A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields

A bibliometric analysis and literature review on emotional skills

The content, management, and implementation of social skills have been developed since the end of the 20th century as a model of capabilities. Thus, as human beings develop and train their basic cognitive and perceptual-motor functions, they increase their ability to solve and cope with difficulties. This article aims to present a bibliometric and systematic review of social skills, using query sources in databases such as Web of Science (WoS) and Scopus between the years 2000 and 2022, with platforms such as Bibliometrix and Gephi. This search yielded a total of 233 records in WoS and 250 records in Scopus that were merged and, after eliminating 143 duplicate data, were consolidated into 340 records that enclose the academic production of 20 years. Through scientific mapping, the main authors, journals, and countries in this field were determined; similarly, the most relevant studies were classified into three categories, namely, classic, structural, and perspectives, which were represented by means of the metaphor of the tree of science. In addition, a program for further studies was planned, such as in-depth qualitative research measuring observationally and directly taking into account emotional expressiveness, emotional understanding, emotion regulation, and behavior, and the impact of social skills training on social problem-solving. Finally, another important aspect to mention is that this research work is useful for the scientific academic community in many areas of knowledge such as psychology, education, and managers of educational institutions.

Data driven flexible supplier network of selfcare essentials during disruptions in supply chain

During disruptive events, supply chains struggle to meet the demand due to limitations posed by logistics, transportation and supply side failures. In the present study, a flexible supplier network of personal protective equipment (PPEs), such as face masks, hand sanitizers, gloves, and face shields, has been modelled using an extensive risk enabled data driven decision making for addressing disruptions in the supply chain. This paper studies various risks which exists in PPE supply chain and evaluates the total supplier risk based on them. Furthermore, the paper proposes a Multi-objective Mixed Integer Linear Program (MOMILP) to optimally select suppliers and the sustainable allocation of orders under various risks, namely disruption, delay, receivables, inventory, and capacity. The proposed MOMILP model is also extended to promptly revise the orders to other suppliers under a disruption scenario enabling an effective response resulting in minimization of stockouts. The criteria-risk matrix is developed with the help of supply chain experts from industry and academia. Conclusively, the numerical case study and its computational analysis is conducted on the PPE data received from distributors to demonstrate the applicability of the proposed model. The findings suggest that the proposed flexible MOMILP can optimally revise allocations during disruptions to drastically reduce the stockouts and minimize overall cost of procurement in the PPE supply network.

The development of a competence framework for artificial intelligence professionals using probabilistic topic modelling

Purpose

The current gap between the required and available artificial intelligence (AI) professionals poses significant challenges for organisations and academia. Organisations are challenged to identify and secure the appropriate AI competencies. Simultaneously, academia is challenged to design, offer and quickly scale academic programmes in line with industry needs and train new generations of AI professionals. Therefore, identifying and structuring AI competencies is necessary to effectively overcome the AI competence shortage.

Design/methodology/approach

A probabilistic topic model was applied to explore the AI competence categories empirically. The authors analysed 1159 AI-related online job ads published on LinkedIn.

Findings

The authors identified five predominant competence categories: (1) Data Science, (2) AI Software Development, (3) AI Product Development and Management, (4) AI Client Servicing, and (5) AI Research. These five competence categories were summarised under the developed AI competence framework.

Originality/value

The AI competence framework contributes to clarifying and structuring the diverse AI landscape. These findings have the potential to aid various stakeholders involved in the process of training, recruiting and selecting AI professionals. They may guide organisations in constructing a complementary portfolio of AI competencies by helping users match the right competence requirements with an organisation's needs and business objectives. Similarly, they can support academia in designing academic programmes aligned with industry needs. Furthermore, while focusing on AI, this study contributes to the research stream of information technology (IT) competencies.

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view

The Internet of Medical Things (IoMT) is an emerging technology in the healthcare revolution which provides real-time healthcare information communication and reasonable medical resource allocation. The COVID-19 pandemic has had a significant effect on people's lives and has affected healthcare capacities. It is important for integrated IoMT platform development to overcome the global pandemic challenges. This study proposed the national IoMT platform strategy portfolio decision-making model from the non-financial (technology, organization, environment) and financial perspectives. As a solution to the decision problem, initially, the decision-making trial and evaluation laboratory (DEMATEL) technology were employed to capture the cause-effect relationship based on the perspectives and criteria obtained from the insight of an expert team. The analytic network process (ANP) and pairwise comparisons were then used to determine the weights for the strategy. Simultaneously, this study incorporated IoMT platform resource limitations into the zero-one goal programming (ZOGP) method to obtain an optimal portfolio selection for IoMT platform strategy planning. The results showed that the integrated MCDM method produced reasonable results for selecting the most appropriate IoMT platform strategy portfolio when considering resource constraints such as system installation costs, consultant fees, infrastructure costs, reduction of medical staff demand, and improvement rates for diagnosis efficiency. The decision-making model of the IoMT platform in this study was conclusive and significantly compelling to aid government decision makers in concentrating their efforts on planning IoMT strategies in response to various pandemic and medical resource allocations.

Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective

This study employs a structured literature analysis considering Industry 4.0 technologies and their adoption stages (intention, adoption, implementation, routinization, continuance, and diffusion). We identify the technology adoption stage for each technology type, which in turn supports a maturity level categorization, as well as future research suggestions and challenging open research questions. By considering an integrated view of all the adoption stages of Industry 4.0 key technologies, we reveal the key technologies and their development stages, as well as a novel maturity level categorization perspective. The proposed categorization brings valuable research insights in the form of guidelines for practitioners and decision-makers interested in gaining a deeper understanding of the maturity level of key Industry 4.0 technologies.

Improving information alignment and coordination in humanitarian supply chain through blockchain technology

Purpose

The coordination among the various entities such as the military, government agencies, civilians, non-governmental agencies, and other commercial enterprises is one of the most challenging aspects of managing the humanitarian supply chain. Blockchain technology (BCT) can facilitate coordination, but the cost and other hindrances have limited their application in disaster relief operations. Despite some studies, the existing literature does not provide a nuanced understanding of the application of blockchain technology to improve information alignment and coordination. Motivated by some recent examples where blockchain technology has been used to trace and mobilize resources in the form of funds and materials from the origin to the destination, the authors develop a theoretical model grounded in the contingent resource-based view.

Design/methodology/approach

To empirically validate the model and test the research hypotheses, the authors gathered cross-sectional data using a structured pre-tested questionnaire. In this study, the authors gathered our responses from international non-governmental organizations from twenty-four countries. The authors performed the statistical analyses using variance-based structural equation modeling (PLS-SEM) with the help of commercial software (WarpPLS 7.0).

Findings

The findings of the study offer some useful implications for theory and practice. The results obtained through statistical analyses suggest that the BCT significantly affects information alignment and coordination. However, contrary to popular beliefs the study suggests that intergroup leadership has no significant moderating effect on the paths joining BCT and information alignment/coordination. Moreover, the authors found that the control variable (interdependence) significantly affects the information alignment and coordination further, which opens the room for further investigation.

Practical implications

The result of the study offers some useful guidance. Firstly, it suggests that humanitarian organizations should invest in BCT to improve information alignment and coordination which is one of the most complex tasks in front of humanitarian organizations. Secondly, intergroup leadership may not have desired influence on the effects of BCT on information alignment/coordination. However, the interdependence of the humanitarian organizations on each other may have a significant influence on the information alignment/coordination.

Originality/value

The study offers some useful implications for theory. For instance, how BCT influences information alignment and coordination was not well understood in the context of humanitarian settings. Hence, this study offers a nuanced understanding of technology-enabled coordination in humanitarian settings.

How to enhance supply chain resilience: a logistics approach

Purpose

Logistics capability is an important enabler of supply chain resilience (SCR). However, few studies have analyzed the underlying influence mechanism of logistics capability on SCR in extreme conditions, such as those of the COVID-19 pandemic. The purpose of this study is to increase understanding of the role of logistics capabilities in constituting a resilient supply chain.

Design/methodology/approach

Drawing upon the dynamic capability perspective and contingency theory, the proposed conceptual framework aims to demonstrate the relationship between a firm's logistics capabilities and SCR. Furthermore, the conceptual framework is illustrated by empirical evidence from a case study of a Chinese manufacturing company, which focuses on extracting practical lessons from the COVID-19 pandemic.

Findings

The findings suggest that digitalization, innovativeness, and modularization comprise potential mediating pathways for firm logistics capability to affect SCR and government policies, risk management culture, trust and cooperation moderate the effect positively. The potential associations are identified and elucidated by detecting the corresponding strategies and practices of a Chinese manufacturer that performed well amid the COVID-19 pandemic.

Practical implications

This study provides specific guidelines for logistics managers to enhance SCR during the COVID-19 pandemic. Seeing SCR as a dynamic capability, the framework is also instructive for manufacturers, supply chain members, and policymakers to achieve the sustained competitive advantage of supply chains.

Originality/value

The findings expand the understanding of enhancing SCR in a logistics approach. The empirical validation of propositions in the case study reveals a new vista for research on SCR.

Learnings from COVID-19 for managing humanitarian supply chains: systematic literature review and future research directions

The COVID-19 pandemic has been experienced as the most significant global disaster after the Spanish flue in 1918. Millions of people lost their life due to a lack of preparedness and ineffective strategies for managing humanitarian supply chains (HSC). Based on the learnings from this pandemic outbreak, different strategies for managing the effective HSC have been explored in the present context of pandemics through a systematic literature review. The findings highlight some of the major challenges faced during the COVID-19 pandemic, such as lack of planning and preparedness, extended shortages of essential lifesaving items, inadequate lab capacity, lack of transparency and visibility, inefficient distribution network, high response time, dependencies on single sourcing for the medical equipment and medicines, lack of the right information on time, and lack of awareness about the protocol for the treatment of the viral disease. Some of the significant learnings observed from this analysis are the use of multiple sourcing of essential items, joint procurement, improving collaboration among all stakeholders, applications of IoT and blockchain technologies for improving tracking and traceability of essential commodities, application of data analytics tools for accurate prediction of next possible COVID wave/disruptions and optimization of distribution network. Limited studies are focused on finding solutions to these problems in managing HSC. Therefore, as a future scope, researchers could find solutions to optimizing the distribution network in context to pandemics, improving tracing and tracking of items during sudden demand, improving trust and collaborations among different agencies involved in HSC.

COVID19 epidemic outbreak: operating rooms scheduling, specialty teams timetabling and emergency patients' assignment using the robust optimization approach

The health care system is characterized by limited resources, including the physical facilities as well as skilled human resources. Due to the extensive fixed cost of medical facilities and the high specialization required by the medical staff, the problem of resource scarcity in a health care supply chain is much more acute than in other industries. In the pandemic of the Coronavirus, where medical services are the most important services in communities, and protective and preventive guidelines impose new restrictions on the system, the issue of resource allocation will be more complicated and significantly affect the efficiency of health care systems. In this paper, the problem of activating the operating rooms in hospitals, assigning active operating rooms to the COVID-19 and non-COVID-19 patients, assigning specialty teams to the operating rooms and assigning the elective and emergency patients to the specialty teams, and scheduling their operations is studied by considering the new constraints of protective and preventive guidelines of the Coronavirus. To address these issues, a mixed-integer mathematical programming model is proposed. Moreover, to consider the uncertainty in the surgery duration of elective and emergency patients, the stochastic robust optimization approach is utilized. The proposed model is applied for the planning of operating rooms in the cardiovascular department of a hospital in Iran, and the results highlight the role of proper management in supplying sufficient medical resources effectively to respond to patients and scheduled surgical team to overcome the pressure on hospital resources and medical staff results from pandemic conditions.

Evaluating the impacts of COVID-19 outbreak on supply chain risks by modified failure mode and effects analysis: a case study in an automotive company

Supply chains have been facing many disruptions due to natural and man-made disasters. Recently, the global pandemic caused by COVID-19 outbreak, has severely hit trade and investment worldwide. Companies around the world faced significant disruption in their supply chains. This study aims to explore the impacts of COVID-19 outbreak on supply chain risks (SCRs). Based on a comprehensive literature review on supply chain risk management, 70 risks are identified and listed in 7 categories including demand, supply, logistics, political, manufacturing, financial and information. Then, a modified failure mode and effects analysis (FMEA) is proposed to assess the identified SCRs, which integrates FMEA and best-worst method to provide a double effectiveness. The results demonstrate the efficiency of the proposed method, and according to the main findings, "insufficient information about demand quantities", "shortages on supply markets", "bullwhip effect", "loss of key suppliers", "transportation breakdowns", "suppliers", "on-time delivery", "government restrictions", "suppliers' temporary closure", "market demand change" and "single supply sourcing" are the top 10 SCRs during the COVID-19 outbreak, respectively. Finally, the practical implications are discussed and useful managerial insights are recommended.

Sustainability practices during COVID-19: an institutional perspective

Purpose

The purpose of this paper is to investigate how sustainable practices in supply chains are affected by the COVID-19 pandemic through the lens of institutional theory.

Design/methodology/approach

This research applies a twofold data collection approach: 1) qualitative semistructured interviews and 2) secondary data collection that includes sustainability reports, newspaper articles, journal articles, strategic plans, research reports and statements made by authorities and stakeholders concerning decision-making. In total, managers representing six companies in Norway were interviewed.

Findings

The study suggests that during the COVID-19 pandemic, companies tend to focus on short-term decisions and economic issues. The long-term focus on sustainability has, however, increased at the cluster level. The research also indicates that the pandemic has led to the development of new business routines that may transform institutional norms. The diversity of institutional contexts can, on the one hand, drive sustainability transitions through pressures and supportive programs but, on the other hand, also hinder the development of sustainability thinking.

Research limitations/implications

Institutional factors must be included when analyzing the effects of crises and sustainable transitions. Researchers are encouraged to explore the COVID-19 implications in the form of longitudinal studies.

Practical implications

Policymakers can benefit from in-depth knowledge on the adverse effects of an institutional environment on sustainability. For managers, the outbreak of the pandemic can afford them additional time to revise their strategies and seek innovation. The pandemic highlights the need to build more resilient and sustainable systems that will aid managers in responding rapidly during future uncertainties and enduring sustainability trajectories in operations.

Originality/value

The paper offers an in-depth investigation of COVID-19 effects on the sustainability of supply chains by drawing on institutional theory.

Financially distressed suppliers: exit, neglect, voice or loyalty?

Purpose

Major crises such as the global financial crisis 2007–08 or the COVID-19 crisis increase the level and likelihood of supplier financial distress. This research expands the understanding of how cooperatively, respectively, uncooperatively buying firms might respond to suppliers who suffer from financial distress in the course of major crises.

Design/methodology/approach

The authors build on a collaborative project with a German automotive OEM, analyze OEM internal “financial quick check data”, questionnaire data and longitudinal supplier financial data and apply regression, mediation and difference-in-difference estimation analyses.

Findings

The results show that the stronger the dependence on the distressed supplier, the more cooperative the buying firm's response. Furthermore, a more cooperative response of the buying firm has a strong positive influence on the suppliers' financial performance and hence recovery from the distress situation. Insights from supplier financial distress in the course of the financial crisis 2007–2008 can serve as learnings for the COVID-19 crisis.

Research limitations/implications

The study fills a gap in the scholarly literature on “response to risk incidents” and response formation. Resource dependence theory and resource dependence dynamics offer a strong rationale for the type of response buying firms are likely to choose.

Practical implications

Besides offering the first menu of response options, this study can help practitioners in figuring out the most appropriate response to distressed suppliers. The findings can assist buying firms in their decisions how to deal with suppliers during major economic and financial crises.

Originality/value

This research is the first to conceptualize buying firms' response options to financially distressed suppliers, to investigate the influence of dependence on buying firm's response and to reveal the consequences of the buying firm's response for the supplier's financial recovery.