Peer influence in the diffusion of iPhone 3G over a large social network

In this paper, we study the effect of peer influence in the diffusion of the iPhone 3G across a number of communities sampled from a large dataset provided by a major European Mobile carrier in one country. We identify tight communities of users in which peer influence may play a role and use instrumental variables to control for potential correlation between unobserved subscriber heterogeneity and friends' adoption. We provide evidence that the propensity of a subscriber to adopt increases with the percentage of friends who have already adopted. During a period of 11 months, we estimate that 14 percent of iPhone 3Gs sold by this carrier were due to peer influence. This result is obtained after controlling for social clustering, gender, previous adoption of mobile Internet data plans, ownership of technologically advanced handsets, and heterogeneity in the regions where subscribers move during the day and spend most of their evenings. This result remains qualitatively unchanged when we control for changes over time in the structure of the social network. We provide results from several policy experiments showing that, with this level of effect of peer influence, the carrier would have hardly benefitted from using traditional marketing strategies to seed the iPhone 3G to benefit from viral marketing.info:eu-repo/semantics/publishedVersio

Asymmetric Peer Influence in Smartphone Adoption in a Large Mobile Network

Understanding adoption patterns of smartphones is of vital importance to telecommunication managers in today’s highly dynamic mobile markets. In this paper, we leverage the network structure and specific position of each individual in the social network to account for and measure the potential heterogeneous role of peer influence in the adoption of the iPhone 3G. We introduce the idea of coreperiphery as a meso-level organizational principle to study the social network, which complements the use of centrality measures derived from either global network properties (macro-level) or from each individual\u27s local social neighbourhood (micro-level). Using millions of call detailed records from a mobile network operator in one country for a period of eleven months, we identify overlapping social communities as well as core and periphery individuals in the network. Our empirical analysis shows that core users exert more influence on periphery users than vice versa. Our findings provide important insights to help identify influential members in the social network, which is potentially useful to design optimal targeting strategies to improve current network-based marketing practices

The Role of Peer Influence in Churn in Wireless Networks

Subscriber churn remains a top challenge for wireless carriers. These carriers need to understand the determinants of churn to confidently apply effective retention strategies to ensure their profitability and growth. In this paper, we look at the effect of peer influence on churn and we try to disentangle it from other effects that drive simultaneous churn across friends but that do not relate to peer influence. We analyze a random sample of roughly 10 thousand subscribers from large dataset from a major wireless carrier over a period of 10 months. We apply survival models and generalized propensity score to identify the role of peer influence. We show that the propensity to churn increases when friends do and that it increases more when many strong friends churn. Therefore, our results suggest that churn managers should consider strategies aimed at preventing group churn. We also show that survival models fail to disentangle homophily from peer influence over-estimating the effect of peer influence.Comment: Accepted in Seventh ASE International Conference on Social Computing (Socialcom 2014), Best Paper Award Winne

Quantifying Social Influence in an Online Music Community

This paper studies two types of social influence in an online music community: observational learning influence based on aggregate consumption data, and social network influence based on music consumption by friends in social proximity. The analysis uses a variety of empirical methods, applied to highly granular user listening and “favoriting” behavior on the largest music blog aggregator site. Our analysis finds positive evidence for observational learning effects, but no evidence for social network influence. Thus, any social influence in this music context is channeled through popularity cues offered by aggregate consumption statistics, rather than contact and communication with friends in close social proximity. We discuss implications of these results for research and practice

Understanding Moderators of Peer Influence for Engineering Viral Marketing Seeding Simulations and Strategies

Seeding as an emerging viral marketing strategy requires a better understanding on how various contextual factors that embedded in social networks affect peer influence and product diffusion. Realistic simulations for seeding need to incorporate empirical insights about the complexities (various moderators) and dynamics (temporal changes) of peer influence by analyzing real-world data. We analyze the impacts of peer influence moderators in a large-scale phone call network of 0.48 million customers with 364 million calls and 3.9 million video-on-demand purchases, to design empirical models and engineer data-driven simulations of product diffusion, as well as developing and evaluating seeding strategies. We intend to contribute to existing research by 1) enriching the theoretical and empirical understanding of peer influence moderators for stakeholders, 2) combining econometric models and analyses with data-driven simulations towards a complex system approach for devising and evaluating effective seeding strategies in different scenarios

Mobile Devices in Social Contexts

The development of mobile devices has occurred with unprecedented pace since the late nineties, and the increase of generic services has proliferated in most developed countries, driven by the expanding technological capabilities and performance of mobile platforms. This dissertation investigates how consumer objectives, orientation, and behavior can aid in explaining the adoption and use of a new type of mobile devices: “app phones”. This dissertation focuses its effort on two focal influences of adoption and use; social influences and competing forces. Through a qualitative case study and field study this dissertation explores early adoption and use of iPhones. The case study is a one-shot cross-sectional case study that investigates five individuals, related through the same social network, and their decision to adopt an iPhone prior to its release in Denmark. This adoption decision engenders high switching costs as adopters lack references to imitate and need skills to unlock and jailbreak their iPhones to make them work on Danish networks. The specific purpose of the case study is to explore how social influences impact mobile users’ early adoption decisions, as it is well known in the literature that people with similar characteristics, tastes, and beliefs often associate in the same social networks and, hence, influence each other. The field study is cross-sectional with multiple snapshots and explores fifteen individuals part of the same university study, who receives an iPhone for a period of seven months short after its release in Denmark. The specific purpose of the field study is to explore how competing forces of iPhone usage influence assimilation, i.e. the degree to which the iPhone is used, over time. The dissertation, furthermore, contains a systematic literature review. The main contribution of this dissertation is reported through four articles and is directed at both academic researchers and practitioners. The study emphasizes the importance of social influences and competing forces in the investigation of adoption and use of certain mobile devices

The effect of friends’ churn on consumer behavior in mobile networks

We study how consumers decide which tariff plan to choose and whether to churn when their friends churn in the mobile industry. We develop a theoretical model showing conditions under which users remain with their carrier and conditions under which they churn when their friends do. We then use a large and rich anonymized longitudinal panel of call detailed records to characterize the consumers’ path to death with unprecedented level of detail. We explore the structure of the network inferred from these data to derive instruments for friends’ churn, which is typically endogenous in network settings. This allows us to econometrically identify the effect of peer influence in our setting. On average, we find that each additional friend that churns increases the monthly churn rate by 0.06 percent. The observed monthly churn rate across our dataset is 2.15 percent. We also find that firms introducing the pre-paid tariff plans that charge the same price to call users inside and outside the carrier help retain consumers that would otherwise churn. In our setting, without this tariff plan the monthly churn rate could have been as high as 8.09 percent. We perform a number of robustness checks, in particular to how we define friends in the social graph, and show that our results remain unchanged. Our paper shows that the traditional definition of customer lifetime value underestimates the value of consumers and, in particular, that of consumers with more friends due to the effect of contagious churn and, therefore, managers should actively take into account the structure of the social network when prioritizing whom to target during retention campaigns.info:eu-repo/semantics/acceptedVersio

Unconsciously Influential. Understanding sociotechnical Influence on social media

Over the last two decades, the rise of social media platforms such as Instagram, YouTube, and TikTok has sparked a global shift in commercial practices worldwide. People are exposed to and influenced by massive amounts of commercial content carefully and strategically integrated into these platforms’ social content. In addition, due to network structures, people’s engagement in the form of likes, comments, and simply viewing content results in the influence of people within and outside their network. In this study, we adopt a sociotechnical perspective and study the interplay between social and technical components in how influence is exercised on social media. Specifically, we identify the actors involved in the influence of commercial content and analyse how they exercise their influence for commercial purposes. Based on our findings and analysis, we present three contributions to Information systems literature: (1) how people have become unconsciously influential in spreading commercial content, which is the premise for social media commercial success, (2) how people’s social and commercial lives and contents are increasingly intertwined and (3) how this interweaving effect removes peoples’ ability to reflect on the content they engage with critically. Our study draws attention to the societal outcomes caused by technologies in practice

Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data

Through seven publications this dissertation shows how anonymized mobile phone data can contribute to the social good and provide insights into human behaviour on a large scale. The size of the datasets analysed ranges from 500 million to 300 billion phone records, covering millions of people. The key contributions are two-fold: 1. Big Data for Social Good: Through prediction algorithms the results show how mobile phone data can be useful to predict important socio-economic indicators, such as income, illiteracy and poverty in developing countries. Such knowledge can be used to identify where vulnerable groups in society are, reduce economic shocks and is a critical component for monitoring poverty rates over time. Further, the dissertation demonstrates how mobile phone data can be used to better understand human behaviour during large shocks in society, exemplified by an analysis of data from the terror attack in Norway and a natural disaster on the south-coast in Bangladesh. This work leads to an increased understanding of how information spreads, and how millions of people move around. The intention is to identify displaced people faster, cheaper and more accurately than existing survey-based methods. 2. Big Data for efficient marketing: Finally, the dissertation offers an insight into how anonymised mobile phone data can be used to map out large social networks, covering millions of people, to understand how products spread inside these networks. Results show that by including social patterns and machine learning techniques in a large-scale marketing experiment in Asia, the adoption rate is increased by 13 times compared to the approach used by experienced marketers. A data-driven and scientific approach to marketing, through more tailored campaigns, contributes to less irrelevant offers for the customers, and better cost efficiency for the companies.Comment: 166 pages, PHD thesi

An Empirical Analysis of Usage Behavior by Content Type and Behavioral Orientation on a Mobile Music App

Recently, many mobile apps have made viable new business models such as in-app purchase. In this paper, we quantify how mobile app usage relates to the unique characteristics of behavioral orientations and content types, focusing on the interrelationship among content usage in the context of in-app purchase. Using a large-scale dataset of individual content usage in a particular music mobile app, we build a simultaneous equation panel data model to examine dynamic interdependent usage of mobile app. We find a positive temporal effect of self-oriented content usage (download) on other-oriented content usage (gift), based on behavioral orientation, and also a temporal interdependence between external (ringtone) and internal usage (mp3) based on types of content. We also find that the 4G communications standard increases content usage in this mobile app. These findings provide useful insights for mobile app developers, mobile network operators, content providers, and mobile device manufacturers