Information overload? Use the RA!

When you are browsing the web in order to buy a certain product, you have got to process a lot of information. As we have been taught in class extensively, a website’s Recommendation Agent (RA) can help consumers to make purchasing decisions. Shoppers are loyal to e-stores that enable them to function efficiently.

Before M. Aljukhadar et. al (2012) conducted their research on consumer’s use of an RA to cope with information overload, little was researched about a consumer’s likelihood of using an RA or conforming to its recommendations. Therefore, they investigated several approaches to measurement of product information load; the relationship between delivered information load and perceived information overload; using an RA to indicate occurrence of information overload; effects on information overload on decision strategy while accounting for consumer’s need for cognition; and information overload and decision strategy on several performance measures.

The researchers created a fictitious retailer website that offered laptops. They chose three levels for the number of alternatives as well as for attributes. As consumer’s normally consider many attributes when shopping for complex search goods such as a laptop, the researchers included a high number of attributes. 466 participants were asked to choose a laptop they would seriously consider buying and needed to rate the importance of each laptop attribute. Afterwards, the participants could choose to click on a link to the recommendation according to their preferences.

Participants also were asked whether there was too much information to make a choice on two seven point scales and on two additional scales on how satisfied they were with the choice they made, in order to test perceived information load and choice confidence respectively. The need for cognition was measured by asking to fill in an 18-item scale, e.g. “I find satisfaction in deliberating hard and for long hours”.

The researchers found that overload was actually experienced by consumers and that the relationship between information load and perceived overload was curvilinear. Participants who experienced high information overload, consulted the RA and accepted its recommendation more often than those who did not experience overload. When overloaded, a consumer with low need for cognition is more likely to consult an RA and vice versa. Moreover, as information overload increases, choice quality improves when consulting an RA. Thus, particularly in complex situations, the use of an RA has a positive effect on choice quality.

While using an RA upholds choice confidence, confidence will decrease for a consumer who contradicts the recommendation. The relationship between perceived information overload and e-store interactivity is curvilinear as well. The researches propose two possible explanations for this: first, the use of an RA makes choice accuracy feedback immediate and tangible. Second, rejecting a personalized and accurate recommendation leads a user to face more choice difficulty and cognitive dissonance, which results in less choice confidence and lower satisfaction with the performance of the webstore.

Given these results, it would be advisable to webstores to proactively show product recommendations when information overload is high. Also, RAs should provide accurate advice. Lastly, apply other measures to increase the number of shoppers that conform to the recommendations.

Aljukhadar, Muhammad, Sylvain Senecal, and Charles-Etienne Daoust. “Using recommendation agents to cope with information overload.” International Journal of Electronic Commerce 17.2 (2012): 41-70.

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