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The purpose of this study is to examine the impact of data analytics, collaboration and flexibility on supply chain resilience (SCR) performance in the current dynamic global market.

This study uses a partial least squares modeling approach to analyze the relationships defined in the conceptual model. This data was organized through a survey questionnaire shared with the professionals working in different industries and belonging to supply chain functions. This survey was designed to measure data analytics capability (DAC), supply chain collaboration (SCC), supply chain flexibility (SCF), industry dynamism (INY) and SCRP, consisting of 29 items. This analysis included involved assessing measurement model for reliability and validity and a structural model for hypothesis testing.

This research empirically examines that collaboration and flexibility are significantly enhanced by advanced DACs, generating superior SCRP. Furthermore, the findings validate that cooperation and adaptability among the supply chain are necessary to reinforce this inherent resilience. The relation of SCC, SCF and the SCRP was significantly moderated with the INY.

This study complements the extant literature by providing empirical evidence of the tangible effects of data analytics on SCR. The study demonstrates the need for the alignment of supply chain strategies with the INY, giving some directions on how businesses can tailor their practices to specific market environments for enhanced resilience.

Artificial intelligence (AI) services are vital in enhancing customer experience and purchase intentions in the international online fashion retail sector. This study explores customers’ intentions to use AI-enabled services, focusing on transaction utility, trust and product uniqueness across the customer journey in the context of international online fashion stores. This study also assesses how privacy moderates customer intentions.

This study adopted a longitudinal research design and purposive sampling technique to collect a total of 566 participants. The final data were analyzed using IBM SPSS Amos version 21 software.

The study highlights the significance of transaction utility, trust and product uniqueness in AI integration across the customer journey (pre-purchase, during purchase and post-purchase stages). Most of the direct relationships are significant, except the relationship between the during purchase and post-purchase stages. With a few exceptions, AI integration commonly does not mediate the relationship between antecedents and intention to use AI-enabled services. Privacy moderates AI integration in post-purchase, during purchase and intention to use AI-enabled services, except in the pre-purchase stage.

This study bridges important gaps in the literature by integrating AI-enabled services and customer behavior, contributing to a broader knowledge of customer interactions in global e-commerce fashion stores. The study examines multiple attributes that impact intention, such as transaction utility, trust, product uniqueness, AI integration in three stages of purchases (pre-purchase, during purchase and post-purchase) and privacy, using three major theories: mental accounting theory, trust commitment theory and commodity theory.

The Industry 4.0 (I4.0) revolution originated in developed countries and has now been promoted worldwide as a powerful tool for improving productivity. However, adopting I4.0 technologies poses significant challenges, especially for emerging economies that are far from the I4.0 frontier. In the context of emerging economies, this paper aims to explore the impact of adopting I4.0 technologies on firms’ total factor productivity (TFP) and its mediation channels.

Using panel data for 2,928 firms in China’s manufacturing industry during the period 2010–2022, this study adopts fixed effects regression model to test the theoretical hypotheses. Endogeneity issues are addressed by the instrumental variable approach and propensity score matching.

The results show that adopting I4.0 technologies can significantly improve emerging economy firms’ TFP, and this effect is achieved by promoting technological innovation and alleviating financial constraints. Furthermore, the findings indicate a heterogeneity in the effects of I4.0 technology adoption. When top managers are long-term oriented, firms are state-owned, industry competition pressure is low or regional manufacturing innovation capability is strong, the positive impact of I4.0 on TFP is weakened.

This paper is one of the first attempts to offer empirical evidence about whether and how the adoption of I4.0 technologies boosts TFP growth among firms in emerging economies. The study expands on the organizational performance consequences of I4.0 adoption and provides implications for decision-makers in developing countries in implementing I4.0.

The study examined a wide range of proactive supply chain practices to demonstrate a cross-linkage among them and to understand their effects on both practitioners of previous decision-making models, frameworks, strategies and policies. Here, six supply chain practices are empirically evaluated based on 28 constructs to investigate a comprehensive model and confirm the connections for achieving performance and competence. The study presents a conceptual model and examines the influence of many crucial factors, i.e. supply chain collaboration, knowledge, information sharing, green human resources (GHR) management and lean-green (LG) practices on supply chain performance.

Structural equation modeling (SEM) examines the conceptual model and allied relationship. A sample of 175 respondents' data was collected to test the hypothesized relations. A resource based view (RBV) was adopted, and the questionnaires-based survey was conducted on the Indian supply chain professionals to explore the effect of LG and green human resource management (GHRM) practices on supply chain performance.

The study presented five constructs for supply chain capabilities (SCCA), five constructs for supply chain collaboration and integration (SCIN), four constructs for supply chain knowledge and information sharing (SCKI), five constructs for GHR, five constructs for LG practices (LGPR) and four constructs for lean-green SCM (LG-SCM) firm performance to be utilized for validation by the specific industry, company size and operational boundaries for attaining sustainability. The outcome emphasizes that SCCA positively influence GHRM, LG practices and LG supply chain firm performance. However, LG practices do not influence LG-SCM firm performance, particularly in India.

The study exploited multiple practices in a conceptual model to provide a widespread understanding of decision-making to assist in developing a holistic approach based on different practices for attaining organizational sustainability. The study stimulates the cross-pollination of ideas between many supply chain practices to better understand SCCA, SCIN, SCKI, GHRM and LG-SCM under a single roof for retaining organization performance.