Big Data and Intellectual Capital: old problems require new solutions

Introduction and problematisation. The rise of knowledge economy and the increasing connectedness of society (Edvinsson, 2013) are having a noticeable impact on Intellectual Capital (IC) research and on IC perspectives (Borin and Donato, 2015). In fact, scholars are now involved in the so-called fourth stage of IC research, which widen the traditional approach in analysing how IC dimensions interact and contribute to the value creation process within the organization considering the social dimension of IC (Secundo et al., 2017). IC may be defined as “the sum of everything everybody in a company knows that gives it competitive edge” and consists of “intellectual material, knowledge, experience, intellectual property and information that can be put to use to create value” (Dumay, 2016, p. 169). The proposed definition suggests that a shift in the focus of IC research is taking place, whereby IC expands its boundaries into the wider ecosystem to include other forms of value, beyond just monetary wealth. One of the most representatives paradigms of the complexity and turbulence of today’s knowledge economy is the so-called Big Data (Secundo et al., 2017). Although there is no unanimous consensus on its definition (Chen et al., 2014), Big Data may be defined as a large volume of complex data (structured and unstructured) from a variety of sources (internal and external) that requires data bases to be stored in, programs and tools to be managed and high-level skilled personnel to retrieve valuable insights for sustained value delivery, measuring performance and establishing competitive advantage (Fredriksson, 2015; Fosso Wanba et al., 2015). The lack of a clear definition of what it is referred to as Big Data may be due to the fact that, in its early stages, the Big Data debate was mostly reserved to practitioners' and consultants' realm. As Gandomi and Haider (2015) observe, in fact, the fast evolution of Big Data technologies and the ready acceptance of this concept by both public and private organisations have impeded the development and the growth of an academic debate on the topic. Authors and practitioners have indeed overlooked the academic research in favour of an immediate and wide circulation of their work on Big Data. According to Andreou et al (2007), data and information have the potential to integrate the intangible asset mix with items such as competitive intelligence, enterprise intelligence and decision effectiveness. The increasing network dimension of society, as well as the rapid growth of technological aids and analytics, have shown that potentially valuable intangible assets may lay both inside and outside the organization (Erickson and Rothberg, 2014; Borin and Donato, 2015). In this scenario, Big Data can significantly contribute to the creation of new forms of value (Brown, 2014) and may thus represent a relevant input in shifting the focus of IC from within the organization to the ecosystem in which it operates to create knowledge on a broader perspective (Dumay, 2013), also generating new opportunities for IC management (Secundo et al., 2017). Albeit some scholars have begun to recognise the opportunity to integrate the Big Data discourse within the fourth stage of IC research (Erickson and Rothberg, 2014; Fredriksson, 2015; Secundo et al., 2017), the way in which organisation can exploit Big Data technologies in order to create value under an IC perspective is still mostly unknown. At present, it may be argued that the research on the integration between Big Data’s opportunities and challenges and the IC management is still in its early stages. Moreover, although most of the extant literature on Big Data focuses on its potential in creating value, some scholars and practitioners also highlight that many firms, which have invested in Big Data project, actually failed to exploit its benefits (Mithas et al., 2013; Erevelles et al., 2016), thus revealing the need for a deeper understanding of the challenges that have to be faced when Big Data technologies are implemented within organisations. So, it becomes of interest to analyse in a systematic way the main contributions from both academics and practitioners about Big Data, IC and their relationship in order to get a deeper understanding and an integrated view of how introducing Big Data technologies may affect IC management. Purpose. The purpose of this study is to give a comprehensive overview of the extant literature on Big Data within the IC domain. Moreover, by linking Big Data attributes and challenges with the three traditional dimensions of IC (human, relational and structural capital), this paper aims to propose an integrated scheme of analysis in order to give a systematic identification and classification of the main threats and opportunities associated with the exploitation of Big Data technologies within the IC management. Design of the study. First, we conducted a systematic review of the extant literature on Big Data an IC from both academic and professional realms. Second, we analysed the distinctive characteristics of Big Data and the related technologies in order to find the links between the traditional dimensions of IC (human, relational and structural capital) and the threats and opportunities that emerge when Big Data technologies are adopted. Findings. The conducted analysis has allowed to develop an interpretive model of the extant literature on Big Data and IC in order to get a systematic identification and classification of the main threats and opportunities associated with the exploitation of Big Data technologies within each of the three dimension of IC system, distinguishing between those related to the phase of knowledge production from those related to the phase of knowledge consumption. The model seems to suggest that the Big Data era have emphasised and amplified many of the risks and opportunities already highlighted in previous literature, such as information security risk (Peltier, 2005), talent recruiting (Lewis et al., 2006) and resistance to change (Burns & Scapens, 2000). At the same time, however, Big Data significantly amplify the magnitude of these threats, thus requiring a revision of the traditional managerial solutions needed to overcome them or at least the way in which they may be applied. It might be argued, therefore, that “old problems” cannot be solved in a traditional way, so the main change Big Data has brought does not affect the “what” have to be faced but the “how” organisations have to face it. Moreover, the scheme of analysis has shown the interdependences between the phase of production and that of consumption of knowledge. This implies that, in designing the system working with Big Data, organisations must take into account both the needs of the users and the technologies available in solving the problems under investigation. Since the newly created data is neither truly known or necessarily well understood by the users of these data, a cyclical, dynamic and iterative process of data collection, data analysis and information extraction is required in order to avoid the failure of Big Data project. This is consistent with Kaisler et al. (2013) as well as with Gandomi & Haider (2015), but this argument is also related to the traditional loop of learning concept (Argyris, 2000). Implications, limitations and further research. Form a theoretical perspective, this study contributes to the IC ecosystem literature and may be used as a starting point in understanding how Big Data can be turned into intangible assets and IC items. Moreover, it contributes to the Information System literature, providing some valuable insights on how the Big Data attributes may be referred to IC dimensions’ management. From a practical perspective, this model might represent a useful decision making tool in identifying and evaluating the main threats and opportunities that must be taken into account when investing in Big Data technology for IC management. Further research is needed, however, to confirm or revise the findings derived from the theoretical analysis. The proposed model might be validated by means of the conduction of case study analyses, which may also allow to understand how the proposed scheme might be implemented within different organisations.