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Record W4405256703 · doi:10.1002/cb.2435

The Role of Augmented Reality Experiences in Consumers' Purchase Intention Toward New Products

2024· article· en· W4405256703 on OpenAlex
Anupama Ambika, Varsha Jain, Russell W. Belk, Dharun Kasilingam, Rajneesh Krishna

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Consumer Behaviour · 2024
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsYork University
Fundersnot available
KeywordsMarketingRevenueConsumption (sociology)PsychologyBusinessAugmented realityCognitionConsumer behaviourSurvey data collectionSociologyComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Augmented reality marketing (ARM) is rapidly emerging as a critical marketing channel to enhance consumer experiences. In the past, researchers have focused on ARM's varied antecedents, mechanisms, and outcomes. However, this study aims to deepen the knowledge by exploring how ARM experiences can drive unique outcomes. Based on the TEAV model of consumption experience, we followed a mixed methods approach, utilizing the data from 22 interview participants and 711 survey respondents, analyzed through PLS‐SEM. The findings indicate that ARM facilitates cognitive, affective, and co‐creation experiences, influencing the purchase intention toward new variants of familiar products. The study's insights establish the role of technology in enabling new experiences and influencing consumption behaviors. The findings expand the academic understanding of the unique outcomes of ARM experiences. Brands, retailers, and marketers can use this research to boost revenue and image by encouraging ARM‐enabled experiences and purchases.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.038
GPT teacher head0.313
Teacher spread0.276 · 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