Purpose – The purpose of this paper is to describe how Nespresso achieved competitive advantage through innovation by changing the rules of the game in its industry. Design/methodology/approach – Nespresso was analyzed based on public available secondary data, in combination with related academic concepts on innovation and competitive advantage. Findings – The company succeeded by the thorough application of a strategy that, through perfect alignment, allowed the company to reach a unique market position. However, as described in the case, it took a relatively long time and the company came close to failure several times. Before the current situation of the company, it remains challenging in the future as well. Hence, the Nespresso story provides interesting space for discussion and learning about what innovation is, how innovation emerges, and under which circumstances innovation can serve as a source for competitive advantage. Research limitations/implications – Especially given the current market situation, the case offers different starting points for discussion about innovation and long-term company success. Practical implications – Especially before the current market situation, the case offers different starting points for discussion about innovation and the success of a company on the long term. The case is designed to give practitioners a better understanding on what an innovation as, and how competitive advantages can be linked to innovation. Originality/value – This case of Nespresso is a unique combination of the concepts of innovation and competitive advantage. It serves as an example of an innovation, which was not successful from the scratch, but evolved over time and is still developing. As many innovations went through such a non-linear process, this case offers interesting lessons learned for academics as well as for practitioners.

No abstract

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions.Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models adjusting for small-study effects. The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods.

Meta-analysis is an important quantitative tool for cumulative science, but its application is frustrated by publication bias. In order to test and adjust for publication bias, we extend model-averaged Bayesian meta-analysis with selection models. The resulting robust Bayesian meta-analysis (RoBMA) methodology does not require all-or-none decisions about the presence of publication bias, can quantify evidence in favor of the absence of publication bias, and performs well under high heterogeneity. By model-averaging over a set of 12 models, RoBMA is relatively robust to model misspecification and simulations show that it outperforms existing methods. We demonstrate that RoBMA finds evidence for the absence of publication bias in Registered Replication Reports and reliably avoids false positives. We provide an implementation in R so that researchers can easily use the new methodology in practice.