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

Information Systems Research (2014), 25 (3), 547-568

Fox School of Business Research Paper No. 14-022

41 Pages Posted: 10 May 2014 Last revised: 31 Dec 2016

See all articles by Zhiqiang (Eric) Zheng

Zhiqiang (Eric) Zheng

UC Riverside

Paul A. Pavlou

Temple University - Department of Management Information Systems; Temple University - Department of Strategic Management

Bin Gu

Arizona State University (ASU) - Department of Information Systems

Date Written: April 1, 2014

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 IS research, specifically how to pose research questions that focus on change over time and also how to implement LGM models to test time-centric hypotheses. Second and more importantly, 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, and 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 the performance of LGM. 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.

Keywords: Latent growth modeling, LGM, Longitudinal data, d-Separation, Word of Mouth

Suggested Citation

Zheng, Zhiqiang (Eric) and Pavlou, Paul A. and Gu, Bin, Latent Growth Modeling for Information Systems: Theoretical Extensions and Practical Applications (April 1, 2014). Information Systems Research (2014), 25 (3), 547-568; Fox School of Business Research Paper No. 14-022. Available at SSRN: https://ssrn.com/abstract=2434743

Zhiqiang (Eric) Zheng

UC Riverside ( email )

900 University Avenue
Riverside, CA 92521
United States

Paul A. Pavlou (Contact Author)

Temple University - Department of Management Information Systems ( email )

1810 N. 13th Street
Floor 2
Philadelphia, PA 19128
United States

Temple University - Department of Strategic Management ( email )

Fox School of Business and Management
Philadelphia, PA 19122
United States

Bin Gu

Arizona State University (ASU) - Department of Information Systems ( email )

Tempe, AZ
United States

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