Schubert, TorbenTorbenSchubertTürkeli, SerdarSerdarTürkeliAshouri, SajadSajadAshouriBäck, AstaAstaBäckDeschryvere, MatthiasMatthiasDeschryvereJäger, AngelaAngelaJägerVisentin, FabianaFabianaVisentinHajikhani, ArashArashHajikhaniSuominen, ArhoArhoSuominen2023-05-032023-05-032022https://publica.fraunhofer.de/handle/publica/441262This final report for Work Package 2 (Deliverable D4) implements the econometric estimations concerning the relationship between the specifics of the innovation process and company productivity as outlined in the interim report (D3). This final report consists of two main parts. In the first, we present the overall project work related to the development of a multistage R&D-innovation-productivity (CDM) model inspired by Crépon et al. (1998). We start with an updated review of the literature relating to the secular stagnation hypothesis, that is, the claim that the depletion of technological opportunities has heralded a phase of low or no productivity growth. Where more recent works have become available, this literature review has been updated. In order to align the literature review with the empirical evidence, we also focus more on the role of digitalization, where web-scraping based data collection has provided very useful indicators in the area of intangibles. Beyond digitalization trends, cooperation, and open innovation, knowledge spillovers and servitization are increasingly important drivers of productivity. We then describe and implement the augmented Crépon-Duguet-Mairesse (CDM)-type model, supplementing the original specification with the influence factors of digitalization, cooperation, spillovers, and servitization. The CDM model is further amended from the original outline in D3 in order to achieve alignment with the existing data. This part also contains a description of the processes underlying data generation and management, including a discussion of data quality issues and missing data, and discusses the potential and limitations of scraping data from company websites for empirical innovation economics. Concluding the first part of this report, we discuss the policy implications resulting from our analysis. In the second part of this report, we present a more detailed analysis of the role of AI use on productivity using a panel on Finnish firms for the period 2013-2019. This exercise was not originally planned as part of the work package. However, the availability of a specialized data source enabling us to infer company use of AI based on data on job offerings provided the opportunity for complementary analysis. Because this data became available as panel data, it also allowed us to control for a number of econometric issues that the web-scraped data from part 1 was unable to resolve.enFTI-PolitikInnovationsmanagementInnovationsstrategienTechnologiemanagementInnovationssystemeMethodenentwicklungÖkonometriereport