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Construction experts acknowledge the adverse effects of rework on project performance. However, the limited understanding of its underlying causes remains a significant challenge. Therefore, this study aimed to thoroughly investigate the sources of construction rework.

A mixed review using bibliometric analysis as a quantitative method and content analysis as a qualitative method was performed to understand the current knowledge in the field. The Web of Science (WoS) was selected for its comprehensive collection of major research articles and integrated analytical tools for generating representative data. The study involved an extensive bibliometric analysis of 107 journal articles on rework causes from 1991 to 2023. RStudio Bibliometrix, an R statistical programming package, was used to analyze rework origins. This method involved mapping the research landscape, identifying research gaps and analyzing emerging trends.

The causes of rework can be classified into three main clusters: human- and contractual-based rework causes, design-, quality- and project management-based rework causes and organizational-based rework causes.

Although several studies have addressed rework causes from various perspectives and methods, the topic has not been investigated holistically. This study is the first to leverage the quantitative and qualitative analytical capabilities of the RStudio Bibliometrix package. Innovative approaches, including the use of metrics, such as the h-index, thematic mapping and trend topic analysis, were employed for a comprehensive understanding of rework causes.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback.

To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.

The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.

This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&A communities.

Drawing upon information processing theory (IPT) and natural resource-based view (NRBV), this study analyses the role of social media technologies (SMT) on internal and external environmental collaboration and green innovation (green product, process and managerial innovation).

This study took in-depth empirical research by developing a survey questionnaire to identify the relationship between SMTs, environmental collaboration and green innovation. The respondents of the questionnaire were supply chain professionals working in the manufacturing industry of Pakistan. The survey collected 475 responses, which were tested through PLS-SEM using Smart-PLS.

The study results indicate that SMTs positively influence both internal and external environmental collaboration. Furthermore, internal environmental collaboration (IEC) fosters green products and green managerial innovation. In contrast, external environmental collaboration (EEC) fosters green processes and green managerial innovation. This study has also tested the mediation of IEC and EEC, which shows that both IEC and EEC mediate all the relationships except green process and green product innovation. The results also revealed that innovation capabilities moderate the relationship between environmental collaboration and green innovation.

Though this study has various practical implications, it is not free of limitations. First, the data were collected from Pakistan, and the results may only be compared with other developing countries. Second, few social media platforms have been considered, but they are increasing in numbers and could be used in upcoming studies. Third, green innovation in the context of products, processes and management is considered, but the concept is evolving, and its other indicators can be taken in upcoming studies.

This study addresses the implication of SMTs, environmental collaboration, innovation capabilities and green innovation, which are helpful for managers and policymakers to design policies.

This study provides the seminal operationalization of SMTs in environmental collaboration and green innovation. This study emphasizes innovation capabilities that firms should adopt.

Research on strategic alliances is concerned with two issues: continuation and reconfiguration. Building on prior research that examines the two issues separately, the paper studies them simultaneously. This paper aims to investigate how strategic alliances may exert the synergetic effect between dynamics and stability as well as to discuss the dynamic evolution process and influence factors of strategic alliances.

This paper describes the construction of a two-party evolutionary game model of alliance and partners. The model is used to analyze the evolution process of synergetic mechanism to determine when to terminate and when to continue with a partnership. Further, numerical simulation is used to quantify the results and to gain insight into the effects of various factors on the dynamic evolution of the synergetic mechanism.

This paper reveals several synergetic states of dynamics and stability in the alliances. The results show that synergy states are positively affected by the collaborative innovation benefits, alliance management capability, the intensity of intellectual property protection, liquidated damages and reputation losses, and negatively affected by the absorptive capacity of partners.

The study helps the alliance to achieve long-term development as well as to balance the paradoxical relationship. The results suggest that managers of strategic alliances should focus on building strong and long-term relationships in order to achieve high performance innovations. Managers should also pay close attention to their partners’ behaviors in previous alliances.

This paper provides new insights into the paradoxical relationship in alliance by revealing the evolution of synergetic mechanism between dynamics and stability. The results remind alliances to understand the relationship between dynamics and stability and to notice the influence factors of synergistic effects when they are making decisions.

Drawing on information foraging theory and the SERVQUAL model, this study built a research model to investigate the roles of middle-aged and elderly short-video creators' online attributes in attracting short-video viewers to be their followers.

Taking Douyin (a famous short-video platform in China) as an example, this study used a sequential triangulation mixed-methods approach (quantitative → qualitative) to examine the proposed model by investigating both creators and viewers.

Viewers who clicked the “like” button for the middle-aged and elderly creators' videos are more likely to follow the creators. Viewers will believe that middle-aged and elderly creators who received more likes are more popular. Thus, middle-aged and elderly creators with more likes usually have more followers. Viewers usually believe that middle-aged and elderly creators who more frequently publish professional and high-quality videos have invested more effort and who have official verification also have a high level of authority and are recognized by the platform. Thus, middle-aged and elderly creators with more professional videos and verification usually have more followers. Moreover, verification, the number of videos and the professionalism of videos can enhance the transformation of viewers who liked middle-aged and elderly creators' videos into their followers, and thus strengthen the positive relationship between the number of likes and the number of followers; however, the number of bio words will have an opposite effect.

These findings have implications for platform managers, middle-aged and elderly creators and the brands aiming to develop a “silver economy” by attracting more followers.

This study researches short-video platforms by using a mixed-methods approach to develop an understanding of viewers' decision-making when following middle-aged and elderly creators based on information foraging theory and the SERVQUAL model from the perspectives of both short-video creators and viewers.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

This study concentrates on assessing the research productivity in the domain of social media security in the past decades. The purpose of this study is to conduct a comprehensive bibliometric review on social media using various bibliometric indicators.

A total of 8,121 scientific publications were retrieved from Scopus database from period 1998 to 2021 using associated keyword search. This study also used VOSviewer^© tool to evaluate the network visualisation.

The result of this study revealed that there is a steady growth of annual publications except for the years 2015 and 2016. A total of 8,121 scientific publications and 80,454 total citations were found with 11.2 average citations per publication. The USA, China and India were top productive countries in terms of publishing research in the field; Chinese Academy of Sciences secured top position with 126 publications in highly productive organisation in the domain. The lecture notes in computer science from Springer Nature received a highly produced title with 553 publications and 4,453 total citations. For co-occurrence of author’s keywords, network visualisation analysis revealed that “Social Media”, “Social Network”, “Security”, “Privacy” and “Trust” found maximum occurrence in the domain of social media security.

This study provides comprehensive research status of social media security by performing a bibliometric analysis. The findings of this study will help relevant researchers to understand the research trend, pursue scientific collaborators and enhance research topic preferences.

The rareness of this study, detailed bibliometric analysis on the domain of social media security, is proven using numerous bibliometric indicators and application of VOSviewer in the domain.

This study aims to synthesize and conceptualize the highly fragmented yet important literature on racial discrimination in entrepreneurship.

A bibliometric analysis and literature review were performed that involved 523 articles containing 26,926 references.

The bibliometric analysis identified three dominant research themes that comprehensively illustrate the state of research in this domain: strategic, sociocultural and individual-level perspectives. The synthesis of extant literature helped in formulating a holistic conceptual model that portrays the genuineness of racial discrimination in entrepreneurship. The sources, factors and impact of racial discrimination faced by entrepreneurs were identified. Based on the review and analysis of keywords, certain fruitful future research directions were formulated that will take the field forward.

This work is the first attempt to review the literature that narrows down the focus to racial discrimination in entrepreneurship (from other discriminations such as gender, cultural and religious discrimination) as one form of discrimination due to its unique origins and consequences.