Purpose – This study aims to examine how open innovation can be effective in changing organizational inertia to create business model innovation and improve firm performance. It also seeks to explore whether the existence of open innovation has a mediating effect and influence. Design/methodology/approach – This study constructs a theoretical model to explore the relationship between latent variables and uses a questionnaire to collect research data. In the conceptual framework, organizational inertia is a second-order latent variable and comprises three first-order latent variables: insight inertia, action inertia, and psychological inertia. Open innovation is also a second-order latent variable, and consists of two first-order latent variables: outbound and inbound open innovation. To clarify the relationship between these latent variables, structural equation modeling (SEM) is used to test the goodness of fit of the theoretical model and research hypotheses. This study uses 141 small to medium-sized manufacturing enterprises (SMEs) in Taiwan as the research subjects. Findings – The SEM analysis revealed that open innovation has a significant mediating effect on the relationship between organizational inertia and business model innovation, and the relationship between organizational inertia and firm performance; business model innovation also has a positive influence on firm performance. Originality/value – This study contributes the empirical analysis of SMEs to illustrate the role of open innovation on business model innovation processes.

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Multivariate discriminant analysis (MDA) has long been used to classify failing and non-failing firms with high accuracy rates, although a number of methodological flaws are well known. The alternative approach based on conditional probability analysis (CPA) models have been applied to forecast mergers and acquisitions and extended to the prediction of corporate failure. This is used here to distinguish between distressed and non-distressed companies in the UK industrial sector for the period 1985-1994. Results show that the CPA model is both efficient and consistent, has high accuracy levels and avoids the biased sampling problems that have been identified in MDA studies.

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