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Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach

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  • Mehmet Güney Celbiş

    (Department of Economics, Yeditepe University, Istanbul 34755, Turkey
    United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), 6211 Maastricht, The Netherlands
    These authors contributed equally to this work.)

  • Pui-Hang Wong

    (United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), 6211 Maastricht, The Netherlands
    Maastricht Graduate School of Governance, School of Business and Economics, Maastricht University, 6211 Maastricht, The Netherlands
    These authors contributed equally to this work.)

  • Karima Kourtit

    (Faculty of Management, Open University, 6419 Heerlen, The Netherlands
    Centre for European Studies, Alexandru Ioan Cuza University, 700506 Iași, Romania
    School of Architecture, Planning and Design, Polytechnic University, Ben Guerir 43150, Morocco
    These authors contributed equally to this work.)

  • Peter Nijkamp

    (Faculty of Management, Open University, 6419 Heerlen, The Netherlands
    Centre for European Studies, Alexandru Ioan Cuza University, 700506 Iași, Romania
    School of Architecture, Planning and Design, Polytechnic University, Ben Guerir 43150, Morocco
    These authors contributed equally to this work.)

Abstract

This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.

Suggested Citation

  • Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13426-:d:694769
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