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Record W3200824117 · doi:10.5267/j.ijdns.2021.3.004

Online sales system and organization outcome

2021· article· en· W3200824117 on OpenAlexvenueno aff
Nader Mohammad Aljawarneh, Khalid talal alhindawi, Ahmed Ghazi Mahafzah, Shadi Mohammad Altahaa, Ebtehal Alzboun, Ibrahim Mohammad Harafsheh

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCopyingMatching (statistics)Order (exchange)Sample (material)Computer scienceUsabilitySet (abstract data type)Test (biology)Information systemOutcome (game theory)MarketingKnowledge managementBusinessEngineeringStatistics

Abstract

fetched live from OpenAlex

The aim of this study is to identify the link between online sales systems, infrastructure, ease of use & information accuracy in improving Jordanian restaurants’ call centers' performance effectiveness (JRCCPE). In order to achieve the study's objectives, a questionnaire was conducted for measuring the link between online sales systems (OSS), infrastructure, ease of use & information accuracy in improving JRCCPE. The study sample was selected by distributing 220 questionnaires to all employees of Jordanian restaurants’ call centers (JRCC) from the set of employees working in an online sales system where 173 were retrieved. Aiming to answer the study questions and test hypotheses, the researcher extracted the means and standard deviations to apply the multiple regression equation. Accordingly, the study reached many results, showing a statistically significant effect for using the OSS, infrastructure, ease of use & information accuracy in improving JRCCPE. The study suggested that JRCC seeks adding various characteristics of editing, deleting, copying, and setting the time on the basis of text messaging through such networks as well as the need to conduct marketing studies in order to enable companies to achieve the customers' wishes in a method matching their expectations.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.347

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 communication0.0000.002
Open science0.0010.001
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 head0.031
GPT teacher head0.295
Teacher spread0.264 · 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
Published2021
Admission routes1
Has abstractyes

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