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The present study aims to provide a practical and robust assessment technique for assessing countries' investability in global supply chains to practitioners. Thus, the proposed approach can help decision-makers evaluate and select appropriate countries in the expansion process of the global supply chains and reduce risks concerning country (market) selection.

The present study proposes a novel decision-making approach, namely the REF-Sort technique. The proposed approach has many valuable contributions to the literature. First, it has an efficient basic algorithm and can be applied to solve highly complicated decision-making problems without requiring advanced mathematical knowledge. Besides, some characteristics differentiate REF-Sort apart from other techniques. REF-Sort employs the value or value range that reflects the most typical characteristic of the relevant class in assignment processes. The reference values in REF-Sort and center profiles are similar in this regard. On the other hand, class references can be defined as ranges in REF-Sort. Secondary values, called successors, can also be employed to assign a value to the appropriate class. REF-Sort can also determine the reference and successor values/ranges independently of the decision matrix. In addition, the proposed model is a maximally stable and consistent decision-making tool, as it is resistant to the rank reversal problem.

The current papers' findings indicate that countries have different features concerning investment. Hence, the current paper pointed out that only 22% of the 95 countries are investable, whereas 19% are risky. Thus, decision-makers should make detailed evaluations using robust, powerful, and practical decision-making tools to make more reasonable and logical decisions concerning country selection.

The current paper proposes a novel decision-making approach to evaluate. According to the authors' information, the proposed model has been applied to evaluate investable countries for the global supply chains for the first time.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

Nowadays, companies have required new alternatives and strategies to handle environmental sustainability difficulties, primarily as ecological and social awareness has grown. In this context, the aim is to determine the green transportation indicators in companies with corporate identity and logistics activities at the international level in Giresun, Ordu, Gümüshane, Artvin, Rize, and Trabzon in the Eastern Black Sea Region in Turkey. At the same time, the study contributes to providing an effective and applicable solution to decision-making problems involving the assessment of green transportation indicators and smart network strategies in the logistics sector, which is a critical sector for countries. The purpose of this paper is to address these issues.

This study aims to propose a model for the selection of smart network strategy and to determine the criteria weights used in green transportation indicators, and establish an ideal smart network strategy. In achieving the outlined goals of the study, the authors believe that the model proposed in the study will draw the focus to green logistics which will aid the environmental, economic and social efforts of businesses and governments through the provision of efficient use of scarce resources, which will, in turn, ensure that we leave a sustainable environment for future generations and businesses enjoy a competitive advantage. At the same time, different smart network strategies and green transportation indicators in companies show the success rate of social, economic and environmental indicators in green logistics practices. In addition to providing innovative, reliable and sustainable transportation systems, smart network strategies are critical for businesses to create cost advantages. Through the green transportation indicators and smart network strategies selection model outlined in this study, it is clear that the contribution will not only be limited to businesses, as the society and governments will also benefit from the important indicators on sustainability, as well as the protection of the environment and nature.

According to the findings, “economic indicators” is the essential green transportation indicator in logistics companies with a corporate identity and worldwide transportation operations. Besides, the “mixed access model strategy” is the most appropriate smart network strategy in logistics firms with corporate identity and worldwide transportation activities. Currently, it is possible to assume that logistics organizations prefer to profit from all smart network strategies in terms of cost optimization and competitiveness rather than from just one. The study, on the other hand, which is a road map that will help sustainability practices in the logistics sector due to green transportation, also examines the similarities and differences of green transportation practices in companies in the relevant sector and to what extent they can be reflected. As a result, the study provides a practical road map for selecting green transport indicators and a smart network strategy process for the logistics industry.

This study examined logistics companies with a corporate identity and international transportation activities in provinces in the Eastern Black Sea Region such as Ordu, Giresun, Trabzon, Rize, Artvin and Gümüshane. Novel picture fuzzy level based weight assessment (PF-LBWA) and picture fuzzy combined compromise solution (PF-CoCoSo) methods are developed to solve the decision-making problem.

Due to the current pandemic, the importance of logistics functions and decisions is well understood both at the level of companies and users. Logistics systems and related decisions are of vital importance in making supply chains effective, efficient and without disruption. Logistic pressure factors may emerge at different points along the logistics process, and given the role of logistics decisions as one of the important indicators of competitiveness, the determination of the logistics pressures that are likely to increase the costs of business, and their causative factors are a vital aspect of the logistics decision-making process. The study aims to provide assistance in the selection of the most ideal logistics decision by ranking the pressure factors affecting the logistics system, especially during the pandemic period for logistics enterprises operating in Ordu and Giresun provinces and which have a corporate identity.

In this study, it is aimed to make the most ideal logistics decision selection by ranking the pressure factors affecting the logistics system, especially during the pandemic period for the logistics enterprises operating in Ordu and Giresun provinces and having a corporate identity. For that purpose interval-valued Pythagorean fuzzy (IVPF)–analytic hierarchy process (AHP) based combinative distance-based assessment (CODAS) methodology was used. Additionally sensitivity and comparison analysis were discussed.

Competitive pressure was found as the most important pressure factor affecting the logistics system during the pandemic period. Change in regulatory rules was the pressure factor found to have the least effect on the logistics system. Using the weights of logistics pressure factors, “Operational Decisions” was found to be the most ideal logistics decision selection.

The findings provide support for the evaluation of logistical pressures and decision options by presenting a decision model capable of processing ambiguous information. During a pandemic or similar period, the study assists decision makers in determining a new route. The findings will also call business managers' attention to logistical pressure factors and lead them toward more realistic and feasible practices in the logistics decision-making process.

This study provided an effective and applicable solution to a decision-making problem in the logistics sector including logistics pressure factors and the selection of logistics decisions. In this context, a methodology was presented that will allow businesses to self-evaluate their own logistics pressure factors and the selection of optimal solutions.

Sustainable supply chain management (SSCM) practices and policies are necessary for businesses that seek to take part in international markets and ensure any form of competitiveness. Over time, and especially in the recent past, researchers, governments, and other policymakers have made use of broad and systematic approaches and come to appreciate the value-enhancing activities of sustainability.

Businesses have embraced the integration of sustainable policies and practices within the supply chain as a critical step in ensuring the efficiency of their operations. It is clear in previous studies and operational programs of enterprises that SSCM practices accord businesses certain benefits including improving their environmental, social, and economic performance, and increasing their ecological awareness by way of influencing performance elements within supply networks in enterprises. The study examines the factors influencing performance and theories of SSCM using a neutrosophic method in the textile industry.

SSCM performance is thus of great importance in ensuring business success and competitiveness, realizing customer satisfaction, and leaving the environment in a desirable state for future generations. Performance management, by assisting in the decision-making by managers and ensuring an adequate level of internal interaction, is an integral part of assimilating sustainability management into businesses. SSCM theories also have a strong impact on the determination of the sources of competitive advantage through effective utilization of business capabilities to solve environmental and social challenges that may affect business performance.

In line with the benefits highlighted, this study seeks to evaluate and select the factors affecting SSCM performance and theory in textile enterprises with corporate identity in Ordu and Giresun provinces following a neutrosophic approach. To this end, the elements obtained from the literature review are evaluated using the MULTIMOORA-mGqNN method.

The purpose of this paper is to develop a scale to measure the level of cyber human values based on the behaviors of social media users in cyberspace.

The paper is based on a scale-development process by following a systematic approach. First, the current scales were examined; following this, focus group interviews were held; next, an item pool was formed; and the validity and reliability of the items were tested. The validity and reliability studies of the scale were conducted with 1,495 social media users. An application on Facebook was used to collect the data.

As a result of the validity study, 25 items under five factors were obtained. These factors were being peaceful, truth, solidarity, respect and tolerance. The items obtained were capable of discriminating the individuals in terms of the features to be measured by the scale. In addition, the scale was confirmed to measure correctly the structure obtained in line with the fit indices. The internal consistency coefficient of the scale was 0.90 and split-half reliability coefficient was 0.88.

The sample has several limitations. Most of the participants were male and the data were collected on social media. Thus, to enhance the validity and reliability of the scale, further in-depth qualitative and cross-cultural studies should be examined.

This study could provide convenience for practitioners about how to diagnose the cyber human values of the internet users in cyberspace.

It was concluded that this scale was valid, reliable and beneficial to measure social media users’ levels of cyber human values.

The advent of social media brought a new perspective for guerrilla marketing since it allows ads to reach more people through the internet. The purpose of this paper is to investigate the influence of guerrilla marketing in social media on brand image.

A conceptual model was developed based on the information acceptance model (IACM). The research model was validated through structural equation modelling based on the surveys of 385 university students.

The results support the proposed model and confirm that guerrilla marketing in social media has a positive effect on both functional and symbolic brand image.

This study was conducted with university students. This sample was deemed appropriate since the study had to be conducted with people who use social media. However, although the age group of university students constitutes the majority of social media users, they may not fully represent the whole population. Also, this study showed four guerrilla marketing examples to participants before they commenced filling in the questionnaire. Although the authors selected the most generic guerrilla advertisements during the pilot tests and eliminated the ones which were difficult to understand, this can still be considered as limitations of the study.

This study has both theoretical and managerial implications. First, most of the guerrilla marketing studies focus on consumers and neglect possible impacts on brands. In order to fulfil this gap in the literature, this study investigates the influence of guerrilla marketing in brand image. Besides, this study contributes to IACM by expanding its scope through testing its determinants on “brand image”. It proves that IACM is valid for use in different contexts. On the managerial side, this study provides marketers with a frame of reference to understand the information adoption process of guerrilla marketing on social media.

Current studies regarding the influence of guerrilla marketing mostly focus on consumers, where the possible impacts on brands have been relatively neglected. This study attempts to fill this gap by focussing on the brand image.