Exploring Organizational Level Continuance of Cloud-Based Enterprise Systems

As cloud computing has become a mature technology broadly being adopted by companies across all industries, cloud service providers are increasingly turning their attention to retaining their customers. However, only little research has been conducted on investigating the antecedents of service continuance in an organizational context. To address this gap in research, we carried out a quantitative-empirical study. We developed a conceptual model that builds on previous research on organizational level continuance. We tested this model, using survey data gathered from IT decision makers of companies which have adopted cloud enterprise systems. The data was analyzed using PLS. The results show that continuance intention can be predicted both by socio-organizational and technology-related factors, explaining 55.9 % of the dependent variable’s variance. Besides cloud specific findings, the study also enhances knowledge in the area of organizational level system continuance as well as its connection to IS success.

[1]  Everett M. Rogers,et al.  Diffusion of innovations (5. ed.) , 2003 .

[2]  E. Burton Swanson,et al.  System Life Expectancy and the Maintenance Effort: Exploring Their Equilibration , 2000, MIS Q..

[3]  Albert H. Segars,et al.  Strategic Information Systems Planning Success: An Investigation of the Construct and Its Measurement , 1998, MIS Q..

[4]  T. Grandon Gill Early Expert Systems : Where Are They Now ? , 2002 .

[5]  R. Anthony,et al.  Planning and Control Systems: A Framework for Analysis , 1965 .

[6]  José Esteves,et al.  An Updated ERP Systems Annotated Bibliography: 2001-2005 , 2007, Commun. Assoc. Inf. Syst..

[7]  William R. King,et al.  Antecedents of Knowledge Transfer from Consultants to Clients in Enterprise System Implementations , 2005, MIS Q..

[8]  Guy Fitzgerald,et al.  The turnaround of the London Ambulance Service Computer-Aided Despatch system (LASCAD) , 2005, Eur. J. Inf. Syst..

[9]  Gerold Riempp,et al.  The State of Research on Information Systems Success , 2009, Bus. Inf. Syst. Eng..

[10]  Ronald T. Cenfetelli,et al.  Interpretation of Formative Measurement in Information Systems Research , 2009, MIS Q..

[11]  Pekka Ahtiala The optimal pricing of computer software and other products with high switching costs , 2006 .

[12]  M. Fleischer,et al.  processes of technological innovation , 1990 .

[13]  Izak Benbasat,et al.  The Use of Information in Decision Making: An Experimental Investigation of the Impact of Computer-Based Decision Aids , 1992, MIS Q..

[14]  I. Ajzen The theory of planned behavior , 1991 .

[15]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[16]  Yi-Shun Wang Assessing e‐commerce systems success: a respecification and validation of the DeLone and McLean model of IS success , 2008, Inf. Syst. J..

[17]  Mark Keil,et al.  Why Software Projects Escalate: An Empirical Analysis and Test of Four Theoretical Models , 2000, MIS Q..

[18]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[19]  Sammy W. Pearson,et al.  Development of a Tool for Measuring and Analyzing Computer User Satisfaction , 1983 .

[20]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[21]  Caroline Chan,et al.  Cloud Enterprise Systems: A Review Of Literature And Its Adoption , 2012, PACIS.

[22]  D. Leonard Implementation as Mutual Adaptation of Technology and Organization , 2011 .

[23]  Yu-Hui Wang,et al.  The role of SaaS privacy and security compliance for continued SaaS use , 2011, The 7th International Conference on Networked Computing and Advanced Information Management.

[24]  Nils Urbach,et al.  Structural Equation Modeling in Information Systems Research Using Partial Least Squares , 2010 .

[25]  Wynne W. Chin,et al.  Extending the technology acceptance model: the influence of perceived user resources , 2001, DATB.

[26]  Seymour Geisser,et al.  The Predictive Sample Reuse Method with Applications , 1975 .

[27]  Scott B. MacKenzie,et al.  Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques , 2011, MIS Q..

[28]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[29]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[30]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[31]  Panayiotis Bozanis,et al.  Business Application Acquisition: On-Premise or SaaS-Based Solutions? , 2012, IEEE Software.

[32]  Torsten Eymann,et al.  Success Factors and Value Propositions of Software as a Service Providers - A Literature Review and Classification , 2012, AMCIS.

[33]  R. Oliver A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

[34]  D. Straub,et al.  Specifying Formative Constructs in Information Systems , 2017 .

[35]  Izak Benbasat,et al.  Predicting Intention to Adopt Interorganizational Linkages: An Institutional Perspective , 2003, MIS Q..

[36]  Ephraim R. McLean,et al.  Measuring information systems success: models, dimensions, measures, and interrelationships , 2008, Eur. J. Inf. Syst..

[37]  Phil Johnson,et al.  Understanding Management Research: An Introduction to Epistemology , 2000 .

[38]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[39]  Panayiotis Bozanis,et al.  SaaS-Based Solutions? , 2012 .

[40]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and Voice Mail Emotion/Adoption Study , 1996, ICIS.

[41]  Anol Bhattacherjee,et al.  Information Technology Continuance: A Theoretic Extension and Empirical Test , 2008, J. Comput. Inf. Syst..

[42]  H. Winklhofer,et al.  Index Construction with Formative Indicators: An Alternative to Scale Development , 2001 .

[43]  Thomas Hess,et al.  Service Quality in Software-as-a-Service: Developing the SaaS-Qual Measure and Examining Its Role in Usage Continuance , 2011, J. Manag. Inf. Syst..

[44]  Cheryl Burke Jarvis,et al.  The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. , 2005, The Journal of applied psychology.

[45]  Straub,et al.  Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research , 2011 .

[46]  Detmar W. Straub,et al.  Validation Guidelines for IS Positivist Research , 2004, Commun. Assoc. Inf. Syst..

[47]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[48]  Andrew B. Whinston,et al.  A Transaction Cost Perspective of the "Software as a Service" Business Model , 2009, J. Manag. Inf. Syst..

[49]  Wynne W. Chin How to Write Up and Report PLS Analyses , 2010 .

[50]  Anand Jeyaraj,et al.  A review of the predictors, linkages, and biases in IT innovation adoption research , 2006, J. Inf. Technol..

[51]  M. Brian Blake,et al.  Service-Oriented Computing and Cloud Computing: Challenges and Opportunities , 2010, IEEE Internet Computing.

[52]  Mingdi Xin,et al.  Software-as-a-Service Model: Elaborating Client-Side Adoption Factors , 2008, ICIS.

[53]  Darshana Sedera,et al.  Re-conceptualizing Information System Success: The IS-Impact Measurement Model , 2008, J. Assoc. Inf. Syst..

[54]  Detmar W. Straub,et al.  Specifying Formative Constructs in Information Systems Research , 2007, MIS Q..

[55]  Thomas H. Davenport,et al.  The New Industrial Engineering: Information Technology and Business Process Redesign , 2011 .

[56]  Michael R. Wade,et al.  An Exploration of Organizational Level Information Systems Discontinuance Intentions , 2011, MIS Q..

[57]  D. Rousseau Issues of level in organizational research: Multi-level and cross-level perspectives. , 1985 .

[58]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[59]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Dependent Variable , 1992, Inf. Syst. Res..

[60]  Thomas Hess,et al.  Opportunities and risks of software-as-a-service: Findings from a survey of IT executives , 2011, Decis. Support Syst..

[61]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic - Mail Emotion/Adoption Study , 2003, Inf. Syst. Res..

[62]  Varun Grover,et al.  Technostress: Technological Antecedents and Implications , 2011, MIS Q..

[63]  Gerold Riempp,et al.  An empirical investigation of employee portal success , 2010, J. Strateg. Inf. Syst..

[64]  Barbara H Wixom,et al.  A Theoretical Integration of User Satisfaction and Technology Acceptance , 2005, Inf. Syst. Res..

[65]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[66]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[67]  BaruaAnitesh,et al.  A Transaction Cost Perspective of the "Software as a Service" Business Model , 2009 .

[68]  H. Arkes,et al.  The Psychology of Sunk Cost , 1985 .

[69]  Richard T. Watson,et al.  Analyzing the Past to Prepare for the Future: Writing a Literature Review , 2002, MIS Q..

[70]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[71]  Torsten Eymann,et al.  The Role of Confirmation on IS Continuance Intention in the Context of On-Demand Enterprise Systems in the Post-Acceptance Phase , 2012, AMCIS.

[72]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .