Small- and medium-sized enterprises (SMEs) have long been recognized as a driving force of economic development (Drucker, 1985; Oliveira et al., 2017). Europe, for instance, has over 21 million SMEs employing 88.8 million people and generating a total of 3.666 billion euros in added value within the European economy (cf. European Commission, 2015). Given the current economic scenario, SMEs are expected to be prepared to respond to the changes imposed by the market, through either their ability to differentiate themselves from competitors or their capability to be innovative and, thereby, survive. Indeed, as Bullinger et al. (2007, p. 17) point out, “SMEs need to innovate in order to survive and create competitive advantages.” The need for SMEs to innovate has resulted from – and/or led to – changes at different levels. First, globalization has exposed SMEs to greater competitiveness, generating a larger number of new competitors. Second, SMEs can no longer focus only on niche markets. Third, technological advances have resulted in rapid progress in information, knowledge and innovation, making SMEs more competitive and more capable of eliminating obsolete products. Last, consumer demand is now focused on higher quality products and services ( for further discussion, see Bullinger et al., 2007; Saunila, 2016; Oliveira et al., 2017). Given these developments, two interrelated questions need to be answered: RQ1. How can SME innovation capability be measured? RQ2. What qualitative and quantitative metrics can be used? In light of the changing economic environment, the methodologies used to evaluate innovation capability within SMEs should be as integrative and close to reality as possible. Therefore, the present study sought to address this issue through the combined use of cognitive mapping and multiple criteria decision analysis (MCDA). According to Zopounidis et al. (2015, p. 339), “a wide range of techniques and approaches can be useful […]. Among such disciplines […] MCDA has appealing distinctive features that are well suited for decision making.” More specifically, we focused on developing a nonparametric method of evaluating SME innovation capability based on a constructivist stance and the results of group meetings with a panel of information technology (IT) entrepreneurs and SME chief executive officers (CEOs). This method used cognitive mapping to identify the evaluation criteria and the analytic hierarchy process (AHP) to calculate the respective trade-offs. Cognitive mapping can bring together multiple decision makers and deal with conflicts of interest and uncertainty, which allows complex problems to be structured more clearly (Ackermann and Eden, 2001; Eden, 2004). In addition, this method helps reduce the number of omitted criteria and identifies the cause-and-effect relationships between variables (Eden and Ackermann, 2004; Damart, 2010; Canas et al., 2015). The AHP was created by Saaty (1980), and it is now probably the most widely known MCDA method. Both approaches have been extensively applied to real-life decision problems, helping generate greater clarity with regard to problem definition and resolution (see Zavadskas et al., 2014). However, we know of no prior work reporting these two methods’ integrated use to evaluate SME innovation capability.