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This paper aimed to introduce a systematic body of knowledge via a scientometric review, guiding the sustainable transition from conventional construction to prefabricated construction. The construction industry currently faces a challenge to balance sustainable development and the construction of new buildings. In this context, one of the most recent debates is prefabricated construction. As an emerging construction approach, although existing knowledge makes contributions to the implementation of prefabricated construction, there is a lack of a comprehensive and in-depth overview of the critical knowledge themes and gaps.

This study uses the scientometric analysis to review the state-of-the-art knowledge of prefabricated construction. It retrieved data from the Web of Science core collection database. CiteSpace software was used to conduct the analysis and visualization; three analysis methods identify the knowledge hotspots, knowledge domains and knowledge topics. Finally, according to integrating the hidden connections among results, a body of knowledge for prefabricated construction application can be inferred.

The results show that 120 knowledge hotspots, five critical knowledge domains and five prominent knowledge topics are vital for promoting implementation of prefabricated construction. Based on the afore analysis, a body of knowledge for prefabricated construction that can systematically cover a broad knowledge of prefabricated construction-related research and activities are integrated and proposed in this paper.

Body of knowledge systematically covers a broad knowledge of prefabricated construction applications and is vital to guide researchers and practitioners to conduct related research and activities, thereby promoting the sustainable transition to prefabricated construction implementation.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

This paper aims to propose an integrated model based on buyer and supplier opportunism to show the mechanism through which current and competing suppliers influence buyer market competitiveness.

Questionnaires were distributed to purchasing staff in listed electronics firms in Taiwan to collect empirical data. Structural equation modeling was used to analyze these data and examine the fitness of the proposed model.

The findings show that current and competing suppliers influence buyer market competitiveness through supplier opportunistic behaviors and buyer commitment. The alternative attractiveness of competing suppliers affects buyer market competitiveness through the influence of asset specificity. Supplier opportunism negatively and indirectly influences buyer market competitiveness through buyer commitment. Nevertheless, buyer opportunism does not influence buyer commitment and market competitiveness.

The investigation focused on only one industry in one country. Future research could investigate other industries and countries to increase the generalizability of the findings.

The results suggest that buyers can focus on utilizing the pressure of alternative suppliers to improve market competitiveness through increased specific investments by the current supplier.

On the basis of buyer–supplier opportunism, this study shows the mechanism through which the asset specificity of current suppliers and alternative attractiveness influence buyer market competitiveness.

The purpose of this study is to examine the impact of unidentifiable individual differences among financial analysts on the cross section of their earnings forecast accuracy.

The paper employs the concept of analyst fixed effects to control for unidentifiable individual differences. Various psychological factors, such as decision style and personality traits, are documented to impact individuals' decision making. However, analysts' individual differences in such psychological factors are not captured by identifiable personal attributes employed in finance literature, such as years of experience. The methodology used addresses this issue and presents a more comprehensive study of analyst forecast accuracy.

The paper documents that unidentifiable analyst‐specific effects are significant, and that controlling for them improves model fitting and changes the explanatory power of some of the traditionally used independent variables in the literature. The paper confirms that the analyst's firm‐specific experience, the intensity of following that a firm receives, and the forecast horizon are all significantly and consistently related to forecast accuracy. However, it is found that analyst general experience and coverage complexity lose explanatory power when individual differences are controlled for. Analyst general experience is not monotonically associated with better accuracy and that analysts only benefit from increased general experience during the early to middle stages of their career. Finally, when analysts' individual differences are controlled for, the boldness of a forecast revision is not associated with the improvement of accuracy.

The documented non‐monotonic relationship between analyst general experience and forecast accuracy is explained as a result of U‐shaped self‐efficacy, decision style and career concerns. This observation is not necessarily in line with the theory of analyst seniority and reputation‐related performance.

The methodology used addresses the issue of analysts' individual differences in psychological factors and presents a more comprehensive study of analyst forecast accuracy.

The ability to forecast a trend is very important in energy consumption prediction and energy production planning. The principle, under which the grey systems theory is applied in our energy consumption prediction, is that the forecasting system can be considered as a grey system. In such a system, unknown system's information can be determined by using known information. Here, the known information consists of energy consumption data, development trend in the consumption system. Based on our study, we eventually make forecast and decisions regarding possible future development. Our method is a whitenization process of a grey course. The model developed is based on the division method established for general data modelling and estimation of parameters of GM(1,1) its standard error coefficient that was applied to judge the accuracy height of the model was put forward; further, the function transform to forecast energy consuming trend and assess GM(1, 1) parameter was established. These two models need not pre‐process the primitive data. It was not only suited for equal interval data modeling, but also for non‐equal interval data modeling. Its calculation was simple and used conveniently, and the oil consumption per unit output analysis was taken as an example. The example showed that the two models were simple and practical, it was worth expanding and applying in the energy consuming prediction and energy programming.

This study aims to address a significant gap in the literature by exploring the individual nature and microfoundation perspective of cultural friction during the integration phase following cross-border mergers and acquisitions (CBMAs). It focuses on the role of face, a pivotal facet of interactions within Asian organizations, elucidating its importance in post-M&A integration outcomes.

Using a conceptual approach, this study draws on three bodies of literature, namely, cultural friction, microfoundations and face concerns. It reconsiders cultural friction as a microfounded construct and introduces face concerns as a pivotal element to understanding the challenges faced by managers at the forefront of CBMAs involving Asian companies. The conceptual approach is illustrated with examples for the world of practice.

This research makes two significant contributions to the fields of CBMAs and cultural friction. First, it demonstrates the relevance of the concept of cultural friction at the individual level, shedding light on the complex post-CBMA integration process. Second, this study demonstrates the critical role of face concerns in the sociocultural integration following CBMAs. This is underexplored in extant literature.

The crucial role of face is well known to those working in Asia. Yet academic inquiry remains underdeveloped on this issue for CBMAs. The friction derived from face concerns provides additional insights into the nature of the cultural challenges confronting managers during sociocultural integration and elucidates the micro-mechanisms influencing individuals’ responses to cultural friction. This research responds to calls to examine the human side of M&As uncovering previously neglected issues within intercultural managerial encounters.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

This study aims to examine members’ perceptions of interactivity in brand communities on social networking sites in the Super Basketball League (SBL) context in Taiwan.

The proposed model was empirically evaluated using survey data collected from 332 followers of the SBL teams’ Facebook pages on their perceptions of brand communities. Structural equation modeling was used to examine the relationships in the research model.

The results suggest significant relationships between perceived interactivity and community benefits, including special treatment, social influence, sense of membership and the notion that community satisfaction has a strong and positive effect on brand loyalty. Both social influence and a sense of membership positively affect community satisfaction. However, special treatment negatively affects community satisfaction. Perceived interactivity positively affects a sense of membership and social influence, which, in turn, positively affect community satisfaction.

This study examines the effects of members’ perceived interactivity and community benefits. The results significantly advance the understanding of the antecedents of members’ loyalty to specific brands. The study offers insights into practical ways of improving community satisfaction and brand loyalty by running brand communities on social networking sites. The findings also augment the theory of brand management.

Lean and Six Sigma are the two most powerful methodologies for process improvement. They have been widely employed in manufacturing and healthcare industries to achieve substantial cost savings and quality improvement. The purpose of this paper is to investigate the implementation of Lean and Six Sigma for improving logistics operations.

A survey was conducted among 410 companies registered under the Singapore Logistics Association and returned 32 usable responses. χ²-tests were used to identify factors affecting the implementation of Lean and Six Sigma.

It was found that 37.5 per cent of respondents had implemented Lean and the majority of them had implemented Six Sigma together. All implementing companies reported varying degrees of cost savings and productivity improvements. Large logistics companies are more likely to implement Lean and Six Sigma. Companies with a higher service standard are more likely to implement Six Sigma.

The survey was limited to Singapore. Future studies should explore a broader geographical coverage and improve the response rate.

Lean and Six Sigma are applicable for improving logistics operations. Senior management must be committed to support an implementation. Effective training and communications are required for overcoming resistance to change and helping employees to understand improvement methodologies and initiatives.

This research is the very first survey study of Lean and Six Sigma in the logistics industry. It reveals similarities and differences in comparison with the implementation of these methodologies in the manufacturing industry. The findings offer valuable insights for improving logistics operations.