1. Sherrie Xiao and Izak Benbasat
The Impact of Internalization and Familiarity on Trust and Adoption of Recommendation Agents
Sherrie Xiao and Izak Benbasat
University of British Columbia, Canada
November 10, 2018

2. Recommendation Agents in Agent-mediated Ecommerce
Companies’ Websites
Recommendation
Agent (RA)
Products
Companies
A customer
Products
product1
Product2
product3
...
RA’s: Firefly; ActiveBuyerGuide.com;
Tete-a-Tete; MySimon.com …
11/10/2018
Sherrie Y. Xiao

3. Relationship Between a Customer and An RA
Personal
Representation
Delegation
A recommendation agent
A customer
The nature of the relationship between a customer
and an IT (e.g. RA) changes
The change may transform IT adoption, customer trust in
the IT, and IT design
Objective: to understand these transformations from the viewpoint of a customer in ecommerce
11/10/2018
Sherrie Y. Xiao

4. The Research Model Cognitive trust Internalization: CT-Comp
CT-Bene
CT-Integ
Emotional trust
Internalization:
low vs. high + +
Int. Delegate
Int. Dec. Aid +
Familiarity:
Initial interaction vs. repeated interactions + +
n/e +
Preference for Quality
11/10/2018
Sherrie Y. Xiao

5. Experimental Design A laboratory experiment
100 student subjects in a business school
Subjects were randomly assigned
Familiarity
Initial
Repeated
Internalization
Low
High
11/10/2018
Sherrie Y. Xiao

6. Pilot tests Pilot test 1 Pilot test 2 Pilot test 3
Objective: look for the right RA’s and pretest the experimental procedure
23 subjects took the experiment individually
Pilot test 2
Objective: to develop measures
18 subjects finished 3 rounds of card-sorting
Pilot test 3
Objective: to test the reliability and validity of measures (factor analysis)
162 subjects tried an RA and answered questionnaires
11/10/2018
Sherrie Y. Xiao

7. Independent Variable: RA Internalization
RA Internalization: low vs. high
RA with low internalization
Important product features, choices, explanations, constraint-satisfaction filtering
RA with high internalization
All features above, plus
Weight questions
The data are more similar to the real data used by a customer
“Get advice” function
A customer expresses her needs from a multiple choice list (e.g., I want a PC to play computer games)
11/10/2018
Sherrie Y. Xiao

8. RA Internalization: low vs. high
Both RA’s from
A selection after
Reviewing the RA’s available online
Conducting a pilot test with 23 subjects
Manipulation was successful
4.4 (low) vs. 5.8 (high), t-test, p<0.001, N=100
11/10/2018
Sherrie Y. Xiao

9. Independent Variable: Familiarity
Familiarity: Initial vs. Repeated Interactions
None of the subjects had used the experimental RA’s
Initial interaction: a notebook
Repeated interactions: a notebook, a desktop PC, and a digital camera
Manipulation was successful
4.7 (initial) vs. 5.5 (repeated), t-test, p<0.01, N=48
11/10/2018
Sherrie Y. Xiao

10. Dependent Variables: Measure Development
New measures developed for
Cognitive trust in an RA’s competence
Cognitive trust in an RA’s benevolence
Cognitive trust in an RA’s integrity
Emotional trust in an RA
The intention to adopt an RA as a delegated agent
The intention to adopt an RA as a decision aid
The preference for decision quality
Three steps (Moore and Benbasat 1991)
Scale creation, development, and testing
PLS measure model (Barclay, Thompson, & Higgins,1995)
11/10/2018
Sherrie Y. Xiao

11. Experimental Procedures
Consent form & background questionnaire
Tutorial about RA
Interacting with an RA
Questionnaire
Manipulation check of Internalization
Subjects tried the other RA and rated both RAs’ internalization
Note: Manipulation check of Familiarity
Each subject rated familiarity after each product shopping
Experimental Setting
Each subject took the experiment individually; hours
11/10/2018
Sherrie Y. Xiao

12. Findings (PLS Analysis Results) (Green: significant; Blue: no effect)M
CT-C (R^=0.39)
Internalization
Int. Delegate (R^=0.56)
CT-B (R^=0.56)
CT-I (R^=0.30)
0.18*
0.22
0.47*** M M
0.75***
Int. Dec. Aid
(R^=0.60) M
Familiarity
0.48***
ET(R^=0.70)
Preference for Quality
11/10/2018
Sherrie Y. Xiao

13. What do we learn? Int. Delegate vs. Int. Dec. Aid
Internalization affects RA adoption through customer trust
RA Representation affects RA adoption
1 SD increase in internalization leads to
0.46 SD increase in Int. Delegate
0.47 SD increase in Int. Dec. Aid
Through
0.55 SD increase in CT-competence
0.68 SD increase in CT-benevolence
0.43 SD increase in CT-integrity
0.60 SD increase in ET
11/10/2018
Sherrie Y. Xiao

14. What do you learn? (cont.)
RA adoption is affected by cognitive & emotional trust
Important to feel comfortable/secure about relying on an RA
CT -> ET -> Intention to adopt: full mediation
The higher dependence, the greater role of emotional trust
0.75 on Int. Delegate vs on Int. Dec. Aid
Familiarity with an RA affects customer trust in an RA
Familiarity -> CT
Familiarity -> CT ->ET
Preference for decision quality
Increases Int. Dec. Aid
Does not affect Int. Delegate
11/10/2018
Sherrie Y. Xiao

15. 11/10/2018
Sherrie Y. Xiao

16. Subjects 100 undergraduate/graduate students in a business school
Felt comfortable shopping online: 4.8 out of 7.0
Spent average of $345 shopping online in 2001
All were interested in buying the three products
Randomly assigned to four groups
Incentives
$15 or ($10 plus one bonus mark)
Lottery: one winner would get $400 toward one product which s/he decided to buy during the experiment
11/10/2018
Sherrie Y. Xiao

17. Internalization (reflective)
Definition: Internalization is a customer’s perception of how well an RA represents the customer’s real needs.
Measure/ manipulation check (Alpha = 0.92)
(zIntern1) This RA understands my needs and preferences.
(zIntern2) This RA knows what I want.
(zIntern3) This RA takes my needs and preferences as its preferences.
11/10/2018
Sherrie Y. Xiao

18. Familiarity (reflective)
Definition: Familiarity refers to a customer’s knowledge of how an RA derives its recommendation
Measure/ manipulation check
(zFami1) I am familiar with how this RA makes its recommendation
11/10/2018
Sherrie Y. Xiao

19. Cognitive trust in an RA’s competence (reflective)
Definition: a customer’s rational assessment that an RA will have capability to give a good product recommendation.
Measures (Alpha = 0.74)
(zCTC1) This RA is a real expert in assessing products.
(zCTC2) This RA has good knowledge about products.
(zCTC3) This RA considers all important product attributes and my needs.
11/10/2018
Sherrie Y. Xiao

20. Cognitive trust in an RA’s benevolence (reflective)
Definition: a customer’s rational assessment that an RA will care about the customer and intend to act in the customer’s interest.
Measures (Alpha = 0.84)
(zCTB1) This RA puts my interest first.
(zCTB2) This RA is concerned with my needs and preferences.
(zCTB3) This RA wants to understand my needs and preferences.
11/10/2018
Sherrie Y. Xiao

21. Cognitive trust in an RA’s integrity (reflective)
Definition: a customer’s rational assessment that an RA will be honest and unbiased.
Measures (Alpha = 0.87)
(zCTI1) This RA provides unbiased product recommendations.
(zCTI2) This RA is honest.
(zCTI3) I consider this RA to be of integrity.
11/10/2018
Sherrie Y. Xiao

22. Emotional trust in an RA (reflective)
Definition: a customer’s feeling secure and comfortable about relying on an RA for the decision about what to buy.
Measure (Alpha = 0.95)
(zET1) I feel secure about relying on this RA for my decision.
(zET2) I feel comfortable about relying on this RA for my decision.
(zET3) I feel confident about relying on this RA for my decision.
(zET4) I feel content about relying on this RA for my decision.
11/10/2018
Sherrie Y. Xiao

23. Intention to adopt an RA as a delegated agent (reflective)
Definition: The extent to which a customer is willing to let an RA make decision on behalf of the customer about what to buy
Measure (Alpha = 0.88)
(zDeleg1) I am willing to delegate to this RA for my decision about which product to buy.
(zDeleg2) I am willing to let this RA decide which product to buy on my behalf.
11/10/2018
Sherrie Y. Xiao

24. Intention to adopt an RA as a decision aid (reflective)
Definition: the extent to which a customer is willing to let an RA narrow down her choices; then she will evaluate product information and make purchase decision.
Measure (Alpha = 0.89)
(zDecAid1) I am willing to use this RA as an aid to help with my decision about which product to buy.
(zDecAid2) I am willing to let this RA assist me decide which product to buy.
(zDecAid3) I am willing to use this RA as a tool that suggests to me a number of products from which I can choose.
11/10/2018
Sherrie Y. Xiao

25. Preference for Decision Quality
Definition: the extent to which a customer would like to improve decision quality at the expense of increased effort for shopping (adapted from Todd & Benbasat, 1992)
Measure (Alpha = 0.78)
(zqlteft1) I am willing to examine the product attributes very carefully in order to make sure that the product fits my preferences perfectly.
(zqlteft2) I prefer to shop hard in order to get exactly what I want.
11/10/2018
Sherrie Y. Xiao

26. 11/10/2018
Sherrie Y. Xiao

27. 11/10/2018
Sherrie Y. Xiao

28. CT->ET->Int.Delegate: full mediation
1. CT-comp -> ET -> Int. Delegate: full mediation
Step 1: IV->DV
CT-comp-> Int.Delegate
CT-comp: 0.57***
Step 2: IV->M
CT-comp-> ET
CT-comp: 0.78***
Step 3: IV+M->DV
CT-comp + ET -> Int. Delegate
CT-comp: -0.03
ET: 0.77***
2. CT-bene -> ET -> Int. Delegate: full mediation
CT-bene-> Int.Delegate
CT-bene: 0.56***
CT-bene-> ET
CT-bene: 0.75***
CT-bene + ET -> Int. Delegate
CT-bene: 0.01
ET: 0.74***
3. CT-bene -> ET -> Int. Delegate: full mediation
CT-integ-> Int.Delegate
CT-integ: 0.47***
CT-integ-> ET
CT-integ: 0.61***
CT-integ + ET -> Int. Delegate
CT-integ: 0.01
11/10/2018
Sherrie Y. Xiao

29. CT->ET->Int.Dec.Aid: full mediation
1. CT-comp -> ET -> Int.Dec.Aid: full mediation
Step 1: IV->DV
CT-comp-> Int.Dec.Aid
CT-comp: 0.65***
Step 2: IV->M
CT-comp-> ET
CT-comp: 0.78***
Step 3: IV+M->DV
CT-comp + ET-> Int.Dec.Aid
CT-comp:0.20(t=1.43)
ET: 0.58***
2. CT-bene -> ET -> Int.Dec.Aid: full mediation
CT-bene-> Int.Dec.Aid
CT-bene: 0.64***
CT-bene-> ET
CT-bene: 0.75***
CT-bene + ET -> Int.Dec.Aid
CT-bene: 0.22 (t=1.50)
ET: 0.57***
3. CT-bene -> ET -> Int.Dec.Aid: partial mediation
CT-integ-> Int.Dec.Aid
CT-integ: 0.56***
CT-integ-> ET
CT-integ: 0.61***
CT-integ + ET -> Int.Dec.Aid
CT-integ: 0.16*
ET: 0.64***
11/10/2018
Sherrie Y. Xiao

30. Internalization -> CT -> ET: full mediation
1. Internalization -> CT-comp -> ET: partial mediation
Step 1: IV->DV
Internalization-> ET
Internalization: 0.62***
Step 2: IV->M
Internalization-> CT-comp
Internalization: 0.64***
Step 3: IV+M->DV
Internalization +CT-comp -> ET
Internalization: 0.26*
CT-comp: 0.62***
2. Internalization -> CT-bene -> ET: full mediation
Internalization: 0.74***
Internalization-> CT-bene
Internalization + CT-bene -> ET
Internalization: 0.19
CT-bene: 0.61***
3. Internalization -> CT-integ -> ET: partial mediation
Internalization: 0.52***
Internalization-> CT-integ
Internalization + CT-integ -> ET
Internalization: 0.44***
CT-integ: 0.39***
11/10/2018
Sherrie Y. Xiao

31. Familiarity -> CT -> ET: full mediation
1. Familiarity -> CT-comp -> ET: full mediation
Step 1: IV->DV
Familiarity-> ET
Familiarity: 0.33**
Step 2: IV->M
Familiarity-> CT-comp
Familiarity: 0.37***
Step 3: IV+M->DV
Familiarity + CT-comp -> ET
Familiarity: 0.05
CT-comp: 0.76***
2. Familiarity -> CT-bene -> ET: full mediation
Familiarity-> CT-bene
Familiarity: 0.42***
Familiarity + CT-bene -> ET
Familiarity: 0.02
CT-bene: 0.74***
3. Familiarity -> CT-integ -> ET: full mediation
Familiarity-> CT-integ
Familiarity: 0.30***
Familiarity + CT-integ -> ET
Familiarity: 0.11
CT-integ: 0.57***
11/10/2018
Sherrie Y. Xiao

32. PLS Structural Model
Path
Direct
T-Statistic
Indirect
Total
Hypothesis
Internalization->CT-competence
0.55
5.47***
H4a: S
Internalization->CT-benevolence
0.68
10.19***
H4b: S
Internalization->CT-integrity
0.43
3.60***
H4c: S
Internalization->ET
0.12
0.86
0.48
0.60
H4d
Familiarity->CT-competence
0.15
1.75*
H5a: S
Familiarity->CT-benevolence
2.00*
H5b: S
Familiarity->CT-integrity
0.21
1.67*
H5c: S
Familiarity->ET
-0.05
-0.96
0.14
0.08
H5d
Note: * t>1.66 and p<0.05; ** t>2.364 and p<0.01; *** t>3.174 and p<0.001.
11/10/2018
Sherrie Y. Xiao

33. PLS Structural Model (cont.)
Path
Direct
T-Statistic
Indirect
Total
Hypothesis
CT-comp->ET
0.47
3.95***
H3a: S
CT-comp->Int.Delegate
-0.04
-0.36
0.35
0.31
H1a
CT-comp->Int.Dec.Aid
0.11
0.75
0.23
0.34
H2a
CT-bene->ET
0.22
1.48
H3b: N
CT-bene->Int.Delegate
0.03
0.26
0.17
0.20
H1b
CT-bene->Int.Dec.Aid
0.66
0.21
H2b
CT-integ->ET
0.18
1.77*
H3c: S
CT-integ->Int.Delegate
0.01
0.14
0.13
0.15
H1c
CT-integ->Int.Dec.Aid
0.10
1.11
0.09
0.19
H2c
ET -> Int. Delegate
7.19***
H1d: S
ET -> Int. Dec. Aid
0.48
3.94***
H2d: S
11/10/2018
Sherrie Y. Xiao

34. PLS Structural Model (cont.)
Path
Direct
T-Statistic
Indirect
Total
Hypothesis
QualityEffort->Int.Delegate
-0.07
-1.05
H6a: S
QualityEffort->Int.Dec.Aid
0.18
2.37**
H6b: S
Internalization->Int.Delegate
0.46
Internalization->Int.Dec.Aid
0.47
Familiarity->Int.Delegate
0.06
Familiarity->Int.Dec.Aid
0.09
Note: * t>1.66 and p<0.05; ** t>2.364 and p<0.01; *** t>3.174 and p<0.001.
11/10/2018
Sherrie Y. Xiao