Journal of value creating in Business Management

Abstract Abstract
The purpose of this study is to design a model for identifying factors of cryptocurrency acceptance by investors with a data-based approach. The research method is fundamental and exploratory according to its purpose, and qualitative in terms of implementation method, based on the data-based method. The statistical population of the study includes 15 financial engineering professors and cryptocurrency market investors, and in selecting the samples, an attempt was made to select the most knowledgeable and in many cases the most active people in the cryptocurrency field. The sample size was determined using the theoretical sampling method. Semi-structured interviews were used to collect information. Data-based techniques and open, axial, and selective coding were used to analyze the data. According to the results obtained from open, axial, and selective coding, the identified variables have been placed in two parts: causes and consequences. The causes identified in the research include cryptocurrency trading characteristics, individual-behavioral characteristics, technology characteristics, and regulatory support; and the consequential factors include specific risks, revenue generation (profit), and the government’s regulatory and operational position.
Introduction
By its nature, the cryptocurrency market experiences extreme volatility. However, currencies able to withstand these fluctuations due to strong backing, high liquidity, and a broad user community are safer options for long-term investment (Carbo et al., 2025).
Meanwhile, investors are exposed to various types of behavioral biases that lead them to cognitive errors and poor decision-making. The tendency effect, defined as investors’ tendency to sell profitable assets faster and hold loss-making assets longer, is one of the most well-documented investor biases in the behavioral finance literature. There are behavioral anomalies in the markets that make investors' decisions appear abnormal and remove the market from regularity. One of these market irregularities stems from the psychological factors of investors that remove the market from an efficient state. Therefore, it is necessary to conduct extensive research with precise scientific foundations to identify such behavioral biases and clarify the various dimensions of market activities in order to provide a stronger basis for judgment and create a basis for market control by legislators and regulators. One of the most important variables that can reduce this bias and investor risks is financial knowledge and attitude. In recent decades, financial knowledge and attitude have aroused significant international interest among government organizations, researchers, professionals and the general population, because this factor can have a huge impact on individuals' financial decisions. Therefore, financial knowledge and attitude not only affect individual financial well-being, but also have positive consequences for the financial system and the economy as a whole (Molina García et al., 2023). Given the importance and role of the variable of financial knowledge and attitude, the number of countries and international organizations that are involved in recognizing and improving the financial knowledge and attitude of their citizens and how it affects their financial decisions is increasing, and paying attention to this variable can support global financial and economic stability (Douissa, 2020). Previous research conducted in this area has focused more on the role of financial knowledge and attitude in financial aspects such as retirement planning (Gallego Losada et al., 2022). However, various researchers in the field of behavioral finance call for more research on the impact that financial knowledge and attitude can have on aspects inherently related to individual behavior; and in particular, are directly related to investors' risk appetite. Because due to the increasing complexity of financial environments in which decisions involve more risk, individuals' financial decisions can entail more risk for them (Molina García et al., 2023). Accordingly, the present study seeks to answer the following question: What is the model for identifying the factors of cryptocurrency adoption by investors with a data-based approach?
Theoretical Framework
Digital Currency
Digital currencies are decentralized digital currencies and therefore do not require the mediation of any financial institution, which indicates a disruption in the financial system. Bitcoin is the first digital currency that has been created and has enabled the emergence of other digital currencies such as Ethereum, Litecoin, Ripple, Dash and Altcoins and many others. Therefore, digital currencies have experienced rapid development and have become popular assets in global financial markets, and considering factors such as media attention, individual investors, institutional investors and governments; the issue of cryptocurrency has become an important and real issue worldwide. However, in the meantime, the issue of buying digital currencies has received more attention in the field of behavioral finance (Almeida & Goncalves, 2023).
Rastegari Basharabadi & Jahanshahi (2024) investigated the impact of market and digital currency strategic orientation on competitive advantage in investment markets among cryptocurrency market players. A general review of the results of hypothesis testing showed that market and digital currency strategic orientation have a direct and significant relationship on competitive advantage.
Sakariyahu et al., (2024) investigated global uncertainty, sentiment factors, and the cryptocurrency market. The results showed that economic and political uncertainty factors significantly affect crypto prices. In addition, the interaction between sentiment dynamics, as expressed by investors on different social platforms, has a significant negative impact on cryptocurrency market returns, and this effect is more pronounced for tokens within an ecosystem. They also showed the existence of a significant contagion between tokens within an ecosystem when bad (or good) news occurs. Given the huge unprotected losses that cryptocurrency investors suffer during crises, their results provide important insights into how portfolio managers can effectively design investment strategies.
Research Methodology
The research method is fundamental and exploratory in terms of purpose, and qualitative in terms of implementation, based on the grounded data method. The statistical population of the research includes 15 financial engineering professors and cryptocurrency market investors, and in selecting the samples, an attempt was made to select the most knowledgeable and, in many cases, the most active people in the cryptocurrency field. The sample size was determined by the theoretical sampling method. Semi-structured interviews were used to collect information.
Research Findings
The grounded data technique and open, axial, and selective coding were used to analyze the data. According to the results obtained from open, axial, and selective coding, the identified variables are placed in two parts: causes and consequences. The causes identified in the study include cryptocurrency trading characteristics, individual-behavioral characteristics, technological characteristics, and regulatory support; and the consequential factors include specific risks, revenue generation (profit), and the government's regulatory and operational position.
Conclusion
The present study aimed to design a model to identify the factors of cryptocurrency acceptance by investors with a data-based approach. These findings are consistent with the results of Rastegari Basharabadi & Jahanshahi (2024), Sakariyahu et al. (2024), Bakhtiari et al. (2024), Amirbeiki Langroodi & Habibi Nodehi (2023), Almeida & Goncalves (2023), Gupta et al. (2021), Paschalie & Santoso (2020), and Raut (2020). Almeida & Goncalves (2023) showed that some socio-demographic characteristics of cryptocurrency investors and some characteristics of the cryptocurrency market such as market inefficiency affect investor behavior. They also provide a structured network analysis for the literature, which helps researchers and academics, investors, and regulators and provides relevant information for future studies on the behavior of cryptocurrency investors. In addition, it shows the most relevant factors that affect the behavior of the cryptocurrency market and its investors, and provides a basis for better regulation and support for investors in the cryptocurrency market.
According to the results of the study, the following suggestions were made:
It is suggested that individuals investing in the cryptocurrency market use online forums on Iranian exchanges to obtain information about a specific cryptocurrency in order to obtain more up-to-date information from the market.
It is suggested that a legal platform be designed for traders first, so that people can easily communicate and interact with exchanges and share their experiences of trading with exchanges. In other words, traders need to not only establish more appropriate interaction with members as the custodian of this platform, but also facilitate relationships between members. A positive user experience is a reassuring factor for the continued use of any system. 

Abstract Abstract The aim of this research is to model the factors affecting the profit response coefficient of companies by combining behavioral finance components using the structural equation method. This research is applicable in terms of its purpose, descriptive-correlational in terms of collecting data, and with a survey method. Using data from 153 companies listed on the Tehran Stock Exchange during the period 2013 to 2022 and through the structural equation modeling method, the factors affecting the profit response coefficient were identified and ranked. The results of the research showed that the financial condition and performance factor with a path coefficient of -0.19, the capital market condition and performance factor with a path coefficient of 0.167, and the investment environment factor with a path coefficient of 0.12 have a significant effect on the profit response coefficient at the 0.05 error level. Accordingly, the structures of earnings per share, financial leverage, information asymmetry, market index return, inflation rate, free float shares, stock turnover rate and stock trading frequency were identified as structures affecting the profit response coefficient. Among the aforementioned structures, the absolute value of the coefficients of the structures showed that the inflation rate has the highest, and earnings per share has the lowest impact on the profit response coefficient. Introduction In investment activity, investors need information to make decisions. The required information can be seen in the company's published financial reports. Financial reports are an excellent tool for obtaining information about the company's financial status and performance. One of the financial statement information used in decision-making is the company's profit. Profit is a comprehensive element for evaluating the overall performance of the business unit (Azizi et al, 2016). Agency theory (Jensen & Meckling, 1976) states a relationship between the owner (the principal) and the delegation of authority and decision-making to management (the agent) to optimize profits. There are opportunities for disagreement between the two sides of this relationship, which leads to information asymmetry. Information asymmetry occurs when a management (agent) has more information about the company's situation and prospects than the owner (principal) (Baroroh et al, 2022). Therefore, information asymmetry occurs when two parties to a contract or transaction have access to different information. Signaling theory is generally useful for explaining the behavior of individuals when two parties have different access to information. According to signaling theory, information asymmetry can be reduced when one party (the sender) chooses what information to send and how to send it (signal), and the other party (the receiver) must choose how to interpret the signal (information) (Nurfadilah et al, 2023). One tool that can be used to measure investor reaction to accounting earnings information is the earnings response coefficient. The earnings response coefficient is an estimate of the changes in a company's stock price as a result of the company's earnings announcement to the market. The earnings response coefficient is another measure of abnormal returns observed in response to unexpected elements of earnings reported by the company. In other words, the earnings response coefficient measures the sensitivity of stock markets to earnings reports through the regression slope coefficient between abnormal returns and unexpected earnings (ViDiat Moko & Indarti, 2018). The importance of research on the earnings response coefficient primarily stems from the need to increase the trust of company shareholders in the disclosure of accounting information, which allows them to make informed decisions about investing in stocks. Therefore, the main goal of this research is to model the factors affecting the earnings response coefficient of companies by combining behavioral finance components in the Iranian stock market. Considering the above, the researcher tries to address the main question of how to model the factors affecting the earnings response coefficient of companies by combining behavioral finance components using the structural equation model. Theoretical Framework Corporate Earnings Response Coefficient One of the tools that evaluate the quality of earnings is the earnings response coefficient. The earnings response coefficient is significantly related to earnings or the responsiveness of the information interpreted or contained in earnings. The earnings response coefficient is another measure of abnormal returns observed in response to unexpected elements of earnings announced by a company that publishes its earnings report. In other words, the earnings response coefficient measures the sensitivity of stock markets to earnings reports through a regression slope coefficient between abnormal returns and unexpected earnings (Nurfadilah et al, 2023). Behavioral Finance Components Investor behavior to buy stocks is influenced by the availability of information that can be used in stock valuation. However, there is still doubt whether the availability of this information, namely earnings information, has been well received by investors. Earnings quality can be shown as the ability of earnings information to respond to the market. In other words, reported earnings have the power to respond. Strong market reaction to earnings information is reflected in a high earnings response coefficient, which indicates that the earnings are reported with quality (Paramita, 2020). Adib Fard & Khan Mohammadi (2024) investigated the effect of management ability on earnings response coefficient considering the role of information asymmetry. The research hypotheses were tested based on a statistical sample of 164 companies over a 10-year period from 2012 to 2021 using multivariate regression models with mixed data. The results indicate a positive effect of management ability on earnings response coefficient; However, the results showed that increasing the difference between the bid and ask price as a measure of information asymmetry leads to the adjustment of this relationship, in a way that weakens the effect of management ability on the real profit response coefficient. Ramineh (2024) examined the effect of conditional conservatism and comparability of financial statements on the profit response coefficient. The statistical population of the study is companies listed on the Tehran Stock Exchange and the sample under study includes 152 companies listed during the years 2018 to 2022. The results obtained from data analysis show that the comparability of financial statements has a positive and significant effect on the profit response coefficient, and conditional conservatism has a positive and significant effect on the profit response coefficient. Research Methodology This research is applicable in terms of purpose, descriptive-correlational in terms of data collection, and with survey method. Using data from 153 companies listed on the Tehran Stock Exchange during the period 2013 to 2022, and through the structural equation modeling method, the factors affecting the earnings response coefficient were identified and ranked. Research findings SPSS software and structural equations were used to analyze the data. The results of the research showed that the financial condition and performance factor with a path coefficient of -0.19, the capital market condition and performance factor with a path coefficient of 0.167, and the investment environment factor with a path coefficient of 0.12 have a significant effect on the earnings response coefficient at the 0.05 error level. Accordingly, the structures of earnings per share, financial leverage, information asymmetry, market index return, inflation rate, free float shares, stock turnover rate, and stock trading frequency were identified as structures affecting the earnings response coefficient. Among the aforementioned structures, the absolute value of the coefficients of the structures showed that the inflation rate has the highest and earnings per share has the lowest effect on the earnings response coefficient. Conclusion The present study aimed to model the factors affecting the profit response coefficient of companies by combining behavioral finance components using the structural equation model. The results of this study are consistent with the results of Adib Fard & Khan Mohammadi (2024), Ramineh (2024), Ahmadi Olyaee (2023), Zareian Kalkhouran & Zareian Kalkhouran (2023), Hajannejad et al, (2022), Kordestani & Abdoli (2022), Bahaghighat & Rezaei (2018), Sandy & Mulya (2024), Kim et al, (2023), Elviani et al, (2022), Niswah et al, (2022), Sun et al, (2021), Awawdeh et al, (2020), and Wijaya et al, (2022). Sandy & Mulya (2024) showed that high earnings quality attracts high investor sentiment in stock trading; good news is quickly processed in the price and the price increases in a short period, while bad news can also lead to price corrections and investor sentiment. Also, a positive relationship was observed between return on equity and earnings reaction coefficient, which strengthens investor sentiment in this relationship. According to the research results, the following suggestions are made: It is suggested that actual and potential investors pay attention to earnings per share and financial leverage as indicators of company performance results at the time of earnings announcement, in order to predict the market reaction to unexpected earnings, as well as the percentage of free float shares and the stock turnover rate and the frequency of stock trading as indicators of the atmosphere prevailing in stock trading in the market, as factors affecting the earnings reaction coefficient.