Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context

Strategies to enhance the corporate innovation resilience in digital era: A cross-organizational collaboration perspective

The current economic environment is characterized by increasing uncertainty, while digitalization has led to profound transformations in both the economy and society. There is a pressing need to conduct an in-depth investigation into the specific effects of cross-organizational cooperation on firms' innovation resilience (FIR) in the digital era, as well as the underlying mechanisms driving these effects. This study utilizes panel data encompassing 30 Chinese provinces and municipalities, intending to analyze the impact of market-based and science-based cross-organizational cooperation on FIR and explore the heterogeneity of this impact. Moreover, the study examines the mediating and moderating mechanisms that influence the relationship between cross-organizational cooperation and FIR. The findings indicate that both market-based cooperation, including cooperation with suppliers (CS) and cooperation with customers (CC), and science-based industry-university-research cooperation (IURC) significantly enhance FIR, with science-based IURC demonstrating the most pronounced effect. Additionally, it is revealed that the impact of market-based and science-based cross-organizational cooperation exhibits spatial heterogeneity in promoting FIR. Market-based and science-based cross-organizational cooperation effectively improves enterprise liquidity, enhances operational efficiency, and consequently enhances innovation resilience (IR). Moreover, the positive impact of cross-organizational cooperation on FIR is amplified by increasing the degree of digital transformation and digital technological innovation.

Management of drug supply chain information based on "artificial intelligence + vendor managed inventory" in China: perspective based on a case study

Objectives

To employ a drug supply chain information system to optimize drug management practices, reducing costs and improving efficiency in financial and asset management.

Methods

A digital artificial intelligence + vendor managed inventory (AI+VMI)-based system for drug supply chain information management in hospitals has been established. The system enables digitalization and intelligentization of purchasing plans, reconciliations, and consumption settlements while generating purchase, sales, inventory reports as well as various query reports. The indicators for evaluating the effectiveness before and after project implementation encompass drug loss reporting, inventory discrepancies, inter-hospital medication retrieval frequency, drug expenditure, and cloud pharmacy service utilization.

Results

The successful implementation of this system has reduced the hospital inventory rate to approximately 20% and decreased the average annual inventory error rate from 0.425‰ to 0.025‰, significantly boosting drug supply chain efficiency by 42.4%. It has also minimized errors in drug application, allocation, and distribution while increasing adverse reaction reports. Drug management across multiple hospital districts has been standardized, leading to improved access to medicines and enhanced patient satisfaction.

Conclusion

The AI+VMI system improves drug supply chain management by ensuring security, reducing costs, enhancing efficiency and safety of drug management, and elevating the professional competence and service level of pharmaceutical personnel.

The impact of AI assimilation on firm performance in small and medium-sized enterprises: A moderated multi-mediation model

Artificial intelligence (AI) and other advanced technologies are increasingly recognized as essential catalysts for enhancing productivity due to their capability to transform nearly all operations within and outside firms. However, the empirical research on how AI assimilation may promote firm-level outcomes such as absorptive capacity (AC), customer agility (CA), and firm performance (FP) is still in its infancy. Drawing from the dynamic capability view and using 417 valid responses collected through cross-sectional methods from small and medium-sized enterprises (SMEs) in Lebanon, this study examines the effect of AI assimilation on firm performance. The mediating roles of AC and CA were investigated. The moderating role of organizational agility (OA) was also explored. The findings support the hypothesized assumptions that continual advancement of technology evolves the industrial organizations' performance with CA and AC as parallel mediators, partially mediating the link between AI assimilation and FP and OA as a moderator, moderating the positive relationship between AI and CA and between AI and FP. The findings provide crucial insights for practitioners and advance the dynamic capability view framework. They provide compelling evidence that enriches the understanding of AI assimilation, demonstrating its positive impact on critical organizational outcomes and yielding performance benefits for SMEs.

Big data analytics capability in healthcare operations and supply chain management: the role of green process innovation

Green approaches remain little disseminated in the healthcare sector despite growing interest in recent years from practitioners and researchers. Big Data Analytics Capability (BDAC) can play a critical role in the integration of environmental concerns into operations and supply chain management (OSCM) and further strengthen the environmental performance of healthcare facilities. According to the literature, the integration of the environment into operations process remains insufficient to achieve high levels of performance and requires efforts in green process innovation. However, this relationship between BDAC and green process innovation remains poorly justified empirically. To address this theoretical gap, we investigated the relationship between BDAC, environmental process integration, green process innovation in OSCM and environmental performance. The main contribution of this study is the valuable knowledge on how BDAC influences environmental process integration and green process innovation to enhance environmental performance. Moreover, the study highlights the mediating role of green process innovation on environmental performance, a finding that has not been mentioned in the extant literature. The paper provides valuable insight for managers and stakeholders that can assist them in supporting the application of BDAC in healthcare OSCM to create sustainable value.

How digitalization in banking improve service supply chain resilience of e-commerce sector? a technological adoption model approach

Supply chain resilience (SCR) is crucial for organizational sustainability and growth. This paper explores the factors of digitalization in the banking sector for achieving resilience in the e-commerce service supply chain. Most previous researchers have explored supply chain resilience in e-commerce using structural equation modeling. This study identifies and evaluates the crucial factors of digital banking that improve resilience in the e-commerce service supply chain using the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. A conceptual framework is created based on the literature review and technology adoption theory. The framework comprises the critical digital technology adoption factors for the resilient service supply chain in Pakistan’s e-commerce sector. The results show that the most critical driving factors are “performance expectancy (F15),” “e-cost effectiveness (F5),” and “Trust (F8)” The research findings have several implications for decision-makers, and practitioners, providing insights into how digitalization in the banking sector can help in designing resilient supply chains in the e-commerce sector.

Fake news on Facebook and their impact on supply chain disruption during COVID-19

Social media (SM) fake news has become a serious concern especially during COVID-19. In this study, we develop a research model to investigate to what extent SM fake news contributes to supply chain disruption (SCD), and what are the different SM affordances that contribute to SM fake news. To test the derived hypotheses with survey data, we have applied partial least square based structural equation modelling (PLS-SEM) technique. Further, to identify how different configurations of SC resilience (SCR) capabilities reduce SCD, we have used fuzzy set qualitative comparative analysis (fsQCA). The results show that SM affordances lead to fake news, which increases consumer panic buying (CPB); CPB in turn increases SCD. In addition, SM fake news directly increases SCD. The moderation test suggests that, SCR capability, as a higher-order construct, decreases the effect of CPB on SCD; however, neither of the capabilities individually moderates. Complimentarily, the fsQCA results suggest that no single capability but their three specific configurations reduce SCD. This work offers a new theoretical perspective to study SCD through SM fake news. Our research advances the knowledge of SCR from a configurational lens by adopting an equifinal means towards mitigating disruption. This research will also assist the operations and SC managers to strategize and understand which combination of resilience capabilities is the most effective in tackling disruptions during a crisis e.g., COVID-19. In addition, by identifying the relative role of different SM affordances, this study provides pragmatic insights into SM affordance measures that combat fake news on SM.

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

Enhancing technology innovation performance through alliance capability: The role of standard alliance network and political skill of TMTs

Given the increasing competition in standards, standard alliances have become a vital choice for enterprises to enhance their competitive advantage. In standard alliances, what decisions must top management teams make to help their enterprises improve their innovation performance? To answer this question, we draw on dynamic capability theory, social network theory, and high-level echelon theory to understand how alliance capabilities and standard alliance networks affect technology innovation performance. We collected questionnaire data from 465 manufacturing enterprises in China, and the empirical findings show that (1) enterprise alliance capabilities and standard alliance networks have a positive impact on technology innovation performance; (2) enterprise alliance capabilities and technology innovation performance are mediated by standard alliance networks; and (3) the political skills of top management teams strengthen this moderating model. The results of this study enrich the literature on standard alliances and provide a reference for enterprises in developing standard alliance strategies, cultivating alliance capabilities, and exercising the requisite political skills of top management teams.

COVID-19 research in management: An updated bibliometric analysis

The unprecedented impact of COVID-19 on the global economy as well as on the academic literature. Since early 2020, management researchers have made exceptional efforts to extend our understanding of the pandemic's effect on consumption, sourcing, the workplace, and corporate strategies. The present study uses a bibliometric design to analyze the extensive database of COVID-19 studies in management literature generated over a 2-year period. The analysis focused on the performance of research constituents, thematic analysis of the literature, categorization of the themes at a societal, organizational, and individual level, and finally, a deep analysis of future research calls in the body of literature.

Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review

Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.

Supply chain management in times of crisis: a systematic review

Complexities of crises force supply chains managers to formulate crisis-induced strategies, which contrast with the conventional strategies that give precedence to competitive priorities. Recent crises, such as the coronavirus outbreaks, large-scale product recalls, and financial crises, underscore the increasing regularity and severity of crises with imperatives for introspective and retrospective socio-economic insights on the contexts, priorities, and themes of supply chain management in times of crisis. The purpose of this article is to review the literature on supply chain management in times of crisis, systematically coalescing the related body of scholarly work; outlining current methods applied by researchers; capturing strategic priorities and themes of complexities in research studies; and highlighting potentials for future studies. Using a systematic review of 250 journal articles published between 1996 and 2021, the review finds four dimensions for restorative priorities that reflect operations strategy during crisis: (i) critical supplies with essential services, (ii) timely response with recovery, (iii) safety with security, and (iv) traceability with transparency. The review also finds that operational complexities during crises originate from network configurations and business cycle complexities, optimal selections and provisioning system complexes, and complex learning processes and demand predictions. Insights from the review aid in the proposal of build-to-cycle, organic capabilities, and operational mindfulness framings for supply chain management in times of crisis. The article concludes by recommending future research studies on supply chain upgrades, diagnosis, solidarity, mapping, temporariness, and thresholds, as well as optimal selection problems on linking crisis systems investments with liabilities and on linking crisis network allotments with cross-functionalities.

Digital technology deployment and firm resilience: Evidence from the COVID-19 pandemic

Although many firms are aggressively deploying diverse digital technologies (DTs) at inter- and intra-organizational levels, not all firms have achieved the anticipated resilience, especially in the face of supply chain disruptions caused by “black swan” events such as the COVID-19 pandemic. To demystify this phenomenon, we draw on the asset orchestration perspective to investigate how breadth (i.e., the scope) and depth (i.e., the scale) of DT deployment influence a firm's resilience to supply chain disruptions. Survey data from 162 Chinese manufacturing firms show that the depth of DT deployment exerts a positive effect on firm resilience. Interestingly, the breadth has a non-significant effect on firm resilience. Moreover, while the breadth and depth of DT deployment both enhance supply chain coordination, supply chain coordination mediates only the relationship between DT deployment depth and firm resilience. Finally, market acuity positively moderates the relationship between supply chain coordination and firm resilience. We contribute to the literature by providing new theoretical explanations for the inconsistency in the reported relationship between technology deployment and resilience. Our study also helps firms reevaluate their DT deployment.

Strategic drivers to overcome the impacts of the COVID-19 pandemic: implications for ensuring resilience in supply chains

The recent coronavirus (COVID-19) pandemic has affected the manufacturing industry’s entire supply chain system. It is important to investigate the strategic drivers to deal with the impacts of COVID-19 in the manufacturing industry. Accordingly, this study aims to identify the strategic drivers to overcome the impacts of the COVID-19 pandemic and improve the resiliency of the Bangladeshi footwear industry, an emerging economy. The strategic drivers are identified after reviewing research papers, reports, blogs, and discussions on social media platforms. The main drivers and their respective sub-drivers are finalized by discussing with domain experts. To offer strategic plans for building resiliency, it is crucial to know the importance of the main drivers and sub-drivers; therefore, the best–worst method is applied to determine the priority importance of the strategic drivers. The findings indicate that the top five drivers to defeat the impacts of COVID-19 are “high capability of reconfigurability,” “enhance the relationship with suppliers,” “develop health protocols to continue manufacturing,” “government support through incentives, subsidy, tax rebate, etc.,” and “set a policy to stable material supply”. Based on the findings, this study also provides practical implications with proposed research themes for policymakers and operations managers towards mitigating the impacts of the COVID-19 pandemic. The study’s contribution is unique and important for the footwear supply chain as the research on COVID-19 in the context of resiliency focusing on the footwear supply chain is non-existent.

Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.

Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis

Increased electricity consumption along with the transformations of the energy systems and interruptions in energy supply can lead to a blackout, i.e., the total loss of power in an area (or a set of areas) of a longer duration. This disruption can be fatal for production, logistics, and retail operations. Depending on the scope of the affected areas and the blackout duration, supply chains (SC) can be impacted to different extent. In this study, we perform a simulation analysis using anyLogistix digital SC twin to identify potential impacts of blackouts on SCs for scenarios of different severity. Distinctively, we triangulate the design and evaluation of experiments with consideration of SC performance, resilience, and viability. The results allow for some generalizations. First, we conceptualize blackout as a special case of SC risks which is distinctively characterized by a simultaneous shutdown of several SC processes, disruption propagations (i.e., the ripple effect), and a danger of viability losses for entire ecosystems. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of blackouts, mitigation and recovery strategies. The major observation from the simulation experiments is that the dynamics of the power loss propagation across different regions, the blackout duration, simultaneous unavailability of supply and logistics along with the unpredictable customer behavior might become major factors that determine the blackout impact and influence selection of an appropriate recovery strategy. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of blackouts on the SCs and viability and develop mitigation and recovery strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda toward SC viability, reconfigurable SC, multi-structural SC dynamics, intertwined supply networks, and cross-structural ripple effects.

Employing Natural Language Processing as Artificial Intelligence for Analyzing Consumer Opinion Toward Advertisement

With the advent and integration of technology in business, marketers started investing in numerous media platforms to influence the consumer's sentiments. Artificial intelligence has been proved as one of the innovative tools of digitalization to change consumer's media habits. Owing to the growing trends of e-commerce, the traditional advertising model is insufficient. Therefore, advertisers are taking advantage of artificial intelligence technology to meet current requirements. Thus, a deeper understanding of product advertisement with reference to consumer sentiments and its implications need to be established. The current research depicts the contribution of artificial intelligence to analyze the consumers' attention, cognition, and emotion. The target product was Samsung Galaxy. Researcher of the current study has employed Think-aloud procedure for data analysis. Tweets dataset was divided into 2 categories. For international consumers' sentiments 30,877 tweets whereas for Pakistani consumers' sentiments tweets dataset was 26,834. For data analysis, authors used Nvivo for generating theme. The Nvivo produced word cloud. The word cloud generated with Pakistani tweets revealed that consumer attachment with Samsung product is based on emotional and attention and the preferred features of Samsung products are linked with emotional and attention. In contrary to that rest of the world tweets unfolded that emotion, attention, and cognition make consumer preferences while selecting Samsung products. This study is useful to the cellular companies for targeting across the world population. The consumer preference varies while selecting cell phones. This study will provide a better idea to cell phone companies for manufacturing consumer oriented cell phones to get better results. Moreover, future research should add more countries separate data and generate a comparative study between developed countries consumer and developing countries consumer preferences. In addition to companies with better insights of consumer can highlight the most attractive features of cell phone in their advertisements.

Obstacles affecting the management innovation process through different actors during the covid-19 crisis: a longitudinal study of Industry 4.0

Industry 4.0 represents the most advanced stage of organization of industrial companies, allowing them to respond to an uncertain and changing environment, particularly as accentuated by the recent crisis resulting from COVID-19. Management innovation (MI) contributes to this process of permanent adaptation. The MI implementation phase is a critical step in MI generation that can generate many potential obstacles. This study focuses on these obstacles while considering the different activities (or subprocesses) embedded in this phase and the different actors involved in this complex process. We conducted a longitudinal case study in real time to investigate the implementation of MI internally generated by a multinational industrial company. Our results show that the obstacles encountered during the MI implementation phase may differ depending on the different activities and actors of this phase, thus leading us to question current implementation frameworks. This paper contributes by refining the theoretical model of MI generation and providing a better understanding of the obstacles that occur during the MI implementation phase. From a managerial perspective, this paper highlights key management principles to overcome the obstacles identified.

Analysis of the COVID-19 pandemic’s impacts on manufacturing: a systematic literature review and future research agenda

The COVID-19 pandemic has affected manufacturing companies and necessitated adaptations of firms’ operations. Despite the increasing interest in this subject, a scarcity of systematic analysis can be observed. The present study systematically reviews the existing research on the COVID-19 pandemic concerning the manufacturing industry. This paper aims to highlight the main impacts of the COVID-19 pandemic on the manufacturing sector from the operations management perspective, the practical adaptation actions, and future research opportunities. Open research questions and directions for further investigation are articulated and triangulated across organisational, process and technology perspectives.

Tourism sustainability during COVID-19: developing value chain resilience

The aim of this study is to evaluate the perceptions of prospective tourists through parameters by which the tourism and hospitality service sector can withstand the widespread implications to the sector as a result of the current pandemic. In turn this will lead to weighing up the means for recovery. The identified parameters are then classified, categorized and linked up with supply chain drivers to obtain a holistic picture that can feed into strategic planning from which the tourism and hospitality service sector could utilize to establish a resilient supply chain. This data can provide deep insight for both theorists and practitioners to utilize. It was found that reforming six supply chain drivers, whilst at the same time developing core competencies, is the central essence of a resilient supply chain within the tourism and hospitality business sector (who are at present working hard to counterbalance the many threats and consequent risks posed due to the pandemic).

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

The application of artificial intelligence (AI) technology has evolved into an influential endeavor to improve firm performance, but little research considers the relationship among artificial intelligence capability (AIC), management (AIM), driven decision making (AIDDM), and firm performance. Based on the resource-based view (RBV) and existing findings, this paper constructs a higher-order model of AIC and suggests a research model of e-commerce firm AIC and firm performance. We collected 394 valid questionnaires and conducted data analysis using partial least squares structural equation modeling (PLS-SEM). As a second-order variable, AIC was formed by three first-order variables: basic, proclivity, and skills. AIC indirectly affects firm performance through creativity, AIM, and AI-driven decision making. Firm creativity, AIM, and AIDDM are essential variables between AIC and firm performance. Innovation culture (IC) positive moderates the relationship between firm creativity and AIDDM as well as the relationship between AIDDM and firm performance. Environmental dynamism (ED) positive mediates the connection between AIM and AIDDM. Among the control variables, firm age negatively affects firm performance, and employee size does not. This study helps enterprises leverage AI to improve firm performance, achieve a competitive advantage, and contribute to theory and management practice.

Supply chain resilience in the UK during the coronavirus pandemic: A resource orchestration perspective

The COVID-19 pandemic caused significant disruptions to global operations and supply chains. While the huge impact of the pandemic has nurtured important literature over the last couple of years, little is being said about the role of resource orchestration in supporting resilience in highly disruptive contexts. Thus, this study aims to this knowledge gap by proposing an original model to explore supply chain resilience (SCRE) antecedents, considering supply chain alertness (SCAL) as a central point to support resilience. This study focuses on the resource orchestration theory (ROT) to design a conceptual model. The partial least squares structural equation modeling (PLS-SEM) served to validate the model, exploring data from the UK supply chain decision-makers. The study reveals a number of both expected and unexpected findings. These include the evidence that supply chain disruption orientation (SCDO) has a strong positive effect on the SCAL. In addition, SCAL plays a strong positive effect in resource reconfiguration (RREC), supply chain efficiency (SCEF) and SCRE. We further identified a partial mediation effect of RREC on the relationship between SCAL and SCRE. Surprisingly, it appeared that SCAL strongly influences SCEF, while SCEF itself does not create any significant effect on SCRE. For managers and practitioners, the importance of resource orchestration as a decisive approach to adequately respond to huge disruptions is clearly highlighted by our results. Finally, this paper helps to grasp better how important resource orchestration in operations and supply chains remains for appropriate responses to high disruptions such as the COVID-19 impacts.

The role of alliance management, big data analytics and information visibility on new-product development capability

Many organizations are increasingly investing in building dynamic capabilities to gain competitive advantage. New products play an important role in gaining competitive advantage and can significantly boost organizational performance. Although new product development (NPD) is widely recognized as a potentially vital source of competitive advantage, organizations face challenges in terms of developing the right antecedents or capabilities to influence NPD performance. Our research suggests that organizations should invest in building alliance management capability (AMC), big data analytics capability (BDAC) and information visibility (IV) to achieve their desired NPD success. Informed by the dynamic capabilities view of the firm (DCV) we have stated seven research hypotheses. We further tested our hypotheses using 219 usable respondents gathered using a pre-tested instrument. The hypotheses were tested using variance based structural equation modelling (PLS-SEM). The results of our study paint an interesting picture. Our study makes some significant contribution to the DCV and offers some useful directions to practitioners engaged in NPD in the big data analytics era. We demonstrate that AMC and BDAC are lower-order dynamic capabilities and that AMC has a positive and significant influence on BDAC. In turn, AMC and BDAC influence NPD under the moderating influence of IV. Ours is one of the first studies to empirically establish an association among three distinct dynamic capabilities which are often considered in isolation: AMC, BDAC and NPD. Our findings support emergent views on dynamic capabilities and their classification into various orders. Lastly, we provide empirical evidence that information visibility acts as a contingent variable to both AMC and BDAC effects on NPD. We end our paper by outlining some limitations of our study and by offering useful future research directions.

Supply chain resilience during the COVID-19: empirical evidence from an emerging economy

Purpose

Although the advances in the supply chain resilience (SCR) literature, there is a critical gap concerning this understanding in a high disruption context, such as in the case of the COVID-19. This paper aims to investigate some dimensions (agility, robustness, disruption orientation and resource reconfiguration) of the SCR during this unprecedented disruption in the Brazilian supply chain context.

Design/methodology/approach

Supported by the resource-based view, dynamic capabilities and the SCR literature, we developed a model, which in turn was analyzed and validated by partial least squares structural equation modelling.

Findings

The results revealed that while resource reconfiguration and supply chain disruption orientation positively affect SCR, we found a non-significant effect of supply chain robustness and agility.

Practical implications

The findings suggest that in a considerable disruption scenario, managers with their supply chain operations in emerging economies should prioritize the development of resources to support the disruption orientation and manage the scarce resources adequately by reconfiguring them.

Originality/value

Our study is one of the first that reported the dynamics of the SCR dimensions in an emerging economy during the COVID-19.

Examining the role of emotional intelligence as a moderator for virtual communication and decision making effectiveness during the COVID-19 crisis: revisiting task technology fit theory

The COVID 19 has brought unprecedented changes in the way we communicate. There is a greater accent on Virtual communication. This paper aims to establish a relationship between Emotional intelligence and the effectiveness of Virtual communication on Decision making. This empirical study is based on a sample drawn from 296 working professionals at five different levels of organizational hierarchy. A standardized questionnaire (ɑ = 0.824) was used to collect the responses of Emotional intelligence, Virtual communication, and Decision-making effectiveness. Hierarchical regression using PROCESS Macro model 1 was used to identify the moderating effect of Emotional intelligence on Virtual communication and Decision making effectiveness. Since the p-value (p ≤ .007) is found significant, Emotional intelligence acts as a moderator that affects the strength of the relationship between Virtual communication effectiveness and Decision making. Validation of Task Technology fit theory is the theoretical implication of the study. Manipulation of individual dimensions in the model can reduce the dependence on technology for task completion with enhanced performance effectiveness. The findings are relevant to educators, consultants, and any professional who need to adapt Virtual communication platforms on an ongoing basis. Since work-life balance is projected as a constraint in this study, policymakers can consider policy amendments to reduce the stress caused due to Virtual communication which intrudes into their personal space. This empirical study is the first of its kind to benchmark the organizational practice of Emotional intelligence training to enhance Virtual communication and Decision making effectiveness during unprecedented times of pandemic.