Adnan Khan, Rohit Sindhwani, Mohd Atif and Ashish Varma
This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during…
Abstract
Purpose
This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.
Design/methodology/approach
Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.
Findings
The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.
Originality/value
The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.
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Ajay Jha, Rohit Sindhwani, Ashish Dwivedi and Venkataramanaiah Saddikuti
The purpose of this study is to identify important criteria for sustainable recovery of digital entrepreneurship from distress situation using shared resources. During pandemic…
Abstract
Purpose
The purpose of this study is to identify important criteria for sustainable recovery of digital entrepreneurship from distress situation using shared resources. During pandemic disruption, the importance of sharing economy in managing business efficiency is reflected through this research.
Design/methodology/approach
The present study advances the knowledge on shared resources in business by integrating case study approach with multi criteria decision-making (MCDM) model. A fuzzy analytic hierarchy process approach is adopted to compute criteria weights, and a fuzzy technique for order performance by similarity to ideal solution (TOPSIS) technique is used to rank the sharing economy entrepreneurial ventures during COVID-19 pandemic in the context of emerging economy.
Findings
The present study identified five most important enablers (technological innovation, technology expertise, convergence of virtual and physical spaces, collaboration rather than competition, and benefits to underserved groups through transparency) for sustainable recovery of sharing economy ventures in emerging economy. For example, the study highlights online tutoring through shared intellect as the most sought after sharing economy venture during pandemic disruption, which fulfills the identified enablers.
Practical implications
The proposed framework provides an accurate decision support tool to rank the various identified potential enablers of sharing economy during disruptions. Further, the approach is practically relevant to sharing economy entrepreneurs in selecting the best approach to recover sustainability during pandemic.
Originality/value
The study is unique in addressing the need of sustainability for digital ventures via sharing economy approach in emerging economy (India). To develop a conceptual framework, the present study incorporates a case based approach together with the hybrid MCDM model. Further, the extant literature on disruptions is enhanced by prioritizing the enablers for sharing economy during pandemic.
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Karambir Singh Dhayal, Arun Kumar Giri, Rohit Agrawal, Shruti Agrawal, Ashutosh Samadhiya and Anil Kumar
Industries have been the most significant contributor to carbon emissions since the beginning of the Industrial Revolution. The transition to Industry 5.0 (I5.0) marks a pivotal…
Abstract
Purpose
Industries have been the most significant contributor to carbon emissions since the beginning of the Industrial Revolution. The transition to Industry 5.0 (I5.0) marks a pivotal moment in the industrial revolution, which aims to reconcile productivity with environmental responsibility. As concerns about the decline of environmental quality increase and the demand for sustainable industrial methods intensifies, experts recognize the shift toward the I5.0 transition as a crucial turning point.
Design/methodology/approach
This review study explores the convergence of green technological advancements with the evolving landscape of I5.0, thereby presenting a roadmap toward carbon neutrality. Through an extensive analysis of literature spanning from 2012 to 2024, sourced from the Scopus database, the research study unravels the transformative potential of green technological innovations, artificial intelligence, green supply chain management and the metaverse.
Findings
The findings underscore the urgent imperative of integrating green technologies into the fabric of I5.0, highlighting the opportunities and challenges inherent in this endeavor. Furthermore, the study provides insights tailored for policymakers, regulators, researchers and environmental stakeholders, fostering informed decision-making toward a carbon-neutral future.
Originality/value
This review serves as a call to action, urging collective efforts to harness innovation for the betterment of industry and the environment.
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Rohit Titiyal, Sujoy Bhattacharya and Jitesh J. Thakkar
The purpose of this paper is to apply a multi-criteria decision-making (MCDM) framework to evaluate distribution strategies for an e-tailer. An application of MCDM method, the…
Abstract
Purpose
The purpose of this paper is to apply a multi-criteria decision-making (MCDM) framework to evaluate distribution strategies for an e-tailer. An application of MCDM method, the hybrid DANP–VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, is used for e-tailers’ distribution strategy evaluation. The choice of distribution strategies under various dimensions is evaluated.
Design/methodology/approach
The authors used a hybrid MCDM model to solve the decision-making framework, which combines Decision-Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based analytic network process and VIKOR method. Data were collected from the experts (e-tail manager, logistics manager, operations manager and distribution center (DC) manager) using two questionnaires, first for the influential relationship among the criteria and dimensions and second for a performance rating of each alternative (distribution strategies) against each criterion.
Findings
DANP with VIKOR method prioritizes the distribution strategies in the following order: DC shipment, drop shipment, click and collect, store shipment and click and reserve. Performance gap was calculated based on the VIKOR method to provide distribution strategies to an e-tailer under different situations. The authors infer that in developing country, product characteristics and transportation have a major influence on deciding the distribution strategy.
Practical implications
Decision-making framework will provide e-tail mangers a knowledge-based understanding to select the distribution strategy under the different situations related to the performance, product, e-tailer and external characteristics for smooth order fulfillment process. The insights developed by this research provide a framework for rational decision making in distribution strategy selection in e-business.
Originality/value
This is the first kind of a study which offers a decision framework for e-tail managers on how to choose distribution strategies under different situations which are related to the performance, product, e-tailer and external characteristics.
Rohit Raj, Vimal Kumar, Arpit Singh and Pratima Verma
This study aims to investigate the relationship between patient satisfaction (PS) and the parameters in healthcare and supply chain management (HLSCM).
Abstract
Purpose
This study aims to investigate the relationship between patient satisfaction (PS) and the parameters in healthcare and supply chain management (HLSCM).
Design/methodology/approach
The structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) method have been employed to identify correlation and possible configuration of causal factors that influence PS, including lack of resilience (LS), lack of visibility (LV), cost management (CM) and integration and interoperability (II).
Findings
The results from SEM confirmed that PS is highly correlated with lack of visibility, CM and II as critical parameters. Moreover, fsQCA findings state that the configuration of high levels of both resilience and lack of visibility, as well as high levels of II, are crucial for PS.
Research limitations/implications
The researchers also identified the configuration of factors that lead to low PS. The study’s results could assist healthcare providers in improving their supply chain operations, resulting in more effective and efficient healthcare service delivery and ultimately improving PS.
Originality/value
The fsQCA method used in the study provides a more nuanced understanding of the complex interplay between these factors. The inclusion of supply chain management characteristics as parameters in the evaluation of PS is a novel aspect of this research. Previous studies largely focused on more traditional factors such as physical care, waiting times and hospital amenities. By considering supply chain management factors, this study provides insights into an under-explored area of PS research, which has important implications for healthcare providers looking to improve their operations and PS.
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Upinder Kumar, Mahender Singh Kaswan, Rakesh Kumar, Rekha Chaudhary, Jose Arturo Garza-Reyes, Rajeev Rathi and Rohit Joshi
The main aim of this study is to review different aspects of Industry 5.0 (I5.0) along with Kaizen measures to foster this novel aspect of industrial sustainability. The study…
Abstract
Purpose
The main aim of this study is to review different aspects of Industry 5.0 (I5.0) along with Kaizen measures to foster this novel aspect of industrial sustainability. The study makes a comprehensive study to explore the implementation status of I5.0 in industries, key technologies, adoption level in different nations and barriers to I5.0 adoption together with mitigation actions.
Design/methodology/approach
To do a systematic study of the literature, the authors have used preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology to extract articles related to the field of the study.
Findings
It has been found that academic literature on the I5.0 is continuously growing as the wheel of time is running. Most of the studies on I5.0 are conceptual-based, and manufacturing and medical industries are the flag bearer in the adoption of this novel aspect. Further, due to I5.0's infancy, many organizations face difficulty to adopt the same due to financial burden, resistive nature, a well-designed standard for cyber-physical systems (CPS) and an effective mechanism for human–robot collaboration. Further studies also provide avenues for future research in terms of the identification of collaborative mechanisms between machines and wells, the establishment of different standards for comparison and the development of I5.0-enabled models for different industrial domains.
Originality/value
The study is the first of its kind that reviews different facets of I5.0in conjunction with Kaizen's measures and application areas and provides avenues for future research to improve an organization's environmental and social sustainability.
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Devnaad Singh, Anupam Sharma, Rohit Kumar Singh and Prashant Singh Rana
Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Asmae El Jaouhari, Jabir Arif, Ashutosh Samadhiya, Anil Kumar, Vranda Jain and Rohit Agrawal
The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing…
Abstract
Purpose
The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.
Design/methodology/approach
The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.
Findings
In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.
Practical implications
This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.
Originality/value
This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.