Distilling the Themes of Knowledge Leakage

1. Distilling the Themes of Knowledge Leakage Andrew Grantham and Raphie Kaplinsky

2. Themes • Dynamic capabilities • Core competences • Capability maturity models • Trust • Knowledge/productivity • References to KL LBS, 2 December 2005

3. Dynamic Capabilities • Dynamic capabilities are the resources and capabilities that a firm draws upon to affect change. • These capabilities are limited by: • the history of the firm such as technological trajectories (Teece, Pisano, and Shuen, 1997); • market (e.g. knowledge of market characteristics deter new market exploration); • relationships (e.g. member of buyer network); • Resources – derived from RBV – rather than the capabilities per se (Eisenhardt and Martin, 2000). LBS, 2 December 2005

4. Dynamic Capabilities • Examples include: • internal capabilities that are explicit and homogeneous such as product development and strategic decision making which pool business, functional and personal expertise (Eisenhardt and Martin, 2000); • internal capabilities that are tacit and heterogeneous such as knowledge resources (Kogut, 1996; Grant, 1996); and • inter-relationship capabilities including commercial alliances/inter-firm cooperation (Eisenhardt and Martin, 2000; Lorenzoni and Lipparini, 1999; Schmitz and Knorringa, 2000; Bessant, Kaplinsky, Lamming, 2003) LBS, 2 December 2005

5. Core Competences • Capabilities that are unique to particular firms, desired in the marketplace and difficult to copy. • The collective learning in an organization, especially how to coordinate diverse production skills and integrate multiple streams of technology. (Prahalad and Hamel, 1990) LBS, 2 December 2005

6. Core Competences • Firms tend to list their capabilities but not core competences. • Firms unlikely to build expertise in more that 5 or 6 fundamental competences. • Core products are ‘lynchpins’ leading to a proliferation of end products – for example, Honda’s engines. (Prahalad and Hamel, 1990) LBS, 2 December 2005

7. Core Competences LBS, 2 December 2005

8. Complementary Concepts • In recent years the capability approach has been supplemented by a wider perspective which recognises the primacy of inter-firm linkages in the attainment of systemic efficiency: • Global Production Networks (Ernst and Kim 2002; Henderson, Dicken et al. 2002); • lean production systems (Womack and Jones 1996); LBS, 2 December 2005

9. Complementary concepts • supply chain management (Bessant, Kaplinsky et al. 2003); • flexible specialisation (Piore and Sable 1984); • new product development (Wheelwright and Clark, 1992; Oliver and Blakeborough, 1998) and • learning networks (Bessant and Tsekouras, 2001). • Value Chain Framework (Gereffi, 1994; Gereffi and Kaplinsky, 2001; Kaplinsky and Morris, 2001). LBS, 2 December 2005

10. Capability Maturity Models - origins • Process maturity models – 1986. • Quality issues for the US government in software procurement. • It funded research (by the Software Engineering Institute, SEI, at Carnegie Mellon University with the assistance of the Mitre Corporation) to develop a framework designed to improve the software development process. • SEI Capability Maturity Model (CMM) finally released in 1991. • The SEI itself has participated in developing models for systems engineering, software acquisition, and HRM (Ferguson, 1999). LBS, 2 December 2005

11. Capability Maturity Models • All capability maturity models are based on one primary concept: it is very difficult consistently to deliver quality products to your customers, while also making a profit, if your product development process is poor (Cusick and Bruce, 1999). • Assumption – Improving the development process helps product quality, customer satisfaction and profits. LBS, 2 December 2005

12. Capability Maturity Models • “The underlying assumptions that organisations mature through various stages, towards rational, controllable processes and that this progress can be measured and assessed (Paulk, 1995)are questionable.” • “If applied uncritically, the CMM can be accused of assuming that the software process is managed wholly ‘from above’ through formal processes and procedures, ignoring ‘management from below’ (i.e. practitioner discretion in the choice and application of methods and procedures). We have found the latter to be common in complex software and extremely important.” (Brady, Davies et al., 2003) LBS, 2 December 2005

13. Capability Maturity Models LBS, 2 December 2005

14. Capability Maturity Models • Managing Risk • Define future products, determine their probability of occurrence and consequence of occurrence, implement mitigations, track the success of the mitigation activities. • Provide skills and knowledge • Determine future skills and knowledge needs, determine whether to hire or train to get the necessary skills, hire or train as necessary. (Cusick and Bruce, 1999) LBS, 2 December 2005

15. Trust • Learning in collaboration depends on high levels of trust between the partners (Buckley & Casson, 1988; 1996). • High levels of trust enhances internal organisational effectiveness (Arrow and Phelps, 1975;Fox, 1974). • Trust facilitates continuing relationships between firms (Macaulay, 1963). • As technological collaboration has become more common, the levels and kind of trust relationships between firms is the focus of attention (Jarillo, 1988; Sako, 1992). (Dodgson, 1993) LBS, 2 December 2005

16. Trust • Saxenian's (1991) study of Silicon Valley firms • This involves “...relationships with suppliers as involving personal and moral commitments which transcend the expectations of simple business relationships”. • Freeman (1990) • cultural factors such as language, educational background, regional loyalties, shared ideologies and experiences and even common leisure interests will continue to play an important role in collaboration. LBS, 2 December 2005

17. Trust • Trust • Macro • Putnam’s (1991) social capital. • Micro • Coleman’s (1990) game theoretic perspective on risk (‘the risk one takes depends on the performance of the other’) LBS, 2 December 2005

18. Trust • Care taken to avoid a functionalist tautology • Mutually beneficial co-operation explains mutually beneficial co-operation! • Confusing origins of trust and presumed benefits for performance. LBS, 2 December 2005

19. Trust • Farrell and Knight (Farrell & Knight, 2003) • Defining trust as: ‘a set of expectations held by one party that another party (or parties) will behave in an appropriate manner with regard to a specific issue.’ • Institutional Theory • using changes within and between organisations to explain levels of trust (often a case of bargaining between relatively powerful actors to achieve outcomes including co-ordination). • Not just institutional compliance (but is an indicator of trustworthiness). LBS, 2 December 2005

20. Trust • Much of the trust debate uses examples from Italian industrial districts • Trust regulates opportunism between co-operating firms. • Notion of community and belonging. • Breaking the ‘rules’ leads to expulsion from the community (Brusco, 1992). • Recent evidence of a breakdown of trust arising out of structural changes in ownership (large firms) and mode of operation (Farrell and Knight, 2003). LBS, 2 December 2005

21. Knowledge and Productivity • Transferring knowledge for productivity (Lapre and Van Wassenhove, 2001) • Mukherjee et al. (1998) analyzed 62 quality improvement projects undertaken in one factory over a decade. • Processes in quality improvement projects exhibit considerable variation along two learning dimensions: conceptual and operational learning. • Conceptual learning is the process of acquiring a better understanding of cause-and-effect relationships, i.e., the acquisition of know-why. Operational learning is the process of obtaining validation of action-outcome links, i.e., the acquisition of know-how. LBS, 2 December 2005

22. Knowledge and Productivity • Only 25% of the projects, the ones that acquired both know-why and know-how, accelerated the factory's learning rate. • Projects that produced know-why without the corresponding know-how were ‘disruptive’. • Projects that failed to acquire know-why did not affect the learning rate. • An organizational structure called model line, a production line run as a learning laboratory, consistently produced know-why and know-how that was successfully transferred to the rest of the factory. LBS, 2 December 2005

23. Knowledge and Productivity • What is the next production frontier? The author argues that it is operating factories as learning "laboratories." These are complex organizational ecosystems that integrate problem solving, internal knowledge, innovation and experimentation, and external information. (Leonard-Barton, 1992) LBS, 2 December 2005

24. Knowledge and Productivity • “For example, in the 1970s he had participated in an R&D project on the ability of tire cord to withstand corrosion. From this R&D project, he remembered that some copper-related variables determined in the brass coating step were relevant for the problem at hand in the WWD department. The MLA team tested the model with controlled experiments. As a result the MLA obtained a sharp improvement in productivity.” LBS, 2 December 2005

25. Knowledge and Productivity • “What we didn't understand when we started model lines in plants B and C is that a model line manager needs to have authority over production and projects, and a young engineer is not a good choice to run a model line. Young engineers lack experience in formal problem solving…” LBS, 2 December 2005

26. Knowledge and Productivity • Taylor was the first person to apply knowledge to work. • There is equal—or even greater—opportunity in the developed countries to organize non-manufacturing production (i.e., production work in services) on the production principles now being developed in manufacturing. • There is equally a tremendous amount of knowledge work—including work requiring highly advanced and thoroughly theoretical knowledge—that includes manual operations. LBS, 2 December 2005

27. Knowledge and Productivity • Six major factors determine knowledge-worker productivity • Knowledge-worker productivity demands that we ask the question: "What is the task?” • It demands that we impose the responsibility for their productivity on the individual knowledge workers themselves. Knowledge Workers have to manage themselves. They have to have autonomy. • Continuing innovation has to be part of the work, the task and the responsibility of knowledge workers. LBS, 2 December 2005

28. Knowledge and Productivity • Knowledge work requires continuous learning on the part of the knowledge worker, but equally continuous teaching on the part of the knowledge worker. • Productivity of the knowledge worker is not—at least not primarily—a matter of the quantity of output. Quality is at least as important. • Finally, knowledge-worker productivity requires that the knowledge worker is both seen and treated as an "asset" rather than a "cost." It requires that knowledge workers want to work for the organization in preference to all other opportunities. (Drucker, 1999) LBS, 2 December 2005

29. References to Knowledge Leakage • In biotech clusters (Wolter, 2003) • The Identification and Assessment of Knowledge Leakage Risks in 3D Environments • Knowledge leakage, refers to the possibility of information or knowledge that is critical to the organization being lost or leaked – whether deliberately or unintentionally – to a competitor or unauthorized personnel. Risk refers to the probability of this occurring (http://isrg.shef.ac.uk/fenio/) • International joint ventures (Tidd and Izumimoto, 2002) • Licensing consortia (negative impact) • Strategic alliances (negative impact) LBS, 2 December 2005

30. References to Knowledge Leakage • Leakage of technical knowledge through outsourcing design work. • Suppliers learn from their experiences and embody these as improvements in their next client's product; • Guest engineers (engineers from supplier firms who permanently reside in the customer company) (Twigg, 1997). • An additional influence on the choice of outsourced design capability will be the long-term relationships that exist with suppliers who are considered development partners “The automotive industry is a surprisingly close-knit community. If a supplier were to leak information of a truly strategic nature from one assembler to another, it would soon be known and the supplier’s credibility would be destroyed. Leakage in the other direction – from one supplier to another via an assembler – is more difficult to detect accurately but appears to be more commonplace. (Lamming, 1993). LBS, 2 December 2005

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