Organisational Performance as a Dependent Variable

A second order meta-analysis and meta-regression


Ann-Marie Nienaber


BA / Leverhulme Small Research Grants




Performance research despite prominent criticism is still one of the most popular areas of research in the field of economics and management. This should come of no surprise as competitive survival of companies is at the heart of both economic and management theory and on top of the agenda for practitioners. While performance research is well established it is also met by severe criticism. This criticism is partly attributable to methodological problems of performance research as well as to content problems concerning different meanings of performance in business and economic.

However, I propose that relatively new tools - second order meta-analysis and meta- regression - allow me to tackle several of the critical points in performance research and provide an efficient mean to compare performance research across fields. It allows me to develop a comprehensive model to analyse what antecedent variables have the strongest effect on organizational performance and give implications for research and business management.


To identify the most important antecedents of organizational performance, I will develop a second order meta-analysis and meta-regression which are relatively new tools. In general a meta-analysis is a method that aims to integrate existing research results in order to explore average effects and reasons for data heterogeneity while the meta-regression proves for moderators. These tools allow me to reduce several critical points in performance research and thus, I am able to identify the main important antecedents of performance. A second order meta regression is very appropriate for several reasons. First, it integrates single meta analytical results and overcomes non-significant results or differing meta-analytic findings (Lipsey & Wilson, 2001; Hunter & Schmidt, 2004). Thus, the advantage of this procedure is to receive a top-level perspective of the entire research field at hand. Secondly, it is able to moderate strong biases in the literature such as publication bias or the measurement of the dependent variable (financial vs. nonfinancial).

Dissemination and knowledge transfer to business and economic academics and students as well as practitioners will be a strong feature of the project. One article has already been published on trust repair and performance in the financial industry (appearing in a special issue on that topic in the International Journal of Bank Marketing) which is read by academics as well as practitioners. Further to this, David Sim from Zurich Insurance Group Ltd., one of the biggest Swiss insurance companies has already asked for the key findings in order to overcome issues of trust in his company. I have also presented some of my findings at the key conference in Meta-analysis MAER-NET 2014 in Athens, Greece. 

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