You can remove an item due to poor loading. Hair et al. (2011) 1 suggested that the measurements with a loading higher than 0.7 should accepted.

whereas Chin (1998)2 argued that the manifest variables with loading value of less than 0.5 should be dropped.

However, the loading value of 0.5 or 0.6 is still acceptable when CR & AVE is in acceptable range ( If the elimination of item increases the CR & AVE then discard or otherwise maintain the factors).

1)Hair, Joe F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

2) Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.

I think the first problem is with the size of the sample. Authors such as Cattell (1978) recommended three to six subjects per item, with a minimum of 250. Comrey and Lee (1992) provided the following guidance in determining the adequacy of sample size: 100 = poor, 200 = fair, 300 = good, 500 = very good, 1000 or more = excellent.

Likewise, according to Hair et al. (2010), factor loading estimates should be higher than 0.5, and ideally, 0.7 or higher. So, there is no obligation that your loading estimates must have higher than 0.7

Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York.

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Thank you Alireza shabani shojaei and Adrián Segura Robles for such a productive contribution. I will surely try my best to incorporate both of the suggestions. Thanks Again