## 3 Empirical Setting: Context of the Research and Methods
### Methodology: Qualitative Comparative Analysis  
Qualitative comparative analysis (QCA) is a methodology that, although initially developed for the field of sociology and political sciences [76; 77; 78], is also used in other areas such as marketing, innovation, and business strategy [79; 80; 81; 82; 83]. The main characteristic of this analysis methodology (QCA) lies in the search for causal recipes that lead to an output ([PERSON], 2008). A simplification of the main mechanism involves locating alternative combinations (recipes) of antecedent conditions (ingredients) that produce a certain outcome or its negation. Therefore, it allows unraveling the causal complexity of the conditions instead of analyzing their net contributions [84]. As a result, the methodology (QCA) is characterized by the presence of equifinality. That is, the same result can be obtained by combining different causal combinations [25; 84]. In order to achieve our main objectives, we used the fuzzy set QCA (fsQCA). This is the last version of this methodology [25; 85; 86; 80] that allows analysis of the relationships among variables based on membership to certain sets. On the basis of Boolean algebra, QCA reveals configurations and combinations of variables (and associated conditions) that result in necessary and/or sufficient conditions to obtain a certain outcome. In short, on the one hand, we are dealing with an outcome (the feature we are aiming to explain), and, on the other hand, with different conditions (or attributes). In the present research, the outcome is related to the technological innovativeness of the companies, as previously stated in the theoretical propositions. The conditions have been defined from a set of variables (or internal features of the companies) that may be related with the outcome. Among them, early adoption and absorptive capacity are suggested to play an important role as previously justified. We selected these specific factors to focus on related characteristics belonging to the same domain of the company, and to describe how these similar factors behave, even interact, in the proposed model. Indeed, we acknowledge the existence of potential interactions among different innovation factors. In this particular research, we suggest that those interactions can be captured through the fsQCA methodology. Rather than to establish simple and direct causal associations between variables, this methodology allows us to uncover combinations of variables (interactions) to obtain a certain outcome, in our case the innovation performance of clustered companies. In Table 1, we can find the outcome scope, the variables, and the conditions. All items used in the survey to build the outcome as well as the variables, which, in turn, will lead to the conditions, are listed in Appendix A with additional details and justifications. Finally, Table 2 identifies the property space in our analysis in terms of outcome and sets (condition) definition. We would like to underline that in order to measure the technological innovation, we use a validated scale from the Community Innovation Survey questionnaire (PITEC). The items used (listed in the appendix) have a technological component including the ones regarding logistics and auxiliary processes. After defining the different sets, we assigned a value to determine membership. In the field of QCA analysis, this process is known as calibration. The calibration can be done directly or indirectly [25]. Regarding this research work, we opted for direct calibration, as recommended by [PERSON] [25], when the samples are small or medium size. Direct calibration involves determining when a certain condition is fully in or fully out of a specific set at the point of maximum ambiguity. To carry out this process [87], we used the fsQCA package in R. The results obtained for the calibration can be found in Table 3. This table shows the membership cut-off points used in each of the conditions defined above.