IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v10y1999i1p87-96.html
   My bibliography  Save this article

Nonlinear and Noncompensatory Models in User Information Satisfaction Measurement

Author

Listed:
  • Vikram Sethi

    (CIS Department, Southwest Missouri State University, Springfield, Missouri 65804)

  • Ruth C. King

    (College of Commerce and Industry, University of Illinois at Urbana-Champaign, Urbana, Illinois)

Abstract

This study applies nonlinear and noncompensatory models to examine how users evaluate their satisfaction with their information systems (IS) environment. Several instruments have been developed in the literature to measure user information satisfaction (UIS). These instruments measure user satisfaction by asking respondents to rate their satisfaction/dissatisfaction with a variety of IS attributes; e.g., EDP services, EDP staff, information product, and involvement in IS development. These responses are then combined linearly to develop a surrogate measure for UIS satisfaction. This linear model is derived from Anderson's information theory (Anderson 1981) and based on the assumption that each attribute judgment has a conditionally monotone relationship with the UIS evaluation. However, the literature on attitude formation and decision making suggests that other nonlinear and noncompensatory models are available to decision makers for combining information and are used frequently in attitude formation. In this study, we use two sets of data to examine the linear model and five nonlinear models of decision making to evaluate whether nonlinear models are more effective in predicting a user's overall satisfaction with information systems. First, responses from faculty members at an academic institution were used to test each model. All the nonlinear models were more efficient predictors than the linear models. In addition, two nonlinear models—the multiplicative and the scatter models—best represented the data with square multiple correlations of 0.69 and 0.68, as compared to the linear model which had an R 2 of 0.61. Second, data from a previous study (Galletta and Lederer 1989) were analyzed to examine whether nonlinear models were more efficient. Data for this study were collected using the short version of the Bailey and Pearson (1983) UIS instrument. Results of the analysis from the full and cross-validation samples show that nonlinear, noncompensatory models performed at par or better than the linear model.

Suggested Citation

  • Vikram Sethi & Ruth C. King, 1999. "Nonlinear and Noncompensatory Models in User Information Satisfaction Measurement," Information Systems Research, INFORMS, vol. 10(1), pages 87-96, March.
    • Handle: RePEc:inm:orisre:v:10:y:1999:i:1:p:87-96
    DOI: 10.1287/isre.10.1.87
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.10.1.87
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.10.1.87?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ganzach, Yoav, 1993. "Goals as Determinants of Nonlinear Noncompensatory Judgment Strategies: Leniency vs Strictness," Organizational Behavior and Human Decision Processes, Elsevier, vol. 56(3), pages 422-440, December.
    2. James E. Bailey & Sammy W. Pearson, 1983. "Development of a Tool for Measuring and Analyzing Computer User Satisfaction," Management Science, INFORMS, vol. 29(5), pages 530-545, May.
    3. Kort, Fred, 1968. "A Nonlinear Model for the Analysis of Judicial Decisions," American Political Science Review, Cambridge University Press, vol. 62(2), pages 546-555, June.
    4. Billings, Robert S. & Scherer, Lisa L., 1988. "The effects of response mode and importance on decision-making strategies: Judgment versus choice," Organizational Behavior and Human Decision Processes, Elsevier, vol. 41(1), pages 1-19, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. William Henry Kaye-Blake & Walt L. Abell & Eva Zellman, 2009. "Respondents' ignoring of attribute information in a choice modelling survey," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 53(4), pages 547-564, October.
    2. Rajiv Sabherwal & Anand Jeyaraj & Charles Chowa, 2006. "Information System Success: Individual and Organizational Determinants," Management Science, INFORMS, vol. 52(12), pages 1849-1864, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Affifa Sardar & Amir Manzoor & Khurram Adeel Shaikh & Liaqat Ali, 2021. "An Empirical Examination of the Impact of eWom Information on Young Consumers’ Online Purchase Intention: Mediating Role of eWom Information Adoption," SAGE Open, , vol. 11(4), pages 21582440211, October.
    2. Ganzach, Yoav & Mazursky, David, 1995. "Time dependent biases in consumer multi-attribute judgment," Journal of Economic Psychology, Elsevier, vol. 16(2), pages 331-349, July.
    3. Sanduni I. Senaratne & Samantha M. Samarasinghe, 2019. "Factors Affecting the Intention to Adopt M-Learning," International Business Research, Canadian Center of Science and Education, vol. 12(2), pages 150-164, February.
    4. Zanakis, Stelios H. & Mandakovic, Tomislav & Gupta, Sushil K. & Sahay, Sundeep & Hong, Sungwan, 1995. "A review of program evaluation and fund allocation methods within the service and government sectors," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 59-79, March.
    5. Un-Kon Lee, 2017. "International Tourism Advertisements on Social Media: Impact of Argument Quality and Source," Sustainability, MDPI, vol. 9(9), pages 1-18, August.
    6. Gelderman, Maarten, 1997. "Task difficulty, task variability and satisfaction with management support systems: consequences and solutions ˜," Serie Research Memoranda 0053, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    7. Olivier Glassey, 2010. "eGovernment: Does IT save time?," Post-Print hal-00599187, HAL.
    8. Jong Uk Kim & Rajiv Kishore, 2019. "Do we Fully Understand Information Systems Failure? An Exploratory Study of the Cognitive Schema of IS Professionals," Information Systems Frontiers, Springer, vol. 21(6), pages 1385-1419, December.
    9. Hunton, James E. & Gibson, Dana, 1999. "Soliciting user-input during the development of an accounting information system: investigating the efficacy of group discussion," Accounting, Organizations and Society, Elsevier, vol. 24(7), pages 597-618, October.
    10. Pierre Berthon & Leyland Pitt & Michael Ewing & Christopher L. Carr, 2002. "Potential Research Space in MIS: A Framework for Envisioning and Evaluating Research Replication, Extension, and Generation," Information Systems Research, INFORMS, vol. 13(4), pages 416-427, December.
    11. Verhagen, Tibert & Meents, Selmar, 2007. "A Framework for Developing Semantic Differentials in IS research: Assessing the Meaning of Electronic Marketplace Quality (EMQ)," Serie Research Memoranda 0016, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    12. Nurlia Dewi & Willy Abdillah & Muhartini Salim & Slamet Widodo, 2021. "The Role of Leadership in Implementation Public Information System of Local Government Institutions in Indonesia," International Review of Management and Marketing, Econjournals, vol. 11(6), pages 1-9.
    13. Steven B. Redd, 2002. "The Influence of Advisers on Foreign Policy Decision Making," Journal of Conflict Resolution, Peace Science Society (International), vol. 46(3), pages 335-364, June.
    14. Stephanie Medlock & Juliette L Parlevliet & Danielle Sent & Saeid Eslami & Marjan Askari & Derk L Arts & Joost B Hoekstra & Sophia E de Rooij & Ameen Abu-Hanna, 2017. "An email-based intervention to improve the number and timeliness of letters sent from the hospital outpatient clinic to the general practitioner: A pair-randomized controlled trial," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-13, October.
    15. repec:jtr:journl:v:4:y:2012:i:1:p:12-37 is not listed on IDEAS
    16. Sharath Sasidharan & Radhika Santhanam & Daniel J. Brass & Vallabh Sambamurthy, 2012. "The Effects of Social Network Structure on Enterprise Systems Success: A Longitudinal Multilevel Analysis," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 658-678, September.
    17. Bonaccio, Silvia & Dalal, Reeshad S., 2006. "Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(2), pages 127-151, November.
    18. Bailey, James R., 1997. "Need for cognition and response mode in the active construction of an information domain," Journal of Economic Psychology, Elsevier, vol. 18(1), pages 69-85, February.
    19. Nripendra P. Rana & Yogesh K. Dwivedi & Michael D. Williams & Vishanth Weerakkody, 2015. "Investigating success of an e-government initiative: Validation of an integrated IS success model," Information Systems Frontiers, Springer, vol. 17(1), pages 127-142, February.
    20. Mario J. Maletta, 1993. "An Examination of Auditors' Decisions to Use Internal Auditors as Assistants: The Effect of Inherent Risk," Contemporary Accounting Research, John Wiley & Sons, vol. 9(2), pages 508-525, March.
    21. Caridi, Maria & Crippa, Luca & Perego, Alessandro & Sianesi, Andrea & Tumino, Angela, 2010. "Do virtuality and complexity affect supply chain visibility?," International Journal of Production Economics, Elsevier, vol. 127(2), pages 372-383, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:10:y:1999:i:1:p:87-96. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.