Article Critique

J. of Multi. Fin. Manag. 36 (2016) 64–74

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Journal of Multinational Financial Management

journal homepage: www.elsevier.com/locate/econbase

The impact of the intellectual capital efficiency on commercial banks performance: Evidence from the US�

Antonio Meles a, Claudio Porzio b, Gabriele Sampagnaro b, Vincenzo Verdoliva c,∗

a Second University of Naples, Italy b University of Naples Parthenope, Italy c Kingston University of London, UK

a r t i c l e i n f o

Article history: Received 31 August 2015 Received in revised form 7 April 2016 Accepted 28 April 2016 Available online 29 April 2016

JEL classification: G21 E24 O34

Keywords: Banks performance Intellectual capital Human capital VAICTM

a b s t r a c t

Using a large sample of 5,749 commercial banks, covering over 40,000 observations over the time window 2005–2012, we find that efficiency in the use of intellectual capital (IC) positively affects the financial performance of US banks. In addition, the results show that human capital (HC) efficiency, a subcomponent of IC efficiency, has a larger impact on financial performance than other IC sub-components. These findings suggest that the devel- opment of effective techniques of knowledge management, enabling banks to accumulate the IC necessary to adapt to a constantly changing environment, represents an effective tool of achieving the goals of both bank managers and policymakers.

© 2016 Elsevier B.V. All rights reserved.

“Intangibles (intellectual capital) resources are now largely recognized as the most important sources of an organizations’ competitive advantage. At the corporate level, intangible investments [. . .] are now unanimously considered as the most important determinants of performance.” World Bank conference, Paris, France, May 28–29, 2009.

1. Introduction

Intellectual Capital (IC) plays an important role in an organisation’s performance. It represents distinctive characteristics that, ceteris paribus, can determinate the success or failure of an organisation relative to its peers (see, among others, Pulic, 1998; de Pablos, 2003; El-Bannany, 2008).

Thus, it is not surprising that over the last decades, many researchers from different disciplines (particularly, management, accounting and finance) have devoted substantial attention to IC, examining it from different viewpoints and for various research purposes. While several studies (e.g., Stewart, 1998; Roos et al., 1997; Bontis, 1998; Wu and Tsai, 2005) have defined

� We thank Jens Hagendorff, the subject editor of Journal Multinational Financial Management, and an anonymous referee for constructive comments on a previous version of this paper.

∗ Corresponding author at: Kingston University of London, Kingston Business School, Kingston Hill, Kingston Upon Thames, KT2 7LB, Surrey, United Kingdom. Tel.: +44 (0)20 8417 9000×66597.

E-mail address: [email protected] (V. Verdoliva).

http://dx.doi.org/10.1016/j.mulfin.2016.04.003 1042-444X/© 2016 Elsevier B.V. All rights reserved.

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A. Meles et al. / J. of Multi. Fin. Manag. 36 (2016) 64–74 65

C from a theoretical perspective, others have developed effective measures of IC-based performance (Stewart, 1998; Pulic, 000), exploring the relationship between IC efficiency and some key characteristics of firms, industries and regions (El- annany, 2008; Liang et al., 2011; Al-Musalli and Ismail, 2012). Finally, a third strand of literature (which includes Chen et al., 005; Firer and Williams, 2003; Chen et al., 2014; Curado et al., 2014; Janošević et al., 2013 among others) has empirically

nvestigated the relationship between firm IC efficiency and financial performance. However, although many studies of firm IC exist, empirical evidence regarding the contribution of IC efficiency to firms’

nancial performance remains confined to certain sectors and geographical areas (Mention and Bontis, 2013). In particular, here appear to be no studies related to bank IC from the US, despite the fact that the US banking system is of notable nterest for its size and the role it plays in global economic growth. Indeed, according to OECD data, total assets managed y US banks were $12,610 billion in 2011, a very high value, especially compared with values recorded in the same year by ther countries with bank-oriented financial systems, for example, Germany ($11,625 billion) and Japan ($11,651 billion).

The present paper aims to fill this gap, mainly by analysing a unique and distinctive dataset of 5749 US banks, covering over 0,000 observations over the time window 2005–2012. The dataset, drawn from the Bankscope Bureau Van Dijk database, is esigned to respond to the following research question: does IC efficiency positively affect a bank’s financial performance?

We are aware that replying this question is of special interest because the banking industry is one of the most knowledge- ntensive industries (Firer and Williams, 2003; Mavridis, 2005) and represents an ideal setting for research on IC. Various tudies (e.g., Boot, 2000; Degryse and Ongena, 2005; Fiordelisi et al., 2014; Sampagnaro et al., 2015) point out that banks can ain valuable competitive advantages by building tight relationships with their customers and making valuable investments n the soft information production. Other studies (among others, Goh, 2005; Kamath, 2007) observe that an efficient uti- ization of IC is more crucial for achieving success in banking than other industries, because the ability of a bank to provide ustomers with high quality products and services depends on its investments in items related to IC such as its human esources, brand building, systems and processes. Therefore, it becomes necessary for banks to manage their IC as efficiently s possible.

We provide evidence that the level of IC efficiency positively affects the financial performance of US commercial banks. his result has multiple positive effects for various agents. First, it helps banks to achieve the target of profitability that anagers and shareholders expect. Second, it permits the policymaker to accomplish the financial stability goal. This because

he banks can achieve a given target of profitability simply by improving IC efficiency that, in turn, would allow to stay way, at least partially, to investing in assets that are particularly risky and for which should be related a good return. Stated ifferently, banks, that engage to improving IC efficiency, can reach their profit target without further increasing the riskiness f their assets. Consequently, it is not surprising whether a number of bank regulations expressly impose specific training or bank employees, including (a) Expedited Funds Availability Act; (b) Bank Protection Act; (c) Anti-money laundering AML) and Customer Identification Program (CIP); (d) Information Security Standards and, (e) Red Flag ID Theft Rules. Given his picture, it is sharable that IC efficiency allows to meeting multiple interests (bank profitability vs. financial stability) of

ultiple agents (managers, shareholders vs. policymaker) that are not easy to reach in a so challenging landscape such as anking sector.

In addition, this is also particularly important because in recent decades, due to financial liberalisation and interna- ionalisation, competition in the banking sector has grown exponentially, and with this development, pressure on bank erformance has increased. While in the recent past, banks sought to improve their profitability mainly by increasing the iskiness of their investments, as a result of the financial crisis and the more stringent rules of Basel 3, they now seek solu- ions that will improve performance without compromising their financial soundness. The results of this paper suggest that anks should not only consider how they select and mix financial assets but also develop specific aptitudes with respect to IC anagement to establish sustainable operations and increase profitability. Therefore, we can conclude that for the banking

ndustry, it has become crucial to develop effective techniques of knowledge management to accumulate and manage the C necessary to address an ever-changing environment.

In an effort to shed additional light on the relation between banks’ IC and their financial performance we decompose the C efficiency. We find that the efficiency in the use of human capital (HC) has a larger impact on financial performance than ther components of IC efficiency (i.e. structural capital). This finding, which is consistent with results reported by several tudies (among others, El-Bannany, 2008), can be explained by considering that HC management is crucial for banks and ther financial service firms. In this sense, it is worth noting that the management of customer relations and the management f financial risk are two key challenges that banks encounter. However, the effective management of both financial risks nd costumer relations by banks may not be possible without employees who possess expertise, obtained both through cademic and on-the-job training (Armenta, 2007), in these areas.

Our paper also contributes to the existing literature on the determinants of banks’ profitability (e.g., DeYoung and ice, 2004; Bonin et al., 2005; Valverde and Fernández, 2007; Albertazzi and Gambacorta, 2010). Previous papers show hat the profitability of a bank depends on both exogenous factors, such as macroeconomic conditions, bank taxation, eposit insurance regulation and banking market structure (among others, Demirgüç -Kunt and Huizinga, 1999; Albertazzi nd Gambacorta, 2010; Mirzaei et al., 2013) and bank characteristics: size, capital ratio, business models and corporate

overnance structure (among others, Aebi et al., 2012; Berger and Bouwman, 2013; Lee and Hsieh, 2013; Mergaerts and ander Vennet, 2016). We extend this literature by documenting that an effective way that banks have, to sustaining their rofitability, is to increase their IC efficiency.

66 A. Meles et al. / J. of Multi. Fin. Manag. 36 (2016) 64–74

The remainder of this paper is organised as follows. Section 2 presents the theoretical background of the study, and Section 3 explains how IC efficiency is measured. Section 4 reviews prior literature and discusses the research hypotheses. Section 5 describes the empirical strategy employed. Section 6 presents the empirical results, and a final section concludes.

2. Theoretical background: definition of IC

Over the last two decades, many definitions of IC have been proposed. Here, we examine some of the definitions most widely accepted in the literature. The seminal paper on IC is Itami (1987), who defines IC as intangible assets, such as technology, brand name, royalties, reputation and so on, that are crucial to a firm’s competitive power. Consistent with this view, Mavridis (2005) states that IC is “an intangible asset with the potential to create value for the enterprise and the society itself”; similarly, Martinez and Garcia-Meca (2005) argue that IC is “the knowledge, information, intellectual property and experience that can be put to use to create wealth”. For Brennan (2001), “IC encompasses intangibles such as patents, intellectual property rights, copyrights and franchises”. Pulic (2001), by contrast, favours the notion that IC consists of employees, their organisation and their capability to create added value. In other words, IC is an intangible asset composed of both the knowledge and know-how that characterise an organisation and give it a competitive advantage over other firms. While many definitions of IC have been proposed over the years, there appears to be a consensus regarding its components. According to the literature, IC consists of three main variables: Human Capital, Relational Capital and Structural Capital (see Petty and Guthrie, 2000; Kujansivu, 2005; among others).

2.1. Human capital (HC)

It is widely recognised in the literature that HC is composed of knowledge possessed by an organisation—knowledge that also is represented by the organisation’s employees (see, among other, Bontis et al., 2002). According to some scholars (e.g., Roos et al., 1997; Hudson, 1993), employees create IC through their competence, capabilities and intellectual agility. While competence is primarily shaped by education and capabilities develop mainly through the behaviour of employees, intellectual agility relates to the ability to solve problems with innovative solutions. Another definition of human capital comes from Sveiby (1997), according to whom HC can be defined as “the capacity to act in a wide variety of situations to create both tangible and intangible assets”.

2.2. Relational capital (RC)

RC can be defined as the complex of relationships that any organisation has with the external world, where the external world consists of customers, banks, shareholders and any other agents that may influence the organisation’s well-being. Gen- erally, it is important for an organisation to expand its relationships with stakeholders to generate access to new resources; thus, it is important to maintain its relational capital over time. Indeed, one method of measuring relational capital is based on its longevity (Bontis et al., 2002) and on strong and lasting relationships with stakeholders, as these positively affect an organisation’s competitive advantage (Håkansson and Snehota, 1995).

2.3. Structural capital (SC)

SC is defined as the complex of goods and knowledge of an organisation, including its procedures, databases, routines, hardware and organisational culture. According to Roos et al. (1997), structural capital can be defined as “what remains in the company when employees go home for the night”. Consistent with these views, examples of structural capital include copyrights, technologies, patents and so on. Bontis (1998) argues that an organisation with strong structural capital will have a supportive culture that allows individuals to try ideas, fail, learn, and try again. If the culture unduly penalises failure, the organisation’s success will be minimal.

3. Measurement of IC-based performance

IC is the sum of a firm’s hidden resources which cannot be entirely captured on the traditional accounting reports because they are difficult to evaluate. As a consequence, measuring IC value and IC-based firms’ performances represents a challenge faced by scholars and practitioners for a long time. Specifically, as highlighted by Clarke et al. (2011), several complications arise with most IC measures: (i) the required information is not available to firm outsider; (ii) the information is habitually qualitative and based on judgements; and (iii) it is hard to translate the information into monetary values.

One easily obtainable measure for IC efficiency that does not suffer from these issues as it uses only publicly available, quantitative, and audited information (Chan Hang, 2009) is the value added intellectual coefficient (VAICTM), developed by

Pulic (1998, 2004). Taking a stakeholder perspective, VAICTM can be considered as a measure of the efficiency with which a firm uses its physical, financial and intellectual capital to enhance stakeholders’ value (e.g., Clarke et al., 2011; Iazzolino and Laise, 2013). Stated differently, this method tries to capture the contribution of human, structural, physical and financial resources to create value added for an organization. The VAICTM methodology has been used in studies that analyse the

A. Meles et al. / J. of Multi. Fin. Manag. 36 (2016) 64–74 67

Table 1 The relation between HC, HCE and SCE.

HC values HCE SCE

HC > VA <1 <0

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mpact of IC efficiency on both bank and non-bank industry performance (Bornemann, 1999; Cabrita and Vaz, 2005; Chen t al., 2005).

For its computation, the VAICTM model follows a number of phases by using balance sheets and income statements. The rst phase aims to evaluate the organization’s ability in creating value added (VA). As such, it concludes by calculating the A as difference between output and input:

VAi,t = OUTi,t − INi,t (1) here, the output (OUT) is the total revenue generated by the services provided by bank to the clients; and the input

IN) includes all the expenses incurred during the production by the bank, without consider the employee costs that are reated as investment in the present model. Stated differently, the VA is calculated as sum between Profit before tax and ayroll expenses, where Profit before tax is the result of the following summation: Net Interest Revenue + Net Gains (Losses) n Trading and Derivatives + Net Gains (Losses) on Assets at fair value through Income Statement + Net Fees and Commis- ions + Remaining Operating Income − Overheads(including overall payroll) − Loan Loss Provisions. In addition, i denotes a ank (i = 1, 2, . . ., 5749), and t the time window 2005–2012. The meaning of these subscripts remains unchanged for the ther phases described below.

The second phase aims to evaluate the relationship between the VA and HC, as defined in Section 2.1. Specifically, it stimates the human capital efficiency (HCE) by showing the marginal contribution per each unit of employee expenses o the value added. The key aspect that leaves this statement makes sense is the fact that in the Pulic (2004) model the mployees expenses are processed as an investment rather than as simply cost of production. HCE, that simply captures the bility—efficiency—of HC to generate VA, is computed as follows:

HCEi,t = VAi,t HCi,t

(2)

here VA is the value added, as computed in the first phase, and HC is the human capital expenses based on bank’s overall ayroll.

The third phase aims to evaluate the relation between the VA and SC, as defined in Section 2.3. Specifically, this phase stimates the structural capital efficiency (SCE) and measures its contribution to the value added. SC is calculated by sub- racting HC from VA. Hence, as is easily untestable from the equation below, there is a reverse relation between HC and SCE. his means that a higher value of HC, the smaller value of SCE. Formally, SCE is computed as follows:

SCEi,t = SCi,t (= VAi,t − HCi,t )

VAi,t (3)

Table 1 reports the relation between HC, HCE and SCE. Specifically, the first column on the left reports the reference alues of HC and the second and third columns report its effect on HCE and SCE, respectively. The main aspect that rises by bserving the table is that as HC decreases, in relation to VA, HCE and SCE increase. In other words, this means that for a iven value of VA, the smaller is HC the much larger is the efficiency both human capital and structure capital.

The fourth phase aims to evaluate the efficiency of intellectual capital (ICE) simultaneously generated both by human apital (HCE) and structural capital (SCE) that, as will be discussed in the next paragraph, are the only two components of C efficiency that the VAICTM model is able to capture. The ICE is simply obtained by summing HCE and SCE.

ICEi,t = HCEi,t + SCEi,t (4) The fifth phase aims to evaluate the relation between VA and physical and financial capital (CA). This latter is the book value

f a bank’s net assets (i.e. the difference between the total assets and intangible assets). The idea behind this consideration is hat the IC cannot generate value added without the instruments: physical and financial capital. For this scope, the VAICTM

odel proposes to take into account also the relation between VA and CA. This relation is measured by the capital employed fficiency (CEE) that indicates the marginal contribution per each unit of physical and financial capital to the value added. tated differently, the CEE reveals the ability—efficiency—of physical and financial capital to generate value added for an rganization. Formally, the CEE is defined as follows:

CEEi,t = VAi,t CAi,t

(5)

Finally, the sixth phase concludes by estimating the VAICTM. It aims to evaluate the combined contribution that comes rom each resource to generating value added. In line with this, VAICTM is the result by summing ICE and CEE. A higher

68 A. Meles et al. / J. of Multi. Fin. Manag. 36 (2016) 64–74

VAICTM value indicates a higher capability in value creating by organization’s resources (human, structural, physical and financial capital). Formally, the VAICTM is computed as follows:

VAICTM i,t

= ICEi,t + CEEi,t (6)

3.1. VAICTM: weaknesses

We are aware that VAICTM shows several drawbacks (for details Ståhle et al., 2011; Iazzolino and Laise, 2013), mainly because the information it uses cannot be completely attributed to IC efficiency (e.g., the employees cost, which is considered to be an investment in human capital). Furthermore, Ståhle et al. (2011, p. 536) state that “the formula for structural capital and human capital contain perfect superimposition and dependency stemming from their definition”. For the authors, another issue is related to the computation of SC that seems to mistaking the use of cash flow and capitalized entities. Finally, as described in Section 2, it is widely recognised that IC is the added value created from intangibles (i.e. HC, SC, and RC) but, VAICTM does not include RC efficiency in its calculation. However, consistent with Iazzolino and Laise (2013), the main weakness point of Pulic’s proposal is not related to the VAICTM’s calculation but to its attempt to qualify VAICTM as a criterion for performance measurement that is alternative to the existing ones (e.g., Economic Value Added—EVA). In fact, as they note, rivalry among VAICTM and others performance indicators does not exist, because VAICTM measures a dimension of performance that is not considered by other traditional measures, that is the value created by IC. As such, it is not surprising that, despite its limitations, a growing number of studies have used, and still use, VAICTM (among others: Cabrita and Vaz, 2005; Chen et al., 2005; Clarke et al., 2011). Nevertheless, some researchers have tried, more recently, to overcome VAICTM

limitations by modifying the original model (e.g., Ulum et al., 2014; Nimtrakoon, 2015). In particular, in order to define a more comprehensive IC-based performance measure, Nimtrakoon (2015) has included marketing costs in the VAICTM

computation to estimate RC.

4. Literature review and research hypotheses

4.1. Literature review

HC, RC and SC are very important to an organisation, as they represent the distinctive characteristics that, ceteris paribus, determine the success or failure of an organisation relative to its peers. Thus, it is not surprising that the magnitude, impact, and role of IC in an organisation’s performance have long been investigated.

One line of research has highlighted the positive relationship between IC efficiency and organisational operating per- formance. A pivotal study is Pulic (2004), who investigates the Australian banking sector, documenting a strong role of IC efficiency in corporate success. In particular, the author observes that banks with higher levels of expenditure on IC exhibit better financial performance, e.g., in terms of profitability, than other banks. Other scholars have obtained similar results in analyses of other countries. Using a sample of commercial banks, Goh (2005) examines the relationship between IC efficiency and bank performance in Malaysia during the 2001–2003 period. With respect to the components of IC, the author finds that the efficiency in the use of HC significantly explains an organisation’s financial performance. In addition, the author reveals that domestic banks are less likely to invest in IC efficiency than foreign banks. A study, appearing two years later of Indian banks, by Kamath (2007), confirms Goh’s findings: there is a large difference in IC performance of Indian banks compared to foreign banks, with the latter achieving better financial performance. Moreover, scholars have shown that the most important component of IC is HC. Thus, HC should strongly influence organisational performance (see, among others, Goh, 2005; Kamath, 2008; Gan and Saleh, 2008; Maditinos et al., 2011; Veltri and Silvestri, 2011).

Unfortunately, although the literature has emphasised the positive effects of IC on organisational performance, some studies have challenged this hypothesis. Using the VAICTM methodology as a measure of IC efficiency, Yalama and Coskun (2007) analyse the impact of IC on bank profitability in a study of Turkish banks. On the one hand, their findings appear to confirm an explanatory role of IC efficiency in banks’ financial performance. On the other hand, the authors cannot generalise these results due to instability across their sample. In other words, they find that only a portion of Turkish banks exhibit a positive correlation between financial performance and IC efficiency. In the same way, Puntillo (2009) analyses the Italian market and finds no significant relationship between ROA (return on assets), ROE (return on equity) and IC efficiency (VAICTM). However, the worst-case scenario is found in Chang and Hsieh (2011). The authors examine the role of innovation capital on value creation in an organisation. The results document a negative relationship between capital used in innovation and organisational financial performance.

Given this literature picture, it is clear that the question of whether the efficiency in the use of IC can explain the financial performance of organisations and, in particular, the financial performance of banks remains open. This motivates the present study to undertake an empirical analysis in order to re-examine this relationship in a large and complex market—that of the US.

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.2. Hypotheses development

A body of literature (see Section 4.1) has highlighted that key to the success of an organisation is its ability—efficiency—to se and exploit IC. One of the seminal papers on this issue is Barney (1991), who observes that common organisational nancial performance measures can capture IC effects and those of the IC components, HC, RC and SC. Consequently, in this tudy, we examine the effects of IC efficiency and its components on banks’ financial performance, using ROAA (return on verage assets) and ROAE (return on average equity) as measures of financial performance. Specifically, in consideration of arney’s arguments, we hypothesise the following:

H1. IC efficiency positively influences banks’ financial performance, as measured by ROAA and ROAE. In the literature, it is widely recognised that HC, which can be summarised as the knowledge, skills, experience and

bilities of members of an organisation, is the most important component of IC (Edvinsson and Malone, 1997; Roslender and incham, 2004). If so, HC efficiency has the largest impact, relative to other IC components, on banks’ financial performance.

Following these indications, the next hypothesis can be summarised as follows: H2. HC efficiency positively influences banks’ financial performance, as measured by ROAA and ROAE, and has the largest

mpact, relative to other IC components, on banks’ financial performance.

. Empirical analysis

In this section, we provide detailed information about the data, the sample and the variables used in the empirical analysis.

.1. Data and sample description

To test our research hypotheses, we have constructed a very large sample of 5749 US commercial banks with a time indow that spans across 2005–2012, covering over 40,000 observations. More specifically, the data useful to compute our easures of IC efficiency, dependent and independent variables come from the Bankscope Bureau Van Dijk, which contains

nformation mainly related to bank balance sheets.

.1.1. Dependent variables

To measure the profitability of banks, we use two proxies that are widely utilised in the financial literature (see, among thers, Chen et al., 2005; Shiu, 2006; Chang, 2007; Gan and Saleh, 2008; Ting and Lean, 2009; Pal and Soriya, 2012; Besharati t al., 2012), which can be summarised as follows:

) ROAA (Return on Average Assets): measured as the ratio of net income to average total assets recorded over one year; ) ROAE (Return on Average Equity): measured as the ratio of net income to average-equity computed as the sum of equity

value at the beginning and end of each year, divided by two.

.1.2. Independent variables

To address our first hypothesis (H1), we use, as a proxy for IC efficiency, the variable VAICTM, computed as described in ection 3. In the model, we control for five variables: size, measured as the natural logarithm of a bank’s total assets; loan oss provisions on total loans, which is an indicator of credit risk that shows how much a bank provisions in year t relative o its total loans; a measure of liquidity, calculated as total loans to total assets and indicating what percentage of the assets f a bank are tied up in loans in year t; and the GDP growth rate between two consecutive years. Furthermore, in an effort o capture additional factors at the regional level that could affect bank financial performance, we add the variable State, a ummy variable set to 1 if the headquarter of the bank is located in the corresponding State and 0 otherwise. In line with our ypothesis (H1), we expect the coefficient for VAICTM to be positive and significant. Following researchers (e.g., de Pablos, 003; El-Bannany, 2008) who have analysed the impact of IC efficiency on organisational performance and in accordance ith our hypothesis (H2), we split the VAICTM variable into its sub-components. Using the same control variables used to test ypothesis H1, we expect HC efficiency to have the largest impact on financial performance compared to other IC efficiency omponents.

.2. Empirical models

This section introduces the econometrical models that we refer to test the hypotheses previously presented. In particular, e specify two linear models, one for each hypothesis, that investigate how IC efficiency and its subcomponents determine

he banks’ performance. Model 1 tests H1, where the dependent variable yi,t (i.e., ROAA i,t and ROAE i,t) is a function of the

ggregate measure of IC efficiency (VAICTM) and various other bank-specific characteristics, which are summarised in the ndependent variables section and Table 2. Leaving unchanged the meanings of the control variables and the dependent ariables, we test hypothesis H2 using Model 2. Specifically, we split the variable VAICTM into its main sub-aggregates: HCE nd SCE.

70 A. Meles et al. / J. of Multi. Fin. Manag. 36 (2016) 64–74

Table 2 Variables.

Variables Description 1 2 3 4 5 6 7

1 VAICTM IC efficiency measurea ; 1.0000 2 SCE Structural Capital

efficiencya ; – 1.0000

3 HCE Human Capital efficiencya ; – 0.0013 1.0000 4 LLP/L Loan loss provisions on

total loans. An indicator of credit risk, showing how much bank provisions in year t relative to its total loansa ;

0.0099 0.0000 0.0096 1.0000

5 LOANS/TA A measure of liquidity, calculated as total loans/total assets. The ratio indicates what percentage of the assets of the bank are tied up in loans in year ta ;

−0.0080 −0.0026 −0.0046 −0.0182 1.0000

6 SIZE The natural logarithm of the accounting value of the total assets of a bank in year ta ;

0.0709 −0.0028 0.0723 0.0027 0.2065 1.0000

7 GDP GDP growth rate between two consecutive yearsb ;

0.0335 0.0126 0.0331 0.0010 −0.0565 −0.0273 1.0000

STATE Dummy variables, equal to 1 if a bank’s headquarters is located in the corresponding State and zero otherwisea

Source: a Bankscope Bureau Van Dijk database; b World Bank data. This table shows the variable description and Pearson pairs-wise correlation matrix. Bold texts indicate statistically significant at 1% level or more.

Model 1

yi,t = ̨ + ˇ1VAICTMi,t + ıXi,t + εi,t Model 2

yi,t = ̨ + ˇ1HCEi,t + ˇ2SCEi,t + ıXi,t + εi,t The subscript i denotes a bank (i = 1, 2, . . ., 5,749), the subscript t denotes the time dimension (time window 2005–2012),

X is a vector of control variables and ε is the error term.

6. Results

The purpose of this paper is to investigate the impact of IC efficiency, measured through VAICTM methodology and its sub-components, such as HCE and SCE, on US commercial banks’ performance. Table 3 reports descriptive statistics for the IC efficiency measures, banks’ financial performance and some variables related to bank characteristics, referred to the time period 2005–2012.

Does IC efficiency positively influence banks’ financial performance? If so, does HC efficiency have a greater impact compared to others subcomponents?

Table 4 reports estimation results for models constructed on the basis of models 1 and 2 using pooled OLS for panel data. The table contains 8 columns, each presenting the results of a test of one of our hypotheses. More specifically, columns 1 and 5 address hypothesis H1, with dependent variables, ROAA and ROAE, respectively. In an effort to understand whether location in a different State affects a bank’s financial performance, we also replicate the analysis without the dummy variable State (columns 2 and 6). The results do not differ significantly to the previous one. Columns 3 and 7 address hypothesis H2, while columns 4 and 8 address the same hypothesis but without the State dummy.

We conduct the multivariate analysis by controlling for various (internal and external) factors, such as LLP/L, LOANS/TA, SIZE and GDP, that the literature commonly treats as explanatory variables with respect to banks’ financial performance. Additionally, we add variables—namely, VAICTM, HCE, and SCE—that mainly allow testing our hypotheses.

Below, we discuss the signs and the relationships of these variables to banks’ financial performance and related theoretical interpretations.

First, the coefficient for VAICTM is positive and significant (see Table 4, columns 1, 2, 5 and 5). This result is fully in line with our expectations and confirms hypothesis H1: IC efficiency positively affects and helps explain bank financial performance.

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Table 3 Descriptive statistics.

Variable Year Obs. Mean Stand. dev. Percentile

25% 50% 75%

Total Assets (th. $) 2005–2012 45,992 1,688,691 3.41e + 07 67,687 139,982.5 310,086.5 2005 5749 1,163,213 2.30e + 07 53,828 109,545 245,609 2006 5749 1,331,053 2.71e + 07 58,601 119,430 266,631 2007 5749 1,520,386 3.10e + 07 62,968 130,353 284,112 2008 5749 1,725,146 3.58e + 07 67,403 141,082 311,448 2009 5749 1,752,233 3.47e + 07 71,814 150,093 332,378 2010 5749 1,878,527 3.69e + 07 74,641 154,290 338,549 2011 5749 2,013,080 3.94e + 07 77,381 158,781 352,555 2012 5749 2,125,893 4.11e + 07 81,589 167,108 367,042

ROAA (%) 2005–2012 45,992 0.901 2.940 0.455 0.883 1.297 2005 5749 1.149 2.401 0.751 1.086 1.461 2006 5749 1.188 2.283 0.719 1.067 1.466 2007 5749 1.178 3.160 0.635 1.008 1.395 2008 5749 0.807 3.251 0.335 0.796 1.230 2009 5749 0.465 2.715 0.092 0.619 1.071 2010 5749 0.649 2.730 0.258 0.701 1.142 2011 5749 0.807 3.346 0.373 0.782 1.198 2012 5749 0.961 3.335 0.479 0.863 1.256

ROAE (%) 2005–2012 45,992 7.548 13.620 4.223 8.278 12.642 2005 5749 11.092 8.720 7.031 10.655 14.788 2006 5749 11.037 8.584 6.671 10.442 14.757 2007 5749 10.122 9.906 5.821 9.583 13.762 2008 5749 6.806 12.664 3.071 7.490 11.729 2009 5749 3.415 15.889 0.898 5.843 10.186 2010 5749 4.806 15.752 2.487 6.695 11.005 2011 5749 5.803 16.416 3.449 7.294 11.415 2012 5749 7.302 15.696 4.427 7.795 11.798

VAICTM

2005–2012 45,959 2.168 27.910 1.637 2.141 2.641 2005 5745 2.365 13.861 1.951 2.365 2.836 2006 5746 2.330 11.281 1.895 2.330 2.800 2007 5744 2.409 5.683 1.802 2.251 2.708 2008 5743 1.892 9.898 1.513 2.035 2.541 2009 5746 0.844 74.563 1.293 1.884 2.430 2010 5743 2.205 8.0513 1.437 1.967 2.523 2011 5746 2.323 11.589 1.544 2.040 2.556 2012 5746 2.158 4.549 1.653 2.114 2.597

HCE 2005–2012 45,986 1.802 5.573 1.317 1.675 2.057 2005 5749 2.163 11.501 1.539 1.852 2.222 2006 5749 2.076 5.660 1.513 1.831 2.199 2007 5748 1.972 4.700 1.449 1.774 2.125 2008 5747 1.665 3.593 1.227 1.598 1.976 2009 5749 1.396 3.465 1.043 1.461 1.853 2010 5747 1.598 3.959 1.172 1.535 1.935 2011 5749 1.721 3.390 1.255 1.600 1.990 2012 5748 1.822 3.087 1.342 1.666 2.034

SCE 2005–2012 45,984 0.335 27.331 0.273 0.420 0.530 2005 5748 0.622 7.714 0.364 0.468 0.559 2006 5749 0.577 9.765 0.342 0.456 0.549 2007 5748 0.401 3.127 0.314 0.438 0.533 2008 5747 0.198 9.180 0.228 0.392 0.512 2009 5749 −0.576 74.446 0.179 0.373 0.517 2010 5746 0.579 7.052 0.214 0.383 0.520 2011 5749 0.573 11.101 0.240 0.396 0.518 2012 5748 0.304 3.251 0.269 0.407 0.518

LOANS/ASSETS (%) 2005–2012 45,992 61.972 16.193 52.900 64.426 73.624

2005 5749 62.385 16.698 80.903 64.910 74.346 2006 5749 63.735 15.983 55.318 66.464 75.019 2007 5749 64.667 16.012 56.262 67.258 76.079 2008 5749 65.128 16.260 56.600 68.151 76.818 2009 5749 62.898 15.655 54.582 65.691 74.051 2010 5749 60.900 15.417 52.379 63.219 71.937 2011 5749 58.581 15.690 49.682 60.604 69.905 2012 5749 57.487 16.125 47.870 59.229 69.284

This table contains descriptive statistics of the banks’ profitability, size and IC efficiency measures. The statistics are provided for each year and for the whole 2005–2012 time period, for all the sample of commercial banks.

72 A. Meles et al. / J. of Multi. Fin. Manag. 36 (2016) 64–74

Table 4 Results from OLS regressions starting from Model 1.

(1) ROAA t (2) ROAA t (3) ROAA t (4) ROAA t (5) ROAE t (6) ROAE t (7) ROAE t (8) ROAE t

VAICTM t 0.113*** 0.113*** 0.536*** 0.556***

(5.14) (5.19) (3.49) (3.57) HCE t 0.109*** 0.110*** 0.526*** 0.546***

(4.88) (4.95) (3.53) (3.61) SCE t −0.000 0.000 −0.001 −0.000

(−0.08) (0.08) (−0.37) (−0.11) LLP/L t 0.037*** 0.039*** 0.037*** 0.039*** 0.008 −0.016 0.009 −0.015

(8.61) (8.64) (8.65) (8.70) (0.17) (−0.32) (0.19) (−0.30) LOANS/TA t −0.693** −0.832*** −0.697** −0.834*** 3.207*** 0.197 3.187*** 0.189

(−2.44) (−3.29) (−2.44) (−3.29) (3.88) (0.27) (3.83) (0.25) SIZE t 0.053*** 0.007 0.054*** 0.008 0.762*** 0.179*** 0.765*** 0.180***

(6.35) (0.96) (6.20) (0.99) (11.80) (3.03) (12.03) (3.08) GDP t 0.076*** 0.075*** 0.077*** 0.075*** 0.892*** 0.865*** 0.894*** 0.867***

(12.62) (12.37) (12.72) (12.47) (24.97) (23.50) (25.16) (23.67) STATE Yes No Yes No Yes No Yes No cons 0.181 0.933*** 0.179 0.937*** −6.310*** 2.917*** −6.309*** 2.947***

(1.21) (6.95) (1.21) (7.00) (−8.25) (4.57) (−8.31) (4.65) Obs. 45,657 45,657 45,676 45,676 45,657 45,657 45,676 45,676 AdjR2 0.0875 0.0734 0.0837 0.0695 0.1227 0.075 0.1208 0.0686 Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Notes: Panel regression analysis of banks’ financial performance of a sample of 5749 commercial banks is reported. Dependent variables: ROAA is defined as ratio of the net income divided by the average total assets recorded over one year and ROAE is defined as ratio of net incomes divided the average-equity computed as the sum of the equity value at the beginning and end of each year, divided by two. VAICTM is the IC efficiency measure; HCE is a measure of human capital efficiency; SCE is a measure of structural capital efficiency; LLP/L is the ratio loan loss provisions on total loans and is an indicator of credit risk, which shows how much a bank is provisioning in year t relative to its total loans; LOANS/TA is a measure of liquidity, calculated as total loans on total assets. The ratio indicates what percentage of the assets of the bank is tied up in loans in year t; SIZE is the natural logarithm of the accounting value of the

total assets of the bank in year t; GDP is the GDP growth rate between two consecutive years; STATE is a set of dummy variables each equal to 1 if the bank’s headquarter is located in the corresponding State and zero otherwise. * , ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively.

Thus, this paper suggests that banks should not only consider how they mix financial assets but also develop an aptitude for IC management, enabling them to achieve sustainable operations and thereby increase profitability.

Second, the results are also in line with the hypothesis H2. Specifically, the sub-aggregate HCE of VAICTM has the largest impact on financial performance among other ones (see Table 4, columns 3, 4, 7 and 8). This finding, which is consistent with El-Bannany (2008), can be explained by reference to the fact that HC management is a key point in the banking industry. Stated differently, it is essential for banks engage employees with strong expertise in this area, both in terms of academic training and on-the-job skills (e.g., Armenta, 2007).

Although the models proposed in our analysis are widely used in literature to test the banks’ performance (e.g., Pasiouras and Kosmidou, 2007), we perform an additional tests to control for endogeneity, i.e. the reverse causality between depen- dent and independent variables, that could affect our results. The robustness check is conducted by using one time lagged independent variables. Specifically, as previous one, Table 5 contains 8 columns, each presenting the results of a test of one of our hypotheses.

Overall, as shown in Table 5, the results are resilient and completely in line with those presented and discussed in Table 4. Specifically, VAICTM coefficient takes positive sign and is highly significant. In the same way, the coefficient and significance of HCE supports the hypothesis H2.

7. Conclusions

In this study, we have provided empirical evidence regarding the contribution of IC efficiency and its sub-components to explain banks’ financial performance, using the US market as an experimental setting. IC efficiency of banks was measured using the VAICTM methodology. The study was conducted on a sample of 5749 US banks over the time period 2005–2012. Overall, the empirical findings, which are based on multivariate regressions of conventional banks’ financial performance measures on VAICTM, using panel data with over 40,000 observations, highlight that IC efficiency plays an important role in bank performance. However, when the measure of IC efficiency is decomposed into its sub-components, the efficiency of HC, in particular, is shown have a major positive effect on banks’ returns.

Additionally, we find that State dummies have no particular impact on profitability. In other words, the coefficients and significance levels of the independent variables are invariant with respect to the inclusion of State dummies.

Our analysis may have important implications for bank managers and policymakers. While the former seek to improve

banks’ financial performance, the latter seek to ensure that such efforts do not inordinately increase the risk that banks take on. Specifically, the findings of this paper suggest that the development by banks of effective techniques of knowledge management, enabling them to accumulate the IC necessary to address an ever-changing environment, can represent an effective means of achieving the goals of both bank managers and policymakers.

A. Meles et al. / J. of Multi. Fin. Manag. 36 (2016) 64–74 73

Table 5 Results from OLS regressions starting from Model 1: One time lagged independent variables.

(1) ROAA t (2) ROAA t (3) ROAA t (4) ROAA t (5) ROAE t (6) ROAE t (7) ROAE t (8) ROAE t

VAICTM t-1 0.098*** 0.099*** 0.411*** 0.436***

(4.14) (4.28) (3.88) (3.88) HCE t-1 0.095*** 0.096*** 0.402*** 0.428***

(3.90) (4.05) (3.95) (3.94) SCE t-1 −0.000 −0.000 −0.002 −0.001

(−1.13) (−0.54) (−1.48) (−0.76) LLP/L t-1 0.054*** 0.055*** 0.054*** 0.055*** 0.114*** 0.090*** 0.115*** 0.091***

(24.47) (24.21) (24.61) (24.43) (4.79) (3.37) (4.83) (3.42) LOANS/TA t-1 −1.102*** −1.218*** −1.105*** −1.220*** −1.056 −3.984*** −1.071 −3.985***

(−3.43) (−4.26) (−3.42) (−4.25) (−1.11) (−4.66) (−1.13) (−4.65) SIZE t-1 0.042*** −0.002 0.043*** −0.001 0.682*** 0.093* 0.685*** 0.094*

(4.33) (−0.20) (4.26) (−0.15) (11.27) (1.66) (11.44) (1.68) GDP t-1 0.078*** 0.076*** 0.078*** 0.077*** 0.917*** 0.890*** 0.919*** 0.891***

(13.22) (12.92) (13.25) (12.96) (25.46) (23.90) (25.60) (24.01) STATE Yes No Yes No Yes No Yes No cons 0.547*** 1.295*** 0.545*** 1.299*** −3.116*** 6.422*** −3.126*** 6.445***

(3.33) (8.68) (3.32) (8.72) (−3.87) (9.38) (−3.91) (9.47) Obs. 39,949 39,949 39,949 39,949 39,949 39,949 39,949 39,949 AdjR2 0.0772 0.0633 0.0742 0.0602 0.1070 0.0532 0.1057 0.0519 Prob > F 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Notes: Panel regression analysis of banks’ financial performance of a sample of 5749 commercial banks is reported. Dependent variables: ROAA is defined as ratio of the net income divided by the average total assets recorded over one year and ROAE is defined as ratio of net incomes divided the average-equity computed as the sum of the equity value at the beginning and end of each year, divided by two. VAICTM is the IC efficiency measure; HCE is a measure of human capital efficiency; SCE is a measure of structural capital efficiency; LLP/L is the ratio loan loss provisions on total loans and is an indicator of credit risk, which shows how much a bank is provisioning in year t relative to its total loans; LOANS/TA is a measure of liquidity, calculated as total loans on total a t h

w s b

R

A

A A A B B B

B B B B B B C C

C

C C

C C C

d D D D

E

ssets. The ratio indicates what percentage of the assets of the bank is tied up in loans in year t; SIZE is the natural logarithm of the accounting value of the otal assets of the bank in year t; GDP is the GDP growth rate between two consecutive years; STATE is a set of dummy variables each equal to 1 if the bank’s eadquarter is located in the corresponding State and zero otherwise. * , ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively.

We are aware that our study could be affected by bias before any generalisation of the results can be made. Specifically, e conduct empirical tests on a large US sample, which raises the question: what about other countries? Further research

hould be undertaken in other countries to obtain a more generalizable result and to capture differences that may exist etween different countries.

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