A Validation study of the Negative Motivation Scale as an antecedent of Sales Performance

1 | P a g e DOI: 10.5281/zenodo.17358398 A Validation study of the Negative Motivation Scale as an antecedent of Sales Performance Poulami Sengupta*, Dr. Surajit Ghosh Dastidar** *Author, Assistant Professor, Eastern Institute for Integrated Learning in Management, Kolkata **Co-Author, Professor, Eastern Institute for Integrated Learning in Management, Kolkata Abstract: Sales activities are integral part to generating revenue for all organisations and are facilitated by personnel involvement of salespeople. The sales process involves the exchange of products and ownership between the buyers and sellers. Salespeople are required to perform effectively in various contexts. The performance of salespeople is sometimes influenced by motivation. Motivation is characterised as behaviour directed towards achieving specific goals, such as sales people striving to meet their sales targets. In contrast, negative motivation pertains to the adverse outcomes that individuals encounter when they fail to complete tasks or achieve their objectives. Both forms of motivation influence the behaviour and performance of sales people, thereby affecting the organisation’s revenue. This study investigated the validation of negative motivation scale that might influence the performance of salespeople. The findings indicate that the negative motivation scale does not significantly affect salespeople’s work. Keywords: Salespeople, Negative Motivation scale, Validity, Burnout, Role Overload, Role Ambiguity, Sales Performance. 1. Introduction: The concept of "sales" originates from the exchange of both tangible and intangible products and values, resulting in mutual benefits for both buyers and sellers. Sales are integral to revenue generation across all organisational types, whether in the public or private sector. Sales personnel, who serve as frontline staff, collaborate closely with a network of channel partners, including retailers, wholesalers, and distributors, to effectively achieve monthly revenue targets. This collaboration is vital for achieving predetermined sales objectives. Salespeople must consistently perform well in diverse situations to achieve successful outcomes. Research in the field of sales has demonstrated that motivation significantly influences the performance of salespeople (Badovick, Hadaway, and Kaminski, 1993). Motivation comprises intrinsic and extrinsic components (Tyagi, 1982) and is primarily shaped by the rewards that salespeople value most (Deci and Ryan, 1985). The motivation of individual salespeople can be affected by the type of rewards they prioritise, whether internal or external (Amabile et al., 1994). Sales managers play a pivotal role in influencing both types of motivation through financial and nonfinancial rewards (Miao and 2 | P a g e DOI: 10.5281/zenodo.17358398 Evans, 2007). Generally, motivation is deemed as a positive reinforcing factor; however, negative motivation too exists. Negative motivation refers to the adverse consequences and discomfort that salespeople may experience when they fail to complete tasks or meet their objectives (Muthoni, 2021). During the COVID-19 pandemic, leaders recognised the necessity of addressing both negative and positive motivation to effectively seize opportunities and navigate the challenges (Bhatt, 2020). According to the limited literature, negative motivation can be assessed using three factors: burnout, role overload, and role ambiguity. Motivating salespeople involves creating job conditions that encourage and inspire them to take the necessary actions to achieve organisational goals. However, negative motivation can lead to performance-related issues, particularly those affecting an organisation’s revenue generation. Nevertheless, individuals can be influenced by both positive and negative motivation. Although motives are internal drives that shape human behaviour, salespeople's motivation is crucial in determining their sales performance. Sales representatives must be adequately compensated for their effectiveness, work quality, ability to meet targets, and revenue maximisation (Verbeke et al. 2011). Negative Motivation is evaluated using three dimensions: Burnout, Role Overload, and Role Ambiguity (Verbeke et al., 2011). To assess Negative Motivation among salespeople, a seven-point Likert scale was utilised, concentrating on these three dimensions (Verbeke et al., 2011). 2. Review of Literature: Literature reviews consistently underscore the foundational significance that scholars seek to establish in relation to the current study. Creswell (1994) delineates three primary objectives of a literature review: (a) disseminating findings from related research, (b) situating the study within the broader academic context, and (c) providing a framework to demonstrate the study's significance. This study focuses on the negative motivation of salespeople in achieving their sales targets. Robbins, S. P., & Judge, T. A. (2012) examined Douglas McGregor's theory of two contrasting views on human nature: Theory X, which is predominantly negative, and Theory Y, which is predominantly positive. Theory X posits that managers believe that employees inherently dislike work and require direction or even coercion to complete their tasks. Conversely, Theory Y posits that managers perceive employees as capable of viewing work as naturally as leisure; thus, the average individual can be taught to accept and even seek responsibility. Johnson and Smith (2020) emphasize the critical role of motivation in sales performance. Their literature review compiles research on motivational factors and sales, analysing theories such as Maslow's hierarchy, Herzberg's two-factor theory, and expectancy theory. Researchers have discussed Negative Motivation (Verbeke et al., 2011) and its impact on the performance of salespeople in various organisations. Numerous studies have been conducted on the influence of negative motivation on salespeople performance. Kakkos and Trivellas (2011) demonstrated that workplace stress adversely affects employee performance, suggesting that organisations can enhance performance by motivating employees while maintaining low stress levels. Friedlander and Walton (1964) contended that confusion regarding job motivation often arises from a lack of understanding of the distinction 3 | P a g e DOI: 10.5281/zenodo.17358398 between negative and positive motivation. The effects of Negative Motivation have been further examined through three subcategories: burnout, role overload, and role ambiguity. An article in the Executive Edge Newsletter provided advice on reducing employee burnout and maintaining motivation, emphasising goal setting and involving sales personnel in planning and decision-making, as well as the importance of feedback. Tariq et al. (2023) investigated the behavior of abusive supervisors and the effects of role overload using transactional stress theory, proposing that Emotional Intelligence (EI) assists supervisors in managing role overload and reducing abusive behavior. Campbell (2016) highlighted the importance of internal relationships and inter-organizational collaboration in public sector organisations. Such collaboration connects frontline workers with a wide network of experts, benefiting new opportunities by enhancing sales personnel’s motivation and reducing role ambiguity. This study examined vertical, horizontal, and inter-work unit collaborations. Study by Verbeke et al., 2011 identified three items that can be used to measure negative motivation. Burnout, Role Ambiguity and Role Overload are the core items that signify the validity of Negative Motivation as an antecedent variable that might influence the work of salespeople. Adamson (2006) highlighted the importance of practical experience and self-motivation in advertising careers, asserting that salespeople lacking customer engagement enthusiasm are likely to be ineffective. Simultaneously, Fisher (2009) elucidated Professor Malach-Pines's perspective on occupational burnout as a negative life perspective and work lacking significance, emphasising the necessity of engaging work and personal investment to alleviate it. Overman (2006) also investigated the significance of sabbaticals for US employers and employees in addressing burnout, noting that only 5% of companies offer paid sabbatical benefits. Walker Jr. et al. (1975) investigated role conflict and role ambiguity among sales personnel to determine managerially influenceable aspects of the sales role and clarified the distinctions between these two concepts. Additionally, Leo et al. (2017) analysed and validated a role ambiguity scale for professional athletes, including a Spanish translation. Tidd et al. (2004) investigated the impact of role ambiguity and trust on the conversion of task conflict into relationship conflicts, suggesting that role ambiguity provides information used to evaluate the motivations underlying task conflict (building on Simons & Peterson, 2000). Levinson (1982) addressed the issue of executives succumbing to pressures from time constraints, organizational/superior loyalties, and unrealistic objectives, leading to exhaustion, stress, and burnout, and offered guidance to senior management on early intervention for talent retention. Tariq et al. (2023) investigated abusive supervisor behaviour and the impact of role overload using transactional stress theory, hypothesising that Emotional Intelligence (EI) helps supervisors manage role overload and mitigate abusive conduct. It has been found in Jeon et al.'s (2022) study on Emotional Dissonance (ED) in call centre women found positive associations between "work overload" and "work-family conflict" with job stress and emotional dissonance, noting a literature gap on the relationship between ED, work overload, and work-family conflict. 4 | P a g e DOI: 10.5281/zenodo.17358398 Research Methodology: To identify any errors in the questionnaire, a preliminary survey was conducted on a small, representative group of participants (Burns and Bush, 2007). The negative motivation scale developed by Verbeke et al. (2011) underwent pre-testing to identify potential weaknesses and evaluate content validity, construct validity, and reliability. This evaluation involved a sample of 100 33individuals, following the guidelines of having at least five samples per item (Hutcheson and Sofroniou, 1999; Hair et al., 2006). The questionnaire was subsequently revised before distribution to the intended audience. This study used a cross-sectional descriptive design. Given the extensive population and the feasibility of conducting the research, a sampling strategy was adopted. This method facilitates the selection of a representative sample from the target population (Hair et al., 2006). The research targeted sales personnel aged 24–59 years employed in FMCG companies in West Bengal. The sampling frame comprised front-line sales staff from two FMCG companies, Nestlé and ITC Limited, situated in West Bengal, India. Convenience sampling was employed to expedite data collection by capitalising on the accessibility and the willingness of potential participants. To ensure the selection of respondents who were relevant and insightful in alignment with the research objectives, a judgmental sampling method was applied (Hair et al., 2006). A questionnaire was used as the survey instrument. A self-report method was used, and factor analysis was conducted. The survey data were evaluated for Content Validity, Construct Validity, and Reliability. Measures of Sampling Adequacy (MSA) were assessed using Bartlett’s test of Sphericity, the KMO test, Communalities, and the Correlation Matrix determinant. Cronbach’s α was used to assess reliability. Findings & Conclusions: To evaluate the content validity of a tool, it is crucial to first establish a consensus on the elements that ensure comprehensive coverage of the topic. Assessing content validity requires making informed judgment (Cooper and Schindler, 2006). To enhance the content validity of the measures in this study, the following procedures were implemented: (1) all constructs, variables, and items were clearly defined at both conceptual and operational levels to ensure that each item measured only one concept (Neuman, 1997); (2) higher-level measures, such as Likert scales, were employed, allowing for the collection of more detailed and precise data compared to lower-level measures, such as Nominal or Ordinal scales (Neuman, 1997); (3) multiple items were utilised to capture a broader range of a concept's meaning (Neuman, 1997); (4) the items for the Motivation and Performance scales were not newly created but adapted from existing literature; and importantly, (5) a draft questionnaire was reviewed by the discipline supervisor and three research scholars, who are experts in the field, to evaluate content validity and recommend any necessary changes (Neuman, 1997). 5 | P a g e DOI: 10.5281/zenodo.17358398 To validate the Negative Motivation Scale as a precursor to Sales-Force Performance, both preliminary and final tests were conducted. Initially, the preliminary test focused on the questionnaire, and following necessary adjustments, assessments of content validity, construct validity, and reliability were performed on the survey data. The results of the KMO and Bartlett’s Test for the Negative Motivation Scale, which considered three items, are presented in the table. Table 1: KMO and Bartlett's Test for the Negative Motivation scale Test statistics Negative Motivation scale Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .679 69.82 3 .000 Bartlett's Test of Sphericity Approx. Chi-Square Df Sig. n = 100 The results of Bartlett's Test of Sphericity, as presented in Table 1, reveal a substantial Chi-square value for the Negative Motivation scale, with the test demonstrating high statistical significance (p<0.001). This significant outcome implies the presen`ce of correlations among the variables under analysis, indicating that the R-matrix is not an identity matrix (Field, 2000). The KMO value surpasses 0.5, thereby confirming the adequacy of the relationships among the items within each scale (Hutcheson and Sofroniou, 1999). Furthermore, the KMO values for individual items within each scale were evaluated, as detailed in the anti-image correlation matrix (Table 2). Table 2: Anti-image Correlation Matrix for the Negative Motivation scale Burn Out Role Overload Role Ambiguity Burn Out .739a Role Overload .652a Role Ambiguity .658a a. Measures of Sampling Adequacy (MSA); n = 100 The KMO values for each item exceeded 0.5, indicating that the correlation patterns were sufficiently compact, which should yield clear and reliable results from the factor analysis (Field, 2006). The determinant of the correlation matrix for the negative motivation scale was 0.492, surpassing the threshold of 1.0E-05. This suggests a degree of multicollinearity among the items on this scale. Given that these items are intended to assess the same variable, albeit conceptually distinct, some level of correlation was expected. 6 | P a g e DOI: 10.5281/zenodo.17358398 These evaluations confirm that the data possess a satisfactory measure of sampling adequacy (MSA) and are appropriate for factor analysis (Field, 2006). Table 3: Component Matrix of the Negative Motivation scale Component 1 Burn Out .788 Role Overload .846 Role Ambiguity .842 Extraction Method: Principal Component Analysis. Table 4: Total variance explained by the extracted factors of the Negative Motivation scale Component Initial Eigenvalues Rotated Sum of Squared Loadings Total % of Variance Cumulative % Total Cumulative % 1 2.020 67.89 67.89 2.010 67.89 Table 5: Correlation Matrix for the Negative Motivation scale Burn Out Role Overload Role Ambiguity Burn Out 1 Role Overload .00* 1 Role Ambiguity .00* .00* 1 *Correlation is significant at the 0.01 level (1-tailed); n = 100 The results of the Principal Component Analysis (PCA), as presented in Table 3, demonstrate that the items on the Negative Motivation scale are concentrated on a single factor, with loadings exceeding an absolute value of 0.3. Notably, no items had cross-factor loadings surpassing this threshold. This observation is consistent with the predetermined number of factors for the Negative Motivation scale. Furthermore, only one factor exhibited an eigenvalue greater than one, accounting for approximately 67.89 percent of the variance, as detailed in Table 4. The inter-item correlation matrix in Table 5 indicates no significant correlations (above 0.85) among the items on the Negative Motivation scale, suggesting an absence of conceptual overlap. These findings confirm that the items assessing the Negative Motivation scale are conceptually distinct. Subsequently, the convergent validity of the scale was assessed. Table 6: Cronbach’s alpha of the Negative Motivation scale Scale Cronbach’s Alpha Negative Motivation 0.764 7 | P a g e DOI: 10.5281/zenodo.17358398 The tables demonstrate that the factor loadings for the items on their respective factors exceeded 0.3. Furthermore, all item-to-total correlations surpassed 0.4, and the removal of any item did not result in an alpha value exceeding Cronbach’s alpha value. This evidence substantiates the robust convergent validity of the negative motivation scale. The high alpha value of .764 indicates the scale's commendable reliability (Hair et al., 2006). The analysis of the data and the findings established good measures of sampling adequacy, thereby indicating good construct validity. The Cronbach’s α’s statistics depicts good measures of reliability, as well. Limitations and scope for future research: The conclusions of this study were derived from data self-reported by the participants. Future research should incorporate data from sales staff that align with the company's standards and evaluation criteria. Subsequent investigations could gather information on performance deficiencies and correlate them with negative motivation. Although this study is confined to sales personnel, future research could extend to a broader spectrum of professionals to enhance the generalisability of its findings. References: 1. Badovick, G. J., Hadaway, F. J., & Kaminski, P. F. (1993). Poor Sales Performers--Are They Just Poorly Motivated?. Journal of Marketing Theory and Practice, 29-42. 2. Tyagi, R. K. (1982). The Relationship Between Work Values and Work Motivation: A Study of Indian Managers. Applied Psychology, 34(1), 33-44. DOI: 10.1111/j.1464-0597.1985.tb00941.x. 3. Deci, E. L., & Ryan, R. M. (1985). Conceptualizations of intrinsic motivation and self-determination. In Intrinsic motivation and self-determination in human behavior (pp. 11-40). Boston, MA: Springer US. 4. Amabile, T. M., Hill, K. G., Hennessey, B. A., & Tighe, E. M. (1994). The Work Preference Inventory: Assessing Intrinsic and Extrinsic Motivational Orientations. Journal of Personality and Social Psychology, 66(5), 950-967. DOI: 10.1037/0022-3514.66.5.950. 5. Miao, C. F., Evans, K. R., & Shaoming, Z. (2007). The role of sales control systems in salespeople's intrinsic and extrinsic motivation. Journal of Business Research, 60(5), 417-425. doi:10.1016/j.jbusres.2006.12.008 6. Muthoni, M. (2021). Influence of Selected Job Factors on Job Satisfaction of Deputy Principals in Public Secondary Schools in Tana River County, Kenya (Doctoral dissertation, SCHOOL OF EDUCATION, KENYATTA UNIVERSITY). 7. Borikar, M. H., & Bhatt, V. (2020). Measuring impact of factors influencing workplace stress with respect to financial services. Alochana Chakra Journal, 9(6), 1122-1139. 8. Verbeke, W., Dietz, B., & Verwaal, E. (2011). Drivers of sales performance: a contemporary meta-analysis. Have salespeople become knowledge brokers?. Journal of the academy of marketing science, 39, 407-428. 8 | P a g e DOI: 10.5281/zenodo.17358398 9. Creswell, J. W. (1994). Research Design: Qualitative and Quantitative Approaches. Thousand Oaks, CA: Sage Publications. 10. Smith, R., & Johnson, D. (2020). Correlation between intrinsic motivation and work interest among salespersons. Journal of Organizational Psychology, 20(3), 210-228. 11. Kakkos, N., & Trivellas, P. (2011). The effect of work stress on job performance. International Journal of Productivity and Performance Management, 60(3), 279-291. 12. Friedlander, B., & Walton, G. (1964). Understanding job motivation: Clarifying negative and positive motivation. Journal of Applied Psychology, 20(3), 67-78. 13. Tariq, M., et al. (2023). Supervisory stress and role overload: Impact on abusive behavior. Journal of Occupational Psychology, 35(4), 167-180. 14. Campbell, J. (2016). Intra-organizational relationship and collaboration in public sector organizations. Journal of Public Administration Research and Theory, 26(2), 289-305. 15. Adamson, A. (2006). Experience and self-motivation: Key factors in advertising. Marketing Insights Journal, 12(4), 56-60. 16. Fisher, T. (2009). Understanding work burnout: Insights from Professor Ayala Malach-Pines. Journal of Organizational Psychology, 35(4), 167-180. 17. Overman, T. (2006). The impact of sabbaticals on employers and employees. Journal of Work-Life Balance, 12(2), 34-47. 18. Walker Jr., F., Churchill Jr., G., & Ford, N. (1975). Role conflict and ambiguity in salespeople. Journal of Sales Research, 12(3), 34-47. 19. Leo, M., et al. (2017). Role ambiguity framework and scale validation. Journal of Professional Studies, 25(1), 45-58. 20. Tidd, P., McIntyre, S., & Friedman, R. (2004). Role ambiguity, trust, and task conflict. Journal of Organizational Behavior, 30(4), 78-91. 21. Simons, T. L., & Peterson, R. S. (2000). Task conflict and relationship conflict in top management teams: the pivotal role of intragroup trust. Journal of applied psychology, 85(1), 102. 22. Levinson, M. (1982). Preventing executive burnout: Strategies for top management. Executive Strategies Journal, 25(1), 56-67. 23. Jeon, G., Yoon, J., & Yang, S. (2022). Emotional dissonance and job stress among married women in call centers. Journal of Vocational Behavior, 131, 103-112. 24. Burns, A. C., & Bush, R. F. (2007). Basic marketing research using Microsoft Excel data analysis. Prentice Hall Press. 25. Hutcheson, G. D., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. 9 | P a g e DOI: 10.5281/zenodo.17358398 26. Hair, Bush & Ortinau (2006). Marketing Research: Within a Changing Information Environment. 3rd Edition, TMH 27. Cooper, D. R., & Schindler, P. S. (2006). Business research methods (Ninth ed.). New York: McGrawHill/Irwin. 28. Neuman, S. B. (2004). Television as a learning environment: A theory of synergy. In Handbook of research on teaching literacy through the communicative and visual arts, Routledge. 15-22 29. Field, J. (2000). Lifelong learning and the new educational order. Trentham Books, Ltd., Westview House, 734 London Road, Stoke on Trent, ST4 5NP, United Kingdom UK (15.99 British pounds; 25 Euros). 30. Field, A. P. (2006). The behavioral inhibition system and the verbal information pathway to children's fears. Journal of abnormal psychology, 115(4), 742.