Generative AI across the Corporate Hierarchy: The Disparity of Emotions, Attitudes, Social Norms and Usage

Patricia Hornstein, Nadja Born, Theresa Treffers, Isabell Welpe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Organizations can unlock numerous benefits by using generative artificial intelligence (AI). Thus, understanding the predictors driving its usage is crucial. Psychological factors such as emotions, attitudes, and norms play a key role. Research has examined these for employees and leaders separately; however, without comparing them across the hierarchy. This is important as research has discovered hierarchical differences in these factors regarding several organizational phenomena, such as digital readiness. Disparities may result in disregarded concerns, leading to a decline in trust and motivation. Building on identity theory, we examine differences in emotions, attitudes, and norms toward generative AI across the hierarchy. Our results show that high-level employees exhibit more excitement, less perceived threat, and stronger norms, yet this does not translate into higher usage. This study highlights the risk of leaders falling into impression management, given their low usage rates, and warns against disconnected levels based on discrepancies in psychological factors.

Original languageEnglish
Title of host publicationProceedings of the 58th Hawaii International Conference on System Sciences, HICSS 2025
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages182-191
Number of pages10
ISBN (Electronic)9780998133188
DOIs
StatePublished - 2025
Event58th Hawaii International Conference on System Sciences, HICSS 2025 - Honolulu, United States
Duration: 7 Jan 202510 Jan 2025

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Conference

Conference58th Hawaii International Conference on System Sciences, HICSS 2025
Country/TerritoryUnited States
CityHonolulu
Period7/01/2510/01/25

Keywords

  • Attitudes
  • Emotions
  • Generative Artificial Intelligence
  • Hierarchical Differences
  • Social Norms

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