Development of an Instrument to Measure Web Site Personality

Qimei Chen

University of Hawaii at Manoa

Shelly Rodgers

University of Missouri-Columbia


A Website Personality Scale (WPS) was developed and validated through a multi-phase process. We investigated the presence of human and brand personality attributes as well as information characteristics in over one hundred websites. A strong presence of information characteristics was found, verified along dimensions of content quality, access, and time. A number of traditional human and brand personality attributes also were present. These attributes correlated with overall attitude and liking of the websites, which helped to validate the scale. There was little evidence to support the presence of interactivity, real time, and customization-characteristics believed to be central to the Internet and Web technology. The findings suggest that the WPS is both a reliable and valid measure of website personality.


The growth of the Internet provides tremendous opportunities for online advertisers who want to establish an Internet presence. However, corporate websites are expensive and over 90 percent of all new websites fail primarily due to bad design (, 2006). Under- standing how website design and by extension, website personality, affects consumers’ attitudes and relationship with the company will help to improve customer relationships and prevent website failure (Palmer and Griffith 1998).

Although website design characteristics have been identified (e.g., Ha and James 1998; McMillan 2002) and explored by a number of advertising and marketing scholars (e.g., Griffith and Chen 2004; Wells and Chen 2000), many of the features overlap or, in some cases, different characteristics are used to describe the same aspects of a website. Website characteristics help to determine how individuals perceive and process site content (Rodgers and Thorson 2000) and serve as determinants of website effectiveness and purchase intentions (Rodgers 2003).

Companies like L.L. Bean, which has a “folksy” personality, have established an online presence that is distinct from competitors (Wagner 1997). However, little is known about which characteristics of a website are used to determine a site’s personality or how these features can be used in an organized fashion to describe the various personalities that might be present among websites. Although previous research has used different ways to measure brand and human personality, we argue that prior personality scales do not adequately account for design features that are unique to the Internet and that may not be present in brand or human personality.

The purpose of this research is to create and validate an instrument called the Website Personality Scale (WPS). We begin by drawing on the well-established literature in advertising and psychology related to human and brand personality, respectively. We argue that websites share human and brand characteristics and subsequently have “personalities” that both attract and detract Internet users; however, websites also have characteristics not present in brands or humans that require measurement items unique to Web technology. We then present the findings of a multi-phase process that entailed identifying the pool of items to be included, follow-up analyses that validated the instrument, as well as a discussion of practical applications of the WPS.

Theoretical Background

Websites have the potential to develop relationships with customers that are characterized by dialogue and customized content (McMillan 2002; Rodgers and Thorson 2000). In this sense, online marketing has much in common with interpersonal face-to-face marketing. The power of this new technology is believed to be its interactive capabilities that allow companies to engage in individualized communication on a massive scale (Chen, Griffith, and Shen 2005; McMillan 2002). Interpersonally speaking, the personality of a salesperson can affect the customer-business relationship and sales effectiveness (Smith 1998). By analogy, the personality (i.e., attributes) of a website could also influence the online customer-business relationship and online sales effectiveness.

A website is also a brand carrier and an extension of the sponsoring organization’s operations (Palmer and Griffith 1998). Hence, it is important that the channel exhibits the personality characteristics of the brand. Previous research has found that brand personality largely influences a consumer’s brand preference and choice (Aaker 1997). Similarly, the personality (characteristics) of a website is (are) expected to influence the preferences and choices of online customers.

Given this, it seems likely that a website will share some characteristics with human beings and with brands that translate to its overall personality. To explore this theory more closely, we examine the literature on human and brand personality. Based on prior findings in the literature, we argue that it is possible for websites to possess human personalities; by extension, examining the literature on human personality is the first building block toward constructing a scale to measure website personality.

Human Personality

Human personality is the “unique, dynamic organization of characteristics of a particular person, physical and psychological, which influence behavior and responses to the social and physical environment” (Liebert and Spiegler 1998, p. 5). It is a set of characteristics that accounts for the ways in which individuals differ from one another. This conceptualization of human personality has been of paramount concern to researchers in personality psychology. The goals include effectively measuring and communicating the important personality dimensions, accurately anticipating the likelihood of various behaviors, and isolating the personality factors that influence future behaviors (Liebert and Spiegler 1998).

Human personality characteristics have long been employed in consumer behavior research. For instance, a number of marketing researchers have theorized that personality characteristics predict brand or store preference and other types of buyer activity (Martineau 1957). Almost any type of buyer decision or choice is believed to be an expression of an individual’s personality, and having knowledge of personality structure is directly applicable to marketing planning (Horton 1979). Consumer psychologists have also found that consumer personality is an effective way to segment markets (e.g., Engel, Kollat and Blackwell 1969; Kassarjian 1971; Kassarjian and Sheffet 1991).

Researchers have attempted to relate a wide variety of marketing variables to various theories of personality. For example, product usage (Cohen 1967; Tucker and Painter 1961), decision behavior (Kernan 1968; Westfall 1962), purchase behavior (Evans 1959, 1961; Koponen 1960), brand loyalty (Brody and Cunningham 1968), innovative buying behavior (Bruce and Witt 1970; Donnelly 1970), response to advertising and design features (Holbrook 1986; Wright 1975), and product acceptance and rejection (Dolich 1969) have all been linked to various consumer personality measures.

The literature also has begun to examine how consumers interact with computers and initial studies suggest that this interaction is much like another human being, so much so that computer personalities are viewed as “real” to consumers (Moon and Nass 1996). However, while the literature on human personality is helpful in establishing potential characteristics that may be present in websites, it is likely that human personality does not cover the range of potential attributes that represent website personalities as a whole. For instance, researchers have discovered that it is possible to create personalities similar to human personalities for computers and that consumers not only prefer to interact with a computer with a similar personality, they also feel more satisfied with the interaction (Nass et al. 1995). This suggests that interaction or interactivity, a feature that is unique to websites, may be viewed by consumers as a unique characteristic that, when present, suggests certain personality traits (e.g., fun, exciting, sociable, etc.).

Although these traits may translate to human personality, the characteristics that are viewed in conjunction with them (e.g., searchable information) will likely differ and, in some ways differ markedly, from characteristics we would expect of human beings. For these reasons, it is important to draw on existing human personality literature, and to extend the literature in the area of personality to include dimensions that may be unique to Internet websites.

Brand Personality

In addition to examining human personality, brand personality offers a number of characteristics that will likely be present in online websites. One personality correlate that has received a considerable amount of attention is brand image or brand/product personality (Aaker 1997). Brand personality is defined as the collection of human characteristics that are associated with a brand (Aaker 1997), and differs from human personality in terms of how it is formed. While human personality traits are inferred on the basis of an individual’s behavior, physical characteristics, attitudes, beliefs, and demographic characteristics (Park 1986), perceptions of brand personality traits are formed and influenced by direct or indirect contact the consumer has with the brand (Plummer 1985).

Martineau (1957) was one of the earliest researchers to discuss the product personality concept. Later research has focused on how the personality of a brand enables a consumer to express his or her own self (Belk 1988) as well as the ideal self (Malhotra 1988) or specific dimensions of the self (Kleine, Kleine, and Kernan 1993) through the use of a brand. Practitioners view brand personality as a key way to differentiate a brand in a product category (Halliday 1996). Brand personalities are also central drivers of consumer preference and usage (Biel 1993), and serve as common denominators that can be used to market a brand across cultures (Plummer 1985).

The symbolic use of brands is possible because consumers often imbue brands with human personality traits, termed animism (Gilmore 1919). Consumers can easily think about brands as if they are celebrities or famous historical figures (Rook 1985) and as they relate to one’s own self (Fournier 1994), due in part to the strategies used by advertisers to imbue a brand with personality traits such as anthropomorphization, personalization, and the creation of user imagery.

Previous research in this area suggests that the greater the congruity between human characteristics that consistently and distinctively describe an individual’s actual or ideal self and those that describe a brand, the greater the preference for the brand (Malhotra 1981). Websites are also a form of advertisement (Griffith and Chen 2004; Singh and Dalal 1999), which serves as a natural carrier of the brand. It is thus essential to not only include brand personality as another building block in the creation of a website personality scale, but to explore potential differences in the characteristics that may exist for brand and website personalities.

Website Personality

Recent Internet research has begun to address the personality issue. For instance, Atkinson and Kydd (1997) found that the individual characteristic of playfulness influences the use of the World Wide Web (WWW). Consumers’ purchase decisions are also influenced by the personality of their service person. Smith (1998) found that similarity in buyer-seller personality had differential effects in facilitating open communication, relationship investment, and customer relationships.

Consistent with research on human and brand personality, consumers imbue websites with personalities. According to Palmer and Griffith (1998), corporate websites are typically used to support marketing activities including promotional activities, sales, service, and support. Like interpersonal communication, websites encompass unique features of an active communicator that represents the corporation as well as brand carrier. Hence, websites are comprised of characteristics that are both brand- and human-oriented, and these characteristics are apparently being used by websites to develop unique personalities for product differentiation (Wagner 1997).

In this study, we have defined website personality as “the set of traits encompassing human characteristics and information technology features associated with a website.” This definition is similar to human personality and brand personality in that it is a set of characteristics that account for the ways in which websites differ from one another. However, it differs from brand and human personality in terms of how it is formed; the formation of website personality is based not only on direct or indirect contact that the consumer has with the website, but also the interface and system design of the site.

Based on this conceptualization, we theorized that website personality would encompass the rational aspects related to website behavior (such as perceived ease of use, perceived usefulness, e.g., Davis 1989), as well as emotional aspects related to human and brand characteristics (such as entertainment, humor, e.g., Aaker and Bruzzone 1981; Wells, Leavitt, and McConville 1971).

To date, the rapid growth of new technology necessitates the need in marketing and advertising fields to explore the characteristics of both the technology itself and the individuals who use it. A few studies have tried to address these needs. For instance, Moon (2000) contends that personal computers actually have either dominant or submissive personalities and PC users tend to prefer the computer with a similar personality. The findings are limited, however, in their application to websites as a whole, or in describing website personalities as more than dominant or submissive.

To examine how the relationship between website and user personality may drive online consumer preference and usage of websites and subsequently fill a gap in the literature, we developed the Website Personality Scale (WPS). Below, we describe the process by which the WPS was developed and validated.


Item Pool Generation

Our final pool of items included 141 adjectives that were collected from a multi-stage process that included: 1) pulling relevant items from existing literature; and 2) asking a group of individuals to list adjectives to describe their best/worst websites.

Literature search. Our pool of items began with a review of the literature on human personality, brand personality, and Internet advertising. Although several personality dimensions have been proposed over the years, we focused on dispositional strategy, which contends that personality is a set of enduring characteristics upon which individuals differ (Cattell 1945). This helped limit the number of items in our pool and provided a parallel universe of items in which to examine website personality.

The “Five-Factor model” or the “Big Five” was employed as the base for the initial pool of items. The Big Five is a desirable model for our purposes because it captures most of the significant variation in human personality (Digman 1990; John 1990a, 1990b) and is robust across measurement domains (Digman and Takemoto-Chock 1981), different cultures and languages (Isaka 1990; John, Goldberg, and Angleitner 1984) and gender, generation, and occupation (John 1990a). The Big Five model consists of the following five factors: Negative Emotionality, Extraversion, Openness, Agreeableness and Conscientiousness.

To rule out the possibility that we had overlooked relevant personality items or factors, we also reviewed a series of scales that have been used to develop and refine the Big Five model including the original scale (Norman 1963; Tupes and Christal 1958), NEO model (McCrae and Costa 1989), Big Five Prototypes (John 1990a), ACL (Piedmont, McCrae, and Costa 1991), and Inter-Circumplex model (McCrae and Costa 1989). A total of 30 unique personality traits (six for each of the five factors) were subsequently retrieved for our pool of items.

We also examined personality dimensions apart from the Big Five model to address the concern of the comprehensiveness of human personality. The following person descriptors orthogonal to the Big Five were included in our adjective pool: Sexuality (Buss 1996), Attractiveness (Henss 1996; Saucier 1997) and Negative Valence (Almagor, Tellegen, and Waller 1995; Benet and Waller 1995; Tellegen and Waller 1987). To limit the total number of items in our pool, three to seven adjectives were selected to represent each of these factors. The factors identified as not subsumable within the Big Five framework were rejected (Saucier and Goldberg 1998), as were factors that, while appropriate for human personality, had little capacity to discriminate among websites (e.g., forcefulness and introspectiveness).

From the few studies conducted on brand personality (Ackoff and Emsoff 1975; Dolich 1969; Martineau 1957; Vitz and Johnson 1965), we used Aaker’s (1997) five dimensions: Sincerity, Excitement, Competence, Sophistication, and Ruggedness. The instrument originally consisted of 42 items. To simplify our questionnaire, we selected two to four adjectives to represent each dimension.

Individual listings. To rule out the possibility that we had overlooked personality traits that might be unique to websites (and therefore not currently present in the literature), a group of individuals were asked to list adjectives that described their favorite/worst websites. This enabled a generation of items in our initial pool that had both positive and negative personality traits, which is consistent with studies that have examined brand and human personality.

Website Sample

A total of 120 websites were selected for the website personality scale design. Websites were selected from four sources: a) random selection of websites from the Internet Source Book (The executive’s guide to the Internet/Intranet world); b) websites recommended by experts (faculty members in the business school at a major mid-western university); c) websites recommended by graduate and undergraduate students that they liked to surf; and, d) websites recommended by graduate and undergraduate students that they disliked to surf.

Using a variety of sources helped increase the variance among the websites and helped to improve the representativeness of the selected websites from the entire website population. Including sites that are liked and disliked helped to ensure that both positive and negative website attributes were included among the personality characteristics of our scale. All websites were corporate, meaning they were run by an identified organization, and no personal websites were included in this study.

Evaluation Criteria

The evaluation criterion was affect, defined as the feelings an individual has toward a stimulus that can lead to relative preferences for the stimulus (Batra and Ray 1986). The criterion scale items were based on affect toward the website, which serves as an indicator of website effectiveness (Chen and Wells 1999). We used a reliable and valid scale, attitude toward the website (AST), which measures consumers’ overall attitude toward a website (Chen and Wells 1999; Chen, Clifford, and Wells 2002). The AST scale included six items. The first item, “This website is easy for me to build a relationship with this company,” was labeled Relationship criterion; the second item “I would like to visit this website again in the future” was labeled Intention criterion; item three “I am satisfied with the service provided by this website” was labeled Satisfaction criterion; item four “I feel comfortable navigating in this website,” was a Comfort criterion; item five “I feel navigating this website is a good way for me to spend my time,” was a Value criterion, and finally, item six “Compared with other websites, I would rate this one as (one of the worst/best), was an Overall Rating criterion.


Each of the 120 websites in our sample was rated by three individuals using 5-point Likert scales for each of the 141 personality items. A choice of Not Applicable was also available to eliminate an adjective that was not useful for describing the website. Attitude toward the site, our evaluation criterion, was rated last.

A total of 120 MBA and undergraduate students from two major state universities were recruited as raters for the websites. Students participated in the website evaluation on a voluntary basis. A total of $300 was offered as an incentive in the form of a random drawing. Raters were given one week to familiarize themselves with each of the three websites and complete the questionnaire. To avoid error caused by fatigue, raters were encouraged to take breaks at any time whenever they felt tired. Each rater evaluated/rated three websites independently and a total of 360 usable responses were obtained.

Data Analysis

Factor Structure

Identifying website personality dimensions. Beginning with the original 141 adjectives, a frequency analysis was run for both universities. This was done to isolate the most relevant traits that described a website’s personality. An arbitrary threshold of 10 percent was used to delete items that were mentioned by the raters as being “not applicable” for describing a website. Any items that were deleted in both data sets (for both universities) were then dropped from final consideration. This procedure decreased the pool of items from 141 to 76.

Next, exploratory factor analysis was conducted to identify the underlying factors or dimensions of website personality that comprise the domain of human personality, brand personality, and advertising-specific factors. We factor analyzed the mean scores calculated on the 76 items with eigenvalues of 1.00 or greater for each of the 120 websites. Using mean scores eliminated the variance due to differences in individuals and left average differences between websites as the determinant of the factor structure (Schlinger 1979).

Using principle component analysis as the extraction technique and Varimax as a method of rotation, 14 factors emerged with eigenvalues greater than one. We retained the first five factors, which accounted for 38 items and explained about 60% of the total variance. The five dimensions were: Intelligent, Fun, Organized, Candid, and Sincere (see Table 1).

Table 1. Varimax Rotation Factorial Structure Varimax Rotation Factorial Structure

Facet identification. A facet identification phase was then conducted to identify the specific items that most reliably, accurately, and comprehensively represented the five dimensions. Individual factor analyses were conducted on the items that comprised each of the five primary factors. A principle component analysis was the extraction technique and oblique was the method of rotation. This choice was based on the assumption that facets that emerge within each factor would not be orthogonal with each other.

To provide a reliable representation of each facet (Nunnally 1978), two criteria were arbitrarily set for item selection for each facet: 1) item loading had to be greater than .40, and 2) no more than four items were selected for each facet. The findings revealed four facets for “Intelligent” (proficient, sophisticated, effective, and systematic), three facets for “Fun” (engaging, exciting, and vital), two facets for “Organized” (confusing and overwhelming), one facet for “Candid” and one facet for “Sincere.” The factors and their facets are shown in Table 2.

Table 2. Rotated Factor-Facet Structure of 38-Item WPS Instrument Rotated Factor-Facet Structure of 38-Item WPS Instrument

The variance explained by each factor is also shown in Table 2, which reports the facet structures under each factor, the variance explained, factor loadings of the final items on each facet, as well as Cronbach’s (1951) alpha reliability, and the corrected item total correlation.

Reliability and Validity

Reliability Tests. As shown in Table 2, the reliability was consistently above .70 within each factor and .60 within each facet. The corrected item total correlation for each item was above .40. Therefore, the instrument’s reliability seems adequate for both academic research and managerial practice, to be discussed later.

To determine whether the adjectives that appeared later on in the questionnaire might be unreliable due to student raters’ fatigue, impatience, or loss of interest, we repeated the adjective “competent” near the beginning and end of the survey to serve as a reliability test. The item had a similar distribution and a moderate correlation (.70 in data set 1, .78 in data set 2) when placed at the beginning and end of the survey, which suggests that the measure was reliable.

Factorial invariance was also tested by splitting the data according to whether the student was an undergraduate or MBA. Similar factorial structures resulted from both data sets, thereby confirming the stability of the original construct.

Criterion-related Validity. Criterion-related validity was tested by using the six-item scale that measured AST, i.e. Attitude toward the Site (Chen and Wells 1999; Chen, Clifford, and Wells 2002). The items were summed for a criterion scale (?lpha=.92), which was subsequently correlated with the final 38 items that comprised the website personality scale. A generally high correlation resulted between AST and the 38 website personality items, which provided a measure of criterion-related validity. To further examine criterion-related validity, a 5-item criterion scale from the AST was formed using the relationship, intention, satisfaction, comfort and value criterion (?lpha=.91), and the 1-item criterion scale was formed using the overall rating criterion. Bivariate correlation was also conducted between each of the 38 final WPS items and the 5- and 1-item criterion scales. All correlations were consistently at or above the .05 significance level with the exception of four items: Intensive, Clutter, Overwhelming, and Simple (See Table 3).

Table 3. Item Criterion Validity

Item Criterion Validity

Nomological Validity. Nomological validity refers to the internal structure of a measure and external relationships with other constructs (Spiro and Weitz 1990). To confirm the nomological validity of the website personality scale, we investigated the relationship between the five website personality dimensions and the criterion variables.

This was accomplished in two steps. The first step pertained to the primary factors and the second step pertained to the facets, which comprised each factor. Table 4 provides the bivariate correlations between each WPS factor level scale and each criterion scale. The findings showed that the correlation between the individual criterion items and the factors were consistently high (p < .05) with Intelligent having the highest correlation (.76) with the Satisfaction criterion, followed by the overall WPS correlation with the overall AST (.71). The scale item Organized negatively correlated with all criterion items. Only one correlation was not significant-the relationship between the item Candid and the Intention criterion, which might indicate that Candid may not be a necessary feature for consumer retention (i.e., sometimes sites with intriguing content might also allure consumers to return to the site).

Table 4. Correlation Matrix (Level One Five Factors) Correlation Matrix (Level One Five Factors)

The findings confirmed the predictive power of the primary factors on overall evaluation of the websites. It is worth noting, however, that the factors also correlated fairly highly with each other with only one exception, i.e., Candid did not significantly correlate with Fun (-.04), although it highly correlated with Intelligent (.71).

Table 5. Correlation Matrix (Level One Five Factors)

Correlation Matrix (Level One Five Factors)

We further investigated the bivariate correlation between each Website Personality facet (items that comprised each factor) and the criterion scale. The results indicated that the correlation between the criterion scale and the facets were also moderate to high (higher than .40). The facet Engaging had the strongest correlational relationship (.70) with the criterion scale; although the facet Proficient also had an equally strong correlation (.69) with the criterion scale. The significant correlation relationship in this matrix confirms the existence of predictive power of the facets on overall evaluation of the website.

We should note that although there were relatively high correlations among the facets, there were some exceptions. Specifically, the facet Exciting did not correlate with Sophisticated or Effective, and was slightly negatively correlated with Systematic. This may indicate that viewers consider a very organized website less stimulating and exciting. The facet Overwhelming did not correlate with Engaging, and Vital did not correlate with Simple and slightly negatively correlated with Overwhelming. This suggests that viewers considered a cluttered website as unattractive and difficult to find what they need.

To further explore the predictive power of the WPS, we regressed the five-factor model on each of the five individual criterion-e.g. relationship, intention, satisfaction, comfort, value, and overall rating.

Table 6. Regression (Level One Five Factors)

Regression (Level One Five Factors)

The regression analysis showed that the five-factor model predicted online consumers’ evaluation toward websites fairly well. Specifically, this model explained 55% of the variance in predicting consumers’ relationship with the websites; 40% of the variance in predicting consumers’ intention to return to the websites; 64% of the variance in predicting consumers’ satisfaction with the websites; 55% of the variance in predicting consumers’ comfort level in surfing the websites; 42% of the variance in predicting consumers’ recognition of value of the websites; and, 61% of the variance in predicting consumers’ overall ranking of the websites.

The analysis also shows that the most important dimensions for predicting consumers’ website evaluations were the factors Fun and Intelligent, with the former being significant (p < .05). Beta values in each regression model were significant (p < .05) with one exception-the factor Intelligent did not seem to be a significant predictor in the regression model that predicted consumer’s intent to visit the website again in the future.

The Organized factor was a significant predictor in the regression model predicting consumers’ appreciation of the websites in assisting them to build relationships with the sponsoring company, consumers’ satisfaction, and consumers’ comfort level in surfing the websites. However, the Beta values of this factor were not significant in the regression model that predicted consumers’ retention, value and overall rating of the websites. The Candid and Sincere factors were the least important factors in predicting consumer evaluations of the websites. Specifically, Candid was not a significant predictor in all regression models, while Sincere only served as a significant predictor for consumers’ belief that surfing the website was a good way for them to spend their time.

These findings seem important. While the Intelligent dimension has been a well-recognized factor in predicting website success (Davis 1989), the Fun factor seems to have been ignored or overlooked in the literature. This may be because advertising and marketing research has emphasized the rational over emotional component or human/brand component which, as evidenced in several empirical studies, is partially responsible for website failure (Lehaney et al. 1999). The findings further stress the importance of integrating users’ interests into website design to achieve success.


The purpose of this research was to develop a valid and reliable scale that measures website personality. Our findings suggest that this was accomplished with the Website Personality Scale, or WPS, which has 38 items that were reliably and validly reduced to five factors: Intelligent, Fun, Organized, Candid, and Sincere. In the framework proposed here, three website personality dimensions related to three of the “Big Five” human and brand personality dimensions. Specifically, Agreeableness (human personality), Sincerity (brand personality) and Sincere (website personality) capture the idea of warmth and acceptance (Aaker 1997). The dimensions Extroversion (human), Excitement (brand) and Fun (website) convey the notion of sociability, energy and activity, whereas the dimensions Conscientiousness (human), Competence (brand) and Intelligent (website) encapsulate responsibility, dependability, and security.

The remaining two dimensions of the WPS (Organized and Candid) differed from the “Big Five” in human personality (Briggs 1992) and the “Big Five” in brand personality. This pattern of findings suggests that while brand and website personality tap innate parts of human personality, website personality taps the domain that encompasses advertising-specific factors. For example, the Organized and Candid factors inferred “perceived ease of use,” which is one of the fundamental determinants of user acceptance of information technology (Davis 1989). These two dimensions might pertain to the information format in website design. For instance, compared to a full-page print ad or a 30- or 60-second TV commercial, a website is virtually free from these limitations. On the one hand, the unlimited shelf space provided by websites gives businesses more freedom to put whatever they want online. On the other hand, this freedom might overwhelm consumers with information overload. Hence, the dimensions Organized and Candid may help to gauge how well a website presents itself and/or guides the user to enhance overall site experience and increase time spent on a site.

It is worth noting that even among the website personality dimensions that related to human and brand personality, a closer look at the facets/items that loaded under each dimension reveals some specific features that are unique to the website and that we probably would not expect to find in brand or human personalities. For instance, consumers buy brands for self-expressive purposes (Aaker 1997); therefore, brand personality taps a dimension that individuals desire but may not necessarily have (Aaker 1997). By comparison, consumers who surf websites are mainly seeking information. This feature of the website is reflected by the facet Proficient under the Intelligent factor that further depicts the feature of the website using items: searchable, satisfying, informative and up-to-date, which parallels “perceived usefulness” -another fundamental determinant of the user’s acceptance of information technology (Davis 1989). These attributes are arguably unique to websites, as we would not expect to find brands or humans searchable or up-to-date.

Interestingly, Sophistication/sophisticated merged both in brand and website personality as a factor/facet. However, the items that loaded under these factors/facets suggest that consumers want a brand to be upper-class, glamorous, good looking, charming, feminine, and smooth in order to be sophisticated; while a website’s sophistication requires it to be comprehensive, knowledgeable, and mature. Again, this difference reveals the unique attributes that consumers assign to a website in comparison to a brand and the findings suggest that existing brand or human personality measures alone do not adequately assess website personality.


The Website Personality Scale (WPS) generated in the current study serves a descriptive and diagnostic purpose in helping e-advertisers to better understand their websites’ characteristics and offers ways to improve customer relationships. First, online advertisers could put the WPS online to capture user perceptions of their website. This information can be used to determine a site’s personality. Promotions for the website could then be created with the site’s personality in mind to create a unique selling proposition that sets the website apart from its competitors.

Additionally, the WPS could be used to capture the personality traits that website users deem important to them. The site can be designed or redesigned to meet the various needs of Internet users. The WPS also can serve as a segmentation tool that can evaluate each area of a website as serving one type of personality or another. Each personality can be matched with segments of the population that have similar personality traits, similar to the brand personality literature. The WPS, used in this way can help to customize both the website and promotion of the website with similar user-website personality attributes in mind.

On a methodological note, the multidimensional WPS could be used in the aggregate or specific level. In the aggregated level, the overall Web personality score would be computed. In the specific level, the score for each factor would be considered, and the score for each facet also would be used for more detailed information about the website. To this end, we recommend the following scoring schema.

To evaluate a website with the 38-item WPS, the website will have a score for each of the 38 items where 1 represents the attribute that does not describe the characteristic of the target website at all and 5 represents the attribute that describes the characteristic of the target website very well. To interpret the raw scores, the data would be formulated according to the construct of our multi-dimensional WPS. First, the scores of the items that loaded under each facet would be averaged to calculate a score for each facet. For instance, the facet:

Proficient = AVERAGE (Searchable + Satisfying + Informative + Up-to-date)

The facet scores would then be averaged within each factor to obtain the score for each factor. For instance, the factor:

Intelligent = AVERAGE (Proficient + Sophisticated + Effective + Systematic)

Finally, the factor scores would be averaged to obtain an overall score of the website’s personality:

Web Personality = AVERAGE (Intelligent + Fun + Organized* + Candid + Sincere)

In the aggregated level, the overall website personality score would be stressed for descriptive purposes. However, if an e-advertiser uses the scale for diagnostic purposes, the score of each factor should be considered to gain a better understanding of the performance of each aspect of the website. E-advertisers also might consider weighing the factor score according to the variance it explains in the factor structure. In this case:

Web Personality = AVERAGE (.32 x Intelligent + .14 x Fun + .07 x Organized* + .04 x Candid + .03 x Sincere)

Based on different criteria, the Beta weight of each factor in the target criterion model would be used to compute the website personality score. For instance, if the target criterion is consumers’ intention to visit the website again, the formula would be:

Web Personality = AVERAGE (.32 x .31 x Intelligent+ .14 x .75 x Fun + .07 x .13 x Organized* + .04 x .09 x Candid + .03 x .17 x Sincere)

Note: *Item Scores are reversed for Organized

In determining a website’s personality, this formula may be useful in competitive analyses where e-advertisers measure their own and their competitors’ site personalities. The overall Web personality score would help to address questions such as: Are there perceived differences between an advertiser’s and competitor’s website and, if so, what? Does the advertiser’s website have a differential advantage and how might a website be changed or updated to reflect the personality of its predominant users? What are consumers’ perceptions of the advertiser’s website and its characteristics inherent in its personality and which of these characteristics associate with the consumer’s evaluation or liking toward the website, either in a positive or negative way?

Note that each of the above strategies and computations is also applicable at the facet level. The analysis can be as detailed as the scale’s user decides. In cases where budgets are limited and survey costs cannot accommodate the 38-item WPS, we suggest a 22-item version of the same scale, which uses the highest and second highest loadings that represent each facet (see Table 7). Used in this way, the 22-item WPS decreases the number of items to be collected and still captures the primary factors as well as the individual facets that comprise each factor.

Table 7. 22-Item Condensed WPS Factorial Structure

22-Item Condensed WPS Factorial Structure


This study developed a measure of website personality and provided a reliable and valid scale. The results of the exploratory analysis suggest that websites have five distinct personality dimensions: Intelligent, Fun, Organized, Candid, and Sincere. Further facet analyses suggest that each factor is comprised of one to four facets (see Figure 1).

Figure 1. A Model For Measuring Web Site Personality (Dimensions and Their Facets)

A Model For Measuring Web Site Personality (Dimensions and Their Facets)

The recognition of human and brand personality characteristics in websites suggests customers will react to those characteristics much as they do salespersons and advertisements. This implies that the design of websites must include individuals who understand human and brand personality as distinct from website personality and in terms of how to use those characteristics to influence consumers’ attitudes and behaviors.

The recognition of human and brand personality attributes in websites further suggests parallels between websites and traditional advertising and marketing applications. People are involved as customers in websites and as users of information systems with the primary difference being the degree of choice. If website designs are to be viewed positively by users of those designs, then website planners and designers must determine the personality attributes that are to define the site and, simultaneously, promote that personality to make the site distinct from its competitors.

The exponential growth of the Internet leaves questions not about whether to have a website, but how to make a “sticky” website that draws and keeps consumers satisfied. We have presented a 38-item scale that will help advertisers and marketers determine their own (and competitor’s) website personality, which should help to increase the competitive position of the website.


Aaker, David A. and Donald E. Bruzzone (1981), “Viewer Perceptions of Prime-Time Television Advertising,” Journal of Advertising Research, 21 (5), 15-23.

Aaker, Jennifer L. (1997), “Dimensions of Brand Personality,” Journal of Marketing Research, 34 (3), 347-356.

Ackoff, Russell L. and James R. Emsoff (1975), “Advertising at Anheuser-Busch,” Sloan Management Review, 16 (3), 1-15.

Almagor, Moshe, Auke Tellegen, and Niels G. Waller (1995), “The Big Seven Model: A Cross-cultural Replication and Further Exploration of the Basic Dimensions of Natural Language Trait Descriptors,” Journal of Personality and Social Psychology, 69 (2), 300-307.

Atkinson, MaryAnne and Christine Kydd (1997), “Individual Characteristics Associated with World Wide Web Use: An Empirical Study of Playfulness and Motivation,” Association for Computing Machinery, 28 (2), 53-62

Batra, Rajeev and Michael L. Ray (1986), “Affective Responses Mediating Acceptance of Advertising,” Journal of Consumer Research, 13 (September), 234-249.

Belk, Russell (1988), “Possessions and the Extended Self,” Journal of Consumer Research, 15 (2), 139-168.

Benet, Veronica and Niels G. Waller (1995), “The Big Five Factor Model of Personality Description: Evidence for Its Cross-cultural Generality in a Spanish Sample,” Journal of Personality and Social Psychology, 69 (4), 701-718.

Biel, Alexander (1993), “Converting Image into Equity,” in Brand Equity and Advertising, David A. Aaker and Alexander Biel, eds., Hillsdale, NJ: Lawrence Erlbaum Associates, 67-82.

Briggs, Steven (1992), “Assessing the Five-Factor Model of Personality Description,” Journal of Personality, 60 (2), 253-293.

Brody, Robert P. and Scott M. Cunningham (1968), “Personality Variables and the Consumer Decision Process, Journal of Marketing Research, 5 (1), 50-57.

Bruce, Grady D. and Robert E. Witt (1970), “Personality Correlates of Innovative Buying Behavior,” Journal of Marketing Research, 7 (2), 259-260.

Buss, David M. (1996), “Social Adaptation and Five Major Factors of Personality,” in Theoretical Perspectives for the Five-Factor Model, J. S. Wiggins, ed., New York: Guilford Press, 180-207.

Cattell, Raymond B. (1945), “The Description of Personality: Principles and Findings in a Factor Analysis,” American Journal of Psychology, 58 (1), 69-90.

Chen, Qimei and William D. Wells (1999), “Attitude Toward the Site,” Journal of Advertising Research, 39 (5), 27-38.

Chen, Qimei, Sandra Clifford, and William D. Wells (2002), “Attitude Toward the Site II-New Information,” Journal of Advertising Research, 42 (2), 33-45.

Chen, Qimei, David A. Griffith, and Fuyuan Shen (2005), “The Effects of Interactivity on Cross-Channel Communication Effectiveness,” Journal of Interactive Advertising, 5 (2), <> (accessed on 1/25/2006).

Cohen, Joel B. (1967), “An Interpersonal Orientation to the Study of Consumer Behavior,” Journal of Marketing Research, 4 (August), 270-278.

Cronbach, Lee J. (1951), “Coefficient Alpha and the Internal Structure of Tests,” Psychometrika, 16 (3), 297-334.

Davis, Fred D. (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13 (3), 319-340.

Digman, John M. (1990), “Personality Structure: Emergence of the Five-Factor Model,” Annual Review of Psychology, 41 (1), 417-440.

— and Naomi K. Takemoto-Chock (1981), “Factors in the Natural Language of Personality: Re-Analysis, Comparison, and Interpretation of Six Major Studies,” Multivariate Behavioral Research, 16 (2), 149-170.

Dolich, Ira J. (1969), “Congruence Relationships between Self-images and Product Brands,” Journal of Marketing Research, 6 (1), 80-84.

Donnelly, James H. (1970), “Social Character and Acceptance of New Products,” Journal of Marketing Research, 7 (1), 111-113.

Engel, James F., David T. Kollat, and Roger D. Blackwell (1969), “Personality Measures and Market Segmentation,” Business Horizons, 12 (3), 61-70.

Evans, Franklin B. (1959), “Psychological and Objective Factors in the Prediction of Brand Choice,” Journal of Business, 32 (October), 340-349.

— (1961), “Reply: You Still Can’t Tell a Ford Owner from a Chevrolet Owner,” Journal of Business, 34 (January), 67-73.

Fournier, Susan (1994), “A Consumer-Brand Relationship Framework for Strategy Brand Management,” unpublished doctoral dissertation, University of Florida.

Gilmore, George W. (1919), Animism, Boston, MA: Marshall Jones Company.

Griffith, David and Qimei Chen (2004), “The Influence of Virtual Direct Experience on Online Ads Message Effectiveness,” Journal of Advertising, 33 (1), 55-69.

Ha, Louisa and Lincoln James (1998), “Interactivity Reexamined: A Baseline Analysis of Early Business Web Sites,” Journal of Broadcasting & Electronic Media, 42 (4), 457-474.

Halliday, Jean (1996), “Chrysler Brings Out Brand Personalities with ’97 Ads,” Advertising Age (September 30), 3.

Henss, R. (1996), “The Big Five and Physical Attractiveness,” Paper presented at the Eighth European Conference on Personality, Ghent, Belgium.

Holbrook, Morris (1986), “Aims, Concepts, and Methods for the Representation of Individual Differences in Esthetic Responses to Advertising,” Journal of Consumer Research, 13 (3), 337-347.

Horton, Raymond L. (1979), “Some Relationships between Personality and Consumer Decision Making,” Journal of Marketing Research, 16 (May), 233-246.

Isaka, Hiroko (1990), “Factor Analysis of Trait Terms in Everyday Japanese Language,” Personality and Individual Differences, 11 (22), 115-124.

John, Oliver P. (1990a), “The ‘Big Five’ Factor Taxonomy: Dimensions of Personality in the Natural Language and in Questionnaires,” in Handbook of Personality: Theory and Research, L.A. Pervin, ed., San Francisco: Harper, 66-100.

— (1990b), “The Search for Basic Dimensions of Personality,” in
Advances in Psychological Assessment, Paul McReynolds, James C. Rosen and Gordon J. Chelune, eds., New York: Plenum, 1-27.

—, Lewis R. Goldberg, and Alois Angleitner (1984), “Better than the Alphabet: Taxonomies of Personality-Descriptive Terms in English, Dutch, and German,” in Personality Psychology in Europe: Theoretical and Empirical Developments, H. Bonarius, G. Van Heck, and N. Smid, eds., Lisse, Netherlands: Swets and Zeitlinger, 83-100.

Kassarjian, Harold H. (1971), “Personality and Consumer Behavior: A Review,” Journal of Marketing Research, 8 (November), 409-418.

— and Mary Jane Sheffet (1991), “Personality and Consumer Behavior: An Update,” in Perspectives in Consumer Behavior, 4th ed., Harold Kassarjian and Jane Sheffet, eds., Englewood Cliffs, NJ: Prentice-Hall, 281-303.

Kernan, Jerome B. (1968), “Choice Criteria, Decision Behavior, and Personality,” Journal of Marketing Research, 5 (May), 155-164.

Kleine, Robert E., Susan Schultz Kleine, and Jerome B. Kernan (1993), “Mundane Consumption and the Self: A Social-Identity Perspective,” Journal of Consumer Psychology, 2 (3), 209-235.

Koponen, Arthur (1960), “Personality Characteristics of Purchasers,” Journal of Advertising Research, 1, 6-12.

Lehaney, Brian, Steven Clarke, Vikki Kimberlee, and Sarah Spencer-Matthews (1999), “The Human Side of Information Development: A Case of an Intervention at a British Visitor Attraction,” Journal of End User Computing, 11 (4), 33-39.

Liebert, Robert M. and Michael D. Spiegler (1998), Personality: Strategies and Issues, Pacific Grove, CA: Brooks/Cole Publishing Company. (2006), “Why Websites Fail,” <> (accessed on 10/15/2006).

Malhotra, Naresh K. (1981), “A Scale to Measure Self-Concepts, Person Concepts and Product Concepts,” Journal of Marketing Research, 23 (November), 456-464.

— (1988), “Self Concept and Product Choice: An Integrated Perspective,” Journal of Economic Psychology, 9 (11), 1-28.

Martineau, Pierre (1957), Motivation in Advertising, New York: McGraw-Hill.

McCrae, Robert R. and Paul T. Costa, Jr. (1989), “The Structure of Interpersonal Traits: Wiggins’s Circumplex and Five-Factor Model,” Journal of Personality and Social Psychology, 56 (4), 586-595.

McMillan, Sally J. (2002), “A Four-Part Model of Cyber-Interactivity: Some Cyber-Places are More Interactive than others,” New Media and Society, 4 (2), 271-291.

Moon, Youngme (2000), “Intimate Exchanges: Using Computers to Elicit Self-disclosure from Consumers,” Journal of Consumer Research 26 (4), 323-339.

— and Clifford I. Nass (1996), “How “Real” Are Computer Personalities? Psychological Responses to Personality Types in Human-Computer Interaction,” Communication Research, 23 (6), 651-674.

Nass, Clifford I., Youngme Moon, B. J. Fogg, Byron Reeves, and D. Chris Dryer. (1995), “Can Computer Personalities Be Human Personalities?” International Journal of Human-Computer Studies, 43 (2), 223-239.

Norman, Warren T. (1963), “Toward an Adequate Taxonomy of Personality Attribute: Replicated Factor Structure in Peer Nomination Personality Ratings,” Journal of Abnormal and Social Psychology, 66 (June), 574-583.

Nunnally, Jura C. (1978), Psychometric Theory. New York: McGraw-Hill, Inc.

Palmer, Jonathan W. and David A. Griffith (1998), “An Emerging Model of Web Site Design for Marketing,” Communications of the ACM, 41 (3), 44-51.

Park, Bernadette (1986), “A Method for Studying the Development of Impressions of Real People,” Journal of Personality and Social Psychology, 51 (5), 907-917.

Piedmont, Ralph L., Robert R. McCrae and Paul T. Costa, Jr. (1991), “Adjective Check List Scales and the Five-Factor Model,” Journal of Personality and Social Psychology, 60 (4), 630-637.

Plummer, Joseph T. (1985), “Brand Personality: A Strategic Concept for Multinational Advertising,” in Marketing Educators’ Conference, New York: Young & Rubicam, 1-31.

Rodgers, Shelly (2003), “The Effects of Sponsor Relevance on Consumer Reactions to Internet Sponsorships,” Journal of Advertising, 32 (4), 67-76.

— and Esther Thorson (2000), “The Interactive Advertising Model: How People Perceive and Process Interactive Ads,” Journal of Interactive Advertising, 1 (1) <> (accessed on 5/10/2004).

Rook, Dennis W. (1985), “The Ritual Dimension of Consumer Behavior,” Journal of Consumer Research, 12 (December), 251-264.

Saucier, Gerard (1997), “Effects of Variable Selection on the Factor Structure of Person-descriptors,” Journal of Personality and Social Psychology, 73 (6), 1296-1312.

— and Lewis R. Goldberg (1998), “What is Beyond the Big Five?” Journal of Personality, 66 (4), 495-525.

Schlinger, Mary Jane (1979), “A Profile of Responses to Commercials,” Journal of Advertising Research, 19 (2), 37-46.

Singh, Surendra N. and Nikunj P. Dalal (1999), “Web Home Pages as Advertisements,” Communications of the ACM, 42 (8), 91-98.

Smith, Brock J. (1998), “Buyer-Seller Relationships: Bonds, Relationship Management, and Sex-type,” Canadian Journal of Administrative Science, 15 (1), 76-92.

Spiro, Rosann L. and Weitz, Barton A. (1990), “Adaptive Selling: Conceptualization, Measurement, and Nomological Validity,” Journal of Marketing Research, 17 (February), 61-69.

Tellegen, Auke and Niles G. Waller (1987), “Re-examining Basic Dimensions of Natural Language Trait Descriptors,” Paper presented at the 95th Annual Convention of the American Psychological Association.

Tucker, W. T. and John J. Painter (1961), “Personality and Product Use,” Journal of Applied Psychology, 45 (5), 325-329.

Tupes, Ernest C. and Raymond E. Christal (1958), “Stability of Personality Trait Rating Factors Obtained Under Diverse Conditions,” USAF WADS Technical Report No. 58-61, Lackland Air Force Base, TX: U.S. Air Force.

Vitz, Paul C. and Donald Johnson (1965), “Masculinity of Smokers and the Masculinity of Cigarette Images,” Journal of Applied Psychology, 49 (3), 155-159.

Wagner, Mitch (1997), “L.L. Bean Puts Folksy Feel into its Web Site,” Computerworld, 31 (25), 47-49.

Wells, William D. and Qimei Chen (2000), “The Dimensions of Commercial Cyberspace,” Journal of Interactive Advertising, 1 (1), <> (accessed on 5/21/2004).

Wells, William D., Clark Leavitt, and Maureen McConville (1971), “A Reaction Profile for TV Commercials,” Journal of Advertising Research, 11 (6), 11-17.

Westfall, Ralph (1962), “Psychological Factors in Predicting Product Choice,” Journal of Marketing, 26 (April), 34-40.

Wright, Peter (1975), “Factors Affecting Cognitive Resistance to Advertising,” Journal of Consumer Research, 2 (June), 1-9.

About the Authors

Qimei Chen (Ph.D.) is an Associate Professor of Marketing in the Shidler College of Business, University of Hawaii at Manoa. Her research interests include advertising effectiveness, e-commerce, IT usage, Internet health, market segmentation, and cross-cultural consumer behavior.

Shelly Rodgers (Ph.D.) is an Associate Professor of Strategic Communication at the University of Missouri-Columbia’s School of Journalism. Her research examines information processing of Internet health care advertising, marketing and communication messages, segmentation of Internet markets, and the impact of new technologies on psychosocial well-being.