The results of the distribution analyses are that all three user groups show, in aggregate, a similar mathematical distribution of response latencies. A closer inspection of the distributions shows that despite the significant differences among the types, purpose, and context of the asynchronous conversations taking place within each group, in all three of them, at least 80% of the responses were sent within the average response latency of that group, and at least 97% of the responses were sent within 10 times that average response latency. In cases where analysis was possible, even individual users show the same skew: At least 70% of almost every individual’s responses were made within that user’s average response latency, and at least 96% within ten times his or her average response latency (RL). These findings allow us to delineate three normative chronemic zones of response latencies in asynchronous CMC, based on the average response latency τ:
Generalizability of the findings
The findings point to common chronemic characteristics of asynchronous CMC. The three datasets described are very diverse in their characteristics: They represent different user populations (business people, students, and varied Internet users in a public arena), assorted asynchronous text-based CMC technologies (email, discussion forum, web pages), a variety of contexts (academic education, major corporation, competitive online bidding), a range of average response latencies (from 1.5 hours to a little over one day) and of cohort sizes (more than 15,000 to more than 100,000, a total of over 170,000 responses), a period spanning at least seven years, and respondents from the U.S. as well as from other countries. Despite these differences, a recurring pattern surfaces when analyzing the aggregates: a power law distribution of the response latencies that can be described by the generalization that regardless of the average response latency (τ), most (at least 80%) of the responses are already created within that average latency, and almost all (at least 97%) of the responses are created within 10τ of the average response latency.
The strength of this generalization is further revealed when drilling down to the level of individual users. We have shown that the generalizations at the aggregate level need to be only slightly relaxed (from 80% to 70% and from 97% to 96%) in order to describe the vast majority of dozens of individual users from the two datasets in which personal identification was possible, and users for whom a sufficiently large sample of response latencies was available. This finding is an indication that users of asynchronous CMC, similar to the users of Internet Relay Chat observed by Bays (1998), tend to create responses within a relatively short time, in the order of magnitude of the average response latency, and are unlikely to respond after a duration longer than one order of magnitude higher than that average response latency.
The robustness of the generalization receives further substantiation when one looks at well established rules describing latencies and response latencies in traditional forms of communication. For example, in Jaffe and Feldstein’s work (1970) on face-to-face contexts, the quantitative results for the duration of pauses by one speaker in a face-to-face dialogue (p. 76, figure IV-9) present the same characteristics as any of the three CMC datasets described here: 70-80% of the pauses are shorter than the average pause length (τ estimated at .97 seconds), and a pause of above10τ, (9.7 seconds) did not occur even once in that 50-minute dialogue. Moreover, when the plot is reconstructed (Figure 2) using modern statistical tools and regression analysis is performed, the power law distribution gives a high R2 value of .82, even better than that for the exponential distribution reported by the authors (the calculation was not performed by the authors, but the reconstruction of the data by us gives an R2 of 0.74 for an exponential distribution). The reconstruction was carried out by scanning the graph from the original book, and using graphical software to measure the coordinates of the pixels of each data point, as well as the pixels of the marks on the axes. Similar behavior apparently appears in telephone-based conversations such as those described by Brady (1968), although precise analysis is difficult due to the partial presentation of results in Brady’s study.
Possible explanations for the findings
Why do people create most of their responses within a relatively short period? One of the promises of online communication was thought to be its asynchronicity: the ability to respond at one’s convenience, even after a relatively long wait (e.g., Lantz, 2003; Newhagen & Rafaeli, 1996). Why then do we see that in practice most responses are created quickly, and that if a response is not created within a short period of time, the probabilities for a response drop precipitously?
One possible answer is the well-documented phenomenon of online information overload (Davenport & Beck, 2001; Shenk, 1999): As messages flow in, people either respond to them at once, or put them aside and rarely return to them. Evidence for this behavior resulting from information overload was presented by Jones, et al. (2004). This possible explanation is further strengthened by a weak but positive correlation (0.19) that we find in the Enron dataset between the total number of responses created by users and the percentage of the responses created within one day. On the assumption that users who create a larger overall number of responses experience more information overload, we observe that the behavior of these busy users tends to be even more skewed than the average user. Further evidence to support this possible explanation is the much lower average response latency in the Google Answers dataset. We can see here evidence that when there is a financial incentive for a quick response, the average response latency drops by more than an order of magnitude. Given information overload, we could expect that activities that carry the potential of immediate financial gain will be less likely to be delegated to a later time than messages that do not have this financial incentive.
Another possible explanation for the behavior pattern identified in this study is linked to the signaling power of a quick response: In asynchronous CMC, a quick response is one of the only non-verbal tools that can be used to signal immediacy, care, and presence.
Thus, there is a preference for quick replies (Aragon, 2003; Danchak, Walther, & Swan, 2001; Feldman & March, 1981; Goodwin, 2002; Walther & Tidwell, 1995). Anecdotal evidence for the positive signaling power of a quick response comes from the observation that late responses tend to include more apologies and/or explanations for the delay in responding. Initial findings reported elsewhere (Kalman, Ravid, Raban, & Rafaeli, 2006) show that responses created after a lengthy wait seem to be more likely to mention the long response latency, often apologize about the delay, and/or provide an explanation. In addition, these are sometimes not actual responses, although they were created by replying to a previous email message. They might show the user sending a reply asking about the progress of an issue mentioned in the original email, or even not connected at all to the text of the original email, possibly as a shortcut to typing an email address. Examples of these responses are reproduced in Table 2.
Table 2. Examples of texts from email responses created after a long latency (Source: Kalman, et al., 2006)
|Response Latency||Quoted Text||Category|
|16 days||sorry for the delay||Apology|
|14 days||Sorry it has taken me so long to write||Apology|
|18 days||i got back from almost three weeks vacation yesterday and am back at work||Explanation|
|14 days||i just got back into town from almost 3 weeks vacation. sorry i didn’t get in touch over the holidays, but…||Explanation + apology|
|23 days||Only took me 3 weeks to respond. That’s pretty good for me. I think things started collapsing the day I got your original email||Humorous apology + explanation|
|16 days||Just following up to see if the recruiting season has started and to make sure everything is going okay. If you need anything, just say||Reference to subject in original email. Not an answer to question|
|51 days||D, how are we coming on this project in relation to the info E sent you? Do you need anything else from E? Thanks.||Reference to subject in original email. Not an answer to question|
|109 days||Hey Mom… thought i would give a call but don’t have your number at work. send it if you get a chance. love,||Email response as probable short-cut to typing email address|
A fuller explanation for the rapid answers probably lies in a combination of both principles mentioned in the previous paragraph: Due to practical constraints on online communication in an age of information overload and constant interruptions (Mark, Gonzalez, & Harris, 2005), a quick response is the best way to ensure that a response will be created. Moreover, by sending a quick response, one conveys rapport, immediacy, and presence. The practicality of interactive communication depends on immediate responses. It is difficult to imagine a world in which every message, even one that was delivered a long time ago, has a high probability of receiving a response.
A third explanation could come from the logging-in habits of CMC users. A study by Dezso, et al. (2006) of an online news portal shows a visitation pattern that is similar to the chronemic pattern we identified in our datasets. Most importantly, the visitation pattern decays as a power law. Dezso, et al. show that a power law chronemic distribution pattern of the time between the posting of a news item and its reading can be explained by the power law distribution of the time intervals between consecutive visits by the same user. This interesting link between the distribution of intervals between user log-ins and the subsequent distribution of visitations to the individual news items might help explain, by analogy, the pattern we see when aggregating response latencies of many online communicative exchanges. We do not have chronemic logging-in information for any of our datasets, but it is reasonable to assume that the same power law distribution describing the frequencies of logging-in to a news portal would also describe (with different slopes) the dynamics of logging-in to check ones’ email, online classroom forum, or the Google Answers website. Thus, by drawing a possible analogy between clicking on a news item and choosing to respond to an online message, we reach another possible explanation for the power law chronemic distributions revealed in our datasets.
Another approach to explaining the results is to extrapolate from the similarity in the distribution of pauses in traditional conversation, and ask how the rules of traditional turn-taking apply to asynchronous CMC. The set of rules suggested by Sacks, et al. (1978) was structured to accommodate 14 facts about any traditional “mouth to ear” conversation (pp. 10-40). Most of these conditions also apply to asynchronous conversational CMC, for example the conditions that state that the sequence, content, distribution, and length of each turn are not specified in advance. There are, however, three important exceptions that result from the asynchronous nature of the conversation: In asynchronous CMC, conditions 2 and 3 (“overwhelmingly, one party talks at a time” and “occurrences of more than one speaker at a time are common, but brief”) do not apply, due to the strict linearity of message posting by most CMC systems (Herring, 1999). At the same time, due to the persistence (Erickson & Herring, 2005) of the conversation, in CMC the message is available as well as retained longer for further and repeated examination. Persistence of messages overcomes the aural and cognitive difficulty of synchronously processing more than one stream of talk, and allows a separation in time between the receipt of the words and their processing. In addition, rule 4 (“transitions from one turn to a next with no gap and no overlap between them are common. Together with transitions characterized by slight gap or slight overlap, they make up the vast majority of transitions”) needs to be restated in light of the findings reported in this article.
Our proposal for the restatement of conditions 2 and 3 is that “The words of each party are presented separately and linearly, and persist for a period of time.” For condition 4, the proposed restatement is “the vast majority of the transitions occur within a relatively short time.” The use of the word “relatively” is intentional. It alludes to the relativity reported in this article, that regardless of whether the average response latency in a specific conversation is a few hours or a few days, the majority of the responses are sent within that average latency, and the vast majority of the rest of the responses are sent shortly thereafter.
It is important to note here that we measured the response latency when there was a response. If there was no response, we considered that no conversation had taken place. In this article we adopt the inclusive definition of “persistent conversation” proposed by Erickson and Herring (2005), a definition that extends the notion of conversation from traditional face-to-face to computer-mediated contexts. In both cases, a conversation is no longer a conversation when silence takes over. The turn-taking rules restated below apply as long as the conversation continues.
After having restated three of the 14 conditions, the rules for turn-allocation in asynchronous written CMC, at least for those types examined in this study, can be restated, and serve to explain the chronemic distribution of asynchronous CMC:
If the sender has selected the next speaker, the party so selected has rights, and is obliged, to send a response as soon as is practicable. Other recepients too have the right to send a response
If the sender has not selected the next speaker, each recipient has the right but not the obligation to send a response
The sender may continue with an additional message
If, after a message is sent by the sender, either 1(a) 1(b) or 1(c) has operated, each party who created a reply is assigned the role of sender and the rule-set (a)-(c) reapplies.
In summary, we have presented here four possible explanations for the highly-skewed distribution patterns of response latencies found in asynchronous CMC. Two of the explanations are direct, and two are based on an analogy. One of the direct explanations is positive, and suggests that a quick response is a way to signal immediacy, care, and closeness. The indirect negative explanation suggests that due to overload, users tend either to reply immediately or not to reply at all. Of the two explanations by analogy, one analogy is to traditional face-to-face conversation, which shows a very similar chronemic distribution; we explore the relation between the rules governing traditional conversational exchanges and those that apply to asynchronous CMC. The last analogy is to online behavior, suggesting that the power law distribution of accumulated CMC response latencies might be a result of the power law distribution of log-ins. None of these four explanations is a sufficient or complete explanation for the chronemic distribution of response latencies in asynchronous CMC, however, and further work will need to be devoted to finding a full explanation of the empirical regularities revealed in this study.
Unresponsiveness and silence in asynchronous CMC
These findings on responsiveness, interactivity, and the maintaining of conversational threads in CMC provide tools to investigate instances when unresponsiveness and silence disrupt a conversation. Extensive research on silence has been conducted in traditional settings, exploring issues such as psychological and ethnographic perspectives on silence, silence as a nonverbal cue, silence in court, and silence in a cross-cultural perspective (Tannen & Saville-Troike, 1985). However, little research on this topic has been carried out in online settings, although a number of studies touch on related issues. Anecdotal evidence of the need to acknowledge silence as a factor in human-computer communication was described as early as1978 by Negroponte (1994). Lurking, a special form of online silence, has been researched by Nonnecke and Preece (2000) and Rafaeli, Ravid, and Soroka, 2004). Unresponsiveness in a chat room in response to different strategies of turn allocation has been analyzed by Panyametheekul and Herring (2003). Cramton (2001) documented the disruptive effect silence can have on teams attempting to collaborate online; and there is clear evidence for the distressful effects of being ignored in online communication (Rintel & Pittam, 1997; Williams, Cheung, & Choi, 2000; Williams, et al., 2002). Williams and his colleagues coined the term “cyberostracism” to describe these distressful effects; they can occur when a person is being ignored in chat, online gaming, and even in phone text messaging (SMS) (Smith & Williams, 2004). One of the factors limiting research on online silence is the lack of a basis for the definition of the length of unresponsiveness that constitutes online silence, such as the three second or more “conversational lapse” described above (McLaughlin & Cody, 1982).
The results reported here allow a quantitative definition of online silence. We can now confidently state that “no response after a period of ten times the average response latency” constitutes silence. This definition yields a better than 95% confidence level that a response is not likely to occur in the future. We base this on our finding that only 3-4% of the responses are created after that time. An inspection of Figure 1 suggests that this is a direct result of the behavior of the power law function at the slopes relevant for our datasets (−1.7 to −2.0). When the average response latency covers 70-85% of the responses, then a move to the right on the x-axis of one order of magnitude translates to a move of roughly two orders of magnitude on the y-axis. Thus, responses that take longer than 10 times the average response latency (10τ) will number a few percentage points or less.
A key strength of this definition is context sensitivity. We believe the “above 10τx average response latency” definition to be conservative, mainly since response rates are usually less than 100%. Moreover, at least a minority of the very late responses created seems not to include actual answers to the original message (Kalman, et al., 2006).
The strength of this definition of a “CMC lapse” is that it combines the rigor of a quantitative, statistical definition with the ability to adjust for qualitative differences among datasets through its context sensitivity. Thus, when researching online silence in a specific context, researchers will identify an average response latency relevant for the context of that specific research. Once that average response latency is identified (through the analysis of a large enough dataset, or through the use of a relevant published average), it can be assumed that if a response was not created within that 10τ period of time, there is a better than 95% confidence level that a response will no longer be created, sent, and received. Nevertheless, whenever possible, it is important that researchers use diligence and look for evidence that the dataset does not show hints of an unusual distribution, especially one that is different from the power law. For example, the email responsiveness profile of an employee who has been away from email due to a two-week holiday will not show a power-law distribution in the first few days after the holiday, and in that case the above definition of online silence is not applicable.
Apparently, these mathematical properties of the chronemics of online and traditional communication are a universal characteristic of typical human response latencies. This finding should be corroborated by further analysis of additional datasets originating in traditional as well as online communication. For example, an additional dataset that originates in an online report (Hamilton, 2005) summarizes response latencies in 199 online surveys in which 523,790 invitations were sent and almost 70,000 responses were received. Though we did not have direct access to the dataset, the report describes a similar pattern to the one observed here, where an estimated 70% of the responses were created within the average response latency (a little less than 3 days), and where over 99% of the responses were created in four weeks (10x the average response latency). Additional published work in various disciplines suggests behavior that is in agreement with these generalizations (Jones, et al., 2004; Matzler, Pechlaner, Abfalter, & Wolf, 2005; Strauss & Hill, 2001). It would be interesting and instructive to find occasions in which the same rules apply, as well as exceptions to the rules. This can be achieved by further analysis of published data, as well as by dedicated original research that focuses on asynchronous CMC, including areas not mentioned here, such as response latencies within blogs. Furthermore, research should measure response latencies in synchronous CMC such as instant messaging, chatting, and text messaging (SMS). For a discussion of synchronous versus asynchronous CMC, see Newhagen and Rafaeli (1996).
It is now also possible to explore the implications of CMC chronemics as a nonverbal cue, in a manner similar to the way proxemics and other nonverbal cues affect interpersonal communication. For example, one could study the correlation between the normative zones described here and the expectations of users. An initial indication that these norms are reflected in the expectations of users is the often quoted (e.g., Tyler & Tang, 2003) expectation in workplace email correspondence of receiving an email reply within “24 hours.” Given the added delay caused by weekends and holidays, the average response latency measured in the Enron dataset (τ= 28.76 hours) is close enough to 24 hours, and it is at the point that separates Zone I from Zone II. Since Zone I defines the range where the majority of the responses actually occur, the 24-hour expectation is in line with the norms of workplace email chronemics revealed in this study. This relationship between chronemic norms and chronemic expectations should be further explored, possibly by leveraging the predictions of a central theory in nonverbal communication, the Expectancy Violations Theory (EVT) (Burgoon, Buller, & Woodall, 1996a). Last, further analysis should explore the distribution of the shorter and most abundant response latencies, in the manner started by Kalman and Rafaeli (2005). In the present study most of these response latencies were bundled in the largest bins.