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The fishing cat's unique hunting strategies, including ambush, detection, diving and trapping, inspired the development of a novel metaheuristic optimization algorithm named the Fishing Cat Optimizer (FCO). The purpose of this paper is to introduce FCO, offering a fresh perspective on metaheuristic optimization and demonstrating its potential for solving complex problems.

The FCO algorithm structures the optimization process into four distinct phases. Each phase incorporates a tailored search strategy to enrich the diversity of the search population and attain an optimal balance between extensive global exploration and focused local exploitation.

To assess the efficacy of the FCO algorithm, we conducted a comparative analysis with state-of-the-art algorithms, including COA, WOA, HHO, SMA, DO and ARO, using a test suite comprising 75 benchmark functions. The findings indicate that the FCO algorithm achieved optimal results on 88% of the test functions, whereas the SMA algorithm, which ranked second, excelled on only 21% of the functions. Furthermore, FCO secured an average ranking of 1.2 across the four benchmark sets of CEC2005, CEC2017, CEC2019 and CEC2022, demonstrating its superior convergence capability and robustness compared to other comparable algorithms.

Although the FCO algorithm performs excellently in solving single-objective optimization problems and constrained optimization problems, it also has some shortcomings and defects. First, the structure of the FCO algorithm is relatively complex and there are many parameters. The value of parameters has a certain impact on solving optimization problems. Second, the computational complexity of the FCO algorithm is relatively high. When solving high-dimensional optimization problems, it takes more time than algorithms such as GWO and WOA. Third, although the FCO algorithm performs excellently in solving multimodal functions, it rarely obtains the theoretical optimal solution when solving combinatorial optimization problems.

The FCO algorithm is applied to the solution process of five common engineering design optimization problems.

This paper innovatively proposes the FCO algorithm, which mimics the unique hunting mechanisms of fishing cats, including strategies such as lurking, perceiving, rapid diving and precise trapping. These mechanisms are abstracted into four closely connected iterative stages, corresponding to extensive and in-depth exploration, multi-dimensional fine detection, rapid and precise developmental search and localized refinement and contraction search. This enables efficient global optimization and local fine-tuning in complex environments, significantly enhancing the algorithm's adaptability and search efficiency.

In the current digital transformation era, guiding employees toward realizing self-leadership has become a critical strategy for enterprises to mitigate the risks of external uncertainty. This study explores the intrinsic relationship between enterprise social media (ESM) communication visibility and self-leadership on the basis of communication visibility theory (CVT) and conservation of resources theory (COR) to support the sustainable growth of employee self-leadership.

Data were collected from 332 Chinese employees across three stages. Hierarchical regression analysis and the bootstrap method were employed to test the hypotheses.

Network translucence and message transparency positively affect self-leadership. Relational energy and job control mediate the relationship between communication visibility and self-leadership. Moreover, response expectations weaken the positive relationship between relational energy and self-leadership as well as between job control and self-leadership.

Managers can use ESM to provide work and social support for employees’ job control and social network development to achieve long-term maintenance of employees’ self-leadership. At the same time, within the context of ESM use, moderately reducing response expectations can offer situational support for employees to achieve self-leadership.

This research sheds light on the internal mechanism by which ESM communication visibility affects self-leadership and explores the key boundary conditions that impact how self-leadership is formed. These findings expand the understanding of self-leadership development in the context of ESM use and offer new technical insights for guiding and fostering sustainable employee self-leadership.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.

With an emphasis on the moderating impact of trust, this study examines determinants influencing the purchase intentions of young consumers in augmented reality (AR) shopping platforms. This research study aims to pinpoint essential elements, including the enjoyment dimensions (i.e. entertainment, visual appeal and hedonic component) and practicality dimensions [i.e. informativeness, navigation and perceived usefulness (PU)], that are imperative in influencing young consumers’ purchase intentions in AR-based shopping platforms.

The present study used a quantitative approach grounded in the stimulus-organism-response model and the extended technology acceptance model, which analysed purchase intention among the youth using AR shopping platforms. One hundred seventy-two samples were gathered through self-administered questionnaires and underwent partial least squares structural equation modelling analysis to predict the relationships between the proposed variables.

The results of the current study suggested the independent variables (e.g. entertainment, visual appeal, informativeness and navigation) had a significant impact on hedonic components and PU. Furthermore, both hedonic components and PU had a significant and positive influence on purchase intention. Nevertheless, it is noteworthy that hedonic components and PU in relation to purchase intention were not moderated by trust.

The developed research framework is significant for understanding the perceptions of shopping behaviour among young consumers in the Borneo region of Malaysia. This is one of the few studies that explored the interplay between enjoyment and practicality’s dimensions on purchase intention via AR shopping platforms in the less explored region of Malaysia. Hence, this study plays a pivotal role in contributing to the existing marketing and technology management literature. Moreover, it holds practical importance for business operators and marketers as it aids in decision-making and strategic planning for the future direction of businesses in the young consumer market.

This study aims to investigate the determinants influencing green mindfulness among university students in Malaysia within the context of the Malaysia 2030 Agenda, focusing on Sustainable Development Goal 17 attainment.

The research uses a quantitative approach with a sample of 203 young adults. It explores the factors of goal difficulty, knowledge and awareness, spirituality, values and perceived university environmental responsibility (PUER), using a novel theoretical framework termed universal identity theory (IT).

Values, knowledge and awareness and PUER significantly contribute to green mindfulness, whereas spirituality and goal difficulty did not exhibit a substantial relationship to green mindfulness.

Limited representation of diverse age groups and the potential influence of seniority on spirituality. Future research should expand the framework to include green behavior and performance, increase the sample size and consider a broader age demographic.

Universities play a crucial role in promoting green mindfulness through the establishment of rules, regulations, environmental initiatives, incentive systems and the introduction of a green mindfulness course. Clear communication channels and top-down approaches are recommended.

This research contributes to understanding the mechanisms that induce green mindfulness among university students in Malaysia, aligning with national and global sustainability goals.

The universal IT provides a comprehensive understanding of how personal, social and community-based identities collectively influence green mindfulness. This theoretical perspective contributes to the environmental psychology and sustainability studies field, offering a culturally sensitive approach.

This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

The purpose of this study is to explore how realistic job previews can mitigate the negative relationship between perceived leader behavioral integrity and employees’ perceptions of psychological contract breach.

A between-subjects field experiment was conducted in the hotel industry. Five waves of data were collected from 104 newcomers over a four-month period.

The results indicated that perceiving leader behavioral integrity reduces newcomer psychological contract breach, turnover intention and increases job performance. The intervention of realistic job previews effectively mitigated the negative relationship between behavioral integrity and psychological contract breach when leader–member value congruence was low.

The findings of this study offer actionable strategies for hotel professionals to improve employment outcomes, particularly during the pre-entry phase and the early socialization process of newcomers.

By integrating social exchange theory and expectancy theory within a framework of employees’ psychological contract breach, this study reveals the positive effects of realistic job previews on creating a positive work experience of newcomers.

Innovative technologies have gained popularity in recent years as a means of improving workers' general well-being at work. Among these exciting new technologies is virtual reality (VR). With a focus on enhancing individual performance, this chapter explores the application of VR as a human resource (HR) intervention to improve spirituality in the workplace. To offer a theoretical foundation for comprehending the possible effects of VR interventions on workplace spirituality, this chapter thoroughly evaluates the literature on mindfulness, quantum consciousness and workplace spirituality. By integrating VR technology with the ideas of these theoretical frameworks, HR professionals may create interventions that foster employee effectiveness, resilience and personal growth. It has also been suggested that HR managers employ VR mindfulness and meditation sessions, virtual retreats, values alignment workshops, empathy-building simulations and spiritual reflection spaces as practical VR interventions to enhance workplace spirituality. To illustrate the usefulness of VR in enhancing workplace spirituality, a few companies that have successfully implemented VR therapies are also cited. Lastly, the challenges and moral dilemmas associated with utilising VR to promote workplace spirituality have been examined. These include privacy difficulties, possible biases in VR content and the requirement for ongoing evaluation and feedback techniques. This chapter highlights how VR has the potential to be a game-changing tool for improving workplace spirituality and boosting individual effectiveness.

This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

Drawing on the pleasure-arousal-dominance (PAD) emotion model, the emotional states of consumers embedded in online reviews can be described through three dimensions, that is, pleasure, arousal and dominance, rather than only the one-dimensional positive and negative polarity, as in previous studies. Therefore, this study aims to explore the effect of online review emotion on perceived review helpfulness based on these three basic emotional dimensions.

A lexicon-based method is developed to analyze PAD emotions of online reviews from JD.com. The zero-inflated negative binomial regression is utilized to empirically validate the study hypothesis. The authors examine the influence of pleasure, arousal, dominance, emotion diversity and emotion deviation on review helpfulness, as well as the moderating effect of product type on the relationship between all independent variables and online review helpfulness.

The study results show that the pleasure emotion impairs the helpfulness of online reviews, while the arousal and dominance emotions have a positive impact. Moreover, the authors find that compared with search products, the effects of pleasure, arousal and dominance on perceived helpfulness are strengthened for experience products. However, the emotional diversity and emotional deviation have opposite effects on the helpfulness of search products and experience products. Additionally, the results show that dominance emotion plays a more important role in the interaction effect.

The empirical findings confirm the applicability of PAD in the online review context and extend the existing knowledge of the influence of review emotion on helpfulness. A feasible scheme for extracting PAD variables from Chinese text is developed. The study findings also have significant implications for reviewers, merchants and platform managers of e-commerce websites.