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The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

Drawing insights from Piaget's theory of cognitive development and exploring their applicability to working adults while assessing the role of tenure, as appreciated by multiple studies associated with adaptability and openness, the current study examines the influence of work experience in the relationship between an individual's adaptability and openness to people's ideas in the context of the Indian workplace. The study followed a cross-sectional survey-based design, and data were gathered from 202 junior, middle and senior executives from Indian manufacturing and service organizations. Using PROCESS macro in SPSS, the moderating effect of work experience on the linkage between adaptability to situations and openness to people's ideas was examined. The study results depicted that adaptability to situations is positively associated with a person's openness to people's ideas. Such a result indicates that promoting adaptability among employees could lead to openness in behaviour towards ideas of their colleagues and other people, which may promote team cohesiveness and learning in the long run. Also, the work experience of employees was found to be moderating the relation between adaptability and openness such that the increasing years of work experience tend to enhance the positive relationship between adaptability and openness. These results suggest that as the work experience increases, the positive association between adaptability and tendency to be open towards people's ideas strengthens. Further, the implications for the domains of research and practice, limitations of the study and directions for future studies have been discussed.

In developing countries such as India, start-ups play an essential role in “industrial output,” “Gross Domestic Product ” and “employment creation.” Evidence suggests that pandemics have risen over the last century due to rising global travel and assimilation, urbanization, alterations in land use, and significantly larger exploitation of the natural environment. These trends are likely to continue and intensify. These pandemic episodes affect businesses, especially start-ups. Supplier selection is among the vital critical elements that start-ups must include in start-ups' strategy procedures during the pandemic episodes. This study's focus is to “identify,” “analyze,” and “categorize” the factors affecting supplier selection in start-ups during frequent pandemic episodes like coronavirus disease 2019 (COVID-19).

Through “literature review” and “experts' opinion” from various start-ups in India, ten affecting factors were identified. Total Interpretative Structural Modeling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) were employed to analyze the interrelationship among the factors affecting the supplier selection on start-ups during frequent pandemic episodes, and these factors were ranked as “autonomous,” “independent,” “linkage,” and “dependent” factors.

The findings show that “performance history,” “service levels,” “technical capability,” and “financial stability” are the most critical factors affecting the supplier selection on start-ups during frequent pandemic episodes. The next importance should be safety and environmental concern” and “quality.”

The factors affecting supplier selection on start-ups during frequent pandemic episodes are the current focus of this study. This study is mainly performed on Indian start-ups and can be extended to other countries.

The start-ups can rely on this study to clearly understand the factors affecting the supplier selection on start-ups during frequent pandemic episodes.

There is no research regarding factors affecting supplier selection on start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to supplier selection in start-ups. This gap inspired the present study, which employs the “Total Interpretive Structural Modeling (TISM)” technique to uncover supplier selection determinants and investigate hierarchical interconnections among factors influencing/affecting supplier selection in start-ups during frequent pandemic episodes.

Collaboration methods are unique strategies that can help organizations hedge against external and internal supply chain risks without stressing their relationships with supply chain partners. However, selecting the most appropriate collaboration method from a given set of strategies is a multifaceted challenge. This paper aims to address this issue.

The decision maker's dilemma of fighting data uncertainty in input parameters to check the efficacy of a given collaboration or mitigation approach is tackled by the integration of Grey theory with the technique for order of preference by similarity to ideal solution (TOPSIS) method. The proposed technique is applied and tested for an Indian diesel generator-set manufacturer to identify the most apposite set of sustainable collaboration strategies.

The results showed that when a firm is bidding for different horizontal collaboration strategies across its supply chain system technology and resource-sharing-centered collaboration strategies are the prominent option. In the case of the company's vertical collaboration deployment, the focus should be kept on information sharing to achieve impactful collaboration. The outcome of the analysis helped the Indian manufacturer to adopt transparent order and production information sharing with its regional distributors and core suppliers within its supply chain.

This study demonstrates from a methodological perspective the successful application of the Grey-TOPSIS approach that effectively captures data uncertainty. It also integrates sustainability parameters in collaboration strategy criteria selections.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

The COVID-19 pandemic has had a significant and worldwide influence on healthcare delivery, and it has significantly increased the pace at which digital technology is being used. Blockchain, one of these developing digital technologies, is distinguished by a number of properties. This study focuses on a blockchain-enabled healthcare supply chain. The purpose of this work is to investigate how blockchain technology (BCT) benefits the performance of healthcare supply chain management (HSCM).

The present study is based on the empirical research. Blockchain Technology (BCT), Healthcare Sustainable Supply Chain Practices (HSSCP), Healthcare Supply Chain Performance (HSCP) and Stakeholders’ Involvement (SI) practices are identified from the literature review and hypotheses are framed to check their interrelationship. For testing of hypothesis, a questionnaire was developed. Data collection was done by healthcare professionals via Google docs. The IBM SPSS version 22.0 was used to analyze the data and IBM SPSS AMOS 22.0 software was used for the development of structural modal. The data was collected through the Google form from the stakeholders of healthcare sector and analyzed through Structural Equation Modelling.

This research is focused on adoption of BCT enabled Healthcare Sustainable Supply Chain to improve HSCP. From the result, it had been found that BCT is positively effecting the stakeholder's involvement (SI) and HSSCP practices. Cumulatively, they positively impact the performance of HSCP. From this study, it is found that adoption of BCT enabled Healthcare Sustainable Supply Chain succours to combat COVID-19 situation.

This study attempts to show the potential benefits of the adoption of BCT enabled HSSCP to improve HSCP.

This study aims to develop a hierarchical model that uncovers the relationships between challenges confronting Africa's organ transplant supply chain systems.

Eleven challenges (variables) were identified after a comprehensive review of the existing literature. The contextual interactions among these variables were analysed from the perspectives of health-care stakeholders in two sub-Saharan Africa (SSA) countries (Nigeria and Uganda), using Delphi-interpretive structural modelling-cross-impact matrix multiplication applied to classification (MICMAC) techniques.

The findings reveal that weak regulatory frameworks, insufficient information systems and a lack of necessary skills make it challenging for critical actors to perform the tasks effectively. The interaction effects of these challenges weaken organ supply chains and make it less efficient, giving rise to negative externalities such as black markets for donated organs and organ tourism/trafficking.

This paper establishes a solid foundation for a critical topic that could significantly impact human health and life once the government or non-profit ecosystem matures. The MICMAC analysis in this paper provides a methodological approach for future studies wishing to further develop the organ supply chain structural models.

The study provides valuable insights for experts and policymakers on where to prioritise efforts in designing interventions to strengthen organ transplantation supply chains in developing countries.

This study is one of the first to empirically examine the challenges of organ transplant supply chains from an SSA perspective, including theoretically grounded explanations from data collected in two developing countries.

A quantitative theoretical model is proposed to measure how productivity performance can be affected by strategic decisions related to specific competitive priorities.

This study proposes the Primary Transformation Model (PTM) and an equation to measure cause-and-effect relationships between productivity and competitive priorities.

The interdependence between productivity and competitive priorities was studied using the PTM and the proposed model indicates that strategies that improve external performance also impact internal productivity. It was also observed that the compatibility between competitive priorities depends on the initial manufacturing conditions and the implementation method adopted.

The proposed model is theoretical and, as such, is an abstraction of reality and does not consider all possible aspects. It consists of a novel approach that still requires further empirical testing. The PTM provides insights about the trade-offs between productivity and strategic objectives, as well, contributes to the ongoing research on manufacturing strategy and can be further developed in future studies.

The main practical implication is to allow companies to relate their strategic decisions to their productivity performance.

This research also contributes to societal issues by enabling firms to better align strategic objectives and operations, which ultimately allows offering products more suited to the needs of customers, thus making better use of the required resources and favoring economic growth.

The model proposed allows objective assessment of actions aiming at operational efficiency and effectiveness, in addition to providing insights into cause-and-effect relationships between productivity and competitive priorities. The model can also be used in empirical investigations on manufacturing strategy.

COVID-19 pandemic has exposed that even the best of the developed nations have surrendered to the devastations imposed on the global supply chains. The purpose of this study is to explore how COVID-19 has exaggerated the supply chain of production and distribution of Taiwan-based face masks and also investigate the conscientious factors and subfactors for it.

In this study, an analytical hierarchy processes (AHP)-based approach has been used to assign the criterion weights and to prioritize the responsible factors. Initially, based on 26 decision-makers, successful factors were categorized into five main categories, and then main categories and their subcategories factors were prioritized through individual and group decision-maker’s contexts by using the AHP approach.

The results of this AHP model suggest that “Safety” is the most important and top-ranked factor, followed by production, price, work environment and distribution. The key informers in this study are stakeholders which consist of managers, volunteers, associations and non-governmental organizations. The results showed that good behavior of the employees under the “Safety” category is the top positioned responsible factor for successful production and distribution of face masks to the other countries with the highest global percentage of 15.7% and using sanitizers to protect health is the second most successful factor with the global percentage of 11.7%.

The limitations faced in this study were limited to only Taiwan-based mask manufacturing companies, and it was dependent on the decisions of the limited company’s decision-makers.

The novelty of this study is that the empirical analysis of this study has been based on a successful Taiwan masks manufacturing company and evaluates the responsible factors for the production and distribution of Taiwan masks to other countries during COVID-19.

To be able to continuously provide affordable services to consumers, managers of critical infrastructure (CI) maintenance supply networks have to balance investments in resilience with costs. At the same time, CI providers need to consider factors that influence resilience such as the geographical spread or the location of the network. This study aims to contextualize supply chain resilience knowledge by exploring how maintenance resource configurations impact resilience and costs in CI supply networks.

An in-depth longitudinal single case study of a representative CI provider that has centralized its maintenance supply network is used. Data were collected before and after the change to evaluate the effect of the changes on the maintenance supply network.

This study shows that in this specific CI maintenance context, structural resource choices such as the quantity or location of spare parts and tools, the creation and exploitation of tacit knowledge and staff motivation impact both resilience and costs due to geographical spread, network location and other network properties.

This study extends general supply chain resilience knowledge to a new setting (i.e. CI) and shows how existing insights apply in this context. More specifically, it is shown that even in engineered supply networks there is a need to consider the effect of human agency on resilience as the creation and exploitation of tacit knowledge are of immense importance in managing the network. In addition, the relationship between normal accidents theory and high reliability theory (HRT) is revisited as findings indicate that HRT is also important after a disruption has taken place.