Purpose: Given the decisive role of knowledge-oriented leadership (KOL) in boosting organizational innovation capacities, the research intends to investigate the effect of KOL on ambidextrous innovation with the mediating effect of knowledge management capability (KMC). Furthermore, technological turbulence (TT) is regarded as a moderator in the relationship between KMC and ambidextrous innovation.Design/methodology/approach: The data obtained from 122 Pakistani manufacturing firms were used to evaluate the proposed relationships using the partial least square structural equation modeling approach.Findings: The empirical findings demonstrate that KOL positively affects both aspects of ambidextrous innovation, namely exploitative innovation (EII) and exploratory innovation (ERI), with a higher effect on EII. Additionally, knowledge management process capability (KMPC) partially mediates the association between KOL and both dimensions of ambidextrous innovation (EII and ERI). Similarly, knowledge management infrastructure capability (KMIC) mediates the link between KOL and ERI but does not mediate the relationship between KOL and EII. The impacts of the KMPC and KMIC on EII are also significantly moderated by TT, although the link between the two components of the KMC (KMPC and KMIC) and ERI is unaffected.Practical implications: The research provides useful knowledge and a novel strategy for policymakers to foster KOL and invest in KMC to improve the capabilities of Pakistani manufacturing firms in terms of innovation.Originality/value: The research has contributed significantly to the resources-based view and knowledge-based view (KBV) literature by examining the various mediation moderation mechanisms and offering greater insights into the relationship between KOL and firms, KMC, and ambidextrous innovations.

Purpose: This study investigates the interaction between organizational strategic factors (Leadership and management support [LMS] and green learning orientation [GLO]) and green innovation performance (GIP), through the lens of resource-based view (RBV) theory. It examines both the direct and indirect impacts of these factors on GIP via green knowledge management (GKM), and explores how green absorptive capacity (GAC) enhances these relationships. Design/methodology/approach: Using Partial least squares structural equation modeling (PLS-SEM) and moderated mediation analysis, we analyzed responses from 419 individuals across 154 manufacturing firms in Pakistan to understand these dynamics. Findings: Results show that LMS and GLO significantly affect GIP, both directly and indirectly, through GKM. Furthermore, GAC intensifies the impact of GLO on GKM and the influence of GKM on GIP, indicating a moderated mediation effect. Practical implications: Highlighting the importance of LMS, GLO, GKM, and GAC, the study suggests that focusing on these areas can help firms align their strategies with sustainability goals, enhancing their GIP. These insights can guide policymakers in creating supportive strategies for businesses to improve their GAC, facilitating better knowledge adoption and application. Originality/value: The research contributes to the RBV theory by clarifying the role of strategic organizational factors in enhancing GIP within manufacturing firms, offering a clearer path to achieving sustainability goals.

Drawing upon social cognitive & social network theories, this study examines individual-level and network-level factors which potentially affect multicultural individuals’ information sharing behavior via social media. The data has been collected from the foreigners (multicultural individuals) who visited visa centers in three different cities in China. The proposed model tests a combined effect of past sharing experience (PE), trust (TR), perceived benefit (PB), perceived richness (RI), & information sharing attitude (ATT), on information sharing behavior (Beh). The data has been analyzed through Structural Equation Modeling (SEM). The results illustrate PE, TR and PB positively affects ATT which further influences Beh. Since scant studies focus on information sharing behavior of multicultural individuals – this research contributes to the IS literature, particularly to the information sharing via social media in a multicultural perspective. The results are likely to be useful guidance for practitioners and scholars intending to evaluate social media information sharing behavior.

Purpose: The study aims to explore the impact of tacit knowledge sharing (TKS) factors and its consequences in the form of technological innovation capabilities (TICs) within Pakistani software small–medium enterprises (SSMEs).Design/methodology/approach: Drawing upon the social exchange theory (SET), the study used a quantitative approach and structural equation modeling (SEM) to test hypotheses with 220 valid data collected from 23 Pakistani software SSMEs.Findings: The peer influence (PI) has positive and significant effect on collaborative culture (CC), willingness to share tacit knowledge (WSTK) and TICs. Organizational trust (OT) has a positive and significant impact on CC and TIC. Whereas, CC possessed positive and significant effect on WSTK and insignificant on TIC. Furthermore, WSTK has positive and significant effect on TIC. Finally, WSTK partially mediates the relationship between PI and TIC whereas WSTK fully mediates the relationship between CC and TIC.Research limitations/implications: The study enriches the research on knowledge sharing and TIC. This research investigates the precursors of tacit knowledge-sharing willingness and their consequences in software SMEs; future studies need to examine tacit knowledge-seeking willingness and its consequences not only in software enterprises but also in other industrial sectors. Besides, it needs to evaluate types of innovative capabilities in software SMEs.Practical implications: The study suggested that the practitioners need to strengthen TKS in the form employees’ updated skills and expertise which ultimately fosters software enterprise’s innovative capabilities to attain competitive advantages in a specific industry.Originality/value: This research is one of the few studies to examine the potential antecedents of WSTK and their final effects within software SMEs in the form of TICs. As currently it is observed, an incredible increase of skills oriented innovations in firms particularly in the software domain and IT industry. Therefore, this study emphasizes how PI, OT and WSTK positively affect TIC of Pakistani software SMEs. However, the study could be considered as a guideline for the academia and practitioners who attempt to strengthen the technological innovations capabilities in software SMEs.

Purpose: Drawing upon social cognitive theory, this study aims to investigate the potential predictors and consequences of social media health-misinformation seeking behavior during the coronavirus (COVID-19) pandemic.Design/methodology/approach: Using a sample of 230 international students studying at Wuhan University and Beijing Language and Cultural University, China, this study employs structural equation modeling to analyze the collected data.Findings: The results indicate that personal factors such as lack of health information literacy, environmental factors, information overload and social media peer influence have a significant effect on behavior, namely social media health-misinformation seeking behavior, which further influences outcomes, namely social media users' anxiety during the COVID-19 pandemic. In addition, both lack of health information literacy and social media peer influence have significant and direct effects on social media users' anxiety. However, the direct effect of information overload on social media users' anxiety is insignificant.Originality/value: First, this study contributes to the literature on the individuals' social media health-misinformation seeking behavior, its precursors and its consequences, specifically on their mental healthcare during a pandemic situation. Second, this research is one of the pioneer studies that extend social cognitive theory to the context of social media health-misinformation seeking behavior and users' anxiety relationship.

Link prediction, which is used to identify the potential relationship between nodes, is an important issue in network science. In existing studies, the traditional methods based on the structural similarity of nodes make it challenging to complete the task of link prediction in large-scale or sparse networks. Although emerging methods based on deep learning can solve this problem, most of the work mainly completes the link prediction through the similarity of the representation vector of network structure information. Many empirical studies show that link formation is affected by node attributes, and similarity is not the only criterion for the formation of links in reality. Accordingly, this paper proposed a two-stage deep-learning model for link prediction (i.e, TDLP), where the node representation vector of the network structure and attributes was obtained in the first stage, while link prediction was realized through supervised learning in the second stage. The empirical results on real networks showed that our model significantly outperforms the traditional methods (e.g., CN and RA), as well as newly proposed deep-learning methods (e.g., GCN and VGAE). This study not only proposed a deep-learning framework for link prediction from the perspective of structure and attribute fusion and link distribution capture, but also lays a methodological foundation for practical applications based on link prediction.