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The purpose of this paper is to investigate the moderating role of cultural factors (concern for face and stability of attribution) in the relationships among service recovery quality, postrecovery satisfaction and repurchase intention. Based on the politeness theory, this paper proposes a theoretical model for understanding how concern for face and stability of attribution may affect collectivists’ consumption behavior.

Data were collected in a field survey of 600 Hong Kong consumers who had experienced a telecommunications service failure. Partial least squares structural equation modeling (PLS-SEM) was used to test the theoretical hypotheses.

A cultural factor of concern for face is found to negatively moderate the relationship between service recovery quality and postrecovery satisfaction. Face also positively influences the relationship between postrecovery satisfaction and repurchase intention. Another cultural factor, stability of attribution, is found to negatively moderate the relationship between service recovery quality and postrecovery satisfaction and to negatively moderate the relationship between postrecovery satisfaction and repurchase intention.

This study contributes to the understanding of the relevance of concern for face and stability of attribution in collectivists’ consumption behavior. The findings have significant implications for managers in a position to exploit the cultural value mechanisms of collectivist consumers.

To the best of the authors’ knowledge, this has been the first research to examine the impact of concern for face and stability of attribution among service recovery quality, postrecovery satisfaction and repurchase intention.

As technology increasingly integrates into the restaurant industry, avatar servers present a promising avenue for promoting healthier dining habits. Grounded in the halo effect theory and social comparison theory, this study aims to delve into the influence of avatars' appearance, humor and persuasion on healthier choices and customer satisfaction.

This paper comprises three experimental studies. Study 1 manipulates avatar appearance (supermodel-looking vs normal-looking) to examine its effects on perceived attractiveness, warmth and relatability. These factors influence customer satisfaction and healthy food choices through the psychological mechanisms of social comparison and aspirational appeal. Studies 2 and 3 further refine this theoretical model by assessing the interplay of appearance with humor (presence vs absence) and persuasion (health-oriented vs beauty-oriented), respectively.

Results suggest that avatars resembling supermodels evoke stronger aspirational appeal and positive social comparison due to their attractiveness, thus bolstering healthier choices and customer satisfaction. Moreover, humor moderates the relationship between appearance and attractiveness, while persuasion moderates the effects of appearance on social comparison and aspirational appeal.

This research bridges the halo effect theory and social comparison theory, offering insights enriching the academic discourse on technology’s role in hospitality.

The findings provide actionable insights for managers, tech developers and health advocates.

Despite its significance, avatar design research in the hospitality sector has been overlooked. This study addresses this gap, offering a guideline for crafting attractive and persuasive avatars.

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Industry 4.0 (I4.0), the Fourth Industrial Revolution, integrates Big Data analytics, blockchain, cloud computing, digitisation and the Internet of Things to enhance supply chain (SC) activities and achieve sustainable growth through dynamic capabilities (DCs). This approach equips businesses with the necessary tools to optimise their operations and remain competitive in a dynamic business environment. The value proposition of a business encompasses a wide range of activities that add value at each stage. By leveraging DCs, a firm can achieve innovation, gain a competitive advantage and enhance its adaptability. Conversely, effective value chain management can amplify the influence of a firm's DCs on SC sustainability, by reducing waste, optimising resource utilisation and fostering strategic partnerships. This mutually beneficial connection takes the form of a dynamic interaction in which I4.0 technologies act as a catalyst to help organisations become more resilient, adaptive and responsive. The adoption of these technologies denotes a comprehensive approach to business shift, not merely technical integration. I4.0 has an impact on several organisational disciplines outside of manufacturing, from automation and efficiency advantages to quality enhancements. This chapter offers an extensive literature review to explore the level of SC sustainability that a business can achieve by combining its DCs and implementing strategic I4.0 adoption. The function of value chain management in moderating the effects of I4.0 and DCs on SC sustainability is also assessed. This study proposes a theoretical model that is grounded in the insights extracted from the literature review.

This paper evaluates intellectual capital (IC) within entrepreneurial environments, towards conceptualising the sequential role of education, its institutions in practice, and wider ecosystems. Well-established attributes of entrepreneurialism, such as idea generation, problem-solving, market expertise and risk awareness are commensurate to that of expected IC practices within enterprising organisations. However, scarce research has been undertaken concerning the confronting of IC practices and activities across collaborative, and sequential, multistakeholder partnerships and activities. This includes alignment to distinct stages of developmental entrepreneurialism inclusive of education and ecosystem support: knowledge exchange and training; mentoring the emergence of the start-up; strategically timing scale-ups; and continued navigation within networks while enduring change.

An integrative review of the relationship between IC, entrepreneurs and new ventures is undertaken to evaluate developmental IC practices as per this paper’s highlighted sequential stages, within entrepreneurial environments and organisational contexts.

Significant roles and responsibilities are evident among collaborative sectors, benefitting the entrepreneurial process and heightening the importance and emergence of IC within entrepreneurial environments. Exposure to enterprise-specific education and support emphasises the developmental human capital process of progressing and protecting ideas and ventures. Latterly, ecosystem engagement leads to consistent intrapreneurialism amongst employees and new venture partners, influencing structured IC systems and enterprising cultures and relational aspects of responsive branding of commercial activity and increased market agility.

Through presenting an attribute-based framework, this paper conceptualises sequential multistakeholder intervention of IC practices and organisational considerations within institutions, as well as guiding the developmental role of education in emboldening individuals and organisations through building IC and evidencing entrepreneurial thinking.

The success of online health communities (OHCs) depends on maintaining long-term relationships with physicians and preventing churn. Even so, the reasons for physician churn are poorly understood. In this study, an empirical model was proposed from a social influence perspective to explore the effects of online social influence and offline social influence on physician churn, as well as the moderating effect of their online returns.

The empirical data of 4,145 physicians from a Chinese OHC, and probit regression models were employed to verify the proposed theoretical model.

The results suggest that physicians' churn intention is influenced by online and offline social influences, and the offline social influence is more powerful. Physicians' reputational and economic returns could weaken the effect of online social influence on churn intention. However, physicians' economic returns could strengthen the effect of offline social influence on churn intention.

This research study is the first attempt to explore physician churn and divides the social influence into online and offline social influences according to the source of social relationship. The findings contribute to the literature on e-Health, user churn and social influence and provide management implications for OHC managers.

This paper aims to identify patterns in the career intentions of PhD students and explore factors influencing these patterns.

Based on data from the Nature 2019 global PhD survey, the paper uses latent class analysis to identify the number and types of patterns in PhD students’ career intentions. Multinomial logistic regressions are used to analyse the influential factors, and means comparisons are used to describe differences in study experiences among career intention patterns.

The paper reveals distinct career intentions among PhD students: Pure Academic Enthusiasts (25.60%), Research-Driven Flexibles (28.64%), Neutralists to Non-research (16.27%), Uncertain Career Explorers (13.63%) and Non-academia Pursuers (15.86%). Research-Driven Flexibles, inclusive of researching roles beyond academia, demonstrate similar engagement and academic skills but have more transferable skills compared to Pure Academic Enthusiasts. Uncertain Career Explorers express positivity but show relatively lower engagement and academic skills. Non-academia Pursuers spend above-average time on learning but have the weakest relationship with supervisors, participation in academic activities, campus environmental support and transferable skills. Older doctoral students with dual degrees are less likely to be Uncertain Career Explorers, while those motivated by academic interests are more likely to be Research-Driven Flexibles or Pure Academic Enthusiasts.

This study provides a more accurate multi-dimensional perspective of PhD students’ career intentions, extending previous research that focused solely on the type of work PhD students sought or the sector in which they desired to work.

Outside of teaching, little knowledge exists about the emotion work of pedagogical professionals, i.e., the emotion work that is performed in kindergartens, residential homes or school counseling. This study addresses this shortcoming by answering the questions (1) how is emotion work experienced and coped with in pedagogical professions? and (2) how does pedagogical professionals’ emotion work relate to burnout?

An exploratory sequential mixed methods approach consisting of an interview and a questionnaire was applied. First, n = 10 interviews were conducted to investigate how emotion work is experienced and managed by pedagogical professionals. Second, hypotheses regarding the relationship between identified resources and burnout were derived and empirically tested in a questionnaire survey with n = 97 participants.

The interviews provided insight into various emotional job demands and resources. Emotion work has been shown to be a key aspect of pedagogical work. Detached concern was identified as an emotion-regulating resource in coping with the resulting emotional job demands. The results of the quantitative phase revealed that pedagogical professionals’ detached concern plays a vital role in preventing burnout.

This study adds new insights to the understanding of emotion work performed in care work professions outside of teaching. The acknowledgement of pedagogical work, as skilled (emotion) work, and the investigation of resources is an important step in improving the working conditions of pedagogical professionals and thus protecting their health and well-being.

With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge. Previous research has improved recommendation effectiveness by using correlations between Web services through graph neural networks (GNNs), while it has not fully leveraged service descriptions, limiting the depth and diversity of learning. To this end, a Web services recommendation method called LLMSARec, based on Large Language Model and semantic alignment, is proposed. This study aims to extract potential semantic information from services and learn deeper relationships between services.

This method consists of two core modules: profile generation and maximizing mutual information. The profile generation module uses LLM to analyze the descriptions of services, infer and construct service profiles. Concurrently, it uses LLM as text encoders to encode inferred service profiles for enhanced service representation learning. The maximizing mutual information model aims to align the semantic features of the services text inferred by LLM with structural semantic features of the services captured by GNNs, thus achieving a more comprehensive representation of services. The aligned representation serves as an input for the model to identify services with superior matching accuracy, thereby enhancing the service recommendation capability.

Experimental comparisons and analyses were conducted on the Programmable Web platform data set, and the results demonstrated that the effectiveness of Web service recommendations can be significantly improved by using LLMSARec.

In this study, the authors propose a Web service recommendation approach based on Large Language Model and semantic alignment. By extracting latent semantic information from services and effectively aligning semantic features with structural features, new representations can be generated to significantly enhance recommendation accuracy.