The role of management practices in closing the productivity gap - PowerPoint PPT Presentation

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The role of management practices in closing the productivity gap

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The role of management practices in closing the productivity gap Uwe Aickelin (Nottingham) Giuliana Battisti (Aston) Chris Clegg (Sheffield), Xiaolan Fu (Cambridge) – PowerPoint PPT presentation

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Title: The role of management practices in closing the productivity gap


1
The role of management practices in closing the
productivity gap
  • Uwe Aickelin (Nottingham)
  • Giuliana Battisti (Aston)
  • Chris Clegg (Sheffield),
  • Xiaolan Fu (Cambridge)

29th September 2005
2
Objectives
  • Assess the role of management practices in the
    productivity gap in selected parts of the service
    sector in the UK
  • Identify key aspects of management activity for
    productivity
  • Develop multi-level models of relevant variables,
    and use them to understand and predict practice
  • Generate ideas on good practice for productivity
    improvements.

3
Rationale
  • Continuing productivity gap, e.g., cf USA
  • Varied explanations
  • Emerging interest at the level of the firm
  • True also for service sectors
  • Likely to be multi-level and multi-disciplinary

4
Research stages
  • Stage 1 Review literatures from range of
    disciplines (months 0-3)
  • Stage 2 Examine existing databases (months 0-6)
  • Stage 3 Identify service sectors in which
    empirical work will be undertaken (months 3-6)
  • Stage 4 Review different modelling approaches to
    select the most appropriate (months 0-12)

5
Research stages cont.
  • Stage 5 Use existing datasets and theory to
    develop first draft multi-level model (months
    3-9)
  • Stage 6 Undertake case studies in relevant
    service sector companies to gain insights
    (months 6-15)
  • Stage 7 Develop and refine second draft of model
    (months 12-15)
  • Stage 8 Operationalise variables in model for
    survey (months 15-18)

6
Research stages cont.
  • Stage 9 Collect data using large-scale survey
    of relevant companies in UK (months
    18-21)
  • Stage 10 Analyse data and refine model
    months 21-27)
  • Stage 11 Identify lessons learned and good
    practice implications (months 24-30)

7
Outputs Best case
  • An inter-disciplinary literature review of the
    area
  • An understanding of the role of management
    practices
  • A valid multi-level model for use in
    understanding and predicting practice
  • A set of lessons learned
  • Ideas on what represents good practice for
    closing the productivity gap

8
Outputs Worst case
  • An inter-disciplinary literature review
  • Better insights into, and understanding of, the
    role of management practices
  • Experience of modelling in this area including
    the difficulties involved
  • A set of comparative empirical findings
  • A set of lessons learned
  • Preliminary ideas on what represents good
    practice for closing the productivity gap

9
Progress
  • Held meetings
  • Staff in place at three Universities from
    01.10.05
  • Staff in Sheffield from 01.01.06
  • Include PhD students?
  • Links with Nick Bloom, CEP
  • Met John Lewis

10
What next
  • Identify and discuss key issues, as raised today
  • Eg comparisons across companies or within
    companies (and across branches)

11
Literature review (1)
  • What do we mean by productivity?
  • How is it currently measured?
  • What do we mean by management practices?
  • How do we measure and assess them?
  • Which ones to include in the study?
  • What evidences is there of the productivity gap?
    How is it measured?
  • What are the current explanations for this gap?

12
Literature Review (2)
  • At which levels has the gap been investigated?
  • What models currently exist?
  • Are there any multi-level and / or
    multi-disciplinary models?

13
Levels
  • Departmental (eg. Supervisory style)
  • Branch / store (eg. Empowerment of staff)
  • Firm / company (eg. Supply chain partnering)
  • External (eg. Availability of skilled labour)

14
Models
  • Mathematical Model
  • Fix Relax (Decision Variables vs. Constraints)
  • Simulation
  • Heuristic Optimisation
  • Stochastic frontier analysis
  • Statistical multi-level model

15
Regular meetings
  • October 11 Cambridge
  • November 15 Nottingham
  • December 6 Aston
  • STAFF
  • China, Italy, Germany, UK, Nigeria, Vietnam,
    Australia, Mexico, Brazil, Thailand
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