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Record W4311785103 · doi:10.5267/j.ijdns.2022.12.012

The effect of multimodality on customers' decision-making and experiencing: A comparative study

2022· article· en· W4311785103 on OpenAlexvenueno aff
Abdullah Alsokkar, Effie L-C. Law, Dmaithan Almajali, Mohammad Alshinwan

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsAvatarPurchasingNaturalismMultimodalityPsychologyNaturalistic observationPresentation (obstetrics)Frame (networking)Applied psychologyComputer scienceSocial psychologyCognitive psychologyHuman–computer interactionMarketingMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

The primary objective of this article is to present the findings of an experimental study on the use of a human-like “naturalistic” avatar (of both genders) for information presentations. The primary goal is to investigate how users' frame of mind, purchasing behavior, and satisfaction are affected by being presented with information by a naturalistic avatar that lacks expressive capabilities. Two different types of information presentations were developed and empirically tested on 48 participants, namely: (i) two dimensional static graphical and textual information presentation and (ii) non-expressive naturalistic avatar. For this comparative research study, a simplified version of the user experience model that was used in our earlier research (the EUX-DM) was selected to serve as the measurement model. Participants' perceived values of the measured qualities were found to be independent of both their own and the naturalistic avatar's gender, and the non-expressive avatar had a positive effect and a stronger encouragement on participants' intention to purchase, usage attitude, and satisfaction than other types of information presentations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.049
GPT teacher head0.375
Teacher spread0.326 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2022
Admission routes1
Has abstractyes

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