Evidence (4189 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Org Design
Remove filter
We provide evidence-based guidance for selecting formulations and metrics in operational decision systems.
Authors' recommendations derived from their empirical analyses and comparisons across Shapley variants, metrics, and human-in-the-loop evaluations.
Explanations consistently increased decision confidence, signaling a critical risk of automation bias in high-stakes settings.
Empirical finding from the analyst study in the fraud-detection environment (3,735 case reviews) reporting increased self-reported decision confidence when explanations were shown.
Highlighting a context-specific set of features rather than a fixed one is a practically appealing and computationally feasible tool for achieving human-algorithm complementarity.
Synthesis of theoretical tractability results for naive agents and empirical illustration; argument in the paper combining theoretical and empirical findings to support practical appeal and feasibility.
Optimizing for naive agents is tractable as long as the maximal bandwidth is fixed.
Algorithmic constructions and complexity analysis in the paper that produce polynomial-time algorithms or show tractability results conditional on fixed maximal bandwidth (theoretical/methodological evidence).
Educators, policymakers, and industry leaders should design AI-inclusive curricula, workforce development strategies, and policies that support sustainable human–AI collaboration.
Policy and practice recommendations derived from the review's synthesis of empirical findings and identified gaps; presented as conclusions and directions.
AI is not simply replacing jobs but is redefining professional identity in IT, emphasizing reskilling, adaptability, and lifelong learning as key determinants of future employability.
Synthesis of reviewed literature and the paper's concluding interpretation summarizing trends across empirical studies, industry reports and conference findings.
There is growing demand for hybrid skill sets that integrate technical expertise with higher-order cognitive, ethical, and socio-emotional competencies among IT professionals.
Reported across reviewed empirical studies and industry reports summarized in the review paper.
Moving beyond traditional theories of the firm rooted in human bounded rationality is necessary because algorithmic decision-making changes the basis of strategic choice and governance.
Theoretical assertion in the paper's argument; presented as a reason for advancing the concept of algorithmic enterprises, grounded in conceptual critique rather than empirical testing in the abstract.
The paper contributes to scholarship on digital capitalism by proposing a redefinition of firm boundaries, strategy formation, and value creation in the age of intelligent systems.
Normative/theoretical claim presented as the paper's intellectual contribution; based on conceptual analysis and literature synthesis rather than empirical validation in the abstract.
Algorithmic decision-making enables new forms of strategic optimization, real-time adaptability, and predictive governance.
Paper asserts this as a normative/theoretical benefit of algorithmic decision-making, derived from conceptual analysis and synthesis of prior work; no empirical test reported in abstract.
Intelligent management systems (IMS) play a central role in shaping organizational strategy, operations, and governance within algorithmic enterprises.
Explicit theoretical claim in the paper; supported by conceptual framework and literature integration rather than reported empirical measurement.
The rapid advancement of AI, ML, and data-driven decision systems has fundamentally transformed the nature of firms and their strategic orientation globally, leading to the evolution of 'algorithmic enterprises'.
Stated as a central premise in the paper's conceptual argument; based on interdisciplinary synthesis of literature (economics, management, digital governance). No empirical sample or original data reported in the abstract.
The study extends resource-based, knowledge-based, and dynamic capabilities perspectives by conceptualising competitive intelligence as a mediating dynamic capability that transforms AI-driven data into actionable strategic knowledge.
Theoretical/conceptual synthesis supported by the study's empirical results (quantitative n = 312; qualitative n = 28).
AI enhances sensing, analytics, and reporting capabilities, and these capabilities are embedded into strategic routines to produce strategic value only when integrated into CI processes and organisational routines.
Mixed-methods evidence: quantitative associations (n = 312) showing AI → CI → growth/sustainability plus qualitative interview evidence (n = 28) describing how AI-enabled sensing/analytics/reporting are embedded into routines.
Qualitative Gioia analysis of 28 semi-structured interviews identifies three aggregate dimensions: AI-enabled competitive intelligence, strategic decision making and growth, and sustainable value creation.
Qualitative data from 28 semi-structured interviews across manufacturing, financial services, telecommunications, and retail sectors; analysis using the Gioia methodology.
CI effectiveness partially mediates the relationship between AI capability and sustainability outcomes.
Mediation analysis reported from the quantitative survey sample (n = 312); mediation described as 'partial'. Exact indirect effect size not provided in summary.
CI effectiveness partially mediates the relationship between AI capability and corporate growth.
Mediation analysis reported from the quantitative survey sample (n = 312); mediation described as 'partial'. Exact indirect effect size not provided in summary.
CI effectiveness significantly predicts sustainability performance (β = 0.47, p < .001).
Quantitative survey (n = 312); reported standardized regression/path coefficient β = 0.47, p < .001.
CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001).
Quantitative survey (n = 312); reported standardized regression/path coefficient β = 0.51, p < .001.
AI capability significantly predicts competitive intelligence (CI) effectiveness (β = 0.62, p < .001).
Quantitative survey (n = 312) of senior managers and strategy professionals from medium and large Zimbabwean firms; reported standardized regression/path coefficient β = 0.62, p < .001.
Policy implications: there is a need for infrastructure support and interoperability standards to enable digitalization for resilient supply chains.
Authors' stated policy implications in the paper, derived from empirical findings on the role of digital technologies and visibility.
Practical implications: strategic digital investment should target visibility as a key intermediate performance goal.
Authors' stated practical implications based on empirical results showing visibility mediates digital technologies' effect on resilience.
Heterogeneity analyses reveal stronger effects of digital technologies on visibility and resilience in technology-intensive industries.
Reported heterogeneity/subgroup analyses in the paper (no subgroup sample sizes provided in the excerpt); methods include regression/SEM.
Heterogeneity analyses reveal stronger effects of digital technologies on visibility and resilience in high-complexity supply chains.
Reported heterogeneity/subgroup analyses in the paper (no subgroup sample sizes provided in the excerpt); methods include regression/SEM.
Supply chain visibility mediates 67.4% of the total effect of digital technologies on supply chain resilience (mediation = 67.4%; bootstrap CI [0.156, 0.253]; Sobel test Z = 8.745, p< .001).
Mediation analysis reported in the paper using bootstrapped confidence intervals and a Sobel test; sample of 742 firms.
Supply chain visibility significantly predicts supply chain resilience (= 0.486, p< .001).
SEM / regression coefficient reported in paper with p-value (< .001); sample of 742 firms.
Digital technologies (IoT, blockchain, AI, big data analytics, and cloud computing) exert a significant positive effect on supply chain resilience (= 0.298, p< .001).
Hierarchical regression and SEM results reported in the paper; sample of 742 firms; p-value reported (< .001).
Digital technologies (IoT, blockchain, AI, big data analytics, and cloud computing) exert a significant positive effect on supply chain visibility (= 0.412, p< .001).
Hierarchical regression and SEM results reported in the paper; sample of 742 firms; p-value reported (< .001).
The emergence of 'Industry 4.0 Inc.' is likely to induce further collaboration among participating incumbents.
Authors' inference based on observed interconnections and overlapping investments in the M&A-based mapping (predictive/interpretive claim; no quantified projection provided in the excerpt).
One consequence of increased M&A activity and overlapping investments is the emergence of interconnections that have given rise to a new structure the authors term 'Industry 4.0 Inc.'
Network mapping of corporate linkages and overlapping investments derived from the M&A deal analysis spanning more than two decades (method: empirical mapping of inter-corporate ties); exact counts not provided in the excerpt.
Mergers and acquisitions are one of the principal tools industrial firms use to overcome this dual challenge.
Authors' argumentation supported by an empirical analysis of more than two decades of M&A deals (method: M&A deal analysis); exact sample size not stated in provided text.
Dynamic combinations of AI and organizational structure can help managers overcome traditional trade-offs between scale and scope, opening pathways for scalable, cross-market expansion.
Managerial implication drawn from the paper's longitudinal case study of ByteDance; qualitative inference from observed organizational practices and AI deployment patterns.
AI transforms the scale–scope nexus from being a trade-off into a source of strategic advantage.
Synthesis and theoretical claim derived from longitudinal case study of ByteDance showing simultaneous scaling and diversification enabled by AI and organizational design.
AI reverses the conventional logic of the resource-based view: rather than valuable resources enabling diversification, diversification amplifies the value of resources.
Theoretical argument supported by the ByteDance case study; paper presents this as a theorized inversion based on observed patterns in the single-case study.
The value of AI learning transfer across domains is contingent on access to structurally related data that allow learning to transfer across domains.
Claim derived from the ByteDance longitudinal case study showing conditions for successful cross-domain AI transfer (qualitative evidence emphasizing data structure/relatedness).
AI evolves and improves through self-learning and cross-fertilization across domains, becoming increasingly valuable as learning accumulates.
Theoretical claim supported by longitudinal observations from the ByteDance case study (qualitative evidence from repeated AI deployments over time).
ByteDance leveraged AI and adaptive organizational design to scale rapidly and diversify across industries and markets without incurring rising costs or coordination complexity.
Longitudinal single-case (qualitative) study of ByteDance described in the paper; method reported as a longitudinal case study of one firm.
Humble leadership indirectly alleviates the negative indirect effect of HAI-C task complexity on work engagement by enhancing employees' AI self-efficacy.
Reported moderated mediation/conditional process findings from hierarchical regression and bootstrapping on the three-wave matched sample of 497 employees.
AI self-efficacy mitigates (buffers) the negative indirect impact of HAI-C task complexity on employees' work engagement.
Moderated mediation analysis conducted on longitudinal survey data (n=497) using hierarchical regression and bootstrapping; reported in Results that AI self-efficacy weakens the negative indirect effect.
HAI-C task complexity increases employees' HAI-C tech-learning anxiety.
Longitudinal survey data (n=497) analyzed with hierarchical regression; reported as a finding in the Results that task complexity amplifies tech-learning anxiety.
GenAI-related benefits are likely to materialize only when AI capabilities are embedded in standardized routines, integrated data infrastructures, and cross-functional governance arrangements (organizational embedding).
Paper's synthesized process model and interpretive case evidence from the three firms indicating organizational conditions required for observed/documented AI effects.
GenAI-related capabilities enhance analysis by translating complex data into more interpretable, scenario-sensitive, and action-oriented outputs (analytical augmentation).
Interpretive finding from analysis of disclosures and literature; presented as a second linked mechanism through which GenAI may influence management accounting.
GenAI-related capabilities broaden the informational basis of management accounting by making operational, service, quality, and ecosystem data more usable in planning and control (information enrichment).
Interpretive inference from corporate disclosures of the three firms and review of AI-and-accounting literature; described as a primary mechanism in the paper.
The findings point to a staged progression of AI utility from low-consequence assistance toward higher-order automation, as trust, infrastructure, and verification mature.
Synthesis of interview responses (over 30) indicating current use cases are lower-risk assistance and that stakeholders expect (or prefer) gradual progression toward automation contingent on trust/infrastructure/verification improvements.
Reliability, verification, and auditability are central requirements for adoption, driving human-in-the-loop frameworks and governance aligned with existing engineering reviews.
Consistent themes from interviews (over 30) indicating stakeholders prioritize reliability, verifiability, and audit trails, leading to preference for human-in-the-loop designs integrated with current review processes.
Higher-value agentic gains come from orchestrating multi-step workflows across tools.
Observed and reported in interviews (over 30) with stakeholders in engineering and manufacturing workflows describing value from agentic orchestration across tools.
Near-term AI gains cluster around structured, repetitive work and data-intensive synthesis.
Qualitative findings from an exploratory state-of-practice study based on over 30 semi-structured interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors).
Because misalignment can occur along each axis -- and affect stakeholders differently -- alignment cannot be 'solved' through technical design alone, but must be managed through ongoing institutional processes that determine how objectives are set, how systems are evaluated, and how affected communities can contest or reshape those decisions.
Normative conclusion drawn from the three-axis framework and discussion of stakeholder impacts (conceptual policy prescription; no empirical testing reported).
Alignment is inherently pluralistic and context-dependent, and resolving misalignment involves trade-offs among competing values.
Theoretical and normative argument in the paper about pluralism and context-dependence of values (conceptual discussion; no empirical quantification).
The three-axis decomposition implies that alignment is fundamentally a problem of governance rather than engineering alone.
Logical inference from the proposed decomposition and normative argument in the paper (theoretical reasoning; no empirical evidence).