Nowadays, the world is changing rapidly, with innovative technology, a new set of challenges need to be considered and addressed. The discussions about using technology in restaurants and food service sector only cover some of the main topics such as information system management and integration, automation, data analytics, and guest-facing technology. However, despite the positive prospects of smart technology in the hospitality industries, they have not been adopted widely in Food and Beverage (F&B) sectors in some Asian or Middle Eastern countries. This study aims to investigate the core motivations for adopting smart restaurant services in developing countries such as Malaysia. The purpose of this study is to explore the relationship between the predictors' variables such as perceived cost, perceived enjoyment, novelty, and customers' acceptance level of smart restaurant services as a dependent variable. A research framework is developed based on the extended Technology Acceptance Model (TAM) and Diffusion of Innovation Theory (DOI). The quantitative research approach was applied to the empirical part of this study to test the conceptual model. Therefore, to achieve this purpose, a survey was conducted in Penang, Malaysia. The data was collected from 150 respondents and analysed by SPSS statistical software. The research results indicate that perceived enjoyment (PE) is the most significant predictor in customers' acceptance of a smart restaurant which is followed by perceived cost (PC). However, Implications of the findings is relevant and helpful to both academia and F&B operators along with future research opportunities with an industrial standard for restaurant entrepreneurs.
A year ago, one thousand USD invested in Bitcoin (BTC) alone would have appreciated to three thousand five hundred USD. Deep reinforcement learning (DRL) recent outstanding performance has opened up the possibilities to predict price fluctuations in changing markets and determine effective trading points, making a significant contribution to the finance sector. Several DRL methods have been tested in the trading domain. However, this research proposes implementing the proximal policy optimisation (PPO) algorithm, which has not been integrated into an automated trading system (ATS). Furthermore, behavioural biases in human decision-making often cloud one’s judgement to perform emotionally. ATS may alleviate these problems by identifying and using the best potential strategy for maximising profit over time. Motivated by the factors mentioned, this research aims to develop a stable, accurate, and robust automated trading system that implements a deep neural network and reinforcement learning to predict price movements to maximise investment returns by performing optimal trading points. Experiments and evaluations illustrated that this research model has outperformed the baseline buy and hold method and exceeded models of other similar works.
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