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Int. J. Electronic Finance, Vol. 7, No. 2, 2013 115

Copyright 2013 Inderscience Enterprises Ltd.

Building social capital with mobile communication services

Juuso Karikoski* and Kalevi Kilkki Department of Communications and Networking, Aalto University, P.O. Box 13000, 00076 Aalto, Finland E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: People may use different kinds of mobile communication services depending on if they are communicating with, for instance, friends, acquaintances or strangers. Thus, in this paper bonding and bridging social capital is studied in the context of two mobile communication services, short message services (SMSs) and voice calls. In Granovetters terms, bridging social capital refers to communication with weak or absent ties, while bonding social capital refers to communication with strong ties. We find that both SMSs and voice calls are used for bonding and bridging social capital, but SMSs are used more for bonding purposes than voice calls. Furthermore, media multiplexity is more associated with bonding than bridging social capital. We also present a method for studying social capital in the context of other, newer mobile communication services, and present results of a pilot study. The implications of the results are discussed from a number of perspectives including communication research, social network analysis (SNA) and mobile operators.

Keywords: social capital; e-finance; social networks; bonding; bridging; strength of ties; short message service; mobile voice calls; handset-based measurements; ESM; experience sampling method.

Reference to this paper should be made as follows: Karikoski, J. and Kilkki, K. (2013) Building social capital with mobile communication services, Int. J. Electronic Finance, Vol. 7, No. 2, pp.115131.

Biographical notes: Juuso Karikoski works as a Doctoral Candidate at the Department of Communications and Networking at Aalto University in Finland. He also holds a position at the Future Internet Graduate School FIGS. His research interests include handset-based measurements, mobile communication service usage and related social networks. He has been actively publishing in academia and is expected to graduate as a Doctor of Science in Technology in 2013. Currently he holds an MSc (Technology) from the Helsinki University of Technology.

Kalevi Kilkki works as a Chief Research Scientist at the Department of Communications and Networking at Aalto University in Finland. He earned MSc and Doctor of Technology degrees at Helsinki University of Technology in 1983 and 1995, respectively. He made a productive career in communications industry in the area of quality of service and performance analysis. His contributions include 25 patents and a textbook

116 J. Karikoski and K. Kilkki

about Differentiated Services. From 2008, he has been responsible for teaching and research in the field of human-centric communications at Aalto University. As a part of that task, he published a textbook in 2012 under the title of An Introduction to Communications Ecosystems.

1 Introduction

Short Message Service (SMS) and mobile voice calls are the most used mobile communication services worldwide. According to the International Telecommunication Union, there were almost six billion mobile cellular subscriptions in the world in 2011, and SMS traffic has grown globally at an astonishing rate with 200,000 SMSs sent every second in 2010 (ITU, 2010, 2011). In addition to these two services, other services, such as Over-the-Top (OTT) communication services or those based on mobile internet (MI) have emerged in the mobile domain. These include e-mail, Voice over IP (VoIP), Instant Messaging (IM) and social media services (e.g., Facebook). SMSs and voice calls have already established themselves as a mobile communication medium, but there is little research on how these services are used for bonding and bridging social capital, as most of the research considers the mobile phone as a single entity. Because of the growing popularity of MI communication services, there is a need to develop methods for studying how social capital is built with these services as well. It is important to study bonding and bridging social capital of mobile communication services from a number of perspectives. From communication research and Social Network Analysis (SNA) perspectives mobile communication services are interesting, since they are used to form and maintain social networks between people. Understanding what factors affect the decision to select or use a given service helps in understanding the underlying communication motivations of users. In addition to the sociological perspective, there are also business related motivations for understanding how and why services are used. Mobile operators need to understand the usage interrelationships between the mobile communication services they are offering or providing access to, since these interrelationships have a direct effect on their revenues and strategy (Karikoski and Luukkainen, 2011; Karikoski and Mkinen, 2012).

We claim that the strength of the tie (Granovetter, 1973) between two persons is one of the factors affecting the decision to select or use a given mobile communication service. Yang et al. (2011) have also claimed that the effects of information and communications technology (ICT) on social capital may differ depending on the technology that is being analysed. Consequently, we study bonding and bridging social capital in the context of SMSs and voice calls via usage logs collected from handset-based measurements (Karikoski, 2012), and present some initial results of a pilot study utilising the Experience Sampling Method (ESM) (Hektner et al., 2007) to study how social capital is built with other communication services. The motivation of the research lies in the fact that mobile phone related social capital research has mostly focused on the mobile phone as a single entity, and there are only a handful of studies which analyse the social capital issues related to specific mobile communication services. Moreover, there are even less studies which study multiple mobile communication services and compare their social capital building properties. Thus, this research aims to

Building social capital with mobile communication services 117

address the gap in the mobile phone related social capital research that we have discovered.

The contributions of this paper are two-fold: first, it gives insight into bonding and bridging social capital in the context of mobile voice calls and SMSs, and second, it presents a method and a pilot study for studying how social capital is built with other communication services. The primary research question is as follows: How are SMSs and voice calls used for bonding and bridging social capital? The paper has the following structure: after the introduction, background and related literature are reviewed first from the perspective of social capital and strength of ties, and then from the perspective of mobile communication services and social networks. Then, the methodology, including handset-based measurements and ESM, and the collected data, are described. Third, the results and analysis are detailed and discussed, and finally the paper is concluded.

2 Background and literature review

2.1 Social capital and the strength of ties

Social capital as a term has been defined in a number of ways and there is no commonly agreed upon definition. Coleman (1988), for instance, refers to social capital as the (individual) resources acquired through the relationships that exist between people. Putnam (2000) on the other hand refers to social capital as connections amidst individuals, and the norms of reciprocity and trust that arise from these social networks. Thus, these two definitions differ in the way social capital is seen as an individual resource in the former and as a collective resource in the latter. As Wilken (2011) discusses, the work of Coleman (1988) and Putnam (2000) has been foundational in the way social capital research has developed in the past decade. More recently, Ellison et al. (2011) have also defined social capital as the benefit that is derived from the relation.

Putnam (2000) has distinguished between two forms of social capital, namely bonding and bridging. Bonding social capital refers to inward looking social capital which tends to reinforce exclusive identities and homogenous groups, such as small groups of friends or family. Thus, bonding social capital is a measure of the strength and usefulness of already established relationships. Bridging social capital, on the other hand, refers to outward looking social capital that networks of people across diverse social backgrounds encompass. Thus, bridging social capital is better for linking between groups, forming new relationships and information diffusion. In Granovetters (1973) terms weak ties form the foundation for bridging social capital, whereas strong ties form the foundation for bonding social capital. Weak ties link acquaintances that move in different social circles, whereas strong ties link friends and family who share the same social circles. In addition to Granovetters (1973) strong, weak, and absent ties, also other types of indicators of relationship strength have been used, such as Boases (2006) someone I feel very close to and someone I feel somewhat close to, and Ellison et al.s (2011) close or actual friends, latent ties and strangers. Moreover, Haythornthwaite (2005) claims that as ties become stronger between people, there is a greater tendency for them to draw on multiple media to communicate with each other (so called media multiplexity).

In this paper we distinguish two aspects of social capital. First, the society as a whole benefits from cooperation between individual members. This aspect of social capital has


been defined by Fukuyama (2002) as the stock of shared norms among a group of people that promote social cooperation, instantiated in actual social relationships. Second, each individual member has the possibility to personally benefit from those social relationships that occur with the people that trust him or her. This individual aspect of social capital can be defined as the stock of trust and indebtedness towards a person that the person can utilise in his or her social relationship in the future. The relationship between these two aspects of social capital is far from simple; in particular, we cannot assume that the social capital of a society is the same as the sum of individual social capitals. There are persons that are able to collect considerable amounts of social capital without affecting positively the social capital of the society, for instance, through cheating or fraud. In contrast, some people might be significant contributors of social capital to their society without being able to personally benefit from their contribution. Moreover, because of the complex nature of the social system, the effect of an individual person on the social capital of the society may considerably depend on the behaviour and contributions of other people.

We concentrate on the characteristics of social capital of individual persons in the circles of acquaintanceship framework of social groups proposed by Hill and Dunbar (2003) and Dunbar (2010). According to their hypothesis, personal social networks consist of a series of layers with roughly 5, 15, 50 and 150 members. These layers are defined by emotional closeness which also is reflected in the patterns of communication as discussed later in this paper. As to the classification of social capital to bonding and bridging, we can safely assume that the first two layers belong to the category of bonding while relationships with people that do not belong to the closest 150 friends shall be classified to the category of bridging. Then as to the possibility of exploiting social capital, it seems that in the case of the closest two groups, the cost of cheating typically exceeds the potential short term benefits, because genuine bonding requires continuous truthful behaviour. In contrast, with bridging relationships the situation is different: a person may fully utilise the social capital of a weak tie in order to achieve a short term gain even at the expense of destroying the tie.

2.2 Mobile communication services and social networks

Wilken (2011) has extensively examined the literature related to mobile phone use and social capital and concluded that the mobile phone is a particularly effective medium for making existing network ties stronger (i.e., bonding social capital), although there is some evidence that it can be used for building wider networks of contacts (i.e., bridging social capital) as well. However, in most of the studies Wilken (2011) has reviewed, the mobile phone is considered as a single entity and the communication services that it enables are not studied separately. Moreover, Yang et al. (2011) report that studies related to the mediating role of ICT in building social capital have tended to focus on single technologies, such as SMSs or voice calls, similar to what we have discovered. Thus, there is definitely a need for more detailed and comparative studies related to social capital in the area of mobile communication services, and especially MI or OTT communication services. Next, we will review some of the research where mobile communication services in particular have been studied from a social capital perspective.

Boase and Kobayashi (2008) analysed how Japanese adolescents use mobile phone e-mail to bond and bridge social capital. They report that mobile phone e-mail is used both to bond with friends and family, as well as bridge with new social ties. In the


Japanese market short text messages are transferred with e-mail protocols (i.e., mobile phone e-mail) and thus as a communication service for the user they are somewhat comparable to SMS in the European market. Ishii (2006) has also studied the use of mobile phone e-mail in Japan and concluded that it is used to maintain existing bonds rather than to bridge to new ones. In a more recent study by Boase and Kobayashi (2011), bonding and bridging nature of SMSs and voice calls were studied. The authors concluded that voice calls are used more for bridging social capital and SMSs for bonding social capital. Furthermore, they report that voice calls and SMSs are used together for bonding social capital. Tossell et al. (2012) also studied a longitudinal dataset of voice calls and SMSs, and concluded that individuals contacted with both SMSs and voice calls are more likely strong ties.

A field of computational social science (Lazer et al., 2009) has emerged, where large-scale datasets of human behaviour are being analysed. For instance, Onnela et al. (2007) analysed a large-scale mobile communication dataset but limited their analysis to voice calls. Palchykov et al. (2012), however, compared voice calls and SMSs. They concluded that voice calls and SMSs have a comparatively low level of overlap when identifying best friends of persons. A possible reason for this is that the services serve different functions in human communication. The only study that we have come across that utilises so called big data to compare the social capital aspects of MI or OTT communication services was conducted by Szabo and Barabasi (2006). They concluded that mobile e-mail is used uniformly across communities while mobile IM/chat is used mainly in tight communities. Thus, from a social capital perspective mobile e-mail is used more for bridging social capital purposes, whereas mobile IM/chat is used more for bonding social capital purposes. Similar results have been obtained by more traditional questionnaire-based methods by, for example, Kim et al. (2007).

3 Methodology and data

3.1 Handset-based measurements

The primary dataset analysed in this paper was collected with handset-based measurements. These measurements are implemented with data collection software installed in the participants mobile phones. Thus, user-level data of mobile phone use in general can be collected. However, in this paper we only analyse the voice call and SMS usage log data in terms of voice calls made/received, and SMSs sent/received. To protect the privacy of the participants the phone numbers were encrypted and no SMS content was collected at all. Naturally informed consent for the data collection was also required from all of the participants. For more information of the data collection method and process, see Karikoski (2012).

The data were collected from a longitudinal panel of 200 participants during October 2009 and December 2010. The participants of these measurements were students or staff of an anonymous university, biased towards technology-oriented male students in their early twenties, and identified as innovators or early adopters of mobile devices and services (Karikoski, 2012). The sample thus creates challenges for the external validity of the results, but it is also possible that the usage patterns observed in this kind of a sample reflect those of the majority in the future. Furthermore, the demographics of the sample are similar to the overall demographics of the university. The dataset is longitudinal in


nature as an average participant has more than three months (104 days) of active data production days in the measurements. Different mobile Operating Systems (OS) are represented as follows: 181 Symbian devices, five Google Android devices, one BlackBerry device, one Windows Mobile device and 12 unidentified devices. As we study only voice calls and SMSs, however, the OS is of no particular importance in the analysis. These services are available for all mobile phones, and thus the overrepresented Symbian is assumed to not to bias the results. The statistics of the raw voice call and SMS datasets are described in Table 1. As we can see, the datasets are quite similar in terms of size.

Table 1 Handset-based dataset statistics

In Out Total No. of voice calls 41,059 43,918 84,977 No. of SMSs 43,889 38,989 82,878 No. of unique phone numbers 7926 8668 11,017

3.2 Data pre-processing and operationalisation

The SMS specifications dictate that there is a limit of 160 characters for a single message. However, in advanced mobile phones (such as the ones used in this research) the message is visible to the user as one single message, although it can consist of several SMSs if the character limit is exceeded. In our dataset this is visible so that there can be multiple SMSs sent or received during a short time interval, although the users themselves might perceive them as single messages. These multipart SMSs (Kovanen, 2009) need to be treated as single messages in our analysis, and thus the dataset needs to be pre-processed before the actual analysis. Kovanen (2009) has analysed a large mobile communication dataset of a mobile operator and observed based on the time interval distribution, that a time window of 10 seconds is suitable for separating the multipart messages from single messages. We will use the same time window in our analysis to identify the multipart messages. Thus, if there are more than one SMSs sent to (received from) the same unique contact within a time window of 10 seconds by a given user, then the SMSs are considered as one SMS and the latter messages are excluded from analysis. In the SMS dataset, there are also clear spikes in the number of SMSs sent on 24th December and 31st December/1st January because of Christmas and New Years Eves, respectively. Interestingly, these spikes are not visible in the voice call dataset, as also observed by Kovanen (2009). Because of the outlier nature of these dates, we will exclude them from both datasets. Thus, after the pre-processing we are left with approximately 98% of the original raw data.

Bonding and bridging social capital will be analysed from the datasets by using an operationalisation similar to the one used by Boase and Kobayashi (2011). We assume that a single individual has a single phone number and thus claim that the larger the number of unique contacts with whom SMSs (voice calls) are exchanged per day, the larger the level of bridging activity of the SMS (voice call) service. Moreover, the larger the number of SMS (voice call) events per unique contact per day, the larger the level of bonding activity of the SMS (voice call) service. In graph theory terminology, the larger the service-specific degree (measured per day) of a node is, the larger the level of


bridging activity of that service. Furthermore, the larger the weight of the service-specific edge (measured per day) is, the larger the level of bonding activity of that service. Haythornthwaite (2005) has also observed that people who have a strong tie between them tend to use more media to communicate to each other. Thus, we also assume that if both SMS and voice calls are used to communicate to a unique contact, then the level of bonding activity is greater.

3.3 Experience Sampling Method

An extensive guide about the ESM has been recently written by Hektner et al. (2007), although the method has been already utilised since the early 1970s. ESM is about studying experience in the natural contexts of everyday life, where experience refers to any of the contents of consciousness of an individual. The data collection occurs by asking questions from individuals at random points of the day, whenever a signalling device prompts them to respond. Thus, ESM is a nonintrusive and precise method for naturalistic behavioural observation. Traditionally ESM has been conducted with beepers as signalling devices and the responses have been documented with pen and paper. However, our approach belongs to the category of computerised ESM (Feldman Barrett and Barrett, 2001) where mobile phones are used as signalling devices. For more information about the method, see Hektner et al. (2007) and Feldman Barrett and Barrett (2001).

In our pilot study we installed a research application to the participants mobile phones and sampled their experiences related to mobile communication services in order to study how different services are used in building social capital. The studied services include, voice calls, SMSs, Multimedia Messaging Service (MMSs), Facebook, and e-mail. The questions were shown after voice calls were disconnected or ended by the user, after a message (SMS, MMS or e-mail) had been sent or received, and after the Facebook application had been running on the foreground of the devices screen. For all the question types we used a minimum threshold of 12 hours between consecutive questions, so that the participants would not be disturbed too much. The sampling in our study has not been purely random, since the questions have been asked after specific events which may or may not happen randomly throughout the day. However, in order to collect the individuals experience related to a certain event, the sampling needs to happen instantly, so that accuracy of the response is not deteriorated, for instance, because of a lapse of memory. In addition to our research, related ESM research has been conducted, for example, in capturing mobile phone user feedback (Froehlich et al., 2007) and in communication research in general (Kubey et al., 1996).

The ESM data were collected between November 2011 and January 2012 during a course at an anonymous university. In total there were responses from 31 participants, with the different devices represented as follows: 15 Symbian or Symbian^3 users, 10 Google Android users and six Apple iOS users. This sample is also biased towards technology-oriented male students in their early twenties. Moreover, contrary to the case of voice calls and SMSs, the OS affects the data that can be collected. From iOS users we were not able to collect messaging data, and e-mail was only accessible with Symbian devices. Otherwise the data were collected from all device platforms. Table 2 presents the statistics of the ESM dataset.


Table 2 ESM dataset statistics

Question type No. of users No. of responses Voice call 31 448 SMS 25 228 Facebook 15 189 MMS 7 16 E-mail 1 2

We used Boases (2006) indicators when prompting the users to answer the ESM questions with additional options for absent ties (option three) and situations where answering is not possible (e.g., dropped calls, option four). The general form of the questions was as follows: What kind of a relationship do you have with the person you just communicated with? and the answer options were the following:

Someone I feel very close to

Someone I feel somewhat close to

Someone I feel not at all close to

Cant say.

Options one to three refer to strong ties (friends), weak ties (acquaintances) and absent ties (strangers), respectively. The fourth option can be used, for instance, when a communication event gets disconnected before actual communication takes place. As previously discussed, this kind of a trichotomy based on relationship strength has been previously used by, for example, Granovetter (1973) and Ellison et al. (2011).

4 Results and analysis

4.1 Handset-based results

Temporal patterns in SMS and voice call usage (Figure 1) show the first hints of the different roles that SMSs and voice calls have in mobile phone communication. Voice calls are used more during the day time and there is a clear peak at 4PM. SMSs, on the other hand, are used earlier in the morning and later at night, but less than voice calls during the day.

The statistics for unique contacts per day and events per unique contact per day are depicted in Tables 3 and 4, respectively. From Table 3 we observe that the number of unique voice call contacts per day is larger than the number of unique SMS contacts per day. Thus, according to the first operationalisation, we conclude that voice calls are used more for bridging social capital when compared to SMSs. Moreover, from Table 4 we observe that the number of voice calls per unique contact per day is smaller than the number of SMSs per unique contact per day. Consequently, according to the second operationalisation, we conclude that SMSs are used more for bonding social capital when compared to voice calls.


Figure 1 Temporal patterns in SMS and voice call usage

Table 3 Unique contacts per day

Unique voice call contacts per day Unique SMS contacts per day

Outbound Inbound Out and inbound Outbound Inbound

Out and inbound

Mean 1.3 1.3 2.1 1.0 1.2 1.4 Median 1.0 1.0 1.0 0.0 1.0 1.0 Skewness 2.3 1.7 1.8 3.9 1.9 3.0

Table 4 Events per unique contacts

Voice calls per unique contact per day SMSs per unique contact per day

Outbound Inbound Out and inbound Outbound Inbound

Out and inbound

Mean 1.5 1.5 1.9 1.8 1.8 2.8 Median 1.0 1.0 1.0 1.0 1.0 2.0 Skewness 4.6 3.9 3.6 5.6 5.6 5.6

To analyse the multiplexity of service usage, we study how many unique contacts were contacted both with voice calls and SMSs on average by a given user. From the dataset we observe, that on average 34% (median 33%) of those who were contacted with voice calls were also contacted with SMSs. On the other hand, on average 49% (median 54%) of those who were contacted with SMSs were also contacted with voice calls. Because


SMS is used more for bonding social capital, and more contacts who were contacted with SMSs were also contacted with voice calls than vice versa, we conclude that media multiplexity is associated more with bonding than bridging social capital. Furthermore, overall voice calls were exchanged with more unique contacts than SMSs. Thus, users are more likely to exchange voice calls with those that they exchange SMSs with, but not necessary exchange SMSs with those that they exchange voice calls with.

Now let us return back to the question whether the collected data would be able to reveal something about the nature of social groups as defined by Hill and Dunbar (2003). In order to study this question we use the long tail model introduced in Kilkki (2007). The long tail formula is defined as follows:

5050

( ; , , )1

F k NNk

=

+

(1)

where

F(k): Popularity covered by items up to rank k N50: Number of items that cover half of the total popularity : Factor that defines the form of the function : Total volume of all items. The popularity of kth item can be easily calculated as the difference between the consecutive cumulative values: f(k) = F(k) F(k 1) in Formula (1). The share of the most popular item f(1) is defined as F(1). Figure 2 shows the average share of voice calls in the order of popularity of the phone number. The distribution includes 175 users, 10762 contacts, and 84582 voice calls. We have calculated for each user the share of voice calls to the most popular contact, then the share to the next most popular contact, etc. Figure 2 shows the average distribution with the following parameters: N50 = 3.88, = 0.766, and = 1.14. The fitting has been done for the first 14 contacts with excellent accuracy.

There seems to be two special points in the curve. First, the difference between the real data and the model remains below 7.5% for all data points from 1 to 14. In contrast, the 15th data point is 12% below the prediction of the model. Then the data fall slowly downwards proportional to the model prediction until contact number 44 at which point the real data are about 24% below the model. After that point the real data start to fall much more rapidly. These two special points match very well with the layers of social groups (15 and 50). However, the data are still limited and do not provide any strong proof for the hypothesis about social group size. Nevertheless, the result is interesting from the viewpoint of social capital and deserves further research.

Similar analysis with SMS data results in the following long tail parameters: N50 = 2.68, = 0.744, and = 1.12. The curve does not indicate as clear turning points as the voice call data shown in Figure 2. However, also the SMS data seem to indicate differences in different levels of group size. Figure 3 shows the difference between the real data and the prediction given by the long tail model. The data and the model start to deviate from each other at roughly 16 but more clearly only after the 22nd contact.


Figure 2 Fitting voice call data to long tail model (see online version for colours)

Figure 3 Real share of SMSs divided by the prediction of the long tail model (fitting of the model for the first 20 contacts) (see online version for colours)

The data indicate that SMS networks are more skewed and concentrated than voice call networks. According to the real data the 3 most popular voice call contacts represent half of the voice calls whereas the same parameter for SMSs is 2. The same difference between voice calls and SMSs can be seen in parameter N50 (3.88 for voice calls and 2.68 for SMSs). Furthermore, we can interpret the results in a way that there is a deficit of voice calls of about 12% (= 1 1/) compared to an ideal situation described by the long tail model. The same deficit for SMSs is about 11%. Part of the deficit might be explained by the length of the study period. Overall the skewness of both voice call


and SMS measures (Tables 3 and 4) have been observed in computational social science studies (e.g., Onnela et al., 2007) and these data follow that trend. Furthermore, it has been discovered that SMS communication among mobile phone users is more reciprocal than voice call communication (Karikoski and Nelimarkka, 2010). Kovanen et al. (2011) have also discovered based on a large-scale dataset that lopsided mobile voice calls are common. Finally, it seems that the voice call distribution conforms better to the circles of acquaintanceship as defined by Hill and Dunbar (2003) and Dunbar (2010), but more detailed research is definitely needed.

4.2 Experience sampling results

The temporal patterns of responses to ESM questions depict that although the questions were not asked randomly throughout the day (as in traditional ESM research), the responses are quite well distributed throughout the normal waking hours of the day (Figure 4). Because of the relatively low sample size, however, there are some peaks in the distribution.

Figure 4 Temporal patterns of selected ESM responses

Figure 5 depicts the results of the analysis of ESM responses. The shares are calculated as the average share of responses to each category per person. These results depict that SMS and voice calls are used most for bonding and bridging social capital, but it must be noted that because of the sample size and the small dataset, these results should only be treated as an illustration and validation of our method. Furthermore, there is a high share of Cant say responses in the Facebook service. This means that mobile Facebook may not be used primarily for interpersonal communication, but, for example, for browsing through friends status updates or killing time purposes. This result is also reflected in a related interview study performed with a sub-sample of the one used in this research (Karikoski and Mkinen, 2012).


Figure 5 Analysis of ESM responses (see online version for colours)

5 Discussion

Studying how mobile communication services are used depending on the strength of the tie and how social capital is built is important from a number of perspectives. For example, from a Uses and Gratifications (U&G) research point of view (Katz et al., 1974), it can be argued that different gratifications are sought from SMSs and voice calls, and that the strength of the tie between the people communicating is one factor affecting the gratifications. For instance, people might perceive SMSs as too personal a form of communication when contacting strangers and thus get more gratifications from using voice calls instead. Up until now, the mobile phone related U&G research has focused mostly on the mobile phone as a single entity (see, e.g., Leung and Wei (2000), and Peters and ben Allouch (2005)). U&G research is traditionally performed with mass communication media, however, and thus also other communication research approaches could also be used when interpreting the results, such as communication technology affordances utilised, for example, by Van Cleemput (2012).

The strength of the tie is naturally not the only factor affecting the decision to select or use a given mobile communication service. Others include use context (Karikoski and Soikkeli, 2011), tariff type (Gerpott, 2011), users own customs, possibilities, preferences, service specificities or qualities, availability, and communication norms (de Bailliencourt et al., 2011; Haddon, 2005). Depending on the service analysed, the social networks of the participants may look a lot different. For instance, Karikoski and Nelimarkka (2011) analysed social networks with data acquired from mobile phone communication and an online social media service. They conclude that the mobile phone communication network and the social network based on the social media service are very different in nature. Thus, they recommend, that in order to really understand the social structure behind the group of people under study, multiple datasets depicting the relations between the persons need to be analysed. Because of media multiplexity this multiple dataset SNA becomes even more salient when ties get stronger. All in all there needs to be more discussion in the future related to what kind of a tie is being analysed


(i.e., the validity of the measurements), instead of just doing SNA research because data are available (Nelimarkka and Karikoski, 2012).

Finally, there is the perspective of a mobile operator. The usage interrelationships between the mobile communication services that the mobile operators are offering themselves, or providing access for, are of major importance when planning, for example, marketing and pricing strategy decisions. In general the usage interrelationships can be substitutive, complementary or independent (Karikoski and Luukkainen, 2011) and understanding how, e.g., voice calls and SMSs are interrelated helps the mobile operators design optimal pricing schemes for those services. The interrelationships are even more salient when MI or OTT communication services come into question, since the mobile operators are usually not providing those services themselves, and thus these services can cannibalise their revenues acquired from more traditional services such as voice calls and SMSs. Furthermore, mobile social phonebooks, where multiple communication services are integrated in the mobile devices phonebook, will definitely have an effect on the usage interrelationships in the future (Karikoski and Mkinen, 2012). Operators need to analyse in detail the practical implications that these phonebooks have on the usage interrelationships.

5.1 Limitations and future research

We have identified some limitations regarding our analysis and results, which will be discussed next. First of all, the samples used in the research limit the external validity of the results. As discussed, the samples are biased towards technology-oriented male students in their early twenties. Moreover, the participants in the primary dataset are identified as innovators or early adopters of mobile devices and services (Karikoski, 2012) and thus cannot represent the general population. However, the demographics are similar to the overall demographics of the university, and thus the results might be representative on a university level. Furthermore, it is possible that the usage patterns observed in this kind of a sample reflect those of the general population in the future. In the case of the secondary dataset, the results should only be treated as an illustration and validation of the method, as the results only represent the participants of a single course. In the future, more focus needs to be placed on acquiring representative samples of the population under study. The main challenge remains that the users of advanced handsets, from whom data collection with these methods is possible, tend to be more technology-savvy than the general population.

Assuming that a single individual has only one phone number is also a limitation of our analysis. People may have multiple phone numbers, and thus we would need to have access to the mobile phonebooks of the participants to know which phone numbers refer to which contacts. Thus, the accuracy of the results could be improved. We also came across several limitations that different OSs pose to the data collection. For instance, we were not able to access the messaging observer in iOS devices. To collect a representative sample of participants from multiple OSs, we need to overcome these practical issues. In the future we plan to deploy the ESM measurements to a larger panel to see how the results of the utilised operationalisation conform to the self-reported data. Finally, we hope to see more studies utilising so called big data to compare the social capital properties of different mobile communication services in the future. It would be especially interesting to see how the distributions of contacts from


different communication services conform to the circles of acquaintanceship defined by Hill and Dunbar (2003).

6 Conclusions

In this paper, we presented two studies that analysed bonding and bridging social capital in the context of mobile communication services. As a primary result we report that SMS is used more for bonding social capital purposes than voice calls, and that media multiplexity is more associated with bonding than bridging social capital. SMSs networks are more skewed and concentrated than voice call networks, but the latter seem to conform better to the circles of acquaintanceship framework. As a secondary result, we depict a pilot study utilising experience sampling in studying social capital properties of newer communication services, and present the results as an illustration and validation of our approach. There is no doubt that voice calls and SMSs are the most established means of interpersonal mobile communication (be it of bonding or bridging type or both), and it remains to be seen how newer communication services are used for social capital building purposes. It might be that the newer services will be used more for bonding than bridging purposes as technology may slow relationship decay rate, but be poor for creating new ones (Dunbar, 2010). The findings of this paper contribute to the literature on mobile communication service usage, and are especially significant for communication scholars, and mobile operators in the area of usage interrelationships. Furthermore, the methods utilised in this paper are novel, and can be instructive to other researchers interested in following a similar research approach.

Acknowledgements

The work has been supported by the OtaSizzle and MoMIE research projects, and the Future Internet Graduate School FIGS. The authors wish to thank MobiTrack Innovations Ltd. and Arbitron Mobile Oy for providing the mobile audience measurement platforms. The sponsoring from Nokia and Elisa to this work is also acknowledged. Finally, the consulting from department colleagues Antti Riikonen and Benjamin Finley are highly appreciated.

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