GENDER DIFFERENCES IN PROCESSING INFORMATION: IMPLICATIONS FOR ONLINE SEARCH PATTERNS AND TASK PERFORMANCE

This research investigated gender differences in search patterns and on-line task performance. 125 subjects, recruited from a panel of consumers, have performed a series of tasks on different websites in a laboratory quasiexperimental setting. When compared to men, women spent significantly more time per page but view less pages (although this latter result is only marginally significant). However, no significant difference were noted in their overall performance as measured by success rate, time to accomplish the task and number of dead-ends encountered. The results support the theory of selectivity model arguing that women are comprehensive processors and men selective ones. Theoretical and managerial implications of these results are provided. Introduction In 2004, online shopping in America progressed by 29% (ComScore Networks Inc. 2005) for total consumer spending in the US of $117.4 billions. Yet, this number is rather modest in comparison of what sales would be if the quality of the sites used by consumers were improved. A recent study by Doubleclick (2005) reports that during the week following Thanksgiving (commonly referred to as Black Friday Week), the proportion of cart abandonment on US sites reached 55%. This means that more than one consumer out of two, who had started a transactional session, abandoned it at one point or another. These figures emphasize the importance of better understanding the information consumers seek on the Web as well as their decision processes. While much research has been done along those lines (Bucklin and al, 2002, Senécal and Nantel 2004, Senécal et al. 2005), it is still little known why consumers drop a task which they initially had decided to undertake. While site’s design and navigability have been shown to have a significant impact upon task completion (Nantel, MekkiBerradda 2004) especially through the impact of dead-ends incurred by customers, little has been done to look at individual variables. The present study represent an effort in this direction by providing a framework and empirical evidences that assess the role of gender as an explicative variable of search patterns and on-line task performance, including the propensity to encounter dead-ends. Gender differences in traits and abilities Men and women vary in their personality traits and their cognitive abilities. For example, men tend to exhibit traits congruent with the masculine stereotype reflecting their agentic orientation: assertiveness, autonomy, achievement, aggression and stoicism (Eagly, Wood and Diekman 2000). Men are also portrayed as more taskoriented and instrumental. In contrast, women are believed to be guided by a communal orientation and exhibits more feminine traits like tenderness and caring. Women are portrayed as being more expressive, interpersonal oriented and socio-emotional (Costa, Terracciano and McCrae 2001, Taylor and Hall, 1982, Minton & Schneider 1980, Carlson 1971, Gill and al 1987) as well as more risk adverse than men in general (Bruce and Johnson 1994, Barber and Odean 1999, Jranakiolos and Barnesek 1998, Stinerock and al 1991, Steger and Witt, 1989 ). Gender differences have also been noted in various cognitive abilities: for example, women excel in verbal and linguistic skills while men exhibit better visual spatial abilities (Geary 1996, Hyde and Linn 1988, Burstein, Bank and Jarvik (1980). Men have also consistently been found to outperform women in mathematical abilities (Maccoby 1998, Maccoby and Jacklin 1974, Solokoff, 2001). In the computer literature and more specifically in the context of the internet, gender differences have received some attention. First, the risk-adversity trait of women that has been studied in various contexts seems also manifest in the Internet one (Garbarino and Strahilevitz, 2004). Substantial literature have also supported the idea that women report higher anxiety with computer (Rosen and Maguire 1990, Igbaria and Chakrabarte 1990) and lower level of perceived self-efficacy or computer-related confidence than men (Brosnan 1998 in Ford and al 2001). Vankatesh and Morris (2000) studied the moderating role of gender in the adoption of a new software system. They revealed that the determinants of adoption vary between gender : Perceived usefulness of the technology (degree to which technology will enhance job performance) was the major factor men consider for acceptance on a new software. In contrast, the perceived ease of use of the software and the normative influence (influence of peers and superior perception) were important determinants for women. Ease of use and normative influence were not even salient for men. These findings support the previous taskorientation of men and confirm that women are more attentive to social cues in the environment (William and Best 1982, Roberts 1991). Studies on computer and Internet usage (Sheehan 1999, Van Slyke and al 2002 ) have also put in perspective the different motives and usage behaviours between gender : where men uses these tools to favour task-oriented or instrumental purposes (information search, downloading or buying), women use them more as a mean of building and sustaining interpersonal relationships, spending a greater portion of their time online using email to stay in touch with others (Jackson, 2001, Thompson, 2001, Akhter, 2003, Garbarino and Strahilevitz, 2004). An explanation of gender differences : the selectivity model How can we explain these differences in traits, abilities, propensity to adopt innovation like internet between men and women? Joan Meyers-Levy (1989) proposed a unifying framework that recognizes and integrates most of these differences under the umbrella of the selectivity hypothesis. According to the model, males and females differ in the strategies they use to process information: males are selective processors, they do not engage in comprehensive, extensive processing of all available information. Instead, they employ various heuristic devices that serve as surrogates for more detailed processing. Men will then tend to focus on the most salient cue or on cues that converge to imply a single inference. They focus on cues that are highly available and particularly salient in the context. They are more likely to be driven by the overall message themes, to use a schema-based or hypothesis-confirming strategy (Chung and Monroe, 1998) and not pay attention to subtle (sometimes disconfirming) details (Maheswaran and Meyers-Levy 1990 and Meyers-Levy and Sternhal 1991 , Graham, J.F. et al 2002). This schema-based orientation of men has also been studied in their interpersonal behaviour : Males interact on the basis of pre-conceived expectancies while females appeared to base their judgments on cues that emerged during the interaction with others (Christensen and Rosenthal 1982). Similarly the direction of males’ conversation is determined by what they process initially upon entering the conversation (Farrell 1974). In contrast, women are comprehensive processors who attend to assimilate all available information, encode more cues and elaborate more on them. Therefore, women attempt to engage in a rather effortful, comprehensive, piecemeal analysis of all information. Women are more likely to notice and elaborate on subtle cues. Meyers-Levy and Sternhal (1991) proposed that women have a lower threshold at which they notice these subtleties. For example, females are more responsive to subtleties that arise in the course of the conversation than men (Farrell, 1974). Furthermore, the selectivity hypothesis holds that males streamline the processing of external world information by focusing on self-relevant information, which acts as a heuristic device on which to base judgments. By contrast, females comprehensively process information, devoting relatively equal processing to information relevant to the self and to the external world of others. The selectivity can manifest itself in various ways. For example, in explaining men’s higher propensity to take risk, the selectivity model proposes that since men consider the most salient cue, they are more likely to focus on task effectiveness of a technology or return of investment in financial environment or convenience with online purchase without considering risk because it does not converge to a single inference. In contrast, women as detailed processors will consider all information available including the ones that are subtle and potentially disconfirming. Women are then more likely to incorporate risk and other secondary information in their decision and behaviour (Graham and al 2002). Another manifestation of the model is women’s tendency to render lower self-evaluations than do males in their ability. For example, females expect to perform less well than do males when clear performance feedback is absent but no gender differences emerge when performance guidelines are clarified (Lenney, Browning and Mitchell 1980). These differences may also be biological in nature. For example, recent clinical studies indicate that these differences may originate in the difference in brain laterization between gender: The two hemispheres are more symmetrically organized (integrated) in females and more specialized in males (Everhart et al 2001, Gorman and al 1992, Saucier and Elias, 2001). Males rely heavily on right-hemisphere processing, denoted by reliance on global rules whereas females primarily rely on left-hemisphere processing which concerns the specificities and intricacies represented or implied by stimulus information. (Meyers-Levy, 1994). These differences in brain functionality suggests that males would favour a more holistic, undifferentiated manner of processing while females should pursue a more detailed, elaborative processing style supporting the selectivity hypothesis. Therefore, this differential information processing between genders seem to be the result of social and evolutionary factors as well as differe