The influence of job, team and organizational level resources on employee well-being, engagement, commitment and extra-role performance : test of a model

Purpose – Worker well-being continues to be fundamental to the study of work and a primary consideration for how organizations can achieve competitive advantage and sustainable and ethical work practices (Cartwright and Holmes; Harter, Schmidt and Keyes; Wright and Cropanzano). The science and practice of employee engagement, a key indicator of employee well-being, continues to evolve with ongoing incremental refinements to existing models and measures. This study aims to elaborate the Job Demands-Resources model of work engagement (Bakker and Demerouti) by examining how organizational, team and job level factors interrelate to influence engagement and well-being and downstream outcome variables such as affective commitment and extra-role behaviour.

Design/methodology/approach – Structural equations modelling of survey data obtained from 3,437 employees of a large multi-national mining company was used to test the important direct and indirect influence of organizational focused resources (a culture of fairness and support), team focused resources (team climate) and job level resources (career development, autonomy, supervisor support, and role clarity) on employee well-being, engagement, extra-role behaviour and organizational commitment.

Findings – The fit of the proposed measurement and structural models met criterion levels and the structural model accounted for sizable proportions of the variance in engagement/wellbeing (66 percent), extra-role-behaviour (52 percent) and commitment (69 percent).

Research limitations/implications – Study limitations (e.g. cross-sectional research design) and future opportunities are outlined.

Originality/value – The study demonstrates important extensions to the Job Demands-Resources model and provides researchers and practitioners with a simple but powerful motivational framework, a suite of measures, and a map of their inter-relationships which can be used to help understand, develop and manage employee well-being and engagement and their outcomes.

A new hybrid method for Bayesian network learning With dependency constraints

A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure.

Associations between neighbourhood and household environmental variables and fruit consumption : exploration of mediation by individual cognitions and habit strength in the GLOBE study

Objective The present study examined associations of several home and neighbourhood environmental variables with fruit consumption and explored whether these associations were mediated by variables derived from the Theory of Planned Behaviour (TPB) and by habit strength.

Design Data of the Dutch GLOBE study on household and neighbourhood environment, fruit intake and related factors were used, obtained by self-administered questionnaires (cross-sectional), face-to-face interviews and audits.

Setting The city of Eindhoven in the Netherlands

Subjects Adults (n 333; mean age 58 years, 54 % female).

Results Multiple mediation analyses were conducted using regression analyses to assess the association between environmental variables and fruit consumption, as well as mediation of these associations by TPB variables and by habit strength. Intention, perceived behaviour control, subjective norm and habit strength were associated with fruit intake. None of the neighbourhood environmental variables was directly or indirectly associated with fruit intake. The home environmental variable ‘modelling behaviour by family members’ was indirectly, but not directly, associated with fruit intake. Habit strength and perceived behaviour control explained most of the mediated effect (71·9 %).

Conclusions Modelling behaviour by family members was indirectly associated with fruit intake through habit strength and perceived behaviour control. None of the neighbourhood variables was directly or indirectly, through any of the proposed mediators, associated with adult fruit intake. These findings suggest that future interventions promoting fruit intake should address a combination of the home environment (especially modelling behaviour by family members), TPB variables and habit strength for fruit intake.

Incremental criterion prediction of personality facets over factors: obtaining unbiased estimates and confidence intervals

 Many researchers have argued that higher order models of personality such as the Five Factor Model are insufficient, and that facet-level analysis is required to better understand criteria such as well-being, job performance, and personality disorders. However, common methods in the extant literature used to estimate the incremental prediction of facets over factors have several shortcomings. This paper delineates these shortcomings by evaluating alternative methods using statistical theory, simulation, and an empirical example. We recommend using differences between Olkin-Pratt adjusted r-squared for factor versus facet regression models to estimate the incremental prediction of facets and present a method for obtaining confidence intervals for such estimates using double adjusted-. r-squared bootstrapping. We also provide an R package that implements the proposed methods.

Application of the gradient boosted method in randomised clinical trials: participant variables that contribute to depression treatment efficacy of duloxetine, SSRIs or placebo

Randomised, placebo-controlled trials of treatments for depression typically collect outcomes data but traditionally only analyse data to demonstrate efficacy and safety. Additional post-hoc statistical techniques may reveal important insights about treatment variables useful when considering inter-individual differences amongst depressed patients. This paper aims to examine the Gradient Boosted Model (GBM), a statistical technique that uses regression tree analyses and can be applied to clinical trial data to identify and measure variables that may influence treatment outcomes.

Exploring associations between parental and peer variables, personal variables and physical activity among adolescents: a mediation analysis

This study aimed to investigate how parental and peer variables are associated with moderate- to-vigorous intensity physical activity (MVPA) on week- and weekend days among Australian adolescents (13-15 y), and whether perceived internal barriers (e.g. lack of time), external barriers (e.g. lack of others to be physically active with) and self-efficacy mediated these associations.

Do we know how to design effective health coaching interventions: a systematic review of the state of the literature

 Objective. To systematically review health coaching interventions regarding effectiveness of health coaching for specific outcomes, optimal intervention approaches, and identification of specific techniques associated with effectiveness.
Data Source. Articles were sourced from CINAHL, Global Health, PsycINFO, Academic Search Complete, Health Source, Psychology and Behavioral Sciences Collection, and Medline.
Study Inclusion and Exclusion Criteria. Randomized controlled trials were included if the study (1) employed health coaching according to a predefined criterion; (2) clearly reported the use of health coaching; or (3) incorporated the use of coaching.

Data Extraction. Aims, participants, approach, behavior change techniques (BCTs), and findings pertaining to each study were summarized. BCTs were classified according to the CALO-RE taxonomy.

Data Synthesis. Data were synthesized by cross-tabulation of BCTs with study outcomes.
Results. Fifteen of 16 eligible studies reported a positive intervention effect in at least one outcome. Nine studies (56%) did not define health coaching; the number of intervention sessions provided ranged from 2 to 48; and in three studies, one or more intervention details were unclear. It was hence difficult to synthesize the studies to adequately address our research questions.
Conclusion. Health coaching is a promising strategy for health improvements; however, future research should ensure clarity in reporting intervention details, clearer definitions of health coaching/theoretical bases, consistency in reporting BCTs, and the inclusion of process variables as outcome measures.