Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word of mouth peer influence and social contagion by incorporating viral features into their products. Word of mouth is generally considered to more effectively promote peer influence and contagion when it is personalized and active. Unfortunately, econometric identification of peer influence is non-trivial. We therefore use a randomized field experiment to test the effectiveness of passive-broadcast and active-personalized viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. Surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion, while adding active-personalized viral messaging only generates an additional 98% increase in contagion. Although active-personalized messaging is more effective per message and is correlated with more user engagement and product use, it is used less often and therefore generates less total peer adoption in the network than passive-broadcast messaging

Experimental auctions, collective induction and choice shift: willingness-to-pay for rice quality in Senegal

We propose a collective induction treatment as an aggregator of information and preferences, which enables testing whether consumer preferences for food quality elicited through experimental auctions are robust to aggregation. We develop a two-stage estimation method based on social judgement scheme theory to identify the determinants of social influence in collective induction. Our method is tested in a market experiment aiming to assess consumers willingness-to-pay for rice quality in Senegal. No significant choice shift was observed after collective induction, which suggests that consumer preferences for rice quality are robust to aggregation. Almost three quarters of social influence captured by the model and the variables was explained by social status, market expertise and information

Network-Based Marketing: Identifying Likely Adopters via Consumer Networks

Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.NYU, Stern School of Business, IOMS, Center for Digital Economy Researc

Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks

Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption

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

Understanding Online Reputation Of Mediterranean Destinations

Destination managers are investing considerable efforts (i.e. time, resources and money) to market their destinations on the internet, often not considering the fact that unofficial information sources are gaining more and more popularity among internet users. Long tail players (such as blogs, wikis, reviews, etc.) are actually appearing in the ranking of search engines, spreading almost the same contents as the official sources, but with very different strategies, goals and styles. Starting from a log files analysis of a given Mediterranean destination, nine keywords have been used to perform search activities on two major search engines (Google and Yahoo!). A content analysis study has been performed on search results in order to examine topics and arguments of the retrieved results, which are shaping the web reputation of the destination. The paper shows that destinations need to manage their brand and online reputation holistically, by listening all players providing information about them, and trying to leverage on their contributions

The role of ICT, eWOM and guest characteristics in loyalty

Purpose - This study aims to examine how technologies contribute to consumer loyalty in the tourist industry. To achieve this objective, information and communication technology (ICT) development and electronic word-of-mouth (eWOM) are analysed to explore their direct and indirect effects on satisfaction and loyalty dimensions. The moderating role of customer characteristics (personal and experience-related variables) is also considered to study the complex relationship between satisfaction and loyalty. Design/methodology/approach - A quantitative study based on a questionnaire structured was developed. The survey was conducted with 386 guests from Spanish hotels. SEM methodology is applied to estimate the structural equation model and multi-group analysis. Findings - Results confirm significant relationships in the sequence 'ICT advancement-satisfaction with ICT-satisfaction with hotel-loyalty', the mediating effect of eWOMand the moderating effects of the customer characteristics. Practical implications - ICT can be a key element to improve loyalty and differentiate from competitors. Managers should recognise that customers will have different loyalty behaviours according to their personal characteristics and type of experience. Originality/value - This paper contributes to the recent and still scanty research line on ICT advancement from the consumer perspective. The novelty lies in the relationships between ICT, satisfaction and loyalty in hotels with particular attention to WOM (both personal and electronic) and the inclusion of different moderating variables

Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies

This research provides an empirical test of the “Twitter effect,” which postulates that microblogging word of mouth (MWOM) shared through Twitter and similar services affects early product adoption behaviors by immediately disseminating consumers’ post-purchase quality evaluations. This is a potentially crucial factor for the success of experiential media products and other products whose distribution strategy relies on a hyped release. Studying the four million MWOM messages sent via Twitter concerning 105 movies on their respective opening weekends, the authors find support for the Twitter effect and report evidence of a negativity bias. In a follow-up incident study of 600 Twitter users who decided not to see a movie based on negative MWOM, the authors shed additional light on the Twitter effect by investigating how consumers use MWOM information in their decision-making processes and describing MWOM’s defining characteristics. They use these insights to position MWOM in the word-of-mouth landscape, to identify future word-of-mouth research opportunities based on this conceptual positioning, and to develop managerial implications

Detecting and interpreting financial stress in the euro area

There is a need to find better models and indicators for large disruptive events, not least in order to be more prepared and mitigate their effects. In this paper we take a step in this direction and discuss the performance of a financial stress indicator with a specific focus on the euro area. As far as we know, our indicator is the first attempt to develop an indicator of financial stress with a specific focus on the euro area. It is also the first to exploit the information contained in central bank communication to help measure stress in financial markets. For use in real time, the indicator is able to efficiently extract information from an otherwise noisy signal and provide information about the level of stress in the markets. JEL Classification: E44, E50, G10behavioural finance, central bank communication, Financial stress, Leading Indicator, logit distribution