Impact of Brand Prominence on Customer Satisfaction: The Moderating Role of Online/Offline Environment

The customer-brand relationship is critical to a company's bottom line, particularly in the service industry that adopts online services. Virtual interfaces are becoming a major point of consumer contact for many companies. Some traditional service quality variables that impact customer satisfaction, such as the physical appearance of buildings, staff, and equipment and the responsiveness and empathy of employees, are not apparent in this setting. However, brand prominence may play a key role in improving consumer satisfaction. To satisfy customers and survive in the marketplace, service providers employ both online and physical purchasing channels wisely. Our research attempts to fill a gap in understanding what motivates customers' contentment and, hence, their expectations by comparing performance quality when consumers buy online vs. offline store. We analyze how brand prominence affects consumer satisfaction through the moderating effect of online and offline environments. The hypotheses were empirically validated using structural equation modeling after collecting 8533 valid responses. The findings show that both direct and indirect channel-induced expectations positively affect satisfaction, with the latter having a stronger influence. The results contribute to the literature by providing empirical evidence of the effect of expectations and performance quality on consumer satisfaction and extend the expectation-confirmation theory by including brand prominence as a component. This study contributes to a better understanding of how consumer satisfaction develops in an online or offline environment.


Introduction
During the last decade, a substantial body of literature has been published on the influence of customer expectations and performance quality on customer satisfaction in both offline environment and online environments. This is particularly essential for firms such as insurance companies that build their innovative plans through the internet as a medium for consumer connections and discourse (PWC, 2017). The online sphere provides greater interactive and personalized marketing options than the offline environment (Wind and Rangaswamy, 2001). Similarly, the pressure exerted by a consumer's immediate environment affects service expectations (Bilgihan et al., 2016). Understanding the factors behind service adoption requires examining the antecedents of individual expectations (López and Sicilia, 2013). Understandably, studies have revealed that people endorse a brand when it significantly exceeds their expectations. This is especially true in the online world, where word of mouth is one of the most trustworthy and reliable sources (Chari et al., 2016). Third-party opinions can alter consumers' feelings about a brand, such as brand love, even without experience-based precedents (Roy et al., 2013). Potential users perceive brands with recommendations as trustworthy and familiar (Wang and Benbasat, 2007). As a result, a brand's reliability increases, and new users are more likely to try a product or service (Hanssens et al., 2015(Hanssens et al., ). al., 2013. Business practices have evolved since the Internet's inception, and they are now being challenged by the opportunities and complications of providing these services online without a physical presence. Customers are more likely to devote more cognitive effort to their decision-making processes when there is more information available because they can see the potential for realizing additional benefits (e.g., more informed or improved choices; lower prices) through additional effort, which in turn is likely to result in a service experience that delivers higher satisfaction when the choices are made online rather than offline (Johnson and Payne, 1985).The online purchasing experience may alter customers' expectations of services if they have more information about a particular brand (including visual information) ahead of time; they know what type of service to receive, and are less likely to be surprised or angry with service failure than when they purchase from that brand offline (Shankar et al., 2003). This implies less expectation disconfirmation (i.e., negative or less positive disconfirmation), which should lead to higher satisfaction than if the decision is made offline. However, the online environment might reduce satisfaction owing to a perceived lack of privacy and financial security, as well as a perceived lack of human contact, technological failure, and poor interface design (Meuter et al., 2000).
The extent to which individuals feel that (1) using a given technology will make it easier to obtain a service and (2) it will be useful to obtain certain benefits are pivotal factors in the formation of individual expectations that lead to customer satisfaction (Yucel and Gulbahar, 2013). Understanding how to generate consumer satisfaction is critical because it can have a positive impact on businesses in a variety of ways, including improving long-term relationships with customers (Kim et al., 2009) and continuous use intentions (Bhattacherjee, 2001;Lin et al., 2012), which results in repurchase behavior (Kim et al., 2009). The expectation-confirmation theory (ECT) explains how satisfaction comes from the expectation-confirmation process (Bhattacherjee, 2001;Oliver, 1977Oliver, , 1980. ECT has long been considered the mainstream paradigm for examining consumer satisfaction for a wide range of products and services (Fan and Suh, 2014;Lin et al., 2017). According to ECT, consumer satisfaction is formed by a combination of expectations and performance quality after receiving a product or service. Consumer satisfaction can be influenced directly or indirectly by expectations. However, we have little understanding about how these relationships is influenced by brand prominence and how the effect of brand prominence could differ in the online and offline contexts.
This study attempts to fill these gaps in the literature. Specifically, we address how brand prominence affects the shaping of customer satisfaction in online versus offline environments. The disconfirmation model (ECT) is used to measure the effect of brand prominence in both settings. Our study is in line with previous studies investigating the antecedents of customer satisfaction in offline and online markets. According to Saini and Lynch (2016), consumer beliefs in a brand differ in the online and offline purchase scenarios. Further, Shankar et al. (2003) discovered that the correlation between brand loyalty and consumer satisfaction is more powerful in the online than the offline due to the "cognitive lock-in" effect, which is a cost-benefit analysis suggesting that the cost of switching away from an incumbent product surpasses the benefit of utilizing an alternative product. Our study extends prior research by including the role of brand prominence.
This study aims to reveal answers of the following two key research questions: (1) How does the effect of brand prominence on customer satisfaction vary in the context of online and offline? (2) What role does brand prominence play regarding customer expectations and performance quality in online and offline insurance? A theoretical model was proposed to investigate the relationship between expectations, perceived quality, and satisfaction, and a survey was conducted to validate the model and hypotheses. By the proposed model, this study discloses the antecedents of online-offline insurance services on expectations and perceived quality, as well as their consequences in terms of WOM intention. The main contributions of this study are two-fold. First, we provide new insights into how brand prominence can affect the development of customer satisfaction in online and offline contexts. Second, our findings could be useful for service retailers who operate their business through online and offline stores to manage customer satisfaction by leveraging the prominence of their brand. the brain's limited processing capacity (Lynch and Srull, 1982), more brand disclosure has a disproportionately greater impact on consumer decision-making process and more memories, which are more potent than merely concepts (Carlson and Smith, 1996). Hoffman and Novak (1996) stated growing interest in understanding the effects of computer-mediated shopping environments. Determining whether there are systematic differences in consumer choice behavior between online and offline stores, and if so, understanding the reasons for these differences, is a topic of particular interest among practitioners and academics (Degeratu et al., 2000). In other words, will the same person behave differently online and offline, and if so, why? Identifying and comprehending these distinctions is critical when developing marketing strategies, particularly for online marketers. Unfortunately, few studies have examined how online consumer behavior differs from the offline one. Pauwels and Dans (2001), Shankar et al. (2003), and Ward and Lee (2000) conducted empirical research on consumer behavior in an online environment. Online consumers' ability to obtain more information about both price and non-price attributes is a key difference between online and offline shopping. According to Degeratu et al. (2000), for some categories, a brand is more essential online than in a traditional buying environment; however, this may be contingent on expectations and performance quality. As a result, these authors ask: "What are the true dynamics of purchase/consumer behavior in this environment?" This is an important research question. More empirical research is needed to determine how the type of media and power of brand prominence affect these consequences.
The American Customer Satisfaction Index (ACSI) has recently shown a decline in satisfaction among American consumers, primarily because of dissatisfaction with services (ACSI, 2021). According to the ACSI study, customer satisfaction is more quality driven than price or value driven. Given that the online channel offers a convenient means of purchasing goods or services, this presents an opportunity for online retailers to improve customer satisfaction. According to previous research, an episode is created in consumer's mind, when a consumer experiences a brand. As the experience with the brand grows, multiple episodes are created, resulting in a brand's level of prominence (Sia et al., 1999). This collection of traces (associative schematics) evolves over time, resulting in brand prominence, and the retrieval ability varies. Consumers evaluate brands based on overall brand knowledge (Raggio et al., 2014); the more coherent episodic memories a brand has, the more it may be retrieved in a specific context to impact consumer's decision-making. Marketers are more concerned with brand prominence, which results in retrieval from long-term memory (Guido,1998). It may be less viable for identity products since the sensitivity of associations with brands chosen to reflect self-concept allows the brand to linger strongly in long-term memory, whereas it may be more practical for instrumentality products. The ability of a brand to be recalled from long-term memory may be directly related to the age of the consumer's relationship with the brand, which makes it prominent in the mind, so that retrieval is conducted in a particular context to accomplish different activities.
Numerous marketing studies have discovered a plethora of cues that influence consumers' decision-making. Consumers evaluate products and services using extrinsic cues such as brand, distribution (online/offline), and price, in addition to intrinsic cues such as physical attributes. When a brand is used as an effective cue, it has a significant impact on consumers' evaluations and decision-making (Brucks et al., 2000). Little research has been conducted on the role of online or offline environments in determining the relationship between brand prominence, consumer expectations, and performance quality. Quality judgment, price evaluation, value interpretation, and purchase probability are influenced by brand. However, brand is not the only factor that consumers consider when evaluating products and services; consumer behavior is also influenced by distribution cues (online/offline) (Donthu et al., 1999).

Customer Expectation on Customer Satisfaction
Customers' expectations serve as cognitive guides that minimize buying uncertainty. The expectations construct captures "experience with the firm's" service includes non-experiential information from sources such as word of mouth and advertising, as well as the prognosis of the supplier's capacity to supply excellence in the future (Fornell et al., 1996). As previously noted, because online purchasing risk is frequently seen to be superior, we predict that purchasers motivated by higher expectations of brand trustworthiness and quality prefer the relative certainty of online shopping to the risk of negative disconfirmation. Customers also commonly assume the risk of purchasing potentially untested products online that are not available in physical locations within a reasonable distance of commuting (Donthu et al., 1999), affecting customer expectations when assessing satisfaction. These expectations are part of the reason why online customer satisfaction is higher than the offline one (Saleh, 2021).

Performance Quality on Customer Satisfaction
major and ongoing concern. Performance quality has key significance in developing personalized offers and enhancing customer satisfaction (Boakye et al., 2014;Namin, 2017). Performance quality is critical in the academic and professional marketing realms. This construct is a sign of market power (Matricon, 2005) because the design of a high-quality offer ensures a company's longevity and evolution over time. Richard-Lanneyrie (2014) defined the concept of service performance quality and claimed that it corresponds to an offer tailored to the needs and expectations of the customer, which leads to customer satisfaction.
Customer satisfaction is defined as the difference between pre-purchase expectations and actual performance after the purchase is made (Jun et al., 2004). According to Jun et al. (2004), perceived performance quality and satisfaction have a significant positive relationship. Consequently, the online retailer's increased convenience positively impacts customer satisfaction (Koo et al., 2006). This means that a more convenient service will increase perceived value; thus, more convenience will result in higher satisfaction (Thuy, 2011). According to Hsu et al. (2010), customers are more likely to be satisfied and reuse services if they can easily and conveniently experience their benefits. Customers will be more satisfied if service providers make their services more convenient (Jih, 2009).

Customer Satisfaction on Intention to WOM
WOM refers to informal consumer communication regarding a provider and/or its product/service features. WOM is received by potential customers before they experience a product or service, and it can be either positive or negative (Tax et al., 1993). There is a lot of research on the impact of contentment on WOM. Based on traditional theory, customer satisfaction plays a decisive role in customers' long-term buying decision behavior: the more satisfied a customer is, the more WOM information they are likely to produce. Thus, customer satisfaction significantly impacts WOM propagation. According to Ranaweera and Prabhu (2003), there is a strong relationship between satisfaction and positive WOM: dissatisfied consumers are more likely to propagate negative WOM, whereas pleased customers are more likely to spread positive WOM. Anderson (1998) confirmed that in addition to a linear link, there is a U-shaped association between customer satisfaction and WOM. Customers' intention to propagate WOM increases when their level of satisfaction is higher or lower, and it decreases when their level of contentment or dissatisfaction is average.

The Moderating Effect of Online/Offline Environment on the Role of Brand Prominence on Customer Satisfaction
Humans use their senses of hearing, seeing, smelling, and touching, which have powerful effects on physical, cognitive, social, and emotional influences on decision-making (Soars, 2009). Sensory attributes in an online environment may be inferior to those in an offline environment (Wood, 2001). Buyers can obtain information on brand attributes through touch, taste, and feel in an offline shopping environment and inquire about relevant product information from sales representatives (Alba et al., 1997). Brand prominence allows the brand to be at the forefront of the consumer's brain when it comes to behavior. A prominent brand in a consumer's mind can harbor purchase intent or future use more clearly and persistently than a lesser-known brand (Barnard and Scriven, 1997).
A measure of a brand's positive differential effect on consumer responses is brand prominence, a component of brand equity (Ailawadi et al., 2003). Consumers' reactions to brand prominence include recognizing brand promotions, recalling when others discuss relevant product categories, and speaking positively about the brand. When it comes to the influence of brand visibility, the online channel has a more significant impact on customer satisfaction for two reasons. Customers can preserve and retrieve purchase histories and search histories and tailor their search and screening processes using online shopping platforms (Degeratu, Wu and Rangaswamy, 2000;Shankar et al., 2003). Many helpful and user-friendly features save future purchase prices (Lynch and Ariely, 2000) and boost the confidence of customer (Shankar et al., 2003). Customers pleased with their previous encounters with a brand are more inclined to return and make future purchases. This happens because of the lowered cognitive effort and buying efficiency (Van Birgelen et al., 2006). Second, when customers are disappointed with their purchase, they may quickly switch brands because switching costs are significantly lower when they shop online (Jones et al., 2000). Undoubtedly, brands will always be more powerful online than offline because the online environment can provide consumers with diagnostic information equal to or better than what they would find in a brick-and-mortar environment when shopping for similar products.
H2: The effect of brand prominence on customer satisfaction is stronger in the online context than in the offline context.

The Moderating Effect of Online/Offline Environment on the Role of Brand Prominence on the Expectation of Individual
Brand is critical in assisting consumers in deducing the consumption benefits of a particular product. This means that a product can be classified as a find good if a consumer purchasing it in a traditional environment can evaluate its quality before purchase. The classification of search and experience attributes used by consumers in the decision-making process effectively explains the role of brand prominence in virtual and non-virtual environments (Nelson, 1974). However, when the same product is sold online, the physical cues that are present in a traditional environment are absent, and the product may be reclassified as an experience good with the brand (Alba et al., 1997;Moore and Andradi, 1996). Customers who are more forgiving of purchasing risks may have lower expectations and be willing to test online purchases for different reasons, such as convenience and value (Zhuang et al., 2018). Brand prominence can fill this gap. In addition, brands that can create additional effective components will have an advantage in the virtual world. It has been hypothesized that large-share brands will favor this task because they provide familiarity, a signal of presence, substance, and commitment (Moore and Andradi, 1996). As a result, larger brands can provide customers with sufficient information to forecast happiness without touching the goods (Alba et al., 1997). As a result, larger brands may have an advantage over smaller, less-well-known firms in an online context (Moore and Andradi, 1996). This logic is reinforced by research (Ernst and Young, 1999) that indicates that 82 percent of respondents consider a product's brand when making an online purchase.
Brand remains a critical competitive tool on the Internet (Brynjolfsson and Smith, 2000). Online channels differ from offline ones in their ability to deliver information to assist consumers in decision-making (Alba et al., 1997). Information is considerably more uneven and imperfect in online situations than in traditional channels because there are no opportunities to taste or touch products or receive in-person recommendations from sellers. As customers fill these knowledge gaps, the brand becomes increasingly essential as an information signal, allowing them to make product judgments. In other words, the brand serves as a signal that reduces the need for physical contact. Andrews et al., (2004) suggested that when shopping online, customers evaluate brands first and then examine fewer brands. Degeratu et al. (2000) claimed that the brand has a stronger influence online than in traditional channels because of the higher level of uncertainty associated with purchases in the former.
H3: Brand prominence positively influences the expectation of an individual.
H4: The effect of brand prominence on individuals' expectations is stronger in the online context than in the offline context.

The Moderating Effect of Online/Offline Environment on the Role of Brand Prominence on Quality of Performance
Customers' impressions of the quality of the product (or service) desired in proportion to its pricing are expressed as perceived quality. It is expected to have a positive impact on their assessment levels (Fornell et al., 1996). It also looks at how purchasers use online and physical channels to strike a balance between affordability and quality significantly. Furthermore, depending on the service supplied to customers, performance quality components may differ. Both technical and functional aspects are regularly encountered in the service industry. Zainuddin et al., (2013), Giovanis (2016), and Agha et al. (2017) recently implemented these components in the offline (health, hospitality, education, banking, and insurance industries) and online (mobile internet) environment. The nature and quality of the services offered by businesses impact quality measurement metrics. However, the widespread adoption of the Internet and the widespread use of online services has enabled businesses to improve the quality of their websites and services. Not surprisingly, when making an online purchase, customers usually consider the purchase value to be more important (Chiu et al., 2014). Indeed, previous research has emphasized the importance of improving electronic service performance (Carlson and O'Cass, 2010), staff responsiveness to information from social media (Berezan, Krishen, Tanford, and Raab, 2017), and ongoing assessment of Internet users' needs and preferences (Carlson and O'Cass, 2010). Customers can enjoy other benefits from shopping online, such as decreased search expenses and the convenience of shopping at any time from the comfort of their own home or office. They can effortlessly preserve and recover their search and shopping histories (Degeratu et al., 2000). Customers can also obtain thorough product information, reviews, tutorials, and the option to customize their search experience based on their preferences through the internet.
Consumers integrate brands into their personalities, thereby creating a strong link between them. However, brand ideas and feelings may have a disproportionately larger impact on consumer decision-making and happiness if they become top-of-mind awareness (Akcura et al., 2004). Because bonding with identity products involves emotionally complicated sensations, the importance of thinking is already significant because self-actualization and brand activation occur at the same time. However, when a brand is identity-based, brand-related thoughts are not significant in influencing consumers to perform difficult behaviors (Park et al., 2010). When the relationship involves functional-based self-connection and the importance of brand-related thoughts and feelings, consumers are more likely to regularly conduct difficult behaviors for items than when the brand prominence is less. When shopping online, by extracting both acquisition and transaction utilities related to the price people pay for goods or services, customers can improve perceived quality of their buying experience (Campo et al., 2015). These unique value offerings, particularly those related to transaction utility in the online purchase context, finally set it apart from the offline environment in terms of value extraction. This suggests that customers may give perceived value judgments more weight in the online purchase context than in the offline one when determining their overall satisfaction.
H5: Brand prominence positively influences performance quality.
H6: The effect of brand prominence on performance quality is stronger in the online context than in the offline context.

Conceptual Model
By extending ECT, we formulate a conceptual structure that forms the framework of this study (see Figure 1), which investigates consumer behavior associated with brand prominence, distribution channels (online/offline), and product or service type. The proposed model uses the ECT framework to incorporate the effects of brand prominence on online/offline service characteristics, resulting in a comprehensive model of customer satisfaction. Although ECT bases its explanation expectation and performance quality on satisfaction, we argue that including the effect of brand prominence in the context of online/offline service improves our understanding of customer behavior from a different perspective. This is because satisfaction is concerned with comparing the actual and expected performance of a product or service, whereas brand prominence impacts the comparison of product/service attributes across competing alternatives. For example, suppose customers believe that viable competing alternatives are available. In that case, they may switch to the new alternative even if they are satisfied with the current services if they believe that the alternative will provide them with a better deal. Therefore, we add brand prominence to the model to measure its impact on satisfaction of customer and WOM intention to further explain consumer behavior by adopting online or offline services.

Sample and data collection
We collected data using an online survey administered by a marketing research company in Japan. Table 1 presents the participants' demographic information, including age and sex. The sample size comprised 8533 participants, of whom 50.88% were male and female were 49.11%. In this study, we focus on customers in the insurance industry for two reasons. First, compared to those in other industries, companies in the insurance industries are rather behind in the adoption of online channels. Thus, many aspects relating to customer satisfaction in the industry remain underexplored. Second, customer preferences toward online and offline channels seem to be vary largely among individuals owing to the heterogeneity in their needs for information and their ability to process information. Thus, we anticipate that brand prominence should play a pivotal role from some consumers. We asked respondents about their adoption of three insurance products (health, cancer, and medical). Those who had adopted these products were also asked. Regarding brand prominence, we asked the respondents how they perceived it on a five-point scale (1 = very applicable, 5 = not at all). For example, one statement reads, "The quality of this brand is high even when compared to competing brands." As we do not have information on firms' marketing activities, we asked respondents to rate how often they encounter insurance products in WOM on a five-point scale (1 = I highly recommend it, 5 = I do not want to recommend at all). Finally, we asked them to rate several questions about expectation and performance quality on a ten-point Likert scale (1 = very dissatisfied, 10 = very satisfied; see Appendix).

Methodology
Structural equation modeling (SEM) was used to evaluate the hypothesized model. First, the scales were assessed for reliability, contractive validity, and discriminant validity. Cronbach's alpha and composite reliability (CR) were used for the reliability of each factor. For convergent validity, factor loadings and average variance extracted (AVE) were used, and for discriminant validity, AVE and the square of inter-factor correlation were compared according to Fornell and Larcker (1981). A two-stage SEM approach was then used to estimate the structural relationships among the latent variables by means of a structural equation model after validating the measurement equation model according to Anderson and Gerbing, (1988). The robust maximum likelihood method was adopted for the analysis because of the use of dummy variables indicating online (offline)).

Results
As a result of the scale evaluation, CS2 was excluded from the construct because of its reliability. The goodness-of-fit indices for the measurement equation model were SRMR = 0.043, RMSEA = 0.085, CFI = 0.898, and GFI = 0.763, indicating acceptable goodness of fit (Brown and Cudeck, 1992;Hu and Bentler, 1999). Table  2 shows the factor loadings, AVE, Cronbach's alpha, and CR for each construct. For each factor, Cronbach's α ≥ 0.70 (Hair et al., 2010) and CR ≥ 0.70 (Bagozzi and Yi, 1988), confirming reliability. Construct validity is acceptable if the factor loadings of each questionnaire item in the CFA are all above 0.50 (Hair et al., 2010) and AVE ≥ 0.50 (Fornell and Larcker, 1981), confirming the contractile validity.  Table 3 shows the correlation coefficients for each factor. A comparison of AVE and the square of the inter-factor correlation proposed by Fornell and Larcker (1981) did not confirm discriminant validity among some factors. In such cases, discriminant validity was confirmed using Anderson and Gerbing's (1988) approach, which compares χ2 in models with and without a constraint on the inter factor correlation of 1.00. Next, the structural equation model was evaluated. The goodness-of-fit indices were SRMR (robust) = 0.169, RMSEA (robust) = 0.082, CFI (robust) = 0.852, and GFI = 0.714. The SRMR and GFI values were not good, but the RMSEA and CFI values were within the acceptable range, and the goodness-of-fit of the model was judged to be within the acceptable range. Thus, we concluded that the model fit was acceptable. Table 4 shows the upper and lower limits of the standardized and unstandardized estimates of the relationship between each variable, P-value, and 95% confidence interval for the unstandardized estimate. Brand prominence had a positive direct effect on expectation and performance quality (β=0.817, p<0.001 and β=0.792, p<0.001, respectively), but no significant effect on customer satisfaction at the 10% significance level (β=0.075, p>0.1). Offline had a negative direct effect on expectations and performance quality (β=-0.016, p<0.05, and β=-0.017, p<0.05) and a positive direct effect on customer satisfaction (β=0.013, p<0.05). Online had no direct effect on expectation and performance quality but suppressed the positive direct effect of brand prominence on expectation and performance quality (β=-0.024, p<0.1, and β=-0.033, p<0.01, respectively). Expectation and performance quality had a positive direct effect on customer satisfaction, but performance quality had a greater effect (β=0.098, p<0.001 and β=0.724, p<0.001). Customer satisfaction had a positive direct effect on positive WOM (β=0.628, p<0.001). Table 5 shows the results of the hypotheses testing.

Discussion
If consumers are interested in a specific product online, they compare it until they feel they have enough knowledge and trust in the brand to make decisions and satisfaction. However, consumers spend more time and energy searching, assessing and comparing products, and postponing purchasing decisions. This study aimed to develop and test the differences between virtual and physical channels and determine the impact of brand prominence on customer expectations, performance quality, and customer satisfaction.
Brand prominence has a positive impact on expectations and performance quality, supporting H3 and H5. However, there is no significant impact on customer satisfaction, which indicates that H2 is not supported, meaning that brand prominence is neither significant nor different across the channel on customer satisfaction.
The effect of brand prominence on customer expectations shows a significantly opposite impact to our expectation. According to the results, the effect of brand prominence on the expectation of an individual is stronger in the offline context than in the online context, which indicates that H4 is supported with the opposite position. This means that the taste, touch on the products, or receiving advice from seller or agents are more important than online cues for customers when they rely on brand to develop quality expectations. We conjecture that this is the case because customers have a high level of faith in the online channel, making brand less important when they build their expectations (Shafiq Obeidat and Young, 2017). Another explanation is that, on the contrary, customers may feel insecure to buy insurance plans from online channel because of low brand trust or familiarity. Thus, although a brand comes into customers'' mind when they evaluate the product, it has limited impact on service expectations. Furthermore, digital immigrants may place the same weight on their expectations in both online and offline channels. They may feel more at ease if they are conscious of the brand or services or products via WOM or advertisements and have faith in the company's ability to deliver regardless of channel.
Similarly, H6 is also supported in the opposite direction. Thus, the effect of brand prominence on performance quality is stronger in the offline context than in the online context. It follows that customers may not want to take risks, although brand prominence is higher online. They may have lower expectations regarding reliability. This might be because customers of online channels do not perceive that they are being "served" by the company owing to limited contacts or physical interfaces. This could lower their understanding about the quality of the products or plans they purchased online. Furthermore, customers may find that the value of brand prominence more salient in offline context because it reduces search costs which are typically higher in this setting than in online channels (Chiu et al., 2014;Zhuang et al., 2018).
We emphasize the most critical insights obtained from our findings, which will help expand the theoretical knowledge of customer satisfaction and assist retail managers in understanding how online/offline influences the influence of brand prominence. As previously stated, the existing multichannel literature has only provided fragmentary answers to the factors that may influence customer satisfaction when customers shop online versus offline. When modeling the satisfaction of customer, we provide evidence of numerous notable and generalizable distinctions as well as the specific settings in which they feel more at ease across the channels. Our research expands on customer adoption theory in a multichannel setting, emphasizes the importance of granular analysis, and, most importantly, offers managers implications for effectively managing customer satisfaction across channels.

Implications
This study has substantial theoretical implications. Firstly, is the creation of a new area in consumer behavior research for product-type contingency. Secondly, conceptual contribution asserts a relationship between customer satisfaction and brand prominence, as well as consumer intent to engage in various activities in two separate environments. Thirdly, there is pragmatic evidence of the moderating effect of brand prominence and consistence length when combined with research variables, which was previously unexplored.
This study has several significant managerial implications as well. To begin with, it would assist managers in understanding the relevance of the channel and prominence in establishing essential customer intentions to engage in tough behaviors in favor of the brand. Because this study gauges insurance products in online/offline environments, managers can use the data to determine the level of adoption and prominence required for the various types of products they deal with. This can assist managers in developing effective marketing strategies for a variety of items. Such information would be useful for firms to determine the efficiency of branding and marketing initiatives related to consumer happiness. It would also assist managers in understanding how to improve the consumer-brand relationship. The current findings could also assist managers in identifying customers who are most inclined to switch brands in near future based on their expectations and quality of performance.

Limitation and Future Research
This study has several limitations: The survey was conducted in Japan, and all the participants were Japanese. To make the proposed model more generalizable, future studies should use more data from other countries and cultural settings. This empirical study allowed for the evaluation of customer behavior in both online and offline settings. It specifically examined the potential use of an online service from the perspective of individual expectations and performance quality form before using the service itself. Future studies should evaluate the consequences of this process by evaluating post-purchase behavior, analyzing reviews, repurchasing and returning products, and switching. Furthermore, it would be interesting to compare multiple users.
The selected sample of insurance business users is the primary limitation of the current study. Following that, further study should focus on putting the concept to the test in a different industry with a variety of products and/or services.