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An amalgamation of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) has been performed to develop a decision-making framework for improving the overall performance of the microfinance institutions. A primary survey was conducted to collect real-time data from the heterogeneous stakeholders of microfinance institutions across India. The validation of the proposed framework is performed by comparing the results against the conventional method of Analytical Hierarchy Process (AHP).

This study identifies various dimensions and indicators for measuring the performance of Indian microfinance institutions. Additionally, the ranking and prioritisation of the performance dimensions and indicators is obtained by considering the mutual interrelation between them.

The study indicates that there exists a significant dyadic relationship between financial performance and social performance for improving the overall performance of the microfinance institutions. Governance is found to unidirectionally influence both financial and social performance. Among all the considered dimensions, financial performance of a microfinance institution is the most critical dimension for improving the overall performance. The top five performance indicators of the Indian microfinance institutions are funding source, borrowing and overhead cost, size of the firm, end-use of the money and depth of outreach.

The study was conducted in the context of Indian microfinance institutions; hence the scope of generalisation of the results is limited. This research considers both subjective and objective aspect of the performance dimensions and indicators from the perspective of multiple stakeholders (i.e. firm, society and regulator). The integrated framework is expected to aid in improving overall performance of microfinance institutions by focusing on the most critical (high prioritised) performance indicators.

An integrated DEMATEL-ANP framework is used in the domain of microfinance to assess the performance dimensions. This study is unique in terms of analysing performance of microfinance institutions from the perspective of heterogeneous stakeholders.

The study aims to find out an appropriate volume of power to be procured through long-term power purchase agreements (PPAs), the volume to be sourced from the power exchange through day-ahead and term-ahead options and also a suitable volume to be sold at different points of time within a day, which would finally lead to the optimum cost of power procurement.

The study has considered a Delhi-based power distribution utility and has collected all relevant data from its archival sources. A stochastic optimization model has been developed to capture the problem of power procurement faced by the distribution utility, which is modelled as a mixed integer linear programming problem. Sensitivity analyses were carried out on the important parameters including hourly demand of power, unit variable cost of power available through PPAs, maximum back-down percentage allowed under PPAs, etc., to investigate their impact on daily cost of power under PPAs, daily cost of power under day-ahead and term-ahead options, daily sales revenue and also the net total daily cost of power procurement.

The findings include the appropriate volume of power procured from different suppliers through PPAs and from the power exchange under day-ahead and term-ahead options and also the surplus volume of power sold under the day-ahead arrangement. It has also computed the total cost of power purchased under PPAs, the cost of power purchased from the power exchange under day-ahead and term-ahead options and also the revenue generated out of the sale of surplus power under the day-ahead arrangement. In addition, it has also presented the results of sensitivity analyses, which provide rich managerial insights.

The paper makes two significant contributions to the existing body of power procurement literature. First, the stochastic mixed-integer linear programming model helps decision makers in determining the right volume of power to be purchased from different sources. Second, based on the findings of the procurement model, a power procurement framework is developed considering the dimensions of uncertainty in power supply and the cost of power procurement. This power procurement framework would aid managers in making procurement decisions under different scenarios.

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.

The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.

Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.

While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

Purpose – We survey the export propensity of firms during the period of economic recession. Our aim is to complement existing literature, by considering the influence of home-country characteristics (corruption, bureaucracy, current financial situation, competition intensity) as predictors of firms’ export decisions.

Methodology – Combining diverse theoretical perspectives, we argue that firms’ export strategy can be reasonably modelled by integrating elements from the environmental determinism theory and the resource-based view of the firm. Research evidence on the factors impacting on export decisions is based on a sample of 136 (local and foreign) firms operating in Greece. A logistic regression model was run with export propensity being the dependent variable.

Findings – Our results indicate that not only firm-level variables (technological competencies and country of origin) are significant determinants of exporting, but considerable attention should also be placed on specific characteristics of the domestic environment. In particular, bureaucracy and the current financial situation seem to influence the managers’ decision to export.

Originality/value – This paper contributes to the field by highlighting the impact of the domestic factors on export propensity, which have been neglected among the micro-level export studies. Further, we present evidence for export propensity in an ‘intermediate-level’ EU peripheral economy during the current economic era.

Implications – Given that this study is based on a country hard hit by the economic recession, it provides useful implications for managers and policy makers.

Warren Buffett asserted that the greatest issue confronting American business and the economy is rising health-care costs, which have risen to 17% of gross domestic product. Public policymakers, health-care providers and other stakeholders grapple with cost-containment and increased health-care delivery efficiencies. There exists a paucity of theory-driven research addressing how information technology vis-à-vis electronic health records (EHR) may supply a managerial mechanism for increasing bottom-line hospital performance, thereby attaining competitive advantage.

A systematic interdisciplinary literature review motivated by resource advantage theory (RAT) offers a conceptual foundation for analyzing the financial, informational and physical workflows that are core elements of supply chain management in a hospital.

RAT links how EHR impacts profitability, competitive advantage and macromarketing factors in hospital supply chains. The literature review provides a research synthesis of the implementation and adoption of EHR to reveal its impact on a hospital’s competitive advantage. Although legislative initiatives like the 2009 Health Information Technology for Economic and Clinical Health Act and the Affordable Care Act encourage EHR adoption, there remains a reluctance for hospitals to do so.

The extant literature precedes the relevant legislation, has incomplete data or focuses solely on patient outcomes.

This study aims to investigate the relationship between strategy intent (product-service innovation intention) and outcome (product-service innovation outcome), and the role that external sources of innovation play in influencing this relationship.

Using data obtained from the community innovation survey, we apply a logit regression to a sample of 1,419 Portuguese firms. By examining the moderating effect of open innovation breadth, we assess how the relationship between differentiation intent and outcome is contingent upon the involvement of external stakeholders.

Our findings reveal that the relationship between differentiation intent and outcome is contingent upon the moderating effect of open innovation breadth. Our analysis suggests that the negative influence of different sources of innovation can be addressed by adopting a paradox lens.

This research provides valuable insights for managers. By simultaneously pursuing a differentiation strategy and engaging in collaboration with external sources, firms may compromise their ability to effectively differentiate their offer. Managers should consider the potential tensions arising from internal and external stakeholder relationships to optimize their innovation strategies.

This study contributes to the existing literature by shedding light on the role of external innovation sources in influencing the relationship between differentiation intent and outcome and the importance that information systems may have in this relationship. By exploring the moderating effect of open innovation breadth, we provide a nuanced understanding of how firms can navigate organizational tensions and leverage innovation for competitive advantage.

The purpose of this paper is to construct the social and environmental disclosure index for Indian companies in order to examine the relationship between social and environmental disclosure and select corporate attributes.

The sample covers annual reports of companies for financial year 2011-2012. The sample represents both financial and non-financial companies that constitute Nifty 50 Index companies as on March 31, 2012. The actual size of the sample analyzed represented 41 companies. The unweighted disclosure index approach has been used to measure the extent of disclosure of social and environmental information where an item scores 1 if disclosed and 0 if not disclosed. The authors built a model using regression which indicates the variables that are significant in determining the social and environmental disclosure of a company. The regression model can be used to predict the degree of disclosure of a company given the values of explanatory variables. Content analysis from annual reports of the companies has been used in constructing the dependent variable.

Regression results indicate that location (place where the registered office of company is located), number of operations of company, turnover, sales and administration expenses, age of company, employee cost and interest paid by company are significant in determining the disclosure index of the company.

Sample size can be increased by considering more companies. In addition, a longitudinal study would enable in drawing comparison over a period of time with respect to disclosure index. The increased sample size would help in validating the disclosure score by dividing the data set into two: one as observation window and the other as validation window.The model explains 23 percent variation in disclosure index. More variation may be explained by incorporating more explanatory variables in the model.

The authors indicate the level of disclosure in case of Indian companies which may prove to be an indicator for prospective investors especially in the present era of global financial and economic downturn. The paper may assist the regulators in framing policies regarding corporate governance. This will enable the regulators of corporate sector to frame laws in order to predict the degree of disclosure of a company based on certain explanatory variables.

The authors focus especially on Indian companies for constructing the disclosure index which to the best of knowledge has not been attempted till date.