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The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors.

Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain.

In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers.

Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias.

The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.

This study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO₂ emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.

Response surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO₂ emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.

The high R² values of 97.38% for CO₂ and 97.28% for service level, along with adjusted R² values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO₂ encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.

This study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

The study seeks to understand the experiences and perceptions of the university youth regarding the democratic atmosphere of a public university located in Turkey. To this end, the objective of this research is twofold: (1) to investigate university students’ level of civic engagement regarding student activism, exercising rights, and interest in politics; and (2) to explore students’ perception of their university environment regarding the promotion of tolerance, respect for ideas, and participation in decision-making. This study was carried out at a public university located in the middle of Turkey. A mixed-method approach was employed, including both qualitative and quantitative data. A total of 332 undergraduate students participated in the quantitative part while 14 undergraduate students were interviewed in the qualitative part of the study. In quantitative data collection, two self-developed scales were used: Civic Engagement Scale and Perceptions of Democratic University Environment Scale. The results of the data analysis indicated that students’ overall civic engagement level was below the average level. In particular, the level of students’ activism was significantly lower than that of students’ interest in politics and exercising rights, respectively. In addition, the level of students’ interest in politics was significantly lower than that of students’ exercising rights. With respect to the students’ perception of democratic university environment, the data revealed that students’ overall perception of the university environment was slightly above average level. Specifically, the students’ perception of university environment regarding respect for ideas was significantly higher than that of university environment regarding participation in decision-making.

Gastronomic festivals are important events to bring people together around food-themed activities. This study aimed at determining the relationships between festival quality, memorable food experience, loyalty, behavioral intention, hedonic well-being, and eudaimonic well-being. In this study, festival quality is the independent variable that affects the memorable food experience, the memorable food experience is the independent variable that affects loyalty, and loyalty is the independent variable that affects behavioral intent. Hedonic well-being and eudaimonic well-being are moderating variables. Behavioral intention is the dependent variable, while memorable food experience and loyalty are both dependent and independent variables.

The population consisted of local tourists visiting Orange Blossom Carnival held in Adana, Turkey. The survey technique and the convenience sampling method were preferred and 545 data were obtained.

The analysis results showed that all dimensions of the memorable food experience are strongly affected by festival quality. Plus, superior service approach and high value perception dimensions of the memorable food experience have significant effects on loyalty. Furthermore, destination loyalty was found to have a strong effect on behavioral intentions. Moreover, higher levels of Hedonic well-being (HWB) and Eudomenic well-being (EWB) were found to increase the effect of loyalty on behavioral intention; accordingly, the moderator roles of HWB and EWB were determined.

This article provides information that the memorable dining experiences of festival visitors who attend the Orange Blossom Carnival in Adana affect the quality of the festival and their intentions to loyalty. In addition, in the study, it was found that the well-being of carnival visitors had a moderating role in the effect of their loyalty on their behavioral intentions. Therefore, this article provides information on how the food experiences and well-being of the visitors at the gastronomy festival will affect which factors.

According to the findings, gastronomy-based events may affect tourists’ experiences, and tourists’ psychological well-being affects loyalty and behavioral intentions. Destination management organizations can learn about gastronomy-based festivals. The results of the study include a number of theoretical and practical findings for destination management organizations, festival managers, policy makers and academics working in the literature.

Along with accounting scandals in the past, academics, researchers, and legislators have focused on fraud. The purpose of this study is to examine postgraduate and doctoral studies, articles, and books about forensic accounting and fraud audit published between the years 2008 and 2018 in Turkey. For this purpose, a total of 96 studies have been examined and 35 of these are master’s theses, 10 of them are PhD theses, 45 of them are articles, and six of them are books. These studies were presented in tables as classified. The studies examined in our research are summarized as year they were published, the author, and the scope of the topic and in terms of results. The conclusions of this study can be summarized as follows: (a) the majority of thesis published about forensic accounting and fraud audit are in 2011 and following years. In addition, most of the theses are focused on forensic accounting review rather than fraud audit. (b) Results in the articles reviewed are in the same direction with theses. (c) There are very few books about fraud audit and forensic accounting. One of them is related to fraud audit, while the rest of them are related to forensic accounting and forensic accounting profession. We suggest extending the scope of the study and making to other countries.

Direct ink writing (DIW) is a robust additive manufacturing technology for the fabrication of fiber-reinforced thermoset composites. However, this technique is currently limited to low design complexity and minimal heights. This study aims to investigate the feasibility of UV-assisted DIW of composites to enhance the green-part strength of the printed inks and resolve the complexity and the height limitations of DIW technology.

The experimental approach involved the preparation of the thermoset inks that are composed of nanoclay, epoxy, photopolymer and glass fiber reinforcement. Composite specimens were fabricated in complex geometries from these ink feedstocks using UV-assisted, hybrid 3D-printing technology. Fabricated specimens were characterized using optical microscopy, three-point bending mechanical tests and numerical simulations.

The introduced hybrid, UV-assisted 3D-printing technology allowed the fabrication of tall and overhanging thermoset composite structures up to 30% glass fiber reinforcement without sagging during or after printing. Glass fiber reinforcement tremendously enhanced the mechanical performance of the composites. UV-curable resin addition led to a reduction in strength (approximately 15%) compared to composites fabricated without UV resin. However, this reduction can be eliminated by increasing the glass fiber content within the hybrid thermoset composite. Numerical simulations indicate that the fiber orientation significantly affects the mechanical performance of the printed composites.

This study showed that the fabrication of high-performing thermoset composites in complex geometries was possible via hybrid DIW technology. This new technology will tremendously expand the application envelope of the additively manufactured thermoset composites and the fabrication of large composite structures with high mechanical performance and dimensional freedom will benefit various engineering fields including the fields of aerospace, automotive and marine engineering.

The main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices.

In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices.

The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort.

In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation.

The finding of this study would help property investors for establishing more effective property management strategies by taking different geographical location conditions into account.

From the government side, knowing future rises, falls and turning points of property prices in different locations can allow the government to monitor the property price changes and control the speculation activities that cause a dramatic change in the market.

There is no previous research paper focusing on neighborhood-based clusters and forecasting house sale price indices in Turkey. At this point, it is the first academic study.