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The purpose of this paper is to analyze the effectiveness of legal protection mechanisms to sustain competitive advantage and appropriate technological intellectual capital, taking into account its degree of complexity.

The empirical analysis was conducted on a sample of Spanish biotechnology firms, devoted to human and animal health, having a specific department for carrying on R&D activities from 2001 to 2003. The hypotheses were tested by non‐parametric techniques after collecting data by sending a questionnaire as the primary source of information.

The results show that the higher the complexity of technological knowledge, the greater the protection of intellectual capital against imitation. In addition, it is proven that legal protection is an effective means of protection against imitation only when technological knowledge has a low degree of complexity.

Within the resource‐based view context, our results provide new empirical evidence about the great impact of the complexity of technological intellectual capital on evaluating the effectiveness of legal protection mechanisms and sustaining competitive advantage.

This research is of great value for strategic technological decision‐making. Although several studies present the theoretical conditions that explain how firms can create imitation “barriers”, the empirical evidence found is very scarce and controversial, especially for the Spanish biotechnology industry. The results obtained here offer new empirical evidence on the resource‐based view.

For better mapping the path of intellectual capital (IC) research, the purpose of this paper is to selectively review empirical studies of IC published, and identify theories, components and three dimensions of analysis: national IC (NIC), regional IC (RIC) and organizational IC (OIC).

The systematic literature review (SLR) subject to analysis is based on empirical studies made between 1960 and 2016, and focuses on three dimensions of analysis: NIC, RIC and OIC. Four research questions were designed, using the following databases, namely, Web of Science, Scopus and Google Scholar, for data collection purposes.

The SLR unveils a multidimensional taxonomy for measuring and classifying the type of IC applicable to the different levels of analysis and provides some recommendations for future studies of NIC, RIC and OIC, by outlining the need for clear definitions of components and measures of IC and identifying strengths, limitations and future research avenues.

In order to fill the gap found in the literature and the non-existence of a study clarifying the multiple dimensions of analysis of IC, this SLR makes a twofold, original contribution to the literature on management: providing an SLR of the main empirical studies dealing with different units of analysis; and identifying a multidimensional taxonomy for measuring and classifying the type of IC applicable to the different levels of analysis.