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This paper aims to explore coopetition within the entrepreneurial ecosystem and answer the following two fundamental questions: How does coopetition affect the entrepreneurial learning and performance of startups? and What learning strategies should startups adopt to promote their growth in the coopetition activities?

Using the structural equation model and instrumental variable, this study used a sample of 371 startups to test the hypotheses. Data comes from startups in Jiangsu, Shanghai and Zhejiang, China.

This study finds that the coopetition-performance relationship of startups is marginally negative. This study also finds that exploitative learning and exploratory learning positively mediate this relationship. Ecosystem’s social capital can enhance the coopetition-exploration relationship, but the coopetition-exploitation relationship is not affected.

Many studies propose that the coopetition-performance relationship is ambiguous, which makes it meaningful to explore startups individually. Based on the resource-based view and the knowledge-based view, this study deepen the works of Bouncken and Fredrich (2016c), that is, how startups can learn and grow through coopetition activities. This study proposes that coopetition is one of the foundations of the ecosystem and explore the coopetition-performance relationship in this special context. Thus, the present paper adds to the budding literature on the effects of the entrepreneurial ecosystem and to the literature on coopetition.

In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm (HBBEPSO). In the proposed HBBEPSO algorithm, we combine the bat algorithm with its capacity for echolocation helping explore the feature space and enhanced version of the particle swarm optimization with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBBEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBBEPSO algorithm to search the feature space for optimal feature combinations.

This study seeks to explore the impact of “fear of missing out” (FOMO) and “psychological enhancement” (PE) on addiction to social media applications, subsequently influencing users' life satisfaction and continuous usage intention.

This research involved the administration of two sets of questionnaires during distinct periods: December 15 to December 30, 2022 and August 26 to September 2, 2023. The participants were college students from three universities in China, and the data collection utilized the “Questionnaire Star” platform. Only responses deemed valid and consistent were included in the subsequent statistical analysis. A total of 1,108 valid samples were used for the final analysis. Analyses including reliability, validity, path analysis, structural equation modeling, mediation effects and moderation effects were conducted using SPSS and AMOS software.

The study revealed that both FOMO and PE exerted positive influences on users' addiction to social media applications. Furthermore, this addiction was found to have a negative effect on users' life satisfaction while simultaneously contributing positively to their intention to continue using these platforms. The mediating effect of social media application addiction and the moderating impact of self-regulation were also substantiated through the analysis.

Firstly, it is important to note that the research population of this study is limited to college students, which may limit its generalizability and representativeness. Although college students are a group known for their familiarity with and frequent use of smartphones and social media apps, the findings may not fully capture the behaviors of social media app users in other age groups. To enhance the understanding of social media app addiction across different age groups, future studies should consider expanding the research population and conducting multi-group difference analyses. Secondly, while focusing on specific users within a particular region can minimize unexplained variance in model estimation, it may also restrict the broader applicability of the study results. Therefore, future studies should consider testing the research model with diverse groups from different regions and cultural backgrounds. This approach will provide valuable insights into how social media app addiction may vary across various contexts, thereby enriching our understanding of this phenomenon.

Our findings reveal that in the “attention economy” environment shaped by addiction, social media app managers should leverage technology to swiftly and accurately target audiences, attract them to their platforms and cultivate long-term relationships. Encouraging users to develop new beneficial habits through app-specific functions and precise services will foster continuous usage and unlock revenue and marketing opportunities for app companies.

Despite the extensive scholarly discourse on social media application addiction, there is a lack of a well-defined framework delineating how addictive user behaviors can be leveraged in the marketing strategies of social media application platforms. The present study seeks to address these gaps, contributing to a better understanding of the formation mechanisms and knowledge systems related to social media application addiction. By investigating the causes and consequences of such addiction, this research offers valuable insights and recommendations for the innovative development of these apps, given their widespread popularity. Concurrently, the study establishes a theoretical basis for the concept that users can mitigate the negative effects of social media addiction by exercising their own self-regulation.

As the functionalities and features of social media apps converge, their individual uniqueness starts to diminish, intensifying the competition among social media companies. This escalating rivalry places higher demands on these companies. This study aims to aid social media app companies in comprehending and analyzing the diverse psychological needs of users. By enriching their platform features and services, leading users towards addiction and gaining an edge in the “Attention Economy” competition. Understanding and catering to users' needs will be instrumental in thriving within this dynamic and evolving attention economy landscape.

This paper aims to recognize whether government policy supervision or social network platform supervision can effectively promote the control of misconducts of web celebrity brand eWOM marketing and to identify the key factors influencing the unhealthy web celebrity marketing environment.

Theoretical research was employed to develop a practical approach for applying evolutionary game theory to eWOM marketing controlling strategies modeling via dynamic visualization, systematic simulation experiments.

Evolutionary game theory combined with dynamic simulation modeling can provide a formal approach to understanding web celebrity brand eWOM marketing decision-making in social media, which can thus support the control of unhealthy web celebrity marketing environment. The results demonstrate that the reasonable control of social platform control costs may be more effective than the government policy on web celebrity fake brand eWOM marketing behaviors.

The study enriches the research on the management and control of eWOM marketing as well as provides guidance for the sustainable development of the web celebrity economy in social media.

Since a single environmental strategy is not sufficient to deal with the various institutional forces that firms are facing, this study proposes ambidextrous environmental strategy and aims to explore its drivers. Based on the awareness-motivation-capability (AMC) framework and resource orchestration theory, the authors investigate the configurational effects of perceived institutional force, green intellectual capital (GIC) and paradox cognition on achieving ambidextrous environmental strategy.

To explore these configurational effects, this study uses two-waved survey data from 317 Chinese manufacturing firms and the fuzzy set qualitative comparative analysis (fsQCA) method.

There are three equivalent configurational paths leading to ambidextrous environmental strategy. The findings suggest that firms with paradox cognition can better orchestrate GIC to achieve ambidextrous environmental strategy under different perceived institutional force. This study also finds three substitution effects between distinct conditions.

This study contributes to the existing literature by introducing the concept of ambidexterity into the field of environmental strategy and using the AMC framework to explore the configurational paths driving ambidextrous environmental strategy.

Drawing upon social capital theory, this research aims to explore the influence of three dimensions of green intellectual capital (GIC) (i.e. green human capital [GHC], green structural capital and green relational capital) on green supply chain integration (GSCI) (i.e. green supplier, internal and customer integration), and the mediating effect of supply chain transformational leadership (SCTL).

To verify the hypothesized relationships, the authors conduct hierarchical regression analysis and bootstrapping method with two-waved survey data collected from 317 Chinese manufacturing firms.

The findings suggest that both green structural and relational capital positively influence three dimensions of GSCI, while GHC only positively affects green internal and customer integration. In addition, SCTL mediates the impacts of green structural and relational capital on three dimensions of GSCI and mediates the impacts of GHC on green supplier and internal integration.

This research contributes to the GSCI literature and practice by deeming GIC as an intangible resource that can enhance GSCI and revealing the mediating role of SCTL.

Manufacturers are facing growing institutional pressures to enhance the manufacturers' sustainability. Establishing appropriate environmental strategy and implementing green supply chain integration (GSCI) are imperative initiatives for them. Nevertheless, prior research has predominantly examined the individual net impacts on sustainable performance. Drawing on the strategy-structure-environment (SSE) framework and configurational perspective, this study investigates the synergistic effects and multiple equivalent combinations of environmental strategy and GSCI under diverse institutional forces.

To empirically validate this relationship, the present study utilizes fuzzy-set qualitative comparative analysis (fsQCA) to analyze two-wave survey data collected from 317 manufacturers in China.

The findings indicate that individual dimension of environmental strategy and GSCI is not independently necessary. However, when combined, this results in seven equifinal configurations that lead to high sustainable performance. Combining all dimensions of environmental strategy and GSCI leads to the simultaneous achievement of high environmental, economic and social performance under perceived social pressure.

This study offers firms the flexibility to select from a range of pathways, allowing the firms to strategically filter and develop diverse combinations of environmental strategy and GSCI. These choices empower firms to enhance the firms' sustainable performance while navigating various institutional forces.

This study contributes to the existing literature by utilizing the SSE framework to investigate the configurational paths that influence sustainable performance. Additionally, this work introduces the fsQCA method to enhance the understanding of sustainable performance in the literature.

An emerging line of research examining the role of numerological superstition in the capital market shows that it has significant impact on investor behavior (Bhattacharya, Kuo, Lin, & Zhao, 2018; Hirshleifer, Jian, & Zhang 2018). However, to the authors’ best knowledge, there is a dearth of evidence on whether numerological superstition affects corporate behavior. This study fills this void by examining the association between investors’ numerological superstition and earnings management using Chinese data.

Chinese culture views 6 and 8 as lucky numbers. Using Chinese publicly traded firms, the authors examine the relation between investors’ numerological superstition and corporate financial reporting behavior.

The results suggest that firms reporting lucky earnings-per-share (EPS) numbers ending with 6 or 8 are more likely to engage in earnings management. These firms also raise more capital through seasoned equity offerings in the following year; however, they do not have more capital investments. Instead, their controlling shareholders siphon a significant amount of capital through related party transactions. Overall, the findings suggest that managers collude with controlling shareholders to manage earnings by exploiting the superstitious beliefs of minority shareholders.

To the authors’ best knowledge, there is a dearth of evidence on whether numerological superstition affects corporate behavior. This study fills this void by examining the association between investors’ numerological superstition and earnings management using Chinese data.

Although blockchain technology holds significant promise in influencing supply chain resilience (SCR), its effectiveness depends on a variety of factors. However, given that blockchain adoption in SCR is still in its infancy, there is a lack of empirical research to reveal the critical success factors maximizing its efficacy. This study aims to apply an organizational information processing theory (OIPT) perspective to explore how transformational supply chain leadership (TSCL) can facilitate the deployment and connection of blockchain technology to meet the imperatives of enhancing SCR.

This study used a two-wave survey method to gather data from 317 Chinese manufacturers to empirically examine the hypothesized relationships.

The findings suggest that the adoption of blockchain technology enhances both the proactive and reactive dimensions of SCR, and these effects can be realized through the mediating role of TSCL. Furthermore, the positive effect of blockchain technology on TSCL is strengthened in the context of dysfunctional competition.

These findings suggest that companies can only enhance the benefits of disruptive technologies, such as blockchain, by fully integrating them into the operational and supply chain processes.

This research offers novel insights into the specific processes of how blockchain technology can be used to enhance SCR. It also deepens our comprehension of how digital technology can be optimally harnessed within the framework of OIPT, thus providing a contribution to the literature on emerging technologies and SCR.

This paper aims to successfully synthesize three-dimensional spindle-like Au functionalized Co₃O₄-ZnO nanocomposites; characterize the structure, morphology and surface chemical properties of the products; study the effect of Au NPs doping concentration, operating temperature different gas to, sensing properties; and introduce an attractive gas sensor for acetone detection.

Au NPs functionalized Co₃O₄-ZnO nanocomposite was prepared by coprecipitation and impregnation methods; the structure and surface chemical property of the products were characterized by XRD, SEM, TEM, UV-Vis, BET and XPS. The sensing ability of Au@Co₃O₄-ZnO for acetone and mechanism was analyzed systematically.

The results of gas sensing tests show that the unique component structure, Schottky junction and catalytic effect of Au functionalization make it have low operating temperature, excellent selectivity, high response (10 ppm, 56) and rapid response recovery time.

All the characterization and test data of the prepared materials are provided in this paper and reveals the gas sensing mechanism of the gas sensor.

The detection limit is 2.92–100 ppb acetone. It is promising to be applied in low-power, micro detection and miniature acetone gas sensors.

The gas sensor prepared has a lower working temperature and low detection limit, so it has promising application prospects in low-concentration acetone detection and early warning.

The unique component structure, Schottky junction and catalytic effect of Au functionalization Co₃O₄-ZnO make it have low operating temperature, excellent selectivity and rapid response recovery time.