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Compared to the occurrence of conflict in general teams in organizations, conflict occurrence in entrepreneurial teams is more prevalent and intense. However, previous studies have found inconsistent relationships between entrepreneurial team conflict and performance, and the mechanisms underlying this relationship remain in the “black box.” Drawing on the motivated information processing in groups theory, this study aims to investigate how and when entrepreneurial team conflict influences entrepreneurial performance.

The authors collected survey data from 190 entrepreneurs across 58 entrepreneurial teams in China. The hypothesized relationships were examined through path analysis using the Mplus7.0 program.

Entrepreneurial team relationship conflict is negatively related to entrepreneurial performance mediated through team behavioral integration. Conversely, there exists a curvilinear (U-shaped) relationship between entrepreneurial team task conflict and entrepreneurial performance, also mediated through team behavioral integration. Furthermore, the curvilinear relationship between entrepreneurial team task conflict and team behavioral integration is strengthened by team contractual governance, whereas the relationship between entrepreneurial team relationship conflict and team behavioral integration is not moderated by team contractual governance.

This study contributes to a deeper understanding of the relationship between entrepreneurial team conflict and performance by identifying the mediating mechanism and boundary condition. The finding of a U-shaped relationship between entrepreneurial team task conflict and entrepreneurial performance underscores the uniqueness of the entrepreneurial team context, offering new empirical insights for future conflict research.

This study aims to explore the impacts of service industry employees’ AI awareness on their voice behavior while also considering the dual mediating roles of voice efficacy and job insecurity, as well as the moderating role of trait competitiveness.

The sample comprises data from a two-wave longitudinal survey of 203 employees in the service sector. This study examined all the hypotheses using Mplus 8.0.

This study confirms that service sector employees’ AI awareness has significant negative effects on both promotive and prohibitive voice behaviors. Voice efficacy can mediate the negative impact of AI awareness on promotive voice. Both voice efficacy and job insecurity can mediate the negative impact of AI awareness on prohibitive voice. Furthermore, employees’ trait competitiveness can weaken the negative impact of employees’ AI awareness on their voice efficacy.

Managers should first investigate employees’ AI awareness and then adopt targeted managerial strategies to promote their voice behavior.

This study contributes to the literature related to the consequences of AI awareness by linking AI awareness to employee voice behavior. Furthermore, this study deepens our understanding of how AI awareness affects employee voice behavior by proposing voice efficacy (i.e. the efficacy pathway) and job insecurity (i.e. the safety pathway) as key mediating mechanisms. Moreover, this study advances our understanding of when AI awareness influences employee voice behavior by identifying the moderating role of trait competitiveness.

This study aims to explore the associations linking employees’ challenge-hindrance appraisals toward artificial intelligence (AI) to service performance while considering the dual mediating roles of job crafting and job insecurity, as well as the moderating role of AI knowledge.

A survey was administered to a sample of 297 service industry employees. This study examined all the hypotheses with Mplus 8.0.

This study confirms that challenge appraisal toward AI has an indirect positive influence on service performance via job crafting (motivation process), whereas hindrance appraisal toward AI has an indirect negative influence on service performance via job insecurity (strain process). Meanwhile, AI knowledge, serving as a key personal resource, could strengthen the positive impacts of challenge appraisal toward AI on job crafting and of hindrance appraisal toward AI on job insecurity.

Organizational decision-makers should first survey employees’ appraisals toward AI and then adopt targeted managerial strategies. From the perspective of service industry employees, employees should adopt proactive coping strategies and enrich their knowledge of AI to meet the challenges brought by this technology.

The primary contribution of this study is that we enrich the literature on AI by exploring the dual mediators (i.e. job crafting and job insecurity) through which AI awareness affects service performance. Moreover, this study advances our understanding of when appraisals toward AI influence job outcomes by identifying the moderating role of AI knowledge.

This study aims to investigate the formation and characteristics of Taylor bubbles resulting from short-time gas injection in liquid-conveying pipelines. Understanding these characteristics is crucial for optimizing pipeline efficiency and enhancing production safety.

The authors conducted short-time gas injection experiments in a vertical rectangular pipe, focusing on Taylor bubble formation time and stable length. Computational fluid dynamics simulations using large eddy simulation and volume of fluid models were used to complement the experiments.

Results reveal that the stable length of Taylor bubbles is significantly influenced by gas injection velocity and duration. Specifically, high injection velocity and duration lead to increased bubble aggregation and recirculation region capture, extending the stable length. Additionally, a higher injection velocity accelerates reaching the critical local gas volume fraction, thereby reducing formation time. The developed fitting formulas for stable length and formation time show good agreement with experimental data, with average errors of 6.5% and 7.39%, respectively. The predicted values of the formulas in glycerol-water and ethanol solutions are also in good agreement with the simulation results.

This research provides new insights into Taylor bubble dynamics under short-time gas injection, offering predictive formulas for bubble formation time and stable length. These findings are valuable for optimizing industrial pipeline designs and mitigating potential safety issues.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.