Ridderprint

Chapter 3 48 2011; Harter, Schmidt, & Hayes, 2002; Jiang, Lepak, & Baer, 2012; Kehoe & Wright, 2013; Subramony, 2009). However, an investigation of this mediational process is complex as it involves measurements at various levels of analysis. The design of HRM policies as well as the implementation of HRM practices commonly occur at either an organizational, functional, departmental or group level whereas the behaviors and cognitions they seek to influence are located at the level of the individual employee (Bowen & Ostroff, 2004; Snape & Redman, 2010; Wright & Boswell, 2002; Wright & Nishii, 2007). Although multi- level techniques provide an avenue to test models with such hierarchical structures (e.g., Snijders & Bosker, 1999), they are developed for models where the outcome variable lies at the lower level of analysis. Problems arise when the outcome occurs at a macro-level (e.g., organizational performance) but the predictors reside at a micro-level (e.g., employee behaviors). Scholars have creatively circumvented modeling such micro-macro processes. For example, studies relating HRM implementation and employee engagement to organizational performance have used three different approaches. Nevertheless, all three have their downsides. First, the micro-level scores can be aggregated to the macro-level. As such, studies have investigated HRM, engagement and performance at the level of the organization or the work group (e.g., Harter et al., 2002; Jiang et al., 2012; Subramony, 2009; Whitman, Van Rooy, & Viswesvaran, 2010). However, after aggregation to the macro-level, the data loses all information on individual variations (Bennink, Croon, & Vermunt, 2013). To illustrate, an aggregated team score of average engagement can be interpreted either as all employees in the team being averagely engaged, or as the team being a mix of highly engaged and highly disengaged employees. Moreover, aggregation has considerable consequences because the reduction of the sample size – to the size of the macro-level sample – significantly decreases the power of the statistical test (Bennink et al., 2013; Krull & MacKinnon, 1999). As a second approach, scholars have restricted the analysis to micro-level variables. For example, studies have examined employees’ individual perceptions of HRM practices and its influence on employees’ engagement and individual performance scores (e.g., Christian et al., 2011; Halbesleben, 2010). However, this approach introduces perspective bias as it looks at the impact of the perceptions employees have of the HRM strategies, policies, and practices. Moreover, no conclusions can be drawn regarding the impact on the actual organizational performance as the models have to rely on micro-level outcomes, such as individual performance evaluations. Finally, disaggregation has been a third approach, in which the macro-level scores (i.e., HRM and organizational performance) are assigned to each micro-level case (i.e., employee). Luckily, this method is not frequently found in published academic studies as disaggregating scores violates the assumption of independent error terms (Keith, 2005), causing biased standard error estimates, overly liberal tests (Krull & MacKinnon, 1999) and artificially high power (Bennink et al., 2013). Bathtub models provide a solution that overcomes the problems specific to the aforementioned approaches by correctly modeling the multi-level processes at hand. They offer an opportunity to investigate the relationship between macro-level variables through micro-level mechanisms. Using a latent variable model, a bathtub model raises