Enhance Service Quality Research Articles

Customer Satisfaction Towards Food Delivery Services in Selangor

This study explores the key factors influencing customer satisfaction with food delivery services at UiTM Puncak Alam, Selangor. Despite the rising demand for food delivery services, issues surrounding the quality and accuracy of delivered food remain significant challenges. This study seeks to address these concerns by identifying and analyzing the determinants that shape customer satisfaction with food delivery services in Malaysia, specifically in Puncak Alam. We adopted a quantitative research approach to gain comprehensive insights into these issues. We collected data using judgment sampling, a non-probability sampling technique and used the Statistical Package for Social Sciences (SPSS) to analyze the responses through descriptive statistics and correlational analysis. The study involved a structured survey of 357 respondents, investigating the impact of three key variables—food quality, app ease of use, and food packaging—on customer satisfaction. The findings provide important implications for businesses and institutions looking to enhance the quality of food delivery services and elevate customer satisfaction levels. By identifying these critical factors, the research offers valuable insights that can help food service providers, policymakers, and industry stakeholders refine their strategies. These insights will not only improve the customer experience but also encourage broader adoption of food delivery services within the Malaysian population. Ultimately, the study’s conclusions hold significance for enhancing service quality and fostering customer loyalty in the increasingly competitive food delivery industry.

Long-Term Passenger Flow Forecasting for Rail Transit Based on Complex Networks and Informer

With the continuous growth of urbanization, passenger flow in urban rail transit systems is steadily increasing, making accurate long-term forecasting essential for optimizing operational scheduling and enhancing service quality. However, passenger flow forecasting becomes increasingly complex due to the intricate structure of rail transit networks and external factors such as seasonal variations. To address these challenges, this paper introduces an optimized Informer model for long-term forecasting that incorporates the influences of other stations based on complex network theory. Compared to the ARIMA, LSTM, and Transformer models, this optimized Informer model excels in processing large-scale complex transit data, particularly in terms of long-term forecasting accuracy and capturing network dependencies. The results demonstrate that this forecasting approach, which integrates complex network theory with the Informer model, significantly improves the accuracy and efficiency of long-term passenger flow predictions, providing robust decision support for urban rail transit planning and management.

Exploring the Impact of Airport Services on Passenger Satisfaction in Tanzania: A Case Study of Julius Nyerere International Airport

Satisfying passengers at the airport has been the cornerstone for airlines and airports in air transport service. This study aimed to understand the effects of airline-related quality service dimensions on overall passenger satisfaction at airports. The study intended to analyse the effect of pre-flight, in-flight, and post-flight services on passenger satisfaction at the airport. Passengers begin to interact with air transport services when booking a ticket, and airline-related service dimensions are considered part of the broader concept of airport services, which should be integrated as they overlap in satisfying passengers at the airport. Previous research focused extensively on airport-related factors but overlooked the specific impacts of airline services on overall passenger satisfaction. The study was guided by Total airport experience framework considering holistic view of passenger satisfaction. The study used a descriptive research design which targeted all international passengers travelled through Julius Nyerere International Airport at the time of conducting this study. In this regard, the study included departing and arriving passengers from Terminal III at the final lounge and baggage claim area. The Webb et al (1996) guidelines were used to determine the sample size which suggests that for population above 2,000, a sample of 1% is appropriate; The population consisted of 18,000 passengers arriving and departing at Julius Nyerere international Airport during the time of conducting survey; thus 180 passenger were selected randomly ensuring that every passenger had an equal chance of being selected. Collection of data invloved questionnaires with close-ended and open-ended questions. Descriptive analysis of collected data was conducted to summarize the main variables through utilizing the Statistical Package for Social Science (SPSS) for data analysis to obtain percentages and frequencies. The findings indicated that several dimensions of airline-related service influence passenger satisfaction: pre-flight, in-flight, and post-flight dimensions. The findings revealed that these three dimensions positively affect passenger satisfaction. For instance, in pre-flight services, respondents reported satisfaction, highlighting the crucial role of effective management of pre-flight services in determining positive initial travel experiences. High satisfaction rates were also observed for in-flight services, emphasizing the importance of onboard amenities and crew responsiveness. However, post-flight services showed areas for improvement to enhance the overall passenger experience. Additionally, the results highlighted the strategic necessity for airlines and airports to prioritize service excellence across all touchpoints of the passenger journey. Enhancing service quality and responsiveness, particularly in mishap resolution and service delivery which are crucial for maintaining a competitive advantage and fostering customer loyalty. The study concluded that passenger satisfaction is highly influenced by three primary dimensions of airline related service: pre-flight, in-flight, and post-flight dimensions. The study recommends the Airline and airport operators to focus on improving service quality delivery across all touch points of passengers journey by implementing an integrated approach such as coordinated decision making.

Enhancing service quality in the insurance industry with AI-powered humanoid chatbots

PurposeThis study aims to explore the potential of artificial intelligence with AI-powered humanoid Chatbots (AIPHC) as transformative tools to improve customer service quality in the insurance sector. The usability and efficiency of integrating advanced AI chatbots that can replicate human-like interactions in insurance services will be examined by taking into consideration customers’ technological readiness and chatbots’ anthropomorphism.Design/methodology/approachThis empirical study analysed 688 customer responses collected through purposive sampling using structural equation modelling. With the help of SmartPLS 4.0, the study determines whether anthropomorphism, that is AIPHC system-specific and customer personality-specific dimensions, can influence the acceptance of AIPHC in the insurance sector.FindingsThe results show that the chatbot’s anthropomorphism positively influenced customers’ optimism and innovativeness but negatively impacted discomfort and security. Further optimism and innovativeness favourably impact AIPHC adoption. Insecurity had a significant negative impact, while discomfort was insignificant for AIPHC adoption.Research limitations/implicationsThe study determines how people will react to AI-powered information systems. The results could help us better understand how the technological readiness of customers can be used in emphasizing the significance of system-specific theories like anthropomorphism in sectors like insurance, where customer interactions and delivery of quality services are important.Practical implicationsThe results highlight chatbots’ potential to raise the quality of service, simplify processes and enhance customers’ overall experiences in the insurance sector. This study contributes to the continuing discussion on using AI technologies in customer service by considering the interplay between technology readiness and anthropomorphism. It also provides insightful information for insurance professionals and technology developers.Social implicationsAnthropomorphic humanoid chatbots can increase the availability, affordability and accessibility of essential services. They have the potential to increase users’ competence, autonomy and—possibly counterintuitively social relatedness.Originality/valueThis empirical research explores the link between anthropomorphism and technology readiness to enhance service quality provided by AI powered chatbots in the insurance sector.

Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts

This research aims to explore the relationship between customer satisfaction and various extracted factors at dive resorts in the Red Sea, Egypt, utilizing a hybrid methodology of descriptive and diagnostic analytics applied to online review data. Employing techniques such as KH coder for text analysis, exploratory factor analysis (EFA), and linear regression, this study systematically identifies key elements that influence customer satisfaction. Findings reveal that activities related to diving and marine life markedly enhance guest satisfaction, underscoring the critical role these aspects play in the overall appeal of Egyptian coastal tourism. Conversely, areas such as dining and amenities were identified as needing improvement. The originality of this study lies in its application of big data analytics to dissect and understand customer feedback in a sector-specific context, providing strategic insights for the sustainable advancement of coastal tourism in Egypt. By focusing on dive resorts, this research highlights their integral role in coastal tourism and offers a model for leveraging online customer reviews to enhance service quality and promote sustainable practices within the tourism industry, contributing to the overall growth and sustainability of coastal tourism.

Deep Learning and Statistical Process Control Approach to Improve Quality in Immigration Services

Technological advancements have profoundly influenced various sectors, including tourism, by simplifying international travel and increasing the demand for passports. In this context, public feedback on the Surabaya Immigration Office's services, gathered from Google Maps reviews, presents an invaluable dataset for analysis. This study introduces a novel approach to quality management systems in immigration services by applying the Convolutional Long Short-Term Memory (Co-LSTM) for sentiment analysis of public reviews and p-attribute control charts for statistical process control. Focusing on the Surabaya Immigration Office, we analyze public feedback from Google Maps to assess service quality. Our methodology preprocesses and classifies opinion data into sentiment classes, employing Co-LSTM for its superior accuracy over traditional models. The sentiment analysis reveals a predominance of positive over negative reviews, with classification accuracy demonstrating Area Under Curve (AUC) values of 98.61% for training and 85.66% for testing data. Furthermore, the p-attribute control charts are utilized to monitor service defects, identifying areas of uncontrolled variability and suggesting the necessity for service improvement interventions. The study uncovers that the primary public grievance relates to the perceived lack of friendliness and politeness from office staff. By integrating sentiment analysis with statistical process control, this research offers a comprehensive approach for immigration service providers to enhance service quality, respond proactively to public sentiment, and ensure customer satisfaction.

Emotional Intelligence as a Strategic Driver of Competitive Advantage and Service Quality in the Banking Industry

This research aims to determine whether emotional intelligence (EI), particularly in government-owned commercial banks, is a valid measure of service quality in the banking sector. The study aims to ascertain how emotional intelligence (EI) raises the caliber of services, with a particular emphasis on the departments of operations, credit, and customer service. Using a quantitative methodology, data were collected through purposive sampling from 284 employees in particular bank branches in Kathmandu. The findings show that emotional intelligence (EI) and service quality are highly correlated. Workers with higher EI are more adept at managing their emotions, positively relating to clients, and offering superior customer service. Self-awareness, social awareness, and relationship management are crucial in enhancing service quality, with relationship management exerting the most significant influence. The study's limited scope focusing only on government-owned banks in a particular area may restrict its applicability opening up new avenues for future studies on emotional intelligence in private banking and other sectors of the economy.

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

Performance Assessment of Health Visitor and Family Medicine Units in Babylon's Main Health Care Centers

Background: The Ministry of Health (MoH) has developed a standardized package of basic health services for all primary healthcare (PHC) facilities to enhance service quality. This study evaluates service availability and facility needs in the family health units in Babylon. Methods: This cross-sectional study was conducted in three family healthcare sectors (the first Al-Hillah sector, Al-Musayyib sector, and Al-Hashemiyyah sector), which includes 6 out of 23 primary healthcare centers in the Babylon Governorate. The data collection started on March 3, 2022, and ended on August 27, 2022, and was randomly chosen from all sectors. Results: According to this study, the family health unit had a poor mean score of 48.03±17.97. Similarly, the health visitor indicators had an inadequate level of assessment, with a mean score of 46.87±14.09. In conclusion, this revealed a study that the primary health care centers evaluated had poor scores in all the final evaluation indicators for the family health unit and the health visitor unit.

Retraction Note: Enhancing quality of service in wireless systems using iterative weighted least squares with fuzzy logic integration algorithm

Retraction Note: Enhancing quality of service in wireless systems using iterative weighted least squares with fuzzy logic integration algorithm

Identification of Customer Through Voice Biometric System in Call Centres

In recent times, there has been a growing emphasis on adjusting communication strategies to foster strong customer relationships. This shift is driven by intensified competition, market maturation, and swift advancements in business technology. Consequently, companies have established call centers to efficiently handle customer support and fulfil customer inquiries. A pivotal aspect of enhancing service quality within these call centers involves accurately identifying customers during their interactions. The primary objective of this study is to introduce a methodology for identifying customers within call centers by analyzing their voice characteristics. Voice authentication (VA) has gained prominence in critical security operations, including banking transactions and conversations within call centers. The susceptibility of automatic speaker verification systems (ASVs) to deceptive spoofing attacks has prompted the development of countermeasures (CMs). These countermeasures are designed to differentiate between authentic and fabricated speech. ASVs and CMs collectively constitute contemporary VA systems, positioned as robust access control mechanisms. To achieve this goal, various customer identification systems within call centers have been examined, along with an analysis of audio signal attributes. Ultimately, the manuscript presents a novel approach to customer identification through voice biometrics. Notably, this method excels in recognizing customers even when provided with limited voice data. Empirical findings demonstrate that the suggested speaker identity confirmation method outperforms alternative techniques utilizing different algorithms, exhibiting a higher recognition rate. The present research work is based on two important perspectives of the call centres: a. call center agents experience and b. customer experience. The data collected separately from customers and agents for understanding the effective usage of voice biometric system in call centres. The data represented and satisfies the effectiveness of voice biometric system from both the perspectives. From the data it is also cleared that, the implementation of voice biometric system in call centres still have long way to go but will be a major technological change for the industries worldwide.

THE IMPACT OF SERVICE QUALITY, PRODUCT QUALITY, AND BRAND IMAGE ON CUSTOMER LOYALTY THROUGH THE MEDIATION OF CUSTOMER SATISFACTION AMONG PRIMA FRESHMART CUSTOMERS

As the retail industry for fresh produce in Indonesia becomes increasingly competitive, it creates an urgency for retailers to maintain relevant position in the market. Retailers need to cultivate loyal customers to ensure sustained performance and growth. Prima Freshmart, a specialized retailer that focuses on poultry products, is currently dealing with the challenge of having low customer loyalty rates based on the results of a preliminary survey. In order to understand how service quality, product quality, and brand image impact customer loyalty among Prima Freshmart's clientele, a study was conducted with 194 participants who completed questionnaires. The data was then analyzed using SmartPLS 4 software. The findings indicate that service quality, product quality, and brand image have a significant and positive influence on customer satisfaction. Furthermore, customer satisfaction has a significant and positive effect on customer loyalty and acts as a mediator in the relationship between service quality, product quality, brand image, and customer loyalty. These discoveries offer potential guidance for Prima Freshmart's management to enhance service quality, product quality, and brand image in order to improve customer satisfaction and loyalty. This research provides valuable insights that can be applied to both theoretical and practical aspects of customer retention strategies in the retail sector.

Determinants of Bank Selection: A Study on Consumer Perceptions and Expectations in Albania

This study investigates the factors influencing consumer perception in selecting banking services, focusing on service quality, customer satisfaction, and brand reputation within the Albanian banking sector. Using a structured questionnaire, 213 valid responses were gathered from customers in major cities, including Durres and Tirana. The analysis involved factor analysis, reliability testing, and regression analysis to explore the relationships between the identified factors and consumer decision-making. The findings reveal that service quality, encompassing tangibles, reliability, responsiveness, assurance, and empathy, plays a critical role in shaping consumer perceptions, with assurance, reliability, and responsiveness having the most significant impact. Additionally, customer satisfaction and the bank's brand influenced the selection process significantly. The results underscore the importance of banks enhancing service quality and maintaining a strong brand reputation to attract and retain customers in a competitive market. These insights provide valuable implications for bank marketers and strategists aiming to develop effective customer-centric approaches.

Contributions to a Theoretical Framework for Evaluating the Supply–Demand Matching of Medical Care Facilities in Mega-Cities: Incorporating Location, Scale, and Quality Factors

The rapid urbanization and population growth in mega-cities have led to a significant increase in the demand for medical services, highlighting the critical need for a more efficient alignment between the supply and demand of medical resources. Previous research often focuses on singular factors, such as accessibility or quantity, as the primary criteria for matching medical services, without comprehensively considering the location, scale, and quality factors of medical facilities. Addressing this gap, this study develops a theoretical framework that integrates these three critical factors to assess the supply–demand matching (SDM) of medical care facilities (MCFs) with population needs. This assessment is conducted using geospatial analysis techniques with ArcGIS and Python. The study includes an empirical analysis of 134 streets within the Chongqing municipality. The empirical results reveal significant disparities in the performance of integrated medical care facilities (MCFs), as well as variations across the dimensions of location, scale, and quality. Central districts like Yuzhong demonstrate high levels of accessibility, appropriate scale matching, and satisfactory service quality, whereas rapidly urbanizing peripheral districts such as Yubei suffer from significant mismatches in resource availability and service quality. The theoretical framework contributes to the field of medical care research, and the corresponding empirical findings provide valuable insights for urban planners and policymakers to optimize the allocation of medical resources, improve healthcare accessibility, and enhance service quality across different urban areas.

A COMPREHENSIVE STUDY OF MACHINE LEARNING APPROACHES FOR CUSTOMER SENTIMENT ANALYSIS IN BANKING SECTOR

This study explores the application of sentiment analysis in the banking sector, focusing on customer feedback to enhance service quality and customer experiences. We collected a comprehensive dataset of approximately 100,000 entries from diverse sources, including customer satisfaction surveys, social media platforms, and direct feedback. A robust preprocessing pipeline was employed to address challenges associated with unstructured data, informal language, and mixed sentiments. We evaluated several machine learning and natural language processing models, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, Long Short-Term Memory (LSTM), and BERT (Bidirectional Encoder Representations from Transformers), using metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and training time. The results revealed that advanced models, particularly BERT, achieved superior performance with an accuracy of 88% and an F1 score of 0.86, demonstrating an exceptional ability to capture nuanced sentiments. This study underscores the importance of employing sophisticated sentiment analysis techniques in banking to derive actionable insights from customer feedback. The findings suggest that leveraging advanced models can significantly improve service quality and customer satisfaction, while also presenting avenues for future research into real-time sentiment analysis and its integration with customer relationship management systems.

The Influence of Service Quality and Brand Image on Customer Satisfaction and Customer Loyalty at the Pratama Marga Ayu Clinic in Badung, Bali

This research investigates the impact of service quality and brand image on customer satisfaction and customer loyalty at the Pratama Marga Ayu Clinic in Badung, Bali. Using SPSS and Smart-PLS for analysis, the study reveals that both service quality and brand image significantly and positively affect customer satisfaction, which in turn influences customer loyalty. Additionally, customer satisfaction mediates the relationship between service quality and customer loyalty. The findings highlight the importance of enhancing service quality and brand image to foster higher customer satisfaction and loyalty. The research concludes with practical suggestions for improving service and brand image, including increasing healthcare personnel, enhancing branding efforts, and implementing customer loyalty programs. Future studies are recommended to explore other influencing factors such as product reviews, location, and social media marketing to further enrich the understanding of customer decision-making.

Stated and Revealed Preferences Towards Bus Transport in Sri Lanka

This study aims to identify the attributes of public transportation buses (PTB) preferred by passengers, which will be perceived as the buses’ quality of service. PTB of Sri Lanka faces several issues impacting its quality, efficiency, and modal share. Understanding user perceptions is essential to enhancing service quality and addressing the challenges that deter bus usage. Thirteen shortlisted attributes were rated by 84 (61 Male and 23 Female) respondents, and the top five attributes were selected. The study gathered user preferences towards PTB using Stated Preference (SP) and Revealed Preference (RP) methods for the five chosen attributes. RP data were modelled using a multiple linear regression model with R language. The data set comprised 192 (127 Male and 65 Female) valid responses. SP data showed that the respondents favoured medium-speed buses (51.2%) that were clean and reliable. Time of arrival was the most crucial attribute (30.3%), followed by vehicle condition (24.3%). Segment analysis revealed that gender and vehicle ownership significantly impacted preferences. Both gender and vehicle ownership segments prioritised arrival time and least valued seat comfort. The study emphasises the importance of prioritising punctuality over comfort. The findings of this study could provide valuable insights for policymakers to design more user-centric strategies, potentially enhancing the efficiency and attractiveness of bus services in Sri Lanka.

Service quality in higher education: A literature review

In a time where the student experience increasingly mirrors a customer-centric approach, universities are tasked with fulfilling the role of comprehensive educational service providers. This paper presents a comprehensive literature review on service quality in higher education, focusing on various generic and industry-specific scales. The primary objective of this article is to analyse and compare prominent measurement scales such as SERVQUAL, SERVPERF, HEdPERF, UnivQual, and HiEduQual. Each scale is defined according to its original literature and provides a foundational understanding of their respective constructs and factors. To achieve a nuanced comparison, the paper examines these scales based on the number of factors extracted from studies and their characteristics. The analysis reveals a great diversity and specificity of factors in measuring service quality across different higher education contexts. Beyond that, the paper analyses a database of 45 selected peer-reviewed articles to understand the practical application of service quality scales in empirical research. This examination considers multiple dimensions such as the country of focus, the statistical procedures employed, and the research methods. These dimensions offer a rich comparative insight into the trends in service quality research within higher education. Additionally, the study delves into the number and names of factors identified in each article, mapping them against the established scales to uncover patterns and deviations in empirical findings. A notable aspect of the review is the exploration of gender differences in service quality perceptions, a critical yet often underexplored area. This gender-based analysis provides valuable insights into how male and female students may perceive service quality differently, thereby informing more nuanced and inclusive service quality enhancement strategies. The findings of this paper underscore the multifaceted nature of service quality in higher education and the importance of context-specific scales. By comparing different measurement approaches and analysing a broad spectrum of empirical studies, this review contributes to a deeper understanding of how service quality is conceptualised and measured in higher education. It also offers practical implications for researchers and practitioners aiming to enhance service quality and align it more closely with student expectations and experiences.

U.S. Airline Customer Complaint Trends: A Decade-Long Analysis (2013-2022) Including COVID-19 Impacts

The Airline Deregulation Act of 1978 significantly reshaped the U.S. aviation industry, transitioning from government-controlled pricing and service standards to market-driven dynamics. This shift led to the rise of low-cost carriers, reduced service quality, and increased customer complaints. In response to fluctuating service standards, the Air Travel Consumer Report (ATCR) was introduced in 1987 to provide transparency in airline service quality. This study leverages ATCR data from 2013 to 2022 to analyze trends in customer complaints across major U.S. airlines. The primary objectives are identifying the major customer complaints and determining which airlines received the most complaints during the studied period including the COVID-19 pandemic era. Results indicate that flight problems, refunds, baggage issues, customer service, and reservations/ticketing/boarding are the top complaints, with refunds peaking during the COVID-19 pandemic due to travel restrictions. Airlines such as Frontier, Spirit, and United consistently received higher complaints, while Alaska, Delta, and SkyWest had fewer complaints. The study underscores the importance of service quality in fostering customer satisfaction and loyalty, suggesting that airlines should focus on reducing complaints in key areas to enhance service quality. The findings provide valuable insights for both airlines and consumers, highlighting areas for improvement and aiding consumers in making informed choices. This study also emphasizes the need for comprehensive service quality measures beyond consumer complaints to assess airline performance accurately.

The Development of Halal Well-Being Experience Quality: A Conceptual Framework

In recent years, the hospitality and tourism industry has increasingly focused on enhancing the well-being of guests by providing tailored experiences that address their physical, mental, and spiritual health. This shift has spurred the development of Halal well-being experiences, specifically designed to cater to Muslim travelers by aligning services and facilities with Islamic principles. Halal well-being experiences include a range of services such as Halal-certified food, prayer facilities, gender-segregated wellness amenities, and activities promoting spiritual health. These offerings are vital for Muslim travelers who seek destinations and accommodations that respect and cater to their faith-based needs. To address these challenges, a comprehensive framework for Halal well-being experience quality is essential. This framework should include clear guidelines for service provision, training programs for staff, and effective marketing strategies. This research aims to explore the current state of Halal well-being experiences in the hospitality industry, identify the key challenges and opportunities, and propose a framework for enhancing service quality. The study provides insights and recommendations for developing and promoting Halal well-being experiences that cater to the needs of Muslim travelers. The research underscores the importance of seven dimensions of tourism experience quality (TEQ) in influencing tourist well-being: Halal hedonism, Muslim-friendly involvement, local culture friendliness, refreshment experience, meaningfulness experience, knowledge experience, and novelty experience. These dimensions collectively enhance the physical, mental, social, and spiritual well-being of Muslim travelers, promoting a holistic approach to well-being in the tourism sector.