An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management

ARTICLE IN PRESS Int. J. Production Economics 120 (2009) 252–269

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Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe

An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management Cheng Zhang a, Jasbir Dhaliwal b, a

Department of Information Management and Information Systems, School of Management, Fudan University, Shanghai, PR China Department of Management Information Systems, Fogelman College of Business & Economics, University of Memphis, Fogelman Administrative Building, FAB 300, Memphis, TN 38152, USA

b

a r t i c l e i n f o

abstract

Article history: Received 1 September 2007 Accepted 1 July 2008 Available online 17 October 2008

There has been inadequate research to date that examined the in-depth processes by which firms adopt technology for operations and supply chain management or critical factors that may influence the operational value firms gain from information technology (IT)-enabled supply chain management. Exploring these questions can contribute knowledge to the field of operations management: how firms can employ their IT capabilities for operations and supply chain management, the impact of competitive and institutional environments on IT-based operations strategy; the relationships between IT-enabled supply chain practices and operations performance. This paper addresses these aspects by examining the factors affecting Chinese firms’ adoption of IT-enabled supply chain operations and the benefits they achieve, by drawing from and integrating the resource-based and institutional theoretic perspectives. It identifies key organizational and institutional factors that influence firms’ technology adoption for supply chain management. Results show that firms can benefit by incorporating the technology within their internal operations processes and by using the technology externally with partners in their supply chain trading community. The results provide evidence that firms’ IT engagement for operations and supply chain management is significantly influenced by institutional factors. Association analysis was conducted to understand the potential influence of external diffusion on internal assimilation. The result supports the theoretical claim. Building on the existing literature, this study demonstrates how resource-based theory and institutional theory can provide a solid theoretical backbone for practitioners, researchers, and policy makers in efforts pertaining to technology adoption for operational supply chain excellence. & 2008 Elsevier B.V. All rights reserved.

Keywords: Institutional theory Resource-based view IT-enabled Supply chain management

1. Introduction Supply chain management is the process by which suppliers, partners, and customers plan, implement and mange the flow of information, service, and products in a way that improves business operations in terms of speed, agility, real-time control, or customer response (Manthou

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E-mail addresses: [email protected] (C. Zhang), [email protected] (J. Dhaliwal). 0925-5273/$ - see front matter & 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2008.07.023

et al., 2004). With intensive internationalization and globalization of markets, multiple dimensions of operational capabilities between suppliers, trading partners, and customers have to be developed for firms to succeed. A key operational capability necessary to meet this challenge is operational information systems that are embedded in a firm’s core business processes and its inter-organizational business transactions (Nurmilaakso, 2008). This paper uses the term ‘IT’ (information technology) for its broad definition to include supply chain technologies and information systems applications supporting both operational processes and business-to-business

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electronic interactions that are central to IT-enabled supply chain networks. IT is an essential enabler of effective supply chain management (Gunasekaran and Chung, 2004; Simchi-Levi et al., 2000), business process excellence (Davenport, 1993), and global competition success (Ngai et al., 2008). Since business transactions about purchasing, production, shipment, and payment accounts for a large volume of a firm’s daily business, ensuring the operational information is recorded and shared seamlessly is critical for operational efficiency and responsiveness in supply chains (Lee, 2002). With larger transactions, the return on IT investment in operations, like ERP systems and e-commerce (e-business) systems, increases. As firms seek to improve supply chain collaboration, they have to see their business processes in a wider context as a part of a larger supply chain in which many firms contribute complementarily to the final product. From this perspective, the firms need to engage any given business partner with effective information sharing and operational collaboration. Consequently, IT is becoming increasingly important (Markus et al., 2006) for excellence in operational strategy development and execution. It can help firms reduce transaction costs and enhance collaboration efficiency among supply chain partners (Chae et al., 2005; Lee et al., 2000; Sambamurthy et al., 2003). Therefore, IT-enabled supply chain management is at the core of operational strategies for firms to manage both their internal operation efficiency and their external relationships, particularly the cross-company workflow, in a supply chain (Manecke and Schoensleben, 2004). This point is emphasized in the 2004 special issue of the International Journal of Production Economics that focused on ‘supply chain management for 21st-century organizational competitiveness’ that also discussed supply chain management design with advanced ITs (Gunasekaran and Chung, 2004). The following diagram

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shows the key relations between IT, operations management, and supply chain management in firms (Diagram 1). Key operations in manufacturing firms usually include production and process development, sales and marketing administration, planning, procurement, production, demand fulfillment, materials management, post-sales service, accounting and human resources, and IT management. The core of IT management is an effective integrated information system (e.g., ERP) to store and process information generated from operations. The integrated information system can also act as an information platform that shares useful information across departments inside the firm, as well as with outside partners to coordinate supply chain activities. These are the two key dimensions of IT-enabled supply chain management and have come to the fore because of the use of internet technology for both purposes—often termed in combination as the Internet-enabling of the supply chain using technology based on the TCP/IP protocol. Very often, industry practitioners use the technical terms of ‘‘e-business intranets’’ and ‘‘e-business extranets’’ to refer to these two dimensions. Therefore in this study, the terms ‘IT-enabled supply chain’ and ‘e-business’ are used interchangeably. This is supported by the fact that in the Chinese business context that we focused on, business managers are much more familiar with e-business technologies as a construct rather that IT-enabled supply chains. To date, few studies have taken an in-depth look at the process by which Chinese firms assimilate IT into operations or at the critical factors that may influence the value these firms gain from supply chain collaboration with business partners. More specifically, there is no unified view on how these factors influence the adoption process or on the interrelationship between technology assimilation into internal operations and the external diffusion of technology amongst supply chain partners.

Supply Chain Management Operation Management Product & Process Development

Sales & Marketing Administration

Production

Demand Fulfillment

Materials Management

Customers

Procurement

Coordination

Coordination

Suppliers

Planning

Post-Sales Service Accounting & Human Resources Integrated Information System Information Technology Management

Diagram 1. The relationship between firms’ IT management, operations management and supply chain management from IT perspective, adapted from SAP Inc’s presentation slides.

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Exploring these questions will contribute knowledge to the field of operations management and help researchers to understand: (1) how Chinese firms can employ their IT capabilities for operations and supply chain management, (2) the impact of the Chinese competitive and institutional environments on IT-enabled supply chain operations and the relationships between IT-enabled supply chain operations and operations performance. Our reading of the literature also suggests that there are genuine (often overlapping) differences of opinion about terms such as deployment, diffusion, assimilation, and adoption. The primary purpose of our paper is not the conceptual clarification of these theoretical dilemmas but rather the empirical examination of the Chinese context in relation to the use of e-business-type technologies for supply chain operations. For definitional purposes, we term internal assimilation as being the assimilation of e-business technologies into internal supply chain operations and external diffusion as being the diffusion of such technologies amongst supply chain partners for the purpose of supply chain collaboration. It is our opinion, that such a conceptualization preserves and the theoretic integrity and the necessary pragmatic from a supply chain operations perspective that is necessary for the purposes of our study. Two key managerial and organizational theories, i.e., resource-based theory and institutional theory, have the potential to offer a wide variety of interesting and helpful perspectives. Although the existing literature in organizational management has suggested that both resource-based capital and institutional capital are indispensable to firms’ sustainable competitive advantage (Oliver, 1997) and new insights may be generated through integrating the two theoretical perspectives, for example, Nevo et al. (2007) apply such an integrative view to analyze the trade-offs between the employment of internal and external IT expertise in IT projects), the integrative view remains largely underdeveloped in the supply chain context. Researchers interested in operations management in general and supply chain management in particular have made limited use of these theories (Ketchen and Hult, 2007). Significant opportunities exist to integrate insights from organizational theory and supply chain management in order to build an understanding of why certain operations strategies (such as those pertaining to developing and exploiting IT capabilities) can bring long-term competitive advantage to supply chains and how firms seek balance between industry best practices and their own unique operational characteristics. This paper addresses these opportunities by examining resource-based and institutional factors affecting firms’ adoption of IT-enabled supply chain collaboration and the benefits they achieve from such deployment. This paper, therefore, also aims to answer how organizational and strategic management theories, like the resource-based view (RBV) and institutional imperatives, can inform the changing landscape of operation strategy and supply chain management.

2. Literature review Firms that engage in inter-organizational collaboration incorporate a wide range of IT into their business processes and operations in order to conduct business with consumers and suppliers electronically (Teo et al., 2006). Many studies have explored diverse aspects of firms’ decision to engage in B2B e-commerce systems. There are two theoretical foundations for the study. One is resource-based theory (Barney, 1991), which is used to define specific firm resources that contribute to supply chain management and lead to IT-enabled value creation. From this view, we would like to study to the extent and manner in which information systems create a long-term competitive advantage in operations and supply chain management. The other is institutional theory (DiMaggio and Powell, 1983), which provides a rich understanding of firms’ adoption behavior from external forces. From this view, we would like to study the extent to which firms’ technology adoption is affected by industry factors and how firms seek balance between industry best-practices and their own unique operational characteristics.

2.1. Resource-based view The RBV holds that firm resources that are valuable, rare, and hard to substitute are a basis for competitive advantage (Barney, 1991; Melville et al., 2004). In the context of IT, RBV can be used to understand the link between IT practices and competitive advantage, that is, how IT applications become one of a firm’s resources and contribute to supply chain management excellence. In line with the RBV, IT resources, and capabilities are valuable, rare, and hard to substitute from an operations strategy perspective. The literature provides a diverse set of ways in which they can be categorized. Mata et al. (1995) define four types of IT resources: capital, proprietary technology, technical IT skills, and managerial IT skills. Powell and Dent-Micallef (1997) divide IT resources into human resources, business resources, and technology resources. Bharadwaj et al. (1998) suggest a six-dimension measure of IT capability and resources: IT/business partnerships, external IT linkages, business IT strategic thinking, IT business process integration, IT management, and IT infrastructure. Bharadwaj (2000) classifies IT resources into IT infrastructure, human IT resources, and IT-enabled intangibles. Melville et al. (2004) describe two types of IT resources: technological IT resources and human IT resources. In sum, a straightforward categorization may include two types of IT resources: technological resources, which consist of infrastructure and related deployment resources, and IT-related organizational resources, which consist of human, business process, and management resources. The external environment, as defined and influenced by trading partners, government, and sociopolitical conditions, also plays an important role in operations value generation (Melville et al., 2004). Melville et al. (2004) consider industry characteristics, tradingpartner resources, and business processes to be the key

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environmental components affecting firms’ business value generation. Teo et al. (2003) conduct an in-depth investigation into institutional factors and find that IT usage by suppliers, customers, and competitors significantly affects firms’ inter-organizational system adoption decisions. Similarly, Gibbs and Kraemer (2004) empirically examine the relationships between firms’ engagement in B2B e-commerce practice and their customers’ demand and their competitors’ online behavior. The study provides evidence of the existence of external pressure on firms’ IT usage. Overall, RBV provides a theoretical basis for understanding the role of IT usage in the firm (Zhu and Kraemer, 2005) and for evaluating the business value of IS resources (Wade and Hulland, 2004). 2.2. Institutional theory Institutional theory provides a rich and complex view of organizational behavior (Berger and Luckmann, 1966; Meyer and Rowan, 1977; Zucker, 1977). From an institutional perspective, organizations can be influenced by varied pressures arising from either the external environment or internal organizational factors. Under certain conditions, these pressures may lead organizations to be ‘‘legitimated’’ in order to survive in the market (Zucker, 1987). Three important legitimization processes are often discussed in relation to innovation diffusion: coercive, imitative, and normative (DiMaggio and Powell, 1983). Coercive legitimization is organizations’ reaction to both formal and informal pressures by other organizations or society. Imitative legitimization is when organizations imitate the success of others when facing uncertainty. Normative legitimization is a result from normative pressure, stemming primarily from professionalization and shared norms amongst organizations (Zucker, 1987). Institutional theory provides a useful research view for the study of firms’ IT adoption. The institutional environment where the organization is embedded exerts an influence on the IT adoption process exhibited by individual organizations. Firms tend to be induced to adopt certain IT practices by external isomorphic pressures form competitors, trading partners, customers, and government. Recent studies have taken an institutional approach to B2B e-commerce system diffusion (Gibbs and Kraemer, 2004; Teo et al., 2003), which reveals the importance of coercive pressures, government promotion, and legislation in IT adoption and use. Imitative legitimization may also play a role, in that firms are likely to imitate their peers’ successful IT practices. Normative legitimization comes into the picture as firms accept normalized best practices for IT adoption in a fastchanging environment. 3. Research model The process of adopting IT for operations and supply chain management can be differentiated into internal assimilation and external diffusion (Ramamurthy and Premkumar, 1995; Ranganathan et al., 2004). Internal assimilation refers to the adoption of IT to support key

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internal organizational operations, while external diffusion refers to the deployment of IT to enhance interorganizational operations with supply chain partners. Together, the internal assimilation and external technology adoption processes constitute the complete process of adopting technology for supply chain operations. However, recent studies show that the two processes cannot be treated similarly (Premkumar and Ramamurthy, 1994; Ramamurthy and Premkumar, 1995); they also show that there is an interrelationship between them (Ranganathan et al., 2004). For example, in field studies, Foster et al. (2006) and Thatcher et al. (2006) find Chinese firms’ IT adoption process, particularly among local SMEs, to be catalyzed by supply chain partners’ requirements and, consequently, begin with inter-organizational electronic linkages rather than internal technology development for operations. In such a situation, electronic linkage with supply chain partners is considered to be a distinct technology diffusion trigger and an enabler of subsequent internal assimilation of technology for supply chainrelated operations. Although several antecedent studies have investigated technological, organizational, and partnership factors that may affect firms’ IT adoption, there is no unified view on how these factors influence the adoption process or on the interrelationship between the internal assimilation and external diffusion processes. This is the focus of this study which focuses on supply chain operations encompassing a range of internal operational activities as well as interorganizational processes that stretch beyond organizational boundaries. Once a firm has adopted and adapted a technology, it begins to use that technology in a comprehensive and integrated manner to support organizational work and transfers of technology both inside and outside the organization. With theoretical grounding in both the RBV and institutional theory, we developed the research model shown in Fig. 1. Three major types of drivers—technological, organizational, and environmental (Melville et al., 2004; Tan and Ouyang, 2004; Wade and Hulland, 2004; Xu et al., 2004)—are proposed, and we analyze their impact on IT adoption for supply chain operations. At the firm level, the deployment of technology as a key resource and capability for supply chain operations is expected to improve internal business process efficiency, while environmental factors are expected to be associated with external diffusion. Focusing on supply chain contexts, the study develops the research model from the perspective of the IT deployment processes. It also introduces partnership as an important organizational resource for operations (Barney, 1991) and builds upon Melville et al.’s (2004) view that defines variables in relation to IT resources, organizational resources, and environment resources. According to Melville et al. (2004)’s conceptual framework, there are two types of IT resources: technological resources and human resources. In this model, we consider two important factors accordingly: formalization of IT deployment and managerial IT knowledge. In relation to organization resources, we introduce partnership as a crucial organizational resource for adopting supply chain

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Managerial IT Knowledge

IT Deployment Capability

H1 H2

Internal Assimilation H7a

H3a H6

Partner Dependence

IT-enabled Operation Improvement

H3b

H8

IT-enabled Strategic Performance Improvement

H7b Competition Intensity

H4

External Difussion

H5 IT Intensity Fig. 1. Research model.

applications. These three factors are expected to be associated with firms’ internal assimilation of IT-enabled supply chain system. Environmental factors represent the characteristics of environment (e.g., industry, competitors, and relationships with partners) in which a firm operates. Since supply chain management at the operational level is not directly influenced by institutional arrangements such as regulation and policy: partners’ influence, competition and IT intensity in industries are the more critical factors for supply chain management. These institutional pressures from the business environment shape the way in which firms adopt electronic linkages with supply chain partners. When firms engage in IT-enabled operations and supply chain management, both operational and strategic performance is improved. IT-enabled operational performance can be measured by improvements in business process efficiency, such as better inventory control, improved customer service, reduced cost, or reduced response time, while strategic performance can be measured by improvement in the firm’s overall performance, as evidenced by competitive advantage and better partner relationships. Fig. 1 is a visual representation of the complete research model. A key driver of technology adoption for supply chain operations is managerial IT knowledge, which is the human component of IT resources (Byrd and Turner, 2000; Melville et al., 2004). Managerial IT knowledge is a set of ‘‘soft’’ abilities that makes it possible for firms to deploy IT in an effective manner (Lee et al., 1995; Swanson, 1994). In order to utilize ‘‘hardware’’ related physical IT assets optimally, senior managers need to ensure that there is a good fit between IT functionality and the firm’s operations and supply chain strategies (Grabowski and Lee, 1993; McLaren and Head, 2004). Operations managers representing both the business functions as well as the IT functions need to pursue well-planned technology adoption strategies , in order to improve operational performance (Markus and Soh, 1993). In sum, the firm requires plentiful IT knowledge at both

strategic and operations management levels. Therefore, we propose: H1. The level of firms’ managerial IT knowledge will be positively associated with their internal assimilation of technology for supply chain operations. IT—computers, networks, databases, and communication platforms—form the core of firms’ IT infrastructure (Duncan, 1995), and that infrastructure can be the source of value in operations if firms use it to develop unique capabilities (Zhu, 2004). For supply chain operations management, system deployment capability rather than technological innovation is of central importance. Therefore, we propose: H2. Firms’ IT deployment capability will be positively associated with their internal assimilation of technology for supply chain operations. Partner dependence is also an important factor affecting firms’ technology adoption (Deeter-Schmelz et al., 2001). Firms with greater market power may put coercive pressure on dependent partners to adopt technological innovations (Hart and Saunders, 1998; Teo et al., 2003). In supply chain management (as characterized by its central component—B2B e-commerce), some recent research indicates that large firms put pressure on smaller supplier firms to adopt technologies for supply chain operations (Chwelos et al., 2001; Iacovou et al., 1995) to assure a high level of chain-wide operational performance. As supply chain management concepts and related e-commerce technologies advance, the more inter-dependence there is between a firm and its suppliers, the greater is the likelihood that they will adopt the same technical applications for supply chain operations (Zhu et al., 2006b). Therefore, the inter-dependence of the firm and its suppliers influences the deployment of supply chain technology applications. Prior research shows that partner and supplier dependence can affect a firm’s external

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diffusion of technology in other contexts such as electronic commerce (Teo et al., 2003). Partner dependence may also influence firms’ internal assimilation of technologies for supply chain operations Institutional theory argues that external pressures can lead organizations to become ‘‘legitimated’’ (Zucker, 1987). A firm may imitate the actions of firms (DiMaggio and Powell, 1983) that are similar to it in position or that share resources with it in the same industry. In a supply chain (B2B e-commerce) trading network, partners, and suppliers play a significant role in influencing a particular firm’s technology adoption. A firm is more likely to follow its partners’ and suppliers’ successful best practices relating to the use of technology for supply chain operations. From a supply chain management perspective, this imitation can be prompted by the promise of operational efficiency and inter-organizational collaboration leading up to enhanced overall supply chain performance. Business-to-business supply chain technologies are not well adopted yet and firms generally lack knowledge and experience related to how to use them to support their operations management practices. Given this reality, firms’ imitation of their supply chain peers and partners may be a part of their learning curve as they move towards excellence in supply chain operations. Therefore, we propose: H3. Firms’ partner dependence will be positively associated with their adoption technology for supply chain operations. H3a. Firms’ partner dependence will be positively associated with their internal assimilation of technologies for supply chain operations. H3b. Firms’ partner dependence will be positively associated with their external diffusion of technologies for supply chain operations. Prior research shows that environmental factors affect the adoption of IT innovations (Kraemer et al., 2002; Tornatzky and Fleischer, 1990). Competitive pressure from the market and industry is one factor (Robertson and Gatignon, 1986). When large-volume transactions need processing, firms in competitive environments are more likely to adopt external electronic linkages with supply chain partners to improve operational efficiency and gain competitive advantage in the market. In a recent study, Zhu et al. (2006b) show that competition positively affects the initiation and adoption of e-commerce technologies. In addition, application integration becomes a strong determinant of technology adoption for supply chain operations. In sum, pressure from industry and competitors may force firms to deploy technology applications to link with supply chain partners. Therefore, we propose: H4. Competitive intensity will be positively associated with firms’ external diffusion of technologies for supply chain operations. IT usage intensity in a supply chain network is another environmental factor that may affect firms’ technology adoption for operations. This includes all forms of IT usage

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by a firm’s partners, suppliers, customers, and competitors when the technology is adopted and deployed for diverse supply chain operations. Greater IT usage intensity in a supply chain network implies greater technological readiness (Zhu and Kraemer, 2005) in relation to both technology infrastructure and IT human resources. This can make it easier for a particular firm to adopt new technologies to deal with its operations partners and suppliers. Trading community influence has been found to be a key driver of network effects which are a determinant of e-commerce technology adoption (Zhu et al., 2006a). With more players in the supply chain network community using IT, firms face heavier normative pressure (Teo et al., 2003) to adopt technology applications with partners and suppliers for a better return on technology investment. Therefore, the more IT usage by industry players, the more likely will be a firm’s external diffusion of technology for supply chain operations. Therefore, we propose: H5. Information technology intensity will be positively associated with firms’ external diffusion of technologies for supply chain operations. Institutional theory offers rich insights into firms’ adoption of supply chain technologies. For instance, regulations and competition from other competing supply chain networks may play a coercive role in firms’ technology management and usage decisions. Firms may also be more willing to imitate successful technology– based operations best-practices from partners and accept normalized best-practices relating to the adoption of automated supply chain operations in a fast-changing environment (Cui et al., 2006). For example, Nurmilaakso (2008) finds that companies having more exchanging standardized data have more e-business functions in supply chain integration. Previous studies of technology adoption also find that strategic environmental factors play a critical role in firms’ technology decisions and usage (e.g., Tan and Ouyang, 2004; Xu et al., 2004). Recognizing this critical ‘‘outside-in’’ influence on technology adoption, we propose: H6. The level of firms’ external diffusion of technologies for supply chain collaboration will be positively correlated with their internal assimilation of technologies for supply chain operations. From a process-oriented perspective (Soh and Markus, 1995), specific IT applications in supply chain management, such as B2B e-commerce, can improve operational performance only when they are used appropriately in the business process, for example, to enable better decision making, to improve operational productivity, and to increase coordination flexibility. Barua et al. (1995) analyze an intermediate process of usage-linked IT and its impact on firm performance. Following Soh and Markus’s logic, Zhu and Xu (2004) further develop the technology value creation model from IT investment and usage to value realization. Our model focuses on two distinct adoption processes in relation to supply chain technologies for operations: internal technology

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assimilation within the firm and external technology diffusion amongst supplier chain partners. We expect the overall supply chain technology adoption process to generate business value for firms. Therefore, we propose: H7. Firms’ adoption of technologies for supply chain operations will be positively associated with firms’ operational performance. H7a. Firms’ internal assimilation of technologies for supply chain operations will be positively associated with firms’ operational performance. H7b. Firms’ external diffusion of technologies for supply chain operations will be positively associated with firms’ operational performance. The literature shows that IT-enabled performance improvements consist of improvements in operational efficiency, increased competitive advantage, and other types of improvement (DeLone and Mclean, 2003; Melville et al., 2004). Generally, firms’ performance can be classified as either operational performance (performance relating to the efficiency of specific business processes, such as inventory control, customer service, cost, and response time) or strategic performance (performance relating to long-term organizational benefits, such as competitive advantage and business partner relationships). From a resource-based theoretic view, technologies for supply chain operations, such as B2B e-commerce, are a strategic resource only when they help the firm gain a competitive advantage. To better understand the link between technologies for supply chain operations and benefit to firms, it is necessary to investigate how these technology applications become a resource for the firm and contribute to its competitive advantage. We hypothesize a positive relationship between technology-enabled operational supply chain performance and strategic performance: H8. Firms’ IT-enabled operational performance will be positively associated with their strategic performance. 4. Data analysis We used the questionnaire survey method for the study which was conducted in 2005 and 2006. An initial sampling frame included firms listed on the China Stock Exchange’s Listed Company Directory, provided by the China Securities Regulatory Commission at www.csrc.gov.cn, and firms with senior operations and technology executives attending a university’s MBA program in Shanghai. From these sources, we mailed out the questionnaire to a total of 1020 potential respondents. We followed this initial distribution of our survey with one reminder coming a few weeks later. Altogether, 101 valid questionnaires were returned. Table 1 presents the demographic profiles of the respondent firms. Distribution of firms’ annual revenue and size shows a balanced representation of small, medium, and large firms. The respondents are also evenly distributed in terms of the type of organization, but as can be expected for the Chinese context, the sample is more oblique to the

Table 1 Sample characteristics (N ¼ 101) % Industry Architecture/Engineering Business services Chemicals Computer/IT related Finance/Banking/Insurance Manufacturing Pharmaceutical/Medical/Health care Real estate/Property Retail/Trading Transportation Travel/Tourism/Hotel Other N/A

6 1 5 17 2 41 3 2 10 3 1 5 5

Organization type Multinational Government owned Locally owned N/A

41 33 25 2

Respondent title IT/Operations Manager Business Unit Manager General Manager/Senior Director N/A

42 39 7 13

Annual revenue (in RMBf millions) o1 1–5 5–10 10–50 50–100 100–1000 41000 N/A

4 7 14 15 12 19 17 13

Number of employees o100 100–500 500–1000 1000–2000 2000–5000 45000 N/A

9 11 11 18 18 21 13

Respondent work experience (in years) o5 5–10 10–20 420 N/A

24 42 21 3 11

Note: N/A refers to missing data.

manufacturing industry than to the service sector. The respondents’ position in the organization covers diverse business functions with a good balance between technology managers and business-unit operations executives. We used the partial least-squares (PLS) approach (Haenlein and Kaplan, 2004; Lohmoller, 1989), a structural equation modeling technique, to examine the model and hypotheses. PLS assesses the relationships between the research constructs as well as the relationships between

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the constructs and their measurement items, so that the error variance is reduced (Ranganathan et al., 2004). PLS involves no assumptions about the population or scale of measurement (Fornell and Bookstein, 1982). Its sample size requirement is either 10 times the largest measurement number within the same construct or 10 times the largest construct number affecting the same construct (Chin and Newsted, 1999). Our sample size is above the minimum needed to satisfy the criteria. The software used to apply PLS to the model was PLS-Graph (Chin, 2001). Most of the constructs in this study are measures from the literature that were adapted to the context of the study. Multiple-item measures were used to assess the research constructs. All items were measured using a 7-point Likert-type scale. We adapted nine items measuring managerial IT knowledge from Armstrong and Sambamurthy (1999) and Boynton et al. (1994). There were also four items assessing top management’s knowledge of, commitment to, and consideration and support for web technologies for supply chain operations and three items assessing functional managers’ knowledge of, commitment to, and support for such technology-based operations. The other two items assessed IT managers’ business knowledge in supply chain automation and implementation. We adapted four items measuring IT deployment capability from Grover and Goslar (1993) and Melville et al. (2004): the extent to which the firm had (1) clear job descriptions for IT personnel, (2) rules and procedures for handling various IT activities, (3) committees and task forces for performing IT tasks, and (4) formal procedures and guidelines for evaluating new technologies and systems. The items for measuring partner dependence examined the extent of the firm’s business and resource dependence on its suppliers along the supply chain: importance of continued business relationship with suppliers, profit proportion related to supplier sale/ service, and suppliers’ dependence on the firm. The items were adapted from Premkumar and Ramamurthy (1995). The items for measuring competition intensity examined the extent to which the firm tracks, monitors, and considers competitors’ activities (Zhu et al., 2006b). The items for measuring IT usage intensity were the extent of IT usage by competitors, suppliers, customers, and partners in the industry. The items were adapted from Purvis et al. (2001). The items for measuring internal assimilation dealt with the extent to which technology applications were used in five key internal operations in supply chain collaboration (Zhu et al., 2006b): supplier selection (getting quotes, bids, etc.), purchase-order processing, procurement from suppliers (distribution, warehousing, logistics, etc.), invoicing and payment processing, and demand management and procurement analysis. The items for measuring external diffusion dealt with breadth and volume of web-based transactions with suppliers (Zhu and Kraemer, 2003): how many suppliers the firm had interacted with, total supplier transactions, and overall supplier interactions handled electronically. The items were adapted from Premkumar

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and Ramamurthy (1994), Ramamurthy and Premkumar (1995), and Zhu and Kraemer (2003). The impact of technologies for supply chain operations on performance was captured at two levels: the operational level and the overall organizational level. Operational performance improvement was measured by the extent of firms’ improvement in customer service, cost reduction, inventory management, and cycle-time reduction. Strategic performance improvement was measured by the extent to which the firms’ supply chain relationships and competitive advantage improved. Items were adapted from Iacovou et al. (1995), Melville et al. (2004), and Ramamurthy and Premkumar (1995). The instruments are summarized in Table 2. To validate the instruments, we examined internal consistency, convergent validity, discriminant validity, and common method variance. We used composite reliability to evaluate internal consistency. In PLS, composite reliability relies on actual loadings to compute the factor scores and is a better indicator of internal consistency than Cronbach’s alpha (Ranganathan et al., 2004). As shown in Table 2, the composite reliability values for the constructs in the model were all above the suggested threshold of 0.7 (Chin, 1998; Straub, 1989) and thus supported the reliability of the measures. We used two tests to examine convergent validity. First, we examined item reliability by factor loading on the construct. As shown in Table 2, all items had a loading above the suggested 0.55 (Falk and Miller, 1992). Second, we examined the construct’s average variance extracted (AVE). The AVE values for all constructs were above the limit of 0.50 advised by Fornell and Larcker (1981). Furthermore, all estimated standard loadings were significant at the 0.01 level (po0.01), suggesting good convergent validity. In summary, convergent validity was supported. We examined discriminant validity at the construct level. The square root of the AVE for each construct should be greater than the correlation between constructs (Fornell and Larcker, 1981). Table 3 presents each construct’s inter-correlations and the square root of its AVE. In every case, the square root of the AVE is greater than the correlation coefficient involving the construct. We tested for multicollinearity before undertaking the data analysis. We analyzed tolerance which is the amount of variability of the selected independent variables not explained by other independent variables (as measured by 1Ri2, and the variation inflation factor (VIF), which measures how much the variance of the estimated regression coefficients are inflated as a result of being related to the other independent variables (as measured by the formula of 1/(1Ri2)) (Neter et al., 1990). The tolerance threshold is above 0.10 and the VIF threshold is less than 5–10. These test outcomes show that the overall independent variables’ tolerance is between 0.543 and 0.738, while their VIF is between 1.35 and 1.84, suggesting that there is no potential problem with multicollinearity. Another weakness of this survey is that all constructs were measured via self reporting by a single informant using the same instrument. Thus, the threat of common method bias may exist. Two tests widely recommended by

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Table 2 Results of confirmatory factor analysis (po0.01) Construct and items

Loading

Managerial IT knowledge (composite reliability ¼ 0.94, AVE ¼ 0.64) Top management’s interest in use of technologies for supply chain operations Top management’s consideration of technology applications for supply chain operations Top management’s commitment to support technology applications for supply chain operations Top management’s knowledge of potential of technologies for supply chain operations to bring future success to firm Functional management’s knowledge of potential of technologies for supply chain operations to bring future success Functional management’s knowledge of potential of technologies for supply chain operations to improve business processes Functional management’s commitment to support supply chain technology applications in firm IT management’s knowledge of business operations of firm IT management’s knowledge of business strategies of firm

0.86 0.88 0.80 0.87 0.75 0.85 0.90 0.61 0.64

IT deployment capability (composite reliability ¼ 0.86, AVE ¼ 0.61) Documented job descriptions for IT personnel Use of operating rules and procedures for IT activities Use of task forces and committees for IT activities Use of formal procedures and guidelines for evaluating new technologies and ideas

0.77 0.82 0.77 0.77

Partner dependence (composite reliability ¼ 0.82, AVE ¼ 0.61) Importance of having continued business relationship with suppliers Significant proportion of total profits depend on relation with suppliers Dependence of suppliers on firm for achievement of their business goals

0.80 0.80 0.74

Competition intensity (composite reliability ¼ 0.91, AVE ¼ 0.77) Tracking new initiatives of competitors Monitoring competitor moves Competitor information considered important for firm’s decisions

0.89 0.86 0.89

IT usage intensity (composite reliability ¼ 0.91, AVE ¼ 0.71) Presence of supply chain IT applications and practices among competitors Use of supply chain IT by key suppliers Use of supply chain IT by key customers Use of supply chain IT by key business partners

0.83 0.86 0.80 0.88

Internal assimilation of technologies for supply chain operations (composite reliability ¼ 0.94, AVE ¼ 0.76) Extent to which such applications (such as web-based B2B operations) are used in— Supplier selection (getting quotes, bids, etc.) Purchase-order processing Procurement from suppliers (distribution, warehouse, logistics, etc.) Invoicing and payment processing Demand management (procurement analysis)

0.84 0.87 0.90 0.87 0.87

External diffusion of technologies for supply chain collaboration (composite reliability ¼ 0.98, AVE ¼ 0.94) Proportion of total suppliers who interact with firm through B2B supply chain applications Proportion of total supplier transactions done through B2B supply chain applications Proportion of overall interactions with suppliers carried out through B2B supply chain applications

0.96 0.98 0.96

Technology-enabled operational improvement (composite reliability ¼ 0.93, AVE ¼ 0.76) Perceived benefits realized Improved customer service Better inventory control Reduced operations costs Reduced cycle time

0.83 0.89 0.88 0.87

Technology-enabled strategic improvement (composite reliability ¼ 0.94, AVE ¼ 0.88) Perceived benefits realized Better supply chain relationships Greater competitive advantage

0.94 0.94

behavioral researchers (Podsakoff et al., 2003; Malhotra et al., 2006), were conducted to determine the extent of method variance. First, a Harmon one-factor test was conducted (Podsakoff and Organ, 1986). Results from this test showed that neither a single factor emerged from

confirmatory factor analysis nor one general factor accounted for the majority of the covariance among the measures, suggesting that common method variance was not a significant concern. To confirm these results, an additional single-method approach (Carlson and Kacmar,

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Table 3 Correlations of latent variables

Managerial IT knowledge (MK) IT deployment capability (ITDC) Partner dependence (PI) Competition intensity (CI) IT usage intensity (ITI) Internal assimilation (IA) External diffusion (EA) Operational improvement (OPI) Strategic improvement (ORI)

MK

ITDC

PI

CI

ITI

IA

EA

OPI

ORI

0.80 0.53 0.33 0.54 0.53 0.45 0.36 0.37 0.28

0.78 0.40 0.56 0.41 0.50 0.37 0.36 0.35

0.78 0.48 0.31 0.45 0.52 0.35 0.37

0.88 0.36 0.62 0.54 0.59 0.51

0.84 0.51 0.39 0.41 0.40

0.87 0.68 0.57 0.54

0.97 0.52 0.44

0.87 0.84

0.94

Note: The numbers in bold in the diagonal row are square roots of the average variance extracted.

Managerial IT Knowledge 0.263*** IT Deployment Capability

0.122

Internal Assimilation R2=0.605

0.405***

0.213*** Partner Dependence

0.408***

IT-Enabled Operation Improvement

0.180*

Competition Intensity

IT Intensity

0.126

External Diffusion

0.413***

0.242**

R2=0.354

0.839*** IT-Enabled Organizational Performance Improvement R2=0.704

R2=0.325

Fig. 2. PLS structure model (*po0.10; **po0.05; ***po0.01, N ¼ 101).

2000; Elangovan and Xie, 1999; Williams et al., 1989) was pursued, i.e., the addition of a method factor was introduced to test the adjusted model. Results showed that while the adjusted model kept significant, the method factor only contributed 1.5% of the variance for one dependent variable but failed to affect the rest of the dependent variables. Together the results of these tests suggest that common method variance is not a pervasive problem in this analysis. Model examination results are shown in Fig. 2. As indicated by path loadings, both managerial IT knowledge (b ¼ 0.263, po0.01) and partner dependence (b ¼ 0.213, po0.01) show significant positive effects on firms’ internal assimilation. The result suggests that firms’ internal knowledge and external partnership relationships have a significant impact their internal assimilation of technologies for supply chain operations, which provides support for H1 and H3a. This result also confirms the theoretical expectation that imitation of peers plays a significant role in firms’ adoption of supply chain technology applications. The path coefficients from partner dependence and IT usage intensity to external diffusion are 0.180 (po0.1) and 0.413 (po0.01), respectively. These results suggest that

partner relationship and supply chain network’s IT usage significantly influence firms’ external diffusion of technologies for supplier chain collaboration. Therefore, H3b and H5 are supported. External diffusion shows a significantly positive correlation with firms’ internal assimilation (b ¼ 0.408, po0.01). Therefore, H6 is also supported. This result helps explain firms’ different adoption actions and the interrelationship of those actions. Both internal assimilation (b ¼ 0.405, po0.01) and external diffusion (b ¼ 0.242, po0.05) have significantly positive effects on firms’ operational improvement. Therefore, H7a and H7b are supported. The path coefficient from operational improvement to organizational improvement is 0.839 (po0.01). The result suggests a close relationship between operational improvement and organizational success. Therefore, H8 is supported. Overall, the results indicate that firms’ adoption of technologies for supplier chain operations and collaboration has a clear positive influence on their performance. However, the impact of IT deployment capability and competitive intensity on firms’ adoption decisions is not significant. Therefore, H2 and H4 are not supported.

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As shown in Fig. 2, the important dependent constructs (internal assimilation, external diffusion, technologyenabled operational improvement, and technologyenabled organizational improvement) have R2-values of 0.605, 0.325, 0.354, and 0.704, respectively. The R2-values offer a reasonable explanation for the data variation from an integrated view combining a resource-based perspective and an institutional perspective. 5. Association analysis While the model illustrates the causal relationships between constructs, current statistical outcomes from empirically assessing these relationships can only reflect associations rather than causal links (Subramani, 2004). To further understand the potential influence from external diffusion to internal assimilation, we set up a competing model, as suggested by Sun and Zhang (2006), with the directional link reversed from our base model, i.e., from internal assimilation to external diffusion. All the factors and the rest relationships remained the same as the original research model we had developed. Then we conducted path analysis on the two models respectively. We compared the estimated and actual correlations between factors in the model (Cohen et al., 1993; Hassanein and Head, 2007; Sun and Zhang, 2006). These results are summarized in Table 4. The total squared error (TSE) is changed by 50.1%. The negative sign of TSE means that when the direction is changed from the original model to the competing model and the TSE is actually reduced. The index suggests that the theoretical

model with the external diffusion-internal assimilation directional link holds better in the data than the reverse associative direction. This association analysis enhances the theoretical claim from an institutional perspective that external diffusion affects firms’ internal assimilation more significantly, rather than the reversed relationship. Determining causal relationships is a complex philosophical, theoretical, and empirical matter. Recognizing this, our supplementary analysis has attempted to provide more confidence for our belief that one associative direction may have had conditional dominance in our empirical context and environment, i.e., the influence from external diffusion to internal assimilation may have been dominant. 6. Implications and discussion In an effort to understand (1) theoretical antecedents, (2) the types, and (3) the impacts of technology adoption for supply chain operations, we developed a research model and examined the model using empirical data from Chinese organizations. Grounded in theoretical perspectives from the RBV and institutional theory, this study investigates key organizational and environmental factors that affect firms’ adoption of these technologies and the resultant business value. Although existing literature has suggested that both resource-based capital and institutional capital are indispensable to firms’ sustainable competitive advantage (Oliver, 1997) and that integrating the two perspectives

Table 4 Association analysis Original theoretical model

Total squared error: 0.410

Associations

Direct paths

Indirect paths

Estimated correlation

Actual correlation

Squared error

MK-IA ITDC-IA PI-IA PI-EA CI-EA ITI-EA EA-IA IA-OPI EA-OPI OPI-ORI

MK-IA ITDC-IA PI-IA PI-EA CI-EA ITI-EA EA-IA IA-OPI EA-OPI OPI-ORI

NA NA PI-EA-IA NA NA NA NA NA EA-IA-OPI NA

0.263 0.122 0.286 0.180 0.126 0.413 0.408 0.405 0.407 0.839

0.085 0.072 0.333 0.055 0.328 0.664 0.663 0.223 0.775 0.982

0.032 0.003 0.002 0.016 0.041 0.063 0.065 0.033 0.135 0.020

Competing model

Total squared error: 0.821

Associations

Direct paths

Indirect paths

Estimated correlation

Actual correlation

Squared error

MK-IA ITDC-IA PI-IA PI-EA CI-EA ITI-EA IA-EA IA-OPI EA-OPI OPI-ORI

MK-IA ITDC-IA PI-IA PI-EA CI-EA ITI-EA EA-IA IA-OPI EA-OPI OPI-ORI

NA NA NA PI-IA-EA NA NA NA IA-EA-OPI NA NA

0.438 0.154 0.295 0.168 0.014 0.263 0.547 0.536 0.242 0.839

0.085 0.072 0.333 0.055 0.328 0.664 0.663 0.223 0.775 0.982

0.125 0.007 0.001 0.013 0.099 0.161 0.013 0.098 0.284 0.020

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can generate new insights on resource selection (Nevo et al., 2007), this is one of the few empirical studies that attempts to integrate institutional and resourcebased perspectives to explain internal and external adoption processes in operations and supply chain management. Through empirical analysis, this study shows that the integration of institutional theory and resource-base theory can provide rich insights into firms’ operational strategies for building technology capabilities for supply chain management. The high overall R2-values in the structural equation model pertaining to factors derived from both the RBV and institutional theory also provide strong support for their explanatory power. Through detailed association analysis, the statistical result enhances the theoretical claim from the institutional perspective that external diffusion affects firms’ internal assimilation, more than the reverse relationship. While acknowledging that possibility of a bi-directional relationship in the adoption process, our analysis supports the possibility of a stronger influence from external diffusion to internal assimilation in supply chain networks. It is also important to consider the positive relationship between internal and external technology adoption. Previous research has only briefly considered the interrelationship between internal and external diffusion, largely because the institutional perspective had been viewed as separate and distinct from the RBV. This study combines and applies both theoretical notions simultaneously in the context of operations strategy for supplier chain management. From a research perspective, the critical link between the internal and external diffusion is important. Previous research in operations management has largely assumed that internal assimilation helps firms improve inter-organizational integration and supplier chain collaboration. While more work is certainly required for determining the specific link direction between internal assimilation and external diffusion, the recognition in this study of the potential reverse influence of external diffusion on internal assimilation may guide future research studies to investigating several factors, such as mimetic imitation and lagged IT usage in operations (Teo et al., 2006). The study also finds significant positive associations between internal and external diffusion of e-business and operational and organizational benefits. That is, greater usage of e-business applications is likely to improve firms’ performance at both the operational level and organizational level, resulting in improved customer service, cost reductions, improved inventory management, and cycletime reductions, and at the organizational level, resulting in improved supplier relationships and enhanced overall competitive advantage. Our research model provides a useful theoretical guide to understanding how firms are adopting IT-enabled supply chain management and our results highlight an important fact that deserves attention from researchers and practitioners: that firms seeking to optimize benefits by engaging in IT-enabled supply chain management need to concentrate as much on internal assimilation of these

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applications in operations as on the external diffusion of such applications across their supplier networks (Frohlich, 2002). The data supports the notion that a dual adoption approach (internal assimilation and external diffusion) generates significant value for firms and that operations strategy needs to incorporate both these aspects in supply chain optimization. In a world of supply chain versus supply chain competition, operations strategy needs to consider and incorporate resource and capacity building dimensions extending outside traditional organizational boundaries to the context of the extended enterprise. Operations strategy development has to be premised on the assumption that the technology sophistication of internal operations management has to stay abreast of the levels of technology adoption by supplier partners while carrying a responsibility for moving the overall excellence of its supplier network forward in its own internal execution. These results are consistent with the mainstream literature on value generation resulting from the use of technology (Soh and Markus, 1995) and use of e-commerce (Zhu and Xu, 2004) in other contexts. According to this prior research (Cooper and Zmud, 1990; DeLone and Mclean, 1992, 2003), firms that deploy appropriate resources to develop and adopt technology innovations rewarded with enhanced business value. Using a structural equation modeling technique, this study verifies the intermediate technology adoption processes—internal assimilation and external diffusion—that connect operational resource planning and capability development to operational excellence and organizational success. Thus, this study directly complements the prior literature that emphasizes technology capability development and use as a significant influence on business value generated. This study also validated theoretically driven antecedents such as firms’ managerial knowledge, partner dependence, and IT intensity in the supply chain trading networks as significantly affecting firms’ technology adoption decisions. The results also indicate that firms’ IT-enabled supply chain management is, to a significant extent, contingent on institutional factors. External institutional factors, like strong partnerships reflecting the closeness among firms, competition intensity, best practice leaders in the supplier chain, and the presence of push and pull channels for disseminating information about new technologies (Rogers, 1995), significantly affect the sophistication of firms’ IT-enabled supply chain management. From the empirical results, we find that partner dependence in supplier chains has a significant influence on firms’ internal e-commerce adoption. The results suggest that that imitation and enforcement may be significant way that firms learn from their supplier chain partners in ramping up the technological sophistication of their operations. IT usage intensity and partner interdependence are among the key environmental factors that were found to have a positive influence on external diffusion of technologies for supplier chain operations. The impact of IT usage intensity is interesting: when partners in a supplier chain begin to adopt new technologies, the effect spreads and

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other firms respond by also adopting the new applications. Thus, both ‘‘inside-out’’ demand for inter-organizational integration and collaboration and ‘‘outside-in’’ institutional pressure in a supply chain drives technology adoption in operations strategy. The influence of partner interdependence suggests that the greater the mutual dependence between a firm and its suppliers, the greater the likelihood that the firm will adopt new technologies for supplier chain operations. Furthermore, the interrelationship between firms’ internal assimilation and external diffusion is found to be significant. The results show clearly how institutional factors influence firms’ adoption. This study identifies internal organizational resource factors and institutional factors from supply chain environments that play a key role in firms’ adoption of technologies for supply chain operations. It simultaneously highlights the importance of sustaining higher levels of managerial IT knowledge and understanding partners’ technology adoption actions and interrelationships as being key variables. Understanding such factors can help operations management practitioners design appropriate operations strategies for success and benefit in an increasingly complex (both globally and technologically) supplier chain network environment. For example, before launching significant supplier chain initiatives whose benefits are premised on sophisticated technical operations (such as business intelligence-based optimization; and radio-frequency tagging-based tracking and optimization), senior operations executives may want to ensure that managerial IT knowledge is at a high level in the organization and that incentives and/or disincentives are in place to ensure supplier chain partner compliance and collaboration. At the organizational level, firms will generally have lesser flexibility when it comes to choosing an appropriate adoption pace to meet market change, because both internal and external diffusion are intricately linked and can contribute significantly to final business value. In environments characterized by high levels of partner dependence and IT usage intensity, operations managers need to plan implementations of technologies for supplier chain operations carefully. Although firms can benefit from technology adoption in internal operations, as well as in external collaboration, it might be difficult to measure the share of benefits accrued to partners with the use of technology (e.g., ERP) as a network. To understand this point better, a dyadicfocus perspective can be applied as an extension to the focal firm perspective in future studies. Prior research and this study have primarily considered the context from a focal firm point of view, i.e., from one side of a buyer–seller dyad. Examples include Chwelos et al. (2001) and Teo et al. (2003), which examined the determinants of EDI adoption, Zhu et al. (2006a, b), which examined e-business adoption and Bala and Venkatesh (2007) which examined the determinants of interorganizational business process standard adoption. However, a focal firm perspective does not fully capture the essence of the dyadic phenomenon in supply chain collaboration. It is valuable to understand the inter-dependent and

joint effects of buyer–seller factors on technology adoption as two sides of the supply chain. From a dyadic perspective, the fit between buyer–seller’s expected benefit, cost-sharing concern, technology compatibility, and relational trust must draw more attention in future research. Paired firms with defined buyer–seller relationships, instead of any single firm, should be surveyed simultaneously in supply chains. Jap and Anderson (2003) and Perrone et al. (2003) have made some advances from this perspective.

7. Conclusion and limitations Reflecting the complexity and multi-facet nature of modern operations and supplier chain management, this paper has integrated two key theoretical perspectives from the strategy literature within the overlapping context of technology implementation for operations management. By investigating the relation between adoption of appropriate technologies internally, for operations such as supplier selection, purchase order processing, invoicing, logistics planning, and demand management, and externally for use in B2B transactions with partners in supply networks, this study presents a clearer understanding of how firms can benefit from both external and internal technology adoption (and their interaction) to improve operational performance. The researchers acknowledge the usual limitations pertaining to cross-sectional analysis of industry phenomenon at a single point in time. While we have worked diligently to protect and strengthen the internal validity of their research model, we acknowledge that the generalizability of our findings may be limited to the context in which their study was undertaken. Firstly, the response rate may challenge the representativeness of the total population, although the distributions of firms’ annual revenue, size, type and respondents’ position is shown to be at reasonable balanced levels. Secondly, sampling is not totally randomized to cover all Chinese firms. This may challenge the generality of the sample, although we have evaluated the distribution of the sample for make-up verification. Thirdly, the sample is more oblique to the manufacturing industry than to the service sector reflecting our population and this can hinder the external validity of our results. Our sample size also limits our ability to further investigate potential sector-by-sector differences in technology adoption.

Acknowledgements This research has been supported by the National Science Fund of China (Grant no. 70572027, 70571016, 70801017) and the Systems Testing Excellence Program of the FedEx Institute of Technology at the University of Memphis. The authors would like to thank the guest editors and anonymous reviewers for their valued feedback in improving the manuscript.

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Appendix. Operationalized questionnaire (as translated from Chinese)

A study of managerial perceptions of the adoption of electronic business Electronic business (EB) is the use of information technology (IT) for facilitating intra- and inter-organizational business operations, which is also widely recognized as electronic commerce (EC). This study seeks to understand the factors impacting the adoption and implementation of electronic business by organizations with established IT departments or divisions. In completing the questionnaire, please answer the questions from your personal knowledge and experience. The questionnaire would require about 20 min of your time. We assure you that total privacy of your responses will be maintained. Your company or yourself will not be identified in any presentation of the survey results. Your completion and return of this survey indicates voluntary consent to participate in this study. As a token of appreciation for your participation, we will make available to you an executive summary of the results of the study, which will be useful to your company in formulating and implementing effective electronic commerce decisions and strategies. Thank you very much for your co-operation! Note: An E-business application (EB) is a web-based information system that is used for transacting business with your customers/suppliers/other partners. Web-based applications that are used purely for providing product/service information (brochure-ware) do NOT qualify as EB applications. How you would rate the extent of adoption of EB applications in your organization? (Check only one) & No consideration of any EB applications & Some discussion on EB applications, but no further action & Some consideration but no decision to have an EB applications yet & Decision to have EB applications made, but implementation in progress & We currently use one or more EB applications Does your company have a formal plan for E-business? Does your company has a dedicated Team/Committee for carrying out EB efforts? In which year did your company implement first major EB application? ___________

Yes No Yes No

Check all the activities in your firm that are currently being performed using EB applications and mention the extent to which the EB applications are being used in that activity Extent of use of E-business in activity None Large extent Supplier selection (getting quotes, bids, etc.)yyyyy 1 2 3 4 5 6 7 Purchase order processingyyyyyyy 1 2 3 4 5 6 7 Procurement from suppliers (distribution, warehouse, shipping, logistics, etc.)yyyyyyy 1 2 3 4 5 6 7 Invoicing and payment processingyyyyyyy 1 2 3 4 5 6 7 Demand management (procurement analysis)yyyyyyy 1 2 3 4 5 6 7 External Environment If your organization currently uses EB applications to transact with customers or suppliers, please complete this section based on the situation when decision to adopt EB was being made. For those who do not have an EB application, please respond based on your current situation. Strongly disagree Strongly agree It is important to have continued business relationship with our suppliers to achieve our business 1 2 3 4 5 6 7 goalsyyyyyyy Significant proportion of our total profits can be related to profits from sale/service to our 1 2 3 4 5 6 7 suppliersyyyyyyy It is easy for our suppliers/partners to switch from our firm to our competitors for their purchasing 1 2 3 4 5 6 7 requirementsyyyyyyy Our suppliers/partners are highly dependant on our supplies/services for achieving their business 1 2 3 4 5 6 7 goalsyyyyyyy Our suppliers/partners have high bargaining poweryyyyyyy 1 2 3 4 5 6 7 Strongly disagree Our organization actively keeps track of new initiatives of competitorsyyyyyyy Competitor moves are monitored very closelyyyyyyyy Information on competitors are considered very important for decision making in our firmyyyyyyy

1 1 1

2 2 2

3 3 3

4 4 4

5 5 5

Strongly agree 6 7 6 7 6 7

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To what extent do you agree with the following statements about your top management, functional area managers and IS Not at all Our top management is interested in the use of EB applications in our firmyyyyyyy 1 2 3 Our top management considers EB applications as important to our organizationyyyyyyy 1 2 3 Our top management is committed to supporting EB applications in our firmyyyyyyy 1 2 3 To what extent does your top management recognize the potential of EB for future success of your 1 2 3 firm?yyyyyyy To what extent do your functional area managers recognize the potential of EB for future success of firm? 1 2 3 To what extent do your functional managers appreciate the potential of EB in improving business 1 2 3 processes?yyyyyyy How informed is your IS staff about the business operations of your firm?yyyyyyy 1 2 3 How informed is your IS staff about the business strategies of your firm?yyyyyyy 1 2 3

staff? 4 4 4 4

5 5 5 5

6 6 6 6

Greatly 7 7 7 7

4 4

5 5

6 6

7 7

4 4

5 5

6 6

7 7

How would you rate the extent of EB diffusion in your organization? Rate the extent of diffusion in a 1–7 scale and also indicate a percentage (%) across each of the following items Low Proportion of total suppliers with whom you interact through EB applications yyyyyyy Proportion of total supplier transactions done through EB applicationsyyyyyyy Proportion of overall interactions with suppliers through EB applicationsyyyyyyy

High

1 1 1

2 2 2

3 3 3

4 4 4

5 5 5

6 6 6

7 7 7

Please circle the appropriate number for the following set of statements about the decision-making responsibilities for the following IT activities in your firm Business unit Joint IT responsibility responsibility responsibility Application development (including outsourcing)yyyyyyy 1 2 3 4 5 6 7 Procurement of hardware/softwareyyyyyyy 1 2 3 4 5 6 7 IT operations and maintenanceyyyyyyy 1 2 3 4 5 6 7 Staffing IT positionsyyyyyyy 1 2 3 4 5 6 7 Capital budgeting decisions related to ITyyyyyyy 1 2 3 4 5 6 7 Please circle the appropriate number for the following set of statements about your IT staff Strongly disagree We have clear, documented job descriptions for all IT staffyyyyyyy Operating rules and procedures play an important roles in how decisions are handled in our IT groupyyyyyyy Task forces and committees are frequently used by our IT group to handle critical issuesyyyyyyy Formal procedures and guidelines exist in our IT group for evaluating new technologies and ideasyyyyyyy

1 1

2 2

3 3

4 4

5 5

Strongly agree 6 7 6 7

1 1

2 2

3 3

4 4

5 5

6 6

Please circle the appropriate number for each of the following statements that best describes the state of the external IT environment commerce (EB) Very low The IT activities and practices of our competitors areyyyyyyy 1 2 3 4 5 The use of IT by our key suppliers isyyyyyyy 1 2 3 4 5 The use of IT by our key customers isyyyyyyy 1 2 3 4 5 The use of IT by key business partners and other agents with whom we interact isyyyyyyy 1 2 3 4 5

7 7

for electronic Very 6 6 6 6

high 7 7 7 7

Expected and realized benefits from electronic business applications Expected benefits Low 1 1 1 1 1 1

High 2 2 2 2 2 2

3 3 3 3 3 3

4 4 4 4 4 4

5 5 5 5 5 5

6 6 6 6 6 6

7 7 7 7 7 7

Improved customer service Better inventory control Reduced operations costs Reduced cycle time Better relationship with suppliers Generate competitive advantage

Benefits realized till now Low

High

1 1 1 1 1 1

6 6 6 6 6 6

7 7 7 7 7 7

( ) YES

( ) NO

2 2 2 2 2 2

3 3 3 3 3 3

4 4 4 4 4 4

Please indicate whether there is/was one individual who enthusiastically championed the cause of EB applications in your firm: If yes, please provide information on that person: Department the person belong to: _______________________ Title/Designation of the person _______________________

5 5 5 5 5 5

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Company Information: Name of your organization (optional): _______________________ Location of your organization (headquarter) __________________ Your designation: ______________________________________ Your work experience (in years)___________________________ Main operating industry: &

Architecture/Engineering

&

& & & & &

Business services Chemicals Computer/IT related Finance/Banking/Insurance Manufacturing

& & & & &

Organization type: ? Multi-national

Pharmaceutical/Medical/ Healthcare Real Estate/Property Retail/Trading Transportation Travel/Tourism/Hotel Others (specify) _____________________

? Government-owned (fully/partly owned) ? Local company with foreign ownership (JV) Total number of IT staff: ______________________

? Locally owned Total number of employees: ______________________

Annual Revenue (in RMBf millions) ______________________ A: o1 B: 1–5 C: 5–10 D: 10–50 E: 50–100 F: 100–1000 G: 41000 Experience of the senior-most IT executive Number of years in IT function: _____________ and in business functions To whom does the senior-most IT executive report to? _______________________

References Armstrong, C.P., Sambamurthy, V., 1999. Information technology assimilation in firms: The influence of senior leadership and IT infrastructures. Information Systems Research 10 (4), 304–327. Bala, H., Venkatesh, V., 2007. Assimilation of interorganizational business process standards. Information Systems Research 18 (3), 340. Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management 17 (1), 99–120. Barua, A., Kriebel, H.C., Mukhopadhyay, T., 1995. Information technology and business value: An analytic and empirical investigation. Information Systems Research 6 (1), 3–23. Berger, P.L., Luckmann, T., 1966. The Social Construction of Reality. Doubleday, New York. Bharadwaj, A.S., 2000. A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly 24 (1), 169–196. Bharadwaj, A.S., Sambamurthy, V., Zmud, R.W., 1998. IT capabilities: Theoretical perspectives and empirical operationalization, In: Hirschheim, R., Newman, M., Degross, J.I. (Ed.), Proceedings of the 19th International Conference on Information Systems, Helsinki, Finland, pp. 378–385. Boynton, A.C., Zmud, R.W., Jacobs, G.C., 1994. The influence of IT management practice on IT use in large organizations. MIS Quarterly 18 (3), 299–318. Byrd, T.A., Turner, D.E., 2000. Measuring the flexibility of information technology infrastructure: Exploratory analysis of a construct. Journal of Management Information Systems 17 (1), 167–208. Carlson, D.S., Kacmar, K.M., 2000. Work–family conflict in the organization: Do life role values make a difference? Journal of Management 26 (5), 1031–1054. Chae, B., Yen, H.R., Sheu, C., 2005. Information technology and supply chain collaboration: Moderating effects of existing relationships between partners. IEEE Transactions on Engineering Management 52 (4), 440–448. Chin, W.W., 1998. Issues and opinion on structure equation modeling. MIS Quarterly 22 (1), vii–xvi. Chin, W.W., 2001. PLS-Graph User’s Guide. Soft Modeling Inc. Chin, W.W., Newsted, P.R., 1999. Structural equation modeling analysis with small samples using partial least squares. In: Hoyle, R. (Ed.), Statistical Strategies for Small Sample Research. Sage Publications, Beverley Hills, CA, pp. 307–341.

Chwelos, P., Benbasat, I., Dexter, A.S., 2001. Research report: Empirical test of an EDI adoption model. Information Systems Research 12 (3), 304–321. Cohen, P.R., Carlson, A., Ballesteros, L., Amant, R.S., 1993. Automating path analysis for building causal models from data. In: Proceedings of the International Workshop on Machine Learning, pp. 57–64. Cooper, R.B., Zmud, R.W., 1990. Information technology implementation research: A technological diffusion approach. Management Science 36 (2), 123–139. Cui, L., Zhang, C., Zhang, C., Huang, L., 2006. Exploring e-government impact on Shanghai firms’ informatization process. Electronic Markets 16 (4), 312–328. Davenport, T.H., 1993. Process Innovation: Reengineering Work through Information Technology. Harvard Business School Press. Deeter-Schmelz, D.R., Bizzari, A., Graham, R., Howdyshell, C., 2001. Business-to-business online purchasing: Suppliers’ impact on buyers’ adoption and usage intent. Journal of Supply Chain Management 37 (1), 4–10. DeLone, W.H., Mclean, E.R., 1992. Information system success: The quest for the dependant variable. Information Systems Research 3 (1), 60–95. DeLone, W.H., Mclean, E.R., 2003. The Delone and Mclean model of information system success: A ten-year update. Journal of Management Information Systems 19 (4), 9–30. DiMaggio, P.J., Powell, W.W., 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review 48 (2), 147–160. Duncan, N.B., 1995. Capturing flexibility of information technology infrastructure: A study of resource characteristics and their measure. Journal of Management Information Systems 12 (2), 37–57. Elangovan, A.R., Xie, J.L., 1999. Effects of perceived power of supervisor on subordinate stress and motivation: The moderating role of subordinate characteristics. Journal of Organizational Behavior 20 (3), 359–373. Falk, R.F., Miller, N.B., 1992. A Primer for Soft Modeling. University of Akron Press, Akron. Fornell, C., Bookstein, F.L., 1982. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research 19, 440–452. Fornell, C., Larcker, D.F., 1981. Evaluating structural models with unobserved variables and measurement errors. Journal of Marketing Research 18 (1), 39–50. Foster, W., Zhang, C., Dedrick, J., Kraemer, K.L., 2006. Technology and organizational factors in the notebook industry supply chain. CAPS

ARTICLE IN PRESS 268

C. Zhang, J. Dhaliwal / Int. J. Production Economics 120 (2009) 252–269

(Center for Strategic Supply Research) Research Report, CAPS (Center for Strategic Supply) Research. Frohlich, M.T., 2002. E-integration in the supply chain: Barriers and performance. Decision Sciences 33 (4), 537–556. Gibbs, J.L., Kraemer, K.L., 2004. A cross-country investigation of the determinants of scope of e-commerce use: An institutional approach. Electronic Markets 14 (2), 124–137. Grabowski, M., Lee, S., 1993. Linking information systems application portfolios and organizational strategy. In: Banker, R.D., Kauffman, R.J., Mahmood, M.A. (Eds.), Strategic Information Technology Management: Perspectives on Organizational Growth and Competitive Advantage. Idea Group Publishing, Harrisburg, PA, pp. 33–54. Grover, V., Goslar, M.D., 1993. The initiation, adoption and implementation of telecommunications technologies in US organizations. Journal of Management Information Systems 10 (1), 141–163. Gunasekaran, A., Chung, W.W.C., 2004. Special issue on supply chain management for the 21st century organizational competitiveness. International Journal of Production Economics 87 (3), 209–212. Haenlein, M., Kaplan, A.M., 2004. A beginner’s guide to partial least squares analysis. Understanding Statistics 3 (4), 283–297. Hart, P.J., Saunders, C.S., 1998. Emerging electronic partnerships: Antecedents and dimensions of EDI use from the supplier’s perspective. Journal of Management Information Systems 14 (4), 87–111. Hassanein, K., Head, M., 2007. Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping. International Journal of Human–Computer Studies 65 (8), 689–708. Iacovou, C.L., Benbasat, I., Dexter, A.S., 1995. Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly 19 (4), 465–485. Jap, S.D., Anderson, E., 2003. Safeguarding interorganizational performance and continuity under ex post opportunism. Management Science 49 (12), 1684–1701. Ketchen Jr., D.J., Hult, G.T.M., 2007. Toward greater integration of insights from organization theory and supply chain management. Journal of Operations Management 25, 455–458. Kraemer, K., Gibbs, J., Dedrick, J., 2002. Environment and policy facilitators shaping e-commerce diffusion: A cross-country comparison. In: Applegate, L., Degross, J.I., R.G. (Eds.), Proceedings of the 23rd International Conference on Information System. Wiley, Barcelona, Spain. Lee, D.M.S., Trauth, E., Farwell, D., 1995. Critical skills and knowledge requirements of is professionals: A joint academic/industry investigation. MIS Quarterly 19 (3), 313–340. Lee, H.L., 2002. Aligning supply chain strategies with product uncertainties. California Management Review 44 (3), 105–119. Lee, H.L., So, K.C., Tang, C.S., 2000. The value of information sharing in a two-level supply chain. Management Science 46 (5), 626–643. Lohmoller, J.B., 1989. Latent Variable Path Modeling with Partial Least Squares. Springer, New York. Malhotra, N.K., Kim, S.S., Patil, A., 2006. Common method variance in is research: A comparison of alternative approaches and a reanalysis of past research. Management Science 52 (12), 1865–1883. Manecke, N., Schoensleben, P., 2004. Cost and benefit of Internet-based support of business processes. International Journal of Production Economics 87 (3), 213–229. Manthou, V., Vlachopoulou, M., Folinas, D., 2004. Virtual e-chain (VeC) model for supply chain collaboration. International Journal of Production Economics 87 (3), 241–250. Markus, L., Steinfield, C., Wigand, R., Minton, G., 2006. Industry-wide information systems standardization as collective action: The case of the US residential mortgage industry. Management Information Systems Quarterly 30 (5), 439–466. Markus, M.L., Soh, C., 1993. Banking on information technology: Converting IT spending into firm performance. In: Banker, R.D., Kauffman, R.J., Mahmood, M.A. (Eds.), Strategic Information Technology Management: Perspectives on Organizational Growth and Competitive Advantage. Idea Group Publishing, Harrisburg, PA, pp. 375–403. Mata, F.J., Fuerst, W.L., Barney, J.B., 1995. Information technology and sustained competitive advantage: A resource-based analysis. MIS Quarterly 19 (4), 487–505. McLaren, T., Head, M.M., 2004. Strategic fit of supply chain management information systems: A measurement model. In: 25th International Conference on Information Systems, pp. 597–606. Melville, N., Kraemer, K., Gurbaxani, V., 2004. Review: Information technology and organizational performance: An integrative model of IT business value. MIS Quarterly 28 (2), 283–322.

Meyer, J.W., Rowan, B., 1977. Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology 83 (2), 340. Neter, J., Wasserman, W., Kutner, M.H., 1990. Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs. Irwin, Homewood, IL. Nevo, S., Wade, M.R., Cook, W.D., 2007. An examination of the trade-off between internal and external IT capabilities. Journal of Strategic Information Systems 16, 5–23. Ngai, E.W.T., Lai, K.H., Cheng, T.C.E., 2008. Logistics information systems: The Hong Kong experience. International Journal of Production Economics 113 (1), 223–234. Nurmilaakso, J., 2008. Adoption of e-business functions and migration from EDI-based to XML-based e-business frameworks in supply chain integration. International Journal of Production Economics 113 (2), 721–733. Oliver, C., 1997. Sustainable competitive advantage: Combining Institutional and resource-based views. Strategic Management Journal 18 (9), 697–713. Perrone, V., Zaheer, A., Mcevily, B., 2003. Free to be trusted? Organizational constraints on trust in boundary spanners. Organization Science 14 (4), 422–439. Podsakoff, P.M., Organ, D.W., 1986. Self-reports in organizational research: Problems and prospects. Journal of Management 12 (4), 531–544. Podsakoff, P.M., Mackenzie, S.B., Lee, J.Y., Podsakoff, N.P., 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology 88 (5), 879–903. Powell, T.C., Dent-Micallef, A., 1997. Information technology as competitive advantage: The role of human, business, and technology resources. Strategic Management Journal 18 (5), 375–405. Premkumar, G., Ramamurthy, K., 1994. Implementation of electronic data interchange: An innovation diffusion perspective. Journal of Management Information Systems 11 (2), 157–186. Premkumar, G., Ramamurthy, K., 1995. The role of interorganizational and organizational factors on the decision mode for adoption of interorganizational systems. Decision Sciences 26 (3), 303–336. Purvis, R.L., Sambamurthy, V., Zmud, R.W., 2001. The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science 12 (2), 117–135. Ramamurthy, K., Premkumar, G., 1995. Determinants and outcomes of electronic data interchange diffusion. IEEE Transactions on Engineering Management 42 (4), 332–351. Ranganathan, C., Dhaliwal, J.S., Teo, T.S.H., 2004. Assimilation and diffusion of web technologies in supply-chain management: An examination of key drivers and performance impacts. International Journal of Electronic Commerce 9 (1), 127–161. Robertson, T.S., Gatignon, H., 1986. Competitive effects on technology diffusion. Journal of Marketing 50 (3), 1–12. Rogers, E.M., 1995. Diffusion of Innovations. The Free Press, New York. Sambamurthy, V., Bharadwaj, A., Grover, V., 2003. Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Quarterly 27 (2), 237–263. Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., 2000. Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. Irwin/McGraw-Hill. Soh, C., Markus, M.L., 1995. How IT creates business value: A process theory synthesis. In: Degross, J.I., Ariav, G., Beath, C., Hoyer, R., Kemerer, C. (Eds.), 16th International Conference on Information Systems, pp. 29–41. Straub, D., 1989. Validating instruments in MIS research. MIS Quarterly 13 (2), 147–169. Subramani, M., 2004. How do suppliers benefit from IT use in supply chain relationships. MIS Quarterly 28 (1), 45–74. Sun, H., Zhang, P., 2006. Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems 7 (9), 618–645. Swanson, E.B., 1994. Information systems innovation among organizations. Management Science 40 (9), 1069–1092. Tan, Z., Ouyang, W., 2004. Diffusion and impacts of the Internet and ecommerce in China. Electronic Markets 14 (1), 25–35. Teo, H.H., Wei, K.K., Benbasat, I., 2003. Predicting intention to adopt interorganizational linkages: An institutional perspective. MIS Quarterly 27 (1), 19–49. Teo, T.S.H., Ranganathan, C., Dhaliwal, J., 2006. Key dimensions of inhibitors for the deployment of web-based business-to-business

ARTICLE IN PRESS C. Zhang, J. Dhaliwal / Int. J. Production Economics 120 (2009) 252–269

electronic commerce. IEEE Transactions on Engineering Management 53 (3), 395–411. Thatcher, S.M.B., Foster, W., Zhu, L., 2006. B2b e-commerce adoption decisions in Taiwan: The interaction of cultural and other institutional factors. Electronic Commerce Research and Applications 5, 92–104. Tornatzky, L.G., Fleischer, M., 1990. The Processes of Technological Innovation. Lexington Books, Lexington, MA. Wade, M., Hulland, J., 2004. Review: The resource-based view and information systems research: Review, extension, and suggestions for future research. MIS Quarterly 28 (1), 75–105. Williams, L.J., Cote, J.A., Ronald Buckley, M., 1989. Lack of method variance in self-reported affect and perceptions and work: Reality or artifact? Journal of Applied Psychology 74 (3), 462–468. Xu, S., Zhu, K., Gibbs, J., 2004. Global technology, local adoption: A crosscountry investigation of Internet adoption by companies in the United States and China. Electronic Markets 14 (1), 13–24. Zhu, K., 2004. The complementarity of information technology infrastructure and e-commerce capability: A resource-based assessment of their business value. Journal of Management Information Systems 21 (1), 167–202.

269

Zhu, K., Kraemer, K., 2005. Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry. Information Systems Research 16 (1), 61–84. Zhu, K., Kraemer, K.L., 2003. E-commerce metrics for net-enhanced organizations: Assessing the value of e-commerce to firm performance in the manufacturing sector. Information Systems Research 13 (3), 275–295. Zhu, K., Xu, S., 2004. The value of information technology in e-business environments: The missing links in the renewed IT value debate. In: 25th International Conference on Information Systems, pp. 955–966. Zhu, K., Kraemer, K.L., Gurbaxani, V., Xu, S.X., 2006a. Migration to openstandard interorganizational systems: Network effects, switching costs, and path dependency. MIS Quarterly 30 (s), 515–539. Zhu, K., Kraemer, K.L., Xu, S., 2006b. The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science 52 (10), 1557–1576. Zucker, L.G., 1977. The role of institutionalization in cultural persistence. American Sociological Review 42 (5), 726–743. Zucker, L.G., 1987. Institutional theories of organization. Annual Review of Sociology 13, 443–464.