Journal of Information Technology

, Volume 30, Issue 4, pp 352–363

An integrated environmental perspective on software as a service adoption in manufacturing and retail firms

Research Article

Abstract

In this study, we examine the influence of a firm’s environmental factors on its intention to adopt software as a service (SaaS). We operationalized our assessment of a firm’s environmental pressures as mimetic, coercive and normative pressures and examined the moderating role of perceived technology complexity. Mimetic forces are pressures to copy or emulate other organizations’ activities, systems or structures. Coercive pressures are formal or informal pressures exerted on organizations by other organizations upon which they are dependent. Normative forces describe the effect of professional standards and the influence of professional communities on an organization. We empirically tested our research model using data from 289 valid survey responses. The results provide support for the assertion that there are both significant direct and interaction effects that influence a firm’s SaaS adoption intention. Most important was the significant interaction effects between mimetic pressure and perceived technology complexity. This suggests that the complex relationships proposed by institutional theory and diffusion of innovation help to describe how environmental pressures and perceived technology complexity combine to affect intention to adopt an emerging technology. The theoretical contributions of this study are (i) we integrated, tested and validated mature theories in today’s supply chain era with a new but rapidly diffusing technology, (ii) and we answered the call to include practical technology artifacts in information systems studies. From a practical perspective, through this work managers may develop a better understanding regarding environmental factors and whether or not they should consider these issues for their firm when formulating an intention to adopt SaaS.

Keywords

environmental factors software as a service technology adoption institutional theory perceived complexity 

References

  1. Abrahamson, E. and Rosenkopf, L. (1993). Institutional and Competitive Bandwagons: Using mathematical modelling as a tool to explore innovation diffusion, The Academy of Management Review 18 (3): 487–517.Google Scholar
  2. Aiken, L.S. and West, S.G. (1991). Multiple Regression: Testing and interpreting interactions, Newbury Park, CA: Sage.Google Scholar
  3. Ajzen, I. (2005). Attitudes, Personality and Behavior, New York, NY: McGraw-Hill.Google Scholar
  4. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Datz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I. and Zaharia, M. (2010). A View of Cloud Computing, Communications of the ACM 53 (4): 50–58.CrossRefGoogle Scholar
  5. Autry, C.W., Grawe, S.J., Daugherty, P. and Richey, Jr. R.G. (2010). The Effects of Technological Turbulence and Breadth on Supply Chain Technology Acceptance and Adoption, Journal of Operations Management 28 (6): 522–536.CrossRefGoogle Scholar
  6. Basaglia, S., Caporarello, L., Magni, M. and Pennarola, F. (2009). Environmental and Organizational Drivers Influencing the Adoption of VoIP, Information Systems and e-Business Management 7 (1): 103–118.CrossRefGoogle Scholar
  7. Benlian, A., Hess, T. and Buxmann, P. (2009). Drivers of SaaS-Adoption – An empirical study of different application types, Business and Information Systems Engineering 1 (5): 357–369.CrossRefGoogle Scholar
  8. Berger, P.L. and Luckmann, T. (1967). The Sociology Construction of Reality: A treatise in the sociology of knowledge. UK: Penguin.Google Scholar
  9. Bradford, M. and Florin, J. (2003). Examining the Role of Innovation Diffusion Factors on the Implementation Success of Enterprise Resource Planning Systems, International Journal of Accounting Information Systems 4 (3): 205–225.CrossRefGoogle Scholar
  10. Brewer, M.B., Campbell, D.T. and Crano, W.D. (1970). Testing a Single-sactor Model as an Alternative to the Misuse of Partial Correlations in Hypothesis-testing Research, Sociometry 33 (1): 1–11.CrossRefGoogle Scholar
  11. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J. and Brandic, I. (2009). Cloud Computing and Emerging IT Platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems 25 (6): 599–616.CrossRefGoogle Scholar
  12. Carroll, G.R. and Delacroix, J. (1982). Organizational Mortality in the Newspaper Industries of Argentina and Ireland: An ecological approach, Administrative Science Quarterly 27 (2): 169–198.CrossRefGoogle Scholar
  13. Chen, M., Zhang, D. and Zhou, L. (2007). Empowering Collaborative Commerce with Web Services Enabled Business Process Management Systems, Decision Support Systems 43 (2): 530–546.CrossRefGoogle Scholar
  14. Damanpour, F. and Schneider, M. (2006). Phases of the Adoption of Iinnovation in Organizations: Effects of environment, organizaiton and top managers, British Journal of Management 17 (3): 215–236.CrossRefGoogle Scholar
  15. DiMaggio, P.J. and Powell, W.W. (1983). The Iron Cage Revisited: Institutional isomorphism and collective rationality in organizational fields, American Sociological Review 48 (2): 147–160.CrossRefGoogle Scholar
  16. Dubey, A. and Wagle, D. (2007). Delivering software as a service, The McKinsey Quarterly. Web exclusive, May. http://static1.squarespace.com/static/52648eb6e4b0f1fda0be4b23/t/535a7b5ee4b0b44fc2cc4cea/1398438750032/Delivering_software_as_a_service.pdf.
  17. Feuerlicht, G. (2010). Next generation SOA: can SOA survive cloud computing? In Advances in Intelligent Web Mastering-2, Berlin Heidelberg: Springer, pp. 19–29.CrossRefGoogle Scholar
  18. Frohlich, M.T. (2002). E-integration in the Supply Chain: Barriers and performance, Decision Sciences 33 (4): 537–556.CrossRefGoogle Scholar
  19. Frohlich, M.T. and Westbrook, R. (2001). Arcs of Integration: An international study of supply chain strategies, Journal of Operations Management 19 (2): 180–200.CrossRefGoogle Scholar
  20. Greene, C.N. and Organ, D.W. (1973). An Evaluation of Causal Models Linking the Received Role with Job Satisfaction, Administrative Science Quarterly 18 (1): 95–103.CrossRefGoogle Scholar
  21. Grundy, J., Kaefer, G., Keung, J. and Liu, A. (2012). Guest Editors’ Introduction: Software engineering for the cloud, Software, IEEE 29 (2): 26–29.CrossRefGoogle Scholar
  22. Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Multivariate Data Analysis, Upper Saddle River, NJ: Pearson Education, Inc.Google Scholar
  23. Harman, H.H. (1960). Modern Factor Analysis, Chicago, IL: University of Chicago Press.Google Scholar
  24. IBM (2009). The benefits of cloud computing’, [WWW document] available at: ftp://public.dhe.ibm.com/common/ssi/ecm/en/diw03004usen/DIW03004USEN.PDF.
  25. Jeyaraj, A., Rottman, J.W. and Lacity, M.C. (2006). A Review of the Predictors, Linkages, and Biases in IT Innovation Adoption Research, Journal of Information Technology 21 (1): 1–23.CrossRefGoogle Scholar
  26. Joo, Y.-B. and Kim, Y.-G. (2004). Determinants of Corporate Adoption of e-Marketplace: An innovation theory perspective, Journal of Purchasing and Supply Management 10 (2): 89–101.CrossRefGoogle Scholar
  27. Jutras, C. (2011). QAD on demand gives manufacturers the tools they need to become global, Aberdeen Research Group. [WWW document] TheStreet.com Inc. (accessed 22nd May 2011).
  28. Khalifa, M. and Davison, R.M. (2006). SME Adoption of IT: The case of electronic trading systems, IEEE Transactions on Engineering Management 53 (2): 275–284.CrossRefGoogle Scholar
  29. Laird, P. (2008). How Oracle, IBM, SAP, Microsoft, and Intuit are responding to the SaaS revolution. Laird OnDemand. Blogspot. (accessed 24th April 2011).Google Scholar
  30. Lee, J.Y., Lee, J.W., Cheun, D.W. and Kim, S.D. (2009). A Quality Model for Evaluating Software-as-a-Service in Cloud Computing, Presentation made at the 2009 Seventh ACIS International Conference on Software Engineering Research, Management and Applications, Haikou, China.Google Scholar
  31. Li, B. and Lin, B. (2006). Accessing Information Sharing and Information Quality in Supply Chain Management, Decision Support Systems 42 (3): 1641–1656.CrossRefGoogle Scholar
  32. Liu, H., Ke, W., Wei, K.K., Gu, J. and Chen, H. (2010). The Role of Institutional Pressures and Organizational Culture in the Firm’s Intention to Adopt Internet-enabled Supply Chain Management Systems, Journal of Operations Management 28 (5): 372–384.CrossRefGoogle Scholar
  33. Makkonen, H. (2008). Beyond Organizational Innovation Adoption – A conceptual and empirical analysis, Journal of Business Market Management 2 (2): 63–77.CrossRefGoogle Scholar
  34. Meyer, J. and Rowan, B. (1977). Institutional Organizations: Formal structure as myth and ceremony, American Journal of Sociology 83 (2): 340–363.CrossRefGoogle Scholar
  35. Mohr, L.B. (1982). Explaining Organizational Behavior, San Francisco, CA: Jossey-Bass.Google Scholar
  36. Morwitz, V.G., Steckel, J.H. and Gupta, A. (2007). When Do Purchase Intentions Predict Sales? International Journal of Forecasting 23 (3): 347–364.CrossRefGoogle Scholar
  37. Nkhoma, M. and Dang, D. (2013). Contributing Factors of Cloud Computing Adoption: A technology-organization-environment framework approach, International Journal of Information Systems and Engineering 1 (1): 38–49.Google Scholar
  38. Oliveira, T. and Martins, M.F. (2011). Literature Review of Information Technology Adoption Models at Firm Level, Journal Information Systems Evaluation 14 (1): 110–121.Google Scholar
  39. Penttinen, E. and Tuunainen, V.K. (2009). Assessing the Effect of External Pressure in Inter-Organizational IS Adoption – Case Electronic Invoicing. Presentation made at the 8th Workshop on eBusiness, Phoenix.Google Scholar
  40. Podsakoff, P.M. and Organ, D.W. (1986). Self Reports in Organizational Research: Problems and prospects, Journal of Management 12 (4): 531–544.CrossRefGoogle Scholar
  41. Powell, W.W. and DiMaggio, P.J. (eds.) (1991). The New Institutionalism in Organizational Analysis, Chicago, IL: University of Chicago Press.Google Scholar
  42. Rieger, P., Gewald, H. and Schumacher, B. (2013). Cloud-computing in Banking Influential Factors, Benefits and Risks from a Decision Maker’s Perspective. Presentation made at the AMCIS 2013, Chicago, IL.Google Scholar
  43. Rogelberg, S.G. and Stanton, J.M. (2007). Introduction: Understanding and dealing with organizational survey nonresponse, Organizational Research Methods 10 (2): 195–209.CrossRefGoogle Scholar
  44. Rogers, E.M. (1995). Diffusion of Innovations, 1st edn, New York, NY: The Free Press.Google Scholar
  45. Scott, W.R. (1987). The Adolescence of Institutional Theory, Administrative Science Quarterly 32 (4): 493–511.CrossRefGoogle Scholar
  46. Scott, W.R. (2004). Institutional Theory, in G. Ritzer (ed.) Encyclopedia of Social Theory, Thousand Oaks, CA: Sage, pp. 408–4414.Google Scholar
  47. Sharma, A., Citurs, A. and Konsynski, B. (2007). Strategic and Institutional Perspectives in the Adoption and Early Integration of Radio Frequency Identification (RFID). Presentation made at the System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference on System Sciences.Google Scholar
  48. Sun, B. and Morwitz, V.G. (2010). Stated Intentions and Purchase Behavior: A unified model, International Journal of Research in Marketing 27 (4): 356–366.CrossRefGoogle Scholar
  49. Teo, H.H., Wei, K.K. and Benbasat, I. (2003). Predicting Intention to Adopt Interorganizational Linkages: An institutional perspective, MIS Quarterly 27 (1): 19–49.Google Scholar
  50. Tolbert, P.S. (1985). Institutional Environments and Resource Dependence: Sources of administrative structure in institutions of higher education, Administrative Science Quarterly 30 (1): 1–13.CrossRefGoogle Scholar
  51. Tolbert, P.S. and Zucker, L.G. (1983). Institutional Sources of Change in the Formal Structure of Organizations: The diffusion of civil service reform, 1880–1935, Administrative Science Quarterly 28 (1): 22–39.CrossRefGoogle Scholar
  52. Tornatzky, L.G. and Fleischer, M. (1990). The Processes of Technological Innovation, Lexington, MS/Toronto: Lexington Books.Google Scholar
  53. Van Ittersum, K. and Feinberg, F.M. (2010). Cumulative Timed Intent: A new predictive tool for technology adoption, Journal of Marketing Research 47 (5): 808–822.CrossRefGoogle Scholar
  54. Wagner, S.M. and Kemmerling, R. (2010). Handling Nonresponse in Logistics Research, Journal of Business Logistics 31 (2): 357–381.CrossRefGoogle Scholar
  55. Weber, M. ed (1946). Bureaucracy, From Max Weber: Essays in Sociology, Routledge, London: HH Gerth & C. Wright Mills.Google Scholar
  56. Wu, W.W. (2011). Mining Significant Factors Affecting the Adoption of SaaS Using the Rough Set Approach, The Journal of Systems and Software 84 (3): 435–441.CrossRefGoogle Scholar
  57. Wu, F. and Lee, Y. (2005). Determinants of e-Communication Adoption: The internal push versus external pull factor, Marketing Theory 5 (1): 7–31.CrossRefGoogle Scholar

Copyright information

© Association for Information Technology Trust 2015

Authors and Affiliations

  • LeeAnn Kung
    • 1
  • Casey G Cegielski
    • 2
  • Hsiang-Jui Kung
    • 3
  1. 1.Rowan UniversityNJUS
  2. 2.Auburn UniversityALUS
  3. 3.Georgia Southern UniversityGAUS

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