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The rising global competition and uncertainty in business environment has led logistics firms to redesign supply chain strategies which boost disaster immunity. Therefore, the current study develops an integrated research framework that combines big data analytics capability, HR practices, resilience and resource reconfiguration to investigate logistic firm disaster immunity. In addition to that, the moderating effect of data-driven culture is tested between disaster immunity and supply chain performance.

The research design of this study is based on quantitative techniques and followed by positivist research paradigm. Data are collected through structured questionnaires. Overall, 219 managers have participated in this logistics research survey. Data are computed with structural equation modeling approach.

Statistical findings have revealed that collectively HR practices, innovation capability, proactive capability, reactive capability, resource configuration and supply chain resilience explained R² 71.5% variance in disaster immunity. Therefore, in the extended model, disaster immunity and data-driven culture have depicted R² 49.2% variance in manufacturing firm supply chain performance.

The integration of big data analytics capability factors with HR practices, supply chain resilience and resource configuration has enriched logistic literature in the disaster immunity setting. Practically, this study has concluded that factors such as data analytics capabilities, resource reconfiguration, supply chain resilience and HR practices need policymakers’ attention to boost disaster immunity and supply chain performance.

This research is original as it unveils three core factors of big data analytics capability, namely proactive, reactive and innovative capability, and examines firm disaster immunity. Similarly, research is unique as it combines capability factors with resilience, HR practices and resource reconfiguration and investigates logistic firm disaster immunity.

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.

The long-term success of internet banking (IB) services is connected with continuous use. Therefore, examining factors which influence IB user continuance intention is important. Grounded in technology continuance theory, bank transparency and task technology fit (TTF) model, the purpose of this study is to develop a unified technology continuance model to investigate IB user continuance intention.

The research model was empirically tested with 348 responses. Respondents included in this study had prior experience with IB services. For hypotheses testing, the structural equation modelling approach was used.

Findings indicate that the research model has substantial power in explaining IB user continuance intention. Importance performance matrix analysis revealed that bank transparency had the highest importance in determining IB user continuance intention. Therefore, factors like user satisfaction and perceived usefulness have shown a medium level of importance in determining IB user continuance intention. Satisfaction is influenced by perceived usefulness and confirmation and established a significant impact on IB user continuance intention.

The findings of this research are useful for bank managers and policymakers to develop and design IB services according to user’s expectations. Results demonstrate that expectation confirmation and perceived usefulness are antecedents of IB user satisfaction and IB user continuance intention. In addition to that bank, transparency has shown a significant effect on user expectation and IB user continuance intention. These findings established that accuracy in sharing bank information and clarity in transaction charges encourage the user to continue the use of IB services.

The current study develops a unified technology continuance model based on the TTF model and technology continuance theory and investigates IB user continuance intention. Moreover, bank transparency is added in the technology continuance model and established a significant impact towards user expectation confirmation and continuance intention. These findings contribute to the limited body of research in the context of IB user continuance intention and enrich information system literature.