Bambang Leo Handoko

This study explains cryptocurrency investment decisions by integrating personality traits, influencer credibility, and social influence within the Stimulus–Organism–Response (SOR) framework. Openness, extraversion, conscientiousness, influencer credibility, and social influence are positioned as stimuli; heuristic bias and herding behaviour as organism states; and cryptocurrency investment decision as the response, with risk tolerance acting as a serial mediating mechanism. Data were collected from 367 Indonesian retail cryptocurrency investors through an online survey and analysed using SEM-PLS. The measurement model demonstrates adequate reliability and convergent validity, while discriminant validity is supported by HTMT values below the recommended threshold. The results indicate that personality traits significantly influence heuristic bias, while influencer credibility and social influence increase herding behaviour. Heuristic bias and herding behaviour both positively affect risk tolerance and cryptocurrency investment decisions, with heuristic bias showing the stronger effect. Risk tolerance also positively influences investment decisions and mediates the effects of heuristic bias and herding behaviour. The model explains a substantial portion of the variance in cryptocurrency investment decisions (Adjusted R² = 0.623). These findings extend the SOR framework to cryptocurrency markets by highlighting how psychological traits and social cues shape risk tolerance and ultimately influence investment behaviour in volatile digital asset environments.

This study explains cryptocurrency investment decisions by integrating personality traits, influencer credibility, and social influence within the Stimulus–Organism–Response (SOR) framework. Openness, extraversion, conscientiousness, influencer credibility, and social influence are positioned as stimuli; heuristic bias and herding behaviour as organism states; and cryptocurrency investment decision as the response, with risk tolerance acting as a serial mediating mechanism. Data were collected from 367 Indonesian retail cryptocurrency investors through an online survey and analysed using SEM-PLS. The measurement model demonstrates adequate reliability and convergent validity, while discriminant validity is supported by HTMT values below the recommended threshold. The results indicate that personality traits significantly influence heuristic bias, while influencer credibility and social influence increase herding behaviour. Heuristic bias and herding behaviour both positively affect risk tolerance and cryptocurrency investment decisions, with heuristic bias showing the stronger effect. Risk tolerance also positively influences investment decisions and mediates the effects of heuristic bias and herding behaviour. The model explains a substantial portion of the variance in cryptocurrency investment decisions (Adjusted R² = 0.623). These findings extend the SOR framework to cryptocurrency markets by highlighting how psychological traits and social cues shape risk tolerance and ultimately influence investment behaviour in volatile digital asset environments.

This study explains cryptocurrency investment decisions by integrating personality traits, influencer credibility, and social influence within the Stimulus–Organism–Response (SOR) framework. The position model of openness, extraversion, conscientiousness, influencer credibility, and social influence as stimuli; heuristic bias and herding behaviour as organism states; and cryptocurrency investment decision as the response, with risk tolerance operating as a serial mediating mechanism. Data were collected from 367 Indonesian retail cryptocurrency investors using an online survey and analysed with SEM-PLS (SmartPLS) and one-tailed significance testing. The measurement model demonstrates adequate convergent validity and reliability, and discriminant validity is supported by HTMT values below the recommended threshold. Structural results support all hypothesized relationships (H1–H12). Openness (β = 0.132), extraversion (β = 0.326), and conscientiousness (β = 0.195) positively influence the tendency toward heuristic bias. Influencer credibility (β = 0.303) and social influence (β = 0.285) positively influence herding behaviour. Heuristic bias positively affects risk tolerance (β = 0.585) and investment decision (β = 0.407), while herding behaviour positively affects risk tolerance (β = 0.185) and investment decision (β = 0.106). Risk tolerance positively affects investment decision (β = 0.354) and mediates the effects of heuristic bias (indirect β = 0.200) and herding behaviour (indirect β = 0.078) on investment decision. The model explains substantial variance in investment decisions (Adjusted R² = 0.623) and moderate variance in risk tolerance (Adjusted R² = 0.507). The findings extend SOR to sentiment-driven digital asset markets by showing that cognitive shortcuts and socially transmitted cues shape risk tolerance before translating into investment actions, and they highlight the importance of behavioural risk-mitigation and disclosure practices in crypto ecosystems.

The rapid rise in cryptocurrency presents both opportunities and challenges for retail investors due to its volatility and technological complexity. Research on investment decisions has primarily focused on behavioural finance, often overlooking how learning and literacy shape investor actions. This study addresses this gap by examining how herding behaviour, financial literacy, and digital literacy impact cryptocurrency investment decisions. Grounded in Social Learning Theory and supported by UTAUT to operationalise digital literacy, this study examines how herding behaviour, financial literacy, and digital literacy shape cryptocurrency investment decisions. We analyse survey data from 138 Indonesian retail investors through PLS-SEM. Key findings show that financial literacy (β = 0.443, t = 5.041) and digital literacy (β = 0.495, t = 4.246) are primary determinants of investment decisions, while herding behaviour (β = 0.016, t = 0.628) does not directly influence them but does so indirectly by enhancing investor literacy. This demonstrates that social observation and learning can convert herd-driven impulses into rational choices when mediated by literacy. By extending Social Learning Theory into digital investment contexts, this study provides insights for investors and policymakers seeking to enhance financial and digital literacy.

This study investigates community attitudes toward sustainable tourism development in Labuan Bajo, a national priority destination in Indonesia. Using survey data from local residents analyzed through Structural Equation Modelling (SEM), the research examines the influence of tourism development potential, sense of place, and perceived tourism impacts on community support. Results indicate that sense of place and development potential significantly strengthen both collective and personal benefit perceptions, which subsequently foster greater support for sustainable tourism. The novelty lies in integrating sense of place with perceived benefits to explain local support, extending existing studies that have predominantly emphasized economic or environmental dimensions. Practically, the findings provide actionable guidance for policymakers and stakeholders by demonstrating that sustainable tourism strategies must incorporate local perceptions and place attachment to secure long-term community endorsement and ensure the success of destination development initiatives.

Cryptocurrency has grown in popularity among investors in recent years, but its high market volatility and technological complexity require deeper comprehension from investors. Most previous research has discussed investment decisions from a behavioural finance perspective underpinning the expectancy theory. As some investors may be guided by others’ investing decisions and investing in cryptocurrency requires their understanding of digital technology, this study attempts to integrate herding behaviour theory and expectancy theory and apply the Unified Theory of Acceptance and Use of Technology (UTAUT) to comprehend digital literacy in cryptocurrency. This study's goal is to analyse into the variables that affect cryptocurrency investment decisions. focusing on herding behaviour, financial literacy and digital literacy. Samples of Indonesian cryptocurrency retail investors were purposively selected to obtain 138 valid responses. Data were analysed using PLS-SEM. The findings demonstrated that digital and financial literacy had an impact on crypto investment decisions, but herding behavior had no effect. This study found that herding behavior significantly affects digital and financial literacy. As such, digital and financial literacy play pivotal roles as mediators between herding behaviour and cryptocurrency investment decisions. The research offers a comprehensive framework to investors, investment influencers, and crypto asset providers for developing educational content that increases financial and digital literacy using social media and crypto investment platforms.

The rapid development of technology in industry 4.0 today has encouraged the integration of Artificial Intelligence (AI), Internet of Things (IoT) and big data in helping the operations of various industrial sectors, especially in the start-up sector. This study aims to determine whether there are factors such as Natural Language Processing, AI-Driven data analysis, risk assessment, and electronic whistle-blowing systems that will affect the way the system detects fraud, and to determine whether these factors cause several start-up companies to use the integration of AI, NLP, and E-WBS to accelerate the fraud disclosure process. This study involved 113 employee respondents who worked in start-up companies. The results of the respondent data were processed using SMART-PLS 4.0 which involved the reliability and validity methods, discriminant, r-square adjusted, and outer loading. The results of the study showed that Natural Language Processing, AI-Driven data analysis, risk assessment, and Electronic Whistle Blowing Systems did have a positive impact or increase the accuracy of fraud disclosure in real-time, effectively and efficiently. Keyword: AI-Driven Data Analysis, Natural Language Processing, Whistle-Blowing Systems

The rapid development of technology in Industry 4.0 today has encouraged the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and big data in helping the operations of various industrial sectors, especially in the start-up sector. This study aims to determine whether there are factors such as Natural Language Processing, AI-driven data analysis, risk assessment, and electronic whistleblowing systems that will affect the way the system detects fraud, and to determine whether these factors cause several start-up companies to use the integration of AI, NLP, and E-WBS to accelerate the fraud disclosure process. This study involved 113 employee respondents who worked in start-up companies. The results of the respondent data were processed using SMART-PLS 4.0, which involved the reliability and validity methods, discriminant analysis, r-squared adjusted, and outer loading. The results of the study showed that Natural Language Processing, AI-driven data analysis, risk assessment, and electronic whistleblowing systems did have a positive impact or increase the accuracy of fraud disclosure in real-time, effectively, and efficiently. Early identification of fraud patterns can prevent greater losses, and parties who are aware of fraudulent actions will report them and have reporting channels that create a sense of security for the reporter.

Financial performance is another requirement for companies to maintain their image, and good financial performance is the key to the company’s standing. Financial performance is influenced by factors such as leadership from the director or chief executive officer (CEO). This study aims to obtain empirical evidence regarding the influence of CEO overconfidence, female directors, and CEO duality on financial performance. The companies studied are industrial companies listed on the Indonesia Stock Exchange (IDX) from 2015 to 2021. This research uses quantitative data collected from annual reports published by the companies. The sampling technique used is purposive sampling, based on criteria relevant to the research needs. The data analysis technique used is multiple linear regression. The results of the study explain that CEO overconfidence and female directors influence financial performance, while CEO duality has no effect on the company’s financial performance. Gender issues have not yet been included in previous CEO antecedent-based research on financial performance. This is the new finding from our study. In our research model, we included antecedents for female directors.