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The aim of the present work is to study the subscales of motivation namely expectancy, performance and rewards on the base of Vroom’s expectancy theory of motivation. This study assumes there is a causal relationship between expectancy, performance, and rewards as the subscales of motivation. Then, the expectancy motivation questionnaire is formed and distributed among 90 IT developers in 90 IT companies in Pune-India. The findings show there is a collinearity of the subscales of motivation (expectancy, performance, and rewards). Further, respondents mostly value training program among the reward categories assigned to IT developers (i.e. monetary, training and family facilities and emotional encouragement). It assumes that due to constant technological changes needing updated knowledge and skills, they prefer to improve their job abilities through training programs to increase their human capital to be upgraded for further job opportunities. Thus, their expectancy toward their abilities of fulfilling tasks would increase leading to the repeat of the motivation cycle. Shortly, the results illustrate a misfit model due to the collinearity of the subscales in this study. However, expectancy shows a positive effect on rewards, performance shows an inverse effect on rewards. On the other hand, the findings show IT developers prefer training program rather than other reward categories to increase their expectation toward job performance to accomplish the tasks satisfactorily. Then according to well-done performance, IT developers would value the rewards assigned to them.

Keywords

Vroom’s Theory, Expectancy, Performance, Rewards.

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References

Bonner, S. E., & Sprinkle, G. B. (2002). The effects of monetary incentives on effort and task performance: Theories, evidence, and a framework for research.Accounting, Organizations and Society, 27(4-5), 303-345.

De Simone, S. (2015). Expectancy value theory: Motivating healthcare workers. American International Journal of Contemporary Research, 5(2), 19-23.

Horst, P. (1982). Expectancy theory modeling. (NPRDC Tech. Rep. 82-56). San Diego: Navy Personnel Research and Development Center (AD-A119 128).

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.

Lawler, E. E., & Suttle, J. L. (1973). Expectancy theory and job behavior. Organizational Behavior and Human Performance, 9(3), 482-503.

Lunenburg, F. C. (2011). Expectancy theory of motivation: Motivating by altering expectations. International Journal of Management, Business and Administration, 15(1), 1-6.

McShane, S. L., Von Glinow, M. A., & Sharma, R. R. (2006). Organizational behavior (3^rd ed.). Tata McGraw-Hill. ISBN10: 0070620091

Mullins, L. J. (2010). Management and organizational behavior (10^th ed.) Harlow, Essex: Financial Times Prentice Hall.

Nebeker, D. M., & Moy, M. C. (1976). Work performance: A new approach to expectancy theory predictions (NPRDC Tech. Rep. 76TQ-47). San Diego: Navy Personnel Research and Development Center (ADA030 451).

Porter, L. W., & Lawler, E. E. (1968). Managerial attitudes and performance. Homewood, IL: Dorsey Press and Richard D. Irwin.

Sevilla, Consuelo G. et al. (2007). Research methods. Rex Printing Company. Quezon City.

Van Eerde, W., & Thierry, H. (1996). Vroom’s expectancy models and work-related criteria: A meta-analysis. Journal of Applied Psychology, 81(5), 575-586.

Vroom, V. H. (1964). Work and Motivation. San Francisco, CA: Jossey-Bass.

A Study of Collective-Work Approach on Perceived Creativity for Enhancing Leadership Qualities in IT Companies

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Authors

Rahil Meymandpour ¹, Zahra Bagheri ², Prafulla Pawar ¹

Affiliations
1 Department of Management Sciences, Savitribai Phule Pune University, Pune, Maharashtra, IN
2 Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, IR

Source

Telecom Business Review, Vol 12, No 1 (2019), Pagination: 11-17

Abstract

The present work aims to examine the effect of collective-work approach on creativity for improving leadership qualities in an unstructured decision-making process especially in uncertainty of IT sections. Perusing related studies surrounding the current issue, it is realized managers can involve team setting policy in developing options stage of a non-programmed decision-making process since the secondary data indicate collective work can elevate creativity sources. Besides, creativity is considered as a moderator for reinforcing leadership attributes. While these qualities enhance leadership performance enabling managers to detect the optimum options when they encounter organizational chaos. Accordingly, a model of an unstructured decisionmaking process is hypothesized in which team-setting is considered as a predictor variable for increasing creativity sources which are recognized as moderators. Then, leadership qualities crenellated by creativity are recognized as a criterion variable for promoting leadership performance. So, the primary data are collected through 38-statements questionnaires involving collective work, creativity and leadership qualities from 86 IT managers in Pune-India being ready to approach collective work policy in their unstructured decision-making. Through correlation, regression analysis and Durbin-Watson test, it is found there is a positive relationship between the variables. The tests between-subjects effects and SEM show the predictor variables have significant interaction with the criterion variables. Besides, ANOVA illustrates there is a significant difference between the means of predictors of this study. It would be concluded team setting approach can enhance creativity to moderate the leadership qualities for an effective unstructured decision-making process.

Keywords

Collective Work, Leadership Attributes, Creativity, Unstructured Decision-Making Process.

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References

Adair, J. (2013). Decision making and problem solving. Kogan Page. ISBN 978-0-7494-6696-1

Amabile, T. M. (2013). Componential theory of creativity. In: Kessler, E.H., Ed., Encyclopedia of Management Theory, pp. 134-139. Sage Publications, London.

Aswathappa, K. (2008). Organizational behavior: Text, cases & games. Himalaya Publishing House.

Feenstra, R. H. (2010). The role of creativity in naturalistic decision-making environments: A systems approach. UNLV Theses, Dissertations, Professional papers, and Capstones, Paper 743.

George, J. M. (2007). Chapter 9: Creativity in organizations. The Academy of Management Annals, 1(1), 439-477.

Ghuman, K., & Aswathappa, K. (2010). Management: Concept, practice and cases. McGraw Hill: New Delhi. ISBN 9780-07-068218-4

Godden, B. (2004, January). Sample size formulas. Retrieved from http://williamgodden.com/samplesizeformula.pdf

Hargadon, A. B, & Bechky, B. A. (2006). When collections of creatives become creative collectives: A field study of problem solving at work. Organization Science, 17(4), 484-500.

Mintzberg, H., Raisinghani, D., & Theoret, A. (1976). The structure of unstructured decision processes. Administrative Science Quarterly, 21(2), 246-275.

Mumford, M. D., Zaccaro, S. J., Harding, F. D., Fleishman, E. A., & Reiter-Palmon, R. (1993). Cognitive and temperament predictors of executive ability: Principles for developing leadership capacity. Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Riordan, N. O., & Reilly, O. P. (2011). Virtual worlds: S(t)imulating creativity in decision making. Journal of Decision Systems, 20(3), 325-351.

Singh, N. (2009). Organizational behaviour: Concepts, theory and practices: Managing people in the 21^st century. Deep and Deep Publications: New Delhi. ISBN 81-7629-287-7

Tongo, C. I. (2015). Collective work motivation in knowledge based organizations. Team Performance Management, 21(7/8), 386-404.

Weihrch, H., & Koontz, H. (2001). Management: A global perspective. Tata Mc Graw-Hill. ISBN 0-07-047378-1

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