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The COVID-19 epidemic caused significant disruptions to numerous supply chains. In order to enhance the resilience of supply chains, Collaboration (CO), Information Alignment (IA), and Big Data Analytics Capability (BDAC) have emerged as contemporary strategies within the humanitarian context. This study was conducted to explore the mechanism via which the effect of BDAC, IA and CO on Humanitarian Supply Chain Resilience (HSCR) in the humanitarian space could be optimized through Organizational Flexibility (OF).

A model of six hypotheses was developed based on the Organizational Information Processing Theory (OIPT). Data from 127 supply chain managers in humanitarian organizations were used to test the hypotheses. The analysis employed both descriptive and inferential statistics using SPSS version 26 and Smart-PLS version 3.

The study revealed that BDAC, IA, and CO individually influence supply chain resilience in the humanitarian setting while OF did not moderate the relationship between BDAC, IA, CO, and HSCR.

It is essential that humanitarian stakeholders prioritize factors that could increase supply chain resilience by employing contemporary BDA technologies, effective information flow, and collaborative strategies to set up a robust humanitarian SC system that could help lessen the impact of disasters.

This presents interesting insights that advance theoretical debates on how CO, IA, and BDAC under varying levels of OF could influence SCR in the humanitarian context. The paper further offers some useful guidance to managers in relief organizations who desire to build resilient supply chains by leveraging BDAC, collaboration and information alignment. Finally, the paper may also provoke future humanitarian scholars to replicate the study using different approaches.

This paper aims to build upon the accountability pyramid model by presenting two studies that examine the dimensionality of individual accountability among Amazon Mechanical Turk (MTurk) gig workers.

For the first study, aimed at investigating the dimensionality of individual accountability of gig workers, the authors collected data from MTurkers by administering two surveys in the English and Mandarin Chinese languages with 185 respondents. The authors implemented principal component analysis and confirmatory factor analysis to analyse the data. For the second study, aimed at establishing validation of the dimensionality found in the first study, the authors collected data from an additional 148 respondents.

The results indicated that the intensity and salience dimensions merged into one factor that the authors labelled Accountability Significance, while the process and outcome dimensions merged into one factor labelled Accountability Focus. Additionally, the authors found that individual accountability is a second-order construct encompassing Accountability Significance and Accountability Focus as first-order factors. The authors validated the findings in the second study.

To the best of the authors’ knowledge, this research is the first quantitative study investigating the individual accountability of gig workers. The validation of individual accountability in MTurkers offers valuable insights into MTurkers’s Hybrid Accountability Focus and Accountability Significance.

Technological tools for knowledge management (KM) actively support and enhance knowledge acquisition and sharing in organizations. However, technology for KM has been understudied, especially in terms of disruptive technologies (DTs). There is a need to identify how DTs, which are becoming increasingly important in industry and society, are applied to KM and their impact. This paper aims to examine the current state of technology and DT adoption in KM.

The analysis involves four steps. First, we examine the current status of DT in academia through a keyword co-occurrence network of literature. Second, we analyze the technological convergence (TC) of KM technology through the cooperative patent classification code co-classification analysis of patents. Third, we explore the main topics of KM technologies using BERTopic, and finally, we explore the introduction of DT into KM technologies and suggest potential TC combinations for the future.

KM technologies can be categorized into four main topics (knowledge acquisition, sharing, searching, and transfer), and DT is most often applied to knowledge transfer and acquisition. The DTs that are attracting attention from academia and industry are artificial intelligence, augmented and virtual reality, and blockchain, which have applications in healthcare, supply chain management, and human resource management.

The findings provide useful insights for organizations to build a technology roadmap for KM. They can also improve the rigid mindset of organization employees toward DT adoption and innovation. By adopting a KM system that leverages DT, organizations will be able to manage and operate efficiently and systematically.

The agricultural supply chain is susceptible to disruptive geopolitical events. Therefore, agri-food firms must devise robust resilience strategies to hasten recovery and mitigate global food security effects. Hence, the central aim of this paper is to investigate how supply chains could leverage digital technologies to design resilience strategies to manage uncertainty stemming from the external environment disrupted by a geopolitical event. The context of the study is the African agri-food supply chain during the Russian invasion of Ukraine.

The authors employ strategic contingency and dynamic capabilities theory arguments to explore the scenario and conditions under which African agri-food firms could leverage digital technologies to formulate contingency strategies and devise mitigation countermeasures. Then, the authors used a multi-case-study analysis of 14 African firms of different sizes and tiers within three main agri-food sectors (i.e. livestock farming, food-crop and fisheries-aquaculture) to explore, interpret and present data and their findings.

Downstream firms (wholesalers and retailers) of the African agri-food supply chain are found to extensively use digital seizing and transforming capabilities to formulate worst-case assumptions amid geopolitical disruption, followed by proactive mitigation actions. These capabilities are mainly supported by advanced technologies such as blockchain and additive manufacturing. On the other hand, smaller upstream partners (SMEs, cooperatives and smallholders) are found to leverage less advanced technologies, such as mobile apps and cloud-based data analytics, to develop sensing capabilities necessary to formulate a “wait-and-see” strategy, allowing them to reduce perceptions of heightened supply chain uncertainty and take mainly reactive mitigation strategies. Finally, the authors integrate their findings into a conceptual framework that advances the research agenda on managing supply chain uncertainty in vulnerable areas.

This study is the first that sought to understand the contextual conditions (supply chain characteristics and firm characteristics) under which companies in the African agri-food supply chain could leverage digital technologies to manage uncertainty. The study advances contingency and dynamic capability theories by providing a new way of interacting in one specific context. In practice, this study assists managers in developing suitable strategies to manage uncertainty during geopolitical disruptions.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

The purpose of this study is to analyse the mediating effect of friends and family as sources of perceived social support in the relationship between loneliness and depressive symptoms.

Survey data sample consisted of 733 university students from January to May 2023. Participants completed the UCLA loneliness scales, Multidimensional Scale of Perceived Social Support (MPSS) and Depression, Anxiety and Stress Scale (DASS-21). The SPSS programme with PROCESS macro (Model 6) was used to test the hypothesis regarding the mediation effect.

The bootstrap analysis found that friends as a source of social support mediated the relationship between loneliness and depressive symptoms. Similarly, loneliness had a significant indirect effect on depressive symptoms through the mediation of family as a source of social support. Moreover, it was found that the relationships of friends and family as sources of social support mediated the association of the aforementioned variables.

This research advances our understanding of social support sources from friends and family amongst university students whilst providing suggestions for interventions tackling loneliness and depressive symptoms in a university setting.

This study aims to validate the Bolton Forgiveness Scale (BFS) created by Amanze and Carson (2019) for the Indian population.

The data for the validation of the BFS was collected (Total N = 813) in two phases (Phase-I, N1 = 613 and Phase-II, N2 = 200) through online surveys. SPSS 26 and AMOS were used to establish the psychometric properties of the scale through internal consistency and confirmatory factor analysis.

The results indicated the validation of the BFS in the Indian context, with a high internal consistency (a = 0.847). Confirmatory factor analysis validated the factor structure and items, along with face validity.

This study offers comprehensive suggestions on the approaches to forgiveness, addresses biases, advocates for qualitative exploration and emphasizes rigour for the future research on forgiveness.

The present study validates the BFS for future use for the Indian population. The authors offer comprehensive suggestions on the approaches to forgiveness, address biases, advocate for qualitative exploration and emphasize rigour for future research on forgiveness.

This study aims to establish an analytical framework to help investors accommodate their environmental, social, and corporate governance (ESG) preferences. The analytical solutions were complemented by empirical analyses to shed light on their benefits and tractability.

This study proposes an expected multi-attribute utility analysis for ESG investors in which stocks can be treated as more green or less green (brown) than the market, represented by an index, all modeled in a one-factor structure. The solution is found via the Hamilton-Jacobi-Bellman (HJB) equation with proper treatment of various sources of risk. For the empirical analysis, we use the RepRisk Rating of US stocks from 2010 to 2020 to select companies that are representative of various ESG ratings.

This study finds closed-form solutions for optimal allocations, wealth and value functions. Our empirical analysis reveals drastic increases in wealth allocation toward high-rated ESG stocks for ESG-sensitive investors, even as the overall level of pecuniary satisfaction remains unchanged.

This study broadens the existing analytical framework by introducing a market portfolio along with green and brown stocks. As by-products, we first demonstrate that investors do not need to reduce their pecuniary satisfaction to increase green investment. Second, we propose a parameterization to capture investors' preferences for green assets over brown or market assets, independent of asset performance.

Drawing upon curiosity drive theory, we examine the effects of epistemic curiosity (EC) on employees' innovative work behavior. Additionally, we explore the potential mediating role of work engagement (WE) and the moderating effects of leader-member exchange (LMX).

The data were collected from 406 employees employed in public service organizations within the United Arab Emirates. Structural equation modeling was employed as the analytical approach to test the hypotheses.

The findings of this study indicate that EC is positively related to innovative work behavior. Further, WE mediates the relationship between EC and creative work behavior. Moreover, LMX moderates the indirect effect of WE on the link between EC (interest and deprivation types) and innovative work behavior. The mediating association is stronger when the leader and member’s exchange is high.

This study will assist practitioners of service organizations in focusing their attention on EC to enhance employees' innovative work behavior and achieve optimal performance. Our research extends work on interest and innovative work behavior in an emerging economic context.

Our study suggests that policymakers should embrace the philosophy of curiosity to nurture innovative behavior in the workplace.

This paper aims to examine the image realism effect, studying how changing the front-of-package visual affects the acceptance of an insect-based food by consumers. By comparing reactions to realistic and less realistic images of an insect as an ingredient, this research investigated how visual imagery can affect consumers’ responses, reducing perceived disgust or increasing expected taste.

Three experiments studied the impact of realistic (photo) versus less realistic (drawing) images for two types of insects (mealworm, cricket) on consumers’ psychological distance from the image, perceived disgust, expected taste, willingness to eat, purchase intention and food choice.

Study 1 demonstrates that using a less realistic insect image reduces perceived disgust, with psychological distance from this image and perceived disgust mediating realism effect on willingness to eat. Study 2 shows that a less realistic insect image, perceived as more remote, improves expected taste and willingness to eat. Study 3 confirms the results by measuring behavior: consumers were more likely to choose the product with the less realistic image.

The research focused on one kind of product and two ways of depicting this product, limiting the generalizability of the findings for other visual representations and product categories.

The findings suggest how brand managers can use the image realism effect on the packaging of novel, sustainable products to influence consumers, reducing their disgust and increasing their expected taste.

This research breaks new ground by explaining how visual cues on packaging affect the acceptance of insect-eating, drawing on construal level theory.