OPUS: HR Journal

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Comparison Between Fresh and Old Employees’ Adoption and Agility in Technological Changes


Affiliations
1 Department of Management Sciences, Savitribai Phule Pune University, Pune, Maharashtra, India
     

   Subscribe/Renew Journal


The aim of this study is to compare fresh and old employees’ adoption and agility in technological changes according to their vintage-specific human capital. It is inquired that who are more adopted and agile in technological changes since fresh employees have updated skills and education related to new vintage and old employees have obsolescent skills related to old vintage. Therefore, this study assumes that whether fresh employees with updated skills are more adopted and agile in technological changes or old employees with obsolescent experience. Two questionnaires about adoption and agility are distributed among 324 top level managers in IT companies in Pune-India. In this perception study, the respondents are asked for filling the questionnaires according to their opinion about their fresh and old employees’ adoption and agility in technological changes. The data analyzed through Wilcoxon Signed Ranks Test show there is a significant difference between fresh and old employees’ adoption and agility in technological changes. It can be inferred that fresh and old employees’ adoption and agility are needed for a satisfactory technological change. Since both of their vintage-specific human capital is complementary to each other to have an optimal technological change.

Keywords

Adoption, Agility, Vintage-Specific Human Capital, Technological Change.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11-39). Heidelberg, Germany: Springer. Retrieved from http://people.umass.edu/aizen/f&a1975.html

  • Attewell, P. (1992). Technology diffusion and organizational learning: The case of business computing. Organization Science, 3(1), 1-19.

  • Bass, F.M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215-227.

  • Bullinger, H. J. (1999). Turbulent times require creative thinking: New European concepts in production management. International Journal of Production Economics, 60(61), 9-27.

  • Chari, V. V., & Hopenhayn, H. (1987). Vintage human capital, growth, and the diffusion of new technology. Federal Reserve Bank of Minneapolis Research Department. Working Paper 375.

  • Comin, D., & Hobijn, B. (2009). The CHAT data set. Harvard Business School and NBER Working Paper 10035.

  • Comin, D., & Mestieri, M. (2014). Technology diffusion: Measurement, causes and consequences. Chapter 02 in Handbook of Economic Growth, Vol. 2, 565-622

  • Conner, D. R. (1992). Managing at the speed of change: How resilient managers succeed and prosper where others fail. Toronto: Random House. ISBN: 0-679-40684-0

  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of Technology.

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley, Reading, MA. ISBN: 0201020890 Retrieved from http://people.umass.edu/aizen/f&a1975.html

  • Giachetti, R. E., Martinez, L. D., Sáenz, O. A., & Chen, C. S. (2003). Analysis of the structural measures of flexibility and agility using a measurement theoretical framework. International Journal of Production Economics, 86(1), 47-62.

  • Godden, B. (2004). Sample Size Formulas (January). Retrieved from http://williamgodden.com/samplesizeformula.pdf

  • Haeckel, S. H. (1999). Adaptive enterprise: Creating and leading sense-and-respond organizations. Boston, Massachusetts: Harvard Business School Press. ISBN 9780875848747

  • Helpman, E., & Rangel, A. (1998). Adjusting to a new technology: Experience and training. National Bureau of Economic Research (NBER) Working Paper 6551.

  • Kotter, J. P. (1996). Leading change. Harvard Business School Press. ISBN: 978-0-87584-747-4

  • Kredler, M. (2008). Experience vs. obsolescence: A vintage-Human-Capital Model. MPRA Paper No. 10200: New York. Retrieved from http://mpra.ub.uni-muenchen.de/10200/

  • Kredler, M. (2014). Experience vs. obsolescence: A vintage-human-capital model. Journal of Economic Theory, 150(1), 709-739.

  • Lyytinen, K., & Rose, G. M. (2004). How agile is agile enough? Towards a theory of agility in software development. Case Western Reserve University, USA. Sprouts: Working Papers on Information Systems, 4(10). ISSN 1535-6078 http://sprouts.aisnet.org/4-10

  • Mathiassen, L. & Pries-Heje, J. (2006). Business agility and diffusion of information technology. European Journal of Information Systems, 15(2), 116-119.

  • Nagel, R. N., & Dove, R. (1991). In Goldman, S. & Preiss, K. (Eds.), 21st Century Manufacturing Enterprise Strategy: An Industry-Led View, Lehigh University, Iacocca Institute, Darby, PA.

  • Pawar, P., & Meymandpour, R. (2017). An exploration of enhancing adoption and agility in technological changes. Journal of Organisation & Human Behaviour, 6(3), 15-20.

  • Plonka, F. E. (1997). Developing a lean and agile workforce. Human Factors and Ergonomics in Manufacturing, 7(1), 11-20.

  • Prahalad, C. K., & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review, 68(3), 79-91.

  • Qin, R., & Nembhard, D. A. (2010). Workforce agility for stochastically diffused conditions: A real options perspective. International Journal of Production Economics, 125(2), 324-334.

  • Rosenberg, M. J. (1956). Cognitive structure and attitudinal affect. Journal of Abnormal and Social Psychology, 53(3), 367-372.

  • Rosenberg, N. (1972). Factors affecting the diffusion of technology. Explorations in Economic History, 10(1), 3-33. Reprinted in N. Rosenberg, (1976). Perspectives on Technology, Cambridge: Cambridge University Press, pp. 189-212.

  • Song, X. (2009). Why do change management strategies fail? Illustrations with case studies. Journal of Cambridge Studies, 4(1), 6-15.

  • Svedaite, E., & Tamosiunas, T. (2013). Investigation of the advantages and disadvantages of temporary employment. Socialiniai tyrimai / Social Research, 1(30), 64-70. ISSN 1392-3110

  • Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. doi:10.1287/isre.6.2.144

  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, 11(1), 167-187.

  • Triandis, H. C. (1971). Attitude and Attitude Change. New York: John Wiley. ISBN 0471888311

  • Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-581.

  • Weinberg, B. A. (2004). Experience and technology adoption. IZA DP No: 1051.

  • Yaghoubi, N. M., & Rahat Dahmardeh, M. (2011). Knowledge management; critical success factor in organizational agility. American Journal of Social and Management Sciences, 2(3), 272-277.

  • Zain, M., Che Rose, R., Abdullah, I., & Masrom, M. (2005). The relationship between information technology acceptance and organizational agility in Malaysia. Information & Management, 42(6), 829-839.

  • Zhang, D. Z. (2011). Towards theory building in agile manufacturing strategies - Case studies of an agility taxonomy. International Journal of Production Economics, 131(1), 303-312.


Abstract Views: 394

PDF Views: 1




  • A Comparison Between Fresh and Old Employees’ Adoption and Agility in Technological Changes

Abstract Views: 394  |  PDF Views: 1

Authors

Rahil Meymandpour
Department of Management Sciences, Savitribai Phule Pune University, Pune, Maharashtra, India
Prafulla Pawar
Department of Management Sciences, Savitribai Phule Pune University, Pune, Maharashtra, India

Abstract


The aim of this study is to compare fresh and old employees’ adoption and agility in technological changes according to their vintage-specific human capital. It is inquired that who are more adopted and agile in technological changes since fresh employees have updated skills and education related to new vintage and old employees have obsolescent skills related to old vintage. Therefore, this study assumes that whether fresh employees with updated skills are more adopted and agile in technological changes or old employees with obsolescent experience. Two questionnaires about adoption and agility are distributed among 324 top level managers in IT companies in Pune-India. In this perception study, the respondents are asked for filling the questionnaires according to their opinion about their fresh and old employees’ adoption and agility in technological changes. The data analyzed through Wilcoxon Signed Ranks Test show there is a significant difference between fresh and old employees’ adoption and agility in technological changes. It can be inferred that fresh and old employees’ adoption and agility are needed for a satisfactory technological change. Since both of their vintage-specific human capital is complementary to each other to have an optimal technological change.

Keywords


Adoption, Agility, Vintage-Specific Human Capital, Technological Change.

References