Prioritizing intentions behind investment in cryptocurrency: a fuzzy analytical framework

Gupta, Swati; Gupta, Sanjay; Mathew, Manoj; Sama, Hanumantha Rao

doi:10.1108/jes-06-2020-0285

Cited by 59 publications

(29 citation statements)

References 52 publications

(64 reference statements)

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“…Within e-banking we can remark [43,48,49,51,55,56]. Regarding cryptos let us outline that [8,10,12,14,[17][18][19][20]22]. In our paper [7] we also found a positive relation but it was not significant, so the following hypothesis is tested: Hypothesis 3 (H3).…”

Section: A Model To Explain the Acceptance Of Cryptocurrencies By Houmentioning

confidence: 64%

“…As far as FindTech is concerned [26,46,47,56] found a positive significant relation. Regarding cryptos and bitcoin [10,16,17,19,21] show that FC influences cryptos IU. In [7] we had also identified FC to be a determinant factor, so the following hypothesis is made: Hypothesis 4 (H4).…”

Section: A Model To Explain the Acceptance Of Cryptocurrencies By Houmentioning

confidence: 99%

“…Literature on the application of TAMs on this topic is not so widespread and, due to the reasons above, all very recent. Let us point out apart from [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Despite all reviewed literature is based in the use of TAMs, the final configuration of hypothesis to test and the use/user of cryptos under consideration have different nuances.…”

Section: Introductionmentioning

confidence: 99%

“…Despite all reviewed literature is based in the use of TAMs, the final configuration of hypothesis to test and the use/user of cryptos under consideration have different nuances. They may come from how TAM is applied but also due to the use of cryptocurrency tested: a generic intention to use by individuals [7][8][9][10][11][12][13][14][15][16][17][18][19][20]; as a payment method in commercial transactions [14][15][16][17][18][19][20][21] or as a purely way of investment [19]. This paper uses the configuration of hypotheses in [7] and also tests the intention to use by households, i.e., their motivation may be either as payment method or as an investment asset.…”

Section: Introductionmentioning

confidence: 99%

“…With the exception of [19], all reviewed papers on the acceptance of cryptos use Partial Least Squares (PLS) to test the influence of factors. This paper supposes a novelty in this context since uses fuzzy set Qualitative Comparative Analysis (fsQCA) developed by Ragin in [23,24] to extend the results that we reached by using PLS in [7].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Set Qualitative Comparative Analysis of Factors Influencing the Use of Cryptocurrencies in Spanish Households

Arias-Oliva¹,

Andrés-Sánchez²,

Borondo

2021

Mathematics

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This paper assesses the variables influencing the expansion of cryptocurrency (crypto for short) use in households. To carry on the study we apply a consumer-behavior focus and so-called fuzzy set Qualitative Comparative Analysis (fsQCA). In a previous research, that was grounded on Unified Theory of Acceptance and Use of Technology (UTAUT) and Partial Least Squares (PLS), we found that main factors to explain the intention to use of cryptos by individuals were performance expectancy (in fact, it was the main factor), effort expectancy and facilitating conditions. We did not found evidences about the relevance of social influence, perceived risk and financial literacy. This study revisits these results by applying fsQCA instead PLS. Empirical research on factors influencing cryto use is relatively scarce due to the novelty of blockchain techs, so the present paper expands the literature on this topic by using an original analytical tool in this context. The main contribution of this paper consists in showing empirically that fsQCA provides a complementary and enriching perspective to interpret data about the use of cryptos. We obtain again that the most relevant factor to explain the intention of using cryptocurrencies is perceived expectancy and that also effort expectancy and facilitation conditions are relevant. But also fsQCA has allowed us discovering that despite social influence, perceived risk and financial literacy were not significant in the PLS model, they impact on the intention to use cryptocurrencies when are combined with other factors. Social influence acts as an “enable factor” for the rest of explanatory variables and it is linked positively with intention to use cryptos. Also financial literacy is relevant because its lack is a sufficient condition for the non-acceptance of that blockchain tech. Likewise we have checked that perceived risk influences the intention of using cryptos. However, this influence may be positive or negative depending of the circumstances.

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Section: A Model To Explain the Acceptance Of Cryptocurrencies By Houmentioning

confidence: 64%

Section: A Model To Explain the Acceptance Of Cryptocurrencies By Houmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy Set Qualitative Comparative Analysis of Factors Influencing the Use of Cryptocurrencies in Spanish Households

Arias-Oliva¹,

Andrés-Sánchez²,

Borondo

2021

Mathematics

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Prioritizing the factors determining the quality in higher educational institutions—An application of fuzzy analytic hierarchy process

Goyal

Gupta

Chauhan³

2021

Journal of Public Affairs

Self Cite

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The aim of this paper is to understand the factors that determine the quality of higher educational institutions and to measure the relative importance of those factors from the students' perspective. A multi-criteria decision-making methodology (MCDM), that is, the fuzzy analytical hierarchical process has been used for measuring the relative importance of factors which determine the student satisfaction with regard to perceived quality in education. The measurement of relative weights of the factors that determine the quality of higher educational institutions as per the perception of the students has been done. Further best preference structure of the students for educational institutions has been found from the study. The study was confined to Punjab state of India that means only the perception of students of this particular region was measured. Moreover, the factors undertaken in this study are also limited which were identified from the extensive review of the literature but there is a possibility that some factors might have been ignored. The results of this study can be used to measure the internal quality by the higher educational institutions. In addition to this, the study may be helpful to higher educational institutions to identify the weak areas to be worked upon and give the students as better as possible quality from the students' perspective. The paper attempts to understand the determinants of quality and to prioritize those for educational institutions as perceived by the students.

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The Innovation Resistance Theory: The Case of Cryptocurrencies

Shishan

Hmoud

Zaidan

et al. 2022

Artificial Intelligence for Sustainable Finance and Sustainable Technology

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Prioritizing intentions behind investment in cryptocurrency: a fuzzy analytical framework

Cited by 59 publications

References 52 publications

Fuzzy Set Qualitative Comparative Analysis of Factors Influencing the Use of Cryptocurrencies in Spanish Households

Fuzzy Set Qualitative Comparative Analysis of Factors Influencing the Use of Cryptocurrencies in Spanish Households

Prioritizing the factors determining the quality in higher educational institutions—An application of fuzzy analytic hierarchy process

The Innovation Resistance Theory: The Case of Cryptocurrencies

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