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The study aims to examine the association between virtual communication effectiveness (VCE), leadership effectiveness (LE) and the role of emotional intelligence (EI) in the post-pandemic era.

A survey was conducted among 305 employees in the Indian IT sector using an online questionnaire. Data were analyzed using reliability, validity and moderated regression analysis.

The findings reveal that VCE is a significant predictor of LE. EI plays a significant moderating role between VCE and LE.

This study establishes the role of EI in pre-empting LE. Furthermore, it results in the advancement of improved tools for the selection, training and development of leadership talent. Research on virtual communication (VC) and EI enhances our understanding of effective leadership. To the best of the authors’ knowledge, the present research is one of the first to link and standardize various practices of VC, and EI to increase LE in the post-pandemic era.

Social robot anthropomorphism is widely recognized for performing crucial tasks in supporting the service industry and enhancing the employee experience. This study elucidates how social anthropomorphic robots can influence the psychological well-being of employees in the hotel industry. The study further examines the impact of social robot warmth, psychological ownership and technology readiness in the above relationship.

The research utilized an online survey questionnaire for data collection and analysis. Data were collected from Indian hotel employees (N = 275). Structural equation modeling was applied to validate the conceptual model. The study examines the results using AMOS v25 and the SPSS PROCESS Macro.

The study empirically finds that social robot anthropomorphism positively impacts the psychological well-being of hotel employees. Further, it substantiates that despite low technology readiness, social robot anthropomorphism enhances psychological well-being through psychological ownership. Additionally, anthropomorphizing social robots emits warmth, which enhances employees' psychological well-being in the hotel industry.

These empirical results and theory suggest a novel dimension of anthropomorphism in social robot interactions, significantly impacting the employees' psychological well-being. A moderated mediation relationship is established, which confirms social robot warmth and psychological ownership as mediators and technology readiness as a moderator of the social robot anthropomorphism and employee psychological well-being relationship. The results validate the conceptual model of research that is grounded in the theory of psychological ownership and the realism maximization theory.

This paper aims to examine the stock market performance of knowledge-intensive employee-owned firms.

It constructs a portfolio comprising stocks of employee-owned wealth management companies listed in the UK Employee Ownership Index. A simple equal-weighted portfolio simulation strategy with annual rebalancing is employed and returns are analysed for the period 01.2002–12.2015.

The employee-owned firms consistently generate significantly higher returns, averaging 13% per annum. During favourable market conditions, the returns are even more significant at 16.40% higher than the market average annual returns. The outperformance persists in single-year and five-year investment periods, full and sub-sample periods, including bullish, stable and challenging economic times and even at high transaction costs and zero dividends. This superior performance is linked to a positive feedback loop created by homogeneous knowledge-workers who are incentivised to perform better in employee-owned business setting through participative decision-making and exhibiting risk aversion skills.

Adoption of the employee ownership model of running a business can be highly rewarding within knowledge-intensive firms. This study emphasises the need for a comprehensive database of employee-owned companies, which is currently lacking in the UK.

No prior study could be found to have studied the relationship between employee-owned knowledge-intensive firms and their stock market performance.

The procedural justice theory of police legitimacy has been examined extensively by previous scholars; yet more expansion of the legitimacy literature is warranted. Specifically, the current study examines the effects of institutional trust and obligation to obey as mediators between procedural justice and perceived legitimacy.

The current study uses structural equation modeling (SEM) to test our hypotheses. Convenience sampling of South Korean university students (N = 2,188) was used to investigate our inquiry.

The results support our hypotheses in that while procedural justice was the primary predictor of legitimacy, institutional trust and obligation to obey were also significant mediators of procedural justice and perceived legitimacy.

A multitude of research examining various predictors of police legitimacy exists (e.g. Baker and Gau, 2018; Boateng and Buckner, 2019; Boateng et al., 2022; Lee and Lee, 2021; Lee et al., 2022, 2024; Lee and Kim, 2023; Nagin and Telep, 2017). More importantly, various researchers have concluded that procedural justice does in fact have a positive impact on the public’s perception of governmental institutions (Hough et al., 2010; Mazerolle et al., 2013a; Murphy and Cherney, 2012; Sunshine and Tyler, 2003). A few research have directly examined institutional trust in the policing context (e.g. Boateng, 2018; Camp et al., 2021). Specifically, Boateng (2018) examined institutional trust and its relationship to police performance, and Camp et al. (2021) examined police officers’ prosody and its impact on the citizens’ institutional trust in police. However, the objective of the current research is to examine the relationship between procedural justice and legitimacy through the double mediating role of the public’s perception of institutional trust and obligation to obey.

Intelligence transformation has hugely influenced business operation in many industries and countries, such as in the emerging market. Generative Artificial Intelligence (GAI) adoption by organisations is a significant result of transformation. However, the influence of GAI adoption on small and medium-sized enterprises (SMEs) has been given less attention in business and management studies. In particular, managing the relationship of employees in GAI adoption is a focal point during the transformation from an ethical, responsible and sustainable perspective. Drawing on organisational socialisation and technology adoption theories, this chapter develops a process of socialising newcomers and/or existing employees in the development of GAI adoption in their workflow with identification of challenges and strategies to the adapt to the change. This discussion can help managers and other key persons to effectively manage the relationship and interactions between employees and technology (GAI) in a more ethical, responsible and sustainable manner.

The growing prevalence of digitalisation in economies has brought attention to the significance of digital transformation and its potential to enhance the competitiveness of enterprises within the emerging market. Nevertheless, it is important to note that disruptive changes are not limited to the organisational level, as they also have broader implications for the environment, society and institutions. The incorporation of technology into the field of education, often known as educational technology (EdTech), has undergone a significant evolution in recent times, fundamentally transforming the methods and processes of teaching and learning. This chapter delves into the multifaceted landscape of digital transformation in the field of EdTech from the perspective of sustainable development, elucidating the wide range of opportunities and challenges that consumer, educators, institutions and technology providers and various stakeholders face when they embark on this journey. Further, this chapter also sheds light on how to overcome the challenges faced by the stakeholders in digital transformation of EdTech for quality education.

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.

This study aims to investigate the impact of artificial intelligence (AI) on customer loyalty in the luxury fashion market. It explores how AI-driven tools influence customer trust, satisfaction, commitment and engagement, which in turn affect loyalty. By examining these relationships, the study provides insights into the acceptance and effectiveness of AI technologies in enhancing customer loyalty within the luxury fashion sector.

This study employs structural equation modelling (SEM) to analyse data collected from 406 luxury consumers in Iran. The data was gathered using a targeted sampling procedure, leveraging DigiKala’s e-commerce platform. A comprehensive literature review informed the measurement items, and a seven-point Likert scale was used. The methodology includes confirmatory factor analysis (CFA) to assess the reliability and validity of the constructs, followed by hypothesis testing through SEM.

The study reveals that AI significantly enhances customer loyalty in the luxury fashion market by positively influencing trust, satisfaction, commitment and engagement. Satisfaction and engagement were found to be key mediators between AI and loyalty, while trust had no direct impact on loyalty. The results underscore the importance of AI-driven personalized experiences in fostering stronger customer relationships and loyalty.

This study is one of the first to explore the impact of AI on customer loyalty in the luxury fashion market, using a comprehensive model that includes trust, satisfaction, commitment and engagement as mediators. It extends the stimulus-organism-response (SOR) and technology acceptance model (TAM) frameworks, offering valuable insights for luxury brands on how AI can be leveraged to enhance customer relationships and loyalty.

This study aims to delve into the ethical challenges in artificial intelligence (AI) technologies to underscore the necessity of establishing principles for ethical AI utilization in hospitality and tourism.

A narrative review of research on ethical AI across diverse realms was conducted to reflect current research progress and examine whether sufficient measures have been taken to address issues pertinent to AI utilization in hospitality and tourism.

Ethical issues including privacy concerns, detrimental stereotypes, manipulation and brutalization pertinent to AI utilization are elaborated. How AI should be properly used and managed ethically, responsibly and sustainably is suggested.

Five fine-tuned principles for regulating AI use in hospitality and tourism are proposed.

A resilient mindset, enhancement of AI context adaptability, equilibrium between development and regulation and collaborative effort of multiple stakeholders are paramount.

Through applying the AI evolution trajectory model, this study contributes to the current discourse of managing AI by proposing a framework that addresses the specific characteristics of hospitality and tourism.

An important consideration in the recovery strategies and performance of any given returned product is to make appropriate decisions for post-use. This paper aims to examine how the small and medium manufacturing enterprises can utilise a model-driven collaborative decision support system to evaluate product recovery strategies and performance.

An optimisation model using a genetic algorithm (GA) approach is developed to assess product recovery plans for any returned products based on the decisions of component reuse, remanufacture and recycle potentials. The model evaluates the key decisions of cost, time, quality and waste, and proposes an optimal recovery plan for manufacturer. A case study was also conducted using the proposed model to evaluate and examine different air compressor piston-types with recovery strategies.

Assessing a product recovery plan for any product is a challenge to the manufacturer due to higher operating costs associated with recovery. The nature of this challenge is complex. In this study, the results indicate that a developed optimisation model using a GA can assess the utilisation value of used products by considering suitable recovery strategies for the components and/or parts to be appropriately reused, remanufactured and recycled upon return.

The developed model assesses utilisation values of returns by considering both key decisions associated with returned products, and aspects of complexity of operational processes.

This research contributes to the practical understanding of product recovery strategies and extended producer responsibility using a case study. Also, the significance of this research is to provide a simple method of proposing an optimal recovery plan for any given returned product within a decision support system. Another innovation of the developed model is that an optimal recovery plan considers the trade-off decisions of cost, time, quality and waste aspects.