Enhance Customer Satisfaction Research Articles

Application of SERVQUAL Model to Gauge Relationship between Service Commitment, Trust, and Price Fairness to Customers Loyalty: Empirical Evidence of Telecommunication Users in Nepal

Background: The service sector, a significant contributor to global GDP, is increasingly focused on innovation, particularly in the telecom industry. Despite its importance, research on how service commitment, service trust, and price fairness affect customer loyalty within Nepal’s telecommunications sector remains limited. Objective: This study aims to investigate the impact of service commitment, service trust, and price fairness on customer loyalty in the mobile telecom sector in Kathmandu Valley. It seeks to apply the SERVQUAL model to assess how these factors influence customer retention and satisfaction. Method: An explanatory research design was employed using a quantitative approach. Data were collected through a structured questionnaire from 404 telecom users selected via purposive sampling. The analysis was conducted using structural equation modeling (SEM) with confirmatory factor analysis and path analysis, employing software tools such as KOBO Toolbox and AMOS to examine the relationships among variables. Result: The findings indicate that service commitment, service trust, and price fairness significantly impact customer loyalty. Service trust and price fairness were found to have a positive and significant effect on loyalty, with price fairness exhibiting a partial mediating role between service commitment and customer loyalty. The study also highlights that telecom firms’ ability to enhance customer satisfaction is closely linked to their service innovation and commitment. Conclusion: The research underscores the importance of service commitment, trust, and price fairness in fostering customer loyalty within Nepal’s telecom sector. Telecom service providers are encouraged to focus on these areas to improve service quality and customer retention. The study offers valuable insights for enhancing customer satisfaction and achieving long-term loyalty in the telecommunications industry. Paper Types: Research Paper Keywords: Telecommunication Service; Commitment; Service Trust; Price Fairness; Customer Loyalty; SERVQUAL model; Structure Equation Model. JEL Classification: D12, L86, L96, M31

Sentiment Mining in E-Commerce

Sentiment analysis is crucial for comprehending customer feedback and enhancing workplace culture, as well as improving products and services. By employing natural language processing (NLP) techniques to meticulously analyze this feedback, organizations can identify specific areas that require improvement, address employee issues, and cultivate a positive work environment. These deep learning models powered by NLP offer invaluable tools for HR and sales departments in the e-commerce sector, enabling them to track sentiment trends among employees and users over time and implement targeted interventions. Focusing on the e-commerce industry, this study employs NLP-driven deep learning methodologies to analyze both employee and user feedback, with the objective of identifying underlying sentiments. The proposed framework leverages these advanced techniques to categorize user feedback into positive, negative, or neutral sentiments. This approach aims to develop a robust and effective system for sentiment analysis, providing significant insights that can help drive organizational improvements and enhance customer satisfaction. The key steps of this framework include data collection, NLP-enhanced feature extraction, sentiment detection, and final classification using finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback collected from an e-commerce industry. Evaluation metrics such as accuracy, precision, and recall were utilized to assess the performance of the system. The results demonstrate the effectiveness of the proposed framework, achieving a 93.75% accuracy rate and surpassing existing benchmark methods. The outcomes of this study are particularly consequential for the e-commerce sector, offering them a strategic advantage in refining their product portfolios and cultivating a more dynamic workplace culture

Customers Perception on Banking Communication in Development Banks of Kathmandu Valley: Evidence from Structural Equation Modelling

Background: Effective communication is vital in banking, influencing customer satisfaction and the overall success of banking operations. While global research on banking communication is extensive, studies specific to Nepalese development banks are limited. Objective: The study aims to analyze how customers perceive the communication strategies employed by development banks, identifying challenges and providing recommendations for improvement. Method: An explanatory research design was used, with data collected from 281 customers using KOBO Toolbox and structured questionnaires. The data was analyzed using SPSS, AMOS, and Excel, with Structural Equation Modeling (SEM) applied for inferential analysis. Result: The study found that both formal and informal communication methods are widely used by development banks. However, major challenges include a lack of proper knowledge in digital communication, inadequate communication technology, and ineffective communication strategies. Despite these challenges, good communication was identified as a key factor in customer satisfaction and loyalty. Conclusion: The study concludes that while banking communication is generally effective, there are significant areas for improvement, particularly in digital communication and strategy formulation. Banks are recommended to adopt user-friendly communication technologies, train employees in communication skills, and conduct educational campaigns for customers. By addressing these issues, banks can enhance customer satisfaction and build stronger relationships with their clientele. Paper Types: Research Paper Key words: Banking Communication, Customer's Perception, Loyalty, Trust, Structural Equation Modeling (SEM), Development Bank, Kathmandu Valley. JEL classification: D83, G21, O14, C39, G53

Assessing Customer Satisfaction towards Electric Vehicle in Mysore Using the KANO Model

The study is entrenched in the recognition that the automotive industry is undergoing a significant transformation, with EVs emerging as a vital solution to reduce carbon emissions and foster sustainable transportation. Understanding customer satisfaction in this context is crucial for manufacturers and policymakers to support the broader adoption of EVs. The research problem is centered on the limited understanding of how specific EV features impact customer satisfaction, particularly in emerging markets like Mysore. This gap in knowledge poses a challenge for manufacturers aiming to align their products with customer expectations and for policymakers promoting sustainable transportation. The study addresses this by applying the Kano Model, a framework that categorizes product attributes into different needs—basic, performance, excitement, indifferent, and reverse—based on their impact on customer satisfaction. The research methodology involves a descriptive study based on a survey of 150 respondents from Mysore. The data were collected through primary and secondary sources adopting a convenient sampling method. The analysis for the study is done by simple percentage method. The results shows that the majority of respondents are young, educated, and earn less than Rs. 50,000 per month, indicating that EV adoption is not limited to higher-income individuals. There is a strong preference for two-wheeled EVs, with most respondents having used their EVs for less than a year, reflecting a growing interest in the market. The Kano Model analysis shows varying levels of satisfaction across different EV attributes. For instance, respondents expressed high satisfaction with the reliability, safety features, and battery life of their EVs, but there were concerns about the availability of charging stations and the maintenance costs. The study also identifies performance attributes, such as EV range and cost savings on fuel, as significant factors driving customer satisfaction. Excitement needs, including advanced technology features and the design of EVs, also play a crucial role in enhancing customer satisfaction. However, certain indifferent needs, such as the color options and brand of the EV, were found to have minimal impact on customer satisfaction.

Proposed Marketing Strategy to Attract Potential Customers and Increase Revenue through Product Diversification in Resik Wangi Auto Detailing

The automotive detailing industry in South Jakarta is experiencing heightened competition, impacting the revenue of Resik Wangi Auto Detailing. This thesis investigates the factors contributing to the revenue decline and proposes a comprehensive marketing strategy focused on product diversification and digital marketing to attract potential customers and increase revenue. The study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather data on customer preferences, market conditions, and business performance. The research identifies that the primary internal challenge is the lack of diversified services, while externally, increased competition and consumer behavior heavily influenced by digital marketing and service quality are critical factors. Key findings indicate that product diversification, particularly through bundled service packages like the “Signature Menu” and “Ultimate Menu,” can significantly enhance customer satisfaction and loyalty. Competitive pricing strategies and a robust digital marketing presence are essential in attracting and retaining customers. The study concludes that by implementing these strategies, Resik Wangi can effectively address its revenue challenges and achieve sustainable business growth.Recommendations for Resik Wangi include the immediate implementation of diversified service packages, enhanced digital marketing efforts, competitive pricing, and a strong focus on service quality. Additionally, future research should explore further diversification opportunities and the impact of emerging technologies on customer engagement and marketing strategies. This research provides a strategic framework for Resik Wangi Auto Detailing to navigate the competitive landscape, improve market positioning, and increase revenue through targeted marketing and service enhancements.

Impact of Artificial Intelligence in Customer Journey

The entire gamut of Customer journey is undergoing a massive transformation due to the rapid advancement of Artificial Intelligence (AI). Leveraging the power of AI , CRM & systems have refined the aspect of how businesses manage and optimize the customer journey. AI-powered systems have significant impact across various stages of the customer lifecycle by use of techniques such as machine learning to empower businesses to use systems that can analyse vast amounts of customer dataset in real-time, enabling them to gain deeper insights in customer behaviours, preferences, & sentiment. The AI-driven techniques help businesses to drive more personalized & targeted marketing campaigns, tailored recommendations, and extend efficient customer service leading ultimately to enhancing customer satisfaction and loyalty. Moreover, AI-powered systems have capabilities of offering predictive analytics which empower businesses to forecast customer behaviours and anticipate their needs. The capabilities help businesses in effective resource optimization and improve efficiency. For customer service AI-powered chatbots and virtual assistants are used to enhance engagement by providing instant responses and ability to handle resolving issues promptly.

Service Quality Variables and Customer Satisfaction at A Nepalese Commercial Bank

Customer satisfaction is a critical component of banking strategy, which reflects customer sentiment towards an organization's offerings, emphasizing emotional attachment over mere satisfaction. This research investigates customer satisfaction in commercial banks of Kathmandu Valley in Nepal. The study conducted a descriptive cross-sectional study using a quantitative research design, explicitly targeting 380 general customers of commercial banks in Kathmandu Valley through convenience sampling. Participants received a semi-structured questionnaire via email in Google Forms, encompassing inquiries on demographics, banking habits, and customer satisfaction. Data analysis was performed using SPSS version 20, and a p-value below 0.05 was considered statistically significant. The investigation found that the study population appears to be young, as 46.6% of the participants are in the 18–30 age range and predominantly male (56.6%). Most respondents (88.4%) hold accounts with private banks. Among those experiencing ATM service-related problems, 45.5% encountered them most frequently. Despite this, overall customer satisfaction remains high. Customers are notably content with the bank's reliability, including timely service and accurate record-keeping. Conversely, dissatisfaction mainly stems from communication issues, particularly regarding timely interest rates and service updates. Customer satisfaction is significantly influenced by service quality factors such as reliability, responsiveness, empathy, assurance, and communication. These findings highlight the necessity of addressing service challenges to enhance customer satisfaction and service quality within Nepal's banking sector.

Pengaruh Kualitas Produk, Desain Produk, dan Brand Image Terhadap Kepuasan Pelanggan Kniel Sports Indonesia

This research aims to identify the effect of product quality, product design, and brand image on customer satisfaction of Kniel Sports Indonesia. A quantitative approach was used with a survey method, and data were collected through questionnaires from 100 respondents. Data analysis utilized multiple linear regression to test the influence of product quality, product design, and brand image on customer satisfaction. The results of the study demonstrate that these three variables simultaneously affect customer satisfaction. Meanwhile, partially, product quality and product design influence customer satisfaction, whereas brand image does not affect customer satisfaction. These findings imply that Kniel Sports Indonesia needs to focus on strengthening the brand image while maintaining product quality and design innovation to enhance customer satisfaction and competitiveness in both domestic and international markets.

The Role of QR Code Applications in Enhancing Customer Satisfaction at Restaurants: A Conceptual Study

This paper aims to analyse the application of QR codes in enhancing customer satisfaction within the restaurant industry. Specifically, the study identifies the impact of QR code usage on customer satisfaction by reviewing existing literature. Data is gathered by reviewing journals, articles, and research studies using argumentative techniques. In today's world, characterized by a shift towards contactless interactions, the restaurant industry has embraced QR codes for seamless, contactless payments and operations. This study addresses the need for a deeper understanding of QR code applications by determining three critical elements: usefulness, acceptability, and feasibility. The findings are expected to guide industry professionals and researchers in identifying the most effective QR code implementations, thereby enhancing customer satisfaction. Through this exploration, the study contributes to bridging the knowledge gap and fostering improved practices in the restaurant industry, ultimately promoting the wide-spread adoption of QR codes as a valuable tool for customer engagement and satisfaction. However, this study profound a limitation with the suggested model leading from limited participant perspectives and experiences. This restricts the ability to form a comprehensive and solid view, potentially affecting the study's findings. Future research should involve a more diverse group of participants with varied experiences to enhance the understanding and application of the model.

The Role of Logistics Innovation in Enhancing Supply Chain Efficiency: A Case Study Analysis

This paper delves into the role of logistics innovation in enhancing supply chain efficiency and demonstrates through case studies the positive impact of logistics innovation on cost control, customer service, environmental sustainability, and corporate competitiveness. The study finds that by introducing automation and information technology, companies can significantly improve the speed and accuracy of logistics operations, reduce operational costs, enhance customer satisfaction, promote environmental sustainability, and enhance market competitiveness. The research also proposes application suggestions for logistics innovation in supply chain management, including continuous investment in technological innovation, strengthening data analysis capabilities, promoting green logistics practices, strengthening customer participation, and establishing a flexible supply chain. Finally, the study points out the limitations of the research and provides suggestions for future research directions.

The Influence of Food Quality, Hygiene, Price and Revisit Intention Towards Street Food in Dannok, Thailand

This study investigates the influence of food quality, hygiene, price, and revisit intention toward street foods in Dannok, Thailand. The research used convenience sampling with a sample size of 150 respondents. The research employed a structured questionnaire, consisting of demographic information, three variables (food quality, hygiene, and price), and revisit intentions through an online survey. A six-point Likert scale was used. Factor analysis and reliability tests were conducted to validate the instruments. The analysis indicated that food quality, hygiene, and price significantly influenced customers' revisit intentions. Then, the Regression Test further highlighted that food hygiene had the most significant impact, followed by food quality and price. The findings suggest that enhancing food quality, price, and hygiene can enhance customer satisfaction and increase the possibility of revisiting intention. The findings of this research provide valuable insights for street food vendors in emergent approaches to boost the street food industry's appeal and sustainability. Future research may explore different samples and incorporate additional variables that will influence revisit intention.

Impact of Chatbot Service on Bank Performance Based on a Case Study of IBM Corporation

Exploring the impact of chatbot on bank performance helps to identify the potential of this technology to revolutionize the bank's business model and enhance customer experience, while it can improve employee efficiency and provide a scientific basis and guidance for bankers to effectively respond to the increasing competitive market and achieve bank performance growth and sustainable development. This paper delineates the definitions of chatbot and business performance, provides an overview of current research, and examines the influence of chatbots on bank performance. Furthermore, it utilizes IBM's chatbot as a case study to investigate its impact on banking performance, focusing primarily on cost reduction, enhanced customer satisfaction, and improved employee efficiency. At the same time, the banking industry also faces the negative risk impact of chatbot, which mainly includes perceived risk, security threat and moral risk. The paper also concludes with recommendations on the negative risk impacts of smart customer service. This study links chatbot with bank performance to fill the research gap. It also provides an outlook and suggestions for the future development prospects of chatbot in the banking industry.

Real-Time Bus Route and Timing Information System using IoT

This system suggests a user-friendly online interface for accessing real-time routes and schedules of the bus. The system provides the most recent information about bus locations, routes, and anticipated arrival times by utilizing the Internet of Things (IoT) and Global Positioning System (GPS) capabilities. The technology continuously collects position data by installing GPS modules on buses and linking them to an IoT network. After that, the data is sent to a central server for processing and display. The web interface serves as the main point of contact for users, it displays the positions of buses in real-time on a map along with integrated route data. By seeing the real-time position and expected arrival time of approaching buses, passengers may easily plan their journeys by accessing the webpage from any device with an internet connection. In addition to reducing wait times, enhancing customer satisfaction levels, and facilitating better travel arrangements, this gives transportation authorities useful information for optimizing bus routes and schedules.

Enhancing Customer Satisfaction in Cross-Cultural Tourism - Exploring the Impact of Employee Foreign Language Proficiency and Beyond -

Enhancing Customer Satisfaction in Cross-Cultural Tourism - Exploring the Impact of Employee Foreign Language Proficiency and Beyond -

Customer Segmentation Using the K-means Clustering Algorithm and Recency Frequency Monetary Model at Pharmaceutical Product Wholesaler

PT Kimia Farma Trading and Distribution (KFTD) is a company engaged in the distribution and trading services of Indonesian health products, on a national scale. In 2022, the company aims to increase sales to be awarded as one of the top 3 national pharmaceutical product distributors by 2024. Their current strategy is to provide customers with delayed payment permission and integrated complaint services. However, the offers and services are the same for all customers which does not consider customer track record hence it is not cost-effective. One way to increase sales is by enhancing customer satisfaction and loyalty by implementing Customer Relationship Management (CRM) strategies. Several strategies can be carried out, namely analysis of associations related to pharmaceutical products, and analysis of customer segmentation and clustering of products. The method used in this study was the K-means clustering algorithm combined with the Recency Frequency Monetary (RFM) model. Experiments showed that the optimal clustering results are 4 therefore they are categorized into 4 customer segments, namely Superstar, Golden, Typical, and Occasional Customers.

The Relationship Between Total Quality Management and Customer Satisfaction Through the Mediation of Employee Performance

The increasing competition in the global market, driven by globalization and technological advancements, has caused many companies to face challenges in maintaining their market position. As a result, effective quality management systems are essential to enhancing employee performance and delivering high-quality products and services to satisfy customers. This study aims to analyze the effect of Total Quality Management (TQM) implementation on employee performance, the influence of employee performance on customer satisfaction, and the mediating role of employee performance between TQM and customer satisfaction. The research was conducted on companies certified with ISO 9001:2015, using questionnaires filled out by 80 respondents. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that TQM significantly improves employee performance, positively affecting customer satisfaction. Additionally, TQM directly enhanced customer satisfaction, with employee performance as an effective mediator in this relationship. These findings suggest that improving TQM practices and employee performance can substantially increase customer satisfaction. The study provides recommendations for companies in Indonesia to optimize TQM implementation, aiming to enhance both employee performance and customer satisfaction.

Utilizing machine learning algorithms to enhance predictive analytics in customer behavior studies

Machine learning (ML) algorithms have revolutionized the field of predictive analytics, particularly in understanding and anticipating customer behavior. This review explores how these advanced algorithms are utilized to enhance predictive analytics in customer behavior studies, driving more informed and strategic decision-making processes within businesses. Predictive analytics leverages historical data to predict future outcomes, and when combined with ML, it becomes significantly more powerful and accurate. ML algorithms can analyze vast amounts of data, identifying patterns and trends that would be impossible for humans to discern. These algorithms, including decision trees, neural networks, and support vector machines, can handle complex and nonlinear relationships within data, making them exceptionally well-suited for customer behavior studies. The application of ML in predictive analytics begins with data collection from various sources, such as transaction records, social media interactions, and customer feedback. This data is then preprocessed to ensure quality and relevance before being fed into ML models. Through techniques like clustering, classification, and regression, ML algorithms can segment customers, predict purchasing behaviors, and identify potential churners. One significant advantage of using ML for predictive analytics in customer behavior is the ability to deliver personalized experiences. By predicting individual customer preferences and needs, businesses can tailor their marketing efforts, product recommendations, and customer service interactions, thereby enhancing customer satisfaction and loyalty. For instance, e-commerce platforms use ML-driven predictive analytics to suggest products that a customer is likely to purchase based on their browsing and buying history. Moreover, ML algorithms continuously learn and improve from new data, allowing for real-time updates and more accurate predictions over time. This dynamic nature is crucial in today’s fast-paced market environments where customer preferences and behaviors can change rapidly. In conclusion, utilizing ML algorithms in predictive analytics significantly enhances the ability to understand and predict customer behavior. This integration not only helps businesses optimize their strategies and operations but also fosters deeper customer relationships through personalized and timely engagements. As ML technology continues to evolve, its impact on predictive analytics and customer behavior studies is expected to grow, offering even more sophisticated and actionable insights.

Impact of Service Quality on Customer Satisfaction in Boutique Hotels

Purpose: The aim of the study was to assess the impact of service quality on customer satisfaction in boutique hotels. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that high-quality service significantly enhances customer satisfaction, leading to increased loyalty and repeat patronage. Key dimensions of service quality, such as responsiveness, reliability, assurance, empathy, and tangibles, play crucial roles in shaping guests' perceptions. For instance, personalized attention and unique, aesthetically pleasing environments typical of boutique hotels contribute positively to the overall guest experience. Additionally, well-trained, courteous staff and prompt service responses further elevate satisfaction levels. The intimate and distinctive atmosphere of boutique hotels allows for tailored experiences, which are highly valued by guests seeking exclusivity and comfort. Ultimately, superior service quality not only meets but often exceeds guest expectations, fostering a strong emotional connection and ensuring that customers are not only satisfied but also likely to advocate for the hotel through positive word-of-mouth and online reviews. This relationship underscores the necessity for boutique hotels to continually invest in service quality improvements to sustain and enhance customer satisfaction in a competitive market. Implications to Theory, Practice and Policy: Servqual model, expectation-confirmation theory (ECT) and Herzberg’s two-factor theory may be used to anchor future studies on assessing the impact of service quality on customer satisfaction in boutique hotels. Boutique hotels should prioritize ongoing training programs that focus on key service quality dimensions, such as responsiveness and empathy. Policymakers should work with industry stakeholders to establish clear service quality standards specific to boutique hotels.

The Influence of Digital Marketing, Physical Evidence, and Price on Customer Satisfaction in the Coffee Shop Industry

The competition in the business world has intensified, with companies in Indonesia striving to increase their market share and attract new consumers. This study aims to explore the impact of digital marketing, physical evidence, and pricing on customer satisfaction at CW Coffee Tanjung Sari in Pontianak City. The research employs a quantitative approach, focusing on individuals who have visited and purchased products from the CW Coffee Tanjung Sari branch. The study uses purposive sampling to select respondents, ensuring that the sample represents the target population effectively. Data were analyzed using descriptive statistics, validity and reliability tests, and multiple linear regression analysis, including the t-test. The findings reveal that digital marketing does not significantly influence customer satisfaction, while physical evidence and pricing have significant and positive effects on customer satisfaction. These results underscore the importance of tangible elements and competitive pricing in enhancing customer satisfaction, while also suggesting that digital marketing may need to be reevaluated or adjusted to better meet customer needs and expectations in this context.

Enhancing productiveness in E-commerce through machine learning: Challenges, Future Perspectives, and a MATLAB-Based Approach

The rapid growth of e-commerce has created an urgent need for more productive and efficient systems to handle the increasing complexity of online transactions and consumer behaviour. Machine Learning (ML) offers a transformative approach to enhancing productiveness in e-commerce by optimizing processes such as personalized recommendations, dynamic pricing, customer segmentation, and supply chain management. This article explores the integration of ML-driven initiatives in e-commerce, addressing key challenges such as data privacy, algorithmic bias, and the computational demands of large-scale ML applications. A MATLAB-based approach is proposed for developing and implementing these ML models, leveraging MATLAB’s robust toolboxes for data analysis, model training, and system simulation. Through an extensive literature review, this study highlights the current state of ML in e-commerce, identifies existing challenges, and discusses future perspectives. The introduction of ML-driven strategies not only improves operational efficiency but also enhances customer satisfaction, ultimately driving productivity in the highly competitive e-commerce landscape. The findings of this article are expected to provide valuable insights for both academic researchers and industry professionals aiming to harness the full potential of ML in e-commerce.