Trust 2.1 – Advancing the trust debate Jens Riegelsberger Google

Mina Vasalou Imperial College London

Trust Debate in HCI ’ CSCW 06 Workshop– organised by Quiping Zhang, John C. Thomas, Dianne Cyr, S. Joon Park

’ CHI 06 Workshop – organised by Jens Riegelsberger, Asimina Vasalou, Philip Bonhard, Anne Adams

’ IJHCS special issue 2003 – edited by Susan Wiedenbeck, Cynthia Corritore, Beverly Kracher

’ CHI 2002 SIG – edited by Susan Wiedenbeck, Cynthia Corritore, Beverly Kracher

’ Communications of the ACM Special Issue 2000- edited by Andrew Rosenbloom

Trust 2.1 – Advancing the Trust Debate Jens Riegelsberger & Asimina Vasalou

’ … and numerous edited books and monographs as well as articles in magazines and popular press

Trust debate Diverse approaches in terms of: ’ ’ ’ ’

Disciplinary background Definitions Methods Objects of trust (e.g. websites, agents, protocols, companies, individuals)

’ Risks

Trust 2.1 – Advancing the Trust Debate Jens Riegelsberger & Asimina Vasalou

Trust debate ’ Changing focus over time (e.g. in e-commerce: safety of transactions to phishing)

’ Any situation is embedded in a web of multiple trust relationships and risks

Trust 2.1 – Advancing the Trust Debate Jens Riegelsberger & Asimina Vasalou

Aims of this SIG ’Review existing models and approaches and their applicability ’Build a framework to achieve common ground on objects and risks ’Discuss goals of trust research and related ethical considerations Trust 2.1 – Advancing the Trust Debate Jens Riegelsberger & Asimina Vasalou

’ Nathan Bos Chemical signals and attributions

’ Cindy Corritore Trust in informational websites

’ Sonja Grabner-Kraeuter Trust in Marketing Research

’ Amjad Hanif eBay Reputation System

’ Ponnurangam Kumaraguru Phishing

’ Gary Olson ’ Jens Riegelsberger A framework for trust in CMC

’ John Thomas Trust and the Myth of a unified agent

’ BJ Fogg Trust 2.1 – Advancing the Trust Debate Jens Riegelsberger & Asimina Vasalou

New developments in longdistance trust Nathan Bos, for CHI 2007 workshop on trust

1. Chemical signals and trust 2. Perception of distance affects attribution

Trust in long distance collaboration • •



Why is trust is harder to achieve at a distance? Working assumption has been that the thin information channels of computer-mediated communications are what makes trust difficult at a distance Two new developments suggests there is more to it

Oxytocin affects trust • Intranasal administration of neuropeptide oxytocin increases trust – Oxytocin is a associated with pair bonding and infant attachment

• Subjects were more trusting and trustworthy in a well-established trust game – Did not lead to general increase in risk behavior

• What does this mean for videoconferencing? Kosfeld, M., Heinrichs, M., Zak, P.J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature 435 (2), 673-677.

Perceptions of distance affects trust • Previous research has shown that at long distance people make different attributions (Cramton) and pay less attention to others • Recent experiments show that people viewing the same information but told they are watching events at a distance make different attributions and perceive fewer distinctions

Henderson, M.D., Fujita, K., Trope, Y., & Liberman, N. (2006). Transcending the “Here”: The Effect of Spatial Distance on Social Judgment. Journal of Personality and Social Psychology, 91 (5), 845-856.

What does this mean? • Do these findings change the trust research agenda?

Online Trust Cindy Corritore Creighton University Beverly Kracher Creighton University

Susan Wiedenbeck Drexel University

Robert Marble Creighton University

the object of trust • the website – research all over the board in different fields • don’t address this explicitly • address it explicitly

• our focus: informational websites – eg. health information (WebMD)

• our basis – Kracher the philosopher – Reeves and Nass CASA (Computers as Social Actors)

• trusting parties – users

risks related to online trust • model of high level online trust of a website • risk is one of three constructs impacting trust that we study –

perception of risk of using the website

• measured by three items: 1. I believe there could be negative consequences from using this website. 2. I feel I must be cautious when using this website. 3. It is risky to interact with this website.

current work • examining online trust in the context of health promotion websites – well individuals seeking health information – diet, exercise, maintenance, etc. – methodology to have participants interact with a well-known website (WebMD), then evaluate their trust using a measurement instrument. – model we propose ….

Q32

Q2

Q4

.88

Q6 .78

.82

Trust

.67

.85

.91

2

R = .74

Cr edibility

2

R =.48 -.27

-.59

2

R =.35

.73

.86

.20

Risk

.54

Q25 Q27

.89

Q30

2

R =.42

PEOU

.65

Predictability

.91 .90

.93 .86

Q20

Q22

Q23

Q17

.78

Q16

Q34

next • different environments …. – MMVW (massively multi-user virtual worlds) – others?

[email protected] [email protected]

CMU Usable Privacy and Security Laboratory http://cups.cs.cmu.edu/

Trust in marketing research c Growing importance of trust in marketing research c Primary focus was on business-to-business relationships (e.g. Moorman et al. 1993; Morgan & Hunt 1994; Doney & Cannon 1997)

c Selected empirical studies with different objects of trust f Trust as important success factor in B2B-relationships

(e.g. Moorman et al. 1993;

Ganesan 1994; Plötner 1995; Doney & Cannon 1997)

f Importance of customer satisfaction and trust in different customer segments (Garbarino & Johnson 1999) f Consumer trust in service provider (frontline employees and management policies and practices) (Sirdeshmukh et al. 2002) f Brand trust (Müller & Wünschmann 2004; Delgado-Ballester 2004; Matzler, Grabner-Kräuter & Bidmon 2006)

f Consumer trust in distribution channels • Retailers and/or department stores (Bauer et al. 2006; Zentes et al. 2006) • Electronic commerce (e.g. Bart et al. 2005; Schlosser et al. 2006)

CHI 2007 SIG Online trust research Sonja Grabner-Kräuter

Risk in marketing research c Different concepts of risk in the marketing and consumer behavior literature

f Perceived risk f Risk aversion f Risk taking

c Dual conception of risk

(e.g. Rousseau et al. 1998)

f uncertainty of an outcome • System-specific and transaction-specific uncertainty

c c

(Grabner-Kräuter 2002)

f importance of negative consequences associated with the outcome of a choice In the marketing literature uncertainty (unknown probability) and risk (known probability) are frequently used synonymously – problems of measurement (Mitchell 1999) Complex relationship between trust and risk (Mitchell 1999; Grabner-Kräuter and

Kaluscha 2003; see also Cheung and Lee 2006 as an example in the IS literature)

f risk is a precondition for the relevance of trust f trust reduces perceived risk f risk taking is a consequence of trust

CHI 2007 SIG Online trust research Sonja Grabner-Kräuter

Perceived risk in consumer behavior c Perceived risk is a well-established concept in consumer behavior f Situational and personal construct that has been defined in several ways (Mitchell 1999)

f Individual and cross-cultural differences

(Harridge-March 2006; Mandrik & Bao 2005;

Park and Jun 2003; Teo and Liu 2007)

c Dimensions of perceived risk f Financial, social, time, performance, psychological, and physical

(Beardon and

Mason 1978)

f Two factors: a combined performance/financial/time risk factor and a psychological/social risk factor (Sweeney et al. 1999)

c Perceived risks of purchasing online, e.g. f f f f f

(Garbarino and Strahilevitz 2004)

Loss of privacy Unauthorized use of credit card information Purchasing from a fraudulent site Having the product not perform as expected Shipping and delivery problems

c Most frequently questionnaires with items for online risk perceptions are used

(e.g. Schlosser et al. 2006; Bart et al. 2005; Park and Jun 2003)

CHI 2007 SIG Online trust research Sonja Grabner-Kräuter

Research interests c Continued use of the Internet as transaction medium for highinvolvement products and/or services

f Different factors influence consumer adoption and continuance behavior (Eriksson and Nilsson 2007)

f Asymmetrical effects of different dimensions of trust

(Sirdeshmukh 2002; Cho 2006)

c Cross-cultural differences in depersonalized trust f Differences in risk perception? f Differences in trust inducing factors?

c Gender differences in bases for online trust f Men are more likely to make more risky decisions than women

(Maddux and

Brewer 2005; Byrnes et al. 1999)

f Women perceive a higher level of risk in online purchasing than men (Garbarino and Strahilevitz 2004)

c Theoretical framework for the relationship between uncertainty, risk and trust

c

Contact information: [email protected]

CHI 2007 SIG Online trust research Sonja Grabner-Kräuter

CMU Usable Privacy and Security Laboratory http://cups.cs.cmu.edu/

Amjad Hanif – eBay Trust & Safety

• Focus on building “trust between strangers” to support commerce • Feedback Forum was launched in 1996 to enable trade in marketplace • Members are able to rate each other based on their performance • Feedback score is one of the primary factors in trust on the site • Over 5 billion ratings in system today with about 4 million left each day • Interested in improving the accuracy of member ratings leading to better information for our community, and improved seller performance

eBay Inc. confidential

Recent Changes to Feedback

• Pilot was underway for last 8 weeks in selected countries • Going live today in all countries • Allows buyers to rate sellers on 4 specific of the transaction • Unlike other feedback, ratings are not attributed to a specific buyer

eBay Inc. confidential

Two Example Sellers

User Name: Positive Feedback %: Feedback Score: Item as Described: Communication: Shipping time: Shipping & Handling Charge:

eBay Inc. confidential

Seller 1 98.3% 226 4.5 4.7 4.8 4.8

Seller 2 98.1% 101 4.1 2.5 2.2 3.5

Site Avg. 99% 4.5 4.4 4.3 4.3

CMU Usable Privacy and Security Laboratory http://cups.cs.cmu.edu/

Object of trust in phishing

AA27

„ Phishing

takes advantage of the way we assign meaning to the content

„ Phishers

make use of the trust that users (trustor) have on organizations (trustee)

„ Victims

falsely trust the fake emails to be from legitimate organizations

„ Victims

falsely trust the fraudulent websites as legitimate organizations

P. Kumaraguru, A. Acquisti, and L. Cranor. Trust modeling for online transactions: A phishing scenario. In Privacy Security Trust, Oct 30 - Nov 1, 2006, Ontario, Canada. • CMU Usable Privacy and Security Laboratory • http://www.cs.cmu.edu/~ponguru

AA28

AA29

AA30

AA31

Risks in phishing

AA32

„ Phishing

is a growing concern among Internet users

„ Cost

involved

• Direct cost: incurred due to phishing attack • Indirect cost: incurred due to increase in support calls and emotional stress for users • Opportunity cost: users refraining from using the Internet „ Important

and hard problem to solve

• CMU Usable Privacy and Security Laboratory • http://www.cs.cmu.edu/~ponguru

AA33

Model representation Not deliberate states

States that affect decision

Misleading signals

AA34

Unknown states

Signals

Meaningful signals

States that affect well-being

Missed signals

• CMU Usable Privacy and Security Laboratory • http://www.cs.cmu.edu/~ponguru

Expert model Unknown states

Not deliberate states

States that affect decision

Misleading signals

Signals

Meaningful signals

States that affect well-being

Missed signals

• CMU Usable Privacy and Security Laboratory • http://www.cs.cmu.edu/~ponguru

Non-expert model Unknown states

Not deliberate states

States that affect decision

Misleading signals

States that affect well-being

Signals

Meaningful signals

Missed signals

• CMU Usable Privacy and Security Laboratory • http://www.cs.cmu.edu/~ponguru

Experiment „ Methodology

• Interview study • Experts and non-experts „ Results

• Significant difference between experts and non-experts in decision making • Non-experts would like to have tools / advice to help them make better trust decisions „ Need

better understanding of trust decisions in phishing scenario to support users make better trust decisions • CMU Usable Privacy and Security Laboratory • http://www.cs.cmu.edu/~ponguru

CMU Usable Privacy and Security Laboratory http://cups.cs.cmu.edu/

Interpersonal Trust at a Distance „

„

What factors promote or impede the formation of trust when people are geographically distributed? Measure of trust – the extent to which people cooperate in a social dilemma game – Has been used widely in the field – Validated by other measures (e.g., questionnaires)

„

Studies – Various media of interaction – Various activities prior to interaction

si.umich.edu

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN

Different conditions for discussion „ „ „ „

Face-to-face Video Audio Text chat Discuss

Round 1-5

Fri

Thurs

Wed

Tues

Mon

Fri

Thurs

Wed

Tues

Mon si.umich.edu

Meet #1

Round 6-10

Discuss etc… Meet #2

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN

Amount invested (trust)

Results by round 90 80 70 60 50 FTF Audio

40 6

11

16

21

Video Text

26

Round si.umich.edu

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN

Different conditions beforehand

Face-to-face Social text chat Seeing a photo of the other person Seeing a brief resume of the other person Nothing Discuss

Round 1-5

Round 6-10 Fri

Thurs

Wed

Tues

Mon

Fri

Thurs

Wed

Tues

Mon si.umich.edu

Meet #1

Text chat Discuss etc… Meet #2

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN

CMU Usable Privacy and Security Laboratory http://cups.cs.cmu.edu/

Objects of Trust Empirical research > User trust in e-commerce web sites [I3E 2001, CHI 2002, CHI 2003, Brit. HCI 2004]

> Users’ ability to identify trustworthy web sites [CHI 2003, Brit. HCI 2004]

> Trust in online advisors [CHI 2005, Brit . HCI 2005]

Conceptual work > Framework for Trust in Mediated Interactions [IJHCS 2005]

Jens Riegelsberger

Risks in Online Interactions Risks > Financial Loss (transaction, credit limit, credit history?)

> Waiting Times, > Spam, > ‘Hassle’

Dis-embedding Interaction is stretched over time and space and involves complex socio-technical systems [Giddens, 1990]

More uncertainty > Inexperienced with decoding cues > Less surface cues are available > Cues might have no significance (“anyone could set up a good-looking site”)

… pervasive in modern societies (e.g. catalogue shopping)

Symbols vs. Symptoms Jens Riegelsberger

TRUSTOR

TRUSTEE

Jens Riegelsberger

TRUSTOR

TRUSTEE

1 Signals

Jens Riegelsberger

TRUSTOR

TRUSTEE

Separation in Space + UNCERTAINTY

1 Signals

Jens Riegelsberger

TRUSTOR

TRUSTEE

Separation in Space + UNCERTAINTY

Outside Option 2b Withdrawal

1 Signals

2a Trusting Action RISK

Jens Riegelsberger

TRUSTOR

TRUSTEE

Separation in Space + UNCERTAINTY

Outside Option 2b Withdrawal

1 Signals

2a Trusting Action RISK

3a Fulfilment

3b Defection Jens Riegelsberger

TRUSTOR

TRUSTEE

Separation in Space + UNCERTAINTY

Outside Option 2b Withdrawal

1 Signals

2a Trusting Action RISK

Separation in Time + UNCERTAINTY

3a Fulfilment

3b Defection Jens Riegelsberger

TRUSTOR Trust

Contextual Properties

TRUSTEE Contextual Incentives

Temporal

Social

Institutional

Context

Signal

Incentive

Jens Riegelsberger

TRUSTOR Trust

Intrinsic Properties

TRUSTEE Contextual Incentives

Temporal

Social

Institutional

Intrinsic Properties

Context

Signal

Incentive

Jens Riegelsberger

TRUSTOR Trust

Intrinsic Properties

TRUSTEE Contextual Incentives

Temporal

Social

Institutional

Ability Internalised Norms Benevolence

Context

Signal

Incentive

Jens Riegelsberger

CMU Usable Privacy and Security Laboratory http://cups.cs.cmu.edu/

Trust and the Myth of a Unified Agent John C. Thomas SIG on on-line trust CHI 2007 San Jose, CA May 1, 2007

Object of Trust in Two Domains •

High Performance Computing Tools: Trust is complexly related to a number of components – Connectivity to high performance facilities – Documentation veracity and completeness – Tool functionality and side-effects



End User Programming via Widget Composition – Widget descriptions are accurate – Composition facility works as stated without hidden side-effects – Ability to comprehend facility – Ability to choose, compose, test, debug

Risks •

High Performance Computing – Wasted time Æ missed deadlines Æ low performance rating | critical failure – Undetected error Ælow performance rating | critical failure – Feeling incompetent, fooled, guilty



End User Programming – Wasted time Æ missed deadlines Æ low performance rating – Undetected error Æ low performance rating – Feeling incompetent, fooled



Modeling focuses on productivity and complexity – Assumption is that if the tools actually “work,” users will come to trust the systems. – Risk minimization comes from careful design, coding, and testing.

Myth of a Unified Agent •

In ordinary speech and writing, we pretend individuals are unitary agents; yet, experience shows this is not true (and advertisers, among others, take advantage of this). – E.g., “Do you want to lose weight (quit smoking, exercise more, etc.) or not?” – “Do you trust me (or this system or this company) or not?”

• • •

In actuality, different environmental frames as well as different emotional states can substantially change our actual behavior. After the fact, we try to generate coherent and consistent “stories” to make us appear unitary and rational. Important in at least two ways, with respect to trust. – How issues are framed and when someone is asked can have huge influence on choices with respect to trust. – Once the person “agrees” to trust, that agreement itself becomes a kind of “twoedged sword.” – On the one hand, the fact of agreement can distort memory and perception to make that agreement of trust rational. – On the other hand, beyond some threshold of irrefutable evidence, the person tends to “switch” to an even less trustful and more hostile stance than if they had never agreed to trust, especially if there is insight into the manipulations of frame and emotion that led to original decision

Implications of Multi-agency • If the desire is to have a truly informed consent, one could try to make sure that the user is asked in several real or imagined contexts and asked to “put them together.” • On the other hand, if the system is trustworthy “enough,” such a thorough procedure might scare away potentially satisfied and productive users.

CMU Usable Privacy and Security Laboratory http://cups.cs.cmu.edu/

Goals of Trust Research Cui bono? ’ Allow sites to acquire more customers? ’ Allow users to make better decisions? ’ Increase trust in online technologies in general? ’ Make everyone act more socially rational?

Trust research poses serious ethical questions. Some examples … Trust 2.1 – Advancing the Trust Debate Jens Riegelsberger & Asimina Vasalou

Scenario 1 Vichy hired a marketing company to maintain a Blog on its new antiaging cream. The Blog posed a woman who was trying out the product reporting on her positive experiences. Eventually consumers discovered this and responded with rage.

Trust 2.1 – Advancing the Trust Debate Jens Riegelsberger & Asimina Vasalou

Scenario 2 Phishing, i.e. using imposter websites or identities to get users to divulge their credentials is a growing problem. Successful phishing relies on ‘trustworthy interface design’ Can malevolent phishers build on the output of HCI trust research?

Trust 2.1 – Advancing the trust debate - Research at Google

May 1, 2007 - transactions to phishing). ✸ Any situation is embedded in a web of multiple trust relationships and risks. Trust 2.1 – Advancing the Trust Debate.

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