Instrumental and experiential attitudes toward (A.I.) augmented decision-making at work

In augmented decision-making, defined as a process wherein human judgment is complemented with decision-support systems powered by artificial intelligence (A.I.-DSS), employees are expected to monitor and sometimes override system outputs to enhance decision-making performance. Despite the growing use of these costly technologies in organizations, they often fail to add value as employees seem unwilling to delegate some of their tasks to A.I.-DSS or monitor its outputs. Past research has shown that employees differ in their attitudes toward (collaborating with) emerging technologies, and that these attitudes can facilitate or hinder effective technology use. Drawing on literature from technology acceptance (TAM) and user experience (UX), this study qualitatively explored whether employees hold both instrumental (i.e., related to consequences like performance) and experiential (i.e., related to experiences of the process) attitudes toward augmented decision-making, and whether these two types of attitudes differ in terms of their antecedents and outcomes. Seventeen semi-structured interviews with A.I.-DSS users from various organizations revealed that experiential attitudes were mentioned more frequently, but were significantly less positive than instrumental attitudes. In terms of antecedents, instrumental attitudes were primarily mentioned in relation to technology (A.I.-DSS) characteristics, whereas experiential attitudes were also related to task and individual characteristics. As for outcomes, instrumental attitudes were solely associated with employees’ intentions to use A.I.-DSS, while experiential attitudes were also mentioned in relation to employee absorption, motivation and stress. These findings highlight the importance of distinguishing between instrumental and experiential attitudes toward augmented decision-making at work. • Workers engage more in experiential than instrumental evaluations of augmented decision-making • Employees engage more often in experiential than instrumental evaluations of working with (AI) decision support systems • Employee experiential attitudes were significantly less positive than instrumental ones. • Instrumental attitudes mostly related to DSS characteristics, experiential ones also to task and individual characteristics • Experiential attitudes also had more varied outcomes (stress, motivation, absorption, usage) than instrumental (usage) ones • We conclude that experiential attitudes are important and should be considered in future research

Authors

Institutions

Publication Details

Journal
Computers in Human Behavior Artificial Humans
Published
2025-08-01
DOI
https://doi.org/10.1016/j.chbah.2025.100188
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Instrumental and experiential attitudes toward (A.I.) augmented decision-making at work

Philippe van de Calseyde, Anna‐Sophie Ulfert, Pascale M. Le Blanc, Kees Maton
Computers in Human Behavior Artificial Humans
Technology Adoption and User Behaviour
article

Instrumental and experiential attitudes toward (A.I.) augmented decision-making at work

Philippe van de Calseyde, Anna‐Sophie Ulfert, Pascale M. Le Blanc, Kees Maton
article en

Abstract

In augmented decision-making, defined as a process wherein human judgment is complemented with decision-support systems powered by artificial intelligence (A.I.-DSS), employees are expected to monitor and sometimes override system outputs to enhance decision-making performance. Despite the growing use of these costly technologies in organizations, they often fail to add value as employees seem unwilling to delegate some of their tasks to A.I.-DSS or monitor its outputs. Past research has shown that employees differ in their attitudes toward (collaborating with) emerging technologies, and that these attitudes can facilitate or hinder effective technology use. Drawing on literature from technology acceptance (TAM) and user experience (UX), this study qualitatively explored whether employees hold both instrumental (i.e., related to consequences like performance) and experiential (i.e., related to experiences of the process) attitudes toward augmented decision-making, and whether these two types of attitudes differ in terms of their antecedents and outcomes. Seventeen semi-structured interviews with A.I.-DSS users from various organizations revealed that experiential attitudes were mentioned more frequently, but were significantly less positive than instrumental attitudes. In terms of antecedents, instrumental attitudes were primarily mentioned in relation to technology (A.I.-DSS) characteristics, whereas experiential attitudes were also related to task and individual characteristics. As for outcomes, instrumental attitudes were solely associated with employees’ intentions to use A.I.-DSS, while experiential attitudes were also mentioned in relation to employee absorption, motivation and stress. These findings highlight the importance of distinguishing between instrumental and experiential attitudes toward augmented decision-making at work. • Workers engage more in experiential than instrumental evaluations of augmented decision-making • Employees engage more often in experiential than instrumental evaluations of working with (AI) decision support systems • Employee experiential attitudes were significantly less positive than instrumental ones. • Instrumental attitudes mostly related to DSS characteristics, experiential ones also to task and individual characteristics • Experiential attitudes also had more varied outcomes (stress, motivation, absorption, usage) than instrumental (usage) ones • We conclude that experiential attitudes are important and should be considered in future research

Computers in Human Behavior Artificial HumansVol. 5
Eindhoven University of Technology (NL)
Peace, Justice and strong institutions
Openalex Percentile: Top 80%
Technology Adoption and User Behaviour
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.