Ridderprint

Expanding the methodological toolbox of HRM researchers 57 a specific pattern. For example, the displayed engagement patterns could function as predictor in leadership, performance, or attrition models. Fourth and final, the cluster output can function as dependent variable for subsequent analysis regarding pattern occurrence. For example, individual differences or HRM practices may explain why certain employees are more likely to display particular engagement patterns. 3.3.4 Limitations of OMA A general shortcoming of OMA is that it aims to summarize a database filled with potentially very complex sequential patterns into a (handful of) categorical variable(s). While this has proven useful in certain research fields – including research on DNA, life courses, and careers – it has yet to be tested whether longitudinal measures of employee behaviors and cognitions can be similarly reduced to a set of patterns. A second challenge relates to setting the substitution costs right. Several studies illustrate how this ratio should be matched to the specific requirements of the research question and the analysis (Hollister, 2009; Lesnard & Kan, 2011). Other scholars have argued that standard and custom cost ratios lead to similar conclusions (Biemann & Datta, 2014). No study provides insights regarding the optimal settings for research on employee experiences, which may be affected by a wide variety of personal and institutional factors. Moreover, there are rightful concerns regarding the symmetrical nature of the substitution costs (Aisenbrey & Fasang, 2010) which causes transitions between states to be regarded as similar, irrespective of their direction. There seems to be no simple solution to the aforementioned issues, apart from some general best practices regarding the cost setting procedure (e.g., Gauthier, Widmer, Bucher, & Notredame, 2009). A third limitation lies in the descriptive nature of OMA. The method can reduce large information volumes into smaller, workable sets of underlying patterns and complementary analysis may provide insights into why these patterns occur and what they result in. However, researchers seeking to test why, how and when patterns occur and trajectories develop may turn to other methods. Here, multi-level and latent growth models can be used to examine the rate of pattern development as well as its causes. Additionally, hazard and Markov models may uncover why and when transitions between states happen. Moreover, time series analysis could be applied to investigate reoccurring patterns and forecast the future state of employees. Finally, the only software that currently provides a means for automated implementation of OMA is R (R Core Team, 2016). The TraMineR package (Gabadinho, Ritschard, Müller, & Studer, 2011) contains functions that automate the process to a large extent and only minor specification and customized programming is required. Additionally, the package includes several visualization functions that facilitate the interpretation of the model’s output. However, getting accustomed to the R language and syntax can be effortful. 3.3.5 Discussion Previous research has heavily relied on GLM to investigate HRM processes and their potential impact on performance. This paper proposes two statistical modeling