Marketing Impact on Diffusion in Social Networks

The paper proposes a way to add marketing into the standard threshold model of social networks. Within this framework, the paper studies logical properties of the influence relation between sets of agents in social networks. Two different forms of this relation are considered: one for promotional marketing and the other for preventive marketing. In each case a sound and complete logical system describing properties of the influence relation is proposed. Both systems could be viewed as extensions of Armstrong's axioms of functional dependency from the database theory

Affinity Paths and Information Diffusion in Social Networks

Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different content. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants' involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the \emph{Affinity Paths} observed in real information diffusion cascades.Comment: 11 pages, 7 figure

Social Media Networks: The Social Influence of Sentiment Content in Online Conversations on Dynamic Patterns of Adoption and Diffusion

The current study is focusing on diffusion and adoption of new digital artifacts. The goal is to explore the social role of user-generated content (UGC) during the diffusion process of digital artifacts in the context of online social networks. The study spans a wide range of analytics methods and tools such as predictive modeling, latent sentiment analysis, data retrieval, and other tools of time-series analysis & visualization. Data collection is conducted on 260 new digital products and more than 105 thousand social network nodes. Results of the study provide a deeper insight into the influence of textual UGC sentiment on new product diffusion and how such a web system (i.e.: online social networks) can help to enable a process of value co-creation. The overall finding shows that Volume of Post and UGC Sentiment have a dynamic impact on Diffusion (Adoption Rate) of digital products. But, the relationships among them depend on certain situations. Specifically, UGC Sentiment has a dynamic impact on Adoption Rate in the early stage of the diffusion process. That is UGC Sentiment and Adoption Rate have a reciprocal relationship during the early stage. However, this relationship was faded out in the later stage. Volume of Post has a positive impact on Adoption Rate throughout the process. Both UGC Sentiment and Volume of Post are also more likely to influence on a single-generation and successful product than a multiple-generation product. Surprisingly, Depth of Post and Ratings did not play a significant role in the diffusion process. The study sheds light on the crowding power and the long-tail effect in online social networks. Findings also offer valuable implications for organizations to set up their strategic vision in terms of targeted marketing, customer relationship management, and information dissemination

Network effects in mass communication - an analysis of information diffusion in markets

In this thesis we investigate the diffusion of information like news, announcements, and commercials in social networks. Such information propagates through a mix of mass communication and interpersonal communication. For example, people who watch a TV spot about a new car will discuss it with their friends. Both communication methods influence the awareness, preferences, and opinions that people display towards certain topics, products, and services. The effects of mass and inter-personal communication on the diffusion process have been studied intensively in several areas, for example, in sociology, economics, social psychology, political science, and marketing. Most of these studies highlight the role of inter-personal relation structures, that is, the network of social ties, in the diffusion process. However, a concise diffusion model that quantifies the effects of social networks and helps to improve mass communication towards structured populations is still in demand. Our purpose is first to analyse the drivers of social networks, then to model the diffusion of information on social networks, and finally to quantify the network effects on the diffusion process. We describe and construct social networks as graphs and present anthropological, psychological, and random factors that shape them. Based on one of these factors, structural balancing, we propose an evolutionary model of social networks, suggesting that the structure of social networks can change dramatically over time. For modelling diffusion processes on social networks, we follow a two-step procedure. We first combine three different generation methods, the generalised random graph, the small-world model, and a third method (random graph with a given assortment structure) to design realistic networks. Then we simulate the propagation of information on these networks. As the computer requirements for such simulations can be expensive, we introduce an efficient computer algorithm that is widely applicable to complex diffusion studies in markets, organisations, and societies. One result of the simulations is a robust closed-form approximation to the diffusion's trajectory in networks. Such an approximation allows marketing and PR managers to predict aggregate market outcomes such as the popularity of a commercial through surveys prior to the launch of a promotional campaign. The simulations also indicate the impact of the network's structure on the diffusion. To measure the network effects on the propagation of information, we run regression analyses with the communication intensity and the different network features as explanatory variables. These network features are the degree distribution, the transitivity (clustering), degree correlation, and the average path length. The regressions show, above all, that network effects are conditional on the intensity of mass communication: the less intensive mass communication, the more important become network effects. For mass communication typical in marketing and PR, the network structure can have a strong impact on the diffusion process. The regressions quantify the respective contribution of each network feature on the diffusion process over time. Our findings confirm and partly reconcile contradictionary results of comparable studies in epidemics and sociology. Finally, our analysis allows us to prioritise different network effects. This can be useful in various situations, for example, when estimating a diffusion process with incomplete network data

Impacts of Tie Characteristics on Online Viral Diffusion

To explain the viral diffusion process, most of previous studies focus on the structure of social networks and the existence of the hub. We extend the scope of analysis from a single node to a tie between the sender and the receiver to explain the impact of tie characteristics on the viral diffusion measured by its speed (i.e., how quickly) and by its volume (i.e., how much viral). Based on our analysis results using a viral marketing data of 30,035 sender-receiver ties, we find that (1) the more heterogeneous the tie is, the quicker the response occurs; and (2) heavy viral generators tend to be connected to each other. Taken together, this research broadens the study of online viral diffusion by applying tie characteristics in terms of the volume and speed of the viral

INFORMATION TRANSPARENCY AND USER BEHAVIOR IN EMERGING ONLINE MARKETPLACES: EMPIRICAL STUDIES OF SOCIAL MEDIA AND OPEN INNOVATION MARKETS

Web 2.0 and social media have significantly increased the amount of information available to users not only about firms and their offerings, but also about the activities of other individuals in their networks and markets. It is widely acknowledged that this increased availability of information is likely to influence a user's behavior and choices. However, there are very few systematic studies of how such increased information transparency influences user behavior in emerging marketplaces. My dissertation seeks to examine the impact of increased information transparency - particularly, information about other individuals - in two emerging platforms. The first essay in my dissertation compares online "social" marketing on Facebook with "non-social" marketing and examines their relative impacts on the likelihood of adoption, usage and diffusion of an "App". While social marketing - wherein a user gets to see which of her other friends have also "liked" the product being marketed- is one of the fastest growing online marketing formats, there are hardly any studies that have examined the value of the social aspect of such marketing. I find that social marketing is associated with increased app adoption, usage, and diffusion as compared to non-social marketing. The study also uncovers interesting tradeoffs between the effects of different types of "social" information on user behavior outcomes. The second essay examines the behavior of contestants in an open innovation design marketplace, wherein firms seek solutions from a crowd through an online contest. The study examines how the availability of information about other contestants as well as the availability of feedback information provided to others by the contest holder, impacts a focal contestant's behavior and outcomes. I find that contestants adopt different strategic behaviors that increase their odds of winning the contest under the different information-transparency regimes. The findings have interesting implications for the design of online contests and crowdsourcing markets. Overall, my dissertation provides a deeper understanding of how the visibility of different types of information in online platforms impacts individual behaviors and outcomes

Social networks and communication behaviour underlying smart home adoption in the UK

Consumer-facing digital innovations with the potential to reduce carbon emissions often exist in small market niches and their impact has been limited thus far. Using the established Diffusion of Innovations theory which considers interpersonal communication amongst social networks to be a vital mechanism for exchanging information, we conducted an online survey in the UK to investigate the social networks and communication behaviours of adopters and non-adopters of three different energy saving smart home technologies. Applying social network analysis and statistically testing hypotheses, our results reveal the potential social barriers to the diffusion of information, with social network structure and characteristics creating obstacles. This research provides necessary insights into real early adopters, confirms the importance of focussing research on the often-neglected social elements of diffusion theory and helps identify marketing strategies and policy actions using social mechanisms to accelerate a low carbon transition

Policy Issues of e-Commerce Technology Diffusion in Southeast Nigeria: The Case of Small Scale Agribusiness

The benefits brought about by the emergence of e-commerce, e-business and other Information Communication Technologies (ICTs) applications have not been fully explored in the developing economies of the world. The less developed economies are still struggling to catch up with ICT application as opposed to its heavy deployment in the developed economies. Empirical evidence suggests that ICTs and other related technologies are increasingly emerging in the communities of the developing economies such as Nigeria. Rural actors engaged in the Agricultural industries (Agribusiness) feel that the implementation of ICTs can influence the development of new business processes and the way existing processes are organised. In the Southeast of Nigeria, which is a typical example of a less developed community, the impact of e-business technologies has yet to be determined. This paper identifies two classical traditional agribusiness supply chains and hence reports on the impact of e-commerce technology diffusion along the equilibrium of the supply chains, focusing on the elimination of intermediary actors from the chain. It provides an assessment of the Governments’ policies and strategies on e-commerce adoption for the sustainability of small-scale agricultural businesses. The paper examines the politics surrounding ICT implementations by actors engaged in the agribusiness sector. This research has motivated The South East State Government, in collaboration with the Federal Government, to give closer attention to their earlier policy of making Nigeria an ICT-enabled country