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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

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.

Governing the Algorithm: A Mediation Analysis of Digital Transformation, Bureaucratic Discretion, and Service Quality in a Developing Democracy Emir Abdullah; Aylin Yermekova; Benyamin Wongso; Ahmad Badruddin
Open Access Indonesia Journal of Social Sciences Vol. 8 No. 4 (2025): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v8i4.293

Governments worldwide are implementing digital transformation policies to enhance public service delivery. However, the impact of these algorithm-driven systems on street-level bureaucrats remains critically under-examined. This study investigates the complex pathways through which Indonesia's e-government policy affects bureaucratic work and service outcomes. This study employed a mixed-methods explanatory sequential design. Quantitative data were collected from 500 public officials across five Indonesian provinces. An E-Government Implementation Index (EGII) was constructed. We used Ordinary Least Squares (OLS) regression and a formal mediation analysis with bootstrapping to analyze the relationships between EGII, Perceived Bureaucratic Discretion (PBD), and Bureaucrat-Perceived Public Service Quality (B-PSQ). This was supplemented by 20 in-depth, semi-structured interviews to explain the statistical findings. Regression analysis confirmed a significant negative association between EGII and PBD (β = -0.47, p < 0.001) and a significant positive association between EGII and B-PSQ (β = 0.62, p < 0.001). The mediation analysis revealed that EGII has a strong, positive direct effect on B-PSQ (Effect = 0.57, p < 0.001) and a small but significant negative indirect effect through the reduction of PBD (Effect = -0.05, p < 0.01). Qualitative data revealed that officials feel constrained by "algorithmic cages" that, while improving efficiency, limit their ability to handle exceptional cases, thereby risking service equity for marginalized citizens. In conclusion, Indonesia’s digital transformation presents a complex trade-off. It successfully enhances administrative efficiency but simultaneously curtails the beneficial discretion of frontline bureaucrats, creating a small but significant drag on service quality. Effective digital governance requires a hybrid model that embeds algorithmic systems within a framework that empowers, rather than replaces, human judgment.

Governing the Algorithm: A Mediation Analysis of Digital Transformation, Bureaucratic Discretion, and Service Quality in a Developing Democracy Emir Abdullah; Aylin Yermekova; Benyamin Wongso; Ahmad Badruddin
Open Access Indonesia Journal of Social Sciences Vol. 8 No. 4 (2025): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v8i4.293

Governments worldwide are implementing digital transformation policies to enhance public service delivery. However, the impact of these algorithm-driven systems on street-level bureaucrats remains critically under-examined. This study investigates the complex pathways through which Indonesia's e-government policy affects bureaucratic work and service outcomes. This study employed a mixed-methods explanatory sequential design. Quantitative data were collected from 500 public officials across five Indonesian provinces. An E-Government Implementation Index (EGII) was constructed. We used Ordinary Least Squares (OLS) regression and a formal mediation analysis with bootstrapping to analyze the relationships between EGII, Perceived Bureaucratic Discretion (PBD), and Bureaucrat-Perceived Public Service Quality (B-PSQ). This was supplemented by 20 in-depth, semi-structured interviews to explain the statistical findings. Regression analysis confirmed a significant negative association between EGII and PBD (β = -0.47, p < 0.001) and a significant positive association between EGII and B-PSQ (β = 0.62, p < 0.001). The mediation analysis revealed that EGII has a strong, positive direct effect on B-PSQ (Effect = 0.57, p < 0.001) and a small but significant negative indirect effect through the reduction of PBD (Effect = -0.05, p < 0.01). Qualitative data revealed that officials feel constrained by "algorithmic cages" that, while improving efficiency, limit their ability to handle exceptional cases, thereby risking service equity for marginalized citizens. In conclusion, Indonesia’s digital transformation presents a complex trade-off. It successfully enhances administrative efficiency but simultaneously curtails the beneficial discretion of frontline bureaucrats, creating a small but significant drag on service quality. Effective digital governance requires a hybrid model that embeds algorithmic systems within a framework that empowers, rather than replaces, human judgment.

Algorithmic Contagion: A Network-SEIR Analysis of Xenophobic Disinformation Diffusion During Indonesia’s 2024 Election Caelin Damayanti; Benyamin Wongso; Emir Abdullah
Open Access Indonesia Journal of Social Sciences Vol. 8 No. 3 (2025): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v8i3.301

The 2024 Indonesian General Election was marked by a sudden, coordinated surge in xenophobic narratives targeting Rohingya refugees. This study investigates the diffusion mechanics of this viral hate, testing the hypothesis that algorithmic architectures on platforms such as TikTok and X (formerly Twitter) accelerate radicalization through specific epidemiological pathways. We employed a Stochastic Network SEIR (Susceptible-Exposed-Infectious-Recovered) model to analyze the Indo-Elect-24 dataset, comprising 2.4 million interaction events across a network of 10.2 million nodes. Unlike traditional aggregate models, we utilized a heterogeneous adjacency matrix to identify super-spreader nodes. Parameters were estimated using Bayesian inference via Markov Chain Monte Carlo sampling to quantify uncertainty. The model achieved a high goodness-of-fit (RMSE = 0.042; R-squared = 0.91). We found the Basic Reproduction Number (R0) for anti-Rohingya narratives was significantly higher on TikTok (R0 = 5.42 [95% CI: 5.12–5.72]) compared to X (R0 = 2.81 [95% CI: 2.65–2.97]). Crucially, the Exposed compartment revealed an Algorithmic Latency period where passive consumption drives radicalization before active sharing. Network analysis identified that 8.2% of nodes accounted for 64.8% of total transmission. In conclusion, the study confirms that hate speech functions as a bio-engineered pathogen with pandemic-level virality, driven by algorithmic amplification rather than organic social consensus.

Algorithmic Contagion: A Network-SEIR Analysis of Xenophobic Disinformation Diffusion During Indonesia’s 2024 Election Caelin Damayanti; Benyamin Wongso; Emir Abdullah
Open Access Indonesia Journal of Social Sciences Vol. 8 No. 3 (2025): Open Access Indonesia Journal of Social Sciences
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/oaijss.v8i3.301

The 2024 Indonesian General Election was marked by a sudden, coordinated surge in xenophobic narratives targeting Rohingya refugees. This study investigates the diffusion mechanics of this viral hate, testing the hypothesis that algorithmic architectures on platforms such as TikTok and X (formerly Twitter) accelerate radicalization through specific epidemiological pathways. We employed a Stochastic Network SEIR (Susceptible-Exposed-Infectious-Recovered) model to analyze the Indo-Elect-24 dataset, comprising 2.4 million interaction events across a network of 10.2 million nodes. Unlike traditional aggregate models, we utilized a heterogeneous adjacency matrix to identify super-spreader nodes. Parameters were estimated using Bayesian inference via Markov Chain Monte Carlo sampling to quantify uncertainty. The model achieved a high goodness-of-fit (RMSE = 0.042; R-squared = 0.91). We found the Basic Reproduction Number (R0) for anti-Rohingya narratives was significantly higher on TikTok (R0 = 5.42 [95% CI: 5.12–5.72]) compared to X (R0 = 2.81 [95% CI: 2.65–2.97]). Crucially, the Exposed compartment revealed an Algorithmic Latency period where passive consumption drives radicalization before active sharing. Network analysis identified that 8.2% of nodes accounted for 64.8% of total transmission. In conclusion, the study confirms that hate speech functions as a bio-engineered pathogen with pandemic-level virality, driven by algorithmic amplification rather than organic social consensus.

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