Keywords

1 Introduction

In light of changing online markets, heightened investigation into group-specific needs is an inevitable part of exploiting new potential and staying afloat in the fast-paced online retail world. For this reason, gender marketing, also called gender commerce, has become increasingly important in recent years [1]. Building upon traditional marketing, gender marketing encourages adapting marketing efforts to the unique needs of each gender [2]. Thus, men and women are assigned specific characteristics and stereotypical behavior as a foundation for implementing more targeted marketing activity [2]. With regard to e-commerce, a variety of studies have already been conducted into gender-specific information research and buying behavior [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]. However, research has yet to evaluate how women’s and men’s information research and buying behavior can be influenced by a feminine or masculine online shop-design.

Multiple experts have noted that predominately “masculine design thinking” [27] has resulted in the existence of primarily masculine online shops [1]. Meanwhile, the expression “pink it and shrink it” [1, 28], in reference to the feminine optimization of online shops, demonstrates a seriously simplified approach to online gender marketing while strengthening stereotypical assumptions. This research therefore aims to explore the following question: Do women and men exhibit different information research and buying behavior in online shopping environments depending on the gender-specific design of the online shop?

2 Conceptual Framework and Hypotheses

2.1 Review of Literature

Regarding research into gender-specific differences in online-shopping, an array of academic work has focused on the constructs of consumer behavior, on the information research and buying process, as well as the online shop-design:

  • Constructs of consumer behavior covers psychological factors not directly observable [29] (e.g. perceived risk, trust, motives, attitude). The authors come to different conclusions, however. While Chen et al. and Pascual-Miguel et al. determine a connection between gender and perceived risk, [3, 4] Nadeem et al. can confirm no such link [5].

  • Studies of the buying decision process (including information search and processing, buying decision). According to Rodgers and Harris, women process information more strongly in their left hemispheres, thus absorbing more detail and intricacy from a website than men do. This substantially influences how a product or website is evaluated [23]. Moreover, women absorb information more comprehensively than men do [6, 7, 26] thereby more intensely dealing with the content of the online shop [15]. Male participants, on the other hand, demonstrate holistic and selective information intake [6, 15, 23]. In sum, men behave more functionally in their buying behavior [24]. Bae and Lee indicate that women more highly value the opinions of others. They were able to determine through their research that women are more strongly influenced by online customer reviews in their buying decisions [17].

  • Studies focusing on online shop design covers, among others, color, design, usability, images, usually shed light on one element of the online shop (e.g. graphics, reviews, language-style). With regard to gender-specific differences, Mahzari and Ahmadzadeh as well as Ellis und Ficek found women to prefer warm colors and harmonious color schemes [9, 30] while men gravitated toward cool colors and neglected any harmony in the coloring [9, 30, 31]. Dittmar et al. have determined buying environment to have a larger effect on women than on men [24].

In addition to these academic papers, a whole host of non-scientific publications exist on the topic as well. Blog posts and similar articles discuss single elements of online shopping including gender differences in shop design. The authors are unaware, however, of any study looking into the influence that feminine or masculine online shop-design has on men and women’s information research and buying behavior.

Scientific studies into gender-specific online information research and buying behavior from recent years focused primarily on surveys (except from the work of Djamasbi et al.) [20]. Since experts point to the necessity of using observations such as eye tracking to examine true behavior, [11, 20] it is of the utmost importance to continue investigating the research question with use of the eye tracking technique.

2.2 Research Object: Online Shops with a Gender-Specific Design

For this study, two online shops were selected. One shop that ranked high on typical female attributes, and one that ranked high on typical masculine attributes. The identification of attributes as well as the selection of these online shops rests on the insights gained from the secondary literature in the previous chapter. Of special interest is the literature on online shop design. The following criteria were used to evaluate the feminine shop design: color scheme, shapes, incorporation of social media, product page, shopping cart/basket, shopping-assistance functionality, trust-building elements, language-style, and sources of inspiration. The masculine shop design was evaluated on functionality, web design, language-style, website construction and product page.

By the end of the screening process, 20 online shops were determined to exhibit gender-typical elements. The first place for feminine design was Zalando, second ABOUT YOU, and third Asos. For the masculine sites, first place was Cyberport, second Amazon and third Conrad.

For the sake of the study, both shops must offer the same type of product, which was, in this case, shoes. After an evaluation, Zalando (www.zalando.de) was selected as the feminine research object. Zalando was originally conceived as a European version of Zappos.com. Amazon (www.amazon.de) was chosen as the typically masculine shop, because the number one, Cyberport, does not offer shoes.

This study did not examine the entirety of both online shops but focused rather on the product page. A customer can access the product page from the product overview page. It must be noted that the customer can also arrive at the product page via external sources e.g. a search engine, affiliated links or social media platforms.

Overall, the product page has two purposes: (1) to present the customer with enough relevant information on the product so that he or she need not look elsewhere and (2) display the product attractively in order to generate a purchase [32].

2.3 Hypotheses Development

As previously explained in more detail, it is widely understood that there are differences between men and women in their information research and buying behavior on online shops. It can be assumed that an online shop with a feminine design will positively influence the information research and buying behavior of a woman. Similarly, it can be assumed that a masculine online shop design will have a positive effect on a man’s information research and buying behavior. The rationale behind these conclusions is that the gender-specific design of the online shop (m/f) better satisfies the needs of each respective gender. This inference, combined with findings in literature, fed the basic hypothesis of this study: “Women and men exhibit differing information research and buying behaviors when online shops are designed to appeal to their particular gender.”

Deriving Hypothesis 1:

It is assumed that female consumers enjoy browsing, often becoming sidetracked during the buying process. In this context, women perceive online shopping as a relaxing experience, opting to take their time in search of inspiration [10, 15]. Studies indicate that men, in comparison, are quicker and more targeted in their approach to online shopping [12, 15, 22, 24]. Kempe also corroborates that men tend to deem online shopping a waste of time and enjoy consumption less than women do. Men are therefore more motivated by efficiency and making a quicker purchase [12]. Contrastingly, women exhibit a more browsing shopping behavior, which can incorporate many diversions [10, 15]. It can therefore be assumed that men require less time than women for the information research process.

  • Hypothesis 1 (H1): Differences can be seen in men and women’s ‘dwell time’ on online shops depending on the gender-specific design of the shop.

Deriving Hypothesis 2:

It is assumed that a gender-specific difference exists regarding the perception and information processing of online shop features (product images, product ratings, recommendations and product descriptions). As a whole, this can be substantiated by the fact that women are more comprehensive information processors while men are more selective [6, 7]. Richard et al. emphasizes that, due to their type of information processing, women are more engaged with a website and even enjoy discovering its content. In comparison, men prefer a more heuristic approach to finding information on a well structured website that enables quick and easy information processing [15]. We predict that these behaviors during online shopping also apply when judging an online shop’s various features.

  • Hypothesis 2 (H2): Differences can be seen in men and women’s use and regard of individual features of a product page depending on the gender-specific design of the online shop.

Hypothesis H2 is divided into the following five sub-hypotheses. Doing so guarantees a detailed and more precise assessment of the differences between the gender-specific online shops.

  • H2a: Differences can be seen in how men and women regard the features of the product page as a whole depending on the gender-specific design of the online shop.

Regarding how women judge the product image, it could already be determined that they are influenced by the size of the image because women tend to notice details more than men [13]. In the context of the product page, it can therefore be inferred that women contemplate an image longer, in more detail and more often than men do. The corresponding hypothesis is:

  • H2b: Differences can be seen in how men and women regard product images depending on the gender-specific design of the online shop.

Women more highly value others’ opinions [22]. In various surveys, women responded that reviews from other customers are important to their decision process when making purchases [17]. We therefore predict that:

  • H2c: Differences can be seen in how men and women regard product reviews depending on the gender-specific design of the online shop.

The authors have not been able to find any scientific studies linked to the influence of product recommendations on genders. On the whole, however, women tend to be more susceptible to impulse purchases, [12] so it can rightly be inferred that recommendations (i.e. for other suitable products) have a stronger effect on women.

  • H2d: Differences can be seen in how men and women regard product recommendations depending on the gender-specific design of the online shop.

Based on the statement that men wish to finalize their purchases quickly and easily, it can be assumed that they more readily look for concise, fact-heavy information and will therefore focus their attention on product descriptions.

  • H2e: Differences can be seen in how men and women regard product descriptions depending on the gender-specific design of the online shop.

3 Methodology

3.1 Design and Participants

The present experiment has a between-subject design [33, 34]. The differences between genders (male/female) are observed as well as the gender-specific design of the online shop (masculine vs. feminine) and the behavior of each gender. This study therefore relies on a two-factorial design with four experimental groups: Gender (male/female) and online shop (masculine design/feminine design).

The experiment is designed to employ an eye-tracker to observe the consumer’s behavior [35]. Since the participant is aware that his or her eye movements are being tracked and behavior in the online shop observed, the experiment can be classified as an open observation. However, the participant is not supposed to know the relevance of gender differences for this study as this could have consequences on the authenticity of their behavior [36]. The experiment took place in the research laboratory of the University of Applied Sciences Niederrhein in Krefeld and utilized a stationary eye-tracker with a number of adjustable features. As a result, the study is classified as a laboratory experiment [35, 36]. Participants of the study were students (N = 80, 40 women, 40 men, ages 18 to 36). Students were selected at random, which is why they represent different fields of study at the university. There was no direct compensation for the participants.

3.2 Eye-Tracking as an Observational Method

The eye-tracking system RED 250 from SensoMotoric Instruments (SMI) was used throughout the course of the experiment and is based on the cornea-reflectometry or the corneal-reflex method, which is common in modern eye-tracking systems [37, 38]. This system is often preferred over other systems because it neither impairs participants with glasses nor by fixing their chin, therefore leaving the head to move freely [38]. Additionally, it captures a set area on the screen, which simplifies the assessment of gathered data. An initial calibration of the device was crucial for each participant in order to collect optimal measurements. For the calibration procedure, the system measured the test person’s eyes. Proper measuring of eye positioning was vital to ensuring the reliability of the data [34].

3.3 Tasks

Duchowski especially highlights the importance of the eye-tracking survey and its function. Eye-tracking surveys are dependent on the tasks given to participants, which is why the assigned task can have a significant influence on the results. Special care should therefore be taken when choosing the task to be carried out [34].

In the present experiment, participants are assigned a task to be accomplished in an online shop. More specifically, the participant is instructed to select one or more items from the online shop and put them in the shopping cart. They are meant to behave as if in a typical online shopping situation. All the while, details are being observed on how the participant reacts to individual features of the (gender-specific) design of the product page. Of interest are the features used – especially by each gender – that therefore influence the buying decision.

The item to be purchased during the experiment was a pair of running shoes. There is a wide selection of running shoes available on both feminine and masculine online shops. They are easy to find in a navigation menu, there is a large quantity of them available, and they are usually accompanied by reviews on the product page. Moreover, the amount of men and women who run recreationally in Germany is at a balanced 53% and 47% respectively [39]. Running is also the second most popular physical activity behind fitness training for ages 18–39 in Germany [40]. Running shoes are available both in the typically masculine online shop (Amazon) and the typically feminine (Zalando).

4 Metrics

The first step will explain how the necessary metrics were extracted using areas of interest (AOIs). The second step will explain which metrics are relevant for this paper.

The AOIs make it possible to analyze selected areas, such as product images, on the online shop. By means of these AOIs, the eye-tracking system determines if and how long the participant examined the relevant area of the screen. The resulting data can then be displayed individually for each subject or as aggregate and median values [37]. For the present study, the product page was divided into four AOIs – product image, product description, recommendations and product rating (see Fig. 1).

Fig. 1.
figure 1

Areas of interest (AOIs) - product page (This figure shows product pages of www.zalando.de (left side) and www.amazon.de (right side). Please note product pages are only examples. The AOIs vary in size and position.)

Full size image

In the context of eye-movement measurements, saccades and fixations are the two fundamental types of eye-movement activity. Saccades are fleeting eye “jumps” that move a person’s gaze to different points. It has been proven that during saccades, no information is processed as perception is interrupted during the movement [38]. Fixations, on the other hand, are resting periods during which the observer’s retinas stabilize and fixate on a point of interest for at least 300 ms [34, 37]. Only during a fixation can information be recognized, analyzed and retained [29, 34, 37, 38].

Due to the established AOIs, other metrics beyond saccades and fixations were collected that both directly and indirectly relate to eye tracking. In this case, other metrics and indicators of interest were those that supplied general insight into information research and buying behavior (e.g. time). Table 1 provides an overview of the areas used in this study and short explanations thereof.

Table 1. Overview eye-tracking metrics
Full size table

5 Results/Hypotheses Testing

In testing the hypotheses, the first step included assessing all variables by means of a two-way ANOVA in order to track the interaction and reciprocal effect between online shops and gender. The conditions for the ANOVA are not met for all variables. According to Bortz and Weber, an ANOVA will be robust for all experimental groups that are of equal size and have at least ten persons per group. The two-way ANOVA can therefore be conducted for all variables [41]. Additionally, due to these circumstances, the data will be assessed individually. In this context, the conditions for a t-test for independent samples are not met, which is why the non-parametric Mann-Whitney-U-Test is utilized. The hypotheses and corresponding sub-hypotheses are tested on a 95% significance level. Should the results reach a higher significance level, it will be articulated in the text as well as in figures.

Hypothesis Testing H1:

The presented hypothesis is to be tested based on the overall time (in secs.) the participant takes to complete the task (dwell time). Figure 2 shows the dwell time for women and men while shopping on the Zalando or Amazon shops.

Fig. 2.
figure 2

Dwell time for women and men in the Zalando or Amazon shop (in seconds) (n = 80,**significant at p < .05 level)

Full size image

The entire ANOVA model exhibits a strong statistically significant result (F (3.76) = 6.99, p = 0.00). Worth noting is that the effect size of the entire model corresponds to a strong effect of f = 0.53, while the main effects and interactions only exhibit a medium effect size.

Main effects and interaction effects can be interpreted in the data. The dwell time is dependent on gender (F (1.76) = 7.82, p = 0.01): Women exhibit on the whole a longer dwell time (Zalando average (Avg.) = 199.25, standard deviation (SD) = 109.49; Amazon Avg. = 114.90, SD = 73.35) than men do (Zalando Avg. = 116.05, SD = 53.63; Amazon Avg. = 105.40, SD = 41.43). Furthermore, a significant difference in dwell time can be detected due to the main effect of the online shop (F (1.76) = 8.21, p = 0.01). The dwell time in the Zalando online-shop is found to be longer in comparison to Amazon. Finally, a significant gender-online shop interaction is present in the results (F (1.76) = 4.94, p = 0.03). This becomes apparent in that women exhibit an unusually high dwell time in combination with the Zalando shop, far beyond what could be expected by virtue of gender and online shops alone.

In an individual review, no significant difference could be determined in comparing the overall dwell time for men on the Zalando and Amazon shops. A significant difference is similarly absent in comparing men and women on the Amazon site. By contrast, women exhibit a high significant difference (p = 0.01) in dwell time on Amazon compared to Zalando. Women also spend all together more time in the Zalando online shop.

Beyond this, a p-value of 0.01 proves the existence of a strong significant difference between men and women in the Zalando shop, as women exhibit a significantly longer dwell time. Table 2 provides an overview of the results for the testing of hypothesis H1.

Table 2. Results of hypothesis testing H1 (n = 80, ***significant at p < .01 level, **significant at p < .05 level, *significant at p < .10 level, A = Amazon.de, Z = Zalando.de)
Full size table

The hypothesis H1 must be partially rejected: the ANOVA demonstrates significant differences. In an individual review of the groups, it can be determined that a significant difference is exclusively seen in women when comparing online shops. Statistically significant differences between men and women are only present in the Zalando shop.

Hypothesis Testing H2:

The focus of testing hypothesis H2 rests on the design of the product page features and their effects on the different genders. In sum, it can be ascertained that the findings, resulting from the ANOVA, do not meet the level of significance necessary to confirm the hypothesis. No statistically significant results were recorded for the average fixation time (in ms) on product images, and the average number of viewed images was equally inconclusive. Statistically significant results were seen, however, in the number of features viewed (AOIs), total number of product images viewed, as well as average fixation time (in ms) for product ratings, recommendations and product descriptions. Again, a clear pattern is apparent since significant differences in the Amazon and Zalando shops are seen solely with women. Furthermore, significant differences between men and women are only present in the Zalando shop.

H2a: Total views of product page features.

According to the U-Test (z = −2.63, p = 0.01), a significant difference between Zalando and Amazon is seen exclusively in women. Women view more product page features in the Zalando shop (median (MD) = 3.00) than on Amazon (MD = 1.00). The effect size amounts to r = 0.42 thereby corresponding to a medium effect (according to Cohen). There are also significant differences between the genders regarding the number of features viewed in the Zalando shop (according to the U-Test z = −2.27, p = 0.02). Compared to men, women view significantly more features in the feminine shop (men MD = 2.00, women MD = 3.00), which corresponds to a medium effect according to Cohen (r = 0.36).

H2b: Product images. This hypothesis was measured by means of two metrics: (1) total number of images viewed and (2) average fixation time (in ms).

For the first metric, the complete model for the total number of images viewed is highly statistically significant F (3.76) = 4.20, p = 0.01. This also applies to the main effect ‘gender’ with F (1.76) = 8.43, p = 0.05. Both significant results indicate a medium to large effect size according to Cohen. The main effect ‘online shop’ as well as the interaction are not significant, however. Gender, on the other hand, does have an effect on the number of images viewed in the online shop. Women view more images in total (Zalando Avg. = 12.55, SD = 10.78; Amazon Avg. = 7.45, SD = 9.97) compared to men (Zalando Avg. = 4.90, SD = 4.67; Amazon Avg. = 4.85, SD = 3.94). In light of these results, the groups were reviewed individually. Only in the Zalando shop a high significant difference was to be seen between the genders (z = −2,52, p = 0,01). Women viewed more total images in the Zalando shop than men did. For women, it can also be determined that they view on average more total images in the Zalando shop than Amazon, even though the p-value of 0.06 fails to indicate statistical significance in this regard.

For metric two, a significant difference in the average fixation time (in ms) on product images can neither be verified through the ANOVA nor through individual review.

H2c: Product ratings (average fixation time in ms).

Regarding the average fixation time on product ratings, the results of the two-way ANOVA show that the complete model, as well as the main effects ‘gender’ and ‘online shop,’ are not significant. Only the interaction, with a p-value of 0.04, exhibits a significant result (F (1.76) = 4.49, p = 0.04). An interaction exists between the main effects ‘online shop’ and ‘gender.’ In the (feminine) Zalando shop, women demonstrate medium to high average fixation times (Avg. = 77.19, SD = 92.41) while men demonstrate lower average times (Avg. = 15.90, SD = 51.55). This distribution flips in the (masculine) Amazon shop, as men exhibit moderate times of 64.53 ms and women lower times of 29.82 ms, although a medium effect of f = 0.25 is present.

In the individual groups, a significant difference (z = −2.37, p = 0.02) in fixation time on product ratings between men and women is solely present in the Zalando online shop. A medium effect size corresponding to r = 0.38 is present here. It can furthermore be determined that women exhibit, on average, a longer view time of product ratings in the Zalando shop than Amazon, even though the p-value of 0.06 fails to prove statistical significance in this regard.

H2d: Recommendations (average fixation time in ms).

With regard to average fixation time on recommendations, only one significant difference can be identified. Women fixate on recommendations on the Zalando shop longer (Avg. = 119.33, SD = 137.90) than those on Amazon (Avg. = 57.34, SD = 87.29). The significant difference for this hypothesis is high (z = −2.65, p = 0.01) corresponding to a medium effect size (r = 0.42).

H2e: Product description (average fixation time in ms).

By means of the Mann-Whitney U-Test, a significant difference in average fixation times on product descriptions between the Amazon (Avg. = 38.04, SD = 79.39) and Zalando shops (Avg. = 99.46, SD = 120,31) could only be established for women. The corresponding p-value is 0.05, and the effect size according to Cohen is medium (r = 0.32).

Table 3 provides an overview of the results for hypothesis test H2. When considered holistically, H2 must be partially rejected on account of the results of some sub-hypotheses.

Table 3. Results of hypothesis test H2 (n = 80, A = Amazon.de, Z = Zalando.de, M = Men, W = Women, ***significant at p < .01 level, **significant at p < .05 level, *significant at p < .10 level, ns not significant)
Full size table

In summary, significant differences between the Amazon and Zalando shops are only seen in female participants. Differences between male and female participants are apparent only in the Zalando shop. Regarding the ANOVA, significant interactions between ‘gender’ and ‘online shop’ can increasingly be seen in both features viewed and product ratings. The main effect ‘gender’ was significant for product images.

Table 4 provides an overview of the results for hypothesis test H1 and H2.

Table 4. Overview of the results for hypothesis test H1 and H2
Full size table

6 Discussion and Limitations

The discussion of the results will take place on the basis of the hypotheses and sub-hypotheses. Subsequently, the limitations of this study will be dicussed.

6.1 Discussing the Results

It could be observed that for the majority of metrics the female participants’ average values were higher in the feminine Zalando shop compared to the masculine shop, Amazon. The male participants exhibited a tendency for higher mean values in the Zalando shop compared to Amazon, however no statistically significant differences can corroborate this. Particularly for total number of viewed features and average fixation time on product ratings, men exhibited lower average values for Zalando than for Amazon.

Product page features.

The present experiment demonstrates that women incorporate more features from the product page into their shopping process in online shops with a feminine design. A significant difference is apparent in the total number of features viewed by women in that more were viewed on Zalando than on Amazon. Throughout the study, it has turned out that female participants in particular exhibit strong differences between online shops. This was similarly seen in the number of product page features women viewed compared to men in the Zalando shop. Based on these results, an online shop with a feminine design appears to strengthen or facilitate such behavior. It can therefore be concluded that women utilize significantly more features during their buying process when the shop reflects their needs. In contrast, men don’t exhibit any changes in use and viewing behavior regarding the features on the product page.

Product images.

There are no significant differences to be seen in average fixation time on product images. This product page feature is of equal importance to both genders. At the same time, it is necessary to recognize that women viewed more images in both shops than men did. Additionally, women’s’ average value was higher for Zalando than for Amazon, even if the difference cannot technically be deemed significant. Hence, the feminine and masculine online shop designs don’t produce the expected effects on participants [42]. One presumption is that online purchasing decisions rely primarily on visuals, especially for clothing and in this case for running shoes. This feature will therefore be viewed and used even when it doesn’t entirely reflect or meet the customer’s needs.

Product ratings.

No unequivocal conclusions could be drawn for this feature from the present experiment. Although the ANOVA points to a significant interaction, no clear results are reflected in the individual reviews by means of the Mann-Whitney U-Test. Though lacking statistical significance, it is apparent that each gender views the product rating longer in their respective gender-specific online shops. A statistically significant difference between men and women is only recognizable in the Zalando shop. It can be inferred that this site’s presentation especially appeals to women.

Recommendations.

The recommendations are an especially attractive feature for female shoppers to generate impulse purchases [42]. The present experiment shows that both genders trend toward higher average values for recommendation views in the Zalando shop. The significant difference for women between Zalando and Amazon speaks to the positive impact on longer viewing times for women. Zalando, in comparison to Amazon, successfully attracts attention to the recommendation feature by positioning it below the shopping-cart button.

Product description.

The experiment shows that women fixate significantly longer on product descriptions for Zalando than for those on Amazon. It can therefore be inferred that superior presentation and organization on the product page support women in their buying process by better supplying them with information. Overall, a significant difference could neither be determined between men and women nor between men shopping on Amazon and Zalando. This speaks to the fact that this feature cannot be exclusively classified as one traditionally used be men during the buying process.

In summary, regarding the initial research question, it can be asserted that gender-specific online shop designs have a significant impact on women only. Although not backed by strong statistical significance, men nevertheless also trend toward higher average values in the typically feminine shop compared to the masculine one.

6.2 Limitations

A number of restrictions arise in conjunction with the research design of the experiment at hand. On account of the chosen design, only two gender-specific online shops were taken into consideration – Amazon for the masculine-design and Zalando for the feminine design.

Furthermore, the experiment only examined the information research and buying behavior as it related to one purchasing object – a pair of running shoes. It would be advisable in future studies to consider purchasing objects that require a higher level of involvement for each respective gender. That way, the influence of the involvement in conjunction with the product could be examined.

Due to the eye-tracking experiment, the corresponding sample size and the selection of experiment participants, the data and results are not representative. The study exclusively examines students, most of who are from the same field of study. Further studies should aim to adjust the circumstances for a more representative sample.

The participants’ task for the experiment covers only one aspect of the buying process (the pre-purchase phase). Gender differences can therefore only be uncovered for this one phase. Further studies should take an expanded look at other phases in order to gain insight into the entire buying process. The actual phase of purchase, such as during checkout in online shops, could be especially interesting for future experiments.

The eye-tracker’s technical conditions as well as the laboratory environment may indicate further constraints to the experiment.

The parameters of the work presented here, which are constrained by factors such as random sampling, experiment design and location, can be built upon in future studies and complimentary research aiming to map out gender-specific behavior during the buying process.

7 Summary

The purpose of this study was to examine gender-specific online information research and buying behavior in online shopping venues. In this context, the subsequent question should be answered: Do women and men exhibit different information research and buying behavior when utilizing online shops designed to appeal to their respective genders?

The ensuing experiment was organized into three steps. The first step covered available literature and studies relating to gender marketing and online shop design. For a better selection of gender-specific sites, an evaluation of online shops with typically masculine and typically feminine design features was conducted. Building upon the insight gained there, the next step involved developing the research design. Purchasing objects and online shops were selected based on criteria derived from the first step that would best guarantee an answer to the research question. The final step comprised conducting the experiment, for which eighty participants were observed in a laboratory using an eye-tracker. The data secured in step three were used for testing the hypotheses and finally answering the research question.

Regarding gender-specific online shop designs, the experiment shows that this has an impact exclusively on women; men remain comparatively unaffected. It is therefore advised that online shops include more feminine design features when targeting a mixed or predominately female customer base. On account of the study’s design, it was able to point to some differences. Nevertheless, further in-depth studies are essential for generating additional insights and optimizing the results at hand.

The experiment was able to show that operators of online shops can better meet the demands of female shoppers by supplying information geared toward a high-quality and varied medial presentation of the product. The buying environment (the online shop) is of special importance for female customers. The general design (e.g. color scheme) more strongly impacts women and their assessment of the online shop.