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Latent Growth Modeling for Information Systems: Theoretical Extensions and Practical Applications

Author

Listed:
  • Zhiqiang (Eric) Zheng

    (Jindal School of Management, University of Texas at Dallas, Dallas, Texas 75080)

  • Paul A. Pavlou

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Bin Gu

    (W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

Abstract

This paper presents and extends Latent Growth Modeling (LGM) as a complementary method for analyzing longitudinal data, modeling the process of change over time, testing time-centric hypotheses, and building longitudinal theories. We first describe the basic tenets of LGM and offer guidelines for applying LGM to Information Systems (IS) research, specifically how to pose research questions that focus on change over time and how to implement LGM models to test time-centric hypotheses. Second and more important, we theoretically extend LGM by proposing a model validation criterion, namely “ d - separation ,” to evaluate why and when LGM works and test its fundamental properties and assumptions. Our d -separation criterion does not rely on any distributional assumptions of the data; it is grounded in the fundamental assumption of the theory of conditional independence. Third, we conduct extensive simulations to examine a multitude of factors that affect LGM performance. Finally, as a practical application, we apply LGM to model the relationship between word-of-mouth communication (online product reviews) and book sales over time with longitudinal 26-week data from Amazon. The paper concludes by discussing the implications of LGM for helping IS researchers develop and test longitudinal theories.

Suggested Citation

  • Zhiqiang (Eric) Zheng & Paul A. Pavlou & Bin Gu, 2014. "Latent Growth Modeling for Information Systems: Theoretical Extensions and Practical Applications," Information Systems Research, INFORMS, vol. 25(3), pages 547-568, September.
    • Handle: RePEc:inm:orisre:v:25:y:2014:i:3:p:547-568
    DOI: 10.1287/isre.2014.0528
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    Cited by:

    1. Mingwen Yang & Zhiqiang (Eric) Zheng & Vijay Mookerjee, 2021. "The Race for Online Reputation: Implications for Platforms, Firms, and Consumers," Information Systems Research, INFORMS, vol. 32(4), pages 1262-1280, December.
    2. Jeremy S. Wolter & Dora Bock & Jeremy Mackey & Pei Xu & Jeffery S. Smith, 2019. "Employee satisfaction trajectories and their effect on customer satisfaction and repatronage intentions," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 815-836, September.
    3. Dmytro Babik & Rahul Singh & Xia Zhao & Eric W. Ford, 2017. "What you think and what I think: Studying intersubjectivity in knowledge artifacts evaluation," Information Systems Frontiers, Springer, vol. 19(1), pages 31-56, February.

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