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Reswanda
Universitas Narotama Surabaya

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Determinants Of Crypto Asset Adoption As A Personal Financial Management Instrument Among Millennial Workers R. Dewi Pertiwi; Riztrya Novedliani; Gema Merdeka Goeyardi; Sengguruh Nilowardono; Reswanda
Journal of Management Vol. 4 No. 2 (2025): July - December
Publisher : Yayasan Pendidikan Belajar Berdikari

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Millennial workers are at the stage of life in which personal financial management decisions, such as saving, investing, and preparing for retirement, become central, and many of them have begun to include crypto assets in their financial plans. This study examines the determinants of crypto asset adoption as a personal financial management instrument among Indonesian millennial workers. Building on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), the model includes performance expectancy, effort expectancy, social influence, facilitating conditions, financial literacy, perceived risk, and trust as determinants of behavioral intention, which in turn drives actual use. Data from 368 employed millennials aged 30 to 45 were analyzed with validity and reliability tests, path analysis using regression with robust standard errors, bootstrapped mediation analysis, and a comparison between income groups. Performance expectancy is the strongest determinant of intention (β = 0.283), followed by perceived risk, which has a significant negative effect, social influence, financial literacy, and trust, while effort expectancy and facilitating conditions have no significant effect on intention. The model explains 37.8% of the variance in intention. Intention is the main driver of use (β = 0.387), and facilitating conditions and financial literacy also affect use directly. Most effects on use are transmitted through intention. The findings suggest that millennial workers treat crypto as a financial instrument whose expected benefits, risks, and trustworthiness matter more than its ease of use, and that financial literacy encourages rather than discourages informed adoption.
Bitcoin Price Prediction Analysis Using Arima And Garch Models Wanda Laksniyunita; Gunawan; Reswanda; Arasy Alimudin; Agus Sukoco
Journal of Management Vol. 2 No. 2 (2023): July - December
Publisher : Yayasan Pendidikan Belajar Berdikari

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Bitcoin is the largest cryptocurrency by market capitalization and one of the most volatile financial assets, which makes forecasting its price both important and difficult. This study evaluates the ability of the Autoregressive Integrated Moving Average (ARIMA) model and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family to predict the price and volatility of Bitcoin. Using daily data from 1 January 2018 to 30 June 2026, the models are estimated on a training sample of 2,737 observations and evaluated on a test sample of 365 days through rolling one step ahead forecasts. The Box and Jenkins procedure selects ARIMA(2,1,0) for the mean, and diagnostic tests reveal strong ARCH effects in its residuals. Among GARCH(1,1), GJR GARCH, and EGARCH specifications, the GARCH(1,1) t model provides the best fit. Out of sample, ARIMA reduces the price forecast error by only 0.28% relative to a random walk, the difference is not significant in the Diebold and Mariano test, and directional accuracy is 51.0%, no better than chance. In contrast, GARCH models forecast volatility significantly better than rolling and EWMA benchmarks, and combining ARIMA with GARCH(1,1) t produces 95% prediction intervals with correct coverage (94.2%), whereas ARIMA with constant variance does not. The results indicate that the Bitcoin price level is close to unpredictable, consistent with weak form efficiency, but that its risk is predictable, so ARIMA and GARCH are more useful for quantifying uncertainty than for forecasting the price itself.