Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

A Window of Opportunity: Radical Versus Repurposing Innovation Under Conditions of Environmental Uncertainty and Crisis

In this article, we extend the innovation literature by examining how firms respond to crisis, specifically exogenous crises. At their early onset, crises may represent a window of opportunity for innovation, but it is not equally allocated across firms. We created a unique database of 636 biopharmaceutical firms, from 24 countries and territories, developing innovative treatments during the early outbreak of the COVID-19 crisis to study this phenomenon. We found that firms acted strategically to the shifting external environment and attempted to capitalize on the opportunity by pursuing different but complementary innovation strategies (i.e., radical versus repurposed). The successful outcome of a chosen strategy was highly dependent upon a firm's accumulated knowledge resources, which varied in degree of diversity (i.e., homogeneous versus heterogeneous). We found that firms with more focused R&D (i.e., homogeneous knowledge) developed more radical innovations, whereas firms with more diverse R&D (i.e., heterogeneous knowledge) repurposed innovations. We controlled for firm size (small versus large), firm age (startup versus mature), and country classification (developing versus emerging). We also controlled for a firm's prior knowledge and expertise in coronavirus research and found that it did not influence innovation. Our results suggest that this unique period of environmental uncertainty and crisis created a window of opportunity and a level playing field for innovation.

Examining collaborative buyer-supplier relationships and social sustainability in the "new normal" era: the moderating effects of justice and big data analytical intelligence

The COVID-19 pandemic has resulted in a slew of new business practices that have put the society and environment under strain. This has drawn the attention of supply chain researchers working to address the COVID-19 pandemic's looming social sustainability issues. Prior literature has indicated that collaborative relationships improve organizational performance. Over the past years, problems related to justice are reported (e.g., between Walmart Canada and the Lego group), which might negatively affect the buyer-supplier relationship. In the new normal, the effect of justice on collaborative buyer-supplier relationships on social sustainability in the COVID-19 context is obviously essential but under-explored. The current study examines buyer-supplier collaborative relationships' influence on social sustainability under the moderating effect of justice and big data analytical intelligence. In this paper, we employ the stakeholder resource-based view, loose coupling theory, and resource dependency theory as the theoretical lens to establish the research hypotheses. Using primary survey data collected from supply chain practitioners in South Africa, hypothesis testing is done using a covariance-based structural equation modelling technique. To enhance research rigor, we have checked the dyadic perspectives of both buyers and suppliers. Our empirical results reveal that collaborative buyer-supplier relationships positively influence supplier social sustainability in the new normal era. However, it is relatively stronger from the suppliers' perspective when compared with the buyers' perspective. Secondly, the moderating effect of perceptions of organizational justice and big data analytical intelligence on the relationship between collaborative buyer-supplier relationships and supplier social sustainability is also statistically significant. However, it is relatively stronger from the buyers' perspective when compared with the suppliers' perspective. These are major findings of this study. Theoretical and managerial implications are further discussed.

Managing the retail operations in the COVID-19 pandemic: Evidence from Morocco

Novel coronavirus disease (COVID-19) and resulting lockdowns have contributed to major retail operational disturbances around the globe, forcing retail organizations to manage their operations effectively. The impact can be measured as a black swan event (BSE). Therefore, to understand its impact on retail operations and enhance operational performance, the study attempts to evaluate retail operations and develop a decision-making model for disruptive events in Morocco. The study develops a three-phase evaluation approach. The approach involves fuzzy logic (to measure the current performance of retail operations), graph theory (to develop an exit strategy for retail operations based on different scenarios), and ANN and random forest-based prediction model with K-cross validation (to predict customer retention for retail operations). This methodology is preferred to develop a unique decision-making model for BSE. From the analysis, the current retail performance index has been computed as "Average" level and the graph-theoretic approach highlighted the critical attributes of retail operations. Further, the study identified triggering attributes for customer retention using machine learning-based prediction models (MLBPM) and develops a contactless payment system for customers' safety and hygiene. The framework can be used on a periodic basis to help retail managers to improve their operational performance level for disruptive events.

Exploring the remote work challenges in the era of COVID-19 pandemic: review and application model

Purpose

Remote work (RW) literature is a megatrend in HRM literature, and the COVID-19 pandemic has highlighted the importance of RW as a concept and an organisational practice. Given the large number of papers being published on remote work, there is a need for a critical review of the extant literature using bibliometric analysis. This paper examines the literature on remote working to identify the factors crucial for managing a remote workforce. This study uses the complex adaptive systems theory as a foundation to build a framework that organisations can use to manage their remote workforce, focusing on three outcomes: employee engagement, collaboration and organisational agility.

Design/methodology/approach

Bibliometric analysis was conducted on the research published in Scopus journal in the area of remote work, followed by critical literature analysis.

Findings

The bibliometric analysis identified five clusters that reflect five organisational factors which the management can align to achieve the desired outcomes of engagement, collaboration and agility: technology orientation, leadership, HRM practices, external processes and organisational culture. The present findings have important implications for managing the remote workforce.

Originality/value

The five factors were mapped to propose a conceptual model on engaging individual employees, fostering team collaboration and building organisational agility while working remotely. We also propose an application model for using technology to achieve the outcomes of engagement, collaboration and agility in the organisation. Practitioners could use this framework to focus on the factors that can create a conducive environment to improve work efficiency in a remote workforce.

Managing the resource allocation for the COVID-19 pandemic in healthcare institutions: a pluralistic perspective

Purpose

As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.

Design/methodology/approach

The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.

Findings

After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.

Research limitations/implications

Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.

Practical implications

The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.

Originality/value

Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.