As EAs play a decisive role in the diffusion of innovations (Rogers, 2003), a better understanding regarding their current usage of established media is of high theoretical
relevance. Research indicates, that these consumers differ from the general public con-cerning personal traits, such as innovativeness (e.g., Bruner & Kumar, 2007; Im, Bayus, & Mason, 2003), opinion leadership (e.g., Goldsmith & Witt, 2003) and their demographics (e.g., Tellis et al., 2009). Therefore, a classification according to these traits was the first step of this study. In correspondence with former literature (Bruner
& Kumar, 2007), our approach was to combine the level of technological innovative-ness (Bruner et al., 2007), independent decision-making (Midgley & Dowling, 1978) and opinion leadership (Goldsmith & Witt, 2003) to profile a technological EA, as these elements accelerate the process of diffusion of technological innovations (Bass, 1969, 2004). Herein, this study additionally endeavors to provide a sufficient answer on the criticism of Rogers’ (2003) time-dependent classification of EAs. To verify our selection approach, we compared the demographic characteristics of the selected po-tential EAs with the outcomes of previous literature and reached similar results to the international studies of Tellis et al. (2009) and Frank, Enkawa, Schvaneveldt, and Her-bas Torrico (2015). Secondly, we used the cross-sectional technique and compared the ownership of digital devices between the selected technological EAs and the majority.
The results indicated that EAs own a greater variety of digital devices and strengthened our classification approach.
Previous research has often selected potential EAs by using demographic data instead of referring to their personality traits (e.g., Frank et al., 2015; Namdeo et al., 2014).
Although this approach seems to be reasonable, EAs of new technologies represent only a small subgroup within the group, which is classified by demographic character-istics such as income, education, age or gender, as these demographic variables predict innovativeness (Tellis et al., 2009). Therefore, this study offers a novel methodology concerning the classification and clusters technological EAs by considering their per-sonal traits in a first step and controlling their demographic data afterwards.
The second goal of our study was to confirm that the chosen personality differences of technological EAs affect their Internet usage behavior. The established theory of the diffusion of innovations (Rogers, 2003) does not offer insights into the subsequent usage differences between technological EAs and the majority of the population, once a broad audience has started to use a technology, such as the Internet. Therefore, we compared the Internet usage behavior of potential EAs for new technologies against
the usage behavior of the majority of the population. While previous academic re-search focused on EAs of domain-specific technologies or products, our study widens the scope of research by providing a general overview about the usage of 15 different Internet channels and therefore illustrates several ways in which this crucial consumer group can be reached on the Internet.
To analyze the Internet usage behavior, we made use of a vast sample of 119,829 par-ticipants, which fully reflects the entirety of the German population. The results of the applied Welch tests confirmed our assumptions and illustrated that EAs use the Inter-net more frequently for information and communication purposes, but also for specific services, such as online banking and online shopping. Additionally, EAs show a higher adoption of mobile Internet compared to the general public. Particularly female EAs can be reached through communication websites on the Internet in Germany.
We showed that personal traits affect the usage of the Internet and that the usage be-havior of the Internet differs significantly between technological EAs and the majority of the population in Germany. Consequently, the Internet offers an additional value for potential EAs of new technologies (Rogers, 2003). Compared to other media, the Internet is of particular value as it can be used for specific in-depth information re-search (Johnson & Kaye, 2000). Other possible reasons for the high usage-frequency might be interactive features (Chung, 2008), which could be particularly interesting for the gadget loving EAs (Thakur et al., 2016). Another aspect may be personalized applications and proposals. Moldovan et al. (2015) emphasized the high need on uniqueness of EAs. According to Clark and Goldsmith (Clark & Goldsmith, 2006), EAs ‘may be less responsive to certain types of advertising, such as testimonials, ce-lebrity endorsement, or expert opinions’, which are often part of the traditional media.
Therefore, personalized offers through the Internet seem to be an efficient way to gain the attention of technological EAs and should be used by marketers to draw attention to their products and services.
In addition to these theoretical implications, the current study provides important prac-tical implications for marketers. One of the key challenges for managers and new com-panies is to identify potential EAs for new products and services (Frank et al., 2015).
Therefore, this study illustrates ways how to identify potential EAs for new technolo-gies by regarding their Internet usage behavior. Herein, the Internet serves as not only a means of identifying but also of reaching EAs. The fact that EAs can be reached
more frequently on the Internet should be taken into consideration by marketing man-agers. Consequently, an appropriate budget for online advertising should be of the highest priority. Additionally, the high activity among several Internet channels should be considered when analyzing user profiles to detect and reach EAs.
With regard to the diffusion process, the communication activity of EAs is of particular interest. They use the Internet for communication purposes more regularly than others.
This frequent usage of social network sites and messengers illustrates the high portance of EAs in the diffusion process postulated by Bass (1969). This gains in im-portance considering the growing influence of electronic word-of-mouth communica-tion on consumer attitude and purchasing decisions (e.g., Brown et al., 2007; Tang, 2017). Burt (1999) identified opinion leaders and therefore our selected group of EAs to be vital elements in the diffusion of information, as these individuals spread news within and between their social clusters and are consequently able to extend the range of the information’s distribution. Within the Internet, opinion leaders spread infor-mation through earned social media (Stephen & Galak, 2012), which, contrary to owned or paid social media, is perceived as trustworthy and helpful among followers (Vries, Gensler, & Leeflang, 2017). As opinion leaders exert influence on other con-sumers (e.g., Gilly, Graham, Wolfinbarger, & Yale, 1998; Sun, Youn, Wu, & Kun-taraporn, 2006) and are viewed as experts due to their product involvement (Jacoby &
Hoyer, 1981), a product recommendation by electronic word of mouth can signifi-cantly accelerate the diffusion process and therefore increase revenues.
Regarding the role of gender among the group of technological EAs in Germany, par-ticularly the influence of female EAs is noteworthy. We were able to show, that they use communication channels more frequently than their male counterparts and indicate the highest need of communication among all examined consumer groups. Conse-quently, channels such as social network sites should be used to target them, for ex-ample with fashion innovations (Beaudoin, Lachance, & Robitaille, 2003).
Another implication can be given considering the approach of the ‘Stage-Gate’ frame-work proposed by Cooper (2001). According to Cooper (2001), the development pro-cess of a new product from idea to launch is characterized as a series of five stages and five gates. Herein, each stage is designed to reduce uncertainties and risks by gathering useful information (Cooper, 2008), for instance regarding the consumers’ needs. As
technological innovations aim to satisfy needs of the consumers (Coccia, 2017), un-derstanding those of technological EAs and considering their crucial role in the ‘Stage-Gate’ systems is of vital importance. The stages of scoping, development and testing of new technological innovations (Cooper, 2008) offer opportunities to integrate tech-nological EAs and therefore reduce the risk of failure. Particularly for high-tech prod-ucts, technological EAs can be integrated through ‘user toolkits’ (Hippel, 2001), as these will satisfy their need for communication as well as assist firms in understanding EAs’ needs. The results of our study indicate that EAs can be reached predominantly by using information and communication channels in the Internet and therefore show ways to integrate them into the process of innovation development. Additionally, they use the newest services, such as online payment or online shopping. An integration of their opinion and skills will not only enhance the product’s technological performance, but also act as an early marketing instrument, thereby increasing the likelihood of a successful market penetration.
Although our study provides essential insights and deepens the understanding of cus-tomers’ behavior, we had to face some limitations, which offer opportunities for fur-ther research. Due to the enormous sample size, all formulated hypotheses show sig-nificant results, despite low respective effect sizes. Additionally, the questionnaire could have been enhanced via several objective questions. Instead of applying a scale from frequently to rarely, a more precise specification of the time one visits the differ-ent types of websites would be advisable. This would enable a more detailed analysis of EAs’ Internet usage behavior. Moreover, we were not able to consider cultural dif-ferences as the sample only consists of German surveyees. As cultural and economic factors moderate the relationship between antecedents of innovative behavior and the consumers’ actual behavior (Frank et al., 2015), an international investigation of tech-nological EAs’ Internet usage behavior could be a promising approach. Particularly the diffusion speed of the Internet differs between countries and cultures (Park
& Yoon, 2005) and hence so does the usage behavior. However, the applied data set of 119,829 Germans considered the regional distribution of Germany as well as the actual dissemination of gender, age and income. Therefore, our results are highly rep-resentative for the German population and can be used to identify technological EAs.
Similar outcomes could be possible for other highly developed European countries and should be investigated by future research.