business strategy bank operation and the role of intellectual capital development luận án tiến sĩ tài chính ngân hàng

Second, when performing the VAIC-separated model, the evidence indicates that while capital employed efficiency becomes the most important component to foster financial intermediation ac

INTRODUCTION

Research motivation and context

It could be said that today’s world has faced increasing threats of the poly-crisis such as the disruption of the global supply chain, soaring energy prices and inflation rates, and geopolitical risk (Lu & Luong, 2022; Phan et al., 2022b; Yusoff et al., 2019) This prospect underscores that most organizations have to find effective ways to sustain their buoyant markets In this case, tapping into knowledge-based resources can be seen as an essential preparedness to open new roads for modern companies because it assists an organization in achieving competitive advantage and stable development (Alvino et al., 2020; Suciu & Năsulea, 2019) This assertion is also true in the banking industry, which usually depends much on intellectual resources rather than physical capital to provide suitable products and services to fulfill the changing needs of their customers and succeed in competitiveness (Adesina, 2019) To some extent, intellectual capital (IC) may be an effective signal to evaluate the degree of banks’ performance compared with their rivals (Meles et al., 2016; Stewert, 1999)

Unsurprisingly, scholars and regulators have recently paid much attention to implementing IC in banking operations While the number of studies in this field has focused mainly on the correlation between IC and banks' productivity (e.g., Alhassan & Asare, 2016; Yalama, 2013; and among others), IC and one’s profitability (e.g., Le & Nguyen, 2020; Poh et al., 2018; and among others), IC and ones’ risks (e.g., Dalwai et al., 2021; Nguyen et al., 2021; and among others), or technical, allocative and cost efficiencies (e.g., Adesina, 2019;

Le et al., 2022), the aspect of financial intermediation in the banking system as well as the link between IC and one of the most critical aspects in banking operations, namely total non- interest incomes, seem to remain an undiscovered area

With that in mind, this research is to tackle the key issue of whether the implementation of IC has fostered the financial intermediation and total non-interest incomes in the banking system or not In this sense, Vietnam may provide one of the ideal countries for finding a clear answer and filling this vital gap in the literature for the following reasons

Vietnam's economic growth has witnessed a fast pace in the ASEAN region, and it is hoped that this country will be the next tiger in Asia (Le, 2021; Le & Nguyen, 2020)

According to the statistics from World Bank Data, from 2006 to 2019, the GDP growth of Vietnam nearly stood at an average of around 6.5% before dropping to about 2.9% in 2020 due to the adverse consequences of COVID-19 Notwithstanding, since the financial market seems to remain undeveloped, sustainable economic growth and development have relied mainly on the effective operations of the banking system, which is also deemed as the backbone of the Vietnamese economy (e.g., Le et al., 2022; Le & Nguyen, 2020; Phan et al., 2022b)

The statistics revealed by World Bank Data illustrate that the economic indicators of domestic credit over the private sphere supported by banks jumped from about 65% to over 116% during the 2006-2020 period This remarkable escalation means that the development of the banking sector plays an imperative role in ensuring and fostering the growth of the Vietnamese economy Therefore, growing financial intermediation activities have become one of the significant factors in ensuring sufficient economic resources and sustaining the developing economy Additionally, the Vietnamese banking market may become fiercely competitive, resulting from the appearance of foreign banks, followed by participating in the World Trade Organization in 2007 (Huynh & Dang, 2021; Le & Nguyen, 2020; Phan et al., 2022a)

Consequently, domestic banks have to find new ways to adapt to changing conditions In this regard, digging more into intellectual resources may be an underlying business strategy to thrive in their business activities Indeed, it is witnessing an increasing evolution from the traditional paradigm of strategy to the knowledge-based paradigm, which deems IC as the driving force of sustainable value creation and sustainable competitive advantage (Alvino et al., 2020; Khan et al., 2019; Marr & Roos, 2012) In this case, the business strategy construction of Vietnamese banks is not the exception, and hence, managers and decision- makers in domestic banks cannot ignore IC perspective in both building their business strategy and managing business operations

On the other hand, as Greenbaum et al (2019) implied, banks traditionally act as financial producers and servicers; they are regularly forced to provide up-to-date products to satisfy their customers' demands, by which they can flourish in today's economic climate Such this scenario underlines the primary driver of IC in generating effective remedies for these requirements In short, discovering the link between IC and financial intermediation in the banking industry would be a requisite study for not only the Vietnamese banking sector but also other emerging countries where the sound operations of banks are seen as the prerequisite for economic development

In addition, the dissertation opts for non-interest incomes as the key objective to explore the effect of IC on the business operations of banks for some main reasons First and foremost, the transformation from traditional incomes into non-interest incomes, including commissions, fees, and trading incomes, is seen as one of the essential strategies driving the operations of banks in the modern economy these days (Bian et al., 2015) Indeed, the shift towards non-traditional sources of income could bring certain benefits for banks, from reducing risk and funding costs (Tran, 2020) to enhancing profitability (Mostak Ahamed, 2017) Furthermore, in tandem with the endeavors to ensure the smoothness and sustainability of the banking sector, some policies have been carried out by the Vietnamese government to motivate domestic banks to further widen their businesses into new areas to seek out new sources of income, leading to enlarge the non-interest income activities of banks (Dang, 2020; Huynh & Dang, 2021) This direction is also quite suitable to the context of increasing intensive competition in the bank market which emerges newcomers such as foreign banks, Fintech firms, and other non-bank institutions (Le & Nguyen, 2020) (see more detail in subsection 3.4.1 of Chapter 3) As a result, taking advantage of IC may become an appropriate business strategy for banks to thrive in such situation in the coming times

At the same time, such this orientation has required banks to possess necessary capabilities of technology, expertise, and human resource (Mostak Ahamed, 2017) In this vein, the emergence of IC would become the bridge to fill these preconditions In addition, to deal with the aftermath of the global financial crisis, operations of the banking industry in particular and the financial system, in general, have been constrained by tighter regulations because such financial deregulation may encourage banks to join more casino-style gambling, leading to growing instability (Tran, 2020) Hence, banks have to navigate their business strategies beyond traditional activities to achieve higher profitability This situation highlights the vital role of both IC and non-interest incomes in banks; thus, discovering the correlation between these factors would provide a major insight into banking operations, especially in emerging markets where the banking system is considered the backbone of the economy

Taken together, it is hoped that the dissertation, namely “Business strategy, bank operation, and the role of intellectual capital development” will may shed new light on the role of IC in both financial intermediation and non-interest income activities and provide valuable helpfulness for not only academicians but also policy-makers in the banking sector.

Main purposes of the dissertation

In general, the dissertation is to conduct an empirical investigation into the role of intellectual capital in financial intermediation and non-interest income activities of Vietnamese domestic banks In this regard, the study will point out the theoretical background to advocate these main concerns before performing an empirical analysis By leaning on the findings, the dissertation is also expected to provide useful implications for managers and decision-makers of Vietnamese banks

In particular, the major purposes of the current study are to focus mainly on some key aspects as follows:

First, one of the scientific endeavors of the dissertation is to elucidate the theoretical foundation of the pivotal role of IC in business operations as well as strategies of banks

Next, the present study aims to determine the association between IC efficiency and financial intermediation activities as well as IC efficiency and non-interest incomes based on the context of the Vietnamese banking industry

Moreover, other effort of the writer is to explore the extent to which the different components of IC exert both financial intermediation activities and non-interest incomes of the Vietnamese commercial banks

In addition, the study also evaluates the role of the bank size factor in the link between

IC, its components, and financial intermediation as well as the nexus between IC, its components, and non-interest incomes of the Vietnamese commercial banks

Eventually, by leaning on the empirical findings, the present research endeavored to propound some major implications under both theoretical and practical views for not only academicians but also policy-makers as well as banking leaders

In other words, as stated early, the current dissertation is carried out to answer the following straightforward questions of:

(i) How does the theoretical background advocate the relationship between IC and financial intermediation activities as well as non-interest incomes?

(ii) Whether does IC play a crucial role in fueling non-interest income activities of

Vietnamese domestic banks or not?

(iii) Which components of IC play a crucial role in fueling the non-interest incomes of Vietnamese domestic banks?

(iv) Whether does IC play a key role in fostering the activities of financial intermediation of Vietnamese domestic banks or not?

(v) Which components of IC capital play a key role in fostering the activities of financial intermediation of Vietnamese domestic banks?

(vi) Whether are the impacts of IC and its components on financial intermediation and non-interest incomes certain differences between large and small banks or not?

To reach clear explanations for these major concerns, the dissertation would perform an empirical investigation to find out the evidence In particular, the author would examine the impact of intellectual capital on total non-interest incomes as well as on financial intermediation activities of banks Due to the importance of these business operations, the findings in the dissertation would provide the implications for multi-stakeholders in Vietnam and, perhaps, other emerging countries, especially against the backdrop of increasingly fierce competition resulting from the technological era.

Research objectives and scope of the dissertation

For the research objectives of the dissertation, as mentioned previously, the study would emphasize the role of intellectual capital in business operations of commercial banks in Vietnam, specifically the non-interest incomes and financial intermediation activities As highlighted in subsection 1.1, these research objectives are selected as the chief purposes of the dissertation since they may play an essential role in building business strategies of local banks as well as the growth of economy in Vietnam these days

First of all, as an emerging country, the economic growth in Vietnam nearly depends on the expansion and sustainable development of the banking system (e.g., Le et al., 2022; Le & Nguyen, 2020; Phan et al., 2022b) Hence, the evaluation of the impact of intellectual capital on the financial intermediation of domestic banks would provide a deep insight into one of the main engines for the expansion of banking activities and economic growth in Vietnam

In addition, the transformation from traditional incomes such as lending interests into non-traditional incomes may become one of crucial business strategies of banks against the backdrop of increasingly competitive market resulting from the emergence of foreign banks, rapid changes in technological innovation, tightening regulations, and so on (e.g., Bian et al., 2015; Huynh & Dang, 2021; Mostak Ahamed, 2017) Therefore, the investigation into the role of intellectual capital in these operating aspects of banks may bring beneficial values to bank managers Taken together, these reasons have necessitated conducting the research to point out the correlations between implementing IC and both non-interest incomes and financial intermediation strategies of domestic banks in Vietnam

In this regard, the research scope of this study would focus on some main items as follows:

For the space scope of the thesis, the author would concentrate on the domestic banks in Vietnam The financial information of each bank would be gathered from the audited financial statements and the notes to the financial statements There are some main reasons why the research has focused mainly on commercial banks in Vietnam to carry out the empirical investigation First, it allows the current analysis to achieve a relatively homogenous research sample (Adesina, 2019) At the same time, it is argued that compared with commercial banks, joint-venture and foreign-controlled ones seemingly account for a relatively small proportion (Huynh & Dang, 2021) Meanwhile, the business operations of non-profit banks seemingly differ from commercial banks (Huynh & Dang, 2021) Taken together, the current research will pay special attention to commercial banks to conduct the empirical analysis

For the time scope of the thesis, the author would select the study period spanning from

2006 to 2020 The period is chosen because it has witnessed many changes in the banking system in Vietnam, including regulations, structures, the emergence of foreign banks, and technology-based orientations (e.g., Huynh & Dang, 2021; Le & Nguyen, 2020; Phan et al.,

2022a; Phan et al., 2022b; Tran, 2022) At the same time, many prominent events have emerged during the same period in the Vietnamese economy (see the detailed analysis in Chapter 3) Thus, to some extent, the findings would draw the general picture of the role of

IC in banking operations, specifically non-interest incomes and financial intermediation activities.

Methodology of the dissertation

In order to tackle the aforementioned concerns and achieve the research objectives, the current research has opted for the quantitative methodology to figure out the extent to which

IC efficiency as well as its elements exert the financial intermediation activities and total non- interest incomes of banks

More specifically, the study utilizes the database of 26 domestic banks in Vietnam during the 2006-2020 period Also, many different regression approaches are performed, including OLS, Fixed-time effect, and GMM estimation, as well as controlling specific characteristics of banks and macro conditions These approaches are still important because they can help to ensure that the research findings withstand various robustness tests conducted It is true that the empirical results of the dissertation still survive after the robustness test stages are being employed, meaning that the findings are quite feasible and plausible Also, the research sample divided into different subsamples based on the bank size will be examined to provide an understanding of the role of IC in both large banks and small ones

At the same time, the dissertation utilizes the prior findings of previous authors in the existing literature to calculate the main explanatory variables including IC efficiency and its ingredients as well as to build the empirical models Accordingly, to estimate the IC efficiency of domestic banks, following many extant studies (for example: Adesina, 2019; Le et al., 2022; Nguyen et al., 2021; Ozkan et al., 2017; Poh et al., 2018, and among others), the study employs VAIC model (the value-added intellectual coefficient model) propounded and developed by Pulic (1998, 2000, 2004) At the same time, VAIC is being separated into three main ingredients being capital employed efficiency (CEE), human capital efficiency (HCE), and structure capital efficiency (SCE) to evaluate the impacts of these components Additionally, the pros and cons of this approach are analyzed clearly and the reasons why the dissertation has chosen this model in Chapter 3, besides these limitations are also stated in the final chapter as one of the research gaps and research directions in the future

Furthermore, the dissertation also controls a variety of variables including bank- specific characteristics and macroeconomic conditions Adding these variables into the analysis model may give a deeper insight into the research findings and, to some extent, they can play a part role in reexamining the results These variables consist of bank size, capital ratio, loan loss reserve ratio, income ratio, GDP growth ratio, and inflation ratio, which have been used by many financial studies in the recent years (e.g., Adesina, 2019; Le & Nguyen, 2020; Nazir et al., 2021; Tran et al., 2021; and among others)

Besides, regarding the first dependent variable, financial intermediation in banks, although there are certain debates on measuring financial intermediation, the ratio of total loans to total deposits is still popular in the banking literature (Boďa & Zimková, 2021) Some existing views scrutinize that macroprudential policies should be built around this

“descriptive indicator” (Satria et al., 2016; Van den End, 2016) In this research, this indicator will be used as the first dependent variable in the analysis models In addition, regarding the second dependent variable, the current study will use the (natural logarithm of) total non- interest incomes (NII) of banks to estimate non-interest incomes of banks, which may mirror most of the non-interest sources of income of banks, from relevant fees and commissions to trading securities, in a direct and absolute way (Phan et al., 2022a; Phan et al., 2022b) Hence, this indicator will be applied as the second dependent variable in the analysis models.

Key contributions of the dissertation

The research is anticipated to contribute to the knowledge gap in this field in the following ways i) Regarding the theoretical view, first and foremost, to the best of the writers' horizon, the research can be seen as the first empirical investigation into the correlation between

IC and banks' financial intermediation as well as the non-interest incomes, at least in the Vietnamese context and perhaps, other developing countries Indeed, the vast majority of related papers emphasize the connection between IC and some business aspects such as productivity, profitability, risk-taking, or technical, allocative, and cost efficiencies (Adesina, 2019; Le et al., 2022; Le & Nguyen, 2020; Meles et al., 2016; and among others), the research makes a difference to the extant studies by digging more into the influence of IC on increasing financial intermediation activities and non- interest incomes in the banking sector ii) Moreover, most studies in this area mainly take the context of the industrialized countries to perform, notably US and China which are considered the most influential economies globally (Alvino et al., 2020) Hence, by conducting an investigation in Vietnam, which has been seen as a pivotal part of the Southeast Asia region and the Asia region as a whole, the study will bring more profound insight into the driver of

IC in rebuilding business strategies of domestic banks to not only economists but also decision-makers in developing economies iii) In addition, as the research conducted by Poh et al (2018) indicated, different results when investigating the impact of IC in the banking industry may come from various measures of banking performance and periods chosen By selecting financial intermediation activities and non-interest incomes as the main research objectives along with the period covering from 2006 to 2020, the author would dig more into the impact of IC on various dimensions of business operations in banks, and therefore, the author adds more illumination to prior findings such as Le et al (2022); Le & Nguyen (2020); Nguyen et al (2021); Poh et al (2018) and among others iv) Besides, the findings of the dissertation also give helpful evidence to shed new light on some important theories such as the resource-based theory, the intellectual capital theory and the knowledge-based theory that underscore a crucial aspect of implementing IC in business management of enterprises these days to stay ahead of the curve in the increasingly intensive competition In other words, the results in the research will contribute to the profound understanding about various theoretical studies in the IC field such as Dierickx & Cool (1989); Harris (2000); Khalique et al (2013); Penrose & Penrose (2009) and among others v) Under other related angle, the dissertation conducted is to respond to the previous calls of Alvino et al (2020); Suciu & Năsulea (2019); Vătămănescu et al (2019) who assert that the urgent need to explore more impacts of IC on different aspects in business operations of modern enterprises Because the center role of IC is increasingly recognized in both academic and practical perspectives, the empirical examination of the study will provide the compelling evidence in advocating this assertation vi) Last but not least, because the transformation from traditional incomes to non- traditional activities is seen as the necessary step in constructing business strategies, and implementation of IC may be the key to opening up new roads for domestic banks in the coming future, the findings would provide certain implications for scholars and regulators in Vietnam and perhaps in other developing markets On the other hand, while the expansion of financial intermediation activities can be seen as the key of banking operations, supporting the allocation of the economic resources and fueling the economic growth of Vietnam, based on the findings, the study will provide productive implications for managers in navigating and expanding these activities.

Structure of the dissertation

As mentioned before, this chapter focuses on some main points including the motivations, the research objectives, the research questions, the approached methodology, and the key contributions of the dissertation In the following chapters, the current study will detail these contents In general, the outline of the study can be summarized as follows

Chapter 2, namely ‘Theoretical background and literature review’, will first provide the concepts and the measure methods of intellectual capital The mentioned information is quite important because it helps to shed light on the ethos, the definition, and the value embedded into IC that many theorists have endeavored to discover At the same time, it shows clearly the ways numerous methods developed are to offer a precise quantification of IC performance in the extant literature After that, this chapter also analyzes the theoretical background and the relevant studies in the banking industry, which in turn will assist to develop the related hypotheses Basically, this chapter is expected to create the important backbone to gain more knowledge about the research trend in the concerned field as well as indicate the research gap that the dissertation can extend and fulfill At the same time, it can give a strong theoretical background that helps to verify the main concerns of the study and validate the research orientation as well as performed approaches

Chapter 3, namely ‘Methodology’, will depict the detailed approaches to address the key issues of the dissertation Accordingly, the chapter first discusses the methodology of the VAIC model and illustrates its detailed calculation as well as the computation of its components Also, the pros and cons of this approach are also analyzed Since this model is being applied in the study as the main explanatory variable to evaluate the role of IC efficiency or value-added creation in banking business operations, analyzing this information will help to provide a deeper insight into how VAIC still stands out in the existing literature compared with other measures proposed, why it is being opted for as the prevalent means to estimate IC efficiency by enormous academicians, although it obtains a variety of drawbacks, and how to formulate its components Afterward, this chapter will give information in a minute way about the data collected, the variables employed, the empirical models performed, and the various stages in performing robustness tests Besides, the backdrop of the research period is also underlined to emphasize the reasons why it is chosen and so important before analyzing descriptive statistics and correlation between employed variables

Chapter 4, namely ‘Empirical findings and discussions’, will illustrate the empirical results of the dissertation based on the methodology presented in the previous chapter By comparing with theoretical views and other empirical studies, the chapter will not only delineate the main findings but also give detailed explanations about the role of IC in the banking operations of domestic banks It can be said that the results illustrated in the chapter will provide a clear answer to the aforementioned research questions

Chapter 5, namely ‘Conclusions and implications’, will summarize the general findings of the dissertation based on the results analyzed in the previous chapter At the same time, by relying on these conclusions, the chapter will propose some main implications under both theoretical as well as practical perspectives which may be helpful for both managers, decision- makers, and researchers in Vietnam and perhaps other emerging countries Besides, the chapter highlights some drawbacks of the dissertation and suggests a variety of future research directions Generally, it is hoped that future scholars can fulfill these limitations as well as pay new paths in this research area

In Chapter 1, the current research has drawn the whole picture of the dissertation in which the research orientation and navigation underlined are to provide a concrete background for reaching a detailed explanation of the chief concerns In this light, the chapter may help readers understand the main purposes, the research objectives, the context, and the research questions that the dissertation will concentrate on It can be said that the first chapter will provide the first key to open the next doors throughout the current study The next chapters will detail the following steps to achieve a clear answer about the research objectives.

THEORETICAL BACKGROUND AND LITERATURE REVIEW

Concepts of intellectual capital

In today's world, economists tend to have a general consensus on the central role of intellectual capital in supporting organizations to gain competitiveness and achieve sustainable growth, especially against the backdrop of a fiercely competitive business environment, rapid changes in technological innovations, and global challenges (e.g., Khan et al., 2019; Kweh et al., 2022; Suciu & Năsulea, 2019; Vătămănescu et al., 2019) In the early stage, the definition of IC has been paid special attention by many economists worldwide According to Khalique et al (2013), the first interpretation is produced by Jon Kenneth Galbraith in 1969 However, Harris (2000) suggests that Schultz was the first economist who introduced the clarification of IC and the economic values of IC in 1963

Even though there are certain debates about the definitions, classifications of IC, and the measures of IC in the existing literature, it is widely recognized that in the knowledge-based era, IC is deemed as the indispensable ingredient in the business strategies of any organization Indeed, although there are certain differences between researchers in interpreting IC in literature, in general, IC is seen as one of the intangible assets to fuel the operations of companies (Ghosh & Mondal, 2009; Mondal & Ghosh, 2012), as one of the main resources to enhance competitive advantage, firm value, the confidence of stakeholders, and sustain economic growth (Caputo et al., 2016; Jardon & Martínez-Cobas, 2019), as one of the reliable sources to ensure prosperity and well-being of individuals, corporations, and countries (Alvino et al., 2020; Suciu and Năsulea, 2019)

At the same time, IC has regularly included three main components: human capital, structural capital, and relational capital (Adesina, 2019; Le & Nguyen, 2020; Meles et al., 2016; Ozkan et al., 2017; Poh et al., 2018) Accordingly, the interpretation of human capital usually involves the competence of people in an organization, such as skills, experiences, knowledge, and so on Meanwhile, the definition of structural capital is related to a firm’s fabric, business strategies, policies, and so on By contrast, the last component, relation capital closely involves extrinsic factors, including clients, suppliers, and relevant stakeholders

Figure 2.1 below presents the heart role of value creation that is blended from three classifications including human capital, structural capital, and relational capital together Besides, Table 2.1 illustrates a variety of definitions related to intellectual capital and its components in the existing literature

Source: Based on the prior studies conducted by Edvinsson & Malone (1997); Harris,

Figure 2.1 The illustration of the value-added creation springing from the three main ingredients of IC

As Figure 2.1 illustrates, the value creation is of the interested concern of enterprises, being the blended and integrated relationships and close connections between three major intangible assets: human capital, relational capital, and structural capital In this light, the more interconnected between these elements, the more maximized value space (Pew Tan et al., 2008) According to Edvinsson & Malone (1997), the firm value only results from the whole combination of all three elements, meaning that by concentrating on one or two of these elements and neglecting the rest of them, a firm cannot optimize the value-added assets

Hence, it is argued that these factors or intellectual resources can be seen as the underlying preconditions and foundations that are essential to perform business and gain competitive leverage (Harris, 2000)

Table 2.1 Some definitions of IC and its components

The author(s) The propounded definitions

Intellectual capital is deemed as the non-physical appearance However, it could bring certain values to a firm

Intellectual capital obtains valuable substances such as experiences, knowledge, IP (intellectual property), and information A company could harness these valuable materials to build wealth and prosperity

3 Sullivan (2000) Intellectual capital has been knowledge that, in turn, could be transformed into the profitability of an organization

4 Bontis (2001) Intellectual capital can be seen as a new resource which assist firms in reaching new achievements on business path

Intellectual capital has been the discrepancy between a company's market and book values At the same time, intellectual capital is the resource that will assist a company to sustain competitive advantages

Intellectual capital is depicted as intangible or non-physical assets that a company can gain value and advantageous competition from these resources

Intellectual capital is seen as an invisible activity consisting of the capability of individuals in learning, namely the human capital, an organizational culture known as the structured capital, and the interactions with extrinsic factors named the relation capital

Intellectual capital is defined as the total of all knowledge that an organization can harness effectively in performing business operations to value-added creation

Intellectual capital is one of the important sources that may enhance the competitiveness of a company and the confidence of multi-stakeholders

Human capital is the distinctive characteristic that individuals or/and teams possess, and it is deemed as intangible assets of an organization By tapping into this factor, any company could gain advantages of competitiveness

The interpretation of human capital is related to the capabilities of employees in solving many problems in enormous circumstances that would ultimately generate tangible as well as intangible assets of companies

Human capital can be deemed as the most important element of IC, supporting the performance, efficiency, and capability of companies

Structural capital is something owned by a company in general Specifically, it is related to data, patents, policy, invention, strategy, and technology From this author's perspective, structure capital is the backbone of IC that would ensure the smoothness of knowledge transmission within a company

Structural capital would be held in companies even when their staffs leave

Structural capital is being controlled by companies, consisting of information technology, culture, innovation, optimized process, and relevant explicit knowledge

Relational capital is identified as the value stemming from individuals and organizations that have relations with a company in selling and buying In other words, this factor is usually related to multi-stakeholders such as customers, suppliers, and other relevant stakeholders

Relational capital can be defined as the connection between companies and their multiple stake-holders

Relational capital is considered the sum of resources (both extant and potential), springing from the relational networks of individuals and organizations as a whole

Relational capital is the combined relationships with the external world that would comprise clients, financial institutions, stakeholders, and other agents

Human capital is the factor reflecting the capabilities of individuals in a company It regularly consists of the skills, experiences, and knowledge of all people in an organization

Structural capital is the factor that emphasizes intrinsic knowledge such as policy, strategy, and structure of a company

Relational capital is the factor related to a firm's extrinsic relationships, such as clients and stakeholders

Source: The author collects from the extant literature

In short, it is an undeniable fact that operating in the technology-led and knowledge-based era, most organizations cannot neglect the role of intellectual resources in constructing business strategies, especially when the competition has been souring and the uncertainty has become more unpredictable This argument is reflected in the scientific endeavors of scholars to codify the role of IC throughout the existing literature

Indeed, based on the existing definitions in the literature and to the limited abilities of the author, it can be concluded that there are the great efforts to identify the concept of IC, however having certain differences in interpreting IC’s definition as well as its elements, depending on the different disciplines and various angles, such as finance and accounting, economics and strategies, marketing and communication, and so on (Le et al., 2022; Le &

Nguyen, 2020) Furthermore, reaching a whole consensus on IC’s interpretation tends to be the inconclusive debate in the extant literature (Bayraktaroglu et al., 2019; Nazir et al., 2021)

Similarly, the definitions of IC’s elements, and perhaps their names, have been still different, but many existing studies have classified IC into three main categories consisting of human capital, structural capital, and relational capital, also called physical capital or capital employed (e.g., Andriessen & Tissen, 2000; Nielsen et al., 2006; G Roos & Roos, 1997; Stewart & Ruckdeschel, 1998; Sveiby, 1997; and among others) Accordingly, human capital is mostly involved the intellectual prowess of individuals in an organization and it can be lost when employees leave companies In contrast to this element, structural capital is mainly engaged with policies, strategies, cultural aspects, possessed by firms Meanwhile, contrary to the first two elements, relational capital is totally related to the external factors such as multiple stake-holders.

Determination of the role of intellectual capital in business strategy development

Historically and conventionally, both academicians and managers have endeavored to find out a clear reason why some businesses perform better while others do not The evident answer to this question may provide the key to opening new paths in managing the business strategy of firms (G Roos, 2005) Based on the theory of business strategy in the extant literature, the aim of this subsection is to highlight the vital role of IC in constructing business strategy of firm industry in general and banking industry in particular

First of all, it is necessary to understand the meaning of “strategy” that a business leans on Theoretically, G Roos (2005) has identified “strategy” is seen as a series of approaches that assist businesses in accomplishing specific goals This author also underscores that the formulation of strategy will depend specifically on the inventiveness coming from the human mind, while the action of strategy is to reach the strategic compromise between business goals and environmental requirements, suggesting that selling products and services has to meet demands of end-users Previously, Andrews (1997) considers that strategy of corporate can be seen as a mechanism of decision-making that helps to clarify the purposes, policies, as well as plans by which companies can reach their business goals From the view of this author, strategy brings significant contributions (both economic and non-economic values) to multiple stakeholders

Now, a question is that why the level of profitability tends to be different between companies, regardless of whether they operate in a same area Looking carefully at the wave of business strategy development, it is argued that there are generally two main perspectives by which strategy may rely on (Marr & Roos, 2012) Accordingly, the first perspective is to focus mainly on the power of market, while the second one is to emphasize the efficiency of internal resources

The former view of business strategy, namely “the paradigm of market power”, is seen as the conventional strategy model leaning specifically on the power of market From this perspective, benefits a firm achieves may spring from the interaction of five major forces including the level of power that buyers have, the level of substitutes for both products and services, the level of power that suppliers possess, the capacity of entry into market, and the level of existing competition (Porter, 1980) These forces are stronger meaning that the profitability of a company will be lower (Marr & Roos, 2012; G Roos, 2005) The main argument of this view is that the fundamental difference in benefits of companies may come from the ability of barriers construction, also named “mobility barriers”, which will assist successful companies in protecting from imitation of their strategic models (Caves & Porter, 1977; Hatten & Hatten, 1987; Marr & Roos, 2012) In this light, the issue that may occur is how to create a set of forces to construct these barriers Rexhepi et al (2013) suggest that the answer may result from harnessing the intellectual capital of an enterprise when building business strategy, especially in industries relying much on this capacity such as educational and financial institutions

The latter view of business strategy is known as “the paradigm of resource-based model” is proposed and developed by some resource-rooted theorists such as Penrose &

Penrose (2009); Wernerfelt (1984) This paradigm explains that the distinction of profitability may originate from the valuable resources that companies possess and the way they employ these resources Also, from this view, both resources and capacities are deemed as “the strategic assets” of enterprises The more strategic assets applied to a huge number of products and services, the higher benefits an organization can achieve (Prahalad & Hamel, 2009) However, it is argued that these resources and capacities cannot themselves produce value-added creation (Penrose & Penrose, 2009), hence, to create value, they have to be embedded into the both products and services that companies offer (Marr & Roos, 2012; G Roos et al., 2001) Such this sense has underscored the center role of IC in business strategy, because by leaning especially on IC view, the resources can be used effectively and companies can determine the way to which value-added creation (G Roos, 2005; G Roos et al., 2001)

To illustrate this issue clearly, it should take the business operations of banks as a typical example G Roos (2005) describes banks serve as conduits between clientele who needs financial support and a group of customers can fulfill this financial gap In this vein, banks have to organize the cash flow appropriately to balance demands of both types of these customers To perform this task well, banks have to transform various kinds of intellectual resources into effective remedies and solutions that will satisfy their clients G Roos (2005) also concludes that possessing valuable resources is not yet enough, instead, banks have to put these resources to value-added creation

Source: Based on the strategy map constructed by Kaplan & Norton (2000, 2004)

Figure 2.2 The role of IC in business strategy map

As Figure 2.2 illustrates above, the strategy map developed by Kaplan & Norton (2000, 2004) has depicted the causal connection between IC drivers and the performance of organizations, indicating the fundamental role of IC in constructing business strategy In other words, the strategy map shows clearly the way by which IC can drive organizational objectives and outcomes of organizations These authors believe that by understanding the readiness of strategic assets, decision-makers can constitute strategic objectives In short, it is clear that by using intellectual capital, the resources of organizations can be transferred into the end products and services which, in turn, will distribute organizational values (Marr & Roos, 2012)

In conclusion, along with the dramatic changes in business conditions, the strategy construction of an organization has to be adapted to suit the market needs, and the business perception of the pivotal role of IC in driving value-added creation also grows gradually (Marr

& Roos, 2012; G Roos et al., 2001) Indeed, approaching business strategy has evolved from the conventional paradigm to strategic assets model, in which, IC has emerged as the key engine for performance outcomes, strategic objectives, and sustainable value-added creations of most companies (Alvino et al., 2020) Therefore, leaders and managers in banks cannot neglect this pivotal factor in their business strategy construction.

Measure methods of intellectual capital efficiency

In tandem with the persistent attempt to explain the concept and the strategic vehicle of

IC as discussed above, it is also witnessed the numerous academicians have put their continuous energies into seeking out clearly the effective measure of IC efficiency It can be said that due to the core role of IC in companies' business strategies and operations, its measurement has attracted many researchers in the financial sector and the existing literature as a whole

The urgent need to measure IC efficiency also originates from the realistic business strategies of firms that desire to manage IC in effective ways and disclosure this information frequently because, as the assertion of Bayraktaroglu et al (2019), managers only manage and control anything that they entirely measure In other words, “when you can measure what you are speaking about and express it in numbers, you know something about it” (Liebowitz

& Suen, 2000) These arguments have underscored the pivotal part of measuring IC in business strategies of enterprises, leading to the fervent desires for conducting and propounding the effective measurement of IC in numerous disciplines

In this vein, various measures have been invented and developed in the extant literature throughout the recent decades In fact, over 42 methods have been developed to estimate intangible assets in the preceding literature and this figure may likely arise (Nazir et al., 2021) Some emblematic methods may include Tobin's Q ratio, the economic value-added indicator, the intellectual capital index, the inclusive value methodology, VAIC model, adjusted/modified/extended-VAIC model and among others

Table 2.2 below states a variety of the typical measure methods of intellectual capital efficiency in the extant literature

Table 2.2 Some measure methods of intellectual capital efficiency

The author(s) The name of propounded measure

1 Kaplan & Norton (1996) The balanced scorecard

2 Brooking (1997) The technology broker’s IC audit

3 Sveiby (1997) The intangible asset monitor

4 Roos & Roos (1997) The intellectual capital index

6 Edvinsson (1997) The Skandia intellectual capital navigator

7 J Roos et al (1997) The holistic value approach

9 Andriessen & Tissen (2000) The value explorer

10 M’Pherson & Pike (2001) The inclusive value methodology

11 Lev (2001) The value chain scoreboard

12 Mouritsen et al (2001) The intellectual capital statements

The intellectual capital benchmarking system

14 Hall et al (2005) The citation-Weighted Patent

16 Bounfour (2003) The intellectual capital dynamic value (IC- dVAL approach)

17 Nazari & Herremans (2007) The extended VAIC model

18 Ulum (2013) The iB-VAIC model

The extended (or additional) VAIC model

20 Ulum et al (2014) The modified VAIC model

Source: The author collects from the extant literature

In the early stage, K.-E Sveiby & Lloyd (2010) and Luthy (1998) categorize the intangible measurement methods into the major four categories including ‘direct intellectual capital methods’, ‘market capitalization methods’, ‘ROA methods’, and ‘scorecard methods’ These categories can be summarized as follows

The first category is directly connected with the aspect of “dollar value” coming from intangible resources, which is estimated by codifying its elements The second involves the calculation of IC’s value through computing the difference between the market capitalization and the book value of the equity The third uses the ROA indicator to calculate the average annual earning springing from intangible assets The final is related to many indices developed from various components of IC to estimate IC’s value, but this approach does not evaluate the economic aspects like the first category

Meanwhile, according to Bayraktaroglu et al (2019) and Pew Tan et al (2008), the existing methods of IC measurement can be classified into the two major kinds consisting of the non-financial calculation or “non-dollar valuation” and the financial calculation or “dollar valuation” These both approaches have naturally embraced both merits and demerits As the given names of these categories suggest, while the former does not estimate the dollar valuation of IC, the latter gives the estimation of the dollar valuation

Some methods in the former group may include the Skandia intellectual capital navigator, intellectual capital index, balanced scorecard, and among others In this line, Kaplan & Norton (1996) can be seen as the typical pioneers trying to propose the IC measurement The authors develop the balanced scorecard as the model to formulate IC, allowing managers to manage the cause-and-effect connections between intangible assets and business performance through the four views: customer, internal process, finance, and learning & growing

Even though this model seems to be in line with the firm-specific view, it may not be applied in general because it may not evaluate the financial value of the intellectual resources (Pew Tan et al., 2008) Hence, many methods have attempted to address this limitation, and different measures are being proposed Among of them, most scholars readily consent that Skandia intellectual capital navigator is deemed as the measurement that provide a wide range of aspects to formulate IC, especially the role of customer relationships in the value-added creation (Bontis, 2001) Accordingly, this approach embraces around 112 indices reflected in five angles: customer, finance, human, process, renewal and development However, this method does not evaluate dollar values of IC

To sum up, the non-financial perspective allows researchers and managers to determine the what type of IC components and their impacts on business operations of firms, but it does not help to formulate the economic values of intangible assets (Bayraktaroglu et al., 2019)

On the other hand, the financial perspective such as the economic value added, Tobin’s

Q, VAIC model, and extended-VAIC model, has focused mainly on the economic value of intangible assets that an organization owns, hence it enables both scholars and business leaders to evaluate IC’s performance and compare it to rivals (Bayraktaroglu et al., 2019)

For instance, the economic value added capturing many variables including financial plan, performance, budget, shareholder communication, and incentive compensation emphasizes the maximation of earnings over costs However, one of this method’s drawbacks is that it leans much on historical expenses that does not capture immediately the current market value (Bontis, 2001; Pew Tan et al., 2008)

Another compelling method is the value-added intellectual coefficient (VAIC) model of Pulic (1998, 2000), which is seen as a relatively simple to calculate and is widely used in the preceding literature (Adesina, 2019; Nazir et al., 2021) As a claim by Poh et al (2018), the value-added intellectual coefficient model (VAIC) is suitable for the banking and financial sector and other industries

This method was created and developed by Pulic (1998, 2000, 2004), and utilized in a wide range of studies on the role of intellectual capital in the banking industry and other disciplines (e.g., Adesina, 2019; Le & Nguyen, 2020; Meles et al., 2016; Nazir et al., 2021; Ozkan et al., 2017; Poh et al., 2018; Yalama, 2013; and among others) This model would consist of three main components: capital employed efficiency (CEE), human capital efficiency (HCE), and structure capital efficiency (SCE) This approach reflects the creation of value in each money a company spends and the efficiency of intellectual capital utilized Therefore, a higher value of VAIC means that using a firm's resources becomes better effective

In this study, the author would utilize VAIC model to measure IC efficiency in banks and acting as the main explanatory variable in the analysis models For calculation, the detailed explanation would be stated in the methodology chapter Along with that, this chapter also acknowledges the drawbacks of VAIC model and gives detailed explanations about some main reasons why this research has chosen this model as measure of IC efficiency In addition, the study will state these limitations as the research gap that future academicians can fulfill in the years coming ahead

In the next subsections, the study will provide theoretical background of intellectual capital before some empirical studies related to IC’s role in the extant literature are reviewed.

Theoretical background of intellectual capital

There are some potential theories which could interpret the connection between IC and financial intermediation and non-interest incomes in banks

Based on the assumption that knowledge embedded in individuals is considered an asset of organizations, the human capital theory developed by Edvinsson & Malone (1997); Stewert (1999) suggests that any company should explore this intangible resource to create and acquire competitive leverage in the market The underlying assumption of this theory is that by tapping into the knowledge embedded into each individual and team, organizations can harness these assets and transform them into advantageous competition in the today’s economy (Stewart & Ruckdeschel, 1998)

According to Harris (2000), the knowledge can be divided into two major types including tacit term and explicit term In terms of the tacit knowledge which is considered the most significant type, Harris states that it may consist of experiences, beliefs, perceptions, learning and similar items that are being embedded into employees and are not spoken

By contrast, the explicit knowledge is appeared clearly and accessible to anyone in firms, which includes procedures, guides, policies written down Allee (1997) asserts that nearly 90% of knowledge owned by firms may spring from the tacit knowledge, therefore to harness intellectual resources effectively, firms need to transform this type of knowledge into the explicit one In other words, an increase in the rate of the explicit knowledge is quite the important issue when exploring intellectual capital

On the other hand, the systems theory considers that each individual has to interact with general processes and businesses to meet the consistent goals of an organization (Heylighen

& Joslyn, 1992) In this vein, IC would become an appropriate environment to ensure constant information flows within the organization and customer communication (Harris, 2000) Relying on this knowledge-sharing mechanism, managers could enhance and even adjust the orientation as well as navigation of business plans to satisfy the demands of their clientele (Harris, 2000)

In other words, each knowledge of each member in a company will emerge as the heart of knowledge for relevant others because individuals become a pivotal part of strategic role in value-added creation (Senge, 1991) More clearly, Harris depicts that the systems approach deems the human capital and systems as two interconnected and integrated entities which serve as the foundation for the performance enhancement of a company

Taken together, implementing IC may assist banks to achieve higher competitiveness and, therefore, achieve an increase in financial intermediation

To identify clearly the intended correlation between IC and financial intermediation as well as non-interest incomes, relying on the models constructed by Edvinsson & Malone (1997) and Suciu & Năsulea (2019), the study would reconstruct these models to draw the possible illustration of these relations in Figure 2.2

As this picture illustrates, through three main ingredients: human capital, structural capital, and relational capital, IC would have certain influences on financial intermediation activities in several channels as follows

First, it is clear that as the human capital theory has indicated, by tapping into the tacit knowledge of employees, any organization may transfer these implicit resources into the innovative capital that is eventually harnessed to accomplish competitive advantages in markets Second, the structural capital would fuel competitiveness through the channel of social capital Indeed, Suciu & Năsulea (2019) argue that in the digital, innovative, and knowledge-based era, structural capital would assist enterprises to build evolutionary adaptations to meet current challenges Such new adjustments could enhance and strengthen the process capital of companies, and therefore, they could achieve substantial improvement in competitive abilities

To explain the last channel, these authors assert that increasingly interacting with multi- stakeholders and facing differences in culture, each organization nowadays has to possess a deeper understanding of the fundamentals of the culture of excellence By relying much on the intangible resource, namely IC, enterprises could construct or re-construct the solid foundation, which would help them explore multi-dimensions of cultures, societies, and environments This improved backbone would bring sustainable development and competitiveness to modern firms

In short, based on these channels, it is indisputable that IC and its components are expected to provide competitive advantages for companies Thus, the activities of financial intermediation and non-interest income would be fueled by these precious resources in the today’s modern world

Source: Based on the models constructed by Edvinsson & Malone (1997) and Suciu &

Figure 2.3 The illustration of the ways IC fuels financial intermediation activities and non-interest incomes of banks

Related empirical studies in the banking industry

As the author mentioned before, although harnessing IC as the key vehicle to accomplish competitiveness in the banking market and perhaps in other disciplines is widely recognized by scholars and theorists in the preceding literature, the empirical evidence in the banking sector seems relatively scarce compared with other industries (Le & Nguyen, 2020) However, regardless of certain obstacles, especially regarding necessary data, there are numerous endeavors to discover this vital field

It can be said that, on the one hand, a wide range of empirical investigations in the banking industry tend to employ VAIC model proposed and developed by Pulic (1998, 2000) as the dominated tool to calculate IC efficiency of banks On the other hand, the empirical results tend to maintain the open questions in which while enormous papers lend to advocate the relationship between VAIC model and banking performance, the opposite view is also found in other studies In addition, the impacts of VAIC’s components seem to be blended, depending on each specific country and perhaps selected financial indicators

Indeed, on the one hand, most studies demonstrate the main driver of IC in fostering banking performance The first attempt to explore the role of IC through applying VAIC model in the banking sector may come from the study conducted by Pulic & Bornemann (1999) In this work, these authors examine the impact of IC efficiency measured by VAIC model on banks’ cost and performance of around 24 big banks in Australia from 1993 to 1995 The results show that VAIC can improve both cost and performance of Australian banks Since then, it can undeniable fact that the existing literature has witnessed the dramatic surge in empirical experiment to find out the role of VAIC in banking performance in both developed and developing countries

For instance, based on the data of 17 banks in Turkey between 1995 and 2006, the empirical evidence of Yalama (2013) shows that VAIC has a positive association with profitability, market value, and productivity in banks, especially in the long term The previous finding of the study conducted by Alhassan & Asare (2016) also obtained a similar result Accordingly, relying on 18 banks in Ghana from 2003 to 2011, the authors find that VAIC positively affects the productivity of Ghanaian banks, in which the most important components driving the productivity are HCE and CEE

In the related study, by using 10 banks in Malaysia between 2007 and 2016, regression results conducted by Poh et al (2018) show that each component of IC has different influences on banking performance depending on measures of performance and the period selected Specifically, whilst HCE and CEE positively affected ROE and ROA indicators respectively during the period 2011-2016, the same relations are found in the cases of SCE and CEE between 2007 and 2016

The regression analysis conducted by Meles et al (2016) shows a similarity for developed markets In particular, by employing a large sample of US banks from 2005 to 2012, the results coming from the OLS estimation indicate the positive relationship between VAIC and ROA and ROE in US banks, in which HCE is the sub-component having more significant effect on banking performance compared to other components The recent study of Neves & Proenỗa (2021), who investigated the relationship between VAIC and the performance of 12 banks in Portugal during the 2009-2016 period, finds that VAIC and its elements contribute to significant improvement in the financial performance of these banks

Tiwari & Vidyarthi (2018) employ about 39 listed banks in India between 1999 and 2015 and perform panel fixed effects technique to point out the connection between IC efficiency and banks’ performance The analysis regression indicates that a positive association is found in the cases of IC and HCE and SCE elements, and private banks tend to use intellectual resources more effectively than public ones The prior study conducted by Singh et al (2016) also confirms that private banks in India harness IC effectively compared to public banks Meanwhile, Kweh et al (2022) utilize the data set of about 24 banks in Taiwan during the 2007-2018 period along with using various econometrics methods and find that intellectual resources help banks to improve harnessing available sources more effectively

On the other hand, the extant financial literature also shows the opposite impacts of VAIC and its components on banking performance The first example is the study of Tran &

Vo (2018) Based on the data of 16 listed banks in Thailand, these authors’ study does not find the association between VAIC and bank profitability At the same time, the empirical evidence implies that while a modest reduction in profitability of banks may result from the HCE component, the CEE component is the key factor fostering banking performance in this nation

These findings seem to reconfirm the prior study conducted Ozkan et al (2017), who employ an empirical analysis based on 44 Turkish banks from 2005 to 2014 and find that CEE has a great impact on banking performance in Turkey Therefore, these authors suggest that Turkish banks should strengthen their financial and physical resources to reach higher profitability

Meanwhile, Haris et al (2019) investigate the connection between the implementation of IC and the performance of 26 banks in Pakistan and find an inverted U-shaped relation between VAIC and bank profitability Also, the authors’ results show that both CEE and HCE assist banks' profits, but an adverse impact is found in the case of SCE Based on the unbalanced panel data of 32 Ghanaian banks from 2000 to 2015, the results of the panel- corrected standard error regression carried out by Duho & Onumah (2019) show that whilst VAIC drives the asset diversification strategy, this factor does not support the income diversification strategy of banks

At the same time, various studies in this field have endeavored to figure out the role of

IC in banks’ business operations based on the cross-countries sample and the general results likely reaffirm that whilst VAIC has supported banking performance, its elements have a mixed impact

For instance, Adesina (2019) utilizes the database of nearly 340 banks in over 30 African countries during the period 2005-2015 and applies the Tobit regression and one- system GMM method to explore the impacts of both VAIC and its ingredients on the technical, allocative, and cost efficiencies of banks The evidence indicates that VAIC has a positive connection with the bank technical, allocative, and cost efficiencies, while a similar relationship is only found in the case of HCE By employing a similar approach, Adesina (2021) also finds that HCE may contribute to mitigation of the negative effects of diversified activities on the performance of African banks

In the recent work of Dalwai et al (2021) who use over the data of 200 listed banks collected from 12 emerging countries in Asia to point out the relationship between IC efficiency and bank risk-taking, the empirical results show that there is no connection between

IC efficiency and banking risk-taking in which HCE has negatively affect banks’ risk Meanwhile, Asare et al (2023) utilize the data set of over 360 banks in 26 countries in Africa between 2007 and 2015 and find that the relationship between VAIC and both NIM indicator and insolvency risk of banks is non-linear

At the same time, there are also scientific endeavors to adjust and extend VAIC model and discovering its role in the banking sector For example, the previous study conducted by Buallay (2019) who employs nearly 60 banks including Islamic and non-Islamic ones in Gulf countries from 2012 to 2016 to find out the influences of the modified VAIC as well as its ingredients on three major financial indicators: ROA, ROE, and Tobin’s Q The modified VAIC is the extended VAIC in which the expenses related to marketing and sales are added into VAIC model Accordingly, the empirical findings suggest that IC efficiency has supported ROE and Tobin’s Q in the Islamic group and only ROE in the non-Islamic one For IC’s components, the study finds that they have a positive association with either financial/operational performance or market performance in both groups

Various recent papers also approach the extended VAIC Soewarno & Tjahjadi (2020) apply both VAIC model and modified-VAIC one to investigate the relationship between IC efficiency and financial performance of 114 Indonesian banks In this light, these authors find that both models have the positive impact on banks’ financial performance, however their elements seem to have different impacts based on the different indicators applied

Hypotheses development

To some extent, it can be said that the evolution of theories related to IC in the extant literature tend to advocate the implementation of intellectual resources as the main vehicle that helps an organization to achieve competitiveness

The first theory is the resource-based theory propounded by Dierickx & Cool (1989); Penrose & Penrose (2009) Accordingly, the resource-based view suggests that the resources of a firm are seen as a collection of both tangible and intangible assets in which the former includes fixed assets such as equipment, land, and other similar items and the latter consists of human resources (Penrose & Penrose, 2009) Meanwhile Barney (1991) figures out that resources of companies may spring from the combination of these factors including human capital, physical capital, and organizational resources, which are seen as the sources of economic benefits of firms if they are appreciated and harnessed effectively by managers and business leaders Zéghal & Maaloul (2010) assert that by leaning on the resource-based view, firms can achieve higher performance

Even though this theory highlights that exploring internal resources, particularly intangible assets, can assist an organization to sustain competitive advantages and stable growth and development, and being seen as a “contemporary and promising ideology”

(Khalique et al., 2013), the resource-based perspective is also criticized by other theorists The major imperfections of this view are that it pays specially and much on the internal resources and capacities of firms and it possesses the static nature, hence rapid changes in the business environment are not captured (Barney, 1991; Bontis, 2001; Penrose & Penrose, 2009) It is not surprising that to fulfill these drawbacks, other approaches have been developed in the extant theoretical literature

The next view labelled as the knowledge-based theory may be seen as the extension of the aforementioned theory because this theory focuses on not only endogenous sources, but also external ones (Khalique et al., 2013) This point of view is seen as a concept developed by Grant (1997); Sveiby (1997) who consider that in contrast to the first perspective, the nature of the knowledge-based view is dynamic because it captures both internal and external aspects of organizations’ resources Like the first view, the former resources include intangible assets and human capacities of organizations, while the latter resources involve cooperation and sharing knowledge with other partners

Curado & Bontis (2006) define the second view as a “special strategic resource” that does not suffer the gradual depreciations in economic values as conventional assets because its nature is dynamic and non-monetary Unsurprisingly, many theorists (e.g., Barney, 1991; Roos et al., 1997; Sveiby, 1997) argue that to meet the changes in requirements of the knowledge-led economy, companies should lean specially on the knowledge-based management to understand deeply the required capacities that managers have to prepare to accomplish competitiveness However, as Khalique et al (2013) assert, even though the knowledge-based theory is considered as the dynamic nature, and contains both interior and exterior facets of the knowledge-led management which are complementary to the resources- based view, it seems to neglect the value creation by harnessing the intangible assets

In this regard, the intellectual capital theory has emerged to bridge this theoretical gap, in which this view focuses nearly on the value-added creation springing from exploring the hidden resources, namely intangible assets

Indeed, in a wave of evolution, the IC theory finally developed is to focalize multidimensional aspects such as skills of employees, reputation of an organization, and multi-stakeholders, and so on, which are classified into three major categories including human capital, structural capital, and relational capital as the aforementioned subsections The

IC theory suggests that most companies can reap a good harvest from harnessing these resources effectively (Harris, 2000; Khalique et al., 2013; Stewart & Ruckdeschel, 1998) Reed et al (2006) highlight that IC is deemed as a unique aspect of firms’ resources that assist them in producing value added, while other capitals may be exchangeable and imitable and may be traded in the market

At the same time, IC perspective is seen as “the art and science” that may assist organizations in maximizing value from their resources and employing them effectively (G Roos, 2005) Furthermore, because IC is considered the driving force of value creation, organizational outcomes, strategic objectives, and financial performance, it can help organizations to fulfill their clientele’s demands, enhance customers’ loyalty, and distribute value to multiple stakeholders (Marr & Roos, 2012) As the conceptual framework illustrated in Figure 2.2, IC can improve the sustainable competitive advantage of banks through three main ways consisting of enhancing innovative capital, process capital, and social capital It is not exaggeration to affirm that the theoretical views tend to underscore the bright side of implementing IC in fostering the sustainable competitiveness, growth, and innovative systems of organizations (Alvino et al., 2020) To sum up, there is an expectation that based on the IC perspective embedded into the business strategy, banking operations in general and both financial intermediation activities and total non-interest incomes will be strengthened significantly

Many empirical findings have tended to support the chief driver of IC in banking operations For instance, Neves & Proenỗa (2021) find that IC can spur financial performance of banks (measured by ROA, ROE, and NIM indicators) The empirical results conducted by Nazir et al (2021) support the view that IC efficiency can foster the productivity of banks’ employees Also, IC can support the market value, profitability, and productivity of banks (Alhassan & Asare, 2016; Yalama, 2013) Furthermore, IC efficiency can enhance the technical, allocative, and cost efficiencies of banks (Adesina, 2019; Le et al., 2022), and support the risk management of banks (Zheng et al., 2022) In addition, the asset diversification of banks can be driven by intellectual resources (Duho & Onumah, 2019) Along with the evolutionary wave of theories related to IC, an appropriate expectation is that IC-rich banks will possess the necessary resources such as well-trained staffs, well-tailored fabrics, customer-oriented products and services, and technological infrastructure to attract more attention of potential customers who can be willingness to use more financial facilities offered by banks Therefore, taken together, it is anticipated that IC can spur the financial intermediation activities and non-interest incomes of banks Based on the aforementioned arguments, the following hypotheses are being constituted:

Hypothesis 1: IC efficiency (measured by VAIC model) is expected to contribute to enhancement in the financial intermediation activities of Vietnamese banks

Hypothesis 2: IC efficiency (measured by VAIC model) is expected to contribute to enhancement in non-interest incomes of Vietnamese banks

Regarding three major components of VAIC model, it is clear that the theoretical perspectives have advocated harnessing sub-resources of IC in business operations to gain ahead of the curve in today’s competitive market Indeed, the resource-based view considers that the human capital including skills, experiences, knowledge, and other similar items embedded in employees and teams can be transferred into the end products and services, and therefore it can deliver values to the clientele of organizations (Grant, 1997; Marr & Roos, 2012) The more well-managed human capital, the more sustainable competitiveness that an organization can achieve (Barney, 1991) Moreover, skilled employees can also assist businesses in enhancing the loyalty as well as satisfying the loyalty and changing demands of customers and other stakeholders (G Roos, 2005; J Roos et al., 1997) Many empirical studies tend to support HCE component in bolstering banking business operations For instance, Meles et al (2016) find that HCE is the most important component spurring financial performance of banks compared to other Similarly, Adesina (2019) find that HCE play a crucial role in improving banks’ technical, allocative, and cost efficiencies Taken together, hence, it is hoped that financial intermediation activities and non-interest incomes of banks will be advanced by HCE The next hypotheses are being built as follows:

Hypothesis 3: HCE (one of VAIC’s components) is expected to contribute to enhancement in the financial intermediation activities of Vietnamese banks

Hypothesis 4: HCE (one of VAIC’s components) is expected to contribute to enhancement in non-interest incomes of Vietnamese banks

Along with the human capital, organizations can take advantage of the structural capital consisting of policies, information, data, structures, and other similar items to strengthen their competitive capacities (Suciu & Năsulea, 2019) Through the considerable improvement in the innovative processes, any change in policies and fabrics of organizations can be adapted immediately to meet new business circumstances (Alvino et al., 2020; Vătămănescu et al., 2019) Such this successful adaption may enhance both products and services that organizations offer, and hence, they can attract more customers and increase their client database Moreover, having higher structural capital means that banks can create a supportive environment and effective knowledge-sharing cultures that incentivize and motivate talented employees to express their abilities (Asutay & Ubaidillah, 2023) It is expected that the stronger SCE, the higher enhancement in banks’ business operations Thus, both financial intermediation activities and non-interest incomes of banks will be propelled by SCE Taken together, the following hypotheses are being constructed:

Hypothesis 5: SCE (one of VAIC’s components) is expected to contribute to enhancement in the financial intermediation activities of Vietnamese banks

Hypothesis 6: SCE (one of VAIC’s components) is expected to contribute to enhancement in non-interest incomes of Vietnamese banks

At the same time, the third component, the capital employed efficiency, also plays a crucial role in the business strategy The resource-based opinion considers valuable resources as “strategic assets”, which seem to be non-substitutable (Teece, 1998) Relying on them, organizations can maintain their economic majority and the power of products, and hence, they can sustain their competitive advantage in business market (Khan et al., 2019; Marr & Roos, 2012) Pulic (1998) and Pulic & Bornemann (1999) assert that to provide value-added for firms, IC may not stand alone but instead it needs to combine itself with physical capital, especially in the financial system Some empirical studies in the banking industry (e.g., Ozkan et al., 2017; Tran & Vo, 2018) also underscore the pivotal role of CEE component in propelling financial performance of banks, especially in the reform period Taken together, it is appropriate to expect that there is a positive impact of this component on both the financial intermediation activities and non-interest incomes of banks Accordingly, the next two hypotheses are being constituted as follows:

Hypothesis 7: CEE (one of VAIC’s components) is expected to contribute to enhancement in the financial intermediation activities of Vietnamese banks

Hypothesis 8: CEE (one of VAIC’s components) is expected to contribute to enhancement in non-interest incomes of Vietnamese banks

Regarding the effect of bank size on the major concerns, it can be ascertained that the theoretical views seemingly tend to be mixed On the one hand, an argument is that large organizations seem to be less motivated to innovate their business activities to satisfy market demands in comparison with their counterparts (Scherer, 2001) On the other hand, by contrast, another viewpoint considers that large companies can take advantage of the economic scale to bolster their performance and reform their business operations (Carter & McNulty, 2005; Maudos & Solís, 2009; Schumpeter & Swedberg, 2014), leading to easy entry into new markets, while small firms may grapple with touch challenges resulting from paradigm shifts in technological development, due to a lack of internal resources (Uddin et al., 2020) From the empirical view, the recent findings of Le & Nguyen (2020) show that generally, the bank size does not affect the correlation between VAIC and financial performance, besides, bigger banks tend to leverage SCE more effectively but they do not harness HCE effectually Taken together, the present study hypothesizes that

Hypothesis 9: The impacts of both VAIC and its ingredients on the financial intermediation activities and non-interest incomes of banks in large and small banks are mixed

In this chapter, a variety of major issues related to the research objectives and purposes are being mentioned and analyzed

METHODOLOGY

VAIC measurement methodology

As mentioned in the previous chapter, there are numerous measures of IC proposed and developed throughout the existing literature Among the IC’s measurements propounded, the VAIC model (the value-added intellectual coefficient model) can be seen as the popular and dominated tool to calculate IC efficiency of banks and other firms in both developed and developing countries (Bayraktaroglu et al., 2019; Le & Nguyen, 2020; Nazir et al., 2021; Poh et al., 2018; and among others) In fact, it is not difficult to find that a large number of studies on different industries has employed VAIC model as the effective measurement of IC since this instrument emerged

Many academicians have a general consensus that VAIC model has been created by Pulic (1998), and then it has been developed by Pulic (2000, 2004) According to Bayraktaroglu et al (2019); and Pew Tan et al (2008), this method is being classified into the group of the financial calculation or “dollar valuation”, meaning that it reflects the dollar valuation or the aspect of economic value in estimation of IC and comparing IC performance of companies to their competitors

Under the view of Pulic, the value-added creation of organizations is being assumed that to promote value creation, most enterprises have to rely heavily on two major resources including capital employed or physical capital and intellectual resources, in which the latter embraces both human and structural capitals In other words, the value-added creation process will spring from capital employed, human capital, and structural capital of organizations, and VAIC measure can be seen as the total of physical capital efficiency, human capital efficiency, and structural capital efficiency (Adesina, 2019; Nazir et al., 2021; Poh et al., 2018) Simply, VAIC model will reflect the level in which organizations can create value added by harnessing their both physical capital and intellectual resources (Bayraktaroglu et al., 2019)

To give a clear illustration for VAIC model proposed by Pulic above, the study applies and adapts the conceptual framework developed by Andriessen (2004) to depict the contributions of the major resources (capital employed efficiency, human capital efficiency, and structural capital efficiency) that may provide a detailed explanation about the formulation of VAIC model

Please see more detail in Figure 3.1 below

Source: Adapted from the study of Andriessen (2004)

Figure 3.1 The major resources in promoting value-added creation

To calculate VAIC model, at the first stage, it is necessary to perform the calculation of value added (VA) To some extent, VA is seen as the most important angle and objective signal, mirroring the successfulness of firms in their business operations and reflecting their capacities in creating value added (Pulic, 2004; Ulum et al., 2014) In a simple way, the calculation of VA is condensed as the differences between output factors and input factors According to Pulic (2000), the former entails all incomes coming from the all the products and services of a firm that are sold on the market, while the latter involves all costs that incur in the business operations of a company except for the personnel expenses

It is argued that excluding the expenses related to employees may be seen as the distinctive signature of the VAIC measure methodology in particular and the ethos of Pulic in general in comparison with other approaches In this light, Pulic considers employees as a precious resource not expense and the special entity which is expected to participate in and play an active role in creating value added of organizations in lieu of taking staffs as costs that other conventional accounting systems have approached (Bayraktaroglu et al., 2019; Ulum et al., 2014) Hence, such prominent facet is quite unique point and contrary to many traditional indicators

In short, the calculation of VA is depicted in the following equations

Where, 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑖𝑖𝑖𝑖 is the total incomes of bank ‘i’ related to selling all products and services on the market during the period of ‘t’ Meanwhile 𝐼𝐼𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 𝑖𝑖𝑖𝑖 is all costs related to operating business of bank ‘i’ at the time ‘t’, except for employment expenses

In a clear way, Pulic (2004) details the aforementioned equation as follows

Accordingly, 𝑂𝑂𝑂𝑂 𝑖𝑖𝑖𝑖 represents the operating profit of bank ‘i’ during the period of ‘t’ 𝑂𝑂𝑃𝑃𝑖𝑖𝑖𝑖 refers personnel expenses (including salaries and other similar items) of bank ‘i’ during the period of ‘t’, and 𝑉𝑉 𝑖𝑖𝑖𝑖 is the amortization and depreciation of bank ‘i’ during the period of

At the next stage, Pulic suggests that VA has a close relationship with the capital employed of an organization The underlining assumption of Pulic is that when a firm spends

1 unit of its capital employed to generate higher returns than its partners, it means that this firm tends to harness the capital employed more effectively In this case, it can be said that using the capital employed better becomes a crucial part of intellectual capital of a company

The existing studies regularly label this association with the capital employed efficiency (CEE), which is seen as the indicator that reflects the ability of a company in creating value added by harnessing 1 unit of its physical capital (Bayraktaroglu et al., 2019; Pew Tan et al., 2008; Pulic, 1998; Ulum et al., 2014) In short, CEE indicates the way in which a firm can produce value added effectively by relying on its financial capital

The calculation of CEE is usually defined as the ratio of VA over the capital employed Accordingly, the capital employed of an organization is the book value of net assets or, in other words, the equity value

The following equation shows how CEE is calculated

Where, 𝑃𝑃𝐶𝐶𝑖𝑖𝑖𝑖 refers the book value of equity of bank ‘i’ during the period of ‘t’, 𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖 represents the value added of bank ‘i’ at time ‘t’, and 𝑃𝑃𝐶𝐶𝐶𝐶 𝑖𝑖𝑖𝑖 is the capital employed efficiency of bank ‘i’ at time ‘t’

In a related angle, there is also an association between VA and human capital of a company In this sense, the human capital efficiency (HCE) will reflect the extent to which

VA can be generated from 1 unit of money spent on employment expenses (Pew Tan et al., 2008) Pulic (2000) suggests that HCE mirrors the compensation for employees due to their abilities, creativity, and incentive

Pulic asserts that using personnel expenses as a signal of human capital is quite in line with other researchers such as Edvinsson (1997) who utilizes the payroll as an indicator for human capital, and Sveiby (1997) who applies the employee renumeration as the proxy for human capital Since then, many related studies also employ total salary and wage costs as an indicator reflecting the human capital capacity of an organization (Bayraktaroglu et al., 2019; Ulum et al., 2014)

Accordingly, the calculation of HCE is depicted in the following equation

Where, 𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖 represents the value added of bank ‘i’ at time ‘t’, and 𝐻𝐻𝑃𝑃𝑖𝑖𝑖𝑖 represents the personnel expenses of bank ‘i’ at time ‘t’ Meanwhile, 𝐻𝐻𝑃𝑃𝐶𝐶 𝑖𝑖𝑖𝑖 reflects the capacity of the human capital of bank ‘i’ during the period ‘t’

Data and Variables

To quantify the role of IC efficiency in both financial intermediation and non-interest income activities of banks, the current research utilizes the secondary data gathered from the two major and reliable sources The first source is the financial information which is being collected directly from the financial statements audited by Vietnamese commercial banks over the period spanning from 2006 to 2020 To calculate VAIC, some relevant detailed costs are also selected from the notes to the financial statements This financial information is publicized annually based on the accounting standards of Vietnam and is regularly presented on the website of each bank The second source is the macro data which is amassed directly from the database publicized by the World Bank through the website: https://data.worldtbank.org/ during the same period

In fact, the banking system in Vietnam has embraced some main types including the policy banks, also known as the non-profit banks, commercial banks, foreign-monitored banks, and joint-venture banks Huynh & Dang (2021) consider that the non-profit banks tend to vary in their business operations compared with the commercial banks In addition, the authors also assert that both joint-venture banks and foreign-controlled ones seem relatively small and only comprise a minor proportion compared to the banking industry Therefore, as mentioned early in the first chapter, the current study is to focus mainly on the commercial banks to investigate the role of IC efficiency As Adesina (2019) suggests, such this concentration on only commercial banks will help to reach a relatively homogeneous research sample

At the same time, according to the prior studies (e.g., Adesina, 2019; Le et al., 2022;

Le & Nguyen, 2020), banks which possess fewer than four consecutive observations will be eliminated from the research sample Furthermore, following Bayraktaroglu et al (2019) and Kai Wah Chu et al (2011), an observation that its VA is negative will be removed because the negative values of VA suggest that not only do the banks tend to expand more input than output resources but also the analysis is quite no meaningfulness Moreover, the negative VA values mean that the calculation of VAIC model can seemingly not be conducted Other studies in the extant literature also have this similar approach, such as Shiu (2006) and Zéghal

Eventually, the research sample in the current research has obtained a total of 26 domestic commercial banks in Vietnam with a period spanning from 2006 to 2020, suggesting that there are around 380 bank-year observations achieved To some extent, it can be said that the research sample can be deemed quite acceptable and representative, especially when comparing it with other studies in different countries in this field

For instance, the prior study conducted by Tran & Vo (2018) uses 16 listed banks in Thailand during the period 1997-2016 to estimate the effects of VAIC and its components on the financial performance of banks It means that there are around 160 bank-year observations obtained in the research sample of this research Based on the main concentration on exploring the causal effects of VAIC and its elements on the financial performance of banks, Poh et al (2018) employ ten banks in Malaysia for the period 2007-2016, meaning that about 100 bank- year observations are obtained in this paper The earlier study carried out by Alhassan & Asare (2016) use18 Ghanian banks from 2003 to 2011 to explore the connection between the productivity of banks and VAIC Hence, over 160 bank-year observations are included in this research Similarly, Yalama (2013) employs 17 Turkish banks between 1995 and 2006 to estimate the role of VAIC and its components in banks’ productivity, suggesting that these authors use the data set of about 200 bank-year observations to conduct the regression analysis

In the recent research, Kweh et al (2022) utilize 24 banks in Taiwan during the period 2007-2018 to investigate the relationship between IC efficiency and the efficiencies of these banks, suggesting that about 288 bank-year observations are employed in this work To examine the impacts of VAIC and its components on the financial performance of banks, Neves & Proenỗa (2021) use the sample of 12 banks in Portugal covering the period 2009-

2016, considering that there are over 100 bank-year observations obtained Nawaz (2019) employs 6 Islamic banks in UK from 2013 to 2017 to estimate the impacts of both VAIC and its elements on the financial performance of these banks It means that the research sample of Nawaz is quite small Haris et al (2019) use sample of 26 domestic banks in Pakistan in 5 years from 2012 to 2016, meaning that over 100 bank-year observations are applied to carry out the research

In comparison with other studies in Vietnam in this field, the representativeness of the research sample in the dissertation still remains acceptable For example, the earliest study carried out by Hang & Trang (2023) uses 24 commercial banks from 2007 to 2020 to investigate impacts of VAIC and its elements on the efficiency of bank stability Both prior studies conducted by Le & Nguyen (2020) and Nguyen et al (2021) utilize 30 banks consisting of both local and foreign banks from 2007 to 2019 It means that there are around

377 bank-year observations employed by these authors The recent research of Le et al (2022) also uses a similar research sample with both studies mentioned above

Meanwhile, as stated in the early stage, this study is to focus mainly on domestic commercial banks and there are totally 26 banks observed during the 2006-2020 period, considering that around 380 bank-year observations are obtained in the research sample Furthermore, based on the calculation of the writer, the total assets of commercial banks selected in the study may comprise roughly 70% of the Vietnamese banking system

Taken together, to some extent, the research sample can be seen as the embodiment of the Vietnamese banking industry as a whole and can be considered the representative sample

As mentioned in the subsection 3.1, VAIC model is being employed to quantify the impact of IC efficiency on two aspects of banks’ business operations: financial intermediation and non-interest income activities The detailed calculations and necessary steps to formulate VAIC model as well as its major components including CEE, HCE, and SCE are expressed in from the equation (3.1) to the equation (3.8) and are also illustrated in Figure 3.2 in the subsection 3.1

In this study, VAIC and its major elements consisting of CEE, HCE, and SCE will be seen as the explanatory variables of interest in all regression models performed

To address the main concerns, the study uses two major proxies as the dependent variables in the regression analysis Basically, one of the main functions of banks is to allocate financial resources, rendering deposits from depositors to borrowers (Allen & Santomero, 1997) In other words, banks have been known as the essential intermediaries in the financial system, playing a crucial role in translating savings of individuals and firms to real investments To give a deeper understanding of the role of these intermediaries, based on resource allocation models, the intermediation theory has been developed This theory has suggested that there are two important issues including costs related transaction and asymmetric information that interpret the function of banks Accordingly, banks have possessed higher advantages compared to individuals to dilute fixed costs and diversify trading costs (Shaw, 1960), while these intermediaries can tackle asymmetric information by acting as “delegated monitors” and having special capacities when deciding new investments (Diamond, 1984) Hence, first, regarding the aspect of financial intermediation, the ratio of total loans to total deposits (FI) is being used as the dependent variable in the analysis model Even though there are certain debates on measuring financial intermediation, the indicator is still prevalent among others and quite simple to calculate in the banking literature as well as remaining one of the most appropriate metrics for the bank-led economies (Boďa & Zimková, 2021) Furthermore, there are some considerations suggesting that the tacet of macroprudential policies should be constructed around this “descriptive indicator” (Satria et al., 2016; Van den End, 2016) 1

On the other hand, Mostak Ahamed (2017) argues that in tandem with the liberalized process in the banking sector, banks tend to seek out other sources of income such as fees and commissions in lieu of concentration mainly on conventional incomes such as lending interests Such a shift toward non-interest incomes means that banks have to possess some necessary preparedness such as skilled and experienced employees, sophisticated technologies, and so on to meet the changing demands of the financial market, especially in the fiercely competitive environment This scenario will underscore the role of IC efficiency in this change in the business strategy of banks So that, to quantify the association between

1 In the unreported results, the study also performs the regression analysis to evaluate the association between IC efficiency and two angles of financial intermediation including deposit and lending Accordingly, the results again confirm the pivotal role of IC efficiency in spurring both intermediation activities

IC efficiency and non-interest comes, the study will use the (natural logarithm of) total non- interest incomes (NII) of banks as the dependent variable in the analysis model This indicator is utilized because it will reflect most of the non-interest sources of income of banks, from fees and commissions to trading securities, in a direct and absolute way (Phan et al., 2022a; Phan et al., 2022b) 2

At the same time, it should be noted again that, the current analysis has paid special attention to financial intermediation and non-interest incomes aspects of banking operations compared to the existing studies This is also a crucial distinction between the present research and others Hence, both FI and NII indicators are being applied to capture the two aspects mentioned above in lieu of other financial indicators such as ROA, ROE, ROAA, Tobin’s Q, EPS, and among others which are popular approaches in the extant literature (see more detail in Chapter 2 before)

Empirical Regression Models

First, to estimate the relationship between the intellectual capital efficiency and financial intermediation of banks, the following regression is being performed as:

Where, 𝐹𝐹𝐼𝐼 𝑖𝑖𝑖𝑖 is the dependent variable of bank i at time t and VAIC is used as the key explanatory proxy in the Model 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝐵𝐵𝐵𝐵𝐼𝐼𝐵𝐵𝑖𝑖𝑖𝑖 is the vector of the control variables comprising SIZE, CAPITAL, EBLTA and LLR 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝑀𝑀𝐵𝐵𝑀𝑀𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 is the vector of the control variables including GDPR and IFL The model obtains time-fixed effects,𝜃𝜃 𝑖𝑖 , to control for the macroeconomic conditions common across banks 𝜀𝜀 𝑖𝑖𝑖𝑖 is the error term

At the next stage, to investigate the effects coming from the different components of intellectual capital on the main concerns, the following regression is being performed as:

Where, 𝐹𝐹𝐼𝐼 𝑖𝑖𝑖𝑖 is the dependent variable of bank i at time t and SCE, HCE, and CEE are utilized as the key explanatory proxy in the Model 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝐵𝐵𝐵𝐵𝐼𝐼𝐵𝐵𝑖𝑖𝑖𝑖 is the vector of the control variables consisting of SIZE, CAPITAL, EBLTA and LLR 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝑀𝑀𝐵𝐵𝑀𝑀𝐶𝐶𝐶𝐶 𝑖𝑖𝑖𝑖 is the vector of the control variables including GDPR and IFL The model obtains time-fixed effects,𝜃𝜃 𝑖𝑖 , to control for the macro-economic conditions, common across banks 𝜀𝜀 𝑖𝑖𝑖𝑖 is the error term

In addition, to evaluate the association between the intellectual capital efficiency and non-interest incomes of banks, the following regression is being applied as:

Where, 𝑁𝑁𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 is the dependent variable, NII, of bank i during the time t and VAIC is used as the main explanatory proxy in the Model 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝐵𝐵𝐵𝐵𝐼𝐼𝐵𝐵𝑖𝑖𝑖𝑖 is the vector of the control variables comprising SIZE, CAPITAL, EBLTA and LLR 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝑀𝑀𝐵𝐵𝑀𝑀𝐶𝐶𝐶𝐶 𝑖𝑖𝑖𝑖 is the vector of the control variables including GDPR and IFL The model obtains time-fixed effects,𝜃𝜃 𝑖𝑖 , to control for the macroeconomic conditions common across banks 𝜀𝜀𝑖𝑖𝑖𝑖 is the error term

Eventually, to examine the impacts of the different components of intellectual capital on non-interest incomes of banks, the following regression is being applied as:

Where, 𝑁𝑁𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 is the dependent variable, NII, of bank i during the time t and SCE, HCE, and CEE are utilized as the main explanatory proxy in the Model 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝐵𝐵𝐵𝐵𝐼𝐼𝐵𝐵𝑖𝑖𝑖𝑖 is the vector of the control variables consisting of SIZE, CAPITAL, EBLTA and LLR 𝑃𝑃𝐶𝐶𝐼𝐼𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝑀𝑀𝐵𝐵𝑀𝑀𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 is the vector of the control variables including GDPR and IFL The model obtains time-fixed effects,𝜃𝜃 𝑖𝑖 , to control for the macro-economic conditions, common across banks 𝜀𝜀 𝑖𝑖𝑖𝑖 is the error term

Regarding the econometric methods, there are several approaches performed as follows

At the first stage, the study will approach the traditional and simple method, namely the ordinary least squares regression, to regress the equations (3.9) – (3.12) As the recent assertion of Kweh et al (2022), this conventional approach still remains an effective method to estimate the role of IC efficiency in the banking industry and perhaps firm industry In fact, these authors have utilized the OLS approach to evaluate the relationship between VAIC and the efficiency of banks in Taiwan

In addition, this traditional method is also found in many studies in the concerned field in both banking sectors and others For instance, Meles et al (2016) use OLS method to quantify the impact of VAIC on the financial performance of commercial banks in US Similarly, Dalwai et al (2021) employ this method to examine the association between IC efficiency and bank risk-taking based on the context of 12 emerging nations in Asia Buallay et al (2020) perform OLS regression to analyze the impacts of IC and its components on the financial performance of listed banks in Gulf nations Many studies have applied the similar approach in the firm industry For example, in the preceding study, Brüggen et al (2009) apply OLS analysis to explore the role of IC based on the sample of listed firms in Australia This approach in the IC field (measured by VAIC) is also found in the cases of non-listed firms in Italy (Ginesti et al., 2018), European firms (Nirino et al., 2020), electronic companies in Taiwan (Ting et al., 2021), and many among others Hence, in the current study, this method is being employed first to point out the main concerns

However, since the approach mentioned above may obtain some limitations such as the possible effects of omitted observations (Tran & Vo, 2018), the current research also performs a variety of robustness tests to ensure the empirical findings Accordingly, a battery of robustness tests is carried out as follows

First of all, the explanatory variables of interest (both VAIC and its components) will be lagged one period of time This approach may possess some efficiencies in several ways Initially, it is argued that approach one-period lagged variable can helps to eliminate endogeneity issues resulting from the causality between the dependent variables and the independent ones in the analyzed models (Huynh & Dang, 2021) Furthermore, as Tran (2020) ascertains, banks regularly need a certain period to absorb the financial information on their financial statements and changes in macroeconomic circumstances and then adapt them to their business operations

In addition, to address limitations coming from the first method employed, other econometric methods are also being performed First, following Ozkan et al (2017) and Tran

& Vo (2018), the current study will use the fixed-effects estimator as an alternative approach to retest the findings To some extent, this estimator can be seen as one of the vehicles that may address problems related to the possible influences of time, unobserved characteristics of bank and cross-section on regression results (Phan et al., 2022a, 2022b; Phan et al., 2021)

Besides, to further ensure the robustness of findings, the dynamic panel of the system GMM method is being applied This estimator may offer some advantages in some ways as follows This technique may help to address the issues related to the potential endogeneity by harnessing internal instruments (e.g., Huynh & Dang, 2021; Le, 2021; Phan et al., 2022a) Also, this approach may help to understand the dynamic characteristics in nature of variables, which are impacted by themselves in the previous period (Tran & Vo, 2018) Moreover, this method can be seen as a robust tool to deal with the important problems, especially the potential endogeneity issue that may incur in the analysis models (e.g., Arellano & Bond, 1991; Blundell & Bond, 1998; Greene, 2012) In fact, it is witnessed an increase in the number of studies that approach this technique as the main method or an alternative one to quantify the role of IC in banking operations (e.g., Adesina, 2021; Le et al., 2022; Le & Nguyen, 2020; Tran & Vo, 2018; Ul Rehman et al., 2023; and among others) Hence, using this method as an alternative tool to recheck the regression results is quite appropriate

In addition, it is argued that this approach can address the potential endogeneity in empirical models by employing the values lagged of dependent variable as well as that of other proxies that may endeavor the endogenous problems as variable instruments (Le & Nguyen, 2020; Tran & Vo, 2018) Utilizing the lagged dependent variable in the model analysis may help to capture the dynamic features in the nature (Huynh & Dang, 2021; Phan et al., 2022a; Phan et al., 2021) Hence, to approach this method, the current research first rewrites the equations mentioned before, from (3.9) to (3.12), as follows:

Besides, following the preceding studies (e.g., Huynh & Dang, 2021; Phan et al., 2022b; Phan et al., 2021), the Arellano-Bond tests (based on the values of AR (1) and AR (2) indicators) will be employed to scrutinize the validation of regression results Accordingly, the analysis results can be seen valid if both AR (1) and AR (2) values indicate the herald of

“first- but no second-order autocorrelation of the residuals” (e.g., Le, 2021; Le & Nguyen, 2020; Tran & Vo, 2018)

On the other hand, eventually, to wipe out the possible influences of outliers, following the prior studies (e.g., Adesina, 2019; Phan et al., 2022a; Tran, 2020; and among others), all financial variables are winsorized at 1 st and 99 th percentiles.

Descriptive statistics and correlations analysis

3.4.1 Context of the Vietnamese banking system during the research period

It can be said that the period selected to carry out the current study has highlighted various pivotal issues as well as major changes in the banking system and the economic structure as a whole in Vietnam

Regarding the economic angle, the economy in Vietnam has witnessed a stable growth during the period 2006-2020 except for the last year which incurred the onset of the Covid-

19 pandemic As Figure 3.3 below indicates, the average annual value of GDP stood at around 6.5% from the first year of the given period to 2019 before dropping to nearly 3% in the final year As the assertion of Le & Nguyen (2020) and Phan et al (2022a), the economic growth of Vietnam can be seen as one of the fastest-growing economies in the ASEAN region in particular and over the world in general Also, it is an expectation that this country will may become a next dragon in the Asia area

Source: Data collected from WB database

Figure 3.3 The economic backdrop of Vietnam

In parallel with growing economy, the inflation ratio of Vietnam has emerged as a salient point Indeed, the average annual of this indicator still stood at over 7% during the given period, meaning that the interest rate of banks has fluctuated strongly It is noticeable that in the aftermath of the economic boom during the period 2006-2011 along with a dramatic surge in the credit supply fueled by the banking system, the inflation rate also reached a peak of the highest value in 2008 and 2011, respectively (see more detail in the thesis of Huỳnh Japan, 2021) Afterwards, the figure was seemingly arrested from 2011 to 2015 before tending to be steady for the remainder of time

In fact, it is true that the economic growth in this nation tends to depend much on the development and sustainability of the banking system (Huynh & Dang, 2021; Phan et al., 2022b) The statistics show that the domestic credit to private sector funded by banks increased steadily over the period More specifically, while this ratio was only around 62% in the beginning period, it had surged rapidly by nearly half by 2020 This figure also partly demonstrates the argument that the expansion of the banking industry is being considered the key engine for growing and sustaining the economy in Vietnam

At the same time, it can be said that during the selected period, the banking industry has also witnessed the major and vital reforms, besides, the emergence of many prominent events has marked the important milestones in the progress of Vietnamese economy

Domestic credit to private sector by banks (% of GDP) GDP growth (annual % - secondary axis)

Inflation, consumer prices (annual % - secondary axis)

The first and the most noticeable example is the participation in WTO (the World Trade Organization) in 2007 This important event has led to have some direct and indirect consequences for the banking sector Initially, the appearance of foreign-owned banks may lead the domestic banking market to be more fierce competition As a result, such these newcomers with higher capacities will have strongly affect the conventional operations of domestic banks, especially the expansion of lending and deposit activities (Le & Nguyen, 2020; Phan et al., 2022b) Additionally, in such scenario, banks have to be forced to adapt to new circumstance and seek out new sources of income (Le & Nguyen, 2020) Besides, it is witnessing a competition in the banking market between private banks and state-controlled ones In other words, both financial intermediation and non-interest income activities of domestic banks are being impacted by this event Consequently, as Le & Nguyen (2020) ascertain, banks have to carefully take IC-based strategy into account in the middle- and- long term

Another compelling event is the announce of the “Decision No.254” in 2012 As Dang (2020) and Huynh & Dang (2021) have stated, the main purpose of this project is to reform and restructure the financial system, especially the banking sector, to enhance the smoothness and stability of the system, which, in turn, will act as the concrete backbone for the economic growth These authors detail that along with the attempt to resolve the issues related to the bad debts of the banking system, the project also tries to incentivize the banks in expanding and finding different sources of income As a result, the non-interest income activities of banks are being fueled and enlarged in the recent times

The given period have also witnessed different events such as the announce of required standards to meet the conditions of Basel in the banking sector, or the technology-oriented development (Phan et al., 2022a; Phan et al., 2022b; Tran, 2022) For instance, the numerous circulars have been announced by the State Bank of Vietnam to meet the Basel standards, including “Circular No.02” and “Circular No.12” in 2013, “Circular No.36” and “Circular No.09” in 2014, “Circular No.41” in 2016 focusing specially on the Pillar I of Basel Regarding the Pillar II, there are some related regulations presented in the “Circular No.44” in 2011, “Circular No.10” in 2012, and “Circular No.07” in 2013 In terms of the Pillar III, some relevant regulations consist of “Circular No.16” in 2010, and “Circular No.41” in 2016 Furthermore, the disclosure information, report quality, and transparence of publicized financial statements have been concerned by national regulators during the given period (see more detail in studies of Tran (2022); Tran Viet Dung et al (2020))

Generally, it is concluded that the banking system has become the concrete bedrock of the economy since the equity market in Vietnam remains undeveloped (Huynh & Dang, 2021;

Le & Nguyen, 2020) Hence, it is not surprisingly that a surge in the number of studies has paid special attention to the banks’ operations in the recent years In this sense, while the role of IC efficiency in particular and intellectual resources in general is increasingly recognized as the center engine for banks’ business, the current study will contribute to the existing studies by exploring two major aspects of banking operations, namely financial intermediation and non-interest income activities

Taken together, based on this period, the findings in the current study are being expected to provide a whole picture of the possible exertion of IC to business operations of the Vietnamese banking system

The descriptive statistics of variables employed is being illustrated in Table 3.2 below

As the given figures in the table indicate, it is easy to see that among three major ingredients of VAIC, HCE has the highest value during the given period in comparison with others Specifically, the average annual value of VAIC stood at around 3.406, while that of three components including HCE, SCE, and CEE were about 2.463, 0.554, and 0.391, respectively

The highest figure of HCE is also found in many existing studies in this field For instance, based on the data set of 44 Turkish banks from 2005 to 2014, the study carried out by Ozkan et al (2017) shows that the average annual values of VAIC, HCE, SCE, and CEE are 3.886, 2.952, 0.675, and 0.259, respectively In the work of Adesina (2019), who uses the database of 339 commercial banks in over 31 countries in Africa, these values of VAIC, HCE, SCE, and CEE are 3.77, 2.72, 0.57, and 0.48 Other studies indicating the highest value of HCE compared to other components consist of Meles et al (2016) based on the sample of US banks, Nazir et al (2021) based on that of financial institutions in three Asia countries (China, Taiwan, and Hongkong), and among others

The studies utilizing the extended-VAIC also obtain a similar result For example, Bayraktaroglu et al (2019) employ the extended-VAIC based on the context of manufacturing firms in Turkey and show that the figures of VAIC, HCE, SCE, and CEE are 2.33, 1.93, 0.05, and 0.34, respectively Some recent studies based on the Vietnamese banking sector have also shown the highest value of HCE, such as the work of Le & Nguyen (2020), the research of Nguyen et al (2021), the study of Le et al (2022), or the recent paper of Hang & Trang (2023)

Other studies investigating the firm industry in Vietnam are also similarity For instance, one of the most recent studies conducted by Nguyen (2023) shows that the average annual values of VAIC, HCE, SCE, and CEE are 3.374, 2.639, 0.494, and 0.241, respectively

It means that the figure of HCE has the highest value compared with other components, indicating that the statistics in the current study may be in consistent with the extant literature in the concerned field

VARIABLES N mean sd min max

Research procedure

In this section, the current study will detail a variety of rigorous steps to demystify the research procedure conducted It is hoped that the procedure will give a deeper understanding of all stages that the research relies on to explore the research objectives The stages are being illustrated clearly below

In general, it is clear that the research procedure consists of nine major steps spanning from identifying the main aims to discussing the empirical results, and then proposing the research significance

Specifically, the initial step embarks on the clarity of the key purposes of the present study This stage is quite important because it assists the author in identifying the direction that the study will pursue It can be said that the research aims are “navigation beacons” for the successfulness of this study

Afterwards, conducting the literature review is the necessary step Reviewing the existing studies in the concerned field will help to illuminate some crucial issues such as the current trends, the formulations of variables, and the research gaps At the same time, this step will provide the pivotal information that based on it, the next stage, namely constructing the theoretical framework, will be constituted The theoretical foundation can be as the backbone of the study because it contributes to clear explanation of the causal-effect relationships paid much attention by the writer In addition, both aforementioned steps also render the fundamental principles for the development of hypotheses created in the fourth stage

In the next consecutive stages, the present study will clarify the variables employed and collect necessary data besides computing variables Based on these steps, the empirical models are being built to quantify the causal-effect relationships Then, along with the empirical findings analyzed, a variety of robustness tests are also performed to ensure that the results withstand In the final step, the detailed discussion as well as related implications stemming from the findings are being stated

In short, all stages mentioned above give a brief summary of the research procedure that reflects each step built to reach an obvious interpretation of the main concerns of the present study

Step 1 Clarifying the main purposes of the study

Step 3 Building the theoretical framework

Step 6 Collecting data & calculating variables

Step 7 Constructing the empirical analysis models

Step 8 Conducting various robustness tests &

Step 9 Discussing the results and propounding related implications

In tandem with the theoretical backbone illustrated in the previous chapter, Chapter 3 details some key points related to the methodology employed in the current study to address the main concerns and reach the clear answers to the research questions In particular, the chapter provides a variety of main contents as follows

First and foremost, the methodology of VAIC measure is being illustrated in the minutest detail This is quite crucial because VAIC and its components not only will play the key role in the analysis models but also are the embodiment of IC efficiency which is the focal point of the current study At the same time, the detailed calculation of VAIC is also depicted through each necessary step Besides, the pros and cons of this method along with the information related to scientific endeavors in the extant literature to bridge its limitations are being stated and analyzed It is expected that the content will give a deeper insight into the ethos of VAIC for readers, and may stimulate more research to approach other extended VAIC in the future

Second, the sample data and variables employed are also mentioned Specifically, the sources of data as well as the definitions and calculations of variables are interpreted clearly in this chapter, which will demonstrate the feasibility and reliability of the analysis models performed and perhaps, the findings portrayed in the next chapter

Third, the model specification as well as the econometric approaches are depicted and explained in the chapter Accordingly, the numerous equations are being constructed to quantify the main concerns in the current analysis In addition, the regression methods selected to carry out the empirical analysis are delineated precisely In parallel with that, the chapter underscores the ways in which a battery of robustness tests applied to ensure the findings

Eventually, the information about the descriptive statistics and correlation analysis between variables employed are described in the final subsection in the chapter This, in turn, may not only help readers to understand clearly the aspect of the correlation of all variables, but also ensure the reliability of analysis model constructed Besides, the backdrop of the research period, which contains the memorable events in both the banking system and the economy as a whole, is being highlighted to explain the key reasons why the period is chosen and to demonstrate its importance

In short, the methodology described in this chapter will provide the direction by which the research questions will be dealt with The expectation is that it also gives the key to open the doors in which the findings will be delineated and analyzed in the next chapter.

EMPIRICAL FINDINGS AND DISCUSSIONS

Financial intermediation of banks and the role of intellectual capital

The section is to focus on figuring out the straightforward question of whether the financial intermediation activities of banks has been boosted by IC efficiency or not, which is also one of two chief concerns in the current study Accordingly, the structure of this section is being organized as follows

First, the main results are described by which the heart of the empirical analysis is to depict the relationship between VAIC and its components, and the financial intermediation Afterwards, the robustness tests performed to ensure the findings presented previously These tests are based on the existing studies and are mentioned in minute detail in the methodology chapter The third subsection provides the estimation of the role of bank size in the empirical analysis Finally, the whole discussion is stated comprehensively to give an evident and general understanding about the results

In the first subsection, the major results of the empirical analysis are being presented Accordingly, these main findings are illustrated in Table 4.1 below, which consists of six models As mentioned in the section 3.3 of Chapter 3, the ordinary least squares regression is being applied first to identify the relationship between VAIC and its components, and the financial intermediation of banks This association is seen as the main concern in all models presented

It is clear that there are numerous studies in the extant literature that have employed this approach to explore the role of IC efficiency (regularly measured by VAIC model) in both the banking sector and the firm industry

For instance, Meles et al (2016) approach this method based on the context of US banks, or the recent work of Kweh et al (2022), who use this approach for banks in Taiwan Buallay et al (2020) also perform OSL method to point out the connection between IC and its elements, and the performance of listed banks in Gulf countries This approach is also found in many existing studies in the IC field based on the non-bank industry For example, Ginesti et al (2018) use it for Italian listed companies, Nirino et al (2020) utilize it for companies in Euro, or Ting et al (2021) employ this tool in the electronic firm industry in Taiwan

In short, as the ascertain of Kweh et al (2022), this traditional method is still effective tool to investigate the impact of IC efficiency in the banking industry So that, Model (1)-(6) in Table 4.1 will be regressed by usage of this approach following the equations (3.9) – (3.10)

It is necessary to state that these equations are being performed in the different combinations of controlled variables

At the beginning step, the model (1) is regressed by using only the explanatory variable of interest, namely VAIC, that will be regularly known as ‘the reduced model’ As noted earlier, this section is to focus mainly on the relationship between IC efficiency reflected by VAIC model and the financial intermediation activities of banks, hence using one key independent variable may help to partly understand this association This approach can be easily found in many existing studies in the financial literature (e.g., Phan et al., 2021; Tran,

More specifically, looking at the detail result presented in the model (1), it is quite easy to see that a positive relationship between VAIC and financial intermediation at the 1% level of statistical significance is found Furthermore, the value of the R-squared is around 14.2%, meaning that the analysis model can explain the causal effect of variables at the level of over 14%

Afterwards, the combination of variables reflecting the bank-specific characteristics controlled is added into the model (1), and the results are presented in the model (2) This step is quite necessary to answer the question of whether the positive association depicted above remains unchanged or not As the model (2) presents, the result again shows a positive connection between IC efficiency and financial intermediation Particularly, the coefficient of VAIC continues to be significantly positive at the 1% level

Contrary to the model (2), the model (3) only controls the macroeconomic variables to give a clear answer of whether the positive impact of VAIC still withstands In the similarity with aforementioned finding, the result in the model (3) continues to indicate that the influence of VAIC remains positively and statistically significant at the 1% level

In the next stage, as depicted in the equation (3.9), a combination of both bank-specific characteristics and country-level features controlled is to evaluate the association between VAIC and financial intermediation of banks The result is described in the model (4), also namely the baseline model, indicating that the coefficient in the model has the statistical significance at the 1% level It means that the vital role of IC efficiency in financial intermediation activities of banks withstands throughout the four models performed

At the same time, it is easy to see that the figure of the R-squared in this model stands at over 25%, which is higher value compared to the three prior models, suggesting that the level of explaining variables in the model is quite better In addition, it is noticeable that according to the result in the baseline model, an increase in one standard deviation of VAIC and holding all other equals would result a rise in FI of 15.1 bps (i.e., the coefficient of VAIC, 0.149, times the standard deviation of VAIC, 1.014)

On the other hand, because the research sample obtains some banks owned by the Vietnamese state, it is quite necessary to evaluate the possible impact of this kind of banks on the aforementioned finding In fact, some existing studies have indicated the different impacts of the role of IC efficiency in these banks For instance, Singh et al (2016) find that the private banks tend to harness intellectual resources more effectively than public ones In a similar result, Tiwari & Vidyarthi (2018) also find that banks operating in the private sector will use these resources more effectively than those in the public sector Performing this step is also seen as the quite necessity to further test the previous finding

So that, to estimate this impact, first, a dummy variable (STATE) will be created This variable will equal one if a bank is a state-owned bank and equal 0 otherwise In fact, the banking industry in Vietnam has had 4 big banks (regularly known as “the Big4 banks”), which can be seen as the largest banks in the banking sector, and are controlled mostly by the Vietnamese state Besides operating as commercial banks, these banks also serve as the bridge to diffuse the policies and aims of the national authorities As the aforementioned studies indicate, intellectual resources are seemingly harnessed effectually by private banks compared to public ones Hence, adding this dummy variable into the baseline model and regressing this model again may help to demystify the impact of IC efficiency in both types of banks Also, performing this step is also seen as the quite necessity to further test the previous finding This approach can be found in some recent studies (e.g., Le & Nguyen, 2020; Phan et al., 2022a and among others) The result will be depicted in the model (5)

As this model indicates, a positive association between VAIC and financial intermediation again is confirmed Specifically, the coefficient of VAIC continues positive and statistical significance at the 1% level Moreover, the R-squared value is also slightly higher compared to the result presented in the baseline model Meanwhile, the coefficient of the dummy variable shows positive but statistical insignificance It means that while the previous finding remains unaltered, the possible impact of state-owned banks on this finding is now seemingly inconclusive evidence

Non-interest incomes of banks and the role of intellectual capital

While the first section emphasizes the aspect of the financial intermediation, this section will examine the extent to which the IC efficiency exerts the non-interest income activities of banks The organization of the section is structured in a way similar to the previous section Accordingly, there are four main contents consisting of the main results, the robustness tests, the effect of bank size, and finally, the detailed discussions

Similarly, the first content is to point out the main concern and the second content is to retest the main findings Afterward, the impact of bank size is evaluated before the whole picture of discussions based on the empirical analysis is mentioned At the same time, each analysis model and each step of the robustness test process will be performed in the same way as described in the prior section

The significant findings, which focus mainly on the association between the non- interest income activities and IC efficiency, are being delineated in this subsection These regression results are illustrated in Table 4.5 below, which includes the six models It is also necessary to restate that the ordinary least squares regression is applied as the first regression method to explore the primary concern in all models

As noted in the previous section as well as in the prior chapter, this approach is still appropriate and suitable to investigate the role of IC efficiency in the financial literature (Kweh et al., 2022) In fact, many recent studies have used this conventional method to discover the impact of IC efficiency in both the banking sector and firm industry (e.g., Buallay et al., 2020; Kweh et al., 2022; Meles et al., 2016; Ting et al., 2021; and among others) So that, this method is utilized to regress all model in table below In addition, a different combination of control variables is also employed as depicted in Table 4.1 previously

At the beginning stage, only the key explanatory variable (VAIC) is employed in model (1), which is also known as “the reduced model” and concentrates deeply on the main relationship This minimized model is being approached in many existing studies (e.g., Phan et al., 2021; Tran, 2020, 2021; and among others) Accordingly, the result shows that VAIC positively affects the dependent variable (NII) at the 1% level of statistical significance Besides, the R-squared value is over 12%, suggesting that the causal effect of employed variables can be explained at a degree of around 12%

Afterwards, the bank-specific characteristics and macroeconomic features are employed in models (2) and (3), respectively The empirical results indicate that while the coefficients of VAIC in both models are positively and statistically significant, the power of statistical significance reduced to the 10% level in model (2) However, the value of the R- squared is quite higher compared to the minimized model Specifically, this figure stands over

80% and nearly 20% in model (2) and model (3), respectively Based on this result, up till now, it can be said that the positive association between NII and VAIC is still unchangeable

In the next model, which is known as the baseline model, the equation (3.11) is being regressed, in which both bank-specific and macro-specific variables are controlled The result continues to indicate a positive relation between VAIC and NII, however this impact only stands at the 10% level of statistical significance, which is quite similar with the finding in model (2) In addition, the R-squared value again shows the higher value, standing at nearly 82%, suggesting that the level that the model can explain the relationships between variables employed is enhanced remarkably Furthermore, as the result depicted in the baseline model, it is quite important to state that according to the result in the baseline model, an increase in one standard deviation of VAIC and holding all other equals would result a rise in NII of 16.02 bps (i.e., the coefficient of VAIC, 0.158, times the standard deviation of VAIC, 1.014)

Resemblance to the investigation on the impact of government-owned banks on the main concern analyzed in Table 4.1, the dummy variable, namely STATE, is being constituted This variable will possess the value of one in the case of state-owned banks, and zero otherwise This examination is quite important because some extant works have considered that the private banks seemingly use intellectual resources more effectually than their counterparts (e.g., Singh et al., 2016; Tiwari & Vidyarthi, 2018; and among others) Also, such step is a way to test the preceding finding in the first four models As stated in subsection 4.1.1, because this step pays attention to public and private banks in the research sample, this dummy variable will reflect the Big 4 banks (equaling 1) and others (equaling 0)

To investigate the impact mentioned above, the dummy variable is being added into the baseline model before carrying out the regression The result in model (5) shows that again the impact of VAIC nearly remains unchanged and possesses the statistical significance at the 1% level At the same time, the value of the R-squared also reaches the higher value of over 82% compared to the result presented in the prior models Interestingly, contrary to the finding mentioned in Table 4.1, which shows no conclusion on the impact of government-owned banks, the coefficient of the dummy variable is negatively and statistically significant at the 1% level, meaning that these banks have fewer incentives to expand their non-traditional incomes To some extent, this result may herald “a quiet life” in these banks, which also reaffirms the preceding findings (e.g., Singh et al., 2016; Tiwari & Vidyarthi, 2018; and among others)

At the eventual stage, to estimate the impact of different components of VAIC, the current analysis re-performs the baseline model in which VAIC is divided into three components consisting of CEE, HCE, and SCE The regression results are illustrated in model (6)

In contrast to the results in Table 4.1, the coefficient of capital employed efficiency is positive but statistically insignificant, while that of other components is statistically significant More specifically, the impact of structural capital efficiency is remarkably positive and statistically significant at the 1% level By contrast, the effect of human capital efficiency is negatively and statistically significant at the 10% level It means that banks can reap the economic benefits from leaning on the former component to expand non-interest income activities However, the latter element can take a toll on this expansion Besides, the degree to which the model can explain the connection between variables stands at nearly 83%, which is the highest value among the six models regressed

In general, based on the findings mentioned above, some main points can be concluded as follows First, the empirical evidence demonstrates that IC efficiency has fueled non-interest incomes of banks In other words, IC efficiency has become one of the main drivers that would enhance the transformation from traditional incomes to non-traditional ones in the banking system Some prior studies suggest that adopting new technologies and harnessing IC may advance the competitiveness and market share of banks (e.g., Singh et al., 2019; Vătămănescu et al., 2019) In this light, the evidence complements that the shift towards non-traditional incomes of banks has been strengthened by IC efficiency

Additionally, when separating VAIC into different components, the empirical result reveals that structure capital employed is the most important component that assist banks to expand into non-interest income activities, while an increase in human capital employed may make banks decrease non-interest incomes Furthermore, there is no evidence that support the relationship between capital employed efficiency and the main concern

In the next subsections, a variety of the robustness tests will be conducted to re- examine the aforementioned preliminary findings before the detailed discussions are stated eventually The discussions will emphasize on not only the role of variables of interest, but also other control variables that are applied in the analysis models

VARIABLES Reduced model Control bank-specific Control macro-specific

Robust standard errors in parentheses: *** p