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All Journal Enigma in Education Enigma in Economics
Gayatri Putri
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Humanities Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Other

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The Future of the Firm: A Comparative Institutional Analysis of Transaction Costs in DAOs versus Traditional Corporations Benyamin Wongso; Caelin Damayanti; Muhammad Faiz; Anies Fatmawati; Aylin Yermekova; Delia Tamim; Dais Susilo; Danila Adi Sanjaya; Gayatri Putri
Enigma in Economics Vol. 3 No. 2 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i2.94

Abstract

The emergence of Decentralized Autonomous Organizations (DAOs) presents a fundamental challenge to the traditional corporate form, which has dominated economic organization for over a century. Built on blockchain technology, DAOs propose a new model for coordinating economic activity. This study addressed the critical question of institutional efficiency by applying the lens of Transaction Cost Economics (TCE) to compare DAOs and traditional corporations. A comparative institutional analysis was conducted using a mixed-methods approach. We employed a multiple case study design, analyzing two representative DAOs and two analogous traditional corporations from Q1 2023 to Q4 2024. Data collection involved the systematic analysis of archival records, including 215 DAO governance proposals and corporate filings, and 32 semi-structured interviews with key participants. A novel analytical framework was developed to categorize transaction costs into ex ante (search, bargaining) and ex post (monitoring, enforcement), further distinguishing between 'on-chain' and 'off-chain' costs. The study revealed significant trade-offs between the two organizational forms. Traditional corporations exhibited high ex ante bargaining costs (legal, negotiation) and ex post monitoring costs (managerial overhead), but benefited from established legal frameworks that reduced enforcement uncertainty. Conversely, DAOs significantly lowered specific transaction costs through automation via smart contracts, particularly in on-chain bargaining and enforcement for codified tasks. However, DAOs incurred substantial, often hidden, new transaction costs related to off-chain social coordination, governance participation, and navigating legal ambiguity. This was termed the 'Governance Overhead Paradox'. In conclusion, DAOs do not represent a universally superior organizational form but rather a new point on an institutional possibility frontier. They are highly efficient for tasks that are global, permissionless, and computationally verifiable. Traditional firms retain advantages in contexts requiring complex, subjective decision-making and legal certainty. The future of the firm is likely not a replacement of one form by the other, but a pluralistic ecosystem where hybrid models emerge.
Building a Profession from the Ground Up: A Longitudinal Study of Teacher Professional Development and Pedagogical Innovation in Papuan Private Schools Iis Sugandhi; Arya Ganendra; Aaliyah El-Hussaini; Gayatri Putri; Evelyn Wang; Anita Havyasari; Muhammad Hasan
Enigma in Education Vol. 3 No. 1 (2025): Enigma in Education
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/edu.v3i1.90

Abstract

Teacher quality is the most significant school-based determinant of student success, yet fostering professional excellence in remote and culturally diverse regions like Papua, Indonesia, presents profound challenges. Private schools often fill critical educational gaps but their teachers can be professionally isolated. This study addressed the gap in long-term, evidence-based research on teacher professional development (TPD) in this unique context. A three-year (2021-2024) concurrent mixed-methods longitudinal study was conducted. The study involved 50 teachers from a network of five private schools in urban, semi-rural, and remote highland regions of Papua. A comprehensive TPD program, focusing on student-centered learning and culturally responsive pedagogy, was implemented. Quantitative data were collected annually using the Teacher Pedagogical Knowledge Test (TPKT), the Teacher Self-Efficacy Scale (TSES), and a structured Classroom Observation Protocol. Qualitative data were gathered through semi-structured interviews, teacher reflective journals, and focus group discussions with Professional Learning Communities (PLCs). Quantitative data were analyzed using repeated measures ANOVA, while qualitative data were analyzed thematically. The longitudinal quantitative analysis revealed statistically significant improvements across all three years. Mean TPKT scores increased from 48.5 (SD=11.2) at baseline to 79.8 (SD=8.5) at endline (F(2, 98) = 157.2, p <0.001). Teacher self-efficacy scores also showed significant growth (F(2, 98) = 112.9, p <0.001). Classroom observations confirmed a marked shift from teacher-centered to student-centered practices. Qualitative findings identified three core themes: (1) "From Transmission to Facilitation: A Pedagogical Awakening," detailing the shift in teachers' core beliefs about learning; (2) "The Power of the Collective," highlighting the crucial role of PLCs in sustaining motivation and collaborative problem-solving; and (3) "Navigating the Cultural Interface," illustrating the teachers' journey in adapting curriculum to be more culturally responsive. In conclusion, sustained, context-specific, and collaborative TPD can foster profound and lasting improvements in teacher knowledge, self-efficacy, and classroom practice, even in highly challenging environments. The findings advocate for a shift away from isolated, short-term workshops towards integrated, long-term models that prioritize peer collaboration and cultural relevance, revealing a clear pathway from knowledge acquisition to a transformed professional identity.
Systemic Contagion or Digital Diversifier? A Dynamic Quantification of the Cryptocurrency Market's Evolving Role in Global Financial Risk Transmission Abdul Malik; Gayatri Putri; Hesti Putri; Ahmad Badruddin
Enigma in Economics Vol. 3 No. 2 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i2.99

Abstract

The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.
Synergistic Alpha: A Deep Learning Framework for Forecasting Cryptocurrency Returns by Fusing On-Chain, Sentiment, and Market Data Gayatri Putri; Sonia Vernanda; Anies Fatmawati; Muhammad Faiz
Enigma in Economics Vol. 3 No. 2 (2025): Enigma in Economics
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/economy.v3i2.103

Abstract

The inherent volatility and unique economic characteristics of cryptocurrencies pose significant challenges to conventional asset-pricing models. This study investigates whether a synergistic fusion of the network’s fundamental data (on-chain metrics), market behavioral dynamics (social media sentiment), and historical market data can uncover statistically and economically significant predictive power when analyzed by advanced deep learning architectures. We developed a sophisticated forecasting and backtesting framework to predict the daily log returns of Bitcoin (BTC). The methodology is grounded in rigorous time-series analysis, beginning with Augmented Dickey-Fuller tests to ensure data stationarity. We constructed a multi-modal dataset from specified, high-frequency sources (Kaiko, Glassnode, and a custom-built FinBERT sentiment model) spanning January 1, 2018, to December 31, 2023. We systematically compared the performance of a state-of-the-art Transformer model against Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and robust econometric baselines, including GARCH(1,1) and ARIMA. The models were evaluated not only on statistical accuracy (such as Root Mean Squared Error and Directional Accuracy) but also on their economic significance via a realistic trading backtest that incorporates transaction costs. The fully integrated Hybrid Transformer model demonstrated superior forecasting accuracy, achieving the highest Directional Accuracy (61.25%). More importantly, in a transaction-cost-aware backtest, a trading strategy guided by this model yielded an annualized Sharpe Ratio of 1.58, significantly outperforming a buy-and-hold benchmark (Sharpe Ratio: 0.72). The strategy generated a statistically significant Jensen's Alpha of 0.18 (p < 0.01), indicating substantial risk-adjusted excess returns. Feature importance analysis via SHAP confirmed that social media sentiment and the NVT Signal were the most influential predictors beyond past returns. In conclusion, the findings provide strong evidence that the cryptocurrency market exhibits exploitable inefficiencies. The fusion of on-chain, sentiment, and market data, when processed by attention-based neural networks, uncovers a statistically and economically significant predictive edge. This work challenges the semi-strong form of market efficiency for digital assets and suggests that alpha is derivable from the complex, high-dimensional data footprints unique to this asset class, providing a robust framework for quantitative investment strategies.
Co-Authors Aaliyah El-Hussaini Abdul Malik Ahmad Badruddin Anies Fatmawati Anita Havyasari Arya Ganendra Aylin Yermekova Benyamin Wongso Caelin Damayanti Dais Susilo Danila Adi Sanjaya Delia Tamim Evelyn Wang Hesti Putri Iis Sugandhi Muhammad Hasan Nurwijayanti Sonia Vernanda