Intra-Firm and Inter-Firm Knowledge Transfers and Productivity in the Retailing Sector

1
Intra-Firm and Inter-Firm Knowledge Transfers and
Productivity in the Retailing Sector

• Dolores Anon Higon, Aston University
• Jeremy Clegg, Leeds University
• Irena Grugulis, Bradford University
• Allan Williams, University of Exeter
• Ödül Bozkurt, Bradford and Leeds Universities
• Nicholas Vasilakos, Aston University
• IDEAS Factory, 25th of January 2005, Nottingham

2
Literature Review

• Productivity
• Productivity in services, and especially
  retailing, is significantly informed by
  non-material inputs
• These include the knowledge embodied in both the
  business plans of individual firms and in the
  skills of their workers
• The relationship of knowledge to labour
  productivity and of this to overall productivity
  is unclear in the food retail sector.
• Knowledge Work
• Knowledge work does not necessarily entail
  knowledgeable workers
• Retail sector may include business strategies
  that vary significantly in terms of the
  generation of knowledge work Those that rely on
  high degrees of personalised (tacit) knowledge in
  workers, and those that rely on extensive
  standardisation.

3

• Knowledge Transfer
• Knowledge transfer inside firms is crucial for
  their productivity
• A central ownership advantage that multinational
  firms rely on is knowledge, which they transfer
  internally in the global organization
• We can therefore expect multinational food
  retailers to have competitive advantages (over
  domestic ones) derived from knowledge transfer.
• Skills
• Knowledge transfer can involve the diffusion of
  business models that rely on knowledge work to
  variable degrees
• Multinationals business models may increase
  skills of the workforce through knowledge
  transfer
• Or these business models may rely on
  standardisation and deskilling of the workforce.

4
Selected Issues on UK Retail Productivity and
Innovation

• Nicholas V. Vasilakos

Dolores Anon
Aston Business School
Nottingham, 25/01/2006
5
Measures and Determinants of Retail Productivity

• Most commonly seen measures of Productivity
• Labour Productivity (Y/L)
• TFP
• Selected factors claimed to affect productivity
• Size of business For UK the bigger, the better
  (Reynolds et al 2005)
• Planning regulation barriers to enter-exit
  (Doms et al, 2001)
• Innovation Technological Sophistication (Doms
  et al, 2002)
• Van Ark et al (2002) and Basu et al (2003) on
  ICT-intensity
• The role of innovation
• Two types of innovation
• fundamental self-service, marketing and ICT.
• Incremental e.g. managerial know-how,
  entrepreneurship etc.

6
Econometric Problems / Concerns

• Measurement errors
• of output positively related to the
  service-intensity of the sector.
• of prices use of CPI may not always be an
  appropriate deflator (e.g. Nakamora 1999 )
• of inputs e.g. if market rigidities (e.g.
  planning regulation refer to trade
  unions/land) distort input prices)
• Put it down to market power.. (e.g. Griffith,
  2004 or Reynolds, 2005)
• Incorporating quality of service in
  productivity measures.

7
Econometrics/ Data

• Y A F(L, K, M)
• Focus
• Model A
• Measurement of Real Output
• Controls for Market structure, Ownership (MNE,
  non-MNE), size, location.
• Datasets
• ARD FDI
• FAME
• Data we look for licenses, skills (ESS ?)

8
Data and Methods II Survey Research Analysis

• Company-Level Questionnaire Survey
• 705 non-specialized retail stores from the FAME
  data base top-30 initially contacted by
  telephone
• Target group Management at head offices
• Target response rate 30-40, ie c 210-280.
• Questionnaire will focus on
• Ownership
• Basic employment data
• Managements assessment of sources of
  productivity in the sector
• Managements evaluation of the relative
  importance of knowledge transfer in productivity
  
• Managements evaluation of the relative
  importance of other factors including human
  mobility, market research, use of external
  consultancies, purchase/leasing/patenting of
  technology, suppliers, intra-company transfers.

9
Data and Methods III Qualitative Case Studies
10

• Thank You
• Gracias
• Efharisto
• Paldeas