Evidence map›Paper›PMID 33868130›Full record

ArticleFrontiers in psychology2021

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model.

Ángel F Villarejo-Ramos, Juan-Pedro Cabrera-Sánchez, Juan Lara-Rubio, Francisco Liébana-Cabanillas

Abstract read
In one paragraph

Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ángel F Villarejo-RamosDepartment of Business Administration and Marketing, Universidad de Sevilla, Sevilla, Spain.
Juan-Pedro Cabrera-SánchezDepartment of Business Administration and Marketing, Universidad de Sevilla, Sevilla, Spain.
Juan Lara-RubioDepartment of Financial Economic and Accounting, Universidad de Granada, Granada, Spain.
Francisco Liébana-CabanillasDepartment of Marketing and Market Research, Universidad de Granada, Granada, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

Indexed as

adoptionbig dataintention to useneural networkspredictive model

Identifiers

PMID33868130
PMCPMC8046906

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.