Online Marketing Communications: Exploring Online Consumer Behavior by Examining Gender Differences and Interactivity within Internet Advertising
Carolynn McMahan
University of North Florida
Roxanne Hovland and Sally McMillan
University of Tennessee
Abstract
To explore gender differences in Internet advertising, this study analyzes gender in relation to interactivity. Specifically, assessments of commercial Web sites help clarify the role of gender for online consumer behavior, its effect on interactivity and advertising effectiveness, and the implications for online marketing communications. This exploration relies on dimensions of consumers' online behavior and consumers' beliefs about the interactive communication environment in relation to three types of features: human-to-human, human-to-computer, and human-to-content. The investigation of gender differences in Internet advertising uses both computer observations with screen-capturing software and a survey. The study subjects are college-aged students, or Generation Y, a group of consumers who tend to go online in great numbers, have considerable spending power, and are computer savvy.
Keywords: Internet advertising, interactivity, online consumer behavior and gender.
Introduction
Despite the U.S. economic downturn and its dramatic impact on traditional forms of media, Internet advertising revenues in the United States totaled $23.45 billion in 2008, an increase of 10.6% from the $21.2 billion earned in 2007 (Interactive Advertising Bureau [IAB] 2009). The President and CEO of IAB further posits that "In this uncertain economy, where marketers know they need to do more with less, interactive advertising provides the tools for them to build deep, engaging relationships with consumers-the experience marketers gain from this will deliver dividends especially after the economy turns around" (IAB 2009). The defining characteristic of such Internet advertising is its interactive capability, which permits user engagement and control over the communication process. Furthermore, the ease of manipulating content and the ability to transfer information grants additional control to the user and enables two-way communication, capabilities that are not available through traditional media (Liu and Shrum 2002). In particular, corporate Web sites can provide a significant form of Internet advertising because of their promise of greater interactivity (Bruner and Kumar 2000; Doren, Van Fechner, and Green-Adelsberger 2000; Liu and Shrum 2009; Macias 2003; Singh and Dalal 1999). Furthermore, Web sites contain more features than most other online ad formats (Rodgers and Thorson 2000), which creates potential for greater interaction opportunities with consumers.
As a dimension of Internet advertising, interactivity enables companies to distinguish themselves from competitors, in that they can offer more engaging Web sites that still give consumers control over and a choice of interactions with the advertising. Many companies use gender segmentation strategies in this interaction; Web sites tend to be targeted to one or both genders, with separate hyperlink headings for men and women's products. However, these efforts appear largely unsuccessful with regard to gender-based strategies in online marketing communications, such as designing distinctive Web site features that can cater to women's and men's unique needs and expectations (Palanisamy 2005; Rogers and Harris 2003).
Furthermore, research indicates some differences in the determinants of men's and women's engagement in and satisfaction with Internet advertising (Dittmar, Long, and Meek 2004; Rodgers and Harris 2003). As more women go online-projections predict that U.S. women will constitute more than half (52%) of the online population, or 109.7 million users by 2011 (CBC News 2007)-sheer numbers require companies to think of both women and men when they design Web site content and online marketing strategies (Murrow 2005). Therefore, advertisers and marketers must understand gender-based differences in behavior and perceptions online to tailor Web site experiences and satisfy consumer needs.
Marketing Implications of Interactivity and Corporate Web Sites
Technology enables two-way communication in ways impossible for traditional media, so the advantages of this medium as a strategic communication tool are numerous. First, a corporate Web site can provide a substantial opportunity for brand building and relationship marketing. The Web site might create a link between the consumer interaction and the brand, likely better than traditional advertising media can (Dahlen, Rasch, and Rosengren 2003). The Internet thus dictates "that advertisers adjust to a new medium that is not bound by either space or time and that has the capability to involve and engage the consumer" (McMillan, Hwang, and Lee 2003, p. 400). According to Arnott and Bridewater (2002, p. 86) though, "marketers are making limited use of the interactive potential of the Internet."
Second, expressive product Web sites provide tremendous opportunities for companies and their advertising efforts, because "consumers want to interact with the brand and enjoy the feeling of it" (Dahlen, Rasch, and Rosengren 2003, p. 32). A site can serve as a strategic communication tool to create and sustain a relationship and positive feeling toward the brand. Researchers also find a positive correlation between brand attitude and time spent on a Web site. Companies should recognize the importance of interactive site elements that offer various interaction opportunities to engage and involve the user (Dahlen, Rasch, and Rosengren 2003).
Third, online retail sales in the United States could increase by 11%, to $156 billion, in 2009 (Forrester Research 2005). Analysts also predict that only those companies that adopt a strong lifestyle branding strategy will survive in this competitive retail marketplace. Even in a difficult economic climate, "interactive media continues to consume a larger piece of the overall advertising pie" (IAB 2009), which reflects the "ongoing secular shift from traditional to online media as marketers recognize that ad dollars invested in interactive media are effective at influencing consumers and delivering measurable results" (IAB 2009).
Fourth, interactivity has key implications in terms of reaching and connecting with target audiences. For the retailers in our study, for example, the target audience is Generation Y consumers. Generation Y represents 25% of the population and has considerable buying power. These college-aged consumers spend a considerable amount of time online and can be categorized as savvy computer users whose interactive activities include downloading, creating content, and chatting online (McMillan 2004). Their expectations for interactivity therefore are very high; they demand interactive elements that will engage them, entertain them, or speed up the process, while also giving them a sense of control. Marketers must realize how these consumers prefer to interact and how gender may influence their behavior if they hope to reach these savvy young consumers.
Interactivity
Previous research into gender differences online is limited, especially for corporate Web sites; research exploring gender and its influence on interactivity is almost nonexistent. Furthermore, the concept of interactivity appears to be changing and evolving over time and with continued technological advancements.
Early attempts to define interactivity were basic and technical, focusing on process aspects and responsiveness. For example, Rafaeli's (1988, p. 111) definition refers to "interactivity as an expression of the extent that in a given series of communication exchanges, any third (or later) transmission (or message) is related to the degree to which previous exchanges referred to even earlier transmission." Researchers have continued to expand on this basic definition.
One stream of research defines it according to features, or the characteristics of the site that make it engaging (e.g., Ahren, Stromer-Galley, and Neuman 2000; Carey 1989; Ha and James 1998; Jensen 1998; Lombard and Snyder-Dutch 2001; McMillan 2000; Novak, Hoffman, and Yung 2000), and emphasizes actual features such as e-mail options, navigational tools, transaction capabilities, customized content, user control, and timeliness. In contrast, studies that define interactivity as a process (e.g., Cho and Leckenby 1999; Ha and James 1998; Haeckel 1998; Heeter 2000; Macias 2003; Pavlik 1998; Steuer 1992) emphasize user control, responsiveness, exchange, two-way communication, and real-time participation for relationship building.
Interactivity also might be defined according to user perceptions of the interactivity of a site (e.g., Jee and Lee 2002; McMillan 2000; McMillan and Hwang 2002; Newhagen, Cordes, and Levy 1996; Schumann, Artis, and Rivera 2001; Wu 1999, 2000). Schumann, Artis, and Rivera (2001) broadly conceptualize interactivity as a characteristic of the consumer, not the medium, and emphasize the consumer's choice to interact. According to Wu (1999, p. 6), perceived interactivity is a "two-component construct consisting of navigation and responsiveness," though Wu (2000) furthers this construct by developing a scale with three dimensions: perceived control, responsiveness, and personalization. Perceived control is a person's power over the communication environment; perceived responsiveness addresses efficiency, speed, and real time capabilities in the computer-mediated environment; and perceived personalization pertains to the one-to-one dimension of customized online communication (Wu 2000).
McMillan (2002) combines features, processes, and perceptions to develop a multifaceted definition of interactivity, then expands each dimension into three unique types of interactivity: human-to-human, human-to-computer, and human-to-content. Features refer to the actual characteristics or functions of the communication environment that identify the Web site as interactive. The process category entails the actual use of the feature, and perceptions involve how the user mentally views the level of interactivity. In turn, human-to-human interaction requires two-way communication between users and other users, as well as between users and the company through features such as e-mail, instant messaging (IM), and live chat. Human-to-computer interactions occur through navigational tools, transactions, downloading, and customization features. Finally, human-to-content interactions occur with the computer and require the user to add content to the site, such as Web logging. In Table 1, we provide some representative examples of each dimension and type; in Table 2, we offer some additional feature examples. In turn, we apply McMillan's (2002) multidimensional conceptualization of interactivity to explore gender differences and interactivity; we also integrate Wu's (2000) perceived interactivity dimensions.
Table 1. McMillan's (2002) Multidimensional Definition of Interactivity
| Human-to-Human | Human-to-Computer | Human-to-Content | |
Features |
Instant messaging | Navigational tools such as menus | Tools that facilitate personalized content |
| Processes | Participating in an IM chat | Navigating a Web site | Creating a personalized home page |
| Perceptions | Believing that IM facilitates communication | Finding a Web site easy to control and engaging | Believing that customized content is interactive |
Table 2. Types of Features and Examples
| Type of Feature | Examples |
| Human-to-human: Features that allow two-way communication between organizations and users and other users | E-mail links, such as "contact us" or "send an e card," online chats, instant messaging |
| Human-to-computer: Features that allow for navigational control, customization and manipulation of content and transaction capabilities |
Hyperlinks, search functions, e-commerce functions, downloading videos, listening to music, playing games, manipulating products (e.g., creating a product by customizing of colors and features), registering |
| Human-to-content: Features that allow for addition of content to the Web site, to be viewed by others |
Web logging, Web debating, online discussion boards, posting photos, wish lists |
| Source: McMillan (2002) |
Gender Differences and Interactivity
Some previously identified differences associated with gender and interactivity warrant further exploration (Jackson et al. 2001; Li 2006; Phillip and Suri 2004; Weiser 2000). Gender refers to the behavioral, psychological, social, and cultural meanings associated with maleness and femaleness, as imposed and expected by society; it is learned in response to experience, whereas sex is biologically determined (Alvesson and Billings 1997; Pryzgoda and Chrisler 2000; Wood 1996). For the purposes of this study, we use gender and sex interchangeably, but we acknowledge that the difference is important and extant in academic literature.
To assess differences associated with gender and interactivity, we adopt the theory of uses and gratifications, which focuses on individual use and choice according to why and how people use media and the satisfaction they obtain from such usage (Baran and Davis 2003; Katz, Gurevitch, and Haas 1973). This theoretical perspective emphasizes how people interact with media uniquely, according to several personal characteristics such as gender (Korgaonkar and Wolin 1999). For example, men use the Internet primarily for entertainment reasons, such as listening to audio broadcasts, building Web pages, searching for products, and participating in online games. In contrast, women use it mainly for interpersonal communication and educational needs, relying on features such as e-mail and online chatting. Furthermore, men indicate that they shop more online than women, whereas women use e-mail more than men (Jackson et al. 2001; Weiser 2000). Weiser (2000) attributes such gender usage patterns to Internet experience; that is, women were less sophisticated in their use of the Internet, which led them to prefer simpler Internet features. This conclusion implies that men use more complex features than do women, a claim that warrants further exploration.
According to Jackson and colleagues (2001), male and female college students use the Internet on an equal basis, but the way they use it differs considerably. Women in their study reported using e-mail more than men, consistent with women's tendency to engage in more interpersonal communication. In using the Internet more for informational searches, men reported greater success than women, which the authors relate to their tendency to want to obtain information and their task orientation.
Phillip and Suri (2004) examine promotional e-mails and find that women evaluate the presence of links to additional sources of information more favorably than do men. Their female respondents also indicate a higher preference for the option to forward e-mail, consistent with prior research on women's need for social connections and desire to communicate online.
A meta-analysis by Li (2006, p. 550) reveals that "females tended to use more engagement approaches, challenge more, were more personal oriented, and tended to remedy or to make suggestions more than males. In contrast, males were more likely to use authoritative language, to present facts, to persevere, and have better access to computer mediated communication." Furthermore, Li (2006) finds that women use engagement approaches, such as graphic elements, rather than the task-oriented and information-driven approaches preferred by men. Hirschmann and Thompson (1997) similarly find significant differences between men's and women's interpretations or perceptions of advertising: Women seem more emotionally charged and include their personal feelings in their interpretations, whereas men appear more detached.
If we apply McMillan's (2002) three types of interactivity, the finding that men are more likely than women to use the Internet for information gathering and entertainment correlates with human-to-computer interactivity. Women's tendency to use the Internet to communicate instead correlates with human-to-human interactivity (Pew Internet and American Life Project 2005; Weiser 2000). Women's greater need for interpersonal relationships may help explain why women are more likely than men to view community and socialization as important reasons to go online and foster relationship building (Phillip and Suri 2004). Furthermore, men's use of the Internet for their informational searches and their success in those searches likely relates to their general information and task orientation, which reflects human-to-computer interactivity (Jackson et al. 2001). Other gender differences pertain to human-to-content interactivity; for example, men are more likely (59%) than women to create Web content (Pew Internet and American Life Project 2005; Weiser 2000).
Thus, gender differences clearly mark interactivity and Internet advertising, but more research is needed to address how gender, a "critical factor in developing marketing strategies via advertising messages," (Wolin 2003, p. 111), should determine the design of Web features that are engaging and interactive. With an awareness of how men and women use Web sites and what they expect and want in terms of their features, businesses can communicate more effectively online. Therefore, we explore two main research questions:
RQ1: What are the differences in the ways men and women engage with Internet advertising?
RQ2: What are the differences in men's and women's perceptions of interactivity in Internet advertising?
Internet Advertising Processing: Cognitive and Affective Needs
Advertising research suggests men and women differ in their cognitive and affective needs (Brunel and Nelson 2003; Meyers-Levy and Maheswaran 1991; Meyers-Levy and Sternthal 1991; Phillip and Suri 2004; Raman, Chattapadhyay, and Hoyer 1995; Weiser 2000). In particular, men exhibit a greater need for cognition, whereas women reveal a need for emotion (Raman, Chattapadhyay, and Hoyer 1995); however, these needs have not been explored in relation to gender and Internet advertising. Theoretically, need for cognition (NFC) emerged from the elaboration likelihood model (ELM) as a variable for exploring motivation and thinking (Liu and Shrum 2009; Macias 2000). The underlying assumption of ELM is that a central route produces more enduring judgments, based on extensive and critical elaboration of message claims, whereas a peripheral route results in simple and intuitive inferences that emerge from exposure to readily processed cues and involve little elaboration (Meyers-Levy and Maheswaran 1991). The ELM focuses on cognition but ignores the emotional aspect of advertising processes.
Traditional models, such as the attention, interest, desire, action (AIDA) concept, rely on the premise that "advertising is something done to consumers rather than something consumers interact with" (Huey 1999). Clarifications of AIDA designate three broader categories: cognition, affection, and conation (CAB) (Li and Leckenby 2007). Cognition refers to knowing, a person's knowledge, and the means of obtaining knowledge; it therefore contains the attention and interest aspects of AIDA. Affection involves both emotional and attitudinal meaning, or the desire aspect. Finally, conation entails behavior or observable acts, that is, the action aspect of AIDA (Li and Leckenby 2007). Whereas in a traditional perspective (Lavidge and Steiner 1961), a linear, step-by-step progression moves from cognition to affection to behavior, Robertson (1971) proposes a revision based on three assumptions: (1) A person may not be perfectly rational in his or her behavior, which means he or she does not necessarily process or carefully evaluate all available information; (2) there is no linear or specified sequence of stages in the process; and (3) the model is multidimensional and must allow for feedback loops (Li and Leckenby 2007). We provide the revised model in Figure 1.
Figure 1. Revised CAB Model
Source: Robertson (1971).
Need for cognition is a person's "tendency to engage in and enjoy effortful cognitive endeavors" (Cacioppo, Petty, and Kao 1984, p. 306). Cognition includes elements of awareness and judgment, and consumers have different NFC levels. For our study, NFC corresponds to a person's use of the Internet for information and thus can influence interactivity (Jee and Lee 2002). That is, the Internet requires a certain amount of cognitive effort, awareness, and judgment, and high NFC users likely enjoy these complicated tasks more (Haugtvedt, Petty, and Cacioppo 1992).
Meyers-Levy and Maheswaran (1991) also find that women respond to nonverbal stimuli with more associative, imagery-laced interpretations and more elaborate descriptions than do men (see also Myers-Levy and Sternthal 1991). The genders also differ in how deeply they process information; women exhibit greater sensitivity and ask more questions, which leads to a more in-depth processing of information (Meyers-Levy and Maheswaran 1991; Myers-Levy and Sternthal 1991). Women also tend to be more dependent on the left hemisphere of their brain, which indicates better adaptability to verbal functions, an ability to differentiate configured elements, and detailed analysis capabilities. In contrast, men depend more on the right hemisphere, which indicates a propensity to comprehend pictures and other nonverbal material and execute visual spatial activities (Myers-Levy 1994).
In contrast, need for emotion (NFE) is "the tendency or propensity for individuals to seek out emotional situations, enjoy emotional stimuli and exhibit a preference to use emotion in interacting with the world" (Raman, Chattopadhyay, and Hoyer 1995, p. 537). Thus, NFE is separate and distinct from NFC; it represents affect, which can influence consumers' interactions and may result in more time spent engaging with features, as well as higher opinions of that site (Coyle and Thorson 2001; Hoffman and Novak 1996). Hoffman and Novak (1996) conclude that positive subjective experiences provide a critical indicator of how involved people will become online, according to the emotions the feel during the interaction process.
Finally, research shows that women are more emotionally oriented than men (Booth-Butterfield and Booth-Butterfield 1990; Dittmar, Long, and Meek 2004; Raman, Chattopadhyay, and Hoyer 1995). Raman, Chattopadhyay, and Hoyer (1995) find that women score significantly higher on the NFE scale (mean 46.62) than do men (mean 43.83). Therefore, this scale might help explain some stable consumer behavior patterns. Dittmar, Long, and Meek (2004), in a qualitative study of offline and online consumer buying motivations, also discover that women exhibit much higher emotional involvement with the overall shopping experience and are more motivated by emotional and social factors in shopping online, whereas men are more motivated by functional factors, such as making a purchase quickly and efficiently. Women also express an experiential need to see, feel, and try products before buying them and note their concern that the Internet shopping experience lacked emotional and experiential dimensions. Rogers and Harris (2003) similarly find women perceive less emotional gratification from online shopping than do men, which they attribute to women's need to experience the product emotionally through more intricate details and dimensions that were not available on the Web site.
The implications from this collected research are that advertisers and marketers must consider not only thoughts and perceptions but also emotions as influences on consumer behavior (Rogers and Harris 2003). Marketers should address male and female consumers differently by communicating content in customized ways that tailor the Web experience to meet the needs of these consumers. Because men and women interact differently with content on the Internet and have different motivations for their behaviors (e.g., Phillip and Suri 2004; Rogers and Harris 2004; Weiser 2000), we propose a distinction between the affective and cognitive needs exhibited by men and women. We further posit that this difference influences their behavior, including purchase intentions, and therefore offer our third research question:
RQ3: In online consumer behavior, do cognitive and affective needs differ according to gender and purchase intention?
Method
To examine differences between men and women in relation to interactivity and Internet advertising, we adopt a two-step methodological approach. A computer observation, using Camtasia software, tracks consumer activity, and a survey explores consumer perceptions. Both methods rely on a laboratory setting, in which a researcher remained present to address issues of validity and reliability. This computer observation method helps overcome some limitations in research methods that use surveys instead of actual records of users' activity to measure online behavior. Camtasia, an onscreen recorder and video production software, can capture users' every movement, including menu selections, downloading, and video and game playing. It unobtrusively tracks such activities without disrupting the user's interaction with the Web site. We also administered a cross-sectional survey to assess the subjects' perceptions of the interactivity of three test Web sites.
Variables
We operationalize gender as a dichotomous variable; participants indicate if they are (1) male or (2) female. We also classify McMillan's (2002) multidimensional definition into two categories: user-based interactivity, which includes processes, actually using an interactive feature, and perceptions, which refer to the mental constructions of beliefs about the interactive communication environment, versus system-based interactivity, which includes features. To operationalize engagement, we measure the activities and amount of time spent with each of the three types of interactive features (i.e., human-to-human, human-to-computer, and human-to-content) with the Camtasia software.
We use three competitive athletic product Web sites for Nike, New Balance, and Reebok. According to feedback we gained from pilot focus groups with college students and industry research, three companies are major competitors (Yahoo Finance 2005).
Nike's (www.nike.com) is the only site that contains a human-to-human feature, that is, live synchronous chat with an e-mail option; Reebok's site (www.reebok.com) offers the human-to-computer features of sports, music and games; the New Balance Web site (www.newbalance.com) is the only one that contains human-to-content features within its Club NB feature. Nike's opening page, and indeed the entire site, is very sleek, with gray color tones and mobile product images that change into active athletes. The initial shopping page contains navigational links on the left, top, and bottom, with images of specific products in the middle. New Balance's opening page is more standardized, using gray and red colors and a white background. One image changes into other images; more informational links highlight features such as the Club NB vote and debate option, which allows users to debate a site-provided question with other uses on a debate blog. The images of people on the New Balance site indicate an active, real-world setting. The initial shopping page contains navigational links on the left, top, and bottom and changing product images in the middle. Finally, Reebok's site is more simplified, with colors that coordinate with changing images of professional athletes. The initial shopping page contains navigational links at the top only. The links include news, what's new, corporate information, social responsibility, and sports, music, and games, all featured in the middle of the screen.
For our operationalization of perceptions of interactivity, we use Wu's (2000) scale of perceived control, perceived responsiveness, and perceived personalization with slight modifications. The perceived control items address aspects of McMillan's (2002) human-to-computer and human-to-content interactions. The perceived responsiveness items pertain to McMillan's (2002) human-to-human type, and perceived personalization questions relate to human-to-human and human-to-content interactions.
We use an 18-item, five-point Likert scale to measure NFC (Cacioppo, Petty, and Kao 1984).For NFE, we use a 7-item, five-point Likert scale developed by Raman, Chattopadhyay, and Hoyer (1995).
Procedures
We recruited 80 college students, 40 men and 40 women, from a large southeastern U.S. university to participate in the computer laboratory study. Students received extra credit for participating, and we scheduled the lab times to accommodate their schedules. Participants first completed Part I of the survey, which included the cognitive and affective scales, product involvement items, and demographic questions, then informed the experimenter when they were ready to begin Part II. Therefore, we knew precisely when to begin the Camtasia recording. Fifteen male and 15 female students were randomly selected to participate in the Camtasia recording. Once a participant indicated that he or she had completed Part I, we started the Camtasia recording, which initiated the screen recording. The Camtasia recording device did not affect participants' surfing capability in any way. Finally, after students completed and submitted their survey, the experimenter stopped the recording, saved it to an external hard drive, and then converted it to a video format.
Part II includes the computer section of the survey, featuring questions about the subjects' perceptions of interactivity, affective and cognitive needs, and purchase intention. The participants visited each site and simulated a purchase of any type of athletic shoe, spending as much time as they wanted on each site. Students were instructed to surf the three Web sites to shop only for athletic shoes, with the intention to evaluate the three sites and purchase from one of them. To minimize participant biases during the survey administration, we randomly ordered the surveys in terms of the questions about the three athletic companies; that is, the respondents did not consistently analyze the same site in the same order or at the same time.
Analysis and Results
In our convenience sample, the subjects' average age is 22 years, and 71% identify themselves as seniors. They represent diverse academic majors, including business, science, engineering, psychology, liberal arts, and communication, though a majority (49%) are majoring in communications. Furthermore, 27% spend an average of 4-6 hours per week on the Internet, followed closely by 24% who spend 1-3 hours, 21% who spend 7-9 hours, and 19% who spend 13 hours or more online every week. Finally, 49% had created a Web site, 64% indicated they were very comfortable using the Internet, and 72% said they were moderately skilled.
To measure the internal consistency of the instrument scales, we use Cronbach's alphas and assess the reliability of the measurement scales. All the Cronbach's alphas fall in the range of .85 to .91, which indicates high reliability.
To explore RQ1, we analyze the Camtasia software recordings for our sample of 30 recorded subjects. For this analysis, we coded the time spent engaging in each type of interactivity, that is, human-to-human, human-to-computer, and human-to-content. We also measured the overall time spent on each Web site.
Before beginning our statistical analyses, we develop descriptive analyses of men's and women's online activities across the three types of interactivity. For each type, we code a list of specific activities, based on the Camtasia recordings of all 30 participants for each Web site and by gender. Thus, we obtain an overall depiction, in which each activity gets represented only once by gender.
The overall activities associated with human-to-human interactivity indicate only one slight difference: Women communicate more with the organization on all three Web sites, using e-mail and/or live chat links. Men only communicated with the organization on the Nike and New Balance sites, not on Reebok's. In Table 3, we summarize these activities by gender and Web site.
Table 3. Summary of Human-to-Human Interactivity by Gender and Web Site
Men |
Women |
|
| Nike | Live chat link
Got a question link Ask the shopping assistant e-mail link |
Live chat option |
| New Balance | Talk to Us e-mail link | Talk to Us |
| Reebok | Contact Us link |
The overall activities for human-to-computer interactivity suggest several gender-based differences. Men play games on the New Balance site; women do not engage in any types of games. Men watch streaming videos of products on the Nike site and commercials on the New Balance site; women only watch running videos on Reebok. Women download songs and wallpaper from Nike, whereas men only download wallpaper. Men engage with professional athletes' links, such as Jordan or the Summer of Lebron, as well as the athletes' training room and statistics links on the Nike site; women visit none of these links. Whereas women visit the corporate communication links on all three sites, searching for jobs and viewing news and promotional information, men visit no corporate communication links. Finally, women engage in activities that provide additional technical information about the product, such as Nike's Pro Fit links and Tech Centers; men do not. We summarize these activities in Table 4.
Table 4. Summary of Human-to-Computer Interactivity by Gender and Web Site
| Men | Women | |
| Nike |
|
|
| New Balance |
|
|
| Reebok |
|
|
In analyzing overall activities for human-to-content interactivity, we find only that men change the content of both the New Balance and Reebok sites by clicking on their China option, whereas women only use the U.S. link and do not customize or change content based on language. In Table 5, we summarize these activities by gender and site.
Table 5. Summary of Human-to-Content Interactivity by Gender and Web Site
Men |
Women |
|
| New Balance |
|
|
| Reebok |
|
To analyze differences in the ways men and women engage with Internet advertising, we also measured the time spent undertaking each of the three types of interactions, along with overall time spent on each site, tracked through Camtasia. For the human-to-computer category, we did not include navigational functions that facilitate shopping within the sites in the time spent measures, because this basic function is required to maneuver through any Web site. We perform statistical tests on the time spent, including repeated measures analysis of variance (ANOVA) to compare gender, time spent on each of the three types, and overall time on each site.
The ANOVA tests indicate no significant interaction between gender and human-to-human interactivity for each Web site (F2, 27 = 1.385, p = .267), though another ANOVA reveals a significant interaction between gender and Web sites for human-to-computer interactivity (F2, 27 = 6.126, p = .006). That is, men interact, across the three Web sites and in terms of human-to-computer interactivity, significantly differently than the way women interact. The graph in Figure 2, which depicts men with a blue line and women with a pink line, depicts this gender interaction effect according to the differences in the means of time spent using human-to-computer interactive features across the three Web sites. We note the great difference in the time men spend interacting with Nike compared with the time women spend on this site.
Figure 2. Means of Time Spent for Human-to-Computer Interactivity
To explore this significant interaction, we examine gender separately and perform separate repeated measures ANOVA for men and women, in which we compare the three Web sites. We find significant differences for men in their human-to-computer interactivity across the three sites (F2, 13 = 4.718, p = .029). The pairwise comparisons in Table 6 show the average time spent on the three sites; men spend significantly more time on human-to-computer activities on the Nike compared with New Balance site and marginally more time with Nike than with Reebok.
Table 6. Pairwise Comparisons: Three Web Sites, Men, and Human-to-Computer Interactivity
| Web Sites | p-Value |
| Nike-New Balance | .008 |
| Nike-Reebok | .062 |
| New Balance-Reebok | .328 |
That is, the average time men spend on human-to-computer interactivity within Nike is 97.67 seconds, followed by Reebok (average 40.47 seconds) and then New Balance, with an average time spent of 23.87 seconds. Table 7 lists the mean values.
Table 7. Mean Comparisons of Time Spent on Human-to-Computer Interactivity
| Web Site | Mean: Men | Mean: Women | Mean: Overall |
| Nike | 97.67 | 38.47 | 68.07 |
| New Balance | 23.87 | 52.60 | 38.23 |
| Reebok | 40.47 | 40.00 | 40.23 |
For women, the repeated measures ANOVA shows no significant differences in the human-to-computer interactivity across the three sites (F2, 13 = 1.402, p = .281).
We also use a repeated measures ANOVA to analyze human-to-content interactivity across sites and genders, according to the time spent. We find no significant interaction between gender and Web sites for human to-content interactivity (F2, 27 = .899, p = .419); that is, men and women do not interact significantly differently in human-to-content contexts across three Web sites.
Within specific activities associated with human-to-human, human-to-computer, and human-to-content interactions, we undertake cross-tabulations and Pearson's chi-square analyses to test for significant differences between men and women and across individual activities. The activities represent all three types of interactivity; the Camtasia-based analysis indicates whether the 30 students clicked on the link and participated in that activity.
The only type of interactivity that indicates significant gender differences is human-to-computer interactivity. Specifically, for the customization feature on Nike's Web site, 40% of male subjects designed and customized an athletic shoe, compared with only 6% of female subjects. The chi-square analysis indicates this difference is significant (c2 = 4.658, p = .031). The New Balance events and sponsorship link gets clicked on by 53% of women, who then spent time viewing the events link; only 6% of men even clicked on the link. This difference also is significant (c2 = 7.778, p = .005). Finally, with regard to their downloading activities (e.g., wallpaper, music, videos, available from both Nike and Reebok), 40% of women clicked on the features compared with only 6% of men. The chi-square analysis indicates that the difference is significant (c2 = 4.658, p = .031). We summarize these results in Table 8.
Table 8. Percentages and p-Values for Specific Interactive Activities
| Interactive Activity |
Men |
Women | p-Value |
| Nike live chat | 20% | 53% | .058 |
| E-mail links for all sites | 53 | 73 | .256 |
| Nike customize | 40 | 6 | .031 |
| New Balance events | 6 | 53 | .005 |
| Nike and Reebok downloading | 6 | 40 | .031 |
| Nike and Reebok videos and games | 20 | 0 | .068 |
| Manipulation of products for all sites | 53 | 80 | .121 |
| New Balance Club NB | 33 | 26 | .659 |
To analyze RQ2, we perform repeated measures ANOVA for each of the three categories of perceptions (control, responsiveness, and personalization) to compare gender and the three Web sites, while controlling for product involvement and Web experience. Independent sample t-tests for men and women and Web experience (t = 1.050, p = .297) return a high average score for Web experience across the whole sample of 4.13.
We also perform independent sample t-tests for product involvement and gender. Product involvement consists of a 10-item, seven-point differential scale for rating athletic shoes, and the results indicate no significant difference across genders (t = .511, p = .611). The overall average composite score for product involvement falls in the middle range, at 4.83.
For perceived control, we run ANOVA tests, with product involvement and Web experience as control variables. The findings show no significant interaction between gender and Web sites (F 2,75= .838, p = .437). A similar ANOVA for perceived responsiveness returns a significant interaction between gender and Web sites (F 2,75= 3,266, p = .044), as we show in Table 9. That is, we find key differences in men's and women's perceptions of responsiveness for each of the three Web sites.
Table 9. ANOVA for Perceptions of Responsiveness
| Effect | Wilks' Lambda | F | Hypothesis df | Error df | Sig. |
| Web site | .999 | .042 | 2.00 | 75.00 | .959 |
| Web site × Involvement | .984 | .601 | 2.00 | 75.00 | .551 |
| Web site × Web experience | .965 | 1.341 | 2.00 | 75.00 | .268 |
| Web site × Gender | .920 | 3.266 | 2.00 | 75.00 | .044* |
| * p < .05. |
The graph in Figure 3 suggests that men perceive Nike and New Balance as much higher in terms of responsiveness, compared with Reebok. Women appear to perceive that Nike rates highest in terms of responsiveness, followed by New Balance and then Reebok.
For perceived personalization, the ANOVA tests again use product involvement and Web experience as control variables; the findings show no significant interaction between gender and Web site (F 2,75= .247, p = .782).
Figure 3. Perceived Responsiveness Means
Finally, to test RQ3, we perform independent sample t-tests for cognition and gender; they indicate no significant difference in men's and women's NFC (t = 1.292, p = .200). However, the independent samples t-tests show significant differences in their NFE (t = .2.217, p = .030); women have a significantly higher level of emotion than men. To examine whether cognitive and affective needs might differ by gender and purchase intention, we perform ANOVAs for both cognition and emotion, comparing gender and purchase intention. The NFC results comparing gender and purchase intention reveal no significant interaction effect (F 2,75 = .151, p = .699). However, the main effect results suggest a significant difference in NFC and purchase intentions (F 2,75 = 7.157, p = .009). Specifically, participants who chose New Balance express a significantly higher NFC level (2.8) compared with those who chose Nike (2.6). The ANOVA test for NFE comparing gender and purchase intention indicates no significant interaction effect (F 2,75 = .066, p = .798) and no significant main effects interaction (F 2,75 = 2.199, p = .142).
Discussion
This study provides several contributions and implications for online marketing communications in terms of interactivity and gender differences in Internet advertising. First, due to changes in the demographic composition of Internet users (CBC News 2007), this study represents a critical contribution because it adds to the limited body of research exploring men's and women's online consumer behavior. The findings indicate that men and women differ in their usage and time spent on the various types of interactive features available on corporate Web site, especially human-to-computer interactions. This finding should warrant further research into gender differences and types of interactivity in different Internet advertising formats and for various consumer groups. Second, the time spent on different types of features on Web sites differs for men and women, but the overall time spent shopping is not significantly different. Further research should consider exploring the concept of stickiness in relation to the types of interactive features, not overall time.
For practitioners, we offer several implications. Advertising efforts should focus on the audience, and the capabilities of the Internet make the connection between the audience and the advertiser much more direct (McMillan 2005). Companies can communicate more effectively with consumers on an individual basis and tailor their message to the interests and expectations of that consumer (Liu and Shrum 2002). The Internet dictates "that advertisers adjust to a new medium that is not bound by either space or time and that has the capability to involve and engage the consumer" (McMillan, Hwang, and Lee 2003, p. 400).
The Internet as an advertising medium is extremely underutilized in current advertising media budgets. Trends indicate significant increases though, which should make individual consumer factors, such as gender, even more critical for understanding how to tailor and customize advertising messages. Therefore, the finding that men and women engage with Internet advertising differently, especially with regard to specific activities within the human-to-computer interactivity context (e.g., downloading, event viewing, product customization), implies that marketers and advertisers should customize their Internet advertising and the online shopping experience to appeal to the differing activities preferred by men and women. Marketers should recognize that it is not the number of interactive features that is important but rather the types that male and female consumers prefer. For example, a Web site could have too many interactive features and thus hinder online communication efforts. Especially in this tough economy, marketers must adopt IAB President Randall Rothenburg's perspective that interactive can enable marketers to build meaningful customer relationships and earn further benefits even after the economy recovers (IAB 2009).
Regarding e-commerce, research is just beginning to explore gender-based differences in shopping patterns and purchase intentions. As women engage in online shopping in increasing numbers, such gendered behavioral patterns should be recognized and considered when designing Web site features. In offline retailing, women influence more than two-thirds of household expenditures, and though this level of influence may not exist online currently, it seems likely to approach that point in the near future (Murrow 2005). In contrast with previous research, we find that women are not just shopping and communicating online using simpler features that indicate their lack of technological competence (Weiser 2000). Rather, women use complex features, such as downloading and event viewing, to enhance their shopping experience. Marketers should start customizing and personalizing online shopping experiences by gender, perhaps by designing features on Web sites that cater to women's needs. Weiser's (2000, p. 170) statement thus appears is even more significant today:
Clearly, the increased presence of women on the Internet has made gender relevant for e-business. Hence, recognizing women's increased Internet presence, investigating specifically what it is they want from the Internet and why they use it, and promptly responding will become a crucial key to success in Internet advertising and e-commerce.
Methodologically, this study should provide an impetus for further research into the capabilities of the Internet to record actual consumers' online behavior, rather than relying on self-recorded data from surveys. Marketers and advertisers can apply our exploratory analysis of actual online consumer behavior that uses screen capture software, which could be replicated in other research endeavors. Many companies already recognize the importance and value of alternative research methods that can complement survey results. Because the Internet allows for greater personalization and customization, this methodology could be very helpful in consumer behavior research. Academic research also could benefit from the use of screen-capturing software to explore online consumer behavior.
Our finding that women and men are not significantly different in their cognitive needs online may begin to dispel some preconceived stereotypical notions-that women lack technological sophistication or Internet experience and therefore do not exhibit levels of judgment or thinking capabilities as high as men's (Weiser 2000). Women also appear to have gained dramatically greater technological capability and confidence, and they go online in numbers that in some cases even surpasses the number of men (Wasserman and Richmond-Abbott 2005).
Women score higher on NFE in Internet advertising, which may imply that marketers and advertisers should address emotions as influential over behavior (Rogers and Harris 2003). Shopping on Web sites can be an emotional experience; because women have a significantly higher NFE than men, marketers should target male and female consumers differently. Such online gender targeting could be accomplished by communicating and customizing content in different ways to tailor Internet advertising to the needs of each gender.
Limitations and Further Research
As does any research, this study has some limitations that should be addressed. First, we use a convenience sample of college students and thus cannot generalize the findings beyond this relatively homogenous group. Second, we consider only one product category, athletic shoes, and three brands. The generalizability of our findings is thus further limited, because Nike, New Balance, and Reebok athletic shoes represent transformational products; many other Web sites' products and services are informational or fall somewhere in between informational and transformational. Third, we administer the survey in a laboratory setting, which increases our control and internal validity but at the expense of external validity. The results might differ, and students might have spent more time shopping on the sites, if the experiment took place in students' normal, comfortable "surfing" environments, such as on a home or dorm computer instead of in a controlled artificial environment.
Much more research is warranted in the area of gender differences and interactivity in Internet advertising. In particular, researchers should explore conditions in which differences may or may not exist. It should especially extend our research to a broader range of product categories and Web sites; gender differences may be more pronounced for some product categories than others.
Finally, additional research might explore different populations, such as Baby Boomers or Matures, who are not as experienced on the Internet, to determine the potential effect of gender differences. Alternatively, research on gender could consider younger teens within Generation Y. It might also be interesting to explore a diverse population, comprising participants from various age groups, race classifications, and cultural backgrounds. Although we focus on gender differences, other demographic variables-such as age, income, race, ethnicity, or marital status-also might have interesting influences on the uses and perceptions of Internet advertising. Such variables could be explored in combination with gender to develop patterns of online consumer behavior and explore whether other variables may make gender differences significant. Additional research could then expand on and examine other covariates that should be controlled for when examining perceptions of Internet advertising, such as situational and motivational factors and satisfaction levels.
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About the Authors
Carolynn McMahan (Ph.D., University of Tennessee) is Assistant Professor of Advertising at the University of North Florida. Her research interests include online marketing and interactivity, cross-cultural gender portrayals, and international marketing and advertising. She has authored numerous journal articles and conference proceedings. E-mail: cmcmahan@unf.edu.
Roxanne Hovland is Professor of Advertising and Public Relations at the University of Tennessee. Her research interests include gender and diversity in advertising, advertising regulatory issues, and the relationship between advertising and consumer culture. She has authored and/or edited several books, along with numerous articles in journals and conference proceedings. E-mail: rhovland@utk.edu.
Sally J. McMillan is Professor of Advertising and Public Relations at the University of Tennessee. Her research focuses on exploring interactivity, definitions and history of new media, online research methods, health communication, and impacts of communication technology on organizations and society. She has published in leading journals and conducted research funded by agencies ranging from the National Cancer Institute to the American Academy of Advertising. E-mail: sjmcmill@utk.edu.
