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Blog user satisfaction: gender differences in preferences and perception of visual design.

The role of Internet technologies in daily life has become more prominent and the World Wide Web is experiencing enormous growth. As users' expectations of websites increase, understanding the process of developing Internet media has become one of the more pressing challenges for both designers and owners of business websites that has resulted from growing user demands. Consequently, there is increasing interest in improving user satisfaction when developing a website as a strategic tool for achieving a competitive advantage (McKinney, Yoon, & Zahedi, 2002; Muylle, Moenaert, & Despontin, 2004).

A website is a visual medium. The characteristics of Web media design should be used effectively to improve user satisfaction. As beauty satisfies a general human need, studies on human-computer interactions increasingly recognize the website user's aesthetic and technical needs in order to extend usability. Therefore, aesthetics, which are defined as beauty in appearance, have become one of the essential elements of visual interface design (Schenkman & Jonsson, 2000; Van der Heijden, 2003) and play an important role in user satisfaction (Lindgaard & Dudek, 2003).

As visual designs are crucial in consumer decision making, researchers have considered the relationship between visual design and usability. A similar phenomenon to the what is beautiful is good effect in social psychology (Dion, Berscheid, & Walster, 1972) also occurs in information technology (IT). Previous researchers have found evidence of the halo effect, in which a positive response to visual design aesthetics is carried over to the evaluation of other attributes of a system (Cyr, Head, & Ivanov, 2006; Tractinsky, Katz, & Ikar, 2000; Van der Heijden, 2003). However, this effect has rarely been examined in relation to gender. In this study, we investigated whether or not gender differences in the influence of visual aesthetics also affect the perception of other website elements, depending on the interest men and women take in the visual stimuli.

The Web 2.0 environment allows anyone to articulate their interests easily on websites. Weblogs, commonly known as blogs, are a good example of this capacity for interaction. Companies are able to maintain a variety of blogs, each designed to appeal to their target customers. Especially with regard to gender-related products, the visual design of the blog may also vary depending on the perceived aesthetic preference of both genders. The research model tested in this study involved empirical testing with a blog.

Gender Differences in Reaction to Visual Design

Males and females view the world from different perspectives (Pease & Pease, 2003). Given that men and women differ significantly in their reaction to visual stimuli (Wrase et al., 2003), researchers have become increasingly interested in responses of men and women to visual design (Moss, Gunn, & Kubacki, 2008). Aesthetics are important in visual design because they are a critical element in creating a positive response, such as the overall impression of a website (Schenkman & Jonsson, 2000; Tuch, Presslaber, Stocklin, Opwis, & Bargas-Avila, 2012), emotional response (Deng & Poole, 2010), satisfaction (Wang, Hernandez, & Minor, 2010), and perception of usability (Cho, Hong, & Moon, 2011; Schmidt, Liu, & Sridharan, 2009; Sonderegger & Sauer, 2010). However, only a few researchers have investigated the differences between men's and women's perception of aesthetics in the context of website design.

Previous researchers have identified gender-related differences in aesthetic perceptions. Using magnetoencephalography, Cela-Conde et al. (2009) found gender differences in the brain activity of men and women when visual beauty was presented. Therefore, in our study we included gender as one of the demographic variables associated with aesthetic preference.

Research Model Development

User Satisfaction Model

DeLone and McLean (1992) presented a user satisfaction model based on the notion that the quality of the system (e.g., information and technical quality) affects user satisfaction. Findings concerning the effect of the quality of attributes of a system on user satisfaction can be applied to maximize system value by considering these from the user's perspective.

A blog is a type of website. We determined the characteristics of a blog based on previous studies about websites. Having identified website elements that were found to affect user satisfaction in the previous studies (Aladwani & Palvia, 2002; Muylle et al., 2004; Palmer, 2002), we established that the blog elements that influence satisfaction can be classified as information, navigation, and visual design.

Gender Differences

Humans cannot process an infinite amount of visual stimuli simultaneously, and thus can perceive only part of the visual scene at any one time (Kastner & Ungerleider, 2000). This essential feature of human performance limits processing capacity and requires that individuals pay selective attention to objects in the visual field (Desimone & Duncan, 1995). Goldstein (2006) described the properties of selective attention, which concerns people's ability to focus on certain sources of information and ignore others because individuals are more likely to look at those objects that they perceive to be interesting.

Therefore, if gender-related differences exist in visual perception, the visual design factors that influence people's selective attention may vary, depending on the level of interest men and women have in the visual stimulus being presented. Such processing differences are likely to influence the aesthetic evaluations made by men and women, respectively. Considering that such evaluation is critical to the judgment process under the condition of a newly presented stimulus, our aim in this study was to investigate gender-related differences in the association between aesthetic-related emotion and the evaluation of blog qualities other than aesthetics. The proposed research model is presented in Figure 1.


Color Aesthetics

Moss and Colman (2001) analyzed the use of colors on name cards and found that women used more colorful name cards than men and that women were more interested in, and more aggressive about, the use of color than men were. In a study of website design, Cyr and Bonanni (2005) found that women were more attracted than men were by the colors of a website. Integrating these different perspectives, we reasoned that women appear to be more interested in the color used on a website and pay more selective attention to color than men do.

Berlyne (1971) and Lindgaard, Fernandes, Dudek, and Brown (2006) found that aesthetic preferences are related to stimuli with arousing attributes. Bagozzi (1996) argued that a strong halo effect emerges when arousal is induced, and the more favorable the attitude toward the arousal, the stronger the belief that positive results will occur. We reasoned that if the degree of arousal differs between men and women, depending on their interest in the visual stimulus, a difference in the halo effect will also be observed. For example, if women are more interested in the colors used on a website than men are, then women should be more influenced than men by color in their evaluations of a blog's other attributes. Thus, we proposed the following hypotheses:

Hypothesis 1: The association between color aesthetics of a blog and information quality will be stronger for women than for men.

Hypothesis 2: The association between color aesthetics of a blog and visual design quality will be stronger for women than for men.

Hypothesis 3: The association between color aesthetics of a blog and navigation quality will be stronger for women than for men.

Layout Aesthetics

Men have been found to outperform women in spatial-perception tasks (Harshman, Hampson, & Berenbaum, 1983). Tuch, Bargas-Avila, and Opwis (2010) reported that women did not differentiate between asymmetric and symmetric conditions in the aesthetic perception of websites. However, websites with vertical symmetry influenced the aesthetic perception of men. These findings suggest that website layout may be stimulating only for men.

Furthermore, men have been found to be more sensitive to the layout structure of a website and to pay more selective attention to it than women do. Given that arousal is related to the halo effect (Bagozzi, 1996), we reasoned that men's arousal caused by layout aesthetics should affect the perception of other website attributes. Therefore, we expected that, compared to women, men would report a stronger association between the layout aesthetics and other blog qualities.

Hypothesis 4: The association between layout aesthetics of a blog and information quality will be stronger for men than for women.

Hypothesis 5: The association between layout aesthetics of a blog and visual design quality will be stronger for men than for women.

Hypothesis 6: The association between layout aesthetics of a blog and navigation quality will be stronger for men than for women.

Overall Visual Design

Darley and Smith (1995) found that men's visual information processing is different to women's visual information processing. Women tend to engage in a comprehensive and detailed analysis of all available information, whereas men focus on highly available cues and attempt to engage in the selective processing of available information (Meyers-Levy & Sternthal, 1991). In other words, researchers have found that women consider as much information as possible, whereas men selectively process information relevant to their goals.

Women are more visually oriented and prefer more visually graphic images than men do (Holbrook, 1986). Therefore, women's evaluation of the visual design quality of a newly encountered blog may be more influential than that of men. Visual design quality may be more arousing to women than to men. Therefore, we predicted that women's evaluation of the association between visual design quality and the other qualities of a blog would be stronger than that of men.

Hypothesis 7: The association between visual design quality of a blog and information quality will be stronger for women than for men.

Hypothesis 8: The association between visual design quality of a blog and navigation quality will be stronger for women than for men.

Hypothesis 9: The association between visual design quality of a blog and satisfaction will be stronger for women than for men.

Information and Navigation

Women have been found to score higher on average than men on verbal and linguistic tasks (Halpern, 1997). It has also been found that, compared to men, women tend to engage in verbal communication more easily and use more descriptive words (Kimura, 1996). Women also grasp the meaning of words more accurately than men do, even within a short time (Meyers-Levy & Sternthal, 1991). Consequently, we proposed the following hypothesis:

Hypothesis 10: The association between information quality of a blog and satisfaction will be stronger for women than for men.

Navigation is an important website design element that improves the user's experience of a website (Palmer, 2002). Ease of use has been identified as the most important factor in evaluating website quality (Hong & Tam, 2006; Vance, Elie-Dit-Cosaque, & Straub, 2008). Harris (1981) found that men were superior to women in navigational ability and Berlyne (1971) reported that men gave themselves a higher self-rating for sense of direction and were more accurate in determining unseen locations. Therefore, we predicted that navigation quality would have more influence on men's satisfaction with a blog than it would on women's satisfaction.

Hypothesis 11: The association between navigation quality and satisfaction with a blog will be stronger for men than for women.


Data Collection and Procedure

The blog we used in this study is one of the most popular business blogs in Korea. Aptly named "THE BLOG" (LG Electronics, n. d.) the site is operated by LG Electronics. This site won the Grand Prix in the corporate section of the Korean Blog Awards in 2009 and 2010.

The data used in this research were collected from a large university in South Korea. Participants were 297 undergraduate business students from four sections of different courses who participated voluntarily for extra course credit. We adapted validated instruments developed for prior studies to compose the new survey items. We made slight wording modifications to adapt the instruments to the context of blog usage. The final list of items we used in the survey is set out in the Appendix along with the sources of these items.

We conducted the experiment in a computer laboratory with 40 computer workstations. It took five minutes for us to explain the purpose and procedure of the test to the participating students. The participants were then allowed to freely browse the blog for 20 minutes. Afterwards, we distributed the survey forms to the participants. Of the 297 forms we collected, 14 incomplete forms and 15 forms completed by participants with previous experience of the blog were discarded, leaving a usable sample of 268 survey forms for the final analysis.

The final sample consisted of 125 male (46.6%) and 143 female (53.4%) students. The average age was 22.4 years. From the final sample, 46.6% of the participants replied that they operated their own blogs.


Instrument Validity and Reliability

We performed confirmatory factor analyses using AMOS 7.0 to check the validity of the measurement model. To demonstrate a reasonable fit for the model, a number of factors were estimated. The fit indices are presented in Table 1. The model showed a reasonably good fit for the data for both men and women. The psychometric properties of the constructs and the items are summarized in Tables 2 and 3, respectively.

Structural Model

AMOS 7.0 was used to assess the structural equation analysis to test the model. Before estimating the gender differences, three structural models for the whole sample and the female and male groups were tested. In Table 4 the path coefficients and [R.sup.2] values for each of the subsamples are presented.

The effects of information quality and navigation quality on user satisfaction were found to be significant in both subsamples. Therefore, a multisample analysis was conducted to test for differences between men and women in the strength of the path coefficients in the model. In Table 5 the results are presented showing the differences between the two subsamples, and in the last column is a summary of the corresponding results related to each of our hypotheses.

Our results show that men and women perceived the visual design stimuli in the blog differently. We expected to find a stronger correlation between information quality and satisfaction among the women than among the men who took part in our study because many researchers have reported that females appear to outperform males in linguistic skills. We found that there was no gender difference in the information quality evaluation of the students who took part in our study. However, differences in language ability may be due to educational level, social influence, and social class rather than to gender (Hoff, 2003). The sample for the present study consisted of undergraduate students who had active language learning experiences.

Navigation quality had a significant influence on the satisfaction of both men and women participants in our study, and this was inconsistent with H11. As the group consisted of university students aged in their twenties who had all been using the Internet for more than five years and, therefore, the women in the group had significant experience of using the Internet, these women exhibited good navigational skills similar to those of the male participants.


Information technology has frequently been characterized as a male-dominated industry because it is a cutting-edge technology field that was developed by young men. Recently, however, the ubiquitous computing environment has integrated seamlessly with users' everyday tasks and nonwork activities, offering new opportunities for people to enhance their lives with the use of information technology. Thus, women now have more opportunities than they did when the technology was new to access computers and the Internet, so that the gender gap in Internet use has narrowed over the past several years (Ono & Zavodny, 2003). However, we found that the most significant differences between women and men in satisfaction with a blog occurred in the perception of the visual design elements. Nevertheless, according to Moss, Gunn, and Kubacki (2007), most commercial websites are designed with male-oriented aesthetics. They found that even beauty-related websites designed specifically to attract women visitors focused mainly on men's aesthetics without considering the aesthetics that women prefer. According to Moss and Colman (2001), men and women tend to be attracted to designs created by their own gender. That is, women prefer websites produced using female aesthetics, whereas men prefer websites produced using male aesthetics. We recommend that website design should be conceptualized considering end-user design preferences. In this study our aim was to shed new light on gender differences related to information technology at a time when females are increasingly visiting more websites.

The Web 2.0 environment enables individuals to take an active role in their use of websites with various designs and content. A good example of this capacity for interaction is the blog, which has become one of the most popular and fashionable of the social media sites available on the Internet. Business blogs provide opportunities to share information and help to build and maintain relationships between customers and companies in a cost-effective way. Thus, business blogs are quickly becoming a core component of business marketing.

Company websites can be improved to enhance the satisfaction of visitors and achieve success in the Web 2.0 environment. When a company produces a number of different product classes this increases its need to keep up with different blogs according to the target customers for those products. The success of blog marketing can be maximized by establishing different strategies for each blog. Our findings indicate that blogs need to be differently designed in order to reflect gender-targeted strategies for gender-related products or services. For example, a marketing strategy for female cosmetics could be matched with female preferences and would be different from a strategy for male cosmetics that would be matched with male preferences. Thus, companies would operate separate blogs for female and male cosmetics and would develop each blog using different content and communication strategies. The visual design of each blog should also be different, depending on the aesthetic preferences of the gender of the targeted user group. Certain limitations should be considered in interpreting our results. Our sample consisted of South Korean university students. Future researchers should, therefore, expand the investigation to different countries and make cross-cultural comparisons.


List of Model Constructs and Items

Construct       Item no.   Question item               Source

Color           CA 1       The colors used on this     Cyr, Head, and
aesthetics                 blog are attractive.        Ivanov (2006);
                                                       Cyr, Head, and
                CA 2       The colors used on this     Larios (2010);
                           blog are pleasing.          Van der Heijden
                CA 3       The colors used on this
                           blog are emotionally

Layout          LA 1       The layout of this blog     Aladwani and
aesthetics                 is clean and clear.         Palvia (2002),
                LA 2       The layout of this blog     Ramasubbu,
                           is visually comforting.     Krishnan, and
                                                       Fornell (2006);
                LA 3       The layout of this blog     Muylle,
                           is attractive.              Moenaert, and

Visual design   VDQ 1      The blog looks              Cyr et al.
quality                    professionally designed     (2006); Vance,
                           and well presented.         Elie-Dit-
                VDQ 2      The overall look and feel   and Straub
                           of this blog is visually    (2008)

                VDQ 3      Overall, this blog's
                           visual design is of high

Information     IQ 1       Overall, I would give the   DeLone and
quality                    information from this       McLean (1992);
                           blog a high mark.           Wixom and Todd
                IQ 2       Overall, I would give the
                           information provided by
                           this blog a high rating
                           in terms of quality.

                IQ 3       In general, this blog
                           provides me with
                           high-quality information.

Navigation      NQ 1       This blog makes it easy     McKinney, Yoon,
quality                    to go back and forth        and Zahedi
                           between pages.              (2002); Palmer
                                                       (2002); Vance
                NQ 1       In general, this blog is    et al. (2008)
                           easy to navigate.

                NQ 3       In terms of navigation
                           quality, I would rate
                           this blog highly.

Satisfaction    SAT 1      This blog satisfies my      DeLone and
                           particular needs well.      McLean (1992);
                                                       Wixom and Todd
                SAT 2      All things considered, I    (2005)
                           am very satisfied with
                           this blog.

                SAT 3      Overall, this blog is
                           very satisfying.

Note. Eighteen items measured on a 7-point Likert scale ranging from
strongly disagree (1) to strongly agree (7).


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Korea University

Sook-Hyun Cho, Institute for Business Research and Education, Korea University; Se-Joon Hong, School of Business, Korea University. Correspondence concerning this article should be addressed to: Se-Joon Hong, School of Business, Korea University, 145 Anam-Ro, Seongbuk Gu, Seoul, Republic of Korea. Email: sejoon@korea.

Table 1. Fit Indices for the Measurement Models

Fit indices                           Threshold

Chi square (df)                           --
Chi square (df)                           --
Goodness-of-fit index       [greater than or equal to] 0.90
Adjusted goodness-of-fit    [greater than or equal to] 0.80
Normed fit index                         0.97
[greater than or equal
Comparative fit index       [greater than or equal to] 0.90
Root mean square error of    [less than or equal to] 0.08

Fit indices                     All            Men           Women

Chi square (df)             106.63 (120)   117.22 (120)   127.16 (120)
Chi square (df)                0.89           0.98           1.06
Goodness-of-fit index          0.96           0.91           0.92
Adjusted goodness-of-fit       0.94           0.88           0.88
Normed fit index               0.93           0.94
[greater than or equal
  to] 0.90
Comparative fit index            1              1            0.99
Root mean square error of        0              0            0.02

Table 2. Factor Loadings, Composite Reliability, Average Variance

Construct   Item   Men    Women     Construct   Item   Men    Women

CA          CA1    .91     .86      LA          LA1    .86     .88
            CA2    .86     .91                  LA2    .91     .95
            CA3    .87     .84                  LA3    .86     .88
             CR    .94     .94                   CR    .94     .96
            AVE    .85     .84                  AVE    .85     .88

IQ          IQ1    .80     .76      VDQ         VDQ1   .92     .90
            IQ2    .93     .93                  VDQ2   .92     .93
            IQ3    .84     .79                  VDQ3   .89     .89
             CR    .93     .91                   CR    .96     .96
            AVE    .82     .78                  AVE    .89     .88

NQ          NQ1    .80     .87      SAT         SAT1   .83     .82
            NQ2    .86     .86                  SAT2   .83     .86
            NQ3    .79     .86                  SAT3   .84     .84
             CR    .91     .94                   CR    .92     .92
            AVE    .78     .83                  AVE    .80     .80

Note. CR = composite reliability; AVE = average variance extracted;
CA = color aesthetics, LA = layout aesthetics, VDQ = visual design
quality, IQ = information quality, NQ = navigation quality, SAT =

Table 3. Convergent and Discriminant Validity

           Correlations between constructs

        CA    CQ    VDQ   LA    NQ    SAT


CA      .92
CQ      .14   .91
VDQ     .19   .24   .94
LA      .23   .32   .47   .92
NQ      .10   .22   .29   .51   .88
SAT     .07   .42   .25   .23   .43   .89


CA      .92
CQ      .54   .88
VDQ     .56   .58   .94
LA      .38   .24   .30   .94
NQ      .48   .38   .48   .35   .91
SAT     .51   .51   .60   .26   .55   .90

Note. The italicized diagonal values refer to the square root of the
average variance extracted for each construct. CA = color
aesthetics), LA = layout aesthetics, VDQ = visual design quality,
IQ = information quality, NQ = navigation quality, SAT = satisfaction.

Table 4. Summary of Three Structural Models

Construct   [R.sup.2]   Whole Sample

SAT         .45
  VDQ                      .15 **
  IQ                       .325 ***
  NQ                       .326 ***
IQ          .26
  CA                       .202 ***
  LA                       .103 **
  VDQ                      .282 ***
NQ          .3
  CA                       .164 **
  LA                       .289 ***
  VDQ                      .278 ***
VDQ         .23
  CA                       .337 ***
  LA                       .237 ***

Construct         Men                Women

SAT         [R.sup.2]=.35       [R.sup.2]=.57
  VDQ                 .058                .314 ***
  IQ                  .338 *              .225 **
  NQ                  .371 *              .272 ***
IQ          [R.sup.2]=.13       [R.sup.2]=.49
  CA                  .033                .329 ***
  LA                  .256 **            -.004
  VDQ                 .101                .385 ***
NQ          [R.sup.2]=.33       [R.sup.2]=.37
  CA                 -.039                .344 **
  LA                  .479 *              .169
  VDQ                 .049                .385 ***
VDQ         [R.sup.2]=.26       [R.sup.2]=.37
  CA                  .097                .616 ***
  LA                  .469 *              .048

Note. ** p < .01, *** p < .001. CA = color aesthetics, LA = layout
aesthetics, VDQ = visual design quality, IQ = information quality, NQ
= navigation quality, SAT = satisfaction.

Table 5. Results of Multigroup Analysis

Constraint                       Chi square   df     Chi square

No structural constraint           249.6      246       --
H1: CA [right arrow] IQ            254.0      247      4.605
H2: CA [right arrow] VDQ           263.9      247     14.307
H3: CA [right arrow] NQ            254.7      247      5.117
H4: LA [right arrow] IQ            254.7      247      5.137
H5: LA [right arrow] VDQ           263.9      247     14.339
H6: LA [right arrow] NQ            255.3      247      5.757
H7: VDQ [right arrow] IQ           254.0      247      4.454
H8: VDQ [right arrow] NQ           254.1      247      4.57
H9: VDQ [right arrow] SAT          253.6      247      4.025
H10: IQ [right arrow] SAT          250.2      247      0.673
H11: NQ [right arrow] SAT          250.2      247      0.677

Constraint                       [DELTA][X.sup.2] Sig. dif   Supported

No structural constraint                    --                  --
H1: CA [right arrow] IQ                  different              Yes
H2: CA [right arrow] VDQ                 different              Yes
H3: CA [right arrow] NQ                  different              Yes
H4: LA [right arrow] IQ                  different              Yes
H5: LA [right arrow] VDQ                 different              Yes
H6: LA [right arrow] NQ                  different              Yes
H7: VDQ [right arrow] IQ                 different              Yes
H8: VDQ [right arrow] NQ                 different              Yes
H9: VDQ [right arrow] SAT                different              Yes
H10: IQ [right arrow] SAT                   ns                  No
H11: NQ [right arrow] SAT                   ns                  No

Note. CA = color aesthetics, LA = layout aesthetics, VDQ = visual
design quality, IQ = information quality, NQ = navigation quality,
SAT = satisfaction.
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Author:Cho, Sook-Hyun; Hong, Se-Joon
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Geographic Code:9SOUT
Date:Sep 1, 2013
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