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This study aims to explore the relationship between elderly users' trust in public digital cultural services (PDCS) and their intention to use PDCS, and reveal the factors affecting their intentions from the perspective of trust to make recommendations that will increase their intention to use PDCS.

Combined with the trust building model and social exchange theory, this study constructed a conceptual model of elderly users' intention to use PDCS. Data collected from Chinese elderly users who have reached the age of 60 through questionnaire surveys were tested using the structural equation model with partial least squares. Finally, the authors proposed a model of elderly users' intention to use PDCS.

This study finds that elderly users' trust positively affects their intention to use PDCS from two aspects: service features and user features of PDCS. Concerning the service features, system quality directly affects elderly users' trust in PDCS most significantly, followed by information quality and service reputation. Concerning the user features, perceived value has a higher impact on elderly users' trust than that of service features, and information literacy and information quality directly affect perceived value.

This study adds new knowledge to the users' behavior of PDCS and enriches the prior description of PDCS. The recommendations made in this study provide a series of strategies for practitioners and researchers to improve the elderly users' intention to use PDCS and bridge the silver digital divide, which offers new ideas for improving the efficiency of PDCS.

The user churn (UC) of rural public digital cultural services (RPDCS) indicates that rural users no longer use RPDCS or have switched to other services. The purpose of this study is to investigate the factors influencing RPDCS's UC and to contribute toward bridging the rural digital divide.

Based on the stimulus-organism-response theory, this study proposes a theoretical framework to investigate the factors influencing RPDCS's user churn. A total of 120 RPDCS users were initially recruited and 90 representative participants were chosen. Data were collected from the 90 respondents and 20 follow-up interviews. To examine the proposed framework and validate the correlations between these factors and UC, an analysis of variance (ANOVA) and multiple regression analysis were used. In addition, recommendations are made to avoid the UC of RPDCS to bridge the rural digital divide.

The results indicate that the UC of RPDCS was stimulated by both physical and ability divides and RPDCS ineffectiveness, resulting in increased churn rates and an exacerbated digital divide. Thus, avoiding the UC of RPDCS is an important way to bridge the rural digital divide, which includes both the physical and ability divides.

This study adds new knowledge about RPDCS and distinguishes it from previous research on public digital cultural services. In addition, the authors discuss how to avoid the UC of RPDCS, to bridge the rural digital divide.

This study aims to develop a robust theoretical framework to explain the impact of hotels’ green brand authenticity on guests’ perceptions of well-being, customer engagement and approach behaviors toward green brands.

In this study, the authors examined the effect of green brand authenticity on perceptions of well-being, customer engagement and approach behaviors toward green brands. For the quantitative empirical analysis, 352 samples were used. Green brand authenticity integrates quality commitment, heritage, uniqueness and symbolism as high-dimensional factors.

The study conceptualizes green brand authenticity as a multi-dimensional phenomenon with four dimensions: quality commitment, heritage, uniqueness and symbolism. The results showed that green brand authenticity has a positive effect on hotel guests’ perceived well-being and behavioral intentions. Interestingly, environmental values did not have a statistically significant regulatory role, while green behavior in everyday life had a partial regulatory role.

This study aims to develop and empirically test a conceptual model that depicts the function of green authenticity in explaining customer responses to green brands. The results and the theoretical framework proposed in this study provide significant insights for researchers and practitioners in the hotel industry.

Further than evaluating brand authenticity generally, this study evaluates the authenticity of a brand's environmental protection efforts. As a result of the empirical analysis conducted in this study, the green brand authenticity of a hotel had a positive effect on customers’ emotional and behavioral aspects. This finding provided valuable and meaningful insights for green hotels and hotel brand-related research.

Co-authored by three South African academics working in higher education development, this chapter uses the transition from professional practice to academic contexts as an entry point into a discussion of transitions broadly. We reflect on the role of mentoring in supporting the multiple transitions which dual professionals navigate and what this means for the provision of inclusive, quality education (SDG 4) with a focus on higher education. In reflecting on this Sustainable Development Goal, we approach the topic of mentoring from a critical perspective which allows us to attend to the themes of power, access and equity that it invokes. The body of scholarship on the transition experiences of practitioners into academia has challenged the assumption that professional expertise translates into teaching expertise in the classroom. The opening vignette contextualizes this challenge. The vignette protagonist offers to support an academic colleague and approaches a mentoring expert to explore her guiding principles for mentoring within and beyond the classroom. The dialogue surfaces the need for mentoring that considers the various transitions that dual professionals navigate. We engage critically with international literature on the role and positionality of dual professionals in academia and reflect on selected concepts from this literature to highlight the importance of an adaptive mentoring approach for meeting academics in transition at their point of need. We offer a synthesis of literature on holistic approaches to mentoring, critically reflecting on how they enable inclusive quality education for the benefit of society.

This study aims to examine the relationships among International Financial Reporting Standards (IFRS) adoption, earnings management, and corporate efficiency.

First, the authors employ the epsilon-based measure (EBM) of the data envelopment analysis to measure the corporate performance of the Taiwanese electronics industry from 2011 to 2014. Second, the authors regress the IFRS adoption and earnings management on corporate efficiency.

The findings show that the corporate efficiency deteriorated after IFRS adoption. Although the regression analysis shows that the relationship between earnings management and corporate efficiency is significantly positive, the authors find that IFRS adoption is effective in unveiling earnings management. Moreover, IFRS adoption moderates the impact earnings management and corporate efficiency.

This study provides reference for decision-makers in the application of accounting principles and in the understanding of the IFRS impact adoption.

IFRS adoption can either facilitate or limit the earnings management that would affect corporate efficiency significantly and help the electronics industry as well as investors to know the changes in accounting principles and their effects on corporate efficiency.

The authors use the EBM of efficiency model to measure corporate efficiency and employ the modified Jones model to measure earnings management.

The presented research explored artificial intelligence (AI) application in the learning and development (L&D) function. Although a few studies reported AI and the people management processes, a systematic and structured study that evaluates the integration of AI with L&D focusing on scope, adoption and affecting factors is mainly absent. This study aims to explore L&D-related AI innovations, AI’s role in L&D processes, advantages of AI adoption and factors leading to effective AI-based learning following the analyse, design, develop, implement and evaluate approach.

The presented research has adopted a systematic literature review method to critically analyse, synthesise and map the extant research by identifying the broad themes involved. The review approach includes determining a time horizon, database selection, article selection and article classification. Databases from Emerald, Sage, Francis and Taylor, etc. were used, and the 81 research articles published between 1996 and 2022 were identified for analysis.

The result shows that AI innovations such as natural language processing, artificial neural networks, interactive voice response and text to speech, speech to text, technology-enhanced learning and robots can improve L&D process efficiency. One can achieve this by facilitating the articulation of learning module, identifying learners through face recognition and speech recognition systems, completing course work, etc. Further, the result also shows that AI can be adopted in evaluating learning aptitude, testing learners’ memory, tracking learning progress, measuring learning effectiveness, helping learners identify mistakes and suggesting corrections. Finally, L&D professionals can use AI to facilitate a quicker, more accurate and cheaper learning process, suitable for a large learning audience at a time, flexible, efficient, convenient and less expensive for learners.

In the absence of any systematic research on AI in L&D function, the result of this study may provide useful insights to researchers and practitioners.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

This paper aims to describe a new method for constructing the FintTech Index that measures the development of FinTech in the conventional and Islamic banking sectors in the Middle East and North Africa (MENA). It also tests the effect of this new proxy on the performance of conventional and Islamic banks in MENA countries.

Using data from Islamic and conventional banks in the MENA region between 2010 and 2020, the authors rely on Text Mining Technology with the help of AntConc, principal component and factor analysis. The study also uses the simultaneous equation model to test the interdependent relationship between FinTech and bank performance.

The study argues that the proposed measure effectively represents the FinTech industry in the MENA financial markets. The results provide micro evidence on the application of FinTech innovation in Islamic and conventional banks to improve their performance, profitability, stability and efficiency. Furthermore, the findings can provide insights for practitioners and researchers interested in implementing FinTech collaboration to enhance the performance of Islamic and conventional banks in the MENA region.

Investors can leverage this FinTech Index in portfolio investments, trading strategy and hedging in MENA countries. In addition, policymakers can benefit from the challenges outlined in this work to support the development and incubation of FinTech in conventional and Islamic banks. Thus, they can better recognize the new generation of banking services with which they need to deal and collaborate.

This paper makes a methodological contribution to the literature on FinTech search patterns by combining factor analysis with corpus processing software. This is the most comprehensive global FinTech index. In addition, to the best of the authors’ knowledge, this study is the first to examine the simultaneous relationship between the FinTech index and the performance of Islamic and conventional banks.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

This study aims to use institutional and upper echelons theories to comprehensively investigate the intricate interplay between TMT legal expertise and firms' adaptive strategies in legal contexts, notably within emerging economies. It explores how upper echelons experiences shape opportunistic compliance strategies, impacting value and risk perceptions. Drawing on upper echelons theory, the research probes how TMT legal expertise molds firms’ involvement in significant lawsuits, accounting for influential roles. It scrutinizes TMT’s impact on legal strategies, positing that managerial discretion emerges from environmental factors, organizational attributes and executive traits. The study underscores TMT’s internal incentives and external factors’ interplay, molding strategic legal engagement.

To validate this framework, statistical analysis is performed on data from 2,584 Chinese-listed firms. The data set spans 2010–2015, with 5,713 material lawsuits. Chosen due to reliable institutional-level incentives data from the China Market Index Database, years 2016–2019 are excluded for methodological disparities. Moreover, 2007–2009 is omitted to mitigate the potential financial crisis impact. This study’s 11,272 observations ensure robust empirical exploration, offering insights into the interplay of TMT legal expertise, institutional factors and firms’ legal strategies.

The study reveals that firms led by executives with legal expertise are more prone to engage in significant lawsuits, indicating strategic use of legal skills. TMT age moderates this, with older teams less likely to engage. TMT tenure’s effect remains unclear due to tenure-risk preference complexity. Institutional factors matter; less legally mature regions reduce managers’ legal risk intention. Results confirm hypotheses and highlight executive human capital’s impact on firms’ legal strategies.

This study acknowledges contributions while highlighting limitations, including the need for detailed distinctions in lawsuit roles and exploration of heterogeneous TMT power dynamics. Further research is proposed for nuanced power dynamics and comprehensive TMT legal background data. The study advances upper echelons theory by introducing TMT legal expertise as a factor influencing strategic lawsuit behavior. It challenges institutional theory by showing the adaptable legal context, beyond fixed constraints. Moderating factors – group risk attitude and external knowledge – deepen understanding of upper echelons’ impact. Enhanced data collection is encouraged to address limitations and refine findings.

This study’s implications extend to managerial practices. Firms should acknowledge the dynamic legal system, using TMT legal expertise for strategic legal challenges. Executives should pragmatically approach regulations. While legal professionals enhance compliance, caution is needed in selecting TMT members with legal expertise due to the risk of misusing it for unnecessary litigation, potentially misaligned with financial performance goals.

This study combines institutional and upper echelons theories to explore TMT legal expertise’s impact on firms’ adaptive strategies in emerging economies. It challenges the idea of a universally constraining legal environment and highlights how TMT legal expertise enhances firms’ management of complex legal risks. The research introduces TMT legal expertise as an influencing factor in strategic lawsuits, revealing nuanced relationships between legal contexts and strategic decisions. The findings enrich upper echelons theory, challenge conventional institutional views and identify moderating factors that deepen the understanding of upper echelons’ influence in legal landscapes.