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Nanda Kumar

Bio: Nanda Kumar is an academic researcher from City University of New York. The author has contributed to research in topics: The Internet & Personalization. The author has an hindex of 17, co-authored 42 publications receiving 1413 citations. Previous affiliations of Nanda Kumar include Baruch College & Mount Saint Mary College.

Papers
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Journal ArticleDOI
TL;DR: A novel approach to generate the experimental conditions by filtering the content of Amazon.com in real time shows that the provision of recommendations and consumer reviews increases both the usefulness and social presence of the website.
Abstract: Recommendations and consumer reviews are universally acknowledged as significant features of a business-to-consumer website. However, because of the well-documented obstacles to measuring the causal impact of these artifacts, there is still a lack of empirical evidence demonstrating their influence on two important outcome variables in the shopping context: perceived usefulness and social presence. To test the existence of a causal link between information technology (IT)-enabled support for the provision of recommendations and consumer reviews on the usefulness and social presence of the website, this study employs a novel approach to generate the experimental conditions by filtering the content of Amazon.com in real time. The results show that the provision of recommendations and consumer reviews increases both the usefulness and social presence of the website.

503 citations

Journal ArticleDOI
TL;DR: The theoretical underpinnings of the construct Para-social Presence are described and an instrument to measure this construct is developed and a research framework is developed that illustrates the impact of new technologies and associated web interface design decisions on perceived communication characteristics of a web site, para-social presence, and subsequent user evaluations of the web site.
Abstract: The goal of this paper is to describe the theoretical underpinnings of the construct Para-social Presence and to develop an instrument to measure this construct. Para-social presence refers to the extent to which a medium facilitates a sense of understanding, connection, involvement and interaction among participating social entities. We make a case for treating a web site as a valid social actor and argue that the relationship between a web site and her visitors should be characterized in much the same way one would characterize an inter-personal relationship. We also argue that a web site could possess different levels of para-social presence depending on how it is configured and used. We then develop a research framework that illustrates the impact of new technologies (such as personalization systems) and associated web interface design decisions on perceived communication characteristics of a web site, para-social presence, and subsequent user evaluations of the web site.

188 citations

Posted Content
TL;DR: A recommender system is designed, developed, and tested, and it is found that the same types of relationships yield the best recommendation accuracy, highlighting the importance of behavioral theory in guiding system design.
Abstract: Social recommender systems utilize data regarding users’ social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data – beyond consumption profile similarity – is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.’s (1992) IS Design Theory by introducing the notion of “applied behavioral theory,” as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient’s advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems.

117 citations

Journal ArticleDOI
TL;DR: In this article, the authors propose a methodology for using behavioral theory to guide recommendation systems design, and apply their theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy.
Abstract: Social recommender systems utilize data regarding users’ social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data – beyond consumption profile similarity – is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.’s (1992) IS Design Theory by introducing the notion of “applied behavioral theory,” as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient’s advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems.

95 citations

Journal ArticleDOI
TL;DR: Recommender systems play a significant role in reducing information overload for people visiting online sites, but their accuracy could be improved by using data from online social networks and electronic communication tools.
Abstract: Recommender systems play a significant role in reducing information overload for people visiting online sites, but their accuracy could be improved by using data from online social networks and electronic communication tools.

95 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

2,639 citations

Book ChapterDOI
01 Jan 2011
TL;DR: The main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.
Abstract: Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

2,160 citations

Journal ArticleDOI
TL;DR: This study draws upon and extends the principal-agent perspective to identify and propose a set of four antecedents of perceived uncertainty in online buyer seller relationship superceived information asymmetry, fears of seller opportunism, information privacy concerns, and information security concerns which facilitate online exchange relationships by overcoming the agency problems of adverse selection and moral hazard.
Abstract: Despite a decade since the inception of B2C e-commerce, the uncertainty of the online environment still makes many consumers reluctant to engage in online exchange relationships. Even if uncertainty has been widely touted as the primary barrier to online transactions, the literature has viewed uncertainty as a "background" mediator with insufficient conceptualization and measurement. To better understand the nature of uncertainty and mitigate its potentially harmful effects on B2C e-commerce adoption (especially for important purchases), this study draws upon and extends the principal-agent perspective to identify and propose a set of four antecedents of perceived uncertainty in online buyer seller relationship superceived information asymmetry, fears of seller opportunism, information privacy concerns, and information security concerns which are drawn from the agency problems of adverse selection (hidden information) and moral hazard (hidden action). To mitigate uncertainty in online exchange relationships, this study builds upon the principal agent perspective to propose a set of four uncertainty mitigating factor-trust, website informativeness, product diagnosticity, and social presence-that facilitate online exchange relationships by overcoming the agency problems of hidden information and hidden action through the logic of signals and incentives. The proposed structural model is empirically tested with longitudinal data from 521 consumers for two products (prescription drugs and books) that differ on their level of purchase involvement. The results support our model, delineating the process by which buyers engage in online exchange relationships by mitigating uncertainty. Interestingly, the proposed model is validated for two distinct targets, a specific website and a class of websites. Implications for understanding and facilitating online exchange relationships for different types of purchases, mitigating uncertainty perceptions, and extending the principal-agent perspective are discussed.

2,151 citations

01 Jan 1997
TL;DR: In this paper, the authors examine the implications of electronic shopping for consumers, retailers, and manufacturers, assuming that near-term technological developments will offer consumers unparalleled opportunities to locate and compare product offerings.
Abstract: The authors examine the implications of electronic shopping for consumers, retailers, and manufacturers. They assume that near-term technological developments will offer consumers unparalleled opportunities to locate and compare product offerings. They examine these advantages as a function of typical consumer goals and the types of products and services being sought and offer conclusions regarding consumer incentives and disincentives to purchase through interactive home shopping vis-à-vis traditional retail formats. The authors discuss implications for industry structure as they pertain to competition among retailers, competition among manufacturers, and retailer-manufacturer relationships.

2,077 citations

Posted Content
TL;DR: Drawing on the paradigm of search and experience goods from information economics, a model of customer review helpfulness is developed and tested and indicates that review extremity, review depth, and product type affect the perceived helpfulness of the review.
Abstract: Customer reviews are increasingly available online for a wide range of products and services. They supplement other information provided by electronic storefronts such as product descriptions, reviews from experts, and personalized advice generated by automated recommendation systems. While researchers have demonstrated the benefits of the presence of customer reviews to an online retailer, a largely uninvestigated issue is what makes customer reviews helpful to a consumer in the process of making a purchase decision. Drawing on the paradigm of search and experience goods from information economics, we develop and test a model of customer review helpfulness. An analysis of 1,587 reviews from Amazon.com across six products indicated that review extremity, review depth, and product type affect the perceived helpfulness of the review. Product type moderates the effect of review extremity on the helpfulness of the review. For experience goods, reviews with extreme ratings are less helpful than reviews with moderate ratings. For both product types, review depth has a positive effect on the helpfulness of the review, but the product type moderates the effect of review depth on the helpfulness of the review. Review depth has a greater positive effect on the helpfulness of the review for search goods than for experience goods. We discuss the implications of our findings for both theory and practice.

2,066 citations