# Lady first? The gender difference in the influence of service quality on online consumer behavior

Weihua Wang (School of Business Administration, Anhui University of Finance and Economics, Benbu, China)
Saebum Kim (BERI, Business Administration, Gyeongsang National University, Jinju, Republic of Korea)

ISSN: 2040-8749

Article publication date: 2 January 2019

Issue publication date: 15 August 2019

## Abstract

### Purpose

This paper aims to articulate the gender differences in the influence of service quality on online consumer behavior.

### Design/methodology/approach

Through data collected via a Web-based questionnaire survey from 330 consumers in China, this study builds and analyzes a structural equation model, using five dimensions of E-service quality, customer satisfaction and customer loyalty, and focuses on the moderation test of gender.

### Findings

This study finds that first, efficiency dimension of e-service quality is of same importance for male and female customers; second, there are significant gender differences in the responsiveness and reliability dimensions of E-service quality, which affect customer satisfaction; third, the impact of female customer satisfaction on customer loyalty is stronger than for male customers.

### Practical implications

Online retailers with limited service resources should preferentially respond to service requests from male customers and provide more reliable services for female consumers under the same condition.

### Originality/value

The research validated the applicability of self-regulation theory in online consumer behavior, explored the occurrence stage and characteristics of gender differences in online consumer behavior under influence of SRT and first found some apparent gender differences in the influence of different dimensions of e-service quality on online consumer behavior.

## Citation

Wang, W. and Kim, S. (2019), "Lady first? The gender difference in the influence of service quality on online consumer behavior", Nankai Business Review International, Vol. 10 No. 3, pp. 408-428. https://doi.org/10.1108/NBRI-07-2017-0039

## Publisher

:

Emerald Publishing Limited

## 1. Introduction

Online retail trade has entered a period of rapid growth in recent years. As online service quality become more and more important, a lot of related research have focused on the studies. Excellent service quality satisfies customers effectively, increases a company’s overall profitability commendably and actual market share (Zeithaml et al., 1996). This is especially important for online retail companies, as they face keener competition. Academia has actively explored the field of online service quality; these extensive studies offer very specific and accurate descriptions of the problem of online service quality. Although it is believed that high-quality service influences significantly customer satisfaction, which leads to customer loyalty (Chiou and Droge, 2006; Kaura et al., 2015), the effects of group differences among customers are ignored; in particular, there are few gender salience studies related to online consumer behavior.

Based on the market segmentation theory, many studies (Afthinos et al., 2005; Frank, 2012; Sharma et al., 2012; Snipes et al., 2006) suggest a need for more systematic research, which focuses on service quality, and proposes highly customized marketing strategies applicable to different consumer markets with unique needs. Gender is one of the most critical factors that influence customer service perception (Afthinos et al., 2005; Sharma et al., 2012; Snipes et al., 2006; Stafford, 1996). Gender is often a basis for segmentation of service in traditional marketing (Putrevu, 2001). In reality, there are many differences in the shopping expectations and consumption behaviors of men and women. Furthermore, there is significant evidence that gender differences in the decision-making process for purchasing (Oly Ndubisi, 2006). However, do these differences also exist in an electronic environment, in the virtual world? After all, the behavior of man and women are usually constrained by social tradition and cultural habits in real environments, but these constraints greatly diminish in a relatively “vacuum” social environment of electronic environment (Cristiano and Tan, 2002; Zhou et al., 2011).

Moreover, because of the individuation of consumer demand, customization of service delivery becomes the mainstream now. The development of online technology and customization helps to enable the effective implementation of the market purposes. Service provider can control the cost of services through the rational allocation of service resources effectively and maximize satisfaction among different customer groups. The customization of service rooting from gender differences is most common problem in the process. Consequently, understanding the gender differences is vitally important in the process that different service dimension effect on consumer behavior, and it is also the foundation of all service differentiation strategies. Obviously, gender differences is the most common and clear customization distinction for a service provider in electronic environment.

On the other hand, “ladies first” is an unwritten rule in the traditional service industry. However, in online business activities, service providers must face multiple customers simultaneously due to the low-cost nature of online retail trade. This makes it difficult to meet the needs of every customer at the same time. Service providers must make such judgments with limited service resources in order to deal with the questions for example who should be got priority service?, what type of services?, and so on. In these circumstances, adhering to the principle of “ladies first” is obviously too hasty. Online marketers need to understand the origins and behavior differences between the two genders, for delivering services that cater to the unique aspirations and needs of each gender. Online service providers also need a considerably more rigorous theory to help them to make decisions. In these circumstances, gender differences is an effective reference for distributing service resources justly. It is only to further clarify the behavioral differences between male and female consumers, limited service resource can be distribute reasonably to improve customer satisfaction and loyalty effectively, even ensure the profitability and long-term development of enterprises. However, few studies examine this aspect. This study attempts to bridge this gap to discuss the major implications of such differences for effective advertising or marketing strategies of retail websites and other online service providers.

We suggest that because of the gender differences the influence of service dimensions on customer satisfaction and even loyalty should be dealt with discriminatively. Therefore, this study creates a research model that contains five dimensions of service quality, and validates the theory using data of male and female online customer groups to find meaningful differences between the two groups. The results sum up both male and female attitudes to service quality and characteristics of consumer behavior. This not only helps researchers prepare for similar studies in future, but also assists online retailers in offering better services to satisfy consumers.

## 2. Theoretical framework

This study uses the self-regulation theory (SRT) as a theoretical framework. The theory holds that “humans are able to control their behavior through a process known as self-regulation” (Bandura, 1991). This is a system of conscious personal management to guide one’s thoughts, feelings and behaviors to reach goals. Simply, when people have a pleasant experience or achieve a goal, desire fulfillment occurs, which leads to satisfaction and positive response even long-term behavior if the goal or event is a positive prospect (Bagozzi, 1992). After a long process of self-regulation, people will develop certain habits. The process consists of three distinct stages: appraisal processes stage, emotional responses stage, and coping responses stage. Many researchers suggested that consumer behavior also follow this process. According to Chang and Wang (2011), under the influence of e-service quality, this system also extensively motivates and regulates online consumer behavior. This integrated process includes the following important stages (see Figure 1): customer’s appraisal of e-service quality (appraisal processes), customer satisfaction (emotional responses), and customer loyalty (coping responses). Compared with the stimulus-response theory, the three stages SRT theory can more clearly explain the developing process of online consumer behavior from judgment to action; and better illustrate the essential difference based on the theoretical concepts of this study.

Meanwhile, the traditional SRT acknowledges some behavioral differences that stem from gender and suggests that there are some differences between male and female in the process of self-regulation. However, few researchers focus on the question about the exact stage and time node at which the differences occurred. Such researches ignored the question of “where” and “when” gender differences lay on the human behavior the process. Most researchers fail to clarify exactly how gender differences happen. Furthermore, in market research, consumer satisfaction and loyalty respectively represent the important standards of current and long-term interests of enterprises. The gender differences in their formative stages are of great significances to the differentiation strategy of enterprise service. However, such differences are often disturbed by cultural, traditional and other social factors, and cannot reflect the original characteristics of itself. This study attempts to use the relatively “vacuum” social environment of online consumer behavior to analyze the occurrence stage and characteristics of gender differences, and ultimately better understand the formation mechanism of gender difference in the influence processes of service quality on online consumer behavior.

### 2.1 E-Service quality

In this study, the customer’s appraisal of e-service quality is at the initial stage of the self-regulation process, namely, the appraisal process. Traditional service quality is generally defined as the difference between expected service and perceived service (Gronroos, 1982; Parasuraman et al., 1988, 1991; Zhuang and Babin, 2015). Therefore, the e-service quality of online retail can be defined as the perception of the degree to which the service meets the customer’s expectations. Excellent service quality attracts and satisfies customers, increases a company’s overall profitability effectively, and actual market share (Zeithaml et al., 1996; Santos, 2003; Salam et al., 2013; Zhuang and Babin, 2015). It is especially important for online retail websites due to the fundamentally different characteristics of online marketing from traditional marketing such as inability to touch products, perceptions of convenience, risk, and shopping enjoyment (Huang and Oppewal, 2006). Online consumers more and more rely on the services offered by websites. Therefore, an important construct receiving significant research attention is E-service quality. In the Internet era, E-service quality has become an important element in customer satisfaction (Herington and Weaven, 2009). However, E-service quality is an elusive and indistinct construct and market researchers have performed much research to clarify and measure it. Zeithaml et al. (2002) identified seven primary factors influencing customer perception of e-service quality in online shopping: efficiency, reliability, fulfillment, privacy, responsiveness, compensation and contact. In addition, Santos (2003) found that reliability, efficiency, support, communication, security and incentive are the six dimensions of e-service quality. Furthermore, Ribbink et al. (2004) show that e-service quality mainly includes the following five aspects: ease of use, e-scape, responsiveness, customization and assurance. Cristobal et al. (2007) take a broader view in dividing the various studies of e-service quality into two major categories: online retailing services, and web site design and quality. The terms used for e-service quality in this paper belongs to the former category. By comparing the results of these prior studies, this study adopted and modified five dimensions (i.e. privacy/security, responsiveness, ease of use, reliability, and efficiency) to explain e-service quality.

First, it is essential to ensure the privacy/security of online customers due to greater uncertainty and risk in making online purchases in comparison to traditional marketing (Zeithaml et al., 2002). Second, safety is also the basic premise of every deal, and customers will only shop when they feel secure (Miyazaki and Fernandez, 2001). Therefore, today, online consumers pay more attention to a website’s privacy policies and data security. Furthermore, usability is another aspect that is important to most customers. The website should be easy to use; it should possess an overview and have a transparent trading system. In addition, correct order fulfillment and billing accuracy (reliability), quick responses to online enquiries or in a promised period (responsiveness), and improving the efficiency of online shopping. All this will provide online customers with an enjoyable and satisfying shopping experience (Santos, 2003).

### 2.2 Customer satisfaction

Customer satisfaction is the mid-stage in the self-regulation process of online shopping. Essentially, it is an emotional response of the customer (Chang and Wang (2011)). In the market theory, customer satisfaction is a key measure of whether retailers are fulfilling the marketing concept (Ellis and Marino, 1992). It is the degree of meeting customer needs during purchase. A satisfying online shopping experience can induce customer loyalty (Bielen and Demoulin, 2007; Chiou et al., 2009). Many online marketing studies suggest that satisfying customers should be the primary goal for firms, because customer’s satisfaction leads to profitability (Keiningham et al., 2005; Anderson et al., 1994). LeHew and Wesley (2007) identified relevant attribute categories through which retail organizations attempt to satisfy customers: merchandise, service, physical characteristics, employees and other shoppers. For online retail organizations, whether a mini website or an online shopping center with multiple retail tenants, offering the appropriate mix of products, services and experiences enhances customer satisfaction. Therefore, customer satisfaction is a function of pre-sale expectations and post-purchase perceived performance (Fornell, 1992). This study measures the customer satisfaction of Chinese online buyers in the following three aspects: overall experience, happiness and achievability.

Customer satisfaction is meeting customer expectations of services by comparing with perceived performance (or outcome). Customers are satisfied if the perceived performance matches their expectations, and dissatisfied if it does not (Wilson et al., 2012). In addition, many studies (Kuo et al., 2009; Chang and Wang, 2011) found that e-service quality has significant positive effect on customer satisfaction.

This study seeks to contribute to the literature on the relationship among the five dimensions of e-service quality and customer satisfaction under the influence of gender differences. Therefore, it focuses on the following five influence paths.

1. The privacy/security dimension of E-service quality affects customer satisfaction.

2. The responsiveness dimension of E-service quality affects customer satisfaction.

3. The ease of use dimension of E-service quality affects customer satisfaction.

4. The trust dimension of E-service quality affects customer satisfaction.

5. The efficiency dimension of E-service quality affects customer satisfaction.

### 2.3 Customer loyalty

Customer loyalty is the final stage in the self-regulation process of online shopping. It is one of the embodiments of coping responses (intent to maintain) in SRT. Building customer loyalty is an important strategy for the success of any retail website. Most marketers focus on customer loyalty because of its positive effect on long-term profitability. The most common definition of customer loyalty is by Jacoby and Kyner (1973), who described it as a biased (non-random), behavioral response (purchase), expressed over time, by some decision-making unit, with respect to one or more alternative brands out of a set of such brands, and is a function of psychological processes. Many researchers (Yang and Peterson, 2004; Chaudhuri and Holbrook, 2001) believe that loyalty can result in repetitive buying, despite marketing or situational effects. This is especially important for the continued progress of an online retailer facing fierce competition. However, customer loyalty is difficult to define and measure; therefore, researchers tend to use behavioral and attitudinal measures to assess it (Bowen and Chen, 2001; Yang and Peterson, 2004). This paper defines loyalty as a customer’s favorable attitude toward a retail website that results in repeating purchase behavior. It measures the customer loyalty of Chinese online buyers in three aspects of long-term repeat purchase, single-minded purchase, and recommendation.

Consumers appraise the service quality of websites they purchase from in the past. If satisfied, the times of repeat buying are high because this experience built loyalty (Hallowell, 1996; Bielen and Demoulin, 2007). Many studies indicate that satisfactory online shopping experiences can induce customer loyalty (Bielen and Demoulin, 2007; Chiou et al., 2009). Therefore, this study also focuses on the effect of customer satisfaction on loyalty.

### 2.4 Gender differences in online consumer behavior

The social cognitive theory suggests that “human differentiation on the basis of gender is a fundamental phenomenon that affects virtually every aspect of people’s daily lives” (Bussey and Bandura, 1999). In SRT literature, some researchers repeatedly mention that males and females demonstrate differences in using self-regulated cognitive strategies in decision behavior (Lee, 2002; Bidjerano, 2005) such as purposes, dynamics of social interactions, motivational factors, frequencies of expression/discussion, feedback, communication skills and so on. Researchers have examined many gender difference issues, for example, males endorsed items that focused on the self while females endorsed items that consider both others and the self (Watts et al., 1982). Empirical evidence also abounds to support gender differences in individual consumption decision-making processes. For instance, Byrnes et al. (1999) found some differences in the risk-taking tendencies of males and females. Males are more assertive than females in financial decision-making (Powell and Ansic, 1997; Francis et al., 2015). Van den Bos et al.’s (2013) results showed that differences exist even in performing gambling tasks.

Marketing literature introduces that gender differences exists in purchase decision-making processes. For example, the differences between gender identity and consumers’ perceptions of masculinity and femininity in services (Kwun, 2011) and products (Allison et al., 1980) and gender differences in shopping drivers and barriers (Lian and Yen, 2014). However, within these studies, there are conflicting results on the relative importance of male and female consumers in explaining the findings. For example, prevailing wisdom assumes that female consumers are more loyal than male consumers are, and it is easier to create customer loyalty among females than it is in males (Oly Ndubisi, 2006; Oakley, 2000); however, there is much evidence to support a opposite result (Melnyk et al., 2009; Sanchez-Franco et al., 2009; Das, 2014).

This study considers that gender differences in consumer behavior do exist, changing with the consumer environment and group (Kim et al., 2007). As a fire-new market environment, the virtual online market obviously differs from the thousand-year-old traditional markets. Hence, it is important to study the change.

Most related studies acknowledge that males and females are different, but one difficulty in research is to clarify that whether biological make-up or social factors (e.g. social tradition, cultural habits) drive these gender differences, because the two driving factors intertwine in the real environment, and it is difficult to separate their influences effectively. However, the virtual and secret nature of the electronic environment allows consumers to indulge their egos more easily, without worrying about gender constraints of social tradition or cultural habits (Freestone and Mitchell, 2004; Pelaprat and Brown, 2012). Online, social factors are remarkably weakened (Dunbar, 2012). This “vacuum” environment provides an opportunity to researchers to better understand the original gender differences (which are more similar to biological differences) in consumer behavior.

This study asks the following six concrete questions, which also form the basis of this study.

Question 1: How does the privacy/security dimension of E-service quality affect customer satisfaction under the influence of gender differences? The privacy/security of E-service quality has significant effect on customer satisfaction (Zeithaml et al., 2000; Zeithaml et al., 2002; Yang and Jun, 2002; Yang and Fang, 2004; Parasuraman et al., 2005; Bressolles et al., 2014). Many studies also suggested that females tend to be more concerned about online privacy/security issues than males (Gauzente, 2004; San Martín and Jiménez, 2011). However, other researches did not find significant gender-differences in online privacy/security (Lightner, 2003; Cyr and Bonanni, 2005). Some studies even produced contradictory findings, which reveal that some female online behaviors are contradictory to their alleged high privacy/security concerns. For instance, females are more likely to disclose personal information on the Internet (Stern, 2004). Hence, researchers have suggested that there is no connection between consumers’ actual behavior and their privacy/security concerns (Joinson et al., 2010). In this paper, we argue that privacy/security is one of the most basics of human needs than just online customers’ needs. Meanwhile, it is the basic insurance of online transaction. It cannot lead to the differences of influence on emotional responses (customer satisfaction) in electronic environment, even if female or male customers’ neglects on the privacy/security appraisal are real in initial phase of online shopping. Namely, a certain level of privacy/security can effect on customer satisfaction, and the impact is near for female consumers and male consumers. There is no obvious difference. Because that privacy/security appraisal is no less important to emotional responses of females than males.

This study tries to explain this privacy/security paradox from the perspective of emotional responses (customer satisfaction). The following hypothesis is proposed:

H1.

The privacy/security dimension of E-service quality has a positive impact on customer satisfaction, and there is no significant difference between female consumer and male consumer groups.

Question 2: How does the responsiveness dimension of E-service quality affect customer satisfaction under the influence of gender differences? Researchers suggest that the responsiveness dimension still plays a vital role in determining customer satisfaction in an electronic environment, as a traditional service quality dimension (Zeithaml et al., 2002; Yang and Fang, 2004; Bressolles et al., 2014). Its importance even exceeds that in the offline environment (Yang and Fang, 2004). Meanwhile, it is generally accepted that females are better at communication than males (Soureshjani, 2013). Psychology research also demonstrates that females are more patient during communication (Eagly, 2013). In contrast, “short-tempered” male consumers are eager for positive responses, and emphasize the responsiveness of online service. Consequently, this study suggests that “ladies first” is only a polite tradition, diverging from human instinct. Compared to females, the satisfaction of male customers is more dependent on the responsiveness dimension of e-service quality. To verify this suggestion, we need to test the following hypothesis:

H2.

The responsiveness dimension of E-service quality has a positive impact on customer satisfaction, and the impact is stronger for female consumers than male consumers.

Question 3: how does the “ease of use” dimension of E-service quality affect customer satisfaction under the influence of gender differences? It is obvious that the improvement in ease of use has a positive impact on customer satisfaction in an electronic environment (Yoo and Donthu, 2001; Zeithaml et al., 2002; Bressolles et al., 2014). In addition, many studies have revealed that males display more instrumental behavior, and are more likely to accept new network techniques than females (Venkatesh and Davis, 2000; Koivisto and Hamari, 2014). Hence, male customers are better at solving complex problems of website interface, while female customers are more satisfied with ease of use when shopping online.

From the above arguments, the following hypothesis is proposed:

H3.

The “ease of use” dimension of E-service quality has a positive impact on customer satisfaction, and the impact is stronger for female consumers than male consumers.

Question 4: How does the reliability dimension of E-service quality affect customer satisfaction under the influence of gender differences? The reliability of e-service is an important precondition for consummating an online transaction successfully. Although the reliability of E-service quality can affect customer satisfaction (Yoo and Donthu, 2001; Yang and Fang, 2004; Bressolles et al., 2014), the gender differences in this effect has largely been ignored. Prior studies have revealed that females are more prudent in decision-making processes (Francis et al., 2015). They are much more sensitive to risk compared to males (Byrnes et al., 1999; Van den Bos et al., 2013). Their decision-making tends to be more defensive. Essentially, reliability is just a reflection of the defense and self-protection strategy to reduce risk. This study believes that female consumers would emphasize this dimension more than male consumers would. Therefore, the following hypothesis is proposed to verify the reasoning:

H4.

The reliability dimension of E-service quality has a positive impact on customer satisfaction, and the impact is stronger for female consumers than male consumers.

Question 5: How does the efficiency dimension of E-service quality affect customer satisfaction under the influence of gender differences? E-business is a very efficient means of organizing transactions and this efficiency is one of its greatest advantages in comparison to traditional business (Zeithaml et al., 2002). Both male and female consumers are in favor of online shopping because they enjoy the experience. However, STR holds that gender differences exist in self-regulated behavior strategies, for example, purposes, and motivational factors (Lee, 2002). In addition, some marketing researchers also suggested the male consumers are more goal-directed and motivated by functional factors (e.g. usefulness, efficiency) than female consumers are when online shopping (Doong and Wang, 2011; Riquelme and Román, 2014). Furthermore, psychology research reveals that males are more impetuous and eager for success in daily life (Eagly, 2013). They often aspire for higher efficiency. Accordingly, we expect the following:

H5.

The efficiency dimension of E-service quality has a positive impact on customer satisfaction, and the impact is stronger for male consumers than female consumers.

These five questions constituted the study’s first stage, which detailed the effect of the appraisal processes on the emotional responses of male and female consumers. In particular, how have the five dimensions of E-service quality affected the satisfaction of male and female customer groups? Question 6 represents the study’s last stage, which discusses the process of coping responses of male and female consumers; namely, how does customer satisfaction affect customer loyalty under the influence of gender differences?

Studies reveal that females give more importance to emotional appeals compared to males (Venkatesh and Agarwal, 2006). Customer satisfaction level is just the most common measure of the emotional appeal of consumers in actual marketing practice (Hsin and Wang, 2011), and it has a remarkable effect on customer loyalty. Early researchers argued that it is easier for female consumers to become loyal customers (Fujita et al., 1991). In contrast, male consumers pay more attention to some experiences other than emotions; their loyalty is harder to achieve. Furthermore, some studies of SRT also mention that the emotional responses of males are harder to translate into coping responses in comparison to females (Lee, 2002; Bidjerano, 2005).

Consequently, we propose the following hypothesis:

H6.

Customer satisfaction has a positive impact on customer loyalty, and the impact is stronger for male consumers than female consumers.

## 3. Research design

### 3.1 Conceptual model

As shown in Figure 2, this study proposes a comprehensive framework to examine the relationships between online e-service quality, customer satisfaction and customer loyalty. The study reviews relevant literature, which serves as the basis for defining the key constructs of the framework. Subsequently, this study describes the existing evidence supporting the relationships in the framework. We use SRT to develop and test specific research hypotheses connecting e-service quality, customer satisfaction, and customer loyalty. In this model, the appraisal process specifically means the customer’s appraisal of the five dimensions of e-service quality. Customer satisfaction and loyalty belong to emotional reaction and coping response separately.

### 3.2 Survey samples

First, this study performs a one-to-one pretest to ensure the validity and reliability of the survey. The pretest was conducted with 28 respondents who were asked to provide opinions on the relevance and wording of the questionnaire items, and it was then adapted based on their opinions. The results of the pilot were tested using Cronbach’s reliability and confirmatory factor analysis. One item of responsiveness dimension and two items of reliability dimension were deleted after the pretest. The final questionnaire contained 25 questions, as shown in Table I (measurement instruments) and Table II (demographic description of sample).

Furthermore, for optimizing the research design to reduce common method bias, the study added some temporal and psychological separation when measuring the independent and dependent variable (e.g. disrupting the order of cause and effect of the questionnaire); eliminated ambiguity in meaning items (e.g. using the familiar online language for survey participants and providing examples when appropriate) (Podsakoff et al., 2012).

Some researchers divide online shopping consumers into two types: browsers who only visit some websites without actual purchases, and actual buyers who shop on websites (Forsythe and Shi, 2003; Chang and Wang, 2011). The participants of this study have actual online purchase experience; therefore, their purchase behaviors have significant research value in recognizing and appraising gender differences.

To find the suitable target group, the study obtained a convenience sample in China via a web-based smart survey and recruited participants through related online shopping communities and Internet forums in October 2016. Respondents completing the survey had a chance to win a gift equivalent to between $1 and$20. Meanwhile, the study would filter out respondents without actual online purchase experiences. Finally, the study obtained a final usable sample of 330 responses across 21 provinces in China.

Based on the former evidence, online shoppers appear to be younger and better educated (Swinyard and Smith, 2003). The primary online shopping users are people in their 20s who are either university students or have just started working but are not high-earners (Chang and Wang, 2011). As shown in Table III, in this study, over half of respondents reflected these characteristics. The demographic description of the sample in the survey result is consistent with the structure of the target group. This fully proves the representativeness of this sample.

### 3.3 Constructs measures

Based on the previous literature, this study measurement items with minor modifications deemed appropriate for inclusion to create a sound basis for specifying the constructs to be measured. The participants were asked to respond using a five-point Likert scale ranging from 1 to 5, where 1 was strongly disagree and 5 was strongly agree. As shown in Table I, the study uses three items to measure the five dimensions of e-service quality, three items to measure customer satisfaction, and three items to measure customer loyalty.

### 3.4 Sample description

As shown in Table II, 171 participants are female (51.90 per cent) while 159 are male (48.1 per cent). The majority of respondents are younger than 30 (60.2 per cent) and 216 respondents (54.2 per cent) hold a college or university degree, while 27 respondents (5.5 per cent) hold advanced degrees, and 192 respondents (58.2 per cent) report that they earn somewhere between 3,000 and 7,000 Yun.

## 4. Data analysis

Data collected for this research were analyzed using SPSS 20.0 and AMOS 21.0 to calculate means, standard deviations, reliability measures, and CFA. SPSS assisted in performing mean, correlation, and reliability tests; however, the research team used AMOS version 21.0 to assess the “goodness of model fit.” The overall fit of the measurement model was assessed based on seven indices associated with cutoff values recommended by Hu and Bentler (1999), including:

1. chi-square/degree of freedom (х2/df<3.0);

2. goodness of ﬁt index (GFI > 0.90);

3. adjusted goodness of fit index (AGFI > 0.80);

4. normed ﬁt index (NFI > 0.90);

5. comparative ﬁt index (CFI > 0.90);

6. standardized root mean square residual (RMR < 0.05); and

7. root mean square error of approximation (RMSEA < 0.08).

### 4.1 Conﬁrmatory factor analysis

After specifying the latent measurement model and employing the 21 questionnaire items that were retained, this study ran a CFA on the remaining 307 cases to uncover the underlying factor structure of the service quality factor, online product factor, online price factor, customer satisfaction, customer loyalty, and the copyright awareness construct. Using AMOS 21.0, this study found that the model showed a good ﬁt to the data (χ2/df = 2.047; p < 0.001; RMR = 0.039; GFI = 0.916; AGFI = 0.880; IFI = 0.952; CFI = 0.951; RMSEA = 0.056) (Hu and Bentler, 1999).

The reliability and convergent validity of the factors were estimated using composite reliability and average variance extracted (AVE). As demonstrated in Table III, the composite reliability for all factors in the research model was above the recommended 0.70 level, and the AVE values were all above the recommended 0.50 level (Fornell and Larcker, 1981). This means that more than half of the variances observed in the items were accounted for by their hypothesized factors. Convergent validity can also be assessed by factor loading. Factor loadings greater than 0.50 are considered significant. All of the factor loadings of the items in the measurement model were significant, ranging from 0.664 to 0.889.

Therefore, all constructs in the model had adequate reliability and convergent validity.

### 4.2 Common method bias test

Harman’s single-factor test is one of the most widely used techniques, which can be used to address the issue of common method bias by many researchers. In this study, we cannot identify the source of the common method bias, so single-factor test was ran, and the result output was shown that cumulative extraction sums of squared loadings of the only factor is 35 per cent, it demonstrated that the common method bias is not significant, does not interfere with the next analyses in this study.

### 4.3 Discriminant validity of constructs

The correlations between constructs were calculated to check the discriminant validity. As Table IV shows, the square root of each factor’s average variance extracted (AVE) is larger than its correlations with other factors. In addition, convergent validity was supported as all AVEs exceeded 0.5 (Fornell and Larcker, 1981). Table III shows this, confirming discriminant validity.

### 4.4 Verification results of the structural model

The structural model was also found to ﬁt the data well, according to the goodness-of-ﬁt indices (χ2/df = 2.086; p < 0.001; RMR = 0.042; GFI = 0.910; AGFI = 0.875; IFI = 0.949; CFI = 0.948; RMSEA = 0.057). Table V presents the results of the analysis; Path 1 (β = 0.186, p = 0.013), Path 2 (β = 0.139, p = 0.005), Path 3 (β = 0.168, p < 0.001), Path 4 (β = 0.192, p = 0.005), Path 5 (β = 0.293, p = 0.007), and Path 6 (β = 0.716, p < 0.001) all are accepted. Therefore, the first-guess structural model is established.

### 4.5 Multiple-group analysis

As Table VI shows, the study splits the sample into two groups (male and female). In the structural model, H2 has values of Δх2 = 3.957, p = 0.047. As the moderation test is significant when Δх2 > 3.84 or p < 0.05 (Byrne, 2013), and after consulting the result of Table V, H2 was accepted. Consequently, the study found that the responsiveness dimension of E-service quality has a signiﬁcant effect on customer satisfaction, although notably, the effect is only in the male group.

Similarly, the reliability dimension of E-service quality has a signiﬁcant effect on customer satisfaction; however, the effect is only in the female group. Customer satisfaction has a significant effect on customer loyalty, but the effect is bigger in the female group. Furthermore, the efficiency dimension of E-service quality has signiﬁcant effects on the customer satisfaction of both males and females, and the gender difference is not signiﬁcant.

## 5. Discussion

The purpose of this study is to understand the gender differences in the influence of service quality on online consumer behaviors. Through the above analyses, several important contributions are shown as follows.

First, this is the first studies to analyze these effects using the SEM model with a multiple-group analysis approach in the processes of the five primary dimensions of e-service quality, which affect online consumer behavior. This study confirms the applicability of SRT in the online retail environment. Namely, the process of attaining online customer loyalty is in accord with the self-regulation process: appraisal processes, coping responses and emotional reactions (Bagozzi, 1992).

Second, this study analyzed the occurrence stage and characteristics of gender differences in online consumer behavior, suggested that gender differences exist widely at every stage in which service quality affects consumer behavior. The result is the first to elucidate the occurrence node of gender difference in STR. Namely, gender difference happen in the ever stage of the self-regulation process of human.

Furthermore, the conceptual model of this study is stable, and similar studies can use it as a source of reference. We hope the combined findings inspire online retailers and market researchers in the future. Based on the results of this study, some concrete implications are found as follows.

First, analyzing the constructs within this model shows that all the relationships in the model are significant for the entire consumer group. The study demonstrates that the five primary dimensions of E-service quality affecting customer satisfaction and leading to customer loyalty online in China is also consistent with Bagozzi’s (1992) self-regulation process. The study also found that the impact of customer satisfaction on customer loyalty is still moderated by the customer’s gender in an electronic environment, and the impact is stronger for female customers than male customers, which is consistent with Han and Ryu (2006). Some correlation studies of SRT suggest that the emotional responses of males are harder to translate into coping responses than they are for females (Lee, 2002; Bidjerano, 2005). Generally, females are more emotional than males, their loyalty more dependent on emotional reaction, for example, “satisfaction”; however, male customers are more rational, and their loyalty is more affected by other factors. Fujita et al. (1991) also suggest that the intense positive emotions of females balance their higher negative effects, and it is easier to develop customer loyalty among them. This finding is useful in designing strategies for better management of customer relationships, and for attracting and developing loyal customers online. Furthermore, the study also found that efficiency in E-service quality could influence customer satisfaction in both males and females, and there are no significant gender differences in the influence process. Therefore, efficient online service is the primary factor in satisfying online retail customers, with equal importance for males or females.

Second, the contrast among the dimensions of E-service quality. This study demonstrated that the different dimension of E-service quality have different influences in the entire consumer group. The influence of the efficiency dimension far outweighs other dimensions of E-service quality, followed by the dimensions of reliability, privacy/security, ease of use, and responsiveness. However, the results are slightly different from the gender group perspective.

For male consumers, the highest impact factor for customer satisfaction is the efficiency dimension of E-service quality, followed by responsiveness and ease of use. Reliability and privacy/security of service have no direct effects on their satisfaction. Therefore, male consumers have more risk resistance capacity when shopping online. The judgment is consistent with the views of many former researchers (Byrnes et al., 1999; Van den Bos et al., 2013; Francis et al., 2015).

For female consumers, the highest impact factor on customer satisfaction is the reliability dimension of E-service quality, followed by efficiency. However, responsiveness, privacy/security, and “ease of use” do not affect their satisfaction. The result for responsiveness is reasonable, as females are better at communication (Soureshjani, 2013), and more patient (Eagly, 2013). Responsiveness is not their primary concern when shopping online. Here at least, the rule of “ladies first” was already useless. Interestingly, why cannot privacy/security and “ease of use” affect females’ satisfaction? This study considers that most online shopping systems have matured with wide approval for their “ease of use” and privacy/security. On one hand, their interfaces are friendly enough to convince female customers about the “ease of use.” For male customers, “ease of use” only is now a key affects online shopping efficiency so can be noticed. In the other hand, the privacy/security levels of such online transaction systems are good enough to reassure female customers that they are ignored.

Third, the contrast between male and female groups under the same dimension of E-service quality. The study found significant gender differences in the process by which the responsiveness and reliability dimension of E-service quality affect customer satisfaction. Male customer satisfaction is more dependent on the responsiveness dimension of E-service quality in comparison to female customers. In reality, females tend to be more patient than males when trying to communicate, and males are more eager for positive response. Network trade is an extension of real life, and the behavioral characteristics of male customers for online shopping change faster and more positive responses. We suggest that online retailers prioritize responding to male customers, especially in limited service resources. In these circumstances, obviously the rule of “Ladies First” is incorrect. However, this should not be ignored for females. This study shows that female customer satisfaction is more dependent on the reliability dimension of E-service quality in comparison to male customers. Females emphasize the reliability of online shopping. This conclusion is diametrically opposite to that of Spathis et al. (2004) who studied bank service quality. This study assumes that the reliability of online shopping services is far worse than bank services due to the intangibility of the Internet. Compared with traditional bank customers, online shopping consumers are likely to face more uncertainty, and even risk of purchasing. Females are obviously more circumspect than males (Oly Ndubisi, 2006). Facing risks, they pay more attention to the reliability of service. Therefore, from the standpoint of advertising campaigns and operations, online retailers should provide reliable services for female consumers under the same condition.

Finally, although the above results provide several interesting findings that further our knowledge of gender difference in influencing service quality when online shopping, which can provide systematic information to practitioners for developing market segmentation and event management strategies; future research can address several limitations. This study examines only cross-sectional rather than longitudinal data, whereas consciousness and behavior of consumers change over time. The authors will further attempt to capture variations in future studies. Besides, this survey was conducted with online shoppers in China, and other countries have different cultures resulting in dissimilar consumer patterns (Chang and Wang, 2011). Therefore, replicating this study in different countries could also help extend the validity of these findings.

## Figures

#### Figure 1.

Theoretical model

Research model

## Table I.

Measurement instruments

Factor and item Previous Literature
E-Service Quality
Privacy/security dimension of e-service quality (PSDE) Parasuraman et al. (2005) and Zeithaml et al. (2002)
The web site is secure
This web site does not give my information away to others
It protects my privacy
Responsiveness dimension of e-service quality (RESDE) Ribbink et al. (2004)
It is easy to get in contact with this online company
This online company is interested in feedback
The online company quickly replies to requests
Ease of use dimension of e-service quality (EUDE) Ribbink et al. (2004) and Parasuraman et al. (2005)
This site is user friendly
This site makes it easy to find what I want
Reliability dimension of e-service quality (RELDE) Collier and Bienstock (2006)
I trust this website
This web site’s billing is accurate
My orders from this site rarely contain the wrong items
Efficiency dimension of e-service quality (EDE) Collier and Bienstock (2006) and Parasuraman et al. (2005)
The time between placing and receiving an order is short
This web site is able to respond to a rush order
This site is well organized
Customer Satisfaction (CS) Wu (2013) and Deng et al. (2010)
I am very satisfied with my overall experience on the website
I have really enjoyed myself on the website
I think I did the right things by buying from this website
Customer Loyalty (CL) Yang and Peterson (2004)
It is likely that I will repurchase from this website
If I could, I will continue to purchase products from this website
I say positive things about the website to other people

## Table II.

Demographic description of sample

Measure Items Frequency (%)
Gender Male 159 48.1
Female 171 51.9
Age in years <20 85 25.7
21-30 114 34.5
31-40 73 22.1
41-50 43 12.9
>50 16 4.8
Education Middle school 35 10.6
High school 98 29.7
College or university 179 54.2
Monthly income (RMB) <3000 60 18.2
3000 ∼ 5000 105 31.8
5001 ∼ 7000 87 26.4
7001 ∼ 9000 42 12.7
>9000 36 10.9
Note:

n = 330

## Table III.

Result of confirmatory factor analysis and AVE

PS3 Privacy/Security dimension 0.723 0.087 11.71 *** 0.793 0.561
PS2 0.757 0.080 12.089 ***
PS1 0.766
res4 Responsiveness dimension 0.771 0.071 13.848 *** 0.829 0.619
res2 0.773 0.074 14.038 ***
res1 0.814
eu4 Ease of use dimension 0.664 0.084 11.016 *** 0.797 0.571
eu3 0.870 0.098 12.54 ***
eu2 0.716
rel5 Reliability dimension 0.812 0.091 12.826 *** 0.838 0.635
rel2 0.874 0.092 13.315 ***
rel1 0.694
eff3 Efficiency dimension 0.693 0.055 13.791 *** 0.835 0.630
eff2 0.783 0.055 16.032 ***
eff1 0.896
cs1 Customer satisfaction 0.825 0.862 0.676
cs2 0.834 0.068 16.729 ***
cs3 0.808 0.068 16.108 ***
cl1 Customer loyalty 0.789 0.846 0.648
cl2 0.841 0.071 15.269 ***
cl3 0.784 0.064 14.356 ***
Notes

: Model ﬁt indices: χ2/df = 2.047; p < 0.001; RMR = 0.039; GFI = 0.916; AGFI = 0.880; IFI = 0.952; CFI = 0.951; RMSEA = 0.056;

***p < 0.001

## Table IV.

Correlations (squared correlations), reliability, and AVE

Variable PSDE RESDE EUDE RELDE EDE CS CL
PSDE 0.750
RESDE 0.569*** 0.787
EUDE 0.304*** 0.401*** 0.756
RELDE 0.396** 0.506*** 0.461*** 0.797
EDE 0.469*** 0.282*** 0.371*** 0.384*** 0.794
CS 0.508*** 0.502*** 0.446*** 0.535** 0.560*** 0.822
CL 0.442*** 0.357*** 0.480*** 0.334*** 0.474*** 0.703*** 0.804
Notes:

All the cross-construct correlation coefﬁcients were statistically signiﬁcant (p < 0.001); the square root of AVE is shown in the diagram (Diagonal);

***p < 0.001;

**p < 0.05; and; PSDEprivacy/security dimension of e-service quality; RESDEresponsiveness dimension of e-service quality; EUDEease of use dimension of e-service quality; RELDEreliability dimension of e-service quality; EDEefficiency dimension of e-service quality; CSCustomer Satisfaction; CL: Customer Loyalty

## Table V.

Path analysis and regression weights

Path Estimate S.E t-value p-value Result
1 Customer Satisfaction ← Privacy/Security 0.186 0.082 2.494 0.013 Support
2 Customer Satisfaction ← Responsiveness 0.139 0.057 1.958 0.050 Support
3 Customer Satisfaction ← Ease of use 0.168 0.057 2.705 *** Support
4 Customer Satisfaction ← Reliability 0.192 0.068 2.812 *** Support
5 Customer Satisfaction ← Efficiency 0.293 0.064 4.519 *** Support
6 Customer Loyalty ← Customer Satisfaction 0.716 0.074 11.136 *** Support
Notes:

Model ﬁt indices: χ2/df = 2.086; p < 0.001; RMR = 0.042; GFI = 0.910; AGFI = 0.875; IFI = 0.949; CFI = 0.948; RMSEA = 0.057; and

***p < 0.001

## Table VI.

Differences between male group and female group

HY Moderating effectsof gender Regression Weights
Male(N = 159) Female(N = 171)Δх² (df = 1)p-value Result
H1 CS ← Privacy/Security 0.192 0.169 0.302 0.582 Support
H2 CS ← Responsiveness 0.273*** 0.029 3.957 0.047 Support
H3 CS ← Ease of use 0.207* 0.307 0.480 0.489 Rejection
H4 CS ← Reliability 0.036 0.320** 4.930 0.026 Support
H5 CS ← Efficiency 0.285*** 0.207*** 0.011 0.916 Rejection
H6 Customer Loyalty ← CS 0.674*** 0.884*** 9.663 0.002 Support
Notes:

Signiﬁcant at:

*p < 0.05;

**p < 0.01;

***p < 0.001; CS: Customer satisfaction

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## Corresponding author

Saebum Kim can be contacted at: wwh0631@gmail.com