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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 assess the oxidative stability of avocado oil (AO) at various temperatures, using butylated hydroxytoluene (BHT) as an artificial antioxidant and different concentrations of ultrasonic extract of Chlorella vulgaris.

Extracts of C. vulgaris were obtained using four solvents: water, acetone, ethanol and 80% ethanol-aqueous. Standard techniques were used to conduct qualitative phytochemical screening of the extracts. The extracted samples were analyzed for total phenolics, total flavonoids, antioxidant activity and phenolic compound fractionation. Some physicochemical parameters of AO treated with various concentrations of C. vulgaris ultrasonic extract compared to a 200 ppm BHT and exposed to different temperatures were measured.

The highest phenolic, flavonoids content and antioxidant activity was achieved by 80% ethanolic extract of C. vulgaris . The results showed that exposure of AO to high temperatures led to significant changes in the oil's physicochemical properties. These changes increased as the temperature increased. On the other hand, adding 80% ethanolic extract of C. vulgaris into AO reduced the effect of heat treatment on the change in physicochemical properties.

Adding 80% ethanolic extract of C. vulgaris into AO can potentially reduce the impact of heat treatment on the alteration of physicochemical properties.

This study investigated the relationship of strengths-based leadership with nurses’ turnover intention and the mediating roles of job crafting and work fatigue in the relationship.

Data comprising 318 valid participants from three hospitals in Beijing were gathered at two points in time, spaced by a two-month interval. Structural equation modeling with a bootstrapping analysis was applied to test hypotheses.

This study found that strengths-based leadership negatively relates to nurses’ turnover intention, and job crafting and work fatigue mediate the relationship of strengths-based leadership with turnover intention, respectively.

The findings of this study highlight the importance of strengths-based leadership in decreasing nurses’ turnover intention and reveal two potential mechanisms through which strengths-based leadership is related to nurses’ turnover intention. In order to retain nursing staff better, nurse leaders should execute more strengths-based leadership behaviors and make more efforts to promote nurses’ job crafting and to reduce nurses’ experience of work fatigue.

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 propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

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.

This paper develops and tests a theoretical framework to explain the effect of guanxi human resource management (HRM), a unique Chinese cultural phenomenon, on employee innovative behavior.

We draw from a sample of 398 employees in 81 teams and test the moderated mediation model using multi-level modeling.

The results show that guanxi HRM can be perceived by employees as being simultaneously an unethical hindrance that stifles innovative behavior and a strategic challenge that is beneficial for innovative behavior. In addition, the results show that these indirect effects are contingent upon the strength of guanxi HRM.

The study advances our understanding of the mechanism and boundary condition underlying the double-edged nature of guanxi HRM practices.