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Journal ArticleDOI

Prioritizing intentions behind investment in cryptocurrency: a fuzzy analytical framework

Swati Gupta, +3 more
- 29 Oct 2021 - 
- Vol. 48, Iss: 8, pp 1442-1459
TLDR
The result indicates that “Social Influence (SI)” is the most influencing factor, while “Effort Expectancy (EE)’ is the least influencing factor considered by investors.
Abstract
The primary objective of this study is to prioritize the main intentions behind investment in cryptocurrency, in spite of its volatile nature and no regulatory framework.,This research paper has worked on collective constructs of the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and social support theory with an added construct of financial literacy. A fuzzy analytical framework has been applied to prioritize the intentions of investors.,The result indicates that “Social Influence (SI)” is the most influencing factor, while “Effort Expectancy (EE)” is the least influencing factor considered by investors. The subdimensions ranked in the top priority by investors are as follows: “I want to invest in cryptocurrencies because I have a good level of financial knowledge (FL1)”; “The people who are important to me will think that I should use cryptocurrencies (SI2)”; “I have the necessary resources to use cryptocurrencies (FC2).” The least importance is given to “It will be easy for me to become an expert in the use of cryptocurrencies (EE3).”,Few of the constructs of the UTAUT, the TAM and social support theory have been considered while prioritizing intentions. Different other intentions also prevail under different theories that need to be researched further.,Unlike previous studies, this research adds the archetype of social commerce, social support and utility theories to analyze and prioritize the behavioral perspective of using cryptocurrencies in digital transactions.,This paper fills the gap in the research study, along with assisting the regulators and cryptocurrency practitioners to widen their knowledge base and to recognize the prioritized intentions.

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Citations
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Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework

TL;DR: The fuzzy Full Consistency Method-Bonferroni (FUCOM-F’B) model is conducted to determine the priorities of drivers for investing in cryptocurrencies and “strong electronic encryption and use of digital signature” are the most significant drivers.
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Fuzzy set qualitative comparative analysis of factors influencing the use of cryptocurrencies in spanish households

TL;DR: In this article, the authors applied a consumer-behavior focus and so-called fuzzy set Qualitative Comparative Analysis (fsQCA) to assess the variables influencing the expansion of cryptocurrency (crypto for short) use in households.
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TL;DR: In this article, the authors performed a two-phase analysis to address the research questions of the study, in the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors, while in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.
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Sustainable cryptocurrency adoption assessment among IT enthusiasts and cryptocurrency social communities

TL;DR: In this paper , the authors focused on the individuals' adoption behavior on the digital currency which is built from the emerging blockchain technology, and the measurement and validation are on technological and non-technological factors that influence individuals, IT enthusiasts, and cryptocurrency social communities to adopt cryptocurrency.
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Influence of contextual factors on investment decision-making: a fuzzy-AHP approach

TL;DR: In this paper , the authors used a technique of Fuzzy-analytical hierarchical process (AHP) to assign relative weights to various contextual factors influencing investment decision-making, and Harman's single factor test was used to check common method bias.
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