Every emerging technology has its pros and cons; health-conscious users pay more importance to healthy and environment-friendly technologies. Based on the UTAUT2 model, we proposed a comprehensive novel model to study the factors influencing consumers’ decision-making to adopt the technology. Compared to prior studies that focused on linear models to investigate consumers’ technology adoption intentions and use behavior. This study used a Structural Equation Modeling-fuzzy set qualitative comparative analysis (SEM-fsQCA) approach to account for the complexity of customers’ decision-making processes in adopting new technology. We collected valid responses from 830 consumers, analyzed them, and evaluated them using a deep learning SEM-fsQCA technique to capture symmetric and asymmetric relations between variables. We have extensively incorporated a health-consciousness attitude as a predictor and mediator to understand better the decision-making toward technology adoption, specifically 5G technology. All the factors tested in our model are statistically significant except the economic factors. Health-consciousness attitude (HCA) and behavioral intention (BI) found significant predictors and valid mediators in the process of 5G technology adoption.FsQCA provided six configurations to achieve high 5G adoption. The findings have significant practical ramifications for telecom corporations, advertisers, government officials, and key policymakers. Additionally, the study added substantial theoretical literature to technology adoption, particularly the adoption of 5G technology.

This study examines the impact of technological integration on sustainable performance in Pakistani manufacturing SMEs from the export sector, focusing on the mediating role of employee capacity building and the moderating role of organizational learning, drawing on dynamic capabilities theory. Data were collected from manufacturing SMEs in Pakistan via a structured questionnaire, and structural equation modeling (SEM) was employed using Smart-PLS (4.1) for analysis. The results reveal that both AI adoption and technological readiness significantly enhance employee capacity building, which, in turn, positively impacts sustainable performance. Employee capacity building significantly mediates the relationships between both AI adoption and sustainable performance as well as between technological readiness and sustainable performance. Notably, organizational learning shows a significant moderating effect between technological readiness and employee capacity building but an insignificant moderating effect between AI adoption and employee capacity building. These findings offer actionable insights for managers in optimizing AI and readiness strategies to enhance capacity building and sustainable outcomes. This study contributes to the literature by being one of the first to integrate AI adoption and technological readiness with employee capacity building as a mediator and organizational learning as a moderator in the context of SMEs. Unlike prior research, which typically examines these factors independently, our framework offers a holistic approach to understanding sustainable performance.

Low user participation in online questions-and-answers (Q&A) communities to contribute knowledge is a serious threat to the sustainability of online forums. This subject is rarely addressed in the context of content shared at these forums. Furthermore, no study has introduced solutions to reduce low participation exclusively considering participants’ age, gender, and education level. User participation in the online question and answer (Q&A) communities to contribute knowledge was mostly examined by applying variance-based methodologies using primary and secondary datasets. This study has targeted the asymmetrical relationship between variables to achieve users’ participation in technical and nontechnical knowledge-sharing communities. We have collected valid responses, 382 from nontechnical and 395 from technical knowledge-sharing communities, and applied fuzzy-set qualitative comparative analysis (fsQCA) to get various equally effective configurations. These configurations explain maximum users’ participation in sharing knowledge in online Q&A communities. fsQCA results have also revealed that to achieve maximum user participation and knowledge contribution in technical knowledge-sharing communities, a sense of reciprocation and online social interaction are the necessary conditions, whereas for nontechnical knowledge-sharing communities, online social interaction is the necessary condition. Study findings are important for online Q&A community managers to minimize low user participation and achieve maximum knowledge contribution in online communities.

Apart from the well-recognized popularity and importance of online question-and-answer (Q&A) communities, the sustainable growth of these platforms is a serious concern faced by online knowledge-sharing platforms. Personality traits play a significant role in shaping human behavior and decision-making. Numerous studies have attempted to understand the volunteer knowledge contribution and provide solutions to improve low participation. However, the role of personality traits of online knowledge-sharing community users in their volunteer knowledge contribution is still unexplored. Based on the big five personality traits, we have proposed a model to understand the role of personality features of online users. We have collected a cross-sectional dataset from online Q&A community users and applied the SEM-ANN model to conclude our model results that will provide insight into online community users’ contribution behavior. Findings show that agreeableness, openness, extrovert, and conscientiousness positively and neuroticism negatively influence online participation to contribute knowledge. We have also observed that the influence of personality traits significantly differs for both genders, and negativity bias is stronger in male users. Study results provide new insight into understanding online users’ behavior and provide a distinct angle to understand and add value to previous studies’ findings on online Q&A community users.