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INTELLECTUAL CAPITAL EFFICIENCY, FAMILY MANAGEMENT AND PROFITABILITY: AN EXPLORATORY STUDY OF SPANISH FIRMS

Yolanda Ramírez Associate Professor

Business Administration Department Faculty of Economics and Business Administration

University of Castilla-La Mancha,

Julio Diéguez-Soto Associate Professor

Finances and Accounting Departament Faculty of Economics and Business Sciences

University of Málaga

Montserrat Manzaneque Associate Professor

Business Administration Department Faculty of Social Sciences,

University of Castilla-La Mancha

Área Temática: c) Dirección y Organización

Palabras Clave: Intellectual Capital Efficiency, Human Capital Efficiency, Structural Capital Efficiency, Family Management, Performance.

Workshop: WORKSHOP 3. EMPRESA FAMILIAR Y PYMES

INTELLECTUAL CAPITAL EFFICIENCY, FAMILY MANAGEMENT AND PROFITABILITY: AN EXPLORATORY STUDY OF SPANISH FIRMS

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Abstract

Is the achieving of intellectual capital efficiency important to obtain greater firm’s

profitability? How does family management change this relationship? Using a

longitudinal analysis of 6,132 observations, 438 firms during the period from 2000-2013,

and controlling for endogeneity, this paper addresses these questions. After confirming

that those firms that achieve greater efficiency from their intangible resources (human

and structural) also obtain greater performance, this study attempts to analyse the

moderating role of family management on that relationship. The results suggest that the

postulates of resources-based and knowledge-based theories, adequately explains the

study issue. Particularly, the results suggest that greater intangible resources efficiency

is a key factor that allow to the firm achieve and maintain competitive advantages,

obtaining greater performance. Additionally, findings also suggest that family

management moderating role can be a double-edged sword depending on the type of

intangible resources. On the base of the SEW perspective, findings suggest that

dysfunctional and conflictive human relationships can make family managed firms

unable to use their human capital efficiency in achieving greater performance while their

focus on SEW preservation can make stronger the impact of structural capital efficiency

on performance. All of these results taken together give new explanation to help to

resolve the mixed findings in prior literature on the influence of family management on

performance.

1. Introduction

In the new global economy, intellectual capital (henceforth, IC) has become a central

issue for academicians and practitioners. The impetus for this interest is a series of

challenges in the knowledge-based business environment that motive a firms to invest

in IC, given that IC has become a major driver for a firm to obtain superior performance

(Youndt, Subramaniam & Snell, 2004; Marr, Schiuma & Neely, 2004).

In the so-called knowledge-based economy, tangible resources have been rendered as

merely insufficient for building and maintaining competitive advantage. A new

competitive dynamic is appearing (Roos, Bainbridge & Jacobsen, 2001; Marr & Chatzkel,

2004), in which firms give increasingly importance to intangible resources and

capabilities (that is, its IC) when they face competitors, recognizing that new knowledge

and its effective implementation are key factors in achieving and maintaining competitive

advantages (Cohen & Kaimenakis, 2007). The IC of an organization is located in its

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relationships, structures and people, and adds value to the organization by creating and

maintaining creativity, innovations, information technology, interpersonal activities and

competitive advantage (Edvinsson & Malone, 1997; Teece, 1998). Extensive academic

literature has stressed the strategic importance of leveraging and managing IC for

reaching performance targets and maintaining high levels of competitiveness. Such an

importance of IC has been especially echoed by the recent shift in the strategic

management thinking, represented by the resource-based and knowledge-based views,

which consider strategic resources and assets related to knowledge as the main sources

for creating and maintaining competitive advantage (Barney, 1991; Grant, 1996).

In this context, IC has emerged as a concept of increased importance for managers and

researchers who are interested in knowing whether the IC is being efficiently utilized by

the companies to achieve better profitability. Despite this recognition of the critical role

of IC much remains to be understood about its role in the value creation process itself

(Schiuma & Lerro, 2008) and its effects on corporate performance (Mention, 2012). In

this sense, a number of empirical studies have attempted to link IC with profitability, but

results are mixed. So, previous studies find significant impact of IC on profitability (Chen,

Cheng & Hwang, 2005; Chu, Chan & Wu, 2011; Rahman, 2012) while other studies have

failed to explain why some firms, despite having experienced top management teams

and employees, sophisticated organizational processes and information systems, and

close connections with customers and suppliers, are still unable to obtain a better

financial performance (Kujansivu & Lönnqvist, 2007; Yu, Ng, Wong, Chu & Chan, 2010;

Maditinos, Chatzoudes, Tsairidis & Theriou, 2011; Purohit & Tandon, 2015). Moreover,

the relationship between IC and profitability has usually been examined drawing on

perceptions rather than facts. Studies frequently utilize a survey instrument to measure

IC instead of using more objective instruments (e.g. Cohen & Kaimenakis, 2007; Cabrita

& Bontis, 2008).

Even when firms show similar levels of IC, they differ in their efficiency turning them into

performance. To fill this gap our study introduces the moderating role of family

management. The family involvement in the management of the firm strengthen family-

focused objectives and increase the common ground between the firm and the family

(Revilla, Pérez-Nuño & Nieto, 2016) and influence on how resources are organized and

disposed (Sirmon & Hitt, 2003). Therefore, family management, defined as the active

involvement in firm management of the controlling family for all those firms that are family

owned, is likely to promote unique resources and also the preservation of the SEW,

becoming the point of reference of decision-making (Gomez-Mejia, Haynes, Nuñez-

Nickel, Jacobson, K. & Moyano-Fuentes, 2007). Thus, we pose that family management

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moderate the effects of IC on performance by introducing particular handling of

resources and taking into account non-financial goals in their decisions.

Taking into consideration the prior reasoning, our work addresses the following research

questions: Does IC efficiency has significant impact on profitability? How does human

capital efficiency affect firm profitability? How does structural capital efficiency influence

profitability? Does family management moderate the expected positive influence of

human and structural capital efficiency on the existence of profitability? To address these

research objectives, we conduct empirical study with different econometric models

covering the hypotheses using a panel data sample of 6,132 paired firm-year

observations from Spanish manufacturing firms in the period of 2000 to 2013. Data are

obtained from the Survey on Business Strategies. Our findings are consistent with the

resources-based and knowledge-based theories. We found evidence showing that IC

efficiency and its components (human and structural capital) have a significantly positive

impact on profitability of Spanish companies. Moreover, the moderating role of family

management on the relationship between structural capital efficiency and performance

is positive and significant.

This study has a twofold contribution. Firstly, the findings of this study lead to a better

understanding of the effect of IC efficiency on profitability by distinguishing between the

effects of human and structural capital efficiency. In this way, it sheds light on the

conflicting views from studies addressing the effects of IC on profitability. As a

replacement for the subjective measurements of IC usually used, we utilize the Value

Added Intellectual Coefficient (henceforth, VAICTM) to measure the efficiency of IC (Pulic,

1998). This uses publicly available and reliable data from financial statements,

minimizing potential data subjectivity, and its output is useful and informative to all

stakeholders, being utilized for benchmarking purposes across firms or industries (Firer

& Williams, 2003). Secondly, we contribute to the literature in family firms, focusing on

the effects of family management on the relationship between IC efficiency and

performance. Many researchers have analysed the effects of family management on

performance (Mazzi, 2011; Basco, 2013). We extend such literature by studying the joint

effects of IC efficiency, distinguishing between human capital and structural capital

efficiency, and family management on performance. We draw on the SEW perspective

to show how family management can positively affect the relationship between structural

capital efficiency and performance.

The remainder of this paper is organized as follows. Section 2 describes both prior

theoretical literature and empirical studies related to impact of IC and its components on

financial performance (profitability) using the VAICTM model and the moderating role of

family management on these relations. Based on this review, this section includes

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hypotheses development. Section 3 presents the methodology followed by this research,

describing the sample, measures of the variables, as well as the two-step GMM system

used. Section 4 discusses the empirical results obtained. Finally, conclusions are

presented in section 5.

2. Literature review and development of hypotheses 2.1. The influence of intellectual capital efficiency on company’s

profitability Issues of IC and financial performance have increasingly attracted the attention of

researchers, policy makers, regulatory bodies and investors both from developed and

emerging countries (Marr et al., 2004; Mention, 2012).

IC, also known as intangible resources and capabilities or knowledge assets, has been

a subject of study since the early 1990s (Marr & Chatzkel, 2004). According to the

resource-based view of the firm, building a sustainable competitive advantage to

maintaining above-average profitability requires a company to create and maintain

strategic resources (Peteraf, 1993; Amit & Schoemaker, 1993). These resources must

meet four attributes: valuable, rare, inimitable or imperfectly imitable and non-

substitutable (Barney, 1991). According to this theoretical model, knowledge is perceived

as a firm’s main resource (Spender & Grant, 1996) and fulfils all the required attributes

proposed by Barney (1991). This idea led to the extension of the knowledge-based view

of the firm, with scholars claiming that how a firm creates, transfers and uses its

knowledge impacts its performance and therefore its ability to compete within an industry

(Grant, 1996; Nonaka, 1994). In the context of the knowledge-based view of the firm,

assets related to knowledge that are perceived as key drivers for a sustainable

competitive advantage are often referred to as IC, or intangible assets (Sydler, Haefliger

& Pruksa, 2014). Following resource-based and knowledge-based theories, Riahi-

Belkaoui (2003) and Chen et al. (2005) stated that organizations with a higher degree of

IC will display higher market values. It implies that IC is an important resource in

generating competitive advantage; hence, it should contribute to firms’ performance.

This assumption is also shared by Youndt et al. (2004) who stated that IC intensive

companies are more competitive than other companies are, therefore, more successful.

Summarizing prior literature (e.g., Edvinsson & Malone, 1997; Stewart, 1997; Sveiby,

1997), we conclude that IC may be defined as knowledge-related intangible assets

embedded in an organization that include intellectual competences, intellectual property,

and intellectual resources. Notwithstanding the variety of definitions of IC, it is often

represented as consisting of three basic and strongly interrelated components: human,

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structural and relational capital (Edvinsson & Malone, 1997; Marr & Roos, 2005). Human

capital is defined as the knowledge, skills, experience and abilities residing with and

utilized by individuals (Roos et al., 2001). Structural capital is the institutionalized

knowledge and codified experience residing within and utilized through databases,

patents, manuals, structures, systems, and processes (Youndt et al., 2004). Structural

capital also embraces corporate culture and management philosophy, which provide a

framework to guide and interpret actions in the firm (Mention, 2012). Relational capital is

broadly defined as all resources linked to the external relationships of the firm with their

stakeholders (e.g. customers, suppliers, investors, and the rest of society) (Marr et al.,

2004; Cabrita & Bontis, 2008). Originally, this form of capital was part of structural capital

(Edvinsson & Malone, 1997). However, it later emerged as a distinctive form of capital,

primarily focused on relationships with customers, thus becoming referred to as

customer capital. The concept was later extended to cover all the various external

connections of the firms and was accordingly renamed relational capital.

Among the 42 methods of measurement summarized by Sveiby (2010), in this paper we

use the VAICTM proposed by Pulic (1998), which divides IC into human capital and

structural capital (comprising items originally developed to reflect organizational capital

and relational capital). Instead of directly measuring IC, Pulic (2000) advocates that a

firm’s market value is created by capital employed and IC. Under Pulic’s VAICTM model,

the efficiency of firms’ inputs; physical and financial, human and structural capital are

measured. The VAICTM model provides an objective, standardized and verifiable

measurement of IC efficiency based on data collected from financial statements. The

VAICTM has been widely used to examine the impact of IC efficiency on financial

performance, in particular on profitability. These studies have been conducted in different

countries (Australia, United Kingdom, Finland, Slovenia, Italy, Greece, Japan, China,

South Africa, Malaysia, India) and across industries (primarily focused on knowledge

intensive sectors, such as ICT (Information and communication technology) and

software, pharmaceutical, manufacturing, firms listed on stock exchanges, and more

recently, banking and financial sector).

In general, the majority of the empirical studies confirm the existence of a positive and

significant relationship between IC efficiency and profitability of companies, but several

of those, especially in developing economies did not reach the same conclusion.

Regarding the studies conducted in the developed economies, Zhang, Nai-Ping and Yu-

Sheng (2006) and Chu et al. (2011) found that IC efficiency (measured using the VAICTM)

significantly influences Chinese company’s profitability (measured by ROA). This result

is in line with that obtained by Zéghal and Maaloul (2010) in the United Kingdom. Also in

the case of UK, Rahman (2012) confirmed that greater IC efficiency leads to better

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financial performance (ROA). However, not all studies show a statistically significant

positive relationship between VAICTM and profitability of the company. These include

analysis conducted by Kujansivu and Lönnqvist (2007) on a sample of 20,000 Finnish

companies and by Chan (2009) on Hong Kong Stock Exchange. In this line, Clarke, Seng

and Whiting (2011) concluded that profitability of Australian companies was mostly

affected by financial and physical capital, and less by IC.

Regarding the studies of IC efficiency and its relationship with firm’s profitability in

emerging economies, the empirical findings are also mixed. Most of researchers cannot

determine strong positive correlation between the IC and the corporate performance. In

this sense, it should be noted that role of the IC in emerging economies is not as visible

in a developed economies. For example, Firer and Williams (2003) and Puhorit and

Tandon (2015) failed to support any relationship between the IC efficiency and

profitability of South African and Indian companies, respectively. These authors conclude

that physical capital remains the most significant underlying resource of corporate

performance. Similarly, Gan and Saleh (2008) also indicated that physical capital

efficiency is the most significant variable related to profitability in Malaysia. Thus, these

studies conclude that firms place greater importance on physical capital over IC. In

contrast, some studies found evidence to support the relationship between IC and

financial performance. In this sense, Phusavat, Comepa, Sitko-Lutek and Ooi (2011)

clearly identified that IC efficiency contributes positively to return on assets (ROA) of

large manufacturing firms in Thailand. Similarly, Chen et al. (2005) find evidence that

Taiwanese companies with better IC efficiency obtain a higher degree of profitability

(measured by ROA) in the current and following years. For the same country, Shiu (2006)

also demonstrates a strong positive relationship between VAICTM and ROA. This result

is in line with those obtained by Ting and Lean (2009) and Muhammad and Ismail (2009)

in the case of Malaysia, Makki and Lodhi (2009) in Pakistan, Mehralian, Rajabzadeh,

Sadeh and Rasekh (2012) in Iran, and Nimtrakoon (2015) for a sample of 213

technological firms listed on five ASEAN stock exchanges (Indonesia, Malaysia,

Philippines, Singapore and Thailand). Also, it should be noted that in the case of India,

the research conducted by Ghosh and Mondal (2009), Pal and Soriya (2012), Vishnu

and Gupta (2014) and Kamath (2015) found that VAICTM had a significant effect on

profitability. These studies have important implications for developing economies and it

further strengthens the underlying importance of IC as a major driver of corporate and

national growth.

Regarding the research conducted in the case of European emerging economies, we

can notice the studies conducted by Javornik, Tekavcic and Marc (2012) in the case of

Slovenia, Kommenic and Pokrajcic (2012) in Serbia and Bryl and Truskolaski (2015) in

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Poland. These studies provide empirical evidence that VAICTM has a positive and

statistically significant effect on ROA. However, most studies failed to validate

hypotheses that VAICTM has positive and significant impact on profitability. Thus, the

studies of Puntillo (2009) and Celenza and Rossi (2014) in the case of Italy, Díez, Ochoa,

Prieto and Santidrián, (2010) in Spain, Maditinos et al. (2011) in the case of Greece,

Morariu (2014) in Romania, concluded that there was no clear relationship between IC

efficiency and profitability measures. In the same line, the results of researches

conducted in Serbia (Janošević & Dženopoljac, 2011; Janošević, Dženopoljac &

Tepavac, 2012; Janošević & Dženopoljac & Bontis, 2013; Janošević & Dženopoljac,

2014) revealed that IC’s impact on profitability is either small or insignificant. Overall,

these studies showed that physical capital was the main predictor of profitability (ROA).

Finally, moving to another continent, Villegas, Hernández and Salazar (2017) confirmed

that VAICTM has a positive impact on financial profitability (measured by ROA) on a

sample of Mexican listed companies.

From this literature review undertaken, it is observed that while on theoretical level,

distinguished authors argue that IC is the main value driver of all companies and

therefore IC investment allows the firm to enhance its financial performance (Riahi-

Belkaoui, 2003; Youndt et al., 2004; Marr et al., 2004; Chen et al., 2005), the empirical

evidence is inconclusive and far from achieving a solid scientific consensus. These

mixed results prompt the researchers to investigate the relationship between IC

efficiency and firm performance. In our paper, the performance is defined by profitability,

an expression of the ability of invested capital to earn a certain level of profit. In this way,

our study proposes for examination the following hypothesis:

Hypothesis 1: There is a positive relationship between Intellectual Capital

Efficiency and profitability (ROA).

Additionally, prior studies have found that different components of IC have a greater

impact on firm performance than others (e.g. Chan, 2009; Ting & Lean, 2009; Maditinos

et al., 2011; Kamath, 2015). Therefore, this paper analyses the effects of human capital

efficiency (HCE) and structural capital efficiency (SCE) on profitability.

The influence of human capital efficiency on company’s profitability

A comprehensive review of theoretical literature (including economic human capital

theory, resource-based theories, human resource management, organizational learning

and knowledge management) shows that human capital is considered the most

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important asset of an organization (Mention, 2012). Having brilliant, motivated, and

experienced human capital should be the base for having better financial performance

(Roos et al., 2001). Also, at the empirical level, the influence of human capital efficiency

(HCE) on the profitability of firms has been widely investigated through VAICTM model.

Almost all research confirms the central role of human capital, as it positively affects

profitability. For example, for baking and financial sector, Mavridis (2004), Goh (2005)

and Joshi, Cahill and Sidhu (2013) found that Japanese, Malaysian and Australian banks

respectively, with the greatest profitability were those who were most efficient in the use

of their human capital. Additionally, using a sample of 213 technology firms listed on five

ASEAN stock exchanges (Indonesia, Malaysia, Philippines, Singapore and Thailand)

and 32 companies listed on the Mexican Stock Exchange, Nimtrakoon (2015) and

Villegas et al. (2017) respectively, found that HCE had a greater influence on profitability

(ROA). In line with their results are those obtained by Kamath (2008) in the case of Indian

pharmaceutical sector and Maditinos et al. (2011) in the case of Greek companies, as

they revealed that companies’ profitability was only significantly associated with the HCE.

On the other hand, some studies revealed that HCE positively affects ROA (Kommenic

& Pokrajčić, 2012; Janošević & Dženopoljac (2015) in their studies on a sample of

Serbian companies, and Bryl and Truskolaski (2015) on a sample of Polish listed

companies). So, all these studies conclude that investment on human capital is more

returnable as compare to physical and structural capital. Likewise, Janošević and

Dženopoljac (2012a) confirmed that ROA is under significant impact of human and

structural capital in 300 Serbian top performing companies in terms of export in 2011.

Similarly, the findings of Phusavat et al. (2011) on large manufacturing firms in Thailand

and Calisir, Gumussoy, Bayraktaroglu and Deniz (2010) on listed companies on the

Istambul Stock Exchange (Turkey) indicated that HCE affects positively ROA. In a similar

vein, Ting and Lean (2009) found that HCE have significant positive effect on profitability

(as measured by ROA) of Malaysian banks. Similar results are obtained by Kamath

(2015) on a sample of 30 Indian firms from across various manufacturing and service

sector. This author indicated that HCE had a greater influence on profitability. Thus, all

these empirical researches conclude that HCE is the most significant variable (compared

to structural and physical capital) and it has positive and significant association with

profitability of the firms. As a result, companies must conduct a substantive investment

on human capital to upgrade the stock of HC through employee training and knowledge

sharing in order to improve their profitability.

While the research mentioned above found positive relationships, surprisingly some

studies found results contrary to expectations. For example, the results obtained by Yu

et al. (2010) in the case of Hong Kong found negative correlations between HCE and the

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financial indicators (ROA), showing that the efficiency with which a firm can use its

human resources impacts negatively on firm performance. Similarly, another study of

Purohit and Tandon (2015) proved that HCE had a negative relationship with ROA in

high-tech and pharmaceutical companies in India.

Finally, some other studies find no evidence of a significant relationship between HCE

and profitability. For example, Bontis et al. (2013) pointed out that human capital was

undervalued and not exploited effectively in Serbian banking sector. So, HCE does not

influence profitability. Similarly, Firer and Williams (2003) and Díez et al. (2010) did not

find any strong association between the efficient use of human capital and firms’

profitability defined as return on assets (ROA) in South Africa and Spain, respectively.

Likewise, Janošević and Dženopoljac (2012b) pointed out that HCE has a weak influence

on ROA to Serbian companies with the highest trade rates on the Belgrade Stock

Exchange (BELEX).

So, in this situation of contradictory support, it is logical to check empirically whether HC

has direct relationship with firm’s profitability or not? Hence, this study proposed the

following hypothesis:

Hypothesis 2: There is a positive relationship between Human Capital Efficiency

and profitability (ROA)

The influence of structural capital efficiency on company’s profitability

Literature has also paid attention to the link between structural capital efficiency (SCE)

and performance in terms of profitability. SCE can be seen as the foundation stone of an

organization in the knowledge age because it creates the tools and architecture for

maintaining, forming, reinforcing, and transferring knowledge along the business

activities (Cabrita & Bontis, 2008). If the organizational culture, rules, procedures and

system are weak, well motivated employee capabilities would not be able to add value

to the firm. However, if a company has efficient systems, database, patents, trademarks,

routines and procedures (as part of the structural capital), then higher IC efficiency might

be attained (Teece, 1998; Bontis, Chua, Keow & Richardson, 2000; Mehralian et al.,

2012) and therefore greater profitability (Shiu, 2006; Tan, Plowman & Hancock, 2007).

However, the effect of SCE on profitability is challenging, as some studies find a positive

relationship (e.g. Chan, 2009; Chu et al., 2011; Clarke et al., 2011; Bontis et al., 2013;

Bryl & Truskolaski, 2015, etc.), while others observe a negative relationship (e.g. Purohit

& Tandon, 2015; Kamath, 2015, etc.). Likewise, the results obtained by Kamath (2008),

Díez et al. (2010), Maditinos et al. (2011) and Janošević and Dženopoljac (2014, 2015)

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revealed that SCE failed to show any significant empirical impact on the firms’

performance in terms of profitability.

Firstly, it is interesting to note that study of Chan (2009) revealed that only SCE has a

statistically significant and positive relationship with profitability measures (ROA). In the

same line, Janošević and Dženopoljac (2012b) suggested that among IC components,

SCE has the most significant impact on ROA. These findings are consistent with those

obtained by Janošević and Dženopoljac (2012a), Vishnu and Gupta (2014) and Villegas

et al. (2017), so they confirmed that ROA was under significant impact of SCE. While,

Firer & Williams (2003) showed only a moderately positive association between the SCE

and profitability.

Given the various results obtained in the former studies and considering the importance

of empirical verification, further studies are necessary for supporting and explaining the

relationship between SCE and profitability. Accordingly, this paper will attempt to

empirically verify the following hypothesis:

Hypothesis 3: There is a positive relationship between Structural Capital

Efficiency and profitability (ROA)

2.2. The moderating role of family management in the relationship between intellectual capital efficiency and profitability

Whilst direct relationships between IC and financial performance have been widely

examined in prior VAICTM research, this study explores another relationship – a

moderating effect of family management

Human Capital Efficiency, Performance and Family Management

Socioemotional issue (SEW) alludes to non-financial aspects of the firm meeting the

family’s affective needs, namely identity, ability to exercise family influence, and the

perpetuation of the family dinasty (Gomez-Mejia et al., 2007; Berrone, Cruz, Gomez-

Mejia & Larraza Kintana, 2010). The preservation of the SEW becomes the point of

reference of family decision-making and family firms consent to taking risks of

inadequate performance (Gomez-Mejia et al., 2007). As a consequence, family

managers evaluate their own decisions based on non-financial goals in order to

maximize the family’s SEW (Zellweger, Kellermanns, Chrisman & Chua, 2012).

Family managers tend to generate or keep specific jobs for family members to continue

family dynasty (Gomez-Mejia et al., 2007) or preserve family dysfunctional and conflictive

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relationships (Berrone, Cruz & Gomez-Mejia 2012). Family-managed firms are willing to

suffer from restrictions in quality and quantity of human resources (Sirmon & Hitt, 2003),

from potential entrenchment of top managers and unqualified human capital (Gomez-

Mejia, Nuñez-Nickel & Gutierrez, 2001; Villalonga & Amit, 2006), or nepotism (Perez-

Gonzalez, 2006), because they make decisions not driven mainly by financial goals but

by the preservation of socioemotional endowment (Berrone, et al., 2010).

However, the particular focus of family-managed businesses on preserving SEW can

also leads to greater performance of human resources. Families are often featured by a

wide range of positive emotions, namely warmth, commitment, tenderness, friendship,

love or consolation, among others, which penetrate the firm impacting on the particular

family manager’s behaviour (Gomez-Mejia, et al., 2007; Baron, 2008). Particular

motivation, cooperation, or even own family language (Tagiuri & Davis, 1996; Collins &

Smith, 2006) collaborate on both the achievement of non-financial and financial goals.

Therefore, the consent by family management of free riding, perquisites or privileges,

among others, in order to preserve family SEW, can weaken the relationship between

Human Capital Efficiency and performance. But, peculiar emotional attachment can also

stimulate both SEW and financial goals, strengthen the link between Human Capital

Efficiency and performance. The net effect is thus ambiguous. Based on the above

arguments, we make the following hypotheses.

Hypothesis 4a1: Family management involvement moderates negatively the relationship

between Human Capital Efficiency and performance.

Hypothesis 4a2: Family management involvement moderates positively the relationship

between Human Capital Efficiency and performance.

Structural Capital Efficiency, Performance and Family Management

The control exerted by family managers over strategic decisions (Schulze, Lubatkin &

Dino, 2003), as it is an integral part of the SEW (Zellweger et al., 2012), is also likely to

influence on the relationship between Structural Capital Efficiency and Performance.

Family managers usually promote a higher identification of the family with the firm, which

not only exert an important impact on human resources, as we have seen above, but

also to the infrastructure, processes, databases of the business or the organization

image. For instance, family management will tend to perpetuate a favourable family

image (Sharma & Manikuti, 2005) because any reputation failure might be devastating

for the firm and the family (Westhead, Cowling & Howorth, 2001). Family managers will

preserve family’s heritage and tradition (Casson, 1999) regarding organization

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philosophy, in order to save dynastic succession (Zellweger & Astrachan, 2008). They

own particular firm-specific knowledge (Donnelley, 1964) and collaborate to generate

collective corporate culture (Arregle, Hitt, Sirmon & Very, 2007). Thus, they are also likely

to cultivate a higher efficiency in the techniques, procedures and programs –process

capital- to improve the quality of the services and products they provide (Carrigan &

Buckley, 2008; Teal, Upton & Seaman, 2003). Furthermore, they will attempt to protect

intellectual properties –innovation capital-, such as patents, copyrights and trademarks,

because of their long term planning horizons (Miller, Le Breton-Miller & Scholnick, 2008)

and their vision of the firm as a long term investment for descendants (Berrone et al.,

2010). In summary, family-managed firms appear to provide higher performance from

their specific structural capital (organizational, process and innovation capital).

Previous literature has confirmed that family management enhance the learning from

environment and external networks (Zahra, 2012) for improving performance. Family

managers tend to engender long-standing and strong social bonds across generations

with stakeholders and the community (Miller & Le Breton-Miller, 2005; Berrone, et al.,

2012). The exchange of new ideas and information (Classen, Carree, Van Gils & Peters,

2014; Llach & Nordqvist, 2010) with close networks and stakeholders will contribute to

the achievement of higher levels of performance. In short, the search of non-financial

goals and the stimulation of trust-based and long-term ties with stakeholders (Berrone et

al., 2012) make family management to intensify the positive relationship between

Structural Capital Efficiency and performance.

Hypothesis 4b: Family management involvement moderates positively the relationship

between Structural Capital Efficiency and performance.

The theoretical model and the proposed hypotheses are presented in Fig. 1.

Fig. 1. Family influence, Intellectual Capital Efficiency, and firm performance.

Intellectual

Capital Efficiency

Firm Performance H1

H2 Human Capital

Efficiency

Structural Capital Efficiency

H3

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3. Methodology

3.1. Sample

The source of data comprising this sample is the Survey on Business Strategies (ESEE),

administered by the State Partnership of Manufacturing Equity (SEPI) foundation, from

the Spanish Ministry of Industry. After removing some firms because of missing,

incomplete or outliers data, financial information and data on family management

involvement were collected from the ESEE database. Our sample includes 438 small

and medium manufacture firms for the period 2000-2013, which results in a balanced

panel data of 6,132 firm-year observations. Table 1 provides a description of the sample

selection process (Panel A) and the sample description (Panel B).

The quality of the data is guaranteed by the fact that they come from a public agency,

following a process that ensures the representativeness of the population. Additionally,

we test the representativeness of the sample from a statistical point of view. The

maximum error for a finite population has been tested resulting in a small error (e=4.49%,

α=0.95) and leading to the conclusion that sample is representative of population.

The proportion of family managed firms on the sample is around 55% against the 44.90%

of non-family managed firms. Panel B shows that a high proportion of firms comes from

metal products (14.38%) and foodstuffs and snuff (10.96%) sub-industrial although a

twenty sub-industries form the manufacture industry are represented on the sample.

Table 1. Sample selection and description

Panel A. Sample selection process ESEE panel of manufacturing firms (2013) 5,304 firms Firms with and incomplete or missing data and outliers for the study variables

(4,866 firms)

Initial sample from ESEE 438 Years 2000-2013 Observations 6,132 Panel B. Sample description N % Family managed 3379 55.10% Non family managed 2753 44.90%

Family management

H4a H4b

15

Total 6132 100.00% N % 1. Meat industry 224 3.65% 2. Foodstuffs and snuff 672 10.96% 3. Drinks 154 2.51% 4. Textiles and clothing 420 6.85% 5. Leather and footwear 98 1.60% 6. Timber industry 140 2.28% 7. Paper Industry 336 5.48% 8. Graphics 196 3.20% 9. Chemical and pharmaceutical products 490 7.99% 10. Rubber and plastic 490 7.99% 11. Non-metallic mineral products 238 3.88% 12. Ferrous and nonferrous metals 210 3.42% 13. Metal products 882 14.38% 14. Agricultural and industrial machinery 406 6.62% 15. Computer, electronic and optical products 84 1.37% 16. Electrical machinery and material 266 4.34% 17. Motor vehicles 392 6.39% 18. Other transport equipment 126 2.05% 19. Furniture industry 182 2.97% 20. Other manufacturing 126 2.05% Total 6132 100.00%

3.2. Measures 3.2.1. Dependent variable

In order to test the research hypotheses, we follow previous studies in the research area

of IC taking as proxy of the firm financial performance1 the return of assets ratio (ROA=

operating income to total assets) (see among others, Firer & Williams, 2003; Kommenic

& Pokrajčić, 2012; Maditinos, et al., 2011).

3.2.2. Independent variables and moderator

As independent variable, we took the Intellectual Capital Efficiency (henceforth, ICE).

We used a holistic approach to IC proposed by Pulic (1998, 2000, 2004) and based on

the efficiency of the firm to create additional value, the Value Added Intellectual

Coefficient (VAICTM). According to that model, the ICE is the sum of: (1) HCE (Human

Capital Efficiency) indicates the amount of value added generated per one monetary unit

invested in human resources (employees); and, (2) SCE (Structural Capital Efficiency)

may be express as the amount of value added generated by the structural capital of the

firm. Although previous studies have used different methods to measure intangibles (for

a review, see Sveiby, 2010), there isn’t consensus about the best option. VAICTM has

1 Due to our sample includes non listed firms, market to book ratio has been omitted and other measures based on market values.

16

been use by a wide number of studies as proxy of ICE (e.g. Chen, et al., 2005;

Manzaneque, Ramírez & Diéguez-Soto, 2017), due to multiples advantages (for a review

see Chan, 2009), highlighting among others the following: (1) it capture the new value

creation by both tangible and intangible assets of the firm; 2) it allows to measure the IC

efficiency through its two components (human and structural capital); 3) it can be easily

applied to unlisted and small and medium firms because its calculation is possible

through the public information of the financial statements; 4) the VAIC measure is

objective, verifiable and comparable, since the data used come from audited and

standardized accounting information; and, 5) it has been widely adopted by researchers

on the IC and corporate performance field of study and it is recognize as a standardized

and integrated measure which allow comparison among different contexts of study.

Pulic (1998) proposes ICE as:

ICE = Human Capital Efficiency (HCE) + Structural Capital Efficiency (SCE)

In the analyses, ICE is split into, HCE and SCE.

HCE is calculated as the ratio of total value added by the resources of the firm to total

salaries and wages spent by the firm (HC). The value added (VA) is calculated by using

information in the annual report by adding the operating profits, total employee expenses,

depreciation, and amortization.

SCE is calculated as the ratio of value added less total salary and wage costs for firm

capital (VA- HC) to total value added (VA).

We consider this measure to be most appropriate because captures the new value

created by the firm’s investment into intellectual resources.

As moderator we used family management involvement. According to the above

literature review, family firms have a particular vision of the firm (Chua, Chrisman &

Sharma, 1999) and differ from other kind of firms in perceptions of management

practices (Poza, Hanlon & Kishida, 2004). In order to test our hypothesis about the

influence of family management involvement on the relationship between ICE and firm

performance, we build a binary measure of family firm management involvement

operationalized as 1 if there is family members or relatives who occupy top managerial

position, and 0 otherwise. Similar measures of family managed firms have been used by

previous studies such as Block (2012), Block, Miller, Jaskiewicz and Spiegel (2013) and

Sirmon, Arregle, Hitt and Webb (2008), among others.

3.2.3. Control variables

17

First we control for firm size measure as the natural logarithm of the book value of total

assets. Previous literature suggests that the ability of the firm to achieve greater

performance depend of the firm size (Riahi-Belkaoui, 2003). We also control for leverage

measures as the ratio of the firm’s debt to total assets. Theoretical and practical

perspectives provide opposing arguments regarding the impact of leverage on

performance. According to Jensen and Meckling (1976) and Myers (1977) higher level

of leverage is linked to “moral hazard problems” between shareholders and debtholders

because of the different interest between them. So, shareholders could take actions in

their own benefit at the expense of debtholders, as an example, investing in riskier

project because they have limited liability in case of bankruptcy (Jensen & Meckling,

1976) or underinvest because probability of project could benefit the debtholders, as

demonstrated by Myers (1977). As a result, greater level of leverage exerts a negative

impact on firm performance. Contrarily, two different arguments point in an opposite

direction. Firstly, if borrowers control managers, it should reduce “free cash-flow” at the

disposal of managers (Jensen, 1986), providing incentives to a better perform of them.

Secondly, debt could be a signal of those firms select by borrowers because of their

higher quality and information transparency (Ross, 1977). Therefore, that point suggests

that the highest leverage firms achieve greater performance. Finally, we control for two

specific characteristics of the firm, industry and territorial context. According to Tan, et

al. (2007) the relationship between IC and financial performance differed across

industries. Therefore, we control the specificities of the industry, including dummy

variables for each sector of the industry (see Table 1 for more details). Additionally, we

control for territorial specificities including dummy variables for seven Spanish territorial

subdivisions according to the Nomenclature des Unités Territoriales Statistiques

(NUTS1) specified by the European Union (2015) because social, legal and cultural

characteristics could influence the profitability of the firm.

3.3. Methodology

In order to deal with the unobservable heterogeneity associated with fixed firm effects

and possible endogeneity linked to dynamic panel data, (dynamic endogeneity,

simultaneity and unobserved heterogeneity) the coefficients are estimated using two-

step system GMM (Arellano & Bond, 1991; Arellano & Bover, 1995; Blundell & Bond,

1998) estimator.

Different variants of the following model of panel data have been estimated:

18

ROAit = 𝛼𝛼 + 𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖−𝑎𝑎 + 𝛽𝛽𝛽𝛽𝛽𝛽𝑖𝑖𝑖𝑖 + 𝛿𝛿𝛽𝛽𝛿𝛿𝑖𝑖𝑖𝑖 + 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖 + 𝜂𝜂𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖, 𝑖𝑖=2000−2013

where ROAit is return of assets ratio, 𝛽𝛽𝛽𝛽𝑖𝑖𝑖𝑖 is the ICE for the business i in the period t (IC

is splited into human capital efficiency and structural capital efficiency) and 𝛽𝛽𝛿𝛿𝑖𝑖𝑖𝑖 are the

control variables (firm size and leverage). 𝜃𝜃𝑖𝑖𝑖𝑖 includes year, industry and territorial

specificities dummies. The instrument used in the two-step system GMM estimation are:

differenced equations: ROAt−2, ICt−2 , Δ𝜃𝜃𝑖𝑖𝑖𝑖 ; level equations: 𝚫𝚫ROAt−2, 𝚫𝚫ICt−2; 𝚫𝚫ROAt−3,

𝚫𝚫ICt−3, 𝜃𝜃𝑖𝑖𝑖𝑖 . Take into account the temporal persistence of performance, including in the

model the first lag of the dependent variable. Year, industry and territorial specificities

are treated as exogenous variables. To test de validity of the model specification two

different test have been used. First, Hansen test of over-identification examines the lack

of correlation between the instruments and the error term. Second, AR(1) and AR(2) are

tests for first and second-order correlation in the firs-differenced residuals (under de null

of no serial correlation). The presence of first order autocorrelation does not invalidate

the results, however we expect no second order correlation.

4. Empirical results

Table 2 (Panel A) provides some descriptive statistics of the variables. The average of

performance is around 11.4%. Particularly, according to the test of difference of means

(Panel B), family managed firms appear to be more profitable than non-family managed

firms (11.8% against 10.9%; T-Tests: -1.798; p<0.1). Regarding ICE, the results show

that non-family managed firms have more ICE than family managed firms (1.843 against

1.800; T-Tests: 3.421; p<0.01). The same circumstance happens regarding human

capital efficiency (1.577 against 1.548; T-Tests: 3.418; p<0.01) and structural capital

efficiency (0.299 against 0.284; T-Tests: 3.418; p<0.01). Finally, average firm size is

higher in non-family managed firms (17.307 against 15.532; T-Tests: 32.258; p<0.01)

while leverage is relatively similar in both cases (0.422 against 0.429; T-Tests: -1.021;

p>0.10).

Pearson correlations are reported in table 2 (Panel C). All bivariate correlations do not

exceed 0.302 (in bold correlation between independent variables). Variance inflation

factor (VIF) values are bellow of 2.5, level suggested as warning for multicollineatiry

problem according to previous studies (see among other Hair, Anderson, Tatham &

Black, 1998). According to both correlation test, multicollinearity concern appear not to

be a serious problem in our models.

19

Table 2. Descriptive statistics and correlation matrix

Panel A. Descriptive statistics Sample

(438 firms/6,132 observations) Mean Median 25% 75% Std. Dev. Performance 0.114 0.100 0.038 0.169 0.142 Intellectual capital efficiency 1.819 1.670 1.268 2.206 0.992

Human capital efficiency 1.561 1.389 1.143 1.771 0.757

Structural capital efficiency 0.291 0.280 0.125 0.435 0.207

Firm size 16.326 16.391 14.566 17.884 2.158 Leverage 0.426 0.420 0.226 0.616 0.251 Panel B. Mean differences family/non-family managed firms Family

managed

firms

Non-Familly

managed firms

Mean Mean T-Tests Performance 0.118 0.109 -1.798* Intellectual capital efficiency 1.800 1.843 3.421*** Human capital efficiency 1.548 1.577 3.418*** Structural capital efficiency 0.284 0.299 3.437*** Firm size 15.532 17.307 32.258*** Leverage 0.429 0.422 -1.021 Panel C. Correlation matrix

20

1 2 3 4 5 6 7 1. Performance 2. Intellectual capital efficiency 0.639***

3. Human capital efficiency 0.590*** 0.988**

*

4. Structural capital efficiency 0.659*** 0.933**

* 0.889**

*

5. Firm size -0.065*** 0.254***

0.245***

0.302***

6. Leverage -0.103

-0.084**

*

-0.078**

*

-0.084**

* 0.084

7. Industry -0.009

-0.098**

*

-0.095**

*

-0.090**

* 0.113**

* 0.013

8. Territorial specificities -0.004 0.045**

* 0.041**

* 0.054**

* 0.013 0.075***

-0.186**

* VIF 1.01 1.01 1.05 1.04

The set of regression results testing the direct effect of Intellectual Capital Efficiency

(Human Capital Efficiency and Structural Capital Efficiency) is reported in Table 3. Year,

industry and territorial specificities dummy variables are included in all models to capture

the potential impact on ROA across all of these particular characteristics of the firm.

Table 3. Intellectual resources productivity Firm performance Dynamic Panel-Data Estimation, Two Step System GMM.

Model 1 Model 2 Model 3 Independent variable Performance_1 (β1) 0.094**

(0.042) 0.098*** (0.036)

0.071** (0.037)

Intellectual Capital efficiency (β2)

0.121*** (0.036)

Human capital efficiency (β2a)

0.129** (0.052)

Structural capital efficiency (β2a)

0.748*** (0.281)

Controls

Firm size (ln) -0.015 (0.044)

-0.012 (0.035)

-0.101 (0.075)

Leverage 0.237 (0.392)

0.343 (0.371)

0.233 (0.355)

Constant and time effects Yes Yes Yes Territorial specificities Yes Yes Yes Industry effects Yes Yes Yes Hansen test of over identification

18.41 (10)

17.13 (10)

13.68 (10)

AR1 (p-value) -3.41*** -3.79*** -3.50*** AR2 (p-value) 0.94 1.18 -0.28 Number of firms 438 438 438

21

Number of observations 5694 5694 5694 No. Instruments 49 49 49

In this table, we report results from dynamic panel-data estimation with two-step system GMM of the model:

ROAit = 𝛼𝛼 + 𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖−𝑎𝑎 + 𝛽𝛽𝛽𝛽𝛽𝛽𝑖𝑖𝑖𝑖 + 𝛿𝛿𝛽𝛽𝛿𝛿𝑖𝑖𝑖𝑖 + 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖 + 𝜂𝜂𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖, 𝑖𝑖=2000−2013 ROAit is return of assets ratio, 𝛽𝛽𝛽𝛽𝑖𝑖𝑖𝑖 is the intellectual capital efficiency for the business I in the period t (IC is splited into human capital efficiency and structural capital efficiency) and 𝛽𝛽𝛿𝛿𝑖𝑖𝑖𝑖 are the control variables (firm size and leverage). 𝜃𝜃𝑖𝑖𝑖𝑖 includes year, industry and territorial specificities dummies. The results are based on a sample of 443 firms and the covered period is 2000-2013. Standard error is reported in parentheses. *, **, ***, respectively, indicate significance levels at 10%, 5%, and 1%. In bold, significant coefficients. AR(1) and AR(2) are tests for first and second-order correlation in the firs-differenced residuals (under de null of no serial correlation). The Hansen test of over identification is under the null hypothesis of zero correlation between the instruments and the error term. That is rejection of the null casts doubt on the validity instruments. The instrument used in the two-step system GMM estimation are: differenced equations: 𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖−2, 𝛽𝛽𝛽𝛽𝑖𝑖−2 , Δ𝜃𝜃𝑖𝑖𝑖𝑖 ; level equations: 𝚫𝚫𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖−2, 𝚫𝚫𝛽𝛽𝛽𝛽𝑖𝑖−2; 𝚫𝚫𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖−3, 𝚫𝚫𝛽𝛽𝛽𝛽𝑖𝑖−3, 𝜃𝜃𝑖𝑖𝑖𝑖. Variables are defined above.

The hypothesis H1 suggests that ICE has positive impact on profitability. Specifically,

our results clearly indicate that firms with better ICE yield greater return on assets (β2=

0.121, ρ<0.01). In conclusion, the obtained results show that ICE has a positive

correlation with ROA. It proves that ICE is an important factor that increases the

profitability of business enterprises. Hence, H1 is supported, which is consistent with the

research conclusions drawn by Chen et al. (2005), Phusavat et al. (2011), Clarke et al.

(2011), Kommenic and Pokrajčić (2012), Rahman (2012), Javornik et al. (2012) and Bryl

and Truskolaski (2015). Based on the arguments of previous literature, the results show

that greater capabilities or knowledge assets efficiency is an important resource of

profitability, which is in line with the postulates of resource-based and knowledge-based

theories.

In hypothesis H2, it is suggested that Human Capital Efficiency would have positive

impact on profitability presented by ROA. Specifically, the results show that Human

Capital Efficiency is significantly correlated with ROA (β2a= 0.129, ρ<0.05). This means

that when companies used efficiently their human capital they recorded a higher ROA.

This results suggests that firms in Spain should recruit and retain employees equipped

with high calibre ability as well as to upgrade their managerial skills in order generate

higher firms’ profitability. These findings significantly support H2, concluding that Human

Capital Efficiency exerts significant impact in improving the ROA. This result is in line

with previous studies such as Kamath (2008), Yu et al. (2010), Kommenic and Pokrajčić

(2012), Bryl and Truskolaski (2015) and Janošević and Dženopoljac (2015).

The third hypothesis regarding a positive relationship between Structural Capital

Efficiency and profitability is strongly statistically supported. Concerning H3, the results

22

show that ROA is positively influenced by SCE (β2b= 0.748, ρ<0.01). These findings

confirming that firms with greater Structural Capital Efficiency tend to have better return

on assets. Thus, the findings significantly support H3, concluding that firms with better

Structural Capital Efficiency yield greater profitability. These results are consistent with

the research conclusions drawn by Chan (2009), Janošević and Dženopoljac (2012b),

Bontis et al. (2013) and Bryl and Truskolaski (2015).

Table 4. Intellectual resources productivity Firm performance Family firmsDynamic Panel-Data Estimation, Two Step System GMM.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Independent variable Performance_1 (β1) 0.080

(0.049) 0.087* (0.049)

0.091** (0.037)

0.097** (0.038)

0.046 (0.039)

0.029 (0.050)

Intellectual Capital efficiency (β2)

0.135** (0.059)

-0.021 (0.082)

Human capital efficiency (β2a)

0.118*** (0.043)

-0.036 (0.120)

Structural capital efficiency (β2a)

0.849*** (0.258)

0.079 (0.373)

Moderator Family management involvement (β3)

-0.318** (0.151)

-0.593*** (0.229)

-0.366** (0.169)

-0.734*** (0.279)

-0.400** (0.209)

-0.525*** (0.205)

Interactions Family management involvement * Intellectual Capital Coefficient (β4)

0.324** (0.145)

Family management involvement * Human capital efficiency (β4a)

0.397 (0.251)

Family management involvement * Structural capital efficiency (β4b)

1.501** (0.786)

Controls Firm size (ln) -0.037

(0.058) 0.001

(0.144) -0.031 (0.043)

-0.011 (0.067)

-0.154 (0.102)

-0.194 (0.139)

Leverage 0.667* (0.404)

0.282 (0.569)

0.788** (0.414)

0.483 (0.478)

0.658 (0.565)

0.437 (0.467)

Constant and time effects

Yes Yes Yes Yes Yes Yes

Territorial specificities Yes Yes Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Yes Yes Hansen test of over identification (p-value)

11.73 (9)

3.21 (8)

11.25 (9)

3.85 (8)

7.02 (9)

2.79 (8)

23

AR1 (p-value) -3.84***

-3.18*** -4.59*** -3.14*** -4.16*** -3.36***

AR2 (p-value) 0.50 -0.97 0.51 0.07 0.95 -1.41 Number of firms 438 438 438 438 438 438 Number of observations 5694 5694 5694 5694 5694 5694 No. Instruments 49 49 49 49 49 49

In this table, we report results from dynamic panel-data estimation with two-step system GMM of the model:

ROAit = 𝛼𝛼 + 𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖−𝑎𝑎 + 𝛽𝛽𝛽𝛽𝛽𝛽𝑖𝑖𝑖𝑖 + 𝛿𝛿𝛽𝛽𝛿𝛿𝑖𝑖𝑖𝑖 + 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖 + 𝜂𝜂𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖, 𝑖𝑖=2000−2013 ROAit is return of assets ratio, 𝛽𝛽𝛽𝛽𝑖𝑖𝑖𝑖 is the intellectual capital efficiency for the business I in the period t (IC is splited into human capital efficiency and structural capital efficiency) and 𝛽𝛽𝛿𝛿𝑖𝑖𝑖𝑖 are the control variables (firm size and leverage). 𝜃𝜃𝑖𝑖𝑖𝑖 includes year, industry and territorial specificities dummies. The results are based on a sample of 443 firms and the covered period is 2000-2013. Standard error is reported in parentheses. *, **, ***, respectively, indicate significance levels at 10%, 5%, and 1%. In bold, significant coefficients. AR(1) and AR(2) are tests for first and second-order correlation in the firs-differenced residuals (under de null of no serial correlation). The Hansen test of overidentification is under the null hypothesis of zero correlation between the instruments and the error term. That is rejection of the null casts doubt on the validity instruments. The instrument used in the two-step system GMM estimation are: differenced equations: 𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖−2, 𝛽𝛽𝛽𝛽𝑖𝑖−2 , Δ𝜃𝜃𝑖𝑖𝑖𝑖 ; level equations: 𝚫𝚫𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖−2, 𝚫𝚫𝛽𝛽𝛽𝛽𝑖𝑖−2; 𝚫𝚫𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖−3, 𝚫𝚫𝛽𝛽𝛽𝛽𝑖𝑖−3, 𝜃𝜃𝑖𝑖𝑖𝑖. Variables are defined above.

Table 4 presents the results of regression models of table 3 adding the family-

management involvement dummy variable and the interactions between family-

management dummy and the variables representatives of ICE (Intellectual Capital

Efficiency, Human Capital Efficiency and Structural Capital Efficiency). First, the

coefficient of Family Management Involvement is negative and statistically significant

(ρ<0.05) in all regressions. That is consistent with previous studies, which points that

family managed firms are less profitable than non-family managed firms. As we

discussed above, the preservation of socioemotional endowment leads to family

managers to make decision not driven mainly by financial goal (Berrone et al., 2010).

Our results appear support that assertion.

In addition, we test the moderator role of family management on the relationship between

ICE and performance. The results show that the coefficient of Family management

involvement * Intellectual Capital Efficiency is positive and significant (β4= 0.324,

ρ<0.05). This finding supports the argument that although family firms management

could impact negatively on the profitability, they are better at transfer the efficiency of

intangible resources to greater performance. But, are this true for each type of intangible

resources? To answer this question, we run models 4 to 6 by splitting the variable

Intellectual Capital Efficiency into Human Capital Efficiency and Structural Capital

Efficiency.

24

The results of model 4 show that the coefficient of Family management involvement *

Human Capital Efficiency is positive but not significant (β4a= 0.397, ρ>0.10). Hypothesis

4a1 and 4a2 are not supported. Finally, the result of model 6 show that the coefficient of

Family management involvement * Structural Capital Efficiency is positive and significant

(β4b= 1.501, ρ<0.05), supporting hypothesis 4b. Both results confirm that socio-

emotional issues appear do not influence in how the efficiency of the human resources

contributes to create greater performance on family managed firms. However, the results

confirm the existence of family management effect on the relationship between Structural

Capital Efficiency and performance. This finding supports the argument that, family-

managed firms are better into transform greater efficiency of organizational, process and

innovation capital into greater performance.

5. Discussion and conclusions

According to resource-based and knowledge-based theories, firms gain competitive

advantage and attain superior performance by controlling both its tangible and intangible

assets (Riahi-Belkaoui, 2003). The IC embodies intangible value drivers and for that

reason, it has an increasingly important role in achieving high business performance

(Kommenic & Pokrajčić, 2012). In addition, it is believed that investors will place a higher

value for firms with greater IC (Firer & Williams, 2003). As such, it is expected that IC

plays an important role in enhancing both corporate value and financial performance

(Chen et al., 2005). However, the inconclusive results from prior empirical studies

suggest the need to conduct more research on the role of IC. Using VAICTM model

developed by Pulic (1998), this paper empirically examines the relationship between IC

efficiency and profitability using a sample of manufacturing companies in Spain for the

period 2000 to 2013. In order to assess this relationship, three main hypotheses are

tested. These research hypotheses are developed in accordance to both theoretical

literature and previous research in the field of IC.

The study results give strong empirical support for the all hypotheses. Our findings reveal

that both Intellectual Capital Efficiency (as measured by VAICTM) and its components

(Human Capital Efficiency and Structural Capital Efficiency) have a significantly positive

impact on profitability (measured by ROA) of Spanish companies over time. It means

that Spanish companies are capable of significantly increasing their profitability using

efficiently their human and structural capital. This is in line with the results of prior

empirical research about the relationship between ICE and financial performance (Chen

et al., 2005; Shiu, 2006; Tan et al., 2007; Chan, 2009; Phusavat et al., 2011; Clarke et

al., 2011; Kommenic & Pokrajčić, 2012; Bryl & Truskolaski, 2015).

25

Our findings also confirm that family management impacts on firm behaviour and

performance (De Massis, Kotlar, Chua, & Chrisman, 2014). We make evident the

moderating role of family management in the relationship between ICE and performance.

Specifically, we demonstrate that family management make stronger the impact of

structural capital efficiency on performance. Our results may thus help resolve the mixed

findings in prior literature on the influence of ICE on performance, which do not account

for the crucial role of family management in the former relationship. We argue that family

management from SEW perspective can be a double-edged sword when impacting on

the influence of human capital efficiency on performance. The family managing of human

resources, from SEW perspective, may produce advantages and disadvantages. Thus,

the pursuit of SEW may generate free riding or hiring family staff regardless their merit,

but also may raise emotional attachment and its positive consequences. As a

consequence, we have not been able to confirm a significant moderating role of family

management. However, financial and non-financial objectives seem to be aligned when

analysing the role of family management in the relationship between structural capital

efficiency and performance. Family managers incentive the identification of the family

with the firm, the preservation of tradition and heritage, the firm-specific knowledge and

collective corporate culture or long-standing alliances across generations with

stakeholders and the community. Consequently, the pursuit of SEW by family managers

usually accords with financial objectives, so family management is associated with the

achievement of better levels of performance from structural capital efficiency.

From an academic perspective, this study contributes to the literature on IC in many

ways. Firstly, this paper provides empirical evidence to support the relationship between

Intellectual Capital Efficiency and firms’ profitability (ROA) by using data from Spanish

manufacturing companies. Such outcome confirms the resource-based and knowledge-

based theories, which emphasize the significant role of IC in creating value. Secondly,

this study enriches IC literature with new empirical evidence and provides a basis for

comparison with the results of the studies conducted in other countries. Thirdly, the value

of this paper is the enrichment of the literature with another investigation that follows the

value-added intellectual coefficient methodology (VAICTM) for the measurement of IC.

Finally, given that this is the first empirical study conducted in Spain testing the

relationship between ICE, family management and profitability it may serve as a platform

for conducting future research on the IC problem area.

Regarding practical implications, the results of this study suggest that it is important that

Spanish firms use human and structural capital efficiently to generate higher profitability

(with a higher return on assets). As a result, companies must conduct a substantive

investment on human capital to enhance employee capability, attitude and satisfaction

26

through employee training and knowledge sharing in order to improve their profitability.

Regarding structural capital, Spanish firms should establish and maintain a positive

organizational culture, develop the management control systems and a strong IT system

to support internal business processes. Moreover, firms may attempt to develop close

relationships with their stakeholders to enhance their structural capital. Accordingly, our

results can influence the awareness raising about the significance of IC for corporate

performance, which in time may result in recognizing the need for the implementation of

some of the IC management and measuring models and methods. This paper provides

a tool for managers to assess the company’s IC using easy accessible data (VAICTM)

and to benchmark against the best competitors in their competition environment. Thus,

our results may give managers an insight to better utilize and manage IC resource

available in their firms in order to improve competitive advantage and ultimately firm

performance. Moreover, VAICTM method can be an important tool for many decision

makers to integrate IC in their decision process. Additionally, investors may use IC

efficiency as a means to assess firms’ ability to create value through IC. Policy makers

should intensify their initiatives in order to encourage greater acceptance and

understanding of the concept of IC and the development of its related assets. Overall,

our results corroborate the initiatives of Spanish government in promoting knowledge-

based economy. Putting continuous emphasis on IC investment is necessary for

sustainable growth and better corporate performance. Finally, family management

should be promoted by academics and practitioners in family firms to obtain larger

performance levels from similar structural capital efficiency.

Limitations and Implications for Future Research

Despite the contributions of this empirical research, this study has a number of limitations

that might benefit from further research. Firstly, this study analyses the relationship

between ICE and profitability in the single social, economic and political Spanish context.

The inclusion of other countries could enable research into the relevance of the

geopolitical context. Hence, it would be interesting in the future to carry out cross-national

IC research in European countries in order to gain more insightful information on IC

efficiency of these countries. Moreover, this study focuses on profitability and it neglects

other kinds of financial performance such as productivity and market valuation, which

deserve to be investigated in the future. Thirdly, the study uses the VAICTM as a proxy

for IC efficiency. The measurement of IC is a complex matter and is still a much-debated

issue in the IC literature. Further research is needed in this area to identify a model that

can correctly measure IC efficiency in firms. Finally, institutional environment can impact

on the moderating role of family management (Liu, Yang & Zhang, 2012). Our sample,

27

however, did not allow us to further analyse this issue. Hence, future research may

investigate whether the conclusions of this study can be the same in other contexts.

28

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