#RAA5: Impact of Web Personalization on User Information Processing and Decision Outcomes

1. APA Citation:

Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. Mis Quarterly, 30(4), 865–890. PDF
2.  Purpose of the Research: To understand the impact of personalized content on user information processing and decision making, because little is known about the effectiveness of web personalization and the link between the IT artifact (the personalization agent) and the effects it exerts on a user’s information processing and decision making.
3. Methods: Theoretically develops and empirically tests a model of web personalization. The model is grounded on social cognition and consumer research theories. The research model depicts the different stages of web processing as (1) attention, (2) cognitive processing, (3) decision, and (4) evaluation of decision. The model highlights two sets of variables hypothesized to have an impact on these four stages. The two sets of variables are related to (1) web personalization and (2) goal specificity. The variables related to web personalization are: self reference and content relevance. Hypotheses were generated from the research model, and were empirically tested in a laboratory experiment and a field study.
The hypotheses are (also refer to the figure bellow):
Hypotheses related to Self Reference in Web Personalization:
H1: Users attend to self-reference web content to a larger extent than they attend to non-self-reference web content.
H2a: Users recall self-referent web content faster and more accurately than they recall non-self-referent web content.
H3a: Users exposed to self-referent web content will seek less information and spend less time on decision making than when they are exposed to non-self-referent web content.
H4a: Users accept offeres associated with self-referent web content to a larger extent than they accept offers associated with non-self-referent web content.
Hypotheses related to Content Relevance in Web Personalization:
H2b: Users recall web content relevant to their processing goal faster and more accurately than they recall irrelevant web content.
H4b: Users accept offers associated with relevant web content to a larger extent than they accept offers associated with irrelevant web content.
Hypotheses related to Processing Goal Specificity:
H2c: There is a larger difference in recall accuracy and response time between relevant and irrelevant web content for users with more-specific processing goals than for those with less-specific processing goals.
Hypotheses related to Evaluation:
H5a: Users evaluate self-referent web content more highly than they evaluate non-self-referent web content.
H5b: Users evaluate relevant web content more highly than they evaluate irrelevant web content.
The controlled lab experiment focuses on the tree variables hypothesized to attract users’ attention, affect their level of cognitive processing, and bias their decisions. The field study is based on a music download site and lasting for 6 weeks. They examined users’ behaviors by analyzing their web activities. Contents of the music site were driven by a commercial personalization agent and all activities of the web site were logged for the entire 6-week period.
4. Main Findings: The findings from the lab experiment and field study indicate that content relevance, self reference, and goal specificity affect the attention, cognitive processes, and decisions of web users in various ways. Also users are found to be receptive to personalized content and find it useful as a decision aid. Major findings are summarized in the table bellow. Only H2a is not supported (while content relevance leads to better re-call of the content, this is not obvious for self-relevance), and all other hypotheses are supported with statistical significance.
5. Analysis: This article provide good information on web personalization and how it impacts users’ decision outcomes. It also provides a snapshot on other related research of this line. Most research on web personalization comes from business management, e-commerce, marketing, etc. No matter what they do is to understand consumers, or to better design the personalization agents, or anything else, the ultimate goal is to maximize business opportunities, to sell products and to gain profit. They do not usually consider other side effect of personalization, such as echo chamber effect. I am not sure whether echo chamber effect will negatively or positively affect companies’ business. This may affect some small companies to get their new products rolling because it’s difficult for new things to get into the bubble of the consumers. However, maybe big companies who already got the consumers around their products love this to happen. As far as I know, the after effect to consumers, and the large impact to society of personalization are usually not considered in this line of research. Indeed, these issues may be a little out of scope of e-commerce research, and they may be usually addressed in other fields.

Personalization: Machine Mirrors the Ugly Us that We don’t Want to See so We Blame the Machine

There is a fair amount of discussion that Google search, Amazon recommendation and Facebook streams are too much personalized and making us closed minded, the so called filter bubble or echo chamber effect. I’ve been thinking this for a while, and I have a new idea about what makes personalization and recommendation bad.

It is not the machine, not the algorithms. It is human nature. The machine is learning from the human beings eventually, and the machine is just augmenting human reality. My argument is that for a person who has relatively open and balanced mind, the machine personalization results will be fairly balanced, and the recommendation results will serve good knowledge discovery. Only for persons who initially themselves have huge biased opinions and worldviews towards certain things, the machine personalization results will be biased. I don’t think the machine is making things worse, the machine is just reflecting the reality, and it is good that machine is making us see the reality so we can figure out a way to solve it. What results these biased and closed opinions are essentially human nature, and it’s not the machine’s responsibility to solve this problem.

Close minded people always exist, no matter whether Google search exists. Even if there is no Google search and other things, these people will not seek or listen opinions outside of their chamber what so ever. We dream that actually machine can solve this problem by providing opposite and diversity opinions (effort like Findory news), but the thing is not that easy, it is difficult to move people out of their comfort zone, so Findory failed.

To solve this problem, we have to open our mind first, or some of us. Then we figure out a way that could open other people’s mind more effectively, and make the machine do it.

To sum up, what I am arguing it that, at this moment, machine personalization and recommendation is not doing bad things (not that good either, just fact) it’s just reflecting human reality. What we have to realize is that the problem is not the machine, it’s ourselves, machine is just letting us see our flaws that we don’t want to face sometimes. In the future, we need to figure out ways to let the machine do good on this.