The Love of Money and Pay Level Satisfaction: Measurement and Functional Equivalence in 29 Geopolitical Entities around the World

Demonstrating the equivalence of constructs is a key requirement for cross-cultural empirical research. The major purpose of this paper is to demonstrate how to assess measurement and functional equivalence or invariance using the 9-item, 3-factor Love of Money Scale (LOMS, a second-order factor model) and the 4-item, 1-factor Pay Level Satisfaction Scale (PLSS, a first-order factor model) across 29 samples in six continents (N = 5973). In step 1, we tested the configural, metric and scalar invariance of the LOMS and 17 samples achieved measurement invariance. In step 2, we applied the same procedures to the PLSS and nine samples achieved measurement invariance. Five samples (Brazil, China, South Africa, Spain and the USA) passed the measurement invariance criteria for both measures. In step 3, we found that for these two measures, common method variance was non-significant. In step 4, we tested the functional equivalence between the Love of Money Scale and Pay Level Satisfaction Scale. We achieved functional equivalence for these two scales in all five samples. The results of this study suggest the critical importance of evaluating and establishing measurement equivalence in cross-cultural studies. Suggestions for remedying measurement non-equivalence are offered

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