Intelligent Personal Assistant in Business-Context: Key-feature Evaluation for User Acceptance

Hüsson Daniel, Holland Alexander and Sánchez Rocío Arteaga
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Hüsson Daniel: UCAM - Catholic University of Murcia, Spain
Holland Alexander: FOM University of Applied Sciences Essen, Germany
Sánchez Rocío Arteaga: UCAM - Catholic University of Murcia, Spain

Business Systems Research, 2020, vol. 11, issue 3, 147-166

Abstract: BackgroundThe usage of intelligent personal assistants (IPA), such as Amazon Alexa or Google Assistant is increasing significantly, and voice-interaction is relevant for workflows in a business context. ObjectivesThis research aims to determine IPA characteristics to evaluate the usefulness of specific functions in a simulated production system of an Enterprise Resource Planning (ERP) software. A new function called explanation-mode is introduced to the scientific community and business world. Methods/ApproachAs part of a design science research, an artefact, i.e. an add-on for speech-interaction in business software, was developed and evaluated using a survey among ERP users and researchers. ResultsIn the area of IPA-features, the search-function and speech input for textual fields were recognised as most useful. The newly introduced feature, the explanation mode, was positively received too. There is no significant correlation between the usefulness of features and participant-characteristics, affinity to technology or previous experience with IPAs in a private context, which is in line with previous studies in the private environment leading to the conclusion that the task attraction is the most important element for usefulness. ConclusionsMost of the participants agreed that the speech-input is not able to fully substitute standard input devices, such as a keyboard or a mouse, so the IPA is recognised as an addition to traditional input methods. The usefulness is rated high especially for speech-input for long text fields, calling up masks and search-functions.

Keywords: intelligent personal assistant; human-computer-interface; enterprise-resource-planning; machine learning; business process; natural language processing (search for similar items in EconPapers)
JEL-codes: O3 O31 O32 O33 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bit:bsrysr:v:11:y:2020:i:3:p:147-166:n:4

DOI: 10.2478/bsrj-2020-0032

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