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Borrowing from the dynamic capabilities theory and augmented by the relational view, the study investigates the criticality of supply chain agility in delivering operational performance while understanding the determinant role of key cross-firm resources. Additionally, based on the contingency theory, the interactive influence of two critical context factors, supply uncertainty and product complexity, is examined to enrich the understanding of the contingent nature of the operational performance implications.

The study draws its conclusions from the survey data collected from a 152-respondent sample of executives from US manufacturing firms. The empirical data analyses using partial least square structural equation modeling (PLS-SEM) relate agility to operational performance enhancements while incorporating the moderating effects of contextual factors.

The study relates agility capability to operational performance enhancements, while resource specificity and resource complementarity emerge as significant determinants of the capability. Results on the contingent impact of contextual factors suggest differential influences of supply uncertainty and product complexity on the agility–performance relationship: while the former enhances, the latter detracts from the relationship.

The study’s contributions suggest theory extensions into supply chains as contexts, reinforcing the importance of market-responsive capabilities and the foundational nature of supply chains as repositories of vital cross-firm resources. The contingent nature of the agility–performance relationship accents the importance of market context factors.

While size asymmetry in buyer–supplier relationships has been studied in non-disruption contexts, this research explores how supplier size influences positive and negative supply chain disruptions. Anchoring on the commitment-trust theory (CTT), we explore buyer commitment as a mediating variable and examine how buying firms' mediated power usage depends on different supplier sizes and types of supplier-induced disruptions.

Through two scenario-based behavioral experiments, we discover different patterns in buyers' use of mediated power, contingent on the types of supplier-induced disruptions.

In negative disruptions, buyers prefer more mediated power with large suppliers to control uncertainties, using reward or coercive power strategies. In positive disruptions, we find opposite results, indicating different buyers' perceptions and actions are contingent on both the supplier size and the types of disruptions. These findings underscore the complex interplay between supplier size, buyer commitment and mediated power strategies, revealing that disruption type significantly shapes buyer responses.

This paper extends the CTT framework by considering new antecedents and outcomes. We also provide a more comprehensive understanding of buyer behavior when facing positive and negative supplier-induced disruptions. Our study has limitations. Through vignette-based behavioral experiments, there is a risk that scenarios may not accurately represent real-life situations and that decision-making dynamics could be oversimplified. Future research should incorporate nuanced measurements and conduct additional qualitative research for a comprehensive understanding.

This study enriches the understanding of the buyer-supplier relationship by expanding the CTT framework for a more comprehensive picture. We also offer nuanced insights into size dynamics and disruption types, emphasizing tailored strategies in supply chain management. The findings underscore the importance of understanding these nuances to employ tailored strategy in a business-to-business (B2B) context, as mediated power is contingent on multiple factors.

The widespread uptake of digital technology tools for online teaching and learning reached its peak during the nationwide lockdown triggered by the COVID-19 pandemic. It transformed the higher education institutions (HEIs) marketplace both in developed and developing countries. However, in this process of digital transformation, several HEIs, specifically from developing countries, faced major challenges. That threatened to affect their sustainability and performance. In this vein, this study conducts a bibliometric review to map the challenges during the COVID-19 pandemic and suggest strategies for HEIs to cope with post-pandemic situations in the future.

This comprehensive review encompasses 343 papers published between 2020 and 2023, employing a systematic approach that combines bibliometrics and content analysis to thoroughly evaluate the articles.

The investigation revealed a lack of published work addressing the specific challenges faced by the faculty members affecting their well-being. The study underscores the importance of e-learning technology adoption for higher education sustainability by compelling both students and teachers to rely heavily on social media platforms to maintain social presence and facilitate remote learning. The reduced interpersonal interaction during the pandemic has had negative consequences for academic engagement and professional advancement for both educators and students.

This has implications for policymakers and the management of HEIs, as it may prove useful in reenvisioning and redesigning future curricula. The paper concludes by developing a sustainable learning framework using a blended approach. Additionally, we also provide directions for future research to scholars.

This study has implications for policymakers and HEI management to rethink the delivery of future courses with a focus on education and institute sustainability. Finally, the research also proposes a hybrid learning framework for sustainability and forms a robust foundation for scholars in future research.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.