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As the complexity and uncertainty of infrastructural megaprojects challenge traditional management models, there is an increasing focus on value co-creation as an organizational strategy to streamline management. However, the role of value co-creation behavior in facilitating the value realization process remains underexplored. This study examines how justice perception (distributive, procedural and interactional justice) improves contractor value co-creation behavior, focusing on the mediating role of psychological ownership.

Ten hypotheses in the proposed research model were tested through partial least squares structural equation modeling using 199 valid questionnaires from China.

The results show that contractor value co-creation behavior is directly and positively influenced by procedural, distributive and interactional justice and indirectly influenced by them through the underlying psychological mechanism of psychological ownership.

The findings fill a knowledge gap by examining the effect of justice perception on contractor value co-creation behavior based on social exchange theory. Discovering justice perception will contribute to contractor value co-creation behavior, and psychological ownership mediates this relationship.

In the realm of open innovation (OI) networks, coopetition—where competition and cooperation coexist—plays a pivotal role in shaping the dynamics between diverse projects. This dual relationship is crucial for the propagation of knowledge and the bolstering of the network's overall resilience. While competition drives the quality of products and services, thereby reinforcing network resilience, cooperation facilitates knowledge diffusion, which is essential for the network's robustness.

We delve into the interplay between coopetition intensity and network resilience through the lens of knowledge diffusion. Our methodology begins with a sensitivity analysis to gauge the direct effects of coopetition on resilience. This is followed by a principal component analysis to identify the key determinants of coopetition intensity among projects. Finally, we utilize linear regression and moderation analysis to explore the mediating role of knowledge diffusion in the resilience of OI networks.

Our work is grounded in network theory, which provides a robust theoretical framework for understanding project coopetition and knowledge diffusion within the OI paradigm. This research not only offers a nuanced understanding of coopetition's impact on OI network resilience but also highlights the significance of knowledge diffusion as a critical mediating variable.

1) Identifies the significant influences in project coopetition (competition and cooperation). (2) Puts the conceptual framework and calculation method of the open innovation network resilience based on the project coopetition and knowledge diffusion. (3) Explores the moderating role of knowledge diffusion in project coopetition influencing open innovation networks resilience. (4) Measures the influence of project coopetition relationship on open innovation network resilience from the perspective of knowledge diffusion. (5) Encourages project management to consider the portfolios of coopetition.

This study aims to explain the privacy paradox, wherein individuals, despite privacy concerns, are willing to share personal information while using AI chatbots. Departing from previous research that primarily viewed AI chatbots from a non-anthropomorphic approach, this paper contends that AI chatbots are taking on an emotional component for humans. This study thus explores this topic by considering both rational and non-rational perspectives, thereby providing a more comprehensive understanding of user behavior in digital environments.

Employing a questionnaire survey (N = 480), this research focuses on young users who regularly engage with AI chatbots. Drawing upon the parasocial interaction theory and privacy calculus theory, the study elucidates the mechanisms governing users’ willingness to disclose information.

Findings show that cognitive, emotional and behavioral dimensions all positively influence perceived benefits of using ChatGPT, which in turn enhances privacy disclosure. While cognitive, emotional and behavioral dimensions negatively impact perceived risks, only the emotional and behavioral dimensions significantly affect perceived risk, which in turn negatively influences privacy disclosure. Notably, the cognitive dimension’s lack of significant mediating effect suggests that users’ awareness of privacy risks does not deter disclosure. Instead, emotional factors drive privacy decisions, with users more likely to disclose personal information based on positive experiences and engagement with ChatGPT. This confirms the existence of the privacy paradox.

This study acknowledges several limitations. While the sample was adequately stratified, the focus was primarily on young users in China. Future research should explore broader demographic groups, including elderly users, to understand how different age groups engage with AI chatbots. Additionally, although the study was conducted within the Chinese context, the findings have broader applicability, highlighting the potential for cross-cultural comparisons. Differences in user attitudes toward AI chatbots may arise due to cultural variations, with East Asian cultures typically exhibiting a more positive attitude toward social AI systems compared to Western cultures. This cultural distinction—rooted in Eastern philosophies such as animism in Shintoism and Buddhism—suggests that East Asians are more likely to anthropomorphize technology, unlike their Western counterparts (Yam et al., 2023; Folk et al., 2023).

The findings of this study offer valuable insights for developers, policymakers and educators navigating the rapidly evolving landscape of intelligent technologies. First, regarding technology design, the study suggests that AI chatbot developers should not focus solely on functional aspects but also consider emotional and social dimensions in user interactions. By enhancing emotional connection and ensuring transparent privacy communication, developers can significantly improve user experiences (Meng and Dai, 2021). Second, there is a pressing need for comprehensive user education programs. As users tend to prioritize perceived benefits over risks, it is essential to raise awareness about privacy risks while also emphasizing the positive outcomes of responsible information sharing. This can help foster a more informed and balanced approach to user engagement (Vimalkumar et al., 2021). Third, cultural and ethical considerations must be incorporated into AI chatbot design. In collectivist societies like China, users may prioritize emotional satisfaction and societal harmony over privacy concerns (Trepte, 2017; Johnston, 2009). Developers and policymakers should account for these cultural factors when designing AI systems. Furthermore, AI systems should communicate privacy policies clearly to users, addressing potential vulnerabilities and ensuring that users are aware of the extent to which their data may be exposed (Wu et al., 2024). Lastly, as AI chatbots become deeply integrated into daily life, there is a growing need for societal discussions on privacy norms and trust in AI systems. This research prompts a reflection on the evolving relationship between technology and personal privacy, especially in societies where trust is shaped by cultural and emotional factors. Developing frameworks to ensure responsible AI practices while fostering user trust is crucial for the long-term societal integration of AI technologies (Nah et al., 2023).

The study’s findings not only draw deeper theoretical insights into the role of emotions in generative artificial intelligence (gAI) chatbot engagement, enriching the emotional research orientation and framework concerning chatbots, but they also contribute to the literature on human–computer interaction and technology acceptance within the framework of the privacy calculus theory, providing practical insights for developers, policymakers and educators navigating the evolving landscape of intelligent technologies.

The building industry is a critical sector that must significantly reduce its carbon emissions for New Zealand (NZ) to meet its 2050 zero-carbon goals. Green Star NZ, a leading Green Building Rating System in NZ, offers a structured framework for assessing and certifying building environmental performance. This research investigates industry professionals' perspectives on Green Star NZ’s effectiveness in achieving NZ’s zero-carbon goals, addressing gaps in existing literature.

Through qualitative analysis of semi-structured interviews, the research identified key areas where Green Star NZ either supports or falls short of zero-carbon practices, according to 22 practising professionals. A thematic analysis method was used to analyse the data.

The results indicate that while Green Star NZ suits NZ, it faces adoption challenges due to few supportive policies, complex certification and material supply issues with sustainable materials. The study addressed these barriers through targeted policies, streamlined processes and market support for sustainable technologies. Moreover, cost is directly or indirectly tied to Green Star NZ.

This study offers insights and recommendations to improve Green Star NZ, assisting NZGBC and stakeholders in advancing towards a zero-carbon future. Implementing these suggestions can boost Green Star NZ’s effectiveness. Through the project experience and the viewpoints of industry professionals, it fills the research gap by assessing Green Star NZ’s framework, identifying challenges and proposing improvements. The findings also position NZ’s experience as a possible model, advancing global green building practices and providing policymakers with recommendations.