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The COVID-19 pandemic has brought dramatic life changes to consumers. From the perspective of fashion shopping, this study aims to provide an understanding of how consumers have coped with the pandemic to maintain their physical and mental well-being.

Utilizing an observational research method, this study extracted and analyzed text data from Twitter, focusing on fashion consumption-related tweets posted by consumers in May 2020. Content analysis was employed to reveal consumers' coping strategies during the pandemic.

Through fashion shopping, consumers have employed various strategies to cope with the problems incurred during the pandemic as well as the associated emotional stress. Specifically, problem-focused strategies included both active coping and restraint coping. Emotion-focused strategies included positive reinterpretation, acceptance, mental disengagement and seeking social support.

Theoretically, this study provides empirical evidence for the Coping Orientation to Problems Experienced (COPE) Inventory in the context of using fashion shopping as a venue to cope with a pandemic. Managerial implications are also provided for the fashion industry as well as human service providers to better prepare for future public health crises.

The primary goal of this review is to identify emerging themes in the cold supply chain (CSC) and their future research directions, methodologies, and theories.

The review looks at CSC related articles from Scopus database published in the years 2000–2020. Thereafter, bibliometric and co-citation analyses have been conducted to identify emerging themes, methodologies, and theoretical perspectives related to CSC management.

This study revealed a clear research gap in CSC literature with emerging themes relevant to diverse aspects. Primarily, the most prominent authors, methodologies, and theories were identified from bibliometric analysis. Next, we generated clusters to identify the insights of each cluster using co-citation analysis. Consequently, the significance of clusters concerning the number of articles, theoretical frameworks, methodologies, and themes was recognized. Finally, a few future research questions regarding emerging themes have been identified.

The importance of co-citation and bibliometric analyses in studying the evolution of research over a definite time is emphasized in this work. As per emerging themes, implementing digital technologies has increased the efficiency of traditional CSC and transformed it into digital CSC.

As per the authors' knowledge, this work is the first in literature to explore the significance of identifying emerging areas and future research directions in managing CSC through literature review based on bibliometric and co-citation analysis.

Design Science (DS) is a relatively new paradigm for addressing complex real-world problems through the design and evaluation of artifacts. Its constituent methodologies are currently being discussed and established in numerous related research fields, such as information systems and management (Hevner et al., 2004). However, a DS methodology that describes the “how to” is largely lacking, not only in the field of OM but in general. The Theory of Constraints (TOC) and its underlying thinking processes (TP) have produced several novel artifacts for addressing ill-structured real-world operations problems (Dettmer, 1997; Goldratt, 1994), but they have not been analyzed from a DS research standpoint. The purpose of this research is to demonstrate how TOC’s thinking process methodology can be used for conducting exploratory DS research in Operations and Supply Chain Management (OSCM).

A case study of spare parts replenishment illustrates the use of TOC’s thinking processes in DS to structure an initially unstructured problem context and to facilitate the design of a novel solution.

TOC’s thinking processes are an effective methodology for problem-solving DS research, enabling the development of novel solutions in initially unstructured and wicked problem situations. Combined with structured CIMO design logic TOC’s thinking process offers a systematic method for exploring wicked problems, designing novel solutions, and demonstrating theoretical contributions.

The implication for research is that TOC’s thinking process methodology can provide important elements of the lacking “how to” methodology for DS research, not only for the field of OM but in general for the field of management.

The practical outcome of the research is a novel design for dynamic buffer-based replenishment that extends beyond organizational boundaries.

This work shows how the thinking processes can be used in DS research to develop rigorous design propositions for ill-structured problems.

This research delves into a comprehensive examination of Amazon’s Vendor Flex (VF) model, seeking to illuminate the intricacies of supply chain innovation through alliances between Amazon and its suppliers. Employing a multiple case study methodology, the study investigates the reduction of transaction costs, the establishment of strategic alliances for supply chain innovation and governance issues within these alliances.

A multiple case study methodology, incorporating personal interviews and triangulation with primary sources, was employed to unravel the dynamics of the VF model.

Results indicate that the VF model aligns with the reduction of transaction costs by leveraging Amazon’s specialized knowledge, although not necessarily through direct knowledge sharing. Amazon suppliers highlight competitive advantages gained through VF, showcasing efficient navigation of peak seasons and a focus on core activities with online retailing integration. The VF alliance represents a collaborative model where Amazon’s technological prowess enables a streamlined and innovative supply chain for online retailing, which resembles a vertical integration process.

This research underscores the potential of strategic alliances to drive innovation by incorporating industry-leading practices. The governance issues within the VF alliance reveal power imbalances, emphasizing the need for managers to govern dynamics, disclose information and build trust in large-scale alliances.

We introduce decision support tools aimed at optimizing perishable food supply chain management, effectively balancing conflicting objectives such as the exporter’s product collection cost and the importer’s profit. This involves considering factors like perishability, selling price, discount rate, and order quantity to achieve optimal outcomes.

This study considered a three-echelon supply chain comprising farmers, a single exporter, and a single importer providing a single, random-lifetime, perishable product under deterministic customer demand. The proposed mathematical model derived the optimal order quantity, selling price, and discount rate for the entire supply chain. This integrated optimization model treats both demand and supply sides as a multi-objective problem, employing a nonlinear program and a two-stage capacitated vehicle routing problem formulation. Numerical examples and a case study focusing on Thailand durian supply chain were conducted to illustrate the approach of the proposed model.

Taking into account both the importer’s profit and the exporter’s product collection cost, the proposed integrated supply chain model and tools maximize profitability, minimizes waste, and meets demand by optimizing perishable product collection costs and proposing a discount system for selling prices.

Limited to a single perishable product in a three-echelon international food supply chain. Future research can explore different products and supply chain contexts.

The tools enhance decision-making for supply chain managers, improving efficiency, reducing costs, and enhancing customer satisfaction in the perishable food industry.

The proposed model aids in local workforce management by forecasting required manpower for upcoming seasons. By factoring in product quality and pricing, it ensures customers receive fresh products at fair prices. Furthermore, the near-zero waste concept enhances storage conditions at importers' facilities, contributing to improved environmental hygiene.

The integrated model and decision support tools offer a novel approach to address complexities and conflicting objectives in perishable food supply chains, providing practical insights for researchers and practitioners.

Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data.

Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis.

Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission.

By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

This paper aims to (1) unveil how artificial intelligence (AI) can be implemented in growth-hacking strategies; and (2) identify the challenges and enabling factors associated with AI’s implementation in these strategies.

The empirical study is based on two distinct groups of analysis units. Firstly, it involves 11 companies (identified as F1 to F11 in Table 1) that employ growth-hacking principles and use AI to support their decision-making and operations. Secondly, interviews were conducted with four businesses and entrepreneurs providing consultancy services in growth and digital strategies. This approach allowed us to gain a broader view of the phenomenon. Data analysis was performed using the Gioia methodology.

The study firstly uncovers the principal benefits and applications of AI in growth hacking, such as enhanced data analysis and user behaviour insights, sales augmentation, traffic and revenue forecasting, campaign development and optimization, and customer service enhancement through chatbots. Secondly, it reveals the challenges and catalysts in AI-driven growth hacking, highlighting the crucial roles of experimentation, creativity and data collection.

This research represents the inaugural scientific investigation into AI’s role in growth-hacking strategies. It uncovers both the challenges and facilitators of AI implementation in this domain. Practically, it offers detailed insights into the operationalization of AI across various phases and aspects of growth hacking, including product-market fit, user acquisition, virality and retention.

The purpose of this study is to examine the extent to which a firm is able to leverage its information system (IS) innovativeness to improve supply chain resilience through developing and employing its analytics capability. It further considers how this mediating effect of analytics capability can be enhanced by internal and external integration.

Building on the logic of organizational information processing theory, a mediated moderation model is developed and tested using structural equation modeling and partial least squares regression based on survey responses from 247 working professionals.

The results indicate that IS innovativeness improves a firm’s supply chain resilience through enhanced analytics capability, with higher levels of internal and external integration further strengthening the effects of this mediating relationship.

This study is among the first to empirically test the effects of IS innovativeness and analytics capability on supply chain resilience and to examine the impacts of internal and external integration as key factors affecting the strength of these relationships. The findings complement existing literature through providing new insights into the linkage between IS strategy and supply chain resilience and highlighting the importance of relationships throughout the supply chain to enhance the efficacy of a firm’s analytics capability within this domain.

This article investigates how the healthcare sector can reorganize its procurement network to better balance its resilience and cost-minimization objectives.

A single case study was conducted on the procurement of personal protective equipment (PPE) during the first COVID-19 pandemic wave in the Quebec public healthcare network. Interviews were conducted with stakeholders from the supply chain management (SCM) departments at eight public healthcare institutions.

Two major challenges in the early months of the pandemic impacted the development of resilience in the healthcare network. First, peripheral actors’ decisions, which orient procurement objectives, limited the deployment of resilience measures in the supply chain (SC). Second, SC resilience included hundreds of products other than PPE that are critical to the delivery of care. The article illustrates the challenges of SCR, which will inevitably be accompanied by additional costs when purchasing in the public healthcare sector is often focused on the lowest price.

Drawing from the network perspective model, this article examines the actions of Quebec supply network stakeholders through the three phases of SCR: anticipation, response to disruption, and recovery. Finally, the article suggests that decision-makers remove the cost of resilience measures from the purchase price of products, in order to maintain these measures over the long term.

This study aims to examine the effect of audit firm tenure (AFT) on corporate tax avoidance (CTA) and the moderating effect of the COVID-19 pandemic.

The sample comprises 41,074 firm-year observations from 32 countries from 2015 to 2020, for which data are collected from various sources: financial data from the Refinitiv database, country corporate tax rates from the Tax Foundation, and other country-level data from the World Bank database. The authors use the book tax difference to measure CTA and multiple proxies for AFT.

This study finds that a longer AFT is associated with higher CTA, confirming the notion that long AFT impairs auditor independence. The findings remain robust when considering various AFT proxies, incorporating Hofstede’s cultural factors, using weighted least-squares estimation and addressing endogeneity through propensity score matching. This study also finds a non-linear relationship between extended client and auditor relationships and CTA, supporting the mandatory audit firm rotation regulation and increasing investors’ caution regarding the consequences of extended client–auditor relationships on firm behaviour.

This study offers new evidence on the effect of the COVID-19 pandemic on the link between AFT and CTA and documents a non-linear relationship between AFT, which has not been addressed in prior studies.

The findings of this study have several significant practical implications. First, governments and policymakers gain insights into the consequences of extended auditor–client relationships, hence calling for a review of auditing and taxation regulations. Second, the findings provide important insights into the issue of auditor independence, especially during long engagements and crises such as COVID-19. Finally, investors and tax authorities should be more cautious about the risks of aggressive tax avoidance during crisis periods.

To the best of the authors’ knowledge, this is the first study to use a global data set to investigate the effect of AFT on CTA during the COVID-19 pandemic.