The Saudi Arabian vehicle market represents a strategically important context for exploring the impact of customer engagement (CE) strategies. Currently the 22nd largest globally [1], this sector is experiencing robust growth, particularly within the automotive repair and maintenance services market. This market, valued at USD 1.51 Billion in 2023, is projected to reach USD 2.31 Billion by 2029, demonstrating a compound annual growth rate (CAGR) of 7.28% [2]. This expansion is fueled by evolving consumer demands and a growing awareness of the benefits of regular maintenance [3].

Within this dynamic environment, CE emerges as a critical driver of sustained growth. CE, a multifaceted concept encompassing psychological, emotional, and behavioral dimensions [4], moves beyond transactional interactions to incorporate active customer participation [5] and brand loyalty [6], [7]. This emphasis on CE has garnered significant attention in recent years [8], highlighting its importance in fostering favorable customer behaviors [9] and achieving a competitive advantage [10] in the rapidly evolving Saudi Arabian automotive market.

In the domain of contemporary marketing research, the complex interplay of Service Quality (SQ), Price Perception (PP), and Customer Experience (CX) concerning Customer Engagement (CE) has yet to be adequately explored. Although individual studies have thoroughly examined these components, a significant empirical gap remains in comprehending their collective influence on CE. Typically, these concepts are analyzed independently, resulting in a fragmented understanding that hinders a comprehensive appreciation of their synergistic effects and intricate interrelations; this fragmentation constrains the formulation of effective strategies to enhance CE across diverse industries. Researchers such as [9] have underscored the necessity for an integrated empirical approach that recognizes these interconnections throughout the customer journey [11]. Moreover, existing literature frequently emphasizes the determinants of customer satisfaction or value, thereby leaving the broader ramifications of CE insufficiently investigated [12]. Addressing these deficiencies is vital for nurturing stronger customer relationships and promoting advancements within the field of CE.

The main research gap is empirical and stems from fragmented studies that overlook the interplay of these concepts, which hinders a comprehensive understanding of their combined effects on fostering customer engagement. This fragmentation limits our ability to identify effective strategies for enhancing engagement within specific contexts or industries [8]. Weng et al. (2022) highlight the need for integrated research that considers these interconnections [9]. Ostrom et al. (2021) call for developing and testing a model that includes both customer engagement and the customer journey [11]. Most studies focus narrowly on satisfaction or value drivers, neglecting the broader constructs of engagement [12]. Sylvia et al. (2020) emphasize the need for understanding cultural and contextual impacts on engagement, advocating for more research across diverse contexts and touchpoints [13]. There's also a scarcity of research on customer engagement in the automotive industry, notably in emerging markets like Saudi Arabia, where existing literature largely centers on Western contexts. This presents an opportunity to empirically study the automotive customer experience and its relationship with engagement in Saudi Arabia’s aftersales industry.

This study proposes an integrated model aimed at addressing the existing research gaps concerning the interconnectedness of key marketing concepts such as Service Quality, Price Perception, and Customer Experience, and their collective impact on Customer Engagement. By developing and empirically testing this model, the study seeks to provide a comprehensive assessment and holistic understanding of the factors influencing Customer Engagement, particularly within the context of the automotive industry.

This cross-sectional study focuses on the Saudi Arabian automotive aftersales sector and collects data from customers recently receiving aftersales services. While the geographical and sector-specific focus may limit its applicability, the study provides valuable insights relevant to similar industry contexts. This research is significant because it has the potential to refine strategic business approaches by highlighting how Service Quality and Price Perception can enhance customer experience and engagement.

Theoretical background

Customer Engagement (CE):

Customer Engagement (CE) is a complex and multifaceted concept that researchers have defined in various ways over time. Bowden (2009) describes CE as a psychological process that influences the formation and maintenance of customer loyalty [6]. Van Doorn et al. (2010) emphasize the behavioral aspects, focusing on valence and customer goals [14]. Brodie et al. (2011) consider CE a multidimensional construct involving cognitive, emotional, and behavioral elements [4], while Vivek et al. (2012) highlight customer participation and connection with a brand's activities [15]. So et al. (2016) expand on this by describing CE in terms of dimensions such as identification and interaction. Raeisi and Lingjie (2017) also view it as a psychological process for maintaining loyalty [7], whereas Harmeling et al. (2017) see CE as voluntary contributions to a firm's marketing efforts that go beyond financial support [16]. Grewal et al. (2017) regard CE as the relational bond between customers and retailers [17]. Sylvia et al. (2020) identify four types of engagement (emotional, behavioral, dispositional, and psychological) [13], while Weng et al. (2022) define CE as a strategic tool to promote positive customer behaviors, including increased brand loyalty and purchase intentions [9].

The dimensions of Customer Engagement (CE) are multifaceted, encompassing various aspects that measure the connection between customers and brands. Below is the definition for each customer engagement dimension. CE includes influence value, which measures the impact of customer recommendations and referrals [10], [18], [19]. Knowledge value assesses the information and insights gained by customers through their engagement with a brand [10], [18], [19]. Customer Lifetime Value measures the economic worth of a customer throughout their duration as a customer [10], [18], [19]. Additionally, cognitive engagement measures a customer's thoughts, beliefs, and knowledge about the brand [20], [21], [22]. Affective engagement assesses emotions, attitudes, and feelings towards the brand [20], [21], [22], while behavioural engagement measures customer actions and behaviors [20], [21], [22]. Other dimensions include emotional attachment, personal involvement, and perceived risk [14], [23], [24]. Customer satisfaction, loyalty, and trust are also crucial, as they evaluate the degree to which customer expectations are met [13], [25], [26]. Furthermore, repeat purchases, brand recommendation, and advocacy are essential in measuring customer engagement [13], [27].

Service Quality (SQ):

Service Quality, according to the SERVQUAL model (Parasuraman et al., 1985) [28], is the perceived gap between customers' expectations and their actual experiences with a service; it encompasses dimensions such as reliability, responsiveness, assurance, empathy, and tangibles, all contributing to the overall customer satisfaction and experience. This concept is supported by Berry, Parasuraman, et al. (1990) [29] and Zeithaml et al. (1996, 2000), emphasizing that both the service outcome and the delivery process are essential [30], [31]. Grönroos (1984) highlights the technical and functional dimensions of Service Quality, concentrating on efficiently meeting customer needs [32].

Service quality is evaluated across several essential dimensions, Tangibility pertains to the tangible and visual aspects of the service, such as the design and appearance of facilities, equipment, and personnel [28], [33], [34], [35]. Reliability assesses the delivered service's accuracy, consistency, and dependability [28], [33], [34], [35]. Responsiveness evaluates how promptly and willingly service providers assist customers [28], [33], [34], [35]. Assurance indicates the expertise, competence, and credibility exhibited by employees within a company [28], [33], [34], [35]. Lastly, empathy encompasses the service providers' ability and willingness to understand and care for customers [28], [33], [34], [35].

Relationship marketing is the foundational theory that connects Service Quality to Customer Engagement (CE) and Customer Experience (CX). High-quality service is essential in relationship marketing, as it meets or surpasses customer expectations, fostering trust, credibility, and emotional connections that strengthen the bond between businesses and their customers [36]. This positive engagement encourages customers to actively interact with the brand, providing feedback and sharing experiences, which enhances loyalty and advocacy [37]. Furthermore, relationship marketing emphasizes that delivering an exceptional Customer Experience requires a comprehensive view of the entire customer journey, from awareness to post-purchase interactions [38]. It involves understanding customer needs and offering tailored, memorable experiences through data-driven insights and proactive support [39].

Price Perception:

Price perception refers to how customers assess the value and quality of a product or service in relation to its price [40], as customers compare the price with the perceived value, which can impact purchase satisfaction if they are not aligned [41]Price perception encompasses a comprehensive view of a retailer's pricing rather than focusing on individual prices [42]; it includes factors such as reference price, comparative price, transaction price, and psychological price [43]. Additionally, it takes into account affordability, reasonableness, value for money, and trust in fair pricing [44].

Price perception includes several dimensions that influence how customers assess the pricing of products and services. "Value for money" evaluates the balance between the sacrifices made and the benefits received from product and service features, reflecting consumers' assessment of the trade-offs involved [45], [46], [47]. Price processing, conversely, pertains to how easily and transparently customers can comprehend and interpret pricing. It encompasses the clarity and accuracy of pricing information, the ease of comparison, and the confidence that the price charged is fair [47]. Finally, Price fairness considers whether customers view prices as reasonable and appropriate for the level of service received [48], [49].

The Value Percept Theory connects Price Perception with Customer Experience (CE) and Customer Experience (CX) by emphasizing how customers assess the trade-off between the price they pay and the perceived value they receive [50], this subjective evaluation, where different customers prioritize factors such as quality or affordability, affects their satisfaction and choice to engage with a brand [51]. A positive Price Perception, in which perceived value outweighs the cost, generally improves Customer Experience and satisfaction [52], resulting in greater brand engagement and repeat purchases [53]. Additionally, a fair and transparent Price Perception can diminish perceived risk and foster trust, nurturing stronger customer relationships [54], throughout the customer journey from pre-purchase considerations, where pricing strategies shape decision-making and perceived value [55], to purchase decisions [56], and post-purchase reflections on the value received [57], Price perception is crucial.

Customer Experience (CX):

Customer Experience refers to the interactions and perceptions customers have with a company or brand throughout their journey [9]. Shaw and Ivens (2002) describe it as a blend of physical performance and emotional responses measured against customer expectations at every touchpoint, which includes objective factors like product quality and subjective emotional reactions [58], encompasses direct and indirect interactions, influencing perceptions at each contact point [59]. Lemke et al. (2011) suggest that CX involves interactions that evoke subjective responses, both cognitive and emotional, across various dimensions [60], including sensory and relational [61].

Several dimensions comprise the CX: Cognitive, Pragmatic, and Ambient; Cognitive experience relates to conscious thought and mental processing during interactions [62], [63]. Pragmatic experience assesses the practicality and usefulness of the interaction [63]. The ambient experience encompasses perceptions influenced by environmental factors such as lighting and sound [64], [65].

Relationship marketing plays a pivotal role in connecting Customer Experience (CX) to engagement by fostering personalized relationships and establishing trust [66]. CX significantly impacts Customer Engagement CE by shaping how customers perceive, feel, and act. Positive experiences boost satisfaction, loyalty, advocacy, and engagement, while negative experiences can result in disengagement and harmful word-of-mouth, hindering engagement [67].

Hypothesis development

CE and Service Quality

Empirical investigations consistently affirm a strong positive relationship between service quality and various Customer Engagement (CE) outcomes. Adnan et al. (2021) [68], Ardini et al. (2022) [69], and Ha et al. (2023) [70]have provided robust evidence demonstrating that high service quality perceptions significantly enhance customer loyalty, these studies underscore the importance of sustained service quality improvements as a critical factor in cultivating long-term customer loyalty. Ha et al. (2023) [70] and Thi et al. (2020) [71]further substantiate the role of customer satisfaction as a mediator in the service quality-loyalty relationship, emphasizing its crucial position in this dynamic.

Moreover, Al-ghifari and Fachira (2021) [72] and Handayani et al. (2022) [73] offer empirical support for service quality's influence on purchase and revisit intentions, indicating that superior service experiences encourage repeat purchases and customer retention. Further, research has explored how service quality influences other key engagement outcomes, W. Fan et al. (2022) show that perceived risk, customer trust, and customer satisfaction mediate this relationship [74]. In the automotive aftersales sector, Balinado et al. (2021) [75] and Abdul Rahman and Saidin (2021) [76] have employed the SERVQUAL and SERVPERF models to evaluate dimensions such as tangibility, reliability, and empathy, examining their effects on customer satisfaction and loyalty.

Collectively, these studies emphasize the critical role of Service Quality in shaping Customer Engagement, particularly in the automotive market. Given the demonstrated influence of service quality on key outcomes, it is reasonable to expect that Service Quality initiatives will significantly impact Customer Engagement. Therefore, the following hypothesis is proposed:

CE and Price Perception

Recent studies have highlighted the significant impact of price perception on various Customer Engagement outcomes. Positive price perception is associated with increased customer loyalty, as supported by research from Adnan et al. (2021)[68] and Ardini et al. (2022) [69]. Lucky et al. (2023) highlight that customers' purchase intentions improve when they view a product as reasonably priced and offering good value [41]. Conversely, if a product is perceived as overpriced, both purchase intentions and word-of-mouth recommendations decline, as shown by Seopela and Zulu (2022) [50]. Furthermore, studies by Ha et al. (2023)[70] and Lucky et al. (2023)[41] indicate that customer satisfaction often mediates the relationship between price perception and engagement outcomes, emphasizing the role of perceived value in fostering Customer Engagement. Additionally, Al-Fadly (2020) notes that price perception influences Customer Experience, with pricing strategies shaping overall perceptions and satisfaction [77]. Research by Mantik et al. (2022)[78] and Rizzon et al. (2023) illustrates how competitive pricing and perceived value mediate and moderate relationships involving customer experience and repurchase intentions [79].

Collectively, these studies emphasize the critical role of price perception in shaping key Customer Engagement. Given the demonstrated influence of positive Price Perception on loyalty, purchase intentions, and overall Customer Engagement, it is reasonable to expect that Price Perception will significantly impact CE. Therefore, the following hypothesis is proposed:

CE and CX

Recent studies have explored CX as a key factor influencing engagement outcomes like loyalty, purchase intentions, and satisfaction. Akram & Kortam (2020) found a positive impact of CX on engagement and purchase intentions [5], while Amenuvor et al. (2019) revealed that hedonic value mediates the relationship between customer experience and behavioral intentions [80]. Lubaba et al. (2022) [81] and Permadi & Silalahi (2021) noted customer loyalty's role in mediating the relationship between CX and CE [82]. Ahmad et al. (2022) demonstrated CE as a mediator between CX and loyalty, complemented by value co-creation [83]. Lestari et al. (2022) [84] and Roy et al. (2022) validated CE's mediating role in building brand loyalty [85], while Wijaya et al. (2023) underscored brand trust's importance in enhancing these effects [86]. Studies by Simbolon & Yanti (2021) [87] and Nugroho & Suprapti (2022) [88] highlighted the mediating role of engagement in the relationship between brand experience, satisfaction, and loyalty, emphasizing that engagement can fully or partially mediate these effects. Additional research examines engagement-related variables, such as trust and behavior, with findings by Hong & Kim (2020) [38] and de Silva & Mahesha (2021) [89] supporting CE's mediating role in broader customer-company relationships.

These studies collectively underscore the critical role of Customer Experience (CX) in shaping various Customer Engagement (CE), it is reasonable to expect that improvements in CX will significantly influence CE. Therefore, the following hypothesis is proposed:

CX mediation role between Service Quality and CE

Recent studies have examined the mediating role of CX in the relationship between Service Quality and CE. Sukendi et al. (2021) discovered that Customer Experience mediates the effects of e-Service Quality on Customer Engagement in B2C e-commerce [90], although it does not significantly influence customer loyalty. Similarly, C. Putri and Ginting (2021) illustrated that user experience mediates the connections between e-Service Quality, relational marketing, and e-satisfaction, with user experience significantly impacting satisfaction [91]. Wulandari et al. (2021) revealed that CX mediates the effect of Service Quality on switching intentions in the hotel sector [92]. Jeloudarlou et al. (2022) emphasized the importance of servicescape in enhancing CE through CX [18]. Additionally, Tay et al. (2020) [93] and Mamakou et al. (2024) [94] confirm the vital role of e-service quality and AI in shaping positive customer perceptions and relationships, emphasizing the imperative of delivering superior customer experiences through service quality.

Collectively, these studies highlight the mediating role of Customer Experience (CX) in the relationship between Service Quality and Customer Engagement (CE). Given the demonstrated ability of CX to mediate these relationships, it is reasonable to expect that improvements in CX will enhance the impact of Service Quality on CE. Therefore, the following hypothesis is proposed:

CX mediation role between Price Perception and CE

Recent research has investigated the mediating role of CX in price perception and CE. Kuppelwieser et al. (2022) identified CX as essential for connecting perceived value with word-of-mouth, particularly for social and utilitarian values [52]. Wulandari et al. (2021) observed limited price influence on switching intentions through CX [92]. Chen et al. (2021) indicated that CX mediates the relationship between customer motivation and engagement, impacting behaviors such as reuse and word-of-mouth on social media [95].

These studies collectively emphasize the mediating role of CX in the relationship between price perception and CE. Given the demonstrated role of CX in connecting perceived value with various CE outcomes, it is reasonable to expect that improvements in CX will significantly influence how price perception impacts CE. Therefore, the following hypothesis is proposed:

Research Framework:

The theoretical framework illustrates the hypothesized relationships between Service Quality, Price Perception, Customer Experience (CX), and Customer Engagement (CE) within the context of the automotive market. The model posits that Service Quality and Price Perception directly influence Customer Engagement. Further, the framework proposes that CX plays a crucial mediating role. Specifically, it is hypothesized that Service Quality directly impacts CX, which, in turn, influences CE. Similarly, it is proposed that Price Perception influences CX, which then affects CE. This framework seeks to clarify the complex interplay of service delivery, pricing strategies, and experiential elements on CE within the competitive automotive sector.

The analysis employed SmartPLS 4 software, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). This software was selected for its effectiveness in managing complicated models involving multiple constructs, as well as its capability to accommodate non-normal data distributions, a frequent challenge in social science research. This study utilizes a cross-sectional design to gather data at one specific moment, offering a timely snapshot of the research topic [96]. This approach effectively captures the current dynamics within the research context without monitoring changes over time, making it suitable for analyzing existing conditions.

Participants

In Saudi Arabia, about 11 million cars need regular maintenance [3], yet not all are included in the study’s target population. The research focuses on Saudi residents who have received automotive aftersales service in the past month, regardless of gender, socioeconomic status, car brand, or model year, ensuring recent feedback. Respondents were profiled according to various categories, including gender, age, nationality, region, and service usage frequency.

Descriptive Statistics of the Respondent

Instruments:

The study utilizes a carefully crafted questionnaire, available in both English and Arabic, to ensure precise response capture. The survey employs a 7-point Likert scale from "Strongly Disagree" to "Strongly Agree," known for yielding nuanced and reliable data [97], [98]. A structured questionnaire served as the main tool for data collection, created through a thorough process that included an extensive literature review and consultations with experts to guarantee its validity and reliability. The questionnaire was organized into distinct sections to gather comprehensive and pertinent information for the study.

The initial section focused on collecting demographic information such as region, gender, age, nationality, and visit frequency, which were vital for understanding the backgrounds of the participants. The following sections aimed to measure the key constructs of the research model. Each construct was assessed using a multi-dimensional scale with items sourced from a prior qualitative study that employed thematic analysis to address customer concerns and feedback, based on the dimensions established in the literature. This approach received validation from a panel of academics and industry professionals. Customer Experience (CE) was evaluated using an 8-item scale, while Service Quality was rated with a 13-item scale. Price Perception was assessed through a 9-item scale, and Customer Experience (CX) was examined using an 11-item scale. These dimensions were chosen because of their proven reliability and validity in earlier studies, ensuring their relevance to the current research context.

Sample and Data Collection:

This study employed a quantitative research approach for data collection through an online survey conducted via Google Forms. The selection of Google Forms was motivated by its accessibility and user-friendliness, as noted by Vasantha Raju and Harinarayana [99], which facilitated efficient data gathering. In compliance with ethical standards, the study received approval from the University Ethics Committee of Management and Science University (Approval No. EA-L1-GSM-2024-12-0035) and adhered to best practices for online survey research [100]. Informed consent was obtained from all participants, ensuring they were fully aware of the study's purpose, their rights, and the limited use of their information for research purposes only. Participants were guaranteed that their involvement was voluntary and that their privacy and anonymity would be maintained throughout the research process, thereby upholding the ethical integrity of the study.

To determine the required sample size, we applied Krejcie & Morgan's [101] popular sample size calculator with a desired precision level of 5% and a confidence level of 95%. Assuming a population proportion of 50%, given the absence of specific information on proportions within the target population, the sample size was subsequently divided by the number of strata for analysis. Data collection occurred from August 10 to September 30, 2024, culminating in a final sample of 395 respondents, which surpassed the recommended threshold and ensured adequate statistical power for subsequent analyses. Stratified sampling was employed to effectively address challenges associated with geographical dispersion by dividing the population into strata, thereby facilitating comprehensive representation [99]. Additionally, to minimize potential biases, efforts were made to recruit a diverse range of respondents across various demographics and geographic locations, enhancing the overall representativeness of the sample.

Data Analysis:

The research utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) through Smart PLS 4.0 [102] to investigate both mediation and moderation effects. The analysis begins with evaluating the measurement model to assess its reliability and validity. This involves analyzing factor loadings, Cronbach's alpha, Composite Reliability (CR), and Average Variance Extracted (AVE).

Following this, the structural model is examined by analyzing path coefficients, R-squared values (R²), effect sizes (f²), and predictive relevance (Q²) [103]. To confirm the reliability of the constructs, both Cronbach's Alpha (CA) and Composite Reliability (CR) indices are assessed to ensure the measures maintain internal consistency. The evaluation of convergent validity employs the Average Variance Extracted (AVE), which verifies that the constructs adequately capture the variance of their corresponding indicators. Additionally, discriminant validity is determined using the Fornell-Larcker criterion and the Heterotrait-Monotrait ratio (HTMT), which confirms that the constructs are separate and not excessively correlated.

Measurement Model Assessment

Construct Reliability and Validity

To evaluate the internal consistency of the constructs, both Cronbach’s Alpha (CA) and composite reliability (CR) were analyzed. These metrics assess how closely related the items are within a construct. While CA provides a lower-bound estimate, CR offers a more accurate measure in structural equation modeling. The study found high reliability for constructs such as Service Quality, Price Perception, Customer Experience, and Customer Engagement, with CR values ranging from 0.950 to 0.925 and CA values from 0.899 to 0.943. All values exceeded the acceptable threshold of 0.70 benchmarks recommended by Bernstein [104], indicating strong internal consistency across all constructs.

To evaluate the reliability and validity of the measurement model, the outer loadings for each construct's indicators were analyzed. These outer loadings reflect the correlation between observed indicators and their corresponding latent constructs. Hair et al. [80] suggest that outer loadings exceeding 0.70 are deemed acceptable, as they indicate that the latent construct accounts for more than 50% of the variance in the indicator, Bernstein (1994) [104] recommends a CR value of ≥ 0.70 for satisfactory internal consistency, while values ≤ 0.60 imply insufficient reliability. In this study, all outer loadings were between 0.609 and 0.839, effectively exceeding the 0.60 threshold [104]. This confirms the strong reliability of the indicators in accurately representing their respective constructs. Such robust indicator reliability enhances the measurement of latent variables, reduces measurement errors, and contributes positively to the model's overall validity.

To evaluate the model’s validity, we assessed convergent validity using Average Variance Extracted (AVE), which measures the degree to which items that evaluate the same construct are correlated. According to Fornell and Larcker [82], an AVE value greater than 0.5 indicates that a construct accounts for more than half of the variance in its indicators, thus confirming convergent validity. In this study, the AVE values for all constructs—Customer Engagement (CE) at 0.624, Customer Experience (CX) at 0.567, Service Quality at 0.597, and Price Perception at 0.634—exceeded the 0.5 threshold. This confirms strong convergent validity, showing that the items within each construct are highly correlated and effectively capture the underlying latent variables.

To evaluate potential collinearity issues, it is important to consider both vertical and lateral collinearity, as noted by Kock and Lynn (2012). While vertical collinearity may be acceptable, lateral collinearity—where two variables that are expected to have a causal relationship measure the same construct—can distort findings [105]. This aspect was assessed using the Variance Inflation Factor (VIF) statistics. Diamantopoulos and Siguaw (2006) indicate that VIF values of 3.3 or higher signal potential collinearity problems [106]. In this study, all VIF values ranged from 1.657 to 2.805, falling below the recommended threshold, which suggests that multicollinearity is not an issue. The low VIF values further reinforce the reliability of the path coefficients, ensuring that the relationships between constructs in the structural model remain unaffected by excessive correlations among predictors.

In summary, the constructs within the measurement model exhibit strong reliability and validity. The significant outer loadings, high internal consistency, and robust convergent validity, along with the lack of multicollinearity, confirm that the latent variables are effectively measured and accurately reflect Customer Engagement within the automotive aftersales context.

Discriminant Validity

Discriminant validity is a crucial aspect of construct validity that ensures each construct in the model represents a distinct concept and does not overlap excessively with others [107], [108]. This validation is vital for providing meaningful insights within the research context. To evaluate discriminant validity, the study utilized the Fornell-Larcker criterion, a common approach in structural equation modeling [82]. According to this method, the square root of the average variance extracted (AVE) for each construct should exceed its correlation with any other construct in the model. In this study, the results showed that the square root of the AVE for each construct was greater than its correlations with other constructs, confirming their distinctiveness. This finding suggests that each construct effectively captures a unique aspect of respondents’ perceptions or behaviors without undue overlap with other latent variables.

To further evaluate discriminant validity, the study analyzed the Heterotrait-Monotrait (HTMT) ratio, which is considered a more rigorous test than the Fornell-Larcker criterion. The HTMT ratio assesses the correlations between constructs to ensure they are not excessively related [108]. Gold and Arvind Malhotra (2001) suggest a more stringent threshold of 0.90. In this study, the HTMT ratios ranged from 0.670 to 0.874, remaining below the 0.90 threshold [108]. This indicates that the constructs are sufficiently distinct from each other. The low HTMT values further support the conclusion that the constructs represent unique theoretical concepts rather than mere variations of one another.

The model's discriminant validity was strongly supported by the Fornell-Larcker criterion and the HTMT ratio, confirming that constructs like interaction, customization, trendiness, entertainment, electronic word-of-mouth, perceived quality, perceived value, and purchase intention measure distinct dimensions of the consumer experience. This validation ensures the model’s sound structure and meaningful insights into consumer interactions with organic cosmetics in social media marketing.

Structural Model Assessment

Hypothesis Testing

The assessment of the relationships among constructs is essential for understanding their interactions within the research framework [109]. This evaluation focuses on path coefficients, t-values, p-values, and confidence intervals (CIs), which indicate the strength, significance, and reliability of the connections between the variables. In this study, the results of hypothesis testing provide valuable insights into how Service Quality, Price Perception, and CX influence CE in the Saudi Arabian automotive aftersales industry.

The first hypothesis is validated, revealing a path coefficient (β) of 0.643, a t-value of 12.892, p < 0.000, and a confidence interval (CI) of [0.453, 0.615]. This indicates that Service Quality significantly affects CE, underscoring its role as a crucial component for enhancing customer involvement. The findings align with existing literature that emphasizes the importance of high-quality service in fostering customer loyalty and engagement [68], [110], [111].

Regarding Price Perception, the second hypothesis demonstrates a significant positive effect on CE, with a path coefficient of β = 0.272, t-value = 7.181, p < 0.000, and CI of [0.125, 0.290]. This finding corroborates previous research that suggests favorable Price Perception enhances customer interaction and commitment [112], [113].

Furthermore, the third hypothesis reveals that CX positively influences CE, with a path coefficient of β = 0.191, t-value = 4.090, p < 0.000, and CI of [0.102, 0.285]. This finding confirms that positive customer experiences are predictive of increased engagement and loyalty and emphasizes the established linkage between customer satisfaction in previous studies [66], [85], [114].

The fourth hypothesis, indicating that Service Quality positively impacts CX, is similarly supported, with a path coefficient of β = 0.558, t-value = 16.080, p < 0.000, and CI of [0.489, 0.623]. This important relationship highlights how superior service quality influences positive customer experiences, as shown in previous studies [38], [93], [115].

The evaluation of mediation relationships further enhances our understanding of the constructs involved. The fifth hypothesis indicates that CX mediates the relationship between Service Quality and CE, with a path coefficient of β = 0.107, t-value = 4.359, p < 0.000, and CI of [0.058, 0.161]. This finding aligns with other previous studies that suggest that the quality of service experienced enhances engagement through improved customer interactions [90], [92], [116].

The sixth hypothesis is also supported, showing a path coefficient of β = 0.323, t-value = 9.050, p < 0.000, and CI of [0.255, 0.395] for the positive impact of Price Perception on CX. This indicates that a favorable perception of pricing significantly enhances the overall brand experience, aligning with studies that highlight the value of fair pricing [78], [117].

The seventh hypothesis is supported as well, showing that CX mediates the effect of Price Perception on CE, with a path coefficient of β = 0.062, t-value = 3.548, p < 0.000, and CI of [0.031, 0.100]. This underlines the role of positive price perceptions in enhancing customer experiences, which in turn promotes greater engagement, consistent with earlier findings [52], [79].

The overarching aim of this research is to explore the relationships between Service Quality, Price Perception, Customer Experience, and engagement in the context of the Saudi Arabian automotive aftersales industry. The findings highlight the pivotal roles that both Service Quality and Price Perception play in shaping customer experiences and fostering engagement. This understanding equips businesses with critical insights to enhance customer satisfaction, trust, and overall engagement through service excellence and pricing strategies.

The explanatory and predictive power of the research model was assessed using R-squared (R²) and Q-squared (Q²) values. The R² values indicate the model's explanatory capability by showing the percentage of variance in each dependent variable accounted for by the independent variables [118]. According to Cohen [118], R² values above 0.26 represent a large effect size. Q² values evaluate the model's predictive accuracy, with a Q² value greater than zero signifying that the model possesses predictive relevance for a specific construct [119], [120]. Additionally, effect size (f²) was calculated to gauge the impact on the R² value when an exogenous construct is excluded from the model, determining whether the omitted construct significantly affects the endogenous constructs. The effect sizes are categorized as small, medium, or large based on thresholds of 0.02, 0.15, and 0.35, respectively [102].

The results of this study indicate that the constructs have a strong explanatory power for CE at 72.1%, suggesting that the independent variables significantly account for a large portion of the variance in CE. Similarly, CX has an R² value of 64.4%, indicating that the model effectively captures the mechanisms influencing CX.

Analyzing the effect sizes for the relationships leading to CE, Service Quality (SQ) demonstrates a substantial impact with an effect size (f²) of 0.406, indicating that improvements in Service Quality have a strong influence on enhancing Customer Engagement. Price Perception (PP) and Customer Experience (CX) also contribute positively to CE, with small effect sizes of 0.081 and 0.047, respectively. This suggests that while Price Perception and Customer Experience have a positive but less influential role, Service Quality is the significant driver of Customer Engagement.

For Customer Experience (CX), the effect of Price Perception (PP) is categorized as medium, with an f² value of 0.177, indicating a meaningful influence on CX. Service Quality (SQ), on the other hand, shows a substantial effect on CX with an effect size of 0.527, reinforcing its critical role in shaping positive customer experiences.

Additionally, the Q² values, which assess predictive relevance, further affirm the model's effectiveness. The Q² value for CX is reported at 0.638, indicating substantial predictive relevance.

Overall, these results underscore the importance of Service Quality in driving Customer Engagement and Customer Experience, while also highlighting the relevant contributions of Price Perception. The substantial R² and Q² values affirm the model's robust ability to explain and predict these key constructs within the research framework.

The combination of strong R² and positive Q² values suggests that the model fits the data well and possesses substantial predictive power for key constructs related to Customer Engagement (CE). The significant explanatory power indicates that the independent variables effectively account for a large proportion of variance in CE and (CX). Meanwhile, the positive Q² values further reinforce the model’s predictive relevance, particularly for essential relationships such as those between Service Quality, Price Perception, and Customer Experience. These results demonstrate that the model is theoretically sound and practically valuable for understanding and forecasting Customer Engagement in the context of automotive aftersales.

This study sheds light on the crucial factors impacting Customer Engagement (CE) in the automotive aftersales industry in Saudi Arabia, focusing on the relationships between Service Quality and Price Perception, with Customer Experience (CX) serving as a mediating factor. The acceptance of all seven hypotheses highlights the intricate dynamics involved in these interactions and offers significant implications for both researchers and practitioners in enhancing Customer Engagement strategies. These findings suggest that prioritizing high service quality and favorable price perceptions can lead to improved customer experiences and, subsequently, higher levels of engagement. Such insights are essential for developing effective marketing strategies and operational improvements to foster customer trust and satisfaction in the industry.

The study results support hypothesis H1, confirming that Service Quality significantly enhances Customer Engagement (CE). This finding is consistent with numerous academic studies that have validated the correlation between service quality and various customer engagement outcomes, such as customer satisfaction [75], [121], [122], [123] et al., 2022). Additionally, research conducted by Shaban & Abdelgawad (2022) [124], and Zygiaris et al. (2022) [125] has similarly validated the positive impact of Service Quality on Customer Engagement within the Saudi Arabian market. Further supporting this perspective, Albayrak et al. (2024) [126] demonstrated that Service Quality attributes can be categorized into basic, excitement, and performance factors based on their varying impacts on Customer Engagement. Additionally, the study by Bacala et al. (2024) [127] also indicated a high level of Service Quality, which corresponded with high levels of Customer Engagement, thereby highlighting a significant relationship between the two constructs. These insights underscore the critical importance of service quality as a foundational element for enhancing customer engagement, suggesting that organizations within the automotive aftersales industry should prioritize service quality improvements to foster better customer relationships and engagement outcomes.

Furthermore, the acceptance of hypothesis H2 confirms that Price Perception significantly impacts Customer Engagement (CE). This finding aligns with Panjaitan (2024) [128], whose research indicates that product quality, price, and promotion significantly affect customer satisfaction, a critical dimension of Customer Engagement in our study. Additionally, the study by Seopela and Zulu (2022)[129] highlights the influence of price perception on word-of-mouth recommendations. Their findings suggest that customers' perceptions of price can significantly affect their overall satisfaction with a product or service and their propensity to recommend it to others. These insights underscore the importance of effective pricing strategies in driving Customer Engagement. They suggest that organizations should focus on enhancing customers' perceptions of price fairness and value to foster higher levels of engagement.

Moreover, the acceptance of hypothesis H3 confirms that Customer Experience (CX) significantly impacts Customer Engagement (CE). This finding aligns with Akram and Kortam (2020) [5], who discovered a direct positive influence of CX on both CE and purchase intentions, as well as its direct effects on brand switching intentions. Similarly, Lubaba et al. (2022) [81] identified that CX influences customer loyalty, suggesting that high-quality experiences can directly enhance brand image and satisfaction, even if they do not have a significant direct impact on loyalty. Additionally, Sudiyono et al. (2022) [114] found that CX significantly affects customer value, loyalty, and satisfaction. In their research on building brand loyalty among luxury automotive product users, Lestari et al. (2022) [84] validated the relationship between CX as an independent variable, CE as a mediating variable, and brand loyalty as the dependent variable. Their findings indicate that both CX and social presence positively and significantly influence brand loyalty, with CE acting as an intervening variable. These insights underscore the critical importance of enhancing Customer Experience to foster greater levels of engagement.

Furthermore, the acceptance of hypothesis H4 confirms that Service Quality positively impacts Customer Experience (CX). This aligns with Mamakou et al. (2024) [94], who highlighted that e-service quality and user experience play critical roles in shaping customer perceptions and satisfaction. Similarly, Izquierdo-Yusta et al. (2021) [130] identified e-service quality as a key determinant of customer experience, with brand influence being significant. Additionally, David et al. (2021) [131] found that both visual and service quality greatly affect the shopping experience and user satisfaction, which in turn influences the intention to recommend a service. The results also support hypothesis H5, indicating that CX mediates the relationship between Service Quality and CE. This is consistent with Sukendi et al. (2021) [90], who emphasized CX mediating role between Service Quality and CE, additionally, Wulandari et al. (2021) [92] and Jeloudarlou et al. (2022) [18] further corroborate these findings, demonstrating the importance of Service Quality and the physical service environment in enhancing CE through improved customer experiences. These insights underscore the critical mediating role of Customer Experience (CX) in the relationship between Service Quality and Customer Engagement (CE). By highlighting how CX acts as a bridge between high service quality and increased CE, the findings emphasize the importance of fostering positive customer experiences. This mediation suggests that enhancing service quality not only directly contributes to customer satisfaction but also indirectly boosts engagement through improved experiences. Ultimately, these results point to the necessity for businesses to prioritize CX as a vital component of their strategies to enhance overall Customer Engagement.

Moreover, the acceptance of hypothesis H6 confirms that Price Perception positively impacts Customer Experience (CX). This finding aligns with Mantik et al. (2022), who noted the influence of information quality, customer experience, price, and service quality on purchase intentions, with perceived value serving as a mediating variable. This suggests that high-quality information, positive customer experiences, competitive pricing, and good service quality collectively enhance purchase intentions by increasing perceived value. Additionally, Rizzon et al. (2023) further demonstrated that customer experience and price sensitivity together influence product price image, perceived value, and repurchase intentions, showing that perceived value acts as a full mediator between product price image and repurchase intention, within the same context, Kusumah et al. (2020) discovered that price perception mediates the relationship between experience quality and customer satisfaction, indicating that involvement in the quality of tourist experience affects price perception.

The results also support hypothesis H7, indicating that CX mediates the relationship between Price Perception and Customer Engagement (CE). This is consistent with Kuppelwieser et al. (2022), who underscore that CX is pivotal in connecting customer perceived value (CPV) with word-of-mouth (WoM), confirming a direct link between CPV—encompassing social, hedonic, and utilitarian value—CX, and WoM. Additionally, Wulandari et al. (2021) examined the mediating role of CX between price and switching intentions, revealing that while price impacts customer experience. In their study, Chen et al. (2021) investigated how CX mediates the connection between customer motivation and customer engagement, finding that factors such as information seeking, entertainment, and social interaction significantly influence the customer brand experience. These insights underscore the critical mediating role of Customer Experience (CX) in the relationship between Price Perception and Customer Engagement (CE), emphasizing how CX acts as a bridge that connects perceived price fairness to enhanced customer engagement outcomes.

This study examines the effects of service quality, price perception, and Customer Experience on Customer Engagement, specifically within the Saudi Arabian automotive aftersales sector. The findings present numerous implications across theoretical, practical, and methodological dimensions, offering valuable insights for researchers and industry practitioners.

Theoretically, this research contributes to the existing body of knowledge by establishing clear linkages between Service Quality, Price Perception, Customer Experience and Customer Engagement. The study deepens our understanding of consumer behavior by demonstrating how Service Quality and Price Perception jointly influence Customer Experience, which in turn enhances Customer Engagement. This elaborates on prior studies, affirming the complex interplay between various service attributes and their cumulative effect on consumer behavior. The research also confirms the mediating role of Customer Experience in translating Price Perception and Service Quality into engagement, marking a significant advancement in consumer behavior theories.

Practically, the study’s findings underscore the strategic importance of businesses investing in service excellence and fair pricing strategies. By enhancing service quality, companies can significantly boost Customer Engagement and trust, which are vital for competitive advantage and customer retention. The research indicates that businesses should monitor and adjust their pricing strategies to align with perceived value, as this promotes positive customer experiences. Managers in the automotive aftersales sector can leverage these insights to optimize their service offerings and pricing models, ensuring they meet customer expectations and drive engagement.

Methodologically, Partial Least Squares Structural Equation Modeling (PLS-SEM) provides a robust framework for analyzing complex relationships between multiple variables. This approach can be applied to similar studies in different contexts, allowing for more comprehensive investigations of consumer behavior dynamics. The study's methodology, which includes stratified sampling and rigorous validity assessments, provides a model for future researchers seeking to ensure representativeness and reliability, particularly in diverse geographical and cultural settings.

This study provides a comprehensive examination of the determinants influencing Customer Engagement (CE) in the Saudi Arabian automotive aftersales sector. By employing a quantitative research approach, data were collected from 395 participants who had their cars serviced within one month of the survey data collection. The participants were distributed across three regions in the kingdom, and a 7-point Likert scale questionnaire was utilized. The survey employed validated scales to assess key constructs, including Customer Experience (CX), Service Quality (SQ), and Price Perception (PP).

Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) statistical technique, which unveiled several significant insights. The findings highlight the critical roles of Service Quality, Price Perception, and Customer Experience on Customer Engagement, while also illustrating the mediating role of CX between SQ and PP on one side and CE on the other. This underscores the necessity for businesses to prioritize these factors in their strategies. Furthermore, the research reveals strong relationships between these constructs, demonstrating that enhancing Service Quality and fostering positive Customer Experiences can substantially drive Customer Engagement in the automotive aftersales industry.

While this research provides valuable insights into customer management in Saudi Arabia's automotive aftersales sector, future studies could further enrich understanding and applicability. Expanding the research to other industries and regions could enhance the generalizability of findings and offer comparative insights across diverse cultural, economic, and regulatory contexts. Additionally, implementing longitudinal research designs would enable tracking changes in customer sentiments and engagement over time, offering a deeper understanding of causal relationships and the evolution of customer interactions with market trends and organizational shifts. Finally, testing and validating these findings in different economic contexts would assess their robustness, revealing how economic fluctuations impact customer engagement and offering strategic insights for businesses navigating these changes. By exploring these avenues, future research could provide a more comprehensive view of customer engagement strategies across various markets.