In the mood: online mood profiling, mood response clusters, and mood-performance relationships in high-risk vocations

In the mood: online mood profiling, mood response clusters, and mood-performance relationships in high-risk vocations

The relationship between mood and performance has long attracted the attention of researchers. Typically, research on the mood construct has had a strong focus on psychometric tests that assess transient emotions (e.g., Profile of Mood States [POMS]; McNair, Lorr, & Dropplemann, 1971, 1992; Terry, Lane, Lane, & Keohane, 1999). Commonly referred to as mood profiling, many inventories have originated using limited normative data (Terry et al., 1999), and cannot be generalised beyond the original population of interest. With brevity being an important factor when assessing mood, Terry et al. (1999) developed a 24-item version of the POMS, now known as the Brunel Mood Scale (BRUMS). Including six subscales (i.e., tension, depression, anger, vigour, fatigue, and confusion), the BRUMS has undergone rigorous validity testing (Terry, Lane, & Fogarty, 2003) making it an appropriate measure in several performance environments. Mood profiling is used extensively for diverse purposes around the world, although Internet-delivered interventions have only recently been made available, being in conjunction with the proliferation of the World Wide Web. Developed by Lim and Terry in 2011, the In The Mood website (http://www.moodprofiling.com) is a web-based mood profiling measure based on the BRUMS and guided by the mood-performance conceptual framework of Lane and Terry (2000). The focus of the website is to facilitate a prompt calculation and interpretation of individual responses to a brief mood scale, and link idiosyncratic feeling states to specific mood regulation strategies with the aim of facilitating improved performance. Although mood profiling has been a popular clinical technique since the 1970s, currently there are no published investigations of whether distinct mood profiles can be identified among the general population. Given this, the underlying aim of the present research was to investigate clusters of mood profiles. The mood responses (N = 2,364) from the In The Mood website were analysed using agglomerative, hierarchical cluster analysis which distinguished six distinct and theoretically meaningful profiles. K-means clustering with a prescribed six-cluster solution was used to further refine the final parameter solution. The mood profiles identified were termed the iceberg, inverse iceberg, inverse Everest, shark fin, surface, and submerged profiles. A multivariate analysis of variance (MANOVA) showed significant differences between clusters on each dimension of mood, and a series of chi-square tests of goodness-of-fit indicated that gender, age, and education were unequally distributed. Further, a simultaneous multiple discriminant function analysis (DFA) showed that cluster membership could be correctly classified with a high degree of accuracy. Following this, a second (N = 2,303) and third (N = 1,865) sample each replicated the results. Given that certain vocations are by nature riskier than others (Khanzode, Maiti, & Ray, 2011) highlighting the importance of performance in the workplace, the present research aimed to further generalise the BRUMS to high-risk industries using a web-based delivery method. Participants from the construction and mining industries were targeted, and the relationship between mood and performance in the context of safety was investigated, together with associated moderating variables (i.e., gender, age, education, occupation, roster, ethnicity, and location).