In Indonesian creative firms, HR policies that build absorptive capacity—training, dynamic performance management, innovative culture and engagement—drive AI adoption, which in turn substantially boosts firms' innovation capability; recruitment/talent mapping was the lone HR dimension not channeling innovation through AI.

Empowering HRM strategies through AI adoption to enhance innovation capability: a mixed-method approach

Sri Wahyu Lelly Hana Setyanti, Khanifatul Khusna, Ni Ketut Seminari, Kamillaeni Jamillah · September 07, 2026 · Cogent Business & Management

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In Indonesia's creative industry, HRM practices—especially training, dynamic performance management, an innovative culture, and engagement/retention—predict AI adoption, and AI adoption strongly mediates and increases firms' innovation capability, while recruitment/talent mapping does not operate through AI.

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This study examines how advances in artificial intelligence (AI) have transformed human resource management (HRM) practices and strategies in relation to innovation capability within Indonesia’s creative industry. A mixed-methods approach was employed, combining a bibliometric analysis of the relevant literature with quantitative validation using field data. The qualitative component consisted of a systematic literature review of the Scopus and Emerald databases (Q1–Q3 journals) from 2021 to 2025, using the PRISMA flow, with VOSviewer used to map bibliometric relationships and thematic clusters. The quantitative component applied SEM-PLS with purposive sampling of 125 respondents. The findings reveal the formation of key HRM practices, including recruitment and talent mapping, training, dynamic performance, innovative culture, and engagement and retention. Quantitative results show that HRM strategies significantly influence AI adoption, which in turn has a strong positive effect on innovation capability. Mediation testing further demonstrates that AI adoption mediates the relationship between HRM practices and innovation capability in four of the five proposed hypotheses, except HR recruitment. Overall, the study confirms that AI has evolved from a supportive tool to a strategic catalyst in HRM transformation within Indonesia’s creative industry.

Summary

Main Finding

AI adoption in Indonesia’s creative industry has shifted from a supportive tool to a strategic catalyst: HRM practices (especially training, dynamic performance management, innovative culture, and engagement/retention) significantly shape AI adoption, and AI adoption in turn strongly increases firms’ innovation capability. AI adoption mediates the relationship between HRM practices and innovation capability for four of five HRM dimensions; recruitment/talent mapping is the exception.

Key Points

Identified HRM practices that interact with AI adoption: recruitment & talent mapping, training, dynamic performance, innovative culture, and engagement & retention.
Quantitative evidence: HRM strategies significantly predict AI adoption; AI adoption strongly predicts innovation capability.
Mediation: AI adoption mediates the effect of training, dynamic performance, innovative culture, and engagement/retention on innovation capability; it does not mediate the recruitment → innovation link.
Conceptual shift: AI is characterized as a strategic enabler of HR transformation and innovation in creative-sector firms rather than just a supportive technology.

Data & Methods

Mixed-methods design:
- Qualitative/bibliometric: Systematic literature review of Scopus and Emerald (Q1–Q3 journals), 2021–2025, following PRISMA procedures. VOSviewer used to map co-authorship, keyword co-occurrence, and thematic clusters.
- Quantitative: Structural Equation Modeling using Partial Least Squares (SEM-PLS).
Sample: Purposive sampling of 125 respondents from Indonesia’s creative industry (field data collected for validation).
Analysis specifics:
- SEM-PLS chosen for model estimation (appropriate for smaller samples and exploratory mediation tests).
- Mediation testing performed to assess whether AI adoption transmits HRM practices’ effects onto innovation capability.

Implications for AI Economics

Productivity and innovation economics: The results imply that investments in HRM (particularly training and culture) amplify the innovation returns to AI, suggesting complementarities between human capital and AI capital in creative industries.
Labor-market effects: Since recruitment/talent mapping did not channel through AI adoption to innovation, bottlenecks may remain in supply-side skill matching. Policymakers and firms may need to address talent pipelines and matching mechanisms to fully realize AI-driven gains.
Investment priorities: Firms and investors should prioritize HR practices that build absorptive capacity (training, performance systems, culture, engagement) to increase the marginal productivity of AI technologies.
Policy design: Public interventions (subsidized training, support for organizational change, incentives for AI diffusion in SMEs) could raise the social returns of AI by accelerating firm-level adoption and innovation.
Measurement and macro linkage: To translate these firm-level findings into macroeconomic forecasts, future work should quantify effects on output, employment composition, wage structure, and aggregate innovation rates across sectors.
Generalizability and external validity: Findings are specific to Indonesia’s creative industry and a purposive sample (n=125); extrapolation to other countries or sectors requires caution and further empirical work.

Suggested next steps for research - Use larger, representative samples and longitudinal designs to establish causal pathways and dynamics of AI adoption. - Compare sectors to assess heterogeneity in HRM–AI complementarities. - Quantify economic magnitudes (productivity, employment, wages) to inform policy and investment decisions. - Investigate why recruitment/talent mapping fails to mediate through AI—e.g., skills shortages, mismatches, or selection frictions—and test interventions to resolve them.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on a small (n=125) purposive cross-sectional sample and SEM-PLS mediation; there is no exogenous variation, longitudinal design, or robustness tests reported to rule out omitted confounding or reverse causality, so causal interpretation is weak. Methods Rigormedium — The mixed-methods design and PRISMA-based literature review plus bibliometric mapping are strengths, and SEM-PLS is an appropriate choice for small-sample exploratory SEM; however, the quantitative analysis is limited by non-probability sampling, small sample size, cross-sectional data, potential common-method bias, and lack of strong causal identification or reported robustness checks. SamplePurposive sample of 125 respondents from Indonesia's creative industry (field data collected for validation); respondents likely firm managers/HR practitioners but exact respondent/firm-level breakdown, sampling frame, representativeness, response rate, and timing are not provided. Themesinnovation adoption skills_training org_design human_ai_collab IdentificationCross-sectional mediation analysis using Structural Equation Modeling with Partial Least Squares (SEM-PLS); identification relies on assumed temporal/causal ordering in the model (HRM → AI adoption → innovation) and measurement validity rather than quasi-experimental or instrumental variation. GeneralizabilitySmall sample size (n=125) limits precision and external validity, Purposive (non-probability) sampling risks selection bias, Single-country (Indonesia) context may not generalize to other economies, Single-sector focus (creative industry) limits applicability to other sectors, Cross-sectional design prevents inference on dynamics or causality, Potential common-method and self-report measurement bias

Claims (6)

Claim	Direction	Outcome	Confidence & Evidence	Details
HRM strategies significantly predict AI adoption in Indonesia’s creative industry. Adoption Rate	positive	AI adoption	Reading fidelity high Study strength medium	n=125 0.3
AI adoption strongly predicts increased innovation capability among firms in Indonesia’s creative industry. Innovation Output	positive	Firm innovation capability	Reading fidelity high Study strength medium	n=125 0.3
AI adoption mediates the relationship between training, dynamic performance management, innovative culture, and engagement and retention practices and innovation capability. Innovation Output	positive	Innovation capability	Reading fidelity high Study strength medium	n=125 0.3
AI adoption does not mediate the relationship between recruitment and talent mapping practices and innovation capability. Innovation Output	null_result	Innovation capability	Reading fidelity high Study strength medium	n=125 0.3
AI adoption functions as a strategic enabler of HR transformation and innovation in creative-sector firms rather than merely as a supportive technology. Innovation Output	positive	Strategic contribution of AI adoption to firm innovation	Reading fidelity high Study strength low	n=125 0.15
The study’s findings are specific to Indonesia’s creative industry and a purposive sample of 125 respondents, so generalization to other countries or sectors requires caution. Other	mixed	External validity and generalizability	Reading fidelity high Study strength high	n=125 0.5