Deploying information technology (IT) in the firm’s interorganizational processes to derive competitive advantage has generated much excitement among information systems (IS) researchers and practitioners (Lui et al. 2013; Lu et al. 2011; Roberts et al. 2012; Tippins et al. 2003). IT competency enables the firm to establish a requisite set of technological resources, providing the foundation for diffusing IT applications along the supply chain (Tu 2010). However, the empirical findings on IT-performance linkages are mixed. For example, some researchers provide empirical support for positive IT-performance linkage (Bhatt et al. 2005; Kim et al. 2013), others show an insignificant relationship (Carr 2003). Therefore, there is a need for fully exploring the relationship between IT and firm performance especially in the supply chain context (Liu et al. 2013).
Although it is well touted that IT competency can lead to superior firm performance, the earlier research has been criticized for lack of a unified conceptualization of IT competency (Tippins et al. 2003). In particular, the mixed findings of previous empirical studies on IT-performance relationship may be due to the incomplete conceptualizations of IT competency (Ray et al. 2005; Wu et al. 2006). Therefore, by extending prior research, the first objective of our study is to refine the conceptualization of IT competency reflected in three dimensions–IT objects, IT operations, and IT knowledge–and investigate how these three types of IT competencies can provide differential benefits to the firm based on the resource-based view (RBV).
Under the context of supply chain, however, firms increasingly engage in interorganizational communication (IOC) to leverage each other’s information and knowledge for achieving mutual goals (Liu et al. 2010; Liu et al. 2012; Lyer et al. 2009). In this view, RBV only stresses the internal resources the firm possesses, which have been criticized for its failure to consider the linkages with external supply chain partners (Priem et al. 2001; Sirmon et al. 2007). While the relational view (RV) posits that a firm’s critical resources not only include those housed with its limits but also those embedded in its external relationships, and thus addressing the limitation in RBV (Powell et al. 1996; Rai et al. 2012). Furthermore, researchers indicate that the value of IT competency can be augmented when it is embedded in interorganizational processes through resource complementarities and co-specialization (Marchildon et al. 2011; Zhu 2004). Wade and Hull and (2004) state that “information systems exert their influence on the firm through complementary relationships with other firm assets and capabilities” (p. 109). They further suggest that key moderating variables should be considered in the studies of IT-performance relationship. Following this call, the second objective of our study is to investigate the moderating effects of IOC on the relationship between the three types of IT competencies and firm performance through the theoretical lens of RV.
To this end, we conceptualize IT competency in terms of IT objects, IT operations, and IT knowledge based on the basic tenets of the RBV (Barney 1991; Bhatt et al. 2005) and the existing IS literature (Lu et al. 2011; Tippins et al. 2003). Drawing upon the RBV and RV, we examine the extent to which the three types of IT competencies affect firm performance and how they are influenced by IOC. Specifically, by drawing on the RBV, we first distinguish between the three types of IT competencies and examine three distinct logics (i.e., the logic of positioning, leverage, and opportunity;Sambamurthy et al. 2003) through which the three different types of IT competencies impact firm performance. Then, we address the moderating role of the IOC from the RV, by investigating the IOC as a manifestation of the relational competency (Paulraj et al. 2008), where IOC represents the degree to which a firm strategically shares information with supply chain partners (Rai et al. 2006; Rosenzweig 2009).
2 Theoretical Framework
2.1 Resource-Based View
RBV indicates that variations of performance among firms can be attributed to the heterogeneity in their possession of valuable, rare, inimitable, and non-substitutable resources and capabilities (Barney 1991). It is based on the assumptions that the resources “needed to conceive, choose, and implement strategies are heterogeneously distributed across firms and these firm differences remain stable over time” (Bharadwaj 2000, p. 171). As such, a firm will be able to attain and sustain competitive advantage by combining its resources in a unique and inimitable way (Marchildon et al. 2011). Following this notion, RBV is useful to provide a cogent perspective for analyzing how IT competency relates to firm performance.
IS researchers thus increasingly adopt the RBV to examine the effect of IT on firm performance (Bharadwaj 2000; Bhatt et al. 2005; Lim et al. 2011). However, the existing research primarily focuses on external positioning or leveraging the firm’s IT to create competitive advantage (Bhatt et al. 2005; Liu et al. 2013; Rai and Tang 2010; Tippins et al. 2003). Less is known about how the IT can help detect the windows of opportunity with proactive stance to gain competitive advantage such as improvisational capabilities, especially in the highly turbulent environment (Lu et al. 2011; Pavlou et al. 2010). As such, we draw from the study by Sambamurthy et al. (2003) and further unpack the underlying casual mechanisms through which IT competency influences firm performance. Accordingly, we identify three distinct logics that can describe the role of IT competency in shaping superior firm performance, namely, the logic of positioning, leverage, and opportunity.
First, the logic of positioning describes superior firm performance in terms of the strategic positions of a firm and the extent to which the positions are executed (Bhatt et al. 2005; Sambamurthy et al. 2003). Strategic positioning helps the firm create uniqueness and value by raising entry barriers, linking closely with suppliers and customers, and providing new products and services through the integrated strategic systems (Bhatt et al. 2005). However, this logic does not explain how firms can build the inimitable differentiators in a dynamic environment with open IT architectures (Bhatt et al. 2005; Sirmon et al. 2007). Second, the logic of leverage frames superior firm performance in terms of the deployment and configuration of idiosyncratic, valuable, inimitable, and firm-specific resources and capabilities that might be difficult to transfer to another firm (Barney 1991; Sambamurthy et al. 2003). Firms generally leverage two distinct strategic mechanisms: resource-picking and capability-building (Makadok 2001). Specifically, resource-picking mechanism creates economic rents when the firm applies superior information and knowledge to gain the value from the resources, while capability-building mechanism highlights the firm’s ability to integrate, build, and reconfigure internal and external resources and competencies to achieve competitive advantage in a rapidly changing business environment (Eisenhardt et al. 2000; Teece et al. 1997). It is also indicated that capability-building process is more significant than the resource-picking process (Oh et al. 2012). Third, the logic of opportunity emphasizes superior firm performance in terms of relentless innovation and exploitation of existing resources and capabilities to create business opportunities (Lu et al. 2011; Sambamurthy et al. 2003). This logic assumes that the firm should be opportunity oriented and has the strategic knowledge and insight to detect and seize the windows of opportunity that emerge in the fast-paced business environments (Lu et al. 2011; Tippins et al. 2003).
2.2 Relational View
RV suggests that a firm can leverage not only the critical resources housed with its limits but also those embedded in its external relationships to create relational value (Dyer et al. 1998; Powell et al. 1996; Rai et al. 2012). Dyer et al. (1998) defined relational value as mutual benefits that were created by two or more firms and further proposed four key components that can determine the relational value: relationship-specific assets, information/knowledge-sharing routines, complementary strategic resources and capabilities, and effective relational governance. Under this condition, the strategic resources of a firm may span its boundaries and embed in interorganizational routines and processes with supply chain partners (Dyer et al. 1998). In the context of supply chain, the increasing interorganizational dependence motivates firms to make more relationship-specific investments, share more idiosyncratic information and knowledge, and build stronger inter-firm capabilities to create relational competency, such as IOC, which in turn, can be positioned, leveraged, or improvised to better explore and exploit the business value of IT (Grover et al. 2012; Rai et al. 2012; Sambamurthy et al. 2003; Saraf et al. 2007). Accordingly, we suggest that IOC can generate relational value by means of relationship-specific assets, complementary resources and capabilities, and information/knowledge-sharing routines (Rosenzweig 2009).
Based on the RV, IOC makes firms work with their supply chain partners to share strategic information to create relational value, which in turn, helps rejuvenate and reconfigure IT competency such as investing updated IT assets, bundling new IT resource portfolios, and exploring new IT innovation opportunities (Saeed et al. 2011). In this view, IOC can leverage the firm’s external resources and push the firm to integrate and reconfigure its IT competency in a unique way, and thus generate a competitive advantage (Piccoli et al. 2005; Wu et al. 2006).
3 Research Model and Hypotheses
Based on the RBV and RV, we develop a research model to examine (1) the impacts of IT competency on firm performance by distinguishing between the three types of IT competencies (i.e., IT objects, IT operations, and IT knowledge), and (2) how the impacts are moderated by IOC as depicted in Figure 1. Specifically, in what follows, we first refine the conceptualization of IT competency by drawing on the Tippins et al. (2003)and argue that the different types of IT competencies enhance firm performance through differential influential mechanism. We then introduce IOC as a relational competency of a firm, and argue that IOC positively moderates the relationships between the three types of IT competencies and firm performance.
3.1 IT Competency
On the basis of the results of prior research, we define IT competency as a firm’s capacity to utilize IT resources to support organizational business processes (Bharadwaj 2000; Sambamurthy et al. 2003; Saraf et al. 2007). IT competency can realize business value by embedding IT-enabled resources in support and enhancement of the firm’s strategies and processes (Devaraj et al. 2007; Li et al. 2009; Lyer et al. 2009; Wu et al. 2006). According to Tippins et al. (2003) and Yoon (2011), we conceptualize IT competency in terms of three co-specialized resources and capabilities: IT objects, IT operations, and IT knowledge. IT objects refer to computer-based hardware, software, and support personnel, which can be reflected in terms of financial investment for IT, a formal IT department, specialized IT managers, and customized software applications. Thus, IT objects act as enablers and provide the technological foundation that can be an effective source of value. IT operations refer to the extent to which a firm utilizes IT to manage market and customer information (Armstrong et al. 1999). It reflects the extent to which IT applications have been diffused into intra- and interorganizational processes (Bhatt et al. 2005; Lu et al. 2011; Tippins et al. 2003). In this view, IT operations are not only valuable but also heterogeneously distributed across firms and hard to imitate. IT knowledge refers to the extent to which a firm possesses a body of technical knowledge on IT (Armstrong et al. 1999; Kearns et al. 2006; Lu et al. 2011; Ranganathan et al. 2004). It reflects the extent to which a firm has recognized the importance of IT (Ranganathan et al. 2004). Thus, IT knowledge helps the firm sense and respond to IT innovations, and thus proactively detect and catch the windows of opportunity in highly turbulent environments in a better manner (Lu et al. 2011; Pavlou et al. 2010; Roberts et al. 2012).
3.2 Impact of IT Competency on Firm Performance
IT objects provide the technological foundation of IT innovations implementation and make information and knowledge more easily accessible and also easy to share between internal functions with a firm and among supply chain partners (Bharadwaj 2000; Rai et al. 2006). The strategically integrated infrastructure represents the firm’s commitments to a specific position (Liu et al. 2013; Lu et al. 2011). As such, IT objects are consistent with the logic of positioning and represent a superior position of a firm with respect to its rivals (Bhatt et al. 2005). Following this logic, the IT objects can be an effective source of value for a firm by locking-out competitors from imitating this position and locking-in itself to keep the position strategic mobility (Bharadwaj 2000; Sambamurthy et al. 2003).
From the perspective of RBV, IT objects, however, evolve over time and make it difficult for competitors to imitate and thus allow the firm to achieve superior performance (Bharadwaj 2000). In particular, such IT infrastructures can permit firms to flexibly and rapidly combine different IT-related resources with business processes, which in turn, positions the firm toward better sense the market, coordinate the operations, and optimize the resources (Rai et al. 2006; Sambamurthy et al. 2003). Under this condition, with high IT objects, firms can quickly build up new competencies for adapting to emerging business paradigms and easily realize the synergies between supply and demand with little added costs (Bharadwaj 2000; Kim et al. 2013; Lu et al. 2011). Thus, we hypothesize the following:
H1: A firm’s IT objects are positively associated with its firm performance.
IT operations emphasize the execution of related business strategies, which enables firms to realize the business value of IT (Armstrong et al. 1999). Therefore, IT operations are consistent with the logic of leverage and highlight the ability to bundle, recombine, and reconfigure IT-enabled resources in managing market and customer information (Sambamurthy et al. 2003; Sirmon et al. 2007). In this view, IT operations can help the firm effectively leverage its existing resources to create value for customers (Morgan et al. 2009; Sirmon et al. 2007; Teece et al. 1997).
For example, high IT operations help firms in the supply chain better integrate each other’s resources and align their goals and processes, and thus improve firms to more efficiently and effectively sense and respond to the market and customer (Roberts et al. 2012). However, firms are heterogeneous in the resource-picking and capability-building processes, and therefore they are likely to have different potential in leveraging IT for their competitiveness (Bhatt et al. 2005). Based on the RBV, the processes that enable IT resources to be combined and integrated into unique functionalities, which enable distinctive capabilities for improving firm performance, are socially complex, path-dependent, and inimitable (Zhu 2004). Thus, we expect the following:
H2: A firm’s IT operations are positively associated with its firm performance.
IT knowledge of a firm will affect its beliefs and participation in the IT innovation projects (Kearns et al. 2006). As such, IT knowledge of a firm helps better search for new ways to explore and exploit the IT infrastructure to create and capitalize on the business opportunities (Lu et al. 2011). In particular, with a high understanding of the strategic potential of IT, the firm would develop appropriate strategies to facilitate the creation of a clear vision for IT, and ensure the strategic use of IT (Bassellier et al. 2001). Under this condition, IT knowledge can help the firm respond quickly to environmental threats and leverage opportunities (Bhatt et al. 2005). Thus, IT knowledge is consistent with the logic of opportunity and describes the proactive stance to identify and select opportunities with IT initiatives to gain competitive advantage (Sambamurthy et al. 2003). Only if the firm has high IT knowledge can they mindfully detect and pursue the windows of opportunity emerging in the market and thus capture positions of competitive advantage (Lu et al. 2011; Sambamurthy et al. 2003).
A firm’s knowledge about IT also enables the firm’s continual learning and renewal, which could be a source of competitive advantage (Lu et al. 2011). Specifically, the firm’s knowledge about IT can be utilized to exploit and explore opportunities and thus prevent it from falling into lock-in technology rigidity, especially in the competitive and dynamic environment (Lu et al. 2011). As such, a firm’s knowledge about IT-related initiatives is firm-specific, durably heterogeneous, and difficult to be imitated by its rivals due to path dependence, and consequently creating a source of sustained competitive advantage (Lim et al. 2011; Tippins et al. 2003). Therefore, we hypothesize the following:
H3: A firm’s IT knowledge is positively associated with its firm performance.
3.3 Interorganizational Communication
Firms are increasingly developing strategic partnerships with their channel partners through IOC to more efficiently and effectively respond to the market needs (Paulraj et al. 2008). IOC refers to the degree to which a firm collaborates with its channel partners to share strategic information and knowledge (Kulp et al. 2004; Lee et al. 2004; Rai et al. 2006). In the context of IOC, firms adopt a “cooperation logic,” which moves away from conventional, arms-length relationships to specific, collaborative interorganizational relationships (Liu et al. 2010; Rai et al. 2012). As such, drawing on the RV, we conceptualize IOC as a relational competency, which may foster mutual learning and knowledge creation between supply chain partners that is critical for the firm’s competitive success (Malhotra et al. 2007; Paulraj et al. 2008). The new information and knowledge acquired from the IOC with external partners (suppliers and customers) can thus serve as a stimulus for more efficiently and effectively realizing the value of internal capabilities such as IT competency (Narasimhan et al. 2010). Under this condition, firms can not only position and configure the resources and capabilities housed within its limits but also detect and leverage the external resources and capabilities beyond its boundary (Marchildon et al. 2011; Rai and Tang 2010). Rai and Tang (2010) further posit that these resources and capabilities “that are embedded in partner relationships have emerged as an increasingly important source of a firm’s capabilities to develop, sustain, and renewal competitive advantage” (p. 519).
3.4 The Moderating Effect of IOC between IT Competency and Firm Performance
Based on the RBV and RV illustrated in Figure 1, we conceptualize the complementarity of IT competency and IOC as a moderation effect (Venkatraman 1989). We recognize that IOC can have a direct effect on firm performance. As such, we focus on how it moderates the relationship between IT competency and firm performance. Specifically, as a relational competency or arrangement, IOC that generates three kinds of relational value, namely, relationship-specific assets, complementary resources and capabilities, and information/knowledge-sharing routines can influence how the three types of IT competencies affect the firm performance in important ways.
Relationship-specific assets involved the firms in the supply chain to contribute specialized IT architectures and applications to better integrate with each other to create mutual value (Grover et al. 2012). IOC enables firms in the supply chain to jointly share information on production planning, inventory replenishment, and about promotions to develop new business process (Cai et al. 2010). In this view, firms participating in IOC can feel confident about further commitment to and investment in idiosyncratic IT infrastructure to seamlessly link with their partners within the supply chain (Liu et al. 2012; Villena et al. 2009). Following this logic, the firm’s strategic position of IT objects can be better established and executed in conjunction with channel partners’ assets through IOC (Grover et al. 2012; Sambamurthy et al. 2003).
Complementary resources and capabilities highlight leveraging the complementary resources and capabilities among the supply chain partners to generate relational value that cannot be generated by either firm in isolation (Dyer et al. 1998). IOC involves synergistic combinations of resources and capabilities across inter-firm boundaries through information sharing and collaborative planning, which may help firms better assimilate IT applications in supporting business processes (Handley 2012). The RBV also indicates that firms’ resources/capabilities can interact with each other, which can be a source of competitive advantage (Marchildon et al. 2011; Narasimhan et al. 2010; Voola et al. 2012). In this view, the impact of a firm’s IT operations on firm performance can be augmented by leveraging its channel partners’ resources and capabilities in virtue of IOC (Flynn et al. 2010; Rai and Tang 2010). For example, IOC helps firms better understand its partner network as well as its resources and capabilities, and thus can more effectively and efficiently deploy and configure IT applications in intra- and interorganizational processes to create value through a process, which is complex and imperfectly imitable process that may lead to sustained competitive advantage (Grover et al. 2012).
Information/knowledge sharing routines focus on sharing of information and knowledge on market environment between supply chain partners to create relational value (Grover et al. 2012). Under the condition of IOC, firms can share direct and real-time information and knowledge about market changes, such as sales, inventory holding, production, and delivery schedules along the supply chain (Kulp et al. 2004; Lee et al. 2004; Rai et al. 2006). In particular, firms in the supply chain that share their strategic insight and knowledge about IT can help firms more proactively sense and respond to IT opportunities emerging in market (Lu et al. 2011). Through these firms’ boundary-spanning activities, firms can help each other better interpret market signals and opportunities and thus develop the knowledge requisite for exploration and exploitation of IT (Malhotra et al. 2007).
In conclusion, drawing upon the RBV and RV, IT competency emphasizes on how to bundle, integrate, and reconfigure intra- and interorganizational IT resources into higher-order competencies. In contrast, IOC focuses on how and to what extent a firm should collaborate with its supply chain partners, which reflects an outwardlooking view of match between internal business processes and external channel partners (Villena et al. 2009; Zhou et al. 2010). As such, a firm’s IOC representing the strategic collaboration with partners through investing relationship-specific assets, bundling complementary resources and capabilities, and sharing information and knowledge should serve as stimuli, promoting IT objects, IT operations, and IT knowledge to better create value for the firm (Li et al. 2009; Rai et al. 2006; Zhou et al. 2010). Additionally, the process of integrating IT competency with IOC is of great complexity and casual ambiguity, which makes it difficult for competitors to imitate, thereby yielding sustained competitive advantage (Barney 1991; Saeed et al. 2011). Therefore, we propose that IOC will positively moderate all the three types of IT competencies impact on firm performance:
H4a: Interorganizational communication positively moderates the relationship between IT objects and firm performance.
H4b: Interorganizational communication positively moderates the relationship between IT operations and firm performance.
H4c: Interorganizational communication positively moderates the relationship between IT knowledge and firm performance.
4 Research Method
4.1 Research Design
To test our hypotheses, we collected data using a questionnaire survey. This survey was conducted in China. With a list provided by government agencies administering major industrial parks, we randomly selected 1,000 firms that had run their supply chains with IT applications. We further identified senior executives from each firm as data sources (Flynn et al. 2010; Liu et al. 2010; Paulraj et al. 2008). After the questionnaires had been sent out for two weeks, we made follow-up phone calls to encourage response. Finally, we received 258 useful questionnaires, with a response rate of 25.8%. To test for possible nonresponse bias, we compared the Chi-squares from the first 25% of the respondents to that of the final 25% and found no significant difference between these two groups on control variables. This result suggested that nonresponse bias was not a serious issue in this study (Armstrong et al. 1977). Table 1 shows the demographic information of the sample.
The current study developed an English questionnaire using the previously validated measures from the existing literature first. In the questionnaire, we used 5-point Likert scales, with options ranging from 1 (“strongly disagree”) to 5 (“strongly agree”) to measure the items. Given that the current research was conducted in China, the English questionnaire was then translated into Chinese by a team consisting of four researchers from different majors. Further, a professional translator, who was unfamiliar with this study, was hired to translate the Chinese questionnaire back to English. No semantic discrepancies were found when the translated questionnaire was compared with the original English version.
Specifically, the items used to measure IT objects, IT operations, and IT knowledge were adapted from Tippins et al. (2003). Meanwhile, IOC was measured by seven items adapted from the work of Paulraj et al. (2008). Finally, the items used to measure firm performance were adapted from Rai et al. (2006) and Ravichandran et al. (2005). All instrument items are shown in Appendix A.
We also included several control variables that might affect firm performance. Specifically, manufacturing and service firms may have differences in firm performance (Frohlich et al. 2002). Thus, we used a dummy variable for the industry, with values of 1 and 0 for the manufacturing and service industries respectively. Furthermore, ownership can also be an important factor that can influence the firm’s activities and performance (Li et al. 2010). As such, dummy variables were also used for ownership types, namely, state-owned, private-owned, and foreign-controlled. In addition, the firm size may also be crucial to a firms performance (Zhou et al. 2010). We included firm size as a control variable and measured it using the number of full-time employees.
5 Analysis and Results
5.1 Common Method Bias
All the data in this study collected were perceptual and from a single source. This may incur the issue of common method bias. To analyze the common method bias, we applied the Harmon’s single-factor test. The results finally revealed that all the items in the dataset could develop five distinct factors with eigenvalues above 1.0 and explain 73.90% of total variance. Meanwhile, the first factor did not account for the majority of the variance (only 22.42%). These results indicated that common method bias was unlikely to be a major issue in the dataset.
5.2 Reliability and Validity
We also assessed the construct reliability and validity of the measurement. Specifically, we assessed Cronbach’s alpha and composite reliability values ranging from 0.8561 to 0.915 and 0.818 to 0.917, respectively, indicating the good reliability of the measurements (Table 2). We further tested construct validity by convergent and discriminant validities. The convergent validity was tested based on the average variance extracted (AVE), which was greater than the recommended level 0.5 (Table 2; (Fornell et al. 1981). Further, as Table 3 shows, all the retained items had loadings greater than 0.60 and that items loaded well on their own factors. These results confirmed the convergent validity of the measures.
To assess the discriminant validity, the square roots of the AVE of each construct were calculated and compared with the correlations among constructs. As shown in Table 4, the square roots of AVEs for all constructs were greater than the correlations between constructs. Further, according to Table 3, all the items loaded well onto their own construct and poorly on other constructs. All these results confirmed the discriminant validity. In addition, one inter-construct correlation value was over the 0.60 criteria, which indicated that multicollinearity may be a potential problem. Generally, multicollinearity is indicated by a variance inflation factor (VIF) value that is higher than 10 or a tolerance value that is less than 0.1 (Mason et al. 1991). We tested these values and found that the highest VIF and the lowest tolerance values were 1.792 and 0.558 respectively. This finding indicated that multicollinearity was not a significant issue in this study.
5.3 Hypothesis Testing
We used hierarchical regression analysis to test our hypotheses. To minimize the possibility of multicollinearity, all the independent variables and moderator variables were mean centered (Aiken and West 1991). We estimated three hierarchical regressions: (i) including just control variables; (ii) adding three types of IT competencies and IOC; and (iii) adding the interaction of three types of IT competencies and IOC.
As shown in Table 5, the results showed that (Model 2) IT operations positively impacted firm performance (β = 0.181, p < 0.05) and IT knowledge was also positively associated with firm performance (β = 0.209, p < 0.01), which supported H2 and H3. Yet, the results showed that IT objects did not influence firm performance significantly (β = 0.027, p > 0.05), and therefore, H1 was not supported.
Furthermore, as expected, H4b, which stated that IOC positively moderates the relationship between IT operations and firm performance, was supported (β = 0.210, p < 0.01; Model 3). H4c was also supported: IOC positively moderates the relationship between IT knowledge and firm performance (β = 0.144, p < 0.05; Model 3). However, the moderating effect of IOC on the relationship between IT objects and firm performance was negative but not significant (β = -0.128, p > 0.05) and thus did not support H4a. In addition, except for the relationship between the ownership of state and the firm, performance was negatively significant (β = -0.178, p < 0.05; Model 1) and all other control variables were found to be insignificant.
To further analyze the moderating effects, we plotted the moderating effects of IOC in Figures 2 and 3. As predicted, at high levels of IOC, firm performance increases rapidly as IT operations (IT knowledge) increase. At low levels of IOC, firm performance does not increase (increases marginally) as IT operations (IT knowledge) increase, confirming the positive moderating effec of IOC on the relationship between both IT operations and IT knowledge and firm performance.
6 Discussion and Implications
6.1 Direct Impacts of IT Competency on Firm Performance
Overall, our study provides the empirical evidence for the theoretical relationships between IT competency and firm performance. However, the results presented that the three types of IT competencies have various impacts on firm performance. In particular, we found that IT objects did not significantly impact firm performance. One possible explanation is that IT objects can be purchased or duplicated fairly easily by rivals, which would make it difficult for a firm to perform better than its competitors (Bharadwaj 2000). Bhatt and Grover (2005) argued that “the existence of open architectures and standardized enterprise packages suggest that this capability might not be heterogeneously distributed across firms-or, even if it is, that access to infrastructure is not restrictive”(p. 260). As such, the logic of positioning IT objects may not directly contribute to differential firm performance due to its failure to explain how firms construct these IT objects, especially in the dynamic environments (Sambamurthy et al. 2003).
As expected, the results indicated that both IT operations and IT knowledge significantly and positively influence firm performance. Our findings suggest that firms can realize the business value of IT competency by utilizing the IT applications in the intra- and interorganizational processes as a driver and mindfully pursuing IT opportunities emerging in the market as a magnifier (Roberts et al. 2012). This finding is consistent with Morgan et al.’s (2009) argument that it is the capabilities by which firms’ resources are deployed and configured, rather than the resources firms possess that explain the variations in firm performance. In this view, the impact of IT competency on firm performance can be better explained through the logics of leverage and opportunity, which highlight how firms combine different IT competency with organizational processes as well as how firms improvise combinations of internal IT resources and external IT opportunities in the market (Sambamurthy et al. 2003).
6.2 Moderating Effects of IOC between IT Competency and Firm Performance
The findings of the moderating effects of IOC on the relationship between IT competency and firm performance support the RV propositions concerning the role of “inter-organizational asset interconnectedness” (Klein et al. 2009). Our results indicated that the interaction effect of IOC and the three types of IT competencies have different effects on firm performance. Specifically, we found that IOC positively moderates the relationship between IT operations and firm performance. As such, firms can leverage channel partners’ complementary resources and capabilities through IOC to better combine IT applications and business processes, thus improving IT operations’ impact on firm performance (Klein et al. 2009). Meanwhile, we also found that IOC positively moderates the relationship between IT knowledge and firm performance. IOC enables the information and knowledge sharing among channel partners, which also includes the sharing of important, proprietary information through some informal ways, such as through their guanxi with government officers and important personnel at partner firms (Sheng et al. 2011). Under this condition, the IT knowledge will permit firms to easily and flexibly adapt to and anticipate future IT needs, and therefore spotting the IT opportunities in market and more quickly capitalizing on these opportunities (Lu et al. 2011). These findings are consistent with the RV, which indicates that firms can combine resources not only at the intraorganizational level but also at the interorganizational level to create a competitive advantage (Dyer et al. 1998).
However, contrary to our hypotheses, we found that the moderating effect of IOC on the relationship between IT objects and firm performance is negatively, though not significantly, associated with firm performance. This result indicated that IOC may negatively moderate the relationship between IT objects and firm performance, rather than what we hypothesize that IOC positively moderates the relationship between IT objects and firm performance. A possible explanation for this unexpected result is that IOC requires firms in the supply chain seamlessly link with each other, and therefore the integrated firms need to invest in relationship-specific assets and infrastructures to “resolve differences in both the syntax and the semantics of the data, reconcile differences in the standards for data exchange and process coordination, and integrate disparate hardware platforms, communication technologies, and applications” (Rai and Tang. 2010, p. 522). However, the value of these relationship-specific infrastructures can be created only in conjunction with the specific partners (Grover et al. 2012). As such, IOC may restrict the firm to benefit from the IT objects.
6.3 Theoretical and Practical Implications
The current research aims to explore how the three types of IT competencies impact firm performance directly as well as the moderating effect of IOC. Overall, our findings support the RBV theoretical propositions on the direct impacts of the three types of IT competencies on firm performance, and explain how IOC moderates the relationship between IT competency and firm performance drawing from the RV. The findings have the following theoretical implications for IS researchers. First, IS research is enriched by distinguishing different dimensions of IT competency. In the existing literature, IT competency has been measured either at an aggregate level or inconsistently (Lim et al. 2011; Lu et al. 2011; Roberts et al. 2012; Tippins et al. 2003). To complement these studies, we explore the impacts of the three types of IT competencies on firm performance from three distinct logics (i.e., logic of positioning, leverage, and opportunity). The results indicated that different types of IT competencies differentially affect firm performance, broadening our understanding of the underlying influential mechanisms through which the three types of IT competencies impact firm performance.
Further, this study provides a more fine-grained insight into the nature of the moderating effect of IOC on the relationship between IT competency and firm performance, which enriches our understanding of the contingent effects of different types of IT competencies on performance outcomes. This finding is consistent with the RV, which indicates that interorganizational resources can interact with each other. That is, the value of a resource/ capability depends on other resources and capabilities partly due to the resource uniqueness attained from the integration and reconfiguration of channel partners’ resources and capabilities (Voola et al. 2012). As such, the resources and capabilities that complement with each other can enhance the casual ambiguity and social complexity, thus generating sustained competitive advantage. Our study therefore provides some support for the RV concerning “inter-organizational asset interconnectedness” effects (Klein et al. 2009).
In addition, our findings could offer some guidelines and directions for mangers who strive to improve the performance of value chain activities through configuring appropriate IT resources and capabilities. Specifically, our findings help managers realize that only positioning IT objects is not an effective way to improve firm performance. To enhance performance through IT investment, managers should extend their IT knowledge, and push the firm to diffuse IT applications in their business processes and supply chain activities. More importantly, this study suggests that it is critical for managers to apply IOC to leverage the value of IT competency, in particular, IT operations and IT knowledge. For example, firms should integrate with their channel partners to share more proactive and strategic information and knowledge about the market and customer, which would help realize the value of the IT competency. In contrast, managers should notice that developing IT objects to create business value when the firm’s degree of IOC is high is not a good choice. That means, a high degree of IOC may prevent the firm from benefiting from the IT objects and thus it is critical to notice the firm’s degree of IOC when configuring and deploying the firm’s IT resources.
7 Limitations and Future Research
Evaluating the contributions of the study along with its limitations is of primary importance. This study has the following limitations, which can be addressed by future research. First, we tested the hypotheses with cross-sectional data. A longitudinal study in the future may help extend our understanding of the time effects. Second, this study used a single respondent as the source of survey data. Given that a firm’s strategic decisions related to IT and supply chain deployment usually involve a group of related executives, future research may collect data from multiple informants to enhance the robustness of the research results. Finally, we only considered the moderating effect of IOC, while future research may consider some other constructs, such as supply chain network (Bellamy et al. 2014).
Based on the RBV and RV, this study examines how different types of IT competencies impact firm performance contingent upon the firm’s degree of IOC with its channel partners in the supply chain context, which sheds new light on the understanding of the underlying influential mechanisms of IT competency. We refined the conceptualization of IT competency in terms of IT objects, IT operations, and IT knowledge and explained three different logics (i.e., logic of positioning, leverage, and opportunity) through which the three types of IT competencies directly impact firm performance. We further proposed that the impact of the three types of IT competencies on firm performance can be augmented by the firm’s IOC. We found that IT operations and IT knowledge enable firm performance directly and that both are positively moderated by IOC, while the direct impact of IT objects as well as its moderating effect of IOC was found to be insignificant. These research findings not only enhance our understanding of the different underlying influential mechanisms through which the different types of IT competency improve firm performance but also address the call for specifying the boundary conditions under which IT competency affects firm performance. We hope this study paves the way for future research on IT business value creation especially in the supply chain context.
The work described in this paper was supported by the grants from theSingapore Ministry of Education Academic Research Tier 1.We would also like to thank the Editors-in-Chief and the anonymous reviewers for their insightful comments and suggestions.
Aiken, L. S., and West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions, (Sage: Newbury Park, CA. Google Scholar
Armstrong, C. P., and Sambamurthy, V. (1999). Information technology assimilation in firms: The influence of senior leadership and IT infrastructures. Information Systems Research 10(4) Dec., 304-327. CrossrefGoogle Scholar
Barney, J. (1991). Firm Resources and Sustained Competitive Advantage, Journal of Management 17(1) Mar, 99-120. Google Scholar
Bassellier, G., Reich, B. H., and Benbasat, I. (2001). Information technology competence of business managers: a definition and research model, Journal of Management Information Systems 17(4), 159-182. CrossrefGoogle Scholar
Bhatt, G. D., and Grover, V. (2005). Types of information technology capabilities and their role in competitive advantage: An empirical study. Journal of Management Information Systems 22(2), 253-277. CrossrefGoogle Scholar
Cai, S., Jun, M., and Yang, Z. (2010). Implementing supply chain information integration in China: The role of institutional forces and trust. Journal of Operations Management 28(3), 257-268. CrossrefGoogle Scholar
Devaraj, S., Krajewski, L., and Wei, J. C. (2007). Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain. Journal of Operations Management 25(6) Nov, 1199-1216. CrossrefGoogle Scholar
Dyer, J. H., and Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review 23(4) Oct, 660-679. Google Scholar
Flynn, B. B., Huo, B. F., and Zhao, X. D. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management 28(1) Jan, 58-71. CrossrefGoogle Scholar
Frohlich, M. T., and Westbrook, R. (2002). Demand chain management in manufacturing and services: web-based integration, drivers and performance. Journal of Operations Management 20(6) Nov, 729-745. CrossrefGoogle Scholar
Grover, V., and Kohli, R. (2012). Cocreating IT value: New capabilities and metrics for multifirm environments. MIS Quarterly 36(1), 225-232. Google Scholar
Kearns, G. S., and Sabherwal, R. (2006). Strategic alignment between business and information technology: A knowledge-based view of behaviors, outcome, and consequences. Journal of Management Information Systems 23(3) Win, 129-162. Google Scholar
Kim, G., Shin, B., and Kwon, O. (2013). Investigating the Value of Sociomaterialism in Conceptualizing IT Capability of a Firm. Journal of Management Information Systems 29(3), 327-362. Google Scholar
Klein, R., and Rai, A. (2009). Interfirm strategic information flows in logistics supply chain relationships. MIS Quarterly 33(4), 735-762. Google Scholar
Lee, H. L., and Whang, S. (2004). e-Business and Supply Chain Integration. Springer: New York. Google Scholar
Li, G., Yang, H., Sun, L., and Sohal, A. S. (2009). The impact of IT implementation on supply chain integration and performance. International Journal of Production Economics 120(1), 125-138. CrossrefGoogle Scholar
Lim, J.-H., Stratopoulos, T. C., and Wirjanto, T. S. (2011). Path dependence of dynamic information technology capability: An empirical investigation. Journal of Management Information Systems 28(3), 45-84. CrossrefGoogle Scholar
Liu, H., Ke, W., Wei, K. K., Gu, J., and Chen, H. (2010). The role of institutional pressures and organizational culture in the firm’s intention to adopt internet-enabled supply chain management systems. Journal of Operations Management 28(5), 372-384. CrossrefGoogle Scholar
Liu, H., Ke, W., Wei, K. K., and Hua, Z. (2013). The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. Decision Support Systems 54(3), 1452-1462. CrossrefGoogle Scholar
Liu, Y., Huang, Y., Luo, Y., and Zhao, Y. (2012). How does justice matter in achieving buyer–supplier relationship performance?. Journal of Operations Management 30(5), 355-367. CrossrefGoogle Scholar
Lu, Y., and Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly 35(4), 931-954. Google Scholar
Lyer, K. N. S., Germain, R., and Claycomb, C. (2009). B2B e-commerce supply chain integration and performance: A contingency fit perspective on the role of environment. Information & Management 46(6), 313-322. CrossrefGoogle Scholar
Malhotra, A., Gosain, S., and El Sawy, O. A. (2007). Leveraging standard electronic business interfaces to enable adaptive supply chain partnerships. Information Systems Research 18(3), 260-279. CrossrefGoogle Scholar
Marchildon, P., and Hadaya, P. (2011). The Strategic Alignment of Supply Chain and IT Resources. The Third International Conference on Information, Process, and Knowledge Management), 55-61. Google Scholar
Narasimhan, R., Swink, M., and Viswanathan, S. (2010). On Decisions for Integration Implementation: An Examination of Complementarities Between Product-Process Technology Integration and Supply Chain Integration. Decision Sciences 41(2), 355-372. CrossrefGoogle Scholar
Oh, L.-B., Teo, H.-H., and Sambamurthy, V. (2012). The effects of retail channel integration through the use of information technologies on firm performance. Journal of Operations Management 30(5), 368-381. CrossrefGoogle Scholar
Paulraj, A., Lado, A. A., and Chen, I. J. (2008). Interorganizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships. Journal of Operations Management 26(1) Jan, 45-64. CrossrefGoogle Scholar
Pavlou, P. A., and El Sawy, O. A. (2010). The “Third Hand”: IT-enabled competitive advantage in turbulence through improvisational capabilities. Information Systems Research 21(3), 443-471. CrossrefGoogle Scholar
Piccoli, G., and Ives, B. (2005). Review: IT-dependent strategic initiatives and sustained competitive advantage: A review and synthesis of the literature. MIS Quarterly 29(4), 747-776. Google Scholar
Powell, W. W., Koput, K. W., and Doerr, L. S. (1996). Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology. Administrative Science Quarterly 41(1), 116-145. CrossrefGoogle Scholar
Rai, A., Patnayakuni, R., and Seth, N. (2006). FIRM PERFORMANCE IMPACTS OF DIGITALLY ENABLED SUPPLY CHAIN INTEGRATION CAPABILITIES. MIS Quarterly 30(2), 225-246. Google Scholar
Rai, A., Pavlou, P. A., Im, G., and Du, S. (2012). Interfirm IT capability profiles and communications for cocreating relational value: Evidence from the logistics industry. MIS Quarterly 36(1), 233-A235. Google Scholar
Rai, A., and Tang, X. (2010). Leveraging IT capabilities and competitive process capabilities for the management of interorganizational relationship portfolios. Information Systems Research 21(3), 516-542. CrossrefGoogle Scholar
Ranganathan, C., Dhaliwal, J. S., and 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. Google Scholar
Ravichandran, T., and Lertwongsatiem, C. (2005). Effect of information systems resources and capabilities on firm performance. Journal of Management Information Systems 21(4), 237-276. CrossrefGoogle Scholar
Ray, G., Muhanna, W. A., and Barney, J. B. (2005). Information technology and the performance of the customer service process: A resource-based analysis. MIS Quarterly 29(4), 625-652. Google Scholar
Roberts, N., and Grover, V. (2012). Leveraging information technology infrastructure to facilitate a firm’s customer agility and competitive activity: An empirical investigation. Journal of Management Information Systems 28(4), 231-270. CrossrefGoogle Scholar
Saeed, K. A., Malhotra, M. K., and Grover, V. (2011). Interorganizational system characteristics and supply chain integration: An empirical assessment. Decision Sciences 42(1), 7-42. CrossrefGoogle Scholar
Sambamurthy, V., Bharadwaj, A., and Grover, V. (2003). Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Quarterly 27(2), 237-263. Google Scholar
Sirmon, D. G., Hitt, M. A., and Ireland, R. D. (2007). Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of Management Review 32(1), 273-292. CrossrefGoogle Scholar
Tu, C. Y. (2010). Balancing exploration and exploitation capabilities in high technology firms: A multi-source multi-context examination. Industrial Marketing Management 39(4), 672-680. CrossrefGoogle Scholar
Venkatraman, N. (1989). The Concept of Fit in Strategy Research - toward Verbal and Statistical Correspondence. Academy of Management Review 14(3), 423-444. Google Scholar
Villena, V. H., Gomez-Mejia, L. R., and Revilla, E. (2009). The Decision of the Supply Chain Executive to Support or Impede Supply Chain Integration: A Multidisciplinary Behavioral Agency Perspective. Decision Sciences 40(4), 635-665. CrossrefGoogle Scholar
Voola, R., Casimir, G., Carlson, J., and Anushree Agnihotri, M. (2012). The effects of market orientation, technological opportunism, and e-business adoption on performance: A moderated mediation analysis. Australasian Marketing Journal (AMJ) 20(2), 136-146. CrossrefGoogle Scholar
Wade, M., and Hulland, J. (2004). The resource-based view and information systems research: Review, extension, and suggestiond for future research. MIS Quarterly 28(1), 107-142. Google Scholar
Wu, F., Yeniyurt, S., Kim, D., and Cavusgil, S. T. (2006). The impact of information technology on supply chain capabilities and firm performance: A resources-based view. Industrial Marketing Management 35, 493-504. CrossrefGoogle Scholar
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. CrossrefGoogle Scholar
Survey Questionnaire Items (Scale 1–5)
About the article
Published Online: 2017-09-29
Citation Information: Data and Information Management, Volume 1, Issue 1, Pages 3–16, ISSN (Online) 2543-9251, DOI: https://doi.org/10.1515/dim-2017-0003.
© 2017 Shaobo Wei, Kwok-Kee Wei. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0