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This study focuses on the Critical Success Factors (CSF) for developing sustainable entrepreneurship in the Pakistani telecommunication industry. Despite the efforts made by governments and stakeholders to stimulate sustainable entrepreneurship initiatives, contributions in the telecommunications sector are lacking. Therefore, this study has the major objective of identifying a transformation path for these firms. This is done by providing a theoretical framework for sustainable entrepreneurship in the telecommunications industry, focusing on managerial and operational practices that should be modified according to a set of CSFs identified by experts in Pakistani firms.

This article proposes a novel Multiple Attribute Decision Making (MADM) approach based on Grey Systems Theory (GST) and Best-Worst Method (BWM) while unveiling endogenous relationships among current managerial/operational practices and the CSFs for sustainable entrepreneurship in the telecommunications industry.

CSFs for achieving sustainable entrepreneurship in the Pakistani telecommunications industry were found to rely on a tripod, based on effectiveness, transparency, and accountability that are embedded within the ambit of managerial and operational practices, such as focusing and reducing digital illiteracy, targeting poor communities, helping the young in structuring start-ups.

This article contributes to the MADM research stream by proposing a novel use of the BWM technique based on GST to promote sustainable entrepreneurship CSFs in Pakistani telecommunications firms.

This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.

To avoid sub-optimization in wheat storage centers, one of the most strategic facilities, it is necessary to review and relocate them to be optimized regularly. The present study aims to propose an integrated method using geographic information systems (GISs) and an appropriate weighting algorithm for the relocation of wheat storage facilities.

To achieve the goal mentioned above, sustainability pillars in facility location and relocation are initially developed; afterward, a set of suitable criteria are obtained from various scientific resources. Then, the weight of each sustainable development pillar and its corresponding sub-criteria were identified through utilizing the best–worst method (BWM). By applying the obtained weights in the ArcGIS software package, various geographical layers were designed, and land-use planning, logistics planning and sustainable logistics planning are carried out in the regions. The regions are ranked based on the scores obtained in the processes, and the best regions are selected for sustainable relocation problem.

A case study including 430 regions (counties) in Iran is conducted to evaluate the efficiency of the suggested approach. The study results indicate that Iran possesses a superior state for establishing wheat storage centers in terms of infrastructural and social aspects. Also, it is established that 16% of counties are recognized as sustainable locations for relocating the wheat storage facilities.

There is no most suitable analysis of the wheat storage facilities, as well as their strategic position in the supply chain, and there is a lack of considering sustainability in wheat storage facility location, despite the particular importance of it to the supply chain.

This framework is applied in an Iranian wheat-bread supply chain to find the best sustainable facilities. It is noted that this algorithm can be applied in other strategic facilities by minor and some major changes.

Decision-makers can apply the proposed methodology to find the best relocation sites for wheat storage facilities as the main part of wheat-bread supply chain in order to prevent sub-optimization and improve the efficiency of their supply chain.

The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).

To determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.

The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).

This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.

This study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

Code smells indicate deep software issues. They have been studied by researchers with different perspectives. The need to study code smells was felt from the perspective of software industry. The authors aim to evaluate the code smells on the basis of their scope of impact on widely used open-source software (OSS) projects.

The authors have proposed a methodology to identify and rank the smells in the source code of 16 versions of Apache Tomcat Software. Further, the authors have analyzed the categorized smells by calculating the weight of the smells using constant weights as well as Best Worst Method (BWM). Consequently, the authors have used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the rank of versions using constant weights as well as BWM.

Version 1 of Apache Tomcat has least smell, and version 8 is reported to contain the maximum code smells. Notable differences in both the cases during the trend analysis are reported by the study. The findings also show that increase is observed in the number of code smells with the release of newer versions. This increment is observed till version 8, followed by a subtle marked depreciation in the number of code smells in further releases.

The focus is to analyze smells and rank several versions of Apache Tomcat, one of the most widely used software for code smell study. This study will act as a significant one for the researchers as it prioritizes the versions and will help in narrowing down the options of the software used to study code smell.

This paper aims to assess and prioritize manufacturing companies in the healthcare industry based on critical success factors (CSFs) of their reverse logistics (RL). The research involves seven medical device companies located in the Tehran Province, Iran.

To identify and prioritize companies based on CSFs of RL, the study proposes a three-phase decision-making framework that integrates the Delphi method, the best-worst method (BWM) and the Additive Ratio Assessment (ARAS) method with Z-numbers. The weights required for this method are obtained by a variant of the BWM based on Z-numbers, denoted as Z-numbers Best-Worst Method, or ZBWM. Since decision-makers face an uncertain environment, Z-numbers, which are a kind of fuzzy numbers, are applied.

First, after customizing CSFs by the Delphi method and obtaining 15 CSFs of RL, these are ranked by the hybrid BWM-ARAS method with Z-numbers. Results reveal which company appears to perform best with respect to their RL implementations. Based on this result, healthcare device companies should choose the highest priority company based on the selected RL CSFs and results from using the BWM-ARAS method with Z-numbers.

The contribution of this paper is using a hybrid ARAS-BWM method based on Z-numbers. Each of these methods has some merits compared to other similar methods. The combination of these methods contributes a new approach for prioritizing companies based on RL CSFs with high accuracy and reliability.

With increasing pressure from the government and private sectors to be more environmentally and socially responsible, sustainable supplier selection has gained enormous currency in recent times. Particularly, in the case of the construction industry, owing to a large amount of industrial wastage generated and extreme workplace conditions, it is even more important to devise strategies to mitigate the harmful consequences. The most crucial step in this regard is the selection of sustainable suppliers that acquire a pivotal position in the supply chain ecosystem. This study aims to identify indicators for three criteria such as economic, environmental and social, and prioritize them according to their level of significance for sustainable supplier selection in the Indian construction industry.

In this study, the best-worst method (BWM) is presented for sustainable supplier selection in Indian construction organizations. Total of 27 indicators was identified for the three criteria of the triple bottom line (TBL) approach namely economic, environmental and social. Using BWM, the most important criterion was found and subsequently, all the indicators under each criterion were ranked in order of importance.

The analysis revealed that the environmental criterion was the most important criteria in the sustainable supplier selection followed by the economic criteria. The indicators that were the most influential in the effective selection process were “Usage of recyclable raw materials for production”, “Adoption of clean and green technologies”, “Waste management” and “Periodical environmental audits” under the environmental criteria; “Efficiency”, “Cost” and “Flexibility and Scalability” from the economic criteria; and “Safety programs” and “Information disclosure” in the social criteria.

The study provides a reference framework for the selection of sustainable suppliers in construction organizations. The findings can also be used for the assessment of suppliers' performance in the supply chains.

The novelty of this work lies in its attempt to model the performance of suppliers in the Indian construction supply chains.

Today, the high cost of e-learning systems’ implementation and the difficulty of managing the infrastructures motivate educational institutions toward application of cloud-based e-learning systems. This new system should be aligned with the academics’ aims and pedagogical principles to be beneficial for learners and instructors. Therefore, the vendor selection of learning systems is one of the most important processes to migrate toward cloud-based e-learning. The purpose of this paper is to develop a new framework to facilitate the vendor selection of cloud-based e-learning systems in the cloud market.

To identify the initial criteria as to the vendor selection of cloud-based e-learning services, a literature review is done. To enrich the initial criteria, a focus group of experts is investigated, and the framework developed; then, a survey analysis is conducted to validate the proposed framework. The extracted criteria and sub-criteria are weighted and prioritized using best-worst method (BWM).

The results indicate that the main dimensions of vendor selection framework as regards cloud-based e-learning systems are managerial, technological and pedagogical factors. The rank orders and weights of the mentioned aspects and their sub-criteria are calculated using the BWM.

The proposed framework helps managers to get a big picture of requirements as to cloud-based e-learning and more effectively to select appropriate vendors in this initiative. In the vendor selection process, managers must pay attention to technological issues as well as managerial and pedagogical considerations.

Cloud-based e-learning systems are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, there are little studies on how educational institutions and organizations could be able to select appropriate cloud-based e-learning systems. This paper explores the ignored but critically important subject of cloud-based e-learning. The main contribution of this paper is to propose a novel and integrated framework containing the important aspects of vendor selection in cloud-based e-learning services. The proposed framework comprises managerial, technological and pedagogical aspects simultaneously as well as sub-criteria denoting each aspect.

Today, social media is counted as an integral part of marketing strategies, which has led to a paradigm change in this field. As reported, social media marketing has been growing over the recent five years and is predicted to be exponentially growing in the future. However, despite the huge promise and intention to adopt social media marketing strategies by organisations, there remain challenges regarding the successful implementation of these new marketing programmes. Accordingly, marketing managers’ awareness of the success factors of social media marketing is essential to return investment in this area. Due to the little research been accomplished in this field, this paper aims to identify the success factors of social networks’ marketing and to rank the factors by using of interval best-worst method (BWM).

To serve the research aims, an extant literature review is accomplished and a focus group approach is conducted to identify the main success factors and sub-factors. To analyse the focus group discussions, a qualitative content analysis approach is applied. Interval BWM is used to calculate the weights of each identified factor.

In the final framework, six main success criteria, including strategy, process, technology, content, performance evaluation and people are identified, for each sub-criteria are developed. The interval BWM results suggest the content criterion as the most important success factor in developing a social media marketing strategy.

First, this research provides a comprehensive insight into the success factors and best practices of social media marketing. This is the first to draw on the critical factors affecting the success of social media marketing, considering people in the organisation such as top management, employees and customers, strategy, process and performance evaluation focussing on the change management requirements for applying social media marketing and technology as the technical factor of the adoption process, simultaneously. Identifying critical success factors of social media marketing will help marketing managers to avoid falling into the trap of developing social media strategies based on less important areas and ignoring the critical ones. Besides, owing to the limited resources of organisations in implementing social media marketing strategies, prioritising and weighing the success factors will lead to a focus on more important areas.

Whilst the related studies have mostly concentrated on the capabilities and activities required to conduct social media marketing and the few research investigated the critical success factors most concentrated on the customer and the content-related factors, the finding of this research goes beyond that and suggests technical, process and human aspects simultaneously in the implementation process in a holistic view.

The purpose of this study is to identify the risks that exist in halal meat supply chain activities and to carry out a risk assessment using the fuzzy best-worst method (FBWM) along with mitigating risks using the risk mitigation number (RMN).

The method used is to collect several literature reviews related to the halal meat supply chain, which has information relevant to the risks of the meat industry in Indonesia. Then, a focus group discussion was held with several experts who play a role in the meat industry in Indonesia, and 33 identified risks were identified in halal meat supply chain activities. The proposed methodology uses FBWM and RMN in conducting risk assessment and mitigation in the meat industry in Indonesia.

The analysis reveals that priority risk is obtained by using the global weight value on the FBWM, and then risk mitigation is carried out with RMN. Priority mitigation strategies can mitigate some of the risks to the meat industry in Indonesia. The proposed mitigation strategy is designed to be more effective and efficient in preventing risks that can interfere with product halalness in halal meat supply chain activities in the Indonesian meat industry.

The implications of this study highlight the need for collaboration among stakeholders, improved risk assessment methodologies and the expansion of research into other halal supply chains. By addressing these implications, the halal industry can enhance its integrity, consumer confidence and overall contribution to the global market.

This research provides an integrated approach to identifying, analyzing, assessing and mitigating risks to the meat industry in Indonesia.