Levers of control and digital innovation in the Indonesian banking sector: A hybrid SEM-ANN approach

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Type of the article: Research Article

Abstract
This study aims to analyze the relationship between management control systems and digital innovation in the Indonesian banking sector through the theory of levers of control (LOC). Unlike previous studies that often assume linear relationships and analyze LOC in aggregate, this study adopts a disaggregated approach and hybrid methods to provide a more comprehensive understanding. The methodology integrates partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) using data from 76 chief financial officers (CFOs) of commercial banks in Indonesia (response rate 77.55%). The PLS-SEM results showed that the belief system (β = 0.234), boundary system (β = 0.231), and interactive control system (β = 0.281) were positively related to digital innovation, with the interactive control system as the most consistent dimension, while the diagnostic control system did not show a significant linear relationship (p = 0.066). The ANN analysis complements these findings by showing that the interactive control system has the highest level of predictive importance, followed by the diagnostic control system, boundary system, and belief system. These findings suggest that PLS-SEM and ANN provide complementary insights, where PLS-SEM describes linear relationships based on theoretical models. In contrast, ANN describes the relative contribution of each predictor to the model’s predictive ability. Overall, interactive control systems are the most consistent LOC dimension related to digital innovation and illustrate the added value of a hybrid approach for a more comprehensive understanding of the role of management control systems in digital transformation in the banking sector.

Acknowledgments
The authors are supported by funding from the Indonesian Education Scholarship, the Center for Higher Education Funding and Assessment, the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia, and the Endowment Fund for Education Agency, the Ministry of Finance of the Republic of Indonesia. This research was supported under the Award Number BPI: 202327092283.

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    • Figure 1. Structural model
    • Figure 2. Artificial neural network (ANN) model
    • Table 1. Demographic characteristics
    • Table 2. Non-response bias test: Early respondents and late respondents
    • Table 3. Reliability and convergent validity
    • Table 4. Discriminant validity tests: Fornell–Larcker
    • Table 5. Discriminant validity tests: HTMT
    • Table 6. Model fit indices
    • Table 7. Path coefficients and p-values
    • Table 8. Root mean square error for ANN model
    • Table 9. Sensitivity analysis of ANN model
    • Table 10. Comparison between PLS-SEM and ANN findings
    • Table A1. Measurement items
    • Conceptualization
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Data curation
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Formal Analysis
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Funding acquisition
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Investigation
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Methodology
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Project administration
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Resources
      Fara Fitriyani, Dwi Ratmono
    • Software
      Fara Fitriyani, Dwi Ratmono
    • Supervision
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Validation
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Visualization
      Fara Fitriyani, Abdul Rohman
    • Writing – original draft
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono
    • Writing – review & editing
      Fara Fitriyani, Abdul Rohman, Dwi Ratmono