Introduction

Cryptoassets represent an emerging innovative class of financial assets. Among these assets, the first to appear is Bitcoin (Pilatin, 2022). Bitcoin is an unregulated, decentralized, peer-to-peer crypto-asset that enables users to process transactions via digital exchanges. The characteristics of Bitcoin and other crypto assets are quite different from traditional investment instruments (Klein et al., 2018). Bitcoin’s market cap is approximately $780 billion as of April 2022, and Bitcoin is the largest of all cryptocurrencies, representing approximately 40% of the total market capitalization of all cryptocurrencies (CoinMerketCap, 2022). Despite having a relatively small market capitalization compared to traditional investment vehicles, research shows that various investors can benefit from growing their portfolios with Bitcoin given the degree of liquidity (Sun et al., 2021).

Crypto assets are seen as speculative financial instruments because they are more volatile than other investment instruments. Although they are called “Cryptocurrency”, it has been revealed that it is not easy to use exactly like money when it is understood that goods and services cannot be traded intensively over “crypto money” (Pilatin, 2022). Global monetary authorities have started to use the term “crypto assets” to express “cryptocurrencies”, which they see as speculative financial assets, with the effect of increasing transaction volume and individual and institutional investor demand for crypto assets (Nishibe, 2016). For this reason, it will be expressed as a crypto asset (CA) instead of cryptocurrency (CC) in the study.

While crypto assets are mostly used by individual investors, they have recently been used by institutional investors as well. Due to the fact that crypto assets are a relatively new and rapidly growing asset class, legal regulations have not yet been made in Turkey, as in many other countries in the world. The reason why it has not yet been put into a legal framework is that states, policy makers and economists have not been able to reach a full consensus on these assets.

Brand awareness is an indicator of the popularity of cryptocurrencies. This awareness can sometimes become a supportive competitive force, especially in the institutional investor market (Presthus & O’Malley, 2017). Therefore, investors tend to decide which crypto assets to add to their portfolios based on their familiarity.

In order to achieve higher expected returns, portfolios with higher risk but higher return potential have been created by developing different asset portfolio strategies with crypto assets (Semra & Doğuş, 2021). Increasing interest and the fact that the market value of crypto assets reached 3 trillion dollars in 2021 (CoinMerketCap, 2022) has also increased the interest in crypto assets in academic terms. Much more work has been done on cryptoassets than in the early days (Sun et al., 2021; Dilek, 2022; Pilatin, 2022).

The value of crypto assets, which was at the level of 120 billion dollars at the beginning of 2019, increased to 200 billion dollars at the end of the year and 500 billion dollars at the end of 2020. In 2021, the total market value of crypto assets has seen 3 trillion dollars (CoinMerketCap, 2022). In this respect, 2021 can be seen as a record year for crypto assets. Compared to the period before 2019, it is noteworthy that crypto assets have gained a lot of value, transaction volumes have increased, while market sizes have increased, volatility has decreased considerably. New investors entering the crypto asset markets contribute to the increase in the trading volume, liquidity level and financial depth in the market, while also supporting the reduction of risk and volatility (Pilatin, 2022).

The market value of crypto assets is approximately $1.8 trillion as of March 1, 2022. Considering that the gold asset in the world is around 18 trillion dollars, it is understood that the value of crypto assets, whose history is very new compared to gold (based on 3 trillion dollars), has reached 1/6 of the world’s registered gold assets (Pilatin, 2022). This is a really high number. In addition, the share of Bitcoin, which had an average of 85% share in the crypto asset market in 2016, decreased to 32% in 2018, then increased to 65% in 2020. Although Bitcoin’s share in the market was 65% until the middle of 2021, it decreased to 40% in the second half of the year. In this time frame, the share of Ethereum and other altcoins has increased. By the end of 2021, the share of Ethereum increased to 20%, Binance’s share to 4%, Tether’s share to 3.5% and Solano’s share to 2.5% (CoinMerketCap, 2022). From this point of view, it can be said that even if crypto assets will not replace value storage instruments such as gold, Dollar and Euro (Klein et al., 2018), it will continue to have its potential to be a valuable investment and an important reserve accumulation tool in the long run.

In this research, the factors that affect individuals’ intentions and real investment behaviors through their psychology at the stages of the adoption of crypto asset technology and investments, which are an innovative investment tool, and investment in these assets are discussed within the framework of the Decomposed Theory of Planned Behavior (DTPB).

After the introduction, in the second part of the study, DTPB and crypto-asset literature are examined. In the third part, data and methodology are given, and then in the fourth part, the process of forming the hypotheses is explained. In the fifth chapter, the results of the analysis are reported, and finally in the sixth chapter, the results and recommendations are given by considering the studies in the literature.

Decomposed theory of planned behavior

This study is one of the first studies in the literature conducted in developing countries to measure investor behavior towards cryptoassets based on the Decomposed Theory of Planned Behavior. In the study, crypto asset investor behavior in Turkey was tested with 11 alternative hypotheses. The Decomposed Theory of Planned Behavior (DTPB) developed by Taylor and Todd (1995) is an improved version of the Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen (1975) and Theory of Planned Behavior (TPB), which is the developed form of TRA by Ajzen (1991).

According to TRA developed by Fishbein and Ajzen (1975), an individual’s intention towards a behavior is affected by attitudes and subjective norms. Attitude expresses the opinion about positive or negative behavior, while subjective norms express the social pressure to perform or not perform a certain behavior. Accordingly, the attitudes of individuals play a decisive role in estimating the resulting intention (Aziz & Afag, 2018: 3).

Ajzen (1985, 1991), who thinks that the Theory of Reasoned Action is insufficient in measuring various abilities, resources and opportunities, predicting the behavior of the individual, exhibiting or determining the intention, has developed the Theory of Planned Behavior (TPB) by adding the perceived behavioral control to the model. The degree to which an individual performs an action does not depend on his intention alone. It also depends on their abilities, psychology, and the opportunities and resources needed to perform the behavior (Ajzen, 2020: 319). In this framework, perceived behavioral control is used to take into account situations in which individuals do not have full control over their behavior. According to TPB, people’s social behavior is caused by certain reasons and occurs in a planned way. In order for a behavior to occur, an intention must first be formed. Intention also influences attitudes, subjective norms, and perceived behavioral control (Ajzen, 1991).

Combining the TPB and the technology acceptance model, Taylor and Todd (1995) came up with the Decomposed Theory of Planned Behavior (DTPB), which describes attitude, subjective norms, and perceived behavioral control. (See Fig. 1)

Fig. 1
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TRA (Fishbein ve Ajzen, 1975), TPB (Ajzen, 1991; 2006) and DTPB (Taylor & Todd, 1995) Drawn with Corel Draw

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According to DTPB, attitude; relative advantage is determined by complexity and compatibility. While the determinants of subjective norms are normative beliefs, the determinants of perceived behavioral control are self-efficacy and facilitating conditions. It has been observed that this model has higher explanatory power in determining intention and behavior compared to the other two models (Shih & Fang, 2004: 216). For this reason, DTPB has been used in many different areas in the literature such as banking (Aziz & Afag, 2018; Nor & Pearson, 2008), online/mobile commerce (Gangwal & Bansal, 2016), purchasing/consumer behavior (Choi & Park, 2020), social networking (Al-Ghaith, 2016), tourism (Garay et al., 2019).There are different studies in the literature on crypto assets. There have been studies investigating the effects of economic news, commodity prices, global uncertainties, trade volume, prices, currencies and other economic and financial variables on crypto asset returns (Al-Khazali et al., 2018; Bouri et al., 2019; Büberkökü, 2021; Bouri & Gupta, 2021; Kim). Al-Khazali et al. (2018) discussed the effects of macroeconomic news on gold and Bitcoin. He concluded that Bitcoin prices and volatility are relatively less responsive to macroeconomic news than gold, whether the impact is positive or negative. Bouri et al. (2019) examined the asymmetric non-linear short/long-term effects of commodity prices on Bitcoin price. It took into account the global uncertainty and revealed that the global financial stress index Granger causes Bitcoin returns. Büberkökü (2021) points out that there is a strong simultaneous interaction between both return rates and volatility values of cryptocurrencies. Bitcoin, Litecoin, XRP etc. Although some crypto assets are highly correlated with each other, it has also been determined that there are crypto assets such as ETH, TRON, BUSD that have a negative correlation with each other (Sun et al., 2021; Bouri & Gupta, 2021) state that the predictability of Bitcoin using internet search-based uncertainty measures is stronger than those taken from newspapers.

In studies on cryptocurrency experts (Ermakova et al., 2017) and investor awareness (Henry et al., 2018), individuals’ intentions to adopt cryptoassets were investigated. In addition, the relevance of using crypto assets as an investment tool and the use of technology and the effect of using these assets as investment opportunities (Presthus & O’Malley, 2017) on the motivation of individuals to invest in these assets are discussed. Nishibe (2016) state that crypto assets are seen as an investment tool rather than a functional currency.

In the study in which the effects of crypto assets on portfolio risk and return are measured and interpreted (Semra & Doğuş, 2021), it has been found that when added to the portfolio content, they positively affect the portfolio in terms of risk-return balancing and offer high returns with the opposite correlation they show. Choi (2021) found in his study that a 1% increase in Tweets results in an approximately 7% increase in liquidity within five to 10 min. This study proves that tweets can significantly increase investors’ buying demand for crypto assets and Bitcoin liquidity in real time.

Some studies have focused on crypto-asset investor behavior. However, most of the studies have addressed institutional investor behavior (Bouri et al., 2019; Henry et al., 2018; Mazambani & Mutambara, 2019; Sun et al., 2021), it was found that price volatility in crypto assets did not reduce the confidence of institutional investors. In addition, it was concluded that crypto-asset units with high awareness and trust can be very suitable in the portfolios of institutional investors. Lin (2021), using Granger Causality tests, concluded that there is an interaction between returns and attention. On the other hand, it was emphasized that if a crypto asset has a higher historical performance, investors may pay more attention to it. Henry et al. (2018) concluded in his study that crypto-asset investors tend to have higher financial literacy. Crypto investors also tend to have experience investing in traditional risky financial assets and use non-cash payment methods.

In the study (Schaupp et al., 2022), which is the only study similar to this study, it was determined that the DTPB model explained 63.5% of the variance in the intention to adopt the cryptocurrency. In this study, all pathways to behavioral intention were found to be significant in hypothetical directions. However, no evaluation has been made regarding the transformation of intention into actual behavior, that is, crypto asset investment. In this study, unlike the studies in the literature, the effects of intention and perceived behavioral control on crypto asset investment behavior were examined. This feature and the fact that it was conducted in a developing country are the most important contributions of the study to the literature.

Data, methodology and research model

Descriptive statistics on variables

Table 1 shows the number of surveys applied in proportion to the population of the regions. In the Marmara Region, which is the most populated region, 29.5% of the questionnaires were applied. Central Anatolia Region ranks second with 16.3%. This is followed by the Mediterranean Region with 13%, the Aegean Region with 12.2%, the Southeastern Anatolia Region with 11%, the Black Sea Region with 10% and the Eastern Anatolia Region with 8%.

Table 1 Population and Sample
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Descriptive statistics of the variables are given in Table 2. Accordingly, the effect levels of the factors affecting the purchase of crypto assets by investors are as in Table 2 in the form of those who do not invest in crypto assets and the total.

Table 2 Descriptive Statistics
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Methodology

In this study, the factors affecting the use of Crypto Assets in Turkey according to TPB were analyzed. TPB argues that the behaviors of individuals occur due to certain reasons and take place in a planned manner. For this reason, in order for a behavior to occur, there must first be an intention for that behavior. There are variables that enable the intention to emerge. Attitude, Subjective Norms, and Perceived Behavioral Control are also factors that affect Intention (Taylor & Tood, 1995). The questionnaire form was created by using the original scale expressions developed for TPB (Beck & Ajzen, 1991) and for DTPB (Taylor & Todd, 1995) and scales in other studies (Chen & Tung, 2014) using this model. The survey prepared to determine the emergence of crypto asset purchasing behavior in Turkey consists of two parts. The questionnaire consists of a total of 57 questions, the first part of which is 7 questions describing the demographic characteristics, and the second part is 50 questions that make up the scale. While preparing the scale questions, TPB (Ajyen, 1991 ), TRA (Fishbein & Ajzen, 1975) and PBC (Taylor & Todd, 1995) models were used.

The data set of the research consists of the cross-sectional data set obtained from the surveys conducted in 7 regions of Turkey. These 6 sets of data were obtained in a 3-month period between March 2021 and May 2021. Convenience sampling method was used in the application phase of the questionnaire. The surveys were conducted through social media platforms and announcements and posts made on investor blogger sites such as Investing, TradingView, foreks, crypto, MyNet stock market throughout Turkey. Due to the Covid-19 global epidemic, the surveys were applied to 1,222 people online.

Attitude scale consists of 5 statements, Subjective Norms scale consists of 4 statements, Perceived Behavioral Control scale consists of 4 statements, Intention Scale consists of 5 statements and Actual Behavior Scale consists of 2 statements (See Table 3). The scale was evaluated with a Likert type scale defined as “1 = Strongly Disagree… 5 = Strongly Agree”. The data were evaluated using reliability (Cronbach’s alpha) and factor analysis.

Table 3 Research Hypotheses
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The Cronbach’s Alpha value indicates the reliability coefficient and takes values between 0 and 1. If this coefficient is 0.7 and above, the reliability of the scale is considered sufficient (Kılıç, 2016). With Bartlett’s Test of Sphericity, it was tested whether the statements forming the scale were consistent with each other (Bartlett, 1954) and whether the KMO value and the sub-dimensions constituting the scale were suitable for the analysis (Kaiser, 1974). A KMO value below 0.5 is unacceptable, while 0.5–0.7 is considered weak, 0.7–0.8 is adequate, and above 0.8 is high (Coşkun et al., 2015).

After the necessary prerequisites were met, confirmatory factor analysis was performed first, and then hypotheses were tested with structural equation modeling (SEM). It was examined whether the values of goodness of fit obtained as a result of confirmatory factor analyzes were within the recommended values (Doll et al., 1994: 456). In order to obtain goodness-of-fit values, covariance was added between the modifications suggested by the AMOS program (See Fig. 2) and the sub-dimensions that needed to be removed were removed from the model (See Table 4).

Fig. 2
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Research Model (Taylor & Todd, 1995). Drawn with Corel Draw

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Hypothesis development

Complexity, relative advantage, compatibility, and attitude

Attitude is determined by three factors. These are relative advantage, compatibility, and complexity (Taylor & Todd, 1995). Relative advantage refers to the degree of benefit from an innovation’s pioneer (Lin, 2021; Rogers, 1983) defines complexity as the degree to which an innovation is perceived as complex to understand, learn, or operate, while adaptability is the degree to which an innovation fits the previous experiences, current values, and needs of potential users. The only study on the use of crypto assets with direct DTPB shows that compliance has a significant and positive effect on attitude (Schaupp et al., 2022). While relative advantage is expected to have a positive effect on attitude towards cryptoassets, complexity is expected to have a negative effect (Shih & Fang, 2004).

Based on this, the following hypotheses were formed.

Hypothesis 1a

Complexity (CL) has a positive and significant impact on attitude (A) towards crypto asset transactions.

Hypothesis 1b

Relative advantage (RA) has a positive and significant impact on attitude (A) towards crypto asset transactions.

Hypothesis 1c

Compatibility (CB) has a positive and significant impact on attitude (A) towards crypto asset transactions.

Fishbein (2001) attitude; people’s feelings towards an object, behavior or event as their positive or negative tendencies. Previous experiences, knowledge gained from these experiences and environmental factors play a role in the formation of attitudes. In many different studies, it is seen that the positive effect of attitude on intention is high. For example, in the use of Islamic banking services (Saptasari & Aji, 2020; Amin et al., 2012; Pilatin & Dilek, 2022), use of mobile banking (Jouda et al., 2020), use of internet banking (Shih & Fang, 2004), investor behavior (Sudarsono, 2015), gift buying behavior (Yoldaş & Dilek, 2020), food consumption (Öztürk et al., 2016).

Studies on the use of crypto-assets have shown that positive attitudes affect the investment intention towards crypto-assets (Schaupp et al., 2022; Soomro et al., 2022; Mazambani & Mutambara, 2019). From this point of view, the hypothesis established to investigate the effect of attitude on the intention to adopt crypto-asset investments in Turkey is as follows.

Hypothesis 1

Attitude (A) has a positive and significant effect on the intention (I) to adopt crypto asset investments.

Normative belief and subjective norms

Subjective norms are people’s perceptions of the social pressures they place on themselves to perform or not perform the behavior in question. (Ajzen, 1985: 12). Normative belief, which is the determinant of subjective norms, has two forms as precautionary normative belief and descriptive normative beliefs. The precautionary normative belief is the expectation that a particular reference individual or group (friends, family, spouse, person’s doctor, etc.) approves or disapproves of performing the behavior in question. Descriptive normative beliefs, on the other hand, are beliefs about whether people whose behavior is important carry out this behavior themselves (Ajzen, 2020: 315). It has been observed that normative beliefs have a significant and positive effect on subjective norms regarding internet banking use (Shih & Fang, 2004) and crypto assets (Schaupp et al., 2022).

From this point of view, the hypothesis regarding the effect of normative beliefs on subjective norms was formed as follows.

Hypothesis 2a

Normative belief (NB) has a positive and significant effect on subjective norms (SN) to crypto asset investments.

Ayedh et al. (2021), Mazambani and Mutambara (2019) studies, it has been observed that subjective norms have a positive effect on investment intention for crypto assets (Schaupp et al., 2022; Huong et al., 2021) in many studies. These results show that individuals can make investment decisions by being influenced by their relatives who advise on crypto-asset investments. From this point of view, the hypothesis regarding the effect of subjective norms on intention was formed as follows.

Hypothesis 2

Subjective norms (SN) have a positive and significant effect on the intention (I) to adopt crypto asset investments.

Self-efficacy, facilitating conditions and perceived behavioral control

Perceived behavioral control is determined by self-efficacy and facilitating conditions. Self-efficacy can be expressed as being sure of the ability to act successfully in a situation (Bandura, 1982: 122). Facilitating conditions can include many factors such as time, money and access to other special resources. It is expected to have a positive impact on crypto asset investments due to the ease of accessibility thanks to the technological infrastructure (Shih & Fang, 2004: 21).

From this point of view, hypotheses regarding the effect of self-efficacy and facilitating conditions on perceived behavioral control were formed as follows.

Hypothesis 3a

Self-Sufficiency (SS) has a positive and significant effect on individuals’ perceived behavioral control (PBC) towards crypto asset investments.

Hypothesis 3b

Facilitating conditions (FC) have a positive and significant effect on individuals’ perceived behavioral control (PBC) towards crypto asset investments.

ADK is people’s perceptions of their ability to perform a certain behavior (Ajzen, 2020: 316). While there are few studies that detect a negative (Huong et al., 2021) relationship between the intention to adopt crypto-asset investments and PBC, there are also studies regarding the existence of a positive relationship between the two variables. For this reason, the existence of the relationship between the related variables was sought in the study. From this point of view, the hypotheses related to this are determined as follows.

Hypothesis 3

Perceived behavioral control (PBC) has a positive and significant effect on the intention (I) to adopt crypto asset investments.

In addition, it is seen in many studies (Albashir et al., 2018; Farah, 2017) that ADK has a direct effect on actual behavior. The hypothesis established to measure the effect of ADK directly on actual behavior;

Hypothesis 4

Perceived behavioral control (PBC) has a positive and significant effect on the actual behavior (AB) of investing in crypto assets.

Mediator variable: intent

Intent; It is related to the effort that the individual is willing to spend while performing a behavior (Cordano & Frieze, 2000: 636). Intention can also be expressed as the tendencies or plans of individuals to perform or not perform the relevant behavior (Kocagöz & Dursun, 2010). Intention as a mediating variable; Attitude is determined by subjective norms and perceived behavioral control and is also the determinant of actual behavior. Rehman et al. (2007) state that strong intentions increase the likelihood of actual behavior. Many studies in the literature (Echchabi & Aziz, 2012; Mahardhika & Zakiyah, 2020) have shown that intention has a positive and significant effect on actual behavior. This study focused on providing an explanation of the mediating role of intentions in the relationship between attitudes, subjective norms, and perceived behavioral control in crypto-asset investments.

A hypothesis measuring the effect of intention on actual behavior towards crypto-asset investment in Turkey.

Hypothesis 5

Intention (I) has a positive and significant impact on the investing actual behavior (AB) of individuals investing in crypto assets.

Actual behavior

Fishbein and Ajzen (1975) define behavior as an observable action performed or not performed towards a product or service in a particular situation. Purchasing behavior is a decision-making process that includes individuals’ purchasing and using products and services (Durmaz & Bahar, 2011: 61). The degree of willingness of individuals to purchase a product or service depends on the effort they plan to spend to use that product or service and the motivation of the intention (Ajzen, 1991). When the model of the study in Fig. 3 is examined, it is seen that the actual behavior, that is, the behavior of investing in crypto assets, is determined by the intention and perceived behavioral control.

Fig. 3
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Most invested crypto assets in Turkey (Drawn with Exel)

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Table 4 Demographic Characteristics
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Findings

Demographic features

As seen in Table 5, 4.5 of the participants are female and 58.5% are male. When their marital status is examined, it is seen that 41% are married and 59% are single. Looking at the age ranges; 34% are 18–25, 31.5% are 26–35, 20.5% are 36–45, 9% are 46–55 years old, and 5% are 56 and over. 25.5% of the participants are high school or below, 51% are associate degree and undergraduate, 23.5% graduate.

Table 5 Status of Investing in Cryptocurrency
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When the occupations are examined, it is seen that 32.5% are public personnel, 29% are students, 23% are private sector employees, 9% are tradesmen and self-employed, 6.5% are workers and retired. 37% of the participants are between 4500 TL and below, 34% between 4501 and 9000 TL, 16.8% between 9001 and 13,500 TL, 9% between 13,501 and 18,000 TL, 2.9% between 18,001 Has an average monthly income of TL or more.

Table 6 Factor Loads of Variables and Other Statistical Values
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As seen in Table 6, it was determined that 33.5% of the participants invested in crypto money, and 66.5% did not invest in crypto money. These results show that approximately one out of every three people in Turkey invest in cryptocurrencies.

As seen in the figure, the most invested crypto assets in Turkey consist of sub-various coins (70.6%). It is followed by Bitcoin (59.7%), Ethereum (56.1%), Ripple (56.1%), Tether (49.5%) and Cardano (45.9%).

Data nad results

The factor loadings of the variables and other statistical values ​​are given in Table 7. A Bartlett’s Test of Sphericity result of 0.000 < 0.001 indicates that the statements that make up the scale are consistent with each other (Bartlett, 1954), and a KMO value of 0.962 > 0.70 indicates the suitability of the data set for analysis (Kaiser, 1974). The Cronbach’s Alpha values ​​calculated for each factor are as follows. Actual behavior (α = 0.935), intention (α = 0.966), attitude (α = 0.975), conformity (α = 0.932), relative advantage (α = 0.875), complexity (α = 0.716), normative belief (α = 0.772), perceived behavioral control (α = 0.919), self-efficacy (α = 0.899), facilitating conditions (α = 0.916), and subjective norm (α = 0.922). The fact that Cronbach’s Alpha results are > 0.70, indicates that the scale is reliable (Altunışık et al., 2016: 184).

Table 7 Goodness of Fit Values of the Research Model
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Following these results, first confirmatory factor analysis was performed with the AMOS21 program and then hypotheses were tested with structural equation modeling (SEM). It was determined that the goodness of fit values ​​obtained as a result of confirmatory factor analysis were not within the recommended values ​​(Doll et al., 1994: 456). For this reason, covariance has been added between e27-28, e31-34, e2-6, e2-3, e12-14, e14-16, e38-39, e42-43, e43-44, e18-21, e19-20 error terms, taking into account the modifications suggested by the AMOS program. In addition, error terms e1, e4, e23 and e26 (respectively, questions K2, K5, NI3, NI6) that were not suitable for the model were removed from the model. The goodness-of-fit values ​​obtained as a result of the modifications were within the recommended values. The results are as shown in Table 8.

Table 8 AVE and CR Values of the Structural Model
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In the tests performed to determine the reliability of the structural equation model, it is required that the mean variance extracted (AVE) value of the dimension is greater than 0.50 (Fornell & Larcker, 1981) and the CR value of the dimension is greater than 0.70 (Hair et al., 2014). As can be seen in Table 4, the AVE and CR values of all variables meet these conditions.

The results of confirmatory factor analysis and reliability tests to be used in this study show that it is suitable for analysis with SEM. In this framework, the analyzes were made with the AMOS program. Structural model results for the relationship between variables are shown in Fig. 4.

Fig. 4
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Structural Equation Model Result (It was produced with the AMOS program. Drawn with Corel Draw)

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The loads of the estimators of the variables in the structural model are shown in Table 4. Accordingly, It was understood that dimension GA3 with highest effect on Relative Advantage, dimension K4 with highest effect on Complexity, dimension U2 with highest effect on Agreement, dimension NI2 with highest effect on Normative Belief, dimension O5 with highest effect on Self-Efficacy, the dimension KK4 with the highest effect on Facilitating Conditions, dimension T3 with the highest effect on Attitude, dimension SN4 with the highest effect on Subjective Norms, and N1 with the highest effect on Intention. It was determined that the dimension with the highest effect on Actual Behavior was D1.

Table 9 Structural model results
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The hypotheses for the existence of the relationship between the variables in the structural model were evaluated. For this purpose, p values showing the direction and strength of the relationship, standardized regression weights and R2 values showing the extent to which independent variables explain the dependent variable were examined. These results are shown in Table 10.

Table 10 Hypothesis Results
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According to the R2 values, it was understood that the variables of complexity, relative advantage and compatibility explained the attitude variable by 85%. While the normative belief variable explained the subjective norm variable with a rate of 46.9%, the variable of self-efficacy and facilitating conditions explained the variable of perceived behavioral control at the rate of 61.7%. It is seen that the variables of attitude, subjective norm and perceived behavioral control explain the intention variable at the rate of 81.1%. In addition, it was determined that the perceived behavioral control and intention variable explained the actual behavior by 69.7%.

According to the model results, the hypotheses H1a, H1b and H1c, which state that the sub-dimensions of complexity, relative advantage and compatibility have an effect on attitude, were accepted. Among these sub-dimensions, it was understood that the relative advantage (0.723) sub-dimension affected the attitude the most. The factor that affected the attitude the least was complexity (0.026). This result may be due to the extremely risky, volatile and intensive technology use of crypto assets, which are an innovative investment tool.

The H2a hypothesis, which states that the normative belief sub-dimension is effective on subjective norms, was accepted. It was understood that the normative belief sub-dimension significantly affected the subjective norm dimension (0.685). The H3a hypothesis, which states that the self-efficacy sub-dimension affects the perceived behavioral control variable, was accepted, while the H3b hypothesis, which states that the facilitating conditions sub-dimension affects the perceived behavioral control variable, was rejected. It was found that the perceived behavioral control variable was mostly affected by the self-efficacy sub-dimension (0.770).

According to the results of SEM, the main hypotheses H1, H2 and H3, which state that the variables of attitude, subjective norm, and perceived behavioral control have an effect on intention, were accepted. It has been understood that these three variables have a significant and positive effect on individuals’ intention to purchase cryptoassets. It has been determined that the attitude variable (0.822) affects intention the most among these three dimensions. Hypotheses H4 and H5, which measure the effect of intention and perceived behavioral control variables on actual behavior, were also accepted. It has been determined that these two variables have a significant and positive effect on crypto asset purchasing behavior. Of these two variables, it is seen that the intention variable affects the actual behavior, that is, buying crypto assets more (0,754).

Results

Today, when changing and developing technology affects every stage of human life, money has also been included in the digitalization process. Crypto assets, which are under the control of a central authority, are not tied to a central authority, are only numbers, can be stored in an external memory and are now very valuable, have emerged. The restrictive regulatory initiatives of some countries for cryptoassets and the existence of some legal loopholes cause cryptoasset investments to be approached with suspicion. However, in addition to the rapid developments in technology, international institutions, companies and investment banks started to add crypto assets to their portfolios, they gave their customers the right to buy and sell, the increase in the number of individual and institutional investors, the decrease in risk and volatility with the transaction volume reaching very high figures, the investors’ interest in crypto assets increased. The total value of crypto assets, which approached 3 trillion dollars in 2021, is around 1.8 trillion dollars as of March 2022. In addition to Bitcoin (BTC), which is the first of the crypto assets and still has the highest share in the market, there are approximately 2,000 crypto assets in the market today.

Despite the increase in the number, use and trading volumes of crypto assets, there are few studies in the literature that focus on investigating the factors that affect the adoption of crypto-asset investments. In addition, the inability to find a comprehensive study in a developing country and the volume of assets in question constitute the main motivations for this study. The demand for crypto assets is truly remarkable, although from a human psychology perspective, new, unknown assets are seen as risky and individuals tend to stay away. In this study, for the first time, Decomposed Theory of Planned Behavior (DTPB) is used to reveal the factors that enable the adoption of investments in crypto assets in Turkey. Crypto assets are very important as a future investment vehicle or currency, even if they are seen as low probability by some (Klein et al., 2018). These assets are, and will continue to be, a good example of disruptive innovation in terms of the impact it has had on some industries. Considering all these, this study will open the door to new studies as well as filling the gap in the literature. According to the DTPB model; While complexity, relative advantage and compatibility variables determine attitude, normative belief affects subjective norms. Perceived behavioral control is determined by self-efficacy and facilitating conditions. Attitude, subjective norms and perceived behavioral control affect intention, while intention and perceived behavioral control affect actual behavior.

According to the R2 values ​​obtained as a result of the analysis; It has been determined that complexity, relative advantage and adaptability variables explain individuals’ attitude variable by 85%, normative belief variable explain subjective norm variable by 46.9%, self-efficacy and facilitating conditions explain perceived behavioral control by 61.7%. It was concluded that the variables of attitude, subjective norm and perceived behavioral control explained 81.1% of the intention variable, while the variable of perceived behavioral control and intention explained the actual behavior by 69.7%. It was determined that the variable that most affected the attitude was relative advantage (0.723), the perceived behavioral control variable was most affected by self-efficacy (0.770), and subjective norms were affected by normative beliefs (0.685).

It has been understood that individuals’ intention to purchase cryptoassets is significantly and positively affected by each of the variables of attitude, subjective norm, and perceived behavioral control. Among these variables, it was determined that the variable with the highest effect on intention is attitude (0.822), which supports other studies (Schaupp at al., 2022; Mazambani & Mutambara, 2019). Considering this high effect of attitude on intention, it is recommended that companies that produce crypto assets or act as intermediaries in buying and selling should focus on activities that will increase their positive attitudes towards investors. For this purpose, in addition to ease of transaction, speed, 24/7 trading and transfer opportunities, giving importance to innovative technology activities will increase positive attitudes towards crypto assets.

Huong et al. Although (2021) concluded that PBC has a negative effect on crypto-asset investment intention, in this study, as in many other studies (Schaupp at al., 2022; Soomro et al., 2022; Mazambani & Mutambara, 2019) It has been determined that it has a positive effect on the attitude and it is the second factor that determines the intention after the attitude. This result means that investors have sufficient knowledge, skills, resources and confidence to invest in crypto assets. Therefore, it can be said that investors do not perceive it as difficult and complex to invest in crypto assets. In addition, with the spread of legal and technical regulations for crypto assets in the upcoming period, it is expected that the impact rate of PBC, which already has a positive effect on crypto asset investment intention, will increase even more.

Analysis results on the intention of SNs to invest in cryptoassets Ayedh et al. (2021) and Mazambani et al. (2019) shows that it has a positive effect on the contrary. This effect, Huong et al. (2021), it was determined that it was not very high and not in the first place, but it was positive and lower as in other studies (Soomro vd., 2022; Schaupp et al., 2022). It is known that the statements made by institutional investors, analysts, global company owners such as Elon Musk, using the power of social media, affect the crypto asset market (Shahzad et al., 2022). Because crypto assets are newer than other investment tools and still have dark spots for investors, it is expected that different opinions will be affected in crypto asset investments. In the light of these data, it can be said that similar statements regarding crypto assets will continue to affect SNs in the coming period.

It has been determined that intention and perceived behavioral control variables have a significant and positive effect on crypto asset purchasing behavior. As in many studies in different fields in the literature (Pilatin & Dilek, 2022; Mahardhika & Zakiyah, 2020; Dilek, 2022; Öztürk et al., 2016, Doğan et al., 2015; Echchabi & Aziz, 2012; Shih & Fang, 2004), it has been observed that the intention variable, which is one of these two variables, has a high effect on the crypto asset purchasing behavior. The study result shows that a one-unit change in intent will cause a 0.754 change in crypto-asset buying behavior. Accordingly, it has been determined that 75 out of every 100 people who intend to invest in crypto assets have invested in crypto assets.

In conclusion, if crypto-asset companies and exchanges want to make crypto-assets more reliable and investable in the future, they need to ensure that individuals, and especially investors, achieve a certain level of crypto-asset literacy, from the technology on which crypto-assets are based, to the applications. In this context, in addition to ease of transaction, safe trading, speed, 24/7 trading, prevention of stock market crashes and transfer opportunities, giving importance to innovative technology activities will increase the attitude and therefore the intention towards crypto asset investment. But if states and policy makers are still undecided about crypto assets, they must first produce announcements, directives and policies for investor attitudes.

This study may have some limitations that should be taken into account. First of all, it is classified as a cross-sectional study, which means that the variables used in the study are collected in a fixed time. However, variables that affect investors’ intention may change over time. Therefore, the results are comprehensive but may represent the current situation. In addition, this research was conducted only in developing Turkey. For this reason, the same research model may change the results to be conducted in countries with different levels of development and culture. Therefore, research needs to be supported in the context of countries with different development and cultures. In future studies, research can be conducted to determine the maturity understanding and investment strategies and psychology of crypto asset investors. Finally, future studies may also focus on the share of crypto assets in investors’ portfolios.