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Generation Z (Gen Z) is entering the hotel workforce and will soon become the dominant group. This has called for a better understanding of this workforce’s attitudes and perceptions towards working in the hotel industry. This study aims to examine the effect of organizational socialization on the retention of Chinese Gen Z employees, the mediating role of person–environment fit (P-E fit) and the moderating effect of career commitment.

Time-lagged data were collected from 426 Gen Z new employees from 20 upscale hotels at two different times (2 weeks and 12 weeks after the employees entered the hotel). Confirmatory factor analysis, structural equation modeling, bootstrapping analysis and moderated hierarchical regression analyses were used for data analysis.

Organizational socialization positively affects employee retention via person–environment fit. Moreover, career commitment positively moderates the relationship between person–environment fit and employee retention.

Hotels must view organizational socialization as a long-term investment in Gen Z talent management by offering effective training through diverse methods, creating a collaborative environment and helping them develop career plans to enhance their career commitment.

This study unpacks the four dimensions of organizational socialization and investigates their differential effects on Gen Z employees’ retention through P-E fit. The moderating role of career commitment is also examined. This study contributes to the growing body of hospitality human resources management research on this new generation of workforce in China.

Previous studies have indicated that career variety offers numerous benefits in the work environment; however, knowledge regarding the mechanisms that retain employees with diverse career experiences is scant. Drawing on person–environment (P–E) fit theory and the job crafting perspective, this study aims to explore the relationship between career variety and turnover intention, and the roles of job crafting and HR innovators and integrators in this relationship.

This study examines the nonlinear effect of career variety on turnover intention using survey data collected from 509 employees (Study 1) and 346 employees and their supervisors (Study 2) in China.

Career variety and turnover intention exhibited an inverted U-shaped relationship – mediated by job crafting. Additionally, the HR innovator and integrator moderated the relationship between career variety and job crafting. When the HR innovator and integrator were strong, employees engaged in more job crafting at intermediate levels of career variety.

The findings not only deepen our understanding of the inherent capabilities and preference traits of employees with diverse career backgrounds but also enrich the body of research on career variety, reconcile inconsistencies across previous studies and offer new insights into strategic organisational interventions for retaining a workforce with varied career experiences.

Researchers have studied supervisors’ proactive personality in its relationship with employees’ attitudes. However, little attention has been given to how employees react to instances of supervisors’ proactive behavior. Drawing from P-E fit theory, we propose that the relationship between supervisor weekly taking charge behavior (TCB, the quintessential proactive behavior) and employees’ weekly job attitudes depends on employees’ proactive personality.

Utilizing a diary study, we investigate how employees’ proactive personality moderates the within-person relationship between supervisor TCB and employees’ attitudes (measured as job satisfaction and affective commitment). We surveyed 39 employees ten times over ten weeks.

Multi-level analyses partially supported our predictions on the differential effects of weekly supervisor TCB on employees’ job attitudes. Supervisors’ above-average TCB was significantly related to higher levels of employees’ job satisfaction and marginally related to affective commitment for employees with high proactive personality, but not for those with low proactive personality. Supplemental analyses revealed that our results are unique to supervisor TCB and not to supervisor helping behavior.

Our study is among the first to utilize a dynamic approach to understand the consequences of supervisors’ proactive work behavior in the context of P-E fit research. Our findings will open several fruitful avenues for future research that continue to understand the powerful effects of supervisors’ proactivity.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

This study aims to understand how female globally mobile employees (GMEs), a group that is gaining a stronger presence in global work, exert their career agency within the context of structural constraints.

Using theoretical perspectives of career-agency theory and person-environment fit as frames of reference, this study collected 113 blog posts written by 19 female GMEs and conducted semi-structured interviews with 11 female GMEs who are currently living and working in Japan.

This study found that the informants exercised career agency as a response to challenges related to foreignness and gender. Through career-related agentic behaviors, the informants strove to increase their fit with their environments while interacting with different aspects of structural constraints. This study also found that people in different stages of global mobility exhibited different mindsets toward their foreignness, consequently influencing their career agency.

This study highlights the dynamic interaction between structural constraints and the agency of female GMEs, advancing the understanding of career agency in women’s global work. In addition, it recognizes the presence of expatriate residents as a distinct subgroup within the GME population, shedding light on the evolving ambiguous boundaries between self-initiated expatriates and other talent categories.

Humans are largely influenced by the environment in which they work, and the positive environment offers positive outcomes. Building on these lines, this study aims to propose and empirically investigate the relationship of fun on employees’ knowledge sharing behavior. The study also proposes and empirically tests the mediation mechanism of psychological empowerment and boundary condition of person–environment fit.

Considering the nature of the study, the data is collected from employees working in information technology firms at two points of time. At first point of time 600 questionnaire were distributed while at the end of second point of time all in all 313 useful responses were available for data analysis purpose.

The statistical analysis reveals that fun improves the psychological state of individual and in turn they share more knowledge. The findings also highlight that person–environment fit strengthens the relationship.

Based on the findings the study highlights some important theoretical and practical implications. The future direction is also provided based on these lines.

Supply chain (SC) professionals are increasingly working alongside business partners of diverse backgrounds, which has been argued to engender both innovation and creativity but also found as potentially detrimental to SC relationships and performance. To reconcile these views, this study explores two mechanisms – supplementary (similarity) and complementary fits – at the surface (observable traits) and deep (unobservable characteristics) levels and their impact on a focal firm representative’s perception of a SC partner’s trustworthiness.

Model was tested using survey data from 285 managers involved in interorganizational SC relationships.

Results indicate that a focal firm representative’s perception of supplementary and complementary fits with a SC partner positively impacts their perception of the partner’s trustworthiness. However, the effects of similarity at both surface and deep levels and complementarity weaken each other.

Understanding the mechanisms of diversity in SC relationships is crucial for fostering trustworthiness and achieving organizational objectives. Firms should evaluate both supplementary and complementary fits when hiring or assigning roles. Embracing a complementary fit not only promotes diversity but also mitigates the negative impact of similarity bias, ultimately strengthening trustworthiness within the organization's SC ecosystem.

By simultaneously examining individual and combined effects of two unique mechanisms of supplementarity and complementarity at the surface and deep levels, this study sheds light on inconsistent findings of the effects of diversity in the SCM literature.

The aim of the study is to investigate the impact of job characteristics on gig worker performance in India. In addition, this study examines the role of work–life balance as a mediator in exploring the impact of job characteristics, namely, flexibility and job autonomy and person–job fit on gig workers performance among location-based gig workers.

A total of 266 responses were collected by gig workers using a questionnaire-based survey. The responses were analyzed using the partial least square structural equation modeling technique.

The study results indicate that factors of job characteristics and person–job fit significantly enhance the gig worker performance and also finds work–life balance as significant mediator. These results provide evidence for the hypotheses, which emphasizes that these aspects will have a beneficial effect on the performance of gig workers.

This research contributes to the field of the gig economy by empirically examining the study factors to understand the work dynamics in flexible work arrangements. Based on the findings, the gig platforms and policymakers can formulate strategies for establishing job design for enhancing work–life balance, which subsequently enhances gig workers performance.

As one of the most energy-intensive sectors, the manufacturing industry is strongly affected by current economic, ecological and political issues and is increasingly looking to reduce its energy consumption through effective management. One important component of energy management is monitoring, which can be improved by using the Internet of Things (IoT). This study aims to identify the factors behind the adoption of IoT energy monitoring systems in manufacturing industries and IoT’s impact on consumption in this context. Moreover, this study explores what constitutes successful implementation and develops recommendations on how companies can best use the generated energy-saving potential.

For this purpose, an exploratory, inductive research approach is taken in which five semi-structured interviews with IoT energy-monitoring platform providers located in Germany are conducted and additional documents about these platforms are analysed.

Based on these interviews and documents, this study defines the relevant factors involved in the increased adoption of IoT energy monitoring systems as desired cost reduction, regulatory requirements and customer expectations. Once implemented, a company’s energy efficiency potential strongly depends on its individual situation; however, the transparency characteristic of IoT supports managers in deriving effective energy-saving measures. Additionally, the used efficiency potential is influenced by the level of data collection and organisational capabilities.

To the best of the authors’ knowledge, as the first study, this research combines findings from the energy management and IoT research streams and places them into an organisational context. The application of the fit-viability model as an overarching framework enhances the theoretical contributions of this study. Moreover, focusing on Germany and its substantial industrial sector enables the gathering of important insights and the analysis of the specific use case of industrial IoT, which yields new findings for practitioners as well.

This study aims to find suitable replacements for hypothesis testing and variable-importance measures.

This study explores under-used predictive methods.

The study's hypothesis testing can and should be replaced by predictive methods. It is the only way to know if models have any value.

This is the first time predictive methods have been used to demonstrate measure and variable importance. Hypothesis testing can never prove the goodness of models. Only predictive methods can.