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There is evidence in the literature suggesting the usage of performance measurement framework (PMF) has a positive impact on organisational performance. This is in line with resource based view (RBV) theory, which argues attaining competitive advantage through internal resources and capabilities. In this regard, PMF can be viewed as a “resource” that can be explored in enabling organisational performance. This paper is aimed at developing PMF for the oil and gas supply chain (SC) as a resource and strategic capability.

Drawing on RBV theory, a questionnaire survey was designed based on prior literature review and exploratory interview with five SC experts. Following this, the questionnaires were distributed to 550 companies in the UK and 120 companies in Malaysia, which resulted in 15% overall response rate.

This study presents the prevalence of performance measures (PM) for the oil and gas industry based on the level of importance. It also reveals the impact of the usage of PMF on overall organisational performance. In addition, it identifies the challenges in managing SC performance and factors to be considered in choosing PM.

This study identifies the challenges in managing SC performance and establishes distinctive factors to consider when choosing PM in the oil and gas SC.

This study aims to explore supply chain resilience (SCR) and provides a unique resilience index. The work measures the resilience status of 37 organizations across 22 industries and provides insight into accessing the supply chain (SC) vulnerability in an uncertain environment.

This study involves measuring the resilience status of 37 organizations across 22 industries based on a subjective decision-making approach using fuzzy logic. Experts from industries rated the importance and level of implementation of 33 attributes of SCR, which are used to develop a fuzzy index of implementation that explains the resilience status of organizations.

A novel coexistent resilience index is computed based on mutualism to exhibit the proportion of contribution or learning of each attribute of an organization in an industry. The research will enhance the response plans and formation of strategic alliances for mutual coexistence by industry.

Evidence-based interpretations and suggestions are provided for each industry to enhance resilience through coexistence.

The work uniquely contributes to academic literature and SC strategy. The novel coexistent resilience index is computed based on mutualism, facilitating researchers to access SC resiliency.

This paper aims to discover and analyze the challenges hampering blockchain technology’s (BT’s) implementation in the Indian health-care sector. A total of 18 challenges have been prioritized and modeled based on an extensive literature search and professional views.

An integrated multi-criteria decision-making approach has been used in two phases. Best worst method (BWM) is used in the first phase to prioritize the challenges with sensitivity analysis to validate the findings and eliminate a few challenges. In the second phase, interpretive structural modeling is applied to the remaining 15 challenges to obtain relative relationships among them with cross-impact matrix multiplication applied to classification analysis for their categorization.

The study’s results reveal that limited knowledge and expertise, cost and risk involved, technical issues, lack of clear regulations, resistance to change and lack of top management support are the top-ranked or high-intensity challenges according to the BWM. Interpretive structural modelling findings suggest that the lack of government initiatives has been driving other challenges with the highest driving power.

This work has been conducted in the Indian context, so careful generalization of the results is needed.

This work will give health-care stakeholders a better perspective regarding blockchain’s adoption. It will help health-care stakeholders, service providers, researchers and policymakers get a glimpse of the strategies for eradicating mentioned challenges. The analysis will help reduce the challenges’ impact on blockchain’s adoption in the Indian health-care sector.

The adoption of BT is a novel concept, especially in developing countries such as India. This is one of the few works addressing the challenges to BT adoption in the Indian health-care sector.

This article aims to understand where industry is in terms of digitalizing their operations, what features of this transformation are essential for practitioners, and what barriers they are facing during their journey. In addition, the authors aim to provide recommendations for organization to start their digital transformation.

Through literature review, the authors summarize the emerging tools and technologies in operations and supply chains to inform the practitioners. Then, the authors use surveys conducted on 183 operations and supply chain professionals, and use statistical tools to examine the association between variables of the data set. The authors present real-life case studies to explain important steps of a digital transformation project.

The survey results indicate that real-time monitoring and data analytics are viewed as the most important and needed tools for organizations. High cost, lack of stakeholder buy-in and lack of successful business use cases are major barriers for companies when starting a digital transformation.

The authors provide recommendations for practitioners based on the survey responses, and outline that starting small, focusing on stakeholder buy-in and implementation of software are the three key steps for a successful transformation journey.

Main contributions of this article are to understand practitioner perspectives in digitalization and provide guidelines for organizations to follow when transforming their operations. This research closes the gap between academic research and practice by collaborating with operations and supply chain professionals.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

Effective leaders have emerged as the cornerstone of project success. The major purpose of this paper is to propose a framework to categorize and prioritize leadership competencies for project managers in megaprojects.

In the first stage, this study utilizes PMBOK 6th Edition, IPMA ICB 4.0 frameworks to develop a hierarchy-based four clusters of leadership competencies. In the second stage, a Fuzzy-AHP (Analytic Hierarchy Process) approach was employed to prioritize the leadership competencies for an organization dealing in megaprojects. Finally, using ordinal priority approach (OPA), the results of Fuzzy-AHP method are validated.

Based on PMBOK, IPMA and literature, the proposed framework deduced twenty-four leadership competencies and grouped them in four clusters. The Fuzzy-AHP results indicate that among clusters, people competencies cluster is ranked most important, followed by perspective, practice, and innovation competencies. Considering the sub-categories and global weights, culture/values, governance, interpersonal skills, and development and growth emerged as the most important leadership competencies. The results from OPA corroborate the findings of Fuzzy-AHP method.

Megaprojects are characterized by massive investments and extensive economic and social impact. The proposed framework would be an important aid for policymakers to develop suitable strategies and programs to inculcate leadership competencies that would lead to successful project managers and improved project performance.

The need for this research stems from the need to integrate popular project management frameworks in leadership competencies development in project based organizations. The proposed integrated framework, based on PMBOK 6th Edition and IPMA ICB 4.0 competency frameworks, is an original contribution to understand and prioritize leadership competencies for megaproject success.

Using a dynamic capability view, this study examined the relationships between big data analytics capability (BDAC), entrepreneurial orientation (EO) and sustainable supply chain performance (SSCP) by exploring the moderating role of trust among supply chain partners.

Questionnaires were collected from 300 manufacturing organizations using snow sampling. The moderating connections and direct relationships were examined using Hays' process macro and structural equation modeling.

BDAC was positively related to EO and SSCP. When supply chain partners experienced low levels of trust, an increase in BDAC did not enhance SSCP. As trust increased, the relationship between BDAC and SSCP became more positive, underpinning the moderating effects of trust. Moreover, trust did not moderate the relationship between BDAC and EO. The moderating effect of trust on the relationship between EO and SSCP showed a positive relationship between EO and SSCP when trust was low; however, the relationship became negative when trust was high.

Developing technology alone may not be sufficient, as supply chain managers need to establish a strong business relationship based on mutual trust. However, they also need to be aware of the dangers of high levels of trust because these may negatively affect performance. Therefore, supply chain managers need to achieve an optimal level of trust that is neither excessive nor insufficient.

Advances in technology and entrepreneurial drive for supply chain sustainability make it pertinent to examine trust levels among supply chain partners and the varying impact on BDAC, EO and SSCP. The current study shows the negative aspects of too much trust among supply chain partners.