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The COVID-19 pandemic has transformed consumer buying behavior across the world. COVID-19 crisis brought a behavioral change in consumers' attitudes toward health, financial and social well-being. The current research work highlights the factors influencing consumer buying behavior during the COVID-19 pandemic considering saving and safety perspectives.

This study attempts to understand the gap in buying behavior with reference to saving and safety. Survey-based study was conducted during the second phase of COVID-19, and the respondents were those who lived in highly affected COVID cities in India. Exploratory factor analysis and multiple regression analysis were carried out for testing the hypotheses.

Seven factors became the prominent factors in consumer buying patterns during the pandemic. Consumers in the times of COVID-19 pandemic spend only on essential items as compared to nice-to-have and non-essential items.

Respondents considered in the research were millennials aged 25–40. The current research is limited to specific geographic location.

The study assessed how savings and safety influence consumer buying behavior. The 2S framework model for consumer buying behavior during pandemic has been developed. The findings of the study provides a road map to the companies, policy makers, managers and consumers in understanding the consumer buying behavior during pandemic.

The current research work observe the changes in the behavioral patterns of consumers in the context of 2S framework, i.e. saving and safety. This study offer novel contribution as there is no available literature that examined the saving and safety aspects together for consumer buying behavior during crisis.

The purpose of this meta-analysis is to encapsulate the outcomes and generate meaningful conclusions by examining the factors that influence consumers' purchase and non-purchase behaviour intention in a virtual reality retailing context.

This study integrates the outcomes from 52 studies, including 403 relationships involving 19,188 samples. The analysis was conducted using R-metafor and AMOS software.

The findings indicate that key factors that influence purchase and non-purchase behavioural intentions are virtual reality (VR)characteristics, virtual reality experience and consumer attitudes. VR experience is the strongest predictor for purchase decisions in virtual environment ,while consumer attitude towards VR most strongly influences the non-purchase behaviour of the consumers. Furthermore, the age of the respondents, cultural backgrounds (high vs low power distance) and gender moderate the relationship between consumers' attitudes and purchase and behaviour intentions.

Marketers can positively influence consumer attitudes and behavioural intentions by prioritizing the design of the virtual environment and facilitating unique experiences (by manipulating different sensory stimuli) in virtual retailing.

The current meta-analysis reconciles and reinforces the findings in the extant literature and provides a robust empirical generalization of the critical factors that influence consumers' purchase or behavioural intentions in a virtual retailing context.

Quantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.

Each decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.

The approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.

The approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.

This approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.

This robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

This paper identifies and models the effect of eight attributes that influence hotel room rents in India. These attributes are conceptually grouped into three factors: (1) site factors including the presence or absence of a “swimming pool,” “free breakfast,” and the “hotel capacity”; (2) situational factors including, “distance from the airport,” “weekend/weekday,” “city population,” “cost of living”; and (3) a reputation factor indicated by “star rating.” Our regression model uses secondary data collected from a hotel booking website for 570 hotels across 18 cities of India. The results indicate that six out of these eight variables namely, presence of swimming pool, free breakfast, hotel capacity, distance from the airport, city population, and hotel star rating have a significant impact on hotel room rents in India.

This paper aims to create a better understanding of the challenges posed by work from home (WFH) during the ongoing COVID-19 pandemic, to investigate the public sentiment toward this transition, and to develop a conceptual model incorporating the relationships among the factors that influence the effectiveness of WFH.

This paper uses netnography method to collect data from the Twitter platform and uses Python programming language, Natural Language Processing techniques and IBM SPSS 26 to conduct sentiment analysis and directed content analysis on the data. The findings are combined with an extensive review of the remote work literature to develop a conceptual model.

Results show the majority of tweets about WFH during the pandemic are positive and objective with technology and cyber security as the most repeated topics in the tweets. New challenges to WFH during pandemic include future uncertainty, health concerns, home workspaces, self-isolation, lack of recreational activities and support mechanisms. In addition, exhaustion and technostress mediate the relationship between the antecedents and outcomes of WFH during the ongoing COVID-19 pandemic. Finally, the fear of pandemic and coping strategies moderates these relationships.

This paper is one of the first efforts to comprehensively investigate the challenges of WFH during a crisis and to extend the remote work literature by developing a conceptual model incorporating the moderating effects of fear of pandemic and coping strategies. Moreover, it is the first paper to investigate the tweeting behavior of different user types on Twitter who shared posts about WFH during the ongoing pandemic.

Block chain technology (BCT) has emerged as a promising solution for the co-ordination and aid mechanism issues in the context of humanitarian supply chain (HSC). However, implementation of BCT in HSC discerns several barriers. Therefore, the purpose of this study is to identify and model the block chain implementation barriers in the context of HSC.

In the present study, 14 potential barriers to BCT adoption in HSC have been identified through literature survey. The survey comprises white papers, pilot studies, conference proceedings and journal articles. Further, the identified barriers were finalised in consultation with a team of experts. The team comprised experienced stakeholders working in the humanitarian domain and BCT development. The barriers were categorised into four (technological, organisational, exogenous and economic) perspectives adopting the kappa statistics. Further, the barriers were prioritised using fuzzy best worst method (FBWM) approach. Later, sensitivity analysis was performed to check the robustness and viability of the model.

The findings from the study indicate that the barriers, such as “data privacy, ownership, and security issues” (B1), “funding issues and cost complexity” (B3) and “technological complexities” (B8), are relatively more influential. The HSC stakeholders and BCT developers are required to identify the safety mechanism against the misuse of victim’s data. The funding issues and technological complexities are interrelated and need synergetic cooperation between blockchain developers, donors, humanitarian organisations (HOs) and other HSC stakeholders. Further, “lack of awareness and understanding among stakeholders” (B6) and “interoperability, collaboration and cross-pollination among HOs” (B5) were identified as least influential barriers to BCT adoption in HSC.

In literature, limited study has been observed on determining barriers to BCT implementation. A more systematic method and statistical confirmation is necessary to establish further new confronting barriers. This study is limited to Indian context.

To the best of the authors’ knowledge, this study is first of its kind to use an FBWM approach for prioritising the barriers to BCT adoption in the context of HSC. The study provides potential barriers to BCT and categorises them into four different perspectives, along with their degree of influence.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

The present study aims at developing a model to quantify supply chain resilience as a single numerical value. The numerical value is called resilience index that measures the resilience capability of the case company's supply chain. The model calculates the index value based on the interactions between the enablers of supply chain resilience and its dimensions.

Graph theoretic approach (GTA) is used to evaluate the resilience index for the case company's supply chain. In GTA, the dimensions of resilience enablers and their interdependencies are modelled through a digraph. The digraph depicting the influence of each dimension is converted into an adjacency matrix. The permanent function value of the adjacency matrix is called the resilience index (RI).

The proposed approach has been illustrated in context of an Indian automobile organization, and value of the RI is evaluated. The best case and the worst-case values are also obtained with the help of GTA. It is noted from the model that strategic level dimension of enablers is most important in contributing towards supply chain resilience. They are followed by tactical and operational level enablers. The GTA framework proposed will help supply chain practitioners to evaluate and benchmark the supply chain resilience of their respective organizations with the best in the industry.

A firm can compare the RI of its own supply chain with other's supply chain or with the best in the industry for benchmarking purpose. Benchmarking of resilience will help organizations in developing strategies to compete in dynamic market scenario.

The study aims at creating a sequence of implementing supply chain enablers to achieve a greater efficiency in the combination by establishing relationship among them.

To build resilience capability in supply chain, enablers have been first identified through literature review and experts' opinion. The shortlisting of enablers is done in a brainstorming session having experts from academia and industry. The methodology Automated Layout Design Program (ALDEP), which is being used for creating facility layout, is applied to understand the relationship among the enablers for a resilient supply chain.

The methodology ALDEP is applied to explore relationship among five enablers shortlisted after discussion with experts. The layout matrix with the highest score between enablers is taken as a basis to establish relationship between two enablers that an organization can use to build a resilient supply chain.

The study uses a novel method helping organizations to build resilient supply chains. This study will not only provide a starting point but also provide an entire model for building a resilient supply chain.