IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v28y2017i3p451-467.html

Thinking About Measures and Measurement in Positivist Research: A Proposal for Refocusing on Fundamentals

Author

Listed:
  • Andrew Burton-Jones

    (University of Queensland Business School and Centre for the Business and Economics of Health, St. Lucia, QLD 4072, Australia)

  • Allen S. Lee

    (School of Business, Virginia Commonwealth University, Richmond, Virginia 23284)

Abstract

We challenge two taken-for-granted assumptions about measurement in positivist research. The first assumption is that measures and measurements are relevant for quantitative, but not qualitative, research. We explain why they apply to both types of research. The second assumption we challenge is that existing measurement practices are unproblematic, even if researchers sometimes vary in how well they enact them. We explain why current norms (both espoused and enacted) are deficient in some important ways because they fail to emphasize the fundamental issues of measures and measurements. Drawing on symbolic logic, we provide a framework to help positivist researchers to assess efforts in measuring and measurement regardless of their quantitative or qualitative orientation. The framework provides more parsimonious and broadly applicable guidance than available to date and suggests the need to refocus on measurement fundamentals.

Suggested Citation

  • Andrew Burton-Jones & Allen S. Lee, 2017. "Thinking About Measures and Measurement in Positivist Research: A Proposal for Refocusing on Fundamentals," Information Systems Research, INFORMS, vol. 28(3), pages 451-467, September.
  • Handle: RePEc:inm:orisre:v:28:y:2017:i:3:p:451-467
    DOI: 10.1287/isre.2017.0704
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2017.0704
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2017.0704?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. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    2. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    3. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
    4. Prasanna Tambe & Lorin M. Hitt, 2014. "Measuring Information Technology Spillovers," Information Systems Research, INFORMS, vol. 25(1), pages 53-71, March.
    5. Spiggle, Susan, 1994. "Analysis and Interpretation of Qualitative Data in Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(3), pages 491-503, December.
    6. Jarvis, Cheryl Burke & MacKenzie, Scott B & Podsakoff, Philip M, 2003. "A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(2), pages 199-218, September.
    7. Houston, Mark B., 2004. "Assessing the validity of secondary data proxies for marketing constructs," Journal of Business Research, Elsevier, vol. 57(2), pages 154-161, February.
    8. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    9. Sartori, Giovanni, 1970. "Concept Misformation in Comparative Politics," American Political Science Review, Cambridge University Press, vol. 64(4), pages 1033-1053, December.
    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. Claudio Vitari & Jean‐charles Pillet, 2023. "Scale adaptation of MIS measures and their implications on MIS scholarship [Adaptation d'échelles de mesure en Systèmes d'Information et leurs implications pratiques]," Post-Print hal-04118246, HAL.
    2. Xi, Mengjie & Liu, Yang & Fang, Wei & Feng, Taiwen, 2024. "Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective," International Journal of Production Economics, Elsevier, vol. 267(C).
    3. Jean-Charles Pillet & Claudio Vitari & Jackie London & Kevin D Matthews, 2022. "Early-Stage Construct Development Practices in IS Research: A 2000-2020 Review," Post-Print hal-03876784, HAL.
    4. Massimo Magni & Manju K. Ahuja & Chiara Trombini, 2023. "Excessive Mobile Use and Family-Work Conflict: A Resource Drain Theory Approach to Examine Their Effects on Productivity and Well-Being," Information Systems Research, INFORMS, vol. 34(1), pages 253-274, March.

    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. Siti Salwa Mohd Ishak & Sidney Newton, 2018. "Testing a Model of User Resistance Towards Technology Adoption in Construction Organizations," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-27, December.
    2. Sun, Jonghak & Teng, James T.C., 2017. "The construct of information systems use benefits: Theoretical explication of its underlying dimensions and the development of a measurement scale," International Journal of Information Management, Elsevier, vol. 37(5), pages 400-416.
    3. Wen-Lung Shiau & Yogesh K. Dwivedi, 2013. "Citation and co-citation analysis to identify core and emerging knowledge in electronic commerce research," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1317-1337, March.
    4. Kuo-Yu Huang & Yea-Ru Chuang, 2016. "A task–technology fit view of job search website impact on performance effects: An empirical analysis from Taiwan," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1253943-125, December.
    5. Philippe Cohard, 2020. "Information Systems Values: A Study of the Intranet in Three French Higher Education Institutions," Post-Print hal-02987225, HAL.
    6. Peng, Zeyu & Sun, Yongqiang & Guo, Xitong, 2018. "Antecedents of employees’ extended use of enterprise systems: An integrative view of person, environment, and technology," International Journal of Information Management, Elsevier, vol. 39(C), pages 104-120.
    7. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    8. Lee, Sang-Yong Tom & Kim, Hee-Woong & Gupta, Sumeet, 2009. "Measuring open source software success," Omega, Elsevier, vol. 37(2), pages 426-438, April.
    9. Dehghani, Milad & William Kennedy, Ryan & Mashatan, Atefeh & Rese, Alexandra & Karavidas, Dionysios, 2022. "High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology," Journal of Business Research, Elsevier, vol. 149(C), pages 393-411.
    10. Morteza Ghobakhloo & Masood Fathi, 2019. "Modeling the Success of Application-Based Mobile Banking," Economies, MDPI, vol. 7(4), pages 1-21, November.
    11. Chi-Yo Huang & Hui-Ya Wang & Chia-Lee Yang & Steven J. H. Shiau, 2020. "A Derivation of Factors Influencing the Diffusion and Adoption of an Open Source Learning Platform," Sustainability, MDPI, vol. 12(18), pages 1-27, September.
    12. Escobar-Rodríguez, T. & Carvajal-Trujillo, E., 2014. "Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model," Tourism Management, Elsevier, vol. 43(C), pages 70-88.
    13. Susan A. Brown & Viswanath Venkatesh & Sandeep Goyal, 2012. "Expectation Confirmation in Technology Use," Information Systems Research, INFORMS, vol. 23(2), pages 474-487, June.
    14. Andrew Burton-Jones & Detmar W. Straub, 2006. "Reconceptualizing System Usage: An Approach and Empirical Test," Information Systems Research, INFORMS, vol. 17(3), pages 228-246, September.
    15. Wasfi Al-Rawabdah & Adel A. Salloum & Serene Zakaria Tarawneh, 2021. "The Moderating Role Of Factors That Influence User Adoption Of Mobile Health Applications: Evidence From Jordan," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 15(1), pages 1-18.
    16. Zhunzhun Liu & Shenglin Ben & Ruidong Zhang, 2019. "Factors affecting consumers’ mobile payment behavior: a meta-analysis," Electronic Commerce Research, Springer, vol. 19(3), pages 575-601, September.
    17. Barbara L. Marcolin & Deborah R. Compeau & Malcolm C. Munro & Sid L. Huff, 2000. "Assessing User Competence: Conceptualization and Measurement," Information Systems Research, INFORMS, vol. 11(1), pages 37-60, March.
    18. Mateus Martins & Josivania Silva Farias & Pedro Henrique Melo Albuquerque & Danilo Santana Pereira, 2018. "Adoption of Technology for Reading Purposes: A Study of E-Books Acceptance," Brazilian Business Review, Fucape Business School, vol. 15(6), pages 568-588, November.
    19. Haque, AKM Bahalul & Islam, A.K.M. Najmul & Mikalef, Patrick, 2023. "Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    20. Haque, Md Ziaul & Qian, Aimin & Hoque, Md Rakibul & Lucky, Suraiea Akter, 2022. "A unified framework for exploring the determinants of online social networks (OSNs) on institutional investors’ capital market investment decision," Technology in Society, Elsevier, vol. 70(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:28:y:2017:i:3:p:451-467. 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.