My Master's Research Presentation. Presented at William Paterson University of New Jersey on May 9, 2016.

1. The Influence of Multimedia
on Recommender System
User’s Perceptions of System
Credibility and Intention to
Accept Recommendations
Ashley Farrell
M.A. Professional Communication
William Paterson University of New Jersey

2. Background
- Technological advancement has led to
an increase in recommender systems
- Recommender systems (RS) are
utilized across the web, both online
and mobile
- So, what are they?
- Algorithms that generate content for a
specific user
- Based on your likes
- Based on stranger’s likes
- Based on search history

3. Recommender Systems, Online

4. Recommender Systems, Mobile

5. Multimedia
- “Multimodal” (Marmolin, 1991)
- Allows user to explore information in
an “active” way
- Mayer’s Multimedia Principle (2009):
- “People learn more deeply from words and
pictures than from words alone”
- Multimedia supports the way our brain
works

6. Multimedia… and Theory
Mayer’s Cognitive Theory of Multimedia Learning [CTML] (2009)
1. Dual-coding: There are two separate channels for processing information
1. Audio
2. Visual
2. Cognitive load: Each channel has limited capacity for storing information
3. Learning is an active process of filtering, selecting, organizing, and
integrating information based on prior knowledge

7. Multimedia… and User Interface
- “Must present recommendations in a
manner that allows users to consider
acting upon the recommendation”
(Murphy-Hill & Murphy, 2013)
- Visual representations of information
have a positive influence on
perception
- How? Enhancing cognition through
aesthetics

8. Problem Statement
- Lack of investigative studies on how
multimedia can influence cognitive
processes
- Lack of investigative studies on
influencing perceptions of movie
recommender systems specifically
- With growing number of RS and movie
streaming sites, it would be useful to
have more insight

9. Perceived Recommender System
Credibility
Intention to Accept the
Recommendation
Multimedia
i. Text
ii. Text and Photo
iii. Text and Video
H1
H2
H3
Research Model

10. Hypotheses
H1:
As the modality of multimedia increases, the perceived credibility of the
recommender system is also increased.
H2:
As the modality of multimedia increases, the user’s intention to accept the
the system’s recommendation is also increased.
H3:
As the perceived credibility of the system increases, the user’s intention to
accept the system’s recommendation is also increased.

11. Research Design
- Basic experimental design
- Three conditions: Condition 1: Text

12. Research Design
- Basic experimental design
- Three conditions: Condition 2: Text-and-Photo

13. Research Design
- Basic experimental design
- Three conditions: Condition 3: Text-and-Video

14. Data from
75 college students
enrolled in a two- or four-year New Jersey university
was collected between
March 3, 2016 — April 10, 2016
with an incentive of
course extra credit offered to WPUNJ students.

15. Data Analysis
- Factor analysis and reliability tests
- To evaluate the measurements of perceived
system credibility and intention to accept the
recommendation
- Descriptive analysis
- To describe participant profile and gain insight
into movie streaming preferences
- One-way between groups ANOVA
- To explore influence of multimedia on perceived
system credibility and intention to accept the
recommendation
- Multiple regression analysis
- To test users’ perceived system credibility on the
intention to accept the recommendation

16. Measures: Perceived Credibility of RS
- Measurement scales for Perceived Credibility (adopted from Yoo, 2010)
- Six items for perceived expertise (Cronbach Alpha = .95)
- Uninformed-informed; Unskilled-Skilled; Inexpert-Expert; Incompetent-Competent;
Unintelligent-Intelligent; Unknowledgeable-Knowledgeable
- Four items for perceived trustworthiness (Cronbach Alpha=.93)
- Undependable-Dependable; Untrustworthy-Trustworthy; Unreliable-Reliable;
Dishonest-Honest
- 7-point semantic differential scale

17. Measures: Intention to Accept Recommendation
- Developed based on previous literature
- Three items on a 7-point Likert scale
- Unidimensionality of scale confirmed high reliability (Cronbach Alpha= .94)
Construct Names and Items Mean Factor Load Eigen Value
% of Vari.
α
Intention to Accept Recommendation 5.35 2.72 90.6% .95
The probability that I would consider watching this movie over spring
break is high.
5.24 .98
The likelihood that I would watch this movie over spring break is high. 5.35 .95
I would be willing to accept the recommendation suggested by this RS. 5.45 .93

18. Sample Profile
- More females (55%) than males
- Mostly 21-24 years old and Caucasian (61%)
- Never used a movie RS before (77%)
- Most use a movie streaming service 1x/week (55%)
- Netflix (89%)

19. - On average, all RS perceived as reasonably credible (M=5.35; SD=1.35)
- No significant influences of multimedia on system user’s perceived
credibility of recommender system
Results: Multimedia –> Perceived System Credibility (H1)
RS Credibility dF F P
RS Expertise 74 0.868 0.424
RS Trustworthiness 74 1.429 0.246

20. Results: Multimedia -> Intention to Accept R (H2)
- On average, all RS show reasonable intention to accept (M=5.28; SD=1.57)
- No significant influence of multimedia on system user’s intention to
accept the recommendation
dF F P
Intention to Accept 74 1.355 0.265

21. Interesting Trend: Mean Plot
As modality of multimedia
increases, the average intention
to accept the recommendation
also increases.
The mean difference between
text and text-and-video RS users is
M=.96 (P=.241).

22. - Significant positive influence of the user’s perceived credibility of a system
on their intention to accept its recommendation
Results: System Credibility –> Intention to Accept R (H3)
RS Credibility Beta P
RS Expertise .355 .033
RS Trustworthiness .341 .039
R Square= 0.45; Adjusted R Square=0.43; F (2, 72)= 28.99, p<.000

23. Conclusions
Multimedia
does not significantly influence the credibility (H1)
of recommender system and multimedia
does not significantly influence a user’s intention to accept (H2)
a system’s recommendation.

24. HOWEVER...

25. Conclusions
Recommender system credibility
(Expertise + Trustworthiness)
does significantly influence (H3)
a user’s intention to accept a recommendation.

26. Limitations
Survey respondents may have
disliked of the movie generated by the RS.
The sample size was smaller than expected.
The RS UI was not optimized for mobile devices.

27. Implications
Designers of recommender system user interfaces
should examine other factors
that contribute to a user’s decision-making process.

28. Future Research
It could be worthwhile to explore the link between
credibility and an RS user’s intention to accept a
recommendation, and also how multimedia
affects RS user’s intention to accept a recommendation.
Future researchers might also
re-test the experiment with manipulations to
the movie option, UI design, or tone of text description.

29. THANK YOU.
Questions?