An effective architecture for monitoring and accessing e-commerce businesses in Ghana for taxation
Abstract
The emergence of electronic commerce has been a game changer in the business market industry especially during and post COVID-19 era. Following the COVID-19 pandemic, most new and existing businesses have resorted to operating either fully or partly online. The outcomes of this move have been very phenomenal and lucrative as it reduces the general cost of operation and distrubtion on the part of the business owners. While these online businesses make huge profits, the government, on the other hand, struggles to general revenue from these businesses in the e-commerce through tax. The underlying factor that makes it difficult to tax online businesses is that there is no effective approach to obtain relevant data on them to aid the taxation process. The existing approach to obtaining the needed data is manual copying of data which is not highly performant. Also, the manual approach can be very tedious and time-consuming since one person must sit down and do all this work. Therefore, this study proposes web scraping approach to extracting and gathering data from the online businesses using "BeautifulSoup" and "Selenium library". The proposed methods proves to be very efficient because it is going to automate the processes involved in data extraction and gathering. The web scraping approach was tested with Instagram platform and extracted relevant data on about two hundred and seventy-two (272) online business cutting across cosmetics, clothing, shoes, computers and accessories, furniture, etc. It took the proposed method an average time of 34.932 seconds to scrape and extract on 272 online businesses. This clearly depicts that web scraping could be the best option for data extraction as compared to manual approach.