan environment leading to satisfaction with the forecasting process tomotivate performance.
2.1.2.2. Forecasting effort. Motivational theories also consistentlyrecognize 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 theoryeach perceives effort as a function of managerial practices thatmotivate 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 exhibithigher levels of effort with higher levels of reward. Ultimately, asseen in Fig. 1, motivational theories advance the belief that the rightcombination of variables impacts a salesperson's level of satisfactionand level of effort. Introducing environmental signals to reducefeelings of incongruity and incompatibility with job expectations,and that provide salespeople with the tools needed to perform theexpected role will result in increased satisfaction with theforecasting process, (Behrman, and Perreault, 1984; Brown, andPeterson, 1993, February) and increased effort expended onforecasting responsibilities.
2.1.2.3. Seriousness placed in the forecasting process. In addition tosatisfaction and effort as dimensions that are impacted by environ-mental signals motivating sales force forecasting behavior, processmodels of behavior suggest that seriousness is related to responsive-ness to a task (Schwartz, 1977, Shiarella, 1998 (1998, January). Whenassessing a task, individuals who perceive higher levels of seriousnessare more likely to direct attention toward the task than thoseperceiving lower levels of seriousness. Describing the job character-istics model of motivation, Pinder (1997) advocates designing workenvironments to generate experiences that are meaningful and, thus,taken seriously. Meaningfulness results from allowing the salespersonto exercise a certain amount of autonomy, and from understandinghow one's actions contribute to the organization (Pinder, 1997). In thecontext of sales forecasting, salespeople who are allowed to use theirown judgment as input into the forecast, and know the impact of theirforecasting efforts on the organization, take their responsibilitiesmore seriously. In addition, Hackman, and Oldham (1980, p. 78)suggest “the degree to which a job requires a variety of differentactivities in carrying out the work, involving the use of a number ofdifferent skills and talents of the person” results in job meaningful-ness. Thus, in order to achieve consistent improvements in forecastingperformance 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 ofliterature on sales forecasting and sales force motivation reveals thathaving the right combination of environmental signals leads tosatisfaction with, effort directed towards, and seriousness placed inthe forecasting process. The conceptual framework presented in Fig. 1reflects the variables and relationships among the variables observedabove in the exploration and description stages of theory develop-ment. In the next section, we move to the explanation stage of theorydevelopment and empirically test the relationship between eachsituational variable and the motivational dimensions associated withsales force forecasting.
2.2. Explanation
In the third stage of theory development, explicit theorydevelopment serves to explain the empirical generalizations observedrepeatedly in the earlier stages of the research. We thereforeempirically test the relationships between the environmental signals
and the motivational dimensions of satisfaction, seriousness, andeffort (see Fig. 1). The environmental signals identified in theinterview data and the literature that were measured and testedinclude: (1) compensation based on forecasting performance, (2)performance evaluation based on forecasting performance, (3) accessto 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) knowledgeof how the forecast impacts the organization, (8) access to forecastingcomputer programs, and (9) others' level of seriousness.
2.2.1. MethodologyTo test the relationships between the environmental signals and
the motivational dimensions, a survey of salespeople was conductedacross a variety of industries and sales settings, as recommended bySchendel, and Hofer (1979). The sample of 1024 was taken from acommercially purchased mailing list of salespeople who indicatedtheir involvement in selling products and/or services to companies(in other words, industrial marketing, not retail sales or sales toindividuals). From this mailing, 382 returned a postcard indicatingthat they were appropriate respondents and willing to complete thesurvey. The survey was mailed to these individuals and 262completed surveys were returned from business-to-business sales-people (68.6% response rate). The survey was designed to capturedata that would allow us to test for patterns in relationships betweenvariables identified in the exploratory and descriptive stages ofresearch that motivate sales force participation in the forecastingprocess, leading to a prescriptive theory of industrial sales force salesforecasting. As such, the survey was developed to collect informationregarding the salesperson's role in sales forecasting, the level ofsatisfaction, effort, and seriousness placed on the forecasting process,and the presence of environmental signals that motivate sales forceforecasting. In addition, demographic data were collected to furtherexplore 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 remainingresults reported in this paper pertain only to those 214 respondentswho are engaged in the forecasting process. The respondents'characteristics indicated that they were well qualified to address theissues under consideration. In addition to indicating that they wereresponsible for forecasting in their territory, the average respondenthad “10 to 15 years” in sales. They also worked in a variety ofindustries, including manufacturing (55.3%), service providers(22.2%), wholesaling (14.0%), publishing (3.7%), health care (2.8%),and financial institutions (2.0%).
The explanatory power of the relationships between the environ-mental signals and motivational variables (Fig. 1) is investigated bythe 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, seriousnessplaced in the forecasting process (Seriousness), satisfaction withforecasting process (Satisfaction), and forecasting effort (Effort),respectively; β0 represents the intercept terms; β1, β2, … β9 denotethe regression coefficients; V1, V2, … V9 denote the predictorvariables described in the Appendix; and E1, E2, and E3 denote theerror terms associated with each equation. Although R2 can beinflated with an increased quantity of independent variables, thenumber of variables in the model falls within the accepted rule ofthumb (i.e., no more than one independent for each 10 cases in thesample) (SAS Institute Inc. 2002). Results from collinearity diagnos-tics indicate model fit is not affected by multicollinearity. Tolerancestatistics range from .683 to .955, all above the .20 threshold (Hair,Black, Babin, Anderson & Tatham, 2005); VIF ranges from 1.05 to1.46 — within acceptable criteria; and proportion of variationparameters reveals none of the variables contribute strongly (0.5 orabove) with two or more variables (Belsley, Kuh & Welsch, 1980). Asshown in Table 1, in each equation the dependent variable is wellfitted by the set of independent variables as reflected by thesignificant R2 coefficients of determination. The set of independentvariables explains the largest percent of variability in the dependentvariable Seriousness (R2=.598), followed by Effort (R2=.198) andSatisfaction (R2=.155) (see1).
Among the environmental signals in the model predicting Serious-ness, the coefficients for training (β=.301,α=.030), knowledge ofhow the forecast is used (β=.286,α=.000), and others' level ofseriousness (β=.488,α=.000), were positive and significant (seeTable 1). Results indicated Satisfaction with the forecasting process isassociated with higher levels of training (β= .949,α=.021) andfeedback (β=.844,α=.001). Three environmental signals were sig-nificantly associatedwith level of Effort. Knowledge of how the forecastis 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 thatreceiving forecasting training may have an interaction with othervariables, specifically, using a forecasting computer program andknowledge of how the forecast is used. When added to the regressionequations, the interaction variables were not significant.
Table 1Survey results.
Variable Variable name SeriousnessR2=.598,α=.000
SatisfactionR2=.155,α=.000
EffortR2=.198,α=.001
β t-statistic β t-statistic β t-statistic
V1 Compensation .025 .227 .005 .019 −.478 −1.122V2 Performance evaluation .147 1.532 −.234 −.986 .625 1.670V3 Use of environmental conditions .153 1.759 −.368 −1.706 .612 1.789V4 Use of judgment −.228 −1.833 .066 .214 −.284 −.582V5 Training .301 2.466a .949 3.145a .429 .897V6 Feedback −.131 −1.459 .844 3.767b −.877 −2.498a
V7 Knowledge .286 5.811b .107 .880 .366 2.008a
V8 Forecasting computer program −.023 −.201 −.212 −.739 1.382 3.035a
Results from the multiple regression analysis indicate that theenvironmental signals account for 59.8% of the variability in the levelof 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 managersstriving to create a serious forecasting environment train theirsalespeople in forecasting processes and techniques. The interviewdata suggest that very few companies provide training, and surveyresults were consistent, with only 14% of respondents receivingforecasting training from their companies. Managers that dedicateresources for forecasting training establish a commitment to theprocess and inculcate performance expectations among salespeople.
Multiple regression results also revealed that salespeople takeforecasting responsibilities more seriously when they are knowledge-able about how their forecast is used to inform other decisionsthroughout the company. Results indicate a wide range of knowledgeamong respondents regarding how their forecasts are used, with 52%indicating they have “a good deal of knowledge” to “a great deal ofknowledge” about what is done with their forecasts after they aresubmitted. As evidenced in the interview data, formally involvingsalespeople in sales and operations planning (S&OP) meetings is apractice that has been successfully used to expose salespeople to thevarious resource allocation decisions for which their forecasts areused, thereby elevating the perceived importance of the task andreducing role ambiguity.
The finding that salespeople are more likely to take theirforecasting responsibilities seriously when others in the organizationalso regard those forecasting efforts seriously confirmed patternsobserved in the interview data. During the interviews, salespeopleidentified two groups of people they often perceived were not takingtheir forecasts seriously—management and downstream users of theforecast such as production and purchasing. Management's unilateralchanges to forecasts developed by the sales force undermined thesalespersons' efforts and discouraged them from taking the processseriously. Downstream users of the forecast often believed thatsalespeople developed forecasts that were intentionally high or low inan effort to achieve some other objective – such as assuring adequateproduct availability or exceeding quota – and thus adjusted theforecasts to align with their beliefs. To create a climate in whichsalespeople perceive others are taking their forecasting effortsseriously, managers can align sales forecasting processes to mitigatelack of seriousness in the forecast by users and managers byestablishing an S&OP process in which a shared interpretation andconsensus agreement on the forecast is established.
4.2. Satisfaction with the forecasting process
Quantitative results indicate that the environmental signalsexplain 15.5% of the variability in salespersons' satisfaction with theforecasting 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 theforecasting process. Training provides direction for the salesperson'sforecasting input and thus reduces role ambiguity, thereby increasingsatisfaction with the process. Training was the only variablesignificantly related to both Satisfaction and Seriousness and, assuch, is an important factor motivating sales force forecasting. Thecomplexity of processes, techniques, and technology can renderforecasting 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 withwhich to disseminate the information.
In addition, salespeople receiving feedback on the accuracy of theirforecasts were found to be more satisfied with the process. Withoutfeedback on forecast accuracy, salespeople are unaware if their effortsare worthwhile or if they need to be improved. Salespeople in theinterviews expressing frustration with absence of feedback perceivedtheir forecasting efforts to be “busy work” that added little value anddetracted from their selling responsibilities. Conversely, one of themanagers in the interviews that provided feedback used forecastaccuracy as a tool to motivate salespeople by relating accuracy tocustomer service levels. Performance feedback can provide a sense ofjustification 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 theenvironmental signals account for 19.8% of variability in the level ofseriousness placed in the forecasting process, with three variables –
feedback, knowledge of how the forecast is used, and forecastingcomputer program – demonstrating a significant relationship. Thenegative relationship between receiving adequate feedback andforecasting effort is counterintuitive. It is possible that the samelevel of feedback may be perceived as adequate by salespeoplespending the most time on the forecast but inadequate by thosespending a lesser amount of effort on the forecast. For example, ifthose spending less time on the forecast experience lower levels offorecast accuracy than those spending more time, they may perceivethe need for more feedback and instruction on what can be done toimprove their performance. Based on the positive relationshipbetween feedback and satisfaction and the negative relationshipbetween feedback and effort, it is important for managers to establisha mechanism to determine if the salesperson perceives the level offeedback adequate.
Salespeople who are knowledgeable about how their forecasts areused throughout the company spend significantly more timeforecasting than those who are less knowledgeable. These regressionanalysis results support the qualitative data suggesting that whenemployees believe their efforts have a direct impact on the company'sperformance, they are more motivated to allocate time and effort tothe task. As discussed in the literature review, motivational theoriessuggest that employee's efforts toward a task are conditioned by theability to achieve desirable goals through those efforts (Ramlall,2004). Managers who create an environment in which salespeopleunderstand the impact of their forecasting efforts on the company –
for example encouraging involvement in S&OP meetings – create anexpectation of attainment of goals such as improved organizationalperformance resulting in increased financial rewards. Furthermore,personal achievements such as improved self esteem can result fromthe knowledge that the salesperson's forecasting efforts are a criticalaspect impacting the performance of several processes throughoutthe organization.
The third environmental signal to exhibit a significant relationshipwith forecasting effort was access to a forecasting computer program.The fact that salespeople who have access to a forecasting computerprogram spend more time and effort developing their forecasts thanthose without a computer program may seem counterintuitivebecause the system should allow them to work more efficiently.However, the system provides immediate access to abundantinformation that the salesperson might not be able to considerusing a simple forecasting spreadsheet. In addition, as salespeopleacquire new demand information from external sources, they may bemore likely to utilize the computer system to analyze the impact ondemand as they acquire the information rather than waiting tocommunicate the information until the next period in which a
forecast update is due. As described in the qualitative research, thesalespersons' time and efforts are more substantive and rewardingwhen making forecasting decisions with the computer program. Thesignificant finding between the environmental signal forecastingcomputer program and the motivational dimension Effort illustratesthat motivational theories can be applied in a sales forecastingcontext, suggesting that work effort is motivated by the perceivedvalue of the reward.
To summarize, among the 9 environmental signals in the model, 5significantly impact the motivational dimensions of sales forceforecasting: training, feedback, knowledge of how the forecast isused, 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 asignificant positive relationship with seriousness and satisfaction;feedback was positively associated with satisfaction but negativelyassociated with effort; and knowledge of how the forecast is used waspositively related to seriousness and effort.
5. Managerial implications
Successfully managing the sales forecasting process involves muchmore than deciding what techniques and computer system to adoptwithin an organization. This research provides a model for managersinterested in motivating the industrial sales force in their salesforecasting efforts. Recognition of the important role of the sales forcein improving the quality of sales forecasts can provide a source ofcompetitive advantage for firms that rely on the forecast to informother organizational processes such as purchasing, manufacturing,warehousing, capacity planning, and logistics scheduling, to name afew. The survey research revealed five environmental signals thatmotivate sales force forecasting. Managers investing in theseresources can alter the prevailing attitude among salespeople thatforecasting is a non-value added activity which constrains the timethey can devote to selling.
5.1. Forecasting training
Providing forecasting training for the sales force increases bothsatisfaction with and seriousness placed on the process. Forecastingtraining provides structure and direction for the sales force to identifysources of information that impact demand and how that informationis most effectively used as an input to the forecast. Training programscan be designed to include those aspects of forecasting that are mostrelevant to the sales force, such as identifying, gathering, andinterpreting qualitative intelligence from customers and otherexternal sources that can impact demand. Salespeople can also betrained to understand how the qualitative customer data cancomplement a statistically generated forecast. Moon, and Mentzer(1999) found that salespeople's forecasting efforts are most effectivewhen they are asked to alter a statistical forecast rather than create anew forecast. However, to be most effective, salespeople should betrained with a basic understanding of the variables that have beenconsidered in the statistical forecast so any changes they make areadding value rather than duplicating the impact of variables alreadyincluded.
5.2. Knowledge of how the forecast is used throughout the organization
Educating the sales force on how their forecasting efforts are usedthroughout the firm increases seriousness placed in and effortdevoted to the process. Managers can establish this environmentalsignal by involving the sales people in cross-functional meetings suchas S&OP to illustrate the systemic effect of their forecast on otherorganizational process decisions. S&OP is the process by which anorganization develops a consensus understanding of demand result-
ing in a single-number forecast that drives the operational plans tomatch supply and demand. Managers are encouraged to involvesalespeople in the S&OP process to demonstrate how their sales forceforecasting efforts directly impact production and order fulfillmentrates. Salespeople who understand that the quality of their forecastingefforts ultimately impacts their order fulfillment and customer servicelevels will take the process more seriously and devote more effort toproducing quality forecasts.
5.3. Feedback on forecasting performance
Providing forecasting feedback increases satisfaction with theprocess. Without regular reports of forecast accuracy, salespeoplehave little incentive to strive for improved accuracy. Forecastingmanagement systems have been developed that track the accuracy offorecasting input for each individual that contributes to the forecast.Managers can utilize these system tools to monitor and providefeedback on the accuracy of forecasting efforts among the sales force.Existing forecast accuracy performance can also be used as abenchmark for setting goals for improvement for each salesperson.
The negative relationship found between feedback and forecastingeffort indicates that managers must gauge the appropriate amountand content of the feedback based on the salespersons' needs andlevel of forecast accuracy. If those spending less time on the forecastexperience 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 accuracyreports at varying levels and varying frequencies based on thefeedback needs of the individual salesperson, and eliminate theburdensome responsibility associated with manual manipulation ofthe numbers.
5.4. Access to a forecasting computer program
Fourth, managers are also encouraged to make the investment in aforecasting computer program that can assist the sales force ineffectively organizing, analyzing, and communicating informationthat can impact demand. Providing forecasting software tools that areinterfacedwith the company's central database enables salespeople tocomplete their forecasting responsibilities efficiently. For example,forecasting software packages eliminate the need for manual input ofhistorical data thereby reducing potential for error, and improve theability for salespeople to view and analyze multiple dimensions of theforecast, such as by product category, customer, channel, timehorizon, or geographic region, to name a few. As described in theinterview, the forecasting efforts become more “substantive” and“rewarding” with a computer program as more effort is dedicated tounderstanding the customer and the business rather than trying todetermine how to manage complex sets of data. However, salesmanagers are cautioned against assuming a forecasting softwarepackage is a panacea for all forecasting problems. The technologybecomes useful when it is used as a tool to enhance an existingforecasting 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 forecastingprocess more seriously, it is important to ensure that others in theorganization also regard the salespeople's forecasting efforts serious-ly. Once again, the S&OP meeting is a forum in which the content andquality of the sales force's forecasts can be shared with a cross-functional group to increase awareness of and seriousness placed inthe forecast. Sales force involvement in S&OP may reduce theincidence of game-playing and thus reduce the skepticism withwhich many users of the forecast view the salespersons' efforts, andthereby increase the level of seriousness conferred upon the forecast.
In addition, communicating the rationale behind “executive over-rides” that alter a salesperson's forecast will “close the loop” andreduce the perception that the salesperson's forecasting efforts are nottaken seriously.
To our knowledge, this is the first study to identify variables thatspecifically motivate sales force behavior related to sales forecasting.The large majority of research on sales force forecasting has focusedon the salesperson's use of a specific technique or practice rather thanan overall assessment of how managers can motivate the industrialsales force to improve their forecasting efforts. The lack of researchrelated to sales forecasting motivation may explain why, in spite ofthe significant findings from our quantitative study, many companiesare not employing these environmental signals in practice. Animplication is that, although not addressed in this study, trainingsales 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 thatorganizations that devote managerial and financial resources toprovide the environmental signals identified in this study foster anatmosphere in which salespeople are more satisfied with and spendmore effort on the forecasting process and take the process moreseriously.
6. Theoretical implications
Our paper developed a theory of industrial sales force forecasting.The theory suggests that salespeople are more motivated to performtheir forecasting responsibilities when the necessary environmentalsignals are provided. Given that studies over the last thirty years haveshown no real improvement in sales forecasting accuracy (McCarthyet al., 2006), and given the fact that a preponderance of salesforecasting research during that time period has concentrated onimproved techniques and systems, perhaps less research attentionshould be paid to the techniques and systems of sales forecasting, andmore to the management of the function. This is the major theoreticalimplication of this study. Industrial salespeople have considerableinsight to provide to the sales forecasting process, if their input isproperly managed, rewarded, and supported. This fact deservesconsiderable future theory development and testing.
The finding that compensation and performance evaluation werenot significantly related to the motivational dimensions of industrialsales force forecasting was surprising given the frequency with whichthey were mentioned in the interviews. In addition, abundantresearch on sales force productivity focuses on how judicious designof compensation plans align salespeople's goals with organizationalobjectives and, thus, motivate behavior to achieve desired outcomes(Brown et al., 2005). However, an explanation for the lack ofsignificance for compensation as a motivator of forecasting behaviormay be found in some of the other environmental signals that werefound to be significant. For example, salespeoplewho, (a) are properlytrained to gather and incorporate customer data into the forecast,(b) receive appropriate levels of feedback in the formof forecast accuracymeasures on the effectiveness of their efforts, and (c) understand theimpact of their forecasting efforts on resource allocations throughout theorganization, may be more likely to understand that the forecastingprocess can be used as a tool to learn more about and consequentlydevelop a better relationshipwith their customers, resulting in increasedsales. That is, the forecasting process becomes a tool to align supply withdemand, increase sales and, in turn, increase commissions and bonuseswithin existing compensation structures and negates the need foradditional motivation in terms of alternative compensation structuresspecific to forecasting.
We found that training, feedback, knowledge of how the forecast isused, forecasting computer program, and others' level of seriousnesshave considerable impact on salesperson seriousness, satisfaction, andeffort 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 firmconsequences of increasing seriousness, satisfaction, and effort. Thistheoretical development (through additional qualitative methods)and testing of the resultant theories (through quantitative methods)should provide practitioners with additional insights on how tomanage the industrial marketing salesperson input to sales forecast-ing, and what benefits these improvements have for firm and supplychain performance. This, in turn, should lead to a wealth of futureresearch.
7. Directions for future research
Our research employed a theory-building approach to identifyfactors that motivate sales force forecasting. Extant literature on thegeneral topic of sales force motivation was placed in a salesforecasting context through the use of depth interviews, and a surveywas developed and administered based on the findings in theliterature and interviews. The interviews revealed thatmany variablesbelieved to motivate behavior are not employed in a forecastingcontext, and the survey results produced some insignificant findingsthat have been found significant in other contexts. Specifically, severalresearchers have found compensation and performance evaluation tobe 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. Futureresearch should explore in more detail the nature of the compensa-tion and performance evaluation used in the sales force forecastingprocess to determine if certain approaches are more successful thanothers in motivating forecasting behavior.
Findings from our depth interviews revealed that many salespeo-ple are unaware of the level of accuracy of their forecasts. Capturingthe impact of environmental signals and motivational dimensions onforecast accuracy is another important approach to understandingand improving sales force forecasting. Structural equation modelingcan be used to examine the strength of relationships among theenvironmental signals, motivational dimensions, and forecast accu-racy performance. Furthermore, a longitudinal study capturingaccuracy levels before and after implementation of environmentalsignals could confirm the direction of the relationships among thevariables, revealing important insights for motivational researchersand for managers attempting to motivate sales force forecastingperformance.
It is also important that future research into sales force forecastingwork to more fully understand the impact that enhanced salespersonperformance has on important corporate outcomes. Ultimately, thereneeds to be a clear link between the resources that are expended toimprove the quality of salesperson contributions to forecastingperformance and enhanced customer service levels, reduced costs,and profitability. Both managers and academics will benefit fromempirical insight into the relationship between salesperson forecast-ing performance and these broader corporate performance metrics.
Finally, additional work is needed to develop and refine reliableand valid measures of some of the constructs introduced here. Thesurvey instrument developed for this study was designed to beexploratory in nature, and given this objective, each of the nineenvironmental signals, as well as the three motivational dimensionsidentified in Fig. 1 are measured using single-item measures. The useof single-item dichotomous variables is a limitation of the currentresearch. More robust, multi-item measures that tap into alldimensions of the constructs can only lead to more precise tests ofthe relationships explored, andmay generate significant relationshipsbetween the environmental signals and motivational dimensions thatwere insignificant in our study. Once this stream of research movesinto a theory testing phase, more robust measures of these constructsshould be developed.
V1 Compensation: Is any part of your compensationtied in any way to the quality of your forecast?[0=no, 1=yes]
No=162, Yes=52
V2 Performance Evaluation: Is your performanceevaluation based in any way on the quality of yourforecast? [0=no, 1=yes]
No=120, Yes=94
V3 Use of Environmental Conditions: Has yourcompany provided you with environmentalinformation (i.e., pricing policies, industry trends,economic forecasts, etc.) to help you do yourforecasting? [0=no, 1=yes]
No=128, Yes=87
V4 Use of Judgment: Do you use your own judgmentto help you forecast? [0=no, 1=yes]
No=28, Yes=187
V5 Training: Has your company provided you withforecasting training to help you do your forecasting?[0=no, 1=yes]
No=185, Yes=30
V6 Feedback: Do you think you receive adequatefeedback on the accuracy of your forecasts?[0=no, 1=yes]
No=87, Yes=126
V7 Knowledge: How much knowledge do you haveabout what is done with your forecasts after yousubmit them? [response option: (1) no knowledgeto (5) a great deal of knowledge]
1=5, 2=22, 3=76,4=60, 5=51
V8 Forecasting Computer Program: Has your companyprovided you with specific forecasting computerprograms to help you do your forecasting?[0=no, 1=yes]
No=181, Yes=34
V9 Others' Level of Seriousness: How seriously are theforecasts that you provide taken by others in yourorganization? [response option: (1) not at allseriously to (5) extremely seriously]
1=3, 2=17, 3=73,4=74, 5=41
Motivational dimensions
Y1 Seriousness: How seriously do you take yourforecasting 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 theforecasting process that you participate in? [responseoptions – (1) extremely dissatisfied to (5) extremelysatisfied]
Range=1 to 5,Mean=3.09, SD = .857
Y3 Effort: Approximately how much time do you spendforecasting in an average month [open-endedresponse 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 attitudesof industrial salespeople. Industrial Marketing Management, 28, 359−368.
Bagozzi, R. P. (1978, Novemberr). Sales force performance and satisfaction as a functionof individual difference, interpersonal, and situational factors. Journal of MarketingResearch, 15, 517−531.
Baldauf, A., & Cravens, D. W. (2002). The effect of moderators on the salespersonbehavior 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 BiscuitCompany. Journal of Business Forecasting, 17(2), 3−6.
Behrman, D. N., & Perreault, W. D. (1984). A role stress model of the performance andsatisfaction of industrial salespersons.Journal of Marketing, 48, 9−12 Fall.
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifyinginfluential 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 PersonalSelling and Sales Management, 25(2), 155−167.
Brown, S. P., & Peterson, R. A. (1993, Februaryy). Antecedents and consequences ofsalesperson job satisfaction: meta-analysis and assessment of causal effects. Journalof Marketing Research, 30, 63−77.
Chakrabarty, S., Oubre, D. T., & Brown, G. (2008). The impact of supervisory adaptiveselling and supervisory feedback on salesperson performance. Industrial MarketingManagement, 37, 447−454.
Champagne, P. J., & McAfee, R. B. (1989). Motivating strategies for performance andproductivity: A guide to human resource development. New York: Quorum Books.
Churchill, G. A. (1979, Februaryy). A paradigm for developing better measures ofmarketing construct. Journal of Marketing Research, 16, 64−73.
Churchill, G. A., Ford, N. M., Hartley, S.W., &Walker, O. C. (1985,Mayy). The determinantsof salesperson performance: a meta-analysis. Journal of Marketing Research, 22,103−118.
Cox, J. E. (1989). Approaches for improving salespersons' forecasts. Industrial MarketingManagement, 18, 307−311.
Dalrymple, D. J. (1975, Decemberr). Sales forecasting methods and accuracy. BusinessHorizons, 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 andOrganizational Studies, 10(4), 58−79.
Igalens, J., & Roussel, P. (1999). A study of the relationships between compensationpackage, 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). Salesmanagement: Analysis and decision making, 4th ed.
Jantan,M. A., Honeycutt, E. D., Thellen, S. T., & Ashraf, M. A. (2004).Managerial perceptionsof 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 ofselling and salesmanagement. Journal of Personal Selling and SalesManagement, 25(2),105−111.
Johlke, M. C., Duhan, D. F., Howell, R. D., & Wilkes, R. W. (2000). An integrated model ofsalesmanagers' communication practices. Journal of the Academy ofMarketing Science,28(2), 263−277.
Locke, E. A., & Latham, G. P. (1990). In U. Kleinbeck (Ed.), Work motivation: the highperformance cycle, in Work Motivation. Hillsdale, NJ: L. Erlbaum.
Makridakis, S., & Wheelwright, S. C. (1977, Octoberr). Forecasting: issues & challenges formarketingmanagement: a framework for relating the available techniques to specificsituations. Journal of Marketing, 24−38.
Mantrala, M. K., & Raman, K. (1990). Analysis of a sales force incentive plan for accuratesales forecasting and performance,. International Journal of Research in Marketing, 7,189−202.
Matsuo, M., & Kusumi, T. (2002). Salesperson's procedural knowledge, experience andperformance: 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 improvesupply chain performance. International Journal of Physical Distribution and LogisticsManagement, 32(6), 431−454.
McCarthy, T. M., Davis, D. F., Golicic, S. L., & Mentzer, J. T. (2006, Augustt). The evolution ofsales 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 demandmanagement
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). Customerdemandplanningat Lucent
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 knowledgeand empowerment on working smart and working hard; the moderating role ofexperience. International Journal of Research in Marketing, 23, 279−293.
Reese, S. (2000). The human aspects of collaborative forecasting. Journal of BusinessForecasting, 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 ResearchAssociation, Houston, TX. Academic Press.
Småros, J., & Hellström, M. (2004). Using the assortment forecasting method to enablesales force involvement in forecasting. International Journal of Physical Distributionand Logistics Management, 34(1/2), 140−157.
Sriram, V., Krapfel, R., & Spekman, R. (1992, Decemberr). Antecedents for buyer-sellercollaboration: 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 ofComposition Theory, 16(2), 313−324.
Teas, K. R., & Horrell, J. F. (1981, Februaryy). Salespeople satisfaction and performancefeedback. 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 andsales 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 activitieswith other firms in their supply chain, such as collaborative forecasting, demandmanagement, and demand planning. Her research has been published in The Journal ofForecasting, International Journal of Physical Distribution and Logistics Management,Transportation Journal, Foresight, Journal of Marketing Education, and Journal of BusinessForecasting. 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 inventoryplanning and control.
Mark A. Moon is an Associate Professor of Marketing and Director of the SalesForecasting Management Forum at the University of Tennessee, Knoxville. Prior tojoining the UT faculty in 1993, Dr. Moon earned his Ph.D. from the University of NorthCarolina at Chapel Hill. He also holds MBA and BA degrees from the University ofMichigan in Ann Arbor. He has published in the International Journal of Forecasting,Supply Chain Management Review, Foresight, Journal of Personal Selling and SalesManagement, Journal of Business Forecasting, Journal of Marketing Education, MarketingEducation Review, Business Horizons, Industrial Marketing Management, Journal ofMarketing Theory and Practice, and several international conference proceedings. Dr.Moon is also the author, along with Dr. John T. (Tom) Mentzer of Sales ForecastingManagement: A Demand Management Approach.
John T. Mentzer (1951-2010) was a Chancellor's Professor and the Harry J. andVivienne R. Bruce Excellence Chair of Business in the Department of Marketing andLogistics at the University of Tennessee. He published 9 books and more than 200articles and papers in the Harvard Business Review, Industrial Marketing Management,Journal of Business Logistics, Journal of Marketing, International Journal of PhysicalDistribution and Logistics Management, Journal of Business Research, Transportation andLogistics Review, Transportation Journal, Journal of the Academy of Marketing Science,Columbia Journal of World Business, Research in Marketing, Business Horizons, and otherjournals. He was the past president of the Academy of Marketing Science and theCouncil of Supply Chain Management Professionals (then the Council of LogisticsManagement). He was the 2004 recipient of the Council of Logistics ManagementDistinguished Service Award, the 2007 recipient of the International Society ofLogistics Armitage Medal, and the 2008 recipient of the Academy of ManagementScience Distinguished Service Award.
