DeFi era: the behavioral intentions toward cryptocurrency in Lebanon

The cryptocurrency market have developed dramatically through the past decade, as it rose in November 2021 to a new record, realizing a market cap of $3 trillion, as Bitcoin and Etherium rushed to score highs (Lau, 2021). Emerged in 2009, cryptocurrency are decentralized digital currencies using encryption to confirm transactions, it is a digital token formed by cryptographic algorithms and moved through cyberspace and blockchain using protocols (Mazikana, 2018). Blockchain is a database where blocks are added to the chain through mining process, i.e. solving mathematical equations and computational puzzles, where the participant motivation is getting coin rewards and transaction fees (Kjærland, Khazal, Krogstad, Nordstrøm, & Oust, 2019). Cyptocurrency leveraged on blockchain disruptive technology which revolutionized the data structure permitting the formation of decentralized digital ledgers where single entities (i.e. governments, organizations, etc.) could not alter or control transactions on the blockchain (D’Alfonso, Langer, & Vandelis, 2016). These technological proficiencies expedite the one-to-one decentralized monetary dealings without a central payment intermediary party, improving the business effectiveness while lessening the operation time and fee (Alqaryouti, Siyam, Alkashri, & Shaalan, 2020b).

Cryptocurrency trading started in 2013, yet, concerns are raised whether the dynamic behavior of crypto assets is predictable or not; and whether forecasts of the parameters of the crypto market can be utilized in trading strategies for obtaining higher profits (Mikhaylov, Danish, & Senjyu, 2021). As of 2022, the estimated global crypto ownership rates at an average of 4.2%, with over 320 million crypto users worldwide (TripleA.io, 2022). Decentralized finance (DeFi) might grow into a podium for more progressive, comprehensive, and transparent financial services, which may carry some potential gains for users but also impose financial stability challenges, especially for developing economies where macro-financial risks apply with respect to asset and currency substitution (IMF, 2021).

Users of cyrptocurrency have various objectives, where some are holding the coins as strategic long term digital assets, some are looking to speculate through short term transactions, and others use it for transferring money around the globe. Also, fast transaction speed, low transaction fees, privacy and security are one of the critical benefits behind adopting cryptocurrency (Gao, Clark, & Lindqvist, 2016); compatibility, awareness and facilitating conditions (Ayedh, Echchabi, Battour, & Omar, 2021); in addition to investment opportunities, businesses acceptance, financial and technological knowledge, performance and effort expectancy (Alzahrani & Daim, 2019; McMorrow & Esfahani, 2021).

However, individuals might have little information about this fledgling technology, and this imprecise atmosphere can adversely influence attitudes to accept coins in daily transactions (Daryaei, Jassbi, Radfar, & Khamseh, 2020). Cryptocurrency markets remain tremendously volatile, with noteworthy and unexpected price swings habitually prejudiced by investor psychology; where research indicate that perceived behavioral control, social norms, and herd behavior (i.e. propensity to imitate others) intensely impact cryptocurrency behavior (Boxer & Thompson, 2020). The influence of trust on cryptocurrency behavioral intentions has not been adequately discovered and researchers argue for the high need for trust-building given their unregulated nature (Jalan, Matkovskyy, Urquhart, & Yarovaya, 2022).

Although Crypto adoption in some emerging markets and unindustrialized economies has outpaced that of developed economies (IMF, 2021); very few studies have been steered to explore the causes behind adopting cryptocurrency in the developing regions in Asia (Xiong & Tang, 2020). This study is one response to this need for investigating the motivation behind adopting and using cryptocurrency in the Lebanese market, and will investigate the demographic characteristics of crypto users as well.

Intention is “an indication of a person’s readiness to perform a given behavior, and it is considered to be the immediate antecedent of behavior” (Ajzen, 2019). The research about crypto adoption intention is relatively new, and researchers in this field used many theories as Diffusion of Innovation Theory, Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Models (TAM), and Unified Theory of Acceptance and Use of Technology (UTAUT) (Alzahrani and Daim, 2019; Arias-Oliva, Pelegrín-Borondo, & Matías-Clavero, 2019; Ayedh et al., 2021; Boxer and Thompson, 2020; Lou and Li, 2017).

However, as a foundation in developing the conceptual framework of the paper, this study will deploy the Signaling Theory (ST) originally theorized by Spence (1973), where his main idea was that “at the time of hiring an employee, the employer will have conditional assessments and informational gaps over productive capacity of employees; given various combinations of signals and indices (e.g. education); with the fact that it takes time to learn an individual’s productive capabilities means that hiring is an investment decision, in addition to the fact that these capabilities are not known beforehand makes the decision one under uncertainty”. Signaling theory focuses mainly on the communication of positive information in an effort to convey positive attributes to reduce information gaps or asymmetry; and has been widely used in different fields to explain customer choice phenomenon (Connelly, Certo, Ireland, & Reutzel, 2011).

ST advocates three chief elements, specifically, the signaler (the provider), the receiver (the customer) and the signal (Boateng, 2019). In cyberspace and through online transactions, there exist a large amount of information asymmetry between buyers and sellers in online transactions (Rao, Lee, Connelly, & Iyengar, 2018); where the provider (here could be considered crypto project owners or crypto trading platforms) has access to the product itself and consequently has a significant amount of information that the possible buyer does not have; whereas the user has information about his trading needs which are unknown by the provider.

In this paper, ST will be utilized to explain the types of signals such as perceived benefits and trust offered to users in order to reduce information asymmetry and uncertainty that assist purchasers in making more accurate judgments of value/attributes when there is incomplete info about crypto. The provider in an effort to influence the opinions of buyers, regularly communicates information regarding the attributes to reduce information asymmetries with the intent of impacting buyers perceptions and behavioral intentions (Boateng, 2019).

Trust might be viewed as “a relationship between two or more parties, whereby one party-the trustor-voluntary decides under a condition of uncertainty, to rely on another party -the trustee-which can be an individual or institution or a system-for the achievement of a particular task, based on the belief that the latter will perform the task in line with the expectations of the former, thereby putting the trustor in a vulnerable position with regard to the trustee; and blockchain technology enables a shift from trusting people to trusting math” (De Filippi, Mannan, & Reijers, 2020). Cryptocurrency biggest hurdle is the trust of the market, which is affected by price fluctuations and instability; and it is shown in emerging countries that the higher market penetration of crypto, the higher the stability of its value which will upsurge acceptance among individuals as well (Connolly & Kick, 2015). The primary design of crypto is to eliminate the necessity of financial institutions, governments, and trusted third parties; and for instance Bitcoin in its essence increases efficiencies and eliminates the probability of fraud via providing proof of work protocols to guarantee transactions’ validity (D’Alfonso et al., 2016). Trust can be seen as “the willingness to take risks based on the belief in, integrity of, competence in and expectations regarding the use of cryptocurrency and is developed based on beliefs regarding the integrity, reliability, trustworthiness, security and privacy of cryptocurrency” (Mendoza-Tello, Mora, Pujol-López, & Lytras, 2019). Trust plays an significant role in promoting innovation (in this case cryptcurrency) when official bodies are absent, and where states of risk and uncertainty exist (Jalan et al., 2022). Earlier research has shown that Trust has a direct significant influence on intention to adopt cryptoassets (Abbasi, Tiew, Tang, Goh, & Thurasamy, 2021; Ayedh et al., 2021; Mendoza-Tello et al., 2019; Voskobojnikov, Abramova, Beznosov, & Böhme, 2021).

Therefore, it is suggested that:

Intentions to adopt Cryptocurrency upsurges if additional benefits are apparent from using it (Xiong & Tang, 2020). Obviously the investment opportunity, digitized currency, time and cost of transaction, ease of use, and security are deliberated to be benefits to consider (Gao et al., 2016), in addition to anonymity and universality (Esmaeilzadeh, Cousins, and Subramanian, 2020) Perceived Benefit refers to “an individual’s perceptual belief that the use of Bitcoin (or another cryptocurrency) will result in both direct and indirect positive outcomes” (Abramova and Böhme, 2016). In this study, perceived benefits will be measured by the three first-order constructs: “1) Transaction Processing (TP) comprises transaction related benefits of using cryptcurrency for payments; 2) Security and Control (SC) refers to perceptions about the overall security of the crypto system; 3) Decentralization (DE) conceptualizes beneficial implications of the cryptocurrency core design principle” (Abramova & Böhme, 2016; Alqaryouti, Siyam, Alkashri, & Shaalan, 2020a). The decentralized nature of cryptocurrency is independent from bodies such as governments and international organizations that are not able to impose financial sanctions and embargos which were executed formerly via traditional means (World Economic Forum, 2021). Number of studies showed the positive influence of perceived benefits on adoption intention of cryptocurrency (Alqaryouti et al., 2020b; Esmaeilzadeh et al., 2020; Gazali, 2019; Muchlis Gazali, Hafiz Bin Che Ismail, & Amboala, 2018). Hence, it is hypothesized that:

In response to the financial markets’ collapse in 2008, some investors wanted enhanced regulation, firmer capital requirements and higher standards of business disclosure and transparency, while others found the notion of substitute, unregulated, and entirely decentralized financial systems and mechanisms mostly pleasing (Jalan et al., 2022). Regulatory frameworks can be defined as “the degree that an individual perceives that the technology is under the authorities’ control” (Daryaei et al., 2020). Crypto makes the cross-border transaction easier and more comfortable with the existence of the ledger, however, it lacks specific regulations that can control it like fiat currencies, and it is no wonder that some people are apprehensive about it. Regulatory risks associated with crypto may influence the attitudes and usage intentions of individuals mainly for reliability matters, and those risks are considered one of the most important barriers for potential users (Esmaeilzadeh et al., 2020).

Regulators and policy makers are evaluating how best to address the novel issues posed by cryptocurrency, which swiftly grew from just a substitute to old-style money and systems, to a well-considered asset to investors, organizations and some countries (World Economic Forum, 2021). One of the main barriers to adopt blockchain tech is the regulatory uncertainties which refers to “the policies and regulations provided by the government to regulate and monitor the industries for the usage of new technology” (Etemadi, Strozzi, Van Gelder, & Etemadi, 2021). With incomplete or insufficient disclosure, the crypto ecosystem is open to users’ fraud and marketplace honesty risks due to the fact that most crypto tokens are speculative assets extremely unpredictable, and investors probably face losses from terminated tokens; something that is less common in regulated securities markets (IMF, 2021). Consequently, the study hypothesizes the following:

Herding as a behavioral bias gained its popularity after considered to be the motive behind the bursting of dotcom bubble in late 1990, the real estate bubble in 2008, and lately the cryptocurrency bubble during the past few years (Dewan & Dharni, 2019). Herding can be labeled as “a group of investors who ignore their private data and follow the investing behavior of other market members or base their investments on a marketplace accord” (Chong, Bany-Ariffin, Nassir, & Muhammad, 2019). Herd behavior is present in crypto market, it is robust during ascending market and periods of high instability (Aydın, Agan, & Aydın, 2022). Many studies tested herding behavior in crypto markets and detected empirical evidence pointing to significant herding behavior, which varies over time and depends on levels of uncertainty and turbulence (Ajaz & Kumar, 2018; Bouri, Gupta, & Roubaud, 2019; da Gama Silva, Klotzle, Pinto, & Gomes, 2019; Jalal, Sargiacomo, Sahar, & Fayyaz, 2020). Henceforth, it is hypothesized that:

Accordingly, the following conceptual framework is suggested by the researcher as illustrated in Figure 1 below:

An online questionnaire was developed and distributed to collect data nationwide. Participants are above 18 and reside in Lebanon. The survey link was shared on internet using social media networks and email marketing techniques, until we reached the needed sample size. According to (Start.io, 2022), only 377,031 individuals residing in Lebanon are crypto-users. This study used Covariance-Based Structural Equation Modeling (CBSEM) for statistical analysis, specifically Maximum Likelihood (ML) estimation method – which is of a confirmatory view unlike Partial Least Squares (PLS-SEM) which is of an exploratory view (Barroso, Carrión, & Roldán, 2010). The minimum sample size for structural equation modeling is at least 200 (Civelek, 2018); yet, the final number of responses for this study was 223 responses, and Table 1 shows the profiling of respondents. The questionnaire shows the measurements embraced for assessing the factors affecting crypto usage intention in Lebanon. The items to measure the constructs were sourced from current validated scales in the literature namely: five items to measure Trust in Cryptocurrency (Mendoza-Tello et al., 2019); nine items to measure perceived benefits (Abramova & Böhme, 2016); two items to measure regulatory support (Amini, 2014); three items for herding behavior (Rejikumar et al., 2022); and five items to measure behavioral Intentions toward Cryptocurrency (Mendoza-Tello et al., 2019).

To evaluate the construct reliability, composite reliability (CR) and Cronbach's alpha (α) were assessed by the researcher. Table 2 shows the reliability scores where α and CR greater than 0.7. In addition, the paper evaluated the constructs’ validity through examining the convergent and discriminant validity. Firstly, a confirmatory factor analysis (CFA) was performed to examine the convergent validity of the measures. The outcomes of CFA confirm that model fit indices are within the thresholds criteria CMIN/DF = 1.999, (CFI) of 0.896, (TLI) of 0.89, (RMSEA) of 0.067; showing a reasonable measurement model fit (Doll, Xia, & Torkzadeh, 1994; Hu & Bentler, 1999). The CFA results shows that the factor loadings are all above 0.5 validating the convergent validity of the measurements (Hair, Black, Babin, & Anderson, 2014) as shown in Table 2.

In addition, to assess the discriminant validity of the measurements, this study applied the heterotrait-monotrait ratio (HTMT) of the correlations, which is “the average of the heterotrait-hetero method correlations (i.e. the correlations of indicators across constructs measuring different phenomena), relative to the average of the monotrait-hetero method correlations (i.e., the correlations of indicators within the same construct)” (Henseler, Ringle, & Sarstedt, 2015), and the HTMT scores should be less than 0.85. As shown in Table 2 all the score are not higher than 0.85, proving the discriminant validity of the measurements.

A full structural analysis was made in order to assess the hypothesized relationships between the constructs. Firstly, the structural model fit results (CMIN/DF = 2.645, CFI = 0.836, TLI = 0.818, RMSEA = 0.086) demonstrated a good fit with the data meeting the most accepted benchmarks (Doll et al., 1994; Hu & Bentler, 1999). Moreover, Table 3 presents the estimated values of the standardized regression weights of the relationship model. It is clear that H1 and H2 are accepted hypotheses namely Perceived Benefits (β = 0.621, p < 0.001) which is having the highest influence, followed by Trust in Crypto (β = 0.424, p < 0.001) whereas H3 and H4 are rejected indicating that herd behavior and regulatory support are of no influence on behavioral intentions toward cryptocurrency.

This study tested a causal model in an attempt to comprehend the behavioral intentions toward cryptocurrency in the Lebanese market. The results indicates that Trust in Crypto and perceived benefits are significant antecedents behavioral intentions toward cryptocurrency; while regulatory support and herd behavior do not possess an influence. The beta score of perceived benefits highlights the vital influence it carries on people intentions. This result matches results of other papers which found that this variable is of high impact on behavioral intentions toward cryptocurrency (Abramova & Böhme, 2016; Alqaryouti et al., 2020a, 2020b; Alqaryouti et al., 2020a, b; Esmaeilzadeh et al., 2020; Gao et al., 2016; Gazali, 2019; Muchlis Gazali et al., 2018). This is a clue that people in Lebanon are mainly driven by benefits of using cryptocurrency rather than something else. Moreover, the results indicated that trust in crypto is influencing the intentions to behave toward cryptocurrency, and this result is similar to earlier mainstream research (Abbasi et al., 2021; Ayedh et al., 2021; Mendoza-Tello et al., 2019; Voskobojnikov et al., 2021).

However, herd behavior was found to possess no impact on behavior intentions, and this result contradicts with earlier research (Ajaz & Kumar, 2018; Aydın et al., 2022; Bouri et al., 2019; da Gama Silva et al., 2019; Jalal et al., 2020). This contradiction can be associated with the comprehension that herd behavior is stronger during extreme situations rather than in normal conditions and it is evident only in bull markets versus bear markets (Kyriazis, 2020), and this study was made during a bear market period.

Lastly, the results did not confirm an impact of regulatory support; although earlier papers articulate that regulatory support can help increasing the intentions toward crypto (Etemadi et al., 2021; IMF, 2021). This might be a consequence of users’ distrust in their government, and they are searching for new ways to detour the governmental financial routes.

The paper attempts to address multiple gaps stated earlier, thus making some contributions theoretically and empirically. First, no previous study to the knowledge of the researcher applied ST theory in cryptocurrency behavioral intentions area of research, and no previous study explored the nominated factors in one research model. Second, the study extends the limited research for understanding the factors affecting behavioral usage intentions in the Middle East region and Lebanon specifically; it is among the first studies in this regard. Third, the outcomes of this research function as an orientation for governments and regulators, crypto project owners, and platform companies. For instance, governments might build on the insights from this study to develop their own central bank digital currencies.

Regarding the study limitations, it should be mentioned that the model was only assessed in the Lebanese market, and other authors may deploy it in cross-cultural dimension with larger data set. Secondly, the domain of herding was examined in a bear market period, thus for future papers it might be investigated in bull market trends; also herding can be inspected in other emerging countries as current literature provides evidence that in developed countries herding is obvious. In future research, other variables could be added to the model to be tested such as financial literacy, perceived ease of use of crypto applications, perceived risk, etc.

Erratum: It has come to the attention of the publisher that the article, Abou Ali, A.A.K. (2024), “DeFi era: the behavioral intentions toward cryptocurrency in Lebanon”, Innovation & Management Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/INMR-02-2023-0022, was published without including the article’s associate editor, Rafael Morais Pereira. This error was introduced in the production process and has now been corrected in the online version. The publisher sincerely apologises for this error and for any inconvenience caused.