Abstract Purpose – Cryptocurrency, an important application of blockchain technology, has gradually circulated, and its use has become widespread. While cryptocurrency is growing rapidly, potential risks are simultaneously emerging. Users thus may abandon their usage behavior of cryptocurrency, hindering the future development of cryptocurrency. While prior studies focus more on the intention to use cryptocurrency in the pre-adoption phase, less studies pay attention to discontinuance usage intention in the post-adoption phase. To fill this knowledge gap, this stfudy aims to explore factors that cause discontinuance usage intention regarding cryptocurrency. Design/methodology/approach – Based on the net valence framework theoretically grounded on the theory of reason action, a dilemmatic dual-factor model is proposed to figure out cryptocurrency users’ discontinuance usage intention from the perceived risk and perceived benefit. This study identifies four potential risks and three potential benefits that affect perceived risk and benefit. The model with nine hypotheses were developed, and research data were collected by a survey method. A total of 343 valid responses were received, and PLS-SEM with SmartPLS was utilized to test the nine hypotheses, with seven hypotheses supported empirically. Findings – Our findings demonstrate that financial, legal and operational risks are critical to increase users’ perceived risk, and perceived usefulness and seamless transactions play important roles in enhancing users’ perceived benefit. Moreover, while perceived risk can increase users’ discontinuance usage intention to cryptocurrency, perceived benefit can mitigate such intention. Originality/value – This study contributes nascent knowledge to the literature by examining factors that influence discontinuous usage intention in regard to cryptocurrencies, to firms that have issued or attempted to issue cryptocurrencies and to the potential users of cryptocurrencies by adjusting the mode of operation and investment strategies and reducing user costs, achieving a win-win situation for firms and users.
Purpose of cryptocurrency (multiple choices) Investment Transaction Vote Others 252 105 10 11 Monthly income (NTD) 10,000 or less 10,00130,000 30,00160,000 60,001100,000 More than 100,000 61 83 149 33 17 Usage experience of cryptocurrency <1 year 13 years 46 years 79 years >10 years 126 174 32 9 2 Frequency usage of cryptocurrency in a week <3 46 79 1012 1315 >16 237 61 16 7 0 22 Total usage time in a week <1 h 13 h 46 h 79 h 1012 h 1315 h >16 h 154 119 31 14 9 2 14 Frequency usage of cryptocurrency in the last 1 month 0 15 610 1115 1620 2125 2630 >31 57 77 148 48 23 19 4 3 American citizens aged 1834 claimed to invest in bitcoin. Although we cannot directly claim that they are all students, the information is still worthful to know that the age population for most users in the investment of cryptocurrency is younger.
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Respondents may have used many types of cryptocurrencies. According to the sample, the most used cryptocurrencies are Bitcoin (BTC) (62.97%), Ether (ETH) (23.91%), BCHABC (BCH) (3.21%) and Litecoin (LTC) (2.33%). This reflects the popularity of BTC, confirming Percent 0.86 66.67 27.78 2.65 2.91 17.78 24.20 43.44 9.62 4.96 36.73 50.73 9.33 2.62 0.58 69.10 17.78 4.66 2.04 0.00 6.41 44.90 34.69 9.04 4.08 2.62 0.58 4.08 28.4 22.45 43.15 13.99 6.71 5.54 1.17 0.87 Factors of discontinuous intention in crypto 579 Table 1. ITP 36,2 580 our assumption. As for experience using cryptocurrency, most respondents used the cryptocurrencies for about three years (63.26%). Of the respondents, 91.54% stated that their total frequency of usage of cryptocurrency in the previous month was less than 10 times. This information can help us to understand the users intention regarding to the discontinuance of adoption of cryptocurrencies.
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4.3 Nonresponse bias Nonresponse bias was assessed using the procedure recommended by Armstrong and Overton (1977). Considering the last group of respondents as most likely to be similar to nonrespondents, a comparison of the first and last quartile of the respondents provides a test of response bias. No significant differences between the first and last quartile samples were found on our key research variables based on the t test (p > 0.05). We also compared the gender and age variables between the first and last quartile samples by a chi-square test (Shiau et al., 2020), resulting in insignificant results (p > 0.05). Accordingly, nonresponse bias should not be a serious concern in this study.
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5. Data analysis and results A partial least squares (PLS) structural equation model (SEM) using SmartPLS Version 3.3.3 was constructed for measurement validation and hypotheses testing. PLS-SEM should be more appropriate for our study than covariance-based SEM because of following reasons. First, our research model is relatively complex. Second, our sample sizes are relatively small due to a small population of cryptocurrency users. Third, the distribution of our research samples is lack of normality. Finally, our research objective is more exploratory foci for theory development on cryptocurrency. Hence, these can be better tackled with PLS-SEM (Khan et al., 2019; Shiau et al., 2019, 2020; Hair et al., 2019; Shiau and Chau, 2016; Gefen et al., 2011). We thus adopted PLS-SEM with SmartPLS to estimate the measurement model with a factor weighting scheme and the structural model with a path weighting scheme (Hair et al., 2017b). Non-parametric bootstrapping with 10,000 replications was
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