Purpose As the pioneer nation to formally integrate data as a factor of production into its national strategy, does China’s research emphasis on data factor market differ from that of other countries? Currently, there is a notable absence of comprehensive comparative analyses addressing this issue. The purpose of this study is to fill this gap by systematically reviewing the development trajectory of research on the data factor market from a governance perspective. Design/methodology/approach In this study, the authors used Citespace software to conduct bibliometric analyses on a corpus of 1,304 international publications and 1,249 Chinese publications. These analyses encompassed various dimensions, including publication quantity and trend analysis, cocitation analysis and keyword cooccurrence analysis, while also facilitating a comparative examination of research disparities between Chinese and international literature. Findings There are significant differences between Chinese and international data factor market studies in terms of governance environment, governance entities, governance technology and governance mechanism. The authors strongly advocate the promotion of cross-border research collaboration to advance the research and application of data factor market governance. Originality/value The authors have innovatively proposed a framework for data factor market governance. Building upon this framework, the authors conducted a comprehensive comparative analysis of research disparities between Chinese and international literature, thereby unveiling emerging research trends in the field of data factor market.

Engineering and technology intensive organizations increasingly adopt AI-enabled recruitment tools to improve selection efficiency, but challenges related to algorithmic fairness and trust influence applicant responses. Despite the use of objective and fair algorithms, applicants may still perceive unfairness, but empirical research on this phenomenon remains limited. Based on expectation disconfirmation (ED) theory, this study defines the gap between applicants' self-assessed qualifications and AI-generated person-job fit scores as person-job fit ED and estimates how it affects job pursuit intention through perceptions of procedural justice. To ensure research rigor, we employed a two-stage online scenario-based experiment, implemented attention checks, and conducted exploratory and confirmatory factor analyses to validate construct reliability and validity. Multiple procedural and statistical remedies were also applied to address potential common method bias. Empirical results show that ED significantly reduces job pursuit intention. Besides, procedural justice partially mediates this relationship, suggesting that weakened fairness perceptions serve as a critical mechanism underlying the negative effect of expectancy disconfirmation. Furthermore, trust in AI exacerbates the negative impact of ED in some cases. These insights deepen the understanding of how AI-generated evaluations shape individuals' behaviors and perceptions. This study extends the applicability of ED theory to algorithmic recruitment contexts and offers novel theoretical and practical implications.

Survival represents a fundamental aspect of business development, particularly for small-sized businesses. This study introduces a comprehensive framework for examining the determinants of restaurant survival from the perspective of word-of-mouth dispersion. Utilizing data collected from Yelp, we analyzed a sample of 9606 randomly selected restaurants across 309 cities in the United States from January 2018 to February 2022. Our findings revealed that topic dispersion exhibits a negative relationship with restaurant survival, albeit with a diminishing marginal effect. Conversely, sentiment dispersion demonstrates a positive but decreasing marginal relationship with restaurant survival. We also observed negative moderating effects of review length on the aforementioned relationships. The findings enhance our theoretical comprehension of the survival among small-sized businesses and offer practical recommendations for both restaurant owners and platform managers.

The emergence of online course platforms (OCPs) provides a convenient channel for individuals to acquire knowledge, and also attracts learners to pay for knowledge. For these platforms, how to attract new learners as well as improve learner's retention is an important issue. The purpose of this study is to analyze the mechanism by which learners' satisfaction with the platform and learning behaviors affect their intention to knowledge cross-buying. In addition, we examine the moderating effect of knowledge product characteristics. To address these issues, we estimate the influence of learners' satisfaction and learning behavior on knowledge cross-buying intention based on the probit regression method. The authors collected data from two online medical course platforms operated by a Chinese company. The dataset includes course information, learner's behavior and login data of 3143 learners from January 2014 to December 2019. Knowledge cross-buying behavior occurs between this new platform and another existing platform. The results reveal that learners' satisfaction and behavior with conviction positively impact knowledge cross-buying intention. Conversely, behavior without conviction does not have a significant impact, which may be attributed to path dependence. Furthermore, the effects of behavior with conviction are amplified when the courses are more difficult, while the effects of learners' satisfaction are comparatively weaker. By examining the influence of OCPs learners' learning environment and their learning behavior on the intention to pay for knowledge, as well as the moderating effect of knowledge product characteristics, this study explains the mechanism behind learners' payment for knowledge.

To explore how the diffusion of digital technologies shapes corporate green innovation, this study uses panel data from A-share listed companies between 2007 and 2021. The empirical findings reveal that digital diffusion significantly enhances the quality, quantity, and efficiency of green innovation. The effects are heterogeneous across firms: in high-tech enterprises, digital diffusion primarily improves innovation quality, while in non-high-tech enterprises, it mainly boosts innovation quantity. Moreover, the positive effects are stronger in heavily polluting industries than in cleaner ones. Mechanism analysis suggests that digital diffusion advances green innovation by strengthening internal corporate capabilities—particularly in production, automation, R&D, and management. These enhanced capabilities lead to more efficient and higher-quality green innovation outcomes. Interestingly, the study uncovers an inverted U-shaped relationship between digital diffusion and the quantity of green innovation, implying that while early-stage diffusion stimulates innovation, its marginal benefits may decline after a certain threshold. This finding offers valuable insights into the stages of technological adoption and their varying impacts. The research provides strategic implications for both policymakers and corporate leaders. For governments, it underscores the need to balance support for digital infrastructure with regulation to avoid diminishing returns. For firms, especially those in high-pollution or low-tech sectors, the study highlights the importance of timing and scale in digital transformation strategies.

Video course previews provide customers with direct product experience, which can reduce information asymmetry relating to knowledge products and promote purchases. Some research has proved the effects of previews. However, little research focuses on the interaction effects between video course previews and extrinsic cues of products, and there is even less research on intrinsic cues provided by video course previews. To address these issues, we study the interaction effects between extrinsic cues and video course previews on purchase decisions. Additionally, we further explore the effects of intrinsic cues provided by video course previews based on cue utilization theory. We empirically test the model using data from a large medical knowledge payment platform. To our knowledge, this is the first study that attempts to provide a better understanding of the effects of intrinsic cues. We find some interesting results. For example, more interrogative sentences in the video course previews can prompt purchase. In contrast, using excessive first-person pronouns in the video course previews negatively affects consumers' purchase behaviors. These new findings have direct implications for platforms to optimize the previews; more broadly, the findings can facilitate better understanding of the influence mechanism of video course previews on knowledge payment platforms.

Reminder messages play a vital role in customer relationship management. Previous e-mails and short messages have received a lot of attention. However, it is not clear how the reminder message in the academic live streaming affects customer participation. In this paper, we explore the influence mechanism of reminder messages in the live streaming platform based on logistic regression and hidden Markov model (HMM). The analysis results suggest that the reminder message has positive effects on participation while "technostress" causes the inverted U-shaped effect between the reminder frequency and customer participation. In addition, compared with working hours, the reminder message has a greater impact on participation when the corresponding live streaming is during after-work hours. The impact of reminder message wears off as the customer's usage level of the platform increases. This study not only contributes to customer relationship management, but also has practical significance for academic live streaming platforms.

As synthetic biology is extensively applied in numerous frontier disciplines, the biosafety and biosecurity concerns with designing and constructing novel biological parts, devices, and systems have inevitably come to the forefront due to potential misuse, abuse, and environmental risks from unintended exposure or potential ecological impacts. The International Genetically Engineered Machine (iGEM) competition often serves as the inception of many synthetic biologists' research careers and plays a pivotal role in the secure progression of the entire synthetic biology field. Even with iGEM's emphasis on biosafety and biosecurity, continuous risk assessment is crucial due to the potential for unforeseen consequences and the relative inexperience of many participants. In this study, possible risk points for the iGEM projects in 2022 were extracted. An attack tree that captures potential risks and threats from experimental procedures, ethical issues, and hardware safety for each iGEM-based attack scenario is constructed. It is found that most of the attack scenarios are related to experimental procedures. The relative likelihood of each scenario is then determined by using an established assessment framework. This research expands the traditionally qualitative analysis of risk society theory, reveals the risk formation in the synthetic biology team, and provides practical implications.

Studies have confirmed the ineffectiveness of sentiment expressions generated by sellers in improving guests' purchasing intentions. However, how sentiment expressions can influence guest behavior and host performance remains unclear, given the relative importance of seller-generated content in peer-to-peer rental platforms. After collecting data from Airbnb and developing empirical models, this study confirmed that hosts' sentiment expressions largely benefit from their online performance. This case is especially true when their properties did not receive high-quality negative reviews. To further reveal the mechanism behind this effect, we further conducted two experiments. Results show that trust plays an intermediary role in the relationship between hosts' sentiment expressions and guests' purchasing intentions. This work contributes to tourism literature and property owners on peer-to-peer rental platforms in practice.

Purpose Blockchain is a distributed ledger technology that uses cryptography to ensure transmission and access security, which provides solutions to numerous challenges to complex supply networks. The purpose of this paper is to empirically test the impact of blockchain implementation on shareholder value varying from internal and external complexity from the complex adaptive systems (CASs) perspective. It further explores how business diversification, supply chain (SC) concentration and environmental complexity affect the relationship between blockchain implementation and shareholder value. Design/methodology/approach Based on 138 blockchain implementation announcements of listed companies on the Chinese A-share stock market, the authors use event study methodology to evaluate the impact of blockchain implementation on shareholder value. Findings The results show that blockchain implementation has a positive impact on shareholder value, and this impact will be moderated by business diversification, SC concentration and environmental complexity. In addition, environmental complexity exerts a moderating effect on SC concentration. In the post hoc analysis, the authors further explore the impact of blockchain implementation on long-term operational performance. Originality/value This is the first research empirically examining the effect of blockchain implementation on shareholder value varying from internal and external complexity from the CASs perspective. This paper provides evidence of the different effects of blockchain implementation on short- and long-term performance. It adds to the interdisciplinary research of information systems (IS) and operations management (OM).

With the digital transformation of the global economy, a new mode of knowledge service has emerged on open innovation platforms such as those for the sharing economy. This mode is the paid knowledge-sharing service, where knowledge providers share knowledge with only those who have paid for it. Since an individual customer's purchases are influenced by others around them, we adopted social influence theory to explain sales of such services on paid knowledge-sharing platforms. A machine learning approach was applied to analyze 27,223 text reviews from the Zhihu Live platform (a well-known and large-scale open knowledge community in China). Hierarchical regression models were built to verify twelve proposed hypotheses about the knowledge providers, knowledge quality, interaction quality, and ratings. The results confirm the positive effect on sales of responsiveness (a dimension of interaction quality), and the negative effect on sales of free provider-driven knowledge contributions.In summary, this study provides a comprehensive framework for antecedent factors of sales of knowledge-sharing services. By introducing to knowledge management notions from the field of e-commerce (e.g., price, quality), this study broadens the understanding of the free-to-paid phenomenon on knowledge-sharing platforms.

Academic engagement with industry is now practiced by more scientists than ever before. Despite broad consensus regarding the positive effect on senior and successful scientists' research productivity, its effects on early career scientists have remained insufficiently investigated. Utilizing a novel dataset drawn from both awardees and nominees in the case of high-tech enterprises' Ph.D. funding programs from 2010 to 2017, we combine difference-in-differences estimation and data mining to explore the future research impact (research productivity and direction) of industrial Ph.D. funding. We find that awardees granted industrial Ph.D. funding outperform nominees in terms of both the quantity and quality of subsequent scientific production, with a more salient improvement of research performance in developing countries. Such discernible improvement in scientific production does not come at the cost of altering the research direction of beneficiaries. The results are robust to different specifications and measurements. Practical and policy implications are discussed for entrepreneurs and science policymakers to favor scientific knowledge production and transfer by strengthening science-industry relations between high-tech enterprises and early career scientists.

Identifying scholars with potentials early in their careers is critical for informed evaluations, effective allocation of funding, and tenure decisions, which in turn propel advancements in science and technology. This paper investigates the impact of social capital features on the identification of such scholars. Utilizing a comprehensive dataset spanning from 1991 to 2020, extracted from the Microsoft Academic Knowledge Graph, we analyze the novelty values of 56,568 scholars' future publications using disruption index. We identify potential scholars as those within the top 1% based on these values. Our approach involves extracting nine key features of structural, relational, and cognitive capital from the dynamic co-authorship networks of these scholars during their early career stages. The influence of these features on scholar identification is assessed through ablation experiments using an LSTM-based predictive model. Our findings underscore the critical importance of cognitive capital features in the identification process. Furthermore, the integration of structural and relational capital features markedly enhances the model's predictive accuracy, achieving significant improvements in precision metrics. Notably, relational capital features demonstrate a greater influence than structural features in predicting scholar potentials. These results provide essential insights and practical implications for strategies aimed at recognizing and fostering outstanding academic talent.

Social networking sites (SNSs) provide users with ample opportunities to share their own information and participate in social browsing to get to know others. Drawing upon signaling theory, this paper investigated how and to what extent recommendation letters' content information (breadth and depth) and external information (field and relationship) affect career mobility. Based on the open data from LinkedIn, this paper adopted the Latent Dirichlet Allocation (LDA) method to capture the breadth and depth of the recommendation letter, and used elaboration likelihood model to test our model. The results show that both content information and external information of the recommendation letter have significant positive impacts on the career mobility. In addition, the social network size shown converse moderating effects between recommendation letter content information and career mobility. This paper extends the boundaries of signaling theory by creatively using machine learning methods to measure textual signals (i.e., recommendation letter). The research results provide practical implications for personal career development and firm talent management.

Using the data of A-share listed companies in Henan Province from 2016 to 2020 and the digital inclusive financial index of Peking University, this paper empirically tests the impact of the development of digital finance in Henan Province on the scientific and technological innovation ability of enterprises. The study found that the development of digital finance helps to improve the scientific and technological innovation ability of listed enterprises in Henan Province. At the same time, financing constraints, as an intermediary variable, mask the impact of digital Finance on scientific and technological innovation of enterprises. Digital finance can more significantly affect the scientific and technological innovation ability of listed enterprises with greater financing constraints.The purpose of this study is to explore how word finance indirectly promotes the improvement of scientific and technological innovation ability of enterprises by alleviating financing constraints and other channels, and to clarify the intermediary role of financing constraints in it. At the same time, this study focuses on this specific region of Henan Province, combines its economic development characteristics and the development status of digital finance, and fills the gap in the research on the impact of digital Finance on scientific and technological innovation at the regional level. By refining the analysis to the provincial level, we can more accurately reveal the regional effect of digital finance.

Synthetic biology (SynBio) is an interdisciplinary field that includes biology, genomics, engineering, and informatics. The introduction of machine learning technology reduces the requirement for human participation in SynBio research, which may lead to increased biosafety and biosecurity concerns within the complex human-technology-management system in SynBio laboratories compared with traditional biology. The rapid development of SynBio and machine learning technology has added to the systemic complexity of management and technology subsystems, the risks of which remain unclear. To fully understand and address these risks, there is an urgent need to develop a quantitative model. In this study, three-round Delphi interviews were conducted with SynBio experts who have published articles in top journals (e.g., Nature, Science, Cell, etc.) and applied the system dynamics method to build a quantitative analysis model. The model explores the potential risk factors and their complex relationships in the SynBio research. We analyzed three subsystems (biotechnology, information technology, and management), identifying seven key risk factors. Among these, the Average Experience of Newcomers and Tolerable Error Frequency play an essential role in laboratory management. Notably, the biosafety atmosphere has the greatest impact on reducing error rate and increasing safety awareness, as well as on the infrastructure safety of SynBio laboratory. In addition, we also consider the impact of the progressiveness of machine learning in the SynBio research and find that using more advanced machine learning technology leads to lower instrument safety. This study provides a framework for quantifying potential risk factors in SynBio research and lays a theoretical foundation for future laboratory safety management and the development of a global code of conduct for interdisciplinary scientists.