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Engineering Leadership Orientation and Perceived Organizational Enablement for Responsible Artificial Intelligence Use in Iraq

Year: 2026

Type: article

Abstract: The integration of artificial intelligence (AI) into engineering practice is bringing new dimensions to the role of a leader, including professional judgment, collaboration, and responsibility for decisions made with AI's support.However, desirable personal leadership orientations do not necessarily correlate with desirable work conditions.This study examines this perceived capability-enablement divide among engineers in Iraq.Survey data were collected from 144 engineers working in Iraq using 39 Likert-scale questions.The primary withinrespondent comparison used bootstrap estimation, the Wilcoxon signed-rank test, and a cumulative ordinal fixedeffects model covering 5,616 item responses.Adjusted associations were examined using ordinary least squares regression with heteroskedasticity-consistent type 3 (HC3) standard errors and complete-profile permutation tests.Individual Leadership Orientation (ILO) was rated more favorably (M = 3.789, standard deviation [SD] = 0.435) than Organizational Enablement (OE) (M = 3.479, SD = 0.848).The mean paired difference was 0.309 points (95% bootstrap confidence interval [CI] [0.187, 0.443], p < .001;Cohen's d z = 0.394).Using the ordinal model, and controlling for respondent and item differences, the same directionality was found; that is, endorsement of Organizational Enablement was associated with lower odds (odds ratio [OR] = 0.450, 95% CI [0.380, 0.534], p < .001).AI-use frequency was associated with ILO, but its association did not differ significantly from that with OE (p = .242).Education was associated much more strongly with individual orientation than enablement.The results suggest that there is a perceived gap in capability and enablement among the engineers participating, rather than any broad opposition to AI.The results indicate that leadership orientation and perceived organizational support are separate factors to consider when designing responsible AI implementation for engineering organizations.

Source: Journal of Engineering Management and Systems Engineering

DOI: 10.56578/jemse050403

Authors: Kasim A. Alomari, Mohammed N. Azeez, Faraj M. Katl

Institutions: Thi Qar University, University of Technology - Iraq

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7225385408

FWCI:

Cites: 30

Cited by:

Related to: 10