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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.

As the complex influencing factors for financing decisions and limited information at the early project stage often render inappropriate financing mode and scheme (FMS) selection in the large-scale urban rail transit (URT) field, this study aims to identify the multiple influencing factors and establish a revised case-based reasoning (CBR) model by drawing on experience in historical URT projects to provide support for effective FMS decisions.

Our research proposes a two-phase, five-step CBR model for FMS decisions. We first establish a case database containing 116 large-scale URT projects and a multi-attribute FMS indicator system. Meanwhile, grey relational analysis (GRA), the entropy-revised G1 method and the time decay function have been employed to precisely revise the simple CBR model for selecting high-similarity cases. Then, the revised CBR model is verified by nine large-scale URT projects and a demonstration project to prove its decision accuracy and effectiveness.

We construct a similarity case indicator system of large-scale URT projects with 11 indicators across three attributes, in which local government fiscal pressure is considered the most influential indicator for FMS decision-making. Through the verification with typical URT projects, the accuracy of our revised CBR model can reach 89%. The identified high-similarity cases have been confirmed to be effective for recommending appropriate financing schemes matched with a specific financing mode.

This is the first study employing the CBR model, an artificial intelligence approach that simulates human cognition by learning from similar past experiences and cases to enhance the accuracy and reliability of FMS decisions. Based on the characteristics of the URT projects, we revise the CBR model in the case retrieval process to achieve a higher accuracy. The revised CBR model utilizes expert experience and historical information to provide a valuable auxiliary tool for guiding the relevant government departments in making systematic decisions at the early project stage with limited and ambiguous project information.

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.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

Financial technology (FinTech) has enhanced the inclusivity and accessibility of traditional finance, offering a novel pathway for rural revitalization and development. The paper aims to assess the rural revitalization development level in prefecture-level cities in China and investigate the potential impact mechanism of FinTech.

This paper develops an index system to evaluate the rural revitalization level across 279 cities in China from 2011 to 2021. In addition, multi-mediation and threshold models are employed to analyze how FinTech influences rural revitalization.

The results reveal that, first, FinTech has significantly promoted rural revitalization. Second, entrepreneurial activeness, innovation capability, and industrial structure advancement are intermediary factors within the benchmark path. Third, FinTech exhibits varied threshold effects in entrepreneurial activeness, innovation capability, and industrial structure advancement, influencing rural revitalization with diverse impacts.

First, this paper expands the rural revitalization evaluation to include 30 indexes, enhancing overall measurement comprehensiveness. Second, in contrast to previous research concentrating on provincial-level assessments, this paper explores rural revitalization across 279 cities in China from 2011 to 2021, broadening the study’s scope and timeline. Third, this paper delves into empirical evidence illustrating how FinTech contributes to rural revitalization through entrepreneurial activeness, urban innovation capability, and industrial structure advancement, thereby deepening research in this domain.

The objective of this research was to test the effect of information and communication technology (ICT) resources, library facilities, teacher lecturing skills and physical classroom environment on student satisfaction and university image. This paper also sought to contribute to the existing body of knowledge by confirming the role of student satisfaction as a mediator among the stated factors and university image.

Data were collected from 314 students at higher education institutions (HEIs) in the United Arab Emirates (UAE) using a survey instrument. Throughout the data analysis stage, the partial least squares structural equation modeling (PLS-SEM) was employed in order to validate the research instrument and test the hypotheses.

The findings verified that teacher lecturing skills and ICT resources have a positive effect on both student satisfaction and university image. Moreover, the study revealed that the library facilities and physical classroom environment positively affect both student satisfaction and university image. Lastly, the analysis showed that student satisfaction mediates the link between the stated factors and university image.

This paper adds to the published literature by investigating the direct and indirect effects of teacher lecturing skills, ICT resources, physical classroom environment and library facilities on university image via student satisfaction at HEIs in the UAE. This study is the first to integrate all of these factors into a single research model.