Anita Tangl

Despite many revolutionary asset pricing models developed over the past decades, traditional finance does not explain investor behavior very well. The purpose of this study is to examine the influence of behavioral biases on the investment decisions of investors of Pakistan Stock Exchange. In addition, the moderating influence of investment experience investigated in this study. The findings were reported using a sample of 230 individual investors, who make their own investments, typically through a mutual fund, bank, or internet broker. They make investments to achieve their unique investment objectives, such as saving for retirement, a child’s education, or increasing their overall wealth. The influence of behavioral biases on investment decisions was calculated using regression analysis. Regression results show that beta and t-values are significant and have a significant impact on investment decisions. Regression findings show that Confirmation Bias, Gamblers Fallacy Bias, Negativity Bias, Bandwagon Effect Bias, Loss Aversion Bias, and Overconfidence Bias all have a substantial impact on Investment Decisions. Status quo prejudice and endowment bias have a favorable but minor influence on Investment Decisions. Investment Experience is regarded as an essential component that contributes to successful decision making under risk and uncertainty, however the results of this study show that moderating variables have a minor influence. According to the findings, the moderating variable had no effect on the connection between behavioral biases and investment decisions. And the reason for this is that behavioral biases persist regardless of investing experience.

Type of the article: Research Article

Abstract
Crypto-assets attract interest as alternatives to centralized financial systems, yet adoption remains limited in advanced economies. This study explores how technological characteristics and human motivations affect adoption intentions, using Czechia as the context of a developed economy. The study applies a dual-stage framework- partial least squares structural equation modelling (PLS-SEM) to test hypotheses on data from 387 Czech adults via face-to-face questionnaires (March-October 2023), and artificial neural network (ANN) analysis to rank factor importance. PLS-SEM findings show Effort Expectancy as the strongest predictor of adoption intention, while Compatibility drives Performance Expectancy, which indirectly drives adoption intention. Relative Advantage significantly shapes Effort Expectancy and indirectly affects adoption intention. However, ANN sensitivity analysis shows technological characteristics dominate overall, with Compatibility showing the highest normalized importance (91.5%), followed by Relative Advantage (79.9%) and Effort Expectancy (50.9%). These results show technological characteristics and human motivations both contribute to adoption intentions, with prominence depending on analytical perspective- ease of use was the strongest direct predictor in PLS-SEM, while Compatibility dominated overall predictive importance in ANN. This indicates that in a stable, resilient financial environment, digital proficiency does not translate into tolerance for complexity; rather, adoption intentions depend on the technology’s ability to align effortlessly with established daily habits. Consequently, the study suggests fostering adoption in developed economies requires reducing structural complexity rather than merely increasing digital literacy.