Bitcoin investment: a mixed methods study of investment motivations

Mattke, Jens; Maier, Christian; Reis, Lea; Weitzel, Tim

doi:10.1080/0960085x.2020.1787109

Cited by 69 publications

(53 citation statements)

References 76 publications

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“…Only three articles were found [23,41,42], dealing with motivational factors as well as barriers. In the past six years, more attention has been paid to motivational factors [43][44][45][46][47][48][49]. In contrast, barriers were not analyzed as much [50][51][52].…”

Section: Related Literaturementioning

confidence: 99%

Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies

2021

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The cryptocurrency market is very young, volatile, and highly risky. By the end of 2020, a new bull run started, and the prices of several cryptocurrencies reached record-breaking highs. The factors affecting this rise of cryptocurrencies include the impacts of the COVID-19 pandemic, the economic crisis and the global increase in the inflation rate, as well as the gradual acceptance and adoption of cryptocurrencies by people worldwide. This exploratory research is focused on this last factor, i.e., using cryptocurrency and with it, the associated support of its ecosystem (e.g., mining, staking). A survey was carried out investigating the motivational factors and barriers to investment in cryptocurrency for Czech representatives of Generations Y and Z (18–42 years; n = 468). The geographic scope was nationwide, and quota sampling was used. Notably, this survey was carried out prior to the global COVID-19 pandemic outbreak, and it is thus not affected by the pandemic and its related economic impacts. The article investigates the dependency between the individual motivational factors and barriers from the perspective of the tendency to take risks (using the risk propensity scale), according to gender and representation of Generations Y and Z. The lack of information on this form of investment is considered as the main barrier to investment in cryptocurrency, with respect to sex and generations. Compared to that, a negative experience with investment in cryptocurrency constitutes the most minor barrier. Respondents that have a tendency to take risks are mostly put off by their lack of experience with investment in general. The main motivational factor for investment in cryptocurrency, with respect to sex and generations, is considered to be the speed of increase in cryptocurrency value. On the other hand, the least encouraging factor is the opportunity to use the high volatility of cryptocurrency for speculative trading. Interestingly, this factor mostly encourages respondents that do not have a tendency to take risks. The findings are discussed, along with the presentation of their implications for practice and the directions of further explanatory research.

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Section: Related Literaturementioning

confidence: 99%

Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies

2021

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“…In this study, the conditions refer to the structural factors and semantic factors of in-app ads and the outcome of interest is clicking behavior. In line with the recommendation of QCA literature (Maier et al, 2021b;Mattke et al, 2020a), conditions and outcomes are represented as fuzzy set membership, whenever possible. There are two extreme situations of fuzzy set memberships.…”

Section: Two-step Qualitative Comparative Analysismentioning

confidence: 99%

“…In this study, we transformed the interval scale values (1 = "strongly disagree," 4 = "neither agree in nor disagree," 7 = "strongly agree") of the structural and semantic factors into fuzzy sets. In line with recommendations in the QCA literature, we used the direct calibration for the survey data using three qualitative anchors (Maier et al, 2021b;Mattke et al, 2020a). We set the Likert-scale value 1 for full non-membership (e.g.…”

Section: Data Analysis: Two-step Qualitative Comparative Analysismentioning

confidence: 99%

“…To reveal how the semantic factors lead to clicking behavior, we apply a frequency threshold and a raw consistency threshold. We use a frequency threshold of three and an acceptable level of raw consistency of 0.80 (Mattke et al, 2020a(Mattke et al, , 2020b, which is higher than the suggested threshold of 0.75 and, thus leads to more robust and reliable results. We also simplify the configurations to reveal more parsimonious results.…”

Section: Ejm 558mentioning

confidence: 99%

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In-app advertising: a two-step qualitative comparative analysis to explain clicking behavior

Mattke

Maier

Reis

et al. 2021

EJM

Self Cite

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Purpose Individuals only click on a very small fraction of the in-app advertisements (ads) they are exposed to. Despite this fact, organizations spend generously placing in-app ads without theoretical knowledge of how the structure and the semantics of in-app ads influence individuals’ clicking behavior. This study aims to identify how the processing of structural and semantic factors leads to clicking behavior. Design/methodology/approach Based on the limited capacity theory, this paper proposes that the sequential processing of structural and semantic factors leads to clicking behavior. To mirror the sequential process, this paper applies a process-oriented configurational approach and performs a two-step qualitative comparative analysis (QCA) using 262 incidents of exposure to in-app ads. Findings The results support the proposed sequential processing and show that neither structural nor semantic factors alone lead to clicking behavior. This paper reveals four different paths of sequential processing of in-app ads that lead to clicking behavior. The results show that individuals click on non-animated in-app ads even though these are perceived as irritating or privacy-concerning. When the in-app ads are animated, individuals do only click on them when these are not irritating, privacy-concerning and personalized. Research limitations/implications Organizations can use these findings to improve their in-app ads and generate more clicks. This study recommends that organizations place in-app ads in a prominent location, design them similar to the design of the app and use bright colors. The advertising message needs to have new and relevant information in a credible and entertaining way. Depending on the degree of personalization, organizations should use different sizes of the in-app ad and only use animation if it is unlikely that the in-app ad caused irritation or privacy concerns. Practical implications Organizations can use these findings to improve their in-app ads and generate more clicks. This paper recommends that organizations place in-app ads in a prominent location, design them similar to the design of the app and with bright colors. The advertising message needs to have new and relevant information in a credible and entertaining way. Depending on the degree of personalization, organizations should use different sizes of the in-app ad and only use animation if it is unlikely that the in-app ad caused irritation or privacy concerns. Originality/value From the in-app ad perspective, this study is the first to theoretically develop and empirically show the sequential processing of structural and semantic factors of in-app ads. From the methodological perspective, this study applies an advanced configurational two-step QCA approach, which is capable of analyzing sequential processes and is new to marketing research.

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“…The research gaps that was established lies with the fact that whereas there are millions of people who invest in BTC, their motives for doing so are not as well understood as those for making other kinds of investments (Mattke, et al, 2021). Furthermore, because of the variety of data…”

Section: Introductionmentioning

confidence: 99%

Examining Heterogeneity Structured on a Large Data Volume with Minimal Incompleteness

Aljojo¹

2021

ARO

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While Big Data analytics can provide a variety of benefits, processing heterogeneous data comes with its own set of limitations. A transaction pattern must be studied independently while working with Bitcoin data, this study examines twitter data related to Bitcoin and investigate communications pattern on bitcoin transactional tweet. Using the hashtags #Bitcoin or #BTC on Twitter, a vast amount of data was gathered, which was mined to uncover a pattern that everyone either (speculators, teaches, or the stakeholders) uses on Twitter to discuss Bitcoin transactions. This aim is to determine the direction of Bitcoin transaction tweets based on historical data. As a result, this research proposes using Big Data analytics to track Bitcoin transaction communications in tweets in order to discover a pattern. Hadoop platform MapReduce was used. The finding indicate that In the map step of the procedure, Hadoop's tokenize the dataset and parse them to the mapper where thirteen patterns were established and reduced to three patterns using the attributes previously stored data in the Hadoop context, one of which is the Emoji data that was left out in previous research discussions, but the text is only one piece of the puzzle on bitcoin transaction interaction, and the key part of it is “No certainty, only possibilities” in Bitcoin transactions

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Bitcoin investment: a mixed methods study of investment motivations

Cited by 69 publications

References 76 publications

Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies

Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies

In-app advertising: a two-step qualitative comparative analysis to explain clicking behavior

Examining Heterogeneity Structured on a Large Data Volume with Minimal Incompleteness

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