Factors Associated with AI Use in a Norwegian Sample
Abstract
1. Introduction
2. Related Works and Study Objectives
2.1. Education
2.2. Job Sector
2.3. Gender
2.4. Age
2.5. Leadership
2.6. Training
2.7. Work Engagement
3. Method
3.1. Participants and Procedure
3.2. Instruments
3.3. Data Analysis
4. Results
4.1. Descriptive Statistics
4.2. Chi-Square Tests
4.3. Independent Sample t-Tests
4.4. Logistic Regression Analyses
5. Discussion
5.1. Limitations
5.2. Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| Variable | Category | n |
|---|---|---|
| AI use | No | 53 |
| Yes | 143 | |
| Gender | Male | 80 |
| Female | 115 | |
| Missing | 1 | |
| Leadership | No | 177 |
| Yes | 19 | |
| Age | 18–34 | 77 |
| 35–49 | 59 | |
| 50+ | 60 | |
| Education | Primary school | 2 |
| Upper secondary school | 21 | |
| Bachelor’s degree | 51 | |
| Master’s degree | 122 | |
| Sector | Health and care | 30 |
| Public administration | 40 | |
| IT and media | 36 | |
| Finance and insurance | 18 | |
| Education | 24 | |
| Other | 24 | |
| Professional services | 7 | |
| Trade and services | 4 | |
| Construction | 11 | |
| Industry | 2 |
| Predictor | χ2(df) | p | Effect Size |
|---|---|---|---|
| Gender | 4.35(1) | 0.037 a | φ = 0.15 |
| Age | 1.81(2) | 0.405 | V = 0.10 |
| Education | 29.42(3) | <0.001 b | V = 0.39 |
| Leadership role | 2.91(1) | 0.088 c | φ = 0.12 |
| Sector | 39.26(9) | <0.001 b | V = 0.45 |
| Variable | Group | n | M | SD | t(df) | p | Cohen’s d |
|---|---|---|---|---|---|---|---|
| Work Training | Non-users | 53 | 4.47 | 1.12 | 0.02(194) | 0.986 | 0.00 |
| AI users | 143 | 4.47 | 1.11 | ||||
| SBL | Non-users | 53 | 4.65 | 1.32 | –2.87(77.34) | 0.005 | 0.52 |
| AI users | 143 | 5.25 | 0.91 | ||||
| Work engagement | Non-users | 53 | 5.29 | 1.54 | 0.04(194) | 0.968 | 0.01 |
| AI users | 143 | 5.28 | 1.36 |
| Predictor | B | SE | OR | 95% CI | p |
|---|---|---|---|---|---|
| Leader | 1.36 | 0.98 | 3.89 | [0.56, 26.44] | 0.172 |
| Age 35–49 | 0.71 | 0.55 | 2.04 | [0.69, 5.99] | 0.196 |
| Age 50+ | −0.27 | 0.50 | 0.77 | [0.29, 2.04] | 0.592 |
| Gender (Female) | −1.03 | 0.45 | 0.36 | [0.15, 0.86] | 0.021 |
| Education (overall) | — | — | — | — | <0.001 |
| Public Admin | 0.22 | 0.62 | 1.25 | [0.37, 4.18] | 0.718 |
| IT & Media | 2.60 | 0.78 | 12.93 | [2.87, 59.15] | <0.001 |
| Finance | — | — | — | — | 0.998 |
| Education sector | 0.84 | 0.74 | 2.32 | [0.55, 9.80] | 0.253 |
| Other | 1.46 | 0.75 | 4.31 | [1.00, 18.58] | 0.050 |
| Prof. Services | 0.16 | 1.05 | 1.18 | [0.15, 9.23] | 0.878 |
| Retail/Service | 1.85 | 1.50 | 6.36 | [0.33, 121.14] | 0.219 |
| Construction | 0.94 | 0.97 | 2.56 | [0.38, 17.14] | 0.333 |
| Industry | −0.90 | 2.00 | 0.41 | [0.01, 20.48] | 0.653 |
| Predictor | B | SE | OR | 95% CI | p |
|---|---|---|---|---|---|
| Leader | 1.08 | 1.06 | 2.95 | [0.37, 23.47] | 0.307 |
| Age 35–49 | 0.50 | 0.58 | 1.65 | [0.53, 5.13] | 0.391 |
| Age 50+ | −0.54 | 0.54 | 0.58 | [0.20, 1.68] | 0.317 |
| Gender (Female) | −1.08 | 0.45 | 0.34 | [0.14, 0.83] | 0.017 |
| Education (overall) | — | — | — | — | <0.001 |
| Public administration | −0.08 | 0.66 | 0.92 | [0.25, 3.38] | 0.899 |
| IT & media | 2.35 | 0.81 | 10.53 | [2.14, 51.82] | 0.004 |
| Finance & insurance | — | — | — | — | 0.998 |
| Education sector | 0.94 | 0.81 | 2.55 | [0.52, 12.43] | 0.248 |
| Other | 1.26 | 0.76 | 3.54 | [0.79, 15.79] | 0.098 |
| Professional services | −0.25 | 1.08 | 0.78 | [0.09, 6.48] | 0.815 |
| Retail/Service | 1.71 | 1.40 | 5.55 | [0.36, 85.51] | 0.219 |
| Construction | 0.81 | 1.02 | 2.24 | [0.31, 16.41] | 0.426 |
| Industry | −0.75 | 2.14 | 0.47 | [0.01, 31.47] | 0.727 |
| Work training | −0.34 | 0.23 | 0.72 | [0.46, 1.12] | 0.146 |
| SBL | 0.71 | 0.24 | 2.03 | [1.28, 3.23] | 0.003 |
| Engagement | −0.05 | 0.17 | 0.95 | [0.68, 1.33] | 0.774 |
| Predictor | B | SE | OR | 95% CI | p |
|---|---|---|---|---|---|
| Strengths-based leadership | 0.64 | 0.17 | 1.89 | [1.35, 2.64] | <0.001 |
| Knowledge sector | 0.92 | 0.42 | 2.52 | [1.12, 5.68] | 0.026 |
| Gender (Female) | −1.09 | 0.44 | 0.34 | [0.14, 0.80] | 0.013 |
| Education (overall) | <0.001 | ||||
| 3 years | 1.29 | 0.60 | 3.64 | [1.12, 11.91] | 0.032 |
| 5 years + | 2.41 | 0.58 | 11.15 | [3.59, 34.92] | <0.001 |
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Thorp, S.O.; Rimol, L.M.; Fleten, M.K.; Hoel, S.K.B. Factors Associated with AI Use in a Norwegian Sample. Behav. Sci. 2026, 16, 537. https://doi.org/10.3390/bs16040537
Thorp SO, Rimol LM, Fleten MK, Hoel SKB. Factors Associated with AI Use in a Norwegian Sample. Behavioral Sciences. 2026; 16(4):537. https://doi.org/10.3390/bs16040537
Chicago/Turabian StyleThorp, Sebastian Oltedal, Lars M. Rimol, Martine Klock Fleten, and Simen Kristoffer Berg Hoel. 2026. "Factors Associated with AI Use in a Norwegian Sample" Behavioral Sciences 16, no. 4: 537. https://doi.org/10.3390/bs16040537
APA StyleThorp, S. O., Rimol, L. M., Fleten, M. K., & Hoel, S. K. B. (2026). Factors Associated with AI Use in a Norwegian Sample. Behavioral Sciences, 16(4), 537. https://doi.org/10.3390/bs16040537

