Latent Growth Modeling for Information Systems : Theoretical Extensions and Practical Applications
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
Year of publication: |
2016
|
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Authors: | Zheng, Zhiqiang (Eric) ; Pavlou, Paul A. ; Gu, Bin |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (41 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: Information Systems Research (2014), 25 (3), 547-568 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 1, 2014 erstellt |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014037251
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