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Andrew Grantham and Raphael Kaplinsky – Brighton Diane Mynors and Souad Mohammed – Brunel Kathryn Walsh and Rhoda Co PowerPoint Presentation
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Andrew Grantham and Raphael Kaplinsky – Brighton Diane Mynors and Souad Mohammed – Brunel Kathryn Walsh and Rhoda Coles – Loughborough Paul Chan – Northumbria. Overview. Aims and objectives of project Knowledge and knowledge leakage Data scoping study Initial findings and conclusion

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slide1

Andrew Grantham and Raphael Kaplinsky – Brighton

Diane Mynors and Souad Mohammed – Brunel

Kathryn Walsh and Rhoda Coles – Loughborough

Paul Chan – Northumbria

overview
Overview
  • Aims and objectives of project
  • Knowledge and knowledge leakage
  • Data scoping study
  • Initial findings and conclusion
  • Themes informing project
    • Theory
    • The nature of knowledge
    • Measurement
  • Summary of literature findings
aims and objectives
Aims and objectives
  • The aims are met by achieving the following objectives:
    • exploration of companies’ appreciation of the significance of knowledge leakage;
    • categorisation of knowledge leakage as a function of firm and inter-firm activities;
    • development of an outline methodology for companies to assess their knowledge leakage holistically and to understand the risks and benefits associated with the leaks;
    • provision of an assessment as to the potential effect of knowledge leakage on productivity.
what is knowledge
What is knowledge?
  • propositional (i.e. Gibbons’ (1994) Mode I – scientific knowledge);
  • procedural (i.e. Gibbons’ (1994) Mode II – application-oriented which and contextually-bound);
  • dispositional – i.e. learned values, attitudes and interests that predispose the acquisition and treatment of knowledge (Billett, 1997; Harrison and Kessels, 2004).
what is knowledge leakage 1
What is knowledge leakage? (1)
  • Premise:
    • The changes [in manufacturing philosophies] have included the:
      • outsourcing of non-core activities
      • introduction of lean
      • increasing requirements on lower tier companies to provide integrated solutions rather than mere components
      • movement of low-value adding activities to low cost base regions.
    • In these and other control-relinquishing activities, including staff retirement and other experience-loss mechanisms, knowledge leaks away from the origin.
what the literature says about kl
What the literature says about KL
  • Developing concepts introduced in studies of outsourcing design work (Twigg, 1997).
    • 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)
  • Tiers in the automotive industry(Lamming, 1993).
  • International joint ventures (Tidd and Izumimoto, 2002)
  • Digital media (Annansingh: http://isrg.shef.ac.uk/fenio/)
  • Spill-overs (Vohinger et al., 2004)
slide7
Data
  • Scoping study
    • Key interviews with seven firms (in process of coding)
      • 5 SMEs/1 Large/Huge
        • Medical equipment design & manufacture
        • Food processing
        • Telecoms equipment & service
        • Design/construction
        • Defence equipment
        • Metal products
    • Turnovers ranging from £500K – 20billion
data key issues emerging
Data – key issues emerging
  • Reliance on individuals with critical knowledge
  • Insufficient knowledge capture procedures
  • Trust relationships evident in subcontracting
  • Information/knowledge as a public good
  • Importance of suppliers for industry knowledge
  • Customer feedback feeding back to product development
  • Knowledge capture – where seen – is through inaccessible paper based systems
data not in any order
Data – not in any order
  • Where individuals leave/retire, poor processes for knowledge transfer to capture knowledge for organisation
  • Back-end sharing but still the risk that they will just walk away (and take knowledge/ideas)
  • Criticality of knowledge based on ‘gut feeling’
  • Reliance on experiential knowledge (know-how rather than know-what)
  • Cultural/social factors increasingly significant for knowledge sharing
  • Transfer rarely uni-directional
  • Awareness of certain types of knowledge leakage, and its criticality – but in cases it is a fact of business life and has to be dealt with.
conclusions
Conclusions
  • Knowledge leakage (flows) are under researched and conceptualised
  • Diverse literatures are complementary
  • Some useful typologies
  • Good case studies
  • Wide variety of indicators available for measurement purposes
  • Challenge to produce taxonomy
  • Operationalise it as a tool/methodology
foundational literature first trawl
Foundational literature – first trawl

Strategy

Knowledge/productivity

HRM

Knowledge intensity

Dynamic capabilities

Lean production

Risk

Supply chains

RBV

Core competences

Trust

value chains

Barriers to entry

CMMs

NPD/R&D

Rents

dynamic capabilities 1
Dynamic Capabilities (1)
  • Dynamic capabilities are the resources and capabilities that a firm draws upon to affect change. (Teece et al., 1997)
    • 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);
dynamic capabilities 2
Dynamic Capabilities (2)
  • 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 et al., 2003)
global value chains gvcs
Global value chains – GVCs
  • Value Chain Framework (Gereffi, 1994; Gereffi and Kaplinsky, 2001; Kaplinsky and Morris, 2001).
    • Schumpeterian rents (Schumpeter, 1961)
      • entrepreneur super-profit exceeding the cost of the invention and the associated innovation as well as the returns to economic activity in other activities which are less well protected from competition.
      • Rents are protected by barriers to entry…
gvcs barriers to entry 2
GVCs – Barriers to entry (2)
  • The most enduring barriers to entry are increasingly found in knowledge-intensive sectors and activities, such as design, chain coordination (Governorship).(Gereffi, 1994; Kaplinsky, 2000; Gereffi and Kaplinsky, 2001)
  • Imitability’ of core technologies - when a firm’s key resources are imitable, the firm cannot realise its full rent potential
towards measurement knowledge intensity 1
Towards measurement – Knowledge intensity (1)
  • Defined as “[The] extent to which a firm depends on the knowledge inherent in its activities and outputs as a source of competitive advantage” (Autio et al, 1999)
    • Rents are maintained at a high level if the KI in production is high. Low KI leads to erosion.
    • The ability to generate and command knowledge resources is a key component of dynamic capabilities and long term and sustainable profitability.
knowledge intensity 2
Knowledge intensity (2)
  • Indicators in the literature
    • R&D expenditure
    • No of patents
    • Stock of managerial and production techniques
    • Audit of current knowledge and future knowledge possibilities based on current knowledge
    • Management assessment questionnaires

(Autio, Sapienza and Almeida, 1999, Smith 2002, Shadbolt and Milton, 1999, Roper and Cronet, 2003, Ndofor and Levitas, 2004)

knowledge flows
Knowledge flows

Highly non-linear, dynamic, complex adaptive systems that differ between supply chains and between entities within supply chains. (A bit of brain work, 2005)

Pre-product flow/post-product flow

Internal flow/external flow

Explicit flow/tacit flow

Knowledge flows

Episodic flow/ continuous flow

Propriety flow/shared flow

nature of knowledge risk
Nature of Knowledge – Risk
  • Intentional
    • Increases time-to-market if poorly managed
    • Increased dependency on suppliers
    • Loss of centralised information control/ maintenance
    • Piracy of confidential knowledge
    • Loss of market share
    • Partial interpretations, forgetting, poor verbal communication, Chinese whispers.

(Bovet, 2005; Yanow, 2004)

trust 1
Trust (1)

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.’

Reducing transaction costs/risk management

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).

trust 2
Trust (2)
  • 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”
    • Social interaction/living proximity.
  • 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.
knowledge and productivity 1
Knowledge and Productivity (1)
  • Competitor imitation has been shown negatively to impact market and accounting performance (Ndofor and Levitas, 2004).
  • A more efficient productivity strategy is to share knowledge about up-to-date activity including process, change in product and services (Baines, 1997)
knowledge and productivity 2
Knowledge and Productivity (2)
  • 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 (know-why) and operational learning (know-how).
    • Only 25% of the projects, the ones that acquired both know-why and know-how, accelerated the factory's learning rate.
knowledge and productivity 3
Knowledge and Productivity (3)
  • Three 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.