Viva 17 May

Presentation of Doctoral Research
Rupert Booth
May 17th 2011

Warwick Business School

Contents
 Research context, references and questions
 Five sets of hypotheses
 Methodology and enablers
 Research findings
 Contributions to literatures
 Publication plans
 Future work


Research context
 Four decades of research into Post‐Acquisition
Performance (PAP) and the ‘Merger Paradox’
 Currently PAP recognised as an intellectual construct,
related to intention and perspective
 Several independent literatures (e.g. finance, organisational
behaviour, finance) but this research opts for:
 strategic management
 comparative efficiency
 Pharmaceutical sector offers clarity of process and
availability of data

Research questions
 Merger paradox – the continued popularity of M&A
despite lack of success– i.e. motive
 ‘Success’ in what way , i.e. measurement (adopt neo‐
positivist philosophical basis)
 ‘Success’ for whom and when, e.g.
 the Firm now
 future Firm
 the Sector
 How to select measures for a Performance
Measurement Framework (PMF)
 Scale effects in R&D, as distinct from M&A

Five sets of hypotheses
 Set 1: Returns to scale
 Sets 2 to 5
 Firm’s M&A History and R&D Efficiency
 M&A History and Financial History
 M&A History & R&D History (Sectoral)
 M&A History and Sales over Assets
 Sets 2 to 5 all consider
 M&A in aggregate
 Cross‐border
 Cross‐product


Methodology and ‘Enablers’
 Develop approach to design of PMF: Design Principles
 Apply to pharmaceutical firm and key competitive process: R&D
 Identify most appropriate process efficiency technique: DEA
 Collect R&D data and analyse efficiency:
 Constant or variable returns to scale
 Various inputs (e.g. include staff or not)
 Various outputs (e.g. compounds or trials)
 Develop merger typology and scale metrics
 Populate typology with M&A data for major firms for full wave
 Undertake hypothesis testing


Enabler 1: Choice of sector/process:
Pharmaceutical R&D


Enabler 2: Resource Based View
 Major strand of strategic Phase Author Contribution to Measurement

management theory
 Focuses on differences between Early Wernerfelt (1984) Resources are the differentiating factors

firms and how these ‘resources’ Rumelt (1984) Isolating mechanisms with examples
are valuable
 To date, measurement has not Consolid Barney (1991) VRIN tests: Valuable Rare Imperfectly imitable
been a priority ation Non-substitutable
Peteraf (1993) Link to value and rent generation
 Useful theoretical basis for
identification of parameters to Dynamic Dierickx and Cool
(1989)
Importance of deployment as opposed to
possession (prelude to process)
be measures from outside the
firm Amit & Capabilities (recognition of intangible aspect to
Schoemaker resources)
 Avoids tendency to ‘measure (1993)

what is available’ Teece
(1997)
et al. Paths, Positions and Processes

 Provides basis for measuring Reasses Peteraf & Barney Efficiency perspective
sment (2003)
long‐term competitiveness


Enabler 3: Data Envelopment Analysis
 Simple efficiency analysis considers Graphical two‐dimensional example:
ratio of output to input
 Many processes have multiple inputs
and outputs
 Data Envelopment Analysis (DEA) is a
method to measure comparative
efficiency of ‘decision making units’
 Does not require knowledge of
‘production function’ relating inputs
to outputs
 Natural choice for application:
 Multiple inputs and outputs
 R&D has no known production function


Application of DEA
Range of Models Use of Results
 Scale: CRS or VRS  DEA results cannot be used in
 Choice of outputs: compounds or
statistical tests as they are not
trials independent observations
 Choice of inputs:  Therefore use results to bisect the
sample (n=48) into two groups of
○ Expenditure (primary)
above‐median and below‐median
○ Staff (affected by M&A)
efficiency
 Relative weight restrictions
 Use statistical tests to examine
 Outputs (pipeline stages) differences in Measure of Central
 Inputs (R&D expenses versus staff) Tendency between groups
 Parametric tests for mean and
non‐parametric tests for median


Enabler 4: Merger History
Principles Results
 Ten year period covers entire  Threshold set at £100m
merger wave and an  Total number deals = 591
economic cycle
 140 deals related to Top 48:
 Analyse all deals above
 64 cross‐border
threshold in relevant  29 cross‐sector
industrial code of acquirer
 Relate deals back to top 48
‘survivors’
 Use industrial code to
identify cross‐border and
cross‐product deals

Summary of hypothesis testing
 Set 1:
 Constant returns to scale refuted
 Relationship established between scale efficiency and size
 Set 2:
 In aggregate, no association between merger history and technical efficiency
 Set 3:
 In aggregate, positive association between merger history and financial
efficiency
 Set 4:
 For the sector as a whole, positive association between sum of deals and
technically efficient companies
 Set 5:
 For cross‐sector deals, new example of biased financial metric
 Further conclusions for cross‐border and cross‐sector in Sets 2 to 4

Research contribution #1
The Paradoxes of Mergers: The Effect of Alternative Measures of Post‐
Acquisition Performance:
 Elaboration on ‘different motive’ hypothesis
 Measures themselves provide divergent interpretations of ‘success’
 Financial measures suggest merger ‘success’ providing both ex ante
justification and ex post ability to continue
 At any a moment in time, merger‐prone companies appear to be more
efficient, technically and financially
 However, technical measures suggest no improvement of efficiency


Application of the Resource Based View to Performance Measurement
Framework Design for use with Data Envelopment Analysis:
Category No. Principle Ref
Scope and Structure
Stakeholders 1 Recognise major stakeholders of the firm (e.g. the firm’s owner and customers). (i)
Leading measures 2 Include at least one leading measure (typically non-financial). (ii)
Risk 3 Risk exposure should be measured. (iv)
Resources and Barriers
Differences 4 The PMF should measure key resources: the crucial few factors that differentiate the firm. (a)
VRIN 5 Measures should be on how resources are VRIN. (c)
Link to value 6 Measures should consider how resources are linked to value creation or economic rent. (d)
Intangibles 7 Capabilities, or intangible resources, should be measured where possible. (f)
Barriers 8 Barriers to imitation that affect the value of a resource should be measured. (b)
Processes and Positions
Deployment 9 Deployment of resources, through processes, should be measured. (e)
Dynamic links 10 Causal links between measures should be identified to provide a time dimension. (iii)
Static positions 11 Static positions are candidates for measurement. (g)
Efficiency and Benchmarking
Benchmarking 12 Efficiency should be measured, after recognising previous paths of development, for benchmarking. (h)


Use of Data Envelopment Analysis to Establish Scale Efficiency Factor in Pharmaceutical
R&D:

 Most comprehensive application of DEA to R&D to date
 Conclusive results on pharmaceutical R&D scale effects

0
3 4 5 6 7 8 9
‐0.5
ln (Scale Efficiency)

‐1

‐1.5

‐2

‐2.5

‐3
ln (mean R&D)


 The Distribution of Size and Frequency of M&A Deals in the
pharmaceutical sector:

Distribution of Deal Value

1000

100
No. Deals

y = 182.71e-0.3888x
R2 = 0.9042

10

1
1 2 3 4 5 6 7 8 9 10 11 12 13
Ln (Deal Value)


Future work
 Select different periods of analysis to test time‐invariance of
conclusions
 Monte Carlo simulation to test robustness of conclusions to
research serendipity
 Use observed statistical distributions to increase power of
statistics test, e.g. Poisson regression
 Apply DEA to other sectors with readily identifiable R&D
inputs and outputs
 Apply Design Principles to design performance measurement
frameworks on other sectors
 Repeat M&A analysis in other sectors

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