What Are the Factors Influencing Customers’ Repurchase Intention?—Taking Smartphone Brands as an Example

Abstract

Customers’ repurchase intention is a key driver of sustained profitability for companies. However, the influencing factors of customers’ repurchase intention and their direct mechanisms of action are not yet clear. This study empirically examines the relationships among intrinsic quality, perceptual quality, brand equity, customer satisfaction, online word-of-mouth, and customers’ repurchase intention using a structural equation model. A customer repurchase intention model is constructed with customer satisfaction as the mediating variable and online word-of-mouth as the moderating variable. The empirical results show that intrinsic quality, perceptual quality, and brand equity have a significant impact on customer satisfaction. Notably, intrinsic quality exerts an indirect effect on customers’ repurchase intention primarily through customer satisfaction, with no significant direct impact, while perceptual quality and brand equity have significant direct effects on repurchase intention. Customer satisfaction plays a partial mediating role between intrinsic quality, perceptual quality, brand equity, and repurchase intention. Online word-of-mouth has a significant moderating effect on the relationship between customer satisfaction and customers’ repurchase intention. These findings provide actionable insights for smartphone enterprises to optimize product development, brand management, and online reputation strategies, thereby enhancing customer loyalty and market competitiveness.

1. Introduction

In the past few years, the penetration rate of smartphones has increased rapidly. As of June 2023, the number of mobile Internet users in China has reached 1.007 billion [1]. In many countries, smartphones have become a necessity in people’s daily lives, and the time people spend on smartphones has exceeded that spent on computers [2]. Many people consider smartphones to be their indispensable tools for various daily activities, including checking emails, chatting with friends, browsing the Internet, managing business, shopping, booking services, etc. Given this context, how to improve customer satisfaction and enhance customer repurchase intention has become a critical issue for smartphone enterprises.

Nowadays, with the continuous development of information technology and the rapid advancement of science and technology, the manufacturing technologies and manufacturing concepts of traditional manufacturing industries have undergone significant changes, and the pace of these changes has gradually accelerated. This has greatly improved the intrinsic quality of products, such as safety and reliability, thereby increasing customer satisfaction and making intrinsic quality the first indicator for customers to evaluate products [3]. Moreover, in the current era of increasingly rich spiritual life, customers are paying more and more attention to the perceptual quality of products; the perceptual quality of products directly attracts customers’ senses through their intrinsic perception (touch, vision, taste, etc.), thereby enhancing customer satisfaction with the products and increasing their repurchase intention [4,5]. In addition, some scholars have summarized the influencing factors of customer repurchase intention from the aspects of aesthetics, practicality, brand equity, etc., and found that factors such as culture and brand equity have a relatively high impact on customer repurchase intention [6]; Harsha (2022) found that brand equity helps create brand value for enterprises and customers and improves customer satisfaction [7]. Therefore, it can be seen that perceptual quality, intrinsic quality, and brand equity have become the influencing factors of customer satisfaction and repurchase intention, but there is still a lack of theoretical research and empirical tests on the relationship between perceptual quality, intrinsic quality, brand equity, customer satisfaction, and repurchase intention.

Despite notable advancements, critical gaps persist in the literature. Firstly, existing research has rarely integrated intrinsic quality, perceptual quality, and brand equity into a unified framework to systematically compare their relative impacts on repurchase intention. It remains unclear how these factors interact—for example, whether perceptual quality can compensate for deficiencies in intrinsic quality, or how brand equity amplifies their combined effects. Secondly, while customer satisfaction is widely recognized as a mediator, its specific role in transmitting the effects of intrinsic quality, perceptual quality, and brand equity to repurchase intention lacks empirical clarification. Lastly, in an era dominated by digital interactions, online word-of-mouth (eWOM) has emerged as a pivotal form of social influence, yet its moderating role in the relationship between customer satisfaction and repurchase intention—particularly in the smartphone context—remains underexplored.

To address these gaps, this study focuses on smartphone consumers and situates itself within the context of widespread online word-of-mouth interactions. Grounded in the interplay of intrinsic quality, perceptual quality, and brand equity, it aims to answer three core research questions:

(1)

What are the direct and indirect effects of intrinsic quality, perceptual quality, brand equity, and customer satisfaction on smartphone repurchase intention?

(2)

What are the underlying mechanisms (e.g., mediation, moderation) that link these variables—specifically, does customer satisfaction mediate the effects of quality dimensions and brand equity on repurchase intention, and does eWOM moderate the satisfaction-repurchase relationship?

(3)

What actionable marketing strategies can smartphone manufacturers derive from these findings to enhance customer loyalty and market competitiveness?

By addressing these questions, this study seeks to contribute to theoretical advancements in consumer behavior research and provide empirically grounded guidance for practitioners.

2. Research Hypotheses and Model Construction

Repeat purchase intention refers to the behavior of customers who are willing to repurchase the same brand of products or services. When the product meets the expectations and recognition of customers, they will repurchase the product and use it for a long time [8].

Many studies have shown that customer repurchase intention is influenced by brand trust, self-image consistency, and usage satisfaction [9]. In China, the influencing factors of customer repurchase intention include five dimensions: aesthetics, functionality, brand value, and social and cultural dimensions [6]. Furthermore, Rajeev (2020) found that customer repurchase intention is a critical driver of corporate profitability; specifically, when the repeat purchase rate of customers increases by 5%, enterprises can increase their profits by 25% to 85% [10]. Therefore, it is essential for enterprises and managers to understand the specific influencing factors of customer repeat purchase intention, as it can generate substantial profit gains and enhance long-term competitiveness.

2.1. Intrinsic Quality, Customer Repurchase Intention, and Customer Satisfaction

Product quality is defined as the sum of all characteristics of a product that meet specified requirements or needs [3]. The intrinsic quality of a product refers to its performance, reliability, safety, lifespan, and economy, etc. [11]. Since the intrinsic quality of a product is fixed, objectively measurable, and does not change with subjective or objective conditions, customers typically regard it as the primary indicator for product evaluation when purchasing [3]. Numerous studies have shown that the improvement of a product’s intrinsic quality will significantly affect customer loyalty, purchase intention, and satisfaction, and good product quality will also enhance the brand image of the enterprise and attract more potential customers. Luo Wei’s research emphasized that the intrinsic quality of a product determined by technical factors is an indispensable quality characteristic for meeting customer needs, and therefore it is crucial for customer satisfaction [3]. Eskildsen et al. (2004) found that intrinsic quality has a direct impact on repurchase intention and satisfaction, and products with higher quality are more likely to be accepted, thus making wholesalers, retailers, and organizations satisfied [12]. Moreover, product quality is positively correlated with customer satisfaction, and products with high intrinsic quality can consistently satisfy customers, thereby further promoting their repurchase intention [13].

Based on these sites, the following hypotheses are proposed.

Hypothesis 1a.

Intrinsic quality has a significant direct positive impact on customer repurchase intention.

Hypothesis 2a.

Intrinsic quality has a significant direct positive impact on customer satisfaction.

2.2. Perceptual Quality, Customer Repurchase Intention, and Customer Satisfaction

Perceptual quality (Kansei quality) is the quality from the inside out, which reflects the characteristics and performance of a product through people’s internal perception (touch, vision, taste, etc.). Products with perceptual quality directly attract customers’ five senses and evoke feelings and impressions of comfort, luxury, and refinement [4]. Nowadays, perceptual quality has become increasingly important in the design of consumer products, as it enhances the value of the product and improves customer satisfaction [5]. After meeting customers’ physical needs, they will urgently seek psychological satisfaction. At this point, enterprises need to improve the overall value of the product through perceptual quality to meet customers’ psychological needs. Han et al. (2004) found that the design of software and hardware (shape, material, texture, color) of smartphones affects customer satisfaction [14]. Lin et al. (2012) found on the basis of Han’s research that the sound quality and touch feel of smartphones have an impact on customers’ emotions [15]. Mahlke (2005) also found that users will be angry when using systems with poor perceptual quality and inconvenience, while they will show positive emotions when using systems with good perceptual quality and high performance [16]. In addition, Hennig-Thurau (2004) found in his research on the perceptual quality of smartphones that when smartphone manufacturers design products based on customer perception, customers will like the product more [17]. Moreover, we believe that customers will continuously repurchase smartphone brands with innovative designs, styles, and consideration of perceptual quality.

Moreover, numerous studies have confirmed that there is a significant positive correlation between perceptual quality and customers’ repurchase intention. When customers have a high perceptual quality of a product, their initial purchase intention will be transformed into recognition of the brand, thereby forming repurchase intention. Hui et al. (2025) systematically explored the influence mechanism between customers’ warm experience (i.e., emotional experience design) of green food and repurchase intention from the perspective of green food, and found that when green food customers have a high perceived value of the product, their repurchase intention is stronger [18].

Based on existing research, we can conclude that the perceptual quality brought by the perceptual design of products has a direct positive effect on customer satisfaction and customer repurchase intention.

In light of the aforementioned factors, the following hypotheses are proposed.

Hypothesis 1b.

Perceptual quality has a significant direct positive impact on customer repurchase intention.

Hypothesis 2b.

Perceptual quality has a significant direct positive impact on customer satisfaction.

2.3. Brand Equity, Customer Repurchase Intention, and Customer Satisfaction

Brand equity refers to “a set of brand assets and liabilities associated with a brand—used to increase or decrease the value of products and services provided to companies and their customers” [7]. This value is constituted by the thoughts and actions of customers when they purchase products [19]; Harsha (2022) [7] proposed that brand equity affects customers’ purchase intentions. When a product has high brand equity, customers will purchase a certain brand of product at a higher price or continue to purchase products of that brand. Empirical research shows that brand equity has a positive impact on satisfaction [7]. When a company’s brand equity is good, customers are more likely to recognize the brand and have a higher level of satisfaction and purchase intention towards it [20]. Numerous experiments have also studied the correlation between brand equity and customer satisfaction and repurchase intention. Ahmad (2015) used a multiple linear regression model to explore the relationship between brand equity and customer satisfaction in the smartphone industry and found a significant positive relationship between brand equity and customer satisfaction [21]. Huang (2022) started from the high-involvement market and established a positive correlation model between brand equity and customer repurchase intention, exploring how brand equity affects customers’ repurchase intention in a high-involvement market [22]. Studies found that brand equity has a positive impact on the use of smartphones and their future repurchase intention [23]. Therefore, we believe that brand equity has a positive impact on customer repurchase intention and customer satisfaction.

Hence, the following hypotheses are proposed.

Hypothesis 1c.

Brand equity has a significant direct positive impact on customer repurchase intention.

Hypothesis 2c.

Brand equity has a significant direct positive impact on customer satisfaction.

2.4. Customer Satisfaction and Customer Repurchase Intention

Customer satisfaction is usually defined as “the subjective evaluation result of customers on whether the product they have chosen meets or exceeds their expectations.” The key to customer retention and repurchase intention is customer satisfaction [24]. More importantly, customers with high satisfaction will repeatedly return to the same supplier for consumption [25]. Customer satisfaction has a positive impact on repurchase intention. When they are satisfied with the quality and value of the product, they will form a purchase intention and thus generate repeat purchase behavior. Evidence found in similar studies that the satisfaction customers obtain after purchasing a product will enhance their positive attitude towards a specific product or service, thereby increasing their repurchase intention. Therefore, researchers believe that customer satisfaction and purchase intention are consistent [26]. Haverila (2011) also pointed out that when the level of customer satisfaction is above a certain level, customers are more willing to repurchase the products of the enterprise [27]; however, other studies have shown that satisfaction is only an occasional factor affecting customer repurchase intention [28].

Based on the above considerations, the following hypothesis is proposed.

Hypothesis 3.

Customer satisfaction has a significant direct positive impact on customer repurchase intention.

2.5. The Mediating Role of Customer Satisfaction

Customer satisfaction is divided into specific transaction satisfaction and overall satisfaction [14]. Specific transaction satisfaction is an immediate evaluation and judgment after purchase [29]; overall satisfaction is the evaluative judgment made by customers during their last purchase of a brand based on all their previous experiences with the brand [30]. Therefore, satisfaction is the final state of a cognitive and emotional process in which customers compare their expectations with the subjective perceived value they obtain from consumption [31]. It reflects a good consistency between customers’ expectations and their perceived consumption experience [32]. Pappas found through the UTAUT2 model that there is a certain correlation between satisfaction and repurchase intention [33]. From a retail perspective, customer satisfaction is a key driver of loyalty and is also considered a prerequisite for repeat purchase intention. Choongsoo (2017) [34] found in their study on the factors influencing customers’ reconfiguration behavior towards smartphone brands that customer satisfaction plays a partial mediating role in the influence of customer inertia and product attributes on customers’ repeat purchase behavior of smartphones [34]. Shin (2015) found in his study on the impact of customer experience on smartphone satisfaction that customer satisfaction is a key factor in regulating the relationship between the intrinsic quality of the phone and customer loyalty [35]. Additionally, many scholars have found that product quality and brand equity have a positive impact on customer satisfaction [36]. Customer satisfaction serves as a bridge connecting external factors and customers’ repurchase intentions. If customer satisfaction is high, customers are more likely to engage in repurchase behavior. Based on existing research, it can be concluded that intrinsic quality, perceptual quality, and brand equity have a direct positive effect on customer satisfaction, and customer satisfaction has a direct positive effect on repurchase intention. However, no model has yet been constructed to illustrate the intrinsic relationships among these concepts.

This paper further infers the relationships among intrinsic quality, perceptual quality, brand equity, customer satisfaction, and repurchase intention, and proposes that intrinsic quality, perceptual quality, and brand equity have an indirect impact on repurchase intention through the mediating variable of customer satisfaction.

Hence, the following hypothesis is proposed.

Hypothesis 4.

Customer satisfaction plays a mediating role between the quality indicators of mobile phones and customers’ repurchase intention.

2.6. The Moderating Role of Online Word-of-Mouth

In the Internet era, an increasing number of customers have begun to use the Internet and social media to express their consumption views and opinions [37]. A new word-of-mouth model composed of online reviews has gradually emerged and become an important part of communication between brands and customers. This new word-of-mouth model is similar to the traditional word-of-mouth model (Word-of-Mouth) and has continuously expanded in the virtual environment to form the current online word-of-mouth [38]. Online word-of-mouth (eWOM) refers to “positive or negative evaluations of a product or company made by potential, current or former customers and provided to other customers and institutions through the Internet” [17]. It has the characteristics of long duration, large traffic, asynchronous communication among users, virtual dissemination, and multi-person sharing [39].

According to Spence’s Signaling Theory, in contexts of information asymmetry, consumers rely on “signals” emitted by products or brands (such as word-of-mouth and certifications) to judge value. As a quality signal, eWOM can enhance consumers’ future purchase intention by improving their satisfaction [40]. A study in the Vietnamese market found that eWOM significantly affects green food purchase intention through the mediation of trust. Specifically, when eWOM frequently mentions signals such as “organic certification” and “sustainable production”, consumers’ trust in the brand increases, thereby strengthening the conversion of satisfaction into repurchase intention. Online word-of-mouth can also continuously influence customers’ purchase intentions. When customers post comments about a brand’s products or services online, the content of the comments provides more information about the brand to other customers and helps potential customers make purchase decisions [41]. El-Baz et al. (2018) also found that online word-of-mouth has a significant positive impact on the purchase intention of smartphones [42]. Online word-of-mouth does not directly affect satisfaction and customer purchase intention, but plays a significant moderating role in the relationship between satisfaction and purchase intention [43].

Hence, the following hypothesis is proposed.

Hypothesis 5.

Online word-of-mouth has a significant positive moderating effect on the relationship between customer satisfaction and customer repurchase intention.

According to the above theoretical hypotheses, the research model of the relationship between intrinsic quality, perceptual quality, brand equity, customer satisfaction, online word-of-mouth, and customer repurchase intention is obtained, as shown in Figure 1.

3. Research Design

3.1. Sample Selection and Questionnaire Survey

The questionnaire was mainly distributed to customers who use mobile phones. The questionnaire was created on the “Questionnaire Star” website and distributed in the form of a link. The initial questionnaire was pre-surveyed in the form of an online questionnaire. Based on the pre-survey results and expert opinions, the initial questionnaire was modified to form the survey questionnaire. To ensure the quality of the questionnaire, after the recovery was completed, the questionnaires were sorted out, and those that did not meet the requirements were eliminated. Finally, 220 questionnaires were obtained for formal analysis, with an effective recovery rate of 80.2%. Meanwhile, a statistical analysis was conducted on the demographic characteristics of the collected samples, as shown in

Table 1. Distribution of Sample Demographic Characteristics.

Variable	Type	Frequency	Percentage (%)
Sex	Female	118	53.6%
	Male	102	46.4%
Age Group	Under 18	21	9.50%
	18–25	187	85.00%
	26–40	3	1.40%
	41–50	8	3.60%
	51–60	1	0.50%
Brand	Huawei	69	31.4%
	Apple	57	25.9%
	vivo	21	13.6%
	Xiaomi	26	11.8%
	BlackBerry	13	5.9%
	Other	15	11.4%

:

3.2. Variable Measurement

The variables in the questionnaire mainly adopt mature scales that have been used in mainstream literature, and are appropriately modified based on the research results and expert opinions. A 5-point Likert scale is used for measurement.

The quality of mobile phones is designed based on the research results of the Korea Quality Association, including “Intrinsic Quality” (IQ), “Perceptual Quality” (KQ), and “Brand Equity” (BQ). Intrinsic quality is measured by 12 items covering functionality (IQF), reliability (IQR), usability (IQU), and safety (IQS). Perceptual quality is measured by four items. Brand equity is measured by brand association and recognition (BQAA), perceived quality (BQP), and brand loyalty (BQL), with a total of 10 items. Online word-of-mouth (eWOM) is measured based on the mature scale by Cheung (2008) [37], with a total of four items. The reason for modifying the EWOM scale is that during the pre-test, it was found that the item “I actively seek out others’ reviews before making a purchase” had a low correlation with other items, so it was excluded. In addition, to enhance the intrinsic consistency of the scale and strengthen its relevance to the online behaviors of the target group, some content was adjusted slightly. Customer satisfaction is measured based on the scale developed by Shin (2015) [35] and the research results of Choongsoo (2017) [34], with a total of three items. Customer repurchase intention is measured based on the scale developed by Chen (2021) [1] and the research results of Choongsoo (2017) [34], with a total of three items. Customer satisfaction and customer repurchase intention have undergone item modifications to ensure their consistency with mobile phone-specific evaluation criteria.

4. Empirical Analysis

4.1. Model Reliability and Validity Analysis

Cronbach’s alpha coefficient is used to measure intrinsic consistency based on the average correlation between items. Generally, the value of Cronbach’s alpha coefficient ranges from 0 to 1. As shown in

Table 2. Variable reliability test.

Dimensions	Cronbach’s Alpha	Number of Items
Intrinsic quality	0.886	10
Perceptual quality	0.884	4
Brand equity	0.943	10
Internet word-of-mouth	0.926	4
Customer satisfaction	0.955	3
Customer repurchase intention	0.887	3

, the standardized reliability coefficient of the dimensions ranges from 0.884 to 0.955, indicating that the reliability of the questionnaire dimensions is relatively high.

To further examine whether common method bias still affects the data, this study employed Harman’s single-factor test. All items were included in the exploratory factor analysis using principal component analysis and varimax rotation. The results showed that the first factor explained 32.6% of the total variance, which is lower than the critical value of 40%, indicating that there is no severe common method bias in the data.

Bartlett’s sphericity test is used to examine whether the variances among K samples are equal. The equal variances among samples are called homogeneity of variance. Applying the Bartlett test rule to this study, the results are shown in

Table 3. Bartlett’s sphericity test.

KMO and Bartlett’s Tests
Kaiser–Meyer–Olkin metric		0.924
Bartlett’s sphericity test	Chi-square value	2426.645
	Df	96
	Sig.	0.000

; the Kaiser–Meyer–Olkin (KMO) test coefficient is 0.924, with a range of 0 to 1. The closer the value is to 1, the more suitable the questionnaire is for factor analysis. The significance of the Bartlett’s sphericity test is 0.000, which is less than 0.05, indicating high significance.

The validity test of the scale includes two parts: convergent validity and discriminant validity. Convergent validity refers to the degree of correlation among the items included in the same factor, which is tested by the average variance extracted (AVE). If the AVE is greater than or equal to 0.5, it is considered that the measurement indicators of the variable have convergent validity. Discriminant validity reflects the uniqueness and distinctiveness of each item in the factor and the degree of non-correlation with other items. The determination of discriminant validity requires that the AVE of each variable must be greater than the square of the correlation coefficient between each pair of variables, which can be called discriminant validity. The verification results are shown in

Table 4. Variable reliability analysis.

Concept	Measurement Attributes (Items)	CFA	AVE	CR
Intrinsic quality	Functionality (2)	0.891	0.629	0.871
	Credibility (3)	0.78
	Usability (3)	0.764
	Safety (2)	0.729
Perceptual quality	Aesthetics	0.714	0.629	0.871
	Joyfulness	0.837
	Novelty	0.838
	Excellence	0.859
Brand equity	Brand association and cognition (4)	0.845	0.726	0.888
	Perceived quality (3)	0.912
	Brand loyalty (3)	0.796
Internet word-of-mouth	Word-of-mouth satisfaction	0.848	0.717	0.910
	Word-of-mouth credibility	0.902
	Word-of-mouth value	0.846
	The degree of influence of word-of-mouth	0.788
Customer satisfaction	Information satisfaction	0.879	0.819	0.931
	Product satisfaction	0.908
	Service satisfaction	0.928
Customer repurchase intention	Recognition	0.779	0.718	0.883
	Repeat purchase	0.945
	Recommended	0.808

, and the AVE values are all greater than 0.5, indicating that the scale has good convergent validity.

4.2. Correlation Analysis

To preliminarily verify the research hypotheses, the Pearson correlation analysis method was used to explore the interrelationships among the latent variables, as shown in

Table 5. Correlation analysis.

Variables	Mean	IQ	KQ	BQ	SM	CS	CRI
IQ	3.8943	1
KQ	4.0232	0.437 **	1
BQ	4.0797	0.276 **	0.308 **	1
SM	3.9687	0.389 **	0.426 **	0.578 **	1
CS	3.99	0.391 **	0.438 **	0.570 **	0.838 **	1
CRI	3.9977	0.336 **	0.433 **	0.577 **	0.682 **	0.665 **	1

** At 0.01 (two-tailed), significant correlation.

. The results show that there is a significant positive correlation among the variables, providing a basis for subsequent hypothesis testing.

4.3. Model Testing Results

4.3.1. Model Fit and Validity

The concept model was tested using the statistical analysis software AMOS 21.0. The fitting results are shown in

Table 6. Confirmatory factor analysis (CFA) model fitness summary.

Category	Indicators	Measurement Level	Evaluation
Absolute Fit Indices	Chi-Square (CMIN)	p-value = 0.014 < 0.05	Acceptable
	RMSEA	RMSEA = 0.075 < 0.08	Acceptable
	GFI	GFI = 0.879 < 0.90	Below threshold
	RMR	RMR = 0.039 < 0.08	Acceptable
Incremental Fit Indices	NFI	NFI = 0.912 > 0.90	Acceptable
	CFI	CFI = 0.950 > 0.90	Acceptable
	TLI	TLI = 0.938 > 0.90	Acceptable
	IFI	IFI = 0.951 > 0.90	Acceptable
Parsimonious Fit Indices	Chi-Square/DF(CMIN/DF)	CMIN/DF = 1.322 < 2	Acceptable

“Acceptable” represents the desired level, and “Below threshold” indicates that the desired level has not been reached.

, with X²/df = 1.322, TLI = 0.938, CFI = 0.950, IFI = 0.951, RMR = 0.039, and RMSE = 0.075, all within acceptable levels, indicating that the concept model has a good fit. Based on this model, the significance level of the path coefficient (p-value) was used as the indicator for theoretical hypothesis verification, and the test results are shown in Table 6.

Additionally, it can be seen from Table 6 that the GFI (Goodness of Fit Index) is 0.879, which is slightly lower than the standard of 0.90. This may be attributed to the GFI’s sensitivity to sample size. The sample size of this study is 220, which is at a moderate level, and the GFI tends to be relatively lower when the sample size does not reach an extremely large scale.

4.3.2. Hypothesis Testing

Table 7. Hypothesis test results.

Category	Std. Estimate	C.R.	p-Value	Accept or Reject
Intrinsic quality → Customer satisfaction	0.311	3.119	0.002 **	Acceptable
Perceptual quality → Customer satisfaction	0.263	3.333	0.000 ***	Acceptable
Brand equity → Customer satisfaction	0.564	8.694	0.000 ***	Acceptable
Customer satisfaction → Customer repurchase intention	0.592	8.483	0.000 ***	Acceptable
Intrinsic quality → Customer repurchase intention	−0.049	−0.681	0.496	Below threshold
Perceptual quality → Customer repurchase intention	0.207	3.479	0.000 ***	Acceptable
Brand equity → Customer repurchase intention	0.143	2.542	0.011 *	Acceptable

*** p < 0.001, ** p < 0.01, * p < 0.05.

and Figure 2 indicate that intrinsic quality, perceptual quality, and brand equity exert significant positive effects on customer satisfaction (β = 0.311, p < 0.05; β = 0.263, p < 0.05; β = 0.564, p < 0.05, respectively), supporting Hypotheses 2a, 2b, and 2c. Furthermore, customer satisfaction significantly predicts customer repurchase intention (β = 0.592, p < 0.001), providing support for H3. Perceptual quality and brand equity also have direct positive effects on customer repurchase intention (β = 0.207, p < 0.05; β = 0.143, p < 0.05, respectively), supporting H1b and 1c.

However, the path from intrinsic quality to customer repurchase intention was not significant (β = −0.049., n.s.), and H1a was not supported. This may be explained by the fact that in today’s competitive and mature smartphone market, fundamental aspects of intrinsic quality, such as hardware configuration and durability, have largely met consumers’ baseline expectations. As a result, the marginal effect of intrinsic quality on repurchase intention is diminishing, and consumers are more likely to be influenced by differentiating factors such as brand image and user experience. Moreover, intrinsic quality may primarily exert its influence on repurchase intention through indirect pathways, such as by enhancing customer satisfaction.

As indicated in

Table 8. Mediation effect test.

Path	Point Estimate	Bias-Corrected 95% Confidence Interval		Percentile 95% Confidence Interval
		Lower Limit	Upper Limit	Lower Limit	Upper Limit
Total effect
Intrinsic quality → Customer repurchase intention	0.172	0.047	0.319	0.044	0.317
Perceptual quality → Customer repurchase intention	0.345	0.194	0.498	0.200	0.504
Brand equity → Customer repurchase intention	0.474	0.379	0.594	0.368	0.581
Indirect effect
Intrinsic quality → Customer satisfaction → Customer repurchase intention	0.172	0.047	0.319	0.044	0.317
Perceptual quality → Customer satisfaction → Customer repurchase intention	0.156	0.053	0.307	0.048	0.300
Brand equity → Customer satisfaction → Customer repurchase intention	0.327	0.208	0.511	0.200	0.489
Direct effect
Intrinsic quality → Customer repurchase intention	0.000	0.000	0.000	0.000	0.000
Perceptual quality → Customer repurchase intention	0.189	0.050	0.322	0.054	0.329
Brand equity → Customer repurchase intention	0.147	−0.008	0.269	−0.010	0.268

, the total effect of intrinsic quality on customer repurchase intention is 0.172, with neither the bias-corrected 95% confidence interval [0.047, 0.319] nor the percentile 95% confidence interval [0.044, 0.317] containing 0, thereby confirming the significance of the total effect. The indirect effect of intrinsic quality on customer repurchase intention via customer satisfaction stands at 0.172, and both confidence intervals exclude 0, signifying a significant indirect effect. However, Table 7 reveals that its direct effect is non-significant. The total effect of perceptual quality on customer repurchase intention is 0.345, with the bias-corrected 95% confidence interval [0.194, 0.498] and the percentile 95% confidence interval [0.200, 0.504] both excluding 0, thus validating the significance of the total effect. The indirect effect of perceptual quality through customer satisfaction is 0.156, and both confidence intervals [0.053, 0.307] and [0.048, 0.300] do not contain 0, indicating a significant indirect effect. Additionally, its direct effect is significant (Table 7). For brand equity, the total effect on customer repurchase intention is 0.474, with the bias-corrected 95% confidence interval [0.379, 0.594] and the percentile 95% confidence interval [0.368, 0.581] both excluding 0, confirming the significance of the total effect. The direct effect of brand equity on customers’ repurchase intention is 0.174, and the direct effect is marginally significant at the 95% confidence level. The indirect effect via customer satisfaction is 0.327, and both confidence intervals [0.208, 0.511] and [0.200, 0.489] exclude 0, demonstrating a significant indirect effect. In conclusion, customer satisfaction plays a mediating role between mobile phone quality indicators and customer repurchase intention, thereby supporting Hypothesis H4.

The indirect effect of brand equity (0.327) accounts for 69% of the total effect (0.474), which is the highest transmission efficiency among the three. This stems from the “emotional-cognitive” dual attributes of brand equity: on the one hand, brand associations directly enhance customer satisfaction through value identification; on the other hand, brand loyalty (such as the “brand habit” of long-term users) reduces the resistance in the process of converting satisfaction into repurchase intention. For example, users with high brand equity are more likely to overlook product defects due to “brand trust”, making it easier for satisfaction to translate into repurchase behavior. Therefore, enterprises should attach more importance to the construction of mobile phone brand equity.

4.3.3. Test of the Moderating Effect of Online Word-of-Mouth

Taking customer satisfaction as the independent variable, online word-of-mouth as the moderating variable, and customer repurchase intention as the dependent variable, hierarchical multiple regression analysis is used to test the moderating effect of online word-of-mouth. To avoid multicollinearity caused by directly generating interaction terms, both the independent and moderating variables are standardized. The results are presented in

Table 9. Moderating effect test.

Variables		Customer Repurchase Intention
		Model (1)	Model (2)	Model (3)
Control variables	Gender	0.253	0.077	0.077
	Age	−0.007	0.015	0.012
	Mobile phone brands	0.105	0.040	0.015
Main effect	Customer satisfaction		0.571	0.574
	Internet word-of-mouth		0.157	0.244
Interaction effect	Customer satisfaction × Internet word-of-mouth			0.121
Indicators	F	6.168	49.406	46.931
	ΔF	6.168	43.238	3.525
	R2	0.086	0.603	0.621
	adj R2	0.072	0.590	0.605
	ΔR2	0.086	0.517	0.018

. From Model 3 in Table 9, it can be seen that after introducing the interaction term customer satisfaction * online word-of-mouth, β = 0.018 (p < 0.05), indicating that online word-of-mouth significantly moderates the relationship between customer satisfaction and customer repurchase intention; thereby, H5 is verified.

To further validate H5, this study re-examined the moderating effect using latent interaction modeling in AMOS. This method accounts for measurement errors in latent variables (such as customer satisfaction, online word-of-mouth, and repurchase intention) and enables more robust estimation of interaction effects by simultaneously integrating the measurement model and the structural model. As shown in

Table 10. Moderating effect test (SEM-based interaction modeling).

Variables		Unstandardized Coefficient	Standardized Coefficient (β)	S.E.	C.R.	p-Value
Main effect	Customer Satisfaction	0.568	0.57	0.042	13.524	0.000 ***
	Internet Word-of-Mouth	0.239	0.241	0.038	6.289	0.000 ***
Interaction Effect Path	Customer Satisfaction × Internet Word-of-Mouth	0.092	0.021	0.041	2.243	0.025 *

*** p < 0.001, * p < 0.05.

, the results of the latent interaction model confirmed the significant moderating effect of online word-of-mouth (β = 0.021, p < 0.05), which is consistent with the findings from hierarchical regression analysis.

The moderating effect of online word-of-mouth between customer satisfaction and customer repurchase intention is obvious. Before the emergence of online word-of-mouth or when enterprises did not sell products online, enterprises could only regulate customer repurchase intention through traditional word-of-mouth, making it difficult to attract additional potential customers to repurchase products. Customers lacked clear information about the company’s products, and firms had limited access to customer feedback or intentions. It is precisely through the development of Internet technology and the growing emphasis on online word-of-mouth by firms that this concept has emerged and developed.

Whether customers’ enthusiasm for a company’s new products and their willingness to repurchase can be fully enhanced is closely related to the quality of the brand’s online word-of-mouth and the company’s emphasis on it. If a company’s online word-of-mouth on social media platforms is poor, customers’ favor towards the company will rapidly decline, thereby reducing their willingness to repurchase. Therefore, in the Internet era, social media will undoubtedly have a significant impact on a company’s marketing strategies, and companies must conduct online marketing communication through online word-of-mouth to enhance customers’ brand loyalty and repurchase intentions.

5. Conclusions and Implications

5.1. Conclusions

Based on the literature review, this paper proposes and verifies the theoretical hypotheses and models of the relationships among intrinsic quality, brand equity, perceptual quality, and customers’ repurchase intentions, with customer satisfaction as the mediating variable and online word-of-mouth as the moderating variable. The main conclusions are as follows:

First, in this study, we found that intrinsic quality, perceptual quality, and brand equity in the smartphone industry play a significant role in enhancing customers’ repurchase intentions. Notably, customer satisfaction functions as a mediating variable in these relationships, but with distinct pathways: intrinsic quality exerts a positive impact on customers’ repurchase intentions only through customer satisfaction, and its direct effect is not significant; in contrast, perceptual quality and brand equity not only positively influence repurchase intentions through customer satisfaction but also have direct positive effects on repurchase intentions. Moreover, based on the path coefficients of the research, it can be seen that brand equity has a more significant impact on customers’ repurchase intentions than intrinsic quality and perceptual quality. In the durable goods sector (such as automobiles and home appliances), existing studies (e.g., Ahmad, 2012) have shown that brand equity is a key factor driving customer loyalty, and this finding aligns with such research [44]. Especially in markets where product functions tend to converge, brands can form a differentiated advantage through emotional identification and value association. This conclusion has been verified in the smartphone industry by this study. Therefore, companies should pay more attention to the brand equity of smartphones.

Second, although intrinsic quality and perceptual quality have a positive impact on repurchase intentions through customer satisfaction, their significance and path coefficient values are relatively low. This may be due to the rapid iteration of manufacturing technology in the smartphone industry, resulting in smaller differences in customers’ perception of intrinsic quality, which leads to a gradual decline in the driving effect of intrinsic quality on customers. Additionally, due to the insufficient innovation ability of companies, customers may not perceive the practicality of new product features or the uniqueness and distinctiveness of the product, resulting in a lower impact of perceptual quality on customers’ repurchase intentions.

Third, online word-of-mouth plays an important moderating role between customer satisfaction and customers’ repurchase intentions, which is consistent with research conclusions in the e-commerce field. However, unlike beauty and clothing brands, online word-of-mouth for smartphones focuses more on “technical details” and long-term experience, while fast-moving consumer goods emphasize immediate experience. Therefore, when managing online word-of-mouth, enterprises need to output technical endorsement content in a targeted manner.

5.2. Implications

First, considering that the Chinese smartphone market has gradually reached saturation, the results of this study have significant value for smartphone R&D personnel and marketing managers in developing and selling smartphones that meet customer needs. This study reveals the importance of factors such as intrinsic quality, perceptual quality, and brand equity in influencing customers’ repurchase intentions. Based on the findings of this study, we believe that smartphone companies should pay attention to the brand of smartphones and the meanings associated with the brand (such as the brand’s quality, reputation, and prestige). Companies need to leverage product advantages to create their own brand characteristics, strengthen the promotion of the brand and products, and enhance the brand image to increase customer satisfaction and repurchase intentions, thereby attracting more potential customers. In addition, companies should pay attention to Gen Z customers. Nowadays, Gen Z customers are increasingly seeking more exclusive smartphone brands to help them gain respect in society. Companies need to provide personalized products or services to Gen Z customers to attract more potential customers.

Second, smartphone companies should improve the intrinsic quality and perceptual quality of their products and attempt to design more innovative and fashionable products. Although intrinsic quality has no significant direct impact on repurchase intentions, as a foundational attribute determined by technical factors, it still reflects the advancement and superiority of products or brands. Therefore, smartphone companies should maintain and further improve the intrinsic quality of their products. Furthermore, the perceptual quality of products is closely related to the profitability of enterprises. We suggest that smartphone enterprises should cultivate their product innovation concepts from the perspective of customers, take innovation as the lifeline of their continuous development, consider the emotions expressed by customers when using the products, and combine perceptual engineering to create innovative products to increase the perceptual quality of the products and enhance their perceptual value.

Third, in the post-pandemic era, the influence of a brand’s online reputation on customers’ decisions to purchase smartphones is gradually increasing. Enterprises should enhance their use of social media and leverage brand reputation to improve customer satisfaction and repurchase intention; they also need to consider how to control the content, timing, and frequency of online comments to enhance the brand’s potential competitiveness and increase customer satisfaction with the brand. However, social media marketing alone is not sufficient to fundamentally increase customers’ repurchase intention. Enterprises must improve the intrinsic quality, perceptual quality, and brand equity of their products to enable online reputation to have a positive impact on repurchase intention.

Finally, as global consumers increasingly rely on online information, the impact of a brand’s online word-of-mouth on purchasing decisions has demonstrated cross-cultural commonality. Whether through Xiaohongshu in China, Amazon reviews in the United States, or Naver blogs in South Korea, consumers tend to reduce purchase risks via online information, which corroborates the conclusion of this study that “online word-of-mouth must work in synergy with product quality.” Therefore, enterprises can only better expand into diverse overseas markets by improving the intrinsic quality of their products and optimizing their brand’s online word-of-mouth.

5.3. Limitations and Future Research Directions

Although this study has ensured the basic validity of the research through rigorous model construction and sample data collection, its limitations still need to be further elaborated in detail:

(1)

Limitations in data collection: The distribution channels of online questionnaires are mainly concentrated on social media platforms (such as WeChat and Weibo), resulting in a sample group that is biased toward young users, with insufficient coverage of middle-aged and elderly users. This may make the research conclusions difficult to fully apply to mobile phone consumers of all age groups. At the same time, self-selection bias is not only reflected in the aspect of external influences, but also in the fact that users with higher attention to mobile phone brands are more likely to participate in the survey, which may amplify the effect intensity of certain variables.

(2)

Potential limitations in variable measurement: Although scales such as perceptual quality and online word-of-mouth have been modified and tested, there is still room for improvement in the contextual adaptation of some items. For example, the item “the matching degree between the mobile phone’s appearance and the usage scenario” in “perceptual quality” does not fully consider the scenario differences among different occupational groups, which may limit the measurement accuracy.

In response to the above limitations, future research can collect data at intervals to observe the dynamic changes in consumers’ satisfaction and repurchase intention during the mobile phone usage cycle, thereby revealing the time-sensitive characteristics of variable relationships. In addition, natural language processing (NLP) technology can be used to analyze unstructured data. For instance, by crawling user reviews from e-commerce platforms and word-of-mouth content on social media, emotional analysis can be employed to quantify the emotional intensity of online word-of-mouth, making the measurement more consistent with the actual form of word-of-mouth communication.