Exploring the Impact of CRM Strategies on Customer Loyalty with the Mediating Role of Relationship Quality

International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6451 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 Exploring the Impact of CRM Strategies on Customer Loyalty with the Mediating Role of Relationship Quality Madhanagopal Ramakrishnan Department of Statistics, The Madura College (Autonomous), Madurai, Tamil Nadu ABSTRACT: The main goal of this study is to decode the complex connections between Consumer relationship marketing strategies (CRMTs), consumer loyalty, and the critical function of relationship quality (Customer trust) as a mediator. Data were collected from 792 students using mobile for communication and other purposes through a carefully structured questionnaire. The study focused on three key consumer relationship marketing strategies namely service quality, price perception and value offer and examined their effects on customer loyalty. The deployment of statistical tools, including descriptive statistics, correlation analysis, and regression analysis, has provided a robust foundation for these conclusions. The results showed that these strategies have a noteworthy impact on customer loyalty, indicating that they are crucial in forming and sustaining customer loyalty in the everchanging mobile telecom market. In addition, the study examined the mediating role of relationship quality through customer trust using the Sobel test. This analysis provided deeper insight into the underlying relationships. The results highlighted relationship quality as a key mediator between customer loyalty and the proposed marketing strategies, showing that customer trust significantly shapes the impact of these strategies on loyalty. Finally, the study not only provided important insights into the factors influencing customer loyalty in the mobile telecom industry but also emphasised the multifaceted relationships between relationship marketing approaches and the role of relationship quality as a moderator. These findings are significant for industry practitioners because they provide actionable knowledge to improve and maximise client loyalty methods in the highly competitive mobile telephony market. KEYWORDS: Customer loyalty, Relationship marketing tactics, Relationship quality, Regression analysis, Mediation, SOBEL test, Telecom industry. 1. INTRODUCTION India's mobile communications story began in the 1990s with a single call and cumbersome phones. Private players, such as Airtel and Reliance Jio, emerged in the 2000s, fuelling intense competition and making mobile services extremely affordable. With the introduction of Reliance Jio's 4G data plan in 2016, India became a global mobile broadband monster with over 1.15 billion users (TRAI, 2022). The mobile service industry in India is extremely competitive, with major competitors vying for the top position. By keeping costs low, they encourage more people to use mobile phones and offer some of the most affordable data plans. However, because they are constantly trying to save on expenses, the network’s quality may suffer. Consumers benefit from this fierce competition because it drives innovation and lowers prices. It is challenging for businesses, however, as they fight for every client in a competitive market. The most important issue for sellers is not only providing excellent, high-quality products or services but also retaining loyal customers who will contribute long-term profit to organisations (Tseng, 2007). As a result, relationship marketing (RM) has emerged as an alternative method for businesses to develop strong and long-term relationships with their customers. Relationship marketing, as part of marketing strategy, seeks to acquire and retain customers by providing high-quality customer service, and has thus become one of the keys to achieving strong competitiveness in today's markets, due to its implications for market access, the generation of repeat purchases, the creation of exit barriers, and the belief that it benefits all parties (Andaleeb, 1996). Knowing that it is less expensive to retain a customer than to acquire a new one, most managers place a high value on keeping their customers coming back for more. As a result, relationship marketing began to dominate the marketing field and has received a lot of attention in the last few decades, both in academics and in practice (Egan, 2001). In the business world, an organisation must cultivate sincere relationships with a range of stakeholders, including suppliers, consumers, workers, distributors, middlemen, and retailers. A company's relationship capital is made up of a variety of factors, International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6452 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 including trust, satisfaction, knowledge, service quality, brand image etc. As Kotler (2000) pointed out, this intangible asset— which is based on strong relationships—has greater value than actual assets and ultimately determines the company's future worth. Hence, organizations consistently question the efficacy of relationship marketing strategies in fostering customer loyalty. If the answer is affirmative, it is necessary to find strategies that can streamline and reinforce this loyalty. As a result, one of the key goals of this research was to examine and improve our understanding of how relationship marketing methods affect customer loyalty, with a particular emphasis on the mediating effect of relationship quality (RQ). The remainder of the research paper is structured as follows: Section 2 offers an overview of relevant literature. Section 3 outlines the conceptual framework employed in this research. In Section 4, a concise explanation of the theoretical framework pertinent to the study is provided. The selection of data and variables for the present study is detailed in Section 5, while Section 6 presents the results and ensuing discussion. Finally, Section 7 brings the study to a conclusion. 2. REVIEW OF RELEVANT LITERATURE The literature review functions as a road map, providing a synthesis of existing information, directing research by identifying major discoveries, debates and gaps and creating the framework for the current investigation. 2.1 Relationship Marketing Strategies Relationship marketing, according to Berry and Parasuraman (1991), is concerned with the acquisition, fostering, and retention of customer relations. It evolved as the dominating concept in strategic marketing planning in the final decade of the twentieth century, embracing both industrial and consumer marketing sectors (Tseng, 2007). Various definitions have been explored to elucidate relationship marketing, including (a) the identification, establishment, preservation, and enhancement of trust-based relationships with customers and stakeholders (Gummesson, 1994), (b) the goal of retaining customers and improving connections with them (Fontenot and Hymon, 2004), (c) the comprehension and management of relationships between a customer and a supplier (Shell by D. Hunt et al., 2006), (d) the endeavour to engage customers (Tseng, 2007) and (e) According to Kotler and Keller (2009), customer relationship marketing is the process of recruiting, growing, and retaining consumers. At its core, relationship marketing comprises a series of actions aimed at establishing long-term and mutually beneficial connections between a business and its customers, with the ultimate aim of creating benefits for both parties (Lovelock and Wright, 2002). Its initiatives are typically designed to collect data, assisting businesses in identifying and retaining their most important customers, ultimately maximising revenue and customer value (Christy et.al 2018). Peng and Wang (2006) investigated the execution of methods relating to service quality, reputation (brand), pricing perception, and value propositions. Bansal et.al (2005) provide a broad perspective on relationship marketing methods, detailing 12 main aspects from which these tactics might be developed. Service quality, satisfaction, value, trust, commitment, pricing perception, alternative appeal, substitution attitude, subjective norms, switching costs, behaviour change, and the pursuit of diversity are examples of these elements. Odekerken-Schroder et al. (2003), emphasize achieving loyalty is dependent on both effective relationship marketing tactics (RMT) and consumer personality, with different RMT approaches having different effects on perceived loyalty. The findings show that marketing techniques could be improved with practical aspects to promote consumer loyalty. Several studies have found links between relationship marketing methods and customer loyalty, emphasising the roles of customer satisfaction and trust as mediators in these relationships. Parasuraman et al (1988) Aydin and Zer (2005) and Ismail et al (2006), all discovered that service quality has a direct impact on customer satisfaction and trust. Studies conducted by Peng and Wang (2005), Kim et al. (2008) and Cheng et al. (2008) indicate that price perception positively impacts customer satisfaction and trust. According to Grönroos (2000) and O'Loughlin et al. (2004) research, customers are attracted to brands they perceive as inclusive, providing satisfaction and instilling trust in them. These research works highlight the importance of the RMT’s. 2.2 Consumer trust According to Mayer et al. (1995), trust is the ability of one party to remain helpless in the face of another's undertakings with the expectation that, independent of the other party's capacity for control, the other party will fulfil a certain duty required by the trustor. Kim et al. (2012) define trust as the conviction that a certain seller is dependable. Customer trust is defined by Moorman et al. (1992) and Ganesan (1994) as the customer's faith in the supplier's kindness, integrity, and ability to behave in the best interests of the particular relationship. Ganesan (1994) states that honesty is a component of trust, meaning that the party putting their faith in International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6453 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 a relationship is dependent on their relationship partner's credibility. Additionally, trust is defined by Anderson and Weitz (1992) as having two fundamental elements: honesty, which implies that the party putting their trust in the relationship partner must be credible, and the belief that the relationship partner will act benevolently in ways that either directly or indirectly affect the relationship. Scheer and Stern (1992) assert that trust is a fundamental element of the customer relationship marketing idea because it demonstrates one party's readiness to take a risk and gives the other party confidence that they will fulfil their obligations going forward. 2.3 Consumer loyalty "Continuous repurchase of (or ongoing supporting behaviour towards) a favoured product or service, regardless of other possibilities and/or marketing efforts to drive switching to competitors" is how Hur et al. (2010) defined loyalty. Oliver's (1999) definition of loyalty and this one are comparable in that they both highlight the desire to maintain long-term patronage in the face of competing market options. Hur et al. (2010) and Oliver (1999), define customer loyalty in their paper as the persistent choice to continue using (or supporting) a preferred telecommunication service provider in the face of alternative options and/or marketing campaigns aimed at encouraging customer switching. Effectively implemented customer retention tactics can provide an organisation with a competitive edge when rival services pose a threat to consumer loyalty, as is the case in the Indian telecom industry. According to Ehrheld and Schefter (2000), businesses with high retention rates yield the highest return on investment because their devoted clientele spreads good word of mouth and refers new business to prospective clients (Hur et al., 2010), which in turn drives down expenses. Businesses that cultivate customer loyalty secure several profitable connections with their business, thereby outpacing inexpert rivals (Reichheld and Schefter, 2000). Therefore, losing customers is a concern that telecom companies shouldn't ignore (Sweeney and Swait, 2008). Thus, cultivating a base of devoted customers is an essential tactical move to stay relevant in the fiercely competitive telecommunications industry today. In the telecom industry, a lot of research has been done on customer loyalty (e.g. Izogo, 2015a; Tarus and Rabach, 2013; Kaur and Soch, 2012; Chen and Cheng, 2012; Edward and Sahadev, 2011; Hur et al., 2010; Lai et al., 2009; Sweeney and Swait, 2008; Gustafsson et al., 2005; Kim et al., 2004; Gerpott et al., 2001). Previous research has found several aspects of loyalty, but two perspectives—behavioural and attitudinal loyalty—dominate. While a behaviourally loyal consumer is willing and prefers to keep buying a specific brand, an attitudinally loyal customer goes above and beyond by promoting the brand through word-of-mouth and business referrals (Rauyruen and Miller, 2007). Customers who repurchase can change, but those who are attitudinally loyal are unlikely to do so. Additionally, committed customers see higher rewards from brand loyalty and higher dangers from switching brands (Evanschitzky et al., 2006). Rauyruen and Miller (2007) suggest that cultivating a devoted clientele entails not just retaining a large number of patrons over time, but also fostering relationships with them to stimulate repeat business and a higher degree of advocacy. Therefore, attracting recurring clients is insufficient for telecommunication service providers. Additionally, companies need to make sure that their clients are so emotionally invested in their brands that they not only make repeat purchases but also spread the word about them and recommend them to others. Accordingly, comprehensive loyalty metrics need to take into account behavioural and attitudinal viewpoints. The traditional method from earlier studies was used in this study, and loyalty was conceptualised to take into account both behavioural and attitudinal factors. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6454 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 3. CONCEPTUAL FRAMEWORK The proposed conceptual model is constructed based on the above literature review, and it is depicted in Fig.1 below. Fig. 1. Proposed theoretical model 3.1 Study Questions and Proposed Hypotheses Although various relationship marketing tactics have been observed by service providers, not all have proven effective. The prevalence of switching behaviour among the majority of targeted customers is evident. As a result, the following research questions and hypotheses for this research are formulated: Questions: • Does the implementation of CRM strategies influence customer loyalty? • Does the mediating role of relationship quality play a significant part in this relationship? Null Hypotheses: • The implementation of CRM strategies has no significant impact on customer loyalty. • The mediating role of relationship quality does not play a significant part in the relationship between relationship marketing tactics and customer loyalty. 4. RESEARCH METHODOLOGY This study adopted a cross-sectional survey research design, which enabled the collection of necessary primary data from target respondents in a single period for analysis and generation of findings. Because the study is from the perspective of the customer, the targeted population includes students who use mobile services from an Indian telecom service provider. The research was carried out through a survey that used quantitative methods. Previous research measurements were used to develop 26 items related to the variables of the questionnaire. Table 1 lists the selected variables with code, the number of questions under each variable and the sources that were used to develop the questions. Table 1. Variable Selection: Items and Their Respective Sources Variables Code Items Sources Service Quality SQ 6 Parasuraman et al. (1988); Peng & Wang (2006) Price Perception PP 5 Peng & Wang (2006); Cheng et al. (2008) Value Offers VO 4 Zeithaml (1988); Peng & Wang (2006) Customer Trust CT 5 Morgan & Hunt (1994); Chu (2009) Customer Loyalty CL 6 De Wulf et al. (2001); Aydin & Özer (2005) * All variables are measured with five likert scale Source: Author compilation International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6455 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 A pilot study with 50 participants from the target population was conducted to assess the reliability of the constructed questionnaire. This aimed to assess the survey instrument's internal consistency across each factor, including relationship marketing tactics, relationship quality, and customer loyalty. The reliability of the framed questionnaire was evaluated in the pilot study using Cronbach's Alpha test. Table 2 provides a summary of the instrument's dependability employed in this study. Table 2. Internal Consistency Assessment Results Variables Code Items Cronbach’s Alpha SQ 6 0.817 PP 5 0.789 VO 4 0.834 CT 5 0.754 CL 6 0.802 Source: Author compilation The pilot study's findings revealed high internal consistency for each variable (0.754 to 0.834), indicating that the survey questions were trustworthy and effectively measured the intended constructs. This preliminary investigation ensured the robustness of the measurement instrument before full-scale data collection, enhancing the overall quality and trustworthiness of the research findings. 4.1 Determination of Sample Size The sample size for this study was determined utilizing the following formula. 𝑛 = 𝑍2 𝑋 𝑃  (1 − 𝑃 ) 𝜀2 Since we are unsure of the precise number of students who used mobile devices, the sample size was determined using an unlimited population, a confidence interval of 95%, a marginal error of 3.5%, and a population proportion of 50%. The calculated result was 784, and to account for potential missing or incomplete data, 850 questionnaires were distributed out of that 792 were received back with fulfilling all the criteria, representing a response rate of 93 per cent. Therefore, used 792 responses for the data analysis and this approach ensures statistical robustness. 4.2 Method of data collection Purposive sampling was used to collect data, due to a lack of knowledge about the specific student population using mobile services from the telecom provider. This method allowed us to purposefully select participants who met specific criteria relevant to the research objectives, ensuring that the individuals chosen possessed valuable insights into the complexities of the relationship marketing variables under investigation. 4.3 Statistical Methods The study aimed to provide a comprehensive understanding of the complex dynamics between relationship marketing, relationship quality, and customer loyalty by incorporating statistical methods. Each method played a unique role in revealing patterns, assessing relationships, and investigating the intricate interplay of variables in the context of the research objectives. To summarise and present the main features of the dataset, descriptive statistics were used, providing an overview of the central tendency, dispersion, and variable distribution. Correlation analysis was used to assess the strength and direction of relationships between variables, as well as to investigate the relationships between relationship marketing tactics, relationship quality, and customer loyalty. This method aids in the identification of potential patterns and connections within the dataset. The relationships between the independent variables (relationship marketing tactics and relationship quality) and the dependent variable (customer loyalty) were modelled using regression analysis. This enabled researchers to investigate the extent to which these variables predict customer loyalty and provided insights into the individual contributions of each factor. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6456 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 The SOBEL mediation test was used to investigate the role of relationship quality as a mediator. This test assesses the significance of an independent variable's indirect effect on a dependent variable via a mediator. Also, aids in determining whether the relationship between relationship marketing tactics and customer loyalty is significantly mediated by relationship quality in this study. This statistical approach deepens the analysis by revealing the underlying mechanisms that influence customer loyalty. 5. FINDINGS AND INTERPRETATIONS This section provides insights into the study's findings, including demographic information, variable correlations, and relationships involving independent, mediating, and dependent variables. 5.1 Characteristics of Respondents From Table 3, the respondents represent a diverse demographic profile across multiple demographic categories. The gender distribution is 50.9% male and 49.1% female, indicating nearly equal representation. Academic backgrounds range from 35.2% in the arts, 31.2% in science, 21.1% in commerce, and 12% in management. Concerning mobile operators, Jio and Airtel have a clear lead, accounting for nearly 87% of students' choices, with 46.1% choosing Jio and 41.5% choosing Airtel. The fact that these two operators were used the most indicates that they were well-liked by the student community. This diverse participant profile ensures a complete representation, allowing for a nuanced exploration of the study variables across different segments of the population. Table 3. Respondents Overview Characteristics Classification Frequency Gender Male 403 Female 389 Department Arts 283 Science 247 Commerce 167 Management 095 Operators Jio 365 Airtel 329 VI 067 BSNL 031 Source: Author compilation 0 10 20 30 40 50 Male Female Percentagee Gender 0 10 20 30 40 Percentage Department 0 10 20 30 40 50 Jio Airtel VI BSNL Percentage Mobile Operator International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6457 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 5.2 Descriptive Analysis According to Table 4, the study examined five variables, with a focus on key summary statistics. The mean scores for the variables ranged from 12.47 (VO) to 19.25 (SQ). This indicates a significant inequality in the study variables' central tendencies. The study variables' standard deviation (SD) values show varying degrees of dispersion around the mean. SQ has the highest SD (4.662), indicating relatively high response variability, whereas VO has the lowest SD (3.121), indicating lower variability. Other variables are all in the middle of the dispersion range. Skewness, a measure of distribution asymmetry, showed values ranging from -0.238 to -0.416. The negative skewness values indicate a slight leftward tail in the distribution, indicating a skewed data tendency. When compared to a normal distribution, negative kurtosis values indicate relatively lighter tails. In a nutshell, these descriptive statistics provide a thorough overview of the central tendency, variability, asymmetry, and distribution shape across the variables studied, providing valuable insights for further analysis and interpretation. Table 4. Summary Statistics Code Min Max Mean SD Skewness Kurtosis SQ 9 30 19.25 4.662 -.238 -.990 PP 8 24 16.39 3.754 -.386 -.856 VO 6 20 12.47 3.266 -.355 -.748 CT 4 23 16.23 3.798 -.416 -.722 CL 8 28 19.10 4.454 -.347 -.787 Source: Author compilation 5.3 Detection of Outliers The computed box plots in Fig. 2 revealed that no observations fell outside the whiskers, indicating that the dataset was outliersfree. This is a good result for the regression analysis because it shows that there are no extreme values, which could distort the model assumptions and results. By making regression coefficient estimates less susceptible to the influence of extreme values, the absence of outliers improves the reliability, validity of statistical inferences, predictability, and the assumptions required for robust regression analysis. Also, the dataset's integrity increases trust in the subsequent regression results. Fig. 2. Boxplot Analysis Source: Author compilation International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6458 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 5.4 Exploring Variable Relationships Finding the relationship between variables in statistical analysis entails examining patterns, dependencies, and trends using techniques like correlation and regression, which offer vital insights into the dynamics of the dataset. 5.4.1 Variable’s Association Correlation analysis is a statistical technique for determining the strength and direction of a relationship between two quantitative variables. The degree of link is indicated by the correlation coefficient, which ranges from -1 to +1. Positive numbers indicate a positive correlation, negative values denote a negative correlation and zero denotes no linear association, Scatter plots depict this link visually. It is appropriate to use parametric Karl-Pearson correlation if the dataset is continuous and the variables are linear. The formula is as follows: 𝑟 = ∑(𝑥𝑖− 𝑥) (𝑦𝑖− 𝑦) √∑(𝑥𝑖− 𝑥)2 √∑(𝑦𝑖− 𝑦)2 Fig. 3. Relationships between Variables Source: Author compilation The study variables' Karl-Pearson correlation coefficients, scatter plots, and smooth frequency curves are shown in Fig. 3. Interestingly, scatter diagrams show that all variables have positive linear relationships, with correlation values ranging from 0.73 (SQ-VO, VO-PP and CT-CL) to 0.83 (SQ and PP). Each variable has a strong correlation with the dependent variable (CL). The correlation values of relationship marketing strategies (SQ, PP and VO), correlate with CL at 0.73 and 0.76 respectively. Relationship Quality, the mediator variable (CT), correlates with CL at 0.73. The left-tailed smooth frequency curve indicates slight asymmetry and lighter tails, which is in line with the negative skewness and negative kurtosis values. Overall, the data shows a strong relation between the variables, highlighting the importance of relationship marketing strategies and the function that relationship quality plays as a mediator of customer loyalty. The distribution of the data is better understood when considering the left-tailed smooth frequency curve, which points to a tendency for responses to cluster towards higher values. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-56, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6459 *Corresponding Author: Madhanagopal Ramakrishnan Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6451-6466 5.5 Unravelling Variable Impacts Regression analysis is a statistical approach employed to investigate the relationship between one dependent variable and one or more independent variables. The fundamental formula of linear regression is represented as 𝑌 = 𝑋𝛽 + 𝜀 where 𝑌 is the dependent variable, 𝑋 is the independent variable, 𝛽 is the parameters (unknown to be estimated) and 𝜀 is the error term. Key assumptions include linearity, independence of errors, homoscedasticity (constant variance of errors), and normality of errors. Validating these assumptions ensures the reliability and accuracy of regression analysis results. 5.5.1 Estimating the Impact of CRM Strategies on Customer Trust The model summary contains three models that were developed using the regression model. The first stage of the hierarchy, known as Model 1, uses only VO as predictors. Model 2 represents the second stage, where VO and SQ are predictor variables. The third and final stage, or "Model 3," contains all the important predictor variables (Table 5). Table 5. Parameter Estimates for CRM Strategies Dependent: CT b SE (b) 𝜷 t LB UB VIF Constant 2.035 0.315 6.453** 1.416 2.654 VO 0.614 0.033 0.528 18.444** 0.550 0.677 2.423 SQ 0.182 0.028 0.224 6.590** 0.128 .236 3.560 PP 0.186 0.034 0.183 5.381** 0.118 0.253 3.590 𝑅 = 0.826 ,𝑅2= 0.682,∆𝑅2= 0.682 for step 1 𝑅 = 0.858 ,𝑅2= 0.736,∆𝑅2= 0.735 for step 2 𝑅 = 0.863 ,𝑅2= 0.745,∆𝑅2= 0.744 for step 3 F-Statistics: 767.240** Durbin-Watson: 2.089 Breusch-Pagan test: 𝜒2= 4.7137, 𝑝 = 0.194 Note: ∆𝑅2 = Adjusted R Square, *p < 0.05, **p < 0.01 Final Model: CL = 2.035 + 0.614(VO) +0.182(SQ)+0.186(PP) Source: Author compilation Based on Table 5, the highly significant F-value (767.240, p < 0.01) supports the regression analysis results, which points to a wellfitted model. Suggesting that the CRM strategies (SQ, VO and PP) collectively have a significant impact on explaining the variation in the RQ(CT). The R-square of 0.682 in Model 1 implies that VO accounts for 68.2% of the variation in the CT. The R-square rises to 0.736 in Model 2, which adds SQ in addition to VO. This indicates that the model explains 73.6% of the variance, or 5.4% more in the Rsquared value, which highlights the second variable's increased explanatory power. Finally, Model 3, which adds PP to VO and SQ, yields an R-square of 0.745. This indicates that, with a smaller increase of 0.9 %, the combined effect of SQ, VO, and PP accounts for roughly 74.5% of the variability in the CT, representing the marginal contribution of the third variable. The increasing R-square values indicate that the overall explanatory power of the model grows with each variable. Moving to the coefficient values, the constant term of 2.035 represents the estimated intercept when all predictor variables are zero. The significant t-value (6.453, (p < 0.01)) for the constant strengthens its importance. 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