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

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

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

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

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

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

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

RA Internalization: low vs. high Both RA’s from http://www.activebuyerguide.com 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

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

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

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; 1.5-2 hours 11/10/2018 Sherrie Y. Xiao

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

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

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. 0.48 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

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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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

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