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The effects of different configurations of online recommendation agents on consumers’ buying decisions = Os efeitos de diferentes configura es de agentes de recomenda o online na tomada de decis o do consumidor  [PDF]
Yuen, Po Yee,Liberali, Jordana Folle de Menezes
Psico , 2011,
Abstract: Este estudo investiga os efeitos de diferentes configura es de agentes de recomenda o online na tomada de decis o do consumidor. 104 consumidores online de roupas participaram desse estudo (66,3% do sexo feminino) respondendo a um questionário online que apresentava diferentes configura es de agentes de recomenda o. Contrariamente à no o de que um grande número de escolhas levaria a uma sobrecarga de informa es e desmotivaria os consumidores (Iyengar e Lepper, 2000; Scheibehenne et al. , 2008), esse artigo mostra que participantes s o mais motivados à pesquisar por produtos e mais dispostos a comprar de um web site com um agente recomendador que apresenta 16 produtos comparado com 4 produtos e s o mais motivados à pesquisar por produtos e mais dispostos a comprar de um web site que apresenta 16 produtos de uma vez ao invés de quatro de cada vez. Tendo como base a teoria do tra o difuso (Reyna, 2008), nós argumentamos que a sobrecarga de informa es n o afeta as escolhas porque as pessoas tendem a raciocinar com base em representa es de essência ao invés de com base em informa es literais. Ainda, a presen a de marcas aumenta a motiva o em pesquisar por produtos e a disposi o em comprar produtos recomendados. Esses resultados podem ser explicados pelo fato de que marcas, assim como estereótipos (Janiszewski e Osselaer, 2000; Keller, 2003), ajudam as pessoas a extrair a essência da informa o sendo exposta (Brainerd e Reyna, 2005). This study investigates the effects of different configurations of online recommendation agents on consumers’ buying decisions. 104 on line consumers of clothing participated in our study (66. 3% female) answering to an online questionnaire displaying different configurations of recommendation agents. Against the assumption that a large number of choices would lead to information overload and demotivate consumers (Iyengar and Lepper, 2000; Scheibehenne et al. , 2008), this article shows that participants are more motivated to search for a product and more willing to buy from a website with a recommendation agent displaying a larger number of products (16), compared with a smaller number (4),and are more motivated to search for a product and more willing to buy from a website displaying 16 products at once instead of four at a time. Based on the fuzzy-trace theory (Reyna, 2008), we argue that information overload do not play a role here because people tend to reason on the basis of simplified representations (gist) rather than on the literal information available (verbatim). Also, compared to the absence of brands, the
Online Impulse Buying and Product Involvement  [cached]
Tsai Chen
Communications of the IBIMA , 2008,
Abstract: Do consumers behavior different on the Internet from other marketing channels? This study investigates impulse buying behaviors in both traditional store and online shopping contexts. The results show that impulsive buying tendency and involvement with clothing products is positively associated with impulse buying behavior of clothing in traditional store shopping, but not online. For computer peripherals, on the other hand, higher impulsive buying tendency and higher product involvement are positively associated with higher impulse buying online, but not in-store shopping.
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior  [PDF]
Farshad Kooti,Kristina Lerman,Luca Maria Aiello,Mihajlo Grbovic,Nemanja Djuric,Vladan Radosavljevic
Computer Science , 2015,
Abstract: Consumer spending accounts for a large fraction of the US economic activity. Increasingly, consumer activity is moving to the web, where digital traces of shopping and purchases provide valuable data about consumer behavior. We analyze these data extracted from emails and combine them with demographic information to characterize, model, and predict consumer behavior. Breaking down purchasing by age and gender, we find that the amount of money spent on online purchases grows sharply with age, peaking in late 30s. Men are more frequent online purchasers and spend more money when compared to women. Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases. We also look at dynamics of purchasing behavior and observe daily and weekly cycles in purchasing behavior, similarly to other online activities. More specifically, we observe temporal patterns in purchasing behavior suggesting shoppers have finite budgets: the more expensive an item, the longer the shopper waits since the last purchase to buy it. We also observe that shoppers who email each other purchase more similar items than socially unconnected shoppers, and this effect is particularly evident among women. Finally, we build a model to predict when shoppers will make a purchase and how much will spend on it. We find that temporal features improve prediction accuracy over competitive baselines. A better understanding of consumer behavior can help improve marketing efforts and make online shopping more pleasant and efficient.
Online Search and Buying Behaviour: Malaysian Experience
Lim Yet Mee,Yap Ching Seng,Lau Teck Chai
Canadian Social Science , 2010,
Abstract: This study examines online search pattern and buying behaviour in Malaysia. Malaysian consumers search moderately for product/service information with company websites being the most popular mode of searching. Books, airline tickets, and hotel room booking are the products and services commonly purchased to satisfy self-fulfillment and affiliation needs. Respondents who have online purchase experiences have a higher intention to make online purchase in the future. There is no gender difference in terms of the frequency of online search and purchase as well as the type of consumer needs being satisfied over the Internet. Implications of the research findings and suggestions for future research are discussed. Keywords: online search behaviour; online purchase; online ads; Malaysia
The Moderating Role of Perceived Self-efficacy in the Context of Online Buying Adoption  [cached]
Claudia Iconaru
BRAND : Broad Research in Accounting, Negotiation, and Distribution , 2013,
Abstract: Previous researchers that have employed Technology Acceptance Model (TAM) in modeling online buying behavior, validated that consumers’ attitude towards online buying is mainly determined by two salient beliefs: perceived usefulness of online buying and perceived ease of buying online. This paper takes a different approach from previous studies and postulates that the relationship between ease of buying online and attitude will be moderated by consumers’ perceived self-efficacy. The results of a PLS-based structural equation modeling analysis validate this assumption, indicating a negative path coefficient for the moderating effect. This means that the direct effect of perceived ease of buying online on attitude will decrease as consumers gain more skills and knowledge about online buying and thus, they perceive an increased selfefficacy. The results of this study implies that consumers’ salient beliefs of online buying must be rethought since perceived ease of buying is losing its importance in determining consumers’ attitude for those highly experienced online buyers. Keywords: online buying, perceived self-efficacy, moderating effect, structural equation modeling
Reciprocal Recommendation System for Online Dating  [PDF]
Peng Xia,Benyuan Liu,Yizhou Sun,Cindy Chen
Computer Science , 2015,
Abstract: Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
Factors that Influence Customers’ Buying Intention on Shopping Online
Yulihasri Eri,Md. Aminul Islam,Ku Amir Ku Daud
International Journal of Marketing Studies , 2011, DOI: 10.5539/ijms.v3n1p128
Abstract: On-line commerce through Internet is gaining attention from students today. The aim of this research is to study the factors influencing student’s buying intention through internet shopping in an institution of higher learning in Malaysia. Several factors such as usefulness, ease of use, compatibility, privacy, security, normative-beliefs and attitude that influence student’s buying intention were analyzed. Respondents who were selected are studying in a public institution of higher learning in Penang, Malaysia. Based on theory of reasoned action (TRA), the technology acceptance model (TAM) concluded that there are two salient beliefs which are ease of use and usefulness. This theory has been applied on the study to adopt technology user different and has been emerged as a model in investigation to increase predictive power. Such theory was used in this study to explain students’ buying intention on-line. Besides the ease of use and usefulness, others factors such as: compatibility, privacy, security, normative beliefs and self-efficacy are utilized at this TAM. The results support seven hypotheses from nine. Compatibility, usefulness, ease of use and security has been found to be important predictors toward attitude in on-line shopping.
The Impact of Online Additional Comments on Consumers’ Information Adoption  [PDF]
Hong Zhou, Sujuan Li
Sociology Mind (SM) , 2017, DOI: 10.4236/sm.2017.72005
Abstract: Online additional reviews, as an effective complement to network commentary system, provide an important reference during the process of consumers’ purchase and businesses’ operation decision-making. In this paper, we explored the impact of additional comments on consumers’ information adoption in different comment combinations. At the same time, we also analyzed the moderating role of consumers’ ambivalent attitude. The results show that, when initial comment is positive, compared with the consistent comments (positive initial comments and positive additional comments), consumers have a higher degree of adoption of inconsistent comments (positive initial comments and negative additional comments). On the contrary, when initial comment is negative, consistent comments (negative initial comments and negative additional comments) are more easily adopted by the consumer. Compared with the low ambivalent attitude consumers, customers with high ambivalent attitude tend to adopt negative comments.
On Differentially Private Online Collaborative Recommendation Systems  [PDF]
Seth Gilbert,Xiao Liu,Haifeng Yu
Computer Science , 2015,
Abstract: In collaborative recommendation systems, privacy may be compromised, as users' opinions are used to generate recommendations for others. In this paper, we consider an online collaborative recommendation system, and we measure users' privacy in terms of the standard differential privacy. We give the first quantitative analysis of the trade-offs between recommendation quality and users' privacy in such a system by showing a lower bound on the best achievable privacy for any non-trivial algorithm, and proposing a near-optimal algorithm. From our results, we find that there is actually little trade-off between recommendation quality and privacy for any non-trivial algorithm. Our results also identify the key parameters that determine the best achievable privacy.
Integrated Expert Recommendation Model for Online Communities  [PDF]
Abeer El-korany
Computer Science , 2013,
Abstract: Online communities have become vital places for Web 2.0 users to share knowledge and experiences. Recently, finding expertise user in community has become an important research issue. This paper proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from enormous contents and social network features. Vector space model is used to compute the relevance of published content with respect to a specific query while PageRank algorithm is applied to rank candidate experts. The experimental results show that the proposed model is an effective recommendation which can guarantee that the most candidate experts are both highly relevant to the specific queries and highly influential in corresponding areas.
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