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Record W2912465083 · doi:10.1080/09593969.2018.1555543

The influence of social capital through social media: a study of the creation of value in shopping behaviour

2019· article· en· W2912465083 on OpenAlexaff
Hyowon Hyun, Frances Gunn, Jungkun Park

Bibliographic record

VenueThe International Review of Retail Distribution and Consumer Research · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBridging (networking)Social capitalPurchasingStructural equation modelingPerceptionSocial mediaSocial network (sociolinguistics)PsychologyValue (mathematics)BusinessMarketingSocial psychologyComputer scienceSociologyWorld Wide WebComputer security

Abstract

fetched live from OpenAlex

When consumers access information from groups through social network sites (SNSs), they develop social capital in the form of bonding and bridging ties with these groups. The purpose of this study is to investigate the influence of these bonding and bridging behaviours on consumers’ use of the social network information (SNI) gained from SNSs in their purchasing decisions. The study integrates constructs from the Technology Acceptance Model and the concept of flow to examine how these behaviours influence consumers’ perceptions of how useful the SNI is, of how easy the SNI is to use, and how they engage with SNI. The study utilizes structural equation modelling to examine questionnaire data from a random sample of social network users. The findings demonstrate that bonding and bridging ties influence consumers’ perceptions of the usefulness and ease of use of the information provided by SNSs, and therefore influence their use of the information when making shopping decisions. In addition, consumers who access SNI through bonding ties are likely to have flow experiences which further contribute to their use of the information. This study makes a theoretical contribution by expanding knowledge of the social capital influences on consumers’ perceptions of the value of the social media shopping experience.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.136
GPT teacher head0.454
Teacher spread0.319 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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