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In the modern context of management and entrepreneurship in the presence of wide access to information and high speed communication systems, an individual is able to accomplish multiple tasks those which might take a department to accomplish when it is compared to the classic concept of business operation. Digitalization, globalization, and robotics are just only a few of the factors that affect the business operation. Nevertheless, the importance of traditional knowledge/skills such as marketing, accounting, finance, and microeconomics is still in the place. A successful construction business in the modern era must be able to adopt the effective factors to the everlasting skill sets essential to a business. The current article is ought to adhere the modern technologies to the classical skill sets in a way to encourage the young generation to develop their entrepreneurship abilities. The uncertainties in the job market and the concern of full-time employment for young graduates are being addressed in the context of entrepreneurship. A survey had been conducted to clarify the status quo in a small population sample composed of small to medium size businesses in construction those are mostly operating in Australia. Recommendations had also been made in order to clarify the entrepreneurial path in the modern context of the construction economy.

This study focuses on the generation of Persian named entity datasets through the application of machine translation on English datasets. The generated datasets were evaluated by experimenting with one monolingual and one multilingual transformer model. Notably, the CoNLL 2003 dataset has achieved the highest F1 score of 85.11%. In contrast, the WNUT 2017 dataset yielded the lowest F1 score of 40.02%. The results of this study highlight the potential of machine translation in creating high-quality named entity recognition datasets for low-resource languages like Persian. The study compares the performance of these generated datasets with English named entity recognition systems and provides insights into the effectiveness of machine translation for this task. Additionally, this approach could be used to augment data in low-resource language or create noisy data to make named entity systems more robust and improve them.