A system dynamics approach for assessing business competitiveness

1 A system dynamics approach for assessing business competitiveness Esteban Lafuente Department of Management, Universitat Politècnica de Catalunya (Barcelona Tech) EPSEB, Av. Gregorio Marañón, 44-50, E-08028 Barcelona, Spain E-mail: [email protected] László Szerb Faculty of Business and Economics, University of Pécs Pécs Rákóczi út 80. 7622-Hungary E-mail: [email protected]e.hu András Rideg Faculty of Business and Economics, University of Pécs Pécs Rákóczi út 80. 7622-Hungary E-mail: [email protected] Draft version: September 2016 Abstract: The analysis of the interconnectedness between resources and capabilities, and the way organizations use them as competitive weapons have become a central element of the strategic management literature. Drawing on the resource-based view and the configuration theory, this study proposes a multidimensional competitiveness index formed by ten competitive pillars that incorporates system dynamics in the analysis. Using a unique sample of 625 Hungarian small and medium-sized firms, we empirically illustrate how the proposed index functions for managerial decision-making. Results show that the impact of competitiveness-enhancing strategies is conditional on the configuration of the businesses’ system of competencies. Low-competitive businesses benefit more from investments in the weakest competitive pillar, while strategies oriented to improve more than one competitive pillar yield higher competitiveness improvements among high-competitive businesses. Keywords: Competitiveness, resource-based view, system dynamics, small business. JEL codes: L25, L19 2 A system dynamics approach for assessing business competitiveness 1. Introduction A central idea in the resource-based view of the firm (RBV) is that businesses acquire or develop specific resources and capabilities that interact with the existing ones for creating competencies as they pursue competitiveness and, consequently, superior performance (Prahalad and Hamel 1990, Barney 1991, 2001). Competitiveness is a construct linked to the development of a competitive advantage, and is often conceptualized as the capacity of the organization to efficiently amalgamate its resources and capabilities seeking to create value-adding competencies (Man et al. 2002, Barney and Mackey 2005). Nevertheless, organizations do not realize the generally positive effects of investments in specific resources of capabilities at the same intensity (see e.g., Newbert 2007). Arguments rooted in the RBV frame emphasize that businesses with superior structures maintain their competitive edge on the basis that their resources and capabilities are not easily duplicable or surpassable (Barney 2001). Additionally, businesses pursuing a competitive advantage must also demonstrate the ability to alter available resources so that their potential can be fully exploited (Mahoney and Pandain 1992, Peteraf 1993, Barney and Mackey 2005). Therefore, the heterogeneous distribution of resources and capabilities among competing firms contributes to explain both the differences in business endowments and the dissimilar ability of businesses to create a resource-based competitive advantage. The analysis of how the associations between resources and capabilities condition business competitiveness is the focus of this study. This paper proposes a managerial tool to evaluate business competitiveness. Specifically, our measure reflects the multidimensional nature of competitiveness by connecting the resource-based view and the configuration theory in a model that considers the mutual dependence of resources and capabilities in shaping organizational competitiveness. Competitiveness is an attractive concept characterized by its long-term orientation, controllability and dynamism (Barney 1995, Man et al. 2002). The construct competitiveness has been analyzed from multiple angles. Previous research has mostly assessed competitiveness using aggregate estimates that capture the contribution of different resources and capabilities to competitiveness or (e.g., Hult et al. 2007, Fernhaber and Patel 2012, Hansen et al. 2013). Despite the rigorous efforts, underlying studies on competitiveness are methodological approaches that ignore the different interactions that might exist between the variables that form business competitiveness. To address this issue, we propose a competitiveness index based on a system dynamics model that incorporates into the analysis system-level constraints between the analyzed resources and capabilities. Building on RBV theory postulates, competitiveness is defined as the mutually dependent bundle of resources and capabilities that allow the creation or development of valuable competencies (Prahalad and Hamel 1990, Barney 2001). Accordingly, the proposed competitiveness measure is formed by 46 variables grouped in ten competitive pillars—human capital, product, domestic market, networks, technology, decision making, strategy, marketing, internationalization, and online presence—which represent different resources and capabilities available for organizations. Furthermore, we show how the proposed managerial tool functions for assessing business competitiveness by analyzing the responsiveness of the competitiveness index to changes in competitive pillars at the businessand industry-level. Additionally, a supplementary analysis examines the relationship between the competitiveness index and business performance metrics, namely employment growth and labor productivity. The empirical application considers a sample of 625 Hungarian SMEs operating in manufacturing, retailing, and professional services sectors during the period 2010-2013. This setting provides an opportunity to assess how different competencies contribute to business competitiveness in contexts where the interactions between resources and capabilities are complex and heterogeneous. This article extends the existing literature on competitiveness in two main ways. First, drawing on the RBV and the configuration theory frames, our comprehensive competitiveness measure employs an index methodology that allows multiple interactions between the different pillars 3 that shape competitiveness, and takes into account the potentially restraining impact of bottleneck pillars on the system’s performance. To the best of our knowledge, this is the first study that assesses competitiveness from a systemic perspective. Second, decisions related to resourceor capability-enhancing actions have important economic, strategic and operational implications for organizations. Existing literature suggests that organizations that couple resource acquisition policies with the development of capabilities show superior results (e.g., Hult et al. 2007, Fernhaber and Patel 2012). We show how an analysis based on our competitiveness measure helps unveil the effects of competitiveness-enhancing actions in organizations with different system configurations, in terms of resources or capabilities. By connecting the RBV and the configuration theory we contribute to further understand the drivers of competitiveness, which is critical for managerial decision-making processes. Additionally, the contribution of this study extends to the small business management literature. Small businesses show significant particularities in terms of organization, resource allocation, managerial styles, strategic choices and the way of competition (Porter 1998, Man et al. 2002). By examining the outcomes that flow from the creation or development of competencies from a system dynamics perspective, managers of small businesses might be in a better position to cope with potential liabilities of newness and smallness, and balance strategic investments with actions that contribute to capitalize on the organization’s resources and capabilities. The remainder of the paper is organized as follow. Section two presents an overview of the resource-based view theory and the existing literature on competitiveness. Section three introduces the competitiveness index in line with the theoretical underpinning. Section four describes the data and variables. Results are presented in section five, and section six offers the discussion and concluding remarks. 2. The resource-based theory of the firm and business competitiveness 2.1 The resource-based theory of the firm Organizations seek to gain and develop bundles of knowledge and skills—capabilities— which enable them to employ their internal resources more effectively (e.g., capital, labor, and materials). Resource-based view (RBV) theorists propose that the associations resulting from connecting resources and capabilities—labeled competencies—contribute to enhance business competitiveness and subsequent performance (Wernerfelt 1984, Prahalad and Hamel 1990). The heterogeneous distribution of resources and capabilities among firms explains both the differences in business endowments and the dissimilar ability of businesses to create a resource-based competitive advantage (Barney 1991). Businesses with superior systems and structures achieve higher performance and maintain their competitive edge on the basis that their resources and capabilities are not easily duplicable or surpassable (Barney and Mackey 2005). Existing research rooted in the RBV is extensive and has mostly evaluated two fundamental assertions of this theory: (1) that some resources and capabilities have the potential to enable businesses to implement valuable strategies, and (2) that such resources and capabilities can be a source of competitive advantage when they possess attributes that make their imitation costly (Newbert 2007). Prior studies offer strong support to RBV postulates (see, e.g., the comprehensive reviews of Barney and Arikan (2001) and Crook et al. (2008)). Perhaps because of the difficulties of measuring competitiveness (Ketchen et al. 2007), most empirical studies have sought to evaluate the individual contribution of different resources or capabilities to performance (Newbert 2007; Crook et al. 2008). Underlying this approach is the assumption that competitiveness is evident in organizations whose resources and capabilities are positively correlated to performance. Organizations are a bundle of resources and capabilities and these ingredients do not work in isolation and, as Newbert (2008, p. 751) points out, ‘it is unlikely that a firm’s competitive position is solely attributable to any one specific resource or capability.’ Instead, businesses pursuing a competitive advantage must demonstrate the ability to exploit their resources and capabilities in such a way that their full potential is realized (Sirmon et al. 2011). Competitiveness is a complex and multidimensional construct and it should be evaluated from a holistic approach to better understand how organizations ‘do business’ (Barney 1995). The core of our analysis is to match resources and capabilities with the creation of value-adding competencies, 4 while acknowledging the multidimensionality of competitiveness as well as the complementarities that exist between the business’ resources and capabilities. 2.2 Competitiveness: competitive strengths and weaknesses Competitiveness is linked to the development of a competitive advantage, and is often conceptualized as the capacity of the organization to amalgamate its resources and capabilities seeking to create value-adding competencies (Barney and Mackey 2005). Competitiveness is a multidimensional construct characterized by its long-term orientation, controllability and dynamism (Grant 1991, Barney 1995). Increased competitiveness allows organizations to create and implement valuable strategies that are hard to imitate by competitors and help to enhance profit margins (Porter and Millar 1985). Theoretical studies coincide in emphasizing both the complexity of measuring competitiveness and the strong relation between competitiveness and business performance in the long-run (Prahalad and Hamel 1990, Barney and Mackey 2005). Studies building on the RBV show a great deal of variation in the resources and capabilities used to operationalize competitiveness, including organizational, operational, technological, customer-oriented and market-oriented variables. For example, variables related to the product/service and business operations are ‘usual suspects’ in competitiveness analyses (Douglas and Ryman 2003, Hult et al. 2007, Fernhaber and Patel 2012). Relevant competencies linked to human capital—e.g., accumulated knowledge stocks, knowledge acquisition and development of technical skills—have also received attention in the literature (Julien and Ramangalahy 2003, Aral and Weill 2007). Additionally, research has analyzed the role on competitiveness of various competencies related to internationalization (Lu and Beamish 2001, Belderbos and Sleuwaegen 2005), networking (Kingsley and Malecki 2004), marketing (O’Cass and Weerawardena 2010), and the business’ strategic orientation (Hansen et al. 2013). Recent technology advances, such as the rapid expansion of the Internet and the drastic decline in computing and communication costs, have allowed the development of IT-based competencies—e.g., use of ITs, database management and e-commerce deepening—which have drawn scholarly attention (Tippins and Sohi 2003, Aral and Weill 2007). Among studies attempting to measure the competitiveness construct using factor analysis and structural equation models, a positive relationship is reported between competitiveness and a variety of performance measures, including: 1) accounting ratios such as return on assets, return on sales or cash flow margins (Zahra and Covin 1993, Douglas and Ryman 2003, Aral and Weill 2007), 2) export-oriented variables (Julien and Ramangalahy 2003), 3) performance constructs computed by factor analysis using financial and managerial variables (Tippins and Sohi 2003, O’Cass and Weerawardena 2010), 4) operating metrics related to cycle time (Hult et al. 2007), 5) growth in sales, employment and market share (Fernhaber and Patel 2012), and 6) shareholder’s value (Hansen et al. 2013). These studies provide widespread support to the notion that competitiveness is a multidimensional construct linked to resources and capabilities, and that competitiveness is positively correlated with performance. This line of thought is consistent with postulates of the RBV frame (Prahalad and Hamel 1990, Barney 1991, Eisenhardt and Schoonhoven 1996). Existing studies also corroborate that the value of resources and capabilities for improving competitiveness is fully realized only when they are effectively capitalized (see, e.g., Hansen et al. 2004, Sirmon and Hitt 2009). This argument has fueled research rooted in the RBV which has mostly analyzed the effects on performance of various competencies (Crook et al. 2008, Sirmon et al. 2010). Organizations capable of acquiring and exploiting a set of valuable competencies will achieve superior performance for two reasons. First, increased competitive strengths allow the business to react to changing market conditions in unique ways and satisfy more consumers (Douglas and Ryman 2003). Second, the positive effects that result from the complementarities between competitive strengths are documented in the literature (Crook et al. 2008). The complementarities between competitive strengths multiply the value that each can create for consumers, and allow businesses to improve the price/quality relationship of their products/services (Sirmon et al. 2010). Following these arguments, it seems logical that businesses with competitive strengths will achieve superior performance than businesses that, regardless of their competitiveness level, do not possess a solid set of competitive strengths. 5 H1: There is a positive relationship between business’ competitive strengths and performance At the business level, competitiveness is not only affected by the exploitation of valuable resource-capability combinations, but also by competitive weaknesses that might tamper the business’ efforts for capitalizing on its available resources or capabilities. Competitive weaknesses represent the dark side of competencies, and different analytical approaches have been proposed within the RBV literature, including resource weaknesses, competitive disadvantage and strategic liabilities (West and De Castro 2001, Powell 2001, Arend 2004, Sirmon et al. 2010). Literature on RBV emphasizes various factors that explain the negative effect of competitive weaknesses on performance. First, competitive weaknesses increase the business’ vulnerability to changing market conditions or competitors’ actions, which is detrimental to sales and, ultimately, performance (West and De Castro 2001). Second, businesses with clear competitive weaknesses have a lower possibility to pursue business opportunities (Sirmon et al. 2010). For example, lack of access to certain resources and capabilities—e.g., financial resources, human capital, networks—negatively affect the business’ capacity to engage in new strategic actions linked to technology regeneration or internationalization. Third, competitive weaknesses create a bottleneck of resources and capabilities that increases the business’ unit cost by limiting the capacity to exploit other valuable competencies. For example, Douglas and Ryman (2003) show how skilled physicians are attracted to hospitals that offer cuttingedge technologies and deliver new services. Therefore, skilled people seek employment in organizations where their abilities are rewarded; while businesses using obsolete technologies will become unattractive to skilled employees, thus increasing the competitive weaknesses of the organization. Consequently, we hypothesize that competitive weaknesses create bottlenecks of resources and capabilities which negatively affect business performance. H2: There is a negative relationship between business’ competitive weaknesses and performance 2.3 The configuration of the system of competencies and performance: Harmonization of competitive pillars So far we have hypothesized that competitive strengths and weaknesses shape business competitiveness and, consequently, performance. In each of these settings, simply examining the relationship between competitive strengths and weaknesses and performance might yield partial conclusions with regard to the RBV theory. Competitiveness is the result of having value-adding resources and capabilities (Peteraf and Barney 2003). Nevertheless, the effective exploitation of resources and capabilities is not only conditioned by their mere availability, but also by the ability of the organization to orchestrate its resources and capabilities seeking to enhance competitiveness. Existing research has mostly adopted the net-effect logic to address the connections between resources and capabilities (Arend 2004, Ray et al. 2004, Sirmon et al. 2010). Underlying this approach in the assumption that competitiveness is a function of available competencies and that, regardless of the overall level of competitiveness, the configuration of competencies (strengths or weaknesses) determines business outcomes. The net-effect logic mostly focuses on the role of the dominant competitive force (strengths or weaknesses) on performance. By acknowledging the interconnectedness of resources and capabilities, we propose an alternative approach to competitiveness based on the configuration of the business’ system of competencies. We argue that the potentially positive value that a focal competency might create is not only a function of its availability or exploitation, but also depends on the configuration of the system of competencies within the organization. In the context of this paper, configuration refers to a multidimensional property that varies across organizations, and is defined as the degree to which organization’s resources and capabilities are amalgamated and connected by a single theme (Miller 1996). Building on the configuration theory originally developed by Miller (1986, 1996), the elements of a system cannot fully be understood in isolation, so the analysis of the system as a whole is inevitable. While it is easy to copy a single element, the competitive advantage lies ‘…in the power of 6 the orchestrating theme and the degree of complementarity it engenders among the elements’ (Miller and Whitney 1999, p. 13). This argument is in line with RBV postulates. Organizations are a bundle of interconnected resources and capabilities (Powell 2001), and accurate analyses should take into account the role on competitiveness of both competitive strengths and weaknesses and the configuration of business competencies. For example, technology and knowledge are highly interconnected resources in professional service businesses, such as financial or knowledge-based consultancy firms. The use of obsolete technology might prove itself ineffectively when it comes to capitalize on human capital resources. Skilled employees will likely struggle with internal procedures in their day-to-day routines. In this example, and regardless of the overall business competitiveness level, poor technology implementation—i.e., in terms of software and hardware—creates a bottleneck that both limits the full exploitation of employees’ knowledge and deteriorates both competitiveness and business operations. On contrary, the contribution of human capital to business competitiveness will increase as the organization harmonizes other competencies—i.e., technology—or develop competitive strengths. Figure 1 illustrates the performance implications of the different configurations of competencies. Organizations can generate important gains from a relatively harmonized system of resources and capabilities. Businesses with a harmonized set of competencies can exploit their resource-capability combinations more efficiently, and performance will result from the value of their competencies. In the case of low-competitive businesses, a harmonized system of competencies might lack critical resources and capabilities that limit their capacity to implement value-adding strategies (Ferrier and Lyon 2004). Although the weak harmonization of competencies, these businesses are in a batter competitive position than businesses with a bottleneck of competencies caused by competitive weaknesses. For high-competitive firms, a harmonized set of competencies constitutes a source of competitive advantage and their effective orchestration contributes to develop strategic actions seeking to differentiate from competitors and, consequently, stimulate performance (Sirmon et al. 2010). In this case, strong harmonization will yield high performance levels. --- Insert Figure 1 about here --- Although their increased vulnerability to competitors’ actions, businesses with a harmonized set of competencies are in a better position to exploit their resources and capabilities; therefore, their performance results are conditioned by the value of their competencies. Therefore, we hypothesize: H3 (a): Among low-competitive businesses, a harmonization strategy leads to greater performance results compared to businesses with bottleneck competencies H3 (b): Among high-competitive businesses, a harmonization strategy constitutes a source of competitive advantage that yields to superior performance results comparable to those generated by businesses with competitive strengths In sum, competitiveness is a multidimensional construct which, to a large extent, results from a complex set of interactions between resources and capabilities. This is the focus on our study. This study seeks to contribute a deeper understanding of how businesses capitalize on their resources and capabilities and the systemic relations that exist between them. Building on the configuration theory, the following section describes the proposed competitiveness index which considers the system dynamics that emerge from the interactions of different competitiveness components. 3. A proposal for assessing business competitiveness Organizations have different strengths and weaknesses, in terms of resources and capabilities, and their identification is critical because the key to a business’ success or its future development lies in its ability to create or develop distinctive competencies (Teece et al. 1997). Competitiveness has been operationalized in a number of different ways. Prior studies underline a number of firm-specific sources of competitiveness; however, previous attempts to measure competitiveness rely on either individual variables or the estimation of aggregate metrics in which the analyzed components individually contribute to competitiveness (see, e.g., Douglas and Ryman 2003, Aral and Weill 2007, O’Cass and Weerawardena 2010, Fernhaber and Patel 2012). 7 These measures capture the level of statistical association between the analyzed variables. Nevertheless, the analysis of competitiveness based on the estimation of aggregate metrics might prove itself ineffective in that they ignore the potential connections between the analyzed resources and capabilities. Based on these arguments and following the theory in section 2.3 we propose that: Competitiveness is the mutually dependent bundle of ten pillars— human capital, product, domestic market, networks, technology, decision making, strategy, marketing, internationalization, and online presence—that allow a firm to effectively compete with other firms and serve customers with valued goods/services. The selected competitiveness pillars match RBV postulates (see e.g., Wernerfelt 1984, Barney 1991, Peteraf 1993, Man et al. 2002), and their relevance flows from the recognition that multiple interactions that can take place within a business and that the nature and intensity of these interdependent relations affect competitiveness. Small and medium-sized enterprises (SMEs) are not scaled-down versions of large firms as the former group shows significant particularities in terms of organization, resource allocation, managerial styles, strategic choices and the way of competition (Porter 1998, Man et al. 2002). Small firms are faced with important resource constraints, thus increasing their vulnerability with respect to environmental changes and uncertainty. Although increased globalization, small firms mainly compete in domestic markets, a fact that implies the adoption of entirely different strategies compared to large firms (Tetteh and Burn 2001). Innovation is another variable frequently used to explain small firms’ differentiating behavior (Malecki and Tootle 1996, Verhees and Meulenberg 2004). Small firms often lack resources which are particularly vital for their survival and performance (Bridge et al. 2003). As a result, networking, external collaborations and efficient knowledge-sharing channels are critical competencies (Eisenhardt and Schoonhoven 1996, Dyer and Singh 1998). Various attempts made way for developing diverse competitiveness measures (see section 2). However, the multidimensional nature of the relations between the analyzed competencies has been mostly ignored in the literature. By interlocking the RBV with configuration theory postulates, we propose a four-step procedure to compute competitiveness. To estimate the competitiveness index (CI), we first normalize in the [0,1] range all variables included in the analysis (j=1,…J) as: , * ,max( ) ij ij j x xx , j = 1,…,J and i = 1,…,N (1) In equation (1) * ,ij x is the normalized value for the jth variable obtained for the ith business, while ,ij x is the original value of the focal variable. The selected benchmarks (max( )) j x are, for each variable (j), the highest score and these proxy the best practices, while all remaining values are related to these benchmarks. In this study we use the distance normalization approach because, contrary to the min-max technique (mean of zero and variance of one), this approach preserves the observed relative difference among the analyzed businesses. In the second step we propose to separate the vector of normalized variables (J) into 10 vectors (v) which correspond to the analyzed competitiveness pillars 1 ( ( ,..., ) ) J J v v Rv . The pillar scores are the average value of the variables included in each pillar (v). Additionally, the values of the pillar scores are normalized in the [0,1] range to ease the interpretation of the results. To compute the normalized competitiveness pillar scores one must solve: * , , K iv ki iv x pK , v = 1,…,10 and k = 1,…,K (2a) , * ,max( ) iv iv v p pp , (2b) 8 Note that the pillar scores , () iv p are computed for each firm (i=1,…,N) and that the number of variables used to estimate each pillar (k=1,…,K) might vary across pillars. The third step considers the mutual dependence of the 10 competitiveness pillars by introducing a penalty for bottleneck in the estimation of the competitiveness index. Following the configuration theory (Miller 1996), improvements can be only achieved by strengthening the weakest link—the bottleneck—that constraints the performance of the whole system. Good performing pillars can only partially and not fully compensate poor performing pillars. This imbalance pulls back the competitive performance of the particular business. Mathematically, the penalty of bottleneck is modeled via a correction form of an exponential function of bx ae (Tarabusi and Guarini 2013). In this study the penalty function is defined as: ** ,, ( min( )) * ,, min( ) (1 ) i v i v pp i v i v h p e (3) where ,iv h is the post-penalty value for the vth pillar and * , min( ) iv p is the lowest pillar value reported for the ith business. Equation (3) shows that, for each business and each pillar, the bottleneck penalty is obtained by adding one minus the base of the natural logarithm of the negative difference between the focal index pillar * , () iv p and the lowest pillar value reported for that business (equations (2a) and (2b)). Finally, in the fourth step we use results from equation (4) to estimate the competitiveness index (CI) for each firm as the sum of the ten pillars as follows: 10 , 1 i i v v CI h (4) Keep in mind that the penalty for bottleneck approach (equation (3)) is particularly suitable for portraying the dynamics of business competitiveness. For illustrative purposes, suppose that a fictitious business has the following unadjusted pillar values (equations (2a) and (2b)): human capital = 0.80, product = 0.75, domestic market = 0.70, networks = 0.75, technology = 0.40, decision making = 0.72, strategy = 0.67, marketing = 0.74, internationalization = 0.65 and online presence = 0.82. A traditional additive approach would yield a competitiveness score of 7.00. Yet, underlying this calculation is the assumption of full substitutability of the competitiveness pillars, which is not a realistic representation of systemic phenomena where the substitutability between system pillars may vary. This concern is addressed by considering the systemic relations through the penalty for bottleneck approach in the estimation of the competitiveness index. In the proposed example the bottleneck pillar is technology (0.40), and by solving equation (3) the post-penalty pillar values are computed. For instance, the final value of the human capital pillar is 0.73 (0.80 0.40) ,1 ( 0.40 (1 )) iv he . The post-penalty values of the rest of the system pillars can be computed analogously, and the resulting competitiveness index is 6.54 instead of 7.00 (equation (4)). This simplified example illustrates the problem with the assumption of full substitutability of index components. Businesses with poor or obsolete technology might not effectively capitalize on their human capital as employees will likely struggle with internal procedures in their day-to-day routines, which limits the full utilization of their knowledge and deteriorates business operations. In line with the example, if the organization improves its technology pillar by 10 index points (0.10)— e.g., through specific investments—the competitiveness index would increase 32 index points (4.94%) from 6.54 to 6.86. To sum up, the penalty for bottleneck method accounts for the partial substitutability between system components, and its inclusion in the analysis increases the capacity of our index to measure competitiveness. Note that in our approach to competitiveness, 1) bottleneck competencies dilute the contribution of other valuable competencies, 2) improvements in bottleneck competencies represent a costly investment, 3) the harmonization of competencies is a source of competitive advantage linked to the exploitation of homogeneously distributed resources and capabilities, and 4) the development of competitive strengths leads to superior performance. The proposed systemic approach to competitiveness is a valuable managerial control tool which not only unveils business weaknesses and 9 their effect on competitiveness, but also captures the multiple relationships that exist among the analyzed competitiveness pillars. 4. Empirical illustration: Data and variables used to build to competitiveness index 4.1 Data For the empirical illustration we use a unique primary dataset drawn from a research project on competitiveness of Hungarian enterprises supported by the European Union (TÁMOP 4.2.2 A– 11/1/KONV-2012-0058). Data were collected specifically for the purpose of this study and the process was entirely supervised by a team of the Faculty of Business and Economics at the University of Pécs (Hungary). The selection process of the surveyed firms was two folded. First, we selected a random sample of firms from the OPTEN company database. The OPTEN database includes all businesses registered in the Hungarian Business Registry. From this dataset nearly 9,500 firms were selected according to size, industry and geographic quotas. In the context of this study, top managers are a relevant respondent group. Therefore, and after an initial telephone call for approval, in the second step a face-to-face interview was carried out to one of the business owners (only if he/she is in top management team) in the case of firms smaller than 20 employees, while for businesses larger than 20 employees a top executive—irrespective of whether the executive has ownership rights or not—was interviewed. The data collection process was achieved through self-administrated, structured interviews where managers were asked to answer essentially close questions. The survey was conducted by a professional market investigation firm, and the information was collected between March and June 2013. The questionnaire was subject to a pre-test to correct potentially misleading or confusing questions. A total number of 662 surveys were obtained (response rate: 6.98%). Yet, in the interest of following a rigorous methodology, only observations for which a complete dataset of the analyzed variables could be constructed were included. Thus, we excluded 37 businesses with incomplete data. This yielded a final sample of 625 businesses. The average business has 26 employees with 15 years of market experience. Also, the analysis of the industry configuration of the final sample reveals that 32% of firms operate in manufacturing sectors, while the proportion of retailing and professional services businesses is 40% and 28%, respectively. We tested non-response bias for early and late respondents in terms of business size (employees), business age and sales across the analyzed industry sectors. We found no significant differences. Additionally, data on sales and assets were obtained from official publicly available sources of the Hungarian Ministry of Justice.1 Based on the unique identification code available from the questionnaire, information was collected for the sampled businesses during the period 2010-2013. Data available allow at computing two performance metrics: employment growth between 2010 and 2013, and labor productivity measured as sales divided by employees. This information was used to carry out the regression analysis linking competitiveness to business outcomes. Details on this analysis and its results are presented in section 5.2. 4.2 Variables used to estimate the competitiveness pillars To compute the competitiveness index we employed two groups of variables. The first set of variables deals with different resources and capabilities, while the second group of variables captures changes in these variables between 2010 and 2013. Similar to Irwin et al. (1998) and Douglas and Ryman (2003), respondents were asked along a five-point scale to value the individual importance of a series of resources and capabilities. These resources and capabilities are only valuable if deemed so by the respondents (Priem and Butler 2001). In the proposed Likert-type scale a value of ‘1’ designates a low relevant variable, while a value of ‘4’ represents a highly relevant variable. The value of ‘0’ indicates that the focal resource or capability has no strategic value whatsoever (Douglas and Ryman 2003), and the remaining points of the scale ensure the uniform evaluation and quantification of the variables’ importance. Also, the division of the positive scale values (from ‘1’ to ‘4’) allows a sufficient degree of differentiation in the valuation of the analyzed variables (Lederer et al. 2013). 1 Data are available at http://www.e-cegjegyzek.hu/index.html 16 Dyer, J.H., Singh, H. (1998). The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage. Academy of Management Review, 23 (4), 660-679. Eisenhardt, K.M., Schoonhoven, C.B. (1996). Resource-Based View of Strategic Alliance Formation: Strategic and Social Effects in Entrepreneurial Firms. Organization Science, 7 (2), 136-150. Fernhaber, S., Patel, P. (2012). How Do Young Firms Manage Product Portfolio Complexity? The Role of Absorptive Capacity and Ambidexterity. Strategic Management Journal, 33, 1516-1539. Ferrier, W., Lyon, D. (2004). Competitive repertoire simplicity and firm performance: the moderating role of top management team heterogeneity. Managerial and Decision Economics, 25 (6-7), 317327. Fisman, R.J., Khurana, R., Rhodes-Kropf, M., Yim, S., 2014. Governance and CEO turnover: Do something or do the right thing? Management Science, 60 (2), 319-337. Grant, R.M (1991). Toward the resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33 (3), 114-135. Grant, R.M. (2008). Contemporary strategy analysis. Sixth edition, Blackwell, Malden. Greene, W. (2003). Econometric Analysis. Fifth edition, Upper Saddle River, New Jersey. Hansen, M.H., Perry, L.T., Reese, C.S. (2004). A Bayesian operationalization of the resource-based view. Strategic Management Journal, 25, 1279-1295. Hansen, J.M., McDonald, R.E., Mitchell, R.K. (2013). Competence resource specialization, causal ambiguity, and the creation and decay of competitiveness: the role of marketing strategy in new product performance and shareholder value. Journal of the Academy of Marketing Science, 41 (3), 300-319. Hult, G.T.M., Ketchen, D., Arrfelt, M. (2007). Strategic supply chain management: Improving performance through a culture of competitiveness and knowledge development. Strategic Management Journal, 28 (10), 1035-1052. Irwin, J., Hoffman, J., Lamont, B. (1998). The effect of the acquisition of technological innovations on organizational performance: a resource-based view. Journal of Engineering and Technology Management, 15, 25-54. Julien, P.A., Ramangalahy, C. (2003). Competitive strategy and performance of exporting SMEs: An empirical investigation of the impact of their export information search and competencies. Entrepreneurship Theory and Practice, 27 (3), 227-245. Kaplan, R.S., Atkinson, A.A. (2000). Advanced management accounting, 3rd edition. Prentice Hall, Upper Saddle River. Ketchen, D.J. Jr, Hult, G.T.M., Slater, S.F. (2007). Toward greater understanding of market orientation and resource-based view. Strategic Management Journal, 28 (9), 961-964. Kingsley, G., Malecki, E.J. (2004). Networking for competitiveness. Small Business Economics, 23 (1), 71-84. Lederer, M., Schott, P., Huber, S., Kurz, M. (2013). Strategic Business Process Analysis: A Procedure Model to Align Business Strategy with Business Process Analysis Methods. In S-BPM ONERunning Processes (pp. 247-263). Springer Berlin Heidelberg. Lu, J., Beamish, P. (2001). The internationalization and performance of SME. Strategic Management Journal, 22 (6-7), 565-586. Mahoney, J.T., Pandain, J.R. (1992). The resource-based view within the conversation of strategic management. Strategic Management Journal, 13 (5), 363-380. Malecki, E.J., Tootle, D.M. (1996). The role of networks in small firm competitiveness. International Journal of Technology Management, 11, (1-2), 43-57. Man, T.W.Y, Lau, T., Chan, K.F. (2002). The competitiveness of small and medium enterprises: A conceptualization with focus on entrepreneurial competencies. Journal of Business Venturing, 17, 123-142. Miller, D. (1986). Configurations of Strategy and Structure: Towards a Synthesis, Strategic Management Journal, 7, 233-249. Miller, D. (1996). Configurations revisited. Strategic Management Journal, 17 (7), 505-512. Miller, D., Whitney, J.O. (1999). Beyond Strategy: Configuration as a Pillar of Competitive Advantage. Business Horizons, May-June, 5-17. Newbert, S. (2007). Empirical research on the Resource-based view of the firm: An assessment and suggestions for future research. Strategic Management Journal, 28 (2), 121-146. 17 Newbert, S. (2008). Value, rareness, competitive advantage, and performance: a conceptual‐level empirical investigation of the resource‐based view of the firm. Strategic Management Journal, 29 (7), 745-768. Nunnally, J.C., Bernstein, I.H. (1994). Psychometric Theory. Third ed. McGraw-Hill, New York. O’Cass, A., Weerawardena, J. (2010). The effects of perceived industry competitive intensity and marketing-related capabilities: Drivers of superior brand performance. Industrial Marketing Management, 39 (4), 571-581. O’Farrell, P.N., Hitchens, D., Moffat, L. (1992). The competitiveness of business services firms: A matched comparison between Scotland and the South East of England. Regional Studies, 26 (6), 519-525. Peteraf, M.A. (1993). The cornerstones of competitive advantage: A resource‐based view. Strategic Management Journal, 14 (3), 179-191. Peteraf, M.A., Barney, J.B. (2003). Unraveling the resource-based tangle. Managerial and Decision Economics, 24 (4), 309-323. Porter, M.E. (1998). Clusters and the new economics of competition. Harvard Business Review, 76 (6), 77-90. Porter, M.E., Millar, V.E. (1985). How information gives you competitive advantage. Harvard Business Review, 63 (4), 149-160. Powell, T.C. (2001). Competitive advantage: logical and philosophical considerations. Strategic Management Journal, 22 (9), 875-888. Prahalad, C.K., Hamel, G. (1990). The Core Competence of the Corporation. Harvard Business Review, 68 (May-June), 79-91. Priem, R., Butler, J. (2001). Is the resource-based ‘view’ a useful perspective for strategic management research? Academy of Management Review, 26, 22-40. Ray, G., Barney, J.B., Muhanna, W.A. (2004). Capabilities, business processes, and competitive advantage: choosing the dependent variable in empirical tests of the resource-based view. Strategic Management Journal, 25 (1), 23-37. Santos-Vijande, M., del Río-Lanza, A. Suárez-Álvarez, L., Díaz-Martín, A. (2013). The brand management system and service firm competitiveness. Journal of Business Research, 66, 148-157. Sirmon, D.G., Hitt, M.A. (2009). Contingencies within dynamic managerial capabilities: Interdependent effects of resource investment and deployment on firm performance. Strategic Management Journal, 30, 1375-1394. Sirmon, D.G., Hitt, M.A., Arregle, J.L., Campbell, J.T. (2010). The dynamic interplay of capability strengths and weaknesses: investigating the bases of temporary competitive advantage. Strategic Management Journal, 31 (13), 1386-1409. Sirmon, D.G., Hitt, M.A., Ireland, R.D., Gilbert, B.A. (2011). Resource orchestration to create competitive advantage breadth, depth, and life cycle effects. Journal of Management, 37 (5), 13901412. Slevin, D.P., Covin, J.G. (1995). New ventures and total competitiveness: a conceptual model, empirical results, and case study examples. Frontiers of Entrepreneurship Research, 574-588. Somaya, D., Williamson, I.O., Zhang, X. (2007). Combining Patent Law Expertise with R&D for Patenting Performance. Organization Science, 18 (6), 922–937. Subramanian, N., Gunasekaran, A., Yu, J., Cheng, J., Ning, K. (2014). Customer satisfaction and competitiveness in the Chinese E-retailing: Structural equation modeling (SEM) approach to identify the role of quality factors. Expert Systems with Applications, 41, 69-80. Tarabusi, E. C., Guarini, G. (2013). An unbalance adjustment method for development indicators. Social Indicators Research, 112 (1), 19-45. Teece, D., Pisano, G., Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18 (7), 509-533. Tetteh, E., Burn, J. (2001). Global strategies for SME-business: applying the small framework. Logistics Information Management, 14 (1-2), 171-180. Tippins, M.J., Sohi, R.S. (2003). IT competency and firm performance: is organizational learning a missing link? Strategic Management Journal, 24 (8), 745-761. Ucbasaran, D., Shepherd, D.A., Lockett, A., Lyon, S.J. (2013). Life after business failure the process and consequences of business failure for entrepreneurs. Journal of Management, 39 (1), 163-202. 18 Verhees, F., Meulenberg, M.T.G. (2004). Market Orientation, Innovativeness, Product Innovation, and Performance in Small Firms. Journal of Small Business Management, 42 (2), 134-154. Wang, Ch.-J., Wu, L.-Y. (2012). Team member commitments and start-up competitiveness. Journal of Business Research, 65, 708-715. Wernerfelt, B. (1984). A Resource-Based View of the Firm. Strategic Management Journal, 5 (2), 171-180. West, G.P., De Castro, J. (2001). The Achilles heel of firm strategy: resource weakness and distinctive inadequacies. Journal of Management Studies, 38 (3), 417-442. Wu, W. (2008). Dimensions of Social Capital and Firm Competitiveness Improvement: The Mediating Role of Information Sharing. Journal of Management Studies, 45 (1), 122-146. Zahra, S., Covin, J. (1993). Business strategy, technology policy and firm performance. Strategic Management Journal, 14, 451-478. Zott, C., Amit, R., Massa, L. (2011). The business model: recent developments and future research. Journal of Management, 37 (4), 1019-1042. 19 List of Figures Figure 1. Response surface of competitiveness and the configuration of competitive pillars Source: Authors’ elaboration Figure 2. Response surface of competitiveness and the configuration of competitive pillars Source: Authors’ elaboration Competitive advantage: Dominance of competitive strengths Configuration of the competitiveness system Weak harmonization: Balanced low-value competencies Bottleneck of competencies: Dominance of competitive weaknesses Performance Low High Strong harmonization: Balanced high-value competencies -2.5 0.0 2.5 Competitive strengths Harmonized Competitive strengths Harmonized Bottleneck Competitive strengths 2.5 5.0 7.5 10.0 Low High growth (in %) Estimated employment competitiveness pillars Configuration of Competitiveness 20 List of Tables Table 1. Competitiveness: Descriptive statistics for the selected competitive pillars Mean Std. dev. Q1 Q3 Competitiveness index (CI) 3.8859 1.3438 2.8783 4.8244 Competitiveness pillars Human capital 0.3941 0.1446 0.2784 0.4863 Product 0.3926 0.1553 0.3062 0.4898 Domestic market 0.3889 0.1584 0.2715 0.4951 Networks 0.3838 0.1982 0.2590 0.5234 Technology 0.3918 0.1566 0.2784 0.4930 Decision making 0.3804 0.1961 0.2263 0.5271 Strategy 0.3817 0.1735 0.2693 0.4947 Marketing 0.3926 0.1572 0.2787 0.4838 Internationalization 0.3837 0.1926 0.2385 0.5269 Online presence 0.3962 0.2881 0.0200 0.6423 Number of observations: 625. 21 Table 2. Competitiveness index: Business-level results Example 1: poor performing business with a clear bottleneck Example 2: high performing business Improvement strategies Improvement strategies Normalized pillar values Penalized pillar values (1) (2) (3) Normalized pillar values Penalized pillar values (1) (2) (3) Human capital 0.2644 0.2404 0.2566 0.3162 0.2495 0.6191 0.6191 0.7156 0.6191 0.6689 Product 0.2252 0.2085 0.2214 0.2085 0.2161 0.6513 0.6508 0.6512 0.6508 0.6513 Domestic market 0.2066 0.1930 0.2042 0.1930 0.1997 0.6344 0.6343 0.6344 0.6343 0.6839 Networks 0.1437 0.1382 0.1437 0.1382 0.1882 0.7330 0.7268 0.7283 0.7268 0.7298 Technology 0.2353 0.2169 0.2306 0.2169 0.2248 0.7079 0.7041 0.7053 0.7041 0.7063 Decision making 0.2186 0.2030 0.2154 0.2030 0.2103 0.7030 0.6996 0.7007 0.6996 0.7017 Strategy 0.2071 0.1934 0.2047 0.1934 0.2002 0.6714 0.6701 0.6707 0.6701 0.6712 Marketing 0.2562 0.2338 0.2494 0.2338 0.2426 0.7125 0.7083 0.7095 0.7083 0.7107 Internationalization 0.1475 0.1416 0.1474 0.1416 0.1457 0.6594 0.6586 0.6591 0.6586 0.6594 Online presence 0.0369 0.0369 0.1369 0.0369 0.0869 0.8110 0.7937 0.7963 0.8723 0.7989 Competitiveness index 1.8057 2.0103 1.8815 1.9640 6.8652 6.9711 6.9438 6.9820 Improvement (index points) 0.2046 0.0758 0.1583 0.1058 0.0785 0.1168 Improvement (%) 11.33% 4.20% 8.77% 1.54% 1.14% 1.70% The normalized pillar values are obtained from equations (2a) and (2b), while the penalized pillar values are computed by solving equation (4). Results in the table refer to the case in which the organization employs 0.10 index-points to enhance its competitiveness by adopting one of the following strategies: 1) improvement of the bottleneck pillar, 2) improvement of the strongest normalized pillar (below 1), and 3) improvement of the two weakest pillars (harmonization approach). 22 Table 3. Regression analysis: Descriptive statistics for the selected variables Mean value Std. dev. Performance variable Employment growth 0.0721 0.6259 Configuration of the competitiveness system Core competencies (competitive strengths) 0.1344 0.3414 Harmonized 0.5056 0.5004 Bottleneck 0.3600 0.4804 Control variables Business size in 2010 (employees) 25.98 77.65 Business size (average employees) 26.01 75.39 Business age (years) 14.59 6.70 Manufacturing 0.3200 0.4668 Retailing 0.3968 0.4896 Professional services sectors 0.2832 0.4509 Budapest 0.1888 0.3917 Central Hungary 0.0848 0.2788 Central Transdanubia 0.0736 0.2613 Western Transdanubia 0.0704 0.2560 Southern Transdanubia 0.2960 0.4569 Northern Hungary 0.0702 0.2587 North Great Plain 0.0926 0.2904 South Great Plain 0.1216 0.3271 Number of observations: 625. 23 Table 4. Regression analysis: The relationship between competitiveness and employment growth Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Competitiveness index 0.0961 *** (0.0191) 0.0940 *** (0.0208) 0.0957 *** (0.0211) 0.0928 *** (0.0207) High competitiveness (above the median) 0.2189 *** (0.0501) 0.1077 * (0.0648) Competitive strengths 0.2110 ** (0.0871) 0.2739 *** (0.0917) 0.1653 ** (0.0843) Harmonized 0.0416 (0.0466) 0.1223 ** (0.0477) 0.0420 (0.0522) Bottleneck –0.1560 *** (0.0479) High competitiveness X Competitive strengths 0.2094 * (0.1184) High competitiveness X Harmonized 0.1643 * (0.0915) Intercept 0.5494 *** (0.1388) 0.5310 *** (0.1375) 0.5304 *** (0.1458) 0.6208 *** (0.1419) 0.7092 *** (0.1296) 0.7516 *** (0.1263) F-test 9.35 *** 8.47 *** 8.76 *** 8.64 *** 7.21 *** 6.52 *** Adjusted R2 0.1384 0.1502 0.1380 0.1508 0.1521 0.1538 Root MSE 0.5810 0.5770 0.5811 0.5768 0.5763 0.5757 Average VIF (min–max) 1.40 (1.18–1.87) 1.37 (1.02–1.87) 1.37 (1.04–1.87) 1.38 (1.05–1.87) 1.38 (1.12–1.86) 1.92 (1.18–3.75) Observations 625 625 625 625 625 625 Robust standard errors are presented in brackets. All model specifications include size (ln employees in 2010), business age (ln years), industry dummies (manufacturing is the reference category) and territorial dummies (Budapest is the reference category) as control variables. *, **, *** indicate significance at the 10%, 5% and 1%, respectively. 24 Appendix Table A1. Description of the variables used to build the pillars that form the competitiveness index Competitiveness pillar Variables included in the pillar 1. Human capital The number and share of employees with higher education degree The problems with employees The share of employees participating in training programs The sophistication of compensation systems The uniqueness of human capital 2. Product Product innovation Activities/effort concerning the introduction of new or amended product The share of new product in sales The uniqueness of firm’s product and continuous innovation 3. Domestic market The geographic scope of selling in Hungary The level of firm’s competition in the market The expected growth of the target market in five years The intensity of competition Quick response to costumers’ demand 4. Networks The number of economic cooperation and innovation agreements The time of networking as compared to the establishment of the firm The reliance to outside help in business development Uniqueness of networking relationship 5. Technology The level of firm’s technology in Hungary The age of available technology used by the firm and technological innovation Environmental investment and quality assurance The level of application of ICT tools Uniqueness of applied technology, possession of license or know-how, product management and quality assurance 6. Decision making The application of the different sources of information The application of financial analyses in the business Information sharing Consultation in decision making Administrative routines/operations knowledge sharing of the business organization 7. Competitive strategy The direction of strategy (defensive, proactive) Growth strategy based on the number of business units The leader’s entrepreneurial traits The uniqueness of firm’ proactive strategy 25 Table A1. Continued Competitiveness pillar Variables included in the pillar 8. Marketing The product The pricing of the main product Sophistication of distribution channels Applied marketing and communication tools Marketing innovation The uniqueness of marketing methods 9. Internationalization The significance of foreign buyers The share of export in sales Language capabilities at business level The uniqueness of location 10. Online presence Webpage technical characteristics Webpage offered services Webpage content Online marketing applications Table A2. Factor analysis: Summary of measurement results Variables Cronbach’s alpha Kaiser-MeyerOlkin (KMO) test Eigenvalue Variance explained (%) Competitiveness index 10 0.8513 0.8984 4.3141 43.14 Competitiveness pillars: Human capital 5 0.7332 0.6146 1.5458 30.92 Product 4 0.6928 0.5899 1.6734 41.84 Domestic market 5 0.7061 0.5258 1.5784 31.57 Networks 4 0.6777 0.5945 1.8105 45.26 Technology 5 0.7297 0.6751 1.7283 34.57 Decision making 5 0.7012 0.7201 2.3121 46.24 Strategy 4 0.7114 0.5005 1.3488 33.72 Marketing 6 0.6451 0.6866 1.8823 31.37 Internationalization 4 0.7042 0.5365 2.0063 50.16 Online presence 4 0.8623 0.7901 2.9434 73.58 Number of observations: 625.