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In the ever-evolving digital landscape, this study aims to explore which specific personality traits contribute to the innovativeness of startups, with a particular emphasis on understanding how technology adoption mediates this relationship. By doing so, the authors strive to unveil the nuanced dynamics of personality, technology adoption and startup innovativeness in the digital era.

The authors conduct a quantitative empirical analysis using a sample of 1,314 German startups. This study utilizes a mediation analysis to examine the effects of personality traits on the innovativeness of startups, taking technology adoption as a mediator into account.

The empirical results show certain personality traits have direct effects on innovativeness. Also, the results show that technology adoption is a driver of startup innovativeness. In addition, these traits are (partially) mediated by technology adoption.

The results shed new light on the interplay of entrepreneurs' personality and technology adoption in relation to startup innovativeness and therefore underline the importance of technology in this triangular relationship. The authors employ secondary data from startups in Germany, which complicates generalization of the results to other geographical and cultural contexts.

This study contributes to the scientific debate on the role of personality traits in entrepreneurship by providing empirical evidence on the mediating effect of technology adoption in the relationship between personality traits and startup innovativeness. The findings offer valuable insights for researchers, entrepreneurs and policymakers interested in understanding and promoting innovativeness in the context of startups.

This commentary presents the analytic development of patient classification, health resource use and outcome research and identifies opportunities to perform longitudinal research.

The authors use a transdisciplinary framework to formulate multilevel models for ascertaining the causal relationship between hospital efficiency and effectiveness in panel data analysis.

The longitudinal design of organization research enables to delineate the relationship between hospital performance and quality of care in future research.

The inclusion of multivariates in health organization research and modeling is pivotal to the identification of a comprehensive set of predictor variables. The authors signify the need to build a systems-oriented theoretical framework to integrate micro- and macro-level predictor variables in conducting data analysis.

The authors signify the need to build a theoretical framework to integrate micro- and macro-level predictor variables in conducting data analysis.

Health organization research is essential to broaden the scope of health services research and policy development, particularly related to global health as noted in the promotion of sustainable development and health goals.

Health organization research should include a complex set of exogenous and endogenous variables in designing and modeling the determinants of hospital performance and patient care outcomes.