This study investigated the instrumental value of resources embedded in online social networks. Forty–nine primary participants solicited a total of 588 secondary participants who were asked to complete a modest task. Approximately 16 percent of all secondary participants responded (N=98) to the request. 8.5 percent of weak ties responded and strong ties were about three times more likely to respond. Perceived reciprocity, contact frequency and a composite measure of tie strength were all positively related to enacted support.Contents
Introduction
Tie strength and mobilization
Communication personalness
Methods
Results
Conclusion
“… humans are social animals that depend in all ways on other humans for their survival, their wellness, and their ability to achieve benefit from the world they live in.” [1] People are becoming more involved with managing their personal lives online via blogs, media sharing sites like Flickr (http://www.flickr.com/) and YouTube (http://www.youtube.com/), and on social network sites (SNSs) like Facebook (http://www.facebook.com/). In particular, the rapid growth of SNSs is prompting the re–examination of some areas of computer–mediated communication (CMC) research and the nature of online social networks. Since SNSs became mainstream, online friendship has drawn the attention of a range of scholars (for example, Ellison, et al., 2007). There is little consensus, however, on how expansive mediated social networks differ from traditional off–line friends, and the utility of SNSs to facilitate the mobilization resources embedded in social networks.
Large–scale examples of the potential for Internet–based communication tools facilitating the mobilization of resources can be found in national–level elections (Stelter, 2008) but our knowledge about the nature of individual relationships and micro–level resource mobilization is less clear. Early research found that compared to online relationships, off–line associations were superior in terms of commitment and understanding (Parks and Roberts, 1998) although more recent findings suggest relationships mediated by SNSs were meaningful because people used these sites to manage networks initiated off–line. For example, typical college–aged users reported using SNSs both for maintaining contact with old friends and getting to know new ones (Lampe, et al., 2006). Still other research is directed at measuring specific dimensions of Internet social capital, albeit it limited to people’s perceptions regarding access to social resources online (Williams, 2006; Ellison, et al., 2007).
However, there is ground to question the value of these online connections. Online networks expand with very little effort and rarely regress; Facebook networks average about 300 people (Stefanone, et al., 2008) and it is highly unusual for people to break these online ties. Considering that social network size is restricted by cognitive limits (Hill and Dunbar, 2003), it is unlikely that many of these online ties have genuine, tangible value because people do not have the energy to invest in most of these relationships.
Traditional relationships build in closeness and intensity via social exchange (Homans, 1961; Befu, 1977). People invest in relationships via reciprocal and equitable self–disclosure over time, and we often use technology to reduce the costs associated with supporting these networks (Nardi, et al., 2004; Stefanone and Jang, 2007). However, friendships maintained via SNSs like Facebook are not as easily understood because many users are promiscuous in their friending behavior; on average approximately 15 percent of users’ networks are people never actually met face–to–face (Stefanone, et al., 2008). So while a subset of these online networks may be composed of traditional close friends, the majority are likely characterized by much lower levels of emotional closeness and intensity placing them on the far end of the weak tie spectrum (Granovetter, 1973). In other words, weak tie relationships on sites like Facebook.com may not represent meaningful connections because generally people invest comparatively little in these relationships. The core of the current study lies in the idea that because people do not have the time or energy to contribute to most of the relationships mediated by SNSs, these networks yield limited resources. Although people may perceive enhanced access to social resources (Ellison, et al., 2007), the actual mobilization potential (or, the likelihood that these ties would actually provide instrumental support) is probably quite low.
In this paper, mobilization of online resources is discussed in the context of social capital. While Lin (2001) and Burt (1997) have written extensively about social structure and opportunity, we adopt Bourdieu’s (1986) definition of social capital as the sum of resources embedded in persistent networks of relationships. This is similar to Wellman and Frank’s (2001) definition of networked capital. These assets are understood as resources available by virtue of interpersonal connections mediated by SNSs. The mobilization of these resources is operationalized as enacted social support. Enacted support has often been measured by selfreport measures (Aneshensel and Frerichs, 1982; Barrera, 1981; Barrera and Balls, 1983; Carveth and Gottlieb, 1979; Ellison, et al., 2007; Lefcourt, et al., 1984; Pearlin and Schooler, 1978; Sandler and Barrera, 1984; Sandler and Lakey, 1982). However, the current research utilizes behavioral measures of enacted, instrumental support in response to small requests for help.
As the popularity of SNSs continues to grow, opportunities for resource mobilization also increase. Because student participation with online social network sites like Facebook is greater than 90 percent (Felts, 2007), research has begun to concentrate on the consequences of social network site use. For example, Ellison, et al. (2007) found that using Facebook helps college students stay in touch with old and new friends. This connectivity resulted in a sense of satisfaction for users.
However, there is likely a disconnect between perceptions about access to social resources and people’s ability to actually tender these assets. The likelihood of mobilizing networked resources is likely a function of relationship strength and the nature of the request. The goal of this research is to establish a baseline response rate to modest requests for instrumental support while controlling for both the personalness of communicated requests and a range of relationship characteristics.
Lin’s (1982) theory of instrumental action is a theory about behavior that benefits the person taking the action, and instrumental behavior is restricted to those actions involving other people. Specifically, the theory of instrumental action addresses access to social resources. Lin outlines the strength of ties proposition which states that for people of equal status, use of weak ties opposed to strong ties should result in access to generally better social resources, or social capital. The strength of weak ties lies in the extent to which they bridge across disparate groups (Granovetter, 1973).
Granovetter (1973) formalized the nature of relationship strength in social networks. He argued that social networks are comprised of relationships ranging from very weak in strength to very strong, and there are systematic differences in resource provision. Weak ties connect people to novel information and resources that propagate across low density networks of non–redundant ties (Hansen, 1999). For example, annual conference meetings allow academics to meet and talk with colleagues from other universities. Through these exchanges they may hear about professional opportunities from these weak tie friends well in advance of public notification. One of the strengths of weak ties is that they function to connect otherwise disparate groups.
Strong ties are characterized by emotional closeness and a long and stable history of frequent reciprocal interaction. On average, people have a limited strong tie (or social support) network size of between six and nine people (Bernard, et al., 1990). Our strong ties consist of very close friends and family, are similar to us in many ways, and represent the subset of our social networks in which the bulk of our resources are invested (see Krackhardt, 1992, for discussion on the importance of strong ties in organizations).
However, Granovetter’s (1973) and Lin’s (1982) tie strength explications do not generalize to all conditions of instrumental action or networked resource mobilization. The strength of weak ties proposition is most applicable to situations where requested resources are limited to information sharing and are unlikely to be successful when requesting other categories of resources (for example, providing a service).
In the example above, a weakly–tied colleague provided information about an opportunity before a general announcement. That behavior had instrumental value because the information may benefit the recipient. But increasingly tangible resources like money, goods and services are more likely to be realized via strong ties. For example, a recent university graduate might ask his parents for a financial loan to complete a real estate transaction, or she might be moving to a new home and ask her family and close friends to help move furniture. In contrast to providing informational resources, these examples represent a categorically different pattern of mobilization. Here, strong ties are committing their time, energy and money to help family members. These behaviors are more intensive then simply sharing information with weak ties. Intuitively then, strong ties should be more likely to do favors when asked because of the long term, reciprocal nature of these relationships. Doing a favor for a friend represents an investment because chances are that friend has done a favor for you in the past, or will in the future. This kind of reciprocity maintains balance (or, equity) in relationships.
Boster, et al., (1995) conducted one of the few studies investigating the intersection between tie strength and request messages. The results of their experiment suggest that friends were more likely than strangers to respond to requests about purchasing raffle tickets for $1USD, and that paying strangers a compliment before initiating requests increased the likelihood that they respond. While Boster, et al., (1995) focused on compliance gaining strategies, the current research is an exploratory study intended to measure baseline response to low–stake, modest non–urgent requests.
In summary, SNSs extend the range of social networks by allowing people to maintain larger, heterogeneous networks. While Granovetter valued weak ties for information diffusion or job seeking behavior and Lin (1982) suggests weak ties provide access to improved resources, less is known about the nature of relationship strength, resource mobilization and instrumental support especially for relationships mediated by SNSs like Facebook. Given that the majority of SNS connections are characterized as increasingly weak ties, the likelihood of mobilizing these contacts for instrumental support is questionable even for modest requests. The evidence cited above does suggest that strong ties should generally be more likely to respond to requests for instrumental help. Thus,
H1: There is a positive relationship between relationship tie strength and the likelihood to enact instrumental support. Homans (1961) defined associations as exchanges “of activity, tangible or intangible, and more or less rewarding or costly, between at least two persons” [2]. He suggests people do things for others with the expectation that there will be some type of reward for doing so. In the absence of rewards such behavior will be enacted less often over time. Like Homans, Blau (1964) also contributed some of the core ideas related to the processes of social exchange and social interaction. Blau (1964) stated that people are anxious to help one another and to reciprocate for the support they receive.
During interpersonal exchange, self–disclosure (SD) is defined as the process by which people provide personal information about their thoughts, feelings and/or needs to others, and functions as a boundary maintenance tool (Derlega and Chaikin, 1977). As relationships mature the boundaries around privately held information tend to relax. Intimacy levels of SD tend to be equal (Kleinke, 1979), viewed as pleasing (Sermat and Smyth, 1973), and lead to higher levels of trust over time (Johnson and Noonan, 1972). SD is elemental to relationship development and maintenance. Considering that relationships grow in strength over time when characterized by frequent communication, it is likely that contact frequency and relationship duration are antecedent to tie strength. Thus,
H2a: There is a positive relationship between relationship duration and tie strength.
H2b: There is a positive relationship between contact frequency and tie strength.Of particular interest to the current study are Kleinke’s (1979) results suggesting that the intimacy of disclosures between people should be equivalent, consistent with equity theory which suggests that people strive for some sense of ‘fairness’ in relationships (Carrell and Dittrich, 1978). For example, Davidson, et al. (1983) suggest that equity in spousal SD is positively related to marital satisfaction. Through the process of reciprocal SD partners try to maximize their rewards so that these rewards outweigh costs. In this sense, equity theory grew from social exchange theory with one modification: inequitable relationships result in distress. People who perceive inequity in a relationship will attempt to eliminate their distress by restoring equity.
Given the characteristics of reciprocal social exchange outlined above, it is likely that people who view relationships as fair and balanced independent from communication frequency and relationship duration will be more likely to respond to instrumental requests for support online. Thus,
H3: There is a positive relationship between the perceived level of equity in relationships and the likelihood to enact instrumental support.
H4: There is a positive relationship between the perceived level of reciprocity in relationships and the likelihood to enact instrumental support.
In addition to tie strength and relational variables, the personalness of requests may also affect the likelihood that others respond to said request. Message personalness is operationalized as a function of whether target audiences are comprised of a single person or groups. Research on message personalness suggests that the way requests are proposed effects receiver’s perceived involvement and eventual motivation. For example, one–to–one communication is more personal than sending an e–mail message to 10 people simultaneously. In terms of SD, communication personalness is linked to interpersonal attraction. Personal, one–to–one disclosures correlate with attraction because the recipient feels that he/she has been singled out as trustworthy and a good candidate for an intimate relationship (Taylor, et al., 1981). Before CMC became popular, interpersonal communication was typically directed at specific others and both parties had a clear understanding that they were engaging in a social exchange and what was expected from their counterparts. This understanding also conveys a sense of personalness.
Personal requests may also increase the recipient’s level of involvement and motivation. The personalness of communication — the extent to which senders direct their messages at specific receivers — is related to the level of attention to and involvement with those messages. Involvement is recognized as an interaction between people and external stimuli (Salmon, 1986) and is a function of the personal relevance of messages (Petty and Cacioppo, 1981). Accordingly, involvement may be a function of the perceived level of personalness during communication with others. Similarly, Cohen (1983) conceptualized involvement as a person’s activation level. Because involvement is closely tied to motivation and activation, the personalness of online requests is relevant to online mobilization research.
At the same time, the personalness resulting from directed communication also creates a sense of exclusivity. When intended receivers are given favorable status via direct messages, people who are not part of the target audience should not be privileged to the content disclosed by the sender. Together, this evidence suggests that the personalness of requests should have a positive relationship with the likelihood that the recipient acts on the request. Considering that SNSs afford a variety of communication channels that differ in personalness,
H5: There is a positive relationship between the personalness of requests and the likelihood to enact instrumental support. The effect of personalness on mobilization also needs to be examined in the context of presentation of self. The process of managing how one is perceived by others is called self–presentation or impression management (Kowalski and Leary, 1990). People prefer that others perceive them in desired ways and work to ensure they communicate a favorable image of themselves. People have a pervasive and ongoing concern with their image and engage in impression management to obtain rewards and to achieve a self–fulfillment (Baumeister, 1982). The reward element of these goals are more relevant to the context of the current study because the dependent variable reflects whether or not participants garnered instrumental support from resources embedded in their online social networks. However, the extent to which behavior is visible to others also affects decision–making.
Satow (1975) demonstrated that people were more likely to donate money when others were watching than when the donor’s identity remained anonymous. This suggests that helping behavior may be motivated by the desire to be recognized as charitable and generous. The notion that people are more likely to help when they are identifiable likely interacts with tie strength. If two people share a strong relationship characterized by frequent, reciprocal interaction over time, they’re also likely to be strongly tied to members of each other’s networks. Put simply, the probability of meeting your friend’s friends increases as the relationship matures over time. As you get to know people better, shared network density — the proportion of existing ties to all possible ties in a network — increases. High density networks are characterized by heightened levels of shared awareness. For example, in the United States immediate family members (networks with density = 1 because everyone knows and communicates with everyone else) typically live in shared spaces. Obviously, a consequence of high density networks like these is a shared awareness of the thoughts, feelings and events (i.e., a request for help) in other people’s lives within the network. In fact, Granovetter (1985) suggests that stable, dense networks result in elevated resource exchange precisely because this behavior is “not atomized from other transactions but embedded in a close–knit community who monitor one another’s behavior closely” [3].
Redundancy in networks like these makes it possible for others to witness the provision (or refusal) of helping behavior. This transparency may promote helping behavior among strong ties particularly in public/non–personal communication. With Facebook–mediated communication, an analogous form of transparency can be achieved via public messaging like wall posts. Here, users can post comments (including requests for help) to their friend’s publicly accessible profile page. On the one hand, such public messages are impersonal and should generally be less likely to result in mobilization. On the other hand, the public nature of these requests should prompt receivers to act because they know the rest of their close friends are aware of the opportunity/request. Thus, public requests should have stronger effects within networks of close friends because these groups are more densely connected and there may be consequences for ignoring public pleas for help. On the contrary, publicly viewable requests are not likely to affect the mobilization of weak ties due to the disconnect between the sender’s and receiver’s networks and the impersonal nature of the communication. Therefore,
H5a: Strong ties who receive public requests for support are the most likely to respond. Based on literature review and hypotheses proposed, Figure 1 below presents the hypothesized model for this study.
This study used a 2 (Tie strength: strong and weak) X 3 (Personalness of request: one–to–one e–mail, one–to–many e–mail, wall notification) design. The participants were drawn in two steps and consist of primary and secondary participants. First, primary participants (N=50) were recruited from communication classes at a large northeastern university and instructed not to discuss this study with anyone else until completion of the study. Participation was voluntary and this project had the approval of the institutional review board for human subjects.
Each primary participant was instructed examine their entire Facebook friend network and to think about their six strongest and six weakest relationships on this site. The strong tie sample size was chosen based on extant research suggesting that people generally have about six very close people in their lives (Bernard, et al., 1990). In turn, six weak ties were chosen to balance the recruitment procedure. Participants were required to record the identities and contact information for these 12 contacts (hereafter referred to as secondary participants). One primary participant did not follow the procedure and was eliminated from the study. As a result, 49 primary participants sent a total of 588 requests to secondary participants.
Next, primary participants completed a brief survey measuring demographic information and their perceptions about a series of relationship characteristics for each of the twelve secondary participants they identified. These characteristics included perceptions about the level of equity and reciprocity, contact frequency, relationship duration, and emotional closeness/tie strength for relationships with each secondary participant.
Finally, they were instructed to send request messages through a variety of channels to secondary participants. Each secondary participant received only one request. Channel selection varied across three levels of personalness and secondary participants (the message targets) were randomly assigned to one of three personalness conditions. Recall that each primary participant selected six strong ties and two requests were sent via direct, one–to–one Facebook message (personal). The Facebook messaging functionality is analogous to e–mail. Two requests were sent via multiple–recipient Facebook messages where four other people could be identified as recipients (less personal), and two requests by wall–to–wall notification (public). In the multiple–recipient condition, each secondary participant was sent a Facebook message with four other fictitious e–mail addresses included in the cc field. These same three personalness conditions were used when messaging weak tie contacts and all communication was mediated by Facebook’s messaging tool; no messages were sent outside of the functionality provided within Facebook.
Task. The request prompted secondary participants to access a Web page and complete a brief survey. Recall that in this study requests were limited to a low urgency, low stakes task to establish a baseline response to modest requests. All primary participants were advised not to discuss the request message with their secondary participants until the researchers could debrief all secondary participants (after a two–week period). The content of the request message primary participants used was as follows: “Hey there! I need your help with a class project I’m working on — it won’t take you long to do, and I’d really appreciate your help! Please go to [URL] and take a quick 10 minute survey for me. Your participation will really help me out.”
Measures. Both primary and secondary participants were asked to complete an online survey. For primary participants, three Likert–type items were used to measure tie strength (Marsden and Campbell, 1984; Wellman and Wortley, 1990), and included “This person is a … (1=casual acquaintance, 7=very good friend),” “How close are you with this person?” (1=very distant, 7=very close), and “Do you interact with this person voluntarily rather than because you are both members of the same social institutions?” (1=not voluntary, 7=completely voluntary). Cronbach’s α for the tie strength scale was .82. The survey also measured contact frequency with two questions measuring how often they communicate with this person daily, and on how many days per week. Relationship duration was measured in months and years. Finally, perceptions about levels of relational reciprocity and equity (e.g., “on the whole, do you give more than you get in this relationship?”; Hatfield, et al., 1979) were also included (α=.78 and .84, respectively).
Each group of 12 secondary participants was not independent of each other because they share a relationship with the primary participant. However, if random selection were utilized, the probability was that the sample of secondary participants would have been overrepresented by weak ties. This would make between–group comparisons difficult.
To explore the possibility that systematic response biases existed, t–tests were used to examine the variability in secondary participant survey responses. There was no observable, systematic pattern in primary participants’ responses based on between–groups factors including age and gender. Within strong and weak tie groups, t–tests revealed no systematic age or gender differences either.
Twenty–six of the 49 primary participants were male, and their mean age was 20.8 years (SD=1.7). Participants had on average 426.9 Facebook friends (SD=148.20), and spent about 35 minutes logged in per session (SD=19.5). The strong tie group of secondary participants had an average relationship history of 8.9 (SD=6.0) years, contact frequency of 4.1 (SD=1.7) times per week, and responses to the perceived reciprocity and equity scales averaged 6.3 (SD=1.8) and 4.3 (SD=0.8) respectively. The weak tie group reported an average history of 5.8 (SD=6.3) years, communicated 2.8 (SD=1.8) times weekly, and indicated reciprocity and equity scores of 4.9 (SD=1.7) and 3.5 (SD=0.7) respectively. T–tests confirmed that the strong tie sample of secondary participants (N=294) was characterized by significantly higher communication frequency, longer relationship duration, and greater emotional closeness opposed to the weak tie sample, as expected (ρ < .01 for all tests). Table 1 below summarizes the relationship between variables used in this study. As expected, reported tie strength had a positive relationship with contact frequency, relationship duration, and perceptions of equity.
Table 1: Item descriptives and zero–order correlation coefficients.
Note: * ρ < .05, ** ρ < .01.
a: edu — education, tie — tie strength, con — contact frequency, dur — duration of relationship, reci — reciprocity, equi — equity, ena — enacted support; b: education (1,freshman — 2, sophomore — 3, junior — 4, senior), sex: (0, male — 1, female)item M(SD) age edu tie con dur reci equi ena Age 20.7 (6.1) 1 .58** .03 -.07 .02 -.09 .06 .01 Edu 2.9 (1.2) 1 -.01 -.05 -.02 -.01 .00 .01 Tie 5.2 (2.2) 1 .59** .23** .45** .18* .18* Con 3.4 (1.8) 1 .10 .22* .16 .06 Dur 7.4 (6.2) 1 .16 .05 .13 Reci 5.6 (1.7) 1 .32** .22* Equi 3.9 (0.7) 1 -.02 Ena 0.7 (0.5) 1
A total of 96 people (16.3 per cent of secondary participants) responded to the requests. Of these, 58 were female. The response rate for strong ties was 24.1 percent and 8.5 percent for weak ties. Two primary participants successfully mobilized all six strong tie Facebook friends. Seven primary participants were not able to mobilize anyone. Table 2 shows the tie strength and personalness of request cross tabulation used in the analysis. Recall that the dependent variable — enacted support — was operationalized as whether or not secondary participants completed the online survey which required about 10 minutes of their time.
Table 2: Tie strength and personalness of request cross tabulation.
Note: Numbers represent the frequency of enacted support and (the total number of requests).Personalness of request High
[one–to–one e–mail]Medium Low
[public wall post]Total Tie strength Strong 23/(98) 25/(98) 23/(98) 71/(294) Weak 9/(98) 9/(98) 7/(98) 25/(294) Total 32/(196) 34/(196) 30/(196) 96/(588)
Path analyses were conducted to test the hypothesized model and to provide insight into the nature of resource mobilization online. The initial conceptual model (Figure 1) was tested using AMOS 4.0 with the maximum likelihood method. Figure 2 summarizes the test results for hypothesized model. The overall model fit of hypothesized model was acceptable (chi–square=13.4, df=6, p=.101, AGFI=.902, NFI=.908, CFI=.939, RMSEA=.048).
This model shows significant effects for tie strength (H1) and reciprocity (H4) on enacted support (ρ<.05), suggesting that secondary participants were more likely to engage in enacted support as perceived tie strength and level of reciprocity between participants increased. As anticipated, duration of relationship (H2a) and contact frequency (H2b) were positively associated with tie strength, indicating that these two traditional scales of interpersonal relationship correspond to tie strength. Perceived equity (H3) was not positively associated with enacted support; hypothesis 3 was not supported. Also, there were no statistically significant differences between the personalness conditions employed in the design of this study. Hypothesis 5 was not supported.
Figure 1: Results for original hypothesized model.
Note: *ρ<.05; the numbers in parentheses indicates standardized error.
Logistic regression was used to test the hypothesized interaction between tie strength and message personalness. Tie strength was dichotomized (0=weak, 1=strong) and personalness was coded as a continuous variable (1=wall posting/public, 2=one–to–many, 3=one–to–one). The dependent variable was whether or not secondary participants participated in the online survey (0=no, 1=yes). Table 3 below summarizes the results of the logistic regression model predicting enacted support. In the omnibus tests of model coefficients, the chi–square value was 26.92 (ρ<.001), and the p–value in the Hosmer and Lemeshow test was greater than .05. The log of the odds of participation was positively related to tie strength (ρ<.001). However, no relationship was found between the interaction of tie strength and personalness, and the dependent variable. H5a was not supported either.
Table 3: Results of logistic regression model predicting enacted support.
Note: * ρ<.001; CI=Confidence interval.B SE Wald Sig. Exp, [CI] tie strength 1.22* .25 23.99 .00 3.40, [2.05, 5.47] personalness -.06 .13 .18 .68 .95, [.72, 1.29] interaction -.10 .12 .58 .45 .91, [.71, 1.16] constant -2.19 .35 39.01 .00 .11
Although the hypothesized model fit the data (as shown in Figure 1), an additional model with enhanced model fit was generated post hoc (Figure 2 below). Correlations between variables were used to specify this model. Message personalness was omitted as it was not significant in previous models and deteriorated overall model fit. This improved the model’s fit (chi–square=8.611, df=9, p=.474, AGFI=.948, NFI=.923, CFI=1.000, RMSEA=.000). According to the final path model, contact frequency, relationship duration and perceived equity were all significant predictors of tie strength. Further, tie strength was positively related to enacted support. These results support H2a and H2b. Although perceived equity was not directly related to survey participation, it did exhibit a positive relationship with reciprocity. In the final model, equitable relationships were associated with adherence to reciprocity norms that were correlated with enacted support.
Figure 2: Final path model.
Note: *ρ<.05; the numbers in parentheses indicates standardized error.
The present study utilized a quasi–experimental research design to investigate Facebook friends’ provision of modest instrumental support. Results suggest that limited access to instrumental resources accrue to Facebook users who make mediated requests for help. While much of the extant body of research on online social capital and mobilization relies on self–report evaluations of access to resources, the current study used an experimental design and measured actual helping behavior online. This study found that a minority of online friends responded to requests for instrumental support underscoring the scarcity of social resources available through mediated networks.
Tie strength explained the likelihood of enacted instrumental support. As expected, close friends and family members were much more likely to respond to the modest request, regardless of personalness. In fact, some strong ties responded instantly; during the experiment two participants received cell phone calls about the request immediately after sending the message through Facebook. In anticipation of communication with secondary participants about the request and the experiment, primary participants were told to pretend they were actually involved with a class project to maintain the ruse. Subsequent communication with primary participants at the end of the project confirmed that the 11 primary participants who were contacted about their request did not reveal the true nature of their behavior.
In the end, about one in four strong ties were willing to give 10 minutes of their time to help their friends. On the other hand, close to 1 in 10 weak ties responded. Results of the preliminary path model also revealed that contact frequency and relationship duration were antecedent to tie strength, as hypothesized. While it is not surprising that people who have known each other for a long time and communicate often are emotionally closer to one another, these results do illustrate the relationships between these variables in a more nuanced way. In this study, equity and reciprocity were proposed to operate independently of traditional social exchange indices like communication frequency and relationship duration. The initial path model supported this. Communication frequency and relationship duration reflect objective, increasingly concrete characteristics while equity and reciprocity are subjective, visceral evaluations of interpersonal relationships. In other words, equity and reciprocity reflect states of relationships. In this case, these states reflect temporary conditions of relationships and suggest that these perceptions are dynamic and fluctuate over time. Variations like these are evident in everyday lives.
Consider this example: a student (Emily) asks friends to help her move to a new apartment. This task requires several hours of labor. Subsequently, one of those friends asks Emily for a favor, and Emily does not reciprocate. In this case, although these friends communicate often and have known each other for some time, the evaluation of relational equity likely would be lower for Emily’s friend until such time that Emily reciprocates the helping behavior (or, gives another equitable resource in return).
Measuring these dimensions reflects an effort to account for the dynamic and complex nature of interpersonal relationships. We proposed that the condition of a relationship — whether or not it is balanced and fair — contributed independently to successful mobilization of networked resources because people respect the reciprocity norm whereby if you do something for me, I’ll owe you one. While it is well known that unbalanced relationships tend to fail over time, the current conceptualization affords ecological benefits by acknowledging fluctuations in relationship evaluations over time. The results support this conceptualization and contribute to the active body of research directed at measuring tie strength (see Petróczi, et al., 2007). While not definitive, this evidence extends the conversation about the nature and measurement of social ties.
In addition to the findings from the initial path model, results of the optimized path model revealed some unexpected relationships. Reciprocity was a significant predictor of enacted support, suggesting that people feel pressure to reciprocate for favors done in the past consistent with previous social exchange research (Homans, 1961; Blau, 1964). On the other hand, while equity was specified as directly related to online mobilization in the original model, it was mediated by reciprocity and indirectly affected enacted support in the final model. This suggests that if people perceive relationships as balanced, they are more likely to subscribe to the reciprocity norm. Maintaining balanced relationships with others requires reciprocation for benefits received in the past. Participants who responded to requests were probably more aware of the give and take in their relationships (as evident by their evaluation of reciprocity), and may have decided to opt in as an investment in the relationship.
No effects of message personalness were observed in this study, and this variable was dropped from the final path model. In the one–to–many e–mail condition, five secondary participants were e–mailed simultaneously. This may not have been a sufficient manipulation to promote the perception of impersonal communication. Likewise, the wall notification condition may not have engendered a sufficiently public perception of the request. Further, we were not able to determine when or if recipients received the wall messages, although all participants reported checking their Facebook accounts daily. Future research should explore the possibility of personalness effects more carefully. For example, response rates would probably increase if requests for help were made face–to–face (or, in real time) or if the communication was characterized by more than a single message exchange. Further, the personalness manipulation was a function of the message audience rather than personalized messages with first names, etc. This study used standardized messages as a form of experimental control, but future research investigating the nature of personal requests would benefit by exploring more nuanced elements of personal messages and requests.
It is noteworthy that while the actual differences between average tie strength in this study were statistically meaningful, participants still reported communicating with their weak ties a couple times per week. In other words, these ties did not represent the very weak end of the relationship strength spectrum. Future research that more effectively targets strong and weak ties would likely result in more extreme between–group differences in enacted support.
While Facebook may be effective at disseminating information, the value of these networks for instrumental mobilization remains questionable. In future work, we hope to address some of the limitations of the current study. First, effort should be made to further distinguish the personalness of request messages as extant literature suggests this is an important variable. While the current research manipulated personalness, all three conditions were asynchronous, mediated communication. Including face–to–face, phone, and computer–mediated requests would be effective between–subjects designs. The current study also limited communication between primary and secondary participants to single requests. It is unclear how sending reminder communiqués to friends about requests would impact the results of this study.
Although statistical tests confirmed that the strong tie sample of secondary participants differed from the weak tie group, primary participants were required to think about their six strongest and weakest ties. Future research would benefit from employing a more systematic sampling approach and test other relationship criteria. This study was also limited in terms of the support requested. This study required participants to ask their friends to provide a service which required a modest time commitment. There are differences, however, in the kinds of resources embedded in strong and weak tie networks (see for example, Resnick, 2002). Recall that this study was designed to provide baseline data on the likelihood of resource mobilization because of the dearth of extant literature on this topic. As such, we used a conservative approach to operationalizing enacted support and used a low stakes, low urgency request.
Future research should explore the differential returns as a function of the type of support requested. Clearly the results in the current study are limited in that they address provision of support for one specific kind of request. Developing a continuous dependent variable would also add strength to these results. To better understand the dynamics of instrumental support and mobilization, continuous variables like time spent helping would be more useful.
However, this study did use an actual behavior measure rather than perceptions of resource availability as the dependent variable. While self–report data are easily and frequently acquired, behavior itself is much less often observed directly. Future research should try to include similar behavior metrics to increase the validity of results in this body of research.
Finally, there are many examples of successful large–scale social mobilization efforts using sites like Facebook, MoveOn.org and Meetup.com particularly for national–level political campaigns and elections. There are fewer cases of micro–level resource mobilization and activation to reflect on. This study raises questions about the functional utility of relationships mediated by SNSs and the extent to which networked capital accrues to users (Wellman and Frank, 2001). As people spend more time managing more of their relationships through these sites, the nature of these relationships and the mechanics of enacted, instrumental support promises to be a worthwhile area for future research.
About the authors
Michael A. Stefanone is an assistant professor in the Department of Communication at the State University of New York at Buffalo. His research focuses on the intersection of people, organizations, and technology. His current research explores how personality differences influence the ways people position themselves within social and task networks, and how social context influence technology adoption and use.
E–mail: ms297 [at] buffalo [dot] eduKyounghee Kwon is a Ph.D. student in the Department of Communication at the State University of New York at Buffalo.
E–mail: Klee0520 [at] gmail [dot] comDerek Lackaff is an assistant professor in the School of Communications at Elon University in Elon, N.C.
E–mail: dlackaff [at] elon [dot] edu
Notes
1. Hobfoll, 2009, p. 96.
2. Homans, 1961, p. 89.
3. Granovetter, 1985, p. 492.
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Editorial history
Received 1 December 2010; accepted 3 January 2011.
This work is in the Public Domain.The value of online friends: Networked resources via social network sites
by Michael A. Stefanone, Kyounghee Kwon, and Derek Lackaff.
First Monday, Volume 16, Number 2 - 7 February 2011
https://firstmonday.org/ojs/index.php/fm/article/download/3314/2763
