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Abstract

This study aims to investigate the relationships of perceived service quality, brand image, customer satisfaction, and purchase intention. The moderated mediating role of brand love is examined on 522 mobile phone consumers in Taiwan. This study found a positive relationship between perceived service quality and purchase intention. Brand image and customer satisfaction play a fully mediated role between perceived service quality and purchase intention. Moderated mediation model one and two found that brand love plays a moderated mediating role on perceived service quality through brand image and customer satisfaction to purchase intention respectively. The results indicate that when the degree of brand love is high, the indirect effect of brand image on the perceived service quality of the purchase intention is strong; on the contrary, when the degree of brand love is low, the indirect effect of brand love on the perceived service quality and purchase intention is weak.

Keywords

perceived service quality purchase intention customer satisfaction brand love brand image moderated mediation model

Introduction

Services are basically intangible, indivisible, un-storable, and volatile (Regan, 1963). Service is an abstract concept. Feeling good or bad often varies from person to person. It also means the service reduces the uncertainty of customers at a psychological level (Smith, 1990). Quality and service are similar and difficult to measure precisely. Service quality is the customer’s perception and subjective response to the overall advantages and disadvantages of the service, which differs from the quality of general physical products, so service quality and product quality greatly differ (Parasuraman et al., 1988). Devlin and Dong (1994) contended service quality is the perception of the service produced by the customer and the service provider during the transmission and interaction. Thus, through the perceived quality belonging to the cognitive level, it is possible to measure the relationship between customers and service quality. Perceived service quality (PSQ) is the customer’s judgment of the overall superiority of the product, that is, the customer obtains the services of expectations and cognition based on the intrinsic and extrinsic attributes of the product and service, and then achieves the perceived service quality (Zeithaml, 1988). The intrinsic attributes mainly refer to information about the product itself, such as the function or nature of the product; the extrinsic attributes are attached to the product but have nothing to do with the actual performance of the product, such as brand, price and after-sales guarantee (Gavilan & Avello, 2020). On the other hand, customers rarely can objectively evaluate a product with all relevant information. Therefore, during the purchase process, if the intrinsic attributes can be known before the purchase, and no personal experience is needed, the intrinsic attributes are relatively important; otherwise if the intrinsic attributes of a product cannot be known or the information they possess is insufficient, consumers will use the extrinsic attributes as a key factor in purchase intentions (Zeithaml & Kirmani, 1993).

Schiffman and Kanuk (2000) found the higher the intention to purchase, the higher the likelihood consumers will purchase; if consumers intend to purchase positively, it will form a brand image, which will then drive consumers to make the actual purchase. Blackwell et al. (2001) believed if consumers have a demand for a product or service, they will evaluate it based on their own experience and external environmental information, and then decide whether to buy the product. Alford and Biswas (2002) argued the higher the consumer’s perceived value of the product, the higher their intention to purchase. By providing good service quality, both online and offline operators can build a positive brand image in addition to building a good consumer PSQ, which can increase purchase intention (PI) (Foroudi et al., 2018). Balaji and Maheswari (2021) defined the possibility customers are willing to buy goods as the PI, that is, the transaction behavior consumers make after evaluating the goods.

The image is the interaction between the subject and the object. The subject perceives the object in a certain perceptual situation. From a psychological point of view, image is a kind of mental schema produced by people reflecting objects (Schenk & Holman, 1980). In terms of brand image (BI), Park et al. (1986) believed the BI is the message the brand provides to consumers, and it is the external image of the company. Consumers create a perception held by the brand from the attributes of the products in their memory, which are mainly the subjective perceptions established by consumers through emotional interpretation (Özcan & Elçi, 2020). BI is the sum of the brand’s impressions and associations that consumers keep in their minds after personally selecting and processing all the information about the brand received during the communication process (Bashir et al., 2020). Consumers have a good BI of the product, leading to purchase willingness, which in turn increases business revenue. Therefore, the importance of the BI in the enterprise can provide an evaluation of the marketing strategy of the business manager in the development of the enterprise (Pedeliento & Kavaratzis, 2019). The intangible content of the BI mainly refers to the unique charm of the brand, which is given to the brand by the marketer, and is personal characteristic consumers perceive and accept. With the development of the social economy and the richness of commodities, consumer demand has also continued to increase. Consumers’ requirements for commodities include not only the tangible manifestations of the functions of the commodities themselves, but also the requirements for the intangible feelings brought by commodities. The intangible content of the BI here mainly reflects people’s emotions, showing the individual requirements of brand preference, such as brand love (BL) (Nawaz et al., 2020).

Carroll and Ahuvia (2006) first proposed the term BL. They believed BL represents a strong emotional connection between a highly satisfied customer and a brand. It includes brand passion, brand admiration, positive brand reviews, positive emotional response and a declaration of love for the brand. To generate BL consumers must personally identify with a brand or product. At the same time, BL only has positive emotions, unlike brand emotions, which include positive and negative reactions (Palusuk et al., 2019). There is a certain degree of influence between BL and BI (Islam & Rahman, 2016). Most studies were based on the cause-and-effect relationships between BI and brand preference. However, few studies have suggested the different degrees of BL will affect consumers’ perceived service quality and brand image, which can strengthen or weaken consumers’ PI.

Although he relation between service quality and purchase intention is mature research topic, mediating roles of BI and CS and moderating of BL are not been considered to investigate on consumer behaviors not only on brand marketing but also on consumer service. Accordingly, in the theoretical model proposed by Taiwanese mobile phone consumers (N = 522), the mediating role of BL is tested. The theoretical model and hypothesis proposed are investigated, and the research results are explained as academic and practical implications. Finally, the conclusion, limitations and future research section concludes the research results.

Theoretical Background and Hypotheses

Perceived Service Quality and Purchase Intention

Past studies have pointed out PSQ is an antecedent variable of PI (e.g., Cronin et al., 2000), and PSQ is positively related to consumer behavioral intentions (Patrick, 2004). For example, Chen and Chen (2010) conducted empirical research on the relationship between PSQ and their PIs based on the experience quality of tourists. On the other hand, Harun et al. (2018) found PSQ positively affects customer PI in in the USA fast-food industry. Khatoon et al. (2020) found that Customer satisfaction tested as a mediator has shown a partial impact on the relationship between information technology (IT), E-banking service quality, and customer purchasing intentions. Therefore, we propose the following hypothesis:

Hypothesis 1 (H1): PSQ is positively related to PI.

Perceived Service Quality and Brand Image

Dirsehan and Kurtuluş (2018) research in the Turkish airline industry found the better the connotation of service quality, the more helpful it is to brand image. Saleem et al. (2018) argued whether consumers are satisfied with the products or services quality provided by the company and establishing a good BI will be the focus of business operations. Dey et al. (2020) contended service quality positively affect corporate image; the better the service quality, the better the brand image. de Ruyter and Wetzels (2000) pointed out that in terms of corporate credibility and expected service quality, consumer service expansion is more favorable for providers with the image of innovative latecomers than for companies with brand image. In regard to these evaluation criteria, it was further found that consumers prefer service brand expansion over related markets. Therefore, we propose the following hypothesis:

Hypothesis 2 (H2): PSQ is positively related to BI.

Perceived Service Quality and Customer Satisfaction

Hallencreutz and Parmler (2019) explored the causal relationship from PSQ to customer satisfaction (CS), and proposed an integrated view for a particular transaction, PSQ is the antecedent of transaction satisfaction, and that is, PSQ is the antecedent variable of CS. Malik et al. (2020) believed product quality, PSQ, price, contextual factors and personal factors all affect CS. Eskiler and Altunışık (2021) found that showed that there were differences in the effects of service quality, perceived value, and customer satisfaction on behavioral intentions among consumer groups in terms of low- or high-involvement levels. Therefore, we propose the following hypothesis:

Hypothesis 3 (H3): PSQ is positively related to CS.

Brand Image and Purchase Intention

Past research found a BI effects consumer purchasing decisions, and many firms have invested heavily to build brand image. Consumers believe products with a high BI may enhance their self-image and achieve the role of promotion (Lee, 2021). Donnelly et al. (2020) used UK fashion products as research products. Their experimental results found regardless of the product category, the better the BI of the product, the higher the consumer’s willingness to buy; another empirical study was conducted by Foroudi et al. (2018) through an experimental method. The experimental results found the consistency of BI and self-image will have a significant positive relationship with consumers’ brand attitude and PI. Therefore, we propose the following hypothesis:

Hypothesis 4 (H4): BI is positively related to PI.

Customer Satisfaction and Purchase Intention

Past research also pointed out CS positively affects PI (Gopalakrishna et al., 2019). Based on the above literature, this study concludes before and after purchasing goods from salespersons, consumers evaluate various consumer benefits and outputs, for example, they believe the consumer surplus is high. After contacting and interacting with the sales staff, consumers have a satisfactory feeling, for example, the salesperson is modest and polite and caring about customers. Both of these will cause customers to have a higher degree of satisfaction and a higher intention to purchase. On the other hand, Khatoon et al. (2020) found that the mediating role of customer satisfaction was established for E-banking service quality and customer purchase intentions. Therefore, we propose the following hypothesis:

Hypothesis 5 (H5): CS is positively related to PI.

Perceived Service Quality, Brand Image, and Purchase Intention

Good PSQ can increase the frequency and willingness of consumers to buy again through BI. For example, Srivastava and Sharma (2013) discussed this issue based on the relationship between buyers and sellers. They found that PSQ is an independent variable of brand image, and BI is a word variable that enhances customer re-purchases intention. Therefore, the PI of the final choice of the online and offline operators will be revised, resulting in the BI of the PSQ of the product/service and existing the mediator on PSQ and consumer PI. Rehman and Al-Ghazali (2022) evaluated the influence of some unique characteristics of social advertising (such as informative, entertainment, credibility, ease of use, privacy, and contents), individual factors (such as market maven, stability, open-minded, agreeable, and materialism), and brand image on the buying behavior of Malaysian consumers. Therefore, we propose the following hypothesis:

Hypothesis 6 (H6): BI mediates the relationship between BI and PI.

Perceived Service Quality, Customer Satisfaction, and Purchase Intention

Lapierre et al. (1999) took the professional services provided by business-to-business e-commerce as the research object, and found the PSQ affects CS, which in turn affects customer PI. DeLone and McLean (2003) contended PSQ significantly affects CS. Khatoon et al. (2020) believed CS does affect customer PI. Therefore, business must pay attention to related factors such as improving CS and increasing customer PI in terms of gaining competitive advantages in a changing environment. Agyei et al. (2021) found that customer-brand identification and customer satisfaction play a key mediating effect in the relationship between CSR and customer engagement. Thus, we propose the following hypothesis:

Hypothesis 7 (H7): CS mediates the relationship between PSQ and PI.

Moderated Mediating Role of Brand Love on Brand Image

Rageh Ismail and Spinelli (2012) found BI can be regarded as a key factor for BL. BL is an important part of building a customer-brand relationship. When customers feel the love and obsession of the brand, they would like to maintain the relationship with the brand and the relationship is established through recognition of the BI (Albert & Merunka, 2013). Junaid et al. (2020) proposed and validated the dimensions of customer engagement (CE) and consumer well-being (CWB) as direct and indirect outcomes of BL, respectively, and investigated the moderating effect of “duration of use” on the BL-CE relationship. The research findings indicated conceptualizing BL as a “perfect two-way” love—dominant in extant research—is the least appropriate option. Hashmi et al. (2021) moderation investigation results show that prevention focused customers moderate the relationship between functional design and self-determined needs satisfaction. Whereas, promotion focused customers moderate the relationship between esthetic design and self-determined needs satisfaction. On the other hand, Balaji and Maheswari (2021) confirmed that the store attributes dimension impacts shoppers’ attitude which in turn determines the perceived value. Further they confirmed that perceived value determines the purchase intention among shoppers in a supermarket outlet. Therefore, we propose the following hypothesis:

Hypothesis 8 (H8): The indirect effect of PSQ on PI through BI is stronger at low degrees of BL than at high degrees of BL.

Moderated Mediating Role of Brand Love on Customer Satisfaction

Bigne et al. (2020) found that the influence of emotion on the post-purchase behavior of active users of social media is more important than that of passive users (lurkers), because it affects their revisit intention, word-of-mouth and electronic word-of-mouth. BL has a mediating effect between the satisfaction and emotion of active social media users. Tsai (2014) considered that the influence of BL on hotel brand conversion resistance and loyalty is greater than the overall CS. In addition, the functional, service, and price fairness satisfaction dimensions of overall CS are positively related with the BL o emotional attachment, passionate love, and self-brand integration dimensions. In these results, BL proved to be a powerful driving force in turning resistance to loyalty into a hotel brand. Please note that overall CS helps strengthen the loyalty driving effect of BL. Therefore, we propose the following hypothesis:

Hypothesis 9 (H9): The indirect effect of PSQ on PI through CS is stronger at low degrees of BL than at high degrees of BL.

Accordingly, although he relation between service quality and purchase intention is mature research topic, mediating roles of BI and CS and moderating of BL are not been considered to investigate on consumer behaviors not only on brand marketing but also on consumer service. Thus, this study investigates the relationships of perceived service quality, brand image, customer satisfaction, and purchase intention. The moderated mediating role of brand love is examined on 522 mobile phone consumers in Taiwan.

Method

Theoretical Model

In this study, Taiwan’s mobile phone consumers are taken as the research subjects to examine the relationships among the variables, including PSQ, PI, BI, CS, and BL and then examine whether there is any mediating effect while considering BI and CS mediating variables and investigating the moderated mediating role of BL. In terms of proposing hypotheses, this study describes the theoretical model in Figure 1.

Figure 1.

The theoretical model.

Subjects and Data Collection Procedure

This study uses a convenient sampling method and the subjects are mobile phone consumers in Taiwan. The data were collected from June 20, 2019 to July 31, 2019. The main locations of mobile phone purchases are at physical stores, including regular chains, dealers, 3C stores, and Telecom firms. The main brand names of mobile phone purchases include APPLE, SAMSUNG ASUS, OPPO, HTC, SONY, HUAWEI, Xiaomi, SUGAR, and NOKIA. The online questionnaire was available as a link on social networking sites for online questionnaire surveys (such as with mobile phone fan groups on WeChat, Line, and Facebook). After completing the online questionnaire, the SurveyCake system and an Excel file will automatically record the respondent’s IP address and the time of completion. When it encounters duplicate IP and the response time is too short, the system will delete the data. A total of 1,294 samples were distributed during the survey period. Since online questionnaires need to set valid rules for the questionnaires to be sent out, a total of 522 valid questionnaires were collected with an effective questionnaire recovery rate of 42.66%.

Measures

Regarding to PSQ, this study modified Cronin et al. (2000) measure according to the characteristics of customers’ perception of service quality. Regarding to PI, this study modified the Schiffman and Kanuk (2000) measurement method, which defines PI as a measure of the likelihood of consumers buying a product. Regarding to brand image, this study modified Park et al. (1986) and Bruhn et al. (2012) measure on the two dimensions of functional BI and hedonic brand image. Regarding to CS, this study modified the measures of Oliver (1993) to show the characteristics of CS with physical store environmental facilities and active information provided by sales staff. Regarding to BL, this study modified Carroll and Ahuvia (2006), that is, BL represents the degree of strong emotional connection of high-satisfaction customers to the brand. It includes enthusiasm for the brand, admiration for the brand, positive evaluation of the brand, positive emotional response, and a declaration of love for the brand. The survey requested respondents to rate their perception of the purchase experience and also to rate their perceptions regarding their preferred mobile phone brand on several scale items related to the above five constructs using a five-point scale ranging from (1 = “strongly disagree” to 5 = “strongly agree”). The section of Appendix included the final questionnaire.

Results

Measurement Model

Confirmatory factor analysis (CFA)

Confirmatory factor analysis mainly examines the degree of model fit between the factors of the variables in this questionnaire and their measurement items. Since the modified index (MI) was considered, the model fit was incomplete, so the initial model of this study was revised. In addition, the lower the item standard estimate on coefficient of the standardized residual (SR), the lower the interpretation of the variable (Chiou, 2003).

Due to the poor fit of the model index, the initial model of this study was modified. The revised standard is mainly evaluated based on standard parameter estimates. There are two deleted items for the PSQ variable, the PI variable has no deleted items, the BI variable has four deleted items, the CS variable has three deleted items, and the brand favorite variable has two deleted items. The final model of the revised indicators shows that the goodness of model fit test of the CFA model as follows: RMSEA (0.058); GFI (0.95); NNFI (0.99); SRMR (0.026); CFI (0.99); chi-square (190.13); χ²/df (2.84); and df (67). All indexes well reach a good model fits (Effelsberg et al., 2014).

Reliability

Cronbach α value proposed by Cronbach (1951) in order to examine the internal consistency and stability of the measures. The Cronbach α of each variable on this study is greater than .6. The value of PSQ is 0.870; the value of BI is 0.904; the value of CS is 0.829; the value of PI is 0.688; the value of BL is 0.952. Therefore, the reliability of the questionnaire in this study is acceptable (see Table 1).

Table 1.

The Reliability.

Construct	Dimension	Items	Item no.	Cronbach’s α
Construct	Dimension	Items	Item no.	Dimension	Variable
PSQ	—	3,4,5	3	—	.870
BI	Functional	6,7,8	3	.837	.904
BI	Hedonic	9,10,11	3	.908	.904
CS	Environmental facility	13,14,15,16	4	.774	.829
CS	Active information	17,19,20,21	4	.731	.829
PI	—	22,24,25	3	—	.688
BL	—	26,27,28,30	4	—	.952

Convergent validity

The results of convergent validity show that the t value in each dimension of the variable ranges from 9.56 to 23.04 (Fornell & Larcker, 1981). As shown in Table 2, the t value of each variable on this study is greater than 1.96, which indicates that the convergent validity of each variable is good.

Table 2.

The Convergent Validity.

Construct	Dimension	Standardized coefficients	Standard error	t value
PSQ	—	SPQ1 0.88	0.02	25.84
		SPQ2 0.91	0.02	27.31
		SPQ3 0.72	0.03	19.01
BI	Hedonic	BIH1 0.81	0.03	21.59
		BIH2 0.77	0.03	20.05
		BIH3 0.81	0.03	21.62
	Functional	BIF1 0.90	0.03	26.51
		BIF2 0.90	0.03	26.55
		BIF3 0.83	0.03	23.52
CS	Environmental facility	CSE1 0.63	0.03	14.81
		CSE2 0.70	0.03	17.03
		CSE3 0.70	0.03	17.03
		CSE4 0.69	0.03	16.83
	Active information	CSA1 0.51	0.04	11.72
		CSA2 0.54	0.04	12.55
		CSA3 0.81	0.03	20.25
		CSA4 0.71	0.03	17.22
PI	—	PI1 0.82	0.03	22.85
		PI2 0.43	0.05	10.28
		PI3 0.85	0.03	23.89
BL	—	BL1 0.93	0.03	28.66
		BL2 0.94	0.03	29.48
		BL3 0.93	0.03	28.65
		BL4 0.85	0.03	24.79

Discriminant validity

This study tested the discriminant validity of Anderson and Gerbing (1988). Since all Δχ² values on this study are greater than 3.84, thus the discriminant validity is good (Table 3).

Table 3.

The Discriminant Validity.

Variable	Model	χ²	df	Δχ²
	Non-restricted model	190.13	67	—
PSQ	PSQ–BI	203.95	68	13.82
	PSQ–CS	321.72	68	131.59
	PSQ–PI	324.36	68	134.23
	PSQ–BL	672.02	68	481.89
BI	BI–CS	284.18	68	94.05
	BI–PI	197.62	68	7.49
	BI–BL	228.3	68	38.17
CS	CS–PI	288.81	68	98.68
CS	CS–BL	299.5	68	109.37
Purchase intention	CS–BL	226.13	68	36

Note. Δχ² = Restricted modelχ² − Non-restricted modelχ². Δχ² > 3.84 is significant.

Common Method Variance Test

In this study, Harman’s single factor test method was used to test whether the measurement has common method variation (CMV). We assume that if the single factor or main variation extracted after factor analysis exceeds 50%, then there is a critical problem on CMV (Mattila & Enz, 2002). According to the test results of this study, five factors have been extracted. Among five factors, the variance explained by the first factor is 38.54% and which is less than 50%. Accordingly, this test result shows that there is no serious common method change problem in this study.

Correlation Analysis

A correlation analysis is mainly based on the linear correlation value between the variables, through the linear substitution method for calculation. In this regard, this study implements a Pearson correlation analysis to test the correlation among proposed variables. The correlation coefficients on this study are listed in Table 4.

Table 4.

The Correlation Analysis.

Construct	Dimension	SPQ	BIF	BIH	CSE	CSA	PI	BL
PSQ	—	1.00
BI	Functional	0.786^**	1.00
BI	Hedonic	0.680^**	0.694^**	1.00
CS	Environmental facility	0.422^**	0.432^**	0.404^**	1.00
CS	Active information	0.407^**	0.431^**	0.474^**	0.589^**	1.00
PI	—	0.630^**	0.629^**	0.671^**	0.418^**	0.461^**	1.00
BL	—	0.706^**	0.690**	0.777^**	0.472^**	0.517^**	0.766^**	1.00

At a significance level of 0.01 (two-tailed), the correlation is significant.

Theoretical Model

The fitness indexes including NNFI, GFI, RMSEA, CFI, and SRMR of the final theoretical model are 0.96, 0.99, 0.98, 0.033, 0.067, respectively, the norm chi-square value is 103.76, and the chi-square degree of freedom is 3.46, in which indicates that the research model has reached the acceptable model fitness. In the relationship between PSQ, BI, and CS, the t value reaches a significant level, and its parameter estimates are 0.36 and 0.60, respectively. In terms of the relationship between PSQ and PI, the t value does not reach a significant level, and its parameter estimated t value is −0.53. Therefore, the mediating roles of BI and CS in this study are fully mediations. The path coefficient of each variable on this study is illustrated in Figure 2.

Figure 2.

The path analysis diagram.

Hypotheses Testing

This study uses maximum likelihood estimation (MLE) to estimate the γ and β on proposed theoretical model, and in addition to test whether the hypothesis is significantly supported. By using LISREL 11.0, the sample size should be between 100 and 150 (Ding et al., 1995). In this regard, the sample size of this study is 552, thus, which meets the sample size requirement. The research results of the structural model are presented as follows:

(1) H1: The relationships between PSQ and PI:

The t-value between the relationship of PSQ and PI is less than 2.66 (γ11 = .53, p < .001), in which indicates there is a significant impact between PSQ and PI. Thus H1 on this study is supported.

(2) H2: The relationships between PSQ and BI:

The t-value between the relationship of PSQ and BI reached a significant level of 23.30 (γ21 = .95, p < .001), which indicates that PSQ and BI have a significant impact. Thus, the hypothesis H2 of this study is supported.

(3) H3: The relationships between PSQ and CS:

The t-value between the relationship of PSQ and CS reached a significant level of 11.11 (γ31 = .60, p < .001), in which indicates that the PSQ and BI have a significant impact. Therefore, the hypothesis H3 of this study is supported.

(4) H4: The relationships between BI and PI:

The t-value between the relationship of BI and PI reaches a significant level of 3.89 (β11 = 1.29, p < .001), which indicates that BI and PI have a significant impact, so that the hypothesis H4 in this study is supported.

(5) H5: The relationships between CS and PI:

The t-value between the relationship of CS and PI reaches a significant level of 4.70 (β21 = .23, p < .001), which can indicate that CS and PI have a significant impact, so that the hypothesis H5 is supported in this study.

Mediating Effect Test

Sobel test implements to examine whether the mediating effect is a significant (Liao et al., 2021). The results found that the relationship between PSQ and PI was a t value of −1.66 (p > .05), which did not reach a significant level. On the other hand, the indirect influence of BI on PSQ and PI was 1.22, and the z value was 3.86 (p < .05). (at a significance level of 0.001), which reaches a significant level. Thus, BI has a fully mediating effect on the impact between PSQ and PI, so that the hypothesis of this research supports H6. On the other hand, the research results can find that the relationship between PSQ and PI is −1.66 (p > .05), which does not reach a significant level. Otherwise, the indirect impact of CS on PSQ and PI is 0.14. The z value is 4.30 (p < .001), which reaches a significant level. Thus, CS has a full mediating effect on the impact of PSQ and PI. Therefore, the hypothesis of this research supports H7.

Moderated Mediating Effect Test—Two Moderated Mediation Models Investigation

Model 1 test

In Model 1, regarding the mediation test, this study tests the degree of BL (the moderating variable) will affect the mediating effect of BI (the mediating variable) on PR and PI. This study uses PROCESS 3.0 to investigate the mediation effect (Hayes, 2013). The parameters are setting respectively as X: perceived service quality (PSQ); Y: purchase intention (PI); M: brand image (BI); W: brand love (BL) (Figure 3).

Figure 3.

The conceptual model of moderated mediation model (Hayes, 2013).

In Table 5, when the degree of BL is high, the BI has a mediating effect, with an indirect effect of 0.3140 (p < .01). On the contrary; when the degree of BL is low, the BI has a mediating effect, with an indirect effect of 0.2252, (p < .01). We further tested the difference between the two indirect effects, it is found that the difference between the two has reached a significant level of indirect effect, which is 0.0888. The above results shows that BL will moderate the indirect influence of BI on PSQ and PI. If the indirect effect of BL is low, then the indirect effect of the BI will be low as well. On the contrary, the higher the degree of BL, then the higher the indirect effect of the BI.

Table 5.

The High and Low Degrees of BL of Moderating Effect on BI.

High and low degrees of moderating effect	Indirect effect	SE	95% CI
Degree of BL is low	0.2252	0.0365	0.1588	0.3031
BL	0.2760	0.0354	0.2110	0.3508
Degree of BL is high	0.3140	0.0389	0.2433	0.3953

Further comparing the indirect effects, it is found that the difference between high BL degree and low BL degree has also reached a significant level (Index = 0.0507, [0.0226, 0.0842]) (Table 6 and Figure 4). The results show that degree of BL is strengthened on the indirect effect of BI between PSQ and PI. When the degree of BL is high, the indirect effect of BI on the PSQ of PI is stronger; on the contrary, when the degree of BL is low, the indirect influence of BL on the PSQ and PI is weak. Therefore, H8 is supported by the analysis results (Liao et al., 2021).

Table 6.

The index of moderated mediation BL on BI.

	Index	SE	95% CI
BL	0.0507	0.0157	0.0226	0.0842

Figure 4.

The moderated effect of the BL on the relationship between PSQ and BI.

Model 2 test

In Model 2, as far as the mediating effect of BL on CS is concerned, we further tested whether the degree of BL (the moderating variable) will affect the mediating effect of CS (the mediating variable) on PSQ and PI. The parameter is setting respectively as X: PSQ; Y: PI; M: CS; W: BL. This study further tested whether there is a conditional indirect effect so that it will change according to the level of the moderating variable. As shown in Table 7, when the degree of BL is high, the BI has a mediating effect, with an indirect effect of 0.0644, (p < .01). However; when the degree of BL is low, BI has a mediating effect and the indirect effect is 0.0192, (p < .01). We further examined the difference between the two indirect effects and found that the difference between the two reached a significant level of indirect effect, which is 0.0452. The above analysis shows that BL will moderate the indirect impact of CS on PSQ and PI. This can explain that if the BL is low, the indirect impact of CS is low as well. On the contrary, the higher the BL, then the higher the indirect effect of CS.

Table 7.

The high and low degrees of BL of moderating effect on CS.

High and low degrees of moderating effect	Indirect effect	SE	95% CI
Degree of BL is low	0.0192	0.0170	−0.0163	0.0515
BL	0.0451	0.0160	0.0136	0.0790
Degree of BL is high	0.0644	0.0206	0.0273	0.1073

This study further compares the indirect effects. The difference between high BL degree and low BL degree has also reached a significant level (Index = 0.0259, [0.0049, 0.0527]) (Table 8 and Figure 5). The results of the analysis show that BL is strengthened on the indirect effect of CS between PSQ and PI. When the degree of BL is high, the indirect effect of CS on the PSQ of the PI is strong; on the contrary, when the degree of BL is low, the indirect effect of CS on the PSQ and PI is weak (Liao et al., 2021). Thus, H9 is thus supported.

Table 8.

The Index of Moderated Mediation BL on CS.

	Index	SE	95% CI
Brand love	0.0259	0.0121	0.0049	0.0527

Figure 5.

The moderated effect of the BL on the relationship between PSQ and CS.

Implications

Theoretical Implications

This study found that PSQ is positively related to PI. Product satisfaction refers to the satisfaction status of the business’s products to customers, including the inherent quality, price, design, packaging, timeliness, creative performance, and other aspects of the product. Guo et al. (2018) found that in addition to the perception of usefulness and brand satisfaction after acceptance, the PSQ and perceived fit of the initial purchase also strongly influence consumers’ intentions to continue buying brand extension products. Among Chinese Xiaomi mobile phone customers, hedonic and utilitarian expectations play a mediating role between the consumer’s consumption outlook after the initial purchase and subsequent purchases of different products under the same brand. Thus, comparing with Guo et al. (2018), this study infers the post consumption view or experience, perceived risk, perceived value and trust might be antecedent variables to build on a positive or negative functional and hedonic BI of the brand extension product and PSQ in addition to influencing purchase and re-purchase intentions. These relationships could be examined in a future study.

CS includes product satisfaction and service satisfaction. Product satisfaction refers to the state of satisfaction given by the company’s products to customers, including the inherent quality, price, design, packaging, timeliness, creative imitation, and other aspects of the product. Product quality satisfaction is the basic factor constituting CS. Service satisfaction refers to the service measures taken at different stages of the product’s life cycle before, during, and after the sale of the product to satisfy customers (Sharma & Kumar, 2021). This is mainly because at every step of the service process, online and offline operators can put themselves in the position of thinking for the customers, so as to facilitate the customer benefits (Ashok et al., 2018). Vigolo et al. (2020) found signage has a positive and significant effect on service satisfaction with the servicescape in the healthcare services. In this study, CS has a fully mediating effect on the PSQ and PI. Without a relationship between PSQ and PI, CS offers another advantage in mediating customers’ PSQ and PI. We use environmental facility and active information to measure CS in terms of generating both product and service satisfaction, which has a positive and full mediation effect on PSQ and PI. It suggests that mobile phone manufactures and sales operators might offer customer products and service satisfaction in terms of conveying and establishing a bridge from perceived service quality to PI. This suggestion might be a valuable implication in terms of social science.

On the other hand, this study found that the indirect influence of PSQ on PI through CS is stronger when the degree of BL is low degree and when the degree of BL is high degree. We further found that the indirect influence of PSQ on PI through BI is stronger when the BL is low degree than when the BL is high degree. When the degree of BL is high, the indirect effect of BI on the PSQ of PI is strong; on the contrary, when the degree of BL is low, the indirect influence of BL on the PSQ and PI is weak. There is the first research finding into the degree of BL strengthening the relationships between PSQ and PI through CS. This finding might be a valuable theoretical implication to social science.

Practical Implications

According to the Nielsen Global Fashion Brand Online Survey, Taiwan has great confidence in the quality of brand names. As many as 51% of respondents believed the quality of brand names is much better than that of ordinary brands, and 55% of respondents believe brand name indicate the status of an individual style (China Times Marketing Knowledge Base, 2019). It can be seen more and more consumers rely on BI as a consideration for purchasing decisions. BI plays an important role between operators and customers. In this study, BI has a fully mediating effect on PSQ and PI. This finding indicates Taiwan mobile phone consumers do not directly build their own perception from operators’ service quality but consider their PI through a BI. This is the first research finding showing Taiwanese mobile phone buyers with mobile phone PI depend on their BI instead of operators’ service quality in the first instance. BI has the advantage of mediating customers’ PSQ and PI. In other words, mobile phone manufactures and operators might consider how to co-operate with mobile phone brand and product operators to build a channel-brand alliance with a competitive advantage.

The life cycle of mobile phone products in the market is shorter than that of other electronic products, so it is necessary to conduct research on consumers to better understand the role of brands and services in customer purchasing decisions. Is brand temporary or permanent? This is a similar question about love. Suppose we believe consumers are fickle, and they like new things and hate old ones on brand/product selection due to market changes. Thus, a brand being temporary is a risk and a brand being permanent is an opportunity in the changing market environment. However, no one can guarantee a brand can be “diamonds are forever” permanent. In terms of retaining BI for a continuous competitive advantage, more efforts can strengthen customers’ emotion and loyalty to a brand, that is, BL. Carroll and Ahuvia (2006) found that satisfied consumers prefer brands in product categories that are considered more hedonic (compared to utilitarian) and brands that provide more iconic benefits. In turn, BL is related to higher brand loyalty and positive word-of-mouth. Therefore, BL can enhance BI and CS. In this study, BL plays a moderated meditating role in PSQ and BI. Mobile phone manufactures and operators might consider establishing customer BL in terms of functional and hedonic BI for both the online and offline environment.

Discussions, Conclusions, Limitations, and Future Research

Regarding discussions, this study finds the relationship between PSQ and PI. BI and CS play a full mediating role between PSQ and PI. In addition, in the proposed theoretical model, BL plays a moderated mediation effect on BI and CS. This is the first study indicates that when the degree of brand love is high, the indirect effect of brand image on the perceived service quality of the purchase intention is strong; on the contrary, when the degree of brand love is low, the indirect effect of brand love on the perceived service quality and purchase intention is weak.

On the other hand, this study concludes that mobile phone operators may consider encouraging customers to participate in brand communities with a high degree of emotion, and to satisfy customers’ PIs in product and service transactions, in order to increase PIs. Therefore, although the brand has a truly lasting quality in the market, which is a risk for mobile phone manufacturers and operators, and harmful to consumers’ intentions to buy, mobile phone manufacturers and operators should make effective use of their marketing efforts. This marketing power might turn risk into opportunity. PSQ, BL, BI, and CS with service industries and enterprises.

Regarding limitations, some limitations and future research directions are worth pursuing. Firstly, this research focuses on the impact of PSQ on BI and CS and PI. However, in examining other types of purchasing emotions and their influence on purchasing attitudes and intentions in the context of purchasing behavior, future research on consumer behavior may find some interesting and valuable results. Secondly, a complete research design is needed to explore the interrelationships between perceptions, attitudes, and behaviors. The results of objective measurements based on perceptions and intentions will reinforce the results of this research report. For example, perceived risk, perceived value, and experience value can be used as other measures to supplement the structure of perceived service quality.

Finally, this research emphasizes that psychological factors are the determinants of consumers’ purchasing attitude and intention. Future research might explore the influence of other determinants, such as emotion, trust, loyalty, and willingness to purchase or not, the interaction between purchasing psychology and behavior, and the reasons for repurchasing after purchase.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Shu-Hsien Liao

References

Agyei

Sun

Penney

E. K.

Abrokwah

Ofori-Boafo

(2021). Linking CSR and customer engagement: The role of customer-brand identification and customer satisfaction. Sage Open, 11(3), 1–16. https://doi.org/10.1177/21582440211040113

Albert

Merunka

(2013). The role of brand love in consumer-brand relationships. Journal of Consumer Marketing, 30(3), 258–266. https://doi.org/10.1108/07363761311328928

Alford

B. L.

Biswas

(2002). The effects of discount level, price consciousness and sale proneness on consumers’ price perception and behavioral intention. Journal of Business Research, 55(9), 775–583. https://doi.org/10.1016/s0148-2963(00)00214-9

Anderson

J. C.

Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Ashok

Day

Narula

(2018). Buyer (dis)satisfaction and process innovation: The case of information technology services provision. Industrial Marketing Management, 68(1), 132–144. https://doi.org/10.1016/j.indmarman.2017.10.004

Balaji

Maheswari

(2021). Impact of store image dimensions on shopper’s attitude, perceived value, and purchase intention. Sage Open, 11(1), 1–13. https://doi.org/10.1177/2158244021994824

Bashir

Khwaja

M. G.

Rashid

Turi

J. A.

Waheed

(2020). Green brand benefits and brand outcomes: The mediating role of green brand image. Sage Open, 10(3), 1–11. https://doi.org/10.1177/2158244020953156

Bigne

Andreu

Perez

Ruiz

(2020). Brand love is all around: Loyalty behaviour, active and passive social media users. Current Issues in Tourism, 23(13), 1613–1630. https://doi.org/10.1080/13683500.2019.1631760

Blackwell

R. D.

Miniard

P. W.

Engel

J. F.

(2001). Consumer behavior (9th ed.). South-Western Thomas Learning.

10.

Bruhn

Schoenmueller

Schäfer

D. B.

(2012). Are social media replacing traditional media in terms of brand equity creation? Management Research Review, 35(9), 770–790. https://doi.org/10.1108/01409171211255948

11.

Carroll

B. A.

Ahuvia

A. C.

(2006). Some antecedents and outcomes of brand love. Marketing Letters, 17(2), 79–89. https://doi.org/10.1007/s11002-006-4219-2

12.

Chen

C. F.

Chen

F. S.

(2010). Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists. Tourism Management, 31(1), 29–35. https://doi.org/10.1016/j.tourman.2009.02.008

13.

China Times Marketing Knowledge Base. (2019). 2019 Nielsen global fashion brand online survey in taiwan. Retrieved February 19, 2020 from https://www.similarweb.com/zh-tw/website/chinatimes.com/#overview

14.

Chiou

F. Y.

(2003). When pivotal politics meets partisan politics. American Journal of Political Science, 47(3), 503–522.

15.

Cronbach

L. J.

(1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/bf02310555

16.

Cronin

J. J.

Brady

M. K.

Hult

G. T. M.

(2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193–218. https://doi.org/10.1016/s0022-4359(00)00028-2

17.

DeLone

W. H.

McLean

E. R.

(2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748

18.

de Ruyter

Wetzels

. (2000). The role of corporate image and extension similarity in service brand extensions. Journal of Economic Psychology, 21(6), 639–659. https://doi.org/10.1016/s0167-4870(00)00024-6

19.

Devlin

S. J.

Dong

H. K.

(1994). Service quality from customers’ perspective. Marketing Research, 6(1), 5–13. https://doi.org/10.1080/10686967.2007.11918032

20.

Dey

B. L.

Al-Karaghouli

Minov

Babu

M. M.

Ayios

Mahammad

S. S.

Binsardi

(2020). The role of speed on customer satisfaction and switching intention: A study of the UK mobile telecom market. Information Systems Management, 37(1), 2–15. https://doi.org/10.1080/10580530.2020.1696526

21.

Ding

Velicer

W. F.

Harlow

L. L.

(1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119–143. https://doi.org/10.1080/10705519509540000

22.

Dirsehan

Kurtuluş

(2018). Measuring brand image using a cognitive approach: Representing brands as a network in the Turkish airline industry. Journal of Air Transport Management, 67(2), 85–93. https://doi.org/10.1016/j.jairtraman.2017.11.010

23.

Donnelly

Gee

Silva

E. S.

(2020). UK mid-market department stores: Is fashion product assortment one key to regaining competitive advantage? Journal of Retailing and Consumer Services, 54(2), 102043. https://doi.org/10.1016/j.jretconser.2020.102043

24.

Effelsberg

Solga

Gurt

(2014). Transformational leadership and follower’s unethical behavior for the benefit of the company: A two-study investigation. Journal of Business Ethics, 120(1), 81–93. https://doi.org/10.1007/s10551-013-1644-z

25.

Eskiler

Altunışık

(2021). The moderating effect of involvement in the relationship between customer behavioral intentions and its antecedents. Sage Open, 11(2), 1–12. https://doi.org/10.1177/21582440211014495

26.

Fornell

C. R.

Larcker

F. F.

(1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–51.

27.

Foroudi

Jin

Gupta

Foroudi

M. M.

Kitchen

P. J.

(2018). Perceptional components of brand equity: Configuring the symmetrical and asymmetrical paths to brand loyalty and brand purchase intention. Journal of Business Research, 89(4), 462–474. https://doi.org/10.1016/j.jbusres.2018.01.031

28.

Gavilan

Avello

(2020). Brand-evoked mental imagery: The role of brands in eliciting mental imagery. Sage Open, 10(4), 1–9. https://doi.org/10.1177/2158244020977484

29.

Gopalakrishna

Malthouse

E. C.

Lawrence

J. M.

(2019). Managing customer engagement at trade shows. Industrial Marketing Management, 81(4), 99–114. https://doi.org/10.1016/j.indmarman.2017.11.015

30.

Guo

Zhu

Barnes

S. J.

Bao

Le-Nguyen

(2018). Understanding cross-product purchase intention in an IT brand extension context. Psychology and Marketing, 35(6), 392–411. https://doi.org/10.1002/mar.21094

31.

Hallencreutz

Parmler

(2019). Important drivers for customer satisfaction – from product focus to image and service quality, 32(5–6), 501–510.

32.

Harun

Prybutok

V. R.

(2018). Insights into the antecedents of fast-food purchase intention and the relative positioning of quality. Journal of Quality Management, 25(2), 83–100. https://doi.org/10.1080/10686967.2018.1436351

33.

Hashmi

H. B. A.

Shu

Haider

S. W.

Khalid

Munir

(2021). Bridging the gap between product design and customer engagement: Role of self-determined needs satisfaction. Sage Open, 11(4), 1–15. https://doi.org/10.1177/21582440211056598

34.

Hayes

A. F.

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

35.

Islam

J. U.

Rahman

(2016). Examining the effects of brand love and brand image on customer engagement: An empirical study of fashion apparel brands. Journal of Global Fashion Marketing, 7(1), 45–59. https://doi.org/10.1080/20932685.2015.1110041

36.

Junaid

Hussain

Basit

Hou

(2020). Nature of brand love: Examining its variable effect on engagement and well-being. Journal of Brand Management, 27(3), 284–299. https://doi.org/10.1057/s41262-019-00179-1

37.

Khatoon

Zhengliang

Hussain

(2020). The mediating effect of customer satisfaction on the relationship between electronic banking service quality and customer purchase intention: Evidence from the Qatar banking sector. Sage Open, 10(2), 1–12. https://doi.org/10.1177/2158244020935887

38.

Lapierre

Jordan

Scott

(1999). What does value mean in businesses-to-business professional services? International Journal of Service Industry Management, 8(3), 377–397.

39.

Lee

U. K.

(2021). The effect of confirmation of Nation brand image in international tourism advertisement on travel intention of foreign tourists: The case of Korean ITA for Chinese tourists. Sage Open, 11(1), 1–15. https://doi.org/10.1177/2158244020988380

40.

Liao

S. H.

D. C.

Chung

Y. C.

Huang

A. P.

(2021). Risk and opportunity for online purchase intention – A moderated mediation model investigation. Telematics and Informatics, 62, 101621. https://doi.org/10.1016/j.tele.2021.101621

41.

Malik

S. A.

Akhtar

Raziq

M. M.

Ahmad

(2020). Measuring service quality perceptions of customers in the hotel industry of Pakistan. Total Quality Management & Business Excellence, 31(3-4), 263–278. https://doi.org/10.1080/14783363.2018.1426451

42.

Mattila

A. S.

Enz

C. A.

(2002). The role of emotions in service encounters. Journal of Service Research, 4(4), 268–277. https://doi.org/10.1177/1094670502004004004

43.

Nawaz

Jiang

Alam

Nawaz

M. Z.

(2020). Role of brand love and consumers’ demographics in building consumer–brand relationship. Sage Open, 10(4), 1–19. https://doi.org/10.1177/2158244020983005

44.

Oliver

R. L.

(1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 418–430. https://doi.org/10.1086/209358

45.

Özcan

Elçi

(2020). Employees’ perception of CSR affecting employer brand, Brand Image, and corporate reputation. Sage Open, 10(4), 1–13. https://doi.org/10.1177/2158244020972372

46.

Palusuk

Koles

Hasan

(2019). All you need is brand love’: A critical review and comprehensive conceptual framework for brand love. Journal of Marketing Management, 35(1-2), 97–129. https://doi.org/10.1080/0267257x.2019.1572025

47.

Parasuraman

Zeithaml

V. A.

Berry

L. L.

(1988). Servqual: A multiple-item scale for measuring customer perceptions of service quality. Journal of Retailing, 64(1), 12–40.

48.

Park

C. W.

Jaworski

B. J.

Maclnnis

D. J.

(1986). Strategic brand concept-image management. Journal of Marketing, 50(4), 135–145. https://doi.org/10.2307/1251291

49.

Patrick

K. D.

(2004). The role of quality, value, and satisfaction in predicting cruise passengers’ behavioral intentions. Journal of Travel Research, 42(4), 307–407.

50.

Pedeliento

Kavaratzis

(2019). Bridging the gap between culture, identity and image: A structurationist conceptualization of place brands and place branding. Journal of Product & Brand Management, 28(3), 348–363. https://doi.org/10.1108/jpbm-01-2018-1735

51.

Rageh Ismail

Spinelli

. (2012). Effects of brand love, personality and image on word of mouth: The case of fashion brands among young consumers. Journal of Fashion Marketing and Management, 16(4), 386–398. https://doi.org/10.1108/13612021211265791

52.

Regan

W. J.

(1963). The service revolution. Journal of Marketing, 27(3), 57–62. https://doi.org/10.1177/002224296302700312

53.

Rehman

F. U.

Al-Ghazali

B. M.

(2022). Evaluating the influence of social advertising, individual factors, and brand image on the buying behavior toward fashion clothing brands. Sage Open, 12(1), 1–18. https://doi.org/10.1177/21582440221088858

54.

Saleem

M. A.

Yaseen

Wasaya

(2018). Drivers of customer loyalty and word of mouth intentions: Moderating role of interactional justice. Journal of Hospitality Marketing & Management, 27(8), 877–904. https://doi.org/10.1080/19368623.2018.1469447

55.

Schenk

C. T.

Holman

R. H.

(1980). A sociological approach to brand choice: The concept of situational self-image. In Olson

(Ed.), Advances in consumer research (Vol. ii, pp. 610–614). Association for Consumer Research.

56.

Schiffman

L. G.

Kanuk

L. L.

(2000). Consumer behavior (7th ed.). Prentice Hall.

57.

Sharma

Kumar

(2021). An analysis of causal relationships among the factors affecting the performance of a service organization. Sage Open, 11(1), 1–14. https://doi.org/10.1177/2158244021999394

58.

Smith

P. D.

(1990). Integrating related services into programs for students with service and multiple handicaps: Kentucky systems change projects. (RRIC Document Reproduction Service No. ED354679).

59.

Srivastava

Sharma

N. K.

(2013). Service quality, corporate brand image, and switching behavior: The mediating role of customer satisfaction and repurchase intention. Services Marketing Quarterly, 34(4), 274–291. https://doi.org/10.1080/15332969.2013.827020

60.

Tsai

S. P.

(2014). Love and satisfaction drive persistent stickiness: Investigating international tourist hotel brands. International Journal of Tourism Research, 16(6), 565–577. https://doi.org/10.1002/jtr.1950

61.

Vigolo

Bonfanti

Sallaku

Douglas

(2020). The effect of signage and emotions on satisfaction with the servicescape: An empirical investigation in a healthcare service setting. Psychology and Marketing, 37(3), 408–417. https://doi.org/10.1002/mar.21307

62.

Zeithaml

V. A.

(1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302

63.

Zeithaml

V. A.

Kirmani

(1993). Advertising, perceived quality, and brand image. In Aaker

D. A.

Biel

A. L.

(Eds.), Brand equity and advertising (pp. 143–161). Lawrence Erlbaum Associates, Inc.

Consumer Perceived Service Quality and Purchase Intention: Two Moderated Mediation Models Investigation

Abstract

Keywords

Introduction

Theoretical Background and Hypotheses

Perceived Service Quality and Purchase Intention

Perceived Service Quality and Brand Image

Perceived Service Quality and Customer Satisfaction

Brand Image and Purchase Intention

Customer Satisfaction and Purchase Intention

Perceived Service Quality, Brand Image, and Purchase Intention

Perceived Service Quality, Customer Satisfaction, and Purchase Intention

Moderated Mediating Role of Brand Love on Brand Image

Moderated Mediating Role of Brand Love on Customer Satisfaction

Method

Theoretical Model

Subjects and Data Collection Procedure

Measures

Results

Measurement Model

Confirmatory factor analysis (CFA)

Reliability

Convergent validity

Discriminant validity

Common Method Variance Test

Correlation Analysis

Theoretical Model

Hypotheses Testing

Mediating Effect Test

Moderated Mediating Effect Test—Two Moderated Mediation Models Investigation

Model 1 test

Model 2 test

Implications

Theoretical Implications

Practical Implications

Discussions, Conclusions, Limitations, and Future Research

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References