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Previous research has largely overlooked the examination of the association between family, work, and personal (FWP) conflict, user dissatisfaction, and subsequent discontinuation intention in the context of social networking sites (SNS). Addressing this research gap, the present study aims to present an integrated theoretical perspective that combines Expectancy Disconfirmation Theory (EDT) and Merton's functions. By doing so, we seek to provide a comprehensive understanding of the factors influencing SNS withdrawal behavior. To achieve this objective, data were collected from 360 SNS users using a time-lag method across three waves, and Structural Equation Modeling (SEM) was employed for data analysis. The findings of our study reveal that all three disconfirmation-based factors (i.e., FWP conflicts) positively contribute to SNS user dissatisfaction, which subsequently leads to users' intention to discontinue their SNS usage. Additionally, we explored the moderating role of Merton's functions, specifically manifest and latent functions, in influencing users' decisions to discontinue SNS use. The results indicate that the manifest functions of social media weaken the relationship between dissatisfaction and discontinuation intention, whereas the latent functions do not exhibit a significant interaction effect. By proposing a dual theoretically integrated mechanism of SNS discontinuation intention, study contributes to the existing literature in the field of information systems. Furthermore, our findings provide valuable insights for managers regarding the timing and manner in which social media FWP conflicts can lead to user dissatisfaction. This knowledge can assist in the development of effective strategies aimed at retaining users in SNS and enhancing their overall user experience.
Assuntos
Mídias Sociais , Humanos , Rede Social , Emoções , IntençãoRESUMO
Although the global green supply chain management (GSCM) practice has attracted considerable scholarly attention, its efficacy for environmental management systems (EMS) and market competitiveness during Covid-19 has not been fully capitalized. Therefore, the existing literature indicates that the important link between GSCM, EMS, and market competitiveness is missing as supply management is crucial to maintaining market competitiveness. To fill this research gap, the current study examines whether EMS affects the relationship between GSCM practices and market competitiveness. We also propose the moderating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility on these associations from a Covid-19 perspective. We tested a proposed model using the primary data (N = 283) from regression-based structural equation modeling (SEM). The results provide empirical support for the impact of GSCM on ESM and market competitiveness. Furthermore, the results show that BDA-AI and environmental visibility strengthen the positive relationship between GSCM-EMS and EMS and market competitiveness, respectively. Current research provides thoughtful insights for supply chain practitioners, policymakers, managers, and academics that organizations should opt for formal EMS, BDA-AI, and environmental visibility to achieve market competitiveness, even in times of crisis such as Covid-19.
RESUMO
Covid-19 has disrupted the lives of employees all over the world. After experiencing a prolonged yet ongoing destructive event (i.e. Covid-19), finding an effective and non-invasive way to get employees back and engage in work is a huge challenge for scholars. Few studies have focused on returning to work after a traumatic event (limited time), but the post-pandemic psychological stress caused by the Covid-19 (PAPIST19) has not received much attention. Current research addresses this gap and uses a comprehensive model drawn from the transactional model of stress and the Kahn psychological framework to advance the work of predicting PAPIST19. Specifically, the current research investigates how PAPIST19 is related to job engagement, and emotional exhaustion and how job reattachment mediates the relationship. In addition, we use health support climate (HSC) as a boundary condition in our model, which can weaken the impact of PAPIST19 and enhance the effectiveness of job reattachment in reducing emotional exhaustion and increasing job engagement. To test our model, we collected data in multiple waves from Chinese seaports, where seafarers came to work after the restrictions were lifted in China. The current research is one of the earliest scholarly contributions. It paved the way for the research to solve the problem of workers returning to work after large-scale destructive events, and discussed important implications.
RESUMO
In this era of technology development, every business wants to equip its salesforce with a sustainable salesforce automation system to improve sales performance and customer relationship management (CRM) capabilities. This study investigates the impact of big data analytics (BDA) on CRM capabilities and the sales performance of pharmaceutical organizations. A research model was tested based on 416 valid responses collected from pharmaceutical companies through a structured questionnaire. Structural equation modeling (SEM) was employed using Smart-PLS3 to confirm the contribution of BDA to improving CRM capabilities and sales performance. The study finds that individual characteristics such as self-efficacy, playfulness, and social norms, along with organizational characteristics such as voluntariness, user involvement, user participation, and management support, are positive predictors of salesforce perception of BDA. This positive perception of BDA increased the person-technology fit in the salesforce, which ultimately increased the CRM capabilities and sales performance.
