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This study examined the effects of organizational justice and citizen respect to support for democratic policing through self-legitimacy among South Korean police officers.

The authors used survey data from 467 South Korean police officers in 2022. Structural equation model analysis was used to examine relationships between each variable.

This study found a positive relationship between organizational justice and self-assessed legitimacy. Also, citizen respect had a positive relationship with both police officers' self-assessed legitimacy and audience legitimacy. Self-assessed legitimacy had a significant effect on support for democratic policing, while perceived-audience legitimacy did not have a significant effect on support for democratic policing.

The current study provides evidence that self-legitimacy affects supporting democratic policing in a non-Western democracy. Additionally, to the best of our knowledge, this study is the first to examine the role of police self-legitimacy as a link between organizational justice and citizen respect and the intended behaviors of police officers toward citizens.

In response to the lack of connection between employee development and employee engagement, this study explores the existing empirical findings regarding these two concepts. Based on the conservation of resources theory, the authors propose a novel theoretical framework that can better leverage the identified antecedents and relationships for future research.

An integrative literature review of 64 empirical studies published in peer-reviewed journals was conducted.

Three different levels of antecedents, including the work environment, social exchange and individual characteristics, are identified. Employee development and employee engagement exhibit reciprocal relationships. Considering the role of job performance as a catalyst, the authors propose an upward gain spiral model to advance both research and practice.

This integrative literature review aims to facilitate discussions based on three distinct sub-dimensions: physical, emotional and cognitive energies, relevant to both employee development and employee engagement. Through this distinction, a more comprehensive understanding of the connection between employee development and employee engagement can be cultivated.

Today, psychological well-being is increasingly valued by organizations because it is integral to employee performance. The style of leaders supervising their subordinates is an important influence on their psychological well-being. Abusive supervision can lead to a depletion of resources among their subordinates by inducing psychological stress, leading to a decline in psychological well-being. In this research, the authors use the conservation of resources (COR) theory and self-determination theory to examine the mechanism between abusive supervision and psychological well-being. This study can contribute to previous research by applying the COR theory and self-determination theory, which were not discussed, to explain the relationship between leader's leadership behavior and psychological well-being of organizational members.

The authors conduct a multi-time data collection method of two waves with six-week intervals. The authors received 322 samples and conducted a confirmatory factor analysis to test result validity and used multiple regression to examine the direct and moderating effects. Additionally, the authors used the bootstrapping method to test mediating effects.

The results show that abusive supervision is negatively related to psychological well-being and self-determination plays the mediating role between them, while perceived person-organization fit is the moderator between self-determination and psychological well-being.

The authors identified self-determination as the mediator between abusive supervision and psychological well-being and perceived person-organization fit plays the moderating role between self-determination and psychological well-being.

The purpose of this study is to investigate the effects of leaders' managerial coaching on followers' organizational citizenship behavior (OCB), creativity and task performance. This study also examined the mediating role of intrinsic motivation and self-efficacy, recognizing the follower’s attitude and cognition as essential elements of behavioral changes.

This study collected data from 20 companies across multiple industries in South Korea, and a total of 386 leader–follower dyads' data were used.

The results show that leaders' coaching is positively associated with OCB directly, but a direct impact of coaching on creativity and task performance was not supported. The results also showed that intrinsic motivation partially mediates the effect of coaching on OCB and fully mediates the effect of coaching on creativity and task performance. Self-efficacy played a role as a full mediator between coaching and task performance.

This study considered both the cognitive and affective aspects of managerial coaching and examined the influence of managerial coaching on the followers' in-role and extra-role behaviors (i.e. OCB, creativity and task performance) using responses from both the leaders and the followers at multiple organizations. Specifically, the results of this study empirically illustrated that managerial coaching by leaders serves as a mechanism mediated through intrinsic motivation and self-efficacy, linking to employees' OCB, creativity and task performance. This provides a clear explanation of the processes through which managerial coaching impacts employees and offers insights into the specific aspects that organizational leaders should focus on when engaging in managerial coaching.

This paper investigates the interaction effects of gender diversity and diversity beliefs on group-level personality fit (“collective personality fit”) and group organizational citizenship behavior (GOCB). It seeks to provide a sufficient understanding of the under-researched area, such as how group composition impacts group behavioral outcomes.

Data were collected from 453 employees and their leaders in 63 teams of Korean private sector firms using a moderated-mediation model.

This study found that high diversity beliefs can weaken the negative effects of gender diversity on collective personality fit and further enhance GOCB. The results confirm the moderated-mediation effect of diversity beliefs in the relationship between gender diversity and GOCB.

Management should realize that the negative effect of workforce diversity on GOCB can be reduced by boosting collective personality fit in the team. In this process, enhancing diversity beliefs may relieve the adverse effects on GOCB caused by workgroup gender differences.

This study develops a group-level model proposing that the interaction effects of gender diversity and high diversity beliefs enable a high level of collective personality fit, enhancing GOCB. It attempts to investigate the effects of gender diversity at the group level under boundary conditions.

This study aims to investigate the impact of overnight off-work relaxation on the performance of frontline service employees (FLEs). Specifically, the authors focused on FLEs' customer-directed extra-role service behavior (C-ERSB) and coworker-directed extra-role service behavior (CW-ERSB) as indicators of outstanding service performance. Drawing on the conservation of resources (Hobfoll, 1989) and ego depletion theories (Baumeister, 2002), the authors hypothesized that the positive effect of overnight relaxation on ERSBs will be mediated by the state of recovery. Additionally, the authors examined the boundary conditions of these relationships by testing the moderating effects of work–family conflict (WFC) and family–work conflict (FWC).

The study employed an episodic sampling method. One hundred thirty-five FLEs completed two daily surveys (before- and after-work) over five consecutive workdays, yielding 636 time-lagged day-level observations. Multilevel path modeling was performed to analyze the mediation and second-stage moderated mediation effects.

Results showed that overnight off-work relaxation was positively related to FLEs' next-day C-ERSB and CW-ERSB via next-morning recovery state. The positive relationship between overnight off-work relaxation and the next-morning recovery state was weaker for FLEs who experienced overnight WFC. FWC during work hours weakened the positive relationship between the next-morning recovery state and CW-ERSB, but not the relationship between the next-morning recovery state and C-ERSB.

The study used an episodic sampling method to reveal the significance of off-work relaxation, recovery and family–work interface on FLEs' ERSBs, a critical yet underexplored phenomenon in service literature. This study sheds light on the pathways to achieve exceptional service performance by revealing the importance of overnight off-work relaxation and the conditions that promote ERSBs.

The next generation of mobility is arising, and various challenging mobilities have entered the limelight. One of the most exciting of these is urban air mobility (UAM), and one of its challenges is constructing effective and efficient UAM service network. This study took a quantitative approach to the problem in an effort to support and facilitate the UAM service industry.

This study derived a multi-objective and multi-period (MOMP) location optimization model to support strategic UAM service network design. The model, based on its long-term service plan, determines where and when to open UAM airports. In addition, this study applied a modified e-constraint algorithm to derive managerial decisions on the Pareto relationship in consideration of multiple objectives and multiple periods.

Each Pareto solution represents a different UAM service network configuration. Thus, the model can analyze the trade-offs between Pareto decisions for the UAM service network. A case study of UAM service network design in South Korea demonstrates the validity of the proposed mathematical model and algorithm.

The design of a UAM service network should consider various aspects. Its construction and operation would require significant investments of time, capital and people, which would redound to society over a significant span of time. The results of this study provide quantitative guidelines for derivation and analysis of various UAM service network configurations in consideration of multiple objectives and multiple periods.

This paper proposes MOMP optimization, which approach is suitable to the fundamental characteristics of expanding UAM service networks and their design. It is expected that the present study will make significant contributions to the efforts of those deriving and analyzing future UAM service networks.

This study aims to examine YouTube comments relevant to sustainable fashion posted on fashion haul videos over the past decade (2011–2021). It is guided by two research questions: (1) How have sustainable fashion-related comments posted on YouTube fashion haul videos changed over time? and (2) What themes are relevant to sustainable fashion in the comments posted on fashion haul videos?

A data set of comments from 110 fashion haul videos posted on YouTube was refined to only include comments with keywords related to sustainable fashion. Leximancer, a machine learning technique, was employed to identify concepts within the data and co-occurrences between concepts. Linguistic Inquiry and Word Count software was employed to assess the prevalence of concepts and identify sentiment over time.

Over the decade, the authors identified increased comments and conversations relevant to sustainable fashion. For instance, conversations surrounding sustainable fashion were linked to “waste” and “addicted” between 2011 and 2013, which evolved to include “environment” and “clothes” between 2014 and 2016, to “buy” and “workers” between 2017 and 2019 and “sustainable” between 2020 and 2021, demonstrating the changes in conversation topics over time.

With increasing engagement from YouTube viewers on sustainable fashion, retail-affiliated content that promotes sustainable fashion is proposed as one approach to engage viewers and promote sustainable practices in the fashion industry, whereby content creators can partner with retailers to feature products and educate viewers on the benefits of sustainable fashion.

The findings suggest that consumers are becoming more aware of and responsive to sustainable fashion. The originality of this research stems from identifying the source of this interest.

This paper aims to examine the impact of metaverse experiences on customers’ offline behavioral intentions: How do customers’ visits to a hospitality business’s virtual property in the metaverse affect their intentions to visit the physical property in the real world?

Based on the general learning model and social cognitive theory, this research hypothesizes the positive impact of metaverse experiences on customers’ visit intentions and explores two boundary conditions for positive impact: user–avatar resemblance and servicescape similarity. Two experimental studies were conducted.

Metaverse experience has a significant impact on customers’ visit intentions, and this impact is moderated by user–avatar resemblance and servicescape similarity.

This research addresses the call for empirical studies regarding the effects of metaverse experience on people’s behavioral intentions.

As one of the earliest empirical studies on the marketing effects of the metaverse, this research provides a basis for future metaverse studies in the hospitality field.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.