Intellectual capital – bank efficiency nexus: evidence from an emerging market

Tu Dq Le; Trang Nt Ho; Nguyen Dat; Thanh Ngo Article Intellectual capital – bank efficiency nexus: evidence from an emerging market Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Tu Dq Le; Trang Nt Ho; Nguyen Dat; Thanh Ngo (2022) : Intellectual capital – bank efficiency nexus: evidence from an emerging market, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-18, https://doi.org/10.1080/23322039.2022.2127485 This Version is available at: https://hdl.handle.net/10419/303817 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Intellectual capital – bank efficiency nexus: evidence from an emerging market Tu DQ Le, Trang NT Ho, Dat T Nguyen & Thanh Ngo To cite this article: Tu DQ Le, Trang NT Ho, Dat T Nguyen & Thanh Ngo (2022) Intellectual capital – bank efficiency nexus: evidence from an emerging market, Cogent Economics & Finance, 10:1, 2127485, DOI: 10.1080/23322039.2022.2127485 To link to this article: https://doi.org/10.1080/23322039.2022.2127485 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 03 Oct 2022. Submit your article to this journal Article views: 1963 View related articles View Crossmark data Citing articles: 9 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Intellectual capital – bank efficiency nexus: evidence from an emerging market Tu DQ Le 1,2 *, Trang NT Ho 3,4 , Dat T Nguyen 1,2 and Thanh Ngo 1,5 Abstract: This paper investigates the effect of intellectual capital (IC) and its components on the efficiency of Vietnamese commercial banks from 2007 to 2019 using the two-step Data Envelopment Analysis approach. Banks’ efficiency scores are firstly estimated, while the relationship between IC and bank efficiency is examined in the second stage. The results indicate a positive relationship between IC and banks’ pure technical efficiency, allocative efficiency, and total cost efficiency. When observing the effect of IC decompositions, the findings show that only human capital enhances all types of bank efficiency. Furthermore, bank size and liquidity risk are significant drivers of Vietnamese bank efficiency. Therefore, our findings suggest that bank managers should focus on intellectual capital, particularly human capital, to strengthen bank efficiency further. Subjects: Microeconomics; Banking; Credit & Credit Institutions Keywords: Intellectual capital; bank efficiency; DEA; physical capital; human capital; structural capital JEL classification: G21; G28; G30 1. Introduction The growth of innovation and technologies and an increasingly competitive environment have made knowledge-based resources crucial in creating value and substituting the traditional factors of firms’ production (Al-Musali & Ismail, 2016; Xu & Li, 2019). In which intellectual capital is seen as a critical resource that could ensure a firm achieves superior economic, financial, and market performance (Sannino et al., 2021) and thereby determine an organization’s success under the resource-based view (RBV) paradigm (Barney, 1991). Since intellectual capital provides additional value to the organization and is considered one of the key sources of sustainable competitive advantage, this topic has attracted much attention from academic researchers and practitioners (Rehman, Bresciani et al., 2021). Indeed, intellectual capital (IC) is even more relevant to the banking sector because it helps to identify the degree of a bank’s performance in comparison to its rivals (Meles et al., 2016; Ting et al., 2021). Tran and Vo (2018) also provided some explanations on the appropriateness of IC in the banking industry. Because banking operations heavily rely on customers and bank products are not manufactured goods, banks must offer the highest quality and various services to customers to survive. Banks have intensively invested in human resources, brand names, systems, and processes. Given that IC is a multi-dimensional resource of experience, knowledge, and practical capabilities, it would help banks enhance their effectiveness and efficiency, thus maintaining longterm competitive advantage. Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 1 of 18 Received: 06 March 2022 Accepted: 19 September 2022 *Corresponding author: Tu DQ Le, Vietnam National University, Ho Chi Minh city 700000, Vietnam E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, Stirling United Kingdom Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. The evidence of the relationship between IC and bank performance is mixed. Early studies focus on bank performance using accounting/market-based measures (e.g., return on assets, return on equity, Tobin’s Q ratio). Several studies using bank data in developed markets have shown that IC is a significant determinant of bank performance (Bollen et al., 2005; Clarke et al., 2011; Joshi et al., 2013; Meles et al., 2016; Mention & Bontis, 2013; Riahi-Belkaoui, 2003; Ståhle et al., 2011; Tan et al., 2007; Youndt et al., 2004; Zeghal & Maaloul, 2010). In the same vein, several studies in emerging markets have claimed the same conclusion (Firer & Williams, 2003; Goh, 2005; Le, Nguyen, McMillan et al., 2020; Mondal & Ghosh, 2012; Pendo, 2020; Poh et al., 2018; Singh et al., 2016; Smriti & Das, 2018; Tran & Vo, 2018) Lotto (2019) stated that operating efficiency is one of the crucial factors determining bank success. However, the evidence of IC on bank efficiency is relatively scanty (Adesina, 2019; Duho, 2020; Ting et al., 2021; Vidyarthi, 2019; Vidyarthi & Tiwari, 2019). The literature in IC components-bank efficiency nexus also yields confounding findings. Several studies have indicated a negative relationship between relation capital and bank efficiency (Ting et al., 2021), while others have found opposite findings (Mondal & Ghosh, 2012). The insignificant impact of IC components on bank efficiency is also documented by Tandon et al. (2016), Alhassan and Asare (2016), and Mention and Bontis (2013). Due to different measures of bank efficiency used and significant differences in regulatory and economic environments in which banks have operated, evidence on IC–bank efficiency nexus is inconclusive. This study will revisit this matter using the Vietnamese banking system in its distinguished setting. Vietnam boasts one of the leaders in the economic performance of the Association of Southeast Asian Nations (ASEAN), with an average annual economic growth of 6.2% from 2007 to 2019. It is not surprising that this country is classified as Asia’s next dragon. As the financial market is still developing, the remarked growth of the Vietnamese economy is attributed to the banking system. Maintaining efficiency is one of main concerns of academics and practitioners and Vietnamese authorities. Because the literature argues that IC is one of the increasingly crucial factors contributing to the success of banks, this necessitates investigating whether IC has any effect on bank efficiency in Vietnam. Since Vietnam entered World Trade Organization in 2007, the presence of foreign banks in the market has increased the challenge to the growth of local banks’ deposits and loans, thus, may affect their efficiency. To overcome this challenge, banks should pay more attention to IC as their long-term goal (Le, Nguyen, McMillan et al., 2020). This study contributes to the literature in several ways. Most prior studies have examined the effect of IC on bank performance using accounting measures, whereas our study looks at the relationship between IC and bank efficiency. Although a few studies have considered the effect of IC on bank efficiency (Ting et al., 2021; Vidyarthi, 2019; Vidyarthi & Tiwari, 2019), the evidence in emerging markets, especially ASEAN region, is limited. Thus, this study will provide a comprehensive understanding of the impact of intellectual capital on the efficiency of the banking system. To the best of our knowledge, this research is the first attempt to examine the relationship between IC (and its components) and bank efficiency (e.g., cost efficiency, technical efficiency and allocative efficiency) in Vietnam using a two-stage DEA framework. This study, therefore, provides significant implications for bank managers and policymakers. Using panel data of 30 commercial banks in Vietnam between 2007 and 2019, the findings show a positive effect of IC on bank efficiency. When observing IC components, the results indicate that only human capital can stimulate three types of bank efficiency. Additionally, allocative efficiency is positively affected by capital employed efficiency and structural capital efficiency. The remainder of this paper is structured as follows. Section 2 reviews the relevant literature. Section 3 describes the methodology and data employed for the study. Section 4 discusses empirical results while Section 5 concludes this study. Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 2 of 18 2. Literature review One may argue that an individual firm in each industry sector has a heterogeneous set of tangible/ physical (e.g., property, plant, equipment, physical technologies, production facilities, and raw materials) and intangible resources (competencies, capabilities, skills, tacit and explicit knowledge, culture, relationships, brands, and patents). The resource-based view paradigm suggests that these resources constantly contribute to an organization’s value creation (Barney, 1991). Alternatively, a firm’s resources and capabilities can result in superior performance and competitive advantage (Rehman, Ashfaq et al., 2021). In which IC, as one of important firm’s resources, plays a crucial roles in the value generating process (Barney, 1991; Kamath, 2007; Sannino et al., 2021). Therefore, many empirical studies have attempted to examine whether IC could improve bank performance. Note that understanding the definition of IC is important since it will determine how IC can be measured. It is acknowledged that the concept of intellectual capital remains controversial (Marr & Moustaghfir, 2005). Different disciplines and perspectives from various fields (e.g., economics, strategy, finance, accounting, human resources, reporting and disclosure, marketing, and communication) define IC differently. More specifically, Giacosa et al. (2017) argued that IC addresses knowledge-related difficulties in businesses. Additionally, Sullivan (2000) defined IC as the total value of intangible assets that are not declared in the corporate’s balance sheet, while Bontis (1998) considers IC as the stock of knowledge in an organization. Ghe and Burz (2008) asserted that IC could convert knowledge into value-generating resources. In a broad view, IC is described as knowledge-related intangible assets that comprise intellectual resources, intellectual property, and intellectual competencies (F.-C. Chen et al., 2014). Given various definitions and data availability, several traditional measurements of IC and its components have been proposed and applied in multiple disciplines. In particular, Saint-Onge (1996) and Stewart (2007) conceptualized IC as the sum of human capital, structural capital, and customer capital. Bontis (1998), however, classified IC into financial capital, human capital, and structural capital. Alternatively, several papers categorize IC as human capital, structural capital, and relational capital (Kujansivu, 2005; Petty & Guthrie, 2000; Sveiby, 1997). Following most prior studies on intellectual capital, we focus on one of the most common approaches as proposed by Pulic (2004) or the so-called value-added intellectual coefficient (VAIC). 1 This method is widely used in many sectors and industries by assuming that the value creation efficiency in organizations is the contribution of all organization’s resources (e.g., human capital, physical capital, and structural capital). Using the VAIC framework, many studies have attempted to investigate the association between the IC and bank performance and yielded conflicting results. Meles et al. (2016) found a strong positive correlation between IC and bank performance. This finding is in line with the studies of Bollen et al. (2005); M. C. Chen et al. (2005); Clarke et al. (2011); Diez et al. (2010); Ng (2006); Tan et al. (2007); Tovstiga et al. (2007); Wang et al. (2005); Youndt et al. (2004), Zeghal and Maaloul (2010), Anifowose et al. (2017), Joshi et al. (2013), Mehralian et al. (2012), Tiwari and Vidyarthi (2018), Rehman, Elrehail, Alsaad and Bhatti (2021), and Rehman, Ashfaq et al. (2021). By contrast, studies by Firer and Williams (2003), Gan and Saleh (2008), Ståhle et al. (2011), Williams et al. (2011), and Zeghal and Maaloul (2010) have failed to prove that organizations with a good source of human capital, well-established capital structures, and well-organizational processes boost firm’s performance. In contrast to using conventional measures of bank performance, bank efficiency has received much attention from academic research. This inspires several studies to examine the effect of intellectual capital on bank efficiency. Vidyarthi and Tiwari (2019), using a panel of 37 Indian banks, indicated the positive but minimal impact of the aggregate IC on cost efficiency, revenue efficiency, and profit efficiency. This result is comparable with Adesina (2019), who claims the positive effect of IC on banks’ efficiency in 31 African countries. When observing the IC Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 3 of 18 components, Onumah and Duho (2020) concluded that human capital is the main determinant of pure technical and cost efficiencies in Ghana, while profit efficiency is positively affected by human capital efficiency and capital employed efficiency. Similarly, Duho (2020) demonstrated the positive relationship between IC and bank efficiency, and human capital is the main driver. On the other hand, Ting et al. (2021) suggested that bank branches’ efficiency in Taiwan is not affected by human capital and structural capital but is negatively associated with relational capital. Due to the mixed evidence of the effect of IC in emerging markets, especially the ASEAN region, this study revisits this disagreement in the context of the Vietnamese banking system It is acknowledged that limited studies examine the impact of intellectual capital on either bank profitability (Le, Nguyen, McMillan et al., 2020; Tran & Vo, 2018) or bank risk (Nguyen et al., 2021). It is argued that the resources from IC, in the form of experience, knowledge, and practical capabilities, may contribute to banks’ efficiency, but their direct impacts have received little attention (Vidyarthi and Tiwari (2019); Dumay (2016)). By assessing the impact of IC on bank efficiency (e.g., cost efficiency, technical efficiency, and allocative efficiency), this study will offer a better understanding of the effect of IC in the Vietnamese banking system. 3. Methodology and data This study examines the relationship between intellectual capital and bank efficiency using a twostage Data Envelopment Analysis (DEA) approach. In the first stage, bank efficiency is estimated using DEA. In the second stage, bank efficiency scores are then regressed against intellectual capital estimated from VAIC framework as proposed by Pulic (2004) while controlling for other factors. 3.1. Measuring bank efficiency—the DEA approach It is worth noting that bank efficiency is primarily calculated using either the parametric method (stochastic frontier approach—SFA) and the non-parametric method (Data Envelopment Analysis —DEA; Berger & Mester, 1997). Bauer et al. (1998) argue that there is no best method to measure efficiency. Weill (2004) affirms the well-matched average efficiency scores measured by DEA and SFA approaches. Similarly, Çelik (2012) emphasize that both SFA and DEA results are similar. Indeed, each of them has its own advantages and disadvantages as documented by many studies such as Ho et al. (2021), Le et al. (2021). DEA is selected to measure bank efficiency in our study since it does not required a priori production function and is more appropriate to our small sample size (Boubaker et al., 2022). DEA was firstly developed by Farrell (1957) and extended by Charnes et al. (1978). The two major DEA models, constant returns to scale (CRS) and variable returns to scale (VRS), are widely used in many empirical studies. This study uses input-oriented DEA under variable returns to scale (VRS) assumption to estimate bank cost efficiency (Le, 2018; Le, 2020; Le et al., 2021; T. Ngo & Le, 2019; T. Ngo & Tripe, 2017). Consequently, the VRS cost minimization DEA model is constructed as follows: Min∑kWjkXjk (1) Subject to Xjk �∑iXikλi"k Yjs �∑iYisλi"s Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 4 of 18 ∑n i¼1λi¼1 λ�0 where j = 1, 2, 3, . . ., n denotes the number of Vietnamese banks, using a vector of j inputs Xi = (Xi1;. . . ;XikÞfor which they pay prices Wi = (Wi1;. . . ;WikÞto generate a vector of s outputs Yi = (Yi1;...;YisÞ; Yi1;. . . ;YisÞillustrates the vector of input and output weights. The optimal value of input demand vector X� j = (X� j1;...;X� jkÞto minimize costs for bank j with the given input prices W can be identified by solving the linear programming problem (1). The cost efficiency (CEj) of bank j is measured by the proportion of the minimum cost and the actual cost: CEj¼C� j Cj¼∑kWjkX� jk ∑kWjkXjk (2) Following Sealey and Lindley (1977), banks are seen as an intermediary employing personnel and physical capital to transform deposits into earning assets. As a result, net loans (Y1) and other earning assets (Y2) are the two outputs, and total funding (X1), fixed assets (X2), and personnel expenses (X3) are used as three inputs. In addition, the corresponding prices of inputs are financial capital price (W1), physical capital price (W2), and labour price W3 ð Þ, respectively. 3.2. Measuring bank intellectual capital and its decompositions The VAIC literature suggests different approaches to measuring IC. 2 However, the conventional VAIC methodology is used in this study as it provides standardized and consistent measures (Shiu, 2006). The conventional VAIC methodology is assessed as an innovative approach both theoretically and methodologically, and it does not contradict or modify any of the fundamental principles of accounting (Iazzolino & Laise, 2013). Following Pulic (2004), Meles et al. (2016), and Tran and Vo (2018), and Le, Nguyen, McMillan et al. (2020), the value-added intellectual coefficient (VAIC) is estimated as follows: VAICit ¼CEEit þHCEit þSCEit (3) where CEEit denotes the capital employed efficiency; HCEit represents the human capital efficiency; HCEit is the structural capital efficiency. Furthermore, the estimation of the total value added (VA) is necessary to measure the VAIC decompositions. Following Le, Nguyen, McMillan et al. (2020), the total value added is obtained as VAit ¼OPit þPCit þAit where OPit is a bank’s operating profit; PCit denotes personnel expenses (salaries, wages, and other benefits), and Ait is the bank’s amortization and depreciation. Once VAit is estimated, the VAICit elements are calculated as follows: CEEit = VAit/CEit where CEit is measured as the book value of equity. HCEit = VAit/HCit, where HCit refers to staff cost. SCEit = SCit/VAit, where SCit represents structural capital and is calculated as SCit=VAit/HCit. 3.3. Model specifications In the next stage, the cost efficiency scores as derived from the DEA approach are regressed against IC, (its subcomponents in a separated model), and other explanatory variables. Following Adesina (2019), Batir et al. (2017), Meles et al. (2016), our following baseline model is formed: EFF ¼αþβ1VAICjt þδBVjt þθCVjt þεjt (5) Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 5 of 18 Where EFF is bank efficiency (TE, AE, and CE) derived from the input-oriented DEA method (see, section 3.1); VAIC is intellectual capital (see, section 3.2); BVjt and CVjt are the vectors of bankspecific features and macroeconomic factors, respectively. The subscript j represents a bank (j = 1, 2, . . ., 30) while t, ε denote the time trend and the error term, respectively. When considering the effect of IC components, VAIC in equation (5) is replaced by HCE, CEE, and SCE (see, section 3.2) For bank-specific characteristics, bank size (SIZE) and liquidity risk (LATA) are used. SIZE;as measured by the natural logarithm of total assets, is used to control for bank size. Large banks with market power should pay less for their inputs, thus improve their efficiency (Le, 2018). Dietrich and Wanzenried (2014) however contend that smaller banks can improve their performance because of reducing the asymmetric information problems. SIZE;, the liquid assets to total assets ratio, is used to control for the effect of liquidity risk (Sharma et al., 2013). Sharma et al. (2013) state that holding more liquid assets tends to reduce bank performance. By contrast, banks that hold more liquid assets are less likely defaulted, thus improving bank performance (Bordeleau & Graham, 2010; Le, 2017c). For macroeconomic variables, BSDev;as measured by the ratio of domestic credit extended to the private sector by banks to GDP is used to control for the impact of banking sector expansion (Adesina, 2019). The Herfindahl-Hirschman Index (HHIA), calculated by the sum of the squared market shares of each bank’s assets for a particular year, is proxied for market concentration 3 (García-Herrero et al., 2009). HHIA is close to 0 for a perfectly competitive market and equals 1 in the case of a monopoly. A lower (higher) value of HHIA means a higher (lower) competition level. We use the annual GDP growth rate (GDP) to examine the influences of economic expansion on bank efficiency (Haris et al., 2019). Finally, the annual inflation rate (INF) is employed to control for inflation consequences (Perry, 1992). Because the value of bank efficiency score ranges between 0 and 1, the Tobit regression is recommended to overcome this limitation (Kumbhakar & Lovell, 2000). The Tobit model also account for the features of the distribution of efficiency metrics and thereby, producing consistent results (Sufian & Noor, 2009). Following Adesina (2019); Batir et al. (2017); Shaban and James (2018), the Tobit regression (so-called one-sided censored Tobit model) is used to accommodate for the narrow [0, 1] range of efficiency values as derived from DEA approach in the first stage which are then regressed against with VAIC and its sub-components in the second stage. To avoid the endogeneity problem that may arise (e.g., the probability of causality between bank efficiency and market concentration and other determinants), the system GMM proposed by Arellano and Bover (1995) is used for robustness checks. The GMM estimator accounts for unobserved heterogeneity and the persistence of the dependent variable into consideration (Arellano, 2002), thus producing consistent parameters. In other words, the use of ample instruments results in estimated coefficients more efficient (Le, 2021a; Le et al., 2022; Le & Ngo, 2020). 3.4. Data Note that several criteria on data collection were applied. Banks with more than five years of data are only included to examine the effect of intellectual capital on bank efficiency. Also, only local commercial banks are chosen to ensure homogenous among DMUs in DEA because they are the most active players, while foreign bank affiliates, wholly foreign-owned banks and joint-venture banks are relatively constrained to operate in the Vietnamese market (Le, 2020, 2021b; Le et al., 2019). This thus arrives at a panel data of 30 Vietnamese commercial banks from 2007 to 2019 which accounted for more than 80% of total assets in the industry. Due to several bank mergers over the studied period, 4 this arrives at an unbalanced panel with 378 observations. Bank-specific information is obtained from banks’ financial statements on consolidated basis following Vietnamese Accounting Standards and the database constructed by Le et al. (2022). Macroeconomic indicators are taken from the World Bank Database. Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 6 of 18 Table 1 presents the descriptive statistics for all variables over the period 2007–2019. A mean (a standard deviation) of TE, AE, and CE are 0.720 (0.196), 0.727 (0.149), and 0.537 (0.216) respectively. 5 The key independent variable, VAIC, has the mean of 4.783 and the standard deviation of 2.279. Moreover, the mean (standard deviation) values of IC sub-elements, CEE, HCE, and SCE are 0.299 (0.138), 3.816 (2.124), and 0.669 (0.280) correspondingly. In addition, variance inflation factors (VIF) test is used to examine the potential multicollinearity problem in this study. 6 The correlation matrix among variables used is displayed in Table 2. Together, these results confirm no multicollinearity issues. 4. Empirical results 4.1. Intellectual capital and bank efficiency Table 3 illustrates the effect of IC on bank efficiency over the period 2007–2019, using the Tobit regression. The positive coefficients on VAIC in all models suggest that intellectual capital can improve bank cost efficiency and its decompositions (pure technical efficiency and allocative efficiency). This finding supports the view of the RBV theory that intellectual resources play a critical role in bank performance. This finding is comparable with those of Onumah and Duho (2020), Vidyarthi (2019), and Vidyarthi and Tiwari (2019), who found that intellectual capital as a significant value-creating resource can improve business activities, thus strengthening their efficiency and generating higher returns. Regarding bank-specific variables, the coefficients on SIZE in all models imply that larger banks are more efficient than smaller counterparts. This finding is comparable with the studies by Peng et al. (2017), Vu and Nahm (2013), and Assaf et al. (2011). Two main explanations are provided. First, large banks can lower costs of their inputs if it associates with market power. Second, large banks may have the advantage of increasing returns to scale by either allocating fixed costs over a greater volume of services or obtaining efficiency gains from a specialized workforce (Hauner, 2005). Additionally, the positive coefficients on LATA in three modes suggest that more liquid Table 1. Descriptive statistics of variables Obs Mean STD Min Max TE 378 0.720 0.196 0.300 1 AE 378 0.727 0.149 0.261 1 CE 378 0.537 0.216 0.131 1 VAIC 378 4.783 2.279 −2.452 19.784 CEE 378 0.299 0.138 −0.047 0.827 HCE 378 3.816 2.124 −0.737 18.636 SCE 378 0.669 0.280 −2.768 2.356 SIZE 378 31.881 1.433 28.493 34.762 LATA 378 0.316 0.130 0.099 0.681 HHIA 378 0.003 0.007 0 0.059 BSDev 378 1.091 0.173 0.829 1.379 GDP 378 0.062 0.007 0.052 0.071 INF 378 0.076 0.063 0.006 0.231 Notes: TE, pure technical efficiency; AE, allocative efficiency; CE, Overall cost efficiency; VAIC, value-added intellectual coefficient; CEE, Capital employed efficiency; HCE, human capital efficiency; SCE, structural capital efficiency; SIZE, the natural logarithm of total assets; LATA, the ratio of liquid assets to total assets; HHIA, Herfindahl–Hirschman Index in terms of total assets; BSDev, domestic credit extended to the private sector by banks as a percentage of GDP; GDP, Real GDP growth rate; INF, annual inflation rate Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 7 of 18 Conference on Performance measurement and management Control (EIASM), Nice, France, September 22-23, 2005. Kumbhakar, S., & Lovell, C. (2000). Stochastic Frontier Analysis. Cambridge University Press. http://dx.doi. org/10.1017/cbo9781139174411 Le, T. D. (2017). The efficiency effects of bank mergers: An analysis of case studies in Vietnam. Risk Governance & Control: Financial Markets & Institutions, 7(1), 61– 70. http://dx.doi.org/10.2139/ssrn.2822944 Le, T. D. (2017b). The interrelationship between net interest margin and non-interest income: Evidence from Vietnam. International Journal of Managerial Finance, 13(5), 521–540. https://doi.org/10.1108/ IJMF-06-2017-0110 Le, T. D. (2018). Bank risk, capitalisation and technical efficiency in the Vietnamese banking system. Australasian Accounting Business & Finance Journal, 12(3), 42–61. http://dx.doi.org/10.14453/aabfj.v12i3.4 Le, T. D. (2020). Multimarket contacts and bank profitability: Do diversification and bank ownership matter? Cogent Economics & Finance, 8(1), 1–21. https://doi.org/10.1080/23322039.2020.1849981 Le, T. D. (2021a). Can foreign ownership reduce bank risk? Evidence from Vietnam. Review of Economic Analysis, 13(2), 1–24. http://dx.doi.org/10.2139/ssrn.3878912 Le, T. D. (2021b). Geographic expansion, income diversification, and bank stability: Evidence from Vietnam. Cogent Business & Management, 8(1), 1–23. https:// doi.org/10.1080/23311975.2021.1885149 Le, T. D. (2022a). The roles of financial inclusion and financial markets development in Fintech credit: Evidence from developing countries. International Journal of Blockchains and Cryptocurrencies, 2(4), 339–349. https://doi.org/10.1504/IJBC.2021.120374 Le, T. D. (2022b). A shift towards household lending during fintech era: The role of financial literacy and credit information sharing. Asia-Pacific Journal of Business Administration, ahead-of-print(ahead-ofprint). https://doi.org/10.1108/APJBA-07-2021-0325 Le, T. D. Q., Ho, T. H., Ngo, T., Nguyen, D. T., & Tran, S. H. (2022). A dataset for the Vietnamese banking system (2002-2021). Data, 7(9), 120. https://doi.org/10.3390/ data7090120 Le, T. D., Ho, T. H., Nguyen, D. T., & Ngo, T. (2021). Fintech credit and bank efficiency: International evidence. International Journal of Financial Studies, 9(3), 1–16. https://doi.org/10.3390/ijfs9030044 Le, T. D., Ho, T. H., Nguyen, D. T., & Ngo, T. (2022). A cross-country analysis on diversification, Sukuk investment, and the performance of Islamic banking systems under the COVID-19 pandemic. Heliyon, 8(3), e09106. https://doi.org/10.1016/j.heliyon.2022.e09106 Le, T. D., & Ngo, T. (2020). The determinants of bank profitability: A cross-country analysis. Central Bank Review, 20(2), 65–73. https://doi.org/10.1016/j.cbrev. 2020.04.001 Le, T. D., Nguyen, D. T., & McMillan, D. (2020). Intellectual capital and bank profitability: New evidence from Vietnam. Cogent Business & Management, 7(1), 1859666. https://doi.org/10.1080/23311975.2020. 1859666 Le, T. D., Nguyen, V. T., Tran, S. H., & McMillan, D. (2020). Geographic loan diversification and bank risk: A cross-country analysis. Cogent Economics & Finance, 8(1), 1–20. https://doi.org/10.1080/23322039.2020. 1809120 Le, T. D., Tran, S. H., & Nguyen, L. T. (2019). The impact of multimarket contacts on bank stability in Vietnam. Pacific Accounting Review, 31(3), 336–357. https://doi. org/10.1108/PAR-04-2018-0033 Levine, R., Loayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46(1), 31–77. https://doi.org/ 10.1016/S0304-3932(00)00017-9 Lotto, J. (2019). Evaluation of factors influencing bank operating efficiency in Tanzanian banking sector. Cogent Economics & Finance, 7(1), 1664192. https:// doi.org/10.1080/23322039.2019.1664192 Marr, B., & Moustaghfir, K. (2005). Defining intellectual capital: A three-dimensional approach. Management Decision, 43(9), 1114–1128. https://doi.org/10.1108/ 00251740510626227 Mehralian, G., Rajabzadeh, A., Sadeh, M. R., & Rasekh, H. R. (2012). Intellectual capital and corporate performance in Iranian pharmaceutical industry. Journal of Intellectual Capital, 13(1), 138–158. https://doi.org/ 10.1108/14691931211196259 Meles, A., Porzio, C., Sampagnaro, G., & Verdoliva, V. (2016). The impact of the intellectual capital efficiency on commercial banks performance: Evidence from the US. Journal of Multinational Financial Management, 36, 64–74. https://doi.org/10.1016/j. mulfin.2016.04.003 Mention, A. L., & Bontis, N. (2013). Intellectual capital and performance within the banking sector of Luxembourg and Belgium. Journal of Intellectual Capital, 14(2), 286–309. https://doi.org/10.1108/ 14691931311323896 Mondal, A., & Ghosh, S. K. (2012). Intellectual capital and financial performance of Indian banks. Journal of Intellectual Capital, 13(4), 515–530. https://doi.org/ 10.1108/14691931211276115 Ng, A. W. (2006). Reporting intellectual capital flow in technology-based companies: Case studies of Canadian wireless technology companies. Journal of Intellectual Capital, 7(4), 492–510. https://doi.org/10. 1108/14691930610709130 Ngo, T., & Le, T. (2019). Capital market development and bank efficiency: A cross-country analysis. International Journal of Managerial Finance, 15(4), 478–491. https://doi.org/10.1108/IJMF-02-2018-0048 Ngo, T., & Tripe, D. (2017). Measuring efficiency of Vietnamese banks: Accounting for nonperforming loans in a single-step stochastic cost frontier analysis. Pacific Accounting Review, 29(2), 171–182. https://doi.org/10.1108/PAR-06-2016-0064 Nguyen, D. T., Le, T. D., & Ho, T. H. (2021). Intellectual capital and bank risk in Vietnam: A quantile regression approach. Journal of Risk and Financial Management, 14(1), 1–15. https://doi.org/10.3390/ jrfm14010027 Onumah, J. M., & Duho, K. C. T. (2020). Impact of intellectual capital on bank efficiency in emerging markets: Evidence from Ghana. International Journal of Banking, Accounting and Finance, 11(4), 435–460. https://doi.org/10.1504/IJBAAF.2020.110303 Pendo, K. S. (2020). Does investing in intellectual capital improve financial performance? Panel evidence from firms listed in Tanzania DSE. Cogent Economics & Finance, 8(1), 1802815. https://doi.org/10.1080/ 23322039.2020.1802815 Peng, J.-L., Jeng, V., Wang, J. L., & Chen, Y.-C. (2017). The impact of bancassurance on efficiency and profitability of banks: Evidence from the banking industry in Taiwan. Journal of Banking & Finance, 80, 1–13. https://doi.org/10.1016/j.jbankfin.2017.03.013 Perry, P. (1992). Do banks gain or lose from inflation? Journal of Retail Banking, 14(2), 25–31. Petty, R., & Guthrie, J. (2000). Intellectual capital literature review. Journal of Intellectual Capital, 1(2), 155– 176. https://doi.org/10.1108/14691930010348731 Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 14 of 18 Phan, H. T. M., Daly, K., & Akhter, S. (2016). Bank efficiency in emerging Asian countries. Research in International Business and Finance, 38, 517–530. https://doi.org/10.1016/j.ribaf.2016.07.012 Poh, L. T., Kilicman, A., Ibrahim, S. N. I., & McMillan, D. (2018). On intellectual capital and financial performances of banks in Malaysia. Cogent Economics & Finance, 6(1), 1453574. https://doi.org/10.1080/ 23322039.2018.1453574 Pulic, A. (2004). Intellectual capital–does it create or destroy value? Measuring Business Excellence, 8(1), 62–68. https://doi.org/10.1108/MF-08-2014-0211 Rehman, S. U., Ashfaq, K., Bresciani, S., Giacosa, E., & Mueller, J. (2021). Nexus among intellectual capital, interorganizational learning, industrial internet of things technology and innovation performance: A resource-based perspective. Journal of Intellectual Capital. https://doi.org/10.1108/JIC-032021-0095 Rehman, S. U., Bresciani, S., Ashfaq, K., & Alam, G. M. (2021). Intellectual capital, knowledge management and competitive advantage: A resource orchestration perspective. Journal of Knowledge Management. https://doi.org/10.1108/JKM-06-2021-0453 Rehman, S. U., Elrehail, H., Alsaad, A., & Bhatti, A. (2021). Intellectual capital and innovative performance: A mediation-moderation perspective. Journal of Intellectual Capital. https://doi.org/10.1108/JIC-042020-0109 Riahi-Belkaoui, A. (2003). Intellectual capital and firm performance of US multinational firms: A study of the resource-based and stakeholder views. Journal of Intellectual Capital, 4(2), 215–226. https://doi.org/10. 1108/14691930310472839 Saint-Onge, H. (1996). Tacit knowledge the key to the strategic alignment of intellectual capital. Planning Review, 24(2), 10–16. https://doi.org/10.1108/ eb054547 Sannino, G., Nicolò, G., & Zampone, G. (2021). The impact of intellectual capital on bank performance during and after the NPLs crisis: Evidence from Italian banks. International Journal of Applied Decision Sciences, 14(4), 419–442. https://doi.org/10.1504/ IJADS.2021.115997 Saona, P. (2016). Intra-and extra-bank determinants of Latin American Banks’ profitability. International Review of Economics & Finance, 45, 197–214. https:// doi.org/10.1016/j.iref.2016.06.004 Sealey, C. W., Jr, & Lindley, J. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. The Journal of Finance, 32(4), 1251–1266. https://doi.org/10.2307/2326527 Shaban, M., & James, G. A. (2018). The effects of ownership change on bank performance and risk exposure: Evidence from Indonesia. Journal of Banking & Finance, 88, 483–497. https://doi.org/10.1016/j.jbank fin.2017.02.002 Sharma, P., Gounder, N., & Xiang, D. (2013). Foreign banks, profits, market power and efficiency in PICs: Some evidence from Fiji. Applied Financial Economics, 23 (22), 1733–1744. https://doi.org/10.1080/09603107. 2013.848026 Shiu, H.-J. (2006). The application of the value added intellectual coefficient to measure corporate performance: Evidence from technological firms. International Journal of Management, 23(2), 356–365. Singh, S., Sidhu, J., Joshi, M., & Kansal, M. (2016). Measuring intellectual capital performance of Indian banks: A public and private sector comparison. Managerial Finance, 42(7), 635–655. https://doi.org/ 10.1108/MF-08-2014-0211 Smriti, N., & Das, N. (2018). The impact of intellectual capital on firm performance: A study of Indian firms listed in COSPI. Journal of Intellectual Capital, 19(5), 935–964. https://doi.org/10.1108/ JIC-11-2017-0156 Ståhle, P., Ståhle, S., & Aho, S. (2011). Value added intellectual coefficient (VAIC): A critical analysis. Journal of Intellectual Capital, 12(4), 531–551. https://doi.org/ 10.1108/14691931111181715 Stewart, T. A. (2007). The wealth of knowledge: Intellectual capital and the twenty-first century organization. Currency. Sufian, F., & Noor, M. A. N. M. (2009). The determinants of Islamic banks’ efficiency changes: Empirical evidence from the MENA and Asian banking sectors. International Journal of Islamic and Middle Eastern Finance and Management, 2(2), 120–138. http://dx.doi.org/10.1108/17538390910965149 Sullivan, P. H. (2000). Value driven intellectual capital: How to convert intangible corporate assets into market value. John Wiley & Sons, Inc. Sveiby, K. E. (1997). The new organizational wealth: Managing & measuring knowledge-based assets. Berrett-Koehler Publishers. Tandon, K., Purohit, H., & Tandon, D. (2016). Measuring intellectual capital and its impact on financial performance: Empirical evidence from CNX nifty companies. Global Business Review, 17(4), 980–997. https://doi.org/10.1177/0972150916645703 Tan, H. P., Plowman, D., & Hancock, P. (2007). Intellectual capital and financial returns of companies. Journal of Intellectual Capital, 8(1), 76–95. https://doi.org/10. 1108/14691930710715079 Ting, I. W. K., Chen, F.-C., Kweh, Q. L., Sui, H. J., & Le, H. T. M. (2021). Intellectual capital and bank branches’ efficiency: An integrated study. Journal of Intellectual Capital. https://doi.org/10.1108/JIC-072020-0245 Tiwari, R., & Vidyarthi, H. (2018). Intellectual capital and corporate performance: A case of Indian banks. Journal of Accounting in Emerging Economies, 8(1), 84–105. https://doi.org/10.1108/ JAEE-07-2016-0067 Tovstiga, G., Tulugurova, E., & Bontis, N. (2007). Intellectual capital practices and performance in Russian enterprises. Journal of Intellectual Capital, 8 (4), 695–707. https://doi.org/10.1108/ 14691930710830846 Tran, D. B., & Vo, D. H. (2018). Should bankers be concerned with Intellectual capital? A study of the Thai banking sector. Journal of Intellectual Capital, 19(5), 897–914. https://doi.org/10.1108/JIC-122017-0185 Vidyarthi, H. (2019). Dynamics of intellectual capitals and bank efficiency in India. The Service Industries Journal, 39(1), 1–24. https://doi.org/10.1080/ 02642069.2018.1435641 Vidyarthi, H., & Tiwari, R. (2019). Cost, revenue, and profit efficiency characteristics, and intellectual capital in Indian Banks. Journal of Intellectual Capital, 21(1), 1–22. https://doi.org/10.1108/JIC-052019-0107 Vu, H., & Nahm, D. (2013). The determinants of profit efficiency of banks in Vietnam. Journal of the Asia Pacific Economy, 18(4), 615–631. https://doi.org/10. 1080/13547860.2013.803847 Wang, W. Y., Chang, C., & Bontis, N. (2005). Intellectual capital and performance in causal models: Evidence from the information technology industry in Taiwan. Journal of Intellectual Capital, 6(2), 222–236. https:// doi.org/10.1108/14691930510592816 Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 15 of 18 Weill, L. (2004). Measuring cost efficiency in European banking: A comparison of frontier techniques. Journal of Productivity Analysis, 21(2), 133–152. https://doi. org/10.1023/B:PROD.0000016869.09423.0c Williams, J., Peypoch, N., & Barros, C. P. (2011). The Luenberger indicator and productivity growth: A note on the European savings banks sector. Applied Economics, 43(6), 747–755. https://doi.org/10.1080/ 00036840802599859 Xu, J., & Li, J. (2019). The impact of intellectual capital on SMEs’ performance in China: Empirical evidence from non-high-tech vs. high-tech SMEs. Journal of Intellectual Capital, 20(4), 488–509. https://doi.org/ 10.1108/JIC-04-2018-0074 Youndt, M. A., Subramaniam, M., & Snell, S. A. (2004). Intellectual capital profiles: An examination of investments and returns. Journal of Management Studies, 41(2), 335–361. https://doi.org/10.1111/j. 1467-6486.2004.00435.x Zeghal, D., & Maaloul, A. (2010). Analysing value added as an indicator of intellectual capital and its consequences on company performance. Journal of Intellectual Capital, 11(1), 39–60. https://doi.org/10. 1108/14691931011013325 Appendices Table A2. The VIFs statistics result Variables VIF VIF VAIC 1.07 CEE 1.82 HCE 1.37 SCE 1.33 SIZE 2.22 2.78 LATA 1.15 1.16 HHIA 1.86 1.94 BSDev 2.85 2.89 GDP 1.63 1.65 INF 1.72 1.78 Mean VIF 1.79 1.86 Table A1. Variables’ measuring used in the efficiency estimation Variables Descriptions Y1Output 1—net loans Y2Output 2—other earning assets W1Price of input 1—Financial capital price, proxied by the ratio of interest expenses to total funding W2Price of input 2—Physical capital price, obtained by the ratio of other operating expenses to fixed assets W3Price of input 3—Labour price, computed by the ratio of personnel expenses to total assets Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 16 of 18 Table A3. The average efficiency scores 2007–2019 Year TE AE CE 2007 0.581 0.517 0.363 2008 0.517 0.600 0.336 2009 0.653 0.656 0.454 2010 0.711 0.666 0.507 2011 0.689 0.730 0.510 2012 0.625 0.797 0.504 2013 0.664 0.772 0.521 2014 0.730 0.734 0.554 2015 0.764 0.736 0.578 2016 0.792 0.739 0.599 2017 0.773 0.717 0.587 2018 0.821 0.772 0.648 2019 0.755 0.729 0.606 Le et al., Cogent Economics & Finance (2022), 10: 2127485 https://doi.org/10.1080/23322039.2022.2127485 Page 17 of 18 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. 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