MétaCan
Menu
Back to cohort
Record W2064650866 · doi:10.1145/1646353.1646386

Designs for effective implementation of trust assurances in internet stores

2010· article· en· W2064650866 on OpenAlexaff
Dong‐Min Kim, Izak Benbasat

Bibliographic record

VenueCommunications of the ACM · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversity of British ColumbiaUniversity of New Brunswick
Fundersnot available
KeywordsThe InternetInternet privacyBusinessTrustworthinessHackerComputer securityCredit cardComputer scienceNothingWorld Wide WebFinance

Abstract

fetched live from OpenAlex

Introduction Improving customer trust in an internet store is an important goal in B2C electronic commerce because it leads to outcomes important for the success of an Internet store, such as reduced customer risk perceptions in transacting with the store and increased willingness to buy from the store. Therefore, one of the critical success factors for Internet stores is to convince customers of the store's trustworthiness, which refers to a set of customers' beliefs regarding the ability, integrity, and benevolence of the online merchant. One mechanism by which such perceptions of trustworthiness on the part of stores can be established is to provide trust assurances on a store's Web site. Trust assurance refers to "a claim and its supporting statements used in an Internet store to address trust-related concerns." An example of trust assurance statements found in internet stores is: We are so confident in our security that we guarantee you'll pay nothing if unauthorized charges are made to your credit card as a result of shopping at circuitcity.com." (Excerpted from a checkout page of www.circuitcity.com) Trust assurances can be provided by an Internet store itself, by customers, or by a third party organization. For example, Simplycheap.com, shown in Figure 1, displays both a store's self-proclaimed assurance (such as "safe shopping our security guarantee") and a third-party assurance (such as Hacker Safe). In this article, we first review finding in previous studies regarding trust assurance. Then we provide a snapshot regarding how often Internet stores use trust assurances and what concerns are addressed in such trust assurances by reporting current usage of trust assurances based on observations of 85 Internet stores. We expect that this snapshot will help business managers to understand how other companies use trust assurances. Second, we suggest two design guidelines for effective implementation of trust assurances for Web developers. Before reporting our findings based on observations from 85 Internet stores, we briefly review the findings of several previous studies regarding trust assurances. First, many studies have reported that displaying trust assurances increases the trustworthiness of an Internet store. A store's own assurance enhances the trustworthiness of an Internet store if they are well-structured. Third-party assurances (or trustmarks), such as TRUSTe and BBBOnLine seals positively influence the favorableness of a store's privacy policies, and are more influential in improving a firm's trustworthiness than a rating by Consumer Reports magazine is. Among third-party assurances, the WebTrust seal appeared to be more influential than BBBOnLine when people chose a vendor. Interestingly, third-party assurances were not considered as important as "security features," such as SET (Secure Electronic Transaction), SSL (Secure Sockets Layer), and a lock symbol, in customer's decisions to buy on the World Wide Web. Second, detailed design/ usability guidelines for building a Web site are already available. For example, best practices in Interaction Design can be accessed at van Welie's Web site (http://www.welie.com/patterns/). In respect to specific implementations of trust assurances, ease of access to assurances was suggested as one of several design considerations. For example, van Duyne et al. suggested that Internet stores needed to make their privacy policy available on each of their Web pages. The finding that only 54% of licensees of the top 500 Internet consumer Web sites display their privacy seal of approval information on both their home and privacy pages indicates that the other 46% has room to improve their customers' ease of access to trust assurances. In this study, based on van Duyne et al. we examine ease of access to trust assurances. In addition, we examine the application of ease of return to the original checkout screen that customer was working on before accessing trust assurances which is important for customers to easily complete the checkout process. These two implementation issues (for example, ease of access and ease of return) are examined in the assurance delivery modes section.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0140.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.189
GPT teacher head0.482
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCommunications of the ACMSame topicTechnology Adoption and User BehaviourFrench-language works237,207