Evidence map›Paper›PMID 36554657›Full record

ArticleInternational journal of environmental research and public health2022

OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor.

Sameena Naaz, Sarah Ali Khan, Farheen Siddiqui, Shahab Saquib Sohail, Dag Øivind Madsen, Asad Ahmad

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. 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

6 authors.

Sameena NaazDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India.ORCID 0000-0003-0080-5063
Sarah Ali KhanDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India.
Farheen SiddiquiDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India.
Shahab Saquib SohailDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India.
Dag Øivind MadsenUSN School of Business, University of South-Eastern Norway, 3511 Hønefoss, Norway.ORCID 0000-0001-8735-3332
Asad AhmadDepartment of Management, School of Management and Business Studies, Jamia Hamdard, New Delhi 110062, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Body odor is a biometric feature unique to each individual, and it can be used for authentication. However, decision makers must learn about the users' level of acceptance of this technology, as well as their thoughts on the system's features and procedures. In this study, a technology acceptance model (TAM) for body-odor-based biometric techniques named OdorTAM was proposed and validated. An English language questionnaire was developed in a web-based, easy-to-read format on Google Forms. The survey consisted of 19 questions, and 150 responses were received. Statistical analysis of the responses was carried out, and it was found that all the hypotheses were supported. Therefore, the OdorTAM model appears to be satisfactory. To this end, we posit that a body-odor-based biometric technique can be one of the alternatives for authentication, and it can also be used along with some other techniques for improved security. The study contributes to the literature on consumers' understanding of biometric technologies, in particular odor detection, which has received relatively less attention in extant research.

Indexed as

Biometric IdentificationBody OdorComputer SecurityHuman BodyHumansOdorantsbiometric authenticationbody odorprivacysecuritytechnology acceptance model (TAM)

Identifiers

PMID36554657
PMCPMC9779205

What Socratic 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 Socratic graph.