Knowledge Leakage: Assessing Risks and Benefits in Companies

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 • Themes informing project • Theory • The nature of knowledge • Measurement • Summary of literature findings

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? • 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) • 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 • 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)

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 • 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 • 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 • 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 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 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) • 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 • 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) • 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) • 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) • 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 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

Inter-firm data sharing – one study Stefansson, 2002

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) 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) • 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) • 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) • 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) • 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.

Knowledge Leakage: Assessing Risks and Benefits in Companies