Evidence map›Paper›PMID 39157374›Full record

ArticleHeliyon2024

Citizen attitudes towards e-government services during the COVID-19 pandemic: A case in Türkiye.

Hasan Serkan Oztaskin, Neslihan Iyit, Omer Alkan

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

3 authors.

Hasan Serkan OztaskinAtaturk University, Department of Economics, Faculty of Economics and Administrative Sciences, Yakutiye, Erzurum, Turkiye.
Neslihan IyitDepartment of Statistics, Faculty of Science, Selçuk University, Konya, Turkiye.
Omer AlkanMaster Araştırma Eğitim ve Danışmanlık Hizmetleri Ltd. Şti., Ata Teknokent, TR-25240, Erzurum, Turkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

E-government services are essential to societies because they save time, reduce corruption, provide efficient, low-cost, and fast service, increase transparency, and enhance trust in the government. These applications save time, which translates to cost savings by reducing bureaucratic crowds and fatigue and eliminating the need for citizens to travel for offline transactions. This study investigates various factors related to citizens' use of e-government services according to gender differences during and before COVID-19. The microdata set from the Survey on Information and Communication Technology (ICT) Usage in Households conducted by TURKSTAT in 2018 and 2021 was used. Additionally, the binary logistic regression method was employed to analyze these factors. According to the research results, it has been determined that variables such as age, education level, occupation, e-commerce use, internet financial transaction status, number of people in the household, and region are associated with women's use of e-government services during the COVID-19 pandemic. The study found that the significance and impact of these variables on the use of e-government services differ based on the gender of individuals and the periods. The study provides recommendations for IT professionals, staff of the interior ministries, and researchers interested in increasing the use of e-government services. This research may also pioneer efforts to identify priority areas for expanding e-government services.

Indexed as

Binary logistic regressionCovid-19 pandemicE-governmentElectronic governmentGender differencesTürkiye

Identifiers

PMID39157374
PMCPMC11328076

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.