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The metaverse has become one of the most popular concepts of recent times. Companies and entrepreneurs are fiercely competing to invest and take part in this virtual world. Millions of people globally are anticipated to spend much of their time in the metaverse, regardless of their age, gender, ethnicity, or culture. There are few comprehensive studies on the positive/negative sentiment and effect of the newly identified, but not well defined, metaverse concept that is already fast evolving the digital landscape. Thereby, this study aimed to better understand the metaverse concept, by, firstly, identifying the positive and negative sentiment characteristics and, secondly, by revealing the associations between the metaverse concept and other related concepts. To do so, this study used Natural Language Processing (NLP) methods, specifically Artificial Intelligence (AI) with computational qualitative analysis. The data comprised metaverse articles from 2021 to 2022 published on The Guardian website, a key global mainstream media outlet. To perform thematic content analysis of the qualitative data, this research used the Leximancer software, and the The Natural Language Toolkit (NLTK) from NLP libraries were used to identify sentiment. Further, an AI-based Monkeylearn API was used to make sectoral classifications of the main topics that emerged in the Leximancer analysis. The key themes which emerged in the Leximancer analysis, included "metaverse", "Facebook", "games" and "platforms". The sentiment analysis revealed that of all articles published in the period of 2021-2022 about the metaverse, 61% (n = 622) were positive, 30% (n = 311) were negative, and 9% (n = 90) were neutral. Positive discourses about the metaverse were found to concern key innovations that the virtual experiences brought to users and companies with the support of the technological infrastructure of blockchain, algorithms, NFTs, led by the gaming world. Negative discourse was found to evidence various problems (misinformation, harmful content, algorithms, data, and equipment) that occur during the use of Facebook and other social media platforms, and that individuals encountered harm in the metaverse or that the metaverse produces new problems. Monkeylearn findings revealed "marketing/advertising/PR" role, "Recreational" business, "Science & Technology" events as the key content topics. This study's contribution is twofold: first, it showcases a novel way to triangulate qualitative data analysis of large unstructured textual data as a method in exploring the metaverse concept; and second, the study reveals the characteristics of the metaverse as a concept, as well as its association with other related concepts. Given that the topic of the metaverse is new, this is the first study, to our knowledge, to do both.

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This study set out to better understand virtual consumerism (VC) by applying natural language processing (NLP) methods for sentiment and content analyses. A total of 318 articles related to VC were identified on theguardian.com Web site and analyzed by text mining methodology. A thematic, content analysis using the Leximancer program was performed to explore VC as a concept, and its related concepts and concept associations. For the purposes of "deep-dive insights," further content and sentiment analyses were performed with MonkeyLearn and valence aware dictionary for sentiment reasoning. This triangulation in methodology enabled a comprehensive unstructured qualitative data analysis. The study identified key themes that characterize and define VC. It uncovered that, although there is predominantly positive sentiment toward VC reported in The Guardian online articles, negative sentiment also exists, presenting challenges for the industry to maneuver. The findings reveal that in the context of VC, a virtual experience is also a social experience in a virtual space, which is becoming and evolving. There are certain industries and sectors that are embracing VC, such as marketing, advertising and public relations, software development/IT, art/design, and entertainment, as well as science/technology. Some sectors and industries are experiencing challenges, such as security/law enforcement and medical, and hence display negative sentiment toward VC. Overall, this study presents a working definition of VC, a synopsis of the state of VC, and highlights areas for potential research to further our understanding of this phenomenon. It contributes to an improved understanding of VC for the industry and academia, and provides impetus for future studies focused on the emergent VC-relevant conceptual relationships.

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With the COVID-19 pandemic, supply chains are today confronted with more uncertainties than ever before. In the face of unanticipated disruptions, being resilient and sustainable has been rewarding for supply chains in terms of competitive advantage. However, literature is still far from possessing an encompassing sustainable supply chain framework (SSCF). As a contribution to the extant literature, the present study expounds a prominent concept termed negative entropy and explores its role in the SSCF. To accomplish this goal, the effect of negative entropy on supply chain sustainability is tested. Following the open systems theory and drawing from the collaboration and information management aspects of the negative entropy, co-creation, open innovation and network governance concepts which are considered to be relevant in this context are selected to be the antecedents of negative entropy. The empirical research is conducted on prominent logistics service providers and firms from various sectors with approved research and development departments in Turkey. The obtained data were subjected to covariance-based structural equation modeling analysis via Lisrel program. According to results, negative entropy is found to be a robust element in explaining supply chain sustainability. Furthermore, whereas co-creation and network governance reflected significant effects on negative entropy, surprisingly, open innovation demonstrated no substantial impact. This paper opens up a new front in sustainable supply chain management studies with a notable empirical study introducing negative entropy in the context of open systems theory.

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OBJECTIVE: This paper empirically analyzes the effects of efficiency and structural quality on patient satisfaction in Turkish public hospitals. It also investigates the controversial relationship between hospital efficiency and structural quality for small, medium and large size hospitals in a comparative perspective. METHODS: Data envelopment analysis (DEA) is used to analyze the efficiencies of hospitals. Data concerning 523 public hospitals is obtained from Turkish Ministry of Health. Due to the missing data of some hospitals, the sample of this study is composed of the remaining 348 observations. Multiple regression analysis is used to evaluate the relationship between patient satisfaction as a dependent variable and structural quality, hospital efficiency and institutional factors as independent variables. Furthermore, stepwise process multiple regression analysis is used to analyze the moderator effects of hospital efficiency on the form of the relationship between quality and satisfaction. FINDINGS: The findings indicate that hospital efficiency changes the form of the relationship between structural quality and patient satisfaction as a moderator variable. The trade-off between quality and efficiency is found to vary depending on the hospital size. Negative correlation is found between quality and efficiency for small-size hospitals. However, positive correlation between efficiency and quality is found significant as indicated by Total Quality Management (TQM) approach for large-size hospitals. This study also provides the empirical evidence on the negative relationship between patient satisfaction and hospital size. CONCLUSIONS: The effect of hospital efficiency on patient satisfaction might be improved for inefficient small and medium size hospitals by taking successful large hospitals as role models.

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PURPOSE: This paper aims to compare two diagnostic performance measures, i.e. signal-to-noise ratio (S/N ratio) and partial area under receiver operating characteristic curves (pAUC). It proposes the use of S/N ratio rather than pAUC for establishing optimal cut-off point for diagnostic biomarkers. DESIGN/METHODOLOGY/APPROACH: This paper discusses the properties, uses, advantages and shortcomings of the two performance measures, namely the partial area under receiver operating characteristic curve (pAUC) and Taguchi's signal-to-noise (S/N) ratio. The benefits of S/N ratio have been illustrated in a sample of four biomarkers, each having five cut-off points. The S/N ratio is compared to the pAUC index. The SAS software is employed to calculate pAUC and AUC. FINDINGS: This paper shows that S/N ratio can be used as a measure of diagnostic accuracy. The cut-off point with the highest S/N ratio is the optimal cut-off point for the biomarker. The proposed method has the advantages of being easier, more practical and less costly than that of pAUC. PRACTICAL IMPLICATIONS: This paper includes implications for the development of a more practical, equally powerful and less costly means of measuring clinical accuracy thereby reducing the costs and risks resulting from wrong selection of cut-off point can be decreased. ORIGINALITY/VALUE: This paper supports suggestions in the recent literature to replace pAUC with a new, more meaningful index.

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PURPOSE: This paper aims to apply the Six Sigma methodology to improve workflow by eliminating the causes of failure in the medical imaging department of a private Turkish hospital. DESIGN/METHODOLOGY/APPROACH: Implementation of the design, measure, analyse, improve and control (DMAIC) improvement cycle, workflow chart, fishbone diagrams and Pareto charts were employed, together with rigorous data collection in the department. The identification of root causes of repeat sessions and delays was followed by failure, mode and effect analysis, hazard analysis and decision tree analysis. FINDINGS: The most frequent causes of failure were malfunction of the RIS/PACS system and improper positioning of patients. Subsequent to extensive training of professionals, the sigma level was increased from 3.5 to 4.2. RESEARCH LIMITATIONS/IMPLICATIONS: The data were collected over only four months. PRACTICAL IMPLICATIONS: Six Sigma's data measurement and process improvement methodology is the impetus for health care organisations to rethink their workflow and reduce malpractice. It involves measuring, recording and reporting data on a regular basis. This enables the administration to monitor workflow continuously. SOCIAL IMPLICATIONS: The improvements in the workflow under study, made by determining the failures and potential risks associated with radiologic care, will have a positive impact on society in terms of patient safety. Having eliminated repeat examinations, the risk of being exposed to more radiation was also minimised. ORIGINALITY/VALUE: This paper supports the need to apply Six Sigma and present an evaluation of the process in an imaging department.

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PURPOSE: The aim of this paper is to propose a new, objective and consistent method for the calculation of the diagnostic efficiency in medical applications. DESIGN/METHODOLOGY/APPROACH: In this study, a hybrid method of Taguchi and DEA is proposed. This method reflects the diversity of inputs and outputs by incorporating the stepwise application of sensitivity, specificity, leveling threshold, and efficiency score. A hypothetical case study is given which involves eight readers of X-ray films in clinical radiology. FINDINGS: The selected pairs of sensitivity and specificity yielded two efficient readers. After super efficiency analysis, Reader 6 is found to be the most efficient reader. ORIGINALITY/VALUE: The paper presents a new, objective and consistent method for the calculation of the diagnostic efficiency in medical applications.

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PURPOSE: The aim of this article is to show how Taguchi methods can be applied to health care to improve the quality of medical images. Quality is often integrated with the performance and parameters of the design of medical applications. Many imaging methods can be designed by setting the correct combination of parameters and estimating the contribution of individual quality influencing factors by means of incorporating parameter design and orthogonal arrays. The performance of any imaging equipment can be measured by signal-to-noise ratio. This inherent index can give a sense of how close the performance is to the ideal. DESIGN/METHODOLOGY/APPROACH: Data were collected from a database of 82 diagnostic thoracic computed tomography (CT) scans. Signal-to-noise ratios (S/N) were calculated. FINDINGS: Given the S/N's, the best CT level was found to be level 4. ORIGINALITY/VALUE: To reduce bias resulting from the observer's readings, robust equipments should be designed incorporating Taguchi's experimental design. Further work is needed to establish imaging protocols and new hardware design.

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