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Bipolar disorder is associated with poor mental and physical health outcomes, and therefore, it is crucial to research and develop effective interventions for this population (Grande et al., 2016). Unfortunately, research on the efficacy of current interventions shows only small improvements in symptoms and quality of life (Oud et al., 2016). Additionally, individuals with bipolar disorder face barriers to accessing care like social stigma, isolation and financial constraints (Blixen et al., 2016). This paper aims to introduce and examine the effectiveness of an accessible, peer-led group program, Mood Lifters (Votta and Deldin, 2022), in those who completed the program and also self-reported a diagnosis of bipolar disorder.

Mood Lifters is a 15-week, peer-led group program that approaches mental wellness from a biopsychosocial framework using strategies from a variety of evidence-based treatment methods (e.g. cognitive-behavioral therapy, dialectical behavior therapy, interpersonal psychotherapy, etc.). Participants meet once a week for 1 hour to review various mental health topics, including behavioral changes and insight into mood patterns. Individuals who participated in nonacademic groups in a company setting and self-reported a bipolar diagnosis were surveyed at the beginning and end of their program to measure various aspects of psychological functioning.

Results suggest that these individuals experienced significant improvements in depression, anxiety, social functioning and perceived stress, along with flourishing and positive and negative affect.

These findings are promising, given that bipolar disorder is historically difficult to treat (Grande et al., 2016). Based on this preliminary evidence, the authors have developed a Mood Lifters program specifically for individuals with bipolar disorder and are launching a randomized control clinical trial.

As a kind of knowledge-intensive team coordinated across physical distance, it is necessary to construct a meta-knowledge driven transactive memory system (TMS) for the knowledge management of distributed agile team (DAT). This study aims to explore the comprehensive antecedents of TMS establishment in DATs and considers how TMS establishment is affected by herding behavior under the artificial intelligence (AI)-related knowledge work environment that emerges with technology penetration.

The data derived from 177 students of 52 DATs in a well-known Chinese business school, which were divided into 26 traditional knowledge work groups and 26 AI-related task groups to conduct a random comparative experiment. The ordinary least squares method was used to analyze the conceptual model and ANOVA was used to examine the differences in herding behavior between the control groups (traditional knowledge work DATs) and treatment groups (DATs engaged in AI-related knowledge work).

The results showed that knowledge diversity, professional knowledge, self-efficacy and social system use had significantly positive effects on the establishment of TMS. Interestingly, the authors also find that herding behavior may promote the process of establishing TMS of the new team, and this effect will be more significant when AI tasks are involved in team knowledge work.

By exploring the comprehensive antecedents of the establishment of TMS, this study provided a theoretical basis for knowledge management of DATs, especially in AI knowledge work teams. From a practical perspective, when the DAT is involved in AI-related knowledge works, managers should appropriately guide the convergence of employees’ behaviors and use the herding effects to accelerate the establishment of TMS, which will improve team knowledge sharing and innovation.

To delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.

Prior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.

The proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.

The case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.

The framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.

Electric cars (EC) adoption represents a strategic action aimed at promoting environmental sustainability. Although Millennials and Gen Z represent the greatest potential market for EC, their adoption remains low; thus, this study focused on examining the role of motive in predicting EC adoption intention within these two generations’ population. Built upon the fundamental motive framework, this research explores the motives that lead to EC adoption intention. Subsequently, this study aims to examine the role of performance expectancy as the mediating variable and EC attributes beliefs as the moderating variable that can promote EC adoption intention.

Both exploratory and confirmatory methods were used in this investigation. Using an exploratory approach, this research explores the fundamental motives and the attributes of EC that influence EC adoption intention. Using a confirmatory approach, this research tests the mediating role of performance expectancy. To collect the data, an online survey was administered to 260 young consumers in Indonesia.

The results of PLS-SEM analysis from the data revealed that self-protection, kin-care, status and affiliative motives influence EC adoption. Furthermore, performance expectancy mediates the relationship between self-protection, mate acquisition, affiliative motives and EC adoption intention. Among EC attributes, the short-haul performance strengthens the indirect relationship between affiliative motive and EC adoption intention.

The main limitation of this study is that it only focuses on the practical attributes of EC, whereas psychological attributes that were found to be more influential in consumer’s purchase decisions were not examined.

Marketers need to explore EC attributes that can strengthen the relationship between consumers’ motives and EC adoption intention by increasing consumers’ evaluation of performance expectancy. In this study, marketers can promote short-haul performance, as it will lead to EC adoption for consumers with affiliative motives.

This study ties together two lines of research on the adoption of EC, exploring EC attributes and examining consumers’ motivation to choose EC, especially Millennials and Gen Z. In this way, EC attributes facilitate the fulfillment of consumers’ needs and promote EC adoption intention.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Researchers who work in partnership with older adults in participatory studies often experience various advantages, but also complex ethical questions or even encounter obstacles during the research process. This paper aims to provide insights into the value of an intersectional lens in participatory research to understand how power plays out within a mixed research team of academic and community co-researchers.

Four academic researchers reflected in a case-study approach in a dialogical way on two critical case examples with the most learning potential by written dialogical and via face-to-face meetings in duos or trios. This study used an intersectionality-informed analysis.

This study shows that the intersectional lens helped the authors to understand the interactions of key players in the study and their different social locations. Intersections of age, gender, ethnicity/class and professional status stood out as categories in conflict. In hindsight, forms of privilege and oppression became more apparent. The authors also understood that they reproduced traditional power dynamics within the group of co-researchers and between academic and community co-researchers that did not match their mission for horizontal relations. This study showed that academics, although they wanted to work toward social inclusion and equality, were bystanders and people who reproduced power relations at several crucial moments. This was disempowering for certain older individuals and social groups and marginalized their voices and interests.

Till now, not many scholars wrote in-depth about race- and age-related tensions in partnerships in participatory action research or related approaches, especially not about tensions in research with older people.