Leveraging Digital Transformation to Enhance Quality Tourism Services in Babylon City, Iraq

3.1 Digital impacts in tourism

Rapid developments in digital technology have profoundly impacted the travel industry. The digital transformation trends in the tourism industry include the usage of mobile devices, social media, big data, artificial intelligence (AI), and the Internet of Things (IoT) [29-31].

The utilization of digital technologies, including mobile devices, social media, big data, artificial intelligence (AI), and the Internet of Things (IoT), has the potential to significantly impact the tourism industry. These technologies offer avenues for market identification, the creation of novel tourism products, the adaptation of existing offerings, and the establishment of innovative customer connections [32]. Such innovations are made feasible through digitalization, with various technological tools such as AI, IoT, robotization, gamification, big data, and cloud computing enabling diverse forms of transformation [32].

Digital technology also plays a crucial role in fostering sustainable development in tourist attractions, especially natural geomorphosites [33]. A sophisticated methodology centered on identifying criteria influencing geomorphosite tourism development can be employed to evaluate the current degradation status and formulate sustainable development proposals from a geomorphological perspective.

Another noteworthy impact of digital transformation in the tourism sector, particularly within the food industry, pertains to its influence on food as a cultural phenomenon for destination branding and stakeholder engagement in culinary tourism [32]. This technological shift allows for the expansion of the existing framework on stakeholder causal scope analysis in culinary tourism, facilitating an examination of the current landscape and exploration of future research agendas and practical implications. However, the following are the significant conclusions from the literature review:

(1) Mobile Devices: The growing usage of mobile devices has revolutionized how tourists plan and schedule their vacations. Tourists utilize mobile devices to look for travel information, make bookings, and discuss their trip experiences on social media platforms [34].

(2) Social Media: Social media platforms have become vital to the tourism industry; tourists utilize social media platforms to discuss their travel experiences, seek advice from other travelers, and communicate with tourism service providers [35].

(3) Big Data: Big data and AI are used to evaluate tourists' behavior and preferences, giving tourism service companies insights to design tailored client experiences. Tourism service providers may utilize AI and big data to study travelers' behavior and interests, delivering tailored suggestions for tourist sites, housing, and cuisine [36].

(4) Artificial Intelligence (AI): AI is utilized in the tourism industry to deliver customized suggestions to tourists [30]. AI-powered chatbots may give tourists real-time support, helping them plan their travels, make bookings, and obtain information about tourist attractions.

(5) Internet of Things (IoT): The IoT is utilized to develop intelligent tourism destinations where tourists may get real-time information on tourist sites and services. Intelligent tourism destinations may assist tourists in making their way through new regions and give customized suggestions based on tourists' tastes [25].

The literature analysis reveals that digital transformation trends in tourism alter visitors' expectations and behavior. Therefore, tourism service providers must leverage digital technology to tailor their services to satisfy tourists' expectations. Therefore, the study's findings can give insights into the influence of digital transformation trends in the tourism industry on visitors to Babylon City and aid tourism service providers and policymakers in designing strategies to improve the quality of tourism services in Babylon City.

3.2 Changing tourist behaviors

The influence of digital transformation on visitors' expectations and behavior has been extensively researched in the literature. For example, digital transformation has altered tourists' behavior in the tourism sector. Mobile devices have become indispensable tools for travelers, serving various purposes such as information retrieval, reservations, and social media sharing [37, 38]. The pervasive use of mobile devices holds the potential to revolutionize the entire tourism industry by creating new opportunities across all sectors of the economy [37]. The Technology Acceptance Model (TAM) offers valuable insights into the relationship between travelers using smartphone travel applications (Apps) and their perceived usefulness, ease of use, usage attitude, usage intentions, and information search behavior [37]. However, the following are the significant conclusions from the literature review:

(1) Personalization: Tourists want individualized experiences, and digital technologies enable tourism service providers to create tailored services to fit tourists' requirements and preferences. Tourists utilize digital technology to search for information, make bookings, and discuss their trip experiences [39]. Therefore, tourism service providers may use this data to customize their services.

(2) Seamless experiences: Tourists want seamless experiences where they may access information and services smoothly across numerous devices and platforms. Tourists utilize mobile devices and social media platforms to obtain travel information, make bookings, and discuss their experiences [40]. Therefore, tourism service providers must create seamless experiences across these platforms to match tourists' expectations.

(3) Real-time information: Tourists want real-time information on tourist sites, hotels, and cuisine, and digital technologies enable tourism service providers to give this information. Intelligent tourism destinations employ the IoT to give real-time information on tourist attractions and services [41].

(4) Social sharing: Social media platforms have become a vital element of the tourism experience, enabling tourists to share their experiences with others and seek advice from other travelers. Social media platforms enable tourists to share their experiences, seek advice, and communicate with tourism service providers [42]. Therefore, tourism service providers must engage with tourists on social media platforms to enhance their services.

(5) Internet reviews: Online reviews have become a crucial source of information for tourists, impacting their decisions on tourist sites, housing, and cuisine. Internet reviews impact travelers' decisions, and tourism service providers must manage their online reputation to attract tourists [43].

The literature analysis reveals that digital technologies have affected visitors' expectations and behavior. Therefore, tourism service providers must employ digital technologies to deliver personalized and seamless experiences and give real-time information to match tourists' expectations. The study's findings can give insights into the influence of digital transformation in the tourism industry on visitors to Babylon City and aid tourism service providers and policymakers in designing strategies to improve the quality of tourism services in Babylon City.

3.3 Tourism service quality

The quality of tourism services has been thoroughly investigated in the literature. Tourism service quality is a crucial aspect of the travel and hospitality industry, encompassing various dimensions that directly impact travelers' experiences. These dimensions, as identified in the literature, include tangibles, dependability, responsiveness, assurance, and empathy [44]. These serve as fundamental benchmarks for evaluating and enhancing the quality of tourism services. Customer happiness and satisfaction are at the core of this assessment, as higher service quality is intrinsically linked to increased customer satisfaction [44]. Ensuring that tourists' needs and expectations are not only met but exceeded is imperative for tourism service providers [45]. Furthermore, service recovery mechanisms play a vital role in preserving customer satisfaction, as they enable providers to address and rectify service failures promptly [45]. Beyond these internal factors, the perception of a destination's image also influences travelers' impressions of service quality [46]. For destinations to thrive in the competitive tourism landscape, they must manage and control their image effectively to attract and retain visitors [45, 46]. Ultimately, a focus on tourism service quality is not only a strategic imperative for service providers but also a key factor in enhancing the overall experience of tourists and promoting sustainable tourism growth [44, 46]. Travelers use mobile devices to look for information, make reservations, and post about their experiences on social media [47, 48]. The use of mobile devices can revolutionize the tourism industry by creating new opportunities for every sector of the economy [47]. However, the following are the significant conclusions from the literature review:

(1) Service quality dimensions: The service quality dimensions constitute a significant aspect in assessing the quality of tourism services. The five pillars of service quality in the tourism industry are tangibles, dependability, responsiveness, assurance, and empathy [49].

(2) Customer happiness: Customer satisfaction is essential in determining the quality of tourism services. Consumer satisfaction is determined by the quality of tourism services, and tourism service providers need to focus on enhancing service quality to boost customer satisfaction [50].

(3) Service recovery: Service recovery is vital in preserving client happiness in tourism. Service recovery may allow tourism service providers to regain consumers' confidence following service failures, and it is vital for increasing the quality of tourism services [51].

(4) Employee training: Employee training is vital for increasing the quality of tourism services. Employee training may increase the quality-of-service delivery and enhance customer satisfaction [52].

(5) Destination image: The destination image is a significant aspect of assessing the quality of tourism services. The destination image impacts travelers' impressions of the quality of tourism services, and tourism service providers need to control the destination image to attract tourists [53].

The literature review shows that the quality of tourism services is affected by factors such as customer satisfaction, service recovery, staff training, and the destination's reputation. Therefore, the study's results can help tourism service providers and policymakers build strategies to improve the quality of tourism services in Babylon City, which can give insights into the value of quality tourism services in improving the experience of visitors to Babylon City.

The most commonly used measure of how customers feel about a company's service is the SERVQUAL instrument developed by Parasuraman et al. [54]. SERVQUAL is a multi-item instrument designed to quantify the gap between a customer's perception of the quality of service they received and their expectations. Parasuraman et al. [54] proposed ten dimensions of service quality as shown in Table 1:

Table 1. The ten dimensions of service quality

The SEVQUAL model analyses the quality of the services provided by various tourism-related industries, including the airline, sports, and dining sectors. These gaps were:

Gap 1: Customer expectation - management perception gap

Sometimes, service providers may lack the information necessary to provide their customers with the features and levels of performance they expect. Customers' perceptions of the quality of those services are affected.

Gap 2: Management perception - service quality specification gap

A chasm forms between a business and its customers when the latter knows what the latter wants but fails to provide it. Several factors may have contributed to this discrepancy, including insufficient resources, bad market conditions, or apathetic management. The client's perceptions of value may be skewed in light of these considerations.

Gap 3: Service quality specifications – service delivery gap

Companies may have policies for handling consumer complaints and providing superior service, but this does not guarantee quality. Even if providing a good impression of the service to customers is crucial, it is impossible to standardize employee performance. This affects how services are delivered, affecting how customers rate those services.

Gap 4: Service delivery – external communications gap

External messaging can affect both the expectations of and satisfaction with customer service. If customers are not aware of the extra steps a business takes to guarantee the high quality of its products and services, they may have different ideas about the value of those measures.

Gap 5: Expected service – perceived service gap

They concluded that the secret to offering high-quality services is doing everything it takes to meet or surpass customers' expectations. The quality of service is judged based on how clients rate its performance relative to their expectations. The SERVQUAL model, developed by Parasuraman et al. [54] in 1988, is a multi-item scale used to evaluate how well a business is doing in terms of its customers' views of the quality of the service they receive. The scale classifies businesses based on how well they perform in five categories related to customer care: sympathy, confidence, dependability, and promptness. It is predicated on the idea that a customer's overall satisfaction is directly proportional to the degree to which their expectations exceed, or are at parity with, their actual experiences. To a greater extent than SERVQUAL, the SERVPERF model implicitly analyses customer experience based on the same criteria as SERVQUAL, consistent with the implications of satisfaction and attitude research [55]. Furthermore, Teas [56] later developed the assessed performance model to deal with problems caused by the conceptualization gap of service quality [54]. However, Figure 1 shown below illustrates the service quality measurement using SERVQUAL model.

1.png

Figure 1. Measuring service quality using SERVQUAL model [57]

The SERVQUAL methodology was developed to analyze customer service quality ratings [54]. Moreover, this is critical for long-term growth and profitability. They also require that we attempt to quantify the relative importance of each dimension when implementing the model. Parasuraman et al. [54] advise that a corporation use this model thrice to assess its service quality over time, identify differences between perceived and actual services, and determine the appropriate response. Additionally, they urge that the model be used in conjunction with other models. For example, in a retail organization, another model may be used to assess employees' perceptions of service quality and elicit recommendations from these employees on enhancing the quality of their services. Although some needs are difficult (or possibly impossible) to examine because they are outside the conscious consciousness of the investigated person (Oliver's Expectancy Disconfirmation and Attribution), the Choice of Theories and techniques helps try to understand the complexity of human wants.

Numerous theoretical models for evaluating consumer satisfaction have been proposed in the tourist literature [58]. The expectancy confirmation model by Oliver [59] is one such model that compares consumer expectations to performance. Emotional satisfaction occurs when performance exceeds expectations; if the contrary is true, emotional discontent results. If tourists' expectations about a destination match what they see, feel, and accomplish when they arrive, they will be satisfied [60]. Chon [60] found that the degree to which a tourist's expectations match what they see, feel, and accomplish at their destination will determine their satisfaction. In the tourism and hospitality industries, the performance-based approach has a better association with tourist satisfaction when it comes to tourism [61]. Empirical investigations have demonstrated that the performance-only technique is more reliable and valid than other approaches, such as disconfirmation and expectation performance, which require expectation. Furthermore, this method optimizes predicting future consumer behavior and overall Satisfaction [62].

Additionally, importance performance analysis is the primary technique utilized in this study to examine the components of the tourism industry, as consumer happiness is a consequence of expectations about specific critical attributes and judgments about attribute performance. Additionally, several academics evaluated the standards used to assess service quality and satisfaction. As a result, they developed alternative service quality and satisfaction measures [63]. Satisfaction must be a fundamental criterion for evaluating tourism products and services. Satisfaction has been a critical factor in developing tourism products and services. Tourism satisfaction is critical for successful destination marketing since it affects destination selection, product and service consumption, and the decision to return [64].

This study examines the factors influencing whether service delivery meets, exceeds, or falls short of expectations. For example, the expectancy disconfirmation hypothesis describes the quality of service. The study aims to investigate attribution theory to understand the causes or factors that lead to a particular outcome for a service to determine the underlying reason or explanation for a particular result or incident. Service users act as psychologists in an attribution study. In other words, attributions are human descriptions of things in a non-scientific language known as naive psychology. According to Heider [65], who says that it considers the concepts related to positioning behaviour within a cause-and-effect relationship, attribution theory may be traced back to that year. When we interpret events as arising from specific elements within a relatively consistent environment, as mentioned in reference [65], it can be considered as engaging in the process of attribution. As a result, the attribution theory outlines a scenario in which the person is "driven to gain a cognitive mastery of the causal structure to his environment". For these reasons, the ten-item original SERVQUAL model's dimensions are used in the current study.

3.4 Relationship between the quality of tourism services and tourists' satisfaction

Numerous studies have examined the correlation between service quality in the tourism industry and customer happiness. The most important findings of the research are as follows:

(1) Contentment with the service received: A tourist's satisfaction is directly related to the quality of the services they receive. Therefore, service quality is a significant factor in how satisfied a tourist is [66]. Customer satisfaction levels rise as service quality improves and fall when it declines.

(2) Dimensions of service quality and clients' happiness: Customer happiness is heavily impacted by the various characteristics of service excellence [67] claim that tangibles, reliability, responsiveness, assurance, and empathy are the five characteristics of service quality that matter most to customers in the tourism business. When these aspects of service are excellent, customers are more satisfied overall.

(3) Destination image and customer satisfaction: The destination image also significantly impacts customer satisfaction. According to Hossain et al. [68], the destination image influences tourists' perceptions of the quality of tourism services and is positively related to customer satisfaction. A favorable destination image leads to increased consumer satisfaction, whereas a negative image leads to lower customer satisfaction.

(4) Service recovery and customer satisfaction: Service recovery is critical in maintaining customer satisfaction in tourism. According to Kelley and Davis [69], good service recovery may boost customer satisfaction following service failures, whereas poor service recovery can lead to reduced customer satisfaction.

(5) Employee training and customer satisfaction: Employee training is vital for increasing the quality of tourism services and raising consumer happiness. Employee training may increase the quality-of-service delivery and enhance customer satisfaction [52].

Previous literature reveals that the quality of tourism services is critical in determining tourists' pleasure. The service quality dimensions, destination image, service recovery, and employee training are critical factors influencing tourist satisfaction. The study's findings can provide insights into the relationship between the quality of tourism services and tourists' satisfaction in Babylon City and help tourism service providers and policymakers to develop strategies to improve the quality of tourism services and enhance customer satisfaction in Babylon City.

Lastly, based on the review of the literature written in this section, it can be concluded that while previous literature discusses various aspects of tourism service quality and its relationship with customer satisfaction, there is a limited exploration of the role of technology and digital platforms in influencing tourists' satisfaction and service quality. Specifically, the impact of digital transformation, such as mobile apps or online reviews, on tourists' perceptions of service quality remains unaddressed. In addition, there is a lack of emphasis on the influence of cultural and societal factors on tourists' satisfaction, which highlights a research gap in how cultural differences and societal norms affect tourists' expectations and evaluations of service quality, particularly in a diverse and multicultural tourism environment like Babylon City. Based on the provided information, the conceptual framework of the study has been designed as depicted in Figure 2.

Table 3. Demographic statistics of the respondents

The data shown in the Table 3 describes the population concerning gender, age, marital status, level of education, and the number of visits. Meanwhile, 73% of the sample is male and 27% female, suggesting that men make up most local travelers. Respondents aged 35-44 equals 41.0%, which is considered the highest among all the age categories. Conversely, the percentage of respondents aged 18-24 was the lowest, with only 1.3% of the total respondents. Those between the ages of 25 and 34 account for 23.8%, while participants aged 45-54 make up 22.9%. However, the percentage of respondents aged 55 and more was 11.1%. However, the frequency of single respondents is 62, which corresponds to a percentage of 19.7%. This means that out of the total sample of 315, 62 individuals reported being single. The frequency of divorce among respondents is 12, corresponding to a percentage of 3.8%. Lastly, most of the sample reported being married with 241 individuals, which equals a percentage of 76.5%. In terms of education, most respondents hold a secondary school qualification, comprising 41.3% of the total sample. Following closely behind are those with a bachelor's degree, constituting 31.4% of the sample. The data also reveal that a significant portion of respondents possess an intermediate school education (11.1%), while a smaller percentage have completed primary school (5.4%). Additionally, there are respondents with certificate diplomas (1.0%) and those with master's or Ph.D. degrees (9.8%). This educational breakdown underscores the diversity within the sample, reflecting a range of educational backgrounds. Lastly, the table showcases the number of times respondents have visited the destination. Remarkably, the largest group of tourists, comprising 34.6% of the sample, has visited Babylon for the second time. The second most common category of visitors includes those on their fourth visit or more, accounting for 23.5%. Meanwhile, 22.2% of respondents have visited Babylon for the third time, while 19.7% are first-time visitors. This distribution highlights the prevalence of repeat visitors to Babylon, suggesting that the destination holds a strong appeal for individuals who choose to return. Overall, the table provides valuable insights into the education levels and travel behaviors of the surveyed individuals, offering a comprehensive snapshot of the sample's demographic characteristics and their relationship with their visitation patterns.

5.1 Expected versus perceived service quality of tourists

The service quality offered to tourists was evaluated using ten dimensions: reliability, security, tangibles, responsiveness, credibility, competence, access, courtesy, communications, and understanding. In addition, the reliability test was used to examine the degree of agreement between separate measurements of the study's variables. The alpha value of 0.8335 suggests that the items in the survey are internally consistent and measure the same construct, as shown in Table 4. The table provides reliability statistics for different service quality dimensions in a study. Reliability is an essential aspect of research, as it reflects the consistency and stability of the measurements. In this study, the researchers have used Cronbach's alpha as a measure of reliability. Indicating how well the items on a scale or instrument assess the same construct, Cronbach's alpha is a statistical measure of internal consistency. The value can be anywhere from 0 to 1, with 1 representing perfect consistency.

Table 4. Reliability statistics of study dimensions

The service quality of pre-arrival services was measured using the ten sub-dimensions of Expectations before visiting (with 30 items) and perception after visiting (with 30 items). Unfortunately, all perceived service quality parameters have mean values below the expected level, as shown in Table 4. Consequently, there is a statistically significant gap between the two service quality measures, as shown by the results of an independent sample t-test. This table shows that the expected dimension has the highest reliability, with an alpha value of .890. This indicates that the items in this dimension are highly consistent in measuring the same construct: the customers' expectations of the service they receive. On the other hand, the perception dimension has a slightly lower reliability, with an alpha value of .777. This suggests that the items in this dimension may not be as consistent in measuring the customers' actual perception of the service.

The total dimension, which combines both expected and perception dimensions, has an alpha value of 0.8335. This indicates that the overall reliability of the scale is good, as it reflects the consistency of the measurements across both dimensions. It is also worth noting that the number of items in each dimension is the same, with 30 items in each. This suggests that the researchers have tried to maintain consistency in the number of items across dimensions, which helps to ensure that each dimension is measured with equal precision. Overall, this table provides valuable information about the reliability of the measurements taken in this study, which is essential for understanding the validity and generalizability of the results.

5.2 SERVQUAL tourist expectations and perceptions

Table 5 displays the respondents' views on what constitutes high-quality service. The table of SERVQUAL scores shows that six items have negative signals, indicating that expectations are higher than performance, leading to a service quality gap and, ultimately, less-than-satisfactory perceived quality. The table shows the descriptive statistics of 30 items (10 variables). Deviation shows the data's degree of variability or dispersion around the mean.

Even though the SERVQUAL score for items credibility, responsiveness, competence, communication, and understanding are harmful, according to the paired samples t-test, means of perception and expectation do not significantly differ in the reliability, access, courtesy, security, and security tangibles. Babylon tourist destinations fall short of expectations.

Table 5 shows the mean scores for various attributes, comparing the expected mean with the perception means and the difference between them. The t-value and significance level are also provided, which can indicate whether the mean difference is statistically significant. Attributes included in the table: There is a discrepancy of 0.1085 between the mean perceived score of 3.8354 and the mean predicted score of 3.9439 for reliability. In this case, the t-value of 1.638 is significant (at the 0.05 level). There is a discrepancy of 0.0317 between the mean perceived score of 2.7937 and the mean predicted score of 2.8254 for responsiveness. The t-value of 0.589 is significant at the 5% level of probability. The gap between the expected and observed means of competence is -0.0043 (or 2.9407 and 2.945, respectively). The t-value of -0.032 is insignificant at the 5% confidence level. There is a discrepancy of 0.0138 between the expected and perceived means of access (mean = 3.563, median = 3.5492). At the 5% significance level, the t-value of 0.085 is insignificant. There is a discrepancy of 0.0222 between the mean perceived score and the mean predicted score for courtesy (3.3365 versus 3.3143). At the 5% significance level, the t-value of 0.559 is insignificant. The mean score for communication (2.9503) and the mean score for perception (3.0709) is -0.1206, indicating poor communication. The t value of -2.079 is insignificant at the 5% confidence level. Regarding believability, the average perceived score is 3.0593, whereas the predicted score is 2.8571 (a difference of -0.2022). At the 5% significance level, the t-value of -3.483% is significant. There is a discrepancy of -0.3322 between the predicted and perceived mean scores of 2.1556 and 2.4878 in safety and security. The t-value of -5.76 is significant at the 05 level of analysis. The average score for perception was 3.1206, whereas the average score for understanding/knowing was 3.0466, for a difference of -0.074. At the 5% significance level, the t-value of -1.167 is insignificant. The average perceived score is 2.4317, while the average expected score is 2.1471. This yields a discrepancy of -0.2846. In this case, the t-value of -3.913 is significant (at the 0.05 level).

MTOTAL: The expected mean score for MTOTAL is 2.9766, while the mean perception score is 3.0608, indicating a difference of -0.0842. The t-value is -2.552, which is not statistically significant at the 0.05 level. The table suggests differences between the expected mean and perception mean scores for specific attributes. Statistically significant differences can indicate areas where improvements are made. However, for each variable, descriptive statistics provide information on the central tendency and spread of the data. These statistics can be used to compare variables and draw inferences about the characteristics of the data. For example, if the data are widely dispersed about the mean, the standard deviation will be considerable; if the observations are densely grouped around the mean, then the standard deviation will be slight. Using the table, we can see how each variable is distributed among the whole data set. The table is helpful since it summarizes the data distribution for each variable and allows us to rapidly compare and contrast them in terms of central tendency and variability.

Table 5. Expectation versus perception service quality in Babylon

The t-value for reliability is 1.638, which is positive, indicating that it is a statistically significant factor in the evaluation. A higher score suggests that the dimension may be counted on to deliver steady performance. In contrast to the reliability, responsiveness (t-value: 0.589) is statistically significant, albeit to a lower extent. It implies that the organization quickly responds to visitors' questions and concerns.

• Performance ability (t-value: -0.032): Competence is not statistically significant because its t-value is close to zero. This factor might not have much of an impact on the rating.
• Gaining Entry (t-value: 0.085): Access's t-value is similar to zero, indicating that it is not statistically significant. Access refers to the ease of reaching or utilizing quality services, but its impact on the overall evaluation may be relatively minor.
• Courtesy (t-value: 0.559): With a positive t-value, courtesy is statistically significant and positively contributes to the evaluation. It implies that the entity treats its customers or users with politeness, respect, and professionalism.
• Communication (t-value: -2.079): This attribute has a negative t-value, indicating that it is statistically significant and negatively affects the evaluation. A lower value suggests the entity's communication practices may be inadequate or ineffective.
• Credibility (t-value: -3.483): The negative t-value for credibility suggests that it is highly statistically significant and negatively impacts the evaluation. It implies that the entity's trustworthiness, believability, or reputation may be questionable.
• Safety and Security (t-value: -5.765): The highly negative t-value indicates that safety and security are statistically significant in the analysis and have a substantial negative impact on the evaluation. It suggests that the entity may have safety measures or security protocol issues.
• Understanding/Knowing (t-value: -1.167): The negative t-value for understanding/knowing suggests that it is statistically significant and negatively influences the evaluation. It implies that the entity may struggle to comprehend customer needs or demonstrate sufficient knowledge.
• Tangibles (t-value: -3.913): The negative t-value for tangibles indicates that it is statistically significant and negatively impacts the evaluation. Tangibles refer to the physical or observable aspects of the entity's services, and a lower value suggests that these aspects may be lacking or of poor quality.

In summary, this analysis provides insights into the significance and impact of various attributes based on their respective t-values. It reveals the areas where the dimensions excel (reliability, responsiveness, courtesy) and the areas that require improvement (communication, credibility, safety, security, understanding/knowing, tangibles). Nevertheless, the subsequent Figure 4 offers insights into the variability of t-values across the ten dimensions under consideration.

4.png

Figure 4. t-value of paired sample t-test

5.3 One-sample test of expectation

Table 6 shows the results of a one-sample test for various dimensions of tourist satisfaction with service. The test value for each dimension is 3, which means the null hypothesis is that the mean satisfaction score for that dimension equals 3. The "t" column shows the calculated t-statistic for each dimension, which measures the difference between the observed mean score and the hypothesized mean score (in this case, 3) in terms of the standard error of the mean. The larger the absolute value of the t-statistic, the more evidence there is against the null hypothesis. The "df" column represents the degrees of freedom, the number of observations minus the estimated parameters. In this case, it appears there are 314 degrees of freedom.

Table 6. One-sample test of expectation

The "Sig. (2-tailed)" column shows the p-value associated with each t-statistic, which is the probability of observing a t-value as extreme or more extreme than the observed t-value, assuming the null hypothesis is actual. A small p-value (typically less than 0.05) indicates strong evidence against the null hypothesis and suggests that the observed mean score significantly differs from the hypothesized mean score of 3. The "Mean Difference" column shows the difference between the observed mean score and the hypothesized mean score (in this case, 3), which is positive for dimensions where the observed mean score is higher than three and negative for dimensions where the observed mean score is lower than 3.

The "95% Confidence Interval of the Difference" column shows the range of values likely to contain the actual difference between the observed and the hypothesized mean scores with 95% confidence. If this interval does not include 0, the difference is statistically significant at 0.05. Overall, customer satisfaction scores appear significantly higher than the hypothesized value of 3 for most dimensions, except for Competence and Understanding/Knowing. However, it is essential to consider the context and specific details of the study to understand and interpret these results fully.

5.4 One sample t-test of perception

Table 7 presents the results of a one-sample t-test, a statistical test used to determine whether a sample mean is significantly different from a known or hypothesized value. In this case, the known or hypothesized value is 3, and the sample means represent participants' perceptions of various aspects of service.

Table 7. One-sample t-test perception

Test Value: This is the hypothesized value, which is 3 in this case. T: This column shows the calculated t-value for each test. A larger absolute t-value indicates a greater difference between the sample mean, and the hypothesized value, and a smaller p-value (see below) indicates stronger evidence against the null hypothesis. Df: This column shows the degrees of freedom for each test. Degrees of freedom represent the number of independent observations in the sample and are used to calculate the t-value. Sig. (2-tailed): This column shows the p-value for each test, representing the probability of obtaining a t-value as extreme or more extreme than the observed value, assuming the null hypothesis is true. A p-value less than .05 is typically considered statistically significant, indicating that the sample mean differs significantly from the hypothesized value.

Mean Difference: This column shows the difference between the sample mean and the hypothesized value. 95% Confidence Interval of the Difference: This column shows the range of values within which the actual population mean difference is likely to fall, with 95% confidence. The results show that several of the tests have very low p-values, indicating strong evidence that the sample mean is significantly different from the hypothesized value of 3. Specifically, the tests for Reliability, Responsiveness, Competence (second test), Courtesy, Security, Understanding/Knowing, and Tangibles all have p-values less than .05, indicating participants' perceptions of these aspects of the service are significantly different from the hypothesized value.

On the other hand, the tests for Competence (first test), Communication, and Credibility all have p-values greater than .05, indicating that there is no strong evidence that participants' perceptions of these aspects of the service are significantly different from the hypothesized value. Finally, the test for MTOTAL has a p-value of .059, which is just above the typical threshold for statistical significance. This suggests that there may be some evidence of a difference between participants' overall perception of the service and the hypothesized value of 3. However, it is not strong enough to reach statistical significance at the .05 level.

Table 8. Differences between the frequency of visits and sample response during the expected period

5.5 Differences between the frequency of visits and sample response during the expected period

Frequency of visits and traits such as reliability, responsiveness, competence, courtesy, communication, credibility, safety and security, understanding/knowing, tangibles, and overall satisfaction are analyzed. The results are displayed in Table 8. The sum of squares, mean square, F-value, and significance level are all dimension-specific statistics (Sig). Variability in answers that may be attributed to varying values of the independent variable is quantified by the sum of squares (SS) (in this case, the various service quality dimensions). The Df is equal to the independent variable's level count minus one. You get the mean square if you divide the SS by the Df. Finally, the F-value is calculated by dividing the variance in replies across all service quality characteristics by the variance in those same responses from the same groups (i.e., the variability in responses not attributable to the different service quality dimensions). If the F-value is large and the Sig. Value is little (less than 0.05), then there is a statistically significant difference between the groups; this suggests that different aspects of service quality are linked to varying degrees of satisfaction among visitors or customers.

Based on the reported F-statistics and significance levels, it appears that there are significant differences between groups for the reliability factor (p < .001), understanding/knowing factor (p = .035), and competence factor (p = .023). For the remaining factors, there are no significant differences between groups (all p-values > .05). The F-values for these dimensions are relatively high (ranging from 1.435 to 5.877), and their significance levels are all less than 0.05, indicating that the differences are statistically significant. For the other dimensions, including courtesy, communication, credibility, safety and security, and tangibles, the F-values are lower, and the significance levels are higher, suggesting that there may not be significant differences in customer or client responses across these dimensions. However, it is essential to note that the significance level is just one indicator of statistical significance, and it should be interpreted in conjunction with other factors such as effect size, sample size, and research context.

5.6 Differences between frequency of visits and sample response during the perceived period

Table 9 shows the results of a statistical analysis of survey responses related to perceived service quality. Specifically, it presents the analysis of variance results, which examines whether the mean differences among groups are statistically significant. The table provides a sum of squares (SS), degree of freedom (df), mean square (MS), F-value (F-statistic), and associated significance level for each dimension of service quality, such as responsiveness, reliability, competence, courtesy, communication, credibility, security, understanding/knowing, and tangibles (Sig). Both the variation between groups and within groups contribute to the SS, representing the total variety in the data. The df value specifies the total number of analyzed classes. The MS is the standard deviation (SS) divided by the number of categories (df), and it is a measure of the typical amount of variation attributable to each classification.

Table 9. Differences between the frequency of visits and sample response during the perceived period