Do we Fully Understand the Critical Success Factors of Employee Portal utilitarianism? - Uncovering and Accounting for Unobserved Heterogeneity

Employee portals are collaborative information systems that are utilized by many companies to improve information exchange, communication, and employee collaboration, as well as to better support their business processes. Although some studies investigate single aspects of portal success, the critical success factors of how employee portals help their users become more productive have to date not been fully explored. To understand the antecedents of realizing employee performance gains though employee portals, we propose and validate a model of factors based on the human-computer interaction literature, particularly to better understand heterogeneity. Accordingly, we apply the finite mixture partial least squares (FIMIX-PLS) approach to uncover different segments of employee portal users and thereby provide a differentiated and more complete segment-specific picture of antecedents of employee portal utilitarianism. Our analysis indicates that the aggregate global model hides the existence of meaningful system user segments that are more homogenous in the productivity drivers. While some users are primarily concerned with ergonomicity, others users’ productivity is the result of functionality. The future research steps we outline include the finding of exploratory variables that characterize different user groups and therefore further improve interpretability.

[1]  Detmar W. Straub,et al.  Validating Instruments in MIS Research , 1989, MIS Q..

[2]  H. Bozdogan Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[3]  W. DeSarbo,et al.  An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data , 1993 .

[4]  Christian M. Ringle,et al.  Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments , 2007 .

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

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

[7]  Jörg Henseler,et al.  A New and Simple Approach to Multi-Group Analysis in Partial Least Squares Path Modeling , 2007 .

[8]  Lawrence A. Gordon,et al.  Information overload: A temporal approach☆ , 1990 .

[9]  Christian M. Ringle,et al.  Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples , 2010 .

[10]  Chang Liu,et al.  Determinants of accepting wireless mobile data services in China , 2008, Inf. Manag..

[11]  H. Murray,et al.  Explorations in Personality , 2007 .

[12]  Marko Sarstedt,et al.  Response-Based Segmentation Using Finite Mixture Partial Least Squares - Theoretical Foundations and an Application to American Customer Satisfaction Index Data , 2010, Data Mining.

[13]  Maiju Markova The DeLone and McLean Model of Information Systems Success – Original and Updated Models , 2006 .

[14]  Gregoris Mentzas,et al.  Review and functional classification of collaborative systems , 2002, Int. J. Inf. Manag..

[15]  H. Akaike A new look at the statistical model identification , 1974 .

[16]  Jong-Ae Kim,et al.  Toward an understanding of Web-based subscription database acceptance , 2006, J. Assoc. Inf. Sci. Technol..

[17]  Mohamad Noorman Masrek,et al.  Measuring campus portal effectiveness and the contributing factors , 2007 .

[18]  Kara A. Latorella,et al.  The Scope and Importance of Human Interruption in Human-Computer Interaction Design , 2002, Hum. Comput. Interact..

[19]  Christian M. Ringle,et al.  A Genetic Algorithm Segmentation Approach for Uncovering and Separating Groups of Data in PLS Path Modeling , 2007 .

[20]  J. Jacoby Perspectives on Information Overload , 1984 .

[21]  Jeffrey K. Liker,et al.  Electronic Meeting Systems: Evidence from a Low Structure Environment , 1992, Inf. Syst. Res..

[22]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[23]  John L. Bennett,et al.  Groupware in Practice: An Interpretation of Work Experiences , 1991, Computerization and Controversy, 2nd Ed..

[24]  Dewi Rooslani Tojib,et al.  User satisfaction with business-to-employee portals: conceptualization and scale development , 2008, Eur. J. Inf. Syst..

[25]  Zheng Zhou,et al.  Development and validation of an instrument to measure user perceived service quality of information presenting Web portals , 2005, Inf. Manag..

[26]  William J. Doll,et al.  The Measurement of End-User Computing Satisfaction , 1988, MIS Q..

[27]  H C Triandis,et al.  Values, attitudes, and interpersonal behavior. , 1980, Nebraska Symposium on Motivation. Nebraska Symposium on Motivation.

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

[29]  Luiz Antonio Joia,et al.  Analysis of the Effects of Technological and Organizational Features on Intranet and Portal Usage , 2008, AMCIS.

[30]  Nils Urbach,et al.  Changing systems to match their users' needs: understanding the realization of utilitarian value from employee portal use , 2011, ECIS.

[31]  Frada Burstein,et al.  A Practical Measure of Employee Satisfaction with B2E Portals , 2007, ICIS.

[32]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[33]  Bengt Muthén,et al.  Latent variable modeling in heterogeneous populations , 1989 .

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

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

[36]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[37]  M. Sarstedt,et al.  Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments? , 2011 .

[38]  W. DeSarbo,et al.  Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity , 1997 .

[39]  C. Ringle,et al.  Segmentation for Path Models and Unobserved Heterogeneity: The Finite Mixture Partial Least Squares Approach , 2006 .

[40]  C. O'Reilly Individuals and Information Overload in Organizations: Is More Necessarily Better? , 1980 .

[41]  James J. Jiang,et al.  E‐commerce user behavior model: an empirical study , 2000 .

[42]  L. Cooper A research agenda to reduce risk in new product development through knowledge management: a practitioner perspective , 2003 .

[43]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[44]  Nassim Belbaly,et al.  Corporate Portal: A Tool for Knowledge Management Synchronization , 2004, Int. J. Inf. Manag..

[45]  M. Wedel,et al.  Market Segmentation: Conceptual and Methodological Foundations , 1997 .

[46]  Wynne W. Chin Issues and Opinion on Structural Equation Modeling by , 2009 .

[47]  D. Broadbent,et al.  What makes interruptions disruptive? A study of length, similarity, and complexity , 1989 .

[48]  Jong-Ae Kim Toward an understanding of Web-based subscription database acceptance , 2006 .

[49]  H. Raghav Rao,et al.  Knowledge Acquisition via Three Learning Processes in Enterprise Information Portals: Learning-by-Investment, Learning-by-Doing, and Learning-from-Others , 2005, MIS Q..

[50]  Kamel Jedidi,et al.  A Hierarchical Bayesian Methodology for Treating Heterogeneity in Structural Equation Models , 2000 .

[51]  Georg Fassott,et al.  Handbuch PLS-Pfadmodellierung. Methode, Anwendung, Praxisbeispiele , 2005 .

[52]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[53]  Marko Sarstedt,et al.  Structural modeling of heterogeneous data with partial least squares , 2010 .

[54]  Marko Sarstedt,et al.  Do We Fully Understand the Critical Success Factors of Customer Satisfaction with Industrial Goods? - Extending Festge and Schwaiger’s Model to Account for Unobserved Heterogeneity , 2009 .

[55]  Robert J. McQueen The effect of voice input on information exchange in computer supported asynchronous group communication , 1992 .

[56]  Frank Huber,et al.  Capturing Customer Heterogeneity using a Finite Mixture PLS Approach , 2002 .

[57]  Robert J. McQueen,et al.  A field study of the effects of asynchronous groupware support on process improvement groups , 1997, J. Inf. Technol..

[58]  Frank Huber,et al.  Capturing Heterogeneity in Customer Satisfaction Models: A finite Mixture PLS Approach , 2002 .

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

[60]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[61]  J. Cellier,et al.  Interference between switched tasks , 1992 .

[62]  M. Sarstedt A review of recent approaches for capturing heterogeneity in partial least squares path modelling , 2008 .

[63]  Franci Pivec Computerization and controversy: value conflicts and social choices , 2003 .

[64]  V. E. Vinzi,et al.  REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modelling , 2008 .

[65]  Na Li,et al.  The Intellectual Development of Human-Computer Interaction Research: A Critical Assessment of the MIS Literature (1990-2002) , 2005, J. Assoc. Inf. Syst..