Intricate Aspects Research Articles

Impact of dental fluorosis on the oral health-related quality of life: a systematic review.

The aim of this systematic review is to investigate the literature to assess the effect of dental fluorosis on the Oral Health-Related Quality of Life (OHRQoL). Included studies were epidemiological investigations, clinical trials, or human studies that directly compared the impact on OHRQoL between individuals with dental fluorosis and those without, utilizing valid OHRQoL assessment tools. The searches were conducted up to March 2024 in databases, including PubMed, Scielo, LILACS, Scopus, Web of Science, Embase, and the Virtual Health Library. Additionally, gray literature sources such as Google Scholar, OpenGrey, Ibict/BDTD, and ProQuest were searched. Reference lists were also manually reviewed for potential eligible studies. The risk of bias in cohort studies was assessed using the Newcastle-Ottawa Scale (NOS), while its modified version was employed for cross-sectional studies. A narrative synthesis of the results was conducted. 16 studies were included in this Systematic Review. In total, 16,314 participants, aged 8 to 18, were assessed across the studies. Most of the included studies were cross-sectional, except for one cohort study. In assessing OHRQoL across different age groups and severities of dental fluorosis, the impact varied. A negative association between dental fluorosis and OHRQoL was found in 9 of the included studies, a positive association was found in 1 study, and no significative association was identified in 6 studies. Limitations in the included studies primarily pertained to cross-sectional designs, with significant issues such as the absence of sample size calculations, lack of non-response rate reporting, limited consideration of confounding factors, and substantial heterogeneity stemming from diverse diagnostic methods and assessment tools. The findings of the systematic review demonstrate a complex and varied relationship between dental fluorosis and OHRQoL in children and adolescents. These conclusions may have limitations in scientific evidence. The findings emphasize the need for further investigations to explore the intricate aspects of this relationship and identify contributing factors to varying outcomes. Dental caries preventive fluoridated measures might be indicated even if there would be a risk for mild dental fluorosis as the fluoridated public water. This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under number: CRD42020186552.

Advancements in 3D Skin Bioprinting: Processes, Bioinks, Applications and Sensor Integration

Abstract This comprehensive review explores the multifaceted landscape of skin bioprinting, revolutionizing dermatological research. The applications of skin bioprinting utilizing techniques like extrusion-, droplet-, laser- and light-based methods, with specialized bioinks for skin biofabrication have been critically reviewed along with the intricate aspects of bioprinting hair follicles, sweat glands, and achieving skin pigmentation. Challenges remain with the need for vascularization, safety concerns, and the integration of automated processes for effective clinical translation. The review further investigates the incorporation of biosensor technologies, emphasizing their role in monitoring and enhancing the wound healing process. While highlighting the remarkable progress in the field, critical limitations and concerns are critically examined to provide a balanced perspective. This synthesis aims to guide scientists, engineers, and healthcare providers, fostering a deeper understanding of the current state, challenges, and future directions in skin bioprinting for transformative applications in tissue engineering and regenerative medicine.

Collaborative online international learning to enhance intercultural effectiveness in social work education

ABSTRACT Using the Collaborative Online International Learning (COIL) model, social work students based in three different countries (U.S. China, U.K.) were invited to participate in intercultural discussions about the development of the welfare systems in these countries. The discussion forum and COIL activities were conducted using synchronous virtual meetings via Zoom which were referred to as ‘virtual cocktail parties.’ Fifteen synchronous sessions were offered, and a total of 91 students participated in the COIL classes. This endeavor aims to enhance students’ utilization of a peer learning format for intercultural effectiveness, thereby fostering a deeper and more comprehensive understanding of the intricate aspects of social work practice. Through discussions with their peers in different countries, social work students recognized how, interculturally, they were impacted by social problems and professional situations. Learners also reflected, dialogued, and shared in contexts marked by cultural diversity. COIL appears to encourage students to develop a sense of global citizenship, fostering a deeper understanding of their roles and responsibilities in an interconnected world.

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis

Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.

Massive fields in AdS from constructive holography

Collective field theory offers a constructive framework for exploring the AdS/CFT duality. In this article, we focus on constructing rotations within the light-front quantized collective field theory for the full set of spatial coordinates in the dual bulk AdS spacetime. Two intricate aspects require attention: how rotations involving the emergent holographic coordinate are implemented, and how rotations that involve the spatial coordinates participating in the construction of the light-cone coordinates X± are realized. Our construction is in agreement with Metsaev’s construction directly in the gravity theory. Additionally, we derive the eigenfunctions of the AdS mass operator, which dictate the GKPW rule for the emergent higher-dimensional theory.

Analyzing the consumer journey for hiking of the John Muir Trail

Most research on the consumer journey has focused on product purchases. This research, however, examines the consumer journey for experiences – in this case a long-distance hike. Two studies examine how people become aware of, learn more about, prepare for, evaluate, and reflect on a 200-mile hike on the John Muir Trail in California. The first study interviewed hikers while on the trail. The second study analyzed online discussion groups dedicated to the trail. These studies reveal that the idea of a long-distance hike often arises from a mention by others where people add the idea to a mental “bucket list”. For those with whom the idea resonates, they may quickly decide to hike the trail, though their motivation may not always be clear, and the trip may not occur for quite some time. Getting a permit is frequently a barrier, but online communities often offer advice. Preparation for the hike is typically a high-involvement process that involves purchasing equipment and physical training. The hike itself is a paradox of aesthetic appreciation in the face of physical struggle. Importantly, many hikers later report the journey as an important experience and serve as evangelists for other hikers. The two studies corroborate certain notions about the consumer decision process while calling others into question. For instance, they validate the significance of word-of-mouth recommendations from individuals with similar backgrounds, which plays a pivotal role in raising awareness. Simultaneously, they challenge conventional thinking by demonstrating that the decision-making process is influenced not solely by rational factors but also by emotional elements. Finally, these findings underscore the importance of employing multiple research methods to comprehensively grasp the intricate aspects of the consumer journey, especially the consumption of experiences.

Deep scene understanding with extended text description for human object interaction detection

Human–object interaction (HOI) detection plays a pivotal role in scene understanding, enabling the identification, localization, and behavioral intention prediction of humans and objects within a visual scene. Conventional approaches, such as graph-based networks, have demonstrated effectiveness in capturing spatio-temporal interaction cues. However, relying solely on visual information within these networks limits their ability to comprehensively grasp the intricate aspects of human interactions. To address this shortcoming, we propose a novel deep scene understanding graph network that harnesses the power of extended text descriptions to represent and interpret interactions between humans and objects effectively. Text serves as a rich source of information, directly conveying the nature of interactions within a visual scene. Our model seamlessly integrates text descriptions with visual features extracted from the entire video sequence, enabling it to capture the context and nuances of human–object interactions. This approach significantly enhances the model’s ability to accurately predict ambiguous human actions and anticipate future interactions. Extensive experiments on two benchmark datasets, CAD-120, and Something-Else, demonstrate that our proposed method achieves remarkable improvements in the F1-Score for both HOI detection and anticipation tasks. This work paves the way for more accurate and comprehensive HOI understanding in various real-world scenarios, highlighting the potential of text-augmented graph networks for effective interaction modeling.

Advancements in Electrocatalysts for Low-Temperature CO2 Electrolysis: Progress and Insights from the ECO2Fuel Project

The Electrochemical CO2 Reduction Reaction (CO2RR) represents a cutting-edge approach for converting CO2 into valuable chemicals and fuels, marking a significant stride in energy storage through chemical bonds. This innovative method ingeniously merges the well-established water splitting reaction with Fischer-Tropsch synthesis, traditionally dependent on high temperatures and pressures. However, uniquely, CO2RR facilitates this process under ambient conditions, combining hydrogenation with water splitting at room temperature and normal atmospheric pressure. In this context, the ECO2Fuel project, an initiative under the European Green Deal, aims to develop, implement, and validate the first-ever 1MW low-temperature direct electrochemical CO2 conversion system, poised to produce economically viable and sustainable e-fuels and chemicals.Despite its transformative potential, the ECO2Fuel project acknowledges and addresses several pivotal challenges, including enhancing catalyst performance and selectivity, electrode longevity, mass transport optimization, averting electrolyte flooding, and mitigating the hydrogen evolution reaction.This presentation will predominantly concentrate on refining the gas diffusion electrode at the cathode, for large-scale fuel and alcohol production via CO2RR. This electrode is composed of a gas diffusion layer, ensuring efficient CO2 transport; a catalyst layer where the reduction transpires; and a top adhesion layer to prevent catalyst delamination. These components collectively influence the electrode’s selectivity, durability, and stability.Our discourse will delve into the intricate aspects of the catalyst layer and porous transport layer design, along with electrode manufacturing techniques, demonstrating their critical roles in achieving high faradaic efficiencies for carbonaceous fuel production. Furthermore, we will explore the nuanced operational modalities of the CO2 electrolyzer cell, focusing on strategies to curtail the parasitic side reaction of hydrogen evolution, thereby enhancing the system's overall efficiency and productivity.This project has received funding from the European Union’s Horizon 2020 research and Innovation program under grant agreement no. 202037389.

Impact of Mobile Learning on Self-Regulated Learning Abilities of Higher Education Students

This study examines the influence of mobile learning (m-learning) on the self-regulated learning skills of college students using the Z-number AROMAN decision-making approach. The rapid integration of mobile technologies into educational practices has transformed traditional learning environments, offering unprecedented opportunities to enhance student autonomy and engagement. This study introduces a novel method for evaluating complex and uncertain educational scenarios through the Z-number AROMAN decision-making process. This approach provides a robust framework for assessing various intricate aspects of self-regulated learning, including goal setting (GS), self-monitoring, and reflective practices. The study underscores the transformative potential of m-learning in education, underscoring the importance of leveraging such technology to foster student-centered learning environments. This study contributes to the body of literature on educational technology, offering valuable insights for educators, policymakers, and scholars seeking to effectively implement m-learning strategies to enhance student learning outcomes.

Positioning and Resistance: a Narrative Inquiry of Undocumented Mexican Domestic Workers in New York City

This study explores the life experiences and empowerment of five emblematic undocumented Mexican domestic workers in New York City (NYC). Employing identity as a conceptual framework, we delve into the intricate aspects of the participants' lives, utilizing narrative inquiry and feminist methodologies to analyze the dynamics between their performative identities and the prevailing ideologies concerning undocumented immigrants. The narratives unveil feelings of rejection and exclusion among the participants in NYC. Additionally, the focus is on their efforts to challenge societal norms, resist dominant ideologies, and devise strategies for resistance. All five participants express a sense of empowerment derived from their experiences, enabling them to reconstruct their past and present themselves as resilient, proactive individuals, activists, and proficient English speakers. Ultimately, the findings demonstrate how the participants assert their narrative identities, reclaiming their subjectivities and subverting patriarchal norms by transcending conventional societal roles.

IMPACT OF GST ON INDIAN BUSINESSES

The Goods and Services Tax (GST) regime in India, implemented in July 2017, represents a transformative overhaul of the country's indirect tax system. This research paper extensively analyzes the multifaceted impact of GST on Indian businesses across various sectors. By harmonizing numerous central and state levies into a unified tax structure, GST aims to simplify compliance, boost economic growth, and cultivate a common national market. The study delves into the intricate aspects of GST, encompassing its objectives, statutory framework, and implementation challenges. It evaluates the profound implications for diverse business entities, ranging from large corporations to micro, small, and medium enterprises (MSMEs). Key areas of focus include supply chain optimization, pricing strategies, working capital management, and transitional complexities. Furthermore, the paper scrutinizes sector-specific ramifications, shedding light on industries experiencing seismic shifts due to GST, such as manufacturing, services, e-commerce, and real estate. It also examines the role of technology in facilitating GST compliance and the digital transformation catalyzed by this tax reform. Through empirical analysis and case studies, the research paper assesses the extent to which GST has achieved its intended objectives, identifying areas of success and ongoing challenges. It provides valuable insights for policymakers, industry stakeholders, and academics, contributing to the discourse on optimizing the GST framework for enhanced economic competitiveness and ease of doing business in India. Keywords: GST, Indian Businesses, Tax Reform, Supply Chain, Compliance, Digital Transformation, Sectoral Impact, Economic Growth, Policy Implications.

166 Beyond solubility: Navigating the complex landscape of dietary fiber in swine digestion and physiology

Abstract Dietary fiber comprises a diverse group of cell wall polysaccharides, along with lignin, that resist digestion by endogenous enzymes in non-ruminants. Over the past 50 yr, our understanding of dietary fiber has significantly advanced, resulting in diverse definitions and analytical methods that have deepened our comprehension of its role in swine nutrition. However, the analysis of dietary fiber remains one of the most intricate, expensive, and resource-intensive aspects of monogastric nutrition research. From the early days of crude fiber and detergent fiber techniques to the more recent methods such as non-starch polysaccharide and total dietary fiber methodologies, analytical techniques have evolved to encompass the complex carbohydrates found in diets. Translating these chemical and analytical understandings into physiological insights poses a formidable challenge for nutritionists. This presentation aims to discuss these analytical concepts and challenges in understanding dietary fiber, with a particular focus on its impact on swine nutrition and physiology. Going beyond the concept of ‘solubility,’ we will delve into the physicochemical and physiological properties of dietary fiber, emphasizing its intricate roles in the swine digestive milieu. This includes aspects such as digestion kinetics, fermentability, rheology, viscosity, and microbial ecosystems. Moreover, research consistently demonstrates that dietary fiber can have positive effects on gastrointestinal health under specific production and dietary conditions. Paradoxically, it can also negatively impact energy digestibility and feed efficiency. By modeling the chemical nature of fiber and its role in digestion physiology, it could be possible to bridge this gap to optimize production goals and swine health in high fiber diets. This presentation will highlight the significance of a holistic comprehension of the intricate aspects of dietary fiber within swine nutrition and future directions for incorporating it into formulation metrics. Such knowledge is pivotal for navigating the complexities of incorporating fiber into diet formulation, effective management, and the pursuit of sustainable practices.

Emerging Trends in Left Ventricular Thrombus: A Comprehensive Review of Non-Ischemic and Ischemic Cardiopathies, Including Eosinophilic Myocarditis, Chagas Cardiomyopathy, Amyloidosis, and Innovative Anticoagulant Approaches.

This comprehensive review explores the intricate aspects of left ventricular thrombus (LVT), a potential complication in both ischemic and non-ischemic cardiomyopathies. It provides a thorough understanding of left ventricular thrombus, revealing its uncommon incidence in the general population (7 cases per 10,000 patients), predominantly linked to ischemic heart diseases (ICMs) at an 80% prevalence rate. Diagnostic tools, notably transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (CMR), demonstrate varying sensitivity but remain indispensable in specific clinical contexts related to LVT as non-invasive diagnostic modalities. A detailed comparison between ICM patients and those with non-ischemic cardiomyopathy (NICM) who have left ventricular thrombus reveals subtle distinctions with significant clinical implications. This analysis underscores the importance of these imaging techniques in distinguishing between the two conditions. Additionally, we explored the occurrence of LVT in specific non-ischemic cardiomyopathies, including Takotsubo syndrome, hypertrophic cardiomyopathy, eosinophilic myocarditis, Chagas disease, cardiac amyloidosis, and several other conditions. The article further delves into anticoagulation strategies, thoroughly examining their impact on LVT regression and patient outcomes. Pharmacological interventions, with a focus on direct oral anticoagulants, emerge as promising alternatives; however, there is insufficient information on their efficiency and safety, especially in NICM population. In conclusion, this review highlights the complex nature of LVT, incorporating a range of etiopathogenic factors, diagnostic complexities, and evolving therapeutic approaches. It emphasizes the pressing need for ongoing research in this field.

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

Abstract: Sentiment analysis is a crucial component of natural language processing that seeks to determine the emotional sentiment expressed in a given text. This study investigates sentiment analysis in the Arabic language through a comprehensive approach that integrates traditional machine learning methods with sophisticated deep learning models. We examine the efficacy of conventional algorithms such as Support Vector Machines (SVM) and Naive Bayes, as well as sophisticated neural network architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and the Arabic variant of Bidirectional Encoder Representations from Transformers (BERT). The primary novelty of our approach is in the ensemble method, which combines many approaches to enhance the precision of sentiment categorization in Arabic text. To address the particular challenges presented by Arabic sentiment analysis, such as the intricate structure of the language and the diverse regional variations, we utilize a tailored preprocessing pipeline to effectively handle the nuances of Arabic text. Our comprehensive analysis of various datasets demonstrates that the ensemble technique outperforms individual model benchmarks and offers novel insights into the interplay between different machine learning paradigms in Arabic NLP. The results emphasize the ability of hybrid approaches to improve Arabic sentiment analysis, providing a solid basis for future research and practical applications in understanding the sentiments of Arabic consumers. This study is a significant addition to the expanding domain of Arabic Natural Language Processing (NLP). This resource offers a comprehensive and advanced methodology for utilizing machine learning and deep learning methods to comprehend and analyse the intricate aspects of sentiment in the Arabic language

Factors Affecting the Adoption of HRIS: An Empirical Study Using Extended UTAUT2 Model

Human Resource Information Systems (HRIS) have become indispensable in modern businesses, reshaping the landscape of Human Resource (HR) service delivery. Despite their crucial role, the adoption and implementation of HRIS present persistent challenges influenced by diverse factors. This study delves into the adoption dynamics of HRIS and their practical utilization in developing countries, specifically in Bangladesh. Leveraging the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the research incorporates Hedonic Motivation (HM) and Personal Innovativeness (PI) as additional factors, forming an extended UTAUT2 model. Employing a quantitative approach, survey data was collected from 255 respondents, including top-level management, HR professionals, and executives from public and private organizations in Chittagong and Dhaka, Bangladesh. Structural Equation Modeling (SEM) with the partial least squares (PLS) method through SmartPLS was employed for model validation. Results reveal that all selected parameters exert a positive and direct influence on HRIS adoption. The results aim to enhance comprehension of the intricate aspects of HRIS adoption, providing valuable insights for organizations to tailor implementation strategies. These findings are pertinent for policymakers and HR managers, offering insights into overcoming obstacles associated with HRIS adoption and optimizing HR practices, thereby contributing to organizational performance improvements.

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation-a review.

Vegetable cultivation stands as a pivotal element in the agricultural transformation illustrating a complex interplay between technological advancements, evolving environmental perspectives, and the growing global demand for food. This comprehensive review delves into the broad spectrum of developments in modern vegetable cultivation practices. Rooted in historical traditions, our exploration commences with conventional cultivation methods and traces the progression toward contemporary practices emphasizing the critical shifts that have refined techniques and outcomes. A significant focus is placed on the evolution of seed selection and quality assessment methods underlining the growing importance of seed treatments in enhancing both germination and plant growth. Transitioning from seeds to the soil, we investigate the transformative journey from traditional soil-based cultivation to the adoption of soilless cultures and the utilization of sustainable substrates like biochar and coir. The review also examines modern environmental controls highlighting the use of advanced greenhouse technologies and artificial intelligence in optimizing plant growth conditions. We underscore the increasing sophistication in water management strategies from advanced irrigation systems to intelligent moisture sensing. Additionally, this paper discusses the intricate aspects of precision fertilization, integrated pest management, and the expanding influence of plant growth regulators in vegetable cultivation. A special segment is dedicated to technological innovations, such as the integration of drones, robots, and state-of-the-art digital monitoring systems, in the cultivation process. While acknowledging these advancements, the review also realistically addresses the challenges and economic considerations involved in adopting cutting-edge technologies. In summary, this review not only provides a comprehensive guide to the current state of vegetable cultivation but also serves as a forward-looking reference emphasizing the critical role of continuous research and the anticipation of future developments in this field.

Effect of Soil-Structure Interaction on the Response of Machine Foundation Subjected to Seismic Loading: A Review Study

This review provides a detailed look at the current knowledge and approaches related to Dynamic Soil-Structure Interaction (DSSI) in machine foundation design, focusing on its substantial impact on seismic response and structural stability. The significance of this interaction in structural design, especially in areas prone to seismic activity, is pivotal. The paper begins by exploring various modeling methods, like the Finite Element Method (FEM), highlighting their importance in understanding the intricate aspects of DSSI, such as energy loss and interface behavior. It is evident from the studies that FEM is particularly effective in analyzing settlement under reciprocating loads. Soil-structure interaction (SSI) is a complex phenomenon that can positively and negatively affect the seismic performance of machine foundations. Several factors, including embedment depth, soil stiffness, and foundation properties, govern the influence of SSI. This review discusses the dual nature of SSI and highlights the importance of considering the interaction between soil properties, foundation design, seismic loads, and interaction effects. In addition, it identifies the limitations of the current research and advocates for more accurate and inclusive models and extensive empirical studies to address real-world complexities and uncertainties. In conclusion, this review offers crucial insights and foundational knowledge for future innovative design solutions and advanced research methodologies and significantly contributes to developing resilient and reliable structural designs in seismic-prone regions. The emphasis is on the need for more nuanced and comprehensive studies to further the understanding and application of DSSI in machine foundation design.

Coloronyms in the phraseological picture of the world in languages of different structure: linguocultural and pragmatic aspect

Relevance. The relevance of this work is determined by the need for a comprehensive study of the national phraseological picture of the world through cultural, social, pragmatic, geographical factors influencing the transmission of extralingual reality through language nominations, including the expression of an ambivalent idea of the world through understanding the specifics of the lexico-semantic and communicative nature of idioms. Purpose. The purpose of this study is to analyze the parameters and characteristics of phraseological units in the Kazakh and English cultural traditions, to study the lexical-semantic field of coloronyms in the declared languages from the point of view of the communicative nature of these units. Methodology. The method of work is based on a comparative study of phraseological units using linguocultural, communicative-pragmatic, and semantic methods. Results. In this article, a comparative analysis of Kazakh and English phraseological units based on coloronymic vocabulary was made. Linguocultural and pragmatic aspects of the study of idiomatic expressions were considered. An idea was formed about the phraseological picture of the world of Kazakhs and Englishmen. Stylistic parameters, including positive and negative connotations, were evaluated. The ambivalence of stable phrases with a core coloronym was examined. Examples of frequently occurring phrases in Kazakh and English were given. Comparisons were made between different idioms. Additionally, the results of the work were compared with the findings of modern linguists. Conclusions. The study reveals profound cultural, social, and pragmatic influences shaping the phraseological landscape in Kazakh and English, with a specific focus on coloronyms. Through comparative analysis, it elucidates the nuanced ambivalence and stylistic connotations inherent in these idioms. This research enhances our understanding of how color-based phraseological units reflect and convey intricate aspects of lexical semantics and communication in both linguistic traditions. Keywords: idiom; color; semantics; connotation; communicative nature

Wellness Tourism Attributes and Tourist Outcomes: An Analysis of Configurational Effects

Prior studies have identified the complexity involved in explaining the relationship between wellness tourism attributes and tourist behavior. This study investigated the configurational effects of 10 constructs in predicting the ambiguous area between overall satisfaction and destination behavioral loyalty. To achieve this, the study used a fuzzy set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA). The results of the fsQCA generated a range of models that effectively predicted tourists’ high or low levels of overall satisfaction and behavioral loyalty. Furthermore, the NCA results identified perceived quality as an essential construct for achieving the combination of overall satisfaction and behavioral loyalty. The study enhances our current knowledge by broadening our comprehension of the intricate aspects of tourists’ decision-making processes and the formation of their behaviors. Additionally, it provides a roadmap for the industry to effectively address challenges in managing wellness tourism resources.

Analyzing the Determinants of Guest Experience in Istanbul's Airbnb Market: An Advanced Topic Modeling Approach via Latent Dirichlet Allocation

This study embraces an inductive approach to comprehensively examine the various factors influencing customer experience and satisfaction in Istanbul's rapidly evolving accommodation-sharing economy, with a specific focus on Airbnb. The research undertakes an extensive analysis of a substantial dataset comprising 508,746 Airbnb reviews collected from Istanbul, marking a significant endeavor in understanding the nuances of customer preferences and expectations in this domain. The process begins with a thorough preprocessing of the textual data, ensuring clarity and relevance in the information analyzed. Following this, the study employs Latent Dirichlet Allocation (LDA), a sophisticated statistical model, to identify and extract 32 distinct topics from the user-generated content. These topics, embedded within the reviews, provide a rich source of insights into the guest experience. The extracted topics are systematically categorized into several key dimensions, offering a structured framework for analysis. These dimensions include detailed assessments made by guests, locational attributes of the accommodations that range from central urban areas to more peripheral locations, and both the tangible and intangible aspects of the Airbnb listings. Additionally, the study examines the management practices of the hosts and the overall quality of service, factors that are crucial in shaping guest satisfaction. Each of these dimensions offers a lens through which the intricate aspects of customer experience in the shared accommodation sector can be understood and evaluated. To explore deeper into the intricate relationships among these topics, the study employs hierarchical Ward Clustering. This statistical technique is instrumental in revealing the complex interplay and subtle connections between the various topics. Such an approach is pivotal in elucidating the multifaceted nature of customer experience in the peer-to-peer accommodation context. The analysis aims to provide a comprehensive and layered understanding of the determinants that shape guest experiences in Istanbul's Airbnb sector. By offering a detailed, multi-faceted perspective on the drivers of customer satisfaction, this study contributes significantly to the body of knowledge in the field, enhancing the understanding of key factors that influence guest experiences and satisfaction in the dynamic and diverse landscape of Istanbul's accommodation-sharing economy.