Harnessing Disruptive

Innovation for the future

Fithra Faisal Hastiadi, Ph.D

Faculty of Economics and Business

Universitas Indonesia

Disruptive Innovations Dynamics

Agung Trisetyarso∗

Department of Computer Science, Bina Nusantara University,

Jl. Kb. Jeruk Raya No.27, Kb. Jeruk, Kota Jakarta Barat,

Daerah Khusus Ibukota Jakarta 11530, Jakarta, INDONESIA

Fithra Faisal Hastiadi†

Faculty of Economics, University of Indonesia,

Kampus Universi tas Indonesia, Depok, INDONESIA(Dated: November 23, 2015)

Theoretical explanations of disruptiv innovations is described beyond Solow-Swan model. Classi-

cal and quantum version of the model is presented: Classical view is based on the theory of evolution,

while quantum view leads into the conclusion that hyperfine splitting of capital is occrred due to the

disruption and as a consequence is the excitation of capital and labour from the old into the new

industry of disruption. The proposal is used to catch the next disruptive innovation.

BACKGROUND

INDONESIAN ECONOMY

New government, new initiative: Nawa Cita

Theme is "Strong Development, Inclusive and Sustainable"

NATURAL RESOURCES/ ENVIRONTMENTALECONOMICS

DEVELOPMENTAGENDA

Strengthening food security, energy and water

Accelerating the marine development

Improvement of environmental quality

Addressing climate change (Mitigation and adaptation)

ECONOMY

Development and technological innovation system

Increased investment and financial sectors

Increase workforce competitiveness and State-Owned Enterprises

Increase the contribution of small and medium enterprises in the economy

Preparation of natural resources for industrial sector

Development of processing industryEfficiency of national logistics and distributionIncrease in non-oil exports and high value-added services

FACILITIES AND INFRASTRUCTURE

Improving connectivity and synergy between sectors

Meeting the needs of basic infrastructure

Creating mass transportation

Source:RPJMN 2015-2019

MACROECONOMICS BACKGROUND

After suffered a lot in Asian Financial Crisis in 1997/1998, Financial

system in Indonesia has improved a lot to be more prudent and more

integrated to the global markets

Prudent macroeconomic policy made Indonesia resilient to Global

Financial Crisis 2008/2009

Now,

• Public and private debt (% of GDP) fallen sharply

• International reserves grown fast

• Inflation under control

• Relative political stability & good demographic trends Strong

economic performance (medium term)

• Rising per capita GDP & low borrowing cost robustness in

private consumption

On problem: inadequate supply of trade finance, affect export

performance and serious deterioration on imports

Country Background

Macroeconomics

Background

Economics Indicators

Composition of

Economy

Economics Prospect

ECONOMIC PROSPECT:GDP GROWTH PROJECTION FOR

INDONESIA

Country Background

Macroeconomics

Background

Economics Indicators

Composition of

Economy

Economics

Prospect

Source: Author’s calculation

5,0999999 5,12

5,4079251

5,1

4,8

4,6

4

4,2

4,4

4,6

4,8

5

5,2

5,4

5,6

2014 2015 2016 2017 2018 2019 2021

GDP Growth GDP Growth Scenario 1 GDP Growth Scenario 2

Source: Author’s calculation

ECONOMIC PROSPECT:EXCHANGE RATE

Country Background

Macroeconomics

Background

Economics Indicators

Composition of

Economy

Economics

Prospect

1500115111

15210 15301

14560 14548 14523 14486

14000

14200

14400

14600

14800

15000

15200

15400

2019Q1 2019Q2 2019Q3 2019Q4

CAD Tidak Terkontrol CAD Terkontrol

The Importance of ICT, Labor and Infrastructure (Ilmi and

Hastiadi, 2019)

Dependent variable : Real Expor Intermediete goods

Independent Variable All Period Machinery ICT Transport

Equip.

Resource

based

Other Mfg

RER 0.776*** 1.173*** 0.742*** 0.886*** 0.905*** 0.604***

LabCost 0.22*** 0.393*** 0.235*** 0.255*** 0.246*** 0.111**

TradeCost 0.259*** 0.141** 0.157*** 0.132* 0.166*** 0.081

Trade Open 0.447*** -0.006 0.762*** 0.338** 0.308** 0.453***

Infrasturcture 0.275** 0.2 0.094 0.658*** 0.246* 0.648***

FDIOpenn 0.317*** 0.288*** 0.282*** 0.382*** 0.263*** 0.371***

VTWR 0.111*** 0.082*** 0.23*** 0.145*** 0.121*** 0.241***

GDPpercap 0.659*** 0.91*** 0.563*** 0.802*** 0.826*** 0.637***

Dummy Euro Currency -0.177** -0.404*** -0.013 -0.42*** -0.508*** -0.486***

Dummy Trade Contraction 0.109** 0.131*** 0.105*** 0.081 0.068 0.01

Observation Number 969 940 966 927 965 960

Adjusted R2 0.947 0.953 0.953 0.938 0.942 0.924

The Order of Participation Level of Global Production Network of each Industrial Groups

Region Participation

Ranking

All Machinery ICT Transport

Equip.

Resource based Other Mfg

Industries

ASEAN5 1 Malaysia Malaysia Malaysia Thailand Malaysia Malaysia

2 Philippines Singapore Philippines Philippines Thailand Thailand

3 Singapore Thailand Singapore Malaysia Singapore Philippines

4 Thailand Philippines Thailand Singapore Philippines Singapore

5 Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia

OTHER ASIA 1 China China China China China China

2 India India India India India India

3 Japan Russian. Japan Japan Israel Japan

4 Israel Japan Israel Russian Japan Russian

5 Russian Israel Korea Israel Russian Israel

6 Korea Korea Russian Korea Korea Korea

EU (5 big) 1 Germany Turkey Germany Turkey Turkey Turkey

2 Turkey Germany Turkey Italy Germany Germany

3 Italy Italy France Germany Italy Italy

4 France France Italy France France France

5 UK UK UK Spain Spain Slovenia

NAFTA 1 USA USA USA USA USA USA

2 Mexico Mexico Mexico Mexico Mexico Mexico

3 Canada Canada Canada Canada Canada Canada

OTHERS 1 Brazil Brazil Brazil Brazil Brazil Brazil

2 Argentina Argentina Australia Argentina Argentina Argentina

3 Australia Australia Argentina South Africa South Africa South Africa

4 South Africa South Africa South Africa Australia Australia Australia

5 Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica Costa Rica

Indonesia is Lagging Behind (Ilmi and Hastiadi, 2019)

Estimation of a Country Moving Up to a Higher Income Category (Hardiana and Hastiadi, 2019)

P(𝐘𝒄𝒕 > j) L to LM LM to UM UM to H

Inovation 0.0327043 0.0378456** 0.0056989

FDI Intensity 0.3077188** 0.1276984 0.0350988

Productivity 1.62827*** 0.3314414*** 0.2070858***

Middle Income Trap (Hardiana and Hastiadi, 2019)

Income Classification of ASEAN and Other Emerging Countries: 1= Low Income, 2 = Lower Middle-Income, 3 = Upper Middle-

Income, 4 = High Income

Indonesia needs new source of

economic growth!

• Economic growth in Indonesia:from consumption-based,to

adoption technology- based ... later disruption technology-

based!

• Lesson from the case of OverTheTop services:telco operator

lost hundred of billions of USD due to a big hit from a new

wave of disruption

• Next wave of technological disruption: Internet ofThings (2014-

2020),Quantum Information and Computation (2020-...),Solar

Photovoltaic (2015-...)

MOTIVATION

DISRUPTIVE INNOVATION

• Poor managementof adoption and disruption -

technology

vs.

• High number of unemployment and

povertyin Indonesia

RECENT PROBLEMS

•

•

•

Because disruption can take time, incumbents frequently

overlook disrupters.

When new technology arises, disruption theory can guide

strategic choices.

Smart disrupters improve their products and drive

upmarket.

WHAT IS DISRUPTIVE

INNOVATION?

(Harvard Business Review, December 2015)

Clayton M. Christensen, Michael E. Raynor,

and Rory McDonald

• Clayton Christensen model is

Moore`s Law in generalization: the

performance of technology (and the

market as well) is in exponential

growth.

CLAYTONCHRISTENSEN MODEL

Bower, Joseph L., and Clayton M. Christensen. Disruptive

technologies: catching the wave.Harvard Business ReviewVideo,

1995.

TRENDS IN TRANSISTORTECHNOLOGY

Size is shrinking (exponentially)

Task is increasing (exponentially)

Quantum effect isemerging

(exponentially??)

INNOVATIONS IN A NUTSHELL

Under the picture, innovation is a Hamiltonian complexity

problem, where:

Disruptive innovation:

Sustaining innovation:

and mainstream market:

CLASSICAL VIEW: TECHNOLOGICAL DISRUPTION

AND THEORY OF EVOLUTION

• Disruption technology obeys theory of evolution: survival of

the fittest,while mainstream technology does not.

• Capital, Knowledge, andLabor in disruption technology wave

survive, while Capital, Knowledge, andLabor in mainstream

technology doesnot.

Anomaly in universe: Old technology will survive in asmall portion

PROPOSED MODEL OF

TECHNOLOGICAL

DISRUPTION

Where:

: the output per unit of labor

: the production function

: the disruption

: the mainstream

: the augmenting technology or knowledge

: the capital stock per unit of effective labor

: the labor : the saved/invested share

• Coupled differential equations

SOLOW-SWAN MODEL OFTECHNOLOGICAL

DISRUPTION (CLASSICALVIEW)

Perf

orm

anc

ePerf

orm

anc

e Perf

orm

anc

e

Time Time Time

Technological Disruption vs. Human

Tidal wave of

technological

disruption

vs.

Human

Interstellar,2014

CAPITAL QUANTIZATION:

DIRAC-SOLOW-SWAN MODEL

Before disruption: After

disruption:

or,

Derivation of Moore`s Law from Dirac-Solow-

Swan model.

Correlation of share vs. number of labor, level of

technology,and constant rate of stock.

Number of labor Level of technology

Share

CATCHING

THE WAVE

Potential Revenue

2014 2020

E-Commerce < $ 2Trillion < $ 7Trillion

Social Media< $ 2Trillion < $ 7Trillion

Internetof Things

$ 2 Trillion $ 14 Trillion

POTENTIAL LOSSES

On the other hand, technological disruption can cause losses of

potential revenue in the short term due to business shifting.

The telecommunications industry is predicted to lose a total

of US$386 billion between 2012 and 2018 due to over-the-top

messaging services such as Skype, WhatsApp and other third-

party internet voice applications.

EMPLOYMENT

Digitization provided a US$193 billion boost to world economic

output and created 6 million jobs globally Digitization creates jobs,

with a 10 point increase in the digitization score leading to a 1.02

percent drop in the unemployment rate

East Asia, South Asia, and Latin America received the most

employment growth of all regions, with more than 4 million jobs

created as a result of these regions’ digitization improvements.

• Source: Booz Company’s Report, 2011

EMPLOYMENT

Digitization provided little employment growth in North America and Western Europe. In these

countries, when digitization increases, their productivity improves; some jobs get replaced by

technology; and lower-value-added, labor-intensive tasks go overseas to emerging markets

where labor is cheaper.

Digitization has more significant employment effects in emerging markets for three main

reasons. First, the digitization gain in some emerging regions is higher than it is in the advanced.

Second, some of these regions have large populations, which means that a marginal

improvement in the unemployment rate leads to a significant number of jobs. Finally, offshoring

grows in tandem with digitization. As companies in digitally advanced countries improve their

productivity thanks to digitization, they transfer jobs to digitally emerging countries.

• Source: Booz Company’s Report, 2011

DIGITIZATION’S SECTORAL IMPACT

Business: Digitization is fundamentally reshaping business models. It is lowering barriers to

entry and expanding market reach for enterprises.

Go-to-market: Digitization is changing how companies build brands and products,

communicate, and provide services to their customers. Companies are increasingly relying

on social media to build brands. More and more, subscribers are forming their purchase

opinions online.

Production: Digitization is also changing the way companies manage their production assets.

It has enabled companies to move labor-intensive tasks to emerging economies while

competing to develop the best design and user interface.

Operations: Finally, digitization has had the greatest impact on the way companies organize

and operate to generate competitive advantage. Digitization has created more global

entities, seamlessly in touch across continents, and has redefined the concept of office space.

This slide is taken from Heru Prasetyo presentation at Australia-Indonesia Leadership Program Course Melbourne, May

24, 2017

This slide is taken from Heru Prasetyo presentation at Australia-Indonesia Leadership Program Course Melbourne,

May 24, 2017

BACKDROP

• Entrepreneurial undertaking has been driven by technology

• IT Enabled ecosystem has emerged as potential solution for market and

employment revitalization

• In emerging economies, this enabling effect of technology in unleashing

the growth of private sector-led micro-businesses has never been more

apparent.

• The surging vitality of micro-businesses such as the eBay/Taobao’s online

retailing and Upwork/freelancer’s service provision has been providing a

wider social-economic implication

NOVELTY

• Our review shows that the knowledge of digital entrepreneurship, while still at its nascent stage, is

currently limited to the context of advanced economies.

• The prior findings offers limited applicability to emerging economies where technological

infrastructure is still developing, and a well-developed and structured institutional environment

can be non-existent or lacking.

• Businesses including micro businesses face conditions that could impede them from

entrepreneurial activity (e.g., Banejee and Duflo 2007), constraints in existing norms and ways of

working, physical distance, information asymmetry, and inaccessibility of institutional finance

(Burger et al. 2015; Tarafdar et al. 2013).

• Given its importance and yet a lack of a cumulative body of knowledge, this study examines digital

entrepreneurship in emerging economies, specifically in facilitating the growth of micro-businesses.

OUR APPROACH

• Given that research into the developmental aspects of digital entrepreneurship is

still nascent and our research question is a ‘how’ question (Walsham 1995), a

case study methodology is appropriate as we seek in-depth answers to an

exploratory and investigative set of ‘how’ questions

• Specifically, we draw on the emancipatory perspective of entrepreneurship that

view “entrepreneuring as change creation through removal of constraints”.

• We adopt a multi-case approach because it allows for comparison, and addresses,

to some extent, the limitations associated with generalizability and observer bias

in single case studies (Leonard-Barton 1990; Miles and Huberman 1994).

STRUCTURED-PRAGMATIC-SITUATIONAL

APPLICATION OF PRINCIPLES FOR INTERPRETIVE FIELD RESEARCH (KLEIN & MYERS, 1999)

HOW WE CHOOSE CASE STUDIES

• The case studies must be anchored by platforms that drive financial inclusion

and social-economic change in agrarian and developing communities.

• The case studies must allow us to examine platforms, digital entrepreneurs and

their constraints and the interoperable processes that support a digital

entrepreneuring community.

• We focused more on start-ups since they have long been recognised as the key

drivers of entrepreneurship and innovation, and generally experimenters that

are willing to try new models and approaches to make innovation work despite

constraints.

DATA COLLECTION (CONT’D)

• Data was collected over a one year period, from early Nov 2015 to late

October 2016. Data from the interviews was supplemented with data

gathered from company publications and corporate websites in order to

provide a more holistic picture of the cases.

• Primary data collection was conducted through face-to-face interviews during

fieldwork and follow-up telephone interviews. Each interview lasted sixty to

ninety minutes.

• We used an interview protocol (Firestone and Herriott 1982) to facilitate the

interview (Robson 1993).

• We used ethnographic interviews (Spradley 1979)

INTERVIEWEES’ ROLE AND DESCRIPTION

RESULT AND DISCUSSION

RESULT AND DISCUSSION

GOJEK

I GROW

RESEARCH AND PRACTICAL IMPLICATIONS

• This study broadens the research context of digital entrepreneurship that has a narrow focus in

advanced economies by contextualizing digital entrepreneurship in emerging economy.

• our findings provide the theoretical elucidation of relationship between contextual factors

(constraints) and the opportunities for digital entrepreneurship, from an emancipation perspective.

• our paper expands the underlying (implicit) conceptualization of digital entrepreneurship as a

process of wealth creation by adopting entrepreneurship as a process of change creation through

removal of constraints (Rindova et al. 2009).

• our findings offer a theoretical explanation for the key contextual determinants of a successful

developmental ICT-based initiative and the corresponding change creation process through digitally

enabled entrepreneurial actions.

RESEARCH AND PRACTICAL IMPLICATIONS (CONT’D)

• This research extends the notion of anecdotal evidences about of digital entrepreneurship for

development by conducting a systematic, multi-case comparison. Importantly, our findings

theorize the role of digital technology (finTech in our case) as a digital enabler in leading to

social-economic benefits.

• We expect our findings to be useful in guiding digital entrepreneurial undertaking in emerging

economies, specifically in creating social change. Small businesses can be important drivers of

growth and innovation (OECD 2016).

• For digital entrepreneurs, our case has shown how constraints faced by the local market can

open up an opportunity for social change, and thus business prospect.

• Our study presents how various digital financial tools (FinTech) can stimulate the growth of

micro-entrepreneurship by resolving different constraints.

CONCLUSION AND RECOMMENDATION

To conclude, creating digital markets and boosting digitization can yield significant

economic benefits and lead to substantial social benefits for societies and communities.

Digitization has the potential to boost productivity, create new jobs, and enhance the

quality of life for society at large.

For example, if emerging markets could double the Digitization Index score for their

poorest citizens over the next 10 years, the result would be a global US$4.4 trillion gain in

nominal GDP, an extra US$930 billion in the cumulative household income for the poorest,

and 64 million new jobs for today’s socially and economically most marginal groups. If

policymakers want to capture these rich returns, then they need to adequately build their

digital markets – the markets where the bulk of the world’s information and goods will be

bought and sold in the upcoming decade of digitization. (Booz Company’s Report, 2011)

Policymakers should work with industry, consumers, and government agencies to jump-

start and continuously monitor an inclusive digitization ecosystem that will encourage the

uptake of digital applications in these sectors and keep them competitive.

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