Risk-adjusted banks' resource-utilization and investment efficiencies: does intellectual capital matter?
| DOI | https://doi.org/10.1108/JIC-03-2020-0106 |
| Published date | 02 February 2021 |
| Date | 02 February 2021 |
| Pages | 687-712 |
| Subject Matter | Information & knowledge management,Knowledge management,HR & organizational behaviour,Organizational structure/dynamics,Accounting & finance,Accounting/accountancy,Behavioural accounting |
| Author | Qian Long Kweh,Wen-Min Lu,Kaoru Tone,Mohammad Nourani |
Risk-adjusted banks’resource-
utilization and investment
efficiencies: does intellectual
capital matter?
Qian Long Kweh
Faculty of Management, Canadian University Dubai, Dubai, United Arab Emirates
Wen-Min Lu
Department of International Business Administration, Chinese Culture University,
Taipei, Taiwan
Kaoru Tone
National Graduate Institute for Policy Studies, Minato-ku, Japan, and
Mohammad Nourani
School of Management, Universiti Sains Malaysia, George Town, Malaysia
Abstract
Purpose –The purpose of this study is twofold. First, this research estimates banks’efficiencies from the
perspectives of resource utilization and investment after incorporating risk measures as an exogenous input in
the investment-efficiency stage. Second, the current study examines the relationship between intellectual
capital (IC) and banks’efficiencies.
Design/methodology/approach –First, this study uses a dynamic network data envelopment analysis
approach in investigating the efficiencies of 24 Taiwanese banks in 2007–2018 from two perspectives. Second,
this research utilizes various regression techniques, namely, ordinary least squares (OLS), robustleast squares
and truncated regression, to gauge the impact of IC on banks’efficiencies. Typically, IC is determined based on
a monetary value-based measure and value-added intellectual coefficient (VAICTM).
Findings –Resource-utilization (investment) efficiencies were observed as 0.941 (0.964), thereby contributing
to the mean overall efficiency of the sample banks at 0.952. However, the related efficiency changes decline over
the sampleperiod, thereby suggesting that the average banks’efficiencies hardly increase. Regression analyses
show a significantly positive relationship between IC and banks’overall resource-utilization and investment
efficiencies.
Research limitations/implications –Overall, this study suggests that researchers should consider risks
when estimating banks’efficiencies owing to their connection to banks’investment performance. From banks’
dynamic two-stage efficiencies, this study demonstrated that investments in IC will bring improved future
economic benefits.
Originality/value –Different from prior studies, this study improves banks’efficiency evaluation models by
incorporating risk measures and assuming weighted periods for the 2007–2008 global financial crisis.
Moreover, the use of monetary value-based measure of IC provides consistent results as the commonly-used
VAICTM does.
Keywords Banks’efficiency, Intellectual capital, Data envelopment analysis, Dynamic measure, Regression
analysis
Paper type Research paper
1. Introduction
Globalization has amplified the importance of intangible assets in value creation compared
with physical assets (Stewart and Ruckdeschel, 1998). These intangible assets include
knowledge embedded in various resource capitals of firms, which is known as intellectual
capital (IC) (Bontis, 1998). By being consistent with a resource-based perspective, companies
can achieve superior performance if strategic resources are well organized (Peteraf, 1993).
These strategic resources have to be rare, valuable and inimitable for companies to have
Risk-adjusted
banks’
efficiency
687
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1469-1930.htm
Received 3 April 2020
Revised 10 August 2020
12 October 2020
Accepted 6 January 2021
Journal of Intellectual Capital
Vol. 23 No. 3, 2022
pp. 687-712
© Emerald Publishing Limited
1469-1930
DOI 10.1108/JIC-03-2020-0106
sustainable competitive advantages (Barney, 1991). The practical implication of companies’
investment in IC stems from the superior management of risks and high profitability and
efficiency (Lu et al., 2010;Sydler et al., 2014). Consequently, IC has been the main focus for
many industries’knowledge capabilities; among financial institutions, banks are
considerably susceptible to IC-related issues owing to their knowledge-intensive structure
(Adesina, 2019;Mavridis, 2004;Mention and Bontis, 2013;Vidyarthi, 2019).
In particular, prior studies that have examined the relationship between IC and banks’
performance (e.g., Alhassan and Asare, 2016;Mondal and Ghosh, 2012;Ozkan et al., 2017)
documented that IC is key to banks’performance. However, when examining the IC
components, studies employing value-added intellectual coefficient (VAIC
TM
) have found
varying effects of these components on banks’performance. One explanation for these
contradictory findings is that VAIC
TM
is subject to criticism (St
ahle et al., 2011). Although
Sydler et al. (2014) can be followed in using the concept of monetary proxies to accurately
measure IC, another potential reason for the mixed effects of IC on banks’performance is the
estimation of banks’performance. That is, measuring banks’performance becomes a
complicated process, in which results can be exposed to subjective judgment.
Before linking IC to banks’performance, this study first estimates banks’performance
from a comprehensive perspective. Seiford and Zhu (1999) introduced a network structure for
banks to assess their performance by simultaneously estimating multiple performance
indicators. Consequently, the banking literature has realized the importance of opening the
black box of banks’performance from the perspective of efficiency. Prior studies have
examined banks’efficiency in stages (e.g., Fukuyama and Matousek, 2017;Lo and Lu, 2006;
Luo, 2003;Wu and Birge, 2012;Zha et al., 2016;Zhou et al., 2019) and accounted for dynamism
(e.g., Avkiran, 2015;Chao et al., 2015;Fukuyama and Weber, 2015,2017;Wanke et al., 2019;
Wu et al., 2016). The current study is founded on the same concept and also opens the black
box within banks’production processes to examine their efficiencies and their components,
namely, resource-utilization and investment efficiencies.
In addition to network structure, this study also accounts for risk when estimating banks’
efficiencies. The reason is that this research finds that risk measures within banks’
production processes have received limited attention in the literature; the exception is Kweh
et al. (2018), which accounted for risk factors in their evaluation of banks’efficiencies, even
though managing risks is crucial for banks to compete well in the rapidly changing business
world (Abdrahamane et al., 2017;Epure and Lafuente, 2015). Since the 2007–2008 global
financial crisis, excessive risk-taking by banks has gained popularity and has become one of
the main factors leading to financial difficulties (Chen et al., 2018). Although many countries
have adopted financial liberalization policies to advance their markets (Hermes and Meesters,
2015), different types of risks subsequently continued to emerge (Luo et al., 2016). In the
globalization era, the banking sector has become an increasingly dynamic and highly
competitive industry. That is, risks should be considered for accurately evaluating banks’
performances (Chen et al., 2018;Chiu and Chen, 2009). Note that a country’s poor regulatory
system may lose control over financial institutions and become the major cause of excessive
risk-taking (Mishkin, 1999).
The current research is different from prior studies because of the inclusion of loan, which
has been overlooked in banks’efficiency evaluation, as an important intermediate product in
a two-stage efficiency framework. Given that banks face different types of risks, including
credit, operating, and legal risks, as a result of intense competition, this study continues the
debate by highlighting the importance of incorporating the aforementioned risks in
estimating banks’efficiency. This study fills in the research gaps in the IC and performance
management fields by linking the monetary value-based IC model to the risk-adjusted
multistage efficiencies. Evidently, substantially informative and accurate model
JIC
23,3
688
To continue reading
Request your trial