Effects of an intelligent virtual assistant on office task performance and workload in a noisy environment.

Background noise Conversational agents Digital personal assistants Human-computer interaction Intelligent virtual assistant Office work

Journal

Applied ergonomics
ISSN: 1872-9126
Titre abrégé: Appl Ergon
Pays: England
ID NLM: 0261412

Informations de publication

Date de publication:
May 2023
Historique:
received: 14 03 2022
revised: 04 01 2023
accepted: 10 01 2023
pubmed: 27 1 2023
medline: 7 3 2023
entrez: 26 1 2023
Statut: ppublish

Résumé

This study examines the effects of noise and the use of an Intelligent Virtual Assistant (IVA) on the task performance and workload of office workers. Data were collected from forty-eight adults across varied office task scenarios (i.e., sending an email, setting up a timer/reminder, and searching for a phone number/address) and noise types (i.e., silence, non-verbal noise, and verbal noise). The baseline for this study is measured without the use of an IVA. Significant differences in performance and workload were found on both objective and subjective measures. In particular, verbal noise emerged as the primary factor affecting performance using an IVA. Task performance was dependent on the task scenario and noise type. Subjective ratings found that participants preferred to use IVA for less complex tasks. Future work can focus more on the effects of tasks, demographics, and learning curves. Furthermore, this work can help guide IVA system designers by highlighting factors affecting performance.

Identifiants

pubmed: 36702001
pii: S0003-6870(23)00007-8
doi: 10.1016/j.apergo.2023.103969
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103969

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Parian Haghighat (P)

Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, USA.

Toan Nguyen (T)

Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, USA.

Mina Valizadeh (M)

Department of Computer Science, University of Illinois at Chicago, USA.

Mohammad Arvan (M)

Department of Computer Science, University of Illinois at Chicago, USA.

Natalie Parde (N)

Department of Computer Science, University of Illinois at Chicago, USA.

Myunghee Kim (M)

Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, USA.

Heejin Jeong (H)

Ira A. Fulton Schools of Engineering, Arizona State University, USA. Electronic address: heejin.jeong@asu.edu.

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Classifications MeSH