I'm not sure what exactly you are asking but any fixed "cut-off value" for eigenvalues for factor selection is arbitrary (e.g., the "Kaiser criterion" according to which factors with eigenvalues > 1 should be retained). Better factor selection criteria than a fixed eigenvalue threshold are provided by parallel analysis and/or model fit statistics (available, e.g., in maximum likelihood factor analysis). See

Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift's electric factor analysis machine. Understanding statistics: Statistical issues in psychology, education, and the social sciences, 2(1), 13-43.

thanks Prof. Christian Geiser for your answer.

"I am currently seeking clear guidance on the procedure for determining the number of factors. Is it feasible to establish the number of factors based on theoretical considerations, rather than solely utilizing all factors with an eigenvalue greater than 1?"

Absolutely. I wouldn't rely exclusively on empirical/statistical criteria and/or arbitrary cut off values for factor selection, especially if they don't result in a meaningful factor solution. In terms of statistical criteria, parallel analysis tends to be among the most reliable procedures. The "eigenvalue > 1" criterion is not a good one.

I have a hypothesis that items will tend to be good when they have a loading factor above 0.40. When normally with EFA it produces 5 components, but with the 5 components it produces items with a loading factor below 0.4. then with the same item the extraction is made into 4 component factors but still found items with a loading factor below 0.4. And then the extraction is made into 3 factors, in the end no loading factor is found below 0.4. and in theory it is also right to be 3 factors.

what is the professor's view regarding my hypothesis? Prof. Christian Geiser