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Tax evasion and money laundering have become important sources of illicit financial flows in developing countries. Foreign capital flows used by shell corporates are generally with no real economic activities but motivated by harmful tax practices, thereby inducing loss of revenue for developing countries. Despite the coercive actions, such as backlisting of noncooperative jurisdictions to anti-money laundering and countering terrorism financing standards, illicit financial activities are still eroding the tax base in developing countries. The purpose of the paper is to assess the blacklisting effectiveness as a coercive policy against illicit financial activities.

This paper applies a propensity score matching strategy to a sample of 118 developing jurisdictions from 2009 to 2017 to evaluate changes in illicit financial activities following the blacklisting.

The results show that rather than altering illicit inflows in blacklisted countries, financial restrictions have produced the inverse, causing a boomerang effect on financial crime activities. The illicit share of capital inflows increases on average by 6 percentage points and 0.7% of GDP following the blacklisting. These results are robust to alternative matching methods and to the hidden bias problem.

Most of the previous research analyzed the link between blacklisting and fiscal revenues. However, here, the study analyzes whether blacklisting makes countries more cooperative in terms of fighting illicit financial flows.

This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.

The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.

The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.

The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.