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

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Název: EVALUATING AND ENHANCING LOCAL INNOVATIVENESS: A NOVEL APPROACH USING PREDICTIVE MODELS.
Autoři: ZASTEMPOWSKI, Maciej, PRZANOWSKI, Karol, KUŚ, Agnieszka
Zdroj: Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie; 2025, Issue 234, p641-677, 37p
Témata: PREDICTION models, SOCIOECONOMIC factors, POLICY analysis, LOCAL government, REGIONAL development, QUANTITATIVE research, TECHNOLOGICAL innovations, METADATA
Abstrakt: 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]
Copyright of Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie is the property of Silesian Technical University, Organisation & Management Faculty and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: EVALUATING AND ENHANCING LOCAL INNOVATIVENESS: A NOVEL APPROACH USING PREDICTIVE MODELS.
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  Data: <searchLink fieldCode="AR" term="%22ZASTEMPOWSKI%2C+Maciej%22">ZASTEMPOWSKI, Maciej</searchLink><br /><searchLink fieldCode="AR" term="%22PRZANOWSKI%2C+Karol%22">PRZANOWSKI, Karol</searchLink><br /><searchLink fieldCode="AR" term="%22KUŚ%2C+Agnieszka%22">KUŚ, Agnieszka</searchLink>
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  Data: Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie; 2025, Issue 234, p641-677, 37p
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  Data: <searchLink fieldCode="DE" term="%22PREDICTION+models%22">PREDICTION models</searchLink><br /><searchLink fieldCode="DE" term="%22SOCIOECONOMIC+factors%22">SOCIOECONOMIC factors</searchLink><br /><searchLink fieldCode="DE" term="%22POLICY+analysis%22">POLICY analysis</searchLink><br /><searchLink fieldCode="DE" term="%22LOCAL+government%22">LOCAL government</searchLink><br /><searchLink fieldCode="DE" term="%22REGIONAL+development%22">REGIONAL development</searchLink><br /><searchLink fieldCode="DE" term="%22QUANTITATIVE+research%22">QUANTITATIVE research</searchLink><br /><searchLink fieldCode="DE" term="%22TECHNOLOGICAL+innovations%22">TECHNOLOGICAL innovations</searchLink><br /><searchLink fieldCode="DE" term="%22METADATA%22">METADATA</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie is the property of Silesian Technical University, Organisation & Management Faculty and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.29119/1641-3466.2025.234.36
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 37
        StartPage: 641
    Subjects:
      – SubjectFull: PREDICTION models
        Type: general
      – SubjectFull: SOCIOECONOMIC factors
        Type: general
      – SubjectFull: POLICY analysis
        Type: general
      – SubjectFull: LOCAL government
        Type: general
      – SubjectFull: REGIONAL development
        Type: general
      – SubjectFull: QUANTITATIVE research
        Type: general
      – SubjectFull: TECHNOLOGICAL innovations
        Type: general
      – SubjectFull: METADATA
        Type: general
    Titles:
      – TitleFull: EVALUATING AND ENHANCING LOCAL INNOVATIVENESS: A NOVEL APPROACH USING PREDICTIVE MODELS.
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            NameFull: ZASTEMPOWSKI, Maciej
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            NameFull: PRZANOWSKI, Karol
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            NameFull: KUŚ, Agnieszka
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            – D: 01
              M: 12
              Text: 2025
              Type: published
              Y: 2025
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              Value: 234
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            – TitleFull: Scientific Papers of Silesian University of Technology. Organization & Management / Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie
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