Multicollinearity test in PLS-SEM?

Does anyone have a better ideas how to explain the used of Heterotrait-Monotrait Ratio of Correlations (HTMT) in assessing the discriminant validity in PLS-SEM model?

 my sample size is 500 customer and my indicator is 24, I run the factor analysis severally deleting the values less than 0.7 . but I later realized I was left with only five indicators 4 are 1 and two are .081 and 0.92 would my result be valid and accepted.

the others questions are:

1. what are the acceptable values for running SMART PLS loadings and cross loading

2. what are the accepted range of value for discriminate reliability, validity, and correlation in SMARTPLS. ALSO R^2 and adjusted R^2

In one of my measurement CFA models (using AMOS) the factor loading of two items are smaller than 0.3. I found some scholars that mentioned only the ones which are smaller than 0.2 should be considered for deletion. However, there are various ideas in this regard. Some said that the items which their factor loading are below 0.3 or even below 0.4 are not valuable and should be deleted.

The measurement I used is a standard one and I do not want to remove any item. What should I do? 

Thank you for your valuable time.

Regards,

when testing Multicollinearity using SEM , should I report the inner or outer model? in other word, testing the measurement or structural model?

should I report the finding of Multicollinearity between the items or the construct ?

please help

Dear researchers,

I have HTMT larger than 0.9, and I have read the article by Henseler et al (2015) :A new criterion for assessing discriminant validity in variance-based structural equation modeling, so I tried to run bootstrapping to check HTMT inference, but the results still look not good. What should I do now? I need to submit thesis in two weeks, so I don't have time to collect data again.

Would you please send me the license key for SmartPLS software? I will be very thankful for this act of kindness..

I have VIF values that greater than 5 (exactly 5.6) in my PLS-SEM results.

Would it be a problem to be accepted into the SCI/SSCI journal?

Reviewers comments are as follows:

Reject this manuscript as there was 4 items had factor loadings below recommended value of 0.70. Hair et al. (2010) require that each item is considered a satisfactory item when item loadings are greater than 0.70. With this flaw, it really affects the whole data analysis, discussion, conclusion and future direction presented in the entire article.

My answer to this comments was: 

There are many studies that reported that factor loadings should be greater than 0.5 for better results (Truong & McColl, 2011; Hulland, 1999), whereas in tourism context Chen & Tsai (2007) were also considered 0.5 as a cut-off for acceptable loadings. Additionally, while exploring pro-environmental consumer behavior, Ertz, Karakas & Sarigollu (2016) have considered the factor loadings of 0.4 and above for their Confirmatory factor analysis.  So, on the above ground, we have not solely chosen this criterion but also as 0.6 is better than these studies cut-offs for factor loadings.  Along in line, the standardized factor loading of all the items ranges was above the threshold limit of .6 and above also suggested by Chin, Gopal & Salisbury (1997) and Hair et al. (2006).