Perspectives on Triple Helix

Fred Phillips DISC 2013, Daegu

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Agenda1. 3-Helix as a meso-level notion

– Epicycle in a grander tech-psych-inst cycle

2. Speed (differentials) as high-level system metric

– Roles of buffering institutions and ICT– Need for smart engagement

3. Applying 3-helix in the developing world

4. SUNY Korea’s joint TS/CS research

3-Helix papers published in Technological Forecasting &

Social Change• Wilfred Dolfsma, Loet Leydesdorff “Lock-in and break-out from

technological trajectories: Modeling and policy implications,” 76( 7), Sept. 2009, 932-941.

• Raul Gouvea, Sul Kassicieh, M.J.R. Montoya “Using the quadruple helix to design strategies for the green economy,” 80(2), Feb. 2013, 221-230.

• Øivind Strand, Loet Leydesdorff “Where is synergy indicated in the Norwegian innovation system? Triple-Helix relations among technology, organization, and geography,” 80(3), Mar. 2013, 471-484.

• Inga A. Ivanova, Loet Leydesdorff “Rotational symmetry and the transformation of innovation systems in a Triple Helix of university–industry–government relations,” In Press, Corrected Proof, Available online 19 Sept. 2013.

In D.S. Oh & F. Phillips (Eds), Technopolis: Best Practices for Science and Technology Cities (Springer, 2014)• E. Becker, B. Burger and T. Hülsmann,

“Regional Innovation and Cooperation among Industries, Universities, R&D Institutes, and Governments”

• F. Phillips, S. Alarakhia and P. Limprayoon,“The Triple Helix: International Cases and Critical Summary”

• José Alberto Sampaio Aranha, “Arrangement of Actors in the Triple Helix Innovation”

IC2 Model• Preceded 3-helix by several years• But only parts were made mathematical (Bard et al)

Academia Industry Government

Community Talent Technology Capital Know-How

Market Needs

Value-Added Economic Development

The math of Academic-Government-Industry

dynamics is interesting, but...

It is just part of a bigger picture.

The cycle of innovation and change: Lab to society & back again

TechnologicalInnovation

New ways to organize (Public & private)

New ways of producing and usingproducts & services

New Products& Services

New desires& dreams

New ways toInteract socially

Note how thisschema extendsEverett Rogers’

more linear model.

We might think all the elements move together in an orderly way.

Technological Change

OrganizationalChange

Psychological Change

Institutional Change

Social Needs

But in a free-market economy, they do not.

• They continually engage and disengage.

• Sometimes they move each other only by friction.

• 90% of MOT and Tech Policy problems stem from the differing speeds of the 3 sectors.

Example: Transportation

• Mobile-web rideshare services– Gain VC investment– Start operations– Get shut down by city

governments trying to regulate them under old taxi rules.

• Institutions have changed slower than technology and social demand.

Example: Health

• An elderly person dies because he was too proud to wear– A medical bracelet– or– An emergency signaller.

• Psychology has changed slower than technology.

Example: Software

• Record companies and publishers– Sue student MP3 pirates– Develop DRP software that further alienates

customers– Can’t adapt away from paper and CD

publishing.• Business organizations change more

slowly than technology and social demand.

Example: More and more often, social/institutional change outpaces

tech change - or will do so soon.• In most of the world, an excess of funds

is chasing too few growth investment opportunities.

• Fewer US companies are making IPOs.• Small-government activists rail

indiscriminately against direct government monetary support for new technologies.

See Phillips (2011).

This can be good.

• Individual creativity may bloom.

• Mistakes... – Can be undone

efficiently.– Don’t necessarily infect

the whole system.

It (disengagement)can be bad.

• Alienation• Lack of coordination and cooperation• Little institutional or organizational

creativity• Waste and pollution• Lives lost

Speed as the system metric• Really, speed

differentials among the sectors.

• A “clutch” and “transmission” are needed.

• The question is less how to engage, but rather, when.

• The key is not engagement per se, but smart (well-timed) engagement.

Not bridging organizations, but buffering organizations

• Civic groups• Workforce training programs• Economic development agencies• Technology brokers• Open innovation integrators• Accountancies • Industry associations• NGOs

• Incubators• Law firms• Venture capital• TTOs

The IC2 Model partially captured this.

3-Helix as meso-level construct: An epicycle within the Technology-

Psychology-Institutional dynamic

(3-Helix)

• Macro: Tech-Psych-Inst• Meso: Aca-Gov-Indus

– “Triple Helix”• Micro:

– Dynamics within people and within organizations;

– Technology life cycles• The buffering institutions

span all 3 levels.

Tech

Psych

Inst

Tech

PsychInst

What causes TOPI* disengagement?*Technological-Organizational-Psychological-

Institutional• Bad marketing, bad market research • Mistrust, bad service• Technology inaccessible to underserved

populations• Competition among de facto standards

(e.g., VHS vs Beta)• Lack of vision• Poor design of information &

communication products and programs.

“Engaging” doesn’t mean “attractive nuisance.”

Intrusive ‘engagement’

Updatethis app!

Marketing guru Geoffrey Moore says,

• “People have disengaged, for ... self-preservation.”

– With “consequences for consumer and brand marketing,

– “and long-term implications for education, health care, citizen participation, and workforce involvement.

• “So engagement is rightfully going to be a big

investment theme.”

Moore: Engagement is taking center stage in business.

• Off-line retailers are using digital interactions/devices in their in-

store experiences. – Example: Starbucks.

• “Social marketing foster[s] engagement around topics that ...

reflect well upon the sponsor.” – Example: Sephora.

• “Big data analytics drive communications that can break through the wall of detachment.” – Example: Obama campaign 2012.

Moore is saying• Advertising used to be like

this.– Annoying! Consumers

disengaged.• Now with social media,

mobile web, Yelp.com,– Consumers share product

reviews & complaints.– Advertisers have to treat

consumers more gently.– To make us want to continually

re-engage.• Engaging doesn’t mean

shouting.

ICT for an Intelligently Engaged Society?

What kinds of IT fosterpositive, voluntary

engagement? Why?

What kinds of IT discourageit? Why?

People are proud to participate electronically.

• Fighting crime– Zapruder film; Rodney King videos

• Supporting favorite businesses, authors– Amazon reviews

• For post-disaster aid– Crowd-mapping of post-earthquake Haiti

• Crowd-funding research projects and entrepreneurs

• Though there are abuses.

Source: Ganti et al, Mobile Crowdsensing: Current State and

Future Challenges.

Micro Level: Workforce Engagement

• Definition: The measure of whether employees merely do the minimum required of them, versus proactively driving innovation and new value for the organization.

• Thus, engagement – “can only ever be partially accounted for by

deploying the latest new collaborative technology, – “and probably significantly less than many of its

proponents would have you believe.”

Source: Hinchcliffe

Current state of worker engagement

ICT for engagement? Summary

• ICT alone cannot create/sustain engagement.– Human intervention, via buffering institutions, can achieve

ICT-aided engagement.

• ICT, especially sensing and crowdsourcing, may assist in deciding when to engage.– Thus achieving smart engagement.

• This applies to all 3 levels (macro, meso, micro) of our multi-level Technology & Society diagram.

For many countries where central government direction is

the norm, 3-helix thinking is premature.

• Indonesia, Mongolia• USA: Industry lobbying government

presents a slightly different problem...

Big man little man game

In sum, the problem is not dis-engagement, but mis-engagement

among governments, people, organizations and products, due to:

• Speed differentials (i.e., poor timing)• Lack of vision• Poor design of information & communication products and

programs.– Lack of feedback– Excess complexity, leading to slow comprehension and adoption– Excess technology push (solutions without problems)– Excess demand pull (unrealistic expectations)– Other factors

SUNY Korea’s research agenda• Combine social science and computer science...• To find principles of IT design that more quickly lead

to engagement that is...– Well-timed– Smart– Satisfying

• Among– Individuals – Businesses– Government institutions – Technology developers

• With secure applications in several techno-policy domains (health, energy, etc.).

Some Implications

• For IT: Meeting users halfway• For managers: Engagement plans for

each constituency• For theorists:

– Modeling the moderating effect of buffering institutions

– Impact of coalitions on the 3-helix dynamic

The math of Academic-Government-Industry

dynamics is interesting, but...

It is just part of a bigger picture.

An aside: Spatializing an innovation

diffusion modelF. Phillips, On S-curves and Tipping Points. Tech.

Forecasting & Social Change, 74(6), July 2007, 715-730.

Alan M. Turing, The chemical basis of morpho-genesis. Philosophical Transactions of the Royal Society of London. B 327, 37–72 (1952)

http://www.cgjennings.ca/toybox/turingmorph/

References• http://davidsasaki.name/2013/01/beyond-technology-for-transparency/ • A. Charnes, S. Littlechild and S. Sorensen, “Core-stem Solutions of N-

person Essential Games.” Socio-Econ. Plan. Sci. Vol. I, pp. 649-660 (1973).

• David Watson The Engaged University. Routledge, 2013.• Dion Hinchcliffe, “Does technology improve employee engagement?”

Enterprise Web 2.0, Nov. 5, 2013. http://www.zdnet.com/does-technology-improve-employee-engagement-7000021695/

• Jonathan Bard, Boaz Golany and Fred Phillips, “Bubble Planning and the Mathematics of Consortia.” Third International Conference on Technology Policy and Innovation, Austin, Texas, September, 1999.

• F. Phillips, The state of technological and social change: Impressions. Technological Forecasting & SocialChange. 78(6), July 2011, 1072-1078.

감사합니다

Thank you

fred.phillips@stonybrook.edufp@generalinformatics.com