Introduction: Decentralized autonomous organizations (DAOs) are an emerging organizational form that operates entirely on blockchain infrastructure. Within a DAO, organizational governance rules are hardcoded in transparent and immutable smart contracts. In principle, these rules are intended to facilitate decentralized decision-making among token holders who collectively create, discuss, and vote on proposals that govern the organization. Despite their promise, the extent to which DAOs achieve true decentralization in practice remains unclear. This study addresses an underexplored area in the literature by empirically investigating key aspects of DAO governance, particularly the degree of decentralization and participant composition.Method: Network analysis is used to examine proposal voting coalitions among participants as a proxy for decentralization. Sentiment analysis is employed to assess trust among participants. The analysis draws on data from 54 DAOs, including 774 unique proposals and 13,085 associated token holder comments.Results: The findings indicate that DAOs may not achieve the level of decentralization originally envisioned. Moreover, decentralization and participant composition within governance structures play a critical role in shaping trust, voting participation, and overall financial performance in DAOs PracticalImplications: Although current voting mechanisms aim to reduce the dominance of large token holders (whales), DAOs may still fall short of the level of decentralization originally envisioned. Accordingly, more advanced voting mechanisms may be required to further mitigate coordination and strategic behavior in proposal voting. In addition, DAOs could benefit from adjusting the threshold requirements for the Foundation to improve accessibility for token holders and encourage broader participation. Leveraging artificial intelligence (AI) may also help streamline the voting process and improve the clarity of proposals.
PurposeGrounded in the dynamic capabilities view and organisational learning theory, this study examines the relative association of big data analytics-enabled dynamic capabilities (BDAEDC) on the quality of the decisions made in organisations through the mediating roles of exploitative and explorative learning and through the moderating role of top-management support for learning (TMSL).Design/Methodology/ApproachUsing cross-sectional survey data collected from 330 software firms in Pakistan, the study tests a moderated mediation model via partial least squares structural equation modelling (PLS-SEM) in WarpPLS 8.0.FindingsIt has been found that BDAEDC is a significant source of improved exploitative and exploratory learning, which are responsible for improved decision-making quality. Explorative learning ends up being a more influential mechanism. Bootstrapped mediation analysis confirms both learning orientations as key pathways. Top-management support of learning has a negative moderating effect on both the associations.Originality/ValueBy combining theory on dynamic capabilities with theory on organizational learning, digital capabilities for analytics integrate technological and organizational perspectives and create a new understanding of the mechanisms and boundary conditions of the effect of analytics-enabled capabilities on decision-making quality.
PurposeRecent studies have identified methodological concerns in data science research, including unvalidated preprocessing assumptions that may contribute to inflated performance claims and irreproducible results. Despite preprocessing consuming 60-80% of analytical effort in business analytics projects, strategy selection typically relies on convention rather than systematic evidence. This study introduces REPROPREP, a methodological framework designed to enable systematic validation of preprocessing effectiveness assumptions.Design/methodology/approachThe REPROPREP framework incorporates conservative statistical analysis with Benjamini-Hochberg false discovery rate correction, systematic quality degradation protocols, and cost-effectiveness assessment. The framework was evaluated across 10 UCI repository datasets using gradient boosting classifiers with 5-fold stratified cross-validation under three simulated quality conditions, resulting in 90 statistical comparisons.FindingsThe analysis found no statistically significant differences between preprocessing strategies after multiple comparisons correction, with negligible effect sizes (mean AUC difference: 0.001). Cost analysis indicated implementation cost differences ranging from $150 to $800 across strategies.Practical implicationsThe framework provides a systematic methodology for preprocessing evaluation, enabling organizations to conduct context-specific validation of preprocessing effectiveness assumptions and support more informed decision-making regarding preprocessing strategies.Originality/valueREPROPREP introduces a reproducible and systematic approach to preprocessing validation that integrates statistical rigor with cost-benefit analysis. The framework is designed for adaptive development, with this initial release focusing on numeric preprocessing and providing a foundation for future extensions.
