Tax avoidance landscape scheme: Scientometrics analysis using Biblioshiny

Abstract

Abstract This study seeks to provide an evaluation of the current status and trends in research on tax avoidance. Many studies have been on tax avoidance schemes, necessitating a systematic overview to summarise the landscape of tax avoidance research. The data analyzed is from 791 cited references from the Web of Science. The search term is "tax avoidance," published from 2002 to 2022, with data updated on September 09, 2022. The analysis technique uses an R-based tool with Biblioshiny scientometric. Numerous sources have released voluminous amounts of research on tax avoidance. The most commonly referenced sources are articles or conversations about theory and concepts. Researchers from the USA, China, Germany, and the U.K. were the most active. International cooperation remains uncommon, except for those involving the USA and China. Research on tax avoidance broadly includes aggressiveness and incentives. The emerging topics have discussed performance, earnings management, corporate governance, and investment. The relevance and suitability of the review with technological approaches and the scope of keywords are less expanded, using only the keyword "tax avoidance." This study recommends further studies on tax systems, tax behavior, and tax disclosure. Future researchers must also systematically investigate the issues of taxable income, financial constraints, risk, investment, information systems, and corporate governance in their tax avoidance strategies. The results reveal the most current scientific advances and future research needs. Greater collaboration is welcomed for future research that will provide information to tax avoidance professionals. Considering the increase in technical progress, there is room for improvement. JEL: D85; H268