Effective supplier selection in e-commerce requires understanding the complex criteria interdependencies that drive company performance. This study addresses this gap by developing a structural framework using a Multiple Criteria Decision Making (MCDM) approach. We integrate the Delphi method with a panel of eight industry experts with Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis to map these critical relationships. The research suggests a seven-level hierarchy that challenges traditional cost-focused evaluations. Foundational criteria like quality, customer service, and manufacturer status are identified as the system's primary drivers, while financial metrics such as price and cost are suggested to be dynamic outcomes. This study proposes a conceptual framework for managers, suggesting a strategic shift from tactical cost-cutting to prioritizing foundational drivers for sustainable business success.

The development of digital supply chains has significantly changed traditional supplier selection models that focus on static and cost-driven criteria. In addition to price, operational standards, service excellence, and contribution to the platform should be taken into account when evaluating sellers operating on dynamic, performance-oriented e-commerce platforms. This study addresses this gap by developing a comprehensive multi-criteria decision-making (MCDM) framework through a systematic literature review according to PRISMA methodology. Searches conducted in Web of Science, ScienceDirect, IEEE Xplore, Google Scholar, and Taylor & Francis yielded 4630 records from 2014 to 2025, of which 123 were analyzed using bibliometric mapping and thematic synthesis. The findings indicate a progressive diversification of evaluation criteria over time: while quality, delivery, and cost remain foundational, recent studies increasingly address customer service, search volume, and refined financial indicators such as profit and markup rate, pointing toward more multidimensional seller evaluation models. Through thematic synthesis of the indicators identified across the reviewed studies, we propose a four-dimensional framework encompassing financial sustainability, operational efficiency, quality and service standards, and market positioning. The study also discusses the implications of integrating artificial intelligence with multi-criteria and hybrid decision-making approaches for developing adaptive seller ranking systems. By synthesizing fragmented research, our framework offers strategic guidance for platform managers designing seller evaluation and allocation mechanisms.

Small and medium-sized enterprises (SMEs) have a critical importance in terms of environmental impact. However, especially in the context of developing countries, there are limited studies on the evaluation of the green capabilities of SMEs, and available data on a regional basis are quite insufficient. This study aims to fill these gaps and contribute to the literature by assessing SMEs in the manufacturing industry with respect to their green capabilities. The required data were collected from 278 SMEs operating in T & uuml;rkiye using a questionnaire designed based on 11 measurement variables, administered through e-mail and face-to-face interviews. An unsupervised machine learning approach consisting of Principal Component Analysis (PCA) and K-Means clustering algorithm was employed to analyse the data. While PCA was used to reduce 11 measurement variables to 3 key dimensions of enterprise green capability, which are green manufacturing capability, green design capability, and green innovation capability, K-means algorithm was used to divide 278 SMEs into three classes: (1) good, (2) medium, and (3) bad, according to their greenness level. Clustering results and performance were measured and assessed by using two metrics, namely silhouette score and Adjusted Rand Index. The distribution of companies for each cluster was examined according to industry, age, number of employees, and capital structure characteristics. The findings of the study provide empirical support for understanding the greenness levels of SMEs in the manufacturing industry in T & uuml;rkiye and form a basis for comparison with companies in other regions.

This study presents a multi criteria decision making (MCDM)-integrated machine learning approach for accurate and reliable clustering of manufacturing firms according to their eco-innovation capabilities. The approach consists of four main phases: data obtaining and preparation, clustering, calculation of performance indices, and performance evaluation of clustering algorithms via MCDM techniques. The required data were collected from manufacturing companies operating in T & uuml;rkiye using a questionnaire through e-mail and face-to-face interviews. The questionnaire includes 16 measurement variables under three dimensions of eco-innovation capability: product capability, process capability, and organizational capability. For classification of the companies, six clustering algorithms were utilized; K-Means from partitioning-based methods, FCM from fuzzy-based methods, AGNES, SPECTRAL, and BIRCH from hierarchical-based methods, and Gaussian Mixture from distribution-based methods. Subsequently, eight performance indices were calculated to measure the performance of the clustering algorithms. In the last phase of the methodology, three MCDM methods, WASPAS, VIKOR, and TOPSIS, were exploited to determine the best clustering algorithm in a reliable and robust manner according to eight performance indices. At the end of the study, AGNES was revealed as the best clustering algorithm according to all of the MCDM methods, and manufacturing companies were divided into five classes according to their ecoinnovation capabilities. The findings of this study provide empirical support for understanding the ecoinnovation capabilities of manufacturing firms in T & uuml;rkiye and form a basis for comparison with companies in other regions. The results also demonstrate the effectiveness of the proposed MCDM-integrated procedure.

The Fourth Industrial Revolution presents transformative opportunities alongside substantial implementation challenges. Understanding these challenges on a sector-specific basis is crucial for successful Industry 4.0 adoption. This study assesses the managerial challenges faced by companies in the lighting industry during their transition to Industry 4.0. Through a literature review and expert consultations, 16 challenges were identified across five categories. To systematically prioritize these challenges, the Step-wise Weight Assessment Ratio Analysis (SWARA) method was extended with Pythagorean fuzzy numbers, enabling a more precise evaluation of expert opinions. Based on input from 10 industry experts, the findings highlight that the most critical challenge is the “lack of skilled workers for implementation of Industry 4.0” followed by the “lack of employees with a clear vision of Industry 4.0 practices”, and the “lack of awareness about Industry 4.0 technologies. Among the challenge categories, human resources challenges emerged as the most significant, followed by technological challenges. These findings underscore the pivotal role of workforce preparedness in Industry 4.0 adoption and the necessity of targeted managerial interventions, such as workforce training, strategic vision alignment, and technology awareness programs. The results also provide practical insights for policymakers and industry leaders, emphasizing the need for sector-specific strategies to facilitate a smoother digital transformation. By employing the Pythagorean Fuzzy SWARA method, this study enhances decision-making precision and provides a practical framework for prioritizing Industry 4.0 challenges. As one of the first studies focusing on the lighting sector, this research fills a critical gap in the literature and serves as a foundation for future sector-specific Industry 4.0 studies.

İşletmeler iş süreçleri gereği tüm faaliyetlerinde bazı stratejik kararlar vermek durumundadır. En önemli kararlardan biri de işletmenin sahip olduğu tedarikçilerle ilişkisine devam etme veya sonlandırma kararıdır. Tedarikçiler ile kurulan iş birliklerinin ürünün kalitesi üzerinde, satın alma maliyetinin düşürülmesinde, üretimde esnekliğin arttırılmasında, müşteri memnuniyetinin artışında olumlu katkılar yaptığı görülmektedir. İlişkinin sürdürülebilirliğine karar vermek için birçok kriter mevcut olup işletmenin bu kriterleri doğru analiz etmesi uygun stratejik kararların verilmesi açısından önem arz etmektedir. Bu amaçla tedarikçilerin değerlendirilmesi stratejik kararların verilmesini kolaylaştıracaktır. Değerlendirmeye dahil olacak tedarikçilerin belirlenmesi için firmada öncelikle stok kalemleri üzerinde ABC analizi gerçekleştirilmiş ve A grubuna giren stok kalemleri belirlenmiştir. Devamında A grubu stok kalemlerini tedarik eden tedarikçilere de ABC analizi uygulanarak A grubuna giren tedarikçiler belirlenmiş ve bu tedarikçiler değerlendirmeye tabi tutulmuştur. Öncelikle tedarikçilerin operasyonel performansları değerlendirilmiş, devamında işletmeler için önem arz eden sürdürülebilir tedarikçiler belirlenmiştir. Kriter ve faktör ağırlıklarının belirlenmesinde CRITIC yöntemi, tedarikçilerin değerlendirilmesinde TOPSIS Yöntemi kullanılmıştır.

The aim of this study to define and analyze the difficulties for the adoption of I4.0 in the white goods sector in Turkey. Firstly, difficulties of I4.0 adoption are determined and finalized by examining the literature. Then a hybrid MCDM approach consisting of the Fuzzy Decision Making Trial and Evaluation Laboratory (F-DEMATEL) method, the Maximum Mean De-Entropy (MMDE) technique, the interpretive structural modelling (ISM) technique and MICMAC analysis is used to evaluate relationships and interactions between difficulties. Findings show that “Lack of technological infrastructure and networks powered by the internet” and “Lack of integration of data and technology platforms” are the difficulties that have the strongest driving power, and they have direct or indirect effects on other difficulties. These are the main difficulties for Industry 4.0 adoption in white good production industry. On the other hand, “Ineffective communication and cooperation amongst supply chain participants” is found out the most affected difficulty directly or indirectly from other difficulties.

Following the industrial revolution, factors such as accelerated industrial processes, increased population, urbanization, and deforestation contributed to a variety of environmental issues, particularly global warming. This study proposes a new Data Envelopment Analysis model to assess the environmental activities of developed and developing countries that have signed the Kyoto Protocol. Using the proposed model, Decision Making Units (DMUs) with similar performance were clustered by creating efficient boundaries, and the efficiency improvement of the effective DMUs in each cluster on each other, as well as the efficiency improvement on the ineffective DMUs in the entire data set, were examined. The final efficiency score of the proposed model is calculated by adding the initial efficiency score, which expresses the initial position of the DMUs, and the efficiency improvement over all DMUs. Finally, the full ranking is obtained by sorting the clusters from smallest to largest, as well as the final efficiency scores of the DMUs within each cluster, from largest to smallest. During the study's implementation phase, the environmental effectiveness of 38 countries that signed the Kyoto Protocol and whose data can be accessed based on determined input/output was assessed using the proposed model. The undesirable input/output problem encountered in environmental effectiveness evaluation was addressed by determining a monotonically decreasing function. The ranking obtained with the proposed model was compared to three different models found in the literature. The proposed model defined seven clusters, and Sweden, Switzerland, Iceland and Norway were in the 1st cluster as the DMUs with the highest efficiency scores.

This study suggests the use of unsupervised and supervised machine learning algorithms to categorize companies according to their innovation capabilities. Companies are categorized into three groups: good, satisfactory, and unsatisfactory, in order to create a thorough and reliable assessment procedure. In this study, unsupervised and supervised machine learning methods are used to solve an innovation capability evaluation problem. Data is provided via a survey which is performed in manufacturing industry in Turkiye Firstly, dimensions of innovation capability were determined Principal Component Analysis (PCA). Then data labels were determined by k-means clustering algorithm which is an unsupervised learning technique. A model is first trained using data provided via questionnaire survey, and it is then tested using fresh, unused data. The model is trained using classification algorithms including KNN, GaussianNB, RandomForest, Gradient Boosting, AdaBoost, DesisionTree, XGBOOST and LightGBMC, MLPC, and SVMC and its performance is evaluated against test data. Each classification techniques are evaluated using the performance metrics. With the highest accuracy rate of 93% and lowest MAE, MSE and RMSE values, The LightGBMC and SVMC methods were found the most efficient supervised learning method for innovation capability evaluation.

In this study, we aimed that influences of eco-determinants and innovation strategies on sustainability performance of SMEs were evaluated in line. Data were collected through a questionnaire-based survey across 152 SMEs from manufacturing industry in Kocaeli known as one of the most industrialized city of Turkey. The empirical model was analyzed by PLS-SEM using Smart-PLS program to verify the hypothetical relationships of the study. In accordance with the derived model, the effects of EI determinants and EI strategies on the sustainable performance (environmental, economic, and social) of SMEs were examined by making use of the second-order PLS-SEM. The results of this study indicate that eco-determinants (technology, market, regulations, and firm-specific factors) of companies had a positive and direct impact on sustainability performance of SMEs, and eco-innovation strategy has a direct impact on sustainability performance and mediating impact between eco-innovation determinants and sustainability performance

Industry 4.0 will radically affect Total Quality Management (TQM) practices and principles, which are still widely used in businesses and one of the management practices. Some TQM principles, with the developed technology and systems, have become “natural management principles” whose application in the organization is fully guaranteed and the system is made to exist on its own. While some principles become easier to implement, some TQM principles and practices may need to be repositioned in terms of business management. For this reason, in this study, after a comprehensive TQM and Industry 4.0 literature research and analysis, the situation and interaction of TQM principles in enterprises that implement Industry 4.0 (I4.0), both a modeling and a survey study were made within the framework of ISO 9001:2015 TQM Principles to determine the changes in TQM principles in the I4.0 environment. Statistical analyses were performed determining relationship among TQM principles and direction of these relationships. In addition, the fuzzy DEMATEL method was applied to determine the interactions of TQM principles in the I4.0 environment. As a result, evidence-based decision making, continuous improvement and process management principles are applied much more easily with I4.0, the principles of leadership and employee participation continue to develop.

The sector of renewable electricity generation in Asia region countries was comprehensively analyzed. A comparative study has been conducted with bar chart. A hierarchical clustering analysis was performed for the various renewable electricity generation types using SPSS software. The countries were clustered into 7 clusters based on the total renewable electricity generation. In hydroelectricity sector Asia Region countries were clustered into six groups in which India and Turkey are taking the lead. In Wind electricity sector four clusters were found in which India and Turkey lead. Countries were analyzed by dividing them into six clusters based on solar energy production. India and Japan is isolated in the first cluster followed by Turkey, while Arab Gulf countries were in the sixth cluster. In biomass electricity sector five clusters were founds in which India and japan in the first cluster. In geothermal electricity, Indonesia is isolated in the first. Four clusters were obtained in the wave-tide electricity sector in which Japan, South Korea and Turkey are isolated in the first cluster. Multiple Regression Model was created to estimate the amount of carbon dioxide emissions in Asia Region countries using the renewable electricity generation as an independent variable. However, the value of the intercept and the hydroelectricity factor were found 51.888 and 3.116 respectively. Moreover, a strong correlation between the GDP and the hydro, solar, wind, and biomass electricity generation was found.

Sanayi devrimi sonrasında hızlanan endüstriyel süreçler, artan nüfus, kentleşme ve ormansızlaşma gibi etkenler atmosferdeki sera gazı miktarını arttırarak başta küresel ısınma olmak üzere çeşitli çevresel sorunlara yol açmıştır. Bu sorunlara uluslararası platformlarda çözüm arayışları başlamış ve ülkeler arasında yaptırım gücüne sahip ilk belge olan Kyoto Protokolü imzalanmıştır. Çalışmada, Kyoto Protokolü’nü imzalamış gelişmiş ve gelişmekte olan ülkelerin çevresel etkinliklerinin değerlendirilmesi için yeni bir Veri Zarflama Analizi (VZA) modeli önerilmiştir. Önerilen model ile benzer performansa sahip Karar Verici Birim (KVB)’ler etkin sınırlar oluşturularak kümelenmiş ve her kümede yer alan etkin KVB’lerin birbirleri üzerindeki etkinlik iyileşmesi ve tüm veri setinde yer alan etkin olmayan KVB’ler üzerindeki etkinlik iyileşmesi analiz edilmiştir. Önerilen modelin nihai etkinlik skoru KVB’lerin başlangıç pozisyonunu ifade eden başlangıç etkinlik skoru ile bütün KVB’ler üzerinde etkinlik iyileşmesinin toplamı ile elde edilmektedir. Son olarak kümelerin küçükten büyüğe sıralanması ve küme içerisinde yer alan KVB’lerin nihai etkinlik skorlarının büyükten küçüğe sıralanması ile tam sıralama elde edilmektedir. Çalışmanın uygulama aşamasında ise Kyoto Protokolünü imzalamış ve belirlenen girdi/çıktı bazında verilerine ulaşılabilen 38 ülkenin çevresel etkinliği önerilen model ile değerlendirilmiştir. Çevresel etkinlik değerlendirmesinde karşılaşılan istenmeyen girdi/çıktı sorunu için monoton azalan fonksiyon belirlenerek veri dönüşümü yapılmıştır. Önerilen model ile elde edilen sıralama literatürde var olan üç farklı model ile karşılaştırılmıştır.

Beyaz eşya sektöründe Türkiye’nin özellikle stratejik bir konumda olması, yerli ve yabancı birçok üretici için cazip hale gelmiştir. Önemli bir üretim ve ihracat pazarı niteliği taşıyan bu sektöre hizmet veren büyük çaplı firmalar kadar ana üreticileri besleyen KOBİ’lerin de üretime sağladıkları katkı dolayısıyla ana sanayi ile paralel olarak gelişmeleri kaçınılmazdır. Özellikle teknolojik gelişmelerden derinden etkilenen beyaz eşya sektöründe faaliyet gösteren firmaların Endüstri 4.0 dijital dönüşüm yolculuğunda başlangıç noktalarını belirlemeleri başarılı bir dönüşüm için atılması gereken ilk adımdır. Bu çalışmada, firmaların dijital dönüşüm yetkinliklerinin ölçülmesi amacı ile bir sistem tasarlanmıştır. Dört ana kriter, yirmi üç alt kriterden oluşan değerlendirme yaklaşımı ile beyaz eşya sektöründe faaliyet gösteren yedi firma belirlenen kriterlere göre puanlanarak değerlendirilmiştir. Çok kriterli karar verme yöntemlerinden MACHBETH ve EDAS kullanılarak firmaların dijital dönüşüm yetkinliklerinin ölçülmesi hedeflenmiştir. Sonuçta firmaların dijital dönüşümün daha başında oldukları belirlenmiştir.

Industry 4.0 has been a revolutionary wave of change over the past decade that has had a variety of impacts on companies. Institutional adoption of the upcoming changes is one of the most important factors in this scenario. In this context, in order for companies to adapt to this wave of change, it is extremely important that they correctly evaluate their current situation in Industry 4.0 and accordingly identify their needs and deficiencies and determine the areas they need to develop correctly. The Turkish white goods sector is one of the leading sectors of the Turkish economy on a global scale with its international competitive power and brands, creating high added value in production and exports, providing positive foreign trade balance. Due to this feature, it is one of the sectors where Industry 4.0 and digitalization will be applied successfully. The first step for companies in the white goods sector, which is deeply affected by technological developments, to be successful in digital transformation is to evaluate their situation and thus determine their starting points. In this study, a measurement and evaluation system consisting of four main criteria and twenty-three sub -criteria was designed with the aim of measuring the digital transformation competencies of companies. Seven companies operating in the white goods sector were scored according to the determined criteria, and the digital transformation competencies of the companies were evaluated with the help of MACHBETH and EDAS methods, which are multi-criteria decision-making methods. As a result, it has been determined that companies are at the very beginning of digital transformation.

Konjoint Analizi, tüketicilerin hizmet veya ürünün çeşitli özelliklerinin kendileri için ne ölçüde önemli olduklarını ölçen pazar araştırması yaklaşımı olarak bilinmektedir. Çalışmanın konusu Kocaeli / Gebze ilçesinde konut seçiminde kişilerin tercihlerini etkileyen faktörlerin konjoint analiz yöntemiyle incelenmesidir. Amacı ise kişinin konut seçiminde etkili olduğu düşünülen faktörlerin önem derecesinin belirlenmesini ve belirlenen düzeylerin yaş, cinsiyet, medeni durum, aile sayısı, meslek durumuna gore nasıl değişiklik gösterdiğinin incelenmesidir. Konut seçiminde önemli olan fiyat, net m2, oda sayısı, bina yaşı, bulunduğu kat faktörlerinden oluşan kartlar hazırlanmıştır. Bu kartlar analizi kolaylaştırmak amacıyla orthogonal tasarım tekniği ile SPSS paket programında oluşturulmuştur. Anket yöntemi ile 24 kişinin yanıtları değerlendirilmiştir. Çalışmanın sonucunda dairenin bulunduğu katın 3, fiyatının 240.000 tl, net m2 ‘nin 93, oda sayısının 3+1, bina yaşının 0-1 aralığında tercih edildiği ortaya çıkmıştır. Konjoint modeli sonucu 17,484 birim fayda sağlayacağı ortaya çıkmıştır.

E-commerce, which is defined as making commercial transactions in an electronic environment, is becoming widespread with the increase of the use of internet and mobile devices. COVID-19 has greatly changed the consumption habits of individuals, increasing interest in electronic sales channels. Regardless of their size, most companies and retailers are currently looking for ways to engage their customers through electronic channels due to the effect of COVID-19. In this process, the rapidly increasing trend of electronic commerce raises an important question for companies, "In which e-marketplace should we sell?” In this study, five criteria that are important in the choice of the right e-marketplace were determined and eight online alternative e-marketplaces were evaluated. The study was carried out using the neutrosophic fuzzy AHP and EDAS methods, which are multi-criteria decision making techniques, and a framework was established for choosing the right e-commerce marketplace for sellers.

Theory of constraints (TOC) is an approach to production planning and control by focusing on the constraints of an organization to increase throughput by effectively managing constraints. TOC approach has been applied in many sectors, and efficient results have been taken. One of the application areas of TOC is product mix decisions. Product mix decisions are important for multi-product manufacturing systems because they affect the performance measures of the companies. This study aims to present how TOC is applied to determine the optimum product mix in a multiple- constraint environment in the textile industry. To achieve this, we first select three basic products of a textile company and examine the production processes of these products from TOC perspective. Next, we perform a bottleneck process and identify three bottlenecks for the problem. Then, based on our bottleneck process results, we generate three scenarios. Upon assessing these scenarios, we determine the most appropriate product mix by implementing the TOC approach. Finally, we employ a goal programming approach to solve the product mix problem and compare its results with those obtained by the TOC.