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Understanding Dynamics of KMS Adoption in Indian ITES Organizations


Affiliations
1 Cognizant Technology Solutions, India
2 Department of Business Administration, Aligarh Muslim University, India
3 All India Management Association- Centre for Management Education, India
4 Dept. of Business Administration, Utkal University, Bhubaneshwar, Odisha, India
 

The design of Knowledge management adoption depicts similarity to the design proposed in 'Technology Acceptance Model (TAM)' and 'Extended Technology Acceptance Model (TAM2)' holding varied adoption enablers, suggested by Davis 1989. The purpose of this paper is to identify the relationship between KM adoption enablers and demographic variables prevalent in Indian ITES organizations within Delhi NCR. Due to ordinal nature of data, 'multiple-ordinal regression' was applied. The demographic variables are considered independent while KM adoption variables/enablers are considered dependent for this research.

The outcomes from of 'multiple-ordinal regression' showcase that maximum number of statistically significant outcomes were in case of the KM adoption enabler 'Perceived Usefulness' holding likelihood of lower cumulative scores in most cases with lowest scores from the independent variables '18-28 years' age group and 'Admin' department.

This research study has proposed a knowledge management adoption framework for Indian ITES organization that can be used as guidelines to develop KM adoption and augmentation strategies.


Keywords

Knowledge Management, Technology Acceptance Model, Multiple-Ordinal Regression.
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  • Anli Suresh; Knowledge Management Adoption, Practice and Innovation in the Indian Organizational Set up: An Empirical Study; Journal of IT and Economic Development 4(2), 31-42, October 2013 31

  • Davis, F.D., R.P. Bagozzi, and P.R. Warshaw. “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” Management Science, 35, 1989, 982-1003.

  • Economic Times Website: http://articles.economictimes.indiatimes.com/2012-06-21/news/32352276_1_ites-exports-ites-sector-sachin-pilot. Retrieved 2012-06-21; Retrieved on 20th Jan 2015

  • Ein-Dor P., 2006. Taxonomies of Knowledge Management. In Schwartz D. and Te’eni D., 2006. Encyclopedia of Knowledge Management. Idea Group Reference, pp. 848 – 854.

  • Field, A.P., 2005. Discovering statistics using SPSS, 2nd ed. London: Sage publications.

  • Hansen, M.T., and Oetinger, B. Introducing T-shaped managers: knowledge management’s next generation. Harvard Business Review (March 2001), 107-116.

  • Hester A.J., 2010. A Comparison of the Influence of Social Factors and Technological Factors on Adoption and Usage of Knowledge Management Systems. Proceedings of the 43rd Hawaii International Conference on System Sciences, IEEE, pp. 1-10.

  • J. R. Perez and P. O. Pablos, “KM and organizational competitiveness: A framework for human capital analy-sis,” Journal of Knowledge Management, Vol. 7, No. 3, pp. 82–91, 2003.

  • Junnarkar, B. (1997). Leveraging collective intellect by building organizational capabilities. Expert Systems with Applications, 13(1), 29–40.

  • Keskin, H. (2005). The relationships between explicit and tacit oriented KM strategy and Firm Performance. Journal of American Academy of Business, Cambridge Hollywood 7 (1), pp 169-176

  • Khoshalhan F. Designing X Control Chart Using DEA Approach, International Multi Conference of Engineers and Computer Scientists, II(2008), pp. 19-21

  • Li-Su Huang, Mohammed Quaddus, Anna L Rowe and Cheng-Po Lai; An investigation into the enablers affecting knowledge management adoption and practice in the life insurance business; Knowledge Management Research & Practice (2011) 9, 58-72. doi:10.1057/kmrp.2011.2

  • Lucier, C. E. & J. D. Torsilieri (2001), Can knowledge management deliver bottomline results? In I. Nonaka and D. Teece (eds.) Managing industrial Knowledge, pp.231-243, London: SAGE Publications.

  • Luis Ernesto Prado Tamez. The adoption of Knowledge Management Systems in Mexico (2014), pp. 19-30

  • Metaxiotis, K., K. Ergazakis, & J. Psarras (2005), “Exploring the world of knowledge management: agreements and disagreements in the academic/practitioner community”, Journal of Knowledge Management, 9 (2), pp. 6-18.

  • Michael Brandt Jones, Bahaudin G. Mujtaba, Albert Williams and Regina A. Greenwood; Organizational Culture Types and Knowledge Management in U.S. Manufacturing Firms; Journal of Knowledge ManagementPractice,Vol.12, No. 4, December 2011

  • Ministry of Corporate Affairs Website: www.mca.gov.in; Retrieved on 15th June 2015.

  • Money, W., Turner, A. (2004) Application of the Technology Acceptance Model to a Knowledge Management System, Proceedings of the 37th Hawaii International Conference on System Sciences (HICSS037), January 5-8, 2004, Hilton Waikoloa Village, Hawaii, USA, IEEE, 1-9.

  • Moore, G.C. and I. Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation,” Information Systems Research, 2, 3 (September 1991), 192-222.

  • NASSCOM Website: “Indian IT-BPO Industry”. Retrieved on 15th December 2012.

  • Prieto, I.M. and Revilla, E. (2006), “Learning capability and business performance: a non-financial and financial assessment”, The Learning Organization, Vol. 12 No. 2, pp. 166-85.

  • Rogers, E.M. (1995), Diffusion of Innovations, The Free Press, New York, NY.

  • Satyendra C. Pandey, Andrew Dutta, (2013) “Role of knowledge infrastructure capabilities in knowledge management”, Journal of Knowledge Management, Vol. 17 Iss: 3, pp.435 – 453

  • Teubner, Alexander and Nietsch, Michael, “Managing Knowledge in Medium Sized Software Companies” (2000). ECIS 2000 Proceedings. Paper 123.

  • Venkatesh, V., and Davis, F. D. “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Management Science (45:2), 2000, pp. 186-204.

  • Website: http://www.itinfo.am/eng/it-enabled-services/; Retrieved on 20th December 2013.

  • Yinglei Wang, Darren B. Meister, Peter H. Gray; “Social influence and knowledge management systems use: evidence from panel data”; MIS Quarterly Volume 37 Issue 1, March 2013; Pages 299-313


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  • Understanding Dynamics of KMS Adoption in Indian ITES Organizations

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Authors

Amit Vikram
Cognizant Technology Solutions, India
Mohammad Israrul Haque
Department of Business Administration, Aligarh Muslim University, India
Ganesh Singh
All India Management Association- Centre for Management Education, India
Sathya Swaroop Debasish
Dept. of Business Administration, Utkal University, Bhubaneshwar, Odisha, India

Abstract


The design of Knowledge management adoption depicts similarity to the design proposed in 'Technology Acceptance Model (TAM)' and 'Extended Technology Acceptance Model (TAM2)' holding varied adoption enablers, suggested by Davis 1989. The purpose of this paper is to identify the relationship between KM adoption enablers and demographic variables prevalent in Indian ITES organizations within Delhi NCR. Due to ordinal nature of data, 'multiple-ordinal regression' was applied. The demographic variables are considered independent while KM adoption variables/enablers are considered dependent for this research.

The outcomes from of 'multiple-ordinal regression' showcase that maximum number of statistically significant outcomes were in case of the KM adoption enabler 'Perceived Usefulness' holding likelihood of lower cumulative scores in most cases with lowest scores from the independent variables '18-28 years' age group and 'Admin' department.

This research study has proposed a knowledge management adoption framework for Indian ITES organization that can be used as guidelines to develop KM adoption and augmentation strategies.


Keywords


Knowledge Management, Technology Acceptance Model, Multiple-Ordinal Regression.

References





DOI: https://doi.org/10.23862/kiit-parikalpana%2F2017%2Fv13%2Fi1%2F151272