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Innovation, open labour markets and brokerage opportunities for knowledge workers in China’s ICT sector. Matias Ramirez Peter Dickenson DIME workshop “ Distributed networks and the knowledge-based economy ” , Juan-le Pins , 10 th -11 th May 2007.

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Innovation, open labour markets and brokerage opportunities for knowledge workers in China’s ICT sector

Matias Ramirez

Peter Dickenson

DIME workshop “Distributed networks and the knowledge-based economy”, Juan-le Pins, 10th-11th May 2007

Conceptualising firm level competence building through knowledge worker networks in Chinese R&D labour markets

1.Intra-firm knowledge transfer: strategic resource-based view

Building unique intangible resource and employment practices to promote intra-organisational knowledge sharing, complementarity of HR practices.

(Ichniowski et al 1997, Osterman 1994, Huselid 1995, Becker and Gerhart 1996Kleinknecht et at 1997, Michie and Sheehan 2000, Michie and Sheenhan 2003)

2. Inter-firm knowledge transfer in the knowledge (networked) economy:

A. Inter-firm knowledge transfer and collaboration through firm level networks(Marshall etc) and/or networks of knowledge workers.

Development of a division of labour associated with working on the boundary of the firm: Gatekeeping, boundary spanning, specialised services (Coleman 1988, Lesser 2000, Grabher 2004, Casper 2005).

Networks of knowledge workers can be closely linked to concepts of social capital and particularly socio-centric networks linked to knowledge brokers activities (Burt 2005).

Dense ties run the risk of redundancy, increases coordination costs for little benefit.
  • Socio-centric emphasizes, not the network, but the position of the individual in the network to fill structural holes.
  • Brokering essential to create variation
  • How to operationalise? By intertwining brokering activities of knowledge workers with labour market features associated with career and rewards.
B. Emphasis on inter-firm mobility (in Anglo-Saxon clusters at least) and flexible careers. Intermediary institutions at cluster level facilitate flexible contracts, matching skills and entrepreneurial opportunities. (OECD 2001, Saxenian 1994, 1996, Arthur and Rousseau 1996).
3 epistemic communities swift innovation exploitation scanning
3.Epistemic communities (swift innovation? exploitation? scanning?)
  • Entrepreneurial “star scientists” (Zucker and Darby 1995) emphasises start-ups and epistemic communities (Hakanson 2005) rather than collaboration on specific projects.
  • Presence of high skilled labour within a geographical area, a milieu where knowledge workers and firms are able to “scan” on managerial and technological developments.
  • In conclusion:
    • “New efficiencies”, a feature of network activity,associated with open labour markets
features of the chinese innovation system
Features of the Chinese Innovation system
  • Size and history of the Chinese science and technology infrastructure, especially the universities and C.A.S.
  • Regional technology parks in china’s innovation system (employ 4 million knowledge workers and account for three-quarters of all exports of high tech products).
  • Labour markets of knowledge workers. 1 million employed in R&D, 20m engineering graduates by 2015.
beijing s zhongguancun park
Beijing’s Zhongguancun Park
  • 1988 Beijing experimental zone became China’s first high technology development zone. Included Founder (Beijing University), Tongfang (Tsinghua), Lenovo (C.A.S).
  • 14,000 high tech companies, 500,000 employees
  • Prime catalysts are NTEs (spin-offs from CAS and others).
  • Financial incentives: Taxes waived first 3 years, 15% thereafter (but only 50% paid for next 3 years).
  • In all science parks, 15% of firms are foreign owned, but make up 42% of valued added.
  • Surge of economic growth in 1990s appears to have by-passed Beijing, focussing mainly in Guandong, Jiangsu and Tianjin.
  • Knowledge workers will derive a wage premium for their tenure with high technology companies.
  • ZGC Park has a highly fluid labour market, that rewards knowledge workers for mobility and experience in the labour market
  • Firms in the ZGN will pay wage premiums for employees to collaborate and network (broker) outside of the firm in innovation projects?
  • Networking activities of knowledge workers will have a positive impact on innovation performance
data analysis
Data analysis
  • Survey 1: Senior R&D managers in 71 Chinese ICT firms undertaking innovation.
  • Survey 2: 381 knowledge workers (R&D employees) from these 71 firms working on specific projects.
  • Sample: Indigenous Chinese, ICT, located in ZGC park, presence of R&D activity, must have introduced a new product in past 3 years.
network constructs




Available n

Inter-organisational problem solving

Sharing knowledge with research institutes

Team members



Sharing knowledge with founder bodies

Team members

Sharing knowledge with standard setting bodies

Team members

Relational problem solving

Sharing knowledge with former classmates

Team members



Sharing knowledge with colleagues

Team members

Scanning activity

Attending conferences

Team members



External communication via chat rooms etc

Team members

Informal contact with external acquaintances

Team members

Network constructs

Inter-organisational problem solving

Benefits from collaboration with suppliers, customers, research institutes, academic institutes and joint ventures. Division of labour includes boundary spanners bridging cognitive gaps (Leonard Barton 1995).

Relational networks

Networking that relies on personal relations of knowledge workers and ties of individuals knowledge workers. Also lowers cost of mobility. “Embeddedness” of labour markets (Macdonald and Piekkari, 2005, Casper 2005, Granovetter (1988, 1995). Lowers the cost of labour mobility and may help innovation, but knowledge belongs to employee.

Scanning Activity

Search and scan activity (Allen 1979, Leonard-Barton 1995, Macdonald 1994, Bucher 2003) associated with learning and understanding latest technologies, managerial techniques, potential for alliances and benchmarking and gate keeping activity.

do knowledge workers network
Do knowledge workers network?

IOPS and REL: Some/very influential in innovation project SCAN : Moderately/very important source of learning

Source: R&D employees

regression 1 are knowledge workers paid a premium for their networking activities
Regression 1: Are knowledge workers paid a premium for their networking activities?

Earnings Function:W= constant+a1EXPi +a2 EXP2i+b1TENUREi +b2TENURE2i + c1Number of PREVIOUS JOBS + d1 SEN+d2 IOPS + d3 REL + d4 SCAN + f1 IOPS*SEN + f2REL*SEN +f3SCAN*SEN where:

W = gross monthly wage including bonuses. EXP = years prior experience to joining present firm,

TENURE = years in current firm,

SEN = level of seniority (we distinguish between manager/senior engineer and non-management technical/commercial).

IOPS = inter-organisational networking,

REL = relational networking,

SCAN = general networking.




interactive terms between seniority and different types of networking

regression results for entire sample of knowledge workers
Regression results for entire sample of knowledge workers

Explanatory Variable Standardised Beta t Sig

Constant 7.692 .000

EXP .278 2.119 .035**

EXPSQ -.120 -.937 .350

TENURE . 698 5.165 .000**

TENURESQ -.412 -3.073 .002**

SEN -.214 -1.302 .194

Number PREVIOUS JOBS .174 3.174 .002**

IOPS .028 .506 .613

REL -.028 -.504 .615

SCAN -.049 -.762 .447

SCAN*SEN .371 2.207 .028 **

IOPS*SEN .052 .445 .180

REL*SEN -.013 -.129 .234

Dependent Variable: gross monthly wage plus bonuses.

Model summary:

R2=.253, adjusted R2=.229, F=4.872, Sig=.028 n=381

do networking activities impact innovation performance innovation performance construct dv
Do networking activities impact innovation performance?Innovation Performance Construct (DV)

Innovation Success function:= constant+a1 Size +b1 Ownership+c1 SCAN + c2IOPS + c3REL where:Size = Number of employees Ownership = Dichotomous variables between cooperative enterprise and privately ownedScanningInter-organisational problem solvingRelational networking Dependent Variable sourced from Senior R&D managers, independent variables sourced from knowledge workers.

regression results for innovation success
Regression results for innovation success

Explanatory Variable Standardised Beta t Sig

Constant 39.77 .00

Size -0.15 -1.32 0.18

Ownership -0.33 -3.31 0.00**

Scan 0.24 2.14 0.03**

IOPS -.165 -1.54 0.13

REL -.12 -1.11 0.27

Dependent Variable: Success on innovation project as measured by degree of success in meeting deadlines, market

share and technical capability.

Model summary:

R2=.20, adjusted R2=.17, F=4.587, Sig=.036 n=71

discussion of results
Discussion of results
  • Firms that engage with their environment will perform better than those that do not do so.
  • Scanning carries the least cost associated with collaboration.
  • Little evidence of innovation driven by formal collaboration (brokering) with other firms or academic institutions or relational networks. Why?
    • innovation is not complex amongst Chinese firms, therefore does not require collaboration?
    • High transaction costs to formal collaboration (poor social capital, low transparency of institutions?)
    • There may be high costs associated with relational networks (Guanxi reciprocity?).
    • Institutions too regimented, inflexible, resistance to reward individuals working on the margins of the organisation?