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Record W3129025901 · doi:10.3390/jtaer16040047

What Deters Online Grocery Shopping? Investigating the Effect of Arithmetic Complexity and Product Type on User Satisfaction

2021· article· en· W3129025901 on OpenAlexaff
Mahdi Mirhoseini, Shirley-Anne Pagé, Pierre‐Majorique Léger, Sylvain Sénécal

Bibliographic record

VenueJournal of theoretical and applied electronic commerce research · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsHEC MontréalConcordia University
Fundersnot available
KeywordsContext (archaeology)Product (mathematics)Grocery shoppingTask (project management)CognitionProduct typeComputer scienceAdvertisingUser satisfactionPsychologyHuman–computer interactionBusinessEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper explores the influence of product type and arithmetic task complexity on users’ perceived mental effort and satisfaction in the context of online grocery shopping. A two-factor within-subject experiment was conducted with 32 participants. Results show that experience products and complex arithmetic tasks are associated with higher perceived mental effort compared to search products and simple arithmetic tasks. Perceived mental effort and satisfaction are negatively related. The more cognitive effort users need to invest in their online shopping tasks, the less satisfied they are likely to be with their online experience. Our results suggest that cognitive absorption mediates the relationship between cognitive effort and satisfaction. The study contributes to our understanding of online grocery shopping by explaining the effect of arithmetic complexity and product type on user satisfaction. It also offers shopping website designers a way to improve consumers’ online grocery shopping experience by implementing simple technology features in their websites to help users reduce their mental effort.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.119
GPT teacher head0.432
Teacher spread0.313 · 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 designTheoretical or conceptual
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

Citations28
Published2021
Admission routes1
Has abstractyes

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