How psychological gender difference influences the relationship between
online shopping motivation and online shopping intention
Faculty of Business and Economics
Msc of Business Studies
March 2014
Name of author: Zengyao Wu
Student Number: 6102778
Supervisor: dhr.drs. ing. A.C.J. Meulemans
Second Supervisor: dhr. prof. dr. J.H.J.P. Tettero
Abstract:
This paper studies how psychological gender difference influences the relationship between online shopping motivation and online shopping intention. By using the 486 questionnaires that collected in China, the study found psychological gender difference has a significant effect on relationship between online shopping motivation and online shopping intention. Moreover, the study also found that Utilitarian shopping motivation is still the major motivation in online shopping environment. Furthermore, several Managerial implications were provided in this article to help E-business attract more target customers.
Table of content 1. Introduction………. 4 1.1 Background ………...4 1.2 Motivation………. 6 2. Theoretic background ………..7 2.1 Gender difference ………..7 2.2 Shopping intention ………9 2.2.1 Consumer-oriented view……… 9 2.2.1.1 Shopping motivation……… 10 2.2.1.2 Innovativeness ………..11 2.2.1.3 Benefit perception ………12 2.2.1.4 Risk perception ……….13 2.2.1.5 Shopping orientation……… 14 2.2.1.6 Normative beliefs ……….15 2.2.2 Technology-oriented view ……….15 2.2.2.1 Website design ………..15 2.2.2.2 Website reputation……… 16 2.2.2.3 Website service ……….16 3. Research model ……….18
4. Methodology and Result………... 22
4.1 Instrument ………22
4.2 Independent variables……….. 22
4.3 Dependent variable ………..23
4.4 Procedure………. 23
4.5 Participants and sample groups selection ………23
4.6 Regression ………...24
5. Discussion and conclusion ………29
5.1 Limitation and future research ………30
5.2 Managerial implications……… 31
6. References ………33
1. Introduction 1.1 Background
Since the 21st century, the information technology revolution has changed almost every aspect of the world. The information technology industry is gradually becoming one of the most important components of national competitiveness. The fast increases of Internet users, who form the foundation for the information technology industry, provide the ability to change the traditional consumption pattern. As a result, various forms of e-commerce have emerged. A prime example of e-commerce is the maturing online shopping experience, which has become more popular along with increase of internet users. In the academic world, a lot of studies have been conducted to investigate the economic impact brought by the dramatic growth of internet users.
The information technology revolution has not only effect on the western countries’ economies, but also the uprising economy in China has come with the fast development of the domestic information technology industry. With the rapid increase of internet users and increasingly maturing internet economic environment, a variety of business models began to grow. Online Business to Customer as one of key model had also achieved an explosive growth, and it becomes a powerful complement of the traditional retailing business.
According to China's online shopping market research report issued in 2011 by the China Internet Network Information Center (CNNIC), which established by Chinese government in 1997, the internet user population in China had reached almost 457.3 million people with approximately 160.5 million online shoppers by 2010. Compared with the number of online shoppers in 2009, there was a 48.62% increase (CNNIC, 2011). In the meantime, the online shopping transaction amount was 523.1 billion RMB in 2010 which had a 109.2% increase compared to 2009’s amount. Table 1 shows more details about recent years’ growth.
However, even though the figures above already seem quite enormous compared to other economies, considering the population and the speed of developing economy in
China there is still great potential for growth in the e-commerce industry.
*China Internet network information center, 2011
1.2 Motivation
The business opportunities in China derived from the exponential increase of online shoppers are attracting many organization and individual merchants that want to join the internet revolution by starting their own online shopping website. To seize the opportunities and win market share, many of these online businesses exert all efforts to attract customers through, for example, price discounts, an attractive online user interface, shopping process optimization, improving delivery procedures and after-sales service. These marketing efforts help online businesses survive in this fierce e-commerce market. Many of these companies and retailers have already known the Chinese domestic market from previous real-life experiences. Especially businesses that focus on gender market segments (e.g., women's cosmetics, clothing, jewellery, fashion or male sports, fitness, and leisure goods) commonly implement specific marketing strategies based on the female and male cognitive and psychological differences.
In theoretical world, numerous had been conducted about factors that influence consumer online shopping intentions, for example age, gender, educational background and income. However, there are no in-depth studies on online shopping intention for specific gender orientated segments. This article argues that men and women have significant differences in cognition, psychology and consumer behaviour based on reasons such as social roles identified. Therefore, it is necessary to have an in-depth exploration in theory.
Furthermore, the result of this paper could provide opportunities for future research and practical recommendations for online business participants. Further theoretic exploration will be given in next chapter.
2 Theoretic background 2.1 Gender difference
As defined by Deaux (1985), sex is the biological based category between male and female, however 'gender' define the psychological features frequently associated with these biological states. The distinction between male and female have been studied by scholars in many aspects. Besides the basic sex differences, most study have found that there are major differences within the cognitive and psychological regions (Maccoby & Jacklin, 1974, Eagly, 1983). Archer and Lloyd (2002) indicate that before public consciousness there are three common beliefs regarding gender differences. First, there are fundamental differences between women and men; second, men are superior and women are inferior; and third, women are more illogical and irrational compared to men (Archer & Lloyd, 2002). These common beliefs are taken for granted by researchers resulted in biases in their studies. Especially the first belief may lead researchers to only seeking tangible differences rather than emphasising our common humanity (Archer & Lloyd, 2002).
To avoiding the bias brought by these common beliefs, Archer & Lloyd (2002) analyse gender differences by incorporating Doise’s (1986) four levels of how social psychology operates (i.e., intrapersonal, interpersonal and situational, the positional, and societal). Moreover, Deutsch and Gerard (1955) indicate that human behaviour is compiled with society expectations and characterize this as normative social influence. Eagly (1983) studies this theory of human behaviour and explains that gender difference also include the normative social influence that arises in role-regulated contexts. Other studies also suggest that personality differences between the genders are more based on the way men and woman are reared rather than biological origin (Barry, Bacon & Child, 1957). Men are trained to be independent thinkers and to assert themselves, while women are generally encouraged to be selfless and concerned with others (Eagly & Steffen, 1984). Hence, these studies explain that social factors are important because they influence the psychological differences between genders.
males and females suit as psychological variables, because they are descriptive rather than conceptual (Deaux, 1977, 1984). Constantinople (1973) laid the foundation for modern gender difference psychology by introducing the concepts of masculinity and femininity as bipolar opposites instead of using the traditional concepts men and women. After Constantinople’s ice breaking study, a number of studies presented scales to evaluate these masculine and feminine characteristics (e.g., Bem, 1974; Spence, Helmreich, & Stapp, 1974; Heilbrun, 1976; Berzins, Welling & Wetter, 1978). Among these measures, the most famous is the Bem Sex Role Inventory (BSRI) developed by Bem (1974).
Bem (1974) first introduced the concept of androgyny, which refers to men and women who possess both masculine and feminine qualities in a relatively equal proportion. She also included it in BSRI measurement tool to enable scholars to characterize a person by masculine, feminine, or androgynous traits (Bam, 1974). The BSRI tool holds four features that make it different from other gender difference measurement tools. First, it includes 20 personality characteristics for each masculinity scale and femininity scale. Second, the selection of these personality characteristics was based on sex-typed social desirability rather than differential endorsement by males and females. Third, the difference between his or her endorsement of masculine and feminine is the essential of characterizing a person. Finally, the BSRI also includes a neutral social desirability scale.
Nevertheless, although the BSRI tool is a well-constructed instrument and has been used in a large number of studies, considering the BSRI was created almost 40 years ago and may therefore be out of date in terms of the representations of masculine and feminine gender roles (Spence, 1993; Holt & Ellis, 1998). Furthermore, even though the BSRI concept has been criticised for methodology (Marsch et al. 1989; Spence 1991) However, Street, Kimmel, & Kromrey (1995a) show that college students’ perceptions of gender roles have not changed since the 1970’s. A second study with the same procedures concluded similar findings for college faculty when analysed the gender role perceptions (Street, Kromrey, & Kimmel, 1995b). These studies indicate
that gender roles such as the masculine and the feminine traits are still valid after 40 years of research. Moreover, Holt & Ellis (1998), who validated the BSRI instrument, also found that, in exception of two feminine measures (i.e. loyal and childlike), the adjectives in BSRI tool were still valid. This also indicates BSRI may still be a suitable tool for measuring gender role differences (Holt & Ellis, 1998; Calvo-Salguero, García-Martínez & Monteoliva, 2008). And its inventory of characteristics for femininity and masculinity is still widely used for the measurement of gender-linked expressive and instrumental personality attributes (Colley, Mulhern, Maltby, & Wood, 2009; McCusker & Galupo, 2011).
Even though the BSRI tool has been proved to be a great instrument for analysing gender difference, it has primarily been used in the western world. However, due to the enormous culture differences between western and eastern countries one may wonder whether it also suitable in China. Qian, Zhang, Luo & Zhang (2000) studied the BSRI model and modified the tool to meet the culture features in China by adding two extra negative scales to test the degree of social desirability and called it Sex Role Inventory for College Students (CSRI). Moreover, in the study of Qian et al., the sample of college students were chosen between 18 to 25 years old – considering the year that this research been conducted, this is similar to the target group of this study. Following Qian, it is more reliable to use CSRI to evaluate the gender differences part of this study.
2.2 Shopping intention
A lot of researches had been conducted within the online shopping intention field. These researches could be divided in to two categories: consumer-oriented view and technology-oriented view (Jarvenpaa and Todd, 1997).
2.2.1 Consumer-oriented view
Davis (1986, 1989) originally formulated the Technology Acceptance Model (TAM) in an attempt to understand why people accept or reject information systems. The
TAM has been widely adopted to explain and predict user behavior in an online shopping environment (Zhou, Dai & Zhang, 2007, Lee, Kozar, & Larsen, 2003). However, the TAM is too general and does not include online shopping factors (Zhou et al., 2007). For example, rather than it being a generic information system, the final goal of an online shopping environment is to attract consumers to purchase products or services online (Zhou et al., 2007). After analyzing the consumer factors related to online shopping in previous studies,Zhou et al. (2007) developed a new model called Online Shopping Acceptance Model (OSAM) as an extension of TAM specifying on online shopping. Figure 1 shows the structure of OSAM.
As showed in Figure 1, OSAM contains five major consumer factors that have a direct effect on the online shopping intention. These five factors are shopping motivation, innovativeness, perceived outcome, shopping orientation, and normative beliefs. The factor related to perceived outcome can, however, be subdivided in to two more factors: benefit perception and risk perception (Zhou et al., 2007). These two indicate the positive or negative attitude toward online shopping. Further traits of these factors are explained as follows:
2.2.1.1 Shopping motivation
Although shopping motivation is well studied by many researchers in the traditional retail environment, it is still considered as a key role in the consumer's efforts to
search for a product or service (Childers, Carr, Peck & Carson, 2001; Joines, Scherer & Scheufele, 2003). In traditional retail shopping, shopping motivation of consumer can be divided into hedonic and utilitarian (Babin, Darden & Griffin, 1994, Childers et al., 2001). Hedonic motivated consumers are more focus on the entertainment and enjoyment of shopping experience (Childers et al., 2001), whereas utilitarian motivated consumers are concerned with purchasing products in an efficient and timely manner to achieve their goals with minimum irritation (Childers et al., 2001).
Both types of shopping motivation were found to exist in the online shopping environment too (Bhatnagar & Ghose, 2004, Wolfinbarger & Gilly, 2001, Bhatnagar, Misra & Rao, 2000). However, utilitarian motivation is considered more important compared to hedonic motivation (Bhatnagar & Ghose, 2004; Bhatnagar et al., 2000). Solomon (1999) found that more than two-third of online shoppers are utilitarian motivated. This may often cause by various factors such as time-starvation (Bellman, Lohse & Johnson, 1999), emphasis of freedom and control for early adopters, and the attributes of online shopping convenience, accessibility, selection, and availability of information (Wolfinbarger & Gilly 2001).
Yet, due to the fast changing e-commerce market, these results from past studies may not represent the resent online shopping motivation. So it is necessary to re-evaluate it and test the relationship with online shopping intention.
2.2.1.2 Innovativeness
Personal innovativeness is a characteristic found to be a good determinant of new-product adoption (Robertson & Kennedy, 1968). Innovativeness measures how fast and to what extent an individual adopts new innovations (Rogers, 1995). Goldsmith (2001) indicates that an innovation is only adopted by few people initially, such as online buying, but if they have favorable reaction, the new practice is likely to spread. Compared with traditional physical stores, personal innovativeness plays a more important role for online shopping because shopping online itself can be treated as an innovative behavior (Zhou et al., 2007).
However, past studies show different results on the effect of personal innovativeness. Personal innovativeness was found to have a positive effect on online shopping intention in some studies (e.g., Donthu & Garcia 1999, Limayem, Khalifa & Frini, 2000) but not in others (e.g., Citrin et al. 2000, Sin & Tse 2002). These conflicting results make it important to restudy the effect on online shopping intention caused by general personal innovativeness in a whole new Chinese environment.
2.2.1.3 Benefit perception
As defined by Davis (1989) in his well-known Technology Acceptance Model, perceived usefulness is "the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989). Based on this definition, the perceived usefulness could also be explained as the benefits that a person believes to receive by using a particular system. The online shopper's Benefit perception and perceived usefulness are therefore alike.
In online shopping, the perceived benefits are concerned as one of the driving forces (Zhou et al., 2007). In general studies, positive results founded between benefit perception and general shopping intention (e.g., Pavlou, 2003; Chen, Gillenson & Sherrell, 2002). Some other studies focused on the benefits perceived through online shopping and decomposed the construct into several elements.
Utility as communication channel: wide selection of goods, updated information and quality information (Li, Kuo & Russell, 1999),
Utility as distribution channel: easy of exchange and returns, prompt access of goods purchased, post-purchase service, security of payment and pre-purchase inspection (Li, Kuo & Russell, 1999),
Time saving (Raijas & Tuunainen, 2001)
product value (Jarvenpaa & Todd, 1997, Mathwick, Malhotra, & Rigdon, 2001, Vijayasarathy, & Jones, 2000) perceived easy to use (Gefen, & Karahanna, 2003), perceived consequence ( Limayem et al., 2000).
2.2.1.4 Risk perception
Risk perception in traditional retailing was first introduced by Bauer (1960) and it can be defined as the consumer’s subjective belief of suffering a loss in pursuit of a desired outcome (Bauer, 1960). Risk perception has also been defined as personal beliefs regarding the inherent risks that involved in every transaction due to the limited information available for consumers (Dowling & Staelin, 1994). Compared to traditional retailing business, the consumer’s risk perception in online shopping environment is an inevitable element due to the distance and impersonal nature of online shopping and the uncertainty of using global open infrastructure for transactions (Pavlou, 2003). Furthermore, the causes of perceived risks could be divided into two uncertainties: behavioral uncertainty and environmental uncertainty (Bensaou & Venkataman, 1996). Behavioral uncertainty appears when online retailers may have the chance to take advantage of the distant and impersonal nature of e-commerce and the lack of legal surveillance of transactions (Pavlou, 2003). For example, online retailers may over promote the advantages of a certain product and hide the disadvantages of it, or the buyers’ private information may be sold to third parties for their own gain. Second, environmental uncertainty appears primarily as a result of the unpredictable nature of Internet (Pavlou, 2003). Recently the largest online shoe store in America, Zappos, was hacked; names, e-mail addresses, addresses, phone numbers and partial credit card numbers of its 24 million customers were leaked, this is a good example of environmental uncertainty (Goldman, 2012).
In the original TAM model, risk perception was not included in the discussion. Pavlou (2003)integrated trust and perceived risk with TAM and proved that perceived risk have great effects on online shopping intention. Other studies also found perceived risk has significant negative impact on online shopping intention (Bhatnagar, Misra & Rao, 2000, Liang & Huang, 1998). Moreover, the perceived risks that created by these two uncertainty can be classified into four categories: economic risk, personal risk, seller performance risk, and privacy risk (Pavlou, 2003).
2.2.1.5 Shopping orientation
During the past 50 years, shopping orientation has been studied in traditional retailing and marketing literature (Stephenson and Willett, 1969, Stone, 1954). Stone (1954) was one of the first to suggest that consumers shop for other than just economic reasons. Four types of shoppers were identified: economic, personalizing, ethical, and apathetic (Stone, 1954). The economic shoppers are majorly concern about the price, quality, variety and efficiency; the personalizing shoppers prefer a closer customer-personnel relationship, so they tend to shop at stores that they are known; the ethical shoppers feel to have obligation to purchase in specific, local stores; and the apathetic shoppers have little interest in shopping, so the type of store does not matter for them (Stone, 1954). Besides the work from Stone, the following studies add another four categories of shopping orientations. There are recreational shoppers, convenience-oriented shoppers (Stephenson & Willett, 1969), highly-involved shoppers (Shim & Kotsiopulos, 1993), and community-oriented shoppers (Brown et al. 2003). The recreational shoppers enjoy the act of shopping regardless of whether a purchase is made or not (Stephenson & Willett, 1969). The convenience-oriented shoppers consider time, space, and effort are important (Stephenson & Willett, 1969). The highly-involved shoppers are the opposite of apathetic shoppers (Zhou et al., 2007). And the community-oriented shoppers tend to shop due to the social motives (Brown et al. 2003). In conclude to those different reasons of shopping, shopping orientations are related to general predisposition toward acts of shopping (Li, Kuo & Russell, 1999). “They are conceptualized as a specific dimension of lifestyle and operationalized on the basis of activities, interests and opinion statements pertaining to acts of shopping (Li et al., 1999).”
In online shopping background, shopping orientation is still the one of the major factor that could influence shopping intention. However, differences do exist when compared with traditional way of shopping. Studies found out positive results about online consumers tend to be convenience-oriented (Donthu & Garcia, 1999, Korgaonkar and Wolin 1999, Li et al., 1999, Swaminathan, Lepkowska-White & Rao, 1999). This may cause by the easy access of online shopping and the massive
products available for choosing and comparing (Korgaonkar and Wolin, 1999, Swaminathan et al., 1999 Zhou et al., 2007).
2.2.1.6 Normative beliefs
Normative beliefs were first developed from social influence theory. Based on the notion of normative social influence, presumes that groups are governed by sets of rules (the norms) that specify appropriate behaviors and actions (Foucault & Scheufele, 2002). Following studies tested this theory in online shopping condition; Limayem et al. (2000) found that perceived norms did play a role in the decision to purchase online, specifically with regard to family influences. One study also found a strong relationship between perceived norms and intentions to shop online (Kraut, Kiesler, Boneva, Cummings & Helgeson, 1996). They found that if people have a supportive social environment, including friends and relatives who shop online, the possibility for them to use the internet for shopping is likely to increase (Kraut et al., 1996). Beside the friends and relatives, the media turned out to be a significant social factor influencing intentions to shop online (Limayem et al., 2000).
2.2.2 Technology-oriented view
The technology-oriented view, other than consumer-oriented view, examining technical specifications of an online store in order to explains and predicts consumer acceptance of online shopping (Zhou et al., 2007). In this study, three factors are included to analysis the shopping intention from technology-oriented view. They are website design, website reputation, and website after-sales service. Further elaboration will be given in following section.
2.2.2.1 Website design
Kent and Taylor (1998) developed a theoretical framework called: dialogic communication to guide online relationship building between organizations and publics. It is reasonable to use Kent and Taylor’s (1998) framework to elaborate the critical points of online store design since the major information that provided by a certain e-commerce business is presented via its website. In order to allow customers
to continue to focus on online shopping site, on one hand, online shopping sites need to maintain good interactive relationship with customers as far as possible, which means to establish a bridge of communication and feedback within shopping website (Kent & Taylor, 1998). This allows users easily obtain the information they needed during browsing the website. On the other hand, online shopping sites should optimization their interface design by improving the navigation, search engine functions, and payment procedure (Kent & Taylor, 1998). These user-friendly design and well organized interface layout also enhance the intention to shopping online (Kent & Taylor, 1998). Moreover, Lohse and Spiller’s (1998) study also support this finding; the user interface is an essential link between the customer and the retail store in web-based shopping environments. The growth of Internet retail sales will at least partially depend on these interface design issues (Lohse & Spiller, 1998).
2.2.2.2 Website reputation
Strader and Shaw (1999) indicated that price is not the only determinate of where to buy. In their study, positive result was found that higher prices may be paid by consumers to offset transaction risk (Strader & Shaw, 1999). In another word, due to higher transaction costs, consumers tend to give up trading with unknown Internet retailers even they offer a lower prices. Reputation is the extent to which buyers believe that the selling organization is honest and concerned about its customers (Doney & Cannon, 1997). Furthermore, Quelch and Klein (1996) and Lohse and Spiller (1998) speculate that the perceptions of a certain online site will be influenced by its reputation.
2.2.2.3 Website service
Genfen and Devine (2001) found that in both traditional and online shops, service quality reduces the effects of perceived risk, cost to switch, and relative price. Since virtual stores are both marketing channels and information systems, service quality is crucial to their success (Chen & Tan, 2004). In other word, improved service quality is the key to retaining the customers of online stores, and thus having these stores show a profit (Reichheld & Schefter, 2000). Other than the traditional dimensions of
services, online stores face the opportunities and challenges from new types of services like: self-service, logistic service, personalization and customization services (Peppers, Rogers & Dorf, 1999). Furthermore, past studies found that website service has a positive relationship with online shopping intention (Chen & Tan, 2004, Lee & Lin, 2005)
3 Research model
According to the related factors that mentioned above, a model is generated to investigate the relationship between gender differences and shopping intention.
Figure 2
However, due to the limited time this model is too broad to analysis in this thesis. It has been narrow down into the model that only located the differences within shopping motivation. Other factors will be elaborated in discussion part of this article.
The reason that shopping motivation has been chosen as the topic of this article is shopping motivation has strong gender related effect on shopping intention. Past studies found that women tend to see traditional shopping as an encompassing and psychologically involving activity and experience, it is comparably less important to actually owning the products (Campbell, 2000; Dittmar & Drury, 2000; Dittmar, Long,
Shopping motivation
Innovativeness
Online shopping risks
Shopping orientation Potential benefit Website design Website reputation Website service Normative beliefs
Online shopping intention Male Female Consumer -oriented Technology-oriented
& Meek ,2004). Men, on the other hand, often describe shopping as a painful task, they would like to shop as quickly and efficiently as possible. In another words, men are goal-oriented when it comes to shopping behavior (Campbell, 2000; Dittmar & Drury, 2000; Dittmar, Long, & Meek ,2004). One study proved that these finding are still valid in online shopping environment. Swaminathan et al. (1999) discovered male Internet buyers were more convenience oriented and less motivated by social interaction than women Internet buyers. These results indicated that online shopping motivation is differ by gender.
Nevertheless, in previous studies, gender was separated by using biological difference: ‘male and female’ not the psychological difference: ‘masculinity and femininity’. Considering the fact that online shopping environment is virtual environment, the psychological difference may better represent gender difference. Based on the above view, the diagram bellow shows the structure of this research:
Figure 3 Group1 Masculinity Group2 Femininity Group4 Female Group3 Male Hedonic Shopping motivation Utilitarian Shopping motivation Hedonic Shopping motivation Utilitarian Shopping motivation Hedonic Shopping motivation Utilitarian Shopping motivation Hedonic Shopping motivation Utilitarian Shopping motivation
Online shopping intention
Based on this structure, 8 hypotheses are summarized as follows:
H1: The online shopping intention of Masculinity group is stronger correlated with utilitarian shopping motivation than hedonic shopping motivation.
H2: The online shopping intention of Femininity group is stronger correlated with hedonic shopping motivation than utilitarian shopping motivation.
H3: The correlation between Hedonic Shopping motivation and online shopping intention is stronger in Femininity group than Masculinity group.
H4: The correlation between Utilitarian Shopping motivation and online shopping intention is stronger in Masculinity group than Femininity group.
H5: The correlation between Hedonic Shopping motivation and online shopping intention is stronger in Male group than Masculinity group.
H6: The correlation between Utilitarian Shopping motivation and online shopping intention is stronger in Masculinity group than Male group.
H7: The correlation between Hedonic Shopping motivation and online shopping intention is stronger in Femininity group than Female group.
H8: The correlation between Utilitarian Shopping motivation and online shopping intention is stronger in Female group than Femininity group.
Moreover, the culture difference between China and western world could also impede the generalizability of the results. Therefore the objective for this research is to answer: How psychological gender difference influences the relationship between online shopping motivation and online shopping intention?
4 Methodology and Result 4.1 Instrument
The instrument used in this study is a modified questionnaire which combines CSRI developed by Qian at al. (2000), online shopping motivation evaluation questions from Overby & Lee (2006) and Arnold & Reynolds (2003), and online shopping intention evaluation questions from Lin (2006).
The first part of the questionnaire contained the CSRI diagram evaluation, which has 12 personality characteristics for each masculinity scale and femininity scale (all measured on a 7-point Likert scale anchored by very suitable and very unsuitable). The second part of the questionnaire contained 12 closed questions that also measured by a 7-point Likert scale anchored by strong agree and strong disagree. In addition gender and age were added in this questionnaire to separate the sample into groups and insure the sample stay in the target population. Appendix 1 shows the detail of this questionnaire.
4.2 Independent variables
Hedonic shopping motivation
4 items were used to measure hedonic shopping motivation. There were taken from studies of Overby & Lee (2006) and Arnold & Reynolds (2003). The items in the survey contained statements like “Online shopping is a way for me to relieve stress”. The mean of these 4 items will be calculated and use in the regression test.
Utilitarian shopping motivation
For this variables another 4 items were selected from study of Overby & Lee (2006) questionnaire. These statements contained questions such as “When I make a purchase online, I save time” and “The products and/or services I purchased online were a good buy.” These 4 items will also be calculated the mean and use in the regression test.
4.3 Dependent variable
Online shopping intention
Online shopping intention was measure by 4 items from study of Lin (2006). Statements such as “I plan to use online shopping again” and “I will strongly recommend my friends to buy product online” were used in this study. Same as the independent variable, the mean of these 4 items were also used in the regression test.
4.4 Procedure
The data will be collected via the distribution of questionnaires through internet questionnaire service group in China. This could make sure that the respondents are not from one certaingeographical location which may damage the generalizability of this study. 300 questionnaires is planned to send to 150 females and 150 males in China in order to get 200 respondents. Due to the majority of online consumers in China belongs to young generation; the range of the sample age will be limited between 20 and 35. Furthermore, all respondents participated voluntarily and were guaranteed anonymity during data collection.
Once the questionnaires were collected, the data will be first divided in to 2 groups based on the biology difference of male and female. Then after evaluate the CSRI in the questionnaire the sample will be further divided into another 2 groups based on the phycology difference of masculinity and femininity. All 4 groups will contain same amount respondents to ensure the reliability of this research. By compare the correlation between online shopping motivation and online shopping intention among the groups the 8 hypotheses will be tested.
4.5 Participants and sample groups selection
486 respondents were received after 2 weeks of distribution. Based on certain questions that can be used to check the validity of the data, 79 invalid surveys were eliminated from the original data set. Then 365 respondents were selected and formed into 4 groups: Group 1 Masculinity (185 respondents), Group 2 Femininity (180
respondents), Group 3 Male (171 respondents) and Group 4 Female (194 respondents) regarding to the model mentioned earlier. The Masculinity Group and Femininity Group were selected based on a method that design specially for this research. Masculinity group: the mean of 12 masculinity personality characteristics is larger than the mean of 12 femininity personality characteristics and also larger than 4, which is the median of the Likert scale. Femininity group: the mean of 12 femininity personality characteristics is greater than the mean of 12 masculinity personality characteristics and also greater than 4. Through this method, the respondents were easily divided into three groups: Masculinity, Femininity and neutral. For this study, only Masculinity and Femininity groups were needed.
The respondents had an average age of 28.4 years (SD=4.23 years) and came from 22 different provinces and municipality which covered almost all developed areas of China. The gender distribution was 46.8% for male and 53.2% of female. Therefore this sample provides a good representation of the generalizability.
4.6 Regression
Table 1: Test 1
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 ,758a ,574 ,570 ,89260
a. Predictors: (Constant), group1_H, group1_U
Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) ,493 ,301 1,636 ,103 group1_U ,672 ,072 ,560 9,319 ,000 group1_H ,269 ,058 ,278 4,624 ,000
a. Dependent Variable: group1_SI
b. Group1_U: Group 1 Masculinity utilitarian shopping motivation c. Group1_H: Group 1 Masculinity hedonic shopping motivation d. Group1_SI: Group 1 Masculinity online shopping intention
Linear regression analyses are used to discover the correlation between a dependent variable and the independent variables that influence it. Hypothesis 1 suggested Utilitarian shopping motivation has a stronger correlation with Online shopping intention of Masculinity group than Hedonic shopping motivation. Table 1: Test 1 shows that both Utilitarian shopping motivation (β = .672, p < .01) and Hedonic shopping motivation (β = .269, p < .01) have a significant impact on Online shopping intention of Masculinity group; however the Utilitarian shopping motivation has a greater influence. Table 1 also shows that the R Square (0.574) and the Adjusted R square (0.570) for test 1 model are sufficient (R>0.30). These results therefore confirm Hypothesis 1.
Table 2: Test 2
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 ,777a ,603 ,599 ,90845
a. Predictors: (Constant), group2_U, group2_H
Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) ,496 ,295 1,685 ,094 group2_H ,355 ,059 ,361 5,974 ,000 group2_U ,613 ,074 ,498 8,238 ,000
a. Dependent Variable: group2_SI
b. Group2_H: Group 2 Femininity hedonic shopping motivation c. Group2_U: Group 2 Femininity utilitarian shopping motivation d. Group2_SI: Group 2 Femininity online shopping intention
Hypothesis 2 predicted that in Femininity group, Hedonic shopping motivation is stronger correlated with Online shopping intention than Utilitarian shopping motivation. As evident in Table2: Test 2, the results demonstrated that Hedonic shopping motivation (β = .355, p < .01) actually has a weaker correlation with Online shopping intention compared with Utilitarian shopping motivation (β = .613, p < .01).
Considered the R Square (0.603) and the Adjusted R square (0.599) for test 2 model are sufficient (R>0.30), the Hypothesis 2 is failed to be supported.
The results from Table 1 and Table 2 can also been used to test the Hypothesis 3 and 4. Hypothesis 3 predicted that Hedonic shopping motivation has a stronger correlation with Online shopping intention in Femininity group (β = .355, p < .01) than Masculinity group (β = .269, p < .01). Moreover, Hypothesis 4 suggested that the correlation between Utilitarian shopping motivation and Online shopping intention is stronger in Masculinity group (β = .672, p < .01) than Femininity group (β = .613, p < .01). Consequently both Hypothesis 3 and 4 are supported.
Table 3: Test 3
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 ,810a ,657 ,653 ,83500
a. Predictors: (Constant), group3_U, group3_H
Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) ,262 ,282 ,930 ,354 group3_H ,340 ,058 ,348 5,830 ,000 group3_U ,645 ,071 ,540 9,046 ,000
a. Dependent Variable: group3_SI
b. Group3_H: Group 3 Male hedonic shopping motivation c. Group3_U: Group 3 Male utilitarian shopping motivation d. Group3_SI: Group 3 Male online shopping intention
Table 3: Test 3 presents the regression analysis of Hedonic shopping motivation and Utilitarian shopping motivation on Online shopping intention within Male group. The results show that, in Male group, Utilitarian shopping motivation (β = .645, p < .01) is stronger correlated with Online shopping intention than Hedonic shopping motivation (β = .340, p < .01). Furthermore, Test 3 model shows an R Square level of 0.657. Considering an R Square value of 1 represents a perfect fit, 0.657 is a very high result.
By comparing Test 3 results with Table 1, Hypothesis 5 and 6 can be tested. Hypothesis 5 proposed that the correlation between Hedonic shopping and online shopping intention is stronger in Male group (β = .340, p < .01) than Masculinity group (β = .269 p < .01). Then Hypothesis 6 predicted that the correlation between Utilitarian shopping motivation and Online shopping intention is stronger in Masculinity group (β = .672, p < .01) than Male group (β = .645, p < .01). Based on these results, both Hypothesis 5 and 6 are supported.
Table 4: Test 4
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 ,730a ,533 ,528 ,94691
a. Predictors: (Constant), group4_U, group4_H
Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) ,677 ,310 2,185 ,030 group4_H ,270 ,059 ,277 4,558 ,000 group4_U ,664 ,075 ,534 8,804 ,000
a. Dependent Variable: group4_SI
b. Group4_H: Group 4 Female hedonic shopping motivation c. Group4_U: Group 4 Female utilitarian shopping motivation d. Group4_SI: Group 4 Female online shopping intention
Table 4: Test 4 shows the regression test of Hedonic shopping motivation and Unitarian shopping motivation effect on Online shopping intention within Female group. In Female group, Utilitarian shopping motivation (β = .664, p < .01) still has a stronger correlation with Online shopping intention than Hedonic shopping motivation (β = .270, p < .01). Additionally the R Square (0.533) and Adjusted R Square (0.528) of Test 4 model are sufficient (R>0.30).
By using the results from Table 2: Test 2 to compare with Table 4: Test 4 results, Hypothesis 7 and 8 are tested. Hypothesis 7 suggested that the correlation between Hedonic shopping motivation and Online shopping intention is stronger in Femininity group (β = .355, p < .01) than Female group (β = .270, p < .01). And Hypothesis 8 suggested that Utilitarian shopping motivation is stronger correlated with Online shopping intention in Female group (β = .664, p < .01) than Femininity group (β = .613, p < .01). So both Hypothesis 7 and 8 are proved to be supported.
5 Discussion and conclusion
This study aimed to assess how psychological gender difference influences the
relationship between online shopping motivation and online shopping intention.
By creating a new online shopping intention model (figure 2) based on past studies, then narrow down into a sub model (figure 3) to analysis more specified area focus only on psychological gender difference toward online shopping motivation, the research question is answered.
As mentioned in the previous chapter, except Hypothesis 2, 7 out of 8 hypotheses were proved to be supported. The proved Hypotheses 5-8 provided a strong support toward the main research question. Compared with Male group, Masculinity group has a stronger Utilitarian shopping motivation and a weaker Hedonic shopping motivation. Compared with Female group, Femininity group has a stronger Hedonic shopping motivation but a weaker Utilitarian shopping motivation. These findings demonstrated psychological genders have more significant influences between online shopping motivation and online shopping intention than biology genders in online shopping environment.
Moreover, the supported Hypotheses 3 and 4 indicate the Femininity group and Masculinity group within psychological gender differences have their own focus. On one hand the Femininity group has the highest correlation between hedonic shopping motivation and online shopping intention (β = .355, p < .01). On the other hand the Masculinity group has the highest correlation between utilitarian shopping motivation and online shopping intention (β = .672, p < .01). These results provide a solid prove that in online environment the general idea of gender is more represented by Femininity and Masculinity. Personal privacy is protected during online shopping. Customers could easily purchase products based on their personal interest regardless the outside view about the products. For intense, male could purchase certain cosmetic product that design for female however also can be used for male. In other
words, biology gender will not become a bias when purchase happened online, then the psychological gender actually play a more important role in online purchase behaviour.
The overall results proved that even after 14 years of information technology development, Utilitarian is still the major shopping motivation in online condition. Finding from this research showed similar results compared with past studies (Bhatnagar and Ghose, 2004; Bhatnagar et al., 2000; Solomon, 1999). Today’s online shopping website still cannot provide the shopping experiences that the hedonic motivated shoppers are looking for. Compared with traditional retail business, although the online stores have already provide enormous amount of products with comprehensive explanations to let the hedonic motivated shoppers to select, it is still very hard for customers to really feel the product in their hands and measure their style and quality. Then the joys of purchasing experiences are limited. This may explain why the Hypothesis 2 was rejected, which expected to have a stronger hedonic shopping motivation over utilitarian motivation.
5.1 Limitation and future research
A few limitations of this study need to be acknowledged. First of all, the age of the sample for this research was limited to the range of 20-35 years old. The reason that this age range was chosen to be target group was due to the internet development period and income status. By using this age group, the results of this study were expected to be more significant. However, more and more elder group now choosing to shop online, because the products range and delivery services. The only bias is the computer skills, which gradually overcome by easy and simple to use software development. Therefore, the exclusion of elder group is one of the limitations of this study. As this paper only sheds light on age range of 20-35 years, future investigations of elder group and its comparison with the results of this study would be much welcomed.
Second, this research only conducts in China, even though the sample covered 22 different provinces and municipality. It is still a limitation because the people’s mentality is different between countries. In other words, the results may be different if this study were taking place in other countries. A wider focusing on both Europe and America is also a quite interesting topic.
Third, after literature review, Figure 2 shows a complete new model for analysis the gender difference effect on online shopping intention. Because of the time limitation of master thesis, this study had to narrow down into a sub model (Figure 3) which only focuses on psychological gender difference effect on the relation between online shopping motivation and online shopping intention. Additional researches that explore the rest parts of new model from Figure 2 might yield additional insides into the gender difference effect on online shopping intention.
Fourth, quantitative research method was used in this study, a survey was sent out for data collection. However, the participants tended to answer the survey as who they think they are instead of who they really are. Therefore, adding a part of qualitative research will generate more reliable results. The reliability of this research could be strengthened by including a qualitative research method such as interview.
5.2 Managerial implications
This research has empirically identified 2 critical domains in the area of online shopping.
If the online store aims to sell masculinity products, they need to help consumers locating their wanted products easily, which means a well-organized website with a clear product catalog and a comprehensive product elaboration are very important. Besides, multiple convenience ways of paying and fast delivery are also critical issues. Because utilitarian motivated shoppers normally want to save time and effort during shopping. These actions can potentially enhance utilitarian motivated shoppers’ satisfaction.
Moreover, online retailers who focus on femininity customers have to not only focus on utilitarian motivated customers but also put more efforts on website design and supporting services to attract hedonic motivated shoppers. They could reinforce the linkage between products by adding handiness linkage, pictures and expressive product explanations, letting hedonic motivated shopper easily switch and compare between products. By this way, they will feel the pleasure of shopping online. Furthermore, it might be beneficial for the online retailers to builds a sophisticated and friendly online customer service team. In order to make the customers feels respected and cared just like in the traditional retail store.
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A p p e n d i x : q u e s t i o n n a i r e
Masculinity
喜欢运动的 Like sports
自立的 Self-reliance
有幽默感的 Have a sense of humor
奔放的 Unrestrained
心宽的 Broad-minded
精干的 Intelligent and capable
狭义心肠的 Enthusiastic 胆大的 Daring 不屈不挠的 Indomitable 开朗的 Optimistic 主动的 Proactive 豪放的 Bold Femininity 心细的 meticulous 娴熟的 adept; skilled 善良的 Kindness
爱整洁的 liking things to be neatly arranged
含蓄的 implicative 纯真的 Innocence 温柔的 Gentle 温顺的 Meek 守本分的 dutiful 柔情的 Tenderness 文雅的 Elegant 有耐心的 Patient Utilitarian value 我在网上购买的产品或者服务在各方面都是很高品质的。 当我在网上购买产品或者服务时，可以节省我的时间。 我在网上购买的产品与服务是价廉物美的。 网店上提供产品与服务具有很高的性价比。
1. The price of the product and/or services I purchased online is at the right level, given the quality. (J. Overby, E. Lee, 2006)
2. When I make a purchase online, I save time. (J. Overby, E. Lee, 2006)
3. The products and/or services I purchased online were a good buy. (J. Overby, E. Lee, 2006) 4. This Internet retailer offers a good economic value. (J. Overby, E. Lee, 2006)
Hedonic value
网上购物是我放松的一种方式。
网上购物不仅仅是购买产品或者服务，也能给我提供乐趣。 我经常去网店看看有没有新的或者有意思的产品。
在网上购物，或者只是浏览网上的商品让我有种从平时生活中解脱的感觉。 1. Online shopping is a way for me to relieve stress (M. Arnold, K. Reynolds, 2003)
2. This Internet retailer doesn't just sell product or services —it entertains me. (J. Overby, E. Lee, 2006)
3. I go online shopping to see what new products are available (M. Arnold, K. Reynolds, 2003) 4. Making a purchase or just browse products online truly feels like “an escape”. (J. Overby, E.
Lee, 2006) Shopping intention 我有计划再次在网上购物。 我愿意在未来一个月内在网上购物。 我会强烈建议我的朋友与身边的人在网上购买产品或者服务。 我每次需要些产品或者服务的时候我都会先去网上搜索。 1. I plan to use online shopping again. (Lin, 2006)
2. I intend to shop online within the next 30 days (Lin, 2006)
3. I will strongly recommend my friends to buy product online. (Lin, 2006) 4. I search online every time when I need something.
