Matching Items (8)
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- All Subjects: Consumer Behavior
- All Subjects: Social networks
- Genre: Academic theses
- 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
It is well understood that decisions made under uncertainty differ from those made without risk in important and significant ways. Yet, there is very little research into how uncertainty manifests itself in the most ubiquitous of decision-making environments: Consumers' day-to-day decisions over where to shop, and what to buy for their daily grocery needs. Facing a choice between stores that either offer relatively stable "everyday low prices" (EDLP) or variable prices that reflect aggressive promotion strategies (HILO), consumers have to choose stores under price-uncertainty. I find that consumers' attitudes toward risk are critically important in determining store-choice, and that heterogeneity in risk attitudes explains the co-existence of EDLP and HILO stores - an equilibrium that was previously explained in somewhat unsatisfying ways. After choosing a store, consumers face another source of risk. While knowing the quality or taste of established brands, consumers have very little information about new products. Consequently, consumers tend to choose smaller package sizes for new products, which limits their exposure to the risk that the product does not meet their prior expectations. While the observation that consumers purchase small amounts of new products is not new, I show how this practice is fully consistent with optimal purchase decision-making by utility-maximizing consumers. I then use this insight to explain how manufacturers of consumer packaged goods (CPGs) respond to higher production costs. Because consumers base their purchase decisions in part on package size, manufacturers can use package size as a competitive tool in order to raise margins in the face of higher production costs. While others have argued that manufacturers reduce package sizes as a means of raising unit-prices (prices per unit of volume) in a hidden way, I show that the more important effect is a competitive one: Changes in package size can soften price competition, so manufacturers need not rely on fooling consumers in order to pass-through cost increases through changes in package size. The broader implications of consumer behavior under risk are dramatic. First, risk perceptions affect consumers' store choice and product choice patterns in ways that can be exploited by both retailers and manufacturers. Second, strategic considerations prevent manufacturers from manipulating package size in ways that seem designed to trick consumers. Third, many services are also offered as packages, and also involve uncertainty, so the effects identified here are likely to be pervasive throughout the consumer economy.
ContributorsYonezawa, Koichi (Author) / Richards, Timothy J. (Thesis advisor) / Grebitus, Carola (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Created2014
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
Private labels command a growing share of food retailers' shelf space. In this dissertation, I explain this phenomenon as resulting from "umbrella branding," or the ability of a single brand to reach across categories. Conceptually, I define umbrella branding as a behavioral attribute that describes a shopper's tendency to ascribe a performance bond to a brand, or to associate certain performance characteristics to a private label brand, across multiple categories. In the second chapter, I describe the performance bond theory in detail, and then test this theory using scanner data in the chapter that follows. Because secondary data has limitations for testing behavioral theories, however, I test the performance bond theory of umbrella branding using a laboratory experiment in the fourth chapter. In this chapter, I find that households tend to transfer their perception of private label performance across categories, or that a manifestation of umbrella branding behavior can indeed explain private labels' success. In the fifth chapter, I extend this theory to compare umbrella branding in international markets, and find that performance transference takes its roots in consumers' cultural backgrounds. Taken together, my results suggest that umbrella branding is an important behavioral mechanism, and one that can be further exploited by retailers across any consumer good category with strong credence attributes.
ContributorsTheron, Sophie (Author) / Richards, Timothy J. (Thesis advisor) / Grebitus, Carola (Committee member) / Hughner, Renee (Committee member) / Arizona State University (Publisher)
Created2014
Description
The popularity of social media has generated abundant large-scale social networks, which advances research on network analytics. Good representations of nodes in a network can facilitate many network mining tasks. The goal of network representation learning (network embedding) is to learn low-dimensional vector representations of social network nodes that capture certain properties of the networks. With the learned node representations, machine learning and data mining algorithms can be applied for network mining tasks such as link prediction and node classification. Because of its ability to learn good node representations, network representation learning is attracting increasing attention and various network embedding algorithms are proposed.
Despite the success of these network embedding methods, the majority of them are dedicated to static plain networks, i.e., networks with fixed nodes and links only; while in social media, networks can present in various formats, such as attributed networks, signed networks, dynamic networks and heterogeneous networks. These social networks contain abundant rich information to alleviate the network sparsity problem and can help learn a better network representation; while plain network embedding approaches cannot tackle such networks. For example, signed social networks can have both positive and negative links. Recent study on signed networks shows that negative links have added value in addition to positive links for many tasks such as link prediction and node classification. However, the existence of negative links challenges the principles used for plain network embedding. Thus, it is important to study signed network embedding. Furthermore, social networks can be dynamic, where new nodes and links can be introduced anytime. Dynamic networks can reveal the concept drift of a user and require efficiently updating the representation when new links or users are introduced. However, static network embedding algorithms cannot deal with dynamic networks. Therefore, it is important and challenging to propose novel algorithms for tackling different types of social networks.
In this dissertation, we investigate network representation learning in social media. In particular, we study representative social networks, which includes attributed network, signed networks, dynamic networks and document networks. We propose novel frameworks to tackle the challenges of these networks and learn representations that not only capture the network structure but also the unique properties of these social networks.
Despite the success of these network embedding methods, the majority of them are dedicated to static plain networks, i.e., networks with fixed nodes and links only; while in social media, networks can present in various formats, such as attributed networks, signed networks, dynamic networks and heterogeneous networks. These social networks contain abundant rich information to alleviate the network sparsity problem and can help learn a better network representation; while plain network embedding approaches cannot tackle such networks. For example, signed social networks can have both positive and negative links. Recent study on signed networks shows that negative links have added value in addition to positive links for many tasks such as link prediction and node classification. However, the existence of negative links challenges the principles used for plain network embedding. Thus, it is important to study signed network embedding. Furthermore, social networks can be dynamic, where new nodes and links can be introduced anytime. Dynamic networks can reveal the concept drift of a user and require efficiently updating the representation when new links or users are introduced. However, static network embedding algorithms cannot deal with dynamic networks. Therefore, it is important and challenging to propose novel algorithms for tackling different types of social networks.
In this dissertation, we investigate network representation learning in social media. In particular, we study representative social networks, which includes attributed network, signed networks, dynamic networks and document networks. We propose novel frameworks to tackle the challenges of these networks and learn representations that not only capture the network structure but also the unique properties of these social networks.
ContributorsWang, Suhang (Author) / Liu, Huan (Thesis advisor) / Aggarwal, Charu (Committee member) / Sen, Arunabha (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2018
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
Description
Decision-making is critical in the livestock supply chain. Understanding how producers and consumers make their decisions requires a sufficient understanding of the process of their decision-making behavior. Based on the processing resources, consumers or producers’ choices could be affected by different processes: affective process, cognitive process or both affective and cognitive processes simultaneously. Applying a variety of experiment methods, this dissertation investigates how producers and consumers make their choices by exploring how the product attributes, and the characteristics of the decision-maker, affect consumers and producers’ choice-making behaviors. In the first essay, I implemented a discrete choice experiment and estimated random parameter logit models with error component to analyze Chinese consumer willingness to pay (WTP) for domestic and imported beef flank labeled with the new quality grades and other relevant beef labels. Results suggest foreign beef producers could compete most closely with domestic beef if it was labeled as premium quality. In the second essay, I investigate Chinese consumer WTP for beef from different countries and the role of ethnocentrism, country image, and product image on the WTP. Results suggest that foreign beef exporters could promote their beef in China by advertising in accordance with positive country and product images.
In the third essay, I attempt to determine hog farmers’ motivations to adopt genomics for breeding hogs that are more resistant to the disease. In doing so I focus on the impact of their risk preferences and related peer effects that might influence potential adoption. This case study provides implications for local governments and companies trying to promote new technologies.
In the fourth essay, I investigate how social influence affects producers’ behavior under disease outbreak using social network analysis. In particular, I focus on how information flows during an epidemic such as African Swine Fever. Findings provide insights into how information flows and how actors communicate during a situation of crisis. This can be used by stakeholders (1) to disseminate information; and (2) to avoid the spread of rumors and false information.
ContributorsGao, Shijun (Author) / Grebitus, Carola (Thesis advisor) / DeLong, Karen (Committee member) / Schmitz, Troy (Committee member) / Arizona State University (Publisher)
Created2022
Description
Food safety incidents have constantly hit society and threatened human health. Hundreds of millions of people become sick after eating contaminated food every year. As the problem continues to emerge, consumers must take action to avoid purchasing risky food products. As one of the solutions, food traceability systems have been developed rapidly in many countries in recent years. More food products can now be provided with traceability information to assist consumers in making purchase decisions. To design services for grocery shoppers to access food information from food traceability systems possibly through modern technologies, this transdisciplinary user research study investigated shopper insights into food traceability information on produce provided at grocery stores, with a fusion of ideas from the disciplines of design and consumer behaviors. Through literature reviews, an online survey study, and an online interview study, this research revealed a series of shopper insights concerning (1) shoppers’ knowledge about food traceability information, (2) shoppers’ behaviors and motivations for using traceability information on produce, (3) shoppers’ perceptions towards providing traceability information on produce to them at grocery stores, (4) shoppers’ perceived important traceability information on produce, (5) shoppers’ behavior intentions of using specific ways to access traceability information on produce, and (6) shoppers’ thresholds to pay for traceability information on produce. Based on the results, this study identified design opportunities for the features, components, and mediums of the service design of future food traceability systems.
ContributorsWang, Anne (Author) / Takamura, John (Thesis advisor) / Fehler, Michelle (Committee member) / Grebitus, Carola (Committee member) / Arizona State University (Publisher)
Created2022