Democratized collaborations in Big Pharma

Benjamin Hughes, Department of Information Systems, ESADE Jonathan Wareham, PhD, Associate Professor of Information Systems, ESADE

August 12, 2008

2008 Academy of management conferenceTIM Section

This study examines the potential of Web 2.0 in Pharma

1

Description

• Web 2.0 tools are poorly defined (McFriedies, 2006 ), but their acceptance as a concept is rapidly rising:

• Big impact in technology and media sectors (Tapscott, 2006)

• Adoption across industries is rising, up to 34% of companies employing wikis or bogs (McKinsey, 2008), to various degrees of satisfaction.

• Potential in Pharmaceutical industry is poorly understood, however Pharmacompanies are embracing open innovation and IT enabled collaboration as a means to drive increasing returns (Gassmann et al., 2003;2005)

• This study looks at the IT innovation strategy of a Big Pharma company taking “a fresh look” at both innovation and IT strategy

Context

Purpose• RQ1: Clarify the scope of Web 2.0

• RQ2: Determine its applicability to the pharmaceutical value chain (with both internal and external participants)

• RQ3: Determine common structural design criteria for these collaborations for managers

Web 2.0, or Democratized collaborations, have high potential and a number of design considerations for management

2

Description

• Web 2.0 is participation in democratized collaborations, enabled by open and participatory web-based tools. Web 2.0 connects participants in an open manner, harnessing the opportunities of collective wisdom and the long tail, to promote knowledge and learning. Many different tools enable this, including blogs, social bookmarking, wikis or social networking.

Scope of Web 2.0

Applicability to Pharma

• High potential in sub-sections of the R&D part of the value chain, and Sales & Marketing

• Amongst external participants academic or clinical collaborators, regulators, health care professionals, consumers and payors are potentially important

• Managers can consider the design criteria they can control, e.g.,

o Collaboration inputs (Use external knowledge and talent, connection of distributed knowledge, cross pollination heterogeneous knowledge types, Intellectual property opportunities)

o Collaboration Process (Ideation, Co-creation)

o Collaboration Structure (Peer-peer, Many-one, Many-one-many)

• Management did not focus on outcomes such as benefits sharing regimes of benefits potential heavily commented on literature, either as it was too early for such predictions or because they were happy pursuing a non-predictive strategy

A research framework for Web 2.0 in Pharma requires combining many different literature types

Web 2.0

•Many IT systems support creative innovation, including user toolkits (Von Hippel & Katz, 2002) or blogs or wikis (Chesbrough et al.,, 2005).

•Web 2.0 includes a) data sources that get richer as more people use them, b) harnessing collective intelligence, or c) levering the “long tail” through customer self service (O'Reilly, 2005).

• Mass collaboration enables new business models through trends such as prosumer, co-creation, etc. (Tapscott , 2006)

3

•Declining R&D productivity, biotechnology interdisciplinary requirements, and shorter exclusivity periods drove trend towards collaborative R&D (Chiaroni at al., 2008; Jones, 2000; Gassmann & Reepmeyer, 2005; Powell et al. 1996 etc.)

•Increasing international dispersion of R&D networks drive reliance on virtual R&D teams in turn is enabled by internet based applications (Gassman & von Zedtwitz, 2003)

Pharma innovation /networks

•Open innovation requires determining which parts of the value chain to be opened (Chesbrough & Appleyard, 2007).

•In firm-led open source considerations includeintellectual property regimes, task modularity, and motivating the participation of contributors (Lakhani & Panetta, 2007)

•Understanding a company’s IP position is key to successful design (Chesbrough, 2006)

Open innovation /source Web 2.0 / User innovation

Bottom line: Internet enabled collaborations are essential to R&D innovation, but literature offers few insights into how management both capitalize and design collaborations with new

Web 2.0 technologies

Data collection and analysis methods

4

Employee interviews

Selection meetings

Final presentation

to board

Define Web 2.0

• With 120 senior managers, divisional or corporate CxOs, and champions of “bottom-up” ideas, covering all major parts of the company’s value chain –their mandate to take “a fresh look at IT and innovation strategy”

• Results were anonymously aggregated and presented back to interviewees in workshops within each division, a multi-workshop process encompassing 2-9 hours input from each group

Description

Analyze documents

• Strategy documents were finalized at the divisional and corporate level, coordinated by the general managers. Only the initiatives presented to the corporate board (researcher not present) were used as archival documents, entailing 10-100 page descriptions of each activity.

• Identification of key Web 2.0 themes via content analysis of to ranked pages

• Ranking of the importance of these themes via Goolge and Yahoo! Search count functions

• We adopted a compromise between pre-ordinate research and the Straussian variant of grounded theory (Strauss & Corbin, 1998), using thematic analysis for both a priori and open code identification

RQ1: Through a literature search and analysis of online material, a clearer definition of Web 2.0 as democratized collaborations was obtained

Web 2.0

5

Tool Total counts Objective/

benefits

Total

counts

Blog 12,800,000 Democracy 1,310,000

Wiki 8,590,000 Collaboration 987,000

Open-source 3,050,000 Knowledge

management,

knowledge

sharing

466,000

RSS Feed 1,760,000 Design

innovation, foster

innovation

106,900

Podcast 1,730,000 Collaborative

learning

32,400

Web services 1,240,000 Harnessing

collective

intelligence

26,000

Search engine 1,040,000 Networking

people

15,800

Social or

collaborative

bookmarking

930,000 Sharing between

users

8,950

File sharing 912,000 Facilitate

creativity

393

• Nebulous phrases like "the web as platform" and "architecture of participation” are often used to describe Web 2.0 (Guistini, 2006) Scholars view Web 2.0 in two ways, as “an initial sensitizing concept, based mainly on the associated tools” (Beer & Burrows, 2007), and also as philosophy of open and user driven participation that is "an attitude not a technology" (Lin, 2007).

• Web 2.0 is participation in democratized collaborations, enabled by open and participatory web-based tools. Web 2.0 connects participants in an open manner, harnessing the opportunities of collective wisdom and the long tail, to promote knowledge and learning.

RQ 2: What to democratized collaborations look like for big Pharma – 2 examples delivering operational cost savings for the company

Web 2.0

6

Example 1: Consumer forums Example 2: R&D collaborative network

Web portal with forums, RSS,

wikis and blogs: run by Internal

Marketing insights and R&D

staff

300 patients

Partner R&D

comp-anies

Partners Health

organiz-ations

50 health profes-sionals

• Aim: to improve R&D and marketing insights

• Inputs: Diverse and distributed knowledge, and some company IP

• Process/Structure: sharing of ideas (no product is produced), where the company is central

Wiki for data standards

Internal R&D unit

2

External R&D

Partner

External clinical

site Partner

Internal R&D unit

1

• Aim: Set data standard for clinical data such as biomarkers (to improve R&D productivity)

• Inputs: Similar but distributed knowledge

• Process/Structure: co-creation of a data standard where all collaborators work as peers

7

All R&D value chain sub-components

HR

Finance

Performance monitoring and control

Supply chain mgt.

Procurement

Product development & lifecycle mgt.

Suppliers

Health care professional

Academic/ Clinical colla-

borator

Competitor

IT

Payor

Provider

Regulator, government

Customer

Consumer

Pricing and health economics

Market a product

#1#2 #3 #4 #5

Ph

arm

ac

eu

tic

al va

lue

ch

ain

Ac

tors

/Sta

ke

ho

lde

r

s in

he

alt

hca

re

va

lue

ch

ain

We

b 2

.0

su

pp

ort

ed

co

lla

bo

rati

on

#7 #6 #9#10 #8

Finance, HR & Administration

Sales, marketing and Commercial Operations

OperationsResearch, discovery and

development

RQ 2: 10 of the 30 IT initiative perused by the company were “Democratized collaborations”, with a high potential in many parts of the value chain

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• Leverage (of

knowledge and

talent)

• Structure

Inputs

• Intellectual

property (or

products)

• Enhance weak position where

R&D costs are high

• Benefit sharing

reginesBenefit

sharing

and

capture • Benefits capture

potential• Modularized (benefits extractable along bit by bit)

• Long tail” or many potential niche solutions

• Big Idea or undefined

• Process • Co-creation

• Ideation

RQ 3: Across these 10 initiatives, we identified design criteria that were deliberately considered by managers (not implicit in their discourse)

Observed patterns

• External talent or knowledge 3

• Distributed knowledge in org.5

Process

and

structure

• Heterogeneous knowledge in org. 2

2

• Peer to peer3

• Many-to-one2

• Many-one-many (e.g., Third party organized) 2

4

2

• “Commons”: deployment (support), hybridization

(add-ons), complements or self service

-

-

-

-

• Other: licensing, IP ownerships-

Area

• Number of

observations of

criteria (of 10)

x

• Drive to find appropriate knowledge, external and internal, customer or partner, is consistent with open source/user driven innovation literature (e.g., von Hippel, Raymond)

• Paying attention to IP inputs is consistent with Open innovation literature (e.g., Chesbrough)

Inputs

• Literature has not highlighted process and structure of collaborations, though partially addressed in intermediaries (e.g., Chesbrough; Huston & Sakkab)

• Not consistent with Tapscotts’ Wikinomics with no support for the concepts of prosumers, new Alexandrians , platforms for participation, global plant floor etc.

Process and

structure

• Not consistent with various literature types:

• Treating outputs as “commons” (e.g., Mahony)

• Designing modularity of tasks and benefits capture (Baldwin & Clark, Fischer & Giaccardi, Lakhani & Panetta)

• Value capture models such as deployment hybridization, complements or self service (Chesbrough & Appleyard)

Benefit sharing

and capture

RQ 3: Implications of design criteria – is strategy unpredictable?

• Why the lack of focus on outcomes such as benefits sharing regimes of benefits potential?

• 1st explanation: initiatives not yet mature, managers saw them as strategic bets, and would seek to define benefits regimes as collaborations evolve

• 2nd explanation: a deliberate strategy of not defining outcomes – driven by uncertainty of knowledge production “shifting areas of scientific internal/external opportunity” (manager interview)

• A non-predictive strategy (Wiltbank, Dew, Sarasvathy& Read, 2006)

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Key takeaways and contributions

Potential in Pharma

Design patterns in pharma

Democratized collaborations

These collaborations are already delivering significant financial benefits in this pharma company

There is high value potential R&D and Sales & Marketing, with external participants including academic or clinical collaborators, regulators, health care professionals, consumers and payors

Design patterns for profiting from democratized collaborations are not well covered in literature – but open source/innovation gives use clues

Managers attempt to control inputs (knowledge of participants, companies IP) consistent with findings from open source

There appears to be clear formulation of structure and process for these collaborations (Ideation, Co-creation; Peer-peer, Many-one, Many-one-many etc.)

Management did not focus on outcomes such as benefits sharing regimes of benefits potential heavily commented on literature, either as it was too early for such predictions or because they were happy pursuing a non-predictive strategy

•This case study is exploratory – both on the definition of web 2.0 and its application to pharma

•In both cases, we examine people’s interpretations of these collaborations, not the collaborations themselves

•As a top 5 pharmaceutical company findings maybe applicable to the industry, but not to others industries with different collaborative R&D characteristics

•There is limited scope to connect this study further to literature

Web 2.0 as participation in democratized collaborations, enabled by open and participatory web-based tools. Web 2.0 connects participants in an open manner, harnessing the opportunities of collective wisdom and the long tail, to promote knowledge and learning.

11

References

Reference

• Beer, D., Burrows, R. (2007). Sociology and, of and in Web 2.0: Some Initial Considerations. Sociological Research Online, 12, 5

• Chesbrough, H. (2006). Open Business Models: How to Thrive in the New Innovation Landscape, Boston: Harvard Business School Press

• Chesbrough, H., Appleyard, M. (2007). Open innovation and strategy. California management review, 50, 1, 57-76.

• Chesbrough, H., Vanhaverberke, W., West, J. (2005). Open innovation: Researching a New Paradigm. Oxford: Oxford University Press

• Chiaroni, D., Chiesa, V., Frattini, F. (2008). Patterns of collaboration along the bio-pharmaceutical innovation process. Journal of Business

Chemistry, 5, 1, 7-22

• Fischer, G., & Giaccardi, E. 2007. Sustaining Social Creativity. Communications of the ACM , 50 (2), 28-29.

• Gassmann, O., Reepmeyer, G. (2005). Organizing Pharmaceutical Innovation: From Science-based Knowledge Creators to Drug-oriented

Knowledge Brokers. Creativity and Innovation Management , 14, 3, 233-245.

• Gassman, O., von Zedtwitz, M. (2003). Organizing virtual R&D teams: Towards a contingency approach. R&D management, 33, 3, 243-262

• Giustini D. How Web 2.0 is changing medicine. BMJ. 2006 Dec 23;333(7582):1283-4. PMID: 17185707

• Huston, L., & N Sakkab, N. 2006. Connect and Develop Inside Procter and Gamble’s New Model of Innovation. Harvard Business Review, March 1 :

58-67.

• Lakhani, K., Panetta, J. (2007). The Principles of Distributed Innovation. Innovations, 2, 3, 97-112.

• Lin KJ. 2007. Building Web 2.0. Computer 40, 5 101-102

• Mahony, S. 2003. Guarding the commons: how community managed software projects protect their work. Research Policy, 32: 1179–1198

• McFredries, P. (2006). Technically Speaking The Web, Take Two. IEEE Spectrum, 43,6,68-68. doi: 10.1109/MSPEC.2006.1638049

• O'Reilly T. (2005). What is Web 2.0. Design patterns and business models. O'Reilly Media.

URL:http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html. Accessed: 2008-02-08. (Archived by WebCite® at

http://www.webcitation.org/5VSDJTyrx)

• Powell, W., Koput, K., Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in Biotechnology.

Administrative science quarterly, 41, 116-146

• Raymond, E. (1999). The Cathedral and the Bazaar. Sebastapol, CA: O'Reilly Media.

• Strauss, A., Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, Calif:

Sage Publications

• Tapon, F. and Thong, M. (1999). Research collaborations by multi-national research oriented pharmaceutical firms: 1988–1997. R&D Management,

29, 3, 219–31.

• Tapscott D, Williams A. Wikinomics. New York, NY: Penguin Group: 2006.

• von Hippel E. Democratizing Innovation. Cambridge, MA: MIT Press: 2005.

• Von Hippel, E., Katz, R (2002). Shifting innovation to users via toolkits. Management science, 48, 7, 821-834

• Wiltbank, R., Dew, N., Sarasvathy, S., Read, S. (2006). What to do next? The case for non-predictive strategy. Strategic Management Journal, 27,

10, 981-998