Interorganizational Relationships, Information Technology, and Firm-level Productivity: An Empirical Analysis

Fardad Zand
Delft University of Technology (TU Delft), Department Econom

*Cees van Beers
Delft University of Technology (TU Delft), Department Economics of Innovation (ECI)

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Abstract
Interorganizational Relationships, Information Technology, and Firm-level Productivity: An Empirical Analysis

Cees van Beers and Fardad Zand*
Delft University of Technology
Department Economics of Innovation
*f.zand@tudelft.nl

1. Research Background

Traditionally, firms faced the “Make-or-Buy Dilemma”. They can buy their inputs in the market or produce them internally. It is now time to spell out this dilemma as “Make-or-Cooperate-or-Buy Decision”. In the field of Transaction Cost Economics, this translates into a firm’s place along a governance spectrum, the ‘Coordination Continuum’. On the one extreme, firms buy input through market transactions. On the other extreme, firms make the inputs within their boundaries. The alternative option is to share strategies and pool resources through cooperation with business partners (Williamson, 1991 and Malone, 1987). While the two extreme options have been investigated for a long time, the notion of interorganizational relationships (IOR) has recently attracted special attention from both scholars and managers.

The use of information technology (IT) is central to the creation, expansion and management of interorganizational relationships (e.g. Venkatraman, 1994). In this regard, Interorganizational Systems (IOS) are defined as “IT-based systems that transcend beyond an organization’s legal boundaries and link otherwise independent organizations together to enable inter-firm relationships (Konsynski, 1990). A common way to classify IOS is to concentrate on the level of information sharing between the participant organizations (Seidmann and Sundararajan, 1998). Kumar and van Dissel (1996) built an interdependence-based typology for IOS.

Another related concept is the “Productivity Paradox”. Despite enormous improvements in the underlying technology, the benefits of IT spending have not been found in aggregate output statistics. This phenomenon has been examined by many researchers, each from a specific perspective (e.g. Triplett, 1999; Brynjolfsson, 1993 and Brynjolfsson and Hitt, 2003). While major success stories exist (e.g. Johnston, 1988), so do equally impressive failures (e.g. Vitale, 1986).

2. Missing Gaps

The current state of research does not provide a comprehensive analysis framework that captures the key characteristics of IOR. The available models mainly investigate inter-firm networks from a single (and mainly interdependency) perspective, with special attention to sharing or pooling organizational resources. A practical framework should incorporate broader aspects of inter-firm collaboration. Such a framework better suits the needs of managers who decide on the governance form of their economic activities and, correspondingly, on the investment in underlying information technologies. We develop a new framework to classify and characterize IOR. This framework enhances the current spectrum to study IOR and IOS and enables us to better understand the rationales and motives behind the forming of networks and alliances.

We also deal with the productivity paradox. Researchers primarily have focused on the direct impact of IT spending on productivity. However, IT as a general-purpose technology can affect productivity in so many indirect (and partly intangible) ways as well. IT is the technological infrastructure for handling and managing interorganizational relationships and thus can affect firms’ productivity through this channel. IT plays the role of both an enabler and a facilitator when it comes to collaborating with other partners. Therefore, it is important to assess how spending on IT and the cooperative status of a company interrelate with each other and, further, how they collectively contribute to productivity. Some studies have considered intangible and complementary effects of information technology (e.g. Brynjolfsson, 2000), while a few other have analyzed the interactive effects of IT and innovation on corporate performance (e.g. Hempell, 2004). To our knowledge, no quantitative research exists that studies the interaction between IT and collaboration and their combined effects on productivity. We address this issue through an empirical analysis of firm data.

3. A New Analysis Framework

Microeconomics and strategic management are two important perspectives from which interorganizational relationships can be studied. Focusing on these perspectives enables us to develop a model with enhanced applicability for both researchers and practitioners. Relying on Transaction Cost Economics and Resource Based View of the firm, we identify two key interorganizational measures (or dimensions) that capture characteristics and motivations of IORs, namely Level of Collaborative Integration (LCI) and Level of Competitive Contribution (LCC).

Level of Collaborative Integration (LCI) concerns the extent to which decisions and actions of an organization are integrated into and depend upon those of its partners. LCI concerns how IORs and their underlying IOSs are operated and, especially, how coordinated they are. This notion relates to what Clemons and Row (1992) refer to as the ‘Level of Explicit Coordination’. They consider the level of explicit coordination as an important dimension of inter-firm interactions that has not received enough attention in the literature and accordingly define it as the degree to which decisions reflect and are tailored to a specific relationship, in contrast to the implicit coordination of the ‘invisible hand’ of the market. The fact that different IORs exhibit different LCIs enables us to classify them on that dimension. In other words, LCI determines the position of a specific IOR along the Coordination Continuum.

Level of Competitive Contribution (LCC) concerns the extent to which a resource is a source of sustained competitive advantage for a company and helps it to gain and/or sustain competitive advantage over its rivals. As LCI measures how coordinated an IOR is, LCC measures how competitive such relationship is. LCC determines the position of a resource with respect to the ‘VRIO Framework’ (Barney, 1991). This framework asserts that particular attributes of a resource determine its competitive ability as well as its potential economic performance. If the resource is valueless, it is a competitive disadvantage and results in below normal economic performance. In case we deal with a resource that is valuable, rare, costly to imitate and well-organized, it is a sustained competitive advantage and results in above normal returns. An IOR can be a core resource, which brings value to an organization and helps it to exploit opportunities or neutralize threats, and/or a complementary resource, which enables or improves the value-realization of other resources through combining or integrating them with those of partners. Hence, similar to other resources, IORs have different LCCs and therefore different positions with regard to the VRIO Framework.

The proposed framework is a model that analyzes IORs through assessing how coordinated and how competitive they are. LCI and LCC are evaluated dichotomously along the X-axis and Y-axis respectively. We say dichotomous since the model distinguishes low and high values of LCI and LCC. Intersecting the axes allows us to divide the LCI-LCC space into four categories. The temporary cooperation of the European aircraft and/or automotive companies (e.g. BMW-Airbus partnership) to co-design a vehicle or its parts through connected CAD systems is an example of the first category, i.e. a noncompetitive uncoordinated IOR/IOS. The bottom-line transactional cooperation between the US retail giant Wal-Mart and its network of suppliers can be considered as the second category, i.e. a competitive uncoordinated IOR/IOS. Supported by EDI systems, this collaboration has enabled Wal-Mart to successfully implement JIT inventory management. The third category or noncompetitive coordinated IOR/IOS is exemplified by many unsuccessful joint ventures or mergers such as that of GM with Fiat. The collaboration between aviation companies to form lifelong strategic alliances is a good example for a competitive coordinated IOR/IOS, which is the fourth category. An example is the SkyTeam alliance, facilitated through TBI technologies, which has resulted in significant advantages for its members.

4. Data Sources and Quantitative Methods

With the help of the Statistics Netherlands (CBS), we designed a questionnaire that was sent to some 800 large Dutch and multinational companies. Focusing on IOR schemes and the role of IT in them, this survey allows us to construct a dataset of 391 firms. We then connect the resulting dataset to productivity and IT spending data as reported in the Dutch standardized annual production and investment statistics.

We build the proposed framework by analyzing the gathered data. This requires that we quantify LCI and LCC. We employ Confirmatory Factor Analysis (CFA) to discover the (latent) determinants of LCI and LCC. Next, we determine the position of each surveyed company according to the model. The second step aims at studying the productivity impacts of information technology, with the focus on IT-induced inter-company networks. We construct a representative panel dataset incorporating a time span of 5 years (2000-2004). Employing Translog and Cobb Douglas production functions, we consider labor productivity as a dynamic function of labor, IT capital, and conventional capital. As the unique thing in this research, we introduce multiplicative terms in the model to reveal interactions between IT capital deepening and the interorganizational measures. After appropriate diagnostic tests, we estimate the parameters of the model, using regression techniques for dynamic linear models.

5. Summary of the Results

We found three primary determinants for LCI and LCC through factor analysis, namely Internal Integration, External Integration, and IT Adoption for LCI, and Product Interrelations, Market Interrelations and Support Activities for LCC. These determinants institutionalize our interorganizational measures of interest. As to the direct productivity impact of IT, our findings show that IT spending has made a substantial and statistically significant contribution to firm output. We found that the gross marginal product for computer capital averaged 172.3% for the firms in our sample. This finding results in an approximate net return on investment of 130% for computer capital. These figures are at least as large as the corresponding values for other (conventional) types of capital and correspond with the results reported in previous empirical studies (e.g. Brynjolfsson and Hitt, 2003).

More interesting is the indirect productivity impact of IT. We find statistically significant evidence for the effects of interorganizational measures on IT capital deepening. In another words, competitive partnerships can significantly help companies to gain more benefits from their IT investments, while coordinated networks can hinder firms from realizing the potential value of IT. This concludes that the LCI and LCC of a company’s interrelationships can substantially change the magnitude and even the direction of IT contribution to its productivity. We thus conclude that the management’s aim should be for competitive but relatively uncoordinated networks unless the partnership is superior enough to compensate for the extra costs of building and maintaining integrated networks.

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