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The purpose of this study is to investigate the influence of varying concentrations of nano-SiO₂ particle doping on the structure and properties of the micro-arc oxidation (MAO) coating of 7075 aluminum alloy. This research aims to provide novel insights and methodologies for the surface treatment and protection of 7075 aluminum alloy.

The surface morphology of the MAO coating was characterized using scanning electron microscope. Energy spectrometer was used to characterize the elemental content and distribution on the surface and cross section of the MAO coating. The phase composition of the MAO coating was characterized using X-ray diffractometer. The corrosion resistance of the MAO coating was characterized using an electrochemical workstation.

The results showed that when the addition of nano-SiO₂ particles is 3 g/L, the corrosion resistance is optimal.

This study investigated the influence of different concentrations of nano-SiO₂ particles on the structure and properties of the MAO coating of 7075 aluminum alloy.

Chat Generative Pre-trained Transformer (ChatGPT) has become everyone’s talk. It frightens many professionals, who worry about losing their jobs. ChatGPT may reconstruct some professions; some occupations may vanish while new ones may appear.

This mixed-methods study explores whether and how the use of ChatGPT impacts English is taught as a foreign language (EFL) students' social and emotional learning (SEL). The study used a questionnaire and collected perception data from 57 EFL students. A discussion with seven EFL professors was also formulated to triangulate the findings.

Results indicate that EFL students have high positive perceptions of using ChatGPT in their learning (M = 3.87). Results also showed that using ChatGPT has a moderate impact on EFL students' SEL (R = 514). This moderate effect was confirmed by the qualitative findings, which indicated that ChatGPT positively impacts EFL students' SEL by allowing them to practice conversation skills, aiding them in managing their emotional intelligence, providing them with feedback and reducing their anxiety. However, findings also indicated that ChatGPT reduces students' creativity and limits their emotional growth. Finally, the findings reported that for better use of ChatGPT, supervision is key.

This study recommends the use of ChatGPT in a way that helps students' creativity and emotional growth.

With over-the-top (OTT) streaming services rapidly transforming the media industry and saturating the market, the authors' study seeks to enrich the goal-directed behaviour model by exploring how perceived risks and descriptive norms influence OTT consumption.

Survey data from OTT subscribers were collected online to assess their risk behaviours. The 353 responses obtained were analysed with SmartPLS, validating the structural equation modelling (SEM) through structural and measurement model verification.

The authors' findings illustrate that descriptive norm, perceived behavioural control, as well as positive and negative anticipated emotion (NEM) and attitude, contribute positively to the desire to engage with OTT streaming services. Interestingly, the authors' study contradicts common assumptions, revealing that subjective norms do not significantly impact the propensity to utilise OTT services. This counterintuitive finding necessitates a reconsideration of prevalent theories and contributes to a nuanced understanding of OTT adoption determinants.

The data gathering for this study were conducted from the perspective of a single nation. Therefore, caution must be exercised when generalising this study's results.

The practical ramifications of this research are vast, providing OTT service providers and marketers with actionable insights to maximise user engagement and navigate perceived risks related to OTT service adoption and consumption.

This study's exploration of perceived risks and descriptive norms enhances the goal-directed behaviour model's breadth, facilitating a holistic comprehension of the constructs shaping OTT consumption behaviours. It would be the first attempt to combine perceptual, affective and behavioural factors and perceived risks to understand the user's predisposition to engage in OTT streaming services.

Although research on how the downstream calculations of a patent’s profit potential influence invention renewal decisions is extensive, the impact of the upstream knowledge creation stages is overlooked. The purpose of this study is to address this theoretical vacuum by examining the intra-organizational configuration of knowledge networks and collaboration networks.

The data consist of 491 global pharmaceutical firms that patent in the USA. Drawing on patent records, the authors simultaneously construct intra-organizational knowledge networks and collaboration networks and identify network cohesion features (i.e. local and global). The authors employ panel fixed-effects models to test the hypotheses.

The results show that local knowledge cohesion and local social cohesion decrease invention renewals, while global knowledge cohesion and global social cohesion increase renewals. Moreover, the marginal effects of local and global social cohesion are stronger than those of local and global knowledge cohesion, respectively.

The hypotheses are tested using the pharmaceutical industry as a research setting, which limits the generalizability of our findings. In addition, potential formal and informal contingencies are not considered.

Despite its limitations, this study provides valuable implications. First, managers are cautioned against the adverse effects of local cohesion structures on invention renewal. Second, firms can dynamically adjust their local and global network configuration strategies to harmonize the generation of valuable inventions and the retention of good ideas.

Complementary to previous research that focused on inventions’ performance feedback, this study delves into upstream knowledge creation stages to understand invention renewals.

The main objective of this article is to visualize the structure and trends of pro-environmental behavior (PEB) between 1999 and 2023 through mapping and in-depth analysis. The aim is to analyze PEB, which has received considerable academic attention in recent years due to its key role in the conservation of the environment and the protection of local communities in tourist destinations. This paper provides an important summary of the recent research that has explored the role that tourists have in protecting the environment through PEB.

This study presents a visual analysis of 2005 scholarly articles between the years 1999 and 2023 related to PEB. Using the knowledge mapping based on VOSviewer it presents the current status of research, which includes the analysis of citation analysis, co-citation analysis, co-citation network and longitudinal analysis.

PEB is an emerging topic due to its relevance to protecting the environment in the context of travel. The citation and co-citation analysis show the relevance of the behavior of tourists with regard to protecting the environment. The co-word analysis highlights the current significance of research concerning green hotels and the destination image of environmentally responsible destinations.

This study sheds light on the current research progress of PEB in the context of tourism through a comprehensive analysis (citation, co-citation and co-word). In addition, we provide theories and factors that have been previously used to study PEB in the context of tourism. The findings contribute to a broad and diverse understanding of the concept of PEB, which can provide important insights for policymakers in formulating management strategies and policies aimed at reducing environmental impacts in destinations.

This study aims to provide insights into the drivers of student engagement in food waste reduction strategies in educational institutions. The proposed research model integrates social media celebrities' attractiveness, expertise and trustworthiness with the value belief norm (VBN) theory to explore their influence on students' behaviour towards food waste reduction.

The data were collected from 417 students enrolled in public and private universities in the Riyadh and Macca regions of Saudi Arabia to evaluate the proposed model. The partial least squares-structural equation modelling (PLS-SEM) was employed to analyse the responses.

The results showed that VBN theory's components, such as values (biospheric, altruistic and egoistic), beliefs (new ecological paradigm, awareness of consequences and aspirations of responsibility) and norms significantly and positively influence food waste reduction behavioural intentions. It was also discovered from the results that social media celebrities' attractiveness, expertise and trustworthiness influence food waste reduction behavioural intentions.

This study contributes significantly to the literature by identifying factors influencing student engagement in food waste reduction strategies in educational institutions where limited research exists. It fills this research gap by developing a novel theoretical framework integrating social media celebrities' attributes with the VBN theory to explain these factors.

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

This paper aims to illustrate the application and effectiveness of a marketing programme co-designed by supply- and demand-side stakeholders to reduce consumer food waste in restaurants.

This stakeholder-based marketing pilot study adopted the co-create, build and engage framework for programme design and implementation. Major stakeholders, interacting at the point-of-sale, participated in a series of focus groups, interviews and co-design. The research process informed the marketing mix, which aimed to provide value for all parties. The four-week pilot programme was delivered in a non-buffet-style commercial restaurant chain for 10 months. The amount of consumer food leftovers was measured and compared with pre-programme baseline data to evaluate programme effectiveness.

The results show that the marketing mix co-designed by restaurant stakeholders and consumers effectively reduced food waste by almost half in the pilot period. The profitability of the pilot restaurant increased as food costs decreased.

This research demonstrates how working with stakeholders from both the supply and demand sides can identify motivations and barriers. Insights gained in the research phase can inform the delivery of a marketing mix that reduces consumer food waste. This study demonstrates the marketing research, design, implementation and evaluation process for a marketing programme that reduced consumer food waste.

To effectively reduce consumer food waste, practitioners should not only focus on changing consumers’ behaviour. Co-designing solutions with food service stakeholders to address business and operation challenges is crucial to the attainment of a positive impact at the point-of-sale.

This research shows how marketing changes behaviour in individuals and business entities, contributing to positive environmental impact through waste reduction in the commercial food service sector.

This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.

This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.

This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.

The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.