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

Xiao, Bo; Benbasat, Izak

doi:10.1007/978-3-642-39747-9_18

Cited by 36 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors conclude the effectiveness of a collaborative filtering-based recommender. Xiao and Benbasat (2014) Theoretical, conceptual model This paper updates the earlier paper written by Xiao and Benbasat (2007). The authors offer an up-to-date review of the literature, considering significant RS-related papers published between 2006 and 2012.…”

Section: Martínezmentioning

confidence: 90%

Consumers’ psychological outcomes linked to the use of an online store’s recommendation system

et al. 2015

View full text Add to dashboard Cite

Article information:To cite this document: Francisco J. Martínez-López Irene Esteban-Millat Ana Argila Francisco Rejón-Guardia , (2015),"Consumers' psychological outcomes linked to the use of an online store's recommendation system", Internet Research, Vol. 25 Iss 4 pp. -Permanent link to this document: http://dx. Access to this document was granted through an Emerald subscription provided by emerald-srm:263496 [] For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

show abstract

Section: Martínezmentioning

confidence: 90%

Consumers’ psychological outcomes linked to the use of an online store’s recommendation system

et al. 2015

View full text Add to dashboard Cite

show abstract

“…For setting selection, a presurvey was conducted to select the product category for the experiment. The initial list of products was selected based on previous e-commerce studies (Xiao and Benbasat, 2014). The final list included book, digital versatile disc (DVD), camera, smartphone, fashion clothing, sports clothing, hotel booking, food and beverage and grocery.…”

Section: Setting Selection and Factor Manipulationmentioning

confidence: 99%

Understanding automated conversational agent as a decision aid: matching agent's conversation with customer's shopping task

Chen

Tran

2021

INTR

View full text Add to dashboard Cite

PurposeTo provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.Design/methodology/approachA 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).FindingsThe findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.Originality/valueThis study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.

show abstract

“…The present section explains review articles, which discuss the utilization of RS in e-commerce applications and summarizes their main advantages and disadvantages. Xiao and Benbasat [33] studied e-commerce product recommendation agents and considered empirical papers on ecommerce product offering agents issued from 2007 to 2012. They selected 34 papers to review such highlighted topics as recommendation agent type, preference-elicitation, explanation, and the social aspects of recommendation agents.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Study on the Recommender Systems in the E-Commerce

et al. 2020

View full text Add to dashboard Cite

Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

show abstract

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

Cited by 36 publications

References 36 publications

Consumers’ psychological outcomes linked to the use of an online store’s recommendation system

Consumers’ psychological outcomes linked to the use of an online store’s recommendation system

Understanding automated conversational agent as a decision aid: matching agent's conversation with customer's shopping task

A Systematic Study on the Recommender Systems in the E-Commerce

Contact Info

Product

Resources

About