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The purpose of this paper is to empirically assess conceptually based arguments that the three-component model (TCM) is not a model of commitment but rather of employee turnover, and that the mindsets that comprise the TCM do not form a unified construct.

A survey design was used that was comprised of 223 staff nurses located in a large, urban hospital in the USA. Data were analyzed using dominance analysis, a variant of multiple linear regression that provides more accurate estimates of the strength of relationships between predictor and criterion variables when multicolinearity among predictors is present.

Results from OLS regression and dominance analysis provided no support for concerns about the viability of the TCM. First, there was no evidence that the continuance and normative mindsets were associated only with employee turnover, and there was strong support that this was not the case. Second, our overall patterns of results indicated that the mindsets that comprise the TCM operated as a unified construct that is consistent with the theory and research underpinning the TCM.

This study indicates that work commitment is multidimensional and must be managed accordingly so that it is important to be mindful of the development and implications of different constellations of work commitment.

Conceptually grounded criticisms of the TCM have not been tested empirically leading to uncertainty about the nature of work commitment. This study adds an empirical perspective that is augmented by an advanced application of multiple regression analysis.

The purpose of this paper is to investigate profiles of employee well-being using multiple components to better understand how well-being is experienced in organizations.

A survey design with 579 health care workers in the USA was administered. Latent profile analysis (LPA) was used to identify well-being profile groups.

Six well-being profile groups based on the relative levels of work stress, carry-over stress and job satisfaction were identified. Profile groups differed with respect to intention to remain in the organization and occupation, and job search behavior.

Models of well-being at work have generated consistently disappointing results that have not enhanced the development of programs to increase well-being at work. By identifying patterns of well-being, this study offers insights into how well-being is experienced so that more targeted programs to promote it can be implemented.

Although there is increased interest in the person-centered model in organizational research, it has not been applied to psychological well-being at work. This study represents an initial attempt to study configurations of well-being based on its components. Results indicate that distinct patterns of well-being are present, and those patterns are useful in gaining a greater understanding of how well-being is experienced and in how it can be more effectively managed.

The purpose of this paper is to explore parallels between scientific management and the new scientific management to gain insight into applications of machine learning and artificial intelligence (AI) to human resource management and employee assessment.

Analysis of Taylor’s work and its interpretation by scholars is contrasted with modern analysis of human resource analytics to demonstrate conceptual and methodological commonalities between the old and the new forms of scientific management.

The analysis demonstrates how the epistemology, ethos and cultural trajectory of scientific management has resulted in a mindset that has influenced the implementation and objectives of the new scientific management with respect to human resources analytics.

This paper offers an alternative to the view that machine learning and AI as applied to work and employees are beneficial and points out why important challenges have been overlooked and how they can be addressed.

Commonalties between Taylorism and the new scientific management have been overlooked so that attempts to gain an understanding of how machine learning is likely to influence work, employees and work organizations are incomplete. This paper provides a new perspective that can be used to address challenges associated with applications of machine learning to work design and employee rights.

While there is research that has explored how institutions have responded to various crises, these are usually locally or regionally situated. However, no event has impacted higher education globally like COVID-19 and it will certainly alter the way top administrators lead and how institutions move forward. Thus, this chapter will explore how to better understand how presidents and top administrators navigate the (inter)national geopolitics as they move the institution forward. In addition, clear and up-to-date communication has proven to be important in battling this crisis. Thus, how presidents at national universities have communicated with students, faculty, staff, and various off-campus communities members regarding COVID-19 and how they have achieved is important to explore. Our findings suggest that presidents and top administrators need to build support to help them navigate the political roadblocks they may encounter. Findings also suggest that communication is the main role they play as leaders on their campus. On/off-campus community members see the presidents as the face of the university and key communicator as relates to communicating what the institutions is doing and how they are addressing the crisis. This chapter helps in better understanding the roles presidents and top administrators play during a global crisis.

This chapter examines a case study of inter-institutional merger in higher education, and explores the complex challenges institutional leaders may face in pursuing a merger process within a university setting where centuries-old tradition frames the context within which new innovations occur. Using the conceptual lens of organizational ambidexterity, findings uncover seven distinct phases of this merger process and propose a pre-merger Affiliation period as a strategy for establishing trust and mutual respect, aligning institutional cultures, and achieving balance between innovation and preservation in order to achieve full merged status. The chapter concludes with implications for theory and opportunities for practice.

Depression and other mental health disorders have a large impact on the quality of life and productivity of millions of individuals worldwide. For employers, mental health disorders are associated with increased health care costs as well as productivity losses in the form of absenteeism, short‐term disability absences and reduced on‐the‐job productivity‐known as presenteeism. The purpose of this paper is to review the association of worker productivity and mental health.

This review summarizes the literature on the prevalence of mental health conditions among working adults, and the association between these disorders and productivity. Finally, the impact of interventions or workplace policies on the productivity of those suffering with mental health conditions is covered and recommendations for employers are suggested.

Depressive disorders are relatively common in most workforces compared to other mental health conditions. The majority of studies on mental health and productivity have been conducted as part of nationwide surveys or in patient populations rather than worksites. The majority of studies show associations between mental health conditions and absenteeism (particularly short‐term disability absences). When presenteeism is measured by a validated questionnaire, results show that depression significantly impacts on‐the‐job productivity (presenteeism). Studies also indicate that the treatment expenditures for employees with depression may be offset by reductions in absenteeism, disability and on the job productivity losses.

Workplace policies and benefits which support employees suffering with mental health disorders and provide access to evidenced‐based care adhering to best practice guidelines may improve the quality of life of employees and lead to reduced absenteeism, disability and lost productivity.