Predict Customer Satisfaction Research Articles

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

Focusing on Key Customers and Customer Knowledge Management as Predictors of Customer Satisfaction among Graduating Students of UMTC

This study aimed to determine if focusing on key customers and customer knowledge management were predictors of customer satisfaction among graduating students of UMTC. 300 graduating students of UMTC were the respondents of this research. The statistical tools used were Mean, Pearson-r, and Regression Analysis. Quantitative non-experimental research utilizing correlational technique with regression analysis was the research design of this paper. Results revealed that the level of focusing on key customers among graduating students in terms of needs assessment, customized services, ongoing dialogue and employee empowerment was high. The level of customer knowledge management yielded a descriptive equivalent of high in terms of knowledge creation, knowledge acquisition, knowledge sharing and knowledge storage. For the level of customer satisfaction of graduating students of UMTC, it yielded a high-level result in terms of customer satisfaction, customer loyalty and customer retention. There was a positive significant relationship between focusing on key customers and customer knowledge management to customer satisfaction among graduating students. In addition, both the focusing on key customers and customer knowledge management was a predictor of customer satisfaction among the graduating students of UMTC.

AN APPROACH TO PREDICT CUSTOMER SATISFACTION WITH CURRENT PRODUCT QUALITY

Improving product quality is still a challenge; therefore, this article aims to propose an approach to predict customer satisfaction. We implemented the following techniques: the SMART(-ER) method, brainstorming (BM), a Likert-scale survey, the Pareto rule, the WSM method, and the Naive Bayes Classifier. Customer expectations were obtained as part of the survey research. Based on these, we determined customers’ satisfaction with the current quality of the criteria and the weights of these criteria. We then applied the Pareto rule, the WSM method, and the Naive Bayes Classifier. In the proposed approach, it was predicted that current product quality is not very satisfactory to customers; that conditioned the need for improvement actions. The originality of the study is the ability to predict customer satisfaction while taking into account the weights of this criterion. The proposed approach can be used for any product.

Modes preču tirdzniecība Covid-19 ietekmē

The internet is gradually becoming the main channel for the distribution of retail goods. It is also a binding distribution channel for the sale of fashion goods. It allows the retailer to post photos, video, and audio material showcasing their latest collections and sell the goods directly to the consumer through a website. According to data from the Interactive Media Retail Group, for example, the UK’s online retail turnover increased by 42% in September 2020, 35.7% in October, and 39% in November compared to the previous year. eBay also confirms this trend – every 7 seconds a product is sold on their site. The aim of this article is to analyze research about online fashion retailers conducted by US and British researchers and the results of surveys among Indian and Latvian students. Who sells fashion items online? A survey of Indian college students (Shanthi, Kannaiah, 2015) and 126 Latvian university students, as well as different researches, show that retailers offering a narrow range of goods are best represented. On the other hand, companies with recognizable brands do not use the internet. There is a conflicting opinion about the involvement of designers in the implementation of their models thru the internet. The next issue addressed in the research and surveys was what helps to create a good fashion website. Then (1999) considers three important visual aspects of a successful website for clothing buyers: - an online image of the goods that most closely matches the actual product; - demonstration of similar products; - ability to view the product from different angles (preferably in three dimensions). In the survey, Latvian students noted the following aspects: - information (information corresponds to the respective task); - possibility to perform electronic transactions; - response time. How do fashion retailers design websites? Ashwort (2005) has summarized different approaches and offers a five-step method. He believes that the success of retailers is determined by the amount of knowledge they have – moving from one stage to another. There are also differences in business planning and strategy choice, and no single dominant strategy exists. For example, some companies take a detailed, formal, well-thought-out approach to decision-making, but some have spontaneous decision-making. Who buys online? There is an opinion in life and also in literature that shopping through a catalog and mail is financially insecure. However, e-commerce is complicated further by the seemingly higher risks of securing on the internet. Respondents of foreign researchers note the possibility of buying goods without leaving home as the main positive reason. For Latvian students, the reasons are much lower prices than in local stores and more original goods unavailable in the local market. Key findings: Competition between websites is based on the way information is presented and what information is provided by website retailers. Information relevant to the task, opportunity to make a purchase, response time, price, and incentive programs are important factors in predicting customer satisfaction. There is no difference in consumer behavior between fashion innovators and ordinary buyers of fashion goods.

‘Stimuli Are All Around’—The Influence of Offline and Online Servicescapes in Customer Satisfaction and Repurchase Intention

In the last decade, multichannel retailing has grown at a significant rate, especially in emerging markets such as Indonesia, where retailers have embraced multichannel marketing to heighten the customer shopping experience. Improved servicescape—the environment where services are provided and where interaction with customers occurs—may help enhance the customer experience. While the role of servicescapes has been analyzed in online or offline contexts previously, this research adopts a multichannel view and pioneers the investigation of the interplay of offline and online (mobile app) servicescapes and their effect on customer satisfaction, as antecedents of repurchase intention, among three types of Indonesian retailers. The study analyzes data from N = 171 Indonesian multichannel customers who have shopped in physical stores and via the store’s mobile app. The results show that ambient conditions are the strongest predictor of perceived quality of offline servicescape, whereas store layout and functionality are the strongest predictor of perceived quality of online servicescape. Both online and physical perceived quality of servicescapes predict customer satisfaction, with the latter having a stronger effect on satisfaction. The study offers practical recommendations for retailers seeking to improve loyalty and customer retention.

Customer Satisfaction and Intention to Use Mobile Financial Services: A Systematic Literature Review

The study aims to provide a comprehensive literature review on customer satisfaction and intention to use mobile financial services, to find out the limitations of existing studies, and to guide a direction for future research. This research is narrative and qualitative in nature. There are 58 relevant and related articles selected for the systematic review which were mostly collected from the Scopus database and a few on snowball systems from other authentic databases. Weight analysis performs to identify the best predictors of the existing literatures and descriptive data are presented in tabular and graphical form. The study revealed that D&M IS Success model, TAM, and UTATUT theories have mostly been employed in existing literatures and SEM as frequently applied statistical techniques. Weight analysis disclosed that perceived usefulness, confirmation of expectation, trust, service quality, system quality, information quality, and perceived cost are the best predictors of customer satisfaction and perceived usefulness, satisfaction, self-efficacy, perceived benefit, hedonic motivation, perceived ease of use, and perceived risks are the best predictors of Intention to use MFS. These findings have implications for academicians to take into account such variables for developing new research frameworks and for practitioners in setting business strategies.

Predicting Customer Satisfaction through Sentiment Analysis on Online Review

User-generated content, such as user reviews, posts, tags, ratings, and opinions on the internet, can be used as a business indicator if collected and appropriately analyzed. One of the examples is predicting customer satisfaction through implementing big data analytics on online reviews. In analyzing the user-generated content to predict customer satisfaction, the author implements machine learning approach using the Sentiment Analysis method. Five-fold cross-validation was performed to train the classification model. The training was performed with a combination of tokenization methods: term frequency-inverse document frequency (tf-idf) and bag-of-words; n-gram types: unigram, bigram, trigram, and combination of unigram, bigram, and trigram; and machine learning algorithms: linear support vector classification (LinearSVC) and multinomial naïve bayes (MultinomialNB). The result was then evaluated using classification performance metrics such as precision, recall, F1 measure, and AUC score. The result shows that the tf-idf vectorizer performs similarly to the bag-of-words method. A similar result was also observed for machine learning algorithm selection. Both MultinomialNB and LinearSVC produce the same performance. Low-level n-grams (such as unigrams and bigrams) tended to have higher precision, recall, F1 measure, and AUC score than high-order n-grams (such as trigrams). The best results were achieved by combining unigrams, bigrams, and trigrams, resulting in an average performance score of 0.94 for all measurements. From the result and analysis, the author finds that predicting customer satisfaction using text and sentiment analysis methods on user-generated content is possible. The model’s performance in this experiment is decent, with high precision, recall, F1, and AUC score.

Predicting Customer Satisfaction through Sentiment Analysis on Online Review

Predicting Customer Satisfaction through Sentiment Analysis on Online Review

Predicting Customer Satisfaction of Online Shoppers Using AI – A Theoretic Framework

Predicting Customer Satisfaction of Online Shoppers Using AI – A Theoretic Framework

The Value of Core and Noncore Activity Fit for Corporate Social Responsibility: An Expectation-Based Study of Nonprofit Hospitals

Corporate social responsibility (CSR) fit is the congruence between an organization's CSR and core activities. Previously, researchers have typically studied CSR fit as a single dimension, often finding that high-fit CSR is more effective than low-fit CSR for for-profit firms. The current research distinguishes between two dimensions of CSR fit—CSR that fits with core or noncore organizational activities—to uncover how the magnitude of investment in each type of CSR affects customer satisfaction. The authors examine the valuable and understudied context of nonprofit organizations, specifically hospitals. Results of a six-year study of over 1,500 nonprofit hospitals suggest that noncore activity CSR positively predicts customer satisfaction, but core activity CSR does not. These findings are best explained using expectation-based theories (expectancy violation and disconfirmation): customers likely expect core activity CSR from hospitals (e.g., charity care for uninsured patients) but are delighted by noncore activity CSR (e.g., green-space renovations). In addition, noncore activity CSR becomes even more important for customer satisfaction for more affluent hospitals, whereas core activity CSR becomes increasingly important for hospitals when the community socioeconomic status is low. These nuanced results provide guidance for hospitals regarding specific CSR investment strategies that ultimately impact downstream financial reimbursements.

Service Quality Dimensions as Predictors of Customer Satisfaction in the Civil Registry

This predictive study examined the service quality dimensions as predictors of customer satisfaction in the Civil Registry of the Island Garden City of Samal, Davao del Norte, Philippines. This study involved 300 customers answering the adapted survey questionnaire via face-to-face data gathering. The descriptive analysis illustrates that the Civil Registry provides a relatively high level of service quality dimensions in terms of reliability, responsiveness, empathy, assurance, tangibility, ease, and timeliness. It also discloses that customers of the Civil Registry are highly satisfied with the process, personnel, facilities, and value for money. Moreover, the relationship test reveals that the correlation coefficients between service quality dimensions and customer satisfaction in the Civil Registry indicate a moderate to a strong positive relationship, suggesting the rejection of the study’s null hypothesis. Furthermore, among the service quality dimensions, reliability, empathy, ease, and timeliness significantly contribute to customer satisfaction, indicating the rejection of the null hypothesis, which states that there are no service quality dimensions that significantly predict customer satisfaction. The multiple correlations on customer satisfaction regarding service quality are very strong, with 77.6%. The estimated model by 60.2% of the variability, which consists of the contributors to customer satisfaction, is strong enough. The Civil Registry may sustain a high level of service quality dimensions to exceed customer expectations and improve its policies on the performance measure. Other researchers may conduct similar studies using the four dimensions of service quality in predicting customer satisfaction in different industries in a more extensive scope and datasets.

Determinants of service quality influencing customer satisfaction in fitness centers: A systematic review

The aim of the present systematic review was to investigate the determinants of fitness center service quality that most influence customer satisfaction. Studies were identified from searches in PubMed, Scopus, Web of Science and EBSCO databases from 2017 through 2022. The search process was carried out by four independent researchers. Titles and abstracts were reviewed to determine whether the studies met the inclusion criteria. The quality of the reporting of the measures was assessed using a tailored list. The electronic search strategy produced 378 studies. A total of 17 articles met the inclusion criteria. Results of the review support the importance of service quality to improve customer satisfaction, leading to customers future intentions with the brand, increasing loyalty and customer retention. Therefore, positive perceptions of service quality influence customer satisfaction, in turn customer loyalty and retention, and are indispensable for the sustainability of these companies. Through the studies included in this systematic review it was possible to identify nine dimensions of service quality as predictors of customer satisfaction: facility quality, instructors and reception staff quality, price, supporting services, service recovery, service assurance, online marketing, and customer relations. This article provides useful and global information on the most current service quality factors. Given the fitness market's competitiveness, understanding the factors that influence service quality allows managers to better choose their strategies for increasing customer satisfaction.

Factors predicting customer satisfaction in online hotel booking using machine learning technique. Evidence from developing countries.

Factors predicting customer satisfaction in online hotel booking using machine learning technique. Evidence from developing countries.

Predicting Customer Satisfaction of Online Shoppers Using AI – A Theoretic Framework

Predicting Customer Satisfaction of Online Shoppers Using AI – A Theoretic Framework

Factors predicting customer satisfaction in online hotel booking using machine learning technique: evidence from developing countries

Factors predicting customer satisfaction in online hotel booking using machine learning technique: evidence from developing countries

Customer and Employee Satisfaction in Hotels

Various academic studies have been conducted on customer and employee satisfaction in the service industries. This research focuses on the hotel sector, exploring 1) job satisfaction (intrinsic, extrinsic, and general) in departments whose employees most frequently come into contact with hotel customers (reception, housekeeping, bar and restaurant, kitchen, and entertainment); and 2) customer satisfaction for these departments. The aim of this research is to examine customer and job satisfaction in the aforementioned departments in greater depth. The research was carried out at the beginning of the 2021 summer season, during the COVID-19 pandemic. A sample of 1,488 individuals was analyzed to find out if employee job satisfaction can predict customer satisfaction. Customer satisfaction was minimally explained by the three dimensions of employee satisfaction (intrinsic, extrinsic, and general), although not in all departments. Extrinsic employee satisfaction achieved higher results than intrinsic employee satisfaction in the reception, kitchen, and bar and restaurant departments. The results show a clear difference between departments that are managed directly by the hotel and those that are outsourced (housekeeping and entertainment), which did not show a predictive relationship between employee and customer satisfaction.

Do quality and business excellence models improve quality and operational results in educational organizations? A repeated cross-sectional analysis

Improving quality and operational practices in education organizations and developing a culture of continuous improvement are crucial yet challenging endeavors because of the complex nature of education organizations. While quality management practices can clearly improve the quality of these organizations in several aspects, empirical research for quality management in educational organizations is sparse because of the lack of suitable frameworks for assessing quality. In this paper, we present the first large-scale empirical study of implementing quality management practices in educational organizations in the United States. It is based on seven years of objective data for quality assessments by external professional reviewers as part of the U.S. government’s Malcolm Baldrige National Quality Award (MBNQA) program. Our findings provide empirical evidence for the reliability and robustness of the Baldrige model as an effective quality assessment for educational organizations. Controlling for the applicant’s year, our findings show that Information and knowledge management is a key influencer and predictor of Customer satisfaction and focus, and Management of process quality is a key influencer and predictor of Quality and operations results. This study also provides effective insight and recommendations for how to improve quality systems and achieve business excellence in educational organizations using the Baldrige model. The findings will therefore help managers and administrators to make informed decisions about key factors that impact quality in educational organizations.

Commuters’ Perception using Public Transport Services and its Impact on Passenger Satisfaction: The Case of Sodo Town, Wolaita Zone, Southern Ethiopia

Public transport should become part of a solution for sustainable transport in the future. However, to keep and attract more passengers, public transport must have high service quality to satisfy and fulfil a wide range of different customer’s needs. The purpose of the study is to examine the influencing factors measuring customers’ satisfaction, especially the impact of the factors on customers’ satisfaction with public transportation in Wolaita Sodo Town of SNNPR. The data was gathered through a structured questionnaire from the public transport commuters. A cross-sectional survey was conducted among 385 randomly selected public transportation passengers in Wolaita Sodo Bus Terminal, Wolaita Zone. Descriptive and Inferential statistics to analyze the data using SPSS version 22. Under Inferential statistics, multiple regression analysis was used to identify the relationships between factors influencing customer satisfaction and commuters’ satisfaction. The results showed that the Fare system has the highest impact on customer satisfaction, followed by Transport comfort, Services provided and Scheduled timings. Three other factors, namely accessibility, Availability of facilities and Terminal environment, were insignificant to customer satisfaction. The analysis of the variance of demographic variables on customer satisfaction with public transport showed that gender, Age, and marital status were statistically insignificant in predicting customer satisfaction. In contrast, Religion, income level, and travelling frequency were statistically significant in predicting customer satisfaction. A flexible and affordable fare rate system should be encouraged and implemented for all routes at different distances to make the service more affordable, satisfy customers’ requirements, and be competent and profitable for the enterprise.

Predicting Customer Satisfaction and Intention to Revisit a Local Coffee Shop with Industrial Concept in Jakarta

This study examines the factors that influence customer satisfaction and visiting intentions at a local industrial coffee shop. This study uses six measured variables: service quality, food quality, variety of menu, café atmosphere, customer satisfaction and revisit intention. The data collection method used a survey with an instrument in the form of a online questionnaire. Respondents are those who live in DKI Jakarta and have made purchases and visits to local coffee shop with industrial concepts. A total of 246 respondents were involved, the data were analyzed using SPSS 26 and SEM from AMOS software version 26 to manage and analyze data. The results of this study based on the hypothesis indicate that the hypothesis of service quality on revisit intention is accepted and significant, service quality on customer satisfaction is accepted and significant, food quality on revisit intention is accepted and significant, food quality on customer satisfaction is accepted and significant, customer satisfaction on revisit intention is accepted. and significant, variety of menu on customer satisfaction is accepted and significant, variety of menu on revisit intention is accepted and significant, café atmosphere on customer satisfaction is accepted and significant, café atmosphere on revisit intention is accepted and significant.