Abstract
Big data and Analytics (BDA) is one of the most talked about technology trend having a widespread impact on organizational value chain. The objective of the study is to explore and examine the key organizational factors that impact the big data adoption in service organizations. A research framework—grounded in organizational theories and IT adoption—examines the impact of four organizational variables on big data adoption and finds that three of them have a strong positive impact. The survey instrument is developed by employing rigorous measurement scales. The study targeted around 500 service organizations headquartered at Mumbai; of which 109 suitable responses are received. Structural equation modeling using the variance based, prediction-oriented PLS model estimation—SmartPLS is applied for testing. The precision estimation and standard errors are evaluated using bootstrapping with 109 cases and 300 samples (resamples).
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References
Checkland, P., & Holwell, S. (1997). Information, Systems and Information Systems: Making Sense of the Field. Wiley.
Beyer, M. A., & Friedman, T. (2013). Big Data Adoption in the Logical Data Warehouse. Gartner Inc.
Rouse, M. (2015, June). Big Data. Retrieved June 16, 2015, from searchcloudcomputing.techtarget.com: http://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data.
Brynjolfsson, E., & McAfee, A. (2013). Is Your Company Ready for Big Data? Retrieved Jan 17, 2015, from http://hbr.org: http://hbr.org/web/2013/06/assessment/is-your-company-ready-for-big-data.
Auschitzky, E., Hammer, M., & Rajagopaul, A. (2014, July). How big data can improve manufacturing. McKinsey & Company.
Gopalkrishnan, S., & Damanpour, F. (2000). The impact of organizational context on innovation adoption in commercial bank. IEEE Transactions on Engineering Management, 47(1), 14–25.
Davenport, T. H. (2014). big data @ work. Boston: Harvard Business School Publishing Corporation.
Economist Intelligence Unit. (2012). The Deciding Factor: Big data and decision-making. Capgemini.
Oracle Corporation, W. H. (2014, September). Oracle Database 12c for Data Warehousing and Big Data. CA, USA.
(2013). The Big Data Readiness Study. Vodafone.
Finos, R. (2015). Wikibon Big Data Analytics Survey: Adoption Maturity by Vertical Market. Wikibon.
Rogers, E. (1995). Diffusion of Innovation (5th Edition ed.). New York: The Free Press.
Thong, J. Y., Yap, C.-S., & Raman, K. S. (1996). Top Management Support, External Expertise and Information Systems Implementation in Small Businesses. Information Systems Research, 7(2), 248–267.
Tornatsky, L., & Fleischer, M. (1990). The Process of Technology Innovation. Lexington, MA.: Lexington Books.
Ajzen, I. (1991). The Theory of Planned Behaviour. Organizational Behaviour and Human Decision Processes, 179–211.
Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing new end-user information systems: Theory and Results. Doctoral Dissertation. Cambridge, MA: Sloan School of Management, Massachusetts Institute of Technology.
Humphrey, W. (1988). Characterizing the software process: A maturity framework. 5(2).
Kwon, T., & Zmud, R. (1987). Unifying the fragmented models of information systems implementation. Critical Issues in Information Systems Research, 227–251.
Oliveira, T., & Martins, M. F. (2011). Literature Review of Information Technology Adoption Models at Firm Level. The Electronic Journal Information Systems Evaluation, 14(1), 110–121.
Paulk, M., & Weber, B. C. (1995). The Capability Maturity Model: Guidelines for Improving the Software Process. MA: Addison-Wesley.
Premkumar, G., Ramamurthy, K., & Crum, M. (1997). Determinants of EDI Adoption in the Transportation Industry. European Journal of Information Systems, 11, 157–186.
Scott, W. (2001). Institutions and organizations, 2 ed. Thousand Oaks, CA: Sage Publications.
Scott, W., & Christensen, S. (1995). (1995) The institutional construction of organizations: International and longitudinal studies. Thousand Oaks, CA: Sage Publications.
Ramamurthy, K., Sen, A., and Sinha, A. P. (2008). An empirical investigation of the key determinants of data warehouse adoption. Decision Support Systems 44, 817–841.
Basole, R., Seuss, D., & Rouse, W. B. (2013). IT innovation adoption by enterprises: Knowledge discovery through text analytics. Decision Support System, 54, 1044–54.
Leidner, D. E., & Kayworth, T. (2006, June). A Review of Culture in Information Systems Research: Toward a Theory of Information Technology Culture Conflict. MIS Quarterly, 30(2), 357–399.
Venkatesh, V., & Bala, H. (2012). Adoption and impacts of inter-organizational business process standards: Role of partnering synergy. Information Systems Research, 23(4), 1131–1157.
Goodhue, D. L., Wybo, M. D., & Kirsch, L. J. (1992). The impact of data integration on the costs and benefits of information systems. MIS Quarterly, 293–311.
Jain, H., Ramamurthy, K., Ryu, H.-S., & Yasai-Ardekani, M. (1998). Success of Data Resource Management in Distributed Environments: An Empirical Investigation. MIS Quarterly, 22(1).
Boynton, A. C., & Zmud, R. W. (1984). An assessment of critical success factors. Sloan Management Review, 17–27.
Ettlie, J. (1986). Implementing manufacturing technologies: Lessons from experience. In D. D. (Eds.), Managing technological innovation. San Francisco: Jossey-Bass.
Hwang, H.-G., Ku, C.-Y., Yen, D. C., & Cheng, C.-C. (2004). Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan. Decision Support Systems, 37(1), 1.
Sanders, G., & Courtney, J. (1985). A Field Study of Organizational Factors Influencing DSS Success. MIS Quarterly, 9(1).
Hartwick, Jon, & Barki, H. (1994). Explaining the Role of User Participation in Information Systems Use. Management Science, 40(4), 440–465.
Chin, W. W., Marcolin, B., & Newsted, P. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from Monte Carlo simulation study and an electronic mail emotion/ adoption study. Information Systems Research, 14(2), 189–217.
Ringle, C. M., Wende, S., and Will, A. (2005). SmartPLS 2.0. Hamburg: University of Hamburg.
Chin, W. (1998b). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, Marcoulides, G.A. (ed.), pp. I295–1336.
Straub, D., Boudreau, M., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the AIS, 380–427.
Gerbing, D., & Anderson, J. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research (JMR), 2, pp. 186–192.
Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the AIS, 16, pp. 91–109.
Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), pp. 297–334.
Werts, C. E., Linn, R. L., & Joreskog, K. (1974). Intra class reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34, 25–33.
Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modelling in international marketing. In R. Sinkovics, & P.N. Ghauri, Advances in International Marketing (pp. 277–320). Bingley: Emerald.
Nunnally, J., & Bernstein, I. (1994). Psychometric Theory. New York: McGraw-Hill.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20, pp. 195–204.
Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7(1), pp 1–26.
Efron, B., & Tibshirani, R. (1993). An Introduction to the Bootstrap. New York: Chapman Hall.
Miller, R. (1974). The jackknife—A review. Biometrika, pp. 1–15.
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Joshi, M., Biswas, P. (2018). An Empirical Investigation of Impact of Organizational Factors on Big Data Adoption. In: Somani, A., Srivastava, S., Mundra, A., Rawat, S. (eds) Proceedings of First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-10-5828-8_77
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