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- All Subjects: Social networks
- Creators: Grebitus, Carola
Description
I compare the effect of anonymous social network ratings (Yelp.com) and peer group recommendations on restaurant demand. I conduct a two-stage choice experiment in which restaurant visits in the first stage are informed by online social network reviews from Yelp.com, and visits in the second stage by peer network reviews. I find that anonymous reviewers have a stronger effect on restaurant preference than peers. I also compare the power of negative reviews with that of positive reviews. I found that negative reviews are more powerful compared to the positive reviews on restaurant preference. More generally, I find that in an environment of high attribute uncertainty, information gained from anonymous experts through social media is likely to be more influential than information obtained from peers.
ContributorsTiwari, Ashutosh (Author) / Richards, Timothy J. (Thesis advisor) / Qiu, Yueming (Committee member) / Grebitus, Carola (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Descriptionyour words
ContributorsWang, Dan, M.S (Author) / Grebitus, Carola (Thesis advisor) / Schroeter, Christiane (Committee member) / Manfredo, Mark (Committee member) / Hughner, Renee (Committee member) / Arizona State University (Publisher)
Created2014
Description
It is well understood that innovation drives productivity growth in agriculture. Innovation, however, is a process that involves activities distributed throughout the supply chain. In this dissertation I investigate three topics that are at the core of the distribution and diffusion of innovation: optimal licensing of university-based inventions, new variety adoption among farmers, and consumers’ choice of new products within a social network environment.
University researchers assume an important role in innovation, particularly as a result of the Bayh-Dole Act, which allowed universities to license inventions funded by federal research dollars, to private industry. Aligning the incentives to innovate at the university level with the incentives to adopt downstream, I show that non-exclusive licensing is preferred under both fixed fee and royalty licensing. Finding support for non-exclusive licensing is important as it provides evidence that the concept underlying the Bayh-Dole Act has economic merit, namely that the goals of university-based researchers are consistent with those of society, and taxpayers, in general.
After licensing, new products enter the diffusion process. Using a case study of small holders in Mozambique, I observe substantial geographic clustering of new-variety adoption decisions. Controlling for the other potential factors, I find that information diffusion through space is largely responsible for variation in adoption. As predicted by a social learning model, spatial effects are not based on geographic distance, but rather on neighbor-relationships that follow from information exchange. My findings are consistent with others who find information to be the primary barrier to adoption, and means that adoption can be accelerated by improving information exchange among farmers.
Ultimately, innovation is only useful when adopted by end consumers. Consumers’ choices of new products are determined by many factors such as personal preferences, the attributes of the products, and more importantly, peer recommendations. My experimental data shows that peers are indeed important, but “weak ties” or information from friends-of-friends is more important than close friends. Further, others regarded as experts in the subject matter exert the strongest influence on peer choices.
University researchers assume an important role in innovation, particularly as a result of the Bayh-Dole Act, which allowed universities to license inventions funded by federal research dollars, to private industry. Aligning the incentives to innovate at the university level with the incentives to adopt downstream, I show that non-exclusive licensing is preferred under both fixed fee and royalty licensing. Finding support for non-exclusive licensing is important as it provides evidence that the concept underlying the Bayh-Dole Act has economic merit, namely that the goals of university-based researchers are consistent with those of society, and taxpayers, in general.
After licensing, new products enter the diffusion process. Using a case study of small holders in Mozambique, I observe substantial geographic clustering of new-variety adoption decisions. Controlling for the other potential factors, I find that information diffusion through space is largely responsible for variation in adoption. As predicted by a social learning model, spatial effects are not based on geographic distance, but rather on neighbor-relationships that follow from information exchange. My findings are consistent with others who find information to be the primary barrier to adoption, and means that adoption can be accelerated by improving information exchange among farmers.
Ultimately, innovation is only useful when adopted by end consumers. Consumers’ choices of new products are determined by many factors such as personal preferences, the attributes of the products, and more importantly, peer recommendations. My experimental data shows that peers are indeed important, but “weak ties” or information from friends-of-friends is more important than close friends. Further, others regarded as experts in the subject matter exert the strongest influence on peer choices.
ContributorsFang, Di (Author) / Richards, Timothy J. (Thesis advisor) / Bolton, Ruth N (Committee member) / Grebitus, Carola (Committee member) / Manfredo, Mark (Committee member) / Arizona State University (Publisher)
Created2015