Purpose Although R&D plays a crucial role in innovativeness and R&D expenditures is the most widely used tool to measure the level of innovativeness of companies, other variables and inputs may be equally interesting. The purpose of this paper is to define an innovative propensity index (IPI) which considers these variables and allows the identification of those companies which have a higher propensity to implement different types of innovativeness. Design/methodology/approach Taking into account, the different criteria that may be considered in an IPI and that the perception of the relative importance of each criterion is subjective, the use of an innovativeness multicriteria decision methodology has been considered appropriate. In particular, an IPI is built from the weighting of the criteria through FAHP methodology. Data mining techniques are subsequently used to establish a non-supervised ranking (clustering) of a sample of firms, considering their IPI values. Findings The application of an IPI to a sample of 1,639 companies operating in different industrial sectors has helped us to find out that this index is useful for identifying those companies which really show an increased innovative capacity. A comparative analysis by sectors has shown that although there are companies from all sectors with a high innovative propensity, the proportion increases in more technological sectors. Moreover, it has been observed that in companies with higher net personnel expenses and high productivity level the innovative propensity is also higher. Originality/value The criteria used to build the index affects innovativeness individually, but the value of the analysis lies in its multicriteria approach and use of fuzzy logic. The validation of the index in a wide sample of firms is another outstanding aspect of the analysis.

中文翻译：

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模糊多准则方法的创新倾向

目的尽管研发在创新中起着至关重要的作用，研发支出是衡量公司创新水平的最广泛使用的工具，但其他变量和投入也可能同样有趣。本文的目的是定义一个创新倾向指数（IPI），该指数考虑了这些变量，并可以识别那些具有更高倾向实施不同类型创新的公司。设计/方法/方法考虑到IPI中可能考虑的不同标准，并且对每个标准的相对重要性的认识是主观的，因此认为使用创新性多标准决策方法是适当的。特别是，通过FAHP方法根据标准的权重构建IPI。考虑到其IPI值，数据挖掘技术随后用于建立公司样本的非监督排名（聚类）。调查结果将IPI应用于在不同工业领域运营的1,639家公司的样本，这帮助我们发现该指数对于确定那些真正显示出更高创新能力的公司很有用。按行业进行的比较分析表明，尽管各行各业的公司具有很高的创新倾向，但在更多技术行业中所占的比例却有所增加。此外，已经观察到，在净人员费用较高且生产率较高的公司中，创新倾向也较高。原创性/价值用来建立索引的标准会分别影响创新性，但是分析的价值在于其多准则方法和模糊逻辑的使用。在众多公司样本中对指数进行验证是分析的另一个突出方面。