Imagine yourself going to the movies together with some family or friends. One person of the group is assigned to pick a movie and that person might be you. To have a look at the current offerings, you go to IMDb and have a look at the suggestions. Recommendations are offered to you based on your preferences. You decide which of these movies might be most suitable for the whole group. Most group consumptions are decided by only a sub-part of the group, but why not offer group recommendations?
The paper by Hennig-Thurau et al. (2012) argues that hedonic products are mostly dominated by experience attributes that are difficult to decide on. In addition, the majority of hedonic products are consumed group wise. Examples are the movies, theatre, vacations, and restaurants. To amplify its importance, this paper states that close to 90% of the movie visits are consumed by groups and 35% were making choices for others within the group. Therefore, this research argues the added value of using group recommenders that consider the preferences of all group members instead of the preferences of one person.
Recommenders, personalized and based on past behaviour and preferences, are argued to reduce the decision complexity for consumers. The paper investigated the discussed phenomenon within two laboratory studies at a German University. Within the first study, the paper argues the value of automated group recommenders for the agent (decision maker), the partner (group member) and the group as a whole. Groups are identified as dyads; two persons. Results show that all of these three significantly increase (positively) due to the use of automated group recommenders. However, automated group recommenders provide no freedom of choice. The second study did provide the freedom to choose for the agent. Due to this phenomenon, the significant positive effect on all three values faded out. However, by including the social relationship quality as moderator, the effect appeared to be significant again. If the social relationship quality within the group is high, it is more likely that the compromise will increase the value for all members due to increased group experience. Second, group recommenders outperform no recommenders, if the intention of using the recommender is high. If the agent does not believe in the recommender, the agent and the partner will experience less value by choosing a less optimal option out of the recommendations.
In summary, this paper provides managerial and academic relevance by developing a conceptual framework of the effects of group recommenders for retailers and other recommenders. In addition, the paper provides a decision tree for practical guidance. Results show that group recommenders outperform no recommenders, if the intention of using the recommendation system by the agent is high. Group recommenders offer more value than single recommenders within automated recommender systems. However, if the agent has freedom of choice, group recommenders only offer value if the relationship quality is high. Nonetheless, recommenders need to distinguish at first between individual and group consumption. Second, non-registered users need to be identified, including their preferences, by asking questions about them. Further research should investigate the use of other algorithms, interfaces, larger groups, and most importantly other products.
- Article: Hennig-Thurau, T., Marchand, A., & Marx, P. (2012). Can automated group recommender systems help consumers make better choices?. Journal of Marketing, 76(5), 89-109.
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