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Agile practices are important for executing business dealings proficiently in today’s scenario, as they thrust on implicating strategies for meeting quick market requirements. These practices are noteworthy from the point of competitiveness and for fulfilling customer’s demands speedily and promptly. The purpose of this paper is to appraise agile supplier selection dilemma based on analytical hierarchy process (AHP), which accompanied grey information. The authors drafted a group of momentous agile supplier selection measures, which can be utilized by the group of industrial and manufacturing industries to measure the status of agile parameters in their partner firms. G-TOPSIS approach to handling the case of agile supplier selection problem is presented by the authors in this work.

The conception of AHP, grey theory and TOPSIS techniques is fused in this study, under the application arena of agile supply chain management (ASCM). The AHP principals are implicated in the first phase to define the priority importance weights of agile measures and, additionally, grey theory and TOPSIS principals are fused in the second phase to fabricate a significant agile supplier selection model.

A merged approach accompanying multiple measures is developed for aiding decision making and for modeling qualitative characteristics of agile arena under grey domain. The present work can be utilized to access the agile performance characteristics of the organization and can define the status of their partner suppliers. The technical guidelines of AHP and G-TOPSIS approach are explained in this study to be implicated in distinguish decision fields. An educational podium for dispensing the theoretical knowledge on supply chain management, ASCM and agility is presented in this study.

A second-level hierarchical structure is built by the authors to facilitate the managers in taking effective decision pertaining to agile measure in their organizations. The lists of qualitative characteristics are catalogued from the literature review in this study. The built model can undertake risk associated in defining the nature of agile criterions as grey concept can undertake risk associated with the system. Thus, the authors implicated G-TOPSIS approach to handling the case of agile supplier selection problem in this study. The presented hierarchical structure will capably assist the industrial and manufacturing firms to react toward random and unpredictable market requirements, along with attaining organizations goals and profits.

The purpose of this study is to identify and prioritize capabilities and practices to ensure a resilient supply chain during an unexpected disruption. In addition, this study ranks maturity factors that influence the main capabilities identified.

This paper is conducted in three stages. First, capabilities and practices are extracted through a literature review. Second, capabilities and practices are ranked using the analytical hierarchical process method. Third, a gray technique for order preference by similarity to ideal solution method is used to rank maturity factors influencing capabilities.

The findings indicate that responsiveness, readiness, flexibility and adaptability are the most important capabilities for supply chain resilience. Also, commitment and communication are the highest maturity factors influencing resilience capabilities.

The findings provide a hierarchical vision of capabilities and practices for industries to increase resilience. Limitations of the paper are related to capabilities, practices and number of experts consulted.

This paper highlights the importance of high-maturity practices in resilience capability adoption. The findings of this study will encourage decisions-makers to increase maturity practices to build resilience against disruption.

The paper reveals that developing powerful capabilities, good practices and a high level of maturity improve supply chain resilience.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

Corporate social responsibility (CSR) rating agencies have arisen with the aim of providing external and reliable information about business behaviour. The purpose of this paper is to present a multi-criteria methodology for integrating CSR valuations with the financial performance of companies in a unique measure of global sustainability performance.

The authors present a hybrid TOPSIS methodology on transformed scores of both the CSR valuations and the financial ratios. The “attribute-specific evaluation” approach into Multi-attribute Prospect Theory (PT) has been applied and the Design of Experiments (DoE) is used with the TOPSIS value of the firm as the response variable.

The proposal has been applied to 118 companies evaluated by Vigeo and Covalence CSR agencies. The authors also have considered five financial ratios of the companies in order to assess their financial performance. Consistent aggregation for firms has been achieved. Relationships between the different rankings, both those of Vigeo and Covalence and the ones constructed in this research, have been analysed. All top 10 Global sustainable firms rank among the top 10 positions in at least one of the remaining rankings. The results show that Vigeo and Covalence provide different information about the CSR behaviour of the companies.

Another interesting question is to study the discrepancies between Vigeo and Covalence, for example, in which areas there is the greatest divergence between the two agencies and what could be the reasons for this.

The results of this research could be of interest for both investors who want a global picture of companies in their selection process and stakeholders concerned with CSR issues who want to take advantage of different CSR ratings.

The application of PT softens the compensatory behaviour of the classical TOPSIS that may prove unsuitable for social evaluation. The DoE allows the aggregation of the weight sets from various decision markers. The combined methodology facilitates the scoring of new firms and the rank reversal problem can be mitigated with this methodology.

The purpose of this paper is to introduce Digital Methods Initiative Twitter Capture and Analysis Toolset, a toolset for capturing and analyzing Twitter data. Instead of just presenting a technical paper detailing the system, however, the authors argue that the type of data used for, as well as the methods encoded in, computational systems have epistemological repercussions for research. The authors thus aim at situating the development of the toolset in relation to methodological debates in the social sciences and humanities.

The authors review the possibilities and limitations of existing approaches to capture and analyze Twitter data in order to address the various ways in which computational systems frame research. The authors then introduce the open-source toolset and put forward an approach that embraces methodological diversity and epistemological plurality.

The authors find that design decisions and more general methodological reasoning can and should go hand in hand when building tools for computational social science or digital humanities.

Besides methodological transparency, the software provides robust and reproducible data capture and analysis, and interlinks with existing analytical software. Epistemic plurality is emphasized by taking into account how Twitter structures information, by allowing for a number of different sampling techniques, by enabling a variety of analytical approaches or paradigms, and by facilitating work at the micro, meso, and macro levels.

The paper opens up critical debate by connecting tool design to fundamental interrogations of methodology and its repercussions for the production of knowledge. The design of the software is inspired by exchanges and debates with scholars from a variety of disciplines and the attempt to propose a flexible and extensible tool that accommodates a wide array of methodological approaches is directly motivated by the desire to keep computational work open for various epistemic sensibilities.

The purpose of this paper is to propose a new MCDM method called ranking based on optimal points (RBOP).

By employing two abstract groups of alternatives as the optimum alternatives and an optimal alternative, in order to offer the most desirable alternative, RBOP imitates human behavior in the decision-making process. RBOP policy is to find the best alternative through measuring alternatives distances from optimum alternatives and optimal alternative, thus, the best alternative must be sitting on the closest distance to its optimum points and the closest distance to the optimal points simultaneously.

In this paper, the author introduced a ten-step gray form of RBOP which is applied in a case of buying running shoes and results compared to the existing MCDM methods. Results showed the considerable differences.

Generally, in order to select the best alternative(s), and to aid decision makers (DMs) to make better decisions for the real-world problems, MCDM methods evaluate a number of alternatives via a number of criteria through the proposed mathematical algorithms. Frequently, for the direct impact of the DMs on the decision-making process, MCDM methods have inflexible algorithms. They only allow DMs to make an impact on the criteria analysis. The inflexibility emerges as a problem when perfect information is available for DMs and MCDM final results are not desirable. The process of the new method completely depends on DMs’ decisions, their interpretation of the periphery and their personal impressions. Hence, the output of RBOP is not necessarily the best alternative, but it offers the most desirable alternative to DM.

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This study aims to identify the intentions of immigrant entrepreneurs to start new projects by investigating the role of influence of institutional support, social context, cultural intelligence, self-efficacy, optimizing personality traits and hierarchy legitimacy on intentions to start new ventures. In addition, the strength of the relationship for such factors and intentions to start new ventures was determined through the moderator role of easy access to venture capital.

To this end, this study complements the academic literature by integrating the structural equation modeling (SEM) and multiple-criteria decision-making (MCDM) techniques. Thus, the MCDM (i.e. analytic hierarchy process and vlsekriterijumska optimizcija i kaompromisno resenje [VIKOR]) is an effective approach to solving the problem of complexity and evaluation (i.e. multiple evaluation criteria, important criteria and data variation). Hence, to complete the strategic guideline solution, this study uses a survey for collecting data from 202 immigrants in Malaysia, Pakistan, Nigeria and Singapore.

The results from SEM prove several critical factors of immigrants’ entrepreneurs. These factors of immigrants’ entrepreneurs can be vital for academics and host countries. By focusing on these aspects and by developing some personality traits (such as self-efficacy and optimal personality traits), these factors can contribute a good deal to increasing the capabilities of immigrant’s entrepreneurs toward entrepreneurial intentions. In the validation, the statistical objective method indicates that the immigrants' prioritizations in all countries are supported by the systematic ranking. Thus, entrepreneurial intentions for immigrants can pursue the order proven by the VIKOR results.

This study has some significant practical and theoretical implications. Practically, the study findings will enable managers to develop strategies to support immigrants for entrepreneurial intentions to start new ventures.

The novelty of the context under given circumstances of global environment adds to the originality of this study. Several previous studies have also emphasized the need for this type of study in other contexts. The findings can call managers’ attention toward a critical issue of immigrants’ entrepreneurial intentions to start new ventures.

The purpose of this paper is to benchmarking the governance performance of Indian states.

This paper provides a framework to measure governance performance at the state level. Using the data on 28 key indicators, the authors evaluate Indian states on seven broad dimensions of governance quality covering several aspects of public service delivery, regulatory quality and law and order. The empirical methodology involves the application of multi-criteria decision making techniques in two steps. The authors, first develop suitable weights of the identified dimensions and criteria under each dimension by applying the inputs of an expert-based decision-panel in a best-worst framework. Next, using these weights, the authors evaluate ranking of each state using TOPSIS and PROMETHEE-II methods.

The results indicate wide disparities in the governance performance of Indian states. Based on different indicators, the paper evaluates the rank of all the major Indian states. Results reveal that “Social Service Delivery(S)” is the most influencing dimension for the development of a state. Overall, the authors find Andra Pradesh, NCT of Delhi and Goa to be the leading states in terms of governance quality.

The paper provides policy makers with easy to use operational indicators to analyse the governance performance of Indian states. These would help in better monitoring of these states through competitive goal-setting for continuous improvement.

To the best of the authors’ knowledge, this study is the first formal assessment of governance quality in the Indian states in a multi-criteria framework. To this end, the paper addresses the issue of wide regional disparities in the country. The findings of the paper provide powerful insights to policy makers in setting up appropriate strategies to eliminate these disparities.

This paper aims to analyze and provide insight on the algorithms for the optimization of construction site layout planning (CSLP). It resolves problems, such as the selection of suitable algorithms, considering the optimality, optimization objectives and representation of layout solutions. The approaches for the better utilization of optimization algorithms are also presented.

To achieve the above, existing records (results = 200) were selected from three databases: Web of Science, ScienceDirect and Scopus. By implementing a systematic protocol, the articles related to the optimization algorithms for the CLSP (results = 75) were identified. Moreover, various related themes were collated and analyzed according to a coding structure.

The results indicate the consistent and increasing interest on the optimization algorithms for the CLSP, revealing that the trend in shifting to smart approaches in the construction industry is significant. Moreover, the interest in metaheuristic algorithms is dominant because 65.3% of the selected articles focus on these algorithms. The optimality, optimization objectives and solution representations are also important in algorithm selection. With the employment of other algorithms, self-developed applications and commercial software, optimization algorithms can be better utilized for solving CSLP problems. The findings also identify the gaps and directions for future research.

The selection of articles in this review does not consider the industrial perspective and practical applications of commercial software. Further comparative analyses of major algorithms are necessary because this review only focuses on algorithm types.

This paper presents a comprehensive systematic review of articles published in the recent decade. It significantly contributes to the demonstration of the status and selection of CLSP algorithms and the benefit of using these algorithms. It also identifies the research gaps in knowledge and reveals potential improvements for future research.