Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system.

The big data analytics (BDA) has dragged tremendous attention in healthcare organizations. Healthcare organizations are investing substantial money and time in big data analytics and want to adopt it to get potential benefits. Thus, this study proposes a BDA adoption model in healthcare organizations to explore the critical factors that can influence its adoption process. The study extends the technology acceptance model (TAM) with the self-efficacy as an external factor and also includes gender and resistance to change (RTC) as moderators to strengthen the research model. The proposed research model has been tested on 283 valid responses which were collected through a structured survey, by applying structural equation modeling. Our results portray that self-efficacy is a strong predictor of intention to use BDA along with other TAM factors. Moreover, it is confirmed by the results that RTC dampens the positive relationship between intention to use and actual use of BDA in healthcare organizations. The outcomes revealed that male employees as compared to female employees are dominant towards the positive intention to use BDA. Furthermore, females create more RTC than males while adopting BDA in healthcare organizations. Theoretical and practical implications, limitations, and future research directions also underlined in this study.

A persistent question for information technology researchers and practitioners is how big data analytics (BDA) can improve sales performance. Therefore, this study proposed a research model to investigate the impact of BDA on perceived sales performance in accordance with the resource-based view (RBV) and dynamic capability theory. The 416 valid responses collected from the employees of pharmaceutical organizations were analyzed using structural equation modelling to test the proposed research model. Results indicated that the BDA and customer relationship management (CRM) capabilities shared a strong positive impact on perceived sales performance. BDA, as organizational resources, creates organizational dynamic capabilities, such as CRM capabilities. BDA and CRM capabilities can influence perceived sales performance. Furthermore, CRM capabilities have a significant mediating impact on the relationships between BDA and perceived sales performance. This study also highlighted the practical and theoretical implications of the proposed model, the research limitations, and the future research directions.

Purpose: This study aims to explore the relationship between financing constraints and the innovation performance of Internet enterprises in the cross-border innovation cooperation network. The study also analyzes the moderating effect of the location of the cross-border innovation cooperation network.Design/methodology/approach: The authors selected patent data, related transaction data and other data of A-share listed companies on Shanghai and Shenzhen stock exchanges from 2014 to 2019. The generalized moment estimation method of instrumental variables (IV-GMM) method was used to analyze the relationship between financing constraints and the innovation performance of Internet firms and the moderating effect of the cross-border innovation cooperation network location. The threshold value of the moderating effect of the network structure hole was calculated with the threshold model.Findings: The empirical results show a significant inverted U-shaped relationship between financing constraints and the innovation performance in the cross-border innovation cooperation network of Internet enterprises. Network centrality positively moderates this relationship. There is a threshold for the adjustment effect of network-structural holes, and the adjustment intensity of structural holes changes before and after the threshold.Originality/value: This study provides a new perspective for Internet firms in innovation cooperation networks to alleviate the negative impact of financing constraints on innovation performance. The inverted U-shaped relationship between financing constraints and the innovation performance of Internet enterprises is in two stages. The moderating range of network centrality and the structural hole besides the threshold of the moderating effect of a structural hole are detailed.