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This paper aims to prepare Poly(Styrene-Butyl acrylate-Methacrylic acid) @Poly Gallic acid-Fe³⁺ photonic crystal composite inks [P(St-BA-MAA)@PGA-Fe³⁺ PCCI, @ means the PGA-Fe³⁺ is loaded on the microspheres] and construct noniridescent structural colors on fabric substrates, with the goal of improving the visibility of structural colors.

P(St-BA-MAA)@PGA-Fe³⁺ PCCI were prepared by coating P(St-BA-MAA) microspheres with a metal-polyphenol network formed by gallic acid (GA, C₇H₆O₅) and Fe³⁺. The assembly effects of the inks were explored under different conditions, including pH, temperature, concentration and surface tension. The optimal self-assembly conditions of the inks were determined using the controlled variable method.

The results demonstrated the successful preparation of P(St-BA-MAA)@PGA-Fe³⁺ PCCI. The metal polyphenol network film composed of GA and Fe³⁺ was successfully coated on the surface of P(St-BA-MAA) seed microspheres. The assembly mechanism of the inks was investigated, indicating that at a diethylene glycol (DEG, C₄H₁₀O₃) concentration of 0.3 wt% and pH of 7, bright noniridescent structural colors could be formed on fabric surfaces after self-assembly by PCCI at 60 °C for 10 min. Furthermore, the mechanical fastness of the structural colors was enhanced due to the adherence of the soft shell composed of P(St-BA-MAA) and GA.

Utilizing a cost-effective approach and a diverse array of readily available raw materials, we have successfully prepared P(St-BA-MAA)@PGA-Fe³⁺ PCCI, which boasts superior performance and offers fabrics a range of unique coloring styles. This innovation paves the way for potential applications of structural colors in practical production, thereby broadening their realm of utility.

The study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.

The study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.

This study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.

The study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

Despite the increasing studies concerning external knowledge for innovative firms or for innovations, little research demonstrates as what are those capabilities that enable firms to explore and assess high-valuable knowledge. The aim of this study is to examine those capabilities that aid innovative firms to search and identify the potential knowledge for open innovation. This study is an empirical and theoretical examination of knowledge search and identification capabilities of the UK-based biotechnology firms. The literature has been drawn from the research streams into knowledge management, open innovation and dynamic capabilities.

Forty-five semi-structured interviews with senior-level innovation and R&D managers were completed, and thematic analysis using NVivo 12 was performed.

Evidence from 45 interviews with senior-level innovation and R&D managers in the UK-based biotechnology firms and thematic analysis was used to develop a process framework capturing the key capabilities. By identifying the role and contribution of each of the capabilities in knowledge identification in the practices of open innovation, this study contributes to open innovation and knowledge management literature.

Managerial implications suggest knowledge-oriented leads greatly influence the development of identification capabilities. Hence, firms with knowledge-oriented leads are better crafted approaches to achieve competitive advantages in the practices of open innovation.

This study offers a new theoretical perspective and empirical evidence to view knowledge identification capabilities as the dynamic capabilities. This study offers a process framework of identification capabilities.

This study aims to investigate the effects of three dimensions of ChatGPT strategic value – organization support (OS), managerial productivity (IM) and decision aids (DA) – on the business sustainability (BS) of Malaysian public universities.

A quantitative methodology was adopted for this study to examine the relationships between ChatGPT strategic value and the BS of Malaysian public universities.

The study found that two dimensions of ChatGPT strategic value, namely, OS and IM, influence BS, whereas DA do not.

To the best of the author’s knowledge, this study is the first to address the relationship between ChatGPT strategic value and BS in a specific context – Malaysian public universities – providing new contributions to theory by extending the literature on the topic.

The findings are expected to guide universities in Malaysia in leveraging ChatGPT strategic value for enhancing BS.

To the best of the author’s knowledge, this empirical study is the first in the literature to examine the relationships between ChatGPT strategic value and BS in the education sector. Supported by an original conceptual model, the insights provided should extend the literature dedicated to ChatGPT strategic value and BS in the context of a South Asian economy.

This paper aims to present a systematic literature review and bibliometric analysis of research papers published on chat generative pre-trained transformer (ChatGPT), an OpenAI-developed large-scale generative language model. The study’s objective is to provide a comprehensive assessment of the present status of research on ChatGPT and identify current trends and themes in the literature.

A total of 328 research article data was extracted from Scopus for bibliometric analysis, to investigate publishing trends, productive countries and keyword analysis around the topic and 34 relevant research publications were selected for an in-depth systematic literature review.

The findings indicate that ChatGPT research is still in its early stages, with the current emphasis on applications such as natural language processing and understanding, dialogue systems, speech processing and recognition, learning systems, chatbots and response generation. The USA is at the forefront of publishing on this topic and new keywords, e.g. “patient care”, “medical”, “higher education” and so on are emerging themes around the topic.

These findings underscore the importance of ongoing research and development to address these limitations and ensure that ChatGPT is used responsibly and ethically. While systematic review research on ChatGPT heralds exciting opportunities, it also demands a careful understanding of its nuances to harness its potential effectively.

Overall, this study provides a valuable resource for researchers and practitioners interested in ChatGPT at this early stage and helps to identify the grey areas around this topic.

Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery merits empirical investigation. This study aims to explore how AI empathic accuracy affects customers’ satisfaction in the context of AI service recovery.

A moderated mediation model was presented to describe the effect of empathic accuracy on customer satisfaction via four scenario-based experiments.

The results reveal the positive impact of AI empathic accuracy on customer satisfaction and the mediating effects of perceived agency and perceived experience. Moreover, anthropomorphism moderates the empathic accuracy effect.

This paper expanded AI service studies by exploring the significance of empathic accuracy in customer recovery satisfaction. The results provide a novel theoretical viewpoint on retaining customers following AI service failure.

人工智能（AI）设备已越来越多地应用于旅游业和酒店业。然而, AI服务失败是不可避免的。因此, AI服务补救值得进一步实证研究。本研究探讨了AI共情准确性如何影响顾客对AI服务补救的满意度。

通过四个基于场景的实验, 提出了一个有调节的中介模型来描述共情准确性对顾客满意度的影响。

结果揭示了AI共情准确性对顾客满意度有积极影响, 感知能动性和感知感受性具有中介效应。此外, 拟人化调节了共情准确性的效应。

本文通过探讨共情准确性在顾客服务补救满意度中的作用, 拓展了AI服务研究。研究结果为AI服务失败后如何留住顾客提供了新的理论视角。

Las agentes de inteligencia artificial (IA) se aplican cada vez más en el sector del turismo y la hostelería. Sin embargo, los fallos de los servicios de IA son inevitables. Por lo tanto, la recuperación de servicios de IA merece una investigación empírica. Esta investigación explora cómo la precisión empática de la IA afecta a la satisfacción de los clientes con la recuperación del servicio de IA.

Se presentó un modelo de mediación moderado para describir el efecto de la precisión empática en la satisfacción del cliente mediante cuatro experimentos basados en escenarios.

Los resultados revelan el impacto positivo de la precisión empática de la IA en la satisfacción del cliente y los efectos mediadores de la agencia percibida y la experiencia percibida. Además, el antropomorfismo modera el efecto de la precisión empática.

Este artículo amplía los estudios sobre los servicios de IA al investigar el papel de la precisión empática en la satisfacción del cliente. Los resultados aportan un punto de vista teórico novedoso sobre la retención de clientes tras el fallo de un servicio de IA.

This research paper aims to examine two hybrid models of logistics service quality (LSQ) and its influence on satisfaction, loyalty and future purchase intention in a business-to-consumer (B2C) e-commerce context. This study extends the literature for LSQ by incorporating the second-order assurance quality construct, which comprises personnel contact quality, order discrepancy handling and order returns, into one of the hybrid models.

A survey-based approach is used to collect data. Participant responses to questions concerning multiple LSQ dimensions and behavioral perceptions from their most recent online shopping experience are measured using structural equation modeling.

Findings highlight the importance of including a second-order construct assurance quality as a more explanatory model. Results illustrate that online ordering procedures and assurance quality impact customer satisfaction more than other prominent LSQ dimensions. Furthermore, the findings revealed a customer loyalty is a partial mediator between customer satisfaction and future purchase intention. This underscores the significance of improved logistics services as a competitive edge for e-commerce retailers.

Implications are limited to the e-commerce B2C domain.

The findings of this study underscore critical LSQ dimensions that garner greater satisfaction and retention in the online shopping experience. The results indicate that the effective and efficient handling of the initial order and any order problem significantly influences customer satisfaction and reaps the long-term benefits of customer retention.

The authors present and empirically test a hybrid model of LSQ in a B2C e-commerce domain that captures many of the important elements of the customer experience as espoused in the literature.

This paper aims to explore the potential of implementing an action competence teaching model as a framework for achieving sustainable development goals (SDGs) in higher education. The paper seeks to draw insights from the students on the potential of this teaching model.

The study adopted a quantitative research design in exploring the potential of an action competence teaching model. This study used self-report measures to obtain insights into the objective of the study.

The action competence teaching model was seen as useful in equipping students with knowledge about a problem, confidence and willingness to act. However, some issues like the design of the projects, teamwork and instructional practices were identified and discussed.

To the best of the authors’ knowledge, this study is the first to implement an action competence teaching model to help draw insights from students on its potential. This paper documents certain aspects of action competence that require attention before being implemented in higher education. This information so far lacking in scientific literature contributes to ongoing discussions on SDGs while unveiling strengths and weaknesses to be considered.

This paper aims to examine whether the cooperation between female chief financial officers (CFO) and the proportion of female directors would impact investment efficiency. The investigation is grounded in the increasing number of female top managers globally and the notion that female tends to cooperate more with other female than with male.

This study uses publicly listed firms in Bursa Malaysia from 2016 to 2020, which yielded a sample of 2,022 firm-year observations. The authors used multivariate ordinary least square regression to test the relationship, and to correct for the selection bias, the Heckman selection and PSM test were used.

The authors find a positive relationship between female CFOs and investment efficiency. A higher proportion of female directors accentuates this result. The findings support the homophily argument that similar characteristics (gender) promote cooperation. This shows that cooperation between female CFOs and directors improves investment efficiency. The results suggest that the improvement in investment efficiency could relate to higher managerial discretion for female CFOs and their ability to collaborate with female directors. These results are robust to a series of additional endogeneity tests. The findings have important implications for policymakers and firms to encourage more appointments of females in top management positions.

By highlighting the cooperation between female CFOs and female directors, this study contributes to the understanding that cooperation among females improves investment efficiency.