Table 3

Factor loadings, reliability, and variance explained (N = 118)

FactorsItemFactor loadingsCronbach's αVariance explained, %
Factor 1: Data protection distrustI am concerned that my personal and academic data might be disclosed to third parties without my consent. (V1)0.6650.79712.580
I fear that stored and processed data could be altered or manipulated without authorization. (V2)0.999
Factor 2: Functional distrustI doubt that the suggestions or recommendations it provides are suitable for learning. (V6)0.6850.75421.881
I am concerned that the technology will not be able to effectively improve specific skills or competencies as promised. (V7)0.642  
I am uncertain about how continuous innovations align with personalized educational needs and preferences. (V9)0.838  
Factor 3: Distrust of AI replacing humansAn AI tool can never compete with the experience of a specialized trainer in entrepreneurship. (V10)0.7230.75823.885
With long-term experience, a trainer can teach more than an AI can. (V11)0.683  
Trainers have a better understanding of what happens in the entrepreneurial process than AI can have. (V12)0.741  
Total variance explained (%)  58.347

Note(s): Extraction method: Maximum Likelihood Factor Analysis (MLFA)

Promax rotation with Kaiser normalization (κ = 4)

Model fit indices: χ2(7) = 21.691, p = 0.003 RMSEA: 0.134

Source(s): Authors’ own work

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