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«NETWORKING IN EVERYDAY LIFE by Bernard J. Hogan A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy ...»

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With my two close colleagues at Ryerson [University], it’s fairly constant.

We’re setting meetings, and someone has a problem so we change the meeting...and the other thing about emailing with my two colleagues is that if we ever have a two-way conversation, we make sure we copy the other. The dyad is never a dyad; it’s always a triad (#431, “Wendy”).

However, the complexities of planning by media might be best expressed by this frazzled quote from the promoter and sales director:

CHAPTER 5. NETWORK PROFILES: COUPLING MEDIA USE AND SOCIAL ACTIVITY 111

I would wake up...on average every Monday to Friday, sometimes Monday to Saturday, around 8 o clock in the morning. I check emails, send faxes, a lot of voicemails at work...I plan my day usually in the morning, I should do it at night but I usually do it in the morning instead. I’m a night owl so

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morning and from my home, because I work from home...So in sales what I do is I I’m gonna wake up in the morning, get in my truck and make a lot additional phone calls confirming appointments for the day (#601 “Clay”).

Here Clay mentions at least four media, (emails, faxes, voicemail, and calling). He also mentions texting, instant messaging and webcamming later in the interview. For Clay, networking is staying in motion and keeping up with whatever is necessary for both his clients and his peers. He typifies the ‘all heavy’ group as someone who is focused

on maintaining accessibility and being a deliberate networker—even explicitly so:

I: What’s your most important work related task? P: Actually it’s completely about what you’re here talking about which is communication. You have to in sales be a good networker. Whether you want to talk to somebody, whether you want to go to that meeting, whether you wanna spend time making a powerpoint presentation or an excel file, you gotta keep in communication with your clients because the competition’s right behind

–  –  –

Clay is in the “Heavy Cell and F2f” group. Unsurprisingly, he could be a torchbearer for Ling’s concept of “microcoordinators”. Ling uses this term to describe the fast and loose networking of the mobile phone (Ling, 2004). For them networking involves many media, lots of scheduling and lots of uncertainty. The “Moderate F2f and telephone” group, by contrast, offer an extreme lifestyle counter point. Theirs is a world of regularity. They are second only to the “Light planning” category for being

CHAPTER 5. NETWORK PROFILES: COUPLING MEDIA USE AND SOCIAL ACTIVITY 112

infrequent planners, but that is not to say they are infrequent socializers. For example “Guy” is an elderly man who is disinterested in computers. One need only read his comparison of daily life to get a sense of how different his world is from Clay’s.

I get up at 8:00 and read the newspaper and do the crossword puzzle. Then some days there’s a breakfast club that meets every Wednesday, and I go there on Wednesdays obviously. And I do Meals on Wheels another day, and there’s a lunch group meets once a month, people I used to work with,

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Perhaps the only similarity is that they awake at 8. Where Clay is constantly trying to keep abreast of his work and social engagements, Guy reads the newspaper and makes sure that if it is Wednesday he is going to the breakfast club. And Guy’s story is not out of sync with others in the ‘light telephone’ partition.

5.4 Individual variations in social activities Planning gives us a glimpse of how individuals orient themselves to future states, how they react to their social structure and maintain their relationships with each other. Yet, planning is not directed towards a specific unique task but to multitude of tasks with a variety of alters. The one constant is merely the individual’s will to seek out and engage others. Thus, to understand networking in everyday life, one must not only look at the medium by which planning is accomplished, but also the goals—what activities do people engage in?

5.4.1 Describing the social activity measures Networking is not merely about the media used to plan activities, but also about the activities themselves. While the Connected Lives data set does not include a comprehensive list of possible activities, it does include a series of questions that get at several

CHAPTER 5. NETWORK PROFILES: COUPLING MEDIA USE AND SOCIAL ACTIVITY 113

dimensions of social activity. The questions and their respective descriptive measures are found in Table 5.4.1.

The criteria for selecting these activities was that they each have a different relationship to the aspects of structure outlined in Chapter 2. There, I categorized affordances according in four dimensions: information, relation, time, and space. The activities listed in the table all vary on these dimensions. Regular meetings are very fixed in time and likely fixed in relation. Neighbouring is very fixed in space, but can happen at any time of the day. Dropping in and hanging out are not fixed in time.

However, dropping in is done with specific known alters (one drops in on a specific person), whereas hanging out is done with specific known places (one hangs outs at

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online chatting is much like hanging out (where the chatroom or buddylist can be considered a context of rotating alters), but it transcends space.

Since this is a purposive rather than comprehensive list of activities, I look forward to future analysis drawing upon the findings here with a more comprehensive, perhaps time-use derived, list of activities and their frequency.

Much like the planning variables, these social activity variables are ordinal categories of time that being with “about daily” and end with “monthly or less”. Also, like the planning variables, they were converted to numeric scores from the ordinal values. However, unlike the planning variables, there was no separate measurement for frequency with very close and somewhat close alters. This means that I did not sum two values (a value for very close alters and a value for somewhat close alters).

So the frequencies here will be understandably smaller. Also, because it is only one global per-individual measure rather than a composite of two distinct measurements, I can refer to the values as days per month rather than times per month.

To clarify some of the spelling herein, note that “hangout” is the noun phrase whereas “hang out” is the verb phrase.

CHAPTER 5. NETWORK PROFILES: COUPLING MEDIA USE AND SOCIAL ACTIVITY 114

–  –  –

Mean values were calculated from a conversion of ordinal scales into number of days per month. ‘Of users’ means the point estimate was calculated only for those who reported doing the activity at least once, whereas ‘of sample’ includes every valid response from the survey.

Of the activities, “having a chat with your neighbours” is the most frequent. However, that is not to suggest that it is the most salient aspect of networking. The respondents included an average of 3.3 neighbours in their personal network (0.9 very close neighbours and 2.4 somewhat close neighbours). The frequency of neighbouring, then, is more likely due to the convenience of seeing one’s neighbour while outside, or the short traveling distance. Neighbours are easily accessed and accessible.

Of the remaining activities, the most popular was going to a local hangout, followed by a regular meeting, dropping in and finally chatting online. As was the case with the earlier discussion on media, the standard deviations are high, suggesting that individuals vary greatly in their styles of sociability. Also like the earlier discussion, those who participate in the activities generally do so more than monthly. Finally, these activities seem to have adequate coverage of the sample, as only 3 individuals reported ‘never’ doing any these particular social activities.

CHAPTER 5. NETWORK PROFILES: COUPLING MEDIA USE AND SOCIAL ACTIVITY 115

–  –  –

The variables in this section are in many ways similar to those in the previous section on media use. I employ k-means clustering in similar ways (iterated runs with Calinski-Harbasz goodness-of-fit) to similar ends—clustering the social activity proles into intelligible partitions. In the earlier clustering, there was a smooth decrease in the CH score, so I selected the cluster with smallest value of k after a big drop in the size of the largest partition. In this case, however, it is not so important to focus so much on the largest partition because the CH score actually increases slightly as the value of k increases between k = 4 and k = 7 (Figure 5.4). Starting with k = 8, the CH score starts decreasing while the size of the largest cluster remains virtually constant.

Thus, k = 7 is the optimal cluster.

–  –  –

Figure 5.4: Calinski-Harabasz scores and largest partition size by values of k

CHAPTER 5.

NETWORK PROFILES: COUPLING MEDIA USE AND SOCIAL ACTIVITY 116

5.4.3 Clustering Part 2: Interpreting the cluster The seven partition solution is characterized by whether respondents do a particular activity daily, weekly or not at all. Accordingly, I have termed the partitions as “Monthly activity”, “Weekly neighbouring”, “Weekly meeting”, “Daily neighbouring”, “Daily online”, “Daily hangout” and “Daily drop-in” (See Figure 5.5). Much like the earlier analysis, each partition is characterized by one or two dominant variables.

However, unlike the earlier analysis, there is no “All social activities” group parallel to the “Heavy all media” group. This is probably due to the cost of in-person interaction. There are only so many places one can go in one day, and while it is easy to send off quick emails or telephone calls daily, activities like regular meetings, drop-ins and even chatting online can take upwards of an hour or more to complete (not including travel). Thus, one can see in these partitions a great deal of contour rather than a simple axis from low activity to high activity.

–  –  –

What stands out firstly about these partitions is that numerous individuals have

CHAPTER 5. NETWORK PROFILES: COUPLING MEDIA USE AND SOCIAL ACTIVITY 117

a daily routine involving social contact. The 107 individuals in the “daily” clusters all interact by a specific primary means on a daily basis, be it neighbouring, online chats, dropping-in or hanging out. Interestingly, there is no daily regular meeting group. That is, regular meetings appear to be more of a weekly ritual than a daily one. Perhaps this is because regular meetings involve a specific organizational commitment that extends through the week, or because individuals can only participate in a handful of voluntary associations, each of which would meet on a weekly or monthly basis. By contrast, the networking strategies other than weekly meeting, are not as easily fixed in time, nor are they as dependent on large social structures. Consequently, I find an interesting relationship to planning in these social activity partitions.

As can be seen in Table 5.4, there is a clear monotonic increase in planning from the “monthly activity group” to the “daily drop-in” group. What is noteworthy about this relationship is that planning variables were not included in this cluster, yet, the optimal solution shows a clear relationship to planning. However, it is not the case that more planning equals greater social outcomes. By contrast, it seems that individuals tend to eschew planning if possible and structure their social activity through more habitual and convenient means. I explore this concept further in the next section.

5.4.4 Clustering Part 3: Interpreting the social locations of the cluster The six social activity partitions do differ from each other in a few salient ways. Many of these differences are significant, but others show either too much variance or too few cases to claim significance. What is important, however, is that the differences between the partitions illustrate a logic that ties many of the social activity patterns to the social locations of the respondents.

The core differences between these partitions is a relationship between social activity and individual opportunities. For example, the three partitions that neighbour

–  –  –

this imply causation? For the sake of brevity, no. People may seek out houses because of the opportunities for neighbouring that houses provide. Yet there is a clear logic of houses affording greater opportunities for neighbouring, while apartment dwellers seeking to maintain personal boundaries. Groups A,B,C, and D are at least 50 percent home dwellers compared to 33 percent or less for groups E,F, and G. Similarly, the two neighbour groups (B and D) are most likely to have children, while B is the most likely group to be coupled. These neighbouring groups illustrate a pattern of social activity that hints at the “ideal type” of suburban life—neighbourly, family oriented and normative. Additionally, B,C and D are the most well off groups, and slightly older than average. By contrast, the daily online (E) and daily drop-in (G) groups are the least well off in the sample and the youngest. However, there is a great deal of difference between daily online and daily drop-in groups in the way they react to their circumstances. The daily online group is primarily young single poor immigrants with disproportionately large close families, while the daily drop-in group are either unemployed, or underemployed, and typically single. The latter group represents ideal-typical young networkers, tied to their cell phones and perpetually negotiating where to go next. Interestingly, despite the fact that this group does the most amount of planning, they are the least well off and the least likely to be employed—hinting at the fact that it is not how much one networks, but how they do it. This group stands in stark contrast to the weekly meeting group, who are financially well off, coupled, and home dwellers.



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