Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4, 570-582 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: Thanuset.c@kkumail.com Emotional intelligence and demographic factors: Moderating effects in service recovery strategy Thanuset Chokpiriyawat1*, Kampanat Siriyota2 1,2Faculty of Business Administration and Accountancy, Khon Kaen University, Khon Kaen, Thailand; Thanuset.c@kkumail.com (T.C.). Abstract: This study investigates the moderating effects of emotional intelligence, gender, income, and region of residence on the relationship between brand image, service recovery strategy, and satisfaction. Emotional intelligence significantly moderates how brand image impacts perceived service recovery strategy, with lower emotional intelligence associated with more negative perceptions of social brand image. Income also plays a crucial role, affecting how brand symbolism and social image are perceived, with lower-income individuals having more positive views. Furthermore, income levels influence satisfaction with a service recovery strategy, with lower-income individuals exhibiting more favourable perceptions. The provincial areas viewed brand experience more positively compared to those from urban regions. Gender did not significantly moderate these relationships. The study also highlights that while emotional intelligence does not substantially impact satisfaction with service recovery, it remains vital for effective communication and brand attachment. The results underscore the intricate dynamics of these variables in shaping consumer attitudes and behaviours toward service recovery strategy. Keywords: Brand image, Demographic factors, Emotional intelligence, Satisfaction, Service recovery strategy, 1. Introduction Emotional intelligence (EI) has been studied in psychology, education, and marketing (Koc 2019). The employees’ EI influences the success of the service recovery strategy in terms of the consumer’s perspective (Fernandes, Morgado, and Rodrigues 2018). Customers with high emotional intelligence tend to understand problems rationally and focus on a step-by-step resolution when experiencing service failures. In contrast, those with lower levels of EI are more likely to exhibit negative behaviours such as a lack of cooperation in problem-solving or displaying irrational reactions (Chaouali, Souiden, and Ringle 2021). There is little research on the influence of customers’ emotional intelligence on the service recovery process (Xu, Liu, and Gursoy 2022a). Personal demographic factors based on consumer behaviour concepts show that gender differences in service usage patterns affect understanding service recovery strategies. High-income customers often have confidence in their social status and tend to express themselves more assertively (Ahn, 2024; Hyun et al., 2024). Understanding consumer behaviour helps organisation tailor their service recovery strategy to meet the needs and satisfaction of customers with quality service. Urban and regional societies exhibit distinct differences in consumer behaviour (Chaihanchanchai and Anantachart 2024). Consumers living in different areas may have varying needs for convenience in services, especially the behaviour of using food delivery applications in Bangkok, which has a different usage rate compared to regional areas (Weiler and Gilitwala 2024). There is evidence that studying the influence of customers’ demographic factors will be beneficial for planning appropriate brand image-building and service recovery strategies tailored to individual customer factors. Therefore, this study aims to test the mailto:ahmed.raza@uos.edu.pk(A.R.C.) 571 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate moderating effect of emotional intelligence and personal demographic factors in the context of the service recovery strategy Personal demographic factors significantly influence consumer behaviour in service industries, particularly regarding service failures and recovery strategies. Emotional intelligence impacts tolerance and reactions to service failures, with more emotionally intelligent customers generally showing greater understanding and appreciation for personalized recovery efforts. Service businesses can enhance customer satisfaction, build loyalty, and ensure long-term sustainability by tailoring service recovery strategies to these demographic factors that align with diverse lifestyle preferences, improving overall customer satisfaction and fostering long-term loyalty. 2. Literature Review 2.1. Emotional Intelligence (EI) Emotional Intelligence (EI) refers to the ability to understand and perceive one’s emotions and those of others. It includes managing and expressing emotions appropriately, effectively building relationships with others, efficiently handling stress, and living a creative and happy life. Emotional intelligence also contributes to achieving more tremendous success in life. Interest in emotional intelligence has increased significantly because success at work does not always equate to happiness in life. Emotional intelligence is essential for achieving both success and happiness. Learning, working, and living all require emotional intelligence as a crucial component, such as managing stress and building relationships with others. As identified by Salovey & Mayer (1990), emotional intelligence involves skills in three areas. The components of emotional intelligence emphasise emotional cognition and the role of emotions in guiding a person’s thoughts towards creative and exciting information (Salovey and Mayer 1990). Past studies on emotional intelligence have explored its context in service recovery. These studies have examined how emotional intelligence affects the effectiveness of service recovery strategies, including how employees’ and customers’ emotional intelligence can influence the outcomes of addressing service failures. Research often focuses on how emotional intelligence contributes to better handling customer complaints, improving satisfaction, and resolving issues more effectively. 2.2. Demographic Factors and Consumer Behavior This study incorporates personal demographic factors of consumers, such as gender, income, and residential area, to test the influence of these moderating variables on service recovery strategies. Understanding consumer behaviour is crucial for predicting business success. In marketing management, personal factors are applied as vital elements in market segmentation to identify target groups and position products or services effectively in the market (Witek 2017). Marketing is increasingly focusing on human psychology. Business units must balance their marketing strategies based on human-to-human (H2H) marketing principles. The goal of service businesses is to achieve customer satisfaction. Meeting the challenge of creating marketing success or individual satisfaction is crucial for modern marketers (Kotler, Pfoertsch, and Sponholz 2021). Demographic factors relate to satisfaction patterns, including age, gender, income, and personality traits, which can affect satisfaction levels (Jamil, Shafique, and Muhammad 2024). Individuals with higher incomes may be more satisfied with high-priced products or services, while younger people may find more satisfaction in toys or activities. Tailoring communication and creating products or services that align with the needs and preferences of these target groups can help businesses enhance satisfaction and build more substantial market confidence. However, individual differences necessitate further analysis to better understand each target group’s needs and satisfaction levels (Monteiro and Borges 2015). Gender is a significant factor influencing consumer behavior. Generally, gender affects purchasing decisions and shopping behavior. For example, men might spend less time selecting products and focus more on features related to the product’s functionality or variety. In contrast, women may spend more 572 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate time choosing products and place greater importance on the experience and emotional aspects related to the purchase. Regarding service usage, such as selecting a hotel, the behaviour of men and women may also differ. For instance, men might prioritizes amenities and efficiency, while women might focus on comfort and service quality (Putachote and Chokpiriyawat 2024). Women may place more importance on the ambience and overall experience, while men may focus on the quality of food and the speed of service. Analyzing gender-specific behaviors can help marketing by creating products or services tailored to specific genders or using targeted advertising. Since gender affects marketing responses differently, conducting detailed analysis and management is essential. This involves understanding the complexities and nuances of behaviours and needs across different gender groups. By grasping these differences, businesses can reach and engage their target audiences more effectively (Zhu et al. 2022). Income level is a crucial factor influencing consumer behavior and purchasing decisions. It affects the types of products or services consumers can afford and their preferences. Higher-income individuals prioritize quality, luxury, and brand prestige, while lower-income individuals might focus more on affordability and value for money. Understanding income-related behavior helps businesses tailor their offerings and marketing strategies to meet the needs and expectations of different income groups (Cicarelli 1974). Consumer behavior can differ significantly between high-income and low-income individuals. Those with higher incomes often prefer to purchase high-priced products or services, focusing on quality, brand reputation, or exclusivity. For instance, they may opt for premium food options or luxury services emphasizes superior quality. In contrast, lower-income individuals prioritise affordability and value for money, making decisions based on cost-effectiveness rather than brand or quality (Putri et al. 2023). High-income consumers are likelier to choose well-known brands and premium products, often valuing brand reputation and quality. In contrast, low-income consumers tend to select more affordable options that fit their budget, focusing on cost-effectiveness rather than brand prestige (Rehman and Jamil 2016). Studies on purchasing behaviors in children show differences in demand and the tendency to conflict with parents, indicating that varying income levels impact both the desires and the expression of conflict. Higher- income levels may lead to greater access to and demand for desired items, which could result in more noticeable conflicts over purchasing decisions. Conversely, lower income levels might limit such demands, potentially reducing the frequency or intensity of disputes (Lapierre 2013). Individuals with higher incomes often prefer high-priced services, such as luxury accommodations or fine dining. In comparison, lower-income people prefer more affordable options, such as travelling by train or dining at budget-friendly restaurants. Analyzing income levels for marketing decisions is widely used in business management. For example, businesses might create products or services tailored to high-income groups or develop promotions and discounts for lower-income groups. Understanding consumer behaviour and needs based on income levels helps companies to develop effective marketing strategies to reach and engage their target audiences appropriately (Dinarso, Qomariah, and Thamrin 2024; Pizzagalli, Sharma, and Lascu 2018; Roy et al. 2021). Linking customer income factors to the context of service recovery strategies is based on the impact of consumer behaviour theories. This connection influences decisions regarding whether to use or not use certain products or services. By understanding how income levels affect consumer behaviour, businesses can better tailor their service recovery strategies to meet different income groups' expectations and preferences, ultimately improving customer satisfaction and loyalty (Aprilia et al. 2024). This includes behavioral responses influenced by social class, which may affect how customers perceive and react to service failures. Social class can impact how individuals handle service errors, influencing their satisfaction and subsequent behaviour in response to such issues. Understanding these dynamics helps businesses address and manage customer reactions more effectively during service recovery. Urban consumers may have different purchasing habits, preferences, and access to products and services than those in regional areas. The differences impact how businesses tailor their marketing strategies and service offerings to meet the specific needs of each demographic (Chen, Zhang, and Bai 2023). In urban areas, heavy traffic and congestion lead people to value their time more, influencing 573 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate their purchasing decisions towards options that save time. For instance, urban residents are more likely to use online services or shop at nearby stores to reduce travel time. Additionally, urban areas often offer more amenities and higher-quality, higher-priced services due to the many available options. In contrast, regional areas might have fewer amenities and options, impacting consumer behaviour and preferences. For example, studies have shown that consumption patterns for fresh milk differ between urban and regional areas due to logistical and technological limitations in transportation. This demonstrates that where people live can significantly affect their perceptions and lifestyle behaviors (Chen et al. 2024). Studies on the service marketing mix (7Ps) have found that applying marketing strategies in different areas requires adjustments to fit the local social and cultural context. This means businesses must tailor their strategies to align with each region’s unique characteristics and preferences to be more effective. For example, local customs, cultural norms, and community values can influence how marketing strategies are received and how services should be presented and delivered to meet local expectations (Wathanakom, Sranrom, and Juicharoen 2023). Competition in urban areas is often intense, with high expectations for results and success. This drives people to seek products or services that help them improve themselves and stand out. Additionally, urban settings provide more opportunities for community engagement and connections with like-minded groups. For instance, people in cities are more likely to participate in activities or initiatives that align with their interests, such as environmental-friendly endeavors or social causes. This enhanced connectivity can influence purchasing decisions and service preferences, as urban residents prioritise alignment with their personal values and community involvement. Understanding consumer behaviour in metropolitan and regional settings helps businesses adapt and create effective marketing strategies tailored to the needs and preferences of each target group. This enables enterprises to meet customer expectations better and enhance satisfaction by aligning their offerings with the specific demands of different areas. Individual personality, values, and cultural background can influence customer behaviour regarding service issues. Effectively managing service dissatisfaction is crucial for businesses to resolve situations and maintain long-term customer satisfaction. Service satisfaction is closely related to decision-making psychology and behaviour. When service failures occur, customers may respond emotionally by contacting service staff or management to seek assistance and resolve the issues. They might inquire or question the problems to understand better or find solutions. In more severe cases, customers may discontinue using the service or product if the issues cannot be resolved satisfactorily (Zhang et al. 2024). This includes expressing dissatisfaction through direct communication or social media platforms, such as leaving harmful comments or reviews. Customers may also exhibit impolite behaviour or vocal frustration when encountering service problems or dissatisfaction. Addressing these forms of feedback effectively is crucial for businesses to mitigate damage to their reputation and improve overall customer experience (Akter and Sultana 2020). The conceptual framework has been illustrated in Figure1. Figure 1. Conceptual framework. 574 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate 3. Method The research design employs a quantitative approach using a survey method to gather data from users of private hospitals in Thailand. The study targets individuals aged 20 and above who have utilised private hospital services, such as routine check-ups or vaccinations, within the past six months, excluding hospital staff. Data will be collected through self-administered online questionnaires via Google Forms, with a Likert scale rating system for responses. The research utilised a modified questionnaire derived from previous studies, translated into Thai to ensure clarity. The section on hospital brand image consisted of 19 questions adapted from Jr et al. (2007) and Lassar, Mittal, & Sharma (1995). The author crafted a service failure scenario, and respondents were instructed to read it thoroughly before completing the questionnaire. Additionally, the 15 questions on service recovery strategies, covering three dimensions, were adapted from Tax et al. (1998), Smith et al. (1999), and Maxham & Netemeyer (2002). Satisfaction was measured using four items from Maxham & Netemeyer (2002). The 33 questions assessing emotional intelligence were adapted from the Schutte Self-Report Emotional Intelligence Test (SSEIT) (Schutte et al., 1998). Emotional intelligence was divided into four sub-categories: ten items for emotion perception, nine items for managing one's own emotions, eight items for managing others' emotions, and six items for utilisation. A 5-point Likert scale was used for responses, ranging from 1 (strongly disagree) to 5 (strongly agree). The data collection period is set from October 10, 2023, to November 11, 2023, ensuring a clear understanding of the survey questions through provided explanations. Multi-group structural equation modeling (SEM) will analyse the moderating effects of emotional intelligence, gender, income, and geographical location on the relationship between service recovery strategy and satisfaction. This analysis aims to elucidate the impact of brand image perception on recovery strategy and user behaviour, highlighting the role of various moderating variables in different contexts. 4. Result The most recent use of private hospital services among the sample group primarily involved visiting relatives, friends, or acquaintances, with 30.8% of respondents indicating this as their purpose. Medical follow-up appointments and annual health check-ups were the second and third most common reasons for using these services, at 22.3% and 20.8%, respectively. A significant majority of respondents, 83.5%, reported no dissatisfaction with the services. Among those who experienced dissatisfaction, issues such as overcrowded facilities (6.0%) and delayed services (4.0%) were noted. The experience level with private hospital services was moderate for 52.2% of respondents, with 24.2% reporting the highest level of experience and 12.0% having considerable experience. Similarly, familiarity with the services was moderate for 49.2% of the sample, with 24.5% indicating high familiarity and 14.0% reporting high familiarity. Regarding private hospitals, 48.7% had a moderate understanding, 24.7% had high knowledge, and 14.0% possessed extensive knowledge. To test the moderating effects of emotional intelligence, gender, income, and location on the relationship between brand image, service recovery strategy, and customer satisfaction. The analysis employed a Multigroup approach, explicitly using two-group analysis to test the hypotheses. This method was used to explain the moderating effects of emotional intelligence by dividing the sample into two groups. After confirming the components, the average scores of the emotional intelligence measurement scale were used to categorise continuous data into categorical data. Six questions were removed by confirmatory factor analysis (CFA), leaving 27 items under four dimensions. The average emotional intelligence score of the sample was found to be 4.10, which was used as a criterion for grouping. Respondents with an average score greater than 4.10 were classified as having high emotional intelligence. In comparison, those with a score less than or equal were classified as having low emotional intelligence. The study identified 378 samples with high emotional intelligence and 222 with low emotional intelligence. 575 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate Table 1. Analysis of the moderating effect of emotional intelligence. Relationship Low EI High EI Group different Sig. Standardized estimates (t-value) Standardized estimates (t-value) (Δ chi square/ degree of freedom) EI -------> SRS 0.123(1.008) 0.452 (1.249) 0.760 0.383 SY -------> SRS 0.504 (4.582) 0.252 (2.004) 2.229 0.135 SOI -------> SRS -0.605 (-4.100) -0.063 (-.501) 6.949 0.008 FI -------> SRS 0.291 (2.046) 0.135 (.445) 0.220 0.639 SRS -------> SAT 0.890 (17.475) *** 0.907 (14.860) *** 0.328 0.567 The analysis presented in Table 1 indicates that emotional intelligence significantly influences the relationship between the social dimension of brand image and service recovery strategy, with a notable p-value of 0.008. Specifically, individuals with lower emotional intelligence exhibit a stronger negative relationship, evidenced by a beta coefficient of -0.605 and a t-statistic of -4.100. In contrast, those with higher emotional intelligence display a much weaker negative relationship, with a beta coefficient of - 0.063 and a t-statistic of -0.501. For other dimensions of brand image, such as experience, symbolism, and function, emotional intelligence does not significantly affect service recovery strategy. Additionally, emotional intelligence does not moderate the impact of service recovery strategy on customer satisfaction. The overall model fit indices, including a chi-square of 3,366, degrees of freedom of 1,519, a p-value of less than 0.000, a Comparative Fit Index (CFI) of 0.935, an Incremental Fit Index (IFI) of 0.934, and a Root Mean Square Error of Approximation (RMSEA) of 0.041, confirm the robustness of the model. These findings highlight the crucial role of emotional intelligence in specific contexts but also underscore its limited impact on other aspects of service compensation and customer satisfaction. 4.1. The Moderating Effect of Gender The data collection revealed a sample of 190 males and 313 females. The analysis of the moderating effect of gender was conducted using a two-group analysis, excluding 97 respondents who did not specify their gender. The results of this analysis are presented in Table 2. Table 2. Analysis of the moderating effect of gender. Relationship Male Female Group different Sig. Standardized estimates (t-value) Standardized estimates (t-value) (Δ chi square/ Degree of freedom) EI -------> SRS 0.037 (.158) 0.042 (.312) 0.000 0.986 SY -------> SRS 0.277 (2.435) * 0.314 (3.347) *** 0.062 0.803 SOI -------> SRS -0.265 (-1.771) -0.195 (-1.580) 0.133 0.716 FI -------> SRS 0.588 (2.123) * 0.470 (3.299) *** 0.148 0.701 SRS -------> SAT 1.017 (12.367) *** 0.907 (14.853) *** 0.036 0.850 The analysis aimed to determine the moderating effect of gender on the relationship between brand image dimensions and service recovery strategy. The results, summarised in Table 2, indicate that gender did not significantly influence this relationship. Specifically, the chi-square change divided by degrees of freedom revealed that gender was not a moderating variable in the following cases: the brand image of experience and service recovery strategy (Δ chi-square/df = .000, p = .986), the brand image of symbol and service recovery strategy (Δ chi-square/df = .062, p = .803), the brand image of social 576 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate aspect and service recovery strategy (Δ chi-square/df = .133, p = .716), and the brand image of usability and service recovery strategy (Δ chi-square/df = .148, p = .701). Furthermore, gender did not moderate the relationship between service recovery strategy and customer satisfaction (Δ chi-square/df = .036, p = .850). The overall model fit indices across groups were as follows: chi-square = 3150, degrees of freedom = 1518, p < .000, CFI = .931, IFI = .931, and RMSEA = .046. These results indicate that gender does not significantly affect the examined relationships in this study. In the income analysis as a moderating variable, data revealed that 362 participants had a monthly income of less than 35,000 baht, while 238 participants had a monthly income greater than 35,000 baht. The results from the two-group analysis are summarised in Table 3. Table 3. Analysis of the moderating effect of income. Relationship Low income High income Group different Sig. Standardized estimates (t-value) Standardized estimates (t-value) (Δ chi square/ degree of freedom) EI -------> SRS 0.249 (2.241) * -0.117 (-.352) 1.687 0.194 SY -------> SRS 0.096 (1.826) 1.826 (1.688) 9.814 0.002 SOI -------> SRS 0.040 (.466) -1.293 (-1.498) 8.528 0.003 FI -------> SRS 0.096 (.900) 0.586 (.056) 3.517 0.061 SRS -------> SAT 1.142 (17.122) *** 0.907 (14.853) *** 6.766 0.009 In examining the influence of income as a moderating variable was found that income did not significantly moderate the relationship between brand image related to experience and service recovery strategy, as indicated by the change in chi-square divided by degrees of freedom, which was not statistically significant (Δ chi-square/df = 1.687, p = .194). Similarly, income did not significantly moderate the relationship between brand image related to usability and service recovery strategy (Δ chi- square/df = 3.517, p = .061). However, income was found to significantly moderate the relationship between brand image related to symbols and service recovery strategy (Δ chi-square/df = 9.814, p = .002) as well as the relationship between brand image related to social aspects and service recovery strategy (Δ chi-square/df = 8.528, p = .003). Additionally, income significantly moderated the relationship between service recovery strategy and customer satisfaction (Δ chi-square/df = 6.766, p = .009). The model fit indices for the analysis across groups were as follows: chi-square = 3044, degrees of freedom = 1518, p < .000, CFI = .936, IFI = .937, and RMSEA = .041. The moderating effect of income on the relationships was notably significant for brand image related to symbols and social aspects, with lower income groups exhibiting lower beta coefficients compared to higher income groups, with coefficients of .096 and .040, and t-statistics of 1.826 and .466, respectively. In comparison, higher income groups had beta coefficients of 1.862 and -1.293 and t- statistics of 1.688 and -1.498, respectively. Additionally, income significantly influenced the relationship between service recovery strategy and customer satisfaction, with lower-income groups showing higher beta coefficients (1.142, t-statistic = 17.122) than higher-income groups (β = .907, t-statistic = 14.853). 4.2. The Moderating Effect of Area The data collection revealed that respondents from Bangkok and the Central Region totalled 184 samples, while respondents from other provinces, including the Northern, Southern, and Northeastern regions, amounted to 416 samples. The analysis of the moderating effect of the area as a variable, using a two-group analysis, is presented in Table 4. 577 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate Table 4. Analysis of the moderating effect of area. Relationship UPC BKK Group different Sig. Standardized estimates (t-value) Standardized estimates (t-value) (Δ chi square/ degree of freedom) EI -------> SRS 0.361 (3.405) *** -7.145 (-0.375) 4.185 0.041 SY -------> SRS 0.392 (4.526) *** 1.165 (0.430) 0.183 0.669 SOI -------> SRS -0.326 (-3.340) *** -2.032 (-0.398) 0.526 0.468 FI -------> SRS 0.078 (0.714) 9.947 (0.422) 3.460 0.063 SRS -------> SAT 1.011 (19.332) *** 0.903 (12.406) *** 1.435 0.231 The analysis of the moderating effect of the region revealed several key findings. The region did not act as a moderating variable in the relationships between brand image (Symbol), brand image (Social), and brand image (Usage) with service compensation strategy, as indicated by the lack of statistical significance in the change in chi-square divided by degrees of freedom (Δ chi-square/df = .181, p = .669; Δ chi-square/df = .526, p = .468; Δ chi-square/df = 3.460, p = .063, respectively). Additionally, the region did not moderate the relationship between service compensation strategy and satisfaction (Δ chi- square/df = 1.435, p = .231). However, the region was found to significantly moderate the relationship between brand image (Experience) and service compensation strategy, with a statistically significant change in chi-square divided by degrees of freedom (Δ chi-square/pdf = 4.185, p = .041). The model fit indices across groups were: chi-square = 3052 / degree of freedom = 1518, p < .000, CFI = .941, IFI = .942, RMSEA = .041. Specifically, the moderating effect was positive for respondents from provincial areas, with a beta coefficient of .361 and a t-statistic of 3.405. Conversely, the beta coefficient was negative for respondents from Bangkok and the UPC Region at -7.145, with a t-statistic of -0.375. These findings suggest that the region significantly shapes the relationship between brand image (Experience) and service compensation strategy, with distinct impacts depending on the respondent’s geographical location. 5. Discussion The analysis of emotional intelligence revealed a significant moderating effect on the relationship between brand image (Social) and service recovery strategy. Specifically, individuals with lower emotional intelligence perceive the impact of brand image (Social) more negatively than those with higher emotional intelligence, as evidenced by a chi-square change divided by degrees of freedom of 6.949 and a p-value of .008. Conversely, gender was not a significant moderating variable in this study. The chi-square changes for the relationships examined did not reach statistical significance, indicating that gender does not moderate the relationship between service recovery strategy and the variables of interest. Income was found to have a substantial moderating effect. For the relationship between brand image (Symbol) and service recovery strategy, the chi-square change divided by degrees of freedom was 9.814 with a p-value of .002. This indicates that lower-income individuals perceive brand image (Symbol) more negatively than those with higher incomes. Similarly, the impact of brand image (Social) on service recovery strategy also varied by income, with a chi-square change of 8.528 and a p-value of .003. Lower-income individuals viewed brand image (Social) more positively, while higher-income individuals viewed it more negatively. Furthermore, income significantly moderated the relationship between service recovery strategy and satisfaction, with a chi-square change of 6.766 and a p-value of .009. Lower-income individuals perceived service recovery strategy more favourably than those with higher incomes. Lastly, the region of residence influenced the relationship between brand image (Experience) and service recovery strategy. The chi-square change divided by degrees of freedom was 4.185 with a p- 578 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate value of .041, indicating that region acts as a moderating variable. Respondents from provincial areas perceived the impact of brand image (Experience) more positively, whereas those from Bangkok and the Central Region perceived it more negatively. 5.1. Influence of Emotional Intelligence on the Relationship Between Service Recovery Strategy and Satisfaction This study examined the role of emotional intelligence as a moderating variable in the relationship between service recovery strategy and satisfaction. The results indicate that the emotional intelligence of service users does not significantly affect the relationship pattern between perceived service recovery strategy and satisfaction in private hospital settings. However, research in service industries has shown that employees’ emotional intelligence can influence the quality of service recovery strategy (Wei et al. 2023; Wei, Hou, and Zhou 2021). Employees with high emotional intelligence in the airline service industry also contribute to the success of the service recovery strategy. The emotional intelligence of airline staff plays a crucial role in effectively implementing these strategies, enhancing overall service quality and customer satisfaction (Lee, Kim, and Jeon 2013). Customers with higher emotional intelligence are generally more tolerant and understanding when facing service mistakes (Xu, Liu, and Gursoy 2022b). Students with higher emotional intelligence experience less stress and are better at solving personal and interpersonal problems. This highlights the importance of emotional intelligence in managing stress and improving problem-solving skills (Delhom, Satorres, and Meléndez 2022; Karaoglan Yilmaz, Yilmaz, and Erdogdu 2022). The study found that customers’ emotional intelligence does not positively influence their satisfaction with service recovery strategies. Therefore, businesses should focus on developing strategies to enhance customer satisfaction primarily through the efforts of service providers. While emotional intelligence is crucial for managing organisational conflicts, it does not significantly impact service recovery satisfaction (Winardi, Prentice, and Weaven 2022). It is a factor that promotes high- quality communication in business operations (Leonidou et al. 2019). However, this study did not find an effect within the customer group in the context of the service recovery strategy. This is because employees’ emotional intelligence is crucial in creating customer satisfaction for those affected by service failures (Al Khoury et al. 2023). This study found that emotional intelligence influences the relationship between social brand image and service recovery strategy. Research explicitly focusing on emotional intelligence’s impact on the perception of social brand image is quite limited. Emotional intelligence plays a role in brand building (Hutchins and Rodriguez 2018). The study found evidence that emotional intelligence is associated with brand attachment through emotional intelligence (Vredeveld 2018).However, there is still very little supporting evidence for these findings, making it an opportunity for further research into compensation strategies, which shows promising potential for continued study (Liu et al. 2024; Sahaf and Fazili 2024). 5.2. Influence of Gender as a Moderating Variable on Service Recovery Strategy and Satisfaction In this study, gender is not a moderating variable in the relationship between Service recovery strategy and satisfaction. This finding is consistent with research showing that male and female customers have similar satisfaction levels with compensation-based service strategies (Mattila, Cho, and Ro 2009). However, some studies have found that gender does affect compensation strategies and perceptions of service failures (Ali et al. 2023; Cambra-Fierro et al. 2013). Therefore, it can be said that personal factors related to gender still show diverse research results. This presents an opportunity for future studies to focus on gender differences in the context of service recovery strategy. 5.3. Influence of Income as a Moderator on the Relationship Between Service Recovery Strategy and Satisfaction This study reveals that individuals with lower incomes perceive the impact of brand symbolism less positively than those with higher incomes. Furthermore, lower-income people view the brand’s social image more positively, while those with higher incomes perceive it negatively. Additionally, individuals with lower incomes have a higher positive perception of service recovery strategy than those with 579 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate higher incomes. The findings indicate that income levels affect attitudes and perceptions regarding service compensation, aligning with studies in the tourism industry, where tourists’ household income influences their satisfaction with service compensation (Akinci and Aksoy 2019). Higher income often reflects a higher social status, influencing behavioural responses when mistakes occur. Individuals with higher social status may be more likely to express negative emotions than those of average social status (Çelik, Özkan Tektaş, and Kavak 2024). Experiencing class-based discrimination in the service response, where higher social status customers receive different treatment, can affect their satisfaction with compensation strategies. (Rohden and Pizzutti 2023). Financial compensation may lead to differing satisfaction levels between social classes (Rohden 2019). The study reveals that individuals in rural areas perceive the brand experience positively, while those in Bangkok perceive it negatively. This indicates differing perceptions of brand experience based on location. Consumers in rural regions value brand experiences more positively, whereas urban residents in Bangkok have a contrasting view, possibly due to differing social and environmental contexts. Although satisfaction studies show that urban residents generally appreciate more comprehensive public utilities than rural areas, these findings highlight the importance of considering regional differences in brand perception and satisfaction. (Rohden and Pizzutti 2023). However, limited research compares differences in attitudes toward brand experience. As a result, it is challenging to determine the extent to which study findings are similar or different. The discussion of the results can be based on the attitudes of entrepreneurs in Sri Lanka toward factors influencing success, which may differ from the perspectives of entrepreneurs in urban areas. This highlights the need for further research to understand regional variations in brand perception and the factors contributing to business success (Rosairo and Potts 2016). In Thailand, attitudes and perceptions of value regarding the acceptance of tablets for children’s education differ between Bangkok and rural areas (Thongpahol 2014). In the private hospital sector, there has been a comparative study of staff models between Bangkok and the rural regions, which may reflect similar trends in service quality and compensation standards (Kittidilokkul and Tangcharoensathien 1997). The political attitudes of people in Bangkok and other provinces are also different (LoGerfo 1996). Therefore, studying consumer behaviour to develop marketing strategies should also study the perceptions and attitudes of local people. Research has compared hospital brand image to the performance perceptions that emerge in consumers’ minds before service errors occur. Hospitals have implemented a service recovery strategy when a mistake appears to offer performance recognition after errors have occurred or been observed. Consumers will compare their internal expectations. The cumulative performance perception of the brand image combined with the performance of the service compensation strategy is higher than expectations. There will be confirmation of expectations. And create satisfaction in the service compensation strategy. Based on this, expectancy confirmation theory has been proposed to explain the variation in expectations between service users with different personalities and attitudes. However, this research suggests that expectancy confirmation theory can also be applied. Let’s define the behaviours of today’s consumers (Chen and Wu 2023; Huang and Ma 2024; Ismail 2023). This research uses social exchange theory to explain the relationship between patterns of behavioral intention exchange between service users and private hospitals. When the service users are satisfied that they have benefited from the service compensation strategy, there will be an exchange of word-of-mouth communication and a revisiting intention to use the services of private hospitals. Past studies in service compensation are based on the social exchange theory, which refers to exchanges in which service users give back, e.g., customer citizenship behaviour, in exchange for satisfaction with service recovery strategy (Zhu et al. 2021). There will be an exchange when service users become loyal to service providers due to a successful service recovery strategy and resilience to switching intention (Ismail 2023). 580 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 570-582, 2024 DOI: 10.55214/25768484.v8i4.1457 © 2024 by the authors; licensee Learning Gate 5.4. Future Research It is recommended to study user factors in the role of controlling variables, such as status, because, at present, in the service business, customer expectations are variable and significantly affect service quality. In addition, a study should be made on the factors of the types of service users. This is because the service user may not need a strategy to compensate for the service just because of a mistake. It should be studied in the concept framework that service users have too many expectations (Expectation customers). The types of errors, such as errors from primary services (Delivery failures) and mistakes of not having the service, can also be studied and gone (Unavailable Service) errors from the uncertainty of the service provider (Unprompted Staff). Copyright: © 2024 by the authors. 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