- Can we use the findings of this paper to apply RAs in B2B environments, in particular in supply chains, or in another domain fit to our interests?
- Can we use the findings of this paper to come up with general design principles of such RAs?
- Do we appreciate the methodology of this paper, do we understand it? Do we need to?
- Do we want to study this paper and (some of) its references in more detail, and what will be our objectives?
- How would (virtual) social networks moderate the impact of and trust in RAs on consumer decision making; should we introduce the concept of multi-RA systems? What are requirements on the (virtual) social network here?
- Important characteristic of an RA is the use of a model of user behavior (similarity). In this sense, descriptive theory on e.g. behavior of actors in financial markets may be a first step towards the design of RAs in that context.
Monday, November 12, 2007
Meeting minutes November 8, 2007
We had a discussion on the paper "E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact" by Bo Xiao and Izak Benbasat that appeared in MIS Quarterly, Vol. 31, No. 1, pp.137-209 (2007). Here are some of the discussion questions and remarks.
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2 comments:
Thanks Rob for your good discussion points! In the next meeting we'll start with these, and I present the follow-up paper and Uzay will discuss the papers about agents and mental capabilities.
Here are some comments in reaction to Rob's questions:
1) A user should be able to select the level of transparency. How much detail does a particular user like, i.e. an expert has different demands than a novice user. There are two ways to present the results: a) Start big and then refine or b) start with a single item and then expand, ask which other features the user likes. Ruud used the example where he asks his brother in law which TV he would recommend, then he looks at that one first and then decides which features he likes and which he doesn't need.
2) There should always be a possibility to give feedback to the agents, so that the agent is able to learn from your feedback explicitly as well as implicitly by reasoning through observation.
Thoughts?
Wolf
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