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International Journal of Information Management 34 (2014) 369–380

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

Supply chain information capabilities and performance outcomes: An empirical study of Korean steel suppliers Sun Hee Youn a , Ma Ga (Mark) Yang b , Jin Hwan Kim c , Paul Hong d,∗ a

College Business Administration, Hanyang University, Seoul, South Korea Management Department, College of Business and Public Affairs, West Chester University, West Chester, PA 19383, USA Global Business School, Soon Chun Hyang University, Chungcheongnam-do, South Korea d Department of Information, Operations and Technology Management, College of Business and Innovation, The University of Toledo, Toledo, OH 43606, USA b c

a r t i c l e

i n f o

Article history: Available online 5 March 2014 Keywords: Supply chain information capabilities Inter-organizational information system capacity Inter-organizational relational competency Resource-based view (RBV) Korean steel companies Empirical study

a b s t r a c t This article discusses how supply chain information capabilities are instrumental to achieve performance outcomes. We identify critical components of supply chain information capabilities in terms of interorganizational information system capacity and inter-organizational relational competency. In view of the high degree of industry concentration in Korea, this article presents and tests a research model using a sample of Korean steel suppliers. Empirical tests are conducted using the structural equation modeling, PLS (partial least squares). The results of this study suggest that Korean manufacturing (e.g., automobile, shipbuilding, construction, and mobile industries) are heavily influenced by the competitiveness of the steel industry in terms of supply chain information capabilities and performance outcomes—supply chain-level (i.e., supply chain flexibility) and firm-level performance outcomes (i.e., customer responsiveness and cost reductions). Future research may extend the findings of this study in other country contexts to accomplish both customer responsiveness and cost reductions through supply chain information capabilities. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction In the past decade, the roles of inter-organizational systems (IOS) in the supply chain have long been advocated among scholars as a means to bring operational, financial, and relational benefits (Gunasekaran & Ngai, 2004; Rai, Patnayakuni, & Patnayakuni, 2006; Chen, Yang, & Li, 2007). However, it has been argued that particular information technology (IT) alone cannot be a source of sustainable competitive advantage for the firm (Powell & Dent-Micallef, 1997; Byrd & Davidson, 2003). Current information system (IS) literature suggests that firms need to develop inter-organizational capabilities that integrate a firm with its supply chain partners to create and deliver value for the firm (Ho, Au, & Newton, 2002; Rai et al., 2006). Such capabilities include a dyadic buyer-seller relationship (Wilson & Vlosky, 1998), high level of inter-organizational knowledge (Scott, 2000), and relational capability (e.g., trust and power) of a focal firm in networking with its supply chain partners (Allen, Colligan, Finnie, & Kern, 2000). In view of this, supply chain information capabilities have

∗ Corresponding author. Tel.: +1 419 530 2054; fax: +1 419 530 2290. E-mail addresses: [email protected] (S.H. Youn), [email protected] (M.G. Yang), [email protected] (J.H. Kim), [email protected], [email protected] (P. Hong). 0268-4012/$ – see front matter © 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijinfomgt.2014.01.008

been suggested as a source of competitive advantage for the firm (Sambamurthy, Bharadwaj, & Grover, 2003; Park, Fujimoto, & Hong, 2012). However, much still remains unclear about how firms’ information capabilities interact with supply chain partners in ways that enhance the overall effectiveness of the firm and its supply chain. Empirical studies that examine information capabilities in the context of the supply chain are insufficient (Li & Lin, 2006). In order to fill this critical research need, the aim of this study is to examine how supply chain information capabilities can create a sustainable competitive advantage for the firm in the context of Korean steel industry. Over the years, the Korean steel industry has been the backbone of Korean manufacturing industries (Huh, 2011). Steel-consuming industries like automobile, shipbuilding, construction, and mobile phones were all heavily influenced by the performance excellence of the steel industry. Studies show that these industries are highly correlated to the competiveness of Korean manufacturing and its economic growth (Huh, 2011; Lee, 2011). Particularly, POSCO (Pohang Iron and Steel Company), one of the Korean global steel giants, has vigorously applied information sharing practices within an organization as well as in the inter-organizational context through developing their supply chain information capabilities. It was possible through their integrated inter-organizational information system, which enabled

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POSCO to share critical and sensitive information with their major suppliers and, therefore, enhanced mutual trust and information quality among them. Such endeavors were instrumental to increase both firm-level and supply chain-level performance outcomes. POSCO implemented POSPIA (POSCO + Utopia) as an integrated enterprise resource planning (ERP) system (Lee & Lee, 2009). The POSPIA system has been instrumental in equipping various functional work units to achieve timely quality decision-making requirements. POSCO’s information integration with various plants and their large domestic and global customers is an example of an enhanced inter-organizational information system. Thus, POSCO could better collaborate with its major suppliers and customers through such intra- and interorganizational information sharing activities (Youn, Yang, & Hong, 2012). In view of the complexity involved with Korean suppliers in supply chain contexts, a theme study was undertaken through a series of parallel steps: (1) the first conceptual and case study paper examined green aspects of Korean steel industries (Youn, Yang, & Roh, 2012); (2) the second paper examined the role of integrative leadership in supply chain context (Youn, Yang, & Hong, 2012); (3) the third paper examined green practices in the broader context (Youn, Yang, Hong, & Park, 2013). In comparison, this paper focuses on how inter-organizational system capacity and relational competency interacts in ways that make a difference in the quality of information flows that, in turn, impact complex supply chain performance outcomes for competitive advantage. In particular, this paper offers a new perspective in conceptualizing two dimensions of supply chain information capabilities: (1) inter-organizational information system capacity (IOISC) and (2) inter-organizational relational competency (IORC). This research framework and the empirical results highlight the role of supply chain information capabilities. Specifically, the following research questions are examined in this article: (1) What are the critical dimensions of supply chain information capabilities? and (2) How do firms utilize supply chain information capabilities for their competitive advantage? Empirical tests with a sample of 74 Korean steel suppliers were conducted using both perceptual measures (i.e., supply chain flexibility and customer responsiveness) and actual financial data (i.e., cost of goods sold). This article is organized as follows: (1) theoretical pespective, (2) literature review, (3) research methods, (4) research results, and (5) implications.

2. Theoretical perspective: the resource-based view of the firm (RBV) This study provides an integrative research model that examines supply chain information capabilities (i.e., inter-organizational information system capacity and inter-organizational relational competency) and competitive advantage for the firm (i.e., supply chain flexibility, customer responsiveness and cost of goods sold) based on the underpinning lens of RBV. Traditionally, RBV posits that a firm’s internal resources can be the primary predictors of sustained competitive advantage for the firm (Corbett & Claridge, 2002). A firm creates value by combining heterogeneous and immobile resources that are valuable, rare, and difficult to imitate and have few substitutes across firms (Barney, 1991). A firm’s capabilities are the unique resources that enable the firm to conceive of and implement value-creating strategies, which improve its efficiency and effectiveness. Extending RBV, recent literature suggests that supply chain capabilities, such as

network/relational capabilities, can be a source of a firm’s competitive advantage (Capaldo, 2007). Capability complementarity exists where resource value is enhanced when a resource generates superior outcomes in the presence of another resource than by itself (Milgrom & Roberts, 1995). In the area of information systems, information systems are regarded as complementary resources that enhance the value of other organizational resources and capabilities (Tippins & Sohi, 2003). Adopting the capability complementarity of RBV, this study argues that inter-organizational information system capacity (IOISC) needs to be complemented with inter-organizational relational competency (IORC). IOISC is the prerequisite for facilitating information sharing among supply chain partners. This IOISC, when established, positively leads to enhanced IORC. Combining IOISC with IORC, firms develop supply chain information capabilities, which bring the competitive advantage for the firm (Wu, Yeniyurt, Kim, & Cavusgil, 2006). Fig. 1 explains the three essential elements of an interorganizational information system mechanism. The first one is a strategic planning capacity that is supported by a not easily imitable system structure and integrative leadership and, unique organizational resources. Strategic planning capacity involves the systematic arrangement of organizational resources toward proactive organizational planning in the context of dynamic market reality. Strategic (organizational wide goal setting) capacity is supported by the resource-based view of the firm. Organizational goal theory also suggests that leadership role and system structure are the basis for goal formulation and implementation. Operational process integrity requires relational trust and information reliability in an information intensive work environment (Youn et al., 2013). It is the people that implement plans and systems for them to function. Thus, relational trust on system structure and the intent of leadership and information quality in terms of content and manner of information exchange is crucial for effective decision making at the work level (Youn, Yang, & Hong, 2012). Information processing theory suggests that the real work is through sharing relevant information in a timely and reliable manner among key stakeholders (Daft & Lengel, 1986; Hong, Doll, & Nahm, 2004). Socio-technical system theory indicates that sound process design requires both highly relational stability (trust factor) and technical accuracy (information quality) (Liu, Shah, & Schroeder, 2006). Socio-technical system theory argues that every organization consists of the technical system (i.e., the tools, techniques, devices, artifacts, methods, configurations, procedures, and knowledge used by organizational members to acquire inputs, transform inputs into outputs, and provide outputs or services to clients or customers) and the social system (i.e., the people who work in the organization and all that is human about their presence) and takes into account how people feel and respond and their social interactions with one another (Pasmore, 1988). Synergistic performance outcomes are the results of both strategic and operational practices (Park et al., 2012; Hong, Doll, Revilla, & Nahm, 2011; Roh, Min, & Hong, 2011). As customer requirements are multiple, it is important for firms to define competitive capabilities in terms of what the organizational system can produce as measured by operational results. All the system efforts are ultimately directed toward efficiency (cost competitiveness). The products and services produced should serve the customer at the right time (responsiveness). Since dynamic market requirements are subject to change, the level of adaptability of products and services in changing contexts (flexibility) is essential. Although other performance outcomes are also important, these three are frequently mentioned as key competitive capabilities at the operational level.


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Fig. 1. Inter-organizational information system mechanism.

3. Literature review and hypotheses development 3.1. Supply chain information capabilities The review of the literature suggests that supply chain capabilities are a key for competitive advantage for a firm (Rai et al., 2006). These capabilities are defined by Wu et al. as “the ability of an organization to identify, utilize, and assimilate both internal and external resources/information to facilitate the entire supply chain activities” (pp. 494–495). Wu et al. (2006) provide an integrative view of supply chain capabilities by encompassing four dimensions, such as information exchange, coordination, inter-firm activity integration, and supply chain responsiveness. Particularly, they examine the impact of an inter-organizational system (e.g., supply chain communication system) on a firm’s supply chain process, arguing that IT facilitates the development of supply chain capabilities, which are highly firm-specific and difficult to copy across organizations. In this study, supply chain information capabilities are conceptualized in such a way that they include two important aspects of information sharing practices: (1) IOISC, which is the cornerstone for facilitating information sharing among supply chain partners; and (2) IORC, which is the outcome of information sharing in the supply chain. Socio-Technical System (STS) theory supports this conceptualization. STS theory argues that every organization and its supply chains have both people (the social system) and technical (the technical system) elements (Liu et al., 2006). According to Pasmore (1988), the extent of the fitness of both the social and technical systems will govern the effectiveness of an organization and supply chains. We argue that IOISC serves the technical element of the supply chain of a focal, firm while IORC functions as the social system. Following the logic of STS and building on the definition of Wu et al. (2006), we define supply chain information capabilities as: the ability of a focal company to identify, utilize, and assimilate both internal and external information to facilitate the information sharing activities among supply chain partners and to develop inter-organizational relational competency. 3.1.1. Inter-organizational information system capacity (IOISC) An inter-organizational information system capacity (IOISC) is “an automated information system shared by two or more companies. An IOISC is built around information technology (i.e., computer and communication technology), which facilitates the creation, storage, transformation, and transmission of information (Johnston & Vitale, 1988, p. 154).” Traditionally, scholars have examined multiple aspects of IOS in the supply chain including strategic planning

of IT, e-commerce, infrastructure for IT, knowledge and IT management, and implementation of IT (Gunasekaran & Ngai, 2004). Among these, the core aspects of IOISC have been identified as: (1) strategic dimensions (IS strategic planning, the alignment of IS, and overall business strategies and strategic priorities of IS) (Cerpa & Verner, 1998) and (2) organizational IS leadership/personnel dimensions (CIO and CEO leadership, and IT team personnel) (Karahanna & Watson, 2006; Youn, Yang, & Roh, 2012). 3.1.1.1. Information system strategic planning capacity. The strategic dimensions of IOISC in the firm have been emphasized as businesses increasingly rely on IS both operationally and strategically (Karahanna & Watson, 2006). For example, IT-enabled businesses (e.g., EDI, E-commerce) have become more prevalent across all organizational functions. Equipping capacity for effective strategic-level IS planning, therefore, is essential for successful supply chain operations (Gunasekaran & Ngai, 2004). Strategic-level IS planning capacity involves designing a supply chain network and maintaining relationships between supply chain members. Strategic planning of IS also needs to support the overall objectives and goals of SCM, by which firms are able to be responsive to volatile market requirements (Gunasekaran & Ngai, 2004). For this, strategic alignment between IS and business strategies requires close interactions between the CIO and the CEO of the firm (Karahanna & Watson, 2006; Youn, Yang, & Roh, 2012). Firms also need to prioritize the strategic planning of IS, since the adoption of inter-organizational systems such as EDI and ERP will bring organizational changes. The overall role of IOS strategic planning is to facilitate the level of information sharing among supply chain partners (Fawcett, Osterhaus, Magnan, Brau, & McCarter, 2007). 3.1.1.2. Information system leadership/personnel capacity. Another dimension of IOS is organization-wide IS leadership/personnel (Karahanna & Watson, 2006). IS leadership involves the CIO’s leadership that understands both technical and business functions across the supply chain (Armstrong & Sambamurthy, 1999). It is critical for the CIO to have a holistic view of an organization, as IS permeates all business functions (Karahanna & Watson, 2006). The role of the CEO’s commitment to IT implementation is also examined in the IS literature (Powell & Dent-Micallef, 1997; Youn, Yang, & Hong, 2012). Such CEOs are willing to integrate IT with business-level strategy and processes (Roepke, Agarwal, & Ferratt, 2000). Also, IT personnel includes the educational level of IT team members, effectiveness of IT team composition, and their business competence (Powell & Dent-Micallef, 1997; Bassellier & Benbasat, 2004). These IT personnel components are of vital importance for

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firms, as they engage in high level information sharing in the supply chain. 3.1.2. Inter-organizational relational competency (IORC) In the context of a supply chain where firms are interconnected with a network of suppliers, strategic collaboration among supply chain partners emerges as an effective tool to bring competitive advantage (Dyer & Singh, 1998). Paulraj, Lado, and Chen (2008) contend that inter-organizational communication is one of the relational competencies that bring strategic advantages for supply chain partners. In this study, IORC is defined as “a firm’s coordinative ability to achieve interactive sharing outcomes among supply chain partners”. This IORC consists of mutual trust and information quality. Mutual trust is required as a relationship quality, by which information quality (i.e., interaction outcome), is achieved (Salaün & Flores, 2001; Hong & Cho, 2011). 3.1.2.1. Mutual trust. Mutual trust is an important relationship asset by which partnering firms willingly engage in information acquisition and sharing practices. In the context of inter-firm relationships, mutual trust is defined as a firm’s expectations regarding the other firm’s responsibilities and behaviors or moral values under the possibility for opportunistic behaviors (Zaheer, McEvily, & Perrone, 1998). In other words, trust is the belief that the other partner will behave based on shared agreement (Currall & Inkpen, 2002; Chai & Kim, 2010). When trust exists between partnering organizations, it will mitigate the opportunisms that may possibly hinder information sharing and thus increase negotiating and monitoring costs (Youn, Hwang, & Yang, 2012). As such, mutual trust serves as a governance mechanism that alleviates opportunistic actions and thus upholds voluntary exchanges. 3.1.2.2. Information quality. Information quality is achieved when a focal firm and its supply chain partners share strategic and operational information in trust relationships. Supply chain performance is dependent on the quality of information shared among partnering firms. Three dimensions of the quality of information sharing are: accuracy, trustworthiness (reliability or credibility), and timeliness (Li & Lin, 2006; Zhou & Benton, 2007). Security is included as one of the essential attributes of information quality (Lee, Strong, Kahn, & Wang, 2002). Thus, this study includes four characteristics of information quality: accuracy, trustworthiness, timeliness, and security of information between a focal firm and its main supply chain partners. 3.2. Competitive advantage In this study, a firm’s competitive advantage includes three performance outcomes. The first one is supply chain flexibility, which is an intermediate inter-organizational performance outcome (Gunasekaran, Patel, & Tirtiroglu, 2001). The other two firm-level operational outcomes are customer responsiveness and cost of goods sold (COGS) (Poston & Grabski, 2001; Reichhart & Holweg, 2007). 3.2.1. Supply chain flexibility With the wide-spread quality implementation, product quality is regarded as an order qualifier in that meeting quality specifications is a necessary condition for a supplier to be considered as viable (Berry, Hill, & Klompmaker, 1995). Increasingly, time-based competition requires the ability to alter volume requirements and offer a variety of products (i.e., flexibility in volume and variety) in response to rapidly changing customer requirements (Liao, Hong, & Rao, 2010). Such flexible changes are attainable only through timely support and effective collaboration of suppliers. Thus, supply chain flexibility is a firm’s capability to effectively adjust and respond to

changes in market realities. This study adopts four dimensions of supply chain flexibility, which include product variety and volume flexibility, new product flexibility, and responsiveness flexibility (Vickery, Calantone, & Droge, 1999; Fantazy, Kumar, & Kumar, 2009). 3.2.2. Firm-level performance 3.2.2.1. Customer responsiveness. Customer responsiveness is the ability of a focal firm to respond to customers’ needs in a timely manner. Customer responsiveness has been identified as one of the most critical objectives of supply chain integration (Narasimhan & Jayaram, 1998). Customer responsiveness is a key dimension of time-based performance. The literature recognizes quick order fulfillments and timely management of customer complaints as two key indicators of customer responsiveness (Stalk & Hout, 1990). 3.2.2.2. Cost of goods sold (COGS). COGS is an important financial performance measure (Poston & Grabski, 2001). The cost of goods of finished products depends on inventory and business processing costs. For the purpose of this study, the actual COGS is measured in terms of the actual improvement rate, not merely the total amount of COGS. Specifically, COGS is measured with the threeyear quarterly averages of COGS (12 periods) of all the participating firms and is calculated by the improvement rate over 12 quarterly periods. Appendix A (Table A.1) shows the calculation details of COGS improvement. 3.3. IOISC to IORC (Hypotheses 1 and 2) Socio-technical system theory argues that firms’ objectives are best met by the joint optimization of the socio and the technical systems (Liu et al., 2006). IOISC is regarded as the technical system of the firm that facilitates information sharing activities. This technical system is the basic foundation that needs to be complemented with the socio system of the firm, which is IORC (mutual trust and information quality) in this article. Thus, IOISC is critical for enhancing IORC because the technical system capacity provides a reasonable assurance of overall inter-organizational information based on mutual trust (Liu, Zhang, & Hu, 2005). At the same time, this system capacity needs to be assured by an interactive information outcome (e.g., information quality) for further improvement (Karahanna & Watson, 2006). Thus, the following hypotheses are suggested: H1.

IOISC is positively related to mutual trust among firms.

H2. IOISC is positively related to information quality among firms. 3.4. Mutual trust to information quality (Hypothesis 3) Mutual trust is a vital relational asset that brings quality of information shared among supply chain partners. Mutual trust mitigates the information asymmetry between trading partners through facilitating information sharing activities (Zaheer et al., 1998; McEvily & Marcus, 2005). When supply chain partners operate in a trust relationship, they are likely to share relevant and timely information. Without trust during the collaborative process, information shared between the partners is likely to be inaccurate, resulting in poor quality of information. On the other hand, having mutual trust facilitates the quality of information in terms of accuracy, timeliness, and openness among partners in the supply chain. Thus, the following hypothesis is suggested: H3.

Mutual trust is positively related to Information quality.


3.5. IORC (mutual trust and information quality) to supply chain flexibility (Hypotheses 4 and 5)

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4. Research methods 4.1. Survey administration and data collection

IORC, characterized as the combination of mutual trust and information quality, is an essential element for supply chain outcomes such as supply chain flexibility (Vickery et al., 1999). Supply chain successes of Wal-Mart (e.g., Vendor Managed Inventory) and Dell (e.g., build-to-order strategy) heavily rely on the trust-based information sharing with their suppliers and customers, achieving supply chain flexibility (Fawcett et al., 2007). Japanese manufacturers also utilize a market flexible customizing system through highly reliable supplier–manufacturer relationship and high quality information sharing in their supply chains (Tomino, Park, Hong, & James, 2009). In sum, firms that exchange quality information with supply chain partners will have a better chance to make its supply chain more flexible. Thus, the following hypotheses are suggested: H4.

Mutual trust is positively related to supply chain flexibility.

H5. Information quality is positively related to supply chain flexibility.

3.6. Supply chain flexibility to firm-level performance (customer responsiveness and COGS) (Hypotheses 6 and 7) As firms interact and operate in the context of a supply chain, their flexibility is measured in terms of the extent to which they collaborate through informational and knowledge resources to fulfill customer requirements and individual firm’s performance standards (Fantazy et al., 2009). Therefore, supply chain flexibility requires IOISC and enhances customer requirements and actual financial outcomes. Narasimhan and Jayaram (1998) argue that supply chain integration impacts customer responsiveness. Supply chain flexibility (supported by information quality about customer and demand changes) allows firms to have more confidence in the just-in-time aspects of inventory and manufacturing process management (Holweg & Pil, 2001). Since fulfilling customer orders requires suppliers to be able to provide necessary component parts, supply chain flexibility is critical for customer responsiveness (Reichhart & Holweg, 2007). Thus, the following hypothesis is suggested: H6. Supply chain flexibility is positively related to customer responsiveness. With enhanced supply chain flexibility, focal firms are able to provide better customer responsiveness and thus inventory volumes are reduced, improving the cost structure of the firm (Reichhart & Holweg, 2007). Specifically, supply chain flexibility in the form of timely processing of customer requirements, efficient inventory management, and processing capability may substantially reduce the cost of goods sold than for firms that have little supply chain flexibility (Reichhart & Holweg, 2007). Thus, the following hypothesis is suggested: H7. Supply chain flexibility is negatively related to cost of goods sold. Fig. 2 summarizes the above mentioned seven hypotheses that explain the inter-relationships between variables: (1) interorganizational information system with (i) mutual trust (H1), (ii) Information quality (H2); (2) Mutual trust with (i) information quality (H3), (ii) supply chain flexibility (H4); (3) information quality with supply chain flexibility (H5); (4) supply chain flexibility with (i) customer responsiveness (H6), (ii) cost of goods sold (H7).

The respondents of the survey were steel suppliers in South Korea (hereafter, “Korea”) from two Korean primary stock markets, KOSPI (Korean Composite Stock Price Index) and KOSDAQ (Korea Securities Dealers Automated Quotation). The criteria for choosing the suppliers in the Korean steel industry were based on: (1) public financial data availability, and (2) adequate firm size requirements that ensure supply chain information capabilities. As of 2006, there were 130 publicly traded steel companies in Korea. In initial contacts, the purpose of the research was explained to managers from all listed companies. To those who showed interest to participate in this research, a survey questionnaire was sent via fax, email, and postal mail according to their preferences. Most of the respondents were managers in a purchasing and manufacturing department. Out of 130, 81 managers responded to the survey, resulting in the response rate of 62.3%. Seven of the survey responses were incomplete, and thus, 74 were used for the test and analysis representing an actual response rate of 56.9% (calculated as [81 − 7]/130). According to Flynn, Sakakibara, Schroeder, Bates, and Flynn (1990), response rates of 50%–60% may support generalization. Particularly, 58 small- and medium-sized enterprises (SMEs)(500) (21.6%) participated in this study. Among them, 35 companies (47.3%) were in KOSPI and 39 companies belonged to KOSDAQ (52.7%). Industries are from the basic metals (37; 50%), the fabricated metal products except machinery and equipment (19; 25.7%), and motor vehicles, trailers and semi-trailers (18; 24.3%). The average tenure of respondents was 9.55 years (standard deviation = 6.73), evidencing the credibility of respondents’ knowledge. Table 1 shows the summary of sample characteristics. 4.2. Non-response bias The primary concern that is typical of the survey methodology is that information collected from respondents might cause a nonresponse bias. Non-respondents change the sample frame and can lead to a sample that does not represent the population (Forza, 2002). In that regard, non-respondents can limit the generalizability of results. Thus, non-response bias testing is an important step before the sample is generalized to the population (Armstrong & Overton, 1977). This research did not investigate non-response bias directly, because it had limited access to any information regarding the organizational details except name, phone, and addresses of the individuals. According to Armstrong and Overton (1977), it is assumed that the late return of surveys represents the opinion of nonrespondents. Following this assumption, this study compared those who responded early (e.g., those who responded after the initial emails) with those who responded late (e.g., those who responded to the follow-up emails). After dividing 74 surveys into two groups (37 for the early response and 37 for the late response), half of the total variables (11 out of 22) were randomly selected and independent t-tests between two groups were conducted. The results showed that there are no statistically significant differences among those variables, indicating that non-response may not be a major concern for this research. 4.3. Common method bias (CMB) CMB occurs because all data are self-reported and collected through the same questionnaire during the same period of time with cross-sectional research design. CMB refers to variance that is attributed to the measurement method rather than the constructs

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Fig. 2. Research model.

of interest and it may cause systematic measurement error and further bias the estimates of the true relationship among theoretical constructs (Podsakoff et al., 2003). In other words, the data collection method is susceptible to CMB when the researcher seeks responses from a single respondent per firm. To test the hypothesis that a single factor accounts for all the variance in the data, Harman’s single factor test using confirmatory factor analysis (CFA) is conducted (Podsakoff, MacKenzi, Lee, & Podsakoff, 2003). If CMB is largely responsible for the relationship among the variables, the single-factor CFA model should fit the data well (Posdakoff et al., 2003). The model fit indicates that a single factor model does not represent the data well (2 /d.f. = 3.002, GFI = 0.518, CFI = 0.452, IFI = 0.466, NFI = 0.368, RMSEA = 0.166, SRMR = 0.145). Furthermore, the AVE by a single factor is 27%, indicating that a lesser than greater proportion of the variance in the data is accounted for by a single factor. Although the results of these analyses do not preclude the possibility of CMB, they do suggest that CMB is not of great concern and, thus, is likely to confound the interpretations of results. 4.4. Measures The initial items for the survey questionnaire were made through comprehensive literature review. Then, items for six variables were adopted and revised from the existing literature to

ensure face and content validity (Nunnally, 1978). Since the literature is mostly based on non-Korean contexts the interviews focused on whether or not these variables are relevant for the Korean steel industry context. To ensure the relevance and clarity of the research instrument, authors consulted with several practitioners who have worked more than three years in the area of supply chain management. The interviews focused on the relevance of these variables in terms of (1) whether inter-organizational information system capacity (e.g., IS strategic planning capacity, IS leadership/personnel capacity) is applied; (2) other relational variables (i.e., mutual trust and information quality) matter; (3) use of performance measures (i.e., supply chain flexibility and customer responsiveness); and (4) most important of all, COGS is the critical cost measure for the steel industry. After translating the original questionnaires into Korean, minor modifications were made in order to improve the acceptance and understanding of survey to respondents in Korea. All questionnaires were based on a five-point Likert scale (e.g., 1: not at all, 3: moderate, 5: very much) (Table 2). Based on the literature of information technology, information systems, and supply chain management, an item pool was developed to measure IS leadership/personnel capacity (Powell and Dent-Micallef, 1997; Karahanna & Watson, 2006), IS strategic planning capacity (Gunasekaran & Ngai, 2004; Karahanna & Watson, 2006), mutual trust (Johnston, McCutcheon, Stuart, & Kerwood, 2004), and information quality (Lee et al., 2002; Li & Lin, 2006; Zhou

Table 1 Respondent profile. Category

n

Size (n = number of employees)

Large (n > 500) Small- and medium-sized (n < 500)

16 58

21.6 78.4

Publicly-traded firms by two major Korean stock markets.

KOSPI KOSDAQ

35 39

47.3 52.7

Industry

Basic metals Fabricated metal products, except machinery and equipment Motor vehicles, trailers and semi-trailers

37 19 18

50.0 25.7 24.3

Years of experience

More than 10 years 5–10 years 3–5 years Less than 3 years No response

32 14 6 17 5

43.2 18.9 8.1 22.9 6.8

74

100.00

Total

%


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Table 2 Summary of measurement scales. Coding

Construct items/scales

Mean

S.D.

Loadinga

CRb

Please indicate the extent of your company’s inter-organizational information system capacity that facilitates information sharing among your major supply chain partners [1: not at all, 3: moderate, 5: very much] Inter-organizational information system capacity (Adapted from Powell & Dent-Micallef, 1997; Karahanna & Watson, 2006) 0.890 IS strategic planning capacity (ISSPC) (˛ = 0.867) Integration of strategic planning and business 3.270 0.799 0.798 ISSPC 1 strategy 3.108 0.820 0.849 Appropriateness of setting priorities in ISSPC 2 strategic planning ISSPC 3 Appropriateness of planning information 3.108 0.769 0.870 resources Fulfilling system development time 3.095 0.995 0.753 ISSPC 4 requirement 0.901 IS leadership/personnel capacity (ISLPC) (˛ = 0.883) ISPC 1 CIO’s leadership 3.581 1.147 0.751 CEO’s commitment toward IT implementation 3.500 0.895 0.875 ISPC 2 Educational level of IT team members 3.351 0.943 0.826 ISPC 3 Effectiveness of IT team composition 3.770 1.080 0.881 ISPC 4 Inter-organizational relational competency Please indicate the level of mutual trust among your major supply chain partners [1: not at all, 3: moderate, 5: very much] 0.831 Mutual trust (MT) (Adapted from Johnston et al., 2004) (˛ = 0.702) 3.081 MT 1 Our firm has strong confidence that our 0.888 0.823 suppliers will provide the best advices in regard to our businesses for our sake. (Johnston et al., 2004) Our firm is able to provide a sincere aid to our 3.446 0.846 0.839 MT 2 suppliers. MT 3 Our suppliers keep their words to our firm. 3.432 0.795 0.678 Please indicate the level of information quality between your firm and your major supply chain partners [1: not at all, 3: moderate, 5: very much] Information quality (IQ) (Adapted from Lee 0.926 et al., 2002; Li & Lin, 2006; Zhou & Benton, 2007) (˛ = 0.892) IQ 1 Information accuracy 3.568 0.829 0.917 Information security 3.392 0.809 0.817 IQ 2 3.622 Information reliability (dependability, 0.806 0.931 IQ 3 trustworthiness) Information timeliness (relevance, recency) 3.216 0.815 0.811 IQ 4 Please indicate the extent of supply chain flexibility [1: not at all, 3: moderate, 5: very much] 0.890 Supply chain flexibility (SCF) (Adapted from Vickery et al., 1999) (˛ = 0.834) 3.811 SCF 1 Our supply chain is able to produce variety of 0.961 0.684 products in terms of options and size. Our supply chain is able to produce sufficient 0.871 0.862 SCF 2 3.811 volume of products according to customer orders. 3.365 Our supply chain is able to handle change 0.915 0.895 SCF 3 requirements of products in time. 3.068 SCF 4 Our supply chain is able to introduce quickly 1.011 0.818 new products to the market. Please indicate the extent of customer responsiveness of your firm [1: not at all, 3: moderate, 5: very much] 0.914 Customer responsiveness (CR) (Adapted from Jayaram, Vickery, & Droge, 1999) (˛ = 0.859) 3.878 CR 1 Our company is able to fulfill customer orders 0.960 0.856 on time 3.622 Our company is capable to maintain short 1.016 0.863 CR 2 customer order cycle time. 3.716 CR 3 Our company is willing to respond to customer 0.958 0.932 requests fast. Cost of goods sold (COGS)c 1.000 a b c

AVE

0.670

0.697

0.623

0.758

0.670

0.782

1.000

All item loadings are statistically significant at p < 0.001. CR = composite reliability. Not perceptual measure; it is an actual secondary data (refer to Table A.1).

& Benton, 2007). Deriving from the RBV literature (Wu et al., 2006), these constructs are conceptualized as supply chain information capabilities, which generate the competitive advantage for the firm. The instrument for measuring supply chain flexibility was derived from the research on supply chain performance literature (Vickery et al., 1999). To measure customer responsiveness and COGS, literature on firm performance was adopted (Narasimhan & Jayaram, 1998). Finally we also include firm size (total assets) as a control variable in our analysis.

4.5. Data analysis The hypotheses were tested by using partial least square (PLS), a structural equation modeling technique. The SmartPLS package version 2.0.M3 was used. PLS is useful when the constraints of data gathering at the firm level is such that securing a large sample is realistically not possible (Chin, 1998). The literature suggests that the “10 times” rule of thumb is the commonly used method for determining the minimum sample size requirement in PLS (Chin

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(H1, H2, H3, H5, H6, and H7) were supported at p < 0.01, except for H4. The indices for explained variability (R2 ) (Fig. 2) and the Q2 test for predictive relevance (redundancy) (Table 3) are included. The endogenous variables achieved R2 values that are 0.645 for IS strategic planning capacity, 0.616 for IS leadership/personnel capacity, 0.152 for mutual trust, 0.265 for information quality, 0.251 for supply chain flexibility, 0.327 for customer responsiveness, and 0.030 for COGS. The Q2 test for predictive relevance (redundancy) measures the quality of the structural model (Tenenhaus, Vinzi, Chatelin, & Lauro, 2005). This test measures how well observed values are reproduced by the model and its parameter estimates (Chin, 1998). A positive value of Q2 implies that the model has predictive relevance, whereas a negative value of Q2 suggests that the model is lacking predictive relevance (Real, Leal, & Roldán, 2006). The range for each construct is from 0.117 to 0.582, which are all positive values, indicating good predictive relevance for the structural model (Fig. 3). The first two hypotheses (H1 and H2) posit that IOISC is positively related to IORC. These hypotheses support the socio-technical system perspective, arguing that to be effective organizations, the technical system (i.e., IOISC) needs to be integrated with the socio system (i.e., IORC). Specifically, H1 assumes a positive relationship between IOISC and mutual trust, while H2 is positively related to IOISC and information quality. The test results show that firms with higher IOISC have greater trust. Based on their capacities for IS strategic planning and IS leadership/personnel, firms build mutual trust among supply chain partners, by which they can satisfy their intra- and inter-organizational information needs. As for H2, as firms clearly establish IOISC, then the information quality (i.e., the level of accuracy and reliability of key information for supply chain decisions) is ensured. A high level of IOISC allows people to have more confidence about the information they receive and, thus, participants tend to use the information for their decision-making practices. These empirical results are similar to the findings of other researchers (Li & Lin, 2006) in the context of the supply chain. In sum, the results (H1 and H2) imply that integration of both strategic (i.e., IS strategic planning capacity) and operational (i.e., IS leadership and personnel capacity) levels are important for IORC (mutual trust and information quality). H3 is about IORC, testing the relationship between mutual trust and information quality. The data analysis suggests that a high level of mutual trust results in information quality, which is the interactive outcome. This enhanced information quality allows supply chain partners to engage in their practical knowledge work, problem solving, and specific operational decisions (Li & Lin, 2006). The next two hypotheses (H4 and H5) posit that both mutual trust (H4) and information quality (H5) are positively related

& Newsted, 1999; Peng & Lai, 2012). In other words, to determine the adequacy of a sample for PLS, researchers should consider two possibilities: (a) the construct with the largest number of formative measures (if the research model includes formative constructs) or (b) the endogenous construct with the largest number of exogenous constructs impacting on it. The sample size should be equal to or more than 10 times either (a) or (b), whichever is greater. The option (b) is employed to evaluate the adequacy of the sample size because the research model used in this study has no formative constructs. By looking at two endogenous constructs (customer responsiveness and cost of goods sold) with four exogenous constructs, the minimum sample size requirement would be 40 (10 × 4). For the present study, 74 respondents with a 56.9% response rate is, therefore, adequate for PLS. 5. Research results and discussion 5.1. Measurement validation The data are analyzed following Anderson and Gerbing’s (1988) recommended two-step approach: (1) measurement model and (2) structural model. In the first step of the analysis, the measurement model is tested and reliability, convergent validity, and discriminant validity are assessed. In order to assess the reliability of the constructs, the values of Cronbach’s ␣ and composite reliability were computed (Fornell & Larcker, 1981). All the values were above 0.70, indicating adequate internal consistency (Table 2). Convergent validity was assessed by examining the standardized factor loadings for each item on their respective constructs. The loadings presented in Table 2 suggest that all the items load significantly on their posited constructs, ranging from 0.684 to 0.932. Also, if the average variance extracted (AVE) for each construct is greater than 0.50, the convergent validity is established (Fornell & Larcker, 1981). The overall values were all well above the 0.5 suggested for each construct (Table 2). Discriminant validity was examined using the method suggested by Fornell and Larcker (1981). Table 3 shows that the square root of the AVE for each construct along the diagonals was larger than the correlation with other constructs (Fornell & Larcker, 1981; Chin, 1998). This suggests that all the constructs are evident in assessing discriminant validity. 5.2. Testing the structural model In the second step of the analysis, the structural model is evaluated (Fig. 2). The hypotheses were tested by assessing the direction, strength, and level of significance of the path coefficients estimated by PLS, using a bootstrap resampling method with 1000 resamples (Chin, 1998). Table 4 provides the summary of findings and indirect effects, examined by Sobel’s test. Overall, all six hypotheses

Table 3 Inter-construct correlations, discriminant, convergent validity, R2 and Q2 test (redundancy) (n = 74).

1. ISSPCa 2. ISLPC 3. MT 4. IQ 5. SCF 6. CUR 7. COGS a

1

2

0.819b 0.443*** 0.279** 0.362*** 0.138 0.142 −0.041

0.835 0.250** 0.379*** 0.323*** 0.320*** −0.100

3

0.789 0.436*** 0.272** 0.208* 0.059

4

0.871 0.499*** 0.320*** −0.024

5

0.819 0.571*** −0.172***

6

0.884 −0.015

7

Q2 c

1.000

0.559 0.582 0.117 0.121 0.167 0.254 0.129

ISSPC refers to information system strategic planning capacity, ISLPC refers to information system leadership/personnel capacity, MT refers to mutual trust, IQ refers to information quality, SCF refers to supply chain flexibility, CUR refers to customer responsiveness, and COGS refers to cost of goods sold. b Diagonal elements in bold: square root of AVE; off-diagonal elements: correlations between constructs. c Q2 test is to measure the quality of the structural model. A positive value of Q2 value implies that the model has predictive relevance (Real et al., 2006). * p < 0.10. ** p < 0.05. *** p < 0.01.


377

Table 4 Summary of findings.

H1(+) H2(+) H3(+) H4(+) H5(+) H6(+) H7(+)

Independent variable

Dependent variable

Path coefficient (t-statistics)

Significance

Sup?

IOISC IOISC MT MT IQ SCF SCF

MT IQ IQ SCF SCF CUR COGS

0.390 (5.889) 0.309 (3.911) 0.309 (3.932) 0.066 (0.758) 0.470 (6.778) 0.572 (9.868) −0.172 (3.123)

p < 0.01 p < 0.01 p < 0.01 p > 0.1 p < 0.01 p < 0.01 p < 0.01

Yes Yes Yes No Yes Yes Yes

Latent variable

Linkages

Path to

Indirect effects and t-statistics (Sobel’s testa ) n = 74 MT IOISC IQ SCF

IQ

IQ

SCF

CUR

0.120 (3.270)*** –

– 0.145 (3.911)***

– –

–

–

0.268 (5.587)***

COGS – – −0.081 (2.836)***

a Sobel’s test is used in testing the statistical significance of indirect relationship between an independent construct and a dependent through a mediator (Preacher & Leonardelli, 2001). The test generates t-statistics and p-values for the indirect path. *** p < 0.01.

Competitive Advantage

Supply Chain Information Capabilities IS Strategic Planning Capacity

R2= 0.645 0.803***

IS Leadership/ Personnel Capacity

R2= 0.616

Mutual Trust R2= 0.152

0.785***

0.066 0.364

0.390*** Inter-organizational Information System Capacity

0.572***

*

Supply Chain Flexibility

0.309***

R2= 0.251

0.309***

0.470*** -0.172 ***

Information Quality R2= 0.265 Inter-organizational Information System Capacity (IOISC)

Customer Responsiveness R2= 0.327

Inter-organizational Relational Competency (IORC)

Supply Chain Performance

Cost of Goods Sold R2= 0.030

Firm-level Performance

Fig. 3. PLS results.

to supply chain flexibility. The results suggest that information quality is directly related to supply chain flexibility (0.470, t = 6.778, p < 0.01), while mutual trust (0.066, t = 0.758, p > 0.1) is not. Mutual trust between a focal company and its suppliers might be an important basis of supply chain flexibility, but it does not necessarily result in actual supply chain flexibility. Instead, mutual trust needs to demonstrate its impact through information quality, which directly influences supply chain flexibility. In fact, information quality mediates the relationship between mutual trust and supply chain flexibility (see Sobel’s test in Table 4). The impact of supply chain flexibility is assumed to be positive on customer responsiveness (H6) and negative on COGS (H7). The statistical results suggest that supply chain flexibility is statistically significant to customer responsiveness (0.572, t = 9.868, p < 0.01) and cost reduction (−0.172, t = 3.123, p < 0.01). In addition, supply chain flexibility is an important variable in mediating the relationships between information quality and both customer responsiveness and COGS (Sobel’s test in Table 4). Finally, these findings indicate that with a higher level of supply chain flexibility, firms achieve two trade-off firm-level performance outcomes: customer responsiveness and COGS. Table 4 summarizes these findings.

5.3. Control variable We include firm size as a control variable in our analysis since firm size can affect a firm’s performance outcome. Instead of sales, total assets are used as a measure of firm size. Sales are often used as a firm size measure, but it does not provide a consistent indicator, while total assets provide a more stable indicator for our purposes (Jacobs, Singhal, & Subramanian, 2010). For that reason, we took the natural log of total assets and used it as a control variable. The results show that firm size has negative effects on performance outcomes such as supply chain flexibility (t = −0.234, p < 0.01), customer responsiveness (t = −0.140, p < 0.05), and COGS (t = −0.183, p < 0.1). 6. Limitations This study examined the supply chain information capabilities of Korean steel suppliers. The research model suggested in this study was tested based on the empirical results of 74 Korean steel suppliers. The fairly small sample size (n = 74) may be, lea limitation of the present study. However, this small sample size reflects the unique nature of Korean steel industry, which is highly focused, consisting of a small numbers of suppliers. The total numbers of

378


registered (thus publicly accessible) firms in the Korean steel industry is about 130. Out of 130, 81 firms responded to our survey, which is a 62.3% response rate. The sample size of 74 is also adequate for structural equation modeling with PLS according to the “10 times” rule of thumb. Future studies in other country contexts may pursue larger samples. In the context of SMEs, not all firms have fully operating IT human resource personnel and technological capacity. Their perspective of the entire supply chain, consequently, could be somewhat limited. In view of the exploratory nature of this research, in addition to our surveys, we have also had additional random follow-up interviews. The randomly selected respondents of the survey understood the strategic, operational, and relational nature of supply chain related information. In addition, they also understood their firm-specific responsibility in achieving their steel industry-level supply chain competence through their own information capabilities (Youn, Yang, & Roh, 2012, 2013). 7. Implications In spite of the above-mentioned limitations, the empirical results of this study can provide rich implications that are of interest beyond the Korean Steel Industry context. First, this study shows the positive influence of supply chain information capabilities on competitive advantage for the firm. Effective information processing is essential for supply chain flexibility (Liao et al., 2010) as well as product innovation success (Hong et al., 2011; Park et al., 2012). Firms need to be aware of the nature of complementary resources that lead to competitive advantages. For example, for an inter-organizational system to be fully functional, firms need to develop along both strategic (IS strategic planning) and human resource dimensions (IS leadership/personnel). These strategic and human resource dimensions of IS also need to be complemented with inter-organizational relational competence (IORC), which enables firms to support operational information practices. Firms utilize supply chain information capabilities, which are facilitated through the socio- (mutual trust and information quality) and the technical aspects of information system in the supply chain. Second, building supply chain flexibility requires both interorganizational information system capacity (IOISC) for indirect effects and inter-organizational relational competence (IORC) for direct effects. Mutual trust alone may not adequately impact supply chain performance outcomes. However, mutual trust via information quality does impact performance outcomes. This suggests that in the context of mutual trust, it is important for firms to sustain information quality so that information is effectively used for performance impact. Third, supply chain flexibility is an important interorganizational linkage performance outcome, from which ultimately firm-level performance (customer responsiveness and cost of goods sold) can be achieved. This finding suggests that the impact of supply chain flexibility on both customer responsiveness and cost of goods sold is significant. This shows how a particular industry enhances its competitive capabilities through strengthening its overall supply chain ability to respond to its external environment. The research results suggest that over the years these Korean steel suppliers have built up their supply chain information capabilities in keeping up with the global supply chain performance requirements of large Korean global firms. Such supply chain information capabilities, in turn, are critical for the overall competitiveness of the Korean steel industry. The implications of this study suggest how firms in other industries may accomplish both customer responsiveness and cost reductions through a widespread implementation of information capabilities and trust-based information sharing practices among supply chain partners. Future studies may examine other critical factors that influence supply

Table A.1 Calculation of cost of goods sold (COGS) improvement.

COGSit % =

COGSit −COGSmt Q

COGSit % = ratio of cost of goods sold improvement of sample i cooperation at t period COGSit = cost of goods sold improvement of sample i cooperation at t period COGSmt = cost of goods sold improvement of industry average at t period Q = during 3 year (2004–2006) quarter (12 quarterly periods)

chain flexibility on downstream performance outcomes. Additional studies might conduct international comparisons of supply chain practices of other industries in North East Asia (e.g., China, Korea, Japan and Taiwan).

Appendix A. See Table A.1.

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Exploring the effects of interorganizational and interpersonal trust on performance. Organization Science, 9(2), 141–159. Zhou, H., & Benton, W. C. (2007). Supply chain practice and information sharing. Journal of Operations Management, 25(6), 1348–1365. Sun Hee Youn is an instructor at the Hanyang University, Seoul, Korea. She received her MBA and Ph.D. in Operations Management from Hongik University in Seoul, Korea. She had two years of post-doc researcher experience at the University of Toledo (2010–2012). Her papers have been published in journals including International Journal of Production Economics, Journal of Cleaner Production, Int. J. Services and Operations Management, Int. J. Logistics Systems and Management, The Korean Production Management Review, Korean Association of Business Education Review, Benchmarking: an International Journal, and Journal of Korean Society for Quality Management. Her research interests are in supply chain management, green supply chain management and operational strategy. Ma Ga (Mark) Yang is an assistant professor of Management of College of Business & Public Affairs at West Chester University of Pennsylvania, USA. He holds a Ph.D. in Manufacturing and Technology Management from the University of Toledo. He holds an MBA from the University of Toledo and a BA from Hankuk University of Foreign Studies in Seoul, Korea. His articles have been published in International Journal of Production Economics, Journal of Cleaner Production, Benchmarking: An International Journal, International Journal of Business Excellence, Computers & Education, and International Journal of Service Operations Management. He is the recipient of 2010 APICS Plossl Doctoral Dissertation Award. His research interests are in sustainability, sustainable operations, sustainable supply chains, environmental management, lean manufacturing, information technology and innovative higher education.

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Jinhwan Kim is an invited professor of Department of Business Administration of Global Business School at the Soon Chun Hyang University, South Korea. Dr. Kim received his MBA and PhD in Management Accounting from Hongik University in Seoul, Korea in 2001. He has been involved in a range of management control system related research projects in Korean firms. His articles have been published in journals including Korean Management Review, Korean Accounting Review, Korean Accounting Journal, Accounting Information Review, and Int. J. Services and Operation Management. His research interests are in BSC (Balanced Scorecard), ABC (Activity Based Costing), strategic performance measurement system, operational strategy and green supply chain management. Paul Hong is a professor of Information Operations and Technology Management of College of Business Administration at the University of Toledo, USA. He holds a

Ph.D. in Manufacturing Management and Engineering from the University of Toledo. He also holds an MBA and an MA in Economics degree from Bowling Green State University, USA and a BA from Yonsei University in Seoul, Korea. His articles have been published in journals including, Journal of Operations Management, Corporate Governance: an International Review, International Journal of Production Research, Journal of Supply Chain Management, International Journal of Information Management, International Journal of Operations and Production Management International Journal of Production Economics, Journal of Business Research, Management Decision, International Journal of Technology Management, Journal of Cleaner Production, and European Journal of Innovation Management. His research interests are in innovation in business ecosystem, public and private partnership and global supply chain management.