Dynamizing intellectual capital through enablers and learning flows
| DOI | https://doi.org/10.1108/IMDS-04-2013-0190 |
| Published date | 28 January 2014 |
| Date | 28 January 2014 |
| Pages | 2-20 |
| Author | M. Nancy Vargas,M. Begoña Lloria |
| Subject Matter | Information & knowledge management,Information systems,Data management systems |
Dynamizing intellectual capital
through enablers and
learning flows
M. Nancy Vargas
University of Santander, Special Projects and Consulting, Bucaramanga,
Colombia, and
M. Begon
˜a Lloria
University of Valencia, Direccio
´n de Empresas Juan Jose
´Renau Piqueras,
Valencia, Spain
Abstract
Purpose – The main aim of this research is to relate intellectual capital (IC) and organizational
performance through intermediate variables, enablers and learning flows. To this end, the paper
defines a new theoretical model of relations and presents an empirical study to contrast the model.
Design/methodology/approach – The proposed theoretical model is contrasted by means of a
quantitative study of Spanish firms from the biotechnology sector. The statistical analysis applies a
method based on variance using partial least squares.
Findings – The theoretical model proposes a total of 15 relations, 13 of which are statistically
significant, which demonstrates the close relationship between IC and performance using enablers and
learning flows as intermediate variables.
Originality/value – From the theoretical model proposed and its subsequent empirical contrast, the
research shows the close relations that exist between areas of knowledge that traditionally appear
separately in the literature. The proposed model thus allows us to explain and predict the
dynamization of the components of IC due to enablers and learning flows, and the effect of these
elements on organizational performance.
Keywords Performance,Intellectual capital, Feedback, Enablers,Feedforward
Paper type Research paper
1. Introduction
The context for this study lies within the vision of the firm based on the theory of
resources and capabilities, and intellectual capital (IC), and approaches the research
from the perspective of learning and knowledge creation in the organization, and its
impact on organizational performance.
The direct, positive relation between IC (and its components) and organizational
performance has been examined in a number of empirical studies (Clarke et al., 2011;
Dı
´ez et al., 2010; Kamukama et al., 2010; Ze
´ghal and Maaloul, 2010; Tovstiga and
Tulugurova, 2007; Bontis et al., 2000; among others). Other studies also examine this
relation, but include one or more intermediate variables, with similar results. For
example, Nold (2012) considers the variable organizational culture; while
Kamukama et al. (2011) and Cheng et al. (2010) use competitive advantage as a
significant mediator. Other authors use various enablers and intermediate variables to
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
Received 16 April 2013
Revised 18 June 2013
Accepted 28 June 2013
Industrial Management & Data
Systems
Vol. 114 No. 1, 2014
pp. 2-20
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/IMDS-04-2013-0190
IMDS
114,1
2
study their impact on performance, but focus on the area of knowledge management
(Lee et al., 2012; Soon and Zainol, 2011; Lee and Choi, 2000; among others).
This study is based upon a similar framework to those mentioned above, though we
broaden the scope and focus on IC. The study attempts to relate the components of IC
(human capital (HC), structural capital (SC) and relational capital (RC)) with
performance, using the enablers of knowledge creation as intermediate or mediating
variables (Von Krogh et al., 2000; Nonaka and Takeuchi, 1995) and knowledge flows
(Bontis, 1999; Crossan et al., 1999). The knowledge and learning-based perspective also
highlights the importance of these processes for the creation of value in the
organization (Crossan and Berdrow, 2003; Bontis, 1999). We thereby propose a new
model of relations between theories that have arisen independently of one another in
the literature: IC, knowledge creation and organizational learning (OL).
The overall structure of the study is as follows: we first consider the basic
theoretical framework and go on to address the complete definition of the model with
its proposed relations, followed by a description of the hypotheses we set out to
contrast. These hypotheses are validated via a quantitative study of firms in the
biotechnology sector in Spain. The final section of the study contains the results, a
discussion of the findings and the most relevant conclusions, along with the main
limitations.
2. Theoretical antecedents and hypotheses
This section presents the basic theoretical framework used to obtain a model of
relations and the hypotheses for contrast in the empirical study.
2.1 IC and its components
IC is still for many an invisible fuzzy dimension, or mainly a measuring and accounting
concept.
The modern knowledge economy demands that we focus more intensely on the real
strategy process. A more productive approach involves the search for multiplying
effects and a deeper understanding of HC and SC, which can be employed to create
connections without frontiers in customer capital (CC) on a global scale (Edvinsson,
2013).
Beginning with a more precise definition of IC allows us to address the concept of
intangible resources (Bontis, 1996; Roos et al., 1997), as well as their interconnections
(Bontis, 1998). From this standpoint, IC is considered as the set of intangible resources
and their flows (Bontis et al., 2002).
More than the definition of the concept in itself, the aspect that has received most
attention in the specialist literature is the classification of the components of IC.
Various models have been developed independently and at different stages over the
past decade and many of them are conceptually similar. However, the major
classificatory contributions, which have led to different models and levels of grouping
of the elements, were carried out at the European schools: Edvinsson and Malone
(1997), Sveiby (1997), Brooking (1998), Bueno (1998) and Can
˜ibano et al. (2000); and in
the USA: Kaplan and Norton (1996), Stewart (1997) and Bontis (1998). Despite the fact
that there is a wide variety of models for categorizing IC, there is still no consolidated
or universally accepted methodology for its classification. These measuring techniques
are still evolving. Consensus does exist, though, on the fact that these models contain
Dynamizing
intellectual
capital
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