American Customer Satisfaction Index Research Articles

Generation Z Satisfaction with Smart Homestays: ASCI and Web Crawler Insights from China

In the digital context, smart homestays have developed rapidly in the post-epidemic era and have become a new form of accommodation. Homestays are favored by many young people in China, such as those belonging to Generation Z. According to data concerning China’s national tourism and related reports, the demand for homestays has increased dramatically in recent years. Thus, we need to consider how to improve the smart homestay user experience. Based on the American Customer Satisfaction Index (ACSI) model, this study explores the factors that affect the user experience of smart homestays. An online survey of 370 respondents of Generation Z in China was conducted, followed by descriptive statistical analysis and hypothesis model validation using SPSS 26.0. The data show that among the five service variables (reservation, check-in, living, check-out, and information sharing), perceived value has a positive and positive impact on service variables in all aspects. Finally, machine learning is used for emotion text analysis, and the results show that users are biased towards smart homestays in the sentiment analysis of the comments. Although smart homestays have a certain amount of attention, there is still a lot of room for progress in technology and services. The purpose of this study is to improve and perfect the rules for making smart homestay service standards based on understanding the satisfaction of Generation Z when using smart homestays while also providing a theoretical basis and practical manuals for the industry to promote the development of the industry and improve user experience. Based on the research results of the above literature, it is imperative to carry out research on Generation Z, the main force of future consumption, especially in the field of artificial intelligence.

Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry

Customer satisfaction (CSAT) is vital in service and marketing, indicating how well products or services meet customer expectations. Traditional CSAT methods like the American Customer Satisfaction Index (ACSI) and Net Promoter Score (NPS) face challenges such as survey fatigue and low response rates. This study introduces a novel framework using advanced machine learning (ML) and deep learning (DL) techniques, specifically Bidirectional Encoder Representations from Transformers (BERT), to classify customer feedback into distinct CSAT drivers. Integrating term frequency-inverse document frequency (TF-IDF) methods with BERT-based embeddings, the framework significantly improves prediction accuracy. Using a proprietary dataset of 5,943 customer feedback responses from 39 companies across 13 industries, the fine-tuned BERT model achieved an F1 score of 0.84, surpassing traditional methods like TF-IDF and support vector machine (SVM) with an F1 score of 0.47, and TF-IDF with multi-layer perceptron (MLP) networks at 0.50. A hybrid approach combining BERT and TF-IDF embeddings with MLP networks yielded an F1 score of 0.71. The results show the transformative potential of DL techniques, particularly fine-tuned BERT models, in enhancing CSAT prediction accuracy. This research bridges the gap between traditional and advanced text mining methods, setting a new standard for CSAT modeling and offering a robust framework for extracting actionable insights from customer feedback. It highlights the importance of adopting advanced ML and DL models for strategic decision-making and improving customer satisfaction measurement.

A Quantitative Study on Factors Influencing User Satisfaction of Micro-Mobility in China in the Post-Sharing Era

In the post-sharing era, the evolution of the shared micro-mobility industry has transitioned from expanding market share to providing precise services. The focus on user satisfaction has shifted from a singular emphasis on functional utility to diversified product values. Product perceived value has emerged as a comprehensive factor for enhancing user satisfaction, aiding companies in formulating precise services, mitigating urban resource wastage, and promoting sustainable urban development. From the perspective of product perceived value, this study combined the analysis of factors affecting user satisfaction of shared micro-mobility and the interaction between these aspects and carried out the following two studies. Research No. 1: By mining the user review data on app platforms related to shared micro-mobility and adopting the latent dirichlet allocation (LDA) algorithm, we have initially identified 17 major factors affecting the satisfaction of users and summarized these factors into four research topics constituting product perceived value. Research No. 2: On the basis of Research No. 1, the content of the American Customer Satisfaction Index (ACSI) was expanded, and a user satisfaction research model focusing on the perceived value of shared micro-mobility products was constructed. Afterwards, by using the data collected in questionnaire surveys, structural equation modeling (SEM) was used to model the user satisfaction of shared micro-mobility through SEM, which was deployed to establish an empirical analysis. It is found that (1) both user expectation and product quality can positively affect the perceived value of products through interactive experience; (2) factors such as user expectation, product quality, interactive experience, and subjective consciousness can positively affect user satisfaction through the perceived value of products, with user expectation delivering the greatest influence; and (3) subjective consciousness has a direct positive effect on users’ willingness to continuously use a product but no significant effect on user satisfaction. These findings expand the user satisfaction theoretical model in the field of shared micro-mobility, constitute suggestions for product development and service promotion in the shared micro-mobility industry, and can provide new ideas and methods for the sustainable development of urban transportation.

Assessing the satisfaction of farmers towards crop insurance scheme using the American customer satisfaction index

Assessing the satisfaction of farmers towards crop insurance scheme using the American customer satisfaction index

The Importance of Perceived Quality for Golf Course Member Satisfaction and Loyalty in Tourism Deprivation Times

This study assessed the relationship between perceived quality, expectations, satisfaction, and loyalty of golf course members, based on an adaptation of the American Customer Satisfaction Index (ACSI). Data collection was performed using a questionnaire, based on Customer Service Quality (CSQ) questionnaire. Structural equation modelling was used to validate the proposed hypotheses. The model presented good fit values, validating the direct, positive, and significant effects between: expectations and perceived quality; perceived quality and satisfaction; and consumer satisfaction and loyalty, which made the ACSI model a reliable tool for golf course managers. This study validated an additional relationship, namely, the positive direct effect of perceived quality on loyalty, which strengthened the model. It also contributed to a better understanding of the factors involved in the management of golf courses regarding the satisfaction and retention of their members.

Customer Perceptions of Firm Innovativeness and Market Performance: A Nation-Level, Longitudinal, Cross-Industry Examination

This paper highlights the importance of innovation in driving economic growth, noting that traditional measures of innovation have focused mainly on manufacturing-related metrics like patents and R&D activities. It addresses the need for new measures that better reflect innovation in service-dominant economies. Specifically, the study highlights nation-level measures of customer perceived firm innovativeness and examines their relationship with firm financial performance. Using data from the American Innovation Index covering 123 publicly traded firms across 20 industries over 5 years (2018–2022), the research finds that customers’ perceptions of a firm’s innovativeness are significant predictors of future abnormal stock returns. Additionally, it reveals a positive relationship between changes in customer satisfaction levels, as measured by the American Customer Satisfaction Index, and abnormal stock returns. Together, these findings point to the importance of customer perceptions on firm performance.

Evaluation of popular science tourism in Guangzhou HEMC based on PLS-SEM

Science popularization is not only a prerequisite for national development, but also an effective means of enhancing citizens' personal quality. All sectors of society, represented by colleges and universities, bear the responsibility of promoting popular science. The integration of popular science and tourism in popular science tourism serves to advance both the field of popular science and the tourism industry simultaneously. The Guangzhou Higher Education Mega Center (HEMC) possesses abundant resources for science popularization and has the potential to develop popular science tourism, yet its current development in this area remains insufficient. This study utilizes Guangzhou HEMC as a case study and modifies the American Customer Satisfaction Index model by incorporating relevant questions pertaining to popular science tourism. A total of 280 valid questionnaires were collected through surveying, which were then analyzed to measure tourist satisfaction using the Tourist Satisfaction Index. The partial least squares structural equation model was employed for analysis, and on the basis of calculation results, the IPA map was constructed. The research revealed that tourists' satisfaction with popular science tourism at Guangzhou HEMC was suboptimal. Among the factors correlating to satisfaction, expectations, quality, and price are all important factors to consider when making a purchase decision; however, prioritizing expectations and quality can lead to greater satisfaction in the long run. Therefore, there is still ample room for improvement in the popular science tourism of HEMC Guangzhou. This can be achieved by intensifying publicity efforts, enhancing infrastructure, improving the quality and safety of catering services, strengthening the introduction and construction of popular science content, as well as appropriately reducing the price of popular science products and services.

Industrial Application of the ANFIS Algorithm—Customer Satisfaction Assessment in the Dairy Industry

As a part of the food industry, the dairy industry is one of the most important sectors of the process industry, keeping in mind the number of employees in that sector, the share in the total industrial production, and the overall value added. Many strategies have been developed over time to satisfy customer needs and assess customer satisfaction. This paper proposes an innovative model based on adaptive neuro-fuzzy inference system (ANFIS) and elements of the ACSI (American customer satisfaction index) for assessing and monitoring the level of customer satisfaction in a dairy manufacturing company where there are no large seasonal variations. In terms of an innovative approach, the base of fuzzy logic rules is determined by applying the fuzzy Delphi technique for the application of the ANFIS algorithm and assessment of customer satisfaction. The verification of the model is delivered by testing a real sample from a company of the dairy industry. As decisions on the strategic company level may be impacted by customer satisfaction, the company management should choose the most precise methodology for customer satisfaction assessment. The results are compared with other methods in terms of mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE). Results show that ANFIS outperformed other methods used for assessing the level of customer satisfaction, such as case-based reasoning and multiple linear regression.

Influencing Factors of Tourist Satisfaction on Tourism Development in Enshi under the Background of Rural Revitalization

The study uses ACSI American Customer Satisfaction Index model to build a theoretical model of tourist satisfaction in Enshi rural tourism resort. Then, taking Tujia Daughter City in Enshi, Hubei Province as a case, the paper analyzes the relevant factors affecting tourist satisfaction and summarizes the relationship between the relevant variables. The empirical study adopted quantitative research method and online questionnaire survey. The data collected in the questionnaire survey were analyzed. This paper mainly adopts descriptive statistics, reliability, validity, factor analysis, correlation analysis, and regression analysis methods. Research has found that there is a significant positive relationship between tourists' satisfaction and their expectations, perceived quality, and perceived value. Customer satisfaction has a significant impact on customer loyalty. There is a significant positive correlation between tourism satisfaction and tourism complaints. To enhance the tourism experience and tourism experience of Enshi city, we should start from two aspects: enhancing the cultural connotation of the tourist destination and improving the surrounding environment of the tourist destination. At the same time, the government will also develop a shared tourism plan and increase efforts to preserve and repair ancient buildings.

Modelling of Passenger Satisfaction and Reuse Intention with Monorail Services in Kuala Lumpur, Malaysia: A Hybrid SEM-ANN Approach

This study employs the fundamental concept of the American Customer Satisfaction Index (ACSI) model to explore the factors influencing passengers’ satisfaction with monorail service in Kuala Lumpur, Malaysia and their reuse intention. The study tests the hypotheses on 417 monorail passengers using a hybrid structural equation modelling based on parameter estimation of partial least squares (PLS-SEM) and an artificial neural network (ANN) method to estimate the proposed model. The results showed that the proposed model explains 70.4% and 59.5% of the variance in passenger satisfaction with the monorail service and reuse intention. The PLS-SEM results for Stage 1 showed that perceived quality and perceived value have a statistically significant influence on passenger satisfaction. Furthermore, all critical factors in the output from Stage 1 were used as the input in the ANN model to overcome the simplistic nature of the SEM model. The results for the ANN model (Stage 2) showed that perceived quality is the most crucial predictor of passenger satisfaction with the monorail service, followed by perceived quality. The outcomes of this study can help service providers, policymakers, and planners develop effective strategies for enhancing user satisfaction and improving monorail ridership.

How Visitors Perceive Heritage Value—A Quantitative Study on Visitors’ Perceived Value and Satisfaction of Architectural Heritage through SEM

In a time when heritage conservation is coordinated with tourism and urban renewal to achieve sustainable development, value is considered as the intrinsic factor of heritage protection, utilization, and management. From the perspective of visitors, this article aims to delve into the key factors of the perceived value of architectural heritage and their relationship with the value types, via structural equation modeling (SEM). This article constructs a research model of the perceived value, perceived enjoyment, visit satisfaction, and post-visit behavioral intention of architectural heritage, based on the American Customer Satisfaction Index (ACSI). The data for the analysis were 271 eligible questionnaires obtained from an anonymous random sample of visitors to architectural heritage sites in Guangzhou, Guangdong (Canton) Province, China. The results show that visitors’ perception of architectural heritage value does not follow the value types proposed by experts, but is based on a comprehensive assessment of personal perceptions. Meanwhile, visitors’ perceived value mainly depends on the artistic value, although the historical value is recognized by experts as dominant, and visitors prefer historical stories that are intertwined with culture. The results also reveal that, as with the perceived value, the perceived enjoyment, which was rarely considered in heritage conservation and management in the past, has a considerable positive impact on visit satisfaction, and can also improve visitors’ perceived value. The proposed theory model and research results can serve as a valuable reference for cultural heritage management and for operators of cultural heritage tourism destinations, and can also provide new ideas and methods for heritage value research.

Park Recreation Intention and Satisfaction of Blue-Collar Workers Based on the ACSI Model: A Case Study of Anning Industrial Park in Yunnan

The negative effects of long working hours and shift work on the physical and mental health of blue-collar groups should not be underestimated. Under intense stress, they have limited time to access recreational green space, and their own health is thus affected. In this study, a conceptual model of recreational satisfaction among blue-collar workers was established based on the American Customer Satisfaction Index (ACSI). The model explores the factors affecting their level of satisfaction when using recreational spaces. Anning Industrial Park in Yunnan Province was used as an example. User data were collected and analyzed using a questionnaire survey and structural equation methods. The results indicate that recreation intention, perceived park quality characteristics, and perceived value all had significant and positive effects on the park recreation satisfaction of blue-collar workers. This study applied social economics theory to the field of landscape planning, identified the typical characteristics of blue-collar workers and their green space use, and strived to optimize the UGS configuration and functional facilities through the evaluation of recreation satisfaction indexes, which provided guidance and reference for improving the service quality of green spaces.

Understanding the link between customer feedback metrics and firm performance

Practitioners utilise customer feedback metrics (CFM's) to monitor business performance. However, the influence of CFM's on firm performance has been ignored. Thus, this paper aims to examine the effects of CFM's on firm performance. Our study collected data about CFM's, marketing efforts, and financial performance over the period 2005–2020 from American Customer Satisfaction Index. The present study used a multiple regression panel analysis to investigate the influence of different CFMs (i.e., SAT, Top-2-Box, NPS proportion, NPS value, and CES) on firm performance (i.e., gross margin, sales growth, and Tobin's Q), moderating by operating environment factors (i.e., munificence, power, and dynamism). Our results revealed that Top-2-box is the best predictor CFM's to compare firms in online booking, hotels, and online shopping industries, while consumer satisfaction is the best predictor for electronic and fixed telecom industries. CES is the best CFM's to compare companies in restaurants industries. Moreover, NPS is the best metric to compare different companies in holiday parks industries. The results provide considerable managerial implications for effective use of resources regarding investing in most suitable CFM's to enhance firm performance.

The Disparity Between Urban andRural Areas in Citizen Satisfaction with Local Public Services

The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate.

Assessing the Satisfaction of farmers towards Crop Insurance Scheme Using the American Customer Satisfaction Index

Assessing the Satisfaction of farmers towards Crop Insurance Scheme Using the American Customer Satisfaction Index

A two-way satisfaction model for sharing economy

Customer satisfaction has been gaining the attention of researchers hoping to develop a model that integrates different constructs to achieve the satisfaction that leads to customer loyalty. Under the sharing economy paradigm, satisfaction is vital to the development of this disruptive business model. Trust between the consumer and the supplier allows its collaboration, and digital platforms connect the consumer with the supplier by creating a framework of trust around transactions. Producer satisfaction is as important as consumer satisfaction; however, it has not had the necessary attention— hence the need and motivation to develop a two-way review system. Based on the American Customer Satisfaction Index, we propose a cause-effect model for the consumer and supplier, connected by the common trust construct. Based on the literature review and choice of relevant publications, we define a conceptual model that will allow the development of a two-way satisfaction model to serve as a contribution to future satisfaction studies in the sharing economy.

Customer Loyalty Analysis On Online Travel Agent (OTA) Using American Customer Satisfaction Index (ACSI) And Structural Equation Modelling (SEM)

Purpose: Knowing what affects customer loyalty in Online Travel Agent (OTA) Services. Which will help OTA Services to understand about customer satisfaction and customer loyalty so that they can develop their business in the future in order to get greater customer satisfaction and loyalty.Design/methodology/approach: Using the American Customer Satisfaction Index (ACSI) Model which explains the antecedents and consequences of customer satisfaction. In Antecedent there are variables of User Expectations, Perceived Quality, and Value benefits that have an impact on customer satisfaction variables then from causing customer complaints and customer loyalty. So that 9 research hypotheses are obtained based on the model used and tested using Structural Equation Modeling (SEM). Research requires data on respondents' answers distributed through digital media with a total need for 385 respondent data.Findings/result: After testing using SEM.The 9 hypotheses proposed show that 2 hypotheses are rejected and 7 hypotheses are accepted.Originality/value/state of the art: Previous research has been done but with different models and different methods.

Construction and validation of a revised satisfaction index model for the Chinese urban and rural resident-based basic medical insurance scheme

BackgroundQuality is the most important factor in satisfaction. However, the existing satisfaction index model of urban and rural resident-based basic medical insurance scheme (SIM_URRBMI) lacks the segmentation of perceived quality elements, it couldn’t provide a reference for quality improvement and satisfaction promotion. This study aims to construct a revised SIM_URRBMI that can accurately and detailly measure perceived quality and provide feasible and scientific suggestions for improving the satisfaction of urban and rural residents' basic medical insurance scheme (URRBMI) in China.MethodsBased on the theoretical framework of the American Customer Satisfaction Index, the elements of perceived quality were refined through literature review and expert consultation, and a pool of alternative measurement variables was formed. A three-stage randomized stratified cluster sampling was adopted. The main decision makers of URRBMI in the families of primary school students in 8 primary schools in Changsha were selected. Both the classic test theory and the item response theory were used for measurement variables selection. The reliability and validity of the model were tested by partial least squares (PLS)-related methods.ResultsA total of 1909 respondents who had URRBMI for their children were investigated. The SIM_URRBMI1.0 consists of 11 latent variables and 28 measurement variables with good reliability and validity. Among the three explanatory variables of public satisfaction, perceived quality had the largest total effect (path coefficient) (0.737). The variable with the greatest effect among the five first-order latent variables on perceived quality was the quality of the medical insurance policy (0.472).ConclusionsThe SIM_URRBMI1.0 consists of 28 measurement variables and 11 latent variables. It is a reliable, valid, and standard satisfaction measurement tool for URRBMI with good prediction ability for public satisfaction. In addition, the model provides an accurate evaluation of the perceived quality, which will greatly help with performance improvement diagnosis. The most critical aspects of satisfaction improvement are optimizing the scope and proportion of reimbursement as well as setting appropriate level of deductible and capitation of URRBMI.

Funding marketing resources and capabilities during a recession: an empirical examination of top corporate advertisers

PurposeThis study aims to ask whether the funding behaviour of companies is different during a recession. Specifically, the authors study whether firms fund marketing resources and capabilities with internal or external financing during a recession and under which conditions of strategic financial flexibility debt might be used to fund marketing resources and capabilities in recessions.Design/methodology/approachThis study estimates empirical models using a newly merged data set covering 17 years, from 2000 to 2016. The authors merge firms’ marketing and financial information from Advertising Age, the American Customer Satisfaction Index, Compustat and the Centre for Research in Security Prices. The sample includes a panel of 653 firm-years of 67 top corporate advertisers.FindingsThe results indicate that firms take recessions as opportunities to be proactive and invest in short- and long-term marketing capabilities, companies with higher strategic financial flexibility relative to their industry peers tend to rely more on debt to fund short- and long-term marketing capabilities during recessions, firms use internal financing to fund their marketing budgets and short-term marketing capabilities in recessionary and non-recessionary periods and firms use internal financing and signals from past stock returns as mechanisms to fund long-term marketing capabilities.Research limitations/implicationsThe findings contribute to the body of knowledge on the antecedents of marketing resources and capabilities. The results extend the pecking order theory to include recessions and provide nuances of the financing drivers of resources and capabilities.Practical implicationsCompanies should be proactive during recessions and invest in short- and long-term marketing capabilities. When negotiating marketing budgets with chief financial officers, marketing practitioners could suggest the sources to finance specific marketing resources and capabilities. Based on the results of top corporate advertisers, the authors recommend companies to fund marketing capabilities with internal resources (e.g. cash flows, retained earnings), and if cash is not available, companies need to rely on their superior strategic financial flexibility to access long-term debt and fund investments in marketing capabilities. The authors also recommend companies to fund long-term marketing capabilities by re-allocating investments. As well, signals from past performance are an important source to gain access to capital and fund investments in long-term marketing capabilities.Originality/valueThis study provides a more complete picture of the financial antecedents of marketing resources and capabilities in general and during a recession. The authors provide light on the moderating role of strategic financial flexibility during recessions. This study also clarifies the potential signalling of past performance for funding marketing resources and capabilities.