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Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems


This study investigates how consumers assess the quality of two types of recommender systems-collaborative filtering and content-based—in the context of e-commerce by using a modified version of the unified theory of acceptance and use of technology (UTAUT) model. Specifically, the concept of trust in the technological artifact is adapted to examine the intention to use recommender systems. Additionally, this study also considers hedonic and utilitarian product characteristics with the goal of presenting a comprehensive picture on recommender systems literature. This study utilized a 2 × 2 crossover within-subjects experimental design involving a total of 80 participants, who all evaluated each recommender system. The results suggest that the type of recommender system significantly moderates many relationships of the determinants of customer behavioral intent on behavioral intention to use recommender systems. Surprisingly, the type of product does not moderate any relationship on behavioral intention. This study holds importance in explaining the factors contributing to the use of recommender systems and understanding the relative influence of the two types of recommender systems on customer behavioral intention to use recommender systems. The finding also sheds light for designers on how to provide more effective recommender systems.

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  1. Content-based recommender systems do use other users’ information to identify and classify profiles and likely preferences for products. For example, Netflix would use their vast database about users and preferences to recommend movies to clients. This is not explicitly social, however, because the algorithms for making these associations are not transparent to the user.

  2. These eight models include the Technology Acceptance Model (TAM) (Davis 1989), the Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1975), the Theory of Planned Behavior (TPB) (Ajzen 1991), the Combined TAM and TPB (C-TAM-TPB) (Taylor and Todd 1995), the Innovation Diffusion Theory (IDT) (Moore and Benbasat 1991), the Social Cognitive Theory (SCT) (Compeau and Higgins 1995), the Motivational Model (MM) (Davis et al. 1992), and the Model of PC Utilization (MPCU) (Thompson et al. 1991).

  3. One time-varying covariate of recommender type familiarity and one time-invariant covariate of age were also included in the analysis. These two variables were not part of the primary analysis and were only included to account for possible differences in behavioral intent due to subject age and/or differing previous experience with the technologies.

  4. The use of the STEST equations involved constructing tests for each treatment effect across models. For example, given that the treatment conditions for the four models were M1: collaborative hedonic, M2: collaborative utilitarian, M3: content-based hedonic, M4: content-based utilitarian, a test of the moderating effect of type of recommender system on PE would be… stest m1.PE1 + m2.PE2 − m3.PE3 − m4.PE4 = 0. If this test was rejected, then there would be evidence on an interaction of recommender type and PE on BI.


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Correspondence to Yen-Yao Wang.


Appendix 1: Product manipulation

Nine product classes: cell phone, Laptop, Digital Camera, MP3 player, TV, Camcorder, Printer, and GPS

  1. 1.

    Hedonic = fun, enjoyable, for pleasure

    Utilitarian = work related, get a job done, accomplishes a task or useful goal

    Please rate these items based on whether you consider each to be closer to being utilitarian or hedonic (Seven point semantic differentials: 1 = Hedonic, 7 = Utilitarian)

  2. 2.

    Please rate these items based on whether you consider each to be closer to being exciting or dull (seven point semantic differentials: 1 = exciting, 7 = dull)

  3. 3.

    Please rate these items based on whether you consider each to be closer to being pleasant or unpleasant (seven point semantic differentials: 1 = pleasant, 7 = unpleasant)

  4. 4.

    Please rate these items based on whether you consider each to be closer to being interesting or boring (seven point semantic differentials: 1 = interesting, 7 = boring)

Appendix 2: Measurement items

Performance expectancy (PE)


I would find the recommender system useful in searching and finding items


Using the recommender system enables me to search and find items more quickly


Using the recommender system increases my productivity in searching and finding items


If I use the recommender system, I will increase my chances of getting better purchasing advice

Effort expectancy (EE)


My interaction with the recommender system is clear and understandable


It would be easy for me to become skillful at using the recommender system


I would find the recommender system easy to use


Learning to operate the recommender system is easy for me

Social influence (SI)


Friends of mine would also find this system attractive


People whose opinion I value would be in favor of using this system


A business professor would recommend using this recommender system


I believe that expert computer users would recommend this system



Even if the system were not monitored, I would trust the recommender system to recommend appropriate items


I trust the recommender system


I trust that the system makes reliable recommendations

Behavioral intentions (BI)


I intend to use this type of recommender system in the next 6 months


I predict I will use this type of recommendation system in the next 6 months


I plan to use this type of recommendation system in the next 6 months

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Wang, YY., Luse, A., Townsend, A.M. et al. Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems. Inf Syst E-Bus Manage 13, 769–799 (2015).

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  • Recommender systems
  • Trust
  • Hedonic product
  • Utilitarian product