Analysis of variables affecting competitiveness of SMEs in the textile industry

Taçoğlu, Caner; Ceylan, Cemil; Kazançoğlu, Yiğit Article Analysis of variables affecting competitiveness of SMEs in the textile industry Journal of Business Economics and Management (JBEM) Provided in Cooperation with: Vilnius Gediminas Technical University (VILNIUS TECH) Suggested Citation: Taçoğlu, Caner; Ceylan, Cemil; Kazançoğlu, Yiğit (2019) : Analysis of variables affecting competitiveness of SMEs in the textile industry, Journal of Business Economics and Management (JBEM), ISSN 2029-4433, Vilnius Gediminas Technical University, Vilnius, Vol. 20, Iss. 4, pp. 648-673, https://doi.org/10.3846/jbem.2019.9853 This Version is available at: https://hdl.handle.net/10419/317346 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author. E-mail: caner.tac[email protected]du.tr Journal of Business Economics and Management ISSN 1611-1699 / eISSN 2029-4433 2019 Volume 20 Issue 4: 648–673 https://doi.org/10.3846/jbem.2019.9853 ANALYSIS OF VARIABLES AFFECTING COMPETITIVENESS OF SMES IN THE TEXTILE INDUSTRY Caner TAÇOĞLU 1*, Cemil CEYLAN 2, Yiğit KAZANÇOĞLU 3 1, 2Industrial Engineering Department, Istanbul Technical University, Istanbul, Turkey 3International Logistics Management Department, Yaşar University, Izmir, Turkey Received 15 January 2018; accepted 27 March 2019 Abstract. This paper1 aims to develop strategy and policy suggestions to increase the competitiveness of SMEs in the textile industry by analyzing the variables that affect competitiveness and contribute to competitiveness literature by adopting a holistic approach to the analysis of competitiveness variables. A hybrid model composed of Delphi and fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) methods were used to gather and analyze the competitiveness variables. This led to the identification of the 15 most important of 73 competitiveness variables relevant to SME competitiveness in the textile industry. These variables were analysed and ranked, and their causal relationships were mapped. The results obtained from the model may function as a reference for SME managers aiming to increase their firm’s competitive power. Keywords: SME, competitiveness, management, Delphi, fuzzy DEMATEL, textile industry, production sector. JEL Classification: L67. Introduction Small and medium-sized enterprises (SMEs) are the major instigators of economic growth in all countries, creating jobs, providing employment opportunities and contributing to large enterprises as suppliers of goods and services. Collectively, SMEs employ the greatest number of employees in a country (Minniti & Bygrave, 1999). This is also true for Turkey. According to YSHI (Annual Industry and Service Statistics) 2017 results, SMEs in Turkey accounted for 75.8% of employment, 55% of salaries and wages, and 65.5% of GDP. SMEs in Turkey formed 99.9% of the total number of enterprises in 2017. The majority of SMEs are simple organizations with flexibility and a short decision chain. Their understanding of customer needs and procedures allows them to have a quick response 1 This article is derived from the PhD thesis of Caner Taçoğlu. Journal of Business Economics and Management, 2019, 20(4): 648–673 649 to the customers. Despite these supportive features of SMEs, they are under great pressure to maintain their competitiveness in domestic and international markets (Singh, Garg, & Deshmukh, 2008). In the production sector, lack of product characteristics provided by SMEs can negatively affect the competitive power of large enterprises. Large companies therefore prefer to collaborate with SMEs that have competitive power. Competitive paradigms are constantly changing due to rapid technological developments, the unstable needs of consumers and the unpredictable environment. These changes have created a challenging competitive setting in the areas of pricing, quality, time and innovation (Lorsuwanrat, 2010). Acquiring knowledge and knowledge management has become crucial (Fang, Wang, & Chen, 2017; Zhou, Kautonen, H. Wang, & L. Wang, 2017). For this reason, it is imperative for SMEs to create flexible business strategies in order to survive in the market. SME owners and managers have begun to focus on firm competencies and human resources management, in particular by attracting valuable employees to the organization and turning them into a competitive advantage. However, today, it is not always possible for SMEs to focus on a small number of competencies to gain competitive advantage. SMEs should therefore make efforts to gain competitive advantage in multiple areas, such as product, manufacturing, design, distribution, communication, marketing, management, human resources, R&D and focus as much as possible on the criteria that increase competitive power. For continuous improvement, it is important that SMEs measure themselves against the very best in the sector (Singh et al., 2008). The competitiveness literature generally focusses on macro environments such as countries, cities or industries. Few studies focus on firm level analysis, therefore there is a need for papers that analyse the competitiveness of small businesses (Szerb & Ulbert, 2009; Cetindamar & Kilitcioglu, 2013). There is still no consensus in the competitiveness literature relating to the factors affecting competitiveness and the importance of these factors (Sirikrai & Tang, 2006). Scholars have studied the SME competitiveness in the isolation of certain competitiveness aspects, generating a gap of holistic approach to analyse the competitiveness of SME’s (Singh et al., 2008). This study addresses the above issues by focusing on SME competitiveness, taking a holistic approach to the identification and analysis of the variables that affect competitiveness. The proposed method in this study has the capability to reveal not only the importance levels, but also the causal relationship among the variables. Although systems theory in management studies is undoubtedly of great complexity, it promotes a better awareness of complex situations and increase the likelihood of taking appropriate actions (Kast & Rosenzweig, 1972). We believe using systems theory to analyse SME competitiveness is more likely to yield results that are practically applicable for managers, thus we gather all competitiveness variables and analyse them holistically. Understanding the interactions of each element is extremely important when using the systems theory, therefore a key issue is selecting an appropriate methodology. We use a hybrid methodology to discover the importance levels and causal relationships of each variable for this purpose. The hybrid methodology is composed of the popular Delphi and fuzzy DEMATEL (DecisionMaking Trial and Evaluation Laboratory) methods. In addition to a specially modified classic Delphi method, fuzzy DEMATEL method is integrated into the last phase of Delphi. Delphi is used to reveal the most important variables and fuzzy DEMATEL to examine the causal 650 C. Taçoğlu et al. Analysis of variables affecting competitiveness of SMEs in the textile industry relationships of variables fulfilling the systems theory requirements. Ensuring competitive advantage for SMEs in the textile industry requires an analysis of the relevant competitiveness variables. For this reason, a competitiveness variable pool was created which, to the best of our knowledge, was not previously done for SMEs, and then filtered by experts in the relevant field via the first phase of the proposed model. In the second phase, the importance of selected variables was analyzed. Finally, causal relations for the most important variables were identified and ranked using fuzzy DEMATEL. The findings may support SME managers in making strategic decisions, and thus increase their organizations’ competitiveness levels. This paper aims to make a significant contribution to competitiveness literature by identifying the importance levels of the variables relevant to SME competitiveness in the textile industry, conducting a relationship analysis and providing SME managers with guidance on the development of competitiveness improvement policies. The hybrid model presented in the study can be used as a framework for future studies aimed at analyzing competitiveness of SMEs in different sectors. This paper is arranged as follows: competitiveness variable pool is presented in section 1 by reviewing the literature on SME competitiveness and variables that affect competitiveness. Section 2 introduces the proposed hybrid model in the paper. In section 3, the methods used in the study are explained and details of the proposed hybrid model are given. Application of the hybrid model, data analysis and the results are displayed in Section 4. The causal relation diagram and the results of the proposed model are also discussed in section 4. Subsequently, the concluding remarks, limitations and future studies are presented in Conclusions Section. 1. The SME competitiveness variable pool An extensive and systematic literature research was conducted to obtain the variables that affect competitiveness in production SME’s. Three types of sources for finding these variables were examined: (1) papers that analyze the impact of selected firm characteristics on competitiveness, (2) papers that measure SME competitiveness and professional competitiveness indexes, (3) papers that investigate the relationship between competitiveness strategies (i.e. Porter’s generic strategies) and various firm characteristics. These papers label the variables as strategy variables, competitiveness variables or business performance variables. However, regardless of their label, all of them affect competitiveness. Previous studies in the competitiveness literature focused on specific competitiveness areas in isolation and derived the competitiveness variables accordingly. Some of the specific areas that are present in the literature are innovation and competitiveness (Salavou, Baltas, & Lioukas, 2004), supply chain competitiveness (Joshi, Nepal, Rathore, & Sharma, 2013), learning orientation and competitiveness (Calantone, Cavusgil, & Zhao, 2002), marketing strategies and competitiveness (Siu, Fang, & Lin, 2004), knowledge management and competitiveness (Perez & Pablos, 2003), information technology and competitiveness (Lai, Zhao, & Wang, 2006). It is indubitable that these studies provide valuable insight to competitiveness literature by analysing the chosen variables on firm competitiveness. However, we believe that, in addition to these area-specific approaches, it is also crucial to be able to analyse the entire range of variables that affect firm competitiveness, deducting a holistic outcome that Journal of Business Economics and Management, 2019, 20(4): 648–673 651 can be invaluable for both scholars and practitioners. The main SME firm characteristics in relevant literature are categorized as production, innovation, marketing, organizational learning, information technology, knowledge management, human resources, entrepreneurship, management, finance and firm competencies. All of the variables previously studied were added to the competitiveness variable pool. For instance, studies that focus on the production aspect of SME competitiveness generally analyze the four most common variables: cost, quality, flexibility and delivery. Two further variables, product design and product service, were studied less frequently, however, in this study these were added to the competitiveness variable pool. Competitiveness variable pool is compiled not only from academic articles, but also the competitiveness indexes created by professional companies or worldwide organizations. The main examples are the World Economic Forum’s Global Competitiveness Index (GCI), and the International Trade Center’s work on SME competitiveness. However, since these global indexes measure competitiveness among countries, the majority of these variables are not relevant to SMEs. In the broad competitiveness literature, Porter’s competitiveness strategies play an important role. Many publications offer the relationship analysis of Porter’s generic strategies and firm performance (Yamin, Mavondo, Gunasekaran, & Sarros, 1997; Powers & Hahn, 2004; Kim, Nam, & Stimpert, 2004; Acquaah & Yasai-Ardekani, 2008; Pertusa‐Ortega, Molina‐ Azorín, & Claver‐Cortés, 2009), marketing strategy (Wu, Lin, & Lee, 2010), organizational learning (Wanto & Suryasaputra, 2012), entrepreneurship (Linton & Kask, 2017) and innovation (Bayraktar, Hancerliogullari, Cetinguc, & Calisir, 2017). The variables that measure different characteristics affecting firm competitiveness were specifically chosen as candidates for the competitiveness variable pool. As a result of this extensive and systematic literature research, a total of 73 variables were accumulated in the competitiveness variable pool. These variables however, needed to be filtered to eliminate duplicates, and to merge those with overlapping meanings. The filtering of the raw competitiveness variable pool through the proposed method in this study yielded 60 unique competitiveness variables shown in Table 1. This is the original SME competitiveness variable pool. Table 1. The SME competitiveness variable pool Competitiveness Variable Reference Product Innovation Yamin et al. (1997), Calantone et al. (2002), Arago´n-Correa, García-Morales, and Cordón-Pozo (2007), Ziegler and Nogareda (2009), Jiménez-Jiménez and Sanz-Valle (2011), García-Morales, Jiménez-Barrionuevo, and GutiérrezGutiérrez (2012), Ollo-López and Aramendía-Muneta (2012), Song (2015), Bayraktar et al. (2017), Wattanapruttipaisan (2002), Gál (2010), Sirikrai and Tang (2006), Szerb and Ulbert (2009), Singh et al. (2008) Process Innovation Yamin et al. (1997), Ziegler and Nogareda (2009), Jiménez-Jiménez and Sanz-Valle (2011), García-Morales et al. (2012), Ollo-López and AramendíaMuneta (2012), Bayraktar et al. (2017), Sirikrai and Tang (2006), Singh et al. (2008) 652 C. Taçoğlu et al. Analysis of variables affecting competitiveness of SMEs in the textile industry Competitiveness Variable Reference Risk Taking Calantone et al. (2002), Arago´n-Correa et al. (2007), Song (2015), Bayraktar et al. (2017), Wattanapruttipaisan (2002), Sirikrai and Tang (2006) Proactiveness Calantone et al. (2002), Arago´n-Correa et al. (2007), Song (2015), Bayraktar et al. (2017) Administrative Innovation Yamin et al. (1997), Jiménez-Jiménez and Sanz-Valle (2011), García-Morales et al. (2012) Invesment for R&D Wattanapruttipaisan (2002), Gál (2010) Product Cost Miller and Roth (1994), Sweeney and Szwejczewski (1996), Avella, Fernandez, and Vazquez (1998), Kathuria (2000), Frohlich and Dixon (2001), Zhao et al. (2006), Cagliano, Acur, and Boer (2005), Rose, Kumar, and Ibrahim (2008), Tian, Jia, and Malik (2010), Joshi et al. (2013), Gál (2010), Sirikrai and Tang (2006), Szerb and Ulbert (2009), Guzmán et al. (2012) Product Quality Miller and Roth (1994), Sweeney and Szwejczewski (1996), Avella et al. (1998), Kathuria (2000), Frohlich and Dixon (2001), Zhao et al. (2006), Cagliano et al. (2005), Rose et al. (2008), Tian et al. (2010), Joshi et al. (2013), Wattanapruttipaisan (2002), Gál (2010), Sirikrai and Tang (2006) Product Flexibility Miller and Roth (1994), Sweeney and Szwejczewski (1996), Avella et al. (1998), Kathuria (2000), Frohlich and Dixon (2001), Zhao et al. (2006), Cagliano et al. (2005), Rose et al. (2008), Tian et al. (2010), Joshi et al. (2013), Gál (2010) Product Delivery Miller and Roth (1994), Sweeney and Szwejczewski (1996), Avella et al. (1998), Kathuria (2000), Frohlich and Dixon (2001), Zhao et al. (2006), Cagliano et al. (2005), Rose et al. (2008), Tian et al. (2010), Joshi et al. (2013), Wattanapruttipaisan (2002), Gál (2010), Sirikrai and Tang (2006) Product Design Miller and Roth (1994), Sweeney and Szwejczewski (1996), Avella et al. (1998), Kathuria (2000), Frohlich and Dixon (2001), Zhao et al. (2006), Tian et al. (2010) Product Service Miller and Roth (1994), Avella et al. (1998), Kathuria (2000), Zhao et al. (2006), Cagliano et al. (2005), Tian et al. (2010), Wattanapruttipaisan (2002), Gál (2010) Green Products Tian et al. (2010) Usage of Production Capacity Wattanapruttipaisan (2002) Knowledge Acquisition Bontis, Crossan, and Hulland (2002), Perez Lopez et al. (2005), Arago´nCorrea et al. (2007), Jiménez-Jiménez and Sanz-Valle (2011), García-Morales et al. (2012), Santos-Vijande et al. (2012), Song (2015), Singh et al. (2008), Carneiro (2000), Perez and Pablos (2003) Knowledge Dissemination Calantone et al. (2002), Bontis et al. (2002), Perez Lopez et al. (2005), Arago´n-Correa et al. (2007), Yeo (2007), Jiménez-Jiménez and Sanz-Valle (2011), García-Morales et al. (2012), Santos-Vijande et al. (2012), Song (2015), Szerb and Ulbert (2009), Singh et al. (2008) Shared Interpretation Calantone et al. (2002), Bontis et al. (2002), Perez Lopez et al. (2005), Arago´n-Correa et al. (2007), Yeo (2007), Jiménez-Jiménez and Sanz-Valle (2011), García-Morales et al. (2012), Wanto and Suryasaputra (2012), SantosVijande et al. (2012), Song (2015), Singh et al. (2008) Continued Table 1 Journal of Business Economics and Management, 2019, 20(4): 648–673 653 Competitiveness Variable Reference Organizational Memory Perez Lopez et al. (2005), Jiménez-Jiménez and Sanz-Valle (2011), SantosVijande et al. (2012), Song (2015) Adaptability to Change Wattanapruttipaisan (2002), Gál (2010), Singh et al. (2008) Open-mindedness Calantone et al. (2002), Bontis et al. (2002), Wattanapruttipaisan (2002) Employee Skills Arago´n-Correa et al. (2007), García-Morales et al. (2012), Wanto and Suryasaputra (2012), Wattanapruttipaisan (2002), Gál (2010), Sirikrai and Tang (2006), Singh et al. (2008), Carneiro (2000) Product Pricing Leonidou, Katsikeas, and Samiee (2002), Rundh (2003), Gonzalez et al. (2004), Siu et al. (2004), Rhee and Mehra (2006), L. C. Leonidou, C. N. Leonidou, Fotiadis, and Zeriti (2013), Martin, Javalgi, and Cavusgil (2017), Chari, Balabanis, Robson, and Slater (2017), Wattanapruttipaisan (2002), Gál (2010) Product Distribution Leonidou et al. (2002), Rundh (2003), Gonzalez et al. (2004), Siu et al. (2004), Rhee and Mehra (2006), Leonidou et al. (2013), Martin et al. (2017), Chari et al. (2017) Product Promotion Leonidou et al. (2002), Gonzalez et al. (2004), Siu et al. (2004), Rhee and Mehra (2006), Leonidou et al. (2013), Martin et al. (2017), Chari et al. (2017), Gál (2010) Product Advertising Leonidou et al. (2002), Siu et al. (2004), Martin et al. (2017), Chari et al. (2017) Communication with Customers Rundh (2003), Leonidou et al. (2013), Martin et al. (2017), Chari et al. (2017), Gál (2010) Customer Satisfaction Wattanapruttipaisan (2002), Gál (2010) Degree of Customer Orientation Global Competitiveness Index, Wattanapruttipaisan (2002) Firm Service Leonidou et al. (2002), Martin et al. (2016), Chari et al. (2017) Technological Infrastructure Melville et al. (2004), Bhatt and Grover (2005), Kalkan et al. (2011), Cohen and Olsen (2013), Mao et al. (2016), Gál (2010) Use of Information Technology Powell and Dent-Micallef (1997), Melville et al. (2004), Kalkan et al. (2011), Chao and Chandra (2012), Mandal and Bagchi (2016), Sirikrai and Tang (2006), Singh et al. (2008) Management of Information Technology Powell and Dent-Micallef (1997), Melville et al. (2004), Lai et al. (2006), Kalkan et al. (2011), Cohen and Olsen (2013), Mao et al. (2016), Mandal and Bagchi (2016), Wattanapruttipaisan (2002), Singh et al. (2008) Availability of Latest Technologies Global Competitiveness Index (2017), Wattanapruttipaisan (2002), Gál (2010), Sirikrai and Tang (2006), Szerb and Ulbert (2009), Singh et al. (2008) Creating Valuable Information Carneiro (2000), Perez and Pablos (2003) Knowledge Transfer Carneiro (2000), Perez and Pablos (2003) Intellectual Capital Carneiro (2000), Perez and Pablos (2003) Continous Education and Training Intracen SME Competitiveness Grid (2016), Wattanapruttipaisan (2002), Gál (2010), Szerb and Ulbert (2009) Continued Table 1 654 C. Taçoğlu et al. Analysis of variables affecting competitiveness of SMEs in the textile industry Competitiveness Variable Reference Performance Management Wattanapruttipaisan (2002), Resurreccion (2012) Career Management Wattanapruttipaisan (2002) Communication with Employees Wattanapruttipaisan (2002), Resurreccion (2012) Employee Motivation Singh et al. (2008), Carneiro (2000) Ethical Behaviour of Firms Global Competitiveness Index (2017), Gál (2010) Employee’s Tertiary Education Ratio Global Competitiveness Index (2017) Employee Benefits Resurreccion (2012) Market Share Wattanapruttipaisan (2002), Gál (2010), Sirikrai and Tang (2006), Szerb and Ulbert (2009), Guzmán et al. (2012) Increase in Sales Wattanapruttipaisan (2002), Sirikrai and Tang (2006) Return on Assets (ROA) Wattanapruttipaisan (2002), Gál (2010) Return on Equity (ROE) Gál (2010) Return on Invesment (ROI) Wattanapruttipaisan (2002), Gál (2010) Audited Financial Statement Intracen SME Competitiveness Grid (2016), Wattanapruttipaisan (2002) Return on Sales (ROS) Sirikrai and Tang (2006), Guzmán et al. (2012) Manager’s Experience Intracen SME Competitiveness Grid (2016), Wattanapruttipaisan (2002) Top Management Support Singh et al. (2008) Professional Management Global Competitiveness Index (2017), Wattanapruttipaisan (2002), Sirikrai and Tang (2006), Szerb and Ulbert (2009) Strategic Alliance Street and Cameron (2007), Szerb and Ulbert (2009), Singh et al. (2008) Manager Attributes Wattanapruttipaisan (2002), Singh et al. (2008) Total Quality Management Wattanapruttipaisan (2002), Singh et al. (2008) International Quality Certificate Intracen SME Competitiveness Grid (2016), Wattanapruttipaisan (2002), Sirikrai and Tang (2006) University-Industry Collobration Projects Global Competitiveness Index (2017) Company Ownership of Patents Global Competitiveness Index (2017), Gál (2010), Guzmán et al. (2012) End of Table 1 Journal of Business Economics and Management, 2019, 20(4): 648–673 655 2. Proposed hybrid model The hybrid model is composed of three elements, competitiveness variable pool (Table 1), Delphi method, and fuzzy DEMATEL method. Competitiveness variable pool was created based on literature research and expert knowledge. Delphi uses expert knowledge to assess the importance levels, and fuzzy DEMATEL uses expert knowledge to identify the causal relationships between variables. The interaction of variables and their importance levels provide SME managers with crucial information. The hybrid model requires a set of experts in the relevant sector, whose knowledge can be generalized to the selected sector and all other SME managers may benefit from the results of the study. The fuzzy DEMATEL method is integrated into a modified version of the Delphi method. There is a dataflow between Delphi and fuzzy DEMATEL methods which operate together to produce a shared result. Third phase of Delphi method is composed of fuzzy DEMATEL, and Delphi results of the second phase serve as an input to fuzzy DEMATEL; thus, we call it a hybrid model. In the proposed hybrid model (Table 2), the three-phased Delphi outline, presented by Schmidt, Lyytinen, Keil, and Cule (2001), has been modified. Table 2. Outline of the proposed hybrid model Phase 1: Literature Review & Brainstorming – Create competitiveness variable pool – Filter the pool Phase 2: Narrowing Down – Gather the most important variables Phase 3: Relationship Analysis & Ranking – Apply fuzzy DEMATEL to the most important variables The proposed hybrid model enables the researchers to explore and analyze the variables that affect SME competitiveness. The SME competitiveness literature currently lacks a systematic, simple holistic framework for the investigation of these variables. Such a framework has been established in this study as the proposed hybrid model. Empirical application of the model reveals the importance levels of competitiveness variables, displays their causal interactions and presents a guide to SME managers for the development of competitiveness improvement policies. The hybrid model proposed in the study can be used as a framework for future analyses of competitiveness of SMEs on different production industries. The basic flow diagram of the proposed hybrid model is presented in Figure 1. At the end of the second round of Delphi study for phase 1, the competitiveness variable pool (Table 1) was completed. DEMATEL was chosen among other MCDM (multiple criteria decision making) methods due to the appropriacy of properties for the purpose of this study. The DEMATEL method supports proposed systems theory approach via forming a structural model to visualize the causal relationship of sub-systems through a causal diagram. DEMATEL also identifies the interaction of each competitiveness variable. The aim is to produce results that can provide effective guidance for SME managers, therefore it is essential to allow visualization of the causal relationships of the competitiveness variables. Using precise numerical values for human judgment may be misleading, fuzzy logic is therefore essential in analysing the issues 662 C. Taçoğlu et al. Analysis of variables affecting competitiveness of SMEs in the textile industry Phase 3: Relationship analysis & ranking The panel of SME Managers applied the fuzzy DEMATEL method for the 15 variables. The relationship analysis and the ranking of the selected variables were obtained through the application of the fuzzy DEMATEL method. Among 60 variables, 15 were chosen as the most important competitiveness variables for SME’s in the textile industry. There were two reasons for limiting the number of variables to 15. First, only 15 scored higher than 4.0, which was midway between the values “Important” and “Very Important” in questionnaires 3 and 4. In questionnaire 3, 19 variables reached an average higher than 4.0, but in questionnaire 4 this number was only 15. All variables that scored slightly less than 4.0 in questionnaire 3, dropped their score in questionnaire 4 (e.g. the investment for R&D averaged 3.97 in questionnaire 3, but fell to 3.89 in questionnaire 4). These results increased our confidence in limiting the number to 15 and confirmed we had not missed any important variables that scored very close to our threshold. The second reason was the required rigorous effort when applying DEMATEL method. Increasing the number of questions would increase the potential of errors in a DEMATEL questionnaire. Conducting DEMATEL with 15 variables resulted in a 15×15 matrix, minus the main diagonals, thus yielding a total of 210 questions for SME managers. 4.2. Data analysis and results The results obtained from Phase 3 represents crucial information for SME managers, especially those in the textile industry. 15 most important competitiveness variables were labeled as V1 (Product Quality), V2 (Product Delivery), V3 (Product Cost), V4 (Product Flexibility), V5 (Product Design), V6 (Product Service), V7 (Communication with Customers), V8 (Degree of Customer Orientation), V9 (Product Pricing), V10 (Customer Satisfaction), V11 (Proactiveness), V12 (Product Innovation), V13 (Knowledge Acquisition), V14 (Employee Skills), V15 (Open-mindedness). The data from each individual assessment was aggregated into the initial direct relation matrix (Table 5) using the CFCS method. The fuzzy linguistic data was converted into crisp values using the formulas (7)–(14) for each assessment. The values given in Table 5 must first be normalized before creating the total relation matrix. To produce the normalized direct relation matrix (Table 6), formulas (1) and (2) were used. (D+R) and (D-R) values cannot be calculated without a total relation matrix, and the values in Table 6 were necessary to create the total relation matrix. As explained in the methodology section, next step was to create the total relation matrix. Applying formula (3) created the total relation matrix (Table 7). Total relation matrix uses formula (3) to create D values and formula (4) to create R values. D and R values were obtained by formulas (4), (5) and (6). The values given in Table 7 were used to calculate (D+R) and (D-R) datasets. Table 8 was created using the net influence matrix calculation as introduced in Step 5 of DEMATEL method. The values of Table 8 show the strength of the causal relations of the variables. In order to map the causal relation diagram (Figure 2), (D+R) and (D-R) datasets obtained from Table 7 were used. The arrows on Figure 2 represent only the crucial relationships, which are higher than a certain threshold value (0.100, twice the average). As seen in Figure 2, the cause group variables consist of V1, V2, V3, V4, V5, V11, V13, V14, and V15. The effect Journal of Business Economics and Management, 2019, 20(4): 648–673 663 group variables consist of V6, V7, V8, V9, V10 and V12. The detailed explanation of every variable presented on Figure 2 are discussed in the subsequent section. Table 5. Initial direct relation matrix Z V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V1 0.000 0.333 0.934 0.567 0.500 0.600 0.867 0.767 0.934 0.934 0.567 0.567 0.400 0.800 0.433 V2 0.333 0.000 0.400 0.500 0.333 0.533 0.733 0.367 0.500 0.834 0.367 0.367 0.400 0.433 0.200 V3 0.767 0.300 0.000 0.500 0.733 0.433 0.200 0.433 0.967 0.800 0.633 0.600 0.333 0.433 0.200 V4 0.667 0.567 0.767 0.000 0.567 0.400 0.333 0.567 0.633 0.500 0.500 0.700 0.567 0.567 0.467 V5 0.600 0.667 0.833 0.667 0.000 0.433 0.367 0.400 0.767 0.667 0.633 0.733 0.500 0.567 0.400 V6 0.200 0.266 0.367 0.233 0.266 0.000 0.633 0.733 0.366 0.800 0.433 0.267 0.567 0.533 0.367 V7 0.467 0.333 0.200 0.433 0.533 0.567 0.000 0.734 0.367 0.733 0.533 0.533 0.667 0.433 0.333 V8 0.600 0.400 0.233 0.467 0.600 0.700 0.800 0.000 0.533 0.733 0.567 0.600 0.600 0.433 0.400 V9 0.767 0.200 0.533 0.267 0.300 0.566 0.400 0.567 0.000 0.800 0.433 0.433 0.367 0.466 0.200 V10 0.533 0.200 0.500 0.300 0.633 0.667 0.800 0.800 0.500 0.000 0.433 0.633 0.400 0.500 0.300 V11 0.500 0.467 0.633 0.567 0.733 0.600 0.567 0.667 0.667 0.633 0.000 0.800 0.667 0.666 0.400 V12 0.733 0.333 0.600 0.633 0.834 0.367 0.433 0.567 0.700 0.600 0.733 0.000 0.466 0.533 0.333 V13 0.500 0.300 0.433 0.567 0.700 0.500 0.567 0.600 0.467 0.567 0.533 0.633 0.000 0.533 0.600 V14 0.800 0.733 0.533 0.700 0.767 0.600 0.533 0.633 0.533 0.567 0.667 0.700 0.633 0.000 0.700 V15 0.667 0.300 0.333 0.467 0.600 0.400 0.467 0.533 0.200 0.467 0.567 0.533 0.567 0.667 0.000 Table 6. Normalized direct relation matrix X V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V1 0.000 0.036 0.101 0.062 0.054 0.065 0.094 0.083 0.101 0.101 0.062 0.062 0.043 0.087 0.047 V2 0.036 0.000 0.043 0.054 0.036 0.058 0.080 0.040 0.054 0.091 0.040 0.040 0.043 0.047 0.022 V3 0.083 0.033 0.000 0.054 0.080 0.047 0.022 0.047 0.105 0.087 0.069 0.065 0.036 0.047 0.022 V4 0.072 0.062 0.083 0.000 0.062 0.043 0.036 0.062 0.069 0.054 0.054 0.076 0.062 0.062 0.051 V5 0.065 0.072 0.091 0.072 0.000 0.047 0.040 0.043 0.083 0.072 0.069 0.080 0.054 0.062 0.043 V6 0.022 0.029 0.040 0.025 0.029 0.000 0.069 0.080 0.040 0.087 0.047 0.029 0.062 0.058 0.040 V7 0.051 0.036 0.022 0.047 0.058 0.062 0.000 0.080 0.040 0.080 0.058 0.058 0.072 0.047 0.036 V8 0.065 0.043 0.025 0.051 0.065 0.076 0.087 0.000 0.058 0.080 0.062 0.065 0.065 0.047 0.043 V9 0.083 0.022 0.058 0.029 0.033 0.062 0.043 0.062 0.000 0.087 0.047 0.047 0.040 0.051 0.022 V10 0.058 0.022 0.054 0.033 0.069 0.072 0.087 0.087 0.054 0.000 0.047 0.069 0.043 0.054 0.033 V11 0.054 0.051 0.069 0.062 0.080 0.065 0.062 0.072 0.072 0.069 0.000 0.087 0.072 0.072 0.043 V12 0.080 0.036 0.065 0.069 0.091 0.040 0.047 0.062 0.076 0.065 0.080 0.000 0.051 0.058 0.036 V13 0.054 0.033 0.047 0.062 0.076 0.054 0.062 0.065 0.051 0.062 0.058 0.069 0.000 0.058 0.065 V14 0.087 0.080 0.058 0.076 0.083 0.065 0.058 0.069 0.058 0.062 0.072 0.076 0.069 0.000 0.076 V15 0.072 0.033 0.036 0.051 0.065 0.043 0.051 0.058 0.022 0.051 0.062 0.058 0.062 0.072 0.000 664 C. Taçoğlu et al. Analysis of variables affecting competitiveness of SMEs in the textile industry Table 7. Total relation matrix T V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V1 0.330 0.254 0.390 0.333 0.379 0.363 0.399 0.418 0.425 0.479 0.366 0.386 0.329 0.384 0.263 V2 0.261 0.151 0.246 0.241 0.261 0.263 0.292 0.275 0.279 0.350 0.250 0.264 0.241 0.254 0.172 V3 0.347 0.211 0.247 0.277 0.341 0.291 0.277 0.323 0.371 0.396 0.315 0.329 0.268 0.294 0.199 V4 0.352 0.248 0.336 0.240 0.341 0.300 0.304 0.350 0.353 0.384 0.317 0.354 0.305 0.321 0.236 V5 0.359 0.266 0.355 0.318 0.296 0.316 0.320 0.348 0.379 0.416 0.342 0.370 0.310 0.333 0.238 V6 0.240 0.174 0.233 0.209 0.248 0.202 0.275 0.303 0.257 0.337 0.250 0.248 0.251 0.257 0.185 V7 0.297 0.202 0.248 0.256 0.305 0.288 0.241 0.334 0.290 0.367 0.289 0.305 0.288 0.277 0.203 V8 0.336 0.226 0.275 0.280 0.336 0.324 0.345 0.287 0.332 0.398 0.316 0.336 0.304 0.301 0.226 V9 0.309 0.175 0.265 0.223 0.263 0.272 0.265 0.300 0.235 0.354 0.262 0.276 0.241 0.263 0.176 V10 0.316 0.197 0.287 0.252 0.325 0.308 0.330 0.352 0.315 0.307 0.290 0.325 0.271 0.293 0.206 V11 0.361 0.255 0.345 0.319 0.382 0.343 0.351 0.386 0.380 0.425 0.289 0.389 0.338 0.353 0.247 V12 0.364 0.229 0.326 0.308 0.371 0.302 0.318 0.356 0.365 0.399 0.344 0.289 0.300 0.323 0.227 V13 0.325 0.215 0.293 0.289 0.344 0.301 0.317 0.344 0.323 0.377 0.311 0.338 0.240 0.308 0.244 V14 0.407 0.294 0.353 0.348 0.403 0.360 0.366 0.402 0.385 0.441 0.374 0.397 0.351 0.304 0.289 V15 0.319 0.202 0.264 0.262 0.313 0.272 0.288 0.315 0.276 0.342 0.294 0.307 0.279 0.302 0.169 Table 8. Net influence matrix T V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V1 0.000 V2 0.007 V3 –0.043 –0.035 V4 0.019 0.007 0.059 V5 –0.020 0.006 0.014 –0.023 V6 –0.123 –0.089 –0.058 –0.092 –0.068 V7 –0.102 –0.089 –0.029 –0.048 –0.015 0.013 V8 –0.083 –0.049 –0.048 –0.070 –0.012 0.022 0.011 V9 –0.116 –0.104 –0.106 –0.130 –0.116 0.015 –0.025 –0.032 V10 –0.163 –0.153 –0.109 –0.132 –0.091 –0.029 –0.036 –0.046 –0.038 V11 –0.005 0.005 0.029 0.001 0.040 0.094 0.062 0.070 0.118 0.135 V12 –0.022 –0.035 –0.004 –0.046 0.001 0.055 0.013 0.019 0.088 0.074 –0.044 V13 –0.004 –0.025 0.025 –0.016 0.034 0.050 0.029 0.040 0.082 0.105 –0.027 0.038 V14 0.023 0.040 0.058 0.027 0.070 0.103 0.089 0.101 0.122 0.148 0.021 0.075 0.043 V15 0.057 0.030 0.065 0.026 0.076 0.087 0.086 0.089 0.100 0.136 0.047 0.080 0.036 0.013 0.000 To sum up, three phased hybrid Delphi and fuzzy DEMATEL model was conducted with academic experts and SME managers. In phase 1 of the proposed model, 60 competitiveness variables were filtered from the competitiveness variable pool. In phase 2, the 15 most important competitiveness variables were identified. In phase 3, the causal relations were acquired from the causal relation diagram. In addition to identifying the cause and effect groups and relations of the variables, D+R values were used to reveal the importance level of each vari- Journal of Business Economics and Management, 2019, 20(4): 648–673 665 able, enabling the ranking step. The most important variable that affects SME competitiveness was found to be Product Quality (V1), followed (in order of importance) by V10-V14-V5V11-V12-V8-V3-V4-V13-V7-V9-V6-V15-V2. The ranking of the cause group variables is V1-V14-V5-V11-V3-V4-V13-V15-V2, and for the effect group, V10-V12-V8-V7-V9-V6. 4.3. Discussion The chosen variables were categorized under four areas as production, marketing, innovation and organizational learning. The results support the idea that organizational learning is a crucial emerging topic for creating business competitiveness and strategy policies (Pérez López, Manuel Montes Peón, & José Vazquez Ordás, 2005). In phase 3, the causal relation diagram (Figure 1) can produce valuable information for making strategic decisions. To improve the variables in the effect group, SME managers need to firstly consider the cause group variables. Thus, in order to increase competitive power of a SME, they need to prioritize variables that have high influence and high intensity of relation, as these are more difficult to assess and enhance. One serious potential misconception when interpreting the critical data given in Figure 2 would be to underestimate the importance of the variables with low D+R values. Although it is true that low D+R value would mean less important compared to other variables, these 15 variables were identified as the most important competitiveness variables and the different Figure 2. Causal relation diagram 666 C. Taçoğlu et al. Analysis of variables affecting competitiveness of SMEs in the textile industry impact of each should be thoroughly analysed. The causal relation diagram in Figure 2 shows the importance levels of each variable, which supports the identification of the cause and effect relationships of the most important variables. According to the results, Open-mindedness is the most influencing variable, followed by Employee Skills. However, the importance level of Open-mindedness is significantly lower than Employee Skills, which holds a strategic position, as both a very high influencer and one of the most important variables. Therefore, Employee Skills is a key variable for SME managers to focus on. Product Quality holds a similar position as most important variable and a high influencer variable. Product Delivery is another high influencer variable; however, it is perceived as the least important of the 15 variables. Before making any improvements to Product Delivery, managers would be advised to focus on another influencer variable with high importance such as Product Quality. Product Flexibility and Knowledge Acquisition both have mediocre importance; however, the former, as the third most influencer variable, has a higher influence level than the latter. Proactiveness is a valuable variable like Product Quality with high influence and importance levels. Product Cost, Product Design and Product Innovation are almost in the neutral area – and can hardly be described as an influencer or influenced variable. Product Cost and Product Design falls slightly in the cause group whereas Product Innovation falls slightly in the effect group. Product Design and Product Innovation are perceived to have almost same importance level with both having higher importance than Product Cost. Customer Satisfaction is the second most important variable and the most influenced variable in the effect group. To increase customer satisfaction levels, SME managers should focus first on the most influencer variables such as Employee Skills or Product Quality. Product Pricing is one of the least important variables and second most influenced variable. Pricing strategy is frequently categorised under marketing and has various dependencies on other variables such as quality or cost (L. Leonidou, C. Leonidou, Fotiadis, & Zeriti, 2013). Product Service, just as Product Pricing falls into same category of less important and most influenced variables. Communication with Customers and Degree of Customer Orientation are both in the effect group, however neither variables are greatly influenced by other variables such as Customer Satisfaction. Degree of Customer Orientation has a high importance level, and exclusively focusing on other variables would not significantly affect this variable. Therefore, to increase their customer orientation performance, SME managers first need to focus specifically on this variable, before the influencer variables. Unlike many of the previous studies, Product Cost was not found to be the most important variable affecting competitiveness; both variables that represent innovation (V11 and V12) scored higher. This might imply that SMEs are willing to invest in innovation to increase their competitive power (Yamin et al., 1997). SME managers should aim to build learning organizations, to nurture new ideas and support proactive activities, and to encourageappropriate risk-taking. Product Quality, Customer Satisfaction and Employee Skills form the three most important variables. Total Quality Management (TQM) is a highly appropriate customer focused, employee involved and quality centered method, as it involves the continuous improvement of these three key variables. Thus, SME managers should consider implementing TQM strategies to increase their firm’s competitive power. Also, long-term relationships with the customers should be emphasized, taking into consideration customer differentiation, in order to providehigh quality services, ensuring customer loyalty. Journal of Business Economics and Management, 2019, 20(4): 648–673 667 The three most influencing variables are Open-mindedness, Employee Skills and Product Flexibility. As these are the most difficult to change, SME managers or human resources department should make efforts to hire open-minded and skillful employees (Wanto & Suryasaputra, 2012). Personality tests can be conductedin the hiring processto reveal employee characteristics.To attract those with the required qualities,SME managers can implement performance rewarding or profit sharing programs to enhance employee benefits. Previous studies suggest that Product Flexibility and Employee Skills are directly related, thus, hiring skillful employees enables flexible production. Suitably skilled employees are better able to respond to environmental changes, allowing the firm to be more proactive. Customer Satisfaction, Product Pricing and Product Service are the variables that are most influenced. Evidently, Customer Satisfaction is influenced by the cause group variables. Cause group variablessuch as Product Quality and Product Flexibilityplay a crucial role in determining product pricing policies, in line withmarketing studies that support the view thatthe price is determined by the market, rather thanby the firm itself. Product service no longer follows predetermined standard procedures, due to the dramatic increase in product variety, and the indeterminate nature of customers. Finally, the importance of Product Flexibility, Product Delivery, and Proactiveness underlines the need forSME managers to focus on logistics and responsiveness. The research implications for this study include contributions to competitiveness literature, by addressing the need for the holistic approach in the field and offering a methodology for the analysis of competitiveness variables (Singh et al., 2008). The competitiveness literature lacks holistic approach to factors affecting competitiveness of SMEs, thus, the SME competitiveness variable pool was created to enable the gathering all of the variables, whether widely known or emerging, in the production field. Scholars and practitioners were able to examine the competitiveness variables holistically through the usage of hybrid model. The competitiveness variable pool is a tool for all SME managers in the production field to consider and reassess the factors that affect the SME competitiveness. Thus, the approach considers new and emerging variables that are attracting the attention of scholars and practitioners globally. This paper reveals the most important 15 variables that are relevant to SME competitiveness in the textile industry. Managers may aim find that a focus on these variables will increase their firm’s competitive power and will gain potentially valuable insight from the causal diagram obtained from the hybrid model. With a clear understanding of the influenced and influencer variables, managers can make strategic decisions on how to address their firm’s competitive weaknesses. The results obtained from the proposed model can be an effective guide for SME managers in the development of competitiveness improvement policies. Conclusions This study suggests eight managerial implications. Five are deducted directly from the fuzzy DEMATEL results, and remaining three are our recommendations to managers operating in the field of textile industry based on the results of our model. Employee Skills is a high influencer and one of the key variables for focus and improvements to Product Quality should be another priority. To increase customer satisfaction, influencer variables, such as these should 668 C. Taçoğlu et al. Analysis of variables affecting competitiveness of SMEs in the textile industry be addressed first. A good pricing strategy is not influenced by Product Cost as much as Product Quality. Influencer variables have no considerable impact on the Degree of Customer Orientation. Therefore, it is crucial for managers to implement TQM strategies to increase their firm’s competitive power. Conducting personality and skill tests before hiring, and implementing performance rewarding systems or programs that focus to improve Employee Skills should be a high priority for SME managers. Lastly, managers should be educated about the importance of logistics and responsiveness. There are also three research implications in this study. Firstly, the competitiveness variable pool, in which all of the variables that affect competitiveness of SME’s in the production field are accumulated, can form the basis for future studies. Secondly, using holistic approach in the analysis of competitiveness variables contributes to addressing the gap in the competitiveness literature. Thirdly, this paper presents a hybrid model that investigates the variables that affect SME competitiveness, as well as their degree of importance, in the textile industry. The hybrid model offers a platform to examine and analyze these variables by focusing on the opinions of valuable academic experts and SME managers. We acknowledge that applying Delphi method has its own limitations and issues; however, the new modifications introduced in our study are designed to reduce these limitations. For the DEMATEL method, we use fuzzy method to further enhance the decision making process of SME managers. Using precise numerical values for human judgment and decisions can cause a lack of clarity, introducing fuzzy logic is essential in respect to issues inherently characterised by ambiguity and imprecision. However, although an appropriate method, fuzzy logic for DEMATEL can never be entirely problem free when dealing with human decisions. The competitiveness variable pool and the model framework can be applied to other sectors in the production field but it should be noted that the results obtained in this specific study apply only to the textile industry in Turkey. Nevertheless, in future studies, this framework can be used in different country or sector contexts for the purposes of comparison. Acknowledgements We are grateful for the support of all valuable SME managers and academicians who took part in this research. Funding This study was not funded by any organization. Author contributions CT was responsible for the design, development, data analysis and data interpretation. CC and YK were responsible for design, development and data interpretation. CT wrote the first draft, CC and YK helped in the correction and forming the final draft. Journal of Business Economics and Management, 2019, 20(4): 648–673 669 Disclosure statement We have no conflicts of interest to disclose. 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