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. 2022 Dec 5:1–11. Online ahead of print. doi: 10.1007/s10341-022-00784-6

Internal Factors Affecting Competitiveness in Agribusinesses: A Case Study in the Hazelnut Sector in Ordu and Giresun Provinces of Turkey

Mehmet Aydoğan 1,✉
PMCID: PMC9734959  PMID: 38625256

Abstract

The study’s primary purpose is to reveal the factors affecting the competitiveness of hazelnut agribusiness enterprises. The data used in the research were obtained from surveys with 51 enterprises in Ordu and Giresun provinces, Turkey. The enterprises were divided into competitive and non-competitive with a two-step cluster analysis. Indices were calculated to measure enterprises’ competitiveness, and the t‑test was used to compare enterprises’ characteristics and competitiveness values. The research results indicated that the size of the enterprises, implementation of corporate governance processes, support for personnel development, employment of qualified personnel, differentiated process management, robust information technologies infrastructure, and using e‑commerce applications increased the competitiveness. The research results confirmed that the competitiveness of enterprises was affected not only by financial indicators but also by internal factors within the enterprise. The non-competitive enterprises focused on low-cost production and overlooked other internal and external factors necessary for competitiveness. The hazelnut enterprises could increase their competitiveness by investing in e‑commerce and information technologies infrastructure by prioritizing personnel and corporate management processes.

Keywords: Competitiveness, Internal factors, Hazelnut agro-companies, COVID-19, Turkey

Introduction

Hazelnut is a strategic product for Turkey, the largest hazelnut producer in the Mediterranean region and the world. Turkey is in the leading position by achieving an average of 66.4% hazelnut production and 70% hazelnut export worldwide. Hazelnut, which is among Turkey’s traditional export products, provides a foreign exchange inflow to the country of approximately 1.3 billion dollars annually. Approximately 9% of Turkey’s annual export of agricultural products and approximately 2% of total exports are obtained from hazelnuts and their products alone (FAOStat 2020). In addition, its cultivation in a specific region (the Black Sea region) has led to the formation of all value chain actors in the region. It is the primary source of livelihood for approximately 3 million people, especially those employed as farmers and other sub-sectors.

Although Turkey ranks first in the world in terms of hazelnut production and export, serious competitors have entered the market in recent years, and Turkey’s hazelnut production and export have decreased significantly. While Turkey had a share of 70% of the world’s total hazelnut production at the beginning of the 2000s, this rate has decreased gradually in recent years. Similarly, at the beginning of the 2000s, 81.9% of the world’s hazelnut exports were carried out by Turkey, while this rate decreased to 66.1% on average over the last 10 years (WTC 2020). Therefore, the decrease in Turkey’s hazelnut production and exports may cause many sectors worldwide to have problems in the supply of raw materials. On the other hand, Italy, Chile, Azerbaijan, and Georgia constantly increase their shares in the world hazelnut market in production and exports. In Italy, hazelnut production has accelerated in recent years due to the desire to guarantee the raw material requirement in the chocolate industry; hazelnut is one of the primary inputs in the chocolate industry. Besides, while some South American countries, such as Chile, have suitable climatic conditions for hazelnut production, Georgia, Azerbaijan, and Iran have the advantages of cheap labor for hazelnut farming. These countries are Turkey’s new competitors in the world hazelnut market (Aktaş et al. 2009). The fact that other countries have become competitive in the world hazelnut market may indirectly mean a loss of income for the producers in Turkey. When income losses are added to the Turkish hazelnut sector, which is currently struggling with many structural problems, the world hazelnut supply may be adversely affected in the short term.

Hazelnut cultivation in Turkey has been struggling with long-term infrastructure problems such as low yield, production with traditional methods, expensive inputs, small farm size, and various marketing problems (Özkan 2011; Öztürk and Kaşko Arıcı 2017; Cansev et al. 2018). When the infrastructural problems are combined with the relative superiority of other countries, Turkey’s role in the world hazelnut market is gradually decreasing. In other words, Turkey may no longer maintain its competitive advantage in hazelnuts with production alone. Therefore, for Turkey to maintain its competitive advantage, a competitive agriculture-based industry should support hazelnut cultivation.

It is difficult to say that the hazelnut processing industry in Turkey is sufficiently developed. However, considering that 37% of the hazelnuts exported by Turkey consist of processed hazelnuts and hazelnut products (Erköse et al. 2020), it can be said that the sector is open to development. Hazelnut enterprises in Turkey generally prepare and market hazelnuts as cracking, roasting, crushing, slicing, and oil. The previous studies on the hazelnut industry in Turkey revealed that there had been a severe decrease in the competitiveness of the Turkish hazelnut industry in recent years (Yenisu 2017), and product diversification and technology usage level is low due to insufficient research and development (R&D) investment works in the industrial enterprises in the sector (Şahinli 2014; Yılmaz 2017). As a result, the Turkish hazelnut industry competes worldwide only with the amount of production and exports but cannot provide good competition for value-added products. The development of the competitiveness of the Turkish hazelnut industry in the category of value-added products is crucial for the world hazelnut supply and the sustainability of the livelihoods of hazelnut producers.

Competitiveness is a multi-dimensional concept in the literature. The competitiveness concept has been examined at three, generally related levels: country, sector, and firm (Ajitabh and Momaya 2004; Windsperger 2006; Allen and Potiowsky 2008; Pigatto et al. 2020). National competitiveness is the comparison of macroeconomic indicators and social welfare parameters among countries (Murtha et al. 1998). Martin et al. (1991) explained sector competitiveness as a sector’s ability to survive and expand sustainably in existing or new markets. On the other hand, Buckley et al. (1988) paraphrased the firm competitiveness as the firm’s long-term profit performance, as well as its ability to satisfy its employees and provide high returns to its owners. Since the concept of competitiveness is multi-dimensional, measurement methods also differ. In the literature, the concept of firm competitiveness has often been measured in comparison with the adoption of innovations, capacity to respond to changing demands (Chodavarapu et al. 2016; Brito and Zapata 2017), private cost ratios, profits, inputs, costs, and operating incomes (Martin et al. 1991; de Freitas et al. 2015; Winarno and Harisudin 2018). On the other hand, in some studies, it was emphasized that in the measurement of competitiveness, it was not only possible to focus on the production factors and market conditions of the firms, but also the interactions and cooperation between the firms were important (Sarturi et al. 2016; Pigatto et al. 2020). Similarly, Man et al. (2002) asserted that competitiveness is affected by internal factors and external environmental factors, and competitiveness at the firm level can only be measured by internal factors. Internal factors affecting competitiveness are financial resources, human and technological resources, quality systems, product diversity, and organizational structure (Buckley et al. 1988; Man et al. 2002; Dlamini et al. 2014).

Firm-level competitiveness is one of the most studied topics in the literature. Siudek and Zawojska (2014) argued that a country’s improving competitiveness is directly related to firm-oriented competitiveness. Firm competitiveness, which was the subject of the study, was explained by the firm’s long-term economic performance, as well as its ability to satisfy its employees and provide sustainable high returns to its owners in literature (Hudori 2013; de Freitas et al. 2015; Winarno and Harisudin 2018; Pigatto et al. 2020). Considering the definition, the concept of competitiveness that is valid today does not only focus on firm profitability. In addition to profitability, two critical concepts, satisfying employees and sustainability of economic return, come to the fore. However, many studies measured competitiveness with traditional methods such as increased profitability, asset return, and equity (Chao-Hung and Li-Chang 2010; Sachitra 2016). In other words, traditional methods ignored factors other than financial indicators in measuring competitiveness. Babu and Shishodia (2018) criticized the methods that did not consider variables other than financial indicators in the measurement of competitiveness, claiming that financial indicators would only provide information about the financial management of companies. Latruffe (2010) suggested that current literature mainly focused on price or cost competitiveness, while non-price components of firms’ competitiveness were often overlooked.

Another issue as crucial as factor selection is the measurement method of competitiveness. Policy analysis matrix, private cost ratio (Winarno and Harisudin 2018), comparative advantages, Balassa index (Sachitra 2016), analytical hierarchy process, linear regression (Delfín-Ortega and Bonales-Valencia 2020), and descriptive statistics (Dlamini et al. 2014; Sarturi et al. 2016) can be indicated among the measurement methods frequently used in competitiveness measurement in previous studies. Although the variables used to measure competitiveness in these studies had different units, analyses were carried out without considering this difference. This problem encourages studies to use variables in different units in the same model. The study de-unitized the variables used in the model by converting them into indices to eliminate the differences stemming from the units and sought a solution to the problem of comparing different units.

The external factors affecting competitiveness usually have a national-level result, which similarly affects the sector and all companies. On the other hand, internal factors affecting competitiveness also affect external factors positively or negatively from bottom to top. So, the study focused on the fact that firms cannot directly control variables such as policies, supports, and market structure, which are external factors that affect competitiveness. However, the human capital, economic structure, cooperation, innovation, and infrastructure variables in their internal structures that directly affect competitiveness can be controlled by enterprises. The research aimed to reveal the internal factors affecting hazelnut enterprises’ competitiveness and the differences between competitive and non-competitive enterprises.

Materials and Methods

Research Area

In Turkey, an average of 580,000 t of shelled hazelnuts are produced annually. The total amount of in-shell hazelnuts produced in the Giresun and Ordu provinces is 39.5% of the hazelnuts produced in Turkey (Turkstat 2020). Therefore, hazelnut agribusinesses enterprises are concentrated in these provinces. Ordu and Giresun provinces were chosen purposefully because hazelnut agricultural enterprises were mainly located in these provinces (Fig. 1).

Fig. 1.

Fig. 1

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Research area and hazelnut producing provinces

Research Data and Sampling Size

The main material of the research consisted of primary data obtained through a survey from hazelnut agribusiness enterprises in Ordu and Giresun provinces. The list of enterprises was obtained from the Turkish Ministry of Agriculture and Forestry (MoAF) databases. The research used the whole count method to determine the number of enterprises to be surveyed, and a survey was conducted with 51 hazelnut agribusiness enterprises. The secondary research data included previous studies, databases, and related reports. The data used in the study belonged to the production period of 2019.

Classifying Enterprises and Calculation of Competitiveness Indices

Previous studies stated that the firm’s competitiveness is related to market share, profitability, and capacity (Martin et al. 1991; Chao-Hung and Li-Chang 2010; Bedek and Njavro 2016). Ajitabh and Momaya (2004) argued that a firm’s share in the competitive market was its competitiveness. Therefore, the enterprises were divided into competitive and non-competitive by two-step cluster analysis using market share and unit profitability variables. According to the cluster analysis results, 54.9% of the enterprises were competitive and 45.1% were non-competitive. The average Silhouette value indicating the clustering quality (Netshipale et al. 2022) was determined as 0.4 and was interpreted as a good clustering.

The variables affecting competitiveness were grouped into human capital, economic capital, corporate governance, cooperation, and innovativeness. In the study, the variables were converted into indices and de-unitized to eliminate the problem of comparing different units. A new method was tried in the study based on previous measurement methods. The procedures for creating and calculating the indices are explained in detail below.

In the first step, the indicators (Xi) used to measure competitiveness and given in Table 1 were converted into scores (SXi) using Eq. 1 to de-unitize them (Barrera-Roldán and Saldivar-Valdés 2002; Aydoğan et al. 2022). Five sub-competitiveness indexes (HCI, ECI, CGI, CI, and InovI) were created by summing the calculated score values (SXi) according to their relevance. The calculating methods were given in the first column of Table 1. In determining the best value and the worst value terms (Eq. 1), the lowest value among all observations of the relevant variable was accepted as the “worst value,” and the highest value was accepted as the “best value.”

SXi=1-Xi's best value-Xi/[Xi's best value-Xi's worst value] 1

Table 1.

Sub-competitiveness indices and indicators used in the calculation

Sub-competitiveness indices Indicators (Xi)

Human Capital Index (HCI)

HCI = SX1 + SX2 + SX3 + SX4 + SX5

 Total number of employees score (X1)
Permanent employee score (X2)
Number of qualified personnel score (X3)
Personnel development support score (X4)
Personnel specialization score (X5)

Economic Capital Index (ECI)

ECI = SX6 + SX7 + SX8 + SX9 + SX10

 Allocated R&D share score (X6)
Capacity utilization rate score (X7)
Equity utilization score (X8)
Market share score (X9)
Revenue growth score (X10)

Corporate Governance Index (CGI)

CGI = SX11 + SX12 + SX13 + SX14 + SX15

 Size of the business area score (X11)
Legal status score (X12)
Corporate governance score (X13)
Quality management processes score (X14)
Operating capacity score (X15)

Cooperation Index (CI)

CI = SX16 + SX17 + SX18 + SX19 + SX20

 Clustering score (X16)
Collaboration score (X17)
Sectoral developments follow-up score (X18)
Technical consulting score (X19)
Joint-production score (X20)

Innovativeness Index (InovI)

InovI = SX21 + SX22 + SX23 + SX24 + SX25

 Brand creation score (X21)
Differentiated product score (X22)
Differentiated process management score (X23)
E‑commerce score (X24)
IT infrastructure score (X25)
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In the next step, the Total Competitiveness Index (TCI) was calculated by taking the sum of the sub-competition indices (Eq. 2), and this value indicated the competitiveness of the enterprises compared to the others.

TCI=HCI+ECI+CGI+CI+InovI 2

In the last step, the values of the Total Competitiveness Index (TCI), Human Capital Index (HCI), Economic Capital Index (ECI), Corporate Governance Index (CGI), Cooperation Index (CI), Innovativeness Index (InovI), and enterprise characteristics were compared according to competitive and non-competitive groups with the t‑test. The t‑test is a widely used hypothesis test that measures the difference between the means of two groups (Demiryürek et al. 2008; Aydoğan et al. 2016). Thus, it was aimed to reveal the factors that positively or negatively affect the competitiveness of the enterprises in the research area. Also, ratios and percentages were used in the analysis of enterprise characteristics. Analyses were performed with SPSS (ver. 25) package program.

Results

Comparison of Agribusiness Enterprise Characteristics

Hazelnut agribusiness enterprises purchase shelled hazelnuts (raw materials) from farmers or intermediaries and transform them into raw hazelnut kernels, roasted hazelnut kernels, sliced hazelnuts, hazelnut puree, hazelnut oil, and hazelnut flour. However, the variety of these products may vary according to the enterprises. Some enterprises produce all of these products, some only produce a few, depending on the operating capacity. Thus, the general characteristics of the enterprises were compared according to competitiveness groups (Table 2). A total of 58.8% of the enterprises in the research were located in Ordu province and 41.2% in Giresun province, Turkey. In all, 17.6% of the companies were sole proprietorships, 58.8% were limited liability companies, and 23.5% were joint stock companies.

Table 2.

Comparison of enterprise characteristics

Characteristics Enterprises Count Mean Std. Err t Value p-Value
Firm age (years) Competitive 28 14.9 1.6 1.753 0.086*
Non-competitive 23 20.2 2.7
The size of the open space area (m2) Competitive 28 5.985 1.910 1.763 0.084*
Non-competitive 23 2.096 708
The size of the closed area (m2) Competitive 28 3.876 1.093 0.623 0.536
Non-competitive 23 2.964 914

The hazelnut (raw material) cost

($/Kg)

 Competitive 28 2.62 0.1 2.235 0.030**
Non-competitive 23 2.80 0.3

Hazelnut (product) sales price

($/Kg)

 Competitive 28 6.25 0.9 1.838 0.072*
Non-competitive 23 5.62 1.2

Total gross income per employee

($/employee)

 Competitive 28 760,316.6 239,111 1.841 0.053*
Non-competitive 23 266,095.9 52,260
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*, **, *** Significant at 10%, 5%, and 1%, respectively

The average firm age was 17.3 years, and the average age of non-competitive enterprises (20.2) was higher than the competitive ones (14.9) (p < 0.10). In the research area, the enterprises’ average open area was 8139.3 m2, and the average indoor area was 3759.7 m2. The competitive enterprises’ open area (5985 m2 vs. 2096 m2) (p < 0.10) and indoor area (3876 m2 vs. 2964 m2) sizes were higher than non-competitive enterprises. In the study, the average hazelnut raw material cost was 2.70 $ kg−1, the average product sales price was 5.99 $ kg−1, and the gross profit per employee was 537,432.7 US dollars. The competitive enterprises’ raw materials cost (shelled hazelnuts) was lower than non-competitive (p < 0.05), and they sold products at higher prices (p < 0.10). Moreover, competitive enterprises’ gross income per employee was higher than non-competitive (p < 0.05).

The attitudes and vision of the enterprise managers are as crucial as the technical indicators in competitiveness. Attitudes of enterprise managers on concepts related to competitiveness are presented in Table 3.

Table 3.

Attitudes of enterprise managers towards some competitiveness indicators

Competitiveness indicators Competitive Non-competitive
Disagree (%) Undecided (%) Agree (%) Total (%) Disagree (%) Undecided (%) Agree (%) Total (%)
Product quality 0.0 0.0 100.0 100.0 0.0 4.3 95.7 100.0
Brand creation 3.60 0.00 96.40 100.0 8.70 4.30 87.00 100.0
Low-cost production 28.6 17.9 53.6 100.0 17.4 26.1 56.5 100.0
Taking quick action 0.0 3.6 96.4 100.0 4.3 21.7 73.9 100.0
Use of advanced technology 0.0 10.7 89.3 100.0 8.7 21.7 69.6 100.0
Cooperation with competitors 21.4 10.7 67.9 100.0 26.1 8.7 65.2 100.0
Cooperation with suppliers 0.0 3.6 96.4 100.0 0.0 4.3 95.7 100.0
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In the study, the product quality variable expressed the products produced in a certain quality and standard. All managers of competitive enterprises and 95.7% of non-competitive enterprises believed that product quality would provide a competitive advantage. Brand creation variable refers to marketing products under a particular brand name. As 96.4% of the managers of competitive enterprises argued that it was essential to create a brand in competition, 87.0% of the managers of non-competitive considered it was essential to create a brand in competition. The number of those who argued that creating a brand would not provide a competitive advantage or had no idea was higher in non-competitive enterprises. One of the most crucial competitiveness indicators in the literature is low-cost production compared to competitors. Those who believed that low-cost production was influential in the competition were more in the non-competitive enterprises. The variable of taking quick action can be defined as adapting quickly to market conditions, innovations, and developments in the sector. Those who thought taking quick action would provide a competitive advantage were competitive enterprises. In other words, it could be concluded that competitive enterprises had a more remarkable ability to adapt to innovations. The rate of those who believed that using advanced technology in production would provide a competitive advantage was higher in competitive enterprises. While enterprises in both groups were aware of the importance of cooperation with suppliers in competitiveness, the indicator of cooperation with competitors was less important than other variables.

Comparison of Competitiveness Indices by Competitiveness Groups

The competitiveness of enterprises in the research area was compared to the calculated competitiveness indices (Table 4). The average values of competitive enterprises’ Corporate Governance Index (p < 0.10), Economic Capital Index (p < 0.05), Human Capital Index (p < 0.01), and Innovativeness Index (p < 0.05) were higher than non-competitive enterprises. Although the enterprises’ average Cooperation Index values differed, this difference was not statistically significant (p > 0.05).

Table 4.

Comparison of competitiveness indices by competitiveness groups

Competitiveness indices Enterprises Count Mean Std. Error t Value p-value
Total Competitiveness Index (TCI) Competitive 28 11.35 0.44 3.904 0.000***
Non-competitive 23 8.87 0.45
Corporate Governance Index (CGI) Competitive 28 1.83 0.17 1.880 0.066*
Non-competitive 23 1.39 0.15
Economic Capital Index (ECI) Competitive 28 2.73 0.14 2.152 0.036**
Non-competitive 23 2.35 0.10
Human Capital Index (HCI) Competitive 28 3.39 0.16 3.164 0.003***
Non-competitive 23 2.73 0.11
Innovativeness Index (InovI) Competitive 28 1.79 0.15 2.385 0.021**
Non-competitive 23 1.19 0.21
Cooperation Index (CI) Competitive 28 1.60 0.16 1.650 0.105
Non-competitive 23 1.21 0.17
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*, **, *** Significant at 10%, 5%, and 1%, respectively

From the findings, it could be concluded that enterprises that were institutionally managed, had good economic indicators, had substantial human capital, and were open to innovations were more competitive. After determining that the leading indices analyzed affect competitiveness, it became necessary to investigate which sub-indicators caused these differences. For this reason, the sub-indicators that made up each leading index were also analyzed and presented below.

The corporate governance index value of competitive and non-competitive enterprises was statistically different (Table 4). In order to determine from which sub-indicators this difference arose, the sub-indicators that make up the corporate governance index, the scores of the business area size, legal status, corporate governance, quality management processes, and operating capacity were compared to the competitiveness groups and presented in Table 5.

Table 5.

Comparison of the sub-indicators of the corporate governance index

Sub-indicators Enterprises Count Mean Std. Error t Value p-Value
Size of the business area score Competitive 28 0.16 0.05 1.596 0.118
Non-competitive 23 0.08 0.03
Operating capacity score Competitive 28 0.30 0.05 1.702 0.095*
Non-competitive 23 0.19 0.04
Corporate governance score Competitive 28 0.36 0.06 2.126 0.039**
Non-competitive 23 0.21 0.04
Quality management processes score Competitive 28 0.45 0.06 0.213 0.646
Non-competitive 23 0.43 0.07
Legal status score Competitive 28 0.57 0.06 0.323 0.218
Non-competitive 23 0.48 0.07
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*, **, *** Significant at 10%, 5%, and 1%, respectively

A comparison of the corporate governance index sub-indicators according to the competitiveness groups determined that the operating capacity (p < 0.10) and the corporate management (p < 0.05) increased the enterprises’ competitiveness. The values of enterprises’ legal status, the size of the business area, and quality processes strategies carried out in the company were similar according to the competitiveness groups. As a result, it can be concluded that large-size and corporate enterprises were more fortunate in competitiveness.

The Economic Capital Index, which indicated the economic competitiveness of the enterprises, consisted of the share allocated to R&D, the capacity utilization rate score, the equity utilization rate score, the increase in market share score, and the revenue growth score in the last 3 years. The analysis results of the sub-indices that made up the economic capital index are given in Table 6. In the last 3 years, the increase in market share and revenue growth rates of competitive enterprises were higher than non-competitive (p < 0.05). On the other hand, the capacity utilization rate, the share allocated to R&D, and the equity utilization rates were not statistically different according to the competitiveness groups. The equity utilization ratio expresses the ratio of equity to total capital. The similarity of this ratio in both groups could be explained by the enterprises that did not have sufficient access to financial resources and avoided borrowing. It could be said that the indifference in capacity utilization rates was due to the instability in the hazelnut supply. In addition, the budget allocated to R&D in food companies in Turkey is generally low (Bakkaloğlu and Güneş 2018). Thus, the R&D budgets of the hazelnut enterprises were similar.

Table 6.

Comparison of the sub-indicators of the economic capital index

Sub-indicators Enterprises Count Mean Std. Error t Value p-Value
Capacity utilization rate score Competitive 28 0.70 0.05 0.002 0.998
Non-competitive 23 0.70 0.06
Allocated R&D share score Competitive 28 0.05 0.04 0.601 0.551
Non-competitive 23 0.03 0.02
Market share increase score Competitive 28 0.67 0.04 3.033 0.004***
Non-competitive 23 0.49 0.04
Revenue growth score Competitive 28 0.65 0.04 2.565 0.013**
Non-competitive 23 0.50 0.04
Equity utilization score Competitive 28 0.66 0.07 0.282 0.779
Non-competitive 23 0.63 0.07
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*, **, *** Significant at 10%, 5%, and 1%, respectively

The sub-indicators that made up the competitiveness of the enterprises according to their human capital and the analysis result are in Table 7. The differences in the human capital index were due to the total number of employees, the number of permanent employees, the number of qualified personnel, and the support the personnel development. From the research findings, it could be concluded that enterprises that employed qualified personnel and adopted policies that support the development of their employees stood out in competitiveness.

Table 7.

Comparison of the sub-indicators of the human capital index

Sub-indicators Enterprises Count Mean Std. Error t Value p-Value
Total number of employees score Competitive 28 0.24 0.05 2.347 0.026**
Non-competitive 23 0.10 0.01
Permanent employee score Competitive 28 0.21 0.05 2.146 0.037**
Non-competitive 23 0.09 0.01
Personnel specialization score Competitive 28 1.93 0.05 0.695 0.491
Non-competitive 23 1.87 0.07
Number of qualified personnel scores Competitive 28 0.14 0.04 2.389 0.040**
Non-competitive 23 0.04 0.02
Personnel development support score Competitive 28 0.88 0.05 2.893 0.006*
Non-competitive 23 0.63 0.07
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*, **, *** Significant at 10%, 5%, and 1%, respectively

In the context of the innovation competition, the differences in brand creation, differentiated product creation, differentiated management processes, e‑commerce applications, and information technology infrastructure variables were compared with the competitiveness group. (Table 8).

Table 8.

Comparison of innovativeness index sub-indicators

Sub-indicators Enterprises Count Mean Std. Error t Value p-Value
Differentiated product score Competitive 28 0.14 0.07 0.607 0.273
Non-competitive 23 0.09 0.06
Brand creation score Competitive 28 0.36 0.04 0.659 0.257
Non-competitive 23 0.32 0.04
Differentiated process management score Competitive 28 0.19 0.05 2.785 0.004*
Non-competitive 23 0.02 0.02
IT infrastructure score Competitive 28 0.62 0.06 1.715 0.094*
Non-competitive 23 0.46 0.07
E‑commerce score Competitive 28 0.48 0.06 1.796 0.079*
Non-competitive 23 0.30 0.08
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*, **, *** Significant at 10%, 5%, and 1%, respectively

Differences in the enterprises’ innovativeness index were due to differentiated process management (p < 0.05), information technologies infrastructure (p < 0.10), and e‑commerce applications (p < 0.10). It can be concluded that establishing a robust information technologies infrastructure and putting differentiated process management and e‑commerce applications in production and marketing makes enterprises more competitive.

The cooperation index values did not differ according to the competitiveness groups, but sub-indicators as the collaboration and following the sectoral developments differed (Table 9). Collaborating with other enterprises (p < 0.05) and following the sectoral developments (p < 0.001) led to increasing enterprises’ competitiveness.

Table 9.

Comparison of cooperation index sub-indicators

Sub-indicators Enterprises Count Mean Std. Error t Value p-Value
Collaboration score Competitive 28 0.36 0.05 2.019 0.049**
Non-competitive 23 0.24 0.03
Clustering score Competitive 28 0.25 0.08 0.425 0.673
Non-competitive 23 0.30 0.10
Sectoral developments follow-up score Competitive 28 0.30 0.05 3.718 0.001***
Non-competitive 23 0.10 0.01
Technical consulting score Competitive 28 0.50 0.10 0.456 0.650
Non-competitive 23 0.43 0.11
Joint-production score Competitive 28 0.20 0.06 0.845 0.402
Non-competitive 23 0.13 0.05
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*, **, *** Significant at 10%, 5%, and 1%, respectively

Discussion

The research determined that corporate governance values, economic capital values, human capital values, and innovativeness values were higher in competitive enterprises. Although studies analyzing enterprise competitiveness based on indices are limited, the division of labor (Siudek and Zawojska 2014), growth of business scale (Armağan 2004), diversification of business income (Savcı 2009), brand management (Altenburg and Meyer-Stamer 1999), product standardization (Lopez-Garcia et al. 2008; Beuchelt and Zeller 2013) and innovative capacity (Chikán 2008; Delfin-Ortega and Valencia 2015; Miniussi et al. 2015; Díaz-Chao et al. 2016) variables were reported as factors positively affecting competitiveness. Also, the research results supported the results of previous studies, in which the factors affecting competitiveness were examined separately, and the cooperation variable differed. This difference stemmed from the focus on a broad definition of the cooperation concept used in the study. In the study, the cooperation concept considered variables such as participating in clusters, receiving technical production support, and joint production. The cooperation index was calculated with the data belonging to these variables. The cooperation index value did not differ according to the groups, but the collaboration indicator, one of the sub-indicators, differed according to the groups. This finding confirmed the results of previous studies (Siudek and Zawojska 2014; Sarturi et al. 2016). The differentiation of the other sub-indicators of the cooperation index can be explained by the research region’s low level of cooperation culture. In Turkey, cooperation between agricultural enterprises and producer organizations, especially companies, is generally low. According to Aydoğan et al. (2016), horizontal cooperation among agricultural producer organizations was low, and their social networks were low-density. Therefore, it can be concluded that the cooperation concept among enterprises may differ according to country and may be affected by the general tendency towards cooperation. As a result, the orientation of hazelnut agribusiness enterprises in Turkey to vertical cooperation would positively affect their competitiveness.

Many studies on competitiveness stated that low-cost production affects competitiveness (Powers and Hahn 2004; González-Benito 2010; Soltanizadeh et al. 2016). However, the degree of influence of this judgment may vary according to the sectors. Taçoğlu et al. (2019), in their study examining the factors affecting the competitiveness of small and medium-sized enterprises (SMEs) in Turkey, stated that the impact of production costs on competitiveness had a medium level of importance. Similarly, the previous studies stated that companies that implemented mixed competition strategies were more competitive than companies that implemented only low-cost strategies (Acquaah and Yasai-Ardekani 2008; Pertusa-Ortega et al. 2009; Martinez-Simarro et al. 2015). In the study, the ratio of those who thought low-cost production provides a competitive advantage was higher in the non-competitive group. It can be explained that the competitive enterprises gave relatively more minor importance to low-cost production due to the hazelnut market structure. On shelled-hazelnut purchasing (raw material), the government determines the hazelnut minimum sales prices, and processed hazelnuts (final product) are generally exported at world prices. Therefore, other than operational improvements, cost-reducing factors are limited. As a result, focusing only on cost advantage may overlook other competitiveness parameters such as quality, brand, use of advanced technology, and cooperation. It can be concluded that non-competitive enterprises should focus on mixed competition strategies rather than entirely low-cost production.

Conclusion

The study intended to determine the factors affecting the competitiveness of hazelnut agribusiness enterprises. Inferences were obtained by comparing the findings with the literature. The research results indicated that corporate governance, economic capital, human capital, and innovativeness increased competitiveness. The hypothesis of that the non-financial indicators affect enterprise competitiveness at least as much as financial indicators were confirmed in the study. The research results could be interpreted that enterprise competitiveness is positively affected by corporate management, a robust financial structure, the investments made in the employees, and the ability to adapt quickly to innovations. The fact that all rules in business management are transparent and open to everyone, and that employees’ job descriptions are clear, increases employee productivity and competitiveness. In addition, the employment of qualified personnel and quality-enhancing activities for personnel development increase competitiveness. Investing in personnel development and employing qualified personnel would increase the competitiveness of hazelnut agribusinesses in Turkey.

Another critical research result was that non-competitive enterprises thought low-cost production increased competitiveness. On the other hand, competitive enterprises emphasize that product quality and standardization are as crucial as low-cost production in competition. As a result, it can be commented that non-competitive enterprises focus on low-cost production and overlook other internal and external factors necessary for competitiveness. Concentrating on product quality and standardization besides low-cost production strategies by non-competitive hazelnut enterprises may elevate them to the forefront of competition.

Another notable result of the research was the relationship between information technologies and e‑commerce with competitiveness. It was determined that competitive enterprises had a robust information technology infrastructure and market their products with e‑commerce. Considering the disruptions in the global agricultural supply chain due to the COVID-19 epidemic, it is likely that hazelnut agricultural enterprises would lose customers and their competitiveness would ultimately be negatively affected. Hazelnut agribusinesses in Turkey may become more competitive globally by developing robust IT infrastructure and e‑commerce applications in order to not be affected by supply chain disruptions, acquiring new customers, and establishing new partnerships. In addition, a robust IT infrastructure and marketing products with e‑commerce can increase resource utilization efficiency.

Acknowledgments

Funding

This work was supported by the Eastern Black Sea Project Regional Development Administration in the period 2016–2019.

Conflict of interest

M. Aydoğan declares that he has no competing interests.

Footnotes

Data, associated metadata, and calculation tools are available from corresponding author Mehmet AYDOĞAN (mehmet.aydogan@ozal.edu.tr).

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