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EVALUATING AND ENHANCING LOCAL INNOVATIVENESS: A NOVEL APPROACH USING PREDICTIVE MODELS.

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Titel: EVALUATING AND ENHANCING LOCAL INNOVATIVENESS: A NOVEL APPROACH USING PREDICTIVE MODELS.
Autoren: ZASTEMPOWSKI, Maciej, PRZANOWSKI, Karol, KUŚ, Agnieszka
Quelle: Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie; 2025, Issue 234, p641-677, 37p
Schlagwörter: PREDICTION models, SOCIOECONOMIC factors, POLICY analysis, LOCAL government, REGIONAL development, QUANTITATIVE research, TECHNOLOGICAL innovations, METADATA
Abstract: Purpose: This study seeks to develop and empirically validate a predictive model for assessing local innovativeness at the municipality level. It responds to the increasing demand among Local Administrative Units for evidence-based insights into the socio-economic and fiscal determinants of innovation and investigates the feasibility of forecasting innovation performance using routinely collected public data. Design/methodology/approach: The research adopts a quantitative methodological framework, integrating objective administrative data sourced from national institutions (e.g., Statistics Poland, the Ministry of Finance, and the Social Insurance Institution) with subjective data obtained via a CAPI survey conducted among 2,418 enterprises in 144 municipalities in the Kuyavian-Pomeranian Voivodeship. A classical risk scorecard modelling approach-widely employed in the financial sector-was adapted to identify determinants of local innovativeness and to construct a statistical model predicting the Predicted Innovation Rate. Findings: The study confirms that publicly accessible data can be effectively utilised to forecast local innovation potential. Six key predictors were identified, including tax base structure, average non-agricultural income, the sectoral composition of local enterprises (notably real estate and education), and VAT data related to service imports. Higher levels of innovativeness were associated with a greater presence of education-oriented businesses and higher service imports, whereas a concentration of real estate firms correlated negatively with innovation. The model demonstrated moderate predictive capability and enabled the stratification of municipalities by innovation potential. Research limitations/implications: As the model was developed using data from a single voivodeship, its generalizability may be constrained. Future research should explore model validation in other regional contexts and incorporate additional qualitative variables to enhance interpretability and predictive accuracy. Practical implications: The proposed model provides municipalities with a cost-efficient, data-driven tool for monitoring and fostering local innovation. It enables the formulation of targeted development strategies and evidence-based policy interventions. Social implications: The model has the potential to inform public policy aimed at enhancing regional innovation capacity, reducing territorial disparities, and supporting socio-economic cohesion through the promotion of knowledge-intensive activities. Originality/value: This paper presents one of the first predictive modelling approaches for evaluating innovativeness at the municipal level, offering a novel, scalable framework of practical relevance to policymakers, local authorities, and development agencies. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:Purpose: This study seeks to develop and empirically validate a predictive model for assessing local innovativeness at the municipality level. It responds to the increasing demand among Local Administrative Units for evidence-based insights into the socio-economic and fiscal determinants of innovation and investigates the feasibility of forecasting innovation performance using routinely collected public data. Design/methodology/approach: The research adopts a quantitative methodological framework, integrating objective administrative data sourced from national institutions (e.g., Statistics Poland, the Ministry of Finance, and the Social Insurance Institution) with subjective data obtained via a CAPI survey conducted among 2,418 enterprises in 144 municipalities in the Kuyavian-Pomeranian Voivodeship. A classical risk scorecard modelling approach-widely employed in the financial sector-was adapted to identify determinants of local innovativeness and to construct a statistical model predicting the Predicted Innovation Rate. Findings: The study confirms that publicly accessible data can be effectively utilised to forecast local innovation potential. Six key predictors were identified, including tax base structure, average non-agricultural income, the sectoral composition of local enterprises (notably real estate and education), and VAT data related to service imports. Higher levels of innovativeness were associated with a greater presence of education-oriented businesses and higher service imports, whereas a concentration of real estate firms correlated negatively with innovation. The model demonstrated moderate predictive capability and enabled the stratification of municipalities by innovation potential. Research limitations/implications: As the model was developed using data from a single voivodeship, its generalizability may be constrained. Future research should explore model validation in other regional contexts and incorporate additional qualitative variables to enhance interpretability and predictive accuracy. Practical implications: The proposed model provides municipalities with a cost-efficient, data-driven tool for monitoring and fostering local innovation. It enables the formulation of targeted development strategies and evidence-based policy interventions. Social implications: The model has the potential to inform public policy aimed at enhancing regional innovation capacity, reducing territorial disparities, and supporting socio-economic cohesion through the promotion of knowledge-intensive activities. Originality/value: This paper presents one of the first predictive modelling approaches for evaluating innovativeness at the municipal level, offering a novel, scalable framework of practical relevance to policymakers, local authorities, and development agencies. [ABSTRACT FROM AUTHOR]
ISSN:16413466
DOI:10.29119/1641-3466.2025.234.36