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Record W2550510245 · doi:10.5539/ibr.v9n12p131

An Empirical Examination of the Relation between Consumption Values, Mobil Trust and Mobile Banking Adoption

2016· article· en· W2550510245 on OpenAlexvenueno aff
Murat Burucuoğlu, Evrim Erdoğan

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsMobile bankingStructural equation modelingValue (mathematics)Consumption (sociology)BusinessRelation (database)Mobile commerceMarketingPsychologyEconometricsEconomicsComputer scienceStatisticsSociologyMathematicsData mining

Abstract

fetched live from OpenAlex

<p>The purpose of this study is to examine the relations among consumption values of the consumers relevant to mobile banking services, adoption to mobile banking and mobile trust. For this purpose, we propose a structural model which demonstrates the relations between consumption values, mobile banking adoption and mobile trust of consumers. The data had been collected through survey applied on individuals who are using mobile banking services in Turkey. It had been reached to 175 participants in total. The obtained data had been analyzed by partial least squares path analysis (PLS-SEM) which is known as second generation structural equation modeling. As the result of the research, it had been concluded that the conditional value, emotional value and epistemic value –from among consumption values- have positive and statistically meaningful effect on adoption to mobile banking, and that the social value has negative and statistically meaningful effect. It is being observed that there is positive and statistically meaningful relation in between trust relevant to mobile banking and conditional value, emotional value and functional value. And there are positive and statistically meaningful relations on trust relevant to mobile banking and adoption to mobile banking.</p>

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)0.005
metaresearch head score (Gemma)0.002
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 score0.101
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.264
GPT teacher head0.492
Teacher spread0.228 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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