Leveraging latent growth models to better understand MIS theory

Kher, Hemant V.; Serva, Mark A.; Davidson, Spring; Monk, Ellen

doi:10.1145/1542130.1542161

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…A structural equation model was also used to investigate users' behaviour within community networks 4 by Kwon and Onwuegbuzie [2005], Bulletin Board Systems by Chen and Chiu [2007], Wikipedia by Cho et al [2010], social network systems by Kipp and Joo [2010] and Park [2014], electronic commerce by Lu and Zhu [2010] and Afzal [2013], library systems by Sin [2010], exploratory search by O'Brien and Toms [2013], agile software development by Senapathi and Srinivasan [2014], and online education by Zhang and Dang [2015]. Kher et al [2009] used a variation of SEM called Latent Growth Modeling to study longitudinal data where time is a relevant variable.…”

Section: Structural Equation Modelling and Other Domainsmentioning

confidence: 99%

Evaluation of information retrieval systems using structural equation modeling

Melucci

Paggiaro

2019

Computer Science Review

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The interpretation of the experimental data collected by testing systems across input datasets and model parameters is of strategic importance for system design and implementation. In particular, finding relationships between variables and detecting the latent variables affecting retrieval performance can provide designers, engineers and experimenters with useful if not necessary information about how a system is performing. This paper discusses the use of Structural Equation Modelling (SEM) in providing an in-depth explanation of evaluation results and an explanation of failures and successes of a system; in particular, we focus on the case of Information Retrieval.

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Section: Structural Equation Modelling and Other Domainsmentioning

confidence: 99%

Evaluation of information retrieval systems using structural equation modeling

Melucci

Paggiaro

2019

Computer Science Review

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“…Mithas et al (2007) tested the effects of website design elements on customer loyalty to a website using the HLM approach to model multi-level and cross-level interactions. Kher et al (2009) examined changes in students' computer self-efficacy over time using a structural equation modelling (SEM)-based multi-level methodology called latent growth (curve) modelling. Similarly, Qureshi (2009) analysed longitudinal data from an international management consulting firm using latent curve modelling and demonstrated that such analysis can identify and explain temporal patterns better than existing methods.…”

Section: Theory and Hypotheses 21 Multi-level Research In Informatimentioning

confidence: 99%

Multi-level analysis in information systems research: the case of enterprise resource planning system usage in China

Sun

Bhattacherjee

2011

Enterprise Information Systems

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Information technology (IT) usage within organisations is a multi-level phenomenon that is influenced by individual-level and organisational-levelvariables. Yet, current theories, such as the unified theory of acceptance and use of technology, describe IT usage as solely an individual-level phenomenon. This article postulates a model of organisational IT usage that integrates salient organisational-level variables such as user training, top management support and technical support within an individual-level model to postulate a multi-level model of IT usage. The multi-level model was then empirically validated using multi-level data collected from 128 end users and 26 managers in 26 firms in China regarding their use of enterprise resource planning systems and analysed using the multi-level structural equation modelling (MSEM) technique. We demonstrate the utility of MSEM analysis of multi-level data relative to the more common structural equation modelling analysis of single-level data and show how single-level data can be aggregated to approximate multi-level analysis when multi-level data collection is not possible. We hope that this article will motivate future scholars to employ multi-level data and multi-level analysis for understanding organisational phenomena that are truly multi-level in nature.

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Changing the way we study change

Kher

Serva

2014

SIGMIS Database

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AcknowledgementThe authors thank the two anonymous reviewers and the SE, Andrew Hardin for providing invaluable feedback that significantly improved the quality of the manuscript. Additionally, the authors would also like to thank John Sawyer and Stewart Shapiro (University of Delaware), and Ron Thompson (Wake Forest University) for reading the initial drafts of the paper and providing important feedback. AbstractRecently, scholars in various disciplines have called for the use of longitudinal research designs to test and build theory. Their argument is that key phenomena in virtually every theory change over time. As such, cross-sectional research designs used to test and extend theories do not provide insights that help understand the nature of temporal relationships between variables that are central to theory. Evidence is emerging that in some cases the strength and the direction of the relationship between variables found using longitudinal data is quite different relative to that found using cross-sectional data. The view expressed in this paper is that longitudinal research brings with it both new opportunities and challenges for information systems (IS) researchers. Opportunities will come in the form of explicitly incorporating time in testing and applying IS theories to cast new light on prior research that has been predominantly based on cross-sectional designs. At the same time, challenges will come in the form of proposing hypotheses on interrelationships between variables over time, and using newer data analytic techniques that are better suited to analyzing longitudinal data.We provide illustrations that highlight both advantages and challenges associated with longitudinal research in the field of IS.

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Leveraging latent growth models to better understand MIS theory

Cited by 3 publications

References 14 publications

Evaluation of information retrieval systems using structural equation modeling

Evaluation of information retrieval systems using structural equation modeling

Multi-level analysis in information systems research: the case of enterprise resource planning system usage in China

Changing the way we study change

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