How does the innovation outsourcing motivation determine the

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How does the innovation outsourcing motivation determine the

    How does the Innovation Outsourcing Goal Determine the Operational

    Method? : A Comparative Study at Siemens

1. Introduction

    There is a widespread trend among firms to increasingly rely on external partners to acquire knowledge (Chesbrough, 2006; De Meyer and Loch, 2007; Terwiesch and Ulrich, 2008). The scope of innovation outsourcing has extended from initial idea generation and prototype development to product testing. With the growing applications of innovation outsourcing in practice there has been a corresponding surge in the attention devoted to the study of why innovation activities are outsourced (Belderbos et al., 2004b, Ulrich and Ellison, 2005) and how such projects should be managed (Valentin et al. 2004, Eppinger and Chitkara, 2007, Cui et. al., 2011).

    The strategic management literature has identified a number of goals that drive firms‟

    outsourcing decisions, including absorption, substitution, complementarity and benchmarking, in an attempt to understand the complex benefits that innovation outsourcing can bring to a firm. Independently, the operations management literature has focused on the project management method once the outsourcing decision has been made. The strategic goal and the operational method are therefore implicitly treated as separate issues, and operational methods are seen as “universally valid regardless of the specific benefit the outsourcer is seeking to achieve. In contrast, the general management literature, which can be traced back to Drucker‟s (1954) “management by objectives (MBO), argues that a proper choice of

    management method should consider the organization‟s specific needs the performance

    review and reward system, for example, should reflect the organization‟s priorities (Drucker,

    1954). In the context of innovation outsourcing, while previous studies have yielded rich insights on the outsourcer‟s objectives (or goals) and the operational methods used, the

    potential contingencies between these two issues remain unclear. In other words, we lack a theory to explain how innovation outsourcing goals determine project management methods.


    This study seeks to fill the gap by exploring the potential contingencies between the innovation outsourcing goals and operational management methods. Based on 31 outsourced R&D projects by Siemens, our study proposes a contingent view of project management in innovation outsourcing: that is, some management methods are indeed universally valid regardless of the outsourcing goals, while some methods are goal-specific. In particular, we identify from the literature four strategic outsourcing goals: absorption, substitution, complementarity and benchmarking, all of which can be observed at Siemens. We further dentify patterns of interaction among these goals. We then summarize ten project i

    management methods from interviews with managers and discuss the extent to which these methods depend on the outsourcer‟s strategic goals.

    Our study has a unique data structure: a single firm (Siemens) embedded with multiple comparative projects (31 outsourced projects with both successful and less successful

    outcomes). Due to the immature state of the existing theory, this study takes a hybrid approach developing grounded theory based on multiple case comparisons, and some simple statistics to ensure robustness of the findings. This combination is common when some theory is available but is immature (Strauss and Corbin 1998; Edmondson and McManus 2007).

2. Review of the Literature

    The literature on strategic alliances has discussed the multi-faceted benefits that innovation outsourcing can bring to the outsourcer. However, little attention has been given to the detailed management issues of strategic alliances. In the operations management literature, by contrast, an independent stream of research has focused on the operational methods to manage outsourced projects. Below we review these two streams of literature.

    2.1 The Goals of Innovation Outsourcing


    The motivation for innovation outsourcing has attracted the attention of various research fields (Hagedoorn, 1993; Doz, 1996; Dyer, 2000; Ulrich and Ellison, 2005; Holcomb and Hitt, 2007; Terwiesch and Ulrich, 2008). Reasons to outsource vary widely from one R&D collaboration project to another (Belderbos et al., 2004a). Looking across a range of studies, we categorize the strategic benefits of innovation outsourcing in the form of four goals: absorption, substitution, complementarity and benchmarking. Table 1 provides a brief definition of these goals and they are described in detail in the following paragraphs.

    Table-1: The Goal of Innovation Outsourcing

    Goal of Absorption. One outsourcing goal often mentioned in the literature is to absorb the know-how of external partners (Holcomb and Hitt, 2007, Terwiesch and Ulrich, 2008). In particular, two types of knowledge are important: the know-how directly associated with the

    focal project, and the know-how of peripheral technologies which are indirectly related but

    essential for the focal project.

    The primary benefit of absorption comes from the increased range of technological solutions available from the knowledge provider. R&D has traditionally been seen as firm-specific


    (Helfat, 1994), whereby the firm exploits the knowledge that exists within its current organizational or technology boundaries. However, this approach carries the risk of becoming trapped in local optimal choices and reducing the range of options available. By exploring a broader set of alternative actions the outsourcing firm may preclude such risks and achieve better performance of the focal technology (Gavetti and Levinthal, 2000; Loch et al., 2006). The second benefit of absorption lies in the interdependencies among various technology domains. Firms that produce a complex product using multiple technologies or multi-products need to acquire knowledge beyond their immediate needs because they cannot fully master all necessary interdependent technologies (Brusoni et al., 2001). By absorbing knowledge of peripheral products/components from other firms, the outsourcing firm can balance uneven knowledge concentrations across different technology domains.

    Goal of Substitution. Another strategic goal considers innovation outsourcing as a substitute for in-house R&D or manufacturing activity. Empirically, Mol (2005) found that Dutch manufacturing companies with a higher in-house R&D intensity had a lower percentage of outsourcing. According to this view, innovation outsourcing is regarded as an effective means to reduce R&D costs (Belderbos et al., 2004b; Holcomb and Hitt, 2007) by sharing R&D investment or exploiting a partner‟s “economies of scale” (Hagedoorn, 1993; Robertson and Gatignon, 1998; Ulrich and Ellison, 2005). By collaborating with partners, a firm may also be able to replace internally developed components with purchased components or subsystems. In some cases these may be market-ready, more reliable, and cheaper to produce than newly-developed components (Hagedoorn, 1993; Belderbos et al., 2004b).

    Goal of Complementarity. In the last decade, a trend has emerged of companies‟ R&D

    outsourcing activities increasing along with their own in-house R&D intensity (Cassiman and Veugelers, 2006; Hagedoorn and Rothaermel, 2007), implying a complementarity between innovation outsourcing and the outsourcer‟s own capabilities. The difference between firms

    driven by the goals of absorption and complementarity goals is that the former seek to


“internalize” external know-how whereas the latter simply “utilize” external providers to

    complement their core activities (Cohen and Levinthal, 1990; Chiesa et. al., 2000). According to this school of thought, the outsourcing firm and the provider should have related but different expertise. Hence it the outsourcer‟s own capacity becomes an important prerequisite

    to ensure successful outsourcing. With respect to the choice of provider, the outsourcing firm should collaborate with partners whose capabilities enhance its own in-house capabilities. For example, a firm with technology-driven expertise should collaborate with external partners such as customers or experienced suppliers to better understand current market needs (Von Hippel and Katz, 2002) and secure access to potential new markets (Robertson and Gatignon, 1998). As evidence, firms attempting to introduce “new to the market” innovations are more

    likely to engage in collaborative arrangements with external partners (Tether, 2002). Goal of Benchmarking. Outsourcing may also serve as an external force to drive behavioral change within an organization. A firm‟s internal performance tends to be assessed against

    existing markets and profit levels, but these measures may become too rigid when the firm encounters new opportunities (Leonard Barton, 1992), thus certain opportunities may be better developed outside the organization in order to avoid such internal barriers (Christensen and Overdorf, 2000). Through outsourcing, managers are exposed to the behavior of other organizations, learn new behaviors, and revise their assessment of the appropriateness of taking certain actions. Using the radio industry as an example, Greve and Taylor (2000) showed that innovations in a related market or by large organizations can have a considerable effect on the rate of managerial change. External partners thus play a dual role: offering benchmarks to imitate and acting as a catalyst for organizational change. The force of imitation drives the outsourcing firm to collaborate with partners with superior performance, and the catalytic impulse drives the firm to collaborate with owners of novel innovations. 2.2 Operational Management Method of Outsourced Projects


    A different stream of research in the operations management literature has examined the specific methods used to manage outsourced projects. For example, Eppinger and Chitkara (2007) identified 10 “success drivers” for managing global product development:

    management commitment and prioritization to spreading innovation activities around, process modularity, product modularity, keeping the core competences in-house, intellectual property (IP) protection, data quality (sufficient articulation of knowledge to share across locations and organizations), infrastructure, governance and project management, a collaborative culture, and structured change management.

    Very few recent studies check the robustness of these operational drivers with respect to specific provider type. A study in Spain (Valentin et al. 2004) found that resource

    commitment and a previous collaboration history were common success drivers across university and corporate partners, while other drivers varied. The authors attributed the provider-specific nature of the operational drivers to variations in the providers‟

    organizational structures. In the same spirit, Cui et al. (2011) studied the contingency between five provider types (university, component supplier, customer, competitor and start-up) and operational drivers. They found that some operational principles could be commonly applied to any type of innovation provider, whereas some were specific to certain provider types.

    In summary, the strategic alliance and operations management literatures look at two important aspects of innovation outsourcing independently why firms outsource and how

    firms should manage outsourced projects without looking at the contingencies between

    them. In our view, the choice of management method should consider the organization‟s

    objective or goal (c.f. Drucker, 1954). In the context of innovation outsourcing, although a few studies refer to the contingent nature of project management methods (e.g., Cui et. al., 2011), to date insufficient research has been devoted to the gap in the theory between the outsourcer‟s strategic goals and the choice of operational methods. The purpose of this study is therefore to explore the missing link between these two important aspects. Owing to the


    immature nature of the existing literature, we explore (rather than test) some qualitative results in the hope this may constitute a starting point for further large-scale quantitative empirical research.

3. Research Methodology and Data Collection

    Our study applies an embedded multiple-case comparison approach to examine how the goal of innovation outsourcing determines the operational management method used. This allows

     to build a new theory and identify underlying processes which cannot be discerned with a us

    quantitative approach alone (Miles and Huberman, 1994). Comparative multiple-case analysis permits replication and systematic comparison (Eisenhardt and Graebner, 2007). We collected our interview data on 31 outsourced projects from Siemens, a leading worldwide developer and manufacturer of electronics. All projects in the sample were randomly selected by our contacts at Siemens.

    Our unit of analysis is a single project with outsourced innovation activity. The dependent variable is project performance as reported by our interviewees (“successful” or “less successful”). In total, we reviewed 20 successful projects and 11 less successful projects. The comparison between successful and less successful cases enabled researchers to clarify whether an observation was consistently replicated across multiple cases (Eisenhardt and Graebner, 2007); such replication is associated with higher data reliability (Yin, 2003). The “independent variables” that explain success emerge from the study. In Sections 4 and 5 we

    report representative instances of the emerging contingencies to illustrate qualitative causality in detail. Further, we take advantage of the relatively large (for this method) sample of 31 cases to triangulate the qualitative case descriptions using simple statistical tests that reduce the risk of obtaining our results “by chance” and help to foster both insight and rigor

    (Edmondson and McManus 2007).

    3.1 Process of Data Collection


    The host organization, Siemens, is one of the largest electronics companies in the world, with gross revenues of ?76.6 billion and a total R&D expenditure of ?3.9 billion in fiscal year 2009.

    The company‟s R&D domains range from such radical technologies as renewable energy,

    artificial intelligence and new light sources to process innovations in such domains as transportation systems and factory design. Siemens collaborates with hundreds of external innovation partners and therefore has all the ingredients of a complex innovation outsourcing environment.

    Our data were collected from interviews at nine different divisions of Siemens in Germany, Austria, and Switzerland from April 2008 through January 2009. We conducted 35 semi-structured field interviews lasting (on average) two hours, covering 31 outsourced projects. The interviewees were project managers, senior engineers, and innovation portfolio managers. Each interview was documented in a standard format within 24 hours. Additional supporting materials included official project documents, pre-interview summaries, and ollow-up questions and e-mails. The authors also discussed the interview reports, project f

    classification, analysis results, and managerial implications (as they became available) with the Siemens partners, who clarified ambiguities and challenged interpretations. Data from 14 projects were based on least two interviewees. In addition, we used available formal documents to back up evaluations whenever possible.

    Our interview questions were open-ended and attempted to avoid imposing implicit assumptions on the interviewees. A typical interview question was, “Why did you decide to

    outsource this project?” or “What are you seeking to achieve from collaborating with this partner?” rather than whether the outsourcing decision was driven by specific goals. This approach enhanced the validity of the field data and allowed us to capture emerging relevant variables (Yin, 2003).

    Because the interviewees were personally involved in the case projects, their success reports may have been positively biased. To reduce any such bias we asked (later in the


    interview) whether the project achieved the original outsourcing goals. In the case of a negative answer in a “successful” project we had intended to reclassify it into the “less successful” category. This proved not to be necessary, which suggests that biases were not strong enough to cause such contradictions. Remaining biases in the successful” category

    were shared by all interviewees and therefore should not invalidate our results, which were based on comparisons across projects.

    3.2 Process of Data Analysis

    To answer our research question, we first summarize the outsourcing goals of each project and the operational management methods undertaken by the interviewees. The four strategic outsourcing goals are all observed in our data sample. In addition, 10 operational methods are named: in-house competency, detailed process control, defined goals, knowledge transfer, organizational stability, expectations management, trust and communication, IP protection, incentive alignment, and flexible decision making. Some of the operational drivers have been identified in the literature and are assumed to be “universally valid” (Eppinger and Chitkara,

    2007). These data form the “raw material” of our contingency search.

    Table-2. Data Table for Implementation of Operational Methods


    in Projects with a Goal of Absorption

    Next, we group the cases by outsourcing goals and compare successful and less successful projects in each group. The interview data are coded in a standard form, as illustrated in Table-2, which shows the implementation of operational methods in projects with a goal of absorption. (For confidentiality reasons, projects are identified only by numbers.) All other interview data for the projects with a goal of substitution, complementarity or benchmarking are shown in Appendix 1. In Table-2, a “+” denotes

    “having implemented this operational method” (e.g., in-house competency is identified as

    being implemented in nine successful and four less successful projects). A “?” represents “not

    having implemented this operational method” (e.g., flexible decision making is not found in 8

    out of 10 projects with a goal of absorption).

    Lastly, we examine how each operational method varies within each data group of an outsourcing goal. Reading down the table columns, some methods show a clear pattern: they appear in all or most of the successful projects but in few of the less successful projects (for example, defined goals and organizational stability), whereas some methods do not appear to make any distinction between successful and less successful cases (for example, expectation management). Reading across the table rows, successful projects have implemented most identified methods, while less successful projects have implemented fewer methods.

4. The Strategic Goal of Innovation Outsourcing at Siemens

    We start the analysis by identifying the strategic goal driving each project‟s outsourcing

    decision. Table-3 summarizes the findings based on the interviewees‟ descriptions.

    From Table-3, we observe that more than half of the outsourced projects at Siemens are driven by at least two goals (17 out of 31 projects). This is consistent with previous findings that innovation outsourcing is often driven by complex strategic considerations (Hagedoorn,


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