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      Exploring the impact of collaboration on eco-innovation in SMEs: a contribution to the business modes of innovation framework

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      European Journal of Innovation Management
      Emerald

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          Abstract

          Purpose

          The purpose of the study is to investigate how small- and medium-sized enterprises (SMEs) can effectively collaborate for eco-innovation using the business modes of innovation framework to emphasise three types of collaboration: “science, technology, and innovation” (STI), “learning by doing, using, and interacting” (DUI)-Vertical and DUI-Horizontal.

          Design/methodology/approach

          This analysis uses data from 838 SMEs in the Basque Country (2018–2020) to evaluate the effects of the three types of collaboration on eco-innovation. The authors employ a propensity score-based method to address potential bias associated with endogeneity in innovation studies.

          Findings

          The findings suggest that DUI-Vertical collaboration has a positive relationship with the development of product, process and marketing eco-innovation. Furthermore, DUI-horizontal collaboration is the most effective collaboration mode for SMEs, positively impacting their overall eco-innovation portfolio. Finally, STI collaboration is positively associated with product eco-innovation.

          Practical implications

          Policymakers should support SMEs by designing programmes that facilitate collaboration between competing firms to stimulate eco-innovation, but potential challenges of coopetition must be addressed. Rather than a generic, one-size-fit-all approach, SMEs' managers should identify the most appropriate partners corresponding to their specific eco-innovation goal, ensuring a more effective and targeted. Collaboration between science partners and SMEs should be reinforced by approximating the SMEs' needs more effectively.

          Originality/value

          This study contributes twofold. Firstly, the authors investigate whether the STI and DUI modes of innovation are determinant factors in the introduction of various types of eco-innovation. Secondly, the authors contribute to the literature on business modes of innovation by differentiating between DUI-Vertical (i.e. suppliers, customers and consultancy) and DUI-Horizontal (i.e. competitors) collaboration, thus highlighting the complexity of DUI collaboration forms.

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          Most cited references76

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          Is Open Access

          Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

          The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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            Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

            The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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              Proximity and Innovation: A Critical Assessment

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                European Journal of Innovation Management
                EJIM
                Emerald
                1460-1060
                December 05 2023
                December 05 2023
                Article
                10.1108/EJIM-05-2023-0435
                63bb9a39-34ce-4544-a207-9af81ede83dd
                © 2023

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