Marketing the Unfamiliar: The Role of Context and Item-Specific Information in Electronic Agent Recommendations

Cooke, Alan; Sujan, Harish; Sujan, Mita; Weitz, Barton A.

doi:10.1509/jmkr.39.4.488.19121

Cited by 95 publications

(79 citation statements)

References 42 publications

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“…Arguably, this recognition effect is an important aspect of inspectability, because knowing recommendations may raise users' trust in the recommender [7,42]. In our experiment, known recom- 6 Conformity bias could be an alternative explanation: "If all my friends know this band, I ought to know it too!"…”

Section: Inspectability and Controlmentioning

confidence: 82%

Inspectability and control in social recommenders

Knijnenburg

Bostandjiev

O’Donovan

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

104

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Users of social recommender systems may want to inspect and control how their social relationships influence the recommendations they receive, especially since recommendations of social recommenders are based on friends rather than anonymous "nearest neighbors". We performed an online user experiment (N=267) with a Facebook music recommender system that gives users control over the recommendations, and explains how they came about. The results show that inspectability and control indeed increase users' perceived understanding of and control over the system, their rating of the recommendation quality, and their satisfaction with the system.

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Section: Inspectability and Controlmentioning

confidence: 82%

Inspectability and control in social recommenders

Knijnenburg

Bostandjiev

O’Donovan

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

104

View full text Add to dashboard Cite

show abstract

“…Whereas a substantial amount of research considers the individual qualities of these recommendations, little research has considered the composition of the list (Hu and Pu 2011;Chen and Pu 2012;Ziegler et al 2005;Cooke et al 2002). The composition may play an important role in the user experience of a recommender system, because it influences the users' decision-making process through context effects (Simonson and Tversky 1992;Tam and Ho 2005).…”

Section: Size and Composition Of Recommendation Setsmentioning

confidence: 99%

Explaining the user experience of recommender systems

Knijnenburg

Willemsen

Gantner

et al. 2012

User Model User-Adap Inter

618

340

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Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated with four field trials and two controlled experiments and analyzed using Structural Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about. In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.

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“…Recent research in marketing has made significant advances in our understanding of various Internet marketing issues, including Web-browsing behavior (Bucklin and Sismeiro 2003), search-engine visits (Telang, Boatwright, and Mukhopadhyay 2004), and recommendation agents (Cooke, Sujan, Sujan, and Weitz 2002;Diehl, Kornish, and Lynch 2003). However, one vexing practitioner's problem has remained virtually untouched by formal analysis: how firm performance is affected by moving from free to free & fee i -that is, from offering all content for free to charging for at least some of it.…”

Section: Introductionmentioning

confidence: 99%

Moving from Free to Fee: How Online Firms Market to Change Their Business Model Successfully

Pauwels

Weiss

2008

Journal of Marketing

124

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Moving from free to 'free & fee' for any product or service represents a challenge to managers, especially when consumers have plenty of free alternatives. For one online content provider, this paper examines (1) the sources of long-run revenue loss (through attracting fewer free subscribers) and (2) how the firm's marketing actions affect its revenue gains (through attracting paid subscribers). The authors quantify revenue loss from several sources, including the direct effects of charging for part of the online content and the reduced effectiveness of search engine referrals and emails. Their analysis suggests several managerial implications. First, managers should focus their price promotions towards stimulating new monthly subscriptions, instead of the current promotional focus on stimulating new yearly contracts. In contrast, email and search engine referrals appear very effective at generating yearly subscriptions. Meanwhile, 'free to fee' conversion email blasts are a double-edged sword: they increase subscription revenue at the expense of advertising revenue. Finally, further analysis shows the move was preceded by the build-up of momentum in new free subscriptions, which appears beneficial for the move's success. The decomposition and comparison of the sources of revenue loss versus gains reveals several tradeoffs facing companies moving from free to 'free & fee'.Keywords: from free to fee, online content, price, marketing communication, searchengine referrals, time series analysis, vector-autoregressive models. IntroductionA decade ago, Peterson, Balasubramanian, and Bronnenberg (1997) A prominent fear among content providers is the likely loss of customers to the many still free competitors. Moreover, the impact of marketing actions to stimulate paid subscriptions is uncertain given the general consensus among web surfers that "content is free" (Dyson 1995). Empirical findings on these issues are scarce, as firms feel uneasy sharing data with academic researchers for confidentiality and competitive reasons. When The NY Times moved to a paid version of its editorial pages, one reader commented, "The only thing the NYTimes will achieve is a reduced readership for its writers. No one will, nor should we, pay for online content. It's a big, free web out there."The ultimate abandonment of the payment system (on September 19, 2007) appears to vindicate these arguments, and illustrates the challenge content providers face online.In sum, despite high managerial importance, little empirical research has addressed the following questions facing companies moving from free to 'free & fee': 1) What are the long-run performance effects of such a move?In particular, what are the sources of revenue loss that may offset the revenue gains from user fees?2) What role do marketing actions play in the free-and fee-user response? In particular, which actions are especially successful in increasing free and fee subscriptions, for both short-term and long-term contracts?This research aims to shed light on these issues by fo...

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Marketing the Unfamiliar: The Role of Context and Item-Specific Information in Electronic Agent Recommendations

Cited by 95 publications

References 42 publications

Inspectability and control in social recommenders

Inspectability and control in social recommenders

Explaining the user experience of recommender systems

Moving from Free to Fee: How Online Firms Market to Change Their Business Model Successfully

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