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This study's goal is to look at how visitors' experiences affect the indirect links between the destination's extrinsic motivations (DEMs) and tourists' intrinsic motives (TIMs), on the one hand, and the perceived destination image (PDI), on the other.

Using structural equation modeling, 613 tourists from different nationalities were used to test the five hypotheses.

The research results revealed that second-order destinations' extrinsic motivations directly impact TIM and PDI. It also showed that tourists' experiences as moderators reduce the direct effect of DEM on PDI for first-time visitors compared to repeat visitors. Moreover, it increases the direct effect of TIM on PDI for repeated visitors.

Destination managers can fix the problems that hurt their reputations and images by hiring police officers in tourist areas and cleaning tourist places. In the same way, destination managers and travel agencies should use AI tools to create social media marketing campaigns focusing on natural and historical monuments. Also, the marketing plans should stress the value for money (for example, lodging, food and attractions’ cost). Finally, destination marketers can make programs for repeat visitors, focusing on DEM and TIM.

This article tries to fill a gap in the research on PDI formation in emerging markets as a modern technique in destination marketing by using the push-intrinsic and pull-extrinsic theories. It also looks at how the tourists' experiences moderate the direct link between DEM, TIM and PDI. Lastly, this study examines how TIM affects a destination's image in emerging markets.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.