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Leveraging Data Analytics for Behavioral Research

Author

Listed:
  • Luvai Motiwalla

    (University of Massachusetts Lowell)

  • Amit V. Deokar

    (University of Massachusetts Lowell)

  • Surendra Sarnikar

    (California State University East Bay)

  • Angelika Dimoka

    (Temple University)

Abstract

Behavioral research in information systems employing quantitative methods has traditionally relied on mainly survey-based approaches to gather subjective user data. With new advances in technology such as mobile computing, wearable devices, and social media, along with computational capabilities, organizations are in a position to leverage objective data in addressing IT issues typically addressed in behavioral research. In this paper, we propose a framework for envisioning how data analytics may be leveraged in conducting behavioral research. Particularly, the framework is explained using examples from extant research along two broad avenues, namely ‘data analytics for data generation’ and ‘data analytics for model generation.’ The article also serves as an introduction to the special issue of the Information Systems Frontiers (ISF) journal on the topic of ‘Data Analytics in Behavioral Research.’

Suggested Citation

  • Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:4:d:10.1007_s10796-019-09928-8
    DOI: 10.1007/s10796-019-09928-8
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    References listed on IDEAS

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    4. Eleanna Kafeza & Christos Makris & Gerasimos Rompolas & Feras Al-Obeidat, 0. "Behavioral and Migration Analysis of the Dynamic Customer Relationships on Twitter," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    5. Ajaya K. Swain & Valeria R. Garza, 2023. "Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident Resolution," Information Systems Frontiers, Springer, vol. 25(2), pages 819-834, April.
    6. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    7. Mengyue Wang & Xin Li & Patrick Y. K. Chau, 2021. "Leveraging Image-Processing Techniques for Empirical Research: Feasibility and Reliability in Online Shopping Context," Information Systems Frontiers, Springer, vol. 23(3), pages 607-626, June.
    8. Benjamin Clapham & Michael Siering & Peter Gomber, 2021. "Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets," Information Systems Frontiers, Springer, vol. 23(2), pages 477-494, April.
    9. Matteo Francia & Patrick Marcel & Verónika Peralta & Stefano Rizzi, 2022. "Enhancing Cubes with Models to Describe Multidimensional Data," Information Systems Frontiers, Springer, vol. 24(1), pages 31-48, February.
    10. Navid Aghakhani & Onook Oh & Dawn G. Gregg & Jahangir Karimi, 0. "Online Review Consistency Matters: An Elaboration Likelihood Model Perspective," Information Systems Frontiers, Springer, vol. 0, pages 1-15.

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