As indicated in the previous sections, our goal was to describe a theory of e-dating development and to explore it empirically. The findings from our preliminary research suggest that:
1. Six distinct stages can be identified empirically in the e-dating career of male and female e-daters. These, as our theory predicted, include: (a) constructing a profile, (b) searching, (c) initiating communication, (d)
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receiving communication, (e) setting face to face dates, (f) conducting dates, and (g) concluding the e-dating process.
2. The sequence of stages for males and females is not the same, with males initiating contact with females earlier and investing more time and energy in initiating contact activities throughout the e-dating process than females do.
3. Even though some males and females exhibit behaviors that are typical ofthe other gender (some males establish a profile and wait for females to contact them and some females start contacting males right away instead of waiting for males to contact them), both genders tend to exhibit the behavior pattern outlined in our theory; namely, males tend to initiate more contact and females tend to initiate less.
4. The inputs that each gender receives from the environment are different and can possibly explain the differences in their behavior initially and over time. Thus, males tend to get fewer approaches from females across all “passive” behavioral categories (e. g., they receive fewer hits on their profile, winks, e-mail messages and text messages).
Obviously, this exploratory research can be
extended in a number of different directions:
First, our e-dating theory proposed a number of hypotheses that we did not test in the preliminary investigation that we are reporting on in this chapter. Future investigations may extend the scope of this research to include questions like: Do the differences between male and female behavior increase over time? Does the input that the two genders receive from the environment change over time? Can the changes in behavior exhibited by e-daters be explained by changes in expectations? Obviously, answering each ofthe above questions in the affirmative would lend more support to the model that we proposed here. Thus, if the differences between the behavior of male and female e-daters increase over time, it would strengthen our contention that e-dating is, indeed, a developmental process in which behaviors change in response to changes in perceptions.
Second, our sample of undergraduate, white, upper middle class, heterosexual students can be extended to include participants of different ages, races, socio-economic classes, and social orientations. Furthermore, the number of participants in each of the above categories can be increased to allow for more generalizable conclusions.
Third, in this exploratory study, we used an open-ended questionnaire to elicit as much information about a range of topics related to e-dating. Future research might triangulate the research methodology by employing, on one hand, more
quantitative measures of some of the variables that we identified here, and on the other, in-depth interviewing to identify additional variables that our inventory did not fully account for.
Fourth, if indeed e-dating services can be seen as different environments for male and female daters, this may have far-reaching implications. Thus, future research might explore the extent to which different e-dating services cater to the unique dating needs of males and females, by categorizing e-dating services as male or female “friendly.”
Following this line of reasoning, male friendly services can be expected to create an environment that is friendlier to males by establishing rewards for females for responding to male contacts. An e-dating service that is already doing it is eHar — mony. One ofthe unique features ofthe eHarmony service is that it correlates the number of “good matches” that an e-dater receives from the service with whether the e-dater responded to contacts from previously provided matches. This system is applied equally to males and females. Since females are less inclined to initiate contact and/ or to respond to male contact than males are, this principle of rewarding e-daters for responding to contacts helps males more than it helps females.
Similarly, a female friendly service would establish an environment that is even friendlier to females than current services are by providing females with additional input on prospective male matches, such as through a ranking of all e-daters. Again, even though ranking would be applied equally to both males and females, since females are more often in a position to screen a large number of prospective matches, they would benefit more from ranking than males will. If a service offers this feature to its customers, the result would be a more attractive environment for females than for males.
Another implication from our research has to do with perceptions. If indeed the reality of e-dating for males and females is so different, are both genders aware of it? Does this awareness affect male and female inclination to use e-dating services? Is this awareness affected by age, social status, marital status or culture? Future research into e-daters perceptions might explore whether they can be modified. For example, if older females were aware that in their age group the ratio of males to females is even greater than in the younger age groups, would they be more inclined to become e-daters than they currently are? Will this awareness remove the stigma that is currently associated with e-dating, particularly in the older age groups?
Another related issue has to do with the strategies that e-daters employ to become successful in this process. Our study indicated that males and females tend to use different strategies. An interesting set of questions that relates to this finding is whether the strategies that each gender uses are affected by individual differences. For example, can a male who is particularly attractive (physically or thanks to a higher level of education or income) use more feminine strategies (initiate less contact) and still be successful? Are e-daters aware ofhow their individual attributes affect their strategies and their success rate? Do they modify their strategies based on this awareness?
Another related issue has to do with education. Given that e-dating is becoming ubiquitous, should the educational system invest resources in training young people to be efficient e-daters? Should males and females get different training on how to conduct themselves on an e-dating Web site given that the environment in which they operate is not the same? Should the educational system consider e-dating skills as important for young adults as driving, cooking, and other “survival skills”?
Another set of questions that the discussion in the previous section raises is related to the exposure that e-dating may involve and the risks that are associated with this exposure. Recent popular publications discuss the fact that the exposure of e-daters might lead (particularly in the case of female e-daters) to risks of violence (Loviglio, 2007; Moraski, 2007). The popular literature is replete with prescriptions on how to conduct “safe” e-dating.
Some e-dating services (e. g., True. com) have based their business model on providing their customers with background checks on other e — daters’ criminal records and matrimonial status, lobbying for such searches to be enforced by law on all services or for services that do not conduct such searchers to acknowledge it on their Web sites (Heydary, 2006). Following this line of reasoning, an interesting direction for future research would be to explore the extent to which different e-dating business models are perceived by e-daters as more or less secure, the extent to which service providers can enhance the sense of security of their customers and the degree to which security enhancing features result in higher revenues for e-dating services.
Privacy concerns and the possible invasion of privacy that some e-dating services involve is another important direction for future research. Thus, ranking or categorizing e-dating services on how “invasive” they are could be an interesting future line of research on e-dating. Such categorization may reveal that some services provide more information to e-daters about other e-daters than is desirable. Indeed, some users of e-dating services (particularly, females) might experience the real-time features of e-dating services (e. g., provision of information to other daters on whether an e-dater is active in real time) as surveillance.
Given the variance in exposure between different e-dating services, it would be interesting to empirically explore how e-daters feel about the invasion of privacy that the various e-dating business models entail. Are males more comfortable with high levels of exposure than females? Are younger e-daters more comfortable with high levels of exposure than older e-daters are? Do demographic variables (such as level of education, ethnicity or income) impact the degree to which e-daters are willing to tolerate different levels of self-exposure or invasion of their privacy?
Another possible direction for future research is to explore how different “types” of e-dating services utilize different business models and how this impacts users. The e-dating arena consists of services that cater to marriage-oriented, friendship-oriented, or sex-oriented users (as well as to users who combine these orientations). Future research might explore the features that differentiate these types of e-dating services from each other, the degree of exposure of daters to each other is involved in each type, the extent to which e-daters are aware of the differences between the “types” and the degree to which the combination of features that each type represents affects the success of the business model used by the service.
Given that this chapter does not focus only on the behavior of e-daters, there are other, wider societal implications that follow from the discussion in the previous sections.
One such implication is the possible scope for abuse of information that e-daters make public. There are references in the popular literature to the use of information in social networking services by employers to spy on their employees (Lavallee, 2007; White, 2007). These reports suggest that many employers use information that individuals have posted on social networking and e-dating services (possibly a long time before the individual joined the labor force) as a basis for selection of candidates for jobs, promotion of employees, and even for harassment of employees on the job. Future research might explore the extent to which employers do indeed engage in spying on their employees by using data from social networking and e-dating services and the impact that this may have on employees’ life at work.
The potential for abuse of social networking and e-dating services raises a set of other issues that involve the political and legal system. If indeed the social networking and e-dating sector poses potential dangers, should society regulate the industry to make sure that customers are more protected than they currently are? Should e — dating services be required to check their customers’ criminal record or marital status? Should they be required to acknowledge on their Web site, in a manner similar to pharmaceutical companies, if they do not conduct such searches? Should e — dating services that involve “ranking” of e-daters by other e-daters in a manner similar to eBay’s rating of buyers and sellers be outlawed because this practice involve the potential for defamation of customers? Should e-dating services be barred from discriminating against groups of e-daters that they do not wish to serve (e. g., gays)?
Only the future will tell how many of these issues will be addressed by researchers and/or by society as a whole and how this will lead to a transformation of e-dating as we know it today.