Social Influence and Multiple Choices: Evidence from Virtual Products Adoption

This study examines the effect of social influence on the adoption of multiple products for the same category (basic choice and upgrade choice). Based on a unique large-scale data set from an online game community and a competing-risk model, the results show that social influence could significantly elevate users’ product adoption on both products, but the effect on users’ upgrade product adoption is greater than that on users’ basic product adoption. Furthermore, users with middle social status are more susceptible to others’ upgrade product adoption than users with low or high social status. In addition, network density positively moderates users’ susceptible to the effect of social influence in upgrade product adoption decisions. These results provide pivotal theoretical and practical implications and should be considered by marketers that aim to predict and affect users’ adoption of multiple products

Social network externalities and price dispersion in online markets.

Ample empirical studies in the e-commerce literature have documented that the price dispersion in online markets is 1) as large as that in offline markets, 2) persistent across time, and 3) only partially explained by observed eretailers’ attributes. Buying on the internet market is risky to consumers. First of all, consumers and the products they purchase are separated in time. There is a delay in time between the time consumers pay and the time they receive the orders. Second, consumers and the products they purchase are separated in space. Consumers cannot physically touch or examine the products at the point of purchase. As such, online markets involve an adoption process based on the interaction of consumers’ experiences in the form of references, recommendations, word of mouth, etc. The social network externalities introduced by the interaction of consumer’s experiences reduces the risk of seller choice and allows some sellers to charge higher prices for even homogeneous products. This research aims to study online market price dispersion from the social network externalities perspective. Our model posits that consumers are risk averse and assess the risk of having a satisfactory transaction from a seller based on the two dimensions of the seller’s social network externalities: quantity externality (i.e., the size of the seller’s social network) and quality externality (i.e., the satisfactory transaction probability of the seller’s social network). We further investigate the moderating effect of product value for consumers on the impact of social network externality on online market price dispersion. Our model yields several important propositions which we empirically test using data sets collected from eBay. We found that 1) both quantity externality and quality externality of social network are salient in driving online price dispersion, and 2) the salience of social network externality is stronger for purchase behavior in higher value product categories.network externalities, price dispersion, online markets, word of mouth

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

Viral Marketing

Viral marketing refers to the application of traditional word-of-mouth marketing to the online environment. Originally developed by Steve Jurvetson and Tim Draper in 1997, the term is used to describe online techniques designed to generate peer-to-peer conversation and buzz about a company, brand, product, or service. A message that contains something of value or appeal is diffused throughout members of a given social network, and ideally across networks, in an exponential fashion, much like the spread of a virus in medical parlance. The rapid adoption of digital and social media tools by politicians has led to an increased visibility and impact of viral marketing efforts in political campaigns, particularly since the 2008 U.S. presidential campaign. Common viral marketing techniques include, but are not limited to, a systematic and strategic deployment of viral e-mail messages, You Tube videos, blogs, microblogs (such as Twitter), social networking Web sites, podcasts, online games, and text messages

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Identifying Social Influence in Networks Using Randomized Experiments

The recent availability of massive amounts of networked data generated by email, instant messaging, mobile phone communications, micro blogs, and online social networks is enabling studies of population-level human interaction on scales orders of magnitude greater than what was previously possible.1\u272 One important goal of applying statistical inference techniques to large networked datasets is to understand how behavioral contagions spread in human social networks. More precisely, understanding how people influence or are influenced by their peers can help us understand the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors such as smoking and exercise, the productivity of information workers, and whether particular individuals in a social network have a disproportion ate amount of influence on the system

Social network externalities and price dispersion in online markets

Ample empirical studies in the e-commerce literature have documented that the price dispersion in online markets is 1) as large as that in offline markets, 2) persistent across time, and 3) only partially explained by observed eretailers’ attributes. Buying on the internet market is risky to consumers. First of all, consumers and the products they purchase are separated in time. There is a delay in time between the time consumers pay and the time they receive the orders. Second, consumers and the products they purchase are separated in space. Consumers cannot physically touch or examine the products at the point of purchase. As such, online markets involve an adoption process based on the interaction of consumers’ experiences in the form of references, recommendations, word of mouth, etc. The social network externalities introduced by the interaction of consumer’s experiences reduces the risk of seller choice and allows some sellers to charge higher prices for even homogeneous products. This research aims to study online market price dispersion from the social network externalities perspective. Our model posits that consumers are risk averse and assess the risk of having a satisfactory transaction from a seller based on the two dimensions of the seller’s social network externalities: quantity externality (i.e., the size of the seller’s social network) and quality externality (i.e., the satisfactory transaction probability of the seller’s social network). We further investigate the moderating effect of product value for consumers on the impact of social network externality on online market price dispersion. Our model yields several important propositions which we empirically test using data sets collected from eBay. We found that 1) both quantity externality and quality externality of social network are salient in driving online price dispersion, and 2) the salience of social network externality is stronger for purchase behavior in higher value product categories

The role of values in collaborative consumption: insights from a product-service system for lending and borrowing in the UK

Collaborative consumption is an emerging socio-economic model based on sharing, renting, gifting, bartering, swapping, lending and borrowing. Made possible through community interaction and, increasingly, use of network technologies, these alternative and more sustainable ways of consuming have attracted growing attention for their potential to prevent new purchases, intensify the use of idle assets and promote reuse of possessions that are no longer wanted. Nonetheless, the uptake of Product- Service Systems (PSSs) that enable collaborative consumption is still very limited. This paper investigates how consumers' values can influence the acceptance, adoption and diffusion of collaborative consumption. It reviews two theoretical frameworks used to understand pro-environmental behaviour, social psychological models of behaviour and social practice theory. Coming from contrasting disciplinary perspectives, these approaches conceptualise values differently. The paper evaluates the possibility of resolving these differences through a mixed methods study. It examines values empirically through a case study of Ecomodo, a UK-based online marketplace where people can lend and borrow each other's objects, spaces and skills, and present the results of a quantitative study which identified and measured value priorities among Ecomodo users through Schwartz's Portrait Value Questionnaire. It concludes with a discussion of the role of values in relation to the introduction and scaling up of PSSs that enable collaborative consumption