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This research aims to examine the link between distributed leadership and team ambidexterity and the sequential mediation of team climate innovation and knowledge management in this relationship.

This study draws on a sample of 546 knowledge workers involved within 157 service research and development (R&D) teams of knowledge-intensive firms (KIFs) and uses partial least squares-structural equation modelling (PLS-SEM) through SMART PLS 4 to analyse the data collected.

Findings reveal that distributed leadership has a significant direct impact on team ambidexterity. Besides, they indicate that team climate innovation and knowledge management in teams mediate this link. Results also highlight the sequential mediation of team climate innovation and knowledge management in teams, linking distributed leadership to team ambidexterity.

This study explores the relationship between distributed leadership and ambidexterity at the team level and proposes a sequential mediation model linking these variables through team climate innovation and knowledge management in teams. It offers practical insights for KIFs’ managers on the importance of using a distributed leadership approach and building a team climate innovation to motivate R&D teams, encourage dynamic participation in knowledge management practices and cultivate both exploitative and exploratory learning activities.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.

This study aims to reveal the mechanism of CEO overconfidence in the digital transformation of specialized, refined, distinctive and innovative (SRDI) enterprises, thereby enriching research related to upper echelons theory and corporate digital transformation.

This study uses listed SRDI companies in China from 2017 to 2022 as a sample and adopts a fixed-effects regression model to analyze the direct, mediating, and moderating effects of CEO overconfidence on corporate digital transformation.

First, CEO overconfidence significantly promotes SRDI enterprises' digital transformation. Second, according to the “cognition-behavior-outcome” model, we found that entrepreneurial orientation plays a mediating role. Third, based on the principle of procedural rationality and the interaction perspective between the CEO and the executive team, we introduce the heterogeneity of the executive team as a moderating variable. Our findings indicate that age heterogeneity within the executive team has a negative moderating effect, whereas educational and occupational heterogeneities have positive moderating effects.

This study expands on earlier research that focuses primarily on CEO demographic characteristics. It enriches the analytical perspective of upper echelons theory on corporate digital transformation by analyzing the psychological characteristics of CEOs, that is, overconfidence and its mediating pathways. Moreover, this study goes beyond the previous literature that does not differentiate between CEOs and executive teams by introducing the concept of CEOs' interactions with the executive team and including the heterogeneity of the executive team as a moderating variable in the literature. Thus, continuing to deepen the application of upper echelons theory to corporate digital transformation. Additionally, this study contributes to the literature on the positive consequences of overconfidence.

The current study investigates the challenges of integrating social responsibility and climate change into sustainable development goals (SDGs).

The study employed a qualitative approach, in which 35 interviews were conducted with the owners and employees in the corporate sector, policymakers in government authorities and some non-government organisations.

The research identified critical issues with the South Asian institutional social responsibility and climate change willingness to approach the SDGs, such as the contrast between reactive and proactive management, dissimilar success indicators, the lack of capital, government interference and employees' resistance, as well as stakeholders' conflict. However, other possible areas of synergy were pointed out regarding organisational image, resource utilisation, reinforcing inclusive economic development, supporting communities in need and collaboration among different spheres of activity.

First, there is a need to synchronise climate and social agenda; second, the calls for financial innovation to unlock sustainable funding to support SDGs; third, the leadership of companies needs to mainstream sustainable practices, and finally, public outreach's role is to inculcate principles of environmental conservation as well as knowledge in climate.

This study contributes to the existing literature by examining the tensions and opportunities for achieving social responsibility and climate change and the SDGs harmoniously in South Asia, incorporating an evaluation of institutions, governments and strategies.

The automobile industry is widely recognized as a key sector with strong industrial linkages that significantly contribute to economic growth. This chapter focuses on the export of energy vehicles in BRICS countries from 2015 to 2021, evaluating export sophistication using panel data. Given that industrial upgrading is a long-term and dynamic economic process, the study employs a dynamic panel model to analyze the relationship between energy vehicle export sophistication and industrial upgrading. The findings reveal a significant positive correlation between the export sophistication of energy vehicles and industrial upgrading in BRICS countries from an economic perspective. Export sophistication emerges as a critical internal factor in advancing the technological structure of export trade. Conversely, external factors such as research and development, foreign direct investment, and GDP growth show comparatively less influence. Therefore, the exportation of energy vehicles presents a valuable opportunity for driving industrial upgrading in BRICS countries.

This study aims to gain a better understanding of artificial serendipity – pre-planned surprises intentionally crafted through deliberate designs – in online marketplaces. By exploring the key features of artificial serendipity, this study investigates whether serendipity can be intentionally designed, particularly with the use of artificial intelligence (AI). The findings from this research broaden the scope of serendipity studies, making them more relevant and applicable in the context of the AI era.

A narrative study was conducted, gathering insights from 32 Chinese online consumers through diaries and interviews. The data were analysed in close collaboration with participants, ensuring an authentic reflection of their perceptions regarding the features of artificial serendipity in online marketplaces.

Findings reveal that artificial serendipity, particularly when designed by AI, is still regarded by online consumers as genuine serendipity. It provides a sense of real surprise and encourages deeper reflection on personal knowledge, affording the two central qualities of genuine serendipity: unexpectedness and valuableness. However, since artificial serendipity is pre-planned through intentional design, consumers cannot have entire control over it. Therefore, compared to natural serendipity – fortune surprises arising from accidental correspondence between individuals and contexts – artificial serendipity tends to be more surprising yet less valuable.

For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity.

Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.

This study stands out as one of the few to provide a nuanced understanding of artificial serendipity, offering valuable insights for both research and practice. For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity. Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.

This study explores how artificial intelligence (AI) has been intertwined with rhetoric and the journey of institutionalization in selected case study firms. The mechanism of institutionalizing AI into organizational processes, future technology transformation and the driving forces behind the implementation of AI is being explored.

It adopts the qualitative methodology and multiple case study approach, drawing evidence from ten leading retail sector organizations that have been practicing AI for over a decade. The main data collection method was face-to-face in-depth interviews, supplemented by focus group discussion and documentary reviews. From a theoretical stance, the paper draws on the notions of rhetoric institutionalism.

Empirical findings revealed that the rhetorical power of the word AI convinces the management of the firm to embrace AI. In contrast to the hype in the media, the real application of AI in the retail sector has not lived up. Therefore, the study delves into the noticeable discrepancy between the buzz surrounding AI and its actual use in retail sectors.

This study contributes to research by postulating that even though AI carries rhetorical power and prompt implementation, the real organizational application is far behind the rhetorical excitements. Foregrounding rhetoric institutionalism, it extends existing institutional theory-inspired management research. The paper also offers learning points to practitioners by illustrating the rise and fall of the AI implementation story. It further showcases how AI tools and techniques could be used by a business, how AI gets implicated in a firm’s business excellence journey and the ensuing management control ramifications.

The globalization of markets poses great challenges, and thus, the manufacturing businesses trying to expand their operations to cater to a global audience have to undergo significant transformations. Therefore, this research aims to identify key challenges and elucidate the critical success factors (CSFs) required for the global growth of manufacturing companies on a worldwide scale.

A range of interval-valued spherical fuzzy sets (IVSFs) and flexible methodologies such as the analytic hierarchy process (AHP) and data envelopment analysis (DEA) have been employed to evaluate the issues in detail. It calculates the effectiveness delivered by each critical success factor (CSF) and identifies the factors acting as a barrier to global market penetration.

This research highlights the transformative potential of smart manufacturing in developing economies, identifying CSFs such as government support, cost optimization and resilient supply chain management as essential for overcoming obstacles like over-reliance on foreign technologies, regulatory rigidity and skill gaps. The integration of IVSFS with AHP and DEA models offers actionable insights to foster localized innovation, reduce foreign dependencies and promote user-centric designs, aligning with the United Nations Sustainable Development Goals.

This study shows that IVSFs, AHP and DEA can be used together to estimate the global challenges of manufacturing firms in developing markets. The combination of efficient decision-making and these strategies is novel as it provides ways in which businesses in developing countries can deal with their obstacles and improve their competitiveness on the global stage.

The purpose of this study was to examine the factors that influence the information seeking behaviors of ChatGPT users. Specifically, we investigated how ChatGPT self-efficacy, ChatGPT characteristics and ChatGPT utility affect the frequency and duration of information seeking via ChatGPT. We also tested the mediating roles of ChatGPT characteristics and utility in the relationship between ChatGPT self-efficacy and information-seeking behaviors.

This study adopts a quantitative approach and collects data from 403 ChatGPT users using an online questionnaire. The data are analyzed using linear regression and structural equation modeling (SEM).

The linear regression analyses revealed that ChatGPT self-efficacy is positively and significantly related to the information seeking behaviors in ChatGPT. Second, mediation analyses also showed that ChatGPT characteristics and utility significantly mediate the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT independently and sequentially.

This study is the first to investigate the factors and mechanisms that influence information-seeking behaviors in ChatGPT, a new phenomenon in the media landscape. The findings in this study suggest that ChatGPT self-efficacy acts as an important motivator for information-seeking behaviors in ChatGPT and that ChatGPT characteristics and utility provide information regarding potential mechanisms in the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT. The study contributes to the literature on information seeking, self-efficacy and generative AI.

The multivariable grey model, a type of multi-output grey model, offers a unified representation of variables from a systemic perspective, carrying significant theoretical implications. However, traditional grey modeling methods generate errors, particularly the jump error from a difference equation to a differential equation. This paper aims to propose an unbiased multivariable grey model to eliminate these inherent errors.

This paper begins by analyzing the sources of errors in the multivariate grey model and subsequently optimizes its parameters to achieve an unbiased outcome. The properties of the unbiased multivariable model are discussed and mathematically proven. The model’s unbiased nature is further validated using data. Finally, the unbiased multivariable grey model is applied to two case studies.

Results indicate the unbiased model aligns completely with simulations and predictions of curves generated by the prediction formula of the multivariable grey model, eliminating its inherent bias. Numerical examples show that the proposed unbiased modeling method enhances the accuracy of the multivariable grey model.

A novel unbiased multivariable grey model is introduced, supported by rigorous mathematical proofs of its properties. Additionally, two case studies compare this model with GM(1,1) and four other multivariable grey models.