Recommendation agent impact on consumer online shopping: The Movie Magic case study

Hostler, R. Eric; Yoon, Victoria; Guimarães, Tor

doi:10.1016/j.eswa.2011.08.160

Cited by 36 publications

(13 citation statements)

References 30 publications

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“…These two motivational factors also affect consumers' intention to spread positive word-of-mouth information on the web about online retailers [8]. Several studies assessed the influence of emotional factors such as satisfaction [9,15,16,17,18,19,20,21,22,23,24,25,26,27], enjoyment [9,28,29,12] and loyalty [25,26,30,31] on consumers' online shopping behavior. Results of the studies showed that consumer satisfaction with the online retailer's website is positively related to consumer's loyalty to it [9,16,17,19] and loyalty has been found to increase the actual website buying frequency [30].…”

Section: Framework Online Consumer Related Studiesmentioning

confidence: 99%

“…Studies classified under this category attempt to assess the influence of online shopping tools on online consumer shopping behavior. Shopping tools assessed by researchers includes recommender agents [16,18,203,204,205], avatars [206], image interactive technologies [70,207,208], social presence tools [209,210], search tools [181,182] and communication tools such as online consumer reviews [211]. Recommender agents are information filtering technology which provides highquality product recommendations for the online consumers.…”

Section: Online Vendor / Store Related Studiesmentioning

confidence: 99%

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Behavioral Issues in B2C E-commerce

Huseynov

Yıldırım

2016

Information Development

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B2C e-commerce is one of the fastest growing industries worldwide. A lot of studies were carried out by both scholars and practitioners in order to assess online consumers’ behavioral issues in B2C e-commerce platforms. This paper aims to organize and classify the accumulated literature on B2C e-commerce in order to determine less-researched areas and provide future research directions. For that purpose 208 peer-reviewed articles from 71 journals published between 2005 and 2014 were retrieved and analyzed. The findings of the studies are discussed within the scope of developed framework.

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Section: Framework Online Consumer Related Studiesmentioning

confidence: 99%

Section: Online Vendor / Store Related Studiesmentioning

confidence: 99%

Behavioral Issues in B2C E-commerce

Huseynov

Yıldırım

2016

Information Development

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“…We have observed in this review exercise that, with a few exceptions (e.g., Hostler et al 2012, which examined the role of RA in improving search effectives; Wang and Benbasat 2012, which explored the effect of user control during preference elicitation on decision effort and perception of RA quality), the majority of the reviewed studies extend, rather than testing, the conceptual model (see Fig. 1) and the supporting propositions (see Table 1) advanced in our MISQ 2007 paper, by introducing other RA characteristics (e.g., other RA types and input/output characteristics), user perception variables (e.g., perceived recommendation quality, enjoyment, perceived social presence, perceived trade-off difficulty), and moderating factors (e.g., regulatory focus, gender, temporal distance, reactance level, decision context, etc.…”

Section: Resultsmentioning

confidence: 94%

Research on the Use, Characteristics, and Impact of e-Commerce Product Recommendation Agents: A Review and Update for 2007–2012

Xiao

Benbasat

2013

Progress in IS

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Five years have passed since the publication of our MISQ 2007 paper on the use, characteristics, and impact of e-commerce product recommendation agents (RAs). We are interested to learn how the research on e-commerce product RAs has progressed since then. More specifically, we are interested to find out whether the conceptual model that we have developed in our MISQ 2007 paper have received further empirical support and how the conceptual model has been extended. In this chapter, we review empirical studies on e-commerce product recommendation agents published between 2007 and 2012, particularly with respect to the theory that we have advanced in the MISQ 2007 paper. In addition, we update our original conceptual model by integrating important additional dimension(s), if any, revealed in the review of empirical studies.

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“…Xu [22] verified that analyzing relevant context information was beneficial to predict customer interests in mobile commerce services more accurately. In addition, the mobile personalized recommendation system can recommend different promotion items under different contexts of shopping venue [23], and can also improve the interest of mobile commerce users and sales performance according to context recommendation [24]. Ren [25] found that context in users' interest mining had a great influence on network users' behaviors, and users were highly dependent on the context when making rational behavior decisions, which produced contextual effects when users adopted mobile personalized recommendation services.…”

Section: Research On Mobile Personalized Recommendation Service Consimentioning

confidence: 99%

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

2020

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A mobile personalized recommendation service satisfies the needs of users and stimulates them to continue to adopt mobile commerce applications. Therefore, how to precisely provide mobile personalized recommendation service is very important for the sustainable development of mobile commerce. However, privacy concerns regarding mobile commerce affect users’ consumption intentions, and also reduce the quality of mobile personalized recommendation services. In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant contextual information to propose a novel mobile personalized recommendation service based on privacy concerns and context analysis. First, this paper puts forward an intensity measurement method to measure the factors that influence privacy concerns, and then realizes a user-based collaborative filtering recommendation integrated with the intensity of privacy concerns. Second, a context similarity algorithm based on a context ontology-tree is proposed, after which this study realizes a user-based collaborative filtering recommendation integrated with context similarity. Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information. After experimental verification, the results show that this model can effectively solve the problems of data sparseness and cold starts. More importantly, it can reduce the influence of users’ privacy concerns on the adoption of mobile personalized recommendation services, and promote the sustainable development of mobile commerce.

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Recommendation agent impact on consumer online shopping: The Movie Magic case study

Cited by 36 publications

References 30 publications

Behavioral Issues in B2C E-commerce

Behavioral Issues in B2C E-commerce

Research on the Use, Characteristics, and Impact of e-Commerce Product Recommendation Agents: A Review and Update for 2007–2012

Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce

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