EXPORT PERFORMANCE IN SOUTH AMERICA: DO INTANGIBLES AFFECT FIRMS’ PERFORMANCE IN DEVELOPING COUNTRIES SUCH AS PERU?

Desempeño de las exportaciones en Sudamérica: ¿Los intangibles afectan el desempeño de las empresas en países en desarrollo como Perú?

ABSTRACT

This study is one of the first empirical studies on intellectual capital (IC) in South American textile exporting companies. Using the resources and capabilities approach, we examine how certain intangibles can provide competitive advantages. We propose and test a model capturing the relationships between IC components and export performance (EP), as well as the various interrelationships among the different dimensions of IC. Using a sample of textile manufacturers operating in Peru, an emerging economy, we find that both human capital and structural capital have a significantly positive influence on EP. However, the data analysis does not confirm that relational capital has any positive influence on export performance.

KEYWORDS:
Intellectual capital; export performance; South American; textile companies; emerging economy

RESUMEN

Este artículo es uno de los primeros estudios empíricos sobre capital intelectual (CI) en empresas exportadoras de textiles de América del Sur. Usando el enfoque de recursos y capacidades, examinamos cómo ciertos intangibles pueden dar ventajas competitivas. Proponemos y probamos un modelo que muestra las relaciones entre el CI y el desempeño de exportación (EP, por su sigla en inglés), así como las diversas interrelaciones entre las diferentes dimensiones del CI. De una muestra de fabricantes de textiles que operan en Perú, se halló que tanto el capital humano (CH) como el capital estructural (CE) tienen una influencia significativamente positiva en el EP. Sin embargo, un análisis de los datos no confirma que el capital relacional (CR) tenga alguna influencia positiva en el desempeño de las exportaciones.

PALABRAS CLAVE:
Capital intelectual; desempeño de exportación; América del Sur; empresas textiles; economía emergente

RESUMO

Este artigo contribui com um dos primeiros estudos empíricos relacionados ao capital intelectual (CI) em empresas exportadoras de têxteis da América do Sul. Utilizando a abordagem de recursos e capacidades, examinamos como certos ativos intangíveis podem trazer vantagens competitivas. Propomos e testamos um modelo que mostra as relações entre o CI e o desempenho de exportação (DE), bem como as várias inter-relações entre as diferentes dimensões do CI. Em uma amostra de fabricantes de têxteis que operam no Peru, uma economia emergente, verificou-se que o capital humano (CH) e o capital estrutural (CE) têm influência significativamente positiva no DE. No entanto, uma análise dos dados não confirma que o capital relacional (CR) tenha influência positiva no desempenho das exportações.

PALAVRAS-CHAVE:
Capital intelectual; desempenho das exportações; América do Sul; empresas de têxteis; economia emergente

INTRODUCTION

Based on a resource-based perspective, a company’s ability to penetrate international markets is linked to its overall competitive advantage (Barney, 1991, 2001; Brouthers, Brouthers, & Werner, 2008; Javalgi & Todd, 2011). Intangible assets such as knowledge and learning contribute to such a competitive advantage (Drenkovska & Redek, 2015; Hoa, Huong, Linh, & Mai, 2018; Muh & Murwaningsari, 2019; Xu & Wang, 2019).

While intellectual capital (IC) is recognized as the dominant factor affecting many companies’ competitive advantage (Alatas & Cakir, 2016; Dumay, 2009; Hejazi, Ghanbari, & Alipour, 2016), the relationship between IC and export performance (EP) remains insufficiently explored throughout Latin America (Calix, Vigier, & Briozzoc, 2015; Pucar, 2012).

This article aims to answer the following two questions: First, which dimensions of IC are linked to the results of companies’ internationalization? Second, how do these dimensions relate to EP in South America?

We address whether and how IC has any relationship with the exports of Peruvian textile companies. We also examine how the dimensions of IC are related to one another. Finally, we propose how dimensions of IC are linked and how they impact EP. Since much of the previous research has focused on competitive, mostly saturated developed markets, the use of our proposed model in Peru, a developing economy, is relevant.

This study advances the knowledge and best practices in the areas of IC and EP by using empirical evidence and obtaining a deeper knowledge of how enterprises in emerging economies leverage IC to improve their export effectiveness.

Our proposed model incorporates the variables of structural capital (SC) and relational capital (RC). While these variables have been studied in other countries, they have not been adequately studied in the South American exporting textile sector.

The contributions of this study are as follows: First, this study is one of the first empirical studies on the impact of IC on EP within the textile industry in emerging markets in Latin America. Little research has been conducted on IC performance within the South American traditional textile manufacturing sector.

Second, the existing IC literature on the textile industry mainly consists of descriptive summaries. However, such research lacks empirical results, focuses almost exclusively on Asian countries, and mostly overlooks South America.

Third, this study expands the extant literature and provides some valuable insights concerning the textile sector, for policy makers as well as academics and business leaders interested in making this sector more competitive.

Other studies have examined firms in the emerging economies of China, South Korea, Vietnam, Slovenia, Indonesia, and western Balkan nations. However, while South America is also characterized by emerging, middle-income economies, only a single relevant study exists, and it was conducted on Honduras.

Peru is a typical emerging economy that is evolving away from its dependence on exports related to the extractive industry into a value-adding economy in sectors such as textile manufacturing. Peru is also a middle-income country whose economy has been steadily growing for more than ten years. Peruvian manufacturers, becoming aware of the possible opportunities in international markets, must grow and adapt to the different characteristics of their export destinations.

This study focuses on companies in the Peruvian textile sector and uses data from 207 firms. The hypotheses were tested using a structural equation model (SEM) and by applying the partial least squares (PLS) method (Hair, Hult, Ringle, & Sarstedt, 2014).

This paper includes six sections: Introduction, Literature Review, Method, Results, Discussion, and Conclusion.

LITERATURE REVIEW AND HYPOTHESES STATEMENTS

Export performance and the resource-based approach

EP is defined as the degree to which an exporting company has achieved its objectives with respect to its international activities. According to the existing literature on the internationalization of companies (Dhanaraj & Beamish, 2003; Florez et al., 2012; Monteiro, Soares, & Rua, 2019; Ramon, Florez, & Araujo, 2019) exports may be understood in two different ways: (1) Exports may be among the first stages of internationalization, with the process starting with irregular exports, then indirect exports, and finally direct exports, and so on (Araya, 2009; Johanson & Vahlne, 2009). (2) Exporting companies are one of the various types of internationalized companies, including multinational, global, and transnational companies (Canals, 1994).

According to Dhanaraj and Beamish (2003) and Fuchs and Köstner (2016), exportation is one of the more attractive ways for a firm to break into international markets. Compared to possible joint ventures or acquisitions, exports require substantially fewer resources. Lu and Beamish (2002) indicate that by changes in the volumes of exported goods, as well as possible changes in destinations, improvements in flexibility and reductions in risk are possible. Lin and Ho (2019) suggest that exportation is the only practical way firms have to internationalize themselves.

Zou, Taylor, and Osland (1998) developed a general model for measuring EP using a scale with three dimensions: economic-financial, strategic, and satisfaction. This construct has been analyzed in the relevant literature (Griffith & Harvey, 2004; Kaleka, 2012). The discussion of EP focuses on the resource-based approach (Calix et al., 2015; Monteiro et al., 2019; Ramon et al., 2019). This approach treats companies as unique entities with accumulated resources, both tangible and intangible (Barney, 1991; Dhanaraj & Beamish, 2003). For example, companies with applicable knowledge, managerial skills, and other strategic intangible assets have resources that are scarce and difficult to create or reproduce. However, these companies are more likely to exhibit a competitive advantage (Pablos, 2004).

The literature suggests that EP correlates with knowledge, attitudes, international commitment, and decision-makers’ abilities (Calix et al., 2015; Johanson & Vahlne, 2009; Monteiro et al., 2019). Therefore, a fundamental prerequisite for a competitive advantage is a company’s ability to acquire and exploit knowledge in foreign markets.

Intellectual capital and export performance

IC refers to the intangible assets that contribute to a company's profitability. These assets include the employees’ expertise, organizational processes, and total individual and organizational knowledge. Recurring references to human capital (HC), SC, and RC have been observed throughout the literature (Asiaei & Jusoh, 2017; Edvinsson & Malone, 1997; Muh & Murwaningsari, 2019; St-Pierre & Audet, 2011; Xu & Wang, 2019).

The various studies that have addressed the relationship between IC and EP (Pucar, 2012; Zhang & Wang, 2018) have indicated that intangible resources become factors that differentiate companies’ performance in foreign markets, resulting in companies gaining a comparative advantage in terms of their EP (Morgan, Kaleka, & Katsikeas, 2004). Drenkovska and Redek (2015) have identified specific skills allowing companies to overcome barriers and develop their export activities. However, the studies providing evidence for a positive relationship between IC and EP have only used proxies to examine these concepts (Calix et al., 2015; Hejazi et al., 2016; Pucar, 2012).

Human capital and export performance

HC includes employees’ knowledge, skills, and attitudes. It is usually considered the most important dimension of IC (Bontis, 2004; Hejazi et al., 2016) (see Figure 1). Previous research has highlighted that HC is positively related to both organizational performance (Muh & Murwaningsari, 2019; Subramaniam & Youndt, 2005) and EP (Johanson & Vahlne, 2009; Monteiro et al., 2019; Pucar, 2012; Ramon et al., 2019). Furthermore, the skills and knowledge of employees responsible for making decisions related to exports are valuable assets that help ensure the proper development of the company's international activities. This specialized knowledge guides internationalization in constructive ways as it reduces uncertainty in decision-making (Hilmersson & Jansson, 2012). Since their emergence, theories of internationalization have suggested that EP correlates positively with the knowledge, attitudes, international commitments, and skills of a company’s decision-makers (Johanson & Vahlne, 2009).

Figure 1
Intellectual capital constructs

Taking into account the general economic conditions in South America, and the textile sector in Peru in particular, as well as Peru’s status as a developing country, the following hypothesis is proposed:

H1: HC in the Peruvian textile sector positively influences EP.

Structural capital and export performance

SC represents codified knowledge owned by the organization that does not exist in the minds of employees. It includes databases, processes, and routines (Bontis, 2004; Jansen, Tempelaar, Bosch, & Volberda, 2009; Kong, 2008). However, this type of capital supports employee activities and helps them learn new tasks and skills, thus improving their individual capacities. Gogan, Duran, and Draghici (2015) affirm that SC represents the competitive intelligence, formulas, information systems, patents, policies, and processes that have resulted in the methods and systems the organization has created over time. Unlike HC, SC remains in the organization when employees leave, thereby contributing to organizational continuity.

SC can be classified into two types: process SC and organizational SC (Edvinsson & Malone, 1997). A company gains process SC when it dedicates a separate division and establishes sales targets that are translated into organizational processes (Muh & Murwaningsari, 2019). Organizational SC results from investment in systems, tools, and operational guidelines that facilitate supply and distribution operations. Applied to foreign markets and operations, SC can be a determining factor of a company’s EP (Johanson & Vahlne, 2009). As suggested by the resource-based approach, SC generates organizational resources that help bolster EP (Laureano & Marques, 2009).

Considering the Peruvian textile sector and the general economic conditions in Peru, the following hypothesis is proposed:

H2: SC in the Peruvian textile sector positively influences EP.

Relational capital and export performance

RC refers to "the knowledge that was accumulated by all the different parties during exchanges, including relations with customers, suppliers, as well as other stakeholders” (Pablos, 2004). Because companies depend on their relations with the people they cooperate with, they cannot be treated as isolated systems. Reagans and Zuckerman (2001) indicate that the teams with the greatest diversity and the most social interconnections are more able to improve organizational performance. The main source of RC in organizations is its clients, suppliers, and other actors or interested members of the surrounding community. Qu and Lu (2013), as cited by Zhang and Wang (2018), state: “Relational capital is the sum of trust, friendship, respect, and mutual understanding which is based on the organizational level.” RC may include elements not directly associated with any of the organization’s direct actions. For example, it may include the corporation’s image and reputation, and its identification with possible partners and other allies in the export sector (Villena, 2019).

RC comprises the capital originating from relationships with clients, as well as the capital from relationships with other businesses (Bontis, 2004; Hoa et al., 2018). Customer relationship management occurs through a company’s interactions with customers. It includes the management of integrated channels, as well as the use of appropriate tools for the analysis of customer data (Ghane & Akhavan, 2014). Therefore, improved management of RC leads to an increase in client and supplier satisfaction, the establishment of mutual trust between parties, and the improvement of relations in general (Xu & Wang, 2019).

Taking this into consideration, the following hypothesis is proposed:

H3: RC in the Peruvian textile sector positively influences EP.

Interrelations among the dimensions of intellectual capital

Relationships between the variables in the aforementioned hypotheses and the organizational performance of exporting companies have been proposed (Edvinsson & Malone, 1997). HC can be identified as a determinant of the other second-order types of IC, including SC and RC (Bontis, 2004). The influence of HC on SC becomes clear because SC is "where the knowledge creation process is assumed to reside" (Boisot, 2002). Additionally, HC influences SC through organizational culture. SC is strongly influenced by HC through the manager’s cognitive capacity (Felício, Couto, & Caiado, 2014).

It is possible to argue that HC influences RC. In particular, some elements of HC, such as those linked to attitude, affect the establishment of relevant relationships (i.e., RC). This, in turn, allows access to valuable resources such as shared knowledge and provides a company with the ability to influence and build solidarity with other companies. Part of that shared knowledge includes cognitive and communication skills necessary in new markets, as well as an understanding of the ways a company can connect itself to these new markets (Hosseini & Owlia, 2016). Finally, the relationship between HC and RC is clarified by Davidsson and Honig (2003), who have indicated that the tacit knowledge acquired by previous experience (by a company’s HC) helps ensure success.

Taking the above into consideration, the following two additional hypotheses are proposed:

H4: HC in the Peruvian textile sector positively influences SC.

H5: HC in the Peruvian textile sector positively influences RC.

Figure 2
Proposed theoretical model

METHODS

Sample

The companies used in this study were selected from a secondary source consisting of a database of importers and exporters provided by the Peruvian Exporters Association (ADEX). Only companies in the Peruvian textile sector were included. Despite experiencing setbacks due to the 2008 financial crisis, this sector has become the single most dynamic industrial export sector in Peru (Sistema Integrado de Información de Comercio Exterior [SIICEX], 2019).

Our primary data were obtained by surveys of company representatives. The latter were quite accessible, enabling us to conduct our primary surveys easily. Since we focused on a single manufacturing sector, which was not subject to supply or demand controls, we were able to control for a larger number of contextual variables. We selected the sample companies based on three broad criteria. First, they needed to be selling their products directly to customers. Second, they had to be involved in both export and domestic markets. Third, they needed to be small or medium-sized companies.

From the ADEX database, we identified 874 companies meeting our criteria but their data lacked information regarding IC and company operations. A questionnaire was prepared that we pre-tested using in-depth, one-on-one interviews. Between March and July 2016, a total of 207 questionnaires were completed.

Variable measurement

As the study employed a questionnaire survey approach, all independent and mediating variables and the dependent variable were measured through five-point Likert-type scales ranging from 1 = “strongly disagree” to 5 = “strongly agree,” with 3 = “neutral.”

A total of 33 items on the three IC constructs, as well as EP, were used (see Tables 1, 2, 3, and 4).

Table 1
Correlations

Variables	1	2	3	4	5	6	7	8
1. Export performance	1
2. Structural capital	0.92***	1
3. Human capital	0.91***	0.42***	1
4. Relational capital	0.007	0.02	0.13*	1
5. Number of employees	-0.06	-0.04	0.11	-0.57***	1
6. Age of the company	-0.23***	-0.15**	-0.10	0.49***	0.48***	1
7. Number of export countries	0.31***	0.36***	0.34***	0.44***	0.54***	0.44***	1
8. Number of export products	0.25***	0.27***	0.23***	0.18**	0.30***	0.12*	0.43***	1

• Notes:
• ^***
  significance at the 1% level (2-tailed);
• ^**
  significance at the 5% level (2-tailed); significance at the 10% level (2-tailed). Number of observations=207.

Table 2
Loadings and measures of validity and reliability

Item	Loading (t-value)
Structural capital (SC):
CR=0.864; AVE=0.680; Cronbach's alpha=0.764
SC2	0.785 (25.355)
SC3	0.811 (42.668)
SC5	0.875 (53.031)
Human capital (HC):
CR=0.918; AVE=0.619; Cronbach's alpha=0.897
HC2	0.919 (92.352)
HC3	0.725 (25.135)
HC6	0.869 (50.367)
HC7	0.860 (52.040)
HC10	0.857 (53.733)
Relational capital (RC):
CR=0.938; AVE=0.619; Cronbach's alpha=0.924
RC1	0.856 (4.141)
RC2	0.768 (2.640)
RC4	0.859 (3.955)
RC5	0.793 (3.314)
RC6	0.772 (3.013)
RC8	0.869 (4.241)
RC9	0.811 (4.466)
Export performance (EP):
CR=0.9893; AVE=0.677; Cronbach's alpha=0.677
EP1	0.932 (144.070)
EP2	0.725 (23.834)
EP3	0.805 (36.106)
EP4	0.817 (37.345)

• Note: Significance established through a resampling procedure (1,000 repetitions), in all cases, at the 1% level.

Table 3
Cross loadings

Item	Structural capital (SC)	Human capital(HC)	Relational capital (RC)	Export performance(EP)
SC2	0.785	0.655	0.141	0.669
SC3	0.811	0.704	-0.064	0.806
SC5	0.875	0.740	-0.003	0.795
HC2	0.716	0.857	-0.008	0.789
HC3	0.815	0.919	-0.050	0.895
HC6	0.620	0.725	0.397	0.571
HC7	0.730	0.869	0.235	0.797
HC10	0.711	0.860	0.031	0.759
RC1	0.008	0.097	0.856	-0.012
RC2	-0.088	0.000	0.768	-0.105
RC4	0.005	0.124	0.859	0.026
RC5	0.008	0.065	0.793	-0.043
RC6	-0.042	0.061	0.772	-0.060
RC8	0.009	0.116	0.869	-0.005
RC9	0.084	0.123	0.811	0.059
EP1	0.868	0.854	-0.115	0.932
EP2	0.688	0.687	0.299	0.725
EP3	0.730	0.684	-0.300	0.805
EP4	0.738	0.753	0.213	0.817

Table 4
Standardized model coefficient estimates and p-values, corrected for bias

			Confidence interval 95%
H0	Effects	Estimate	Low	High	P-Value	Sig.
	Direct Effects:
H1	EP←HC	0.465	0.386	0.537	0.000	***
H2	EP←SC	0.482	0.399	0.565	0.000	**
H3	EP←RC	0.020	-0.026	0.091	0.507
H4	SC←HC	0.850	0.809	0.878	0.000	***
H5	RC←HC	0.127	-0.234	0.238	0.315
	Indirect Effects:
	EP←HC	0.412	0.352	0.476	0.000	***
Controls	EP←EMPLOYEES	-0.085	-0.146	-0.007	0.019	**
	EP←TIME	-0.109	-0.162	-0.050	0.000	***
	EP←CONTRIES	0.069	0.011	0.125	0.375
	EP←PRODUCTS	0.019	-0.027	0.057	0.017	**

• Notes: Standard error based on resampling.
• ^*
  p <0.10;
• ^**
  p <0.05;
• ^***
  p <0.01. EP: export performance; HC: human capital; SC: structural capital; RC: relational capital; EMPLOYEES: Number of employees; TIME: Time during which company has been engaged in export activities; COUNTRIES: Number of countries exported to; PRODUCTS: umber of products exported.

Dependent variable

EP depends on a company’s relative success in utilizing the various dimensions of IC. The measurement of EP did not include export volumes but four indicators were used to capture characteristics such as a company’s export intensity, export growth, market share, and the company’s perceived success and strategic position (see Exhibit 1).

Exhibit 1
Dependent variable: Export performance (EP)

Item	Statement	Relevant Literature
EP1	In recent years, exports have accounted for the bulk of the company's sales.	Zou et al. (1998)
EP2	The growth in the volume of exports in recent years has been considerable.
EP3	The company is satisfied with the results achieved regarding its export activity in recent years.
EP4	In recent years, the company has achieved the forecasted objectives with respect to its foreign markets and export volumes.

Independent variables

Human capital

Following the existing literature, we measure HC in terms of knowledge, skills, and attitudes (see Exhibit 2).

Exhibit 2
Independent variable: Human capital (HC)

Dimension	Item	Statement	Literature Review
Knowledge	HC1	Employees have sufficient knowledge about the textile and/or garment industry.	Pedrini (2007), Nieto & Fernández (2006), Katsikea et al. (2007), Cadogan et al. (2002), Knowles et al. (2006), and Andersen (2006)
	HC2	Managers have sufficient knowledge about economics, business, and finance.
	HC3	Employees have sufficient knowledge about exports (contractual conditions, currencies, tariffs, and regulations).
	HC4	Employees in the sales area have sufficient knowledge about the target markets.
	HC5	Salespeople have sufficient knowledge of the relevant foreign languages.
Skills	HC6	Employees can analyze and solve problems.	Daghfous & Kah (2006), Pedrini (2007), Chong et al. (2014), and Wong & Aspinwall (2005)
	HC7	Employees can adapt to new situations.
	HC8	Managers have leadership skills and the ability to delegate work.
Attitudes	HC9	Employees cooperate with their colleagues.	Wong & Aspinwall (2005), Pedrini (2007), Wang & Chang (2005), and Diamantopoulos & Kakkos (2007)
	HC10	Employees show interest in remaining at the company.
	HC11	Managers are emotionally willing to persevere during the internationalization process.

Structural capital

Following the existing literature, we measure SC by considering processes and organization (see Exhibit 3).

Exhibit 3
Independent variable: Structural capital (SC)

Dimension	Item	Statement	Relevant Literature
Process	SC1	The company has formal processes (for production, administration, sales, exportation, and finance) within the value chain it operates.	Bontis et al. (2000), Chen et al. (2004), Tseng & Goo (2005), Pedrini (2007), Wong & Aspinwall (2005), Lee & Steen (2010), Hutchinson & Quintas (2008), and Cadogan et al. (2002)
	SC2	Company processes are standardized and are recorded in management documents.
	SC3	The company constantly incorporates improvements into its products and processes.
	SC4	In the company, there is a continuous concern for the quality of the products or services exported.
Organization	SC5	The company can impart to new employees the experience it has gained.	Wong & Aspinwall (2005), Chirico (2008), Janet & Alton (2013), Hutchinson & Quintas (2008), and Jones & Crompton (2009)
	SC6	The company can impart its organizational culture to its employees.
	SC7	Company information flows effectively for decision-making.

Relational capital

Following the existing literature, we measure RC through questionnaire items on customers and businesses (see Exhibit 4).

Exhibit 4
Independent variable: Relational Capital (RC)

Dimension	Item	Statement	Relevant Literature
Clients	RC1	The company focuses on maintaining its image with current customers.	Shane & Cable (2002), Pablos (2004), Pedrini (2007), Blyler & Coff (2003), Blomstermo et al. (2004), Hilmersson & Jansson (2012), and Fletcher & Harris (2012)
	RC2	The company cultivates a positive image for prospective customers.
	RC3	The company maintains an updated database of customers and suppliers.
	RC4	The company participates in events to find customers in current and new markets (e.g., fairs and exhibitions).
	RC5	The company analyzes the cultural, business, and political backgrounds of the countries to which it wants to export.
	RC6	The company analyzes customers' preferences in the countries to which it wants to export.
Businesses	RC7	The company obtains strategic information from its agents and distributors in the destination countries.	Blyler & Coff (2003), Andersen (2006), Pedrini (2007), and Souchon et al. (2012)
	RC8	The company cultivates and maintains good relations with agents and distributors in the destination countries.
	RC9	The company belongs to business networks (e.g., chambers of commerce and industry-wide associations).
	RC10	The company has cooperation agreements with other exporting companies.
	RC11	The company has cooperation agreements with its suppliers.

Control variables

The following four control variables were considered: time of operation, number of employees, number of products exported, and number of export destination countries. Although these do influence EP, they were not included because they were not the emphasis of the study.

PLS

We utilize an SEM and the PLS method (Hair et al., 2014) for five main reasons: (1) The present study seeks evidence regarding the impact of IC on EP in South America. (2) The model proposed is a complex, multivariable model and includes three independent variables with seven dimensions, in addition to the dependent variable. (3) The five hypotheses are predictive but not causal. While causality implies the ability to predict events with 100% accuracy, prediction only allows a limited degree of accuracy. (4) PLS is recommended for small sample sizes; in this case, n = 207 companies. (5) The data obtained on our sample companies were not assumed to follow a normal distribution. PLS-SEM does not assume that the data are normally distributed; instead, a nonparametric bootstrapping procedure is used (Henseler, Ringle, & Sarstedt, 2012). In this procedure, repeated random samples are taken from the original sample with replacement, to create a bootstrap sample. Then, standard errors are obtained to test the hypotheses (the process assumes that the distribution of the sample reasonably represents the distribution of the population). The bootstrap samples allow the level of significance of the estimated coefficients to be obtained through the PLS-SEM algorithm. In this case, a bootstrapping procedure of 1,000 subsamples representing the standard errors was applied to assess the statistical significance of the established structural relationships.

RESULTS

Measurement model

Table 1 shows the outcome of the correlation analysis of the model variables. The correlations between the dependent variable and the independent variables were positive and significant.

The model has four latent variables. The Cronbach's alpha values for all the latent variables are shown in Table 2. In the evaluation of the measurement model, the composite reliability was checked to evaluate the model’s internal consistency. To assess convergent validity, individual reliability indicators and the average variance extracted (AVE) were used. In addition, the evaluation of the model included the examination of discriminant validity through the cross-loadings.

Cronbach's alpha was used to measure the reliability of each of the measurement scales of the latent variables. The measurement scales of all the variables exceeded the threshold value of 0.70. However, it is technically more appropriate to use the composite reliability measure. Composite reliability varies between 0.0 and 1.0, with higher values indicating high levels of reliability and generally interpreted in the same way as Cronbach's alpha. In general, composite reliability values less than 0.60 indicate a lack of internal consistency while values of 0.60 to 0.70 are acceptable, and values greater than 0.70 are considered satisfactory (see Table 5). In the present model, composite reliability exceeds 0.70 in all cases.

The external loading (that is, the estimates of the relationships between the latent variables and their indicators) are presented in Table 2, and for the EP, HC, SC, and RC constructs, they are all significant and well above the threshold, 0.70. This suggests a sufficient level of reliability for each indicator. The AVE values ​​of the four latent constructs exceed the minimum level of 0.50 (see Table 2). Therefore, the measures of the four constructs exhibit high levels of convergent validity.

As can be seen in Table 2, all the external loadings of the EP, HC, SC, and RC constructs are significant and well above the threshold value of 0.70, suggesting adequate levels of reliability for the indicators.

An additional measure to establish the convergent validity of each construct is the AVE. Using the same logic as with the individual indicators, an AVE of 0.50 or higher indicates that, on average, the construct explains more than half of the variance of its indicators. On the other hand, an AVE of less than 0.50 indicates that, on average, the variance of the indicators is larger than the variance explained by the construct. The AVE values of the four latent constructs exceed the minimum level of 0.50 (see Table 2). Therefore, the measures of the four constructs exhibit high levels of convergent validity.

CR: component reliability; AVE: average variance extracted It is also possible to assess the discriminant validity of the indicators by examining their loadings and cross loadings (see Table 3.) As can be seen in Table 3, the loadings always exceed the cross loadings. For example, the item RC1 generates a high loading in its corresponding RC construct (0.856) but is much lower in other constructs (0.008, 0.0097, and -0.012). The same is observed for the other RC items, including for the items of the SC, HC, and EP constructs. Therefore, the cross loading analysis suggests that discriminant validity has been established.

Structural model

Statistical evidence in favor of three of the five hypotheses proposed was found (see Table 4). The impacts of HC and SC on EP were statistically significant and positive, supporting hypotheses H1 and H2, respectively. In addition, it was found that the relationship between HC and EP is mediated by SC, as proposed in hypothesis H4. On the contrary, no evidence was found in support of hypothesis H3 since the relationship between RC and EP was not significant. Hypothesis H5 was also not supported, given that HC did not exhibit a significant effect on RC. Finally, given that SC shows a pronounced and significant effect on EP, SC partially mediates the relationship between HC and EP. Since these direct and indirect effects are both positive, the product of the two (i.e., 0.412) is also positive. In addition, the indirect effect from HC on EP through SC (that is, the product of the path coefficients from HC to SC and from SC to EP) is significant since the 95% confidence interval does not include zero. Therefore, it can be concluded that SC is a complementary mediator of the influence of HC on EP. These results offer evidence for the mediating role of SC in the model. Specifically, SC represents a mechanism that underlies the relationship between HC and EP. HC affects SC, and SC, in turn, affects EP.

These results support much of the theoretical basis presented above, as interactions among the different components of IC have been identified. Based on the results, SC partially mediates the relationship between HC and EP. Furthermore, the results also corroborate that HC and SC are the dimensions that contribute most significantly to EP.

DISCUSSION

In the past, the literature on the management of intangible resources focused on large organizations within developed countries (Cuervo-Cazurra & Genc, 2008). However, in recent years, researchers have broadened their interest to small- and medium-sized organizations in developing countries (Drenkovska & Redek, 2015; Hejazi et al., 2016; Hoa et al., 2018; Xu & Wang, 2019).

This study aimed to analyze the impact of IC on EP in the developing countries of South America. The literature suggests that IC resources often drive performance and, hence, there must be a causal relationship between those resources and value creation (Drenkovska & Redek, 2015). In general, the results suggest that intangible resources make important contributions to EP. Further, the results confirm that there is a positive relationship between the dimensions of IC and EP. Additionally, there exists a positive relationship between IC and EP within small- and medium-sized firms. These findings validate the resource-based theory in the South American context. Hence, this supports the theory that IC influences EP, which is consistent with Calix et al.’s (2015) findings about Honduras.

Regarding hypothesis 1, the results show that HC is the most important component of IC. Edvinsson and Malone (1997) also argue that “human capital is the primary component of IC as it is the critical source of intangible value for firms.” It is a significant determinant of EP. This finding is consistent with prior research regarding the key role of HC in the performance of firms operating in Indonesia (Muh & Murwaningsari, 2019; Ngatno, Apriatni, & Widayanto, 2016), in South Asia (Bhavan, 2017), and in Latin America (Calix et al., 2015). However, the results seem different in countries such as India and China. Both Smiriti and Das (2018) and Xu and Wang (2019) have shown that the HC of companies in these countries’ textile industries does not affect EP as strongly as it does in emerging countries. The reason may be what Bhavan (2017) affirms: “The countries suffering from trade deficits can concentrate on human capital development in the external sector to enhance its economic performance.”

Regarding hypothesis 2, SC is a significant determinant of EP. This result is consistent with the findings of Xu and Wang (2019) in China and South Korea, Smiriti and Das (2018) in India Hejazi et al. (2016). However, no significant associations are found in Iran (Muh and Murwaningsari, 2019) or Indonesia, especially in the province of Banten. An explanation may be that “the manufacturing companies in the province of Banten have not optimized their routines, procedures and bureaucratic systems.”

Regarding hypothesis 4, HC influences SC. This reinforces the role of HC in enhancing the other intangible assets. However, the empirical results indicate that it does not have a significant influence on EP.

Hypothesis 3, concerning RC, is not supported, despite our expectations that it would be (Hoa et al., 2018). However, Xu and Wang (2019) found that in China, RC significantly influences textile companies’ EP. Nevertheless, according to Xu and Wang, this is not the case for South Korea. Further, Xu and Wang state, “Overall, CEE, SCE, and RCE are found to be the main value-added drivers for Chinese textile companies. In South Korea, CEE and HCE are major contributors to the performance of the textile industry.” In addition, studies conducted in Slovenia and the Western Balkans (Drenkovska & Redek, 2015) and in Vietnam (Hoa et al., 2018; Xu & Wang, 2019) confirm the importance of RC in company performance. However, in studies conducted in Russia, the importance of RC is not found to be as significant (Andreeva & Garanina, 2016).

CONCLUSIONS

The present study examined the influence of intangible capital on EP in the textile manufacturing sector of South American countries. The results suggest that intangible assets make important contributions to companies’ EP. Specifically, the findings reveal that both HC and SC are significant determinants of EP. However, RC is not a significant factor affecting the South American textile industry. These results are similar to those found in emerging countries in Southern Asia. Comparing Asian countries with Peru, it seems that the political and economic context of the latter influences whether RC is important or not. It appears that some countries exhibit greater reliance on RC than others.

This study has contributed to the literature concerning IC. First, it has drawn from previous research on IC and has specifically included textile manufacturers from Peru, an emerging South American economy. Second, it has confirmed that by driving business opportunities in foreign markets, HC is the most important factor driving EP. Finally, we have used more than one indicator for each component of IC. This has overcome some of the possible limitations of previous studies, which only used accounting data as an indicator.

This study helps balance the attention between tangible and intangible assets, thereby creating a more holistic view of resources. This can provide researchers with a more comprehensive vision of the resources at a company’s disposal. It can also aid policy makers who work to support and encourage the development of export activities, especially in small- and medium-sized enterprises. We conclude that Peruvian firms in the textile sector must focus on strategic resources to improve their EP.

This study has some limitations. First, the questionnaire may introduce possible bias due to the self-reporting. Those receiving the questionnaire were often unable to delegate the task of completing it because they were the only ones at the organization with the knowledge necessary to complete it. Second, because it was decided to focus on a single industrial sector, to control and manage the number of contextual variables, the findings cannot be extrapolated to a wider context.

The proposed model provides a future direction for researchers to continue and explore the role of IC in internationalization. Based on the findings of this study, it is necessary to consider how these factors may play a role in companies’ internationalization.

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Edited by

Evaluated through a double-blind review process. Scientific Editor: Teresia Diana Macedo-Soares

Publication Dates

• Publication in this collection
  03 May 2021
• Date of issue
  2021

History

• Received
  26 Aug 2019
• Accepted
  04 May 2020