Interactivity Perceptions and Online Newspaper Preference

Thijs Broekhuizen

University of Groningen

Arvid Hoffmann

Maastricht University and Netspar


The present article examines the relevance and importance of perceived interactivity for online newspapers. A large-scale survey of online newspaper readers highlights the influencers and consequences of online interactivity in a real-life setting. Perceived interactivity positively influences online newspaper readers’ flow experiences and quality of information processing, but only the latter consequence enhances online newspaper preference. Although interactive websites typically demand greater user skills and effort, readers with lower levels of online skills and need for cognition benefit more strongly from perceived interactivity, because perceived interactivity has a stronger effect on the quality of their information processing.

Keywords: Online newspapers, interactivity, flow, quality of information processing.


Interactivity is vital for online newspaper success (Chyi and Sylvie 1998), stimulates user loyalty (Varadarajan et al. 2010), and sets online newspapers apart from other mass media (Song and Zinkhan 2008). With Internet technology, publishers can communicate digitally through news feeds, blogs, and online versions of traditional newspapers (Flavián and Gurrea 2009). Online newspapers rely on hypertext markup language to deliver information in a dynamic, nonhierarchical format (Stromer-Galley 2004), which facilitates customized media messages (Kalyanaraman and Sundar 2006). On top of these elements come multimedia features, such as audio and video downloads, and other functions to communicate bilaterally. Although developers preprogram such content, users choose whether, when, and how to experience such interactive features (Sundar 2004).

Consumers increasingly embrace online newspapers (Kaye and Johnson 2004). From 2007 to 2010, the number of unique monthly visitors to U.S. newspaper websites increased by 18.2% to 71 million, and the number of page views increased by 10% (Newspaper Association of America 2011). Despite these increases, online newspaper customer loyalty remains difficult to obtain; only 35% of online consumers have one favorite news site (State of the Media 2010). One of the challenges for customer loyalty and success in the online newspaper market is the need to differentiate; simply putting offline content online is not enough (Chyi and Sylvie 1998). That is, companies must provide interactive features, such as two-way communication, searchable databases, real-time data transmission, hyperlinking, and multimedia content.

The notion and importance of interactivity is well established; however, the understanding of the concept remains deficient (Yadav and Varadarajan 2005), mainly due to the lack of a common definition, different abstraction levels, and its context-specific nature. Little work theorizes about the constituents, causes, or effects of interactivity (Lowry et al. 2009). Many observers assume that interactivity builds online attitudes and preferences, but research rarely examines these assumptions in real-life business settings (Yoon, Choi, and Sohn 2008). As a result, the mechanisms through which interactivity influences newspaper readers’ preference for online versions remain unclear.

This article analyzes how perceptions of online interactivity may affect online preferences in a real-life online newspaper context. We define online preference as customers’ preference for the online newspaper, relative to its offline and physical counterpart. This study builds on previous research that argues that need for cognition and online skills influence the extent to which consumers perceive their interaction with a medium as interactive (Tremayne 2005). To add to existing work, empirical tests reveal how consumer characteristics affect the degree to which perceived interactivity influences consumer preferences (Bucy and Tao 2007; Martin, Sherrard, and Wentzel 2005). The consequences of perceived interactivity involve hedonic benefits, such as flow experiences (Hoffman and Novak 1996; Mathwick and Rigdon 2004), and utilitarian benefits, such as improved information processing quality (Widing and Talarzyk 1993). The results show that perceived interactivity positively influences online newspaper readers’ flow experiences and quality of information processing, but only the latter consequence enhances preferences for the online newspaper. Although interactive websites typically demand greater skills and effort from users, readers with low online skills and need for cognition benefit more from perceived interactivity than higher-scoring counterparts, through a greater improvement in their quality of information processing.

Background and Hypotheses

Interactivity and Online Newspapers

A family of related concepts and definitions define interactivity at three levels: the message exchange process (message-centered approach), technology features (structuralist approach), and users’ perceptions of a technology (perceptual approach). The message-centered approach focuses on activities, such as interchange and responsiveness, as key elements of interactivity (McMillan and Hwang 2002; Rafaeli 1988). The structuralist approach views interactivity as responses to the structural properties of the online medium or website, with a focus on a medium’s objective interactive opportunities (Bucy and Tao 2007; Liu and Shrum 2002; Mollen and Wilson 2010). Here, technology features are the focal object, and the specific features of the technology, such as the availability of search engines, chat rooms, and hyperlinks, constitute important influencers or elements of interactivity (Ghose and Dou 1998; McMillan and Hwang 2002). Finally, perceptual research measures users’ perceptions of interactivity to explain behaviors and attitudes by analyzing process and website features (Lee et al. 2004). This approach assumes that consumers interpret interactive features to form perceptions of interactivity, which influence their cognitive, affective, and conative responses (Bucy and Tao 2007). This approach implicitly takes consumers’ cognitive processing and involvement in the activity into account and thus can explain empirical findings better (Bucy and Tao 2007; Mollen and Wilson 2010). More interactive features may not always lead to higher perceptions of interactivity, especially if consumers do not use the features (Lee et al. 2004) or resist websites with too many interactive features that make excessive demands on their cognitive processing capabilities (Liu and Shrum 2002).

Extant studies that empirically examine the influence of interactivity generally follow a structuralist approach and manipulate the medium’s interactivity in lab settings to investigate the degree of influence on interactivity perceptions, attitudes, and behaviors (Fortin and Dholakia 2003; Mazursky and Vinitzky 2005; Song and Zinkhan 2008; Sundar, Kalyanaraman, and Brown 2003; Tremayne 2005). Experiments demonstrate that changing the stimulus (in the well-known stimulus-organism-response framework) alters interactivity perceptions and subsequent attitudes and behaviors (Mollen and Wilson, 2010). However, prior studies consistently use extreme cases to isolate interactivity effects, such as by comparing very static (text) with highly dynamic (3-D images and routing, virtual agents, virtual models) websites (Mazursky and Vinitzky 2005). In contrast, most companies offer websites with intermediate interactivity levels (Lee et al. 2004). Furthermore, laboratory studies investigate the influence of (perceived) interactivity for simple visits or tasks (Fiore, Kim, and Lee 2005; Jee and Lee 2002; Mazursky and Vinitzky 2005; Song and Zinkhan 2008), whereas in reality, consumers often need to experiment and accumulate sufficient experience with the website’s features through multiple visits before they can accurately interpret the level of interactivity that determines their attitudes and behavior. Finally, most experimental studies rely on student samples (e.g., Fiore, Kim, and Lee 2005; Song and Zinkhan 2008; Wu 2005), but these are not typical consumers, considering their extended cognitive and online skills. The studies’ generalizability to business settings thus is limited.

Other empirical research links perceptions of interactivity to loyalty or purchase intentions for business websites, but it often excludes measures of perceived interactivity and resorts instead to measuring related constructs, such as telepresence or socialness (Fiore, Kim, and Lee. 2005; Suh and Chang 2006), or treats the multidimensional interactivity construct as unidimensional (Kim and Niehm 2009).

To investigate the influence of customers’ perceptions of interactivity on online newspaper preferences, we investigate how readers’ perceptions of interactivity affect their perceived utilitarian and hedonic benefits, which ultimately shape their preferences for an online newspaper. This study also examines how individual differences in need for cognition and online skills might moderate the relative impact of perceived interactivity on these benefits.

Dimensions of Online Interactivity

This study adopts a perceptual approach to explore the influences on and consequences of perceived online newspaper interactivity. Following previous work (Bucy and Tao 2007; Yadav and Varadarajan 2005), we conceptualize interactivity as a user-perceived characteristic of a computer-mediated communication that comprises four key elements: two-way communication, user control, responsiveness, and multimedia usage and fulfillment.

Two-way communication refers to mutual discourse (Ball-Rokeach and Reardon 1988; Hanssen, Etienne, and Jankowski 1996; Liu and Shrum 2002; Williams, Rogers, and Rice 1988) or a user’s ability to provide feedback and interact with other users (Day 1998; Ha and James 1998; Newhagen, Levy, and Cordes 1996). Online newspapers differ in the degree to which readers can to react to articles and engage in reciprocal communication with journalists and other readers. Users might interact either synchronously, as in chat rooms, or asynchronously, as on discussion forums (Bucy and Tao 2007). Communicating and sharing common interests tends to raise consumer commitment and involvement and the potential for a more compelling and interactive online experience.

User control refers to the level of control that persons perceive in computer-mediated interactions (Huhtamo 1999). High perceived interactivity requires a feeling of control, as when consumers sense the freedom of individual choice and a lack of obligation. Most online newspapers provide readers with tools to control the content, aesthetics, and navigational structure (e.g., search engines, website customization, topic filters). Interfaces and input devices (Baecker 1980; Biocca 1992; Nielsen 2000; Schneiderman 1998), navigation tools (Heeter 2000; Nielsen 2000), user choice and input features (Daft, Klebe Trevino, and Lengel 1987; Steuer 1992; Zeltzer 1992), and system activity (Milheim 1996; Valacich et al. 1993) all shape user control.

Responsiveness is the speed of response in the interface and the degree of correspondence between the response and the information solicited (Alba et al. 1997). In computer-mediated environments, responsiveness refers to message delivery speed and the pace at which people process messages (McMillan and Hwang 2002). Interactive systems enable users to work in their own time and at their own pace, choose preferred navigational pathways and delivery systems, and develop mental models and schemata (Latchem, Henderson-Lancett, and Williamson 1993). This dimension thus refers not only to download speed but also to the user’s ability to search and navigate through a wealth of information and receive prompt responses (Mahmood, Hall, and Swanberg 2001; Nielsen 2000; Wu 2000). Low responsiveness reduces perceived interactivity by hindering communication flows and leading users to turn their attention elsewhere.

Finally, multimedia usage and fulfillment, a website feature, refers to the degree to which different kinds of multimedia exist and satisfy user needs (Sundar 2004). This aspect covers the technology elements that make information consumption more interactive. The mere presence of certain cues, such as movies, can alter users’ perceptions of interactivity (Sundar 2004).

Conceptual Model

This study builds on Bucy and Tao’s (2007) work to model the influences on and consequences of interactivity (Figure 1). These authors argue that a systematic investigation of interactivity requires consideration of media stimuli, user perceptions, individual differences, and media effects to explain the individual-level consequences of interactive media. Perceived interactivity mediates the link between a medium’s level of structural interactivity (i.e., objective interactive features) and media effects (e.g., user preferences). In addition, individual differences moderate the relationship between structural and perceived interactivity, because similar interactive features produce different levels of perceived interactivity among different users.

By including consumers’ need for cognition and online skills, the proposed model acknowledges individual differences that may explain differences in the perceived interactivity of a website (Tremayne 2005). The level of perceived interactivity determines the degree to which consumers experience positive intermediate states, whether utilitarian or hedonic (Batra and Ahtola 1990; Dhar and Wertenbroch 2000; Fiore, Kim, and Lee 2005; Mathwick and Rigdon 2004). In the model, quality of information processing is the utilitarian and flow is the hedonic benefit of perceived interactivity (Babin, Darden, and Griffin 1994). Consumers reflect on these benefits to form their preference for online newspapers, compared with physical versions.

However, not all consumers desire interactivity (Sundar 2004), so reactions to perceived interactivity vary across individual consumers (Tremayne and Dunwoody 2001). Consumers’ need for cognition and online skills might moderate the effect of interactivity on positive intermediate states.

Figure 1. Conceptual Model

Conceptual Model


Influences on Perceived Interactivity

Need for cognition (NFC) refers to a person’s tendency to engage in and enjoy effortful cognitive endeavors (Cacioppo and Petty 1982). People with high NFC put more effort into their consumption experiences and search more widely and deeply for new information (Levin, Jasper, and Huneke 2000). Because interactive websites are more complex than static ones, high-NFC consumers should search for and experiment with interactive website features to satisfy their cognitive needs. By using these features effectively, these consumers experience more interactivity. That is, for a website with interactive features, high-NFC consumers likely recognize and use those features and perceive the website as interactive.

H1: For an interactive website, perceptions of online interactivity are higher among consumers with a high need for cognition than among those with a low need for cognition.

According to Novak, Yung, and Hoffman (2000, p. 27), online skills represent “a consumer’s capacity for action during the online navigation process.” Greater skills allow consumers to experience the more complex and challenging features of interactive websites, which may lead to more satisfying consumer experiences (Csikszentmihalyi 1997; Hoffman and Novak 1996). Online newspapers offer interactive features and customize the news to individual readers’ needs through real-time news updates, search functions, and options to react to articles (Chyi and Sylvie 1998). To recognize and take advantage of these enhancements, consumers must have sufficient online skills (Wu 2000).

H2: For an interactive website, perceptions of online interactivity are higher among consumers with greater online skills than among those with lesser online skills.

Consequences of Perceived Interactivity on Intermediate States

According to the elaboration likelihood model (Petty, Schumann, and Cacioppo 1983), the quality and depth of information processing depends on the amount of elaboration people dedicate to communication with the medium. The amount, complexity, and range of activity occasioned by a stimulus (McQuarrie and Mick 1999) reflect elaboration, including the cognitive subprocesses of encoding, storage, and retrieval (Lang, Wise, and Borse 2002). The level of interactivity that consumers experience influences their elaboration of information in online newspapers. First, interactive systems help consumers process information more easily, by reducing or eliminating unwanted information and organizing wanted information to facilitate search processes (Widing and Talarzyk 1993; Wu 2006). On websites, consumers can exploit advanced search engines and determine the order, speed, and format (text, image, video) for the information presentation (Bezjian-Avery, Calder, and Iacobucci 1998; Rodgers and Thorson 2000). Therefore, users enjoy easier encoding, storage, and retrieval of information (Cho 1999; Eveland and Dunwoody 2001). Second, the enactment effect in cognitive psychology (Nilsson 2000) implies that greater interactivity facilitates learning by increasing the level of interaction with the interface (Sundar 2004). Interactively transmitted information thus enhances user involvement and leads to more purposeful and conscious information processing (Sundar 2004).

H3: Perceived online interactivity is positively associated with quality of information processing.

Flow is the process of optimal experience (Csikszentmihalyi 1997) that arises when consumers experience balance between their skills and the challenges of the computer-mediated interaction (Hoffman and Novak 1996). In this very enjoyable state, which results from a seamless sequence of responses facilitated by the system, consumers may lose self-consciousness and a sense of time. Online newspapers that appear interactive should enable consumers to explore background and related stories, engage in interpersonal discussions, experience several multimedia formats, and become deeply involved with and indulged in the medium. Thus flow experiences are more likely than in systems with lesser interactivity.

H4: Perceived online interactivity is positively associated with the state of flow.

Determinants of Online Preference

Apart from its indirect influence through the intermediate states of quality of information processing and feelings of flow, perceived interactivity also should directly influence online preference. Previous research has demonstrated that interactivity positively influences the probability of future hypermedia use (Hoffman and Novak 1996), visit and purchase intentions (Hausman and Siepke 2009; Luna, Peracchio, and De Juan 2003), and relationship quality and behavioral loyalty (Yoon, Choi, and Sohn 2008).

Previous research also has demonstrated that the quality of information processing, including the ease of finding the desired information, is an important website criterion that determines website loyalty (Wolfinbarger and Gilly 2003). Readers prefer websites that allow them to find their desired information quickly. Furthermore, when people experience flow during an activity, they develop a tendency to repeat that activity (Csikszentmihalyi 1997; Webster, Trevino, and Ryan 1993).

H5: Perceived interactivity is positively associated with online preference.

H6: The quality of information processing is positively associated with online preference.

H7: Flow is positively associated with online preference.

Moderating Influence of Consumer Characteristics

Consumers’ needs and skills can influence not only perceived interactivity but also the impact of perceived interactivity on desired end states (Bucy and Tao 2007). Fortin and Dholakia (2003) argue that NFC moderates the relationship between interactivity and flow: Low-NFC consumers perceive interactivity as difficult, complex, and unwanted, whereas high-NFC consumers perceive interactivity as positive and challenging, as well as a means to reach flow. In this complementary moderation effect (Voss, Godfrey, and Seiders 2010), NFC increases relationship strength. Petty, Schumann, and Cacioppo (1983) suggest that consumers with high levels of NFC generate more inferences and elaborations in response to persuasive messages than consumers with low levels of NFC. Website interactivity thus should prompt a greater increase in information processing among consumers with a high need for cognition than among users with a low need for cognition (Meyers-Levy and Peracchio 1992).

H8: The positive effects of interactivity on (a) the state of flow and (b) the quality of information processing are stronger for users with high rather than low need for cognition.

Similar complementary moderation effects should arise for online skills. Highly skilled online users are familiar with, make more frequent use of, and more effectively employ the interactive features of online newspapers (Rodgers and Thorson 2000). Therefore, these users should seek interactivity to attain an experience of flow and improve quality of information. Skilled online users may prefer more interactive and complex websites, which offer higher optimum stimulation and embody arousing qualities that challenge online skills (Martin, Sherrard, and Wentzel 2005). Consequently, skilled online users attach greater importance to interactivity as an enabler of flow states than do less skilled users. Simultaneously, people with more accumulated experience with online newspapers can assess the consequences of the use of interactive tools more accurately and rely more on such tools to improve information processing.

H9: The positive effects of interactivity on (a) the state of flow and (b) the quality of information processing are stronger among skilled than among less skilled online users.


Sample and Procedure

An online survey collected data from readers of a well-known Dutch newspaper that publishes both an online and a print version. The online version contains several interactive features, such as search engines, customizable looks, multimedia (pictures, sound bites, videos), and an opportunity to interact with journalists and other readers. In 2010, the publisher’s website ranked second among Dutch online newspapers with more than 1.6 million unique readers per year. An e-mail invitation to participate in the survey to a random sample of 1,500 readers produced a response of 314 usable questionnaires. Most respondents were men (61.8%), and two-thirds were older than 35 years. More than 40% visited the website daily. A comparison of early and late respondents revealed no significant differences in the variables of interest and thus no evidence of nonresponse bias (Armstrong and Overton 1977).

Construct Measurement

The five-point Likert scales ranged from 1 (“totally disagree”) to 5 (“totally agree”). Two items from Sicilia, Ruiz, and Munuera (2005) measure NFC. Two items from Novak, Yung, and Hoffman (2000) provided the measure of online skills. Ten items measured perceived interactivity: seven that cover the dimensions of two-way communication, control, and responsiveness (Liu 2003) and three that address multimedia usage and fulfillment. This study used three items from Swaminathan, Lepkowska-White, and Rao’s (1999) measure of quality of information processing. Two items from Csikszentmihalyi (1997) gauged flow. Finally, three items assessed online preference, that is, consumers’ preference for the newspaper’s online version over the physical version.

Because the data are not multivariate normally distributed, a partial least squares (PLS) approach is more appropriate than covariance-based structural equation modeling techniques to analyze the data (Ringle, Wende, and Will 2005; Tenenhaus et al. 2005). As a component-based approach, PLS does not require multivariate normality, has minimum measurement level requirements, and is suitable for complex models (Chin, Marcolin, and Newsted 2003).

Construct Validity and Reliability

All constructs have reflective indicators (Table 1), because the manifest items should be highly correlated, and removing any item does not alter the meaning of each construct (Jarvis, MacKenzie, and Podsakoff 2003). We measure interactivity as a second-order construct, reflected by four dimensions, through the repeated use of manifest variables (Kleijnen, De Ruyter, and Wetzels 2007; Tenenhaus et al. 2005). Prior to testing the structural model, we established the measurement model. The convergent validity tests show that for all constructs except interactivity, the average variance extracted (AVE) is greater than .50. Table 2 indicates that the square roots of the AVEs exceed the construct intercorrelations, in support of discriminant validity. Finally, the constructs demonstrate sufficient reliability; the construct reliabilities are greater than .60 (Bagozzi and Yi 1988). The assessment of potential multicollinearity according to the variable inflation factor for each dependent variable in a set of regressions reveals a highest value of 1.08, so multicollinearity is not an issue.

Table 1. Measurement Model

Constructs and Items

Mean (SD)




Need for cognition





I would prefer complex to simple problems.

3.05 (1.26)




I find satisfaction in deliberating hard and for long hours.

3.19 (1.27)









Using an online newspaper is too much of a challenge to me (r).

2.64 (1.09)




Using the online newspaper is an excellent test of my skills.

3.82 (1.07)




Two-way communication





The website provides sufficient response possibilities to ask a question.

2.39 (1.59)




The website provides sufficient possibilities to provide feedback to a journalist.

2.10 (1.48)




I am satisfied with the possibilities to add information to the website.

2.06 (1.53)




User control





I am in control of my navigation on this website.

3.87 (0.81)




I have control over the content of this website that I wanted to see.

3.80 (0.87)









This website processed my input very quickly.

3.89 (1.03)




I was able to obtain the information I want without any delay.

3.92 (1.01)









The website offers a wide range of movies and pictures.

3.16 (1.48)




The website offers sufficient sound bites.

3.43 (1.49)




The website allows me to see a wide range of pictures.

2.75 (1.23)




Interactivity (second-order, reflective construct)





Two-way communication





User control




















The website gets me in a pleasant state that makes me forget my surroundings.

3.91 (0.89)




The website gets me in an enjoyable state that distorts my sense of time.

3.78 (0.92)




Quality of information processing





The online version is easy to understand.

3.77 (0.92)




The information online is easy to remember.

3.75 (0.95)




The online version is more structured than the physical newspaper.

3.15 (1.14)




Online preference





I prefer this online newspaper more than the printed version.

2.14 (1.43)




I read the online newspaper more frequently than the printed version.

3.33 (1.28)




I can find more relevant information in the online than in the physical newspaper.

2.61 (1.17)




Notes: SL = standardized loading, CR = composite reliability, AVE = average variance extracted, (r) = reverse-coded item.

Nevertheless, common method variance may be a concern. The survey design uses reverse-coded items (Lindell and Whitney 2001) and a short questionnaire to limit acquiescence effects. The check for potential method bias relied on a Lindell-Whitney (2001) marker variable test that links a theoretically unrelated variable to the study’s principal constructs. The respondents’ self-reported frequency of reading traditional (offline) print newspapers (1 = “never,” 5 = “daily”) provides the marker variable. High correlation of the marker variable with any of the principal constructs would indicate common method bias. The average correlations of the marker variable with the principal constructs are low (maximum r = .04) and insignificant. In addition, the highest correlation among all principal constructs is .58, below Bagozzi, Yi, and Phillips’s (1991) limit. All tests thus indicate common method variance is not a serious threat.

Table 2. Correlations Between Constructs


















































Notes: Numbers below the diagonal represent the correlations between two latent constructs. Numbers in bold on the diagonal represent the square root of the average variance extracted.

* p < .05.

** p < .01.

*** p < .001.


Because this study uses a single interactive website, no test can assess the moderating effects of individual characteristics on the relationship between structural interactivity and perceived interactivity for different websites with varying levels of interactivity. Instead, the analysis focuses on the mean differences of one-way analyses of variance to show how NFC and online skills moderate perceived interactivity. The mean scores of interactivity do not vary with different levels of NFC (F(8, 306) = .88, p = .54) but do vary by online skills (F(8, 306) = 3.56, p = .001). A general linear model indicates that online skills positively influence (p = .001) perceived interactivity, which explains the limited amount of variance (R2 = .09) in perceived interactivity. Thus the results support H2 but not H1.

The PLS test of the main effects (H3-H7) uses the statistical significance of the structural coefficients, with a bootstrapping procedure with 500 subsamples. The test of the moderation effects (H8-H9) uses a two-step score construction procedure (Chin, Marcolin, and Newsted 2003). Because PLS supports an explicit estimation of latent variable scores, this method calculates the significance of the interaction terms by creating a new construct that consists of the multiplied indicator scores of the manifest items of the predictor and mediator variables (Tenenhaus et al. 2005). This two-step procedure can test many interaction effects while also correcting for measurement error (Chin, Marcolin, and Newsted 2003).

Although PLS path modeling lacks an index for global validation of the model (Chin, Marcolin, and Newsted 2003), Tenenhaus et al. (2005) propose a global goodness-of-fit (GoF) criterion that can serve as a diagnostic tool. The GoF measure represents the geometric mean of the average communality and average R-square (for endogenous constructs): Equation. Wetzels, Odekerken-Schroder, and van Oppen (2009) also formulate indicative GoF values as baseline values for global validations of a PLS model: GoFsmall = .1, GoFmedium = .25, and GoFlarge = .36. With a GoF of .35, the proposed model performs well compared with baseline values.

The structural results largely confirm the direct effect hypotheses, whereas the moderation effects are either insignificant or contrary to expectations (Figure 2). Perceived interactivity positively and significantly explains both quality of information processing (β = .35, p < .001, H3) and flow (β = .30, p < .001, H4). Perceived interactivity (β = .23, p < .01, H5) and quality of information processing (β = .52, p < .001, H6), positively affect online preference, but flow has no such effect (β = -.07, p > .10, H7).

The tests for full, partial, or no mediation of the effects of perceived interactivity on online preferences follow the procedures of Baron and Kenny (1986). The results show partial mediation of the effects of interactivity by quality of information processing but not by flow. Sobel (1982) tests confirm mediation for quality of information processing (p < .001) but not for flow (p > .10).

This study tests the moderation effects in isolation of the remaining factors of the conceptual model. In contrast with H8a and H9a, neither NFC nor online skills significantly affects the relationship between interactivity and flow (p > .10). However, NFC (βNFC´QUALINFO = -.22, p < .001, H8b; ΔR2 = 4.3%) and online skills (βSKILLS´QUALINFO = -.16, p < .01, H9b; ΔR2 = 2.1%) significantly attenuate the relationship between interactivity and quality of information processing. These significant moderation effects are contrary to the hypothesized direction, so the results reject both H8b and H9b.

Figure 2. Structural PLS Results

Structural PLS Results

Notes: The figure shows standardized coefficients. The squared multiple correlations (in bold) appear without moderation effects. n.s. = not significantly different from 0, based on two-sided t-tests.

* p < .05.

** p < .01.

*** p < .001.


This study’s examination of the influences on and consequences of consumers’ perceptions of online newspapers’ interactivity in a realistic business setting confirms previous findings that indicate perceived interactivity has both utilitarian (quality of information processing) and hedonic (flow) benefits. However, in contrast with previous studies (Hausman and Siepke 2009), this research reveals that only quality of information processing, not flow experiences, mediates the positive effect of perceived interactivity on consumers’ preference for the online newspaper. Although perceived interactivity positively affects the strength of flow experiences, flow experiences on their own are not sufficient to induce consumer loyalty to an online newspaper. Many studies address the central role of flow in online experiences and propose a causal relationship with consumer attitudes or loyalty intentions (Hausman and Siepke 2009; Huang 2006; Mathwick and Rigdon 2004; Novak, Yung, and Hoffman 2000; Richard and Chandra 2005). This study suggests-in line with prior conceptual studies (Finneran and Zhang 2005; Zeithaml, Parasuraman, and Malhotra 2002)-that the relationship of flow to positive commercial outcomes is unclear. This finding is not to suggest that the hedonic consequences of interactivity play no role in shaping loyalty intentions. As Mollen and Wilson (2010) explain, similar to telepresence, flow could be an antecedent of another experiential construct with a strong direct influence on consumer preferences. Another possible explanation is that flow is a special case of perceived interactivity and a similar psychological state at the high end of the interactivity continuum (Wu 2006).

As a further contribution to extant literature, we show that not all consumers react to the same degree to perceived interactivity. Consumer characteristics moderate the degree to which perceived interactivity leads to utilitarian benefits and thus influence online preferences. The results of the moderation analyses are surprising, suggesting substitutive rather than complementary effects. That is, NFC and online skills weaken the positive effect of perceived interactivity on newspaper readers’ quality of information processing. Although online newspaper interactivity generally has a positive influence on this quality, interactivity may appeal particularly to readers with low NFC and poor online skills. For such consumers, perceived interactivity, expressed in interactive features such as search engines and multimedia, provides a “tool” that enables them to select, understand, and store online news content better. In contrast with contentions that interactive website features demand greater skill and more effort (Ariely 2000) or produce less desirability among less-skilled and low-NFC readers (Fortin and Dholakia 2003), this study suggests perceived interactivity is particularly effective for these readers and helps them select, encode, store, and retrieve information in their desired processing format. Compared with those with strong NFC, readers with low NFC perceive similar levels of interactivity but attach greater importance to this perceived interactivity as an information facilitator. High-NFC users instead may be distracted in their information processing by high perceived interactivity, in line with the preference to process information in a different (e.g., less interactive) manner. Martin, Sherrard, and Wentzel (2005) report that people with high NFC prefer less complex visual and high verbal websites and are less attracted by multimedia features such as video, sound bites, and pictures.

As a practical implication, managers of (media) companies can stimulate readers’ loyalty by improving perceptions of interactivity, especially among readers with low NFC and poor online skills. Managers worried about audience migration from revenue-generating content (i.e., paid newspapers, online or print) to free content (i.e., popular news aggregator websites) may find these results particularly useful. Only quality of information processing mediates the effect of interactivity on consumers’ preferences for online newspapers. Therefore, readers’ motives for consuming online newspapers may be primarily utilitarian (quality of information processing) rather than hedonic (flow) (Flavián and Gurrea 2009). Because readers often use online newspapers as an online search activity, rather than as a leisure activity (Mathwick and Rigdon 2004), managers should help facilitate readers’ goal-directed online search behavior. Developing websites with interactive features that readers can adjust to their information processing mode (e.g., visual vs. non-visual) appeals to different reader groups and can enhance loyalty. Furthermore, the perceived interactivity of online newspapers can be enhanced by presenting cognitive social presence cues (the provision of information about other consumers that are also online) and affective social presence cues (the use of emoticons in online consumer comments) (Cui, Wang, and Xu 2010).

This research uses a between-subjects design to investigate the role of perceived interactivity in an online newspaper context. This approach and the use of an actual consumer sample demonstrate the influences and consequences of perceived interactivity in a realistic setting. However, we cannot test the influences of specific interactive website features on online preferences (Song and Zinkhan 2008). Laboratory experiments using eye-tracking or skin conductance devices might demonstrate how users’ interaction with specific features (e.g., search engines, videos, sound bites) specifically influence perceived interactivity and other cognitive and affective user responses. This study also examines preferences for online newspapers, without studying integration with offline versions. Further research should address possible synergetic or counterproductive consequences of integrating online and offline channels (Berry et al. 2010). By investigating two potential moderators and two positive outcomes of perceived interactivity, this study ignores other potential moderators, such as consumer search strategy or patterns, which could reveal more clearly how search strategies affect online preference. Extant studies offer mixed results about whether flow experiences are more likely during experiential versus goal-directed search (Novak, Yung, and Hoffman 2000). Investigating other outcomes of perceived interactivity, including negative outcomes such as cognitive effort and confusion (Bucy and Tao 2007), may provide a finer-grained understanding of the impact of perceived interactivity, allowing managers to more closely match individual customer needs and enhance customer loyalty.


Alba, J., John Lynch, Barton Weitz, C. Janiszewski, R. Lutz, A. Sawyer, and S. Wood (1997), “Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces,” Journal of Marketing, 61 (July), 38-53.

Ariely, Dan (2000), “Controlling the Information Flow: Effects on Consumers’ Decision Making and Preference,” Journal of Consumer Research, 27 (2), 233-48.

Armstrong, J.S. and T.S. Overton (1977), “Estimating Nonresponse Bias in Mail Surveys,” Journal of Marketing Research, 14, 396-402.

Babin, Barry J., William R. Darden, and Mitch Griffin (1994), “Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Values,” Journal of Consumer Research, 20 (March), 644-56.

Baecker, R. (1980), “Towards an Effective Characterization of Graphical Interaction,” in Methodology of Interaction, R.A. Guedj, P.J.W. ten Hagen, F.R. Hopgood, H.A. Tucker, and D.A. Duce, eds. Amsterdam: North-Holland Publishing Company, 127-47.

Bagozzi, Richard P. and Youjae Yi (1988), “On the Evaluation of Structural Equation Models,” Journal of the Academy of Marketing Science, 16, 74-94.

—, —, and L. W. Phillips (1991), “Assessing Construct Validity in Organizational Research,” Administrative Science Quarterly, 36 (3), 421-58.

Ball-Rokeach, S.J. and K. Reardon (1988), “Monologue, Dialogue, and Telelog: Comparing an Emergent Form of Communication with Traditional Forms,” in Advancing Communication Science: Merging Mass and Interpersonal Process, R.P. Hawkins, J.M. Wiemann, and S. Pingree, eds. Newbury Park, CA: Sage, 135-61.

Baron, R.M. and D.A. Kenny (1986), “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations,” Journal of Personality and Social Psychology, 51 (6), 1173-82.

Batra, R. and O.T. Ahtola (1990), “Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes,” Marketing Letters, 2 (2), 159-70.

Berry, Leonard L., Ruth N. Bolton, C.H. Bridges, J. Meyer, A. Parasuraman, and Kathleen Seiders (2010), “Opportunities for Innovation in the Delivery of Interactive Retail Services,” Journal of Interactive Marketing, 24 (2), 155-67.

Bezjian-Avery, A., B. Calder, and Dawn Iacobucci (1998), “New Media Interactive Advertising vs. Traditional Advertising,” Journal of Advertising Research, 38 (4), 23-32.

Biocca, Frank (1992), “Communication Within Virtual Reality: Creating a Space for Research,” Journal of Communication, 42 (4), 5-22.

Bucy, E.P. and C.-C. Tao (2007), “The Mediated Moderation Model of Interactivity,” Media Psychology, 9 (3), 647-72.

Cacioppo, John T. and R.E. Petty (1982), “The Need for Cognition,” Journal of Personality and Social Psychology, 42, 116-31.

Chin, W.W., B.L. Marcolin, and P.R. Newsted (2003), “A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study,” Information Systems Research, 14 (2), 189-217.

Cho, C.H. (1999), “How Advertising Works on the WWW: Modified Elaboration Likelihood Model,” Journal of Current Issues and Research in Advertising, 21 (1), 33-50.

Chyi, H.I. and G. Sylvie (1998), “Competing With Whom? Where? And How? A Structural Analysis of the Electronic Newspaper Market,” Journal of Media Economics, 11 (2), 11-18.

Csikszentmihalyi, M. (1997), Finding Flow. New York: Harper Collins.

Cui, Nan, Tao Wang, and Shuang Xu (2010), “The Influence of Social Presence on Consumers’ Perceptions of the Interactivity of Web Sites,” Journal of Interactive Advertising, 11 (1), 36-49.

Daft, R.L., L. Klebe Trevino, and R.H. Lengel (1987), “Message Equivocality, Media Selection, and Manager Performance; Implications for Information Systems,” MIS Quarterly, 11 (2), 355-66.

Day, G. S. (1998), “Organizing for Interactivity,” Journal of Interactive Marketing, 12 (1), 47-58.

Dhar, R. and K. Wertenbroch (2000), “Consumer Choice Between Hedonic and Utilitarian Goods,” Journal of Marketing Research, 37 (February), 60-71.

Eveland, W.P. and S. Dunwoody (2001), “An Investigation of Elaboration and Selective Scanning as Mediators of Learning from the Web versus Print,” Journal of Broadcasting and Electronic Media, 46 (1), 34-53.

Finneran, C.M. and P. Zhang (2005), “Flow in Computer Mediated Environments: Promises and Challenges,” Communications of the Association for Information Systems, 15, 82-101.

Fiore, A.M., J. Kim and Y.-H. Lee (2005), “For Fun and Profit: Hedonic Value from Image Interactivity and Responses toward an Online Store,” Psychology & Marketing, 22 (8), 669-94.

Flavián, Carlos and R. Gurrea (2009), “Users’ Motivations and Attitude towards the Online Press,” Journal of Consumer Marketing, 26 (3), 164-74.

Fortin, D.R. and R.R. Dholakia (2003), “Interactivity and Vividness Effects on Social Presence and Involvement with a Web-Based Advertisement,” Journal of Business Research, 58, 387-396.

Ghose, Sanjay and Wenyu Dou (1998), “Interactive Functions and Their Impacts on the Appeal of Internet Presence Sites,” Journal of Advertising Research, 38 (2), 29-43.

Ha, I. and E.L. James (1998), “Interactivity Reexamined: A Baseline Analysis of Early Business Web Sites,” Journal of Broadcasting and Electronic Media, 42, 457-74.

Hanssen, L., R. Etienne, and N.W. Jankowski (1996), Interactivity from the Perspective of Communication Studies. Luton, UK: University of Luton Press.

Hausman, A. and J.S. Siepke (2009), “The Effect of Web Interface Features on Consumer Online Purchase Intentions,” Journal of Business Research, 62 (1), 5-13.

Heeter, C. (2000), “Interactivity in the Context of Designed Experiences,” Journal of Interactive Advertising, 1 (1), 4-15.

Hoffman, Donna L. and T.P. Novak (1996), “Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations,” Journal of Marketing, 60, 50-68.

Huang, M.-H. (2006), “Flow, Enduring, and Situational Involvement in the Web Environment: A Tripartite Second-Order Examination,” Psychology & Marketing, 23 (5), 383-411.

Huhtamo, E. (1999), “From Cybernation to Interaction: A Contribution to an Archaeology of Interactivity,” in The Digital Dialectic: New Essays on New Media, P. Lunenfeld, ed. Cambridge, MA: MIT Press, 96-110.

Jarvis, C.B., Scott B. MacKenzie, and P.M. Podsakoff (2003), “A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research,” Journal of Consumer Research, 30 (2), 199-218.

Jee, J. and W.-N. Lee (2002), “Antecedents and Consequences of Perceived Interactivity: An Exploratory Study,” Journal of Interactive Advertising, 3 (1), 34-45.

Kalyanaraman, S. and S.S. Sundar (2006), “The Psychological Appeal of Personalized Content in Web Portals: Does Customization Affect Attitudes and Behavior?” Journal of Communication, 56 (1), 110-32.

Kaye, B. and T. Johnson (2004), “A Web for All Reasons: Uses and Gratifications of Internet Components for Political Information,” Telematics and Informatics, 21, 197-223.

Kim, H. and L.S. Niehm (2009), “The Impact of Website Quality on Information Quality, Value, and Loyalty Intentions in Apparel Retailing,” Journal of Interactive Marketing, 23 (3), 221-33.

Kleijnen, Mirella H.P., Ko de Ruyter, and Martin G.M. Wetzels (2007), “An Assessment of Value Creation in Mobile Service Delivery and the Moderating Role of Time Consciousness,” Journal of Retailing, 83 (1), 33-46.

Lang, A., P. David, K. Wise, and J. Borse (2002), “Captured by the World Wide Web: Orienting to Structural and Content Features of Computer-Presented Information,” Communication Research, 29 (3), 215-45.

Latchem, C., L. Henderson-Lancett, and J. Williamson (1993), Interactive Multimedia: Practice and Purpose. London: Kogan Page Limited.

Lee, S.-J., W.-N. Lee, H. Kim, and P. Stout (2004), “A Comparison of Objective Characteristics and User Perception of Web Sites,” Journal of Interactive Advertising, 4 (2), 61-75.

Levin, I.P., J.D. Jasper, and M.E. Huneke (2000), “Information Processing at Successive Stages of Decision Making: Need for Cognition and Inclusion-Exclusion Effects,” Organizational Behavior and Human Decision Processes, 82 (2), 171-93.

Lindell, M.K. and D.J. Whitney (2001), “Accounting for Common Method Variance in Cross-Sectional Research Designs,” Journal of Applied Psychology, 86 (1), 114-21.

Liu, Y. (2003), “Developing a Scale to Measure the Interactivity of Websites,” Journal of Advertising, 43 (2), 207-16.

Liu, Y. and L.J. Shrum (2002), “What Is Interactivity and Is It Always Such a Good Thing? Implications of Definition, Person, and Situation for the Influence of Interactivity on Advertising Effectiveness,” Journal of Advertising, 31 (4), 53-64.

Lowry, P.B., N.C. Romano Jr., J.L. Jenkins, and R.W. Guthrie (2009), “The CMC Interactivity Model: How Interactivity Enhances Communication Quality and Process Satisfaction in Lean-Media Groups,” Journal of Management Information Systems, 26 (1), 155-95.

Luna, D., Laura A. Peracchio, and M. De Juan (2003), “Flow in Individual Web Sites: Model Estimation and Cross-Cultural Validation,” Advances in Consumer Research, 30, 280-81.

Mahmood, M.A., L. Hall, and D.L. Swanberg (2001), “Factors Affecting Information Technology Usage: A Meta-Analysis of the Empirical Literature,” Journal of Organizational Computing and Electronic Commerce, 11 (2), 107-30.

Martin, B.A.S., M.J. Sherrard, and D. Wentzel (2005), “The Role of Sensation Seeking and Need for Cognition on Web-Site Evaluations: A Resource-Matching Perspective,” Psychology & Marketing, 22 (2), 109-26.

Mathwick, Charla and Edward Rigdon (2004), “Play, Flow, and the Online Search Experience,” Journal of Consumer Research, 31 (2), 324-32.

Mazursky, D. and G. Vinitzky (2005), “Modifying Consumer Search Processes in Enhanced On-Line Interfaces,” Journal of Business Research, 58 (10), 1299-309.

McMillan, S.J. and J.S. Hwang (2002), “Measures of Perceived Interactivity: An Exploration of the Role of Direction of Communication, User Control, and Time in Shaping Perceptions of Interactivity,” Journal of Advertising, 31 (3), 29-42.

McQuarrie, E.F. and D.G. Mick (1999), “Visual Rhetoric in Advertising: Text-Interpretive Experimental and Reader-Response Analyses,” Journal of Consumer Research, 26 (1), 37-54.

Meyers-Levy, Joan and Laura Peracchio (1992), “Getting an Angle in Advertising: The Effect of Camera Angle on Product Evaluations,” Journal of Marketing Research, 29 (November), 454-61.

Milheim, W.D. (1996), “Interactivity and Computer-Based Instruction,” Journal of Educational Technology Systems, 24 (3), 225-33.

Mollen, A. and H. Wilson (2010), “Engagement, Telepresence and Interactivity in Online Consumer Experience: Reconciling Scholastic and Managerial Perspectives,” Journal of Business Research, 63 (9-10), 919-25.

Newhagen, J.E., M.R. Levy, and J.W. Cordes (1996), “[email protected]: Audience Scope and the Perception of Interactivity in Viewer Mail on the Internet,” Journal of Communication, 45 (3), 164-75.

Newspaper Association of America (2011), “Trends and Numbers,”

Nielsen, J. (2000), Designing Web Usability. Indianapolis: New Riders Publishing.

Nilsson, L. (2000), “Remembering Actions and Words,” in The Oxford Handbook of Memory, E. Tulving and F.I.M. Craik, eds. New York: Oxford University Press, 137-48.

Novak, T.P., Y.F. Yung, and D.L. Hoffman (2000), “Measuring the Customer Experience in Online Environments: A Structural Modeling Approach,” Marketing Science, 19 (1), 22-42.

Petty, R.E., D. Schumann, and J.T. Cacioppo (1983), “Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement,” Journal of Consumer Research, 10 (2), 135-46.

Rafaeli, S. (1988), “Interactivity: From New Media to Communication?” Sage Annual Review of Communication Research: Advancing Communication Science, 16, 110-34.

Richard, M-O. and R. Chandra (2005), “A Model of Consumer Web Navigational Behavior: Conceptual Development and Application,” Journal of Business Research, 58 (8), 1019-29.

Ringle, C.M., S. Wende, and A. Will (2005), SmartPLS 2.0 (M3). Hamburg:

Rodgers, S. and E. Thorson (2000), “The Interactive Advertising Model: How Users Perceive and Process Online Ads,” Journal of Interactive Advertising, 1 (1), 42-61.

Schneiderman, B. (1998), Designing the User Interface: Strategies for Effective Human-Computer Interaction. Reading, MA: Addison-Wesley Publishing Co.

Sicilia, M., S. Ruiz, and J.L. Munuera (2005), “Effects of Interactivity in a Web Site: The Moderating Effect of Need for Cognition,” Journal of Advertising, 34 (3), 31-45.

Sobel, M.E. (1982), “Asymptotic Confidence Intervals for Indirect Effects in Structural Equations Models,” in Sociological Methodology, S. Leinhart, ed. San Francisco: Jossey-Bass, 290-312.

Song, J.H. and George M. Zinkhan (2008), “Determinants of Perceived Web Site Interactivity,” Journal of Marketing, 72 (2), 99-113.

State of the Media (2010), “Report,”

Steuer, J. (1992), “Defining Virtual Reality: Dimensions Determining Telepresence,” Journal of Communication, 42 (4), 73-93.

Stromer-Galley, J. (2004), “Interactivity-as-Product and Interactivity-as-Process,” The Information Society, 20, 391-94.

Suh, K.-S. and S. Chang (2006), “User Interfaces and Consumer Perceptions of Online Stores: The Role of Telepresence,” Behaviour & Information Technology, 25 (2), 99-113.

Sundar, S.S. (2004), “Theorizing Interactivity’s Effects,” The Information Society, 20, 385-89.

—, S. Kalyanaraman, and J. Brown (2003), “Explicating Web Site Interactivity: Impression Formation Effects in Political Campaign Sites,” Communication Research, 30 (1), 30-59.

Swaminathan, Vanita, Elzbieta Lepkowska-White, and Bharat P. Rao (1999), “Browsers or Buyers in Cyberspace? An Investigation of Factors Influencing Electronic Exchange,” Journal of Computer-Mediated Communication, 5(2).

Tenenhaus, M., V.E. Vinzi, Y.-M. Chatelin, and C. Lauro (2005), “PLS Path Modeling,” Computational Statistics & Data Analysis, 48 (1), 159-205.

Tremayne, M. (2005), “Lessons Learned from Experiments with Interactivity on the Web,” Journal of Interactive Advertising, 5 (2), 40-46.

— and S. Dunwoody (2001), “Interactivity, Information Processing, and Learning on the World Wide Web,” Science Communication, 23 (2), 111-34.

Valacich, J.S., J.F. Nunamaker Jr., J.F. George, and D. Paranka (1993), “Communication Concurrency and the New Media: A New Dimension for Media Richness,” Communication Research, 20 (2), 249-76.

Varadarajan, R.R. G.G. Srinivasan, M.S. Vadakkepat, P.A. Yadav, S. Pavlou, S. Krishnamurthy, and T. Krause (2010), “Interactive Technologies and Retailing Strategy: A Review, Conceptual Framework and Future Research Directions,” Journal of Interactive Marketing, 24, 96-110.

Voss, C.A., Andrea Godfrey, and Kathleen Seiders (2010), “How Complementarity and Substitution Alter the Customer Satisfaction-Repurchase Link,” Journal of Marketing, 74 (6), 111-27.

Webster, J., K.L. Trevino, and L. Ryan (1993), “The Dimensionality and Correlates of Flow in Human-Computer Interactions,” Computers in Human Behavior, 9, 411-26.

Wetzels, Martin, Gaby Odekerken-Schroder, and C. van Oppen (2009), “Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration,” MIS Quarterly, 33 (1), 177-95.

Widing, R.E. and W. Talarzyk (1993), “Electronic Information Systems for Consumers: An Evaluation of Computer-Assisted Formats in Multiple Decision Environments,” Journal of Marketing Research, 30 (2), 125-41.

Williams, F., E. Rogers, and R.E. Rice (1988), Research Methods and the New Media. New York: The Free Press.

Wolfinbarger, M. and Mary C. Gilly (2003), “eTailQ: Dimensionalizing, Measuring and Predicting Etail Quality,” Journal of Retailing, 79 (3), 183-98.

Wu, G. (2000), “The Role of Perceived Interactivity in Interactive Ad Processing,” working paper, University of Texas Austin.

— (2005), “The Mediating Role of Perceived Interactivity in the Effect of Actual Interactivity on Attitude toward the Website,” Journal of Interactive Advertising, 5 (2), 29-39.

— (2006), “Conceptualizing and Measuring the Perceived Interactivity of Websites,” Journal of Current Issues & Research in Advertising, 28 (1), 87-104.

Yadav, M.S. and R. Varadarajan (2005), “Interactivity in the Electronic Marketplace: An Exposition of the Concept and Implications for Research,” Journal of the Academy of Marketing Science, 33 (4), 585-603.

Yoon, D., S.M. Choi, and D. Sohn (2008), “Building Customer Relationships in an Electronic Age: The Role of Interactivity of E-Commerce Web Sites,” Psychology & Marketing, 25 (7), 602-18.

Zeithaml Valarie A., A. Parasuraman, and Naresh Malhotra (2002), “Service Quality Delivery through Web Sites: A Critical Review of Extant Knowledge,” Journal of the Academy of Marketing Science, 30 (4), 362-75.

Zeltzer, D. (1992), “Autonomy, Interaction, and Presence,” Teleoperators and Virtual Environments, 1 (1), 127-32.

About the Authors

Dr. Thijs Broekhuizen (Ph.D., Marketing, 2006, University of Groningen, The Netherlands) is an assistant professor in the Department of Innovation Management & Strategy and coordinator of the MScBA Strategic Innovation Management of the Faculty of Economics and Business at the University of Groningen. During 2006-2008, he participated in the Sixth Framework (FP6) research project “eRep,” which investigated the reputational effects in online auction markets. His main research interests are in innovation adoption, diffusion, e-commerce, mass customization, lead users, and social influence. His articles have appeared in marketing and innovation journals, including International Journal of Research in Marketing, Journal of Product Innovation Management, and Journal of the Academy of Marketing Science. More information on his publications and media coverage can be found on his personal website, His LinkedIn profile can be found here:

Dr. Arvid Hoffmann (Ph.D., Marketing, 2007, University of Groningen, The Netherlands) is Assistant Professor in Finance, Maastricht University, The Netherlands. He also serves as Program Co-Director of the Marketing-Finance Master of Science program at Maastricht University and is a research fellow at the Network for Studies on Pensions, Aging and Retirement (Netspar), as well as at the Meteor Research School of Maastricht University. Moreover, he is a founding co-director of the Marketing-Finance Research Lab at Maastricht University. His research interests lie in the area of consumer decision making, in particular with respect to their financial and investment decisions. He has published in leading journals in marketing and finance, such as the International Journal of Research in Marketing, Journal of the Academy of Marketing Science, Journal of Business Research, Journal of Banking and Finance, Journal of Behavioral Finance, and International Journal of Bank Marketing. He also has held various visiting positions at renowned business schools around the world, including the Leavey School of Business (Santa Clara University), the Foster School of Business (University of Washington), and the Helsinki School of Economics (Aalto University). More information on his publications and media coverage can be found on his personal website, His LinkedIn profile can be found here: