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Behaviors and Trends in E-Purchasing: Case of Turkey

2013· article· en· W1702127950 on OpenAlexvenueno aff
Lamiha Gün, Şirin Atakan-Duman

Bibliographic record

VenueCanadian social science · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingThe InternetBusinessMarketingAdvertisingQuality (philosophy)PopulationDemocratizationConsumer behaviourPurchasing processSociologyComputer sciencePolitical scienceDemocracy

Abstract

fetched live from OpenAlex

In Turkey, as in many countries, online shopping has experienced modest changes in 90’s. In addition to the doubts about the quality of products purchased online, the lack of consumer confidence in financial transactions made on commercial sites account for this slow evolution. However, democratization of the Internet, development of social media and evolution of e-marketing has changed the purchasing habits of consumers. The purpose of this research is to analyze how the Internet as a tool has or may have a concrete effect on online purchasing behaviors of economic agents and to highlight the key factors influencing the decision process of e-consumer. To this end, we conducted a population survey of 1055 consumers. The data collected were analyzed using the SPSS factor analysis was then applied to the data. The results of our analysis indicate that, prior to any decision, consumers get information about the products they wish to acquire from not only the search engines, but also the decisions of other consumers. In fact, consumers trust substantially the past experiences of other consumers shared on the Internet. Noting that various discussion forums or websites can be used in the comparison of products and influence the decision of purchasing of e-customer. Our research indicates that low-price variable and/or special offers available only on the Internet are the real factor that encourages purchase online in Turkey.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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