Determinants of social media website attractiveness

Abstract

The technologies and functions of social media have significantly changed interaction on the Internet. These changes affect the perceived attractiveness of websites. Prior research regarding classic Internet offers has only partly considered these specifics. The determinants of attractive social media websites and corresponding online instruments remain under-investigated. Therefore, this study describes a conceptualization of website attractiveness in the context of social media and its relevance for potential usage. The research model was empirically tested by a standardized user survey (n=237) with the help of a structural equation model. The results show that social media website attractiveness is determined by the 2nd-order dimensions interaction orientation, social networking and user-added value. Moreover, a link to the intention to use social media offers can be established. Overall, the results shed light on the key aspects of users' expectations towards the integration of social media into electronic commerce and provide insights into how the corresponding social media instruments are to be evaluated.

Full text

Journal of Electronic Commerce Research, VOL 14, NO 1, 2013
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DETERMINANTS OF SOCIAL MEDIA WEBSITE ATTRACTIVENESS
Bernd W. Wirtz
German University of Administrative Sciences Speyer
Chair for Information and Communication Management
Freiherr-vom-Stein-Str. 2, 67346 Speyer, Germany
wirtz@uni-speyer.de
Robert Piehler
German Research Institute for Public Administration Speyer
Freiherr-vom-Stein-Str. 2, 67346 Speyer, Germany
piehler@foev-speyer.de
Sebastian Ullrich
German Research Institute for Public Administration Speyer
Freiherr-vom-Stein-Str. 2, 67346 Speyer, Germany
ABSTRACT
The technologies and functions of social media have significantly changed interaction on the Internet. These
changes affect the perceived attractiveness of websites. Prior research regarding classic Internet offers has only
partly considered these specifics. The determinants of attractive social media websites and corresponding online
instruments remain under-investigated. Therefore, this study describes a conceptualization of website attractiveness
in the context of social media and its relevance for potential usage. The research model was empirically tested by a
standardized user survey (n=237) with the help of a structural equation model. The results show that social media
website attractiveness is determined by the 2nd-order dimensions interaction orientation, social networking and
user-added value. Moreover, a link to the intention to use social media offers can be established. Overall, the results
shed light on the key aspects of users’ expectations towards the integration of social media into electronic commerce
and provide insights into how the corresponding social media instruments are to be evaluated.
Keywords: Web 2.0, social media, attractiveness of websites, structural equation modeling
1. Introduction
The user's handling of the Internet as a medium, as well as his own self-image, have undergone major changes
in the past few years. The increasing emphasis of interactive, social and networked phenomena of this technology is
summarized under the term Web 2.0 [Hoegg et al. 2006, p. 12] or social media, which is used interchangeably
[Constantinides & Fountain 2008]. Applications of social media have had an impact on a large variety of areas of
life even if they are not directly linked to internet usage like public health surveillance or organizing vacations
[Parra-López et al. 2011; Yang et al. 2011]. Furthermore the relevance of social media applications can be verified
based on the number of users and the intensity of use. On the one hand, social media platforms are growing at an
above-average rate. For example, Facebook shows a monthly increase in users of nearly 10 % in markets not fully
penetrated, such as Brazil or India [SocialBakers 2011]. On the other hand, the term of use for social media offers is
longer than that of classic web contents [Nielsen 2010], so that, for example, there is a higher potential for online
advertising revenues. However, for marketers in the field of electronic commerce an integration of social media
features is challenging, since it is often unclear which features are accepted by the users. Furthermore, it is hard to
determine which instruments yield corresponding returns in terms of corporate image. To address this knowledge
gap is a central motivation of this article.
The development of the terms social media and Web 2.0 has been characterized by a lack of conceptual clarity
from the start [O'Reilly 2006]. The phenomenon of Web 2.0 which is often interchangeably referred to as social
media has been analyzed in various fields of research such as computer science, business management, or sociology.
As a result an extensive, uniform and precise definition has hardly yet been made [Song 2010]. It shows, however,
that various fundamental dimensions are almost continuously used in relevant scientific literature. This includes
concepts like networks, platforms, applications, interaction, user profiles, information distribution and active
participation [Boyd & Ellison 2007, p. 210; Koh et al. 2007, p. 68; Park 2007, p. 175; Allen 2008]. In accordance

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with [Kaplan & Haenlein 2010, p. 61] social media is defined as “a group of internet-based applications that build
on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User
Generated Content”. In that regard, web 2.0 refers to the basic technical platform of social media and user generated
content refers to its underlying purpose. Therefore, this definition integrates technological, action-theoretical as well
as interaction-related aspects which are classified on subject-related, functional as well as teleological levels.
Social media offers include various instruments [Anderson 2007, p. 7] such as blogs, wikis, social bookmarking
or content sharing. They are characterized by user integration, social interaction, personalization and the exchange
of content [Wirtz et al. 2010, p. 279]. In addition, the instruments are used on the respective platforms in part as a
supplement or in the form of a mash up [Benslimane et al. 2008, p. 13]. The success of the offers depends on various
design factors as well as individual traits of the users.
There is already a number of articles regarding this topic for classic Internet offers, especially in the area of
usability, online service quality and electronic commerce, which examine the success of websites using empirically
multivariate methods [Zhang & von Dran 2001; Loiacono et al. 2002; Braddy et al. 2005; Lee & Kozar 2006; Kuan
et al. 2008; Liang & Chen 2009; Yoon & Kim 2009; Chiou et al. 2010; Gregg & Walczak 2010]. In this context, a
customer-oriented research perspective is pursued which is geared towards the satisfaction, trust, and acceptance of
users [Bressolles & Durrieu 2007, p. 3048; Cenfetelli et al. 2008, p. 162]. The corresponding studies mostly refer to
motivation-based theories on an individual level. The theoretical approaches most commonly used are the theory of
planned behavior [Ajzen 1991], the theory of reasoned action [Ajzen & Fishbein 1973], the diffusion of innovations
theory [Rogers 1962], the DeLone & McLean IS success model [DeLone & McLean 2003] as well as various online
service quality approaches such as SITEQUAL [Yoo & Donthu 2001], WebQual [Loiacono et al. 2002], WebQual
[Barnes & Vidgen 2002], E-S-Qual [Parasuraman et al. 2005] and Netqual [Bressolles 2006].
However, important aspects of social media like user integration or social interaction are often not referred to in
these models. The change of the Internet users’ self-image through social media offers has been insufficiently taken
into account for example [Cormode & Krishnamurthy 2008, p. 18]. Also, the integration of user-generated values
into existing value creation and the resultant additional benefit derived cannot be completely comprehended. Hence,
additional conceptual considerations are necessary to transfer these aspects to an adequate model. On the whole it
can be said that there is a lack of conceptual approach for website attractiveness in the context of social media offers
[Constantinides & Fountain 2008, p. 243; Pilgrim 2008, p. 239]: “Although the impact of customer participation and
inter-customer support on service quality is recognized, e service quality conceptualizations and measurement
models have failed to incorporate the impact of Web 2.0 on e service delivery” [Sigala 2009, p. 1341]. The basis and
dimensions of the success of social media communities to that effect have been insufficiently explained up to now.
The concept of attractiveness provides relevant insights in this regard for two reasons. On the one hand, it
constitutes a potential theoretical extension for determinant models of usage intentions that mainly refer to
technological aspects and user acceptance. On the other hand, the developed scales can be used by social media
managers as alternative success measures in order to complement online service quality analyses.
The current state of social media research represents the starting point of this study. A literature review
highlights important social media concepts and builds the foundation for the research model. Based on a user survey,
the derived model of website attractiveness in social media is empirically tested by the method of structural equation
modeling. In this regard, the goal of this article is to establish attractiveness of interactive systems as a variable of
success and identify drivers of attractive social media websites and instruments. Various dimensions of related
influencing factors are emphasized in the process and the effect sizes of relationships are examined. The article is
structured as follows: In the next section, the concept of attractiveness is introduced, a literature review regarding
social media is presented and a typology of attractiveness determinants of social media is derived. Afterwards, the
structure and results of the empirical study are explained. Finally, the implications are derived from it and the
limitations of the study and further research potential are shown.
2. Definition, State of Research and Potential Related Concepts
2.1. Definition of Attractiveness
Attractiveness is a concept that originated in the field of interpersonal psychology, where it describes a positive
attitude or orientation towards other people. It is based on individual expectations and is hence also subject to social
trends [Umberson & Hughes 1987]. Evolutionary approaches of attractiveness measurement emphasize the
importance of individual traits in this context [Gangestad & Scheyd 2005]. Often researchers measure attractiveness
by a simple confirmatory approach which is called truth-of-consensus-methodology [Donovan et al. 1989]. The test
subjects are required to evaluate the level of attractiveness and inter-coder reliability is controlled. Attractiveness as
a psychological construct has an effect on self-perception as well as behavior like social interaction [Langlois et al.
2000]. The theoretical background of these effects can be deduced from socialization theory, social expectancy

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theory and fitness-related evolutionary theories like mate selection theory [Langlois et al. 2000]. The concept of
attractiveness is also used in the context of objects. In this research stream studies with different levels of
abstractness can be found as is shown in figure 1.
Figure 1: Examples of attractiveness research regarding objects
In the field of object-related attractiveness research, the analysed objects are heterogeneous, ranging from
concrete items like toys to more abstract objects and object-related information. There are only few scientific
contributions dealing with attractiveness in the context of interactive systems. Most of them are either too specific
[Chippendale et al. 2008] to be adapted for social media or cover only basic conceptual considerations [Hartmann
2006]. Therefore, definitional attributes of physical attractiveness are transferred to digital systems in order to give a
working definition. Physical attractiveness can be defined as a socially shared orientation towards other human
beings that is characterized by a positive valence [Byrne & Griffitt 1973]. In the context of digital systems and
social media, attractiveness hence can be understood as a shared aggregated positive valence of users. As a holistic
judgement it covers rational and affective aspects of potential or actual usage scenarios and usage experiences. In
this regard, the concept bears implications on an attitudinal level and an action-based level. On the one hand,
attractiveness of a digital system should affect individual perceptions and attitudes, for example in the context of
user satisfaction. On the other hand, it should also positively affect usage behaviour, for example in the context of
continued usage or user loyalty.
Furthermore, when using social networks the priming of mental models of social interaction can trigger a halo
effect on the evaluation of those platforms. This means that potential benefits of social interactions may be attributed
to the platforms. The concepts of online service quality and acceptance can only partly consider this affective
component. The emphasis on social interaction in social networks therefore makes a strong argument for integrating
attractiveness as an analytical concept in the body of literature. On a methodological level, the concept of

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attractiveness can be used complementing user studies regarding technology acceptance or online service quality
since it is compatible to attitudinal measurement.
2.2. Literature Review Social Media
As a first step, a conceptual analysis of scientific literature was conducted in the areas of usability, Web 2.0 and
electronic commerce, in order to determine relevant influencing factors for website attractiveness in the context of
social media. A systematic EBSCO-search inquiry of peer reviewed articles using the term Social Media in the title
or abstract from 2002 until 2011 yielded 2533 results in the databases Academic Search Premier, Business Source
Complete and Communication Mass Media Complete. A further optimized search term covering only quantitative
contributions resulted in 235 hits. These articles were screened and analyzed. In the context of social media offers,
there is only a limited portfolio of well-founded scientific publications. This can be ascribed to the relatively early
state of this research area. Some research methods and study designs must therefore first be adapted to the new
environment [Rathi & Given 2010]. In addition, relevant research literature frequently falls back on qualitative
approaches which hardly allow conclusions about the concrete strength of effects and their significance beyond
individual cases [Cho & Khang 2006; Wei 2009]. The current state of research often lacks statistical rigor [Khang et
al. 2012, p. 293]. Figure 2 shows the current state of research in the field of social media by highlighting key
research subjects.
Figure 2: Evaluation of Research in the field of Social Media
With regard to contents a number of contribution deals with individual traits of users. For instance, [Correa et
al. 2010] found positive effects of the individual traits extroversion and openness to experience on social media use.
Emotional stability on the other hand was found to be a negative predictor. The effects were moderated by gender
and age. [Durukan et al. 2012] tested a model of individual determinants of word of mouth communication in social
media. They found extroverted personality, parameters of social media use, attitudes towards social media, computer
using anxiety and social media credibility to be related to positive or negative word of mouth.
Another important research stream deals with the characteristics of social media. [Figueiredo et al. 2012]
proposed a method of evaluating the quality of social media content by analyzing textual features like tags,

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descriptions and comments. Furthermore, with the help of experiments they showed how classification and
recommendation tasks are influenced by these features. [Khang et al. 2012] conducted an extensive scientific
literature review in communication, public relations, advertising and marketing. They found that there is an
increasing number of articles, a trend which is connected to the emergence of new popular social media platforms
like Facebook or Twitter. However, most recent studies focus on social media itself, uses and users or effects,
whereas only few address potential improvements of social media.
A third relevant research stream is usage behavior. [Valenzuela et al. 2009] found that the intensity of students’
Facebook use is positively related to their life satisfaction, social trust, civic engagement and political participation.
[Lorenzo-Romero et al. 2011] showed that quantitative measures based on concepts of technology acceptance can
also be applied to social networking sites. In this context, the relevance of perceived risk seems to be questionable
since the influence on the intention to use was very low and on perceived usefulness it was even not significant.
Concluding, it can be stated that the field of social media is tackled by a variety of research perspectives and
methods. The analysis of the relevant research contributions showed that there is a growing body of literature, which
has a strong focus on conceptual work. Substantial quantitative research is in an early stage, where especially the
field of success factors in the context of social media use needs further examination. All in all, it can be said that the
systematic multivariate study of essential parts of the attractiveness of social media offers has not yet been
sufficiently represented in scientific research from a strategic point of view [Wirtz et al. 2010, p. 286]. With regard
to the relevance of social media for internet users it remains a topic that needs further scientific analysis. Social
media attractiveness constitutes a phenomenon that is rooted in all these research streams.
2.3. Potential Related Concepts of Social Media Attractiveness
To integrate social media attractiveness in a meaningful research framework, antecedents and consequences
were necessary to add. By analyzing theoretical and empirical contributions, three major sets of potential
determinants and one potential dependent concept could be identified. The discussion regarding the determinants
interaction orientation, social networking and user-added value is based on the empirical findings of [Wirtz et al.
2010]. Each concept and its relevant subtopics are discussed in the following.
Interaction is a crucial element of social media and social media managers even treat artifacts of interaction in
the form of Likes or Shares as success measures. The corresponding properties of an attractive social media offer
can be summarized as interaction orientation, which hence is hypothesized to be a determinant of social media
attractiveness. Interaction orientation covers a user’s need for interactive content and the corresponding expectations
regarding the provider of these offers. By using modern information and communication media, individual customer
interactions can continually be systematically compiled. This data is processed and serves as the orientation basis of
the added value of a company. Such an interaction-related strategy is referred to as interaction orientation [Ramani
& Kumar 2008]. In this context, relevant management aspects include general customer orientation, the
configuration of customer interfaces [Piller et al. 2003; Frutos & Borenstein 2004], the potential of reaction to
customer inquiries [Jayachandran et al. 2004] as well as cooperative added value [Prahalad & Ramaswamy 2004;
Payne et al. 2008; Potts et al. 2008]. Regarding content, interaction orientation covers the focus of companies on
individual customer interactions through instruments of social media. In this article, a positive correlation is
assumed with regard to the effect of interaction orientation on social media website attractiveness. Therefore, an
Internet user who has a high orientation on individual customer interactions should also show a high level of
perceived attractiveness of social media websites.
The online interaction between individual users is frequently referred to as social networking and is considered
to be a central aspect of social media [Hoegg et al. 2006, p. 6]. The motivation of users to participate in social
networking services is due to several reasons, for example self-reflection, image cultivation as well as access
information. [Coyle & Vaughn 2008, p. 14]. The hypothesized construct social networking combines, on a
conceptual level, different user-related aspects of social online networks. In so doing, social network effects are
viewed on an individual level. Constructs related to this phenomenon include customer power [Constantinides &
Fountain 2008; Wei et al. 2011], virtual word of mouth [Dwyer 2007; Chen & Xie 2008], social identity
[Gangadharbatla 2008; Pentina et al. 2008] and social trust [Valenzuela et al. 2009]. It is also hypothesized to be a
determinant of social media attractiveness. The directional effect of social networking on social media website
attractiveness is formulated positively in the research model of this article. Accordingly, an Internet user should
show a high degree of attractiveness for social media websites, if social networking experiences are appreciated.
The generating of values by users is an aspect of social media which is relevant for business models and has
received a lot of attention in literature [Franke et al. 2006; Füller et al. 2006; Bilgram et al. 2008; Daugherty et al.
2008; Strube et al. 2011]. In this regard, various levels of value generation are taken into account. This way user
integration in the context of value generation can occur as part of a value proposition on the basis of content such as
the inclusion of user-generated videos on a website. User integration can also take place on the level of product

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innovation, e. g. with interactive product design systems in the fashion sector. Lastly, user integration can also occur
via a marketplace-based system, which makes expansions of existing offers available by providing access to various
components of the business model, e. g. the forms of revenue. Such a development can be found at app-
marketplaces in the smartphone sector. Based on these considerations, user-added value is conceptualized as a
construct that consists of user-generated content, user-generated innovation and user-generated revenue/contacts.
The integration of corresponding applications at companies shows an increasing degree of complexity from user-
generated content and user-generated innovation to user-generated contacts/revenue. Regarding content, user
integration is included in the goods and services process of companies by this conceptualization in the context of
social media. In the research model of this article, a positive correlation to social media website attractiveness is
proclaimed for perceptions regarding user-added value. Hence, it can be concluded that an Internet user, who shows
a high appreciation of customer integration into the value added process, should also show a high degree of social
media website attractiveness.
As a potential dependent variable, the concept of intention to use was identified. Intention to use is regularly
applied in the context of technology acceptance and information system success measurement [DeLone & McLean
2003; Venkatesh et al. 2003]. It describes user attitudes towards actions concerning the technological system and
thereby constitutes a measurement concept in the tradition of the theory of reasoned action [Ajzen & Fishbein 1973].
Intention to use can also be used in post-adoption scenarios of empirical research, where in most cases it captures an
intention to continue usage [Bhattacherjee 2001]. Since social media attractiveness is also understood as an
attitudinal concept in this article, a direct link between both concepts is assumed to explain a potential link between
attractiveness and behavior. In this regard, intention to use also provides a good basis to connect and compare the
research model of this article to other scientific examinations.
3. Definition, State of Research and Potential Related Concepts
3.1. Definition of Attractiveness
After the literature review, a screening of existing social media offers was performed. The set of factors derived
were first qualitatively tested by using semi-standardized expert discussions. In the process, in-depth interviews
were conducted with 26 social media users. The data gained was open, axial and selectively codified and analyzed
by applying the grounded theory [Glaser 1978; Strauss & Corbin 2008]. A temporary model structure was identified
from this two-step process and items were generated according to the literature review. Next, an item-sorting pretest
[Anderson & Gerbing 1991] was conducted with 12 scientists to test for validity of the item batteries before the data
gathering. Five items had to be changed. The final research model consists of three 2nd-order constructs, comprising
a total of 11 1st-order constructs, which affect Social Media Website Attractiveness. This involves the constructs
Interaction Orientation, Social Networking as well as User-added value. Social Media Website Attractiveness is
hypothesized to have a direct effect on Intention to Use, which was therefore selected as another latent variable and
endogenous construct of the research model. Building on basic structural considerations from the theory of reasoned
action [Ajzen & Fishbein 1973] as well as the theory of planned behaviour [Ajzen 1991], attitudinal measurements
are aimed for. In the following, the 2nd-order constructs and their components are shown before the final research
model is presented.
3.2. Definition of Attractiveness
A system of hypotheses has been derived from the preceding conceptual considerations and research process.
The hypotheses of the research model serve to explain the structure and effect of website attractiveness on social
media. They exhibit either a confirmatory-descriptive or a confirmatory-explicative character and are presented
below. Three confirmatory-descriptive hypotheses were formulated, targeted to the structure of relevant influencing
factors of social media website attractiveness:
H1: Interaction Orientation is a latent, reflective construct of 2nd order that consists of the dimensions
Customer Centricity, Interaction Configuration, Customer Response and Cooperative Value Generation.
H2: Social Networking is a latent, reflective construct of 2nd order that consists of the dimensions Customer
Power, Virtual Word of Mouth, Social Identity and Social Trust.
H3: User-added Value is a latent, reflective construct of 2nd order that consists of the dimensions User-
generated Content, User-generated Innovation and User-generated Revenue/Contacts.
The effect of these constructs on social media website attractiveness is covered in four additional hypotheses.
These are therefore to be classified as confirmatory-explicative and are as follows:
H4: The more pronounced the Interaction Orientation of a social media user is, the higher his or her Social
Media Website Attractiveness will be.
H5: The more pronounced Social Networking in a social media user is, the higher his or her Social Media
Website Attractiveness will be.

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H6: The more important the possibilities for User-added Value within a social media offer for a user are, the
higher his or her Social Media Website Attractiveness will be.
H7: The higher the Social Media Website Attractiveness of a social media user is, the stronger the Intention to
Use will be.
These hypotheses can be depicted in aggregated form in the overall model, as seen in Figure 3. Social Media
Website Attractiveness is conceptualized as a 1st-order construct that is influenced by the 2nd-order dimensions
Interaction Orientation, Social Networking and User-added Value. This Social Media Website Attractiveness has an
effect on the intention of use of a social media offer, which also represents a dependent, latent success variable of
this study.
Figure 3: Research Model
4. Empirical Study
Due to the conceptualization of website attractiveness as well as its influencing factors as latent constructs of
1st or 2nd order and the path relationships to be studied, the use of a powerful multivariate method of analysis is
necessary in order to manage the complexity of the model. In the following, the fundamentals of this research
methodology are explained, the database is described, the implementation of the constructs, including considerations
of reliability and validity, is presented and an analysis of postulated cause and effect is done on the level of the
overall model. Thus, the significance of individual influencing factors can be concluded.
4.1. Research Methodology & Study Design
Structural equation models are proven instruments of empirical social research for theory-based studies of
complex multivariate phenomena [Bollen 1989, p. 4; Steenkamp & Baumgartner 2000, p. 195; Kline 2011, p. 18;
Hair et al. 2010, p. 706]. Variance and covariance-based approaches differ from one another, exhibiting specific
advantages and disadvantages, so that their use depends on the particular research objective [Chin & Newsted 1999,
p. 314]. While variance-based methods are mainly used for the purpose of prognosis, since they are targeted to a
preferably accurate prediction of the empirical data matrix in regard to the target variables [Fornell 1987, p. 413],

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the covariance-based methods are mainly used in studies of confirmatory character. In this context, a distinction can
be drawn between hard and soft modeling [Tenenhaus et al. 2005]. Since the present study’s focus is on testing the
hypothesized model structure and not prediction, a covariance approach was taken by using the EQS. 6.1 software
package and the ML-Method. The requirements on the size of the sample as well as the multi-normal distribution of
data are fulfilled.
4.2. Database & Sample Characteristics
To gather suitable data, the method of standardized online questionnaires using 7-point-Likert scales was
applied. For use in the context of social media, biases in data for technically-savvy users are not expected, because
the use of an online questionnaire does not differ from the use of social media offers in regard to cognitive
requirements. On the contrary, it is even an ideal survey instrument for the study, since the entry of answers from
non-onliners is prevented.
From April to August 2009, 1339 social media users were contacted per e-mail for participation in the survey.
The respondent pool was randomly extracted from a sample of a previous large-n study with a focus on social
media. A reminder was used to increase the response rate. This kind of sampling served to ensure sufficient
expertise regarding social media. A total of 237 completed questionnaires could be used for the analysis, which
corresponds to a response rate of 17,7 %. Data was collected about the online experience and self-assessment of
users. Those questioned had a mean value of 8.3 years of experience with the Internet and even 2.9 years of
experience in dealing with social media offers. More than 73 % were members of several online networks and used
them more than three times per week. 83 % of those surveyed classified themselves as heavy social media users.
To start with, it was tested for potential systematic bias of data [Groves 2004, p. 9]. The first step was to
calculate the squared Mahalanobis Distances by an SPSS script, in order to identify completed questionnaires which
made no sense [Kline 2011, p. 54, Hair et al. 2010, p. 75]. There were no conspicuities determined. Secondly, data
was examined for non-response bias. Under this phenomenon, data bias is understood based on missing information
from the population, arising from non-responses [Ruxton 2006, p. 690]. For testing purposes, the responses of
former participants in the study were compared with the questionnaires submitted later on. Inasmuch as there were
significant differences in the response behavior with only 3.4 % of the items, the parametric t-test as well as the non-
parametric Mann-Whitney U test showed that it can be assumed that non-response bias is absent [Armstrong &
Overton 1977]. However, it cannot be definitely excluded because the motives for a late participation in the study
may differ from those for refusing to participate [Mentzer & Flint 1997].
4.3. Operationalization of Constructs
Based on the analysis of literature, research was conducted for the individual factors for already existing scales
and indicators, supplemented by data from expert discussions and an item-sorting pretest [Anderson & Gerbing
1991]. With some scales an adjustment was made to social media specifics. It should be noted that only a few
relevant scales were identified in the studied area. To measure the individual constructs, the study model was
transferred to a questionnaire containing items for the individual factors. All factors were collected via a 7-stage
Likert scale. To ensure reliable and valid operationalization, recommendations concerning a multistage testing
process have been taken into account [Hinkin 1995; DeVellis 2003]. All factors in the model are specified
reflectively.
With the data gathered a number of statistical tests were carried out. As a first step, Cronbach alpha values and
the item-to-total-correlation were assessed using SPSS to test for scale reliability. Items which did not meet the
corresponding cutoff-criteria were eliminated from further analysis [Kaiser 1974; Hair et al. 2010, p. 92; Bearden et
al. 2011, p. 7]. Afterwards, an exploratory factor analysis using principal axis factoring and direct oblimin rotation
was carried out. The following table shows all constructs, working definitions [Wirtz et al. 2010], the corresponding
conceptual sources, items and a summary of the 1st-generation reliability indices of the measurement models used in
the study. Furthermore, the number of eliminated items is shown. For each construct, there was only one factor
extracted in the exploratory factor analysis. For all constructs a Cronbach alpha value > 0.8 and more than 50 % of
explained variance could be reached.

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Table 1: Reliability and Validity of the Measurement Models
Construct
Definition
Item
It.-total
corr.
Factor
loading
Cronb.
alpha
Explained
variance
Items
elimin.
Customer
Centricity (Based
on [Wagner &
Majchrzak 2007;
Ramani & Kumar
2008])
 Perceived organizational
structure of companies using
social media
 Customers as focal points of
all business activities
It feels good when I receive product
recommendations tailored to my needs.
I want the social media companies, where I buy
products, to know exactly what my wishes are.
I really appreciate it when individual advertising
efforts are made for me.
0.635
0.712
0.646
0.835
0.882
0.845
0.813
72.964 %
4
Interaction
Configuration
(Based on [Chung
& Austria 2010;
Lee & Cho 2011])
 Perceived structure of
interaction processes of
companies using social media
 Social media as a
communication channel
Social media companies have to provide
applications for interactive communication among
users.
Social media companies have to allow for
connecting with other users on their websites.
Chats or forums are an important part of companies
which are active in social media.
0.602
0.711
0.681
0.812
0.882
0.866
0.813
72.900 %
4
Customer
Response (Based
on [Ramani &
Kumar 2008;
Pagani &
Mirabello 2011])
 Perceived capacity of a firm to
manage customer dialogs
 Reaction to customer
feedback
I value quick responses to my inquiries by the
companies.
An individual reply to my inquiries goes without
saying on social media.
A prompt reply to my inquiries goes without
saying on social media.
Contact with companies should offer an honest and
authentic response.
I wish to get into contact with persons responsible
within a company.
Customers and companies are communicatively
equal on social media.
Companies must communicate directly,
individually and authentically.
0.756
0.816
0.736
0.749
0.667
0.435
0.777
0.852
0.903
0.848
0.843
0.756
0.518
0.838
0.885
64.447 %
1

Wirtz et al.: Determinants of Social Media Website
Page 20
Construct
Definition
Item
It.-total
corr.
Factor
loading
Cronb.
alpha
Explained
variance
Items
elimin.
Co-operative
Value Generation
(Based on
[Nambisan &
Baron 2007;
Payne et al.
2008])
 Attitude towards participating
in shared value generation
projects of firms via social
media.
I would like to make my contribution on social
media to help support companies.
I enjoy solving problems together with other users.
When users and companies work together, it brings
about true values.
I am thrilled when companies motivate me to
cooperate.
Mutual generation of value is especially important
to me.
I like taking part in the cooperative generation of
value on social media.
0.600
0.801
0.641
0.827
0.767
0.799
0.704
0.875
0.746
0.894
0.848
0.869
0.904
68.182 %
1
Customer Power
(Based on [Park
2007; Füller et al.
2009; Leung
2010])
 Perceived increase of power
for consumers related to social
media
Social media has increased my influence on
companies.
Social media allows me to find the information I
need with low search costs (e.g. time).
Users know better how to use social media than the
companies themselves.
Social media has significantly improved the cost-
/price transparency for customers.
Through the influence of social media I less
frequently buy from the same companies on the
Internet.
All in all, through the influence of social media
chances increase that I, as a customer, gain more
power over companies.
0.696
0.545
0.597
0.694
0.540
0.656
0.813
0.681
0.727
0.809
0.675
0.780
0.842
56.195 %
1

Journal of Electronic Commerce Research, VOL 14, NO 1, 2013
Page 21
Construct
Definition
Item
It.-total
corr.
Factor
loading
Cronb.
alpha
Explained
variance
Items
elimin.
Virtual Word of
Mouth (Based on
[Riegner 2007;
Durukan et al.
2012; Pitt et al.
2012])
 Perceived relevance of
social media for information
transfer between different
parties via electronic
applications such as blogs,
review websites or even
e-mail
Social media allows to make the right buying
decisions by user comments.
Information about products and services supplied
by other social media users are more valuable than
those of companies.
Through the user comments on products or services
on social media websites one gets information a lot
faster than from the companies themselves.
To gather information about products the
experiences of other social media users are of
particular importance.
Social media allows to solve problems regarding
products faster through the experiences of others.
Through the comments of other social media users
one gets the right answers to questions much faster.
0.720
0743
0.800
0.856
0.836
0.771
0.803
0.821
0.864
0.907
0.892
0.845
0.927
73.304 %
2
Social Identity
(Based on [Bailey
2005; Kim et al.
2010; Chai et al.
2011])
 Perceived membership of
the specific web interest
group of social media users
It makes me feel better when someone mentions
the social media community in a positive way.
I behave like a typical social media user.
When talking about social media users, I frequently
use the word "we".
I combine a lot of skills of social media users.
When the social media community succeeds, then I
succeed as well.
Should the social media community ever be spoken
ill of in the media, I would take it personally.
0.752
0.750
0.839
0.789
0.797
0.812
0.827
0.827
0.895
0.860
0.863
0.872
0.927
73.545 %
3
Social Trust
(Adapted from
[Mathwick et al.
2008; Valenzuela
et al. 2009])
 Perceived confidence in
reciprocative beneficial
behaviour in interactions
with others using social
media
The more a social media user publishes or
contributes to a project (e.g. Wikipedia), the more
trustworthy he is.
A strong willingness to become active for the
community on social media makes the user
trustworthy.
The trust in the community of users shapes social
media as a whole.
0.717
0.792
0.588
0.881
0.920
0.795
0.834
75.137 %
4

Wirtz et al.: Determinants of Social Media Website
Page 22
Construct
Definition
Item
It.-total
corr.
Factor
loading
Cronb.
alpha
Explained
variance
Items
elimin.
User-generated-
Content (Based on
[Daugherty et al.
2008; Imran &
Zaheer 2012])
 Perceived relevance of user-
generated content for social
media
The generation of content is an essential part of
social media.
Social media is largely dependent on the supply of
content by the user.
User-generated content is very important for the
forming of opinions.
Without the provision of user-generated content
there would be no social media.
The information provided by the user is
comparable to that of professional news agencies.
0.783
0.891
0.664
0.629
0.669
0.876
0.899
0.784
0.757
0.790
0.878
67.714 %
2
User-generated
Innovation (Based
on [Füller et al.
2008; Hung et al.
2011])
 Perceived relevance of user-
generated innovations for
the further development of
social media offers
For the new and further development of offers on
social media, it is the users who especially
contribute to problem solving.
The problem-solving expertise of social media
users is of great importance for the new and further
development of offers.
It is mostly the social media users who have good
and new ideas for offers.
The ideas of social media users to improve offers
of companies have high potential of also being
implemented.
Innovations initiated by social media users greatly
benefit other Internet users as well.
On the whole, user-generated innovations on social
media are of major importance for the new and
further development of offers.
0.817
0.851
0.826
0.747
0.821
0.867
0.876
0.901
0.881
0.821
0.879
0.911
0.940
77.219 %
-

Journal of Electronic Commerce Research, VOL 14, NO 1, 2013
Page 23
Construct
Definition
Item
It.-total
corr.
Factor
loading
Cronb.
alpha
Explained
variance
Items
elimin.
User-generated
Revenue /
Contacts (Based
on [Vilpponen et
al. 2006; Ryu &
Feick 2007;
Hanna et al.
2011])
 Perceived relevance of user-
generated contacts for value
creation of companies using
social media
Companies obtain new users through my
contributions on social media.
My invitations for friendship help to increase the
advertising revenue of companies.
The inviting of friends by the user generates value
for companies.
Articles provided by users increase the
attractiveness of the website for other users.
User recommendations enhance sales for the
companies.
0.724
0.842
0.844
0.806
0.863
0.813
0.901
0.907
0.883
0.921
0.927
78.467 %
1
Social Media
Website
Attractiveness
(Based on
[Sutcliffe 2002;
Hartmann 2006;
Hartmann et al.
2007])
 Perceived individual
preference to use social
media offers
I find websites that allow users to interact with
each other or with the companies that provide the
service very attractive.
I find websites that allow users to connect with
other users or companies in the form of networks
very attractive.
I find social media websites very attractive.
I find social media websites that allow users to get
in touch with a company or other users very
attractive.
0.880
0.843
0.869
0.762
0.938
0.914
0.931
0.860
0.930
83.036 %
2
Intention to Use
(Adapted from
[Venkatesh et al.
2003; Java et al.
2007; Parra-López
et al. 2011])
 Attitude towards using or
continuing use of social
media offers
If I had the chance to use social media websites I
would do so.
It is probable that I am going to use social media
websites.
I am ready to use social media websites at any
time.
If and when the occasion arises I will use social
media websites.
Even if there are alternative websites, I prefer using
social media websites.
0.879
0.874
0.851
0.870
0.797
0.927
0.924
0.905
0.920
0.865
0.946
82.546 %
-

Wirtz et al.: Determinants of Social Media Website
Page 24
The process of the scale reduction can be classified as acceptable. Most concepts were measured adequately
since only few items had to be eliminated. However, the operationalization of Customer Centricity, Interaction
Configuration and Social Trust seems to be critical, since only three items can be retained. Further research is
needed to develop more measurement items for these concepts. For all measurement models, discriminant validity
was checked using the Fornell/Larcker criterion as a next step. According to this method, the average variance
extracted of each construct is set into relation to the squared crossloadings with the other constructs [Hair et al.
2010, p. 710]. For all constructs the average variance extracted was higher than the crossloadings and therefore the
criterion is met. The results are shown in Table 2.
Table 2: Fornell/Larcker Criterion for all Measurement Models
1
2
3
4
5
6
7
8
9
10
11
12
13
Customer
Centricity (1)
0.599
Interaction
Configuration
(2)
0.491
0.599
Customer
Response (3)
0.141
0.153
0.517
Cooperative
Value
Generation (4)
0.362
0.531
0.111
0.624
Customer Power
(5)
0.222
0.426
0.135
0.312
0.513
Virtual Word of
Mouth (6)
0.094
0.295
0.194
0.228
0.461
0.683
Social Identity
(7)
0.340
0.315
0.010
0.296
0.371
0.179
0.684
Social Trust (8)
0.130
0.397
0.033
0.194
0.423
0.217
0.602
0.667
User-generated
Content (9)
0.077
0.327
0.138
0.185
0.367
0.352
0.181
0.397
0.603
User-generated
Innovation (10)
0.125
0.345
0.147
0.266
0.441
0.298
0.218
0.458
0.569
0.727
User-generated
Revenue /
Contacts (11)
0.060
0.426
0.089
0.248
0.373
0.372
0.271
0.408
0.475
0.537
0.721
Social Media
Website Attrac-
tiveness (12)
0.371
0.325
0.121
0.195
0.357
0.301
0.254
0.382
0.273
0.283
0.147
0.769
Intention to Use
(13)
0.215
0.427
0.078
0.261
0.328
0.252
0.194
0.177
0.193
0.264
0.351
0.492
0.777
4.4. Discussion of Cause and Effect Relations
The last step of analysis of the research model is the examination of the overall model fit as well as the
evaluation of cause and effect relations. Regarding the global quality levels, the overall model has sufficient fit (see

Journal of Electronic Commerce Research, VOL 14, NO 1, 2013
Page 25
Fig. 4). The indices GFI, AGFI, RMSEA as well as the chi-square value are below or above the required values for
complex models in method literature [Byrne 1989], p. 5; Kline 2011, p. 193]. Only for CFI und TLI, the required
values of ≥ 0.9 are not achieved by a small margin [Bollen 1989, p. 273; Bentler 1990, p. 238]. The first order
constructs all sufficiently reflect their superordinate construct. The only close exception is the concept of Customer
Response, whose path coefficient is 0.445. The factor was kept due to concerns of content validity, but further
research may be needed to come to a clearer picture, whether or not Customer Response is a part of Interaction
Orientation. The hypotheses H1 to H3 were not rejected in the context of these results.
In regard to the path relationships of the model, there is a heterogenous significance of individual influencing
factors on website attractiveness on social media. While Interaction Orientation (0.255) and Social Networking
(0.351) exhibit low to middle effect size, the strongest influence comes from User-added Value (0.581). User
integration in added value is the most important aspect of social media offers from the user perspective. Since all
relationships exhibit positive signs, the Hypotheses H4, H5 und H6 are not to be rejected. The effect of Website
Attractiveness on Intention to Use (0.708) can be classified as middle to high. For this reason, Hypothesis H7 cannot
be rejected and the postulated model structure is confirmed. The results of the analysis are shown in Figure 8, where
manifest variables were omitted to enhance readability.
Figure 4: Cause and Effect Relationships of the Model
5. Conclusion
This study constitutes a first concept-driven contribution to the empirical identification and measurement of
determinants for the attractiveness and usage of digital systems using social media as an example. So far, the
concept of attractiveness primarily has been used in the context of physical attraction. A definition has been
transferred and measures were developed with regard to social media research. The starting point of the study was a
conceptional derivation and empirical examination of factors which influence the attractiveness of social media

Wirtz et al.: Determinants of Social Media Website
Page 26
offers. These target specifically features of social media that go beyond classic Internet offers. Thereto, 11 1st-order
factors and three 2nd-order factors were identified from expert discussions, by screening existing offers and from an
analysis of literature. Corresponding impact hypotheses were also formulated and intention to use was used as an
endogenous construct.
5.1. Discussion of Findings
The developed measures prove to be reliable and valid. These scales can be used to differentiate the concept of
digital attractiveness in further empirical contributions. Overall, the research model shows an acceptable fit and all
hypotheses concerning the structure of constructs cannot be rejected. However, the role of Customer Response needs
to addressed in further research. It was found that its path coefficient in the final model is only moderate, but in the
data there is no conceptual overlap detected. Customer response could hint to another phenomenon that is not
covered by the model of social media attractiveness in this regard.
The postulated 2nd-order structure of the factors and the impact hypotheses were also confirmed by the data.
The Interaction Orientation of users has a positive effect on the perceived attractiveness of a social media offer via
Customer Centricity, Interaction Configuration, Customer Response and Cooperative Value Generation. Attitudes
towards Social Networking also have a positive effect on Social Media Website Attractiveness. The dimensions of
this construct include perceived Customer Power, individual Virtual Word of Mouth, opportunities to build up
Social Identity and Social Trust. Finally, User-added Value shows positive effects on the perceived attractiveness of
social media offers as well via the dimensions of User-generated Content, User-generated Innovation and User-
generated Revenue/Contacts. Furthermore, User-added Value is the most important factor influencing the
attractiveness of social media. Perceived Social Media Website Attractiveness in turn has a strong effect on the
intention to use respective offers and covers a substantial proportion of explained variance. With this model the
structure of individual proclivity for social media offers and the basic related affective causal effect chain can be
explained.
In context of electronic commerce and the attractiveness of social media offers, this study is one of the few
complex-multivariate contributions with a confirmatory study design. For the area of social media, it shows essential
success-relevant factors from the user perspective. Accordingly, the results can serve as a starting point for other
theoretical and empirical studies. Besides validation studies, in particular the integration of scales from the areas of
technology acceptance, online service quality as well as information system success can be stated on a research
agenda to classify the results in more general research streams. Also, the development of short scales and single
measure items can be fruitful to apply the concept of social media attractiveness in a more complex setting.
Moreover, the features of specific application scenarios of social media can also be an object of continued user-
oriented studies [Schaffers et al. 2007; Purdy 2010] or cultural features of certain user groups may be taken into
account [Ji et al. 2010].
5.2. Limitations
The limitations of the study are mainly the result of methodical considerations. For one thing, a potential
distortion through common method bias cannot be completely excluded even though a Harman’s single factor test
showed no irregularities because the data for exogenous and endogenous variables stems from the same source
[Podsakoff et al. 2003]. In this context, a triangulation or multi-informant design would be interesting for future
studies. On the other hand, there are no conclusions to be drawn about changes in chronological sequence due to the
cross-sectional design of the study. Long-term effects of individual factors can probably therefore only inadequately
be depicted in the model of this study. To address this issue, for instance, the RET-procedure could be adapted in
this context [Carbon & Leder 2005]. Moreover, it cannot be completely excluded that individual aspects have
inadequately entered into the conceptualization, due to the partially inductive procedure with the generating of
factors. Also, the low sample size for such a complex model and the corresponding response rate have to be
mentioned as limiting factors.
5.3. Implications
The special significance of fundamental aspects of social media platforms for users has been shown by this
study. Practical implications of this research can be derived for two groups of business stakeholders. On the one
hand, implications for companies which are platform providers in the area of social media can be summarized as
follows: Different aspects of individual perception have a significant influence on the attractiveness of social media
offers, which in turn determines the intention to use these offers. A systematic analysis of social media offers is
possible by means of the proposed influencing factors. Accordingly, instruments und concepts from the areas of
Interaction Orientation, Social Networking and User-added Value contribute to the attractiveness of social media
platforms. The expectations of users have to be considered when social media strategies are developed. They cannot
be successful if they are not aligned to an active role of the user in the process of value generation and user
feedback. The most important success factor is identified as providing interfaces for user-added value applications

Journal of Electronic Commerce Research, VOL 14, NO 1, 2013
Page 27
and the communication of corresponding offers. Since this aspect is the most relevant for users, companies have to
make sure they allocate a sufficient amount of resources in this sector. Furthermore, based on results of this study,
design guidelines and checklists for social media offers can be adapted. In coordination with basic considerations
about the business model, an analysis of strengths and weaknesses can be created.
On the other hand, implications can be concluded for marketers in the area of electronic commerce. When
integrating social media platforms into an electronic commerce online offer, the main focus should be on creating
possibilities for users to contribute or interact in the context of value creation. This will yield the biggest returns in
terms of attractiveness. Instruments to enhance Social Networking and Interaction Orientation should accompany
this process however. These three dimensions drive the attractiveness of social media and are necessary to increase
the intention to use.
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