Engagement of employees in a research organisation: A relational perspective

Abstract

Background: Increasing work engagement in a sustainable way remains a challenge despite years of research on the topic. Relationships at work are vital to foster engagement or disengagement. While the relational model by Kahn and Heaphy is conceptually appealing to explain work engagement, it lacks empirical support.Aims: The aims of this study were to investigate the associations among relational factors, psychological conditions (psychological meaningfulness, availability and safety) and work engagement and to test a structural model of work engagement.Setting: A total of 443 individuals in an agricultural research organisation participated in a cross-sectional study.Methods: Four scales that measured relational factors, the Psychological Conditions Scale and the Work Engagement Scale were administered. Latent variable modelling was used to test the measurement and structural models.Results: The results confirmed a structural model in which relational facets of job design contributed to psychological meaningfulness. Emotional exhaustion (inverse) and co-worker relationships contributed to psychological availability. Supervisor relationships contributed to psychological safety. Psychological meaningfulness and psychological availability contributed to work engagement, while emotional exhaustion contributed to disengagement.Conclusion: The relational context is an important target for intervention to affect the psychological conditions which precede work engagement. To promote work engagement, it is vital to focus on psychological meaningfulness, psychological availability and emotional exhaustion.

Job Crafting and Work Engagement: The Mediating Role of Work Meaning

“…This relationship was observed to be stronger in participants with high levels of well-being [ 33 ]. A second study with 443 participants also confirmed the strong and positive relationship between these two variables [ 34 ]. Likewise, in Fairli’s [ 35 ] study with a sample of 574 employees, meaningfulness at work was shown to have the strongest relationship with engagement, above other personal and organizational variables such as organizational support, peer and/or supervisor relationships or intrinsic rewards (like autonomy, task identity, self-efficacy, etc.)…”

“…Later studies yielded similar results, demonstrating that work meaning had a mediating influence on predictive variables such as peer relationships, job characteristics, feedback, skills development and utilization or the work-role fit and engagement [ 9 , 36 , 39 , 40 ]. Asiwe et al [ 34 ] showed that the positive relationship between engagement and job design—as a job characteristic or resource—was mediated by the perceived meaning. More recently, Bakker and Albrecht [ 41 ] proposed job crafting as an appropriate strategy to increase engagement as it also enhances the meaningfulness experienced by the worker and the fit between job demands and the person’s resources to address the task.…”

Meaningfulness of Work and Employee Engagement: The Role of Affective Commitment

“…The above-mentioned justification indicates a dynamic that demands the collective effort of both the employee and organization to achieve a feeling of meaningfulness [13,14], also asserting that an individual's exploration of meaning is considered the main motivation in their life and can be achieved only by the individual alone. Every employee is well versed in his/her strengths and weaknesses, and consequently, he/she chooses the prospect that is compatible with his/her strengths [15]. This self-determined behavior of individuals has a significant impact on the quality of their experience in all areas of their behavior.…”

Predicting intention to share news through social media: An empirical analysis in Indonesian youth context

“…though SEM is usually applied as a confirmatory techniques, but it can be used for exploratory purposes (Schreiber et al, 2006). Furthermore, indices in SEM that are suggested for one time analyses are the Root Mean Square Error of Approximation (RMSEA), the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI), and CMIN/DF because these indices are the most insensitive to sample size, model misspecification, and parameter estimates (Asiwe et al, 2017;Hooper et al, 2008;Schreiber et al, 2006).…”