Roll No:- 34
Kohinoor Business School
Customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Customers should be managed as assets, and that customers vary in their needs, preferences, and buying behavior. This study applied the Taiwan Customer Satisfaction Index model to a tourism factory to analyze customer satisfaction and loyalty. We surveyed 242 customers served by one tourism factory organizations in Taiwan. A partial least squares was performed to analyze and test the theoretical model. The results show that perceived quality had the greatest influence on the customer satisfaction for satisfied and dissatisfied customers. In addition, in terms of customer loyalty, the customer satisfaction is more important than image for satisfied and dissatisfied customers. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.
National customer satisfaction index (CSI)
The CSI model includes a structural equation with estimated parameters of hidden categories and category relationships. The CSI can clearly define the relationships between different categories and provide predictions. The basic CSI model is a structural equation model with latent variables which are calculated as weighted averages of their measurement variables, and the PLS estimation method calculates the weights and provide maximum predictive power of the ultimate dependent variable (Kristiansen et al. 2001). Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005; Malhotra et al. 1994).
Although the core of the models are in most respects standard, they have some obvious distinctions in model’s structure and variable’s selection so that their results cannot be compared with each other and some variations between the SCSB (Swedish), the ACSI (American), the ECSI (European), the NCSB (Norwegian) and other indices. For example, the image factor is not employed in the ACSI model (Johnson et al. 2001); the NCSB eliminated customer expectation and replaced with corporate image; the ECSI model does not include the customer complaint as a consequence of satisfaction. Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005; Malhotra et al. 1994). The ECSI model distinguishes service quality from product quality (Kristiansen et al. 2001) and the NCSB model applies SERVQUAL instrument to evaluate service quality (Johnson et al. 2001). A quality measure of a single customer satisfaction index is typically developed according to a certain type of culture or the culture of a certain country. When developing a system for measuring or evaluating a certain country or district’s customer satisfaction level, a specialized customer satisfaction index should be developed.
As such, the ACSI and ECSI were used as a foundation to develop the TCSI. The TCSI was developed by Chung Hua University and the Chinese Society for Quality. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs. The TCSI assumes that currently: (1) Taiwan corporations have ability of dealing with customer complaints; customer complaints have already changed from a factor that influences customer satisfaction results to a factor that affects quality perception; (2) The expectations, satisfaction and loyalty of customers are affected by the image of the corporation. The concept that customer complaints are not calculated into the TCSI model is that they were removed based on the ECSI model (Lee et al. 2005, 2006, 2014a, b; Guo and Tsai 2015; Tsai et al. 2015a, b; 2016a).
TCSI model and service quality:-
Service quality is frequently used by both researchers and practitioners to evaluate customer satisfaction. It is generally accepted that customer satisfaction depends on the quality of the product or service offered (Anderson and Sullivan 1993). Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the NCSI model (e.g., Ryzin et al. 2004; Hsu 2008; Yazdanpanah et al. 2013; Chiu et al. 2011; Temizer and Turkyilmaz 2012; Mutua et al. 2012; Dutta and Singh 2014). Ryzin et al. (2004) applied the ACSI to U.S. local government services and indicated that the perceived quality of public schools, police, road conditions, and subway service were the most salient drivers of satisfaction, but that the significance of each service varied among income, race, and geography. Hsu (2008) proposed an index for online customer satisfaction based on the ACSI and found that e-service quality was more determinative than other factors (e.g., trust and perceived value) for customer satisfaction. To deliver superior service quality, an online business must first understand how customers perceive and evaluate its service quality. This study developed a basic model for using the TCSI to analyze Taiwan’s tourism factory services. The theoretical model comprised 14 observation variables and the following six constructs: image, customer expectations, perceived quality, perceived value, customer satisfaction, and loyalty.
How do you measure customer satisfaction and loyalty?
Customer satisfaction and loyalty are key indicators of how well you are delivering value to your customers and building long-term relationships. But how do you measure them effectively in the context of Business Relationship Management (BRM)? BRM is a strategic approach to aligning business needs and IT capabilities, fostering collaboration and innovation, and creating value for both parties. In this article, we will explore some of the methods and tools you can use to assess and improve your customer engagement and value creation in BRM.
Define your goals and metrics
Before you start measuring customer satisfaction and loyalty, you need to define what they mean for your specific BRM context and objectives. What are the expectations and outcomes that you and your customers have agreed on? How do you align them with your business value proposition and IT service delivery? How do you communicate and report on them? You need to establish clear and measurable goals and metrics that reflect your BRM strategy and value drivers, and that can be tracked and evaluated over time.
Use surveys and feedback
One of the most common and effective ways to measure customer satisfaction and loyalty is to ask them directly. You can use surveys and feedback tools to collect and analyze data on how your customers perceive and experience your BRM services and interactions. You can ask them to rate various aspects of your BRM performance, such as quality, timeliness, responsiveness, innovation, and alignment. You can also ask them to provide comments and suggestions for improvement. You should conduct surveys and feedback regularly and consistently, and use the results to identify gaps and opportunities for enhancement.
Monitor key performance indicators
Another way to measure customer satisfaction and loyalty is to monitor the key performance indicators (KPIs) that reflect the value and impact of your BRM services and initiatives. These can include quantitative and qualitative measures, such as revenue, cost, productivity, efficiency, customer retention, customer advocacy, customer satisfaction score, net promoter score, and value realization score. You should align your KPIs with your goals and metrics, and use them to track and evaluate your BRM progress and outcomes. You should also share and discuss your KPIs with your customers, and use them to demonstrate and communicate your BRM value proposition and achievements.
Apply value management techniques
A third way to measure customer satisfaction and loyalty is to apply value management techniques that help you assess and optimize the value creation and delivery of your BRM services and initiatives. These can include value mapping, value modeling, value scoring, value portfolio management, and value harvesting. These techniques help you identify and prioritize the sources and drivers of value for your customers, and align them with your BRM capabilities and resources. They also help you measure and optimize the value realization and benefits of your BRM services and initiatives, and ensure that they are aligned with your customers’ needs and expectations.
Engage in continuous improvement
A final way to measure customer satisfaction and loyalty is to engage in continuous improvement activities that help you enhance your BRM performance and value creation. These can include benchmarking, best practices, lessons learned, feedback loops, and innovation cycles. These activities help you compare and learn from your own and others’ BRM experiences and results, and identify and implement improvements and innovations that can increase your BRM effectiveness and efficiency. They also help you foster a culture of collaboration and co-creation with your customers, and build trust and loyalty that can sustain and grow your BRM relationships.
The measurement scale items for this study were primarily designed using the questionnaire from the TCSI model. In designing the questionnaire, a 10-point Likert scale (with anchors ranging from strongly disagree to strongly agree) was used to reduce the statistical problem of extreme skewness (Fornell et al. 1996; Quetal. 2015; Tsai 2016; Tsai et al. 2016b; Zhou et al. 2016). A total of 14 items, organized into six constructs, were included in the questionnaire. The primary questionnaire was pretested on 30 customers who had visited a tourism factory. Because the TCSI model is preliminary research in the tourism factory, this study convened a focus group to decide final attributes of model. The focus group was composed of one manager of tourism factory, one professor in Hospitality Management, and two customers with experience of tourism factory.
We used the TCSI model (Fig. 1) To structure our research. From this structure and the basic theories of the ACSI and ECSI, we established the following hypotheses:
Image has a strong influence on tourist expectations.
Image has a strong influence on tourist satisfaction.
Image has a strong influence on tourist loyalty.
Tourist expectations have a strong influence on perceived quality.
Tourist expectations have a strong influence on perceived values.
Tourist expectations have a strong influence on tourist satisfaction.
Perceived quality has a strong influence on perceived value.
Perceived quality has a strong influence on tourist satisfaction.
Perceived value has a strong influence on tourist satisfaction.
Customer satisfaction has a strong influence on tourist loyalty.
The content of our surveys were separated into two parts; customer satisfaction and personal information. The definitions and processing of above categories are listed below:
Part 1 of the survey assessed customer satisfaction by measuring customer levels of tourism factory image, expectations, quality perceptions, value perceptions, satisfaction, and loyalty toward their experience, and used these constructs to indirectly survey the customer’s overall evaluation of the services provided by the tourism factory.
Part 2 of the survey collected personal information: gender, age, family situation, education, income, profession, and residence.
The six constructs are defined as follows:
Image reflects the levels of overall impression of the tourism factory as measured by two items: (1) word-of-mouth reputation, (2) responsibility toward concerned parties that the tourist had toward the tourism factory before traveling.
Customer expectations refer to the levels of overall expectations as measured by two items: (1) expectations regarding the service of employees, (2) expectations regarding reliability that the tourist had before the experience at the tourism factory.
Perceived quality was measured using three survey measures: (1) the overall evaluation, (2) perceptions of reliability, (3) perceptions of customization that the tourist had after the experience at the tourism factory.
Perceived value was measured using two items: (1) the cost in terms of money and time (2) a comparison with other tourism factories.
Customer satisfaction represents the levels of overall satisfaction was captured by two items: (1) meeting of expectations, (2) closeness to the ideal tourism factory.
Loyalty was measured using three survey measures: (1) the probabilities of visiting the tourism factory again (2) attending another activity held by the tourism factory, (3) recommending the tourism factory to others.
Data Collection & Analysis:-
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 300 tourists invited to complete the questionnaire, 242 effective responses were obtained (usable response rate of 80.6 %). The sample of tourists contained more females (55.7 %) than males (44.35 %). More than half of the respondents had a college degree or higher, 28 % were students, and 36.8 % had an annual household income of US $10,000–$20,000. The majority of the respondents (63.7 %) were aged 20–40 years.
Comparison of the TCSI models for satisfied and dissatisfied customers
Researchers have claimed that satisfaction levels differ according to gender, age, socioeconomic status, and residence (Bryant and Cha 1996). Moreover, the needs, preferences, buying behavior, and price sensitivity of customers vary (Kutner and Cripps 1997). 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. 2003, 2004). 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 3.0 software, which is suited for highly complex predictive models (Wold 1985; Barclay et al. 1995). 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 R2 values. The path coefficients indicate the strengths of the relationships between the dependent and independent variables, and the R2 values represent the amount of variance explained by the independent variables. Using Smart PLS, we determined the path coefficients. Figures 2 and 3 show ten path estimates corresponding to the ten research hypothesis of TCSI model for satisfied and dissatisfied customers. Every path coefficient was obtained by bootstrapping the computation of R2 and performing a t test for each hypothesis. Fornell et al. (1996) demonstrated that the ability to explain the influential latent variables in a model is an indicator of model performance, in particular the customer satisfaction and customer loyalty variables. From the results shown, the R2 values for the customer satisfaction were 0.53 vs. 0.50, respectively; and the R2 value for customer loyalty were 0.64 vs. 0.60, respectively. Thus, the TCSI model explained 53 vs. 50 % of the variance in customer satisfaction; 64 vs. 60 % of that in customer loyalty as well.
According to the path coefficients shown in Figs. 2 and 3, image positively affected customer expectations (β = 0.58 vs. 0.37), the customer satisfaction (β = 0.16 vs. 0.11), and customer loyalty (β = 0.47 vs. 0.16). Therefore, H1–H3 were accepted. Customer expectations were significantly related to perceived quality (β = 0.94 vs. 0.83). However, customer expectations were not significantly related to perceived value shown as dotted line (β = −0.01 vs. −0.20) or the customer satisfaction, shown as dotted line (β = −0.21 vs. −0.32). Thus, H4 was accepted but H5 and H6 were not accepted. Perceived value positively affected the customer satisfaction (β = 0.27 vs. 0.14), supporting H7. Accordingly, the analysis showed that each of the antecedent constructs had a reasonable power to explain the overall customer satisfaction. Furthermore, perceived quality positively affected the customer satisfaction (β = 0.70 vs. 0.62), as did perceived value (β = 0.83 vs. 0.74). These results confirm H8 and H9. The path coefficient between the customer satisfaction and customer loyalty was positive and significant (β = 0.63 vs. 0.53). This study tested the suitability of two TCSI models by analyzing the tourism factories in Taiwan. 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.g., customer expectation and perceived quality), customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Furthermore, satisfied customers were affected more by perceived quality (β = 0.83 vs. 0.74), as shown in Table 1. Regarding the effect of the antecedents of customer satisfaction (e.g., image, customer expectations, perceived value and perceived quality), the total effects of perceived quality on the customer satisfaction of satisfied and dissatisfied customers were 0.92 and 0.72. The total effects of image on the customer satisfaction of satisfied and dissatisfied customers were 0.45 and 0.19. 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.g., Ryzin et al. 2004; Hsu 2008; Yazdanpanah et al. 2013; Chiu et al. 2011; Temizer and Turkyilmaz 2012; Mutua et al. 2012; Dutta and Singh 2014). This is consistent with the results of previous research ( O’Loughlin and Coenders 2002; Yazdanpanah et al. 2013; Chiu et al. 2011; Chin and Liu 2015; Chin et al. 2016).
With respect to the effect of the antecedents of customer loyalty (e.g., image and customer satisfaction), the total effects of image on customer loyalty for satisfied and dissatisfied customers were 0.57 and 0.21. In other words, the customer loyalty of satisfied customers was affected more by customer satisfaction. Customer satisfaction was significantly related to the customer loyalty of both satisfied and dissatisfied customers, and satisfied customers were affected more by customer satisfaction (β = 0.63 vs. 0.14). Consequently, regarding customer loyalty, customer satisfaction is more important than image for both satisfied and dissatisfied customers. Numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992; Smith and Bolton 1998; Hallowell 1996; Grønholdt et al. 2000). 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.g., satisfied vs. dissatisfied); because the purchase behaviors of customers differ, they must be treated differently. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively.
Fornell et al. (1996) demonstrated that the ability to explain influential latent variables in a model, particularly customer satisfaction and customer loyalty variables, is an indicator of model performance. 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. 1996, 2001; Martensen et al. 2000; Anderson and Sullivan 1993).
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 (1992), Cronin et al. (2000), Hsu (2008), Ladhari (2009), Terblanche and Boshoff (2010), Deng et al. (2013), and Yazdanpanah et al. (2013).
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 (2015) 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 1992; Smith and Bolton 1998; Hallowell 1996; Su 2004; Deng et al. 2013). 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. 2000). In early attempts to build the ECSI model, image was defined as a variable involving not only a company’s overall image but products or brand awareness; thus image is readily connected with customer expectation and perception. 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.
Second, this study not only indicated that each of the antecedent constructs had reasonable power to explain customer satisfaction and loyalty but also showed that perceived quality exerts the largest influence on the customer satisfaction of Taiwan’s tourism factory industry. Therefore, continually, Taiwan’s tourism factories must endeavor to enhance their customer satisfaction, ideally by improving service quality. Managers of Taiwan’s tourism factories must ensure that service providers deliver consistently high service quality.
Third, this research determined that the factors having the most influence on customer loyalty were image and customer satisfaction. Therefore, managers of Taiwan’s tourism factories should allow customer expectations to be fulfilled through experiences, thereby raising their overall level of 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. This leads to a higher level of customer satisfaction and increased chances of customers’ reattending tourism factory activities.
Rights & Permissions:-
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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.g., satisfied vs. dissatisfied) expressed differences toward service quality and customer satisfaction. Thus, the management of Taiwan’s tourism factories must notice the needs of different market segments to meet their individual expectations. This study proposes two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.
Limitations and suggestions for future research
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. Second, the sample size was quite small for tourists (N = 242). 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.
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