Demystifying crypto-asset adoption intention: What matters more, technology or human motivation?

The financial markets are found to be finite Hilbert space, inside which the stocks are displaying their wave-particle duality. The Reynolds number, an age old fluid mechanics theory, has been redefined in investment finance domain to identify possible explosive moments in the stock exchange. CNX Nifty Index, a known index on the National Stock Exchange of India Ltd., has been put to the test under this situation. The Reynolds number (its financial version) has been predicted, as well as connected with plausible behavioral rationale. While predicting, both econometric and machine-learning approaches have been put into use. The primary objective of this paper is to set up an efficient econophysics’ proxy for stock exchange explosion. The secondary objective of the paper is to predict the Reynolds number for the future. Last but not least, this paper aims to trace back the behavioral links as well.

Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.

Indonesia is one of the world’s biggest halal food product and service consumers. The halal industry will continue to expand as the Muslim community’s needs grow. Therefore, application development for online halal food providers through the Go Food and Shopee Food platforms is in high demand. This paper aims to analyze the determinants of Go Food and Shopee Food users’ halal food purchase decisions. The theory of planned behavior (TPB), the theory of consumer behavior, and the unified theory of acceptance and utilization of technology (UTAUT2) were used. The research sample consists of Go Food and Shopee Food users chosen randomly from a pool of 104 respondents. The data were collected using a questionnaire developed from previous studies and the theories applied (TPB and UTAUT2). Respondents received questionnaires online via Google Forms. Path analysis was used in this study. The findings show that TPB constructs can adequately explain halal food purchase behavior. The attitude toward the purchase of halal food and subjective norms affect the user’s intentions to purchase halal food. The coefficients are 0.291 and 0.379, with a p-value < 0.001. The user’s intention determines the positive decision to purchase halal food with a coefficient of 0.843 and a p-value < 0.001. Halal awareness is a powerful predictor with a coefficient of 0.206 and a p-value of 0.014. However, perceived behavioral control, halal literacy, religious commitment, financial literacy, and UTAUT2 constructs (price value, hedonic motivation, and habit) were not found to determine the intention to purchase halal food.