The survey sites selected for this study was the parking lots of one food tourism factory in Taipei, Taiwan. A domestic group package and individual tourists were a major source of respondents who were willing to participate in the survey and completed the questionnaires themselves based on their perceptions of their factory tour experience.
Four research assistants were trained to conduct the survey regarding to questionnaire distribution and sampling. To minimize prospective biases of visiting patterns, the survey was conducted at different times of day and days of week—Tuesday, Thursday, Saturday for the first week; Monday, Wednesday, Friday and Sunday for the next week.
The afternoon time period was used first then the morning time period in the following weeks. The data were collected over 1 month period.
Of tourists invited to complete the questionnaire, effective responses were obtained usable response rate of The sample of tourists contained more females The majority of the respondents Researchers have claimed that satisfaction levels differ according to gender, age, socioeconomic status, and residence Bryant and Cha Moreover, the needs, preferences, buying behavior, and price sensitivity of customers vary Kutner and Cripps Previous studies have demonstrated that it is crucial to measure the relative impact of each attribute for high and low performance satisfaction Matzler et al.
To determine the reasons for differences, a satisfaction scale was used to group the sample into satisfied 8—10 and dissatisfied 1—7 customers. The research model was tested using SmartPLS 3. In particular, it has been successfully applied to customer satisfaction analysis. The PLS method is a useful tool for obtaining indicator weights and predicting latent variables and includes estimating path coefficients and R 2 values.
The path coefficients indicate the strengths of the relationships between the dependent and independent variables, and the R 2 values represent the amount of variance explained by the independent variables. Using Smart PLS, we determined the path coefficients. Every path coefficient was obtained by bootstrapping the computation of R 2 and performing a t test for each hypothesis. Fornell et al. From the results shown, the R 2 values for the customer satisfaction were 0.
Thus, the TCSI model explained 53 vs. Path estimate of the TCSI model for satisfied customers. Path estimate of the TCSI model for dissatisfied customers. According to the path coefficients shown in Figs. Therefore, H1—H3 were accepted. Thus, H4 was accepted but H5 and H6 were not accepted.
Accordingly, the analysis showed that each of the antecedent constructs had a reasonable power to explain the overall customer satisfaction. These results confirm H8 and H9. The results showed that the TCSI models were all close fit for this type of research.
This study provides empirical evidence of the causal relationships among perceived quality, image, perceived value, perceived expectations, customer satisfaction, and customer loyalty. To observe the effects of antecedent constructs of perceived value e. Regarding the effect of the antecedents of customer satisfaction e. The total effects of image on the customer satisfaction of satisfied and dissatisfied customers were 0.
Thus, the satisfaction level of satisfied customers was affected more by perceived quality. Consequently, regarding customer satisfaction, perceived quality is more important than image for satisfied and dissatisfied customers. Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the CSI model e.
With respect to the effect of the antecedents of customer loyalty e. In other words, the customer loyalty of satisfied customers was affected more by customer satisfaction. Consequently, regarding customer loyalty, customer satisfaction is more important than image for both satisfied and dissatisfied customers.
This study empirically supports the notion that customer satisfaction is positively related to customer loyalty. The TCSI model has a predictive capability that can help tourism factory managers improve customer satisfaction based on different performance levels. Our model enables managers to determine the specific factors that significantly affect overall customer satisfaction and loyalty within a tourism factory. This study also helps managers to address different customer segments e.
However, the results of this study indicate that customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Moreover, they were affected more by perceived quality of customer satisfaction. Numerous researchers have found that the construct of customer expectations used in the ACSI model does not significantly affect the level of customer satisfaction Johnson et al. Through the overall effects, this study derived several theoretical findings.
First, the factors with the largest influence on customer satisfaction were perceived quality and perceived expectations, despite the results showing that customer expectations were not significantly related to perceived value or customer satisfaction. Hence, customer expectations indirectly affected customer satisfaction through perceived quality.
Accordingly, perceived quality had the greatest influence on customer satisfaction. Likewise, our results also show that satisfied customers were affected more by perceived quality than dissatisfied customers. This study determined that perceived quality, whether directly or indirectly, positively influenced customer satisfaction.
This result is consistent with those of Cronin and Taylor , Cronin et al. Second, the factors with the most influence on customer loyalty were image and customer satisfaction. The results of this study demonstrate that the customer loyalty of satisfied customers was affected more by customer satisfaction.
Consequently, regarding customer loyalty, customer satisfaction is more important than image for satisfied customers. Lee found that higher overall satisfaction increased the possibility that visitors will recommend and reattend tourism factory activities.
Moreover, numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty Barsky ; Smith and Bolton ; Hallowell ; Su ; Deng et al. In initial experiments on ECSI, corporate image was assumed to have direct influences on customer expectation, satisfaction, and loyalty. Subsequent experiments in Denmark proved that image affected only expectation and satisfaction and had no relationship with loyalty Martensen et al.
Therefore, this study contributes to relevant research by providing empirical support for the notion that customer satisfaction is positively related to customer loyalty. In addition to theoretical implications, this study has several managerial implications. First, the TCSI model has a satisfactory predictive capability that can help tourism factory managers to examine customer satisfaction more closely and to understand explicit influences on customer satisfaction for different customer segments by assessing the accurate causal relationships involved.
In contrast to general customer satisfaction surveys, the TCSI model cannot obtain information on post-purchase customer behavior to improve customer satisfaction and achieve competitive advantage. Third, this research determined that the factors having the most influence on customer loyalty were image and customer satisfaction.
Regarding image, which refers to a brand name and its related associations, when tourists regard a tourism factory as having a positive image, they tend to perceive higher value of its products and services. Different performance levels exist in how tourists express their opinions about various aspects of service quality and satisfaction with tourism factories.
Customer segments can have different preferences depending on their needs and purchase behavior. Our findings indicate that tourists belonging to different customer segments e. This study proposes two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. This study has some limitations. First, the tourism factory surveyed in this study was a food tourism factory operating in Taipei, Taiwan, and the present findings cannot be generalized to the all tourism factory industries.
Future research should collect a greater number of samples and include a more diverse range of tourists. Third, this study was preliminary research on tourism factories, and domestic group package tourists were a major source of the respondents. Future studies should collect data from international tourists as well. All authors read and approved the final manuscript. Yu-Cheng Lee, Email: moc. Yu-Che Wang, Email: wt. Shu-Chiung Lu, Email: moc. Yi-Fang Hsieh, Email: moc.
Chih-Hung Chien, Email: moc. Weiwei Dong, Email: moc. National Center for Biotechnology Information , U. Published online Sep Author information Article notes Copyright and License information Disclaimer. Corresponding author. Received Apr 25; Accepted Sep 2. This article has been cited by other articles in PMC.
Abstract Customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Keywords: Customer satisfaction, Tourism factory industry, Partial least squares, Business management, Service management.
Background Traditional manufacturing factories converted for tourism purposes, have become a popular leisure industry in Taiwan. Open in a separate window. Literature review National customer satisfaction index CSI The CSI model includes a structural equation with estimated parameters of hidden categories and category relationships.
TCSI model and service quality Service quality is frequently used by both researchers and practitioners to evaluate customer satisfaction. Further, with the dynamic industry organizational performance [2, 4, 5, 6, 9, 22]. The art of achieving a competitive advantage.
Such Any research into the performance of a firm needs to strategies are capital, human and time intensive based on consider the two key elements of profit and growth, which Product, Placing, Packaging and Promotion hence the need are the key to the very existence of a business. In addition, for each firm to conduct its own analysis.
The findings will ensuring business performance outcomes are satisfactory to help the University of Baghdad since there are no literature stakeholders also determines continuation [5, 10]. Firms on marketing strategies and customer satisfaction on the who are eager to sustain long-term growth and overall University of Baghdad. These competitive strategies strategy to assess Customer Satisfaction. Businesses are therefore constantly assessment of customer satisfaction of distance looking to determine the factors for success and identify education?
Literature review and theoretical framework Bowen and Chen [9] opined that a high level of customer satisfaction decreases the perceived need to switch service 2. Performance is the level of quality of 2. Fornell [17] emphasised that increased 2. Kapiki [22], observed that excellent services quality by changing trends of the industry.
Customer satisfaction has been given different in maintaining long-term customer relationship behaviours definitions by many gurus in the area of marketing [38]. A study by Walsh et al. Zeithaml [49, 50, 51] findings prove a product or service in terms of whether that product or relationship between customer satisfaction and business service has met their needs and expectations.
Bagram and performance. Hennig-Thurau et al. Oliva et al. Sherry [37] believes that communications [32] provided evidence to show that customer satisfaction from faculty that directly engages students and offers timely enhances the financial performance of a company.
A study feedback contributes to interchanges between the by Cronin and Morris [12] and Innis and La Londe [19] organization and students and leads to success in the course found that a satisfied customer is more likely to repurchase, of study.
Nelson et al. Anton [6] higher profitability. Sivadas and Baker-Prewitt [40] further maintained that customer 2. Marketing strategy according to Wood [47] adds that if the value is present for both Walker, Boyd and Larreche [44] involves specifying the organization and consumer, the resulting relationship may target segments to be pursued and the breadth of the product take the form of a one-time purchase and referrals to other line to offer.
Hence, for customers to be satisfied, effective potential customers. Thus, effective marketing covers marketing strategies that deal with the process of planning everything about an organization and must consistently and executing programs designed to influence the behaviour provide value to win customers and earn their loyalty [47].
Thus, the price is used to judge parallel To influence the decision of consumers on the type of products or services that offer similar satisfaction. Wells, Farley and 2. According to Moore [29] and Young and buying behavior. The authors contend that these three purchase decision. Durling, Cross and Johnson [15] also echo that understand more their customers in terms of needs, instructors should understand the diverse nature of students behavior, satisfaction and perception towards the services and encourage them to interact regularly to promote and products and consider customers as important assets to effective learning.
Kurtenbach [26] concludes that them. From the viewpoint of Table 1: Previous studies on e-learning in Iraq the customer, the social bonding strategy seems to provide an important psychological benefit [11]. Damkuviene and Virvilaite [13] investigating the e- university culture learning Learning also noted that the elements in a relationship should be Management System interdependence, long-term orientation, commitment and LMS acceptance trust.
Hence, without these elements in the marketing game, composed of it will be difficult for the university to build a strong Technology Acceptance Model customer base in the distance education market. Kotler et al. Individual Imran, et institutions in Iraq impact, al, through designing a organizational website includes impact. Ahmed Proposed a framework University culture.
Dheyaa for using ICT in E- 2. Conceptual Framework Basha, et learning. They solution MOOC determined three main reduce the cost and barriers including does not require Figure 1: The Conceptual Framework technical costly infrastructure.
Research methodology and motivation Students offering both Degree and Diploma programmes at Elameer Proposed a modified Organizational or and Idrus Khan e-learning university tasks the University of Education, Winneba, and Kumasi Girls framework. They Study Centre constituted the population for the study.
A focused on individual sample size of one hundred and twenty students was tasks, group work and selected based on the convenience sampling method. Of the whole class activities based on both content students selected for the study, 73 were males, whiles and collaboration 47 were females.
The mean age for the respondents was 32 al universities was found usability of e- high, due to measuring learning years. Students who were present at both degree and the overall reaction to diploma lecture rooms at the time of collection of data were the e-learning system given self-designed questionnaires.
The students were given by participants and the twenty minutes to answer the questions on the questionnaire e-learning system terminologies and after which all were collecting. The Anter, et universities university culture respondents were asked to respond to the questions on the al, considering that the questionnaire by showing the degree to which they strongly main success factors in disagree or strongly agree with each question on the e-learning are services and information questionnaire.
The respondents were required to fill the and materials in order to manage the learning questionnaire as sincerely as practicable. The study environment and employed SPSS in analyzing the data collected and reduce the expenses of frequency tables were drawn from the data analyzed. Reliability Statistics Descriptive statistics were conducted to determine the means, standard deviations and the correlations among the determinants of customer satisfaction in this study.
The Cronbach alpha coefficient was used to test the internal Table Error! No text of specified style in document.. In the current study, the Cronbach alpha Frequenci Technique coefficient was 0. Total Cases Started the Survey 4. Result Cases with non-completed - 31 In this study, the data collected online via an online status survey. The student who responds and starts practising Multi-variate Outliers - 08 the survey were students, but 31 of them decided to quit Cleaned Dataset without completion.
The Final data set after data cleaning are Detecting outliers is very import to make the dataset clear and accurate for further analysis.
Outliers defined as cases that have data values that are very different from the data values for the majority of cases in the dataset. One of them called detecting univariate outliers, The final dataset for analysis is from and the other called detecting multivariate outliers. The uncompleted cases are 6. The single variable. In this study analyses, we detected respondent's profiles are consist of six characteristics: univariate outliers for all the variables. The rule of thumb as Hair [53] is to delete any values 2.
The numbers of cases that are deleted are 17 based on this test. The value surveyed were females. The tool that used in this study to Table Error! Mahalanobis D2 is a multidimensional version of a z-score. It measures the Gender Frequency Percent distance of a case from the centroid multidimensional Male A case is a multivariate outlier if the probability associated with its D2 is 0. D2 follows a chi-square distribution with 2.
The numbers of cases that are Most of the respondents in this study were deleted are 8 based on this test. Finally, no respondents joined the survey with an age older than The least respondents are from year 1 with the percentage of 9.
The study level statistics. Four Table Error! Year 1 36 9. Discussion Political Science. There is a greater need to elevate their products and services Faculty of Law. Currently, the Faculty of Medicine 22 5. Since Faculty of Pharmacology. Further, Technology. This means the products strategy are currently poorly positioned hence customers are unsatisfied.
Others A majority of the respondents who participated are E-newsletters, Breakfast meetings and Newspaper in this study are studying Bachelor, which constitutes articles respectively.
According to Henning-Thurau and Klee the conceptualization of customer retention needs further clarification. According to Wolfinbarger and Gilly , customer retention has a direct impact on profitability. Naturally then, considerable time and money is spent in many organizations to develop strategies to retain customers. The costs of attracting new customers include advertising and promotion, but loyal customers also act as word-of-mouth ambassadors.
Financial gains from customer acquisitions can be much larger than gains through defections. Other studies seem to have contrary findings. The study by Smith and Higgins found a weak link between satisfaction and retention.
Reinartz and Kumar also dispute the view that long-term customers are more tolerant of prices. East, Hammond and Gendall also argue that in some cases, long-term customers recommend significantly less customers compared to short-term customers. In a hyper-competitive market all commercial banks are faced with challenges of retaining existing and attracting new customers.
If an organization is not able to keep customers and build long-term relationships it will continue to operate with discrete once-off transactions. Discussions of customer retention seem to be dominated by loyalty programmes and customer discounts.
However research such as one undertaken by Ba and Pavlou shows that what really drives repurchase is high-quality customer service which is well-managed and strategically delivered. Customers do not remain with an organization just because of the discounts offered or the loyalty programs that are available.
The service provided must also meet the expectations of the customer. A desired outcome of providing quality in all transactions is customer retention. Bank managers must therefore understand customer perceptions and expectations of quality. Bank management must identify and improve upon factors that can limit customer defection.
These include employee performance and professionalism, willingness to solve problems, friendliness, level of knowledge, communication skills and selling skills, among others. Literature on retention in banks has tended to focus on the impact of individual constructs, without attempting to link them in a model to further explore or explain retention.
If retention criteria are not well managed, customers might still leave their banks, no matter how hard bankers try to retain those Griffins, From the literature reviewed, service as well bank related have been identified as major causes of customer satisfaction and retention of the bank customer. Literature on demographic factors and their correlation with satisfaction and retention is still limited and therefore the findings of this study will add valuable contribution to the body of knowledge in the area of satisfaction of the bank customer in Kenya.
Research Problem Customer satisfaction remains one of the biggest challenges for the Kenyan banking industry. Empirical studies that attempt to explore how various aspects in the macro environment affect customer satisfaction are few.
Customer attrition in the Kenyan banking industry is still high and the causes have not been exhausted. This gap presented the motivation for this study. The study sought to establish the correlation between demographic factors and customer satisfaction and retention in the Kenya banking industry.
To achieve this objective, the study adopted a descriptive survey design which was chosen because the target population was dispersed over the whole country covering a wide geographical area. Samples drawn from the 43 Kenyan banks were taken and structured questionnaires were issued to customers in all the 43 banks in Kenya. Customer satisfaction and retention were the dependent variables while the demographic factors were the independent variable. The sampling process used the multi-stage sampling technique.
In the first stage, cluster sampling was used to group the target population using their banks of affiliation. The second stage involved stratified sampling which grouped the bank customers in either urban or rural branch settings. In the third stage, proportions were determined based on branch customer portfolios to ensure representation.
Finally, systematic random sampling was used to select the individual respondents for the study. Once the data was collected, it was entered into Statistical Package for Social Sciences SPSS computer software which was used to carry out the data analysis.
The types of statistics or indices used depended on the variables in the study and the scale of measurement used such as ratio, interval, ordinal and normal. The tests were used because the variables were measured at interval or ratio scales. Pearson correlation was used where the data was continuous while Spearman correlation was used where the data was ranked. There is no relationship between demographic factors and customer satisfaction in Kenyan banks. There is a relationship between demographic factors and customer satisfaction in Kenyan banks H0: There is no correlation between demographic factors and customer retention in Kenyan banks.
H2: There is a correlation between demographic factors and customer retention in Kenyan banks. It has been suggested by Sekaran that a range of a minimum sample size of 30 to a maximum of is sufficient and acceptable for a scientific investigation.
The questionnaires were administered by use of field agents who distributed the questionnaires to bank customers in various banks and work places in the various towns according to the sampling design. A total of questionnaires were completed out of which 70 were annulled due to errors leaving a sample of which formed the basis of this analysis. All the questionnaires were analyzed in order to enrich the findings.
Cronbach Alpha was computed to assess the reliability of the data collected. According to Leedy and Ormrod , a Cronbach Alpha value greater than 0.
The overall Cronbach Alpha value was 0. It was therefore concluded that the data collected were reliable for the subsequent stages of analysis. Discussion of the Findings 4. Regarding the gender of the respondents, the findings show that the majority The other age categories accounted for According to the monthly income figures of the respondents, a large proportion Other income categories accounted for a cumulative percentage of The highest level of education findings shows that a large proportion Civil servants accounted for Based on the marital status of the respondents, the findings show that almost half The respondents who were single accounted for The mean and standard deviations were calculated as shown in the Table 1.
The first objective of the study sought to assess the relationship between demographic factors and customer satisfaction in Kenyan banks. The Pearson Chi-square tests analyses were conducted on each of the demographic variables. The test was performed at the 0. This means a relationship exists between age and customer satisfaction, monthly income and customer satisfaction, highest level of education and customer satisfaction.
A value of 0. If the significance level P-value is very small less than 0. If the significance level is relatively large greater than 0. The findings in Table 3 show that there is very low association between customer satisfaction and gender and customer satisfaction and marital status. This implies that gender and marital status do not affect customer satisfaction of the Kenyan bank customer.
H2: There is a relationship between demographic factors and customer retention in Kenyan banks. This section sought to test the existence of a relationship between the various demographic factors and customer retention. This shows that there is a relationship between: age and customer retention, monthly income and customer retention, level of education and customer retention in Kenyan banks.
Implications A relationship between demographic factors and customer satisfaction in Kenyan banks was established by the study. Gender and marital status were not significant factors to the satisfaction of the Kenyan bank customer. Age, monthly income and level of education were significant factors affecting customer satisfaction of the Kenyan bank customer.
On customer retention, gender, occupation and marital status were of low significance whereas age, monthly income and level of education were of higher significance as factors affecting customer retention of the Kenyan bank customer.
From the study, age, monthly income and level of education are significant factors affecting both the satisfaction and retention of the bank customer.
The three variables however have more significance in affecting customer satisfaction than customer retention of the Kenyan bank customer. Banks may adopt strategies that target age, monthly income and level of education as these are significant variables for the satisfaction and retention of the bank customer. This age group requires technology driven products like mobile telephone banking and internet banking which are now available in some banks.
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