Dynamic Capabilities And Companies’ Performance In The Textile, Apparel And Leather And Related Products Industry Alica Grilec Kauric, Josip Mikulic, Mislav Ante Omazic University of Zagreb, Faculty of Textile Technology, Zagreb, Croatia University of Zagreb, Faculty of Economics and Business, Zagreb, Croatia
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Abstract In the age of turbulent and frequent changes on complex markets, dynamic capabilities represent contemporary, vivid and propulsive interdisciplinary topic of strategic management research in the field of industrial management. Furthermore, recent theoretical development placed it in the field of the supply chain management, where it is identified as the main promoter of value building throughout supply chain. Despite this fact, scientific literature lacks empirical research that would integrate dynamic capabilities into a single theoretical concept together with the performance within supply chain. Building on this literature gap, and taking into account all the specifics of the textile, apparel and footwear industry, this paper establishes its presence in the selected industries and suggests ways of possible application in order to improve both, companies’ dynamic capabilities and its supply chain performance. An empirical study is conducted to test the positive impact of selected dynamic capabilities. The paper concludes with a series of important managerial implications.
Keywords: Dynamic capabilitie, Companies’ performance, Supply chain performance, Textile apparel, Footwear industry Track: Management Word count: 6.336 1. Introduction Although the world's oldest continuously ongoing independent company is operating for over 1,400 years (Kongō Gumi Co., Ltd. in Japan) average life span of companies is shortening (Foster and Kaplan, 2001; Zook, 2007; Deloitte, 2013; Knight, 2014). In order to survive their managers must take advantage of its capabilities and current resources at all operational levels and to take into account that probably these scarce resources will not be available in the near future. Dynamic capabilities (hereafter DC) point to two seemingly conflicting actions. On one hand, the company must be able to deliver value in present, on the other hand it must adjust to the changing environment in the future. Majority of business organisations practically can and do not affect their environment. That requires companies to create business model stable enough to deliver value in their recognizable way but in the same time be flexible enough to allow development of the inventions when the circumstances demand it. One of the fields that has shown a great interest in researching the sources of competitiveness is resource-based view (hereafter RBV) because firms that are seeking positional advantage in the marketplace should identify all potential resources that can lead them to competitive 1231
differentiation (Esper and Crook, 2014). Both, RBV and dynamic capabilities (DC) frameworks are often mentioned as success stories in the historical evolution of strategic management (Katkalo, Pitelis and Teece, 2010). RVB argues that organizations may achieve competitive advantage through the bundling of resources to create capabilities (Barney, 1991) and that high-performing companies are those that can effectively combine, access, develop, and utilize strategic resources that are valuable, rare, and difficult to imitate (Esper and Crook, 2014: 3). While resources are defined as something a firm possesses or has access to, not what a firm is able to do (Brandon-Jones et al., 2014: 57), organizational capabilities are defined as a higher- order construct which relies on the bundling of resources (Brandon-Jones et al., 2014: 57). In recent years, theoretical advances in the field of strategy management have started to highlight the importance of DC in building the companies’ competitive position (Teece, Pisano and Shuen, 1997). Lummus and Vokurka (1999) traced back the history of the supply chain to the early beginnings of the textile industry. Intense competition in the textile and apparel industry induced leading US companies to form strategic alliance named Crafted With Pride in 1984 (then become Kurt Salmon Associates in 1993). In 1985, agency Kurt Salmon Associates conducted analysis of textile supply chain. The research concluded that delivery time in the textile supply chains was 66 weeks long, and that 40 weeks were spent on warehousing and transport. Therefore, textile industry experienced major losses due to financing inventories and lack of in time delivery. Hence, strategies directed towards the supply chain partners (the quick response strategy and efficient consumer response) were developed.
2. Dynamic capabilities Capabilities can be static (given at a point in time) or dynamic (changing over time) (Katkalo, Pitelis and Teece, 2010). As stated in Ambrosini and Bowman (2009: 33) the turbulent and changing nature of the environment suggests that resources cannot remain static and still be valuable. They must be continually evolving and developing, otherwise firms may only be able to be competitive in the short and limited time framework. To have a persistent competitive advantage, business organizations must continue to invest in and upgrade their resources to create new strategic growth options (Ambrosini, Bowman, and Collier, 2009). DCs represent companies’ capacities to integrate, build, and reconfigure internal and external resources/competences to address and shape rapidly changing business environments (Teece, Pisano and Shuen, 1997). DC builds framework where companies develop dynamic capabilities among themselves and employees learn from each other, in order to create competitive advantages, solve burning problems and ultimately create lasting value for consumers. Although the DCs have shown predominant focus in the context of local companies, many authors have studied DCs in the context of supply chains (Wu et al, 2006; Chmielewski and Paladino, 2007; Wilden et al, 2013). However, despite the growing interest of supply chain researchers for DCs, the literature is still scarce on these issues (Beske, 2012). The essence of DC’s is that they cannot generally be bought - they must be built (Katkalo, Pitelis and Teece, 2010). 2.1. Sensing, seizing and reconfiguring According Teece (2007: 1319), DC’s can be disaggregated into the three capacities: (1) to sense and shape opportunities and threats (2) to seize opportunities, and (3) to maintain competitiveness through enhancing, combining, protecting, and, when necessary, reconfiguring the business enterprise’s intangible and tangible assets. 1230
Sensing capability refers to the gathering relevant marketing intelligence and is crucial for gaining competitive advantage - to be able to scan, both, global and local markets, evaluate last customers’ preferences, and inside the company - capture ideas from employees (Kindström, Kowalkowski and Sandberg, 2013). It is similar to the term of market-focused learning, which refers to a company’s market-searching efforts and is “connected to the processes that allow a firm to anticipate market developments and customer requirements” (Gebauer, 2011: 1240). Function of sensing is to “scan and monitor changes in the operating environment, identify new ideas” (Jantunen, Ellonen, and Johansson, 2012: 144). Seizing capability suggest that it is not enough only to invest in technology but company have to be capable of sustaining and exploiting new opportunities (Kindström, Kowalkowski and Sandberg, 2013). It also relates to “taking advantage of investments realized in the sensed opportunities” (Gebauer, 2011: 1240). Function of seizing is to “link the innovativeness to products and markets” (Jantunen, Ellonen, and Johansson, 2012: 144). Reconfiguring capability reminds companies that it is necessary reconfigure fundamental elements of business models and all current resources (Kindström, Kowalkowski and Sandberg, 2013). It allows companies to “continuously realign the operational capabilities with the (seized) opportunities and is embedded in the notion of internally focused learning” (Gebauer, 2011: 1240). Function of reconfiguring is to “align the firm’s resources and capabilities” (Jantunen, Ellonen, and Johansson, 2012: 144). While sensing seeks searching and exploring markets and technologies both local to and distal from the organization, seizing making high-quality, interdependent investment decisions, such as those involved in selecting product architectures and business models, reconfiguring implies continuously transforming the firm in response to market and technological changes, such that it retains evolutionary fitness (Hodgkinson and Healey, 2011: 1501). According Wilden et al. (2013: 74) DC positively impact company’s performance in multiple ways: “they match the resource base with changing environments, create market change, support both the resource-picking and capability-building rent-generating mechanisms and improve inter-firm performance, improve the effectiveness, speed and efficiency of organizational responses to environmental turbulence which ultimately strengthens performance”. And competitive intensity is defined as “a situation where a firm operates in markets that are characterized by a high number of manifestly competing organizations, limiting potential growth opportunities” (Wilden et al., 2013: 76). 2.2. Supply chain visibility and supply chain responsiveness In the supply chain context DCs are defined as the ability of the companies to identify, use and adapt to internal and external resources/information in order to facilitate all activities in the SC (Wu et al, 2006: 494). Due to its importance, in this research supply chain visibility and responsiveness will be discussed. Visibility is an important antecedent to risk reduction - it helps companies to track products and identify potential disruptions and its absence can create new risks and and a consistent definition of supply chain visibility is still absent (Brandon-Jones et al., 2014). Barrat and Oke (2007: 1218) defined supply chain visibility as “the extent to which actors within a supply chain have access to or share information which they consider as key or useful to their 1231
operations and which they consider will be of mutual benefit”. Francis (2008: 182) defined supply chain visibility as the identity, location and status of entities transiting the supply chain, captured in timely messages about events, along with the planned and actual dates/times for these events. Chan (2003) explained that visibility is important for a supply chain because accurate and fast delivery of information are crucial for successful supply chains. According Wu (2006, p. 495), supply chain responsiveness is defined as “the extent to which channel members respond cooperatively to environmental changes” and is one of the key dimensions of companies’ supply chain capabilities. Supply chain responsiveness involve that a company’s ability to remain responsive does not come from only the company itself but also its supply chain partners and it can also result from strategic collaboration at the strategic level of activities between supply chain partners that indicates their strong commitment to success, and thereby leads to a more responsive supply chain (Kim and Lee, 2010). Flexibility as a measure of supply chain performance, “can measure a system's ability to accommodate volume and schedule fluctuations from suppliers, manufacturers, and customers” (Beamon, 1999, p. 284). There are many definitions on flexibility, but it can be concluded that it is about the ability or the adaptability of the company to respond to diversity or change (Chan, 2003). According Blome, Schoenherr and Eckstein (2014) flexibility has been the most important ingredient for companies’ ability to adapt to changing market conditions and at the same time, to remain competitive. The authors explained that researching related to flexibility has broadened due to increasing globalization, the trend towards outsourcing and increased environmental complexity and it resulted in more encompassing concept of supply chain flexibility. Authors Duclos, Vokurka and Lummus (2003: 450) explain that a definition of supply chain flexibility components includes the flexibility dimensions required by all the participants in the supply chain to successfully meet customer demand and that flexibility in the supply chain adds the requirement of flexibility within and between all partners in the chain, including departments within an organization, and the external partners, including suppliers, carriers, third-party companies, and information systems providers.
3. Textile, apparel and footwear industry in EU and in the Republic of Croatia Textile, apparel and leather (footwear) industry are still attracting attention when considering the prospects of industrial production development in the European Union and in the Republic of Croatia. The main available indicators of textiles, clothing and leather at the EU level are shown in Table 1.
Table 1: The main indicators of textiles, clothing manufacturing industry at EU level in 2010 Nr of Nr of companies employees C Manufacturing industry (in (in (u (in 000) %) 000) %) Textiles manufacturing
62.0
2.91
662.5
2.2 1
and leather production in the total Revenue (in (in mil.EUR %) ) 1.2 80 000 5
Gross wages (in mil. (in EUR) %) 15 000
1.4 8 1232
Table 1: The main indicators of textiles, clothing and leather production in the total manufacturing industry at EU level in 2010 Apparel 1 3.5 1.1 1.3 129.4 6.98 73 000 13 800 manufacturing 059.8 3 4 7 Leather and related 1.3 0.6 0.7 products 36.5 1.71 414,1 43 471 7 283 8 8 2 manufacturing Textiles, clothing 10.6 2 7.1 3.0 2.2 and leather and 227.9 196 471 23 083 7 136.4 2 7 8 related products manufact. in total Manufacturing 2 30 10 6 410 10 1 010 100 100 industry in total 130.0 000 0 000 0 000 Source: Sectoral analysis of key indicators, manufacturing (NACE Section C), EU-27, 2010 A.png - Statistics Explained, Eurostat. Available at: http://epp.eurostat.ec.europa.eu, (October 2nd 2015) Table 1 shows that textiles manufacturers participate with 2.91% (62,000 companies) in the total number of companies, apparel manufacturers with 6.98% (129,400 companies), and leather and related products manufacturers with 1.71% (36,500 companies) in the total number of companies operating in manufacturing industry. Companies from the three observed branches of manufacturing industry, participate with 10.67% (227,900 companies) in the total of 2.13 million manufacturing companies. According the number of employees, textile production participates with 2.21% (662,500 employees), manufacture of apparel with 3.53% (1,059,800 employees), and the manufacture of leather and related products with 1.38% (414,100 employees) in total employment in manufacturing industry. In total, manufacture of textiles, apparel and leather and related products employ 7.12% (2,136,400) of the total number of employed in the manufacturing industry. In the total revenue, the three selected branches participate with a share of 3.6%. Looking at the gross wages, the highest wages when comparing textile, apparel and leather manufacturing industry are in the textile manufacturing. Number of employees in textile, apparel and leather and related products manufacturing according to their size are shown in Table 2.
Table 2: Sector analysis of employment according companies size, EU-27, 2010. in % C Manufacturing industry Micro Small Medium Big Textiles manufacturing 17.8 31.7 31.4 19.1 Apparel manufacturing 50.2 29.0 20.8 Leather and related products manufacturing 18.1 26.3 34.2 21.4 Manufacturing industry in total 14.3 20.5 25.3 40.0 Source: Sectoral analysis of key indicators, manufacturing (NACE Section C), EU-27, 2010 A.png - Statistics Explained, Eurostat. Available at: http://epp.eurostat.ec.europa.eu, (October 2nd 2015) According to Table 2 it can be concluded that the largest concentration of employees are in small and medium-sized companies, while medium companies manufacturers of leather and related products have the highest employment rate in the observed categories. It can be also 1233
concluded that, although weakened branches of manufacturing industry - textiles, apparel and leather manufacturing - still significantly participate in the total employment, the total number of companies, as well as in the total realized revenue in manufacturing industry of the European Union. The following is situation analysis of textiles, apparel and leather and related products industry in the Republic of Croatia. Data on the number and structure of employees in the Republic of Croatia can be found in Table 3.
Table 3: The number and structure of employees in the manufacturing industry of the Republic of Croatia according to NACE 2007 (in thousands) 2014 C Manufacturing industry 2011 2012 2013 3.1 Textiles manufacturing 4.4 4.1 3.7 14.7 Apparel manufacturing 17.0 15.6 13.8 10.6 Leather and related products manufacturing 9.0 8.5 9.8 Textiles, clothing and leather and related 30.5 28.5 27.2 28.4 products manufacturing in total Manufacturing industry in total 214.3 207.3 197.9 220.6 Source: CBS (2012); CBS (2013); CCC (2015) According to Table 3, it is concluded that the production of textiles, apparel and leather follow trends in the overall manufacturing industry, a constant decrease in the number of employees observed in the four-year period. In the total employment in the manufacturing industry in November 2014, production of textiles, apparel and leather goods participated with 12.8% in total employment, which is still a significant proportion. Number of business entities by sector in 2012, is shown in Table 4.
Table 4: Number of business entities by sector in the Republic of Croatia, 2014 C Manufacturing industry Small Medium Large Total Textiles manufacturing 231 10 0 241 Apparel manufacturing 498 10 5 513 Leather and related products manufacturing 126 4 2 132 Textiles, clothing and leather and related 816 37 6 859 products manufacturing in total Manufacturing industry in total 11 150 384 110 11 644 Source: CCC (2015) Table 4 shows that companies manufacturers of textiles, apparel and leather present 7.4% of the total number of business entities of manufacturing industry in Croatia.
4. Methodology This research on dynamic capabilities in the textile, clothing and leather companies is part of a broader research of manufacturing industry. The study was conducted in the form of an online questionnaire. A questionnaire was sent to the companies from the Business Registry of the Croatian Chamber of Commerce. The list included all companies classified in the textile, 1234
clothing and leather industry. There were 455 registered companies with e-mail contact and e-mail was firwarded to all 455 e-mail adreses. The effective response rate to this survey was 10.11% (n=46). Table 5 presents the companies that participated in this research.
Table 5: Respondents according their business activity Business activity Frequency Textiles manufacturing 9 Apparel manufacturing 24 Leather and related products manufacturing 13 Total 46 Source: authors
% 19.57 52.17 28.26 100
We considered it satisfactory for the data processing program SmartPLS that is based on variance analysis for structural equation modelling (Hair et al., 2014). The important note is that this study is considered as an exploratory research and the positive impact of dynamic capabilities on companies’ performance and supply chains performance in the textile, clothing and leather industry is predicted. Therefore, the goal of this research is developing a theory that has not been developed in the textile, clothing and leather industry. Chosen statistical method is Partial Least Squares Structural Equation Modelling (PLS-SEM) analysed with SmartPLS (Ringle, Wende and Will, 2005). The decision was made based on the fact that the PLS-SEM overcomes the disadvantages of abnormalities of data and a much smaller sample and it is focused on the explanation of the variance – it clarifies the model by prediction (constructs prognosis). This program is appropriate when the goal of researchers is to develop a theory to predict the structural relationship (not just strictly confirm it), to identify the key drivers of the model and, in studies such as this, to ensure the enrichment of existing theory (Hair et al., 2009; Hair, Ringle and Sarstedt, 2011). Based on previous theoretical and empirical evidence, it is possible to set up three main hypotheses for two defined models. Model 1: H1. Dynamic capabilities positively impact competitive advantage. H1a. Sensing positively impacts companies’ competitive advantage. H1b. Seizing positively impacts companies’ competitive advantage. H1c. Reconfiguring positively impacts companies’ competitive advantage. H2. Dynamic capabilities positively impact business performance. H2a. Sensing positively impacts companies’ business performance. H2b. Seizing positively impacts companies’ business performance. H2c. Reconfiguring positively impacts companies’ business performance. Model 2: H3. Supply chain dynamic capabilities positively impact supply chain performance. H3a. Supply chain visibility positively impacts supply chain performance. H3b. Supply chain responsiveness positively impacts supply chain performance.
5. Construct operationalization 1235
Sensing, seizing and reconfiguring measurement scales were developed based on measurement scales from Wilden, R. et al. (2013) and are presented in Table 6.
Table 6: Sensing, seizing and reconfiguring indicators Sensing SEN1 In my organization people participate in professional association activities. SEN2 In my organization we use established processes to identify target market
segments. SEN2a In my organization we use established processes to identify changing customer needs. SEN3 In my organization we observe best practices in our sector and learn from them. SEN4 In my organization we gather economic information on our operations and operational environment. Seizing SEI1 In my organization we invest in finding solutions for our customers. SEI2 In my organization we adopt the best practices in our sector. SEI3 In my organization we change our practices when customer feedback gives us a reason to change. Reconfiguring Between 2010 and 2015 we carried out the following activities in our company: REC1 Implementation of new kinds of management methods. REC2 New or substantially changed marketing method or strategy. REC3 Substantial renewal of business processes.
REC4 New or substantially changed ways of achieving our targets and objectives. Source: authors according to Wilden, R. et al. (2013) Competitive advantage can be measured by subjective data and this research measures competitive advantage by questions, reflected by five financial indicators and non-financial indicators compared with competitors in the same industry (Li and Liu, 2014). Measurement scale was developed based on measurement scale from Li and Liu (2014) and is presented in Table 7. Table 7: Competitive advantage indicators Competitive advantage CA1 Compared with our competitors, we have higher profit growth rate. CA2 Compared with our competitors, we have higher sales revenue growth rate. CA3 Compared with our competitors, we have lower operating costs
. CA4 Compared with our competitors, we have better product and service quality. CA5 Compared with our competitors, we have increasingly higher market share. Source: Li and Liu (2014) Business performance can be measured by subjective assessment of managers and/or analysis of financial statements, such as balance sheet and income statement. Researches from Dess and Robinson (1984: 271), Fynes Voss and de Búrca (2005: 9) and Venkatraman and Ramanujam (1986) concluded that the expert assessment of managers on business results of the company does not deviate significantly from indicators drawn from the financial reports of companies. Business performance measurement scales were developed based on measurement scale from Krohmer, Homburg and Workman Jr. (2002), Lee and Choi (2003) 1236
and Kim (2006) and are presented in Table 8. Table 8: Business performance indicators Business performance BP1 Our company achieves the desired growth. BP2 Our company provides the desired market share. BP3 Our company make profit. BP4 Our company has a shorter response time needed for changes in product design. BP5 Our company has a shorter response time to changes in the volume of products. BP6 Our company processes orders within the foreseen deadlines. Source: authors according to Krohmer, Homburg and Workman Jr. (2002); Lee and Choi (2003); Kim (2006) Supply chain visibility measurement scales were developed based on measurement scale from Brandon-Jones, E. et al. (2014) and are presented in Table 9.
Table 9: Supply chain visibility indicators
Supply chain visibility SCV1 Inventory levels are visible throughout our supply chain. SCV2 Demand levels are visible throughout our supply chain Source: authors according to Brandon-Jones et al. (2014) Supply chain responsiveness measurement scales were developed based on measurement scale from Rajaguru and Jekanyika Matanda (2013) and are presented in Table 10.
Table 10: Supply chain responsiveness indicators Supply chain responsiveness SCR1 Our supply chain responds quickly to changing consumer needs. SCR2 Our supply chain responds effectively to changing consumer needs. SCR3 Our supply chain responds quickly to changing competitors' strategies. SCR4 Our supply chain develops new products quickly. SCR5 Our supply chain responds effectively to changing competitors' strategies. Source: authors according: Rajaguru and Jekanyika Matanda (2013) Supply chain performance measurement scales were developed based on measurement scale from Blome Schoenherr and Eckstein (2014) and are presented in Table 11.
Table 11: Supply chain performance indicators Supply chain performance SCF1 Our supply chain is able to short-term adjust supplier's order of goods and services according to the last information from the market. SCF2 Our supply chain adjusts deliveries to customer changes. SCF3 Our supply chain reduces manufacturing leadtime. 1237
Table 11: Supply chain performance indicators SCF4 Our supply chain reduces development cycle times. SCF5 Our supply chain adjusts manufacturing process capabilities. SCF6 Our supply chain increases production volume capacity. SCF7 Our supply chain increases frequencies of new product introductions. Source: authors according to Blome, Schoenherr and Eckstein (2014)
6. Analysis and results To test hypothesized relationships this study uses variance-based structural equations modeling (SEM) using SmartPLS software. Variance-based SEM was preferred over covariance-based SEM because of the small sample size, on the one hand, and the formative specification of predictor variables, on the other hand. 6.1. Assessments of model 1: Influence of dynamic capabilities on company performance An overview of measurement model assessments for model 1 is provided in Table 12. A graphical presentation of structural model results is provided by Figure 1.
Table 12: Overview of model 1 assessments Composite R2 Variable AVE Reliability DC_REC n.a. n.a. n.a. DC_SEI n.a. n.a. n.a. DC_SEN n.a. n.a. n.a. 0 PERF_BP 0.8176 0.4203 .4331 PERF_CA 0.7147 0.9084 0.3217 Source: authors
Cronbach Alpha n.a. n.a. n.a.
Communality Redundancy 0.5362 0.6270 0.4442
n.a. n.a. n.a.
0.7359
0.4331
0.0735
0.8629
0.7147
0.1140
1238
Figure 1: Influence of dynamic capabilities on competitive advantages and business performance. The composite reliability scores and Cronbach alpha coefficients indicate sufficient levels of internal consistency and reliability of the reflectively identified dependent variables. The Stone-Geisser’s Q2 statistic (Stone, 1974; Geisser, 1975; reported as cross-validated redundancy in SmartPLS) is above zero, thus indicating that endogenous variables are well explained by exogenous variables in the structural model (Chin, 1998; Hair et al., 2014). The cross-validated redundancy measure q2 is above 0.35 for each variable, which indicates strong predictive relevance of each effect (Tenenhaus et al., 2005). With regard to explanatory power, the independent variables explain 42.0% variance in the variable business performance (PERF_BP) and 32.2% in the variable competitive advantages (PERF_CA). Path coefficients with respective significance-levels (t-values) are presented in Table 13. With exception of DC_SEI, all predictors were found to have a statistically significant influence on business performance and competitive advantages. Surprisingly, DC_SEI was further found to have a significant negative influence on competitive advantage.
Table 13: Significance-levels for model 1 path coefficients Std. path Relationship Std. error T-statistic coefficient DC_REC -> PERF_BP 0.257 0.0868 2.9581 DC_REC -> PERF_CA 0.310 0.0964 3.2113 DC_SEI -> PERF_BP 0.074 0.0889 0.8291 DC_SEI -> PERF_CA -0.242 0.1147 2.1099 DC_SEN -> PERF_BP 0.453 0.0937 4.8356 DC_SEN -> PERF_CA 0.431 0.1218 3.5391 1239
Source: authors 6.2. Assessments of model 2: Influence of supply chain visibility and responsiveness on supply chain flexibility (performance) Results of the measurement model assessments for model 2 are provided in Table 14. A graphical presentation of structural model results is provided in Figure 2.
Table 14: Overview of model 2 assessments Composite AVE R2 Reliability DC_SCR n.a. n.a. n.a. DC_SCV n.a. n.a. n.a. SCPERF 0.6151 0.9170 0.6205 Source: authors
Cronbach Alpha n.a. n.a. 0.8926
Communality Redundancy 0.5566 0.8479 0.6151
n.a. n.a. 0.3648
Figure 2: Influence of supply chain visibility and responsiveness on supply chain flexibility (performance). The composite reliability score and Cronbach alpha indicate sufficient levels of internal consistency and reliability of the reflectively identified dependent variable. The StoneGeisser’s Q2 statistic (Stone, 1974; Geisser, 1975; reported as cross-validated redundancy in SmartPLS) is above zero, thus indicating predictive relevance independent variables in the structural model (Chin, 1998; Hair et al., 2014). The cross-validated redundancy measure q2 is above 0.35 for the dependent variable, which indicates strong predictive relevance of each effect (Tenenhaus et al., 2005). A look at the coefficient of determination further reveals that the two independent variables explain high 62.1% of variance in the dependent variable. A look at Table 15 further reveals that this effect can be attributed to the variable supply chain responsiveness (DC_SCR) which has a statistically significant path coefficient, while supply chain visibility (DC_SCV) emerges not to be a significant predictor of supply 1240
chain performance (SCPERF).
Table 15: Significance-levels for model 2 path coefficients. Relationship Std. path coefficient Std. error T-statistic DC_SCR -> SCPERF 0.834 0.0649 12.8411 DC_SCV -> SCPERF -0.080 0.0762 1.0554 Source: authors The results of the hypotheses are presented in Table 16.
Table 16: Results of hypotheses Hypothesis H1a. Sensing-> Competitive advantage H1b. Seizing-> Competitive advantage H1c. Reconfiguring-> Competitive advantage H2a. Sensing -> Business performance H2b. Seizing -> Business performance H2c. Reconfiguring -> Business performance H3a. Supply chain visibility -> Supply chain performance H3b. Supply chain responsiveness -> Supply chain performance Source: authors
Supported/not supported supported not supported supported supported supported supported not supported supported
7. Discussion and conclusion The hypotheses relating to DCs impact on competitive advantage was partially sup6orted, as shown in Table 15. This can be explained by referring to the results of additional hypotheses. Sensing and reconfiguring influence competitive advantage (H1a and H1c) and are supported by path coefficients (β = 0.431, β = 0.310). These positive relationships were significant (p
