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Online food delivery has been prevalent in recent years worldwide, especially during the COVID-19 pandemic, and people's consumption behaviors have changed significantly. This study aims to investigate the consumption behavior of young adults using online food delivery platforms during the COVID-19 pandemic and focuses on the dominant factors influencing their decision to use online food delivery platforms.

Semi-structured interviews including 14 young adults aged 18–25 living in Hong Kong were conducted to collect data about their perspectives on online food delivery platforms in five areas. This research adopted the stimulus-organism-response model (S-O-R model) to analyze how the factors influence young adult users' loyalty and satisfaction with online food delivery platforms.

Thematic analyses revealed that young adults were attracted to online food delivery platforms for their numerous benefits. They had a high frequency of usage and significant spending. Usability, usefulness, satisfaction and loyalty influenced young adults' behaviors on online food delivery platforms. Participants were overall satisfied with their experiences, but platforms still had room for improvement.

Few prior studies investigated the factors affecting the consumer experience and behavioral intention of online food delivery for young adults in Asia. This study contributes to understanding young adults' experiences and problems with online food delivery platforms. It provides practical insights for system engineers and designers to improve the current services and for the governments to enhance the existing regulatory loopholes.

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.

Characterised by simultaneous food waste and shortages, our current food system is far from sustainable. Industry 4.0 has responded with technology-enabled innovations, including digital food-sharing platforms aimed at facilitating the efficient redistribution of surplus food. However, potential users often express reluctance to adopt such platforms, prompting this study to explore the underlying reasons for their hesitations.

This study was conducted in China, the world’s largest platform economy, where food-sharing platforms are notably absent. Using a vignette-based qualitative approach, semi-structured interviews were conducted with 35 potential users. The data were analysed through thematic analysis to uncover insights into adoption intentions.

The findings highlight the relevance of factors identified in existing technology acceptance theories, such as performance expectancy, effort expectancy, social influence, hedonic motivation, facilitating conditions and price value, in shaping adoption intentions. Additionally, content-specific and context-specific factors – such as trust in other users and the platform, concerns about “losing face” (mianzi) and safety concerns during the pandemic – emerged as critical influences on users' decisions to engage with these platforms.

This study contributes to scholarly discussions on enhancing the effectiveness of new technological innovations for food supply chain sustainability. The theoretical contributions expand the technology acceptance literature by incorporating factors related to platform service content and operating context.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

Currently, many studies have shown an increasing interest in owner-dynamic capabilities (ODCs). Existing studies mainly focus on the dynamic capability basis and capability development within the owner organization, whereas they rarely analyze the capability mobilization within the network of participants in megaprojects. Therefore, this study aims to explain the interaction and evolution of the mobilization strategies of ODCs and the cooperative strategies of other participants.

This study develops a tripartite evolutionary game model to analyze the evolutionarily stable strategy of the owner, the reciprocal participants and the general participants. Results are numerically simulated with a validation case. The asymptotic stability of multiple group strategies is discussed under the replicator dynamic system.

This study suggests that resource complementarity significantly reduces the difficulty of mobilization. Moreover, these strategies are only effective with sufficient ODCs. The results indicate that reciprocal participants are more sensitive to the change in resource complementarity.

This study provides strategic guidance for mobilizing ODCs in megaprojects to better embrace uncertainty and stress, contributing to the dynamic capability literature with an evolutionary game approach. And new insight for the study of reciprocity preference in megaprojects is also provided.

This study investigates the determinants of eco-buying behavior by incorporating individual attributes such as personality traits and personal values and the theory of planned behavior (TPB). It also explores status consumption’s potential role in motivating eco-fashion purchases.

Conducted through an online survey with 436 respondents in the UK, the study utilizes structural equation modeling (SEM) to analyze the data.

The study contributes to the eco-friendly consumer behavior literature in fashion, revealing that social influence, need for uniqueness, public self-consciousness, green consumption values and prosocial attitudes significantly influence both eco-behavior and status consumption. Interestingly, while no substantial impact of status consumption on eco-behavior was observed overall, a multi-group analysis unveils distinctive perceptions and intentions. A clustering analysis identifies two significant segments (status buyers and eco-savvy fashionistas) exhibiting markedly different relationships between variables necessitating tailored marketing approaches concerning eco-fashion buying.

Theoretically, the study highlights the limitations of the TPB in capturing evolving consumer dynamics, calling for refined models that incorporate personal values and specific motivations to better understand sustainable consumption.

For “Status Buyers,” marketers should focus on their desire for uniqueness and present eco-fashion as a status symbol using social proof and influencer marketing. For “Eco-Savvy Fashionistas,” strategies should highlight environmental benefits, provide educational resources and offer tools to track environmental impact.

This study deepens our understanding of eco-friendly fashion purchases by accounting for the diverse perceptions and values that drive consumer behavior. Recognizing the heterogeneity within the sample reveals previously overlooked nuances in this complex decision-making process.

This study aims to investigate the interrelationship among consumers, celebrities and brands within social media environments. It proposes a theoretical framework based on an integrative approach that combines parasocial relationship theory and the meaning transfer model.

This study collected data from 342 Generation Z fans, and structural equation modeling was used for the analysis.

The results revealed that social media use strongly affects celebrity worship, but its impacts on brand love and online brand advocacy are weak. Celebrity worship significantly influences brand love; however, the relationship with online brand advocacy is nonsignificant, highlighting the crucial mediating role of brand love. The mediating effects of celebrity worship and brand love significantly strengthened the relationship between social media use and online brand advocacy.

By using an integrative approach, this study advances the understanding of the dynamics of consumer–brand relationships, revealing the serial mediating path that highlights the roles of celebrity worship and brand love.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

Digital transformation and innovation-driven development have become an international consensus. The purpose of this paper is to examine the effects of relationships, mechanisms and economic consequences between digital transformation and enterprise innovation quality in order to provide a benchmark for developing countries to implement digital transformation strategies and innovation-driven strategies and provide a major support for economic recovery in the post-coronavirus disease 2019 (COVID-19) era.

Using microdata from A-share listed enterprises in Shanghai and Shenzhen from 2010 to 2021, this study examines the relationship between digital transformation and enterprise innovation quality and further reveals the internal logic and economic consequences of digital transformation to improve enterprise innovation quality through the mediating effect and moderating effect models.

The results demonstrate that digital transformation is beneficial for improving enterprise innovation quality. The heterogeneity test demonstrates that digital transformation has a larger effect on improving enterprise innovation quality in non-state-owned enterprises and eastern enterprises in China. The mechanism test demonstrates that digital transformation can improve enterprise innovation quality by improving internal control quality and analyst attention. Furthermore, with the increase in enterprise innovation inputs, digital transformation plays a significantly stronger role in improving enterprise innovation quality. The extended analysis demonstrates that digital transformation can significantly improve enterprise financial performance by improving innovation quality.

First, the construction of the core explanatory variable digital transformation index in this study is based on the Python data analysis software, which calculates the frequency of digital transformation in the text of the business situation analysis portion of the annual report of the listed companies and then obtains the degree of digital transformation of the company in this year. There may be some deviation from the degree of digital transformation in the actual production and operation of enterprises. Second, in addition to internal control quality and analyst attention, are there other mediating mechanisms for the impact of digital transformation on the quality of enterprise innovation? Third, whether the moderating effect of innovation input on digital transformation and innovation quality is related to human capital factors of the research and development (R&D) team, such as the technical background of R&D personnel, etc.

This study enriches the relevant theories of digital transformation and broadens the research boundaries of digital transformation and enterprise innovation. This study's result provides an empirical basis for enterprises to improve enterprise innovation quality and financial performance from the perspective of digital transformation at the micro level and points out specific practical directions, combining theory with practice.

The purpose of this study was to understand knowledge-sharing behavior of nonacademic staff. In relation to the theory of reasoned action (TRA), the study investigated demographic characteristic differences in attitude, subjective norms, behavioral intention and knowledge-sharing behavior of nonacademic staff from India and the United Arab Emirates (UAE).

The study used a survey method and questionnaire instrument to collect data from 467 participants. The participants were nonacademic staff working for two academic universities in India and the UAE. The two universities were Pondicherry University (266 respondents) and Al Ain University (201 respondents). The collected data were treated and analyzed using Microsoft Excel and SPSS software for statistical analyses and frequency distributions.

The findings of the study showed that nonacademic staff from India and the UAE have a positive attitude toward knowledge-sharing, subjective norms and positive behavioral intention and are frequently involved in knowledge-sharing behavior. In addition, the study’s findings indicated statistically significant demographic differences in attitude, subjective norms and behavior intense knowledge-sharing behavior of nonacademic staff from India and the UAE. Nonacademic participants from India expressed a higher positive attitude toward knowledge-sharing and demonstrated more involvement in knowledge-sharing behavior than the respondents from the UAE. The findings of the study accepted 8 out of 20 stated hypotheses. The accepted hypotheses support the TRA theory and suggest the impact of age, job position, tenure of work and geographical diversity on attitude, subjective norms, behavioral intention and knowledge-sharing behavior. The study’s results suggested that nonacademic staff of 50 and above age groups are more likely to share knowledge than the nonacademic staff of 49 and fewer age groups. Likewise, the findings of the study suggested that nonacademic staff with a long tenure of work experience are more likely to share their knowledge with their coworkers than those with a short tenure of work experience.

Studies targeting knowledge sharing among nonacademic staff are scarce as compared to academic staff and students. Nonacademic staff are different from students and academic staff in attitudes and behavior. This study contributes to the expansion of knowledge sharing through the investigation of knowledge-sharing behavior of nonacademic staff. Moreover, the study expands the understanding of knowledge-sharing behavior through the use of demographic characteristics in relation to the TRA theory. In addition, most of the previous studies are based on data collected from one country, and this study is based on data collected from two countries.