Predict Customer Satisfaction Research Articles

The impact of digital banking service quality on customer loyalty: An interplay between customer experience and customer satisfaction

The purpose of this study is to examine the impact of service quality dimensions on customer experience, satisfaction and loyalty in the context of digital banking in Indian public sector banks. Data collection involved purposeful sampling, resulting in 524 valid survey responses. Psychometric characteristics were examined, and the hypotheses were tested using the Hayes process approach. Statistical analysis was conducted using SPSS 25 and AMOS 22 (structural equation modeling software). The study uses the Cognitive-Motivational-Relational (CMR) theory to examine the dynamics of service quality dimensions (RATER) in the context of digital banking, as well as their impact on customer experience and, ultimately, customer loyalty. The study’s findings reveal that service quality dimensions play a significant role in promoting customer experience. The study emphasizes the importance of customer experience as a predictor of customer satisfaction and loyalty. Customer satisfaction has been identified as a strong predictor of customer loyalty. It has also been identified as a mediating factor in the relationship between customer experience and loyalty. Banks and financial institutions can use these findings to optimize their digital banking services and enhance customer experience. Audits of the quality of digital banking services provided by Indian public sector banks will help them to strategize their operations in a highly competitive and digitized environment to meet the ever-increasing demands of today’s tech-savvy, digitally empowered customers who expect a seamless customer experience, thereby attracting new customers and strengthening the relationships with existing ones.

Customers on the Move: Predicting Customer Satisfaction among Mobile Banking Users in Malaysia

In recent decades, the financial industry has seen rapid modernization of information technology and increased competition. Nevertheless, numerous past studies have shown that technological adoption in mobile banking is relatively low in certain Southeast Asian countries. Customers' opposition to mobile banking services is mostly attributed to a lack of knowledge, security worries, and technological difficulties. Therefore, financial service providers need to comprehend and meet customer expectations to enhance the mobile banking experience. This, the present research seeks to examine the determinants of customer satisfaction with mobile banking among Malaysian consumers. The findings demonstrate that perceived ease of use, efficiency, and responsiveness were significant in influencing customer satisfaction. However, interoperability, privacy and security, and reliability were not significant in affecting mobile banking satisfaction. The findings have implications for both theoretical and practical perspectives, highlighting the need for efficient mobile banking services, enhancing financial inclusion, and empowering individuals.

Performance of Machine Learning Models to Predict Customer Satisfaction Scores for Lao National Convention Center

This research paper investigates the effectiveness of various machine learning (ML) models in predicting customer satisfaction scores for the Lao National Convention Center (LNCC). By utilizing a dataset containing customer feedback, demographic data, facilities, and service-related information, the study aims to identify the best performing model for predicting customer satisfaction. In this paper, the performance of ML to predict customer satisfaction scores from the questionnaire survey dataset is evaluated. The customer satisfaction score is categorized into five classes: strongly agree, agree, neutral, disagree and strongly disagree. The analysis will compare the performance of different models, including linear regression (LR), k-nearest neighbor (KNN), support vector machines (SVM), decision trees (DT) and random forest (RF). The results show that SVM model achieved the highest accuracy rate of 95.91%, followed by KNN and LR with an accuracy rate of 95.55% and 95.49%, respectively. The findings of this study have important implications for the use of ML in improving the service-related information for LNCC and providing valuable insights for decision-makers and developers.

Impact of AI on Customer Experience in Video Streaming Services: A Focus on Personalization and Trust

In the digital entertainment landscape, AI-enhanced video streaming services like Netflix and Hulu significantly shape user experiences through AI-based recommendations. This study examines customer perceptions of these AI-driven suggestions, focusing on factors such as Perceived Interactivity, Service Quality, Commitment, Trust, and Personalization. Using data from a diverse sample in Klang Valley, Malaysia, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). PLS-SEM models complex relationships to predict customer satisfaction, while NCA identifies essential conditions for desired outcomes in customer experience. Findings reveal that Personalization significantly influences satisfaction (path coefficient = 0.34), with Trust enhancing the impact of Perceived Interactivity (mediation effect size = 0.15). Expectancy confirmation also moderates the relationship between service attributes and satisfaction (β = 0.09). The results highlight the importance of tailored personalization and robust trust mechanisms in AI systems, emphasizing strategies to improve viewer retention and overall satisfaction.

Forecasting airline passengers’ satisfaction based on sentiments and ratings: An application of VADER and machine learning techniques

To the best of the authors' knowledge, research predicting airline passengers' satisfaction based on their sentiments and ratings is seldom sighted. Additionally, the literature reveals that most studies have primarily concentrated on specific airlines or routes, neglecting to conduct a comparative analysis of satisfaction levels across numerous airlines and routes. Hence, this research aims to predict passengers' satisfaction by combining the sentiment of their reviews and ratings on various parameters like food, entertainment, seat comfort, ground service, and value for money. Using the "Skytrax Airline Reviews" dataset, which contains data about 81 airlines and 64440 reviews, our research analyzes and predicts airline passengers' satisfaction based on sentiments and ratings using nine popular machine learning techniques. The study found that the LightGBM obtains an accuracy of 97 percent in predicting customer satisfaction. The results further reveal that 'value for money' and 'ground service' are crucial factors in determining the passengers' satisfaction, whereas 'entertainment' had no significant impact. Our study thus provides a valuable tool for predicting airline industry customer satisfaction and gives insight into the factors contributing to passenger satisfaction. These findings can further help airlines better understand their passengers' needs and improve their services accordingly.

Determinants of customer satisfaction in Nepalese banking sector

This study attempts to explore the factors impacting customer satisfaction in Nepal’s banking industry with a focus on the service quality that the banks offer. The respondent and the study area of the authors are 20 banks located in the Kathmandu Metropolis. Descriptive statistics, Pearson correlation analysis and regression analysis were employed to assess the subjects under study. The Pearson correlation results indicated a positive correlation between customer satisfaction and service quality characteristics. The ordinary Least Squares (OLS) regression model showed tangibility and empathy as key factors. The regression model, however demonstrates tangibility, responsiveness, and empathy as significant in influencing customer satisfaction. Study findings affirm the validity of hypothesis (H1) and highlight the importance of tangibility, responsiveness, and empathy in predicting customer satisfaction, suggests the enhancement of banks’ competitive advantage by improving their service quality.

Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches

The rapid growth of e-commerce has increased the need for retailers to understand and predict customer satisfaction to support data-driven managerial decisions. This study analyzes online consumer behavior through a comparative machine learning modeling approach to forecast future customer satisfaction based on review ratings. Using a large dataset of over 100 k online orders from a major retailer, traditional machine learning models including random forest and support vector machines are benchmarked against deep learning techniques like multi-layer perceptrons. The predictive models are assessed for their ability to accurately predict customer satisfaction scores for the next orders based on key e-commerce features including delivery time, order value, and location. The findings demonstrate that the random forest model can predict future satisfaction with 92% accuracy, outperforming deep learning. The analysis further identifies core drivers of satisfaction such as delivery time and order accuracy. These insights enable retail managers to make targeted improvements, like optimizing logistics, to increase customer loyalty and revenue. This study provides a framework for leveraging predictive analytics and machine learning to unlock data-driven insights into online consumer behavior and satisfaction for superior retail decision-making. The focus on generalizable insights across a major retailer enhances the practical applicability of the machine learning approach for the retail sector.

Measuring the impact of healthcare service quality of hospitals on customer satisfaction

Various studies have been conducted to measure the level of service provided by an institution or service, including hospitals, in order to determine the level of customer satisfaction. The purpose of this research is to assess the role of the quality of services offered by a hospital in predicting customer satisfaction. This study was conducted at Bunda Purwokerto Hospital. The participants consisted of 380 samples. Hypothesis testing using the Smart PLS method yielded an R-square value of 0.413 for the customer satisfaction variable. The results of H1 revealed a favourable influence on the satisfaction of outpatient patients at Bunda Purwokerto Hospital. The original sample produced a coefficient of 0.130, a t statistic of 2.163 (> 1.96), and a p-value of 0.031 (< 0.05). The relationship between trust and job performance was deemed positive and significant, as evidenced by the t-statistic exceeding 1.96. This study confirms that an improvement in the quality of healthcare services shapes patient satisfaction and loyalty. Aspects encompassing healthcare service quality, such as the physical environment, customer-oriented atmosphere, responsiveness, effective communication, privacy maintenance, and safety, demonstrate a positive correlation with patient loyalty towards the hospital’s image.

PREDIKSI KEPUASAN PELANGGAN HOTEL: STUDI PERBANDINGAN ALGORITMA DECISION TREE DAN KNEAREST NEIGHBOR

Customer satisfaction has become an important aspect for every business in today's competitive market. Understanding customer needs, wants, and expectations is critical for a business to provide outstanding customer service and retain customers. Therefore, this research represents a comparative study between two machine learning algorithms, Decision Tree and K-Nearest Neighbor, to predict hotel customer satisfaction. This study aims to identify which algorithm is more effective in predicting customer satisfaction by evaluating their performance using various metrics. The methodology used includes data preprocessing, feature selection, and machine learning model creation. The results show that the Decision Tree algorithm is superior to the K-Nearest Neighbor in terms of accuracy and precision. The findings from this study provide insights for businesses in the hospitality industry on how to predict customer satisfaction and improve their services.

Use of Factor Scores in Multiple Regression Model for Predicting Customer Satisfaction in Online Shopping

Online shopping can be done from our convenient places like home, office, etc., and the product will be delivered to the respective places. There are many factors influencing online shopping. The purpose of this study is to develop a statistical model that is used to determine the factors that influence online shopping. In this study, using factor analysis five main factors have been obtained from 15 variables that influence online shopping. These five factors have significant effects on satisfaction of customers and accounted up to 56% of total variation. Using the factor scores as independent variables, multiple regression model has been developed for predicting customers satisfaction in online shopping. Customer satisfaction has been used as dependent variable in the regression model. The five main factors that contribute online shopping are: preference of consumers towards online shopping, the risk involved in purchasing products through online, time effectiveness in online shopping, difficulties faced during online shopping and getting products from trustworthy websites.

Analyzing Restaurant Reviews to Predict Customer Satisfaction Trends

Numerous studies comparing the quality of various restaurants' cuisines have been conducted. Area, average cost for two people, votes, cuisines, mostly flavour, and restaurant type are some of the criteria used to evaluate restaurants. Finding out which restaurants people like to eat at and reading reviews of such places is the primary objective here. The goal of this study is to develop a model that can foretell if a review of the eatery will be favourable or negative. A number of prediction algorithms, including Multinomial Naive Bayes, SVC, XGB Regressor, Pipeline, and Logistic Regression, will be utilised to achieve this goal. Finally, we'd like to identify the "best" model that can forecast the reviewer's emotional tone.

Factors that Influence to Costumers Sharia Bank Loyalty: An Empirical Study of Students at Universitas Darussalam Gontor Ponorogo

Introduction: The purpose of this research is to predict customer satisfaction, customer perceived value, and customer Islamic religiosity on Sharia bank customer loyalty. Research Methods: The research used a quantitative approach with 140 respondents who were Sharia bank customers, specifically students at Universitas Darussalam Gontor, Ponorogo. A Likert scale of 1-5 was employed for measurement, and partial least squares–structural equation modeling was used to analyze the data. Results: The results of this research demonstrate that customer satisfaction, perceived value, and Islamic religiosity have a significantly positive impact on Sharia bank customer loyalty. Conclusion: To build and maintain customer loyalty, Islamic banks must leverage customer satisfaction, value, and religiosity by offering a variety of Sharia-based products and services that truly differentiate them from conventional banks.

Improved Random Forest Based on Grid Search for Customer Satisfaction Prediction

Customer satisfaction is an important influencing factor that affects the survival and development of an enterprise, especially a service-oriented enterprise, then predicting customer satisfaction can achieve a continuous and steady improvement of customer satisfaction of an enterprise. In this paper, we improve the random forest algorithm based on grid search, and use the improved random forest model to predict airline customer satisfaction scores and rank the importance of customer satisfaction influencing factors by minimizing the average root mean square error of the model as the goal of the preferred parameters. The research results show that airlines should focus on Online boarding, In-flight Wi-Fi Service, Type of Travel and Class when improving customer satisfaction, which provides a reference for service-oriented companies to improve their economic efficiency.

Enhancing customer satisfaction in the ecuadorian food and beverage delivery sector: a SERVQUAL-Based Analysis

This paper explores the pivotal role of service quality in shaping customer satisfaction within the context of the Ecuadorian food and beverage delivery sector. Grounded in the SERVQUAL model, this research examines five essential dimensions of service quality: tangibility, reliability, responsiveness, assurance, and empathy. The study employs a quantitative research methodology, utilizing a Likert scale-based questionnaire to collect data from 323 respondents in the provinces of El Oro and Loja. The study reveals substantial positive correlations between all dimensions of service quality and customer satisfaction, underscoring the importance of these dimensions in influencing customer perceptions. A multiple regression analysis demonstrates that each dimension significantly predicts customer satisfaction. Specifically, tangibility, including physical presentation and aesthetics, reliability, emphasizing order accuracy and consistency, responsiveness, focusing on prompt customer service, assurance, which includes employee knowledge and courtesy, and empathy, characterized by personalized attention, all contribute significantly to customer satisfaction. The multiple regression model explains 62.3% of the variance in customer satisfaction. These findings have significant implications for businesses in the Ecuadorian food and beverage delivery sector. They highlight the necessity for companies to prioritize and invest in improving service quality across these dimensions to enhance customer satisfaction and maintain a competitive edge in the market. Staff training and motivation are identified as critical factors in consistently delivering exceptional service in alignment with customer expectations. While the study provides valuable insights, it is essential to acknowledge its geographic limitations, as the research focuses exclusively on two provinces. Future research endeavors should expand the sample size and geographic scope to obtain a more comprehensive understanding of customer preferences and service quality dynamics across various regions and segments of the industry.

Tourist personality, value co‐creation, and emotional well‐being

AbstractThis study aimed to examine value co‐creation as a predictor of customer satisfaction and emotional well‐being using a comprehensive research model. It investigated the personal (customer extraversion and agreeableness) and situational (customer–employee rapport) variables that influence value co‐creation (customer participation and citizenship behaviors) and, consequently, lead to customer satisfaction and emotional well‐being. The tourism sector was selected for the study because value co‐creation, satisfaction, and emotional well‐being are crucial for hotel managers and academic researchers in the dynamic and competitive service environment. Around 400 tourists in Turkey participated in the field research. Data analysis with partial least squares structural equation modeling showed that extraversion and agreeableness positively influenced customer–employee rapport and value co‐creation; agreeable and extroverted customers are more likely to positively interact with the service staff and engage in value co‐creation. Customer–employee rapport positively affects value co‐creation, customer satisfaction, and emotional well‐being. Value co‐creation has a significant positive impact on customer satisfaction and emotional well‐being. The results contribute to the literature by testing an original model illustrating customers' role in value co‐creation. Customer personalities shape value co‐creation in dyadic exchange relationships. Their personalities, behaviors, and interactions during the service process shape their satisfaction and emotional well‐being. This study contributes to the social exchange theory and service‐dominant logic perspectives by providing empirical evidence regarding the significant direct impact of value co‐creation on tourists' emotional well‐being.

Data-driven implicit design preference prediction model for product concept evaluation via BP neural network and EEG

Customer preference-involved product concept evaluation is a vital stage for developing new products. One of the key factors in this stage is to mine the potential relationships between customers’ needs and technical requirements. In most new product development (NPD), design characteristics (DCs) are employed as performance indicators to describe the technical requirements of a scheme. Previous studies have focused on evaluating the degree to which a scheme meets the performance value of DCs, disregarding the relationship between DCs and potential user preferences. This oversight leads to an inability to accurately predict customer satisfaction with the candidate schemes based on their preferences. Previous research, which was aimed at ensuring the preference value of schemes by utilizing mapping data between design preference (DP) and DCs, overlooked users' psychological states and thus failed to accurately capture their cognitive preferences for the schemes. To address these issues, an electroencephalogram (EEG) data-driven, implicit DP prediction approach is proposed. The method characterizes the implicit cognitive relationship between DP and DCs by mining EEG data and utilizes a back propagation (BP) neural network to predict DP. First, a user's DP word pairs were constructed based on the approach of statistical analysis, and the best-worst method (BWM) was introduced to obtain the importance of DCs. Second, an experiment was designed based on the extracted preference word pairs to collect EEG data that reflect a user's DC preference, record the user's stimulation state toward the DCs, and label their DP based on the segmentation results of preference evaluation. Third, a BP neural network was employed to construct a preference prediction model for DCs, and the reliability of the proposed preference prediction model was validated by using the forklift design example. The prediction results further exhibited the potential association between user preferences and DCs that can be acquired by EEG data mining. The results also provide support for designers to promptly acquire design concepts that satisfy user requirements (URs), thereby enhancing the efficiency and quality of product development in a highly competitive market.

The Influence of Service and Product Quality on Customer Satisfaction: A Study of UBA-Ventures PLC Bambili, Cameroon

Purpose: Service quality and customer satisfaction are very important concepts that companies must understand if they are to grow and remain competitive in the business environment. Service and product quality is considered to be very critical to any modern business because it contributes to higher customer’s satisfaction. The purpose of this study was to find out the implication of service and product quality on consumers’ satisfaction, particular focus was placed on variables like responsiveness, reliability, empathy and how they affect consumers’ satisfaction.
 Methodology: To accomplish this task both quantitative and qualitative research approaches were applied. A sample of 150 respondents was selected from the customers of UBa ventures PLC Bambili using a simple random sampling technique. Questionnaire was the main instrument for data collection for the study. The data collected was analyzed using both descriptive and inferential statistics. The ordinary least square (OLS) was use to verify the
 Findings: We observed that, responsiveness, reliability has a positive effect and empathy has a negative effect in predicting customer satisfaction. From the finding, the results also reveal an interesting relationship between service and product quality on customer satisfaction. We observe that all the indicators of service and product quality have significant effect on customer satisfaction, thus this findings can help organization gain competitive advantage by improving their service and product quality in terms of reliability, responsiveness and empathy
 Unique Contribution to Theory, Policy and Practice: It was recommended that, hospitality industries should create a complaint desk where customers can register their complaints and a systematic procedure to handle customer’s complaints. Also, proper explanation and use of a well understood language is vital for effective communication of employees. Management should put in place strict guidelines to be followed while dealing with customer’s complaints to be satisfactorily handled and thus increase the level of customer satisfaction.

Predictors of Customer Satisfaction with Mass Transit Transport Scheme: Evidence from an Emerging Economy

Predictors of Customer Satisfaction with Mass Transit Transport Scheme: Evidence from an Emerging Economy

An empirical assessment of customer satisfaction of internet banking service quality – Hybrid model approach

PurposeThe present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.Design/methodology/approachThe qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.FindingsThe success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.Research limitations/implicationsThis research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.Originality/valueThe conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

An Adaptive Layer to Leverage Both Domain and Task Specific Information from Scarce Data

Many companies make use of customer service chats to help the customer and try to solve their problem. However, customer service data is confidential and as such, cannot easily be shared in the research community. This also implies that these data are rarely labeled, making it difficult to take advantage of it with machine learning methods. In this paper we present the first work on a customer’s problem status prediction and identification of problematic conversations. Given very small subsets of labeled textual conversations and unlabeled ones, we propose a semi-supervised framework dedicated to customer service data leveraging speaker role information to adapt the model to the domain and the task using a two-step process. Our framework, Task-Adaptive Fine-tuning, goes from predicting customer satisfaction to identifying the status of the customer’s problem, with the latter being the main objective of the multi-task setting. It outperforms recent inductive semi-supervised approaches on this novel task while only considering a relatively low number of parameters to train on during the final target task. We believe it can not only serve models dedicated to customer service but also to any other application making use of confidential conversational data where labeled sets are rare. Source code is available at https://github.com/gguibon/taft