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Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to the lack of knowledge regarding its key drivers. To address this gap, this study examines the factors driving GenAI implementation in an SCM environment and how these factors optimize SCM performance.

A thorough literature review was followed to identify the drivers. The resultant model from the drivers was validated using a quantitative study based on partial least squares structural equation modeling (PLS-SEM) that used responses from 315 expert respondents from the field of SCM.

The results confirmed the positive effect of performance expectancy, output quality and reliability, organizational innovativeness and management commitment to GenAI usage. Further, they showed that successful GenAI usage improved SCM performance through improved transparency, better decision-making, innovative design, robust development and responsiveness.

This study reports the potential drivers for the contemporary development of GenAI in SCM and highlights an action plan for GenAI’s optimal performance. The findings suggest that by increasing the rate of GenAI implementation, organizations can continuously improve their strategies and practices for better SCM performance.

This study establishes the first step toward empirically testing and validating a theoretical model for GenAI implementation and its effect on SCM performance.

The objective of this research is to evaluate and rank the factors influencing omnichannel (OC) logistics, while also investigating the significant impact of big data analytics in improving these drivers of OC logistics.

Using exploratory sequential mixed method design, an in-person interview survey was conducted to identify and stratifies the enablers of OC retailing. These interviews were supplemented with a case study in an apparel firm to prioritise the enablers of OC logistics. Further, a survey was conducted to understand the role of big data analytics in improving drivers of OC logistics as well as the role of Individual capability and organisational capability in big data usage for omnichannel retailing.

Findings represent that information management is the most important driver followed by inventory management and network design for improving OC logistics. Further, significant relationship between big data analytics and drivers of omnichannel logistics has been reported.

This study identifies and classifies the drivers of OC retailing relating to their level of criticality in OC logistics which will assists practitioners to prioritise their tasks for the successful development of OC logistics. The study will also help practitioners to use BDA for developing the drivers of OC.

The study substantiates and adds to the BDA literature by emphasising the positive role of BDA in development of OC driver and highlighting the significant role of drivers of BDA in its usage.

This bibliometric study is steered to compute the impact of literature published on cloud computing within the fields of information science and library science. The research has been conducted on concentrating the term “Cloud Computing” to search the literature published in both fields, i.e. information science and library science from the time span 2007 to August 2023. This study aims to investigate the top productive country, organizations and highly cited publications.

The period of the exploration was from 2007 to August 2023 for bibliometric analysis and data was collected from the ISI Web of Science. Total 401 documents were retrieved and analyzed to highlight the year-wise distribution of documents type, year-wise most cited articles, prominent journals of the subjects, productivity of organizations, impact of countries and cooccurrences of keywords. The results are grounded on the basis of documents types (articles, early access articles, proceeding papers, book review, editorial material, news items and reviews).

The findings reveal that the most productive year of publication on cloud computing services was 2013. The top productive source is “International Journal of Information Management.” The articles entitled “Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors” found as the most cited article and the significant increase in citations is also noteworthy. The most productive organizations on the topic include “Islamic Azad University of Iran,” “University Cologne of Germany” and “University Nova Lisboa of Portugal.” The results confirmed that the USA dominates in the production of research on “Cloud Computing Services” and the most repeated keyword in the literature is cloud computing. The research articles are the most cited sources of research.

This bibliometric research is an original piece of work that has been conducted to measure the research production in the field of information science and library science during 2007−2023. This piece of work is valuable for those who want to study the literature on cloud computing in the area of information science and library science.