Intellectual capital in the age of Big Data: establishing a research agenda
| Pages | 242-261 |
| DOI | https://doi.org/10.1108/JIC-10-2016-0097 |
| Published date | 10 April 2017 |
| Date | 10 April 2017 |
| Author | Giustina Secundo,Pasquale Del Vecchio,John Dumay,Giuseppina Passiante |
| Subject Matter | Information & knowledge management,Knowledge management,HR & organizational behaviour,Organizational structure/dynamics,Accounting & Finance,Accounting/accountancy,Behavioural accounting |
Intellectual capital in the
age of Big Data: establishing
a research agenda
Giustina Secundo and Pasquale Del Vecchio
Department of Innovation Engineering, University of Salento, Lecce, Italy
John Dumay
Department of Accounting and Corporate Governance,
Macquarie University, Sydney, Australia, and
Giuseppina Passiante
Department of Innovation Engineering, University of Salento, Lecce, Italy
Abstract
Purpose –The purpose of this paper is to contribute to the literature on intellectualcapital (IC) in light of the
emerging paradigm of Big Data. Through a literature review, this paper provides momentum for researchers
and scholars to explore the emerging trends and implications of the Big Data movement in the field of IC.
Design/methodology/approach –A literature review highlights novel and emerging issues in IC and Big
Data research, focussing on: IC for organisational value, the staged evolution of IC research, and Big Data
research from the technological to the managerial paradigm. It is expected that identifying these
contributions will help establish future research directions.
Findings –A conceptual multi-level framework demonstrates how Big Data validates the need to shift the
focus of IC researchfrom organisations to ecosystems.The framework is organised into four sections:“why”–
the managerial reasonsfor incorporating Big Data into IC; “what”–the Big Data typologies that enhance IC
practice;“who”–the stakeholders involvedin and impacted by Big DataIC value creation; and “how”–theBig
Data processes suitable for IC management.
Research limitations/implications –The paper provides many avenues for future research in this
emerging area of investigation. The key research questions posed aim to advance the contribution of Big Data
to research on IC approaches.
Practical implications –The paper outlines the socio-economic value of Big Data generated by and about
organisational ecosystems. It identifies opportunities for existing companies to renew their value propositions
through Big Data, and discusses new tools for managing Big Data to support disclosing IC value drivers and
creating new intangible assets.
Originality/value –This paper investigates the effects and implications Big Data offers for IC management,
in support of the fourth stage of IC research. Additionally, it provides an original interpretation of IC research
through the lens of Big Data.
Keywords Stakeholders, Value creation, Ecosystem, Intellectual capital, Big Data, IC fourth stage
Paper type Conceptual paper
1. Introduction
This study aims to systematise the main areas of research emerging from the intersection of
Big Data and intellectual capital (IC) management to establish a future research agenda.
This section describes the pillars of the conceptual framework adopted in Big Data approaches
to managing IC. Over recent decades there has been a rapid global transition from industrial
economies to knowledge-based economies (Romano et al., 2014), in which wealth is created by
developing and managing knowledge and intangible assets –commonly known as IC
(Andriessen, 2004; Ricceri and Guthrie, 2009; Dumay and Garanina, 2013). The field of IC has
always distinguished between data, information, and knowledge.One of its basic foundations
is that IC deals with valuable organisational assets, and this deserves great attention by
managers (Erickson and Rothberg, 2014). IC is valuable enough to identify, measure, manage,
and protect. It may also contribute to competitive advantage (Erickson and Rothberg, 2014).
Journal of Intellectual Capital
Vol. 18 No. 2, 2017
pp. 242-261
© Emerald PublishingLimited
1469-1930
DOI 10.1108/JIC-10-2016-0097
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1469-1930.htm
242
JIC
18,2
A new development in organisational management is the emergence of Big Data, which
offers a new interpretative lens for IC management (Erickson and Rothberg, 2014;
Fredriksson, 2015). Big Data is one of the most representative paradigms of the complexity
and turbulence of the knowledge economy. However, the concept of Big Data seems
abstract. Even if the importance of Big Data is generally recognised, it does not yet have an
agreed definition (Chen et al., 2014; Ward and Barker, 2013). In this paper, Big Data means
the massive amounts of data derived from a variety of sources, including social media
accounts, web pages, purchases in stores, video and picture downloading, customer
feedback, health records, maps, protocols, and geographic locations in applications.
In other words, it is data that generate valuable knowledge and tangible benefits for
organisations (Fredriksson, 2015; Gandomi and Haider, 2015; McAfee and Brynjolfsson,
2012). Wu et al. (2014) and Kaisler et al. (2013) define Big Data as the large, complex, and
growing volume of data continuously generated by multiple, autonomous, and smart
sources. Gartner (2012) identifies the high volume, velocity, and/or variety of information
assets as the main features of Big Data, and asserts that new processes to support decision
making, insight discovery and process optimisation are required. The volume, velocity, and
nature of the information Big Data encompasses requires adopting performative tools
(Latour, 1987; Mouritsen, 2006) and systems to manage it (Xiaofeng and Xiang, 2013).
As the volume,velocity, and varietyof data (Laney, 2001)increase, takingadvantage of that
data has become a more compelling proposition than in the past. Additionally, scholars and
researchers have identified veracity, variability, and value as additional dimensions of Big Data
(Gandomi and Haider, 2015), and highlight the need to recognise the challenges associated with
translating data available into organisational value. All this highlights the relevance of
Big Data into the debate on IC; this is the conceptual premise at the basis of this study.
For the purposes of this paper, IC is defined as “the sum of everything everybody in a
company knows that givesit competitive edge […] Intellectualcapital is intellectual material,
knowledge, experience, intellectual property, information […] that can be put to use to
create value”(Dumay, 2016). Accordingly, Big Data is interpreted as “information assets
characterised by such a high volume, velocity and variety [as] to require specific technology
and analytical methods for its transformation into value”(De Mauro et al., 2016, p. 103).
The word “value”is included in both definitions because, although value includes
monetary wealth, outputs for the organisation’s stakeholders should not be expressed solely
in terms of monetary assets. More and more, creating value is the result of managing
knowledge assets, and this is reflected in both fields of research. Expanding the concept of
value creation beyond organisational wealth creation into wider society aligns with Dumay
and Garanina’s (2013) concept of the fourth stage IC research which requires to “navigate the
knowledgecreated by countries, citiesand communities and advocateshow knowledge can be
widely developedthus switching from a managerial to an ecosystem focus”(p. 21).Similarly,
Big Data is now a central part of many organisation’s strategicagenda –public and private,
and government. The huge amount of information generated from, and about, people and
things represents both an ocean of opportunity and a source of input for more effective
decision-making processes ( Jin et al., 2015; Kaisler et al., 2013), as well as for developing
knowledge-intensive entrepreneurship (Del Vecchio et al., 2014). However, most org anisations
have only identified a small portionof the potential value of this data,and limited tools exist to
create, manage, and measure it. Models that qualitatively measure IC to support decision
making (Kujansivu, 2009; Lönnqvist et al., 2009) and the use of narrative to explain the
numbers (Dumay and Rooney, 2016) are two emerging approaches to this problem.
However, at this stage, synthesising a Big Data approach into an effective IC strategy
that is suitable for transforming data into knowledgeable inputs is challenging.
The emergence of Big Data has seen the blurring of boundaries between the internal and
external knowledge assets that companies leverage to gain and sustain their competitive
243
IC in the age
of Big Data
To continue reading
Request your trial