Description
On this paper talk how do new business models affect existing players in an industry.
RESEARCH REPORT
HOW DO NEW BUSINESS
MODELS AFFECT EXISTING
PLAYERS IN AN INDUSTRY?
Prof. dr. Marion Debruyne
Bart Devoldere
Knowl edge partner
October 2008
l 1
Flanders District of Creativity is the Flemish organization for entrepreneurial creativity. It was
founded in 2004 by the Flemish Government as a non-pro?t organization and enjoys broad support.
Flemish businesses, academia, and public institutions use Flanders DC as a platform for cooperation
in the pursuit of a more creative Flanders region.
Creativity is the key ingredient in making companies more successful and in helping regional
governments ensure a healthy economy with more jobs. Flanders DC inspires creativity and
innovation:
1. by learning from the most creative regions in the world,
2. by igniting creative sparks in everyday life and business, and
3. by providing research, practical business tools and business training, in cooperation with
the Flanders DC Knowledge Centre.
1. Districts of Creativity: Inspiration from the most creative regions
Responses to global challenges are best found within
an international network of excellence. With the single
aim of learning from the very best, Flanders DC aims to
unite the most dynamic regions in the world within the
'Districts of Creativity' network. Every two years, Flanders
DC convenes the Creativity World Forum, bringing together government leaders, entrepreneurs, and
knowledge institutions to exchange ideas about how to tackle pressing economic problems and
make their regions hotbeds for innovation and creativity.
FLANDERS DISTRICT OF CREATIVITY
November 19-20, 2008 - Antwerp, Belgium
Quebec
Catalonia
Lombardy
Karnataka
Queensland*
Shanghai
Rhône-Alpes
Baden-Württemberg
Flanders
Scotland
Nord-Pas-de-Calais
Oklahoma*
*: Candidate members
Qingdao
Kanagawa*
Quebec
Catalonia
Lombardy
Karnataka
Queensland*
Shanghai
Rhône-Alpes
Baden-Württemberg
Flanders
Scotland
Nord-Pas-de-Calais
Oklahoma*
*: Candidate members
Qingdao
Kanagawa*
l 2
2. Raising awareness: The best way to predict the future is to invent it
Flanders DC encourages entrepreneurs and citizens to look
ahead and ?nd creative solutions today for tomorrow's problems.
Flanders DC has developed an idea-generation tool to encourage
people and organizations to take the ?rst step toward innovation. In
addition, Flanders DC runs a general awareness-raising campaign
entitled “Flanders’ Future”.
3. The Flanders DC Knowledge Centre: Academic support
The Flanders DC Knowledge Centre serves as a link between Flanders
DC and Vlerick Leuven Gent Management School. Each year, the Flanders
DC Knowledge Centre publishes several reports and develops various tools,
case studies and courses. All these projects focus on the role of creativity
in a business environment and identify obstacles to, and accelerators of
competitive growth.
The Creativity Talks ? brief monthly, interactive info sessions ? update you on these research
activities. See www.creativitytalks.be for a current calendar and subscription information.
ONDERZOEKSRAPPORT
HET INNOVATIEPROCES
IN GROTE BEDRIJVEN
EN KMO’S
Geert Devos, Mieke Van De Woestyne, Herman Van den Broeck
Februari 2007
Kenni spartner
Kennispartner
ISBN-NUMMER : 9789080712195
EAN : 9789080712195
HET INNOVATIEPROCES IN GROTE BEDRIJVEN EN KMO’S - Februari 2007
l 3
Research reports:
? De Vlaamse economie in 2015: Uitdagingen voor de toekomst, Koen De Backer en Leo
Sleuwaegen, September 2005, Published in Dutch
? Ondernemingscreativiteit als motor van groei voor Vlaamse steden en Brussel, Isabelle
De Voldere, Eva Janssens en Jonas Onkelinx, November 2005, Published in Dutch
? The Creative Economy: challenges and opportunities for the DC-regions, Isabelle De
Voldere, Eva Janssens, Jonas Onkelinx en Leo Sleuwaegen, April 2006, Published in English
? Spelers uit de televisiesector getuigen: een verkennende studie in de creatieve industrie,
Marc Buelens en Mieke Van De Woestyne, Juni 2006, Published in Dutch
? Mobiliseren, dynamiseren en enthousiasmeren van onze toekomstige zilvervloot, Thomas
Dewilde, Annick Vlaminckx, Ans De Vos en Dirk Buyens, Juni 2006, Published in Dutch
? Development of a regional competitiveness index, Harry Bowen, Wim Moesen and Leo
Sleuwaegen, September 2006, Published in English
? Innovation outside the lab: strategic innovation as the alternative, Marion Debruyne and
Marie Schoovaerts, November 2006, Published in English
? De creatieve industrie in Vlaanderen, Tine Maenhout, Isabelle De Voldere, Jonas Onkelinx en
Leo Sleuwaegen, December 2006, Published in Dutch
? Het innovatieproces in grote bedrijven en KMO’s, Geert Devos, Mieke Van De Woestyne en
Herman Van den Broeck, Februari 2007, Published in Dutch
? Creatief ondernemen in Vlaanderen, Tine Maenhout, Jonas Onkelinx en Hans Crijns, Maart
2007, Published in Dutch
? Hoe ondernemers in Vlaanderen opportuniteiten identi?ceren. Een rapport met tips
en tools voor de ondernemer in de praktijk, Eva Cools, Herman Van den Broeck, Sabine
Vermeulen, Hans Crijns, Deva Rangarajan, Mei 2007, published in Dutch
? Networking in multinational manufacturing companies, Ann Vereecke, July 2007, published
in English
? How entrepreneurial are our Flemish students, Hans Crijns and Sabine Vermeulen, November
2007, published in English
? Fashionate about Creativity, Isabelle De Voldere, Tine Maenhout en Marion Debruyne,
December 2007, published in Dutch
? Find the innovator. Identifying and understanding adopters of innovative consumer
technologies in Flanders, Marion De Bruyne and Bert Weijters, December 2007, published in
English
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? De case Arteconomy, Eva Cools, Herman Van den Broeck en Tine Maenhout, December 2007,
published in Dutch
? Entrepreneurship and globalization, Italo Colantone and Leo Sleuwaegen, December 2007,
published in English
? HR Tools als stimulans voor creativiteit bij uw werknemers, Kristien Van Bruystegem, Vickie
Decocker, Koen Dewettinck, Xavier Baeten, December 2007, published in Dutch
? Internationalization of SMEs, Jonas Onkelinx, Leo Sleuwaegen, April 2008, published in
English
? HRM-uitdagingen voor groeiende ondernemingen, Mieke Van De Woestyne, Kristien Van
Bruystegem, Prof. Dr. Koen Dewettinck, Maart 2008, published in Dutch
? Sociaal Ondernemerschap in Vlaanderen, Hans Crijns, Frank Verzele, Sabine Vermeulen,
April 2008, published in Dutch
? Foreign direct investments. Trends and developments, Frederik De Witte, Isabelle De
Voldere, Leo Sleuwaegen, June 2008, published in English
Published research reports can be downloaded via the Vlerick Leuven Gent
Management School library catalogue or via www.?andersdc.be.
In addition to these research projects, the Flanders DC Knowledge
Centre has also developed the following tools and training sessions:
? Ondernemen.meerdan.ondernemen, an online learning platform
? Creativity Class for young high-potentials
? Flanders DC Fellows, inspiring role models in business creativity
? Creativity Talks, monthly seminars on business creativity and innovation
? Innovix, online innovation management game
? Flanders DC Academic Seminars: research seminars on business creativity and innovation
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
Board of Directors
of Flanders DC
l 5
TABLE OF CONTENT
List of Tables ................................................................................................................................7
List of Figures ................................................................................................................................8
Introduction ................................................................................................................................9
1. Research setting: the market research industry .................................................................10
1.1 Industry background ........................................................................................................10
1.2 Online market research: a business model innovation? .....................................................10
2 Literature review and conceptual framework ......................................................................12
2.1 Conceptual framework .....................................................................................................12
2.2 Ability to respond .............................................................................................................13
2.2.1 The role of existing complementary assets .............................................................13
2.2.2 The role of dynamic capabilities .............................................................................14
2.2.3 Structural inertia ....................................................................................................16
2.3 Motivation to respond ......................................................................................................16
2.3.1 New business model attractiveness .......................................................................17
2.3.2 Degree of threat to core competence ....................................................................17
2.3.3 Competitive behavior .............................................................................................17
2.4 Role of embeddedness ....................................................................................................18
3 Methodology and data .........................................................................................................20
3.1 Dataset and measures .....................................................................................................20
3.1.1 Introduction ...........................................................................................................20
3.1.2 Measures of the dependent variable ......................................................................20
3.1.3 Measures of the incumbent’s ability to respond ......................................................22
3.1.3.1 Complementary assets .............................................................................22
3.1.3.2 Dynamic capabilities .................................................................................22
3.1.3.3 Structural inertia .......................................................................................23
3.1.4 Measures of the incumbent’s motivation to respond ..............................................23
3.1.4.1 Attractiveness of online market research...................................................23
3.1.4.2 Threat to core competence ......................................................................23
3.1.4.3 Competitive behavior ................................................................................23
3.1.5 Embeddedness .....................................................................................................23
3.2 Empirical model ...............................................................................................................24
4 Discussion of results ............................................................................................................25
4.1 Introduction ................................................................................................................25
4.2 Testing the conceptual framework ....................................................................................28
4.2.1 Correlations and descriptives .................................................................................28
4.2.2 Basic copy results .................................................................................................32
4.2.3 Full copy results .....................................................................................................34
4.2.4 Test for completeness ............................................................................................37
4.3 Hypotheses discussion ....................................................................................................38
l 6
4.3.1 Complementary assets ..........................................................................................38
4.3.2 Dynamic capabilities ..............................................................................................39
4.3.3 Structural inertia ....................................................................................................39
4.3.4 New business model attractiveness .......................................................................40
4.3.5 Degree of threat .....................................................................................................40
4.3.6 Competitive behavior .............................................................................................41
4.3.7 Embeddedness .....................................................................................................41
5 Discussion and managerial implications .............................................................................43
5.1 Two golden questions ......................................................................................................43
5.2 The ability to respond .......................................................................................................43
5.3 The motivation to respond................................................................................................43
5.4 Further research ...............................................................................................................44
6 Acknowledgements ..............................................................................................................45
References ..................................................................................................................................46
l 7
LIST OF TABLES
Table 01: ESOMAR offering variables ........................................................................................... 21
Table 02: Correlation matrix for probability tests ........................................................................... 30
Table 03: Descriptives for probability tests ................................................................................... 30
Table 04: Correlation matrix for timing tests ................................................................................. 31
Table 05: Descriptives for timing tests .......................................................................................... 31
Table 06: cases in timing analysis ................................................................................................ 32
Table 07: test results for basic copy (without Embeddedness) ..................................................... 33
Table 08: test results for basic copy (with Embeddedness) .......................................................... 34
Table 09: test results for full copy (without Embeddedness) ......................................................... 35
Table 10: test results for full copy (with Embeddedness) .............................................................. 36
Table 11: Test results for completeness (with Embeddedness) ..................................................... 37
Table 12: Overview tests for peripheral complementary assets..................................................... 39
l 8
Figure 1: Conceptual Framework ................................................................................................. 13
Figure 2: Adoption behavior in Belgium ........................................................................................ 25
Figure 3: Adoption behavior of online as basic market research tool ............................................ 26
Figure 4: Adoption behavior of online as specialism ..................................................................... 27
Figure 5: Adoption behavior of online as basic market research tool (in %) ................................... 27
Figure 6: Adoption behavior of online as specialism (in %) ............................................................ 28
LIST OF FIGURES
l 9
INTRODUCTION
Since Gary Hamel’s bestseller Leading the Revolution (2000) and Kim & Mauborgne’s Blue Ocean
Strategy (2005), and the ever-returning examples like Cirque du Soleil, Southwest Airlines, IBM’s
solutions orientation, Canon, Starbucks coffee, etc. a new type of innovation is getting mounting
attention as a viable source for creating new growth businesses. Companies are not only looking
into product innovation anymore, but also consider re-inventing their business model. The stream
of research that investigates this new type of innovation gave birth to several terms, among them
strategic innovation and business model innovation
1
.
A business model is a combination of the company’s core strategy, strategic resources, value
network, and customer interface that is put into practice (Hamel, 2000). A business model indicates
then among others a company’s target customers, value proposition and product/service attributes,
based on the different assets, capabilities, and competences possessed. A company’s existing
business model is however mostly based on the industry’s generally accepted de?nition or industry
recipe of “how to do business in the industry” (Markides, 1997).
Existing literature on business model innovations focused strongly on descriptions about what
business model innovation is, which players initiate a new business model, how to come up with
new business models, how to make them work, what barriers exist towards implementing a new
business model and ways of overcoming those barriers (Christensen, Johnson, and Rigby, 2002;
Kim and Mauborgne, 1997; Markides, 1997, 1998; Matthyssens et al., 2006). Debruyne and
Schoovaerts (2006) serves as a basis for this study and posits that strategic innovation consists
of 4 key elements, namely value innovation, new market creation, go-to-market innovation, and
competitive disruption. Debruyne and Schoovaerts (2006) also found that ?rms that operate in highly
competitive environments (where strategic innovation or business model innovation matters the
most) are less likely to be strategically innovative. So “a substantial effort is needed to educate ?rms
about the nature and potential of strategic innovation” to which we also want to contribute with this
study.
Taking into account this focus of previous research efforts, which is focused on the innovator, we
will focus on the incumbent dealing with business model innovation in its industry. Our quantitative
study tackles untapped or less developed research questions about how new business models
diffuse within industries, how incumbents
2
can react to new business models, and what the role is
of companies’ complementary assets to deal effectively with new business models. The research
questions that are speci?cally treated are:
-How do existing industry players, named incumbents, respond to the emergence of new business
models?
-How does the imitation of business models develop over time?
-To what extent do existing complementary assets of incumbents affect the response of incumbents
after the emergence of a new business model?
1
In this study we use Markides’ de?nition of business model innovations (2006), but in the existing literature business
model innovation is strongly related, even sometimes similar, to the terms strategic innovation, strategy innovation,
business concept innovation, and value innovation.
2
‘Incumbency’ re?ects whether a ?rm participated in the previous generation of products. (Chandy and Tellis, 2000)
l 10
1.1 Industry background
The market research industry, ‘invented’ in the US and introduced in Europe before the 2
nd
World War,
was a mature and stable growing industry back in the nineties. With the advent and breakthrough
of internet however, the market research industry also encountered a big change, namely the
introduction of online data collection. Suddenly, everybody who could get two PCs and a server
could start-up a small, online market research agency and attack vested incumbents, almost without
any barriers…at ?rst sight.
The use of internet in market research, what we call online market research, is considered to have
started around 1995. Before 1995 it was virtually nonexistent, but the worldwide spend on online
is estimated by Inside Research at US$3,6 Billion in 2007 and increasing to US$4,3 Billion in 2008
(ESOMAR, 2008). In the Netherlands one of the ?rst commercial uses of online market research was
in 1996 with NSS/Market Research that investigated which online payment methods were preferred
by internet users. It proved to be a very easy and quick way to conduct research, and it didn’t
take long for lots of entrepreneurs to set up their own online market research agency. In Belgium
Insites Consulting (founded in 1997) is considered to be the pioneer in the online market research
?eld. Previous to commercial online market research, online research was already used in academic
environments.
1.2 Online market research: a business model innovation?
Online market research has some characteristics that indicate it represents a business model
innovation. Over time incumbents develop an industry recipe about their industry through education
and experience (Markides, 1997). This industry recipe is a combination of culture, routines, and
unwritten rules of behavior that de?nes “the way business is done in a certain industry”. Business
model innovations break through this industry recipe.
Firstly, industry experts agree that online enlarges the total market size of market research by creating
a ‘new market’ of e.g. smaller companies that didn’t use to have suf?cient means to conduct market
research and that are now facing lower boundaries to order some market intelligence data and
advice by using often cheaper online market research.
Moreover, the online market research business model emphasizes different product/service attributes
to customers that indicate the character of a value innovation. Whereas online emphasizes speed, low
cost, more objective response, and ease-of-use, of?ine points e.g. to higher validity in sampling and
interviewing, the possibility to track qualitative information, and the experience of the interviewer.
However, the new, online market is not expected to overtake the whole market research market.
Industry players are convinced that online will take in its position in the spectrum of research
methods and techniques, but will not replace all existing methods to conduct market research.
Traditional methods are still recommended in certain situations, but a shift towards online is felt by
some methods like e.g. in-home face-to-face personal interviews that are very expensive.
1 RESEARCH SETTING: THE MARKET RESEARCH INDUSTRY
l 11
It is also considered to be dif?cult to compete both in traditional and online research methods. In
the period 1999-2000 online was a ‘hot’ topic in the industry, and some incumbents started up their
own online panel because the visible, direct costs associated with going online were low. The indirect
costs however of investing in appropriate software and in building up a panel community with less
biased response proved to be hard, which makes it dif?cult to build up a competitive advantage in
both ways of doing research. Some have stopped their attempts to ?ght in both markets and are
now outsourcing web panels to agencies with appropriate core competences.
Finally, the old business model (traditional of?ine methods of market research) is not necessarily
worse than the new business model of using internet in methods and techniques to perform market
research. Since the advent of online, there are large debates in the industry about the validity of
online market research, but online gets more and more accepted by the industry. The discussion
centers on the premise that online panels would have less validity because of the bias created by
online respondents who would be more incentive-driven than respondents from of?ine panels and
the observation that a small number of people completes a large number of online questionnaires.
Three main questions are important when deciding to undertake online market research instead
of traditional of?ine methods: population characteristics (who is online?), channel characteristics
(what are they doing there?), and method characteristics (how do you measure on the internet?).
These questions and the on-going discussions about validity indicate that online is expected to be a
research method next to of?ine, without making of?ine research obsolete.
l 12
2.1 Conceptual framework
In an industry there are several viable choices of a business model and a particular business model
is then just one possible way to go to market (Moore, 2004). A company should come up with a
business model that is maximally aligned with its strengths and weaknesses, and its customer needs
(Markides, 1997). On the one hand this implies that if a new business model emerges, incumbents
should not blindly follow the new trend and automatically commit themselves to copy this new
business model. On the other hand, as strengths and weaknesses, as well as customer needs
evolve over time, the company’s decision towards copying a particular business model can also be
subjected to evolutions over time.
When confronted with a new business model in their industry, incumbents have many ways to
respond. Copying a new business model is just one way. A company could also decide to stick to
their current business model with heavy investments in this current business model to withstand the
new business model. A company could even totally ignore a business model innovation as this could
be perceived as not their business, because it targets different customers, pronounces a different
value proposition, and/or stresses different skills and competences. Incumbents can also come
up themselves with an innovation towards the new business model, and thus emphasizing again
different product/service attributes. A last response strategy could be focusing on and investing in
the traditional way of competing (Charitou and Markides, 2003; Markides, 2006).
In this study we focus on an incumbent’s copy response and we make the distinction between basic
and full copy of the new business model. We de?ne basic and full copy as two points on the axis of
completeness of the response, meaning that incumbents could only copy parts of the new business
model (basic) or replace their existing business model completely by the new business model (full
copy). Next to the completeness of the incumbent’s response, we also look into the timing and
probability of basic or full copy response.
We propose and test an overarching framework (Figure 1) that identi?es drivers and enablers for the
incumbent’s response to a new business model. The response decision is split up in three parts.
A ?rst response action is whether the incumbent does or does not adopt the new business model.
This is a simple yes/no question and speci?es the probability of either basic or full copy response.
A second part of the incumbent’s response behavior we investigate is when the incumbent takes
action, and expresses the timing of the response.
A third part of the response is the completeness. Incumbents can fully embrace the new business
model (full copy) or they can copy it only partly (basic copy).
In what follows we discuss this conceptual framework based on our literature study. We can identify
2 main drivers that have an impact on the response behavior of incumbents, namely the ?rm’s ability
to respond and the ?rm’s motivation to respond (Charitou and Markides, 2003). These 2 drivers are
reinforced by the industry embeddedness of the incumbent. We now discuss each of these building
blocks of the framework.
2 LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
l 13
Figure 1: Conceptual Framework
2.2 Ability to respond
The incumbent’s ability to respond (“Can I do it?”) consists of three main factors: its existing
complementary assets, its possession of dynamic capabilities, and its degree of structural inertia.
2.2.1 The role of existing complementary assets
Each company has certain strengths and weaknesses, relatively seen towards a particular market and
competitor(s). Those strengths and weaknesses make up the ‘resources’ of a ?rm and form together
with a ?rm’s skills and routines the basis for a potential competitive advantage (Wernerfelt, 1984).
Resources on their turn consist of tangible and intangible assets which are tied semi-permanently to
the ?rm (Barney, 1991).
We propose in this study that the complementarity of a ?rm’s assets with certain business model-
related activities of that ?rm, will be of major importance in deciding which business model to adopt
and when to adopt it. We propose this because companies will strive for maximum complementarity
between their resources and business model (Markides, 1997). Therefore the role of complementary
assets can not be underestimated and should be considered (Rothaermel and Hill, 2005; Dutta et
al., 2004; Helfat, 1997).
Teece (1986) untangles three sorts of complementary assets: generic, specialized and cospecialized
ones. Generic complementary assets are general purpose assets that do not need to be tailored to
the innovation or business model in question. They are commodity-type assets that can be transacted
for in the open market. Specialized complementary assets have a unilateral dependence with the
innovation, whereas cospecialized assets contain a bilateral dependence with the innovation. Both
specialized and cospecialized assets are built over a long time period.
Incumbent’s response
behavior:
-probability
-timing
-completeness
Motivation to respond:
-new business model attractiveness
-threat to core competence
-competitive behavior
Ability to respond:
-existing complementary assets
-possession of dynamic capabilities
-degree of structural inertia
Embeddedness in an industry
l 14
Early theory (and examples) on complementary assets focuses especially on the importance of
these assets to the commercialization of an innovation (Teece, 1986). Examples of (co)specialized
complementary assets in the commercialization of an innovation are extensive infrastructure of
switching networks in telecom industry, regulatory and legal expertise, large sales forces of detail
people in pharmaceutical industry, etc. The possession of complementary assets is considered to
play a role in the following situations:
-predicting who wins in case of innovation (Teece, 1986)
-assessing the industry and ?rm performance evolution following a new technology (Rothaermel and
Hill, 2005)
-predicting the probability of incumbents entering an emerging technological sub?eld (Mitchell,
1989).
We propose that complementary assets have interesting repercussions on the incumbent’s response
behavior to business model innovations. On the one hand, complementary assets increase entry
barriers for incumbents to copy a new business model, because an incumbent’s strengths and
weaknesses are not maximally aligned with the newly needed strategic resources to compete
in the new business model. On the other hand, complementary assets that are still useful in the
new business model buy the incumbent some time to decide whether or not to respond, because
newcomers will ?rst have to build those complementary assets needed.
Building on the theory of complementary assets and our propositions, we hypothesize:
H1: Complementary assets that an incumbent possesses towards a business model innovation
increase the probability that the incumbent copies the new business model, the later it does so, and
the more complete the copy is.
H2: The degree of complementarity of assets has a bigger in?uence than the number of complementary
assets on the probability, the timing, and the completeness with which an incumbent copies the new
business model.
To test for these hypotheses, we will use two variables: core complementary assets and peripheral
complementary assets. Core complementary assets are a small amount of assets that have a high
degree of complementarity with the business model innovation. Peripheral complementary assets
are a larger amount of assets that have a lower degree of complementarity with the new business
model. The measures of these two variables are presented in section 3.1.3.1.
2.2.2 The role of dynamic capabilities
Because business model innovations fundamentally reconceptualize what the business is all about by
breaking the industry’s recipe and by questioning the whole go-to-market approach of incumbents,
being responsive to this type of innovation demands a high ability to change or to adapt to change
from incumbents. The ability to continually integrate, build, and recon?gure internal and external
resources, competences, and capabilities to achieve new forms of competitive advantage and
congruence with rapidly changing business environments, has been de?ned as ‘dynamic capabilities’
(Teece et al., 1997).
Like with many of the constructs developed in the research ?eld originating from the resource-
based view (RBV) of the ?rm (e.g. resources, competences, and capabilities), conceptualization
l 15
and measurement is one of the most criticized aspects of the concept of dynamic capabilities. This
is re?ected in a lack of convincing quantitative support (Newbert, 2007). There are however some
efforts to universally de?ne these constructs and to make them more operational and measurable
(Ray and Ramakrishnan, 2006; Dutta et al., 2004; Armstrong and Shimizu, 2007).
Based on Teece (2007) we try to capture some key types of dynamic capabilities in this study to
partly explain response behavior of incumbents towards business model innovations. We suggest
market orientation, absorptive capacity, and entrepreneurial character of a company as key types of
dynamic capabilities.
Recognizing the potential of a business model innovation is not easy. A new business model can have
the strategy of creating a new market targeted towards non-customers, or of disrupting the existing
business model from the low end targeted towards ‘low-pro?t’ customers. These situations of attack
on non- or low-pro?t-customers increase the probability (the former even more than the latter) that
incumbents overlook or neglect the new business model. Therefore we hypothesize that the more
market oriented a company is, the better it knows its market and the faster it sees the new business
model. This entails a faster reaction towards the business model innovation. Market orientation is
even considered as part of kick-starting strategic innovation (Markides, 1997). We though want to
remark that a too ‘narrow’ market orientation could also negatively in?uence incumbent reaction.
That is because incumbents that focus too much on the market as de?ned in their industry recipe,
could overlook the new business model. Such a ‘narrow’ market orientation can thus postpone an
incumbent’s reaction and its probability of copying the business model innovation. A distinction can
be made between reactive and pro-active market orientation. A reactive market orientation focuses
on the manifest and expressed needs of customers, whereas a pro-active market orientation focuses
on the latent, and nascent needs of customers. The key difference between reactive and pro-active
market orientation is the forward-looking nature of pro-active market orientation. It is not about
responding to customer’s current needs, but about anticipating future needs.
A new business model is innovative in terms of the applied business model to serve a market. If a
company wants to adopt a new business model, the company’s industry recipe (cf. the assumption
of which business it thinks it is in) should change and this asks a particular capability to value,
assimilate, and apply new knowledge (cf. not necessarily technological knowledge). This is called
‘absorptive capacity’ (Rothaermel and Hill, 2005). Absorptive capacity could be a main determinant
of the organizational learning degree of a company. Researchers have commented extensively on
the tendency of decision-makers to rely on old frameworks to assess new information (Barr and
Huff, 1997). Change only happens at the point when new information impels a change in cognitive
frameworks (Kiesler and Sproull, 1982). However, cognitive frameworks are built up over time,
based on past experiences and established beliefs, and are deeply rooted within the organization.
Organizational cognition in?uences decision-making through its effect on the interpretation of new
information, but also through its effect as information ?lter. We propose a high absorptive capacity
as being a key type of dynamic capabilities. If an incumbent then has a high absorptive capacity, its
response would probably be more copy-oriented, more complete, and earlier.
Together with the absorptive capacity of an incumbent, we propose that also entrepreneurial behavior
of the incumbent plays a role as key type of dynamic capabilities. An incumbent having a high degree
of entrepreneurial behavior is proposed to have a more probable intention to copy the new business
model, to react more timely, and to be more complete in its response.
l 16
Building on the theory of dynamic capabilities and our propositions, we hypothesize:
H3: The higher the incumbent’s dynamic capabilities, the more likely, the quicker, and the more
complete it will respond to a business model innovation. A company’s market orientation, absorptive
capacity, and entrepreneurial character are key types of dynamic capabilities.
2.2.3 Structural inertia
Every organization develops over time certain routines and procedures to optimize working processes
that have a repetitive character. Even adapting to major changes can become a routine (cf. dynamic
capabilities) suggesting transformational experience of incumbents (King and Tucci, 2002). However,
establishing change within an organization and making it work is not easy especially in the case of
a major turnaround like a new business model. Questions like “Why should I change? What shall I
change into? What if I jump into a new position and it turns out to be a mistake?” are only a few ever-
returning issues that pop up as obstacles for business model innovation (Markides, 1998).
We propose structural inertia as being a major postponing and non-copying in?uence towards the
incumbent’s response in the advent of a business model innovation. The preferred state of the
organization is to remain inert. Organizational inertia has been de?ned as the propensity of a ?rm
to sustain the status quo and maintain the current course of action (Chandrashekaran et al., 1999).
Along with the increased interest for the sources of organizational dynamics, the notion of inertia
has received much attention from organizational theorists in the recent two decades. Scholars
adopting an inertial view to organizational and strategic change argue that organizations generally
resist change and it is their nature to preserve the status quo (Hannan and Freeman, 1984; Boeker,
1997). The emphasis of organization research on inertia has been primarily devoted to structural,
institutional and political barriers to change within the organization, often related to factors such as
the organization size and age (Kelly and Amburgey, 1991).
There are a few reasons why we observe this type of structural inertia which also explains why
business model innovators are mostly entrepreneurial start-ups or new market entrants, and not
incumbents (Markides, 1998). Firstly, the impact or return of projects in existing markets is ‘easier’
to assess than the impact of new ‘adventures’. Secondly, incumbents often seem paralyzed by their
current thinking and this further strengthens their traditional go-to-market approach (Matthyssens et
al., 2005). Breaking through this industry recipe is however a prerequisite to come to business model
innovation. A last component of structural inertia is the dif?culty of overcoming negative responses
internally that is driven by established product/service lines in large organizations and by the necessity
to achieve managerial consensus (Teece, 2007).
Based on the theory of structural inertia and our propositions, we hypothesize:
H4: The higher the structural inertia of an incumbent, the lower its probability of copying a new
business model, the later and the less complete it does so.
2.3 Motivation to respond
The incumbent’s motivation to respond to business model innovations is considered to be in?uenced
by 3 main factors, namely the attractiveness of the new business model, the threat it introduces to
the core competence(s) of incumbents, and the competitive behavior in the industry.
l 17
2.3.1 New business model attractiveness
The attractiveness of a market and pro?ts that are made by its market players attract other players. In
case of a business model innovation, the growth of the new market that causes as well substitution as
market expansion has a big in?uence on incumbent entry (Debruyne and Reibstein, 2005). When the
new business model is highly attractive, incumbents not only ‘feel’ that the new market substitutes
some pieces of the existing market, but they also ‘see’ that business model innovators are gaining
attractive pro?ts. This leads us to propose that the new business model attractiveness has a positive
in?uence on the incumbent’s adoption behavior towards business model innovations.
H5: A new business model’s attractiveness has a positive in?uence on the incumbent’s copy behavior
in terms of probability, timing and completeness.
2.3.2 Degree of threat to core competence
The substitution potential of a new business model leads us to the threat a business model innovation
has on incumbents and their core competences. This threat lies especially in the cannibalization
upon a ?rm’s existing markets served and products/services offered, and is more important for
incumbents whose core competence is directly linked to that part of the market that is ‘under attack’
of the new business model.
An incumbent’s response towards a new business model is thus expected to be in?uenced by the
cannibalization potential of the business model innovation (Debruyne and Reibstein, 2005). And
the greater the competitive threat, the less likely an incumbent will enter but the earlier it will do so
(Mitchell, 1989).
Based on the theory, we hypothesize:
H6: The greater the threat of a new business model towards the incumbent’s core competence(s),
the less likely an incumbent will copy the new business model but the earlier it will do so.
2.3.3 Competitive behavior
In their industry recipe of the business they are in, incumbents not only have a ?xed idea of target
markets, ideal customers, possible go-to-market approaches and a viable business model, but they
also develop an image of who their competitors are and who they are not. Each company de?nes
its key competitors and monitors their actions (in a structured or unstructured way) to know what’s
going on in the business. These competitive actions (or non actions) not only increase the incumbent’s
knowledge of the industry, but it also stimulates incumbent’s action (or non action).
In the advent of business model innovation there is on the one hand high uncertainty, because of
infringement on the very business logic of an entire industry. On the other hand there is high risk
aversion, because large, strategic key issues are at stake with the choice of a business model.
Therefore, industry players tend to show herd behavior or organizational imitation behavior towards
competitors. Herd behavior is an organizational behavior in which each response to a new practice
by a particular industry player makes the imitation of that response from another industry players
more likely (Burt, 1987). This process is called ‘contagion’ and serves as the dynamic behind
l 18
diffusion of new products (Bass, 1969; Rogers, 1962). Synonyms of herd behavior are bandwagon
or demonstration effect, and organizational imitation (Kennedy, 2002).
Organizational imitation behavior is considered to play an important role in the response towards
business model innovations by incumbents, especially among incumbents similar in size and
resources (Debruyne and Reibstein, 2005; Porac and Thomas, 1990; Haveman, 1993; Kraatz,
1998). We also propose that incumbent’s are in?uenced by the response behavior of market leaders,
and put this as standard in?uencer in our conceptual framework.
This imitation behavior is explained by the reduced uncertainty, and thus reduced risk, of incumbents
about the value of a particular business model because other incumbents also copy (or don’t copy)
it. The adoption of incumbent ?rms of the new business model may also create legitimacy for it.
However, the entry of other incumbents also changes the competition between the existing and new
markets with the possibility to accelerate the level of substitution between the old and the new, and
thus increasing the threat to the incumbent’s core competences (King and Tucci, 2002). Next to that,
complementary assets that are still useful in the new business model buy the incumbent some time
to decide whether or not to respond (section 2.2.1). But once other incumbents enter, the incumbent
will need to follow quickly not to lose its competitive advantage if the formers also possess those
complementary assets (Mitchell, 1989, 1991).
Based on the theory of organizational imitation behavior, we hypothesize:
H7: Incumbents are signi?cantly in?uenced by incumbent market leaders in responding towards a
new business model.
H8: Incumbents are not signi?cantly in?uenced by newcomers in responding towards a new
business model.
H9: Incumbents experience a bigger imitation behavior effect from incumbents that are similar in
size and/or resources.
H10: Incumbents experience a bigger imitation behavior effect from incumbents that have similar
complementary assets.
2.4 Role of embeddedness
The embeddedness of a company signi?es to what extent a company is anchored in an industry,
potentially re?ected in the strength of an incumbent’s ties with customers and suppliers, the strength
of an incumbent’s idea of the business (industry recipe), and the magnitude of an incumbent’s
inventory of path-dependent assets. An indicator of embeddedness is e.g. the period of time a
company has been around in a certain industry.
In our framework we suggest that embeddedness is more a moderator than a direct independent
variable. We thus propose that embeddedness changes the effect of existing complementary assets,
the effect of dynamic capabilities, the effect of structural inertia, the effect of a new business model’s
attractiveness, the effect of threat to core competence(s), and the effect of competitive behavior.
If an incumbent possesses path-dependent complementary assets, it is very dif?cult to make these
assets obsolete (redundant) in the event of an innovation that poses a threat to these assets (Teece,
l 19
2007). The more embedded an incumbent thus is in the industry, the stronger this path-dependency
and the stronger a threat to core competence(s) could play a role.
The longer an incumbent is present in an industry, the more engrained the dynamic (or non-dynamic)
character of the company is in the hearts of employees. Embeddedness thus renders the company’s
dynamic capabilities more extreme. Low dynamic capabilities become very low and high dynamic
capabilities become very high.
The time an incumbent has been around in an industry also impacts its degree of structural inertia.
The longer and the more an industry recipe has been con?rmed and has brought success, the more
that industry recipe is engrained in the minds and hearts of people as being ‘true’. So the bigger
an organization, the more time it consumes to convince the whole organization of another industry
recipe.
The existence of established assets and routines exacerbate problems of excessive risk aversion
(Teece, 2007). This increased risk aversion together with the increased attributed value of market
actions of long-time survivors over time, suggests a moderating effect of embeddedness on
organizational imitation behavior.
Based on the theory of industry embeddedness and our propositions, we hypothesize:
H11: Embeddedness has signi?cant effects as moderating variable and no or less signi?cant effects
as a direct independent variable.
l 20
3 METHODOLOGY AND DATA
3.1 Dataset and measures
3.1.1 Introduction
The data used for modeling the incumbent’s response behavior in this study is drawn from ESOMAR
3
directories. These global, yearly directories contain general information like e.g. year of foundation,
number of employees, turnover, etc. per market research agency that is member of ESOMAR. The
directories also disclose yearly information about a market research agency’s offering in terms of
methods/techniques used (for the period 1990-2005), operational ?elds of research (for the period
1990-2005), market sectors (for 2006-2007), research solutions presented (for 2006-2007), and
research services (for 2006-2007). We entered this hard-copy information in an electronic database
consisting of directory information of Belgium, the Netherlands, and the UK throughout the period
1990-2007. As such, we obtained a cross-country, longitudinal dataset.
Table 1 gives an overview of all relevant offerings presented in the ESOMAR directories together with
their appropriated values necessary for calculating some measures.
3.1.2 Measures of the dependent variable
We tested each time for two models, namely basic copy and full copy of online market research.
Incumbents in the period 1999-2005 (t = 1999, 2000,… 2005) were considered to offer a basic
copy of online market research if they offered internet research (1999-2005) in their operational ?elds
of research. Incumbents in the period 2006-2007 were considered to offer a basic copy of online
market research if they offered ‘online quantitative’ or ‘online focus groups’ as research services, or
if they offered ‘web panel’ as research solution.
Incumbents in the period 1999-2005 were considered to offer a full copy of online market research if
they offered internet research (1999-2005) in their operational ?elds of research when indicating that
internet research is their specialism. Incumbents in the period 2006-2007 were considered to offer a
full copy of online market research if they offered ‘web panel’ as research solution.
We want to remark that incumbents that offer a full copy of online market research are also included
in the analysis of incumbents that offer a basic copy of online market research.
3
ESOMAR is a worldwide organization for enabling better research into markets, consumers and societies. The or-
ganization currently has 4.400 individual members in more than 100 countries, and was founded in 1948. We highly
appreciate the willingness of ESOMAR to provide us all directories from the period 1990-2007.
l 21
Table 1: ESOMAR offering variables
Methods/Techniques Used
(1990-2005)
Operational Fields of Research
(1990-2005)
Research
Services
(2006-2007)
Market Sectors
(2006-2007)
Research Solutions
(2006-2007)
Personal ?eldwork and/or mail
surveys***
Advertising / Packaging Desk
Research**
Advertising/Public Relations* Advertising Research*
Personal Fieldwork*** Advertising Research* Agriculture Audience Research
Telephone ?eldwork*** Agricultural Research CAPI*** Automotive Brand Research***
Mail Surveys*** Automotive Research CATI*** Beverages Business-to-Business**
Panel and/or continuous survey
research*
Business/Industrial Research Mail*** Catering/Hospitality Children/Youth’s
Research**
Omnibus surveys* Business-to-Business Research** Online Charity/Non For Pro?t Consumer Research***
Qualitative/Psychological marketing
research
Child Studies** Mystery
Shoppers
Chemicals Concept Testing
Laboratory test facilities Consumer Marketing Research*** Focus Groups Confectionery Customer Satisfaction
Studies
Desk research, market analysis and/or
operations research**
Customer Satisfaction Recruiting Consultancy*** Data Mining**
Data processing, computer facilities** Financial/Corporate image** In-Depth
Interviews
Cosmetic/Hygiene Demographic Research
Datamining** Food&Drink Online Focus
Groups
Durables/Electrical Goods Employee Research**
Statistical analysis, interpretation of
survey data**
Industrial Research Detergents Ethnographic Research
Business and/or research consultancy International Marketing Research Energy/Utilities Image Studies
Educational services Internet Financial Services** International Studies
Market modelling Fragrance Industry Media Testing*
Segmentation/Typology Food New Product
Development
Media* Healthcare/Pharmaceutical*** Omnibus*
Medical/Pharmaceutical Research*** IT/Software/Hardware* Opinion Polling*
Packaging Research*** Legal/Lawyers Packaging/Design***
Personnel/Staff** Logistic/Mail/Transportation Panels*
Pricing/Promotions Media/Entertainment* Pricing Studies**
Pricing Research** Petrol/Oil/Gas Projective Techniques
Product Testing/NPD Public Sector/Government Product Testing
Promotions Research** Retail/Wholesale** Retail Audit
Social/Opinion* Telecommunication** Scenario Planning
Tracking/Brand Image*** Textile/Fashion/Clothing Segmentation Research
Travel/Tourism/Motorist Research Tobacco/Cigarettes Semiotic and Cultural
Analysis
Travel/Tourism Research Toys/Games Senior Citizen/Mid-Life
Wholesale/Retail** Travel/Tourism/Sport/Leisure Statistic Analysis**
Syndicated Research
Tracking Studies***
Usage&Attitude
Studies***
Web Panel
0 = not offered; 1 = basic offer; 2 = offered as specialization 0 = not offered; 2 = offered as specialization
*included in the measure of existing core complementary assets
**included in the measure of existing peripheral complementary assets
***included in the measure of threat to core competence(s)
l 22
3.1.3 Measures of the incumbent’s ability to respond
3.1.3.1 Complementary assets
The core complementary assets of an incumbent were calculated as the sum of variables in Table 1
marked with*. The peripheral complementary assets of an incumbent were calculated as the sum of
variables in Table 1 marked with **.
The selection of the different variables in our measures for complementary assets is based upon
qualitative interviews and desk research. We tried to balance the measures throughout the change
in data collection (1990-2005 versus 2006-2007) by looking for similar variables across periods and
by striving for a higher number of variables from the period 2006-2007 because those variables
have less chance of being indicated for a market research agency in the directories (cf. it are
specializations). Only for peripheral complementary assets is the number of variables from the period
1990-2005 larger than those from the period 2006-2007. This could however only impact our testing
of hypotheses 1 and 2.
Core complementary assets are suggested to have a higher degree of complementarity towards
the online market research business model than peripheral complementary assets. Peripheral
complementary assets are though larger in number than core complementary assets. We use core
complementary assets in our basic tests for ?t of our conceptual framework.
3.1.3.2 Dynamic capabilities
We measured an incumbent’s market orientation by the number of ‘key people’ or ‘managing
director(s)’ of that incumbent that were also presented as ‘ESOMAR member’. This measure indicates
the degree to which the market research agency is aware of the newest trends and industry news in
the market research world.
Absorptive capacity is measured by the degree to which an incumbent is being focused on
specializations in the product offering or markets served. We calculate for each year of the period
1999-2007 the number of specializations of each incumbent from the 14 ‘methods and techniques
used’ variables, the 29 ‘operational ?elds of research’ variables, the 29 ‘market sectors’ variables,
and the 33 ‘research solutions’ variables (see Table 1). We then rescale the variable ‘absorptive
capacity’ for the years 2006 and 2007 by multiplying it with 43/62 to balance for the number of
variables in our measure.
Entrepreneurial behavior is considered to be re?ected by the change of the operational ?elds of
research offering throughout the years (see Table 1). The variable ‘entrepreneurship’ is then calculated
as follows. For each 10-year-timeframe, starting with the period 1990-1999 till 1998-2007, we
calculated every incumbent’s standard deviation of its 29 ‘operational ?elds of research’ and made the
sum of those standard deviations. We then coded the variable entrepreneurship for each incumbent
for the period 1999-2007 as follows:
? the value for year 1999 = sum of standard deviations for timeframe 1990-1999
? the value for year 2000 = sum of standard deviations for timeframe 1991-2000
? etc.
l 23
3.1.3.3 Structural inertia
Structural inertia is measured by the number of ‘key people’ or ‘managing director(s)’ divided by the
number of ‘employees’.
3.1.4 Measures of the incumbent’s motivation to respond
3.1.4.1 Attractiveness of online market research
We measure the attractiveness of online market research for each year in the period 1999-2007
by dividing the global spend on online market research for each year by the number of newcomers
in our dataset in the period 1995-2007. The spend of online market research is estimated by
Inside Research at US$3,6 Billion in 2007, US$3,1 in 2006, and US$2,66 Billion in 2005 (ESOMAR,
2008). Because we missed data from 1999 till 2004, we assumed a linear market growth from
1999 onwards till 2005. So we started in 1999 with US$0,38 Billion, in 2000 with US$0,76 Billion,
etc.
3.1.4.2 Threat to core competence
The threat to core competence(s) was calculated by the sum of variables in Table 1 marked with***.
3.1.4.3 Competitive behavior
We use the cumulative basic copy of online market research by market leaders as basic competitive
behavior variable in our tests for ?t of our conceptual framework.
To test for imitation behavior among groups of incumbents with similar size and complementary
assets (cf. H9 and H10), we split our analyses each time for two groups. We then compare the
explanatory strength of our competitive behavior variable in the case of different groups based on
size and complementary assets versus the case of no formation of different groups (cf. standard test
for ?t of our conceptual framework).
To test for similar size, group 1 is the group with market research agencies that have a size in terms
of mean number of employees for the period 1999-2007 under the median value of all incumbents,
so the group with ‘smaller’ agencies. Group 2 is then the group with market research agencies that
have a size in terms of mean number of employees for the period 1999-2007 above the median
value of all incumbents, so the group with ‘bigger’ agencies.
To test for similar complementary assets, group 1 is the group with market research agencies that
have a value for the mean of their core complementary assets for the period 1999-2007 lower than
the appropriate median value of all mean values of market research agencies. Group 2 is then the
group with a mean value higher than the median value.
3.1.5 Embeddedness
We measure the incumbent’s embeddedness by its year of foundation.
l 24
3.2 Empirical model
Our conceptual framework (Figure 1) has a dependent variable, the incumbent’s response behavior
towards online market research, which is split up in three parts. We not only test for the probability
an incumbent will copy the online business model and for the timing of the response. We also test
the completeness of that response (basic or full copy).
The timing of the incumbent’s response is tested using a hazard model. The probability that an
incumbent copies the new business model and the completeness of its response are tested using
a binary logistic regression model. Time-varying covariates were averaged over the measurement
period to enter the logistic regression equation.
As all our hypotheses pertain to an incumbent’s response behavior, we only considered cases in
which (basic or full) copy of the online business model was possible and incumbents were involved.
The probability of offering online market research is estimated starting from 1999 when the ?rst
recording of online occurs in the dataset was. A company is considered an incumbent if it was
present in the market before 1995. Our total dataset contains 304 cases from three countries:
Belgium, the Netherlands, and the UK. Please notice that our dataset covers the period from 1990
till 2007.
l 25
Adoption Behavior Belgium
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B-Basic B-Specialist B-Basic-Incumbent B-Specialist-Incumbent
4.1 Introduction
The ESOMAR dataset contains company information for the period 1990-2007. Information about
online adoption behavior starts from 1999 onwards. We distinguish among two types of adoption
behavior. On the one hand the ‘basic adoption’ referring to market research agencies that offer online
methods (e.g. online focus groups or online quantitative research methods) or internet research,
but not commercialized as a specialism of that agency. On the other hand there is a ‘specialist
adoption’ referring to market research agencies that offer online market research as one of their core
competences (e.g. internet research and web panels).
Figure 2 sets out the adoption behavior in Belgium in cumulative numbers of market research agencies
for the two types of adoption (basic vs specialist), and for two samples for the period 1999-2007. The
?rst sample is the total ESOMAR population in Belgium active in the period 1999-2007, consisting
of incumbents (founded before 1995) and startups (founded in or after 1995). The second sample is
the ESOMAR incumbents that are active in the period 1999-2007.
We are interested in the cumulative number of adopters of online, so online adopters disappearing
from the market are not taken into account as negative values for adoption. Also startups (agencies
that are founded in or later than 1995) that adopted online and that are later acquired by incumbents
are not taken into account as a separate adoption. These remarks remain valid throughout our whole
analysis.
Figure 2: Adoption behavior in Belgium
4 DISCUSSION OF RESULTS
Adoption Behavior Belgium
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Adoption Behavior Belgium
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Figure 2 shows that there is a large increase in the number of agencies adopting online as a basic
offering in the period 1999-2001,and the period 2005-2006. The number of agencies adopting
online as a core competence stays throughout the whole period stable with in total less than 5
agencies that adopted online as their specialism.
Figure 3 and Figure 4 represent the evolution of adoption behavior for incumbents of Belgium, the
Netherlands and the UK for respectively a basic adoption, and a specialist adoption. There is a large
increase of the number of basic adopters in the UK in the period 1999-2001, followed by stability and
a small increase in 2005-2006. Belgium has a more stable growth over time just like the Netherlands,
but the latter has a larger number of adopters which is normal if we know that the Netherlands has
more market research agencies than Belgium. To make comparisons across countries possible,
we can correct for the number of market research agencies present in each country. Therefore we
introduce Figure 5 and Figure 6 where the cumulative number of adopters in a country is expressed
in relative terms to the total number of incumbent market research agencies in that country.
Figure 3: Adoption behavior of online as basic market research tool
The Netherlands seems good ground for the specialist adoption of online (Figure 4), and this becomes
very clear when we look at Figure 6. The Netherlands takes the lead in specialist adoption in relative
cumulative numbers of adopters with a large increase in 1999-2000. Belgium and the UK are lagging
behind, and in 2007 Belgium almost closes the gap with the UK.
Figure 5 indicates that Belgium, the Netherlands, and the UK are experiencing a similar basic adoption
evolution, but it is again clear that the Netherlands was the quickest to adopt relatively seen towards
Adoption behavior of online as basic market
research tool in an incumbent's o?er in Belgium,
Netherlands, and UK
0
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60
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100
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Adoption behavior of online as basic market
research tool in an incumbent’s offer in Belgium,
Netherlands, and UK
l 27
the number of players in each country. Figure 5 also shows that it is possible to have more than
100% of cumulative adoption due to consolidation of the market and bankruptcies.
Figure 4: Adoption behavior of online as specialism
Figure 5: Adoption behavior of online as basic market research tool (in %)
Adoption behavior of online as specialism in an
incumbent's market research tools ofer in
Belgium, Netherlands, and UK
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UK-Specialist-Incumbent
Adoption behavior of online as specialism in an
incumbent's market research tools ofer in
Belgium, Netherlands, and UK
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UK-Specialist-Incumbent
Adoption behavior of online as basic market research tool in an
incumbent's o?er in Belgium, Netherlands, and UK
0%
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120%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
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Adoption behavior of online as basic market research tool in an
incumbent's o?er in Belgium, Netherlands, and UK
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Adoption behavior of online as specialism in an
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Belgium, Netherlands, and UK
Adoption behavior of online as basic market research tool in an
incumbent’s offer in Belgium, Netherlands, and UK
l 28
Figure 6: Adoption behavior of online as specialism (in %)
4.2 Testing the conceptual framework
4
We tested two models based on our conceptual framework. On the one hand we tested if the
probability and timing of a ‘basic copy’ is in?uenced by the determinants of an incumbent’s ability to
respond and its motivation to respond, with and without embeddedness as a moderating variable.
On the other hand we tested the probability and timing also for a ‘full copy’. Basic copy and full copy
are the two points on our axis of response completeness.
We ?rst present correlations and descriptive statistics of our framework variables. Thereafter, we
discuss the results of our framework for basic copy and then for full copy.
4.2.1 Correlations and descriptives
Table 2 and Table 4 represent the Pearson correlations (and the number of cases included in the
correlation calculation) between the variables of our empirical model.
Further investigation of Table 2 and Table 4 indicates that there is only one case of excessive
correlation (cut-off rate set at 0,400) between variables in our framework. The correlation between
the mean of existing complementary assets and the mean of absorptive capacity is 0,415. Therefore
4
We want to remind that all time-varying covariates were averaged over the measurement period to enter the logis-
tic regression equation used for probability testing. Thus the variables discussed in the timing tests are not time-
averaged whereas the variables in the probability tests are time-averaged (although we use in the tables the same
names).
Adoption behavior of online as specialism in an incumbent's
market research tools o?er in Belgium, Netherlands, and UK
0%
5%
10%
15%
20%
25%
30%
35%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
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Adoption behavior of online as specialism in an
incumbent’s market research tools offer in
Belgium, Netherlands, and UK
l 29
we have not included absorptive capabity in our probability testing. We choose to exclude absorptive
capacity and not the mean of existing complementary assets, because the latter is the only variable
indicating complementary assets whereas the former is only one of three variables indicating dynamic
capabilities.
For our timing tests, there is no problem of correlation among independent variables.
We also want to remark that there is a high, but not problematic, correlation between the mean of
existing complementary assets and the mean of threat to core competence(s). This corresponds to
the dif?culty of making a clear distinction between those two variables, and will be further discussed
upon in section 5.
Table 3 and Table 5 are representing some key descriptive statistics for our incumbents, mentioning
number of cases (N), minimum value, maximum value, mean, and standard deviation.
We see that there is an overall probability among incumbents to offer a basic or full copy of online
market research during the period 1999-2007 of 74% (Table 3). For only a full copy of online market
research this percentage decreases till 17%.
Among the three variables indicating different types of dynamic capabilities, absorptive capacity
has the largest standard deviation, meaning that this variable makes the clearest distinction among
incumbents of how their degree of dynamic capabilities could be.
Table 3 and Table 5 also indicate that the ‘normal’ age of an incumbent is 30 years, because the
mean value of year of foundation is 1979.
Table 6 indicates that there are suf?cient cases available in our ESOMAR dataset to test our
framework for timing. There are 1647 cases covering the period 1999-2007 and countries Belgium,
the Netherlands, and the UK. Note however that the number of cases available in the analysis
(namely 1463), thus cases with no missing values, was not enough to test separately for countries.
Table 6 also shows that basic copy (52,7% of all cases) is more experienced than full copy (9,9% of
all cases) which is logical if we look at the probability means in Table 3. ‘Censored’ expresses the
number of cases that not yet experienced a copy action.
Dropped cases with missing values are kept low enough. In total there are 1647 cases in our sample
for testing for response timing. Note that the number of cases for probability testing is 304 (Table
3).
l 30
Table 2: Correlation matrix for probability tests
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Complementary
Assets
Pearson
Correlation
1 0,091 ,415 ,180 -,162 -,127 ,367 0,070
N 303 303 303 268 294 303 303 303
Market
Orientation
Pearson
Correlation
0,091 1 0,035 0,042 ,131 -0,077 0,041 0,098
N 303 304 304 268 295 304 303 304
Absorptive
Capacity
Pearson
Correlation
,415 0,035 1 ,234 -,170 -0,048 ,377 -,126
N 303 304 304 268 295 304 303 304
Entrepreneurial
behavior
Pearson
Correlation
,180 0,042 ,234 1 -0,100 -,171 ,145 0,004
N 268 268 268 268 262 268 268 268
Structural Inertia Pearson
Correlation
-,162 ,131 -,170 -0,100 1 0,105 -,149 -0,008
N 294 295 295 262 295 295 294 295
Attractiveness Pearson
Correlation
-,127 -0,077 -0,048 -,171 0,105 1 ,166 -,239
N 303 304 304 268 295 304 303 304
Threat Pearson
Correlation
,367 0,041 ,377 ,145 -,149 ,166 1 0,056
N 303 303 303 268 294 303 303 303
Competitive
Behavior
Pearson
Correlation
0,070 0,098 -,126 0,004 -0,008 -,239 0,056 1
N 303 304 304 268 295 304 303 304
Table 3: Descriptives for probability tests
N Minimum Maximum Mean Std. Deviation
Probability basic and
full copy
303 0 1 0,74 0,44
Probability full copy 303 0 1 0,17 0,38
Complementary
Assets
303 0 9,33 3,08 1,80
Market Orientation 304 0 3,78 0,96 0,57
Absorptive Capacity 304 0 38 8,63 5,15
Entrepreneurial
Behavior
268 0 17,50 4,32 3,20
Structural Inertia 295 0 1 0,13 0,16
Attractiveness 304 1,52 270 65,47 61,92
Threat 303 0 17 6,44 2,46
Competitive Behavior 304 1 3 2,15 0,43
Embeddedness 304 1923 1994 1979 13,32
l 31
Table 4: Correlation matrix for timing tests
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Complementary
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Pearson
Correlation
1 ,109 ,343 ,164 -,148 -,091 ,275 ,066
N 1.639 1.639 1.639 1.529 1.570 1.639 1.639 1.639
Market
Orientation
Pearson
Correlation
,109 1 0,042 0,014 ,078 -0,034 0,024 ,103
N 1.639 1.644 1.644 1.533 1.574 1.644 1.639 1.644
Absorptive
Capacity
Pearson
Correlation
,343 0,042 1 ,222 -,119 -,083 ,288 -,103
N 1.639 1.644 1.645 1.534 1.574 1.645 1.639 1.645
Entrepreneurial
Behavior
Pearson
Correlation
,164 0,014 ,222 1 -,120 -,137 ,107 0,024
N 1.529 1.533 1.534 1.536 1.469 1.536 1.529 1.536
Structural Inertia Pearson
Correlation
-,148 ,078 -,119 -,120 1 ,098 -,115 -0,023
N 1.570 1.574 1.574 1.469 1.574 1.574 1.570 1.574
Attractiveness Pearson
Correlation
-,091 -0,034 -,083 -,137 ,098 1 ,259 -,193
N 1.639 1.644 1.645 1.536 1.574 1.647 1.639 1.647
Threat Pearson
Correlation
,275 0,024 ,288 ,107 -,115 ,259 1 ,076
N 1.639 1.639 1.639 1.529 1.570 1.639 1.639 1.639
Competitive
Behavior
Pearson
Correlation
,066 ,103 -,103 0,024 -0,023 -,193 ,076 1
N 1.639 1.644 1.645 1.536 1.574 1.647 1.639 1.647
Table 5: Descriptives for timing tests
N Minimum Maximum Mean Std.Deviation
Complementary Assets 1639 0 16 3,27 2,07
Market Orientation 1644 0 6 1,03 0,69
Absorptive Capacity 1645 0 58 8,69 6,30
Entrepreneurial Behavior 1536 0 19,14 4,53 3,34
Structural Inertia 1574 0 1 0,12 0,15
Attractiveness 1647 1,52 270 65,18 73,52
Threat 1639 0 22 6,70 2,78
Competitive Behavior 1647 1 3 2,20 0,52
Embeddedness 1647 1923 1994 1979 12,95
l 32
Table 6: cases in timing analysis
Basic copy Full copy
N Percent N Percent
Cases available in analysis Event 868 52,7 163 9,9
Censored 595 36,1 1.353 82,1
Cases dropped Cases with missing values 184 11,2 131 8
Total 1.647 100 1.647 100
4.2.2 Basic copy results
Table 7 shows the probability and timing test results of our conceptual framework without
embeddedness as a moderating independent variable. Table 8 presents the results for our framework
including embeddedness as a moderating independent variable. Both tables present Chi-square
values, signi?cance level of variables, coef?cient estimates per variable (B), and standard error per
variable (SE).
All test results indicate by the Chi-square value that our overall framework contributes signi?cantly at
level 0,001 to explaining timing and probability of the incumbent’s response of offering a basic copy
of online market research throughout the period 1999-2007 and across the countries Belgium, the
Netherlands, and the UK. Especially the ‘change from block 0’ is interesting, because it shows the
contribution of our model relative to the situation that all our variables would be zero.
The results in Table 7 and Table 8 also show that more variables are signi?cant in the estimation
of the timing than the probability of response. A possible explanation is the high overall probability
percentage, which enables us to discriminate less between those adopters and non-adopters. In
other words, if eventually almost all incumbents adopt the new business model, when they do so is
more insightful then if they do so.
There is a signi?cant positive impact of Complementary Assets, Absorptive Capacity, and
Entrepreneurial Behavior for the incumbent’s response timing, indicating that the higher the value for
these variables, the earlier an incumbent will offer a basic copy of online market research. Signi?cant
negative impacts are assessed for Market Orientation, Structural Inertia, Attractiveness, Threat, and
Competitive Behavior. Embeddedness has also a signi?cant moderating impact with Complementary
Assets, Market Orientation, Entrepreneurial Behavior, Structural Inertia, Threat, and Competitive
Behavior on an incumbent’s response timing. We note that the inclusion of Embeddedness as a
moderating independent variable has not much in?uence on the B-values for the direct independent
variables. Including Embeddedness-moderating variables also shows a small increase of 6,33% of
our framework’s overall contribution in explaining response timing behavior.
We see a signi?cant positive in?uence of Entrepreneurial Behavior, Attractiveness, Threat, and
Competitive Behavior to an incumbent’s probability of offering basic online market research. A
signi?cant negative impact on basic copy probability is noted for Structural Inertia. Embeddedness
has a signi?cant moderating impact with Attractiveness and Threat on basic copy probability. Including
Embeddedness as a moderating independent variable in the model increases overall contribution of
the model with 25,84%, but has an important impact on the signi?cance and attributed value of the
direct independent variables.
A further discussion of all variables and their impact follows in section 4.3.
l 33
Table 7
5
: test results for basic copy (without Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets 0,174 0,116 Complementary Assets** 0,039 0,016
Market Orientation 0,363 0,324 Market Orientation**** -0,203 0,051
Absorptive Capacity Not included Absorptive Capacity**** 0,043 0,006
Entrepreneurial Behavior* 0,092 0,055 Entrepreneurial Behavior**** 0,055 0,010
Structural Inertia** -2,964 1,193 Structural Inertia**** -1,591 0,327
Motivation Motivation
Attractiveness**** 0,018 0,004 Attractiveness**** -0,010 0,001
Threat ** 0,174 0,084 Threat **** -0,048 0,013
Competitive Behavior** 1,013 0,398 Competitive Behavior**** -1,113 0,082
Constant**** -3,814 1,073
Overall ?t Overall ?t
Chi Square**** 54,429 Chi Square**** 575,267
Change from block 0 Change from block 0
Chi Square**** 54,429 Chi Square**** 563,033
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
5
Signi?cance levels are calculated based on the Wald statistic. The Wald statistic is calculated per variable via the
division of the estimated coef?cient (B) by the standard error (SE).
l 34
Table 8: test results for basic copy (with Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets** 0,248 0,124 Complementary Assets*** 0,054 0,018
Market Orientation 0,389 0,328 Market Orientation**** -0,222 0,053
Absorptive Capacity Not included Absorptive Capacity**** 0,055 0,007
Entrepreneurial Behavior 0,072 0,058 Entrepreneurial Behavior**** 0,050 0,011
Structural Inertia** -3,201 1,317 Structural Inertia**** -1,689 0,329
Motivation Motivation
Attractiveness**** 0,020 0,004 Attractiveness**** -0,010 0,001
Threat** 0,207 0,091 Threat*** -0,040 0,013
Competitive Behavior** 1,335 0,459 Competitive Behavior**** -1,130 0,084
Embeddedness Embeddedness
Complementary Assets
x Embeddedness
-0,104 0,211 Complementary Assets x
Embeddedness**
0,061 0,029
Market Orientation
x Embeddedness
-0,055 0,184 Market Orientation x
Embeddedness**
-0,070 0,031
Absorptive Capacity
x Embeddedness
Not included Absorptive Capacity x
Embeddedness
0,042 0,031
Entrepreneurial Behavior
x Embeddedness
-0,070 0,186 Entrepreneurial Behavior x
Embeddedness**
-0,069 0,034
Structural Inertia
x Embeddedness
0,006 0,310 Structural Inertia x
Embeddedness***
0,170 0,055
Attractiveness
x Embeddedness*
-0,491 0,296 Attractiveness x Embeddedness 0,041 0,036
Threat
x Embeddedness**
0,617 0,249 Threat x Embeddedness** 0,084 0,038
Competitive Behavior
x Embeddedness
0,145 0,163 Competitive Behavior x
Embeddedness**
-0,086 0,035
Constant**** -4,777 1,217
Overall ?t Overall ?t
Chi
Square****
68,494 Chi
Square****
611,693
Change from block 0 Change from block 0
Chi Square**** 68,494 Chi Square**** 605,907
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
4.2.3 Full copy results
Table 9 and Table 10 give an overview of the test results for full copy behavior of incumbents towards
online market research.
The lower Chi-square values and the lower signi?cance levels of our variables indicate that the overall
contribution of our framework to explaining the timing and the probability of full copy behavior of
l 35
incumbents is much less than for basic copy behavior. The overall contribution of our framework is
again much bigger for explaining the timing than the probability of incumbents offering a full copy of
online market research.
Towards the incumbent’s response timing, there is a signi?cant positive impact of Complementary
Assets, Absorptive Capacity, and Entrepreneurial Behavior. A signi?cant negative impact is noted for
Structural Inertia, Attractiveness, Threat, and Competitive Behavior. These results are comparable
with the results obtained for basic copy behavior, but there is no signi?cant impact of Market
Orientation for full copy. We can also notice signi?cant effects of the moderating independent
variable Embeddedness with all variables except Market Orientation. Including Embeddedness as
moderating independent variable has a minor impact on variable values and their signi?cant levels.
The incumbent’s probability of offering a full copy of online market research is positively in?uenced
by Complementary Assets, Entrepreneurial Behavior, Attractiveness, and Competitive Behavior.
No signi?cant negative and moderating impact is observed, except for a negative constant. The
introduction of Embeddedness as a moderating independent variable is thus not adding value to our
framework. The Chi-square is however increasing, but this is merely because of the larger amount
of variables included in the model.
A further discussion of all variables and their impact follows in section 4.3.
Table 9: test results for full copy (without Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets** 0,261 0,107 Complementary Assets**** 0,181 0,039
Market Orientation 0,190 0,309 Market Orientation -0,037 0,106
Absorptive Capacity Not included Absorptive Capacity**** 0,076 0,009
Entrepreneurial Behavior*** 0,147 0,056 Entrepreneurial Behavior**** 0,137 0,023
Structural Inertia -1,584 1,728 Structural Inertia* -1,790 0,923
Motivation Motivation
Attractiveness* 0,008 0,004 Attractiveness**** -0,015 0,002
Threat 0,069 0,098 Threat **** -0,173 0,038
Competitive Behavior*** 1,304 0,420 Competitive Behavior** -0,415 0,179
Constant**** -6,930 1,363
Overall ?t Overall ?t
Chi Square**** 35,068 Chi Square**** 356,377
Change from block 0 Change from block 0
Chi Square**** 35,068 Chi Square**** 295,269
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
l 36
Table 10: test results for full copy (with Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets** 0,247 0,117 Complementary Assets**** 0,242 0,049
Market Orientation 0,179 0,326 Market Orientation -0,172 0,130
Absorptive Capacity Not included Absorptive Capacity**** 0,086 0,014
Entrepreneurial Behavior** 0,141 0,057 Entrepreneurial Behavior**** 0,141 0,025
Structural Inertia -1,950 1,754 Structural Inertia** -1,869 0,951
Motivation Motivation
Attractiveness* 0,008 0,004 Attractiveness**** -0,016 0,002
Threat 0,062 0,100 Threat**** -0,149 0,042
Competitive Behavior*** 1,364 0,432 Competitive Behavior** -0,460 0,190
Embeddedness Embeddedness
Complementary Assets
x Embeddedness
0,112 0,160 Complementary Assets x
Embeddedness****
0,250 0,068
Market Orientation
x Embeddedness
-0,004 0,153 Market Orientation x
Embeddedness****
-0,181 0,053
Absorptive Capacity
x Embeddedness
Not included Absorptive Capacity x
Embeddedness
0,013 0,053
Entrepreneurial Behavior
x Embeddedness
0,059 0,168 Entrepreneurial Behavior x
Embeddedness**
0,134 0,068
Structural Inertia
x Embeddedness
0,267 0,325 Structural Inertia x
Embeddedness***
0,432 0,153
Attractiveness
x Embeddedness
-0,141 0,284 Attractiveness x Embeddedness* 0,223 0,130
Threat
x Embeddedness
-0,202 0,275 Threat x Embeddedness -0,089 0,103
Competitive Behavior
x Embeddedness
0,043 0,178 Competitive Behavior x
Embeddedness
-0,004 0,073
Constant**** -6,927 1,400
Overall ?t Overall ?t
Chi
Square****
37,139 Chi
Square****
437,575
Change from block 0 Change from block 0
Chi Square**** 37,139 Chi Square**** 331,645
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
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4.2.4 Test for completeness
We also test for completeness of the incumbent’s copy behavior (cf. basic versus full copy of online
market research). We do this by again running a binary logistic regression analysis. We note that only
responding incumbents (either basic or full copy) are included in the completeness test.
A positive impact in our test means that a responding incumbent is more inclined to offer a full copy
of online market research.
Table 11 shows that our overall ?t of the empirical model for explaining the response completeness
of incumbents is the lowest compared to other tests using binary logistic regression. The number
of signi?cantly contributing covariates is thus also low with only two covariates signi?cant (at 0.05
level). The test indicates that Entrepreneurial Behavior and Competitive Behavior are signi?cantly and
positively in?uencing incumbents towards responding in a full copy way.
Table 11: Test results for completeness (with Embeddedness)
Completeness
Variables B SE
Ability
Complementary Assets 0,176 0,119
Market Orientation 0,160 0,319
Absorptive Capacity Not included
Entrepreneurial Behavior** 0,126 0,058
Structural Inertia -1,886 1,954
Motivation
Attractiveness 0,003 0,005
Threat 0,035 0,108
Competitive Behavior** 1,062 0,445
Embeddedness
Complementary Assets x Embeddedness 0,115 0,165
Market Orientation x Embeddedness 0,015 0,150
Absorptive Capacity x Embeddedness Not included
Entrepreneurial Behavior x Embeddedness 0,051 0,170
Structural Inertia x Embeddedness 0,405 0,357
Attractiveness x Embeddedness -0,009 0,296
Threat x Embeddedness -0,317 0,284
Competitive Behavior x Embeddedness 0,028 0,191
Constant**** -5,232 1,430
Overall ?t
Chi Square** 25,632
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
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4.3 Hypotheses discussion
4.3.1 Complementary assets
H1: Complementary assets that an incumbent possesses towards a business model innovation
increase the probability that the incumbent copies the new business model, the later it does so, and
the more complete the copy is.
Our results show that complementary assets always have a positive impact on the probability of
offering a basic or full copy of online market research among incumbents. The second part of H1 is
not supported by our results. It appears that complementary assets have always a signi?cant positive
effect on the incumbent’s response timing, meaning that the bigger the value of complementary
assets, the earlier the incumbent will respond. When testing for completeness of the incumbent’s
response, there was no signi?cant effect found of complementary assets.
The results thus demonstrate that possessing complementary assets helps incumbents to deal with
new business models in their industry. Incumbents that have a wide range of complementary assets
not only have a higher likelihood to respond to a new business model, they also do it earlier than
others.
H2: The degree of complementarity of assets has a bigger in?uence than the number of complementary
assets on the probability, the timing, and the completeness with which an incumbent copies the new
business model.
To test for H2 we used the variable peripheral Complementary Assets (See section 3.1.3.1).
Peripheral complementary assets have less complementarity with online market research and are
wider in range than core complementary assets (See Table 1). When testing our framework with
peripheral Complementary Assets we excluded core Complementary Assets, Absorptive Capacity,
and their time-averaged measures. We also excluded time-averaged Threat from the analysis. These
exclusions were needed because of high correlation with peripheral Complementary Assets. We
always tested excluding the moderating independent variable Embeddedness. An overview of the
test results is given in Table 12.
From Table 12 we see that peripheral complementary assets have a highly positive and signi?cant
impact on an incumbent’s probability and timing. There is also indication, just like with core
complementary assets but to a lesser extent, that the bigger peripheral complementary assets are
the more probable an incumbent will copy the new business model in a more complete way. Overall,
we remark that complementary assets play a bigger role in the case of a full copy.
Table 12 also demonstrates that the probability effect of peripheral complementary assets, although
they are bigger in number, is lower compared to the effect of core complementary assets. This
is seen for basic and for full copy. It means that the complementarity rather than the number of
complementary assets plays a bigger role in the probability of copying a new business model.
With respect to timing, we see that the number of complementary assets is more important in the
advent of basic copy. The degree of complementarity is then more vital when deciding about a full
copy reaction.
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We conclude that part 1 and 3 of H2 are supported. The trade-off however between number and
degree of complementarity of complementary assets is more ambiguous with respect to explaining
the timing of an incumbent’s response towards new business models.
Table 12: Overview tests for peripheral complementary assets
Probability Timing Completeness
Peripheral
Complementary
Assets (B)
Basic copy 0,169** 0,116****
Full copy 0,204*** 0,213**** 0,178**
Core Complementary
Assets (B)
Basic copy 0,276*** 0,080****
Full copy 0,285*** 0,281**** 0,231**
4.3.2 Dynamic capabilities
H3: The higher the incumbent’s dynamic capabilities, the more likely, the quicker, and the more
complete it will respond to a business model innovation. A company’s market orientation, absorptive
capacity, and entrepreneurial character are key types of dynamic capabilities.
The effect of the incumbent’s dynamic capabilities is supported by our results. However, only
entrepreneurial behavior has signi?cant positive effect on an incumbent’s probability to respond;
even bigger and more signi?cant in case of full copy. We also note that absorptive capacity was not
included in the ?rst analyses because of high correlation with complementary assets. If we include
absorptive capacity (and thus exclude complementary assets), we see however also no signi?cant
effects of absorptive capacity.
Results show that the impact of Absorptive Capacity and Entrepreneurial Behavior is positive and
highly signi?cantly supporting that higher dynamic capabilities lead to a quicker response of an
incumbent. The effect is even bigger in case of full copy. We also see that Market Orientation slows
down the incumbent’s response, supporting our remark of the danger of a too narrowly de?ned
market orientation of incumbents (See section 2.2.2).
In terms of completeness of the incumbent’s response, we notice a signi?cant, positive impact
of Entrepreneurial Behavior (B = 0,126**). This supports H3, because a positive impact in our
completeness test means that an incumbent is more inclined to adopt a full offering of online market
research.
The results indicate the importance for incumbents to possess dynamic capabilities to deal with
the changing environment created by a new business model in their industry. Entrepreneurship in
particular seems to enable incumbents to respond swiftly.
4.3.3 Structural inertia
H4: The higher the structural inertia of an incumbent, the lower its probability of copying a new
business model, the later and the less complete it does so.
Results show that the higher the structural inertia of an incumbent, the lower its probability of copying
a new business model is. Signi?cance is however only found in the case of a basic copy.
l 40
Our tests present a very strongly signi?cant negative effect of structural inertia towards the incumbent’s
response timing. This means that the bigger an incumbent’s structural inertia, the later it will respond
towards a business model innovation.
Our completeness analysis does not give a signi?cant result for structural inertia, but the indication
is that higher structural inertia drives incumbents towards lower response completeness. Another
indication for this lower completeness when having more structural inertia is that the negative impact
of structural inertia is bigger in the case of a full copy than for a basic copy.
We thus ?nd evidence that inertia hampers an incumbent’s response to new business models.
4.3.4 New business model attractiveness
H5: A new business model’s attractiveness has a positive in?uence on the incumbent’s copy behavior
in terms of probability, timing and completeness.
Our results show that the online market research’s attractiveness has a signi?cant positive impact on
an incumbent’s response behavior. The higher the attractiveness of a business model innovation, the
higher the probability that an incumbent will copy the new business model.
In terms of timing we don’t ?nd support for H5 in our data. There is a very signi?cant negative, but
again very small in?uence of the attractiveness of online market research on response timing of the
incumbent.
In our completeness test there is no signi?cant effect of market attractiveness at all.
4.3.5 Degree of threat
H6: The greater the threat of a new business model towards the incumbent’s core competence(s),
the less likely an incumbent will copy the new business model but the earlier it will do so.
Our test results don’t support H6, but the opposite.
The greater the threat of a business model innovation, the more likely an incumbent will copy the
online offering. This is especially true for a basic online market research offering. The threat of a new
business model also signi?cantly postpones a reaction of incumbents to copy the business model
innovation. These results contradict earlier ?ndings on the response of incumbents to innovation in
their industry (Mitchell, 1989).
A potential explanation for our ?ndings is that business model innovations represent a different type
of innovation than a technological innovation for incumbents to tackle. Our ?ndings demonstrate that
incumbents who are highly threatened by a business model innovation do respond to it, but respond
late. This result, combined with the non signi?cant effect of the attractiveness of the market, allow us
to speculate about the process that underlies these ?ndings. It appears that incumbents, in an effort
to minimize the effect of the new business model, act as late as possible. In fact, incumbents can, by
entering a new business model, provide it with additional legitimacy and accelerate its cannibalization
effect. Given this, it makes sense for incumbents to delay entry.
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4.3.6 Competitive behavior
H7: Incumbents are signi?cantly in?uenced by incumbent market leaders in responding towards a
new business model.
H8: Incumbents are not signi?cantly in?uenced by newcomers in responding towards a new business
model.
We see in Table 7 till 10 a very big (minimum B = 1,013**) and signi?cantly positive effect of incumbent’s
copy probability being in?uenced by market leaders. The more market leaders copy (basic or full)
online market research, the more likely and more complete an incumbent will also copy online market
research, but the later it will do so.
If we test for the in?uence of copy behavior of newcomers we leave out Attractiveness because
of high correlations, but we can still apply Competitive Behavior, because of no high correlations
between market leaders’ and newcomers response behavior. From our results we see that there
is no effect of newcomers’ response behavior on the copy completeness of incumbents towards
online market research. On probability and timing of the incumbent’s response, there are very small
but highly signi?cant results. The more newcomers copy the online market research business model,
the more likely an incumbent will also offer online market research (B = 0,063**** for basic copy and
B = 0,031* for full copy). The more newcomers however copy the online market research business
model, the later incumbents will react (B = -0,032**** for basic copy and B = -0,046 for full copy).
H9: Incumbents experience a bigger imitation behavior effect among incumbents that are similar in
size and/or resources.
H10: Incumbents experience a bigger imitation behavior effect among incumbents that have similar
complementary assets.
Our results for probability tests show that there is a very small but signi?cantly positive imitation
behavior based on similarity in size (B = 0,017** for small agencies and B = 0,024** for larger
agencies) and complementary assets (B = 0,021*** for low complementary assets and B = 0,022**
for high complementary assets). This signi?cant effect occurs only in the advent of a basic copy
of online market research and is much smaller than the imitation effect based on market leaders’
behavior (cf. minimum B = 1,013).
In terms of timing we see a very small but signi?cantly negative imitation behavior effect for
incumbents similar in complementary assets especially in the advent of a full copy (B = -0,012** for
low complementary assets and B = -0,013** for high complementary assets). This effect is very weak
in comparison with the highly negative imitation effect based on market leaders’ copy response (cf
minimum B = -0,415**).
H9 and H10 are thus not supported by our dataset.
4.3.7 Embeddedness
H11: Embeddedness has signi?cant effects as moderating variable and no or less signi?cant effects
as a direct independent variable.
l 42
Table 7 and Table 10 indicate that there are signi?cant effects of Embeddedness as a moderating
independent variable especially for the timing of incumbents to respond towards online market
research. The biggest signi?cant effect (positive) is of Structural Inertia x Embeddedness meaning that
embeddedness decreases the negative effect of structural inertia. When testing for embeddedness
as a direct independent variable for probability, timing, and completeness of an incumbent’s reaction
towards online market research, we ?nd not any signi?cant effect.
The moderated effect of Threat on the probability of responding is very large for a basic copy (B
= 0,617**) when we include Embeddedness as a moderating independent variable. This means
that the more embedded or the older a market research agency is, the less a threat towards the
existing market offering triggers the agency to respond. The moderating effect however is not found
signi?cant for a full copy of online market research.
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5 DISCUSSION AND MANAGERIAL IMPLICATIONS
5.1 Two golden questions
There are three basic strategic options for incumbents when confronted with an upcoming new
business model: no reaction, basic copy of the new business model, or full copy (cf. complete
imitation). The type of reaction and the appropriate timing is driven by two strategic questions: “Am
I able to do it?” and “Why should I do it?”
Companies should ask themselves those two questions before deciding if, how, and when to react.
5.2 The ability to respond
If incumbents want to be able to increase their responsiveness towards heavily changing environments
and deal effectively with new business models, they should monitor and manage their complementary
assets, dynamic capabilities, and structural inertia.
Incumbents should assess their complementary assets and manage them. Results indicate that
the possession of complementary assets helps incumbents to deal with new business models in
their industry. Incumbents that have a wide range of complementary assets not only have a higher
likelihood to respond to a new business model, they also do it earlier than others. However, it is not
always needed to have a high number of complementary assets. The degree of complementarity
of an incumbent’s assets towards a new business model has even more importance in terms of
response likelihood and completeness. Incumbents that have assets highly complementary with the
business model innovation respond more quickly than others in a complete way.
Important to note is that we could speculate (further research here is needed) that the degree of
complementarity and the number of complementary assets is key in the incumbent’s management
of complementary assets. Number and complementarity of assets could thus be seen as two axes
of a decision matrix used in the management of complementarity assets.
Results also show the importance of building up dynamic capabilities (especially entrepreneurial
behavior) and avoiding structural inertia to respond timely to changing business models. This pleads
for ?at and entrepreneurial organizations.
5.3 The motivation to respond
The motivation of incumbents to react is especially motivated by competitive behavior. However,
results also seem to let us speculate that an interaction between threat and market attractiveness
could be a defendable argument for incumbents to delay action. This interaction could be e.g. that
a copy creates increased legitimacy which could decrease threat, and that in turn could increase
attractiveness. This creates again increased copy, etc.
Organization imitation behavior is not new and certainly not gone. Results demonstrate that
incumbents are heavily watching incumbents with market leadership rather than incumbents similar
l 44
in size and/or complementary assets. Newcomers are not ignored by incumbents, but don’t have
much motivation in?uence either.
Results however indicate that incumbents have, next to pointing the ?nger towards competitors,
other possibilities to motivate certain response types. These possibilities include indications of the
attractiveness and threat of a new business model. Apart from the monitoring of competitors, it is
therefore important for incumbents to investigate the attractiveness of a new business model and
the threat it poses.
5.4 Further research
Although we built up a unique dataset covering 17 years of industry knowledge and response
behavior, we acknowledge that there is still work to do in terms of cross-country analysis and in
terms of coupling performance-related data with the existing dataset. Especially the relationship of
response behavior with performance-related outcomes is highly interesting to further investigate.
Also further investigation and re?nement of the empirical model over more industries and countries
is needed to untangle the speci?c nature of the drivers behind strategic response behavior in the
advent of business model innovations. There is already clear indication (See section 4.3.5) that
different effects can be expected in the advent of technological innovation versus business model
innovation.
We also remark that entrepreneurial behavior appears to be the most robust type of dynamic
capabilities we de?ned in our conceptual framework. Further research and thorough conceptualization
and measurement of other types of dynamic capabilities like market orientation and absorptive
capacity is certainly needed. This research is not only to better understand the concept of dynamic
capabilities and the different types, but also to be able to measure and steer these types of dynamic
capabilities.
l 45
6 ACKNOWLEDGEMENTS
We would like to express our sincerest thankfulness towards Flanders District of Creativity for the
?nancial support to make this research study possible.
Also ESOMAR helped us a lot in building up our longitudinal dataset covering 17 years of unique
industry data, and in understanding the market research industry by providing us with their market
and price studies.
A last word of gratitude towards Ludovic Depoortere (ROGIL ?eld research), Tim Rofessart and Stijn
De Rammelaere (PROFACTS), and Niels Schillewaert and Kristof Dewulf (INSITES Consulting) for
their qualitative insights and feedback.
l 46
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Debruyne, M. and M. Schoovaerts, (2006) “Innovation outside the lab: Strategic innovation as the
alternative”. FDC Research Report, October.
Dutta, S., O. Narashimhan and S. Rajiv, (2004) “Conceptualizing and measuring capabilities:
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Books:
Hamel, G., (2000). Leading the Revolution. Harvard Business School Press, Boston.
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Boston.
Lingeman, H. and M. Berntsen, (2004). Online Marktonderzoek. Kluwer, Amsterdam.
Rogers, Everett M., (1962). Diffusion of Innovations. Free Press, New York.
Knowledge partner
ISBN-NUMMER : 9789078858003
doc_489909174.pdf
On this paper talk how do new business models affect existing players in an industry.
RESEARCH REPORT
HOW DO NEW BUSINESS
MODELS AFFECT EXISTING
PLAYERS IN AN INDUSTRY?
Prof. dr. Marion Debruyne
Bart Devoldere
Knowl edge partner
October 2008
l 1
Flanders District of Creativity is the Flemish organization for entrepreneurial creativity. It was
founded in 2004 by the Flemish Government as a non-pro?t organization and enjoys broad support.
Flemish businesses, academia, and public institutions use Flanders DC as a platform for cooperation
in the pursuit of a more creative Flanders region.
Creativity is the key ingredient in making companies more successful and in helping regional
governments ensure a healthy economy with more jobs. Flanders DC inspires creativity and
innovation:
1. by learning from the most creative regions in the world,
2. by igniting creative sparks in everyday life and business, and
3. by providing research, practical business tools and business training, in cooperation with
the Flanders DC Knowledge Centre.
1. Districts of Creativity: Inspiration from the most creative regions
Responses to global challenges are best found within
an international network of excellence. With the single
aim of learning from the very best, Flanders DC aims to
unite the most dynamic regions in the world within the
'Districts of Creativity' network. Every two years, Flanders
DC convenes the Creativity World Forum, bringing together government leaders, entrepreneurs, and
knowledge institutions to exchange ideas about how to tackle pressing economic problems and
make their regions hotbeds for innovation and creativity.
FLANDERS DISTRICT OF CREATIVITY
November 19-20, 2008 - Antwerp, Belgium
Quebec
Catalonia
Lombardy
Karnataka
Queensland*
Shanghai
Rhône-Alpes
Baden-Württemberg
Flanders
Scotland
Nord-Pas-de-Calais
Oklahoma*
*: Candidate members
Qingdao
Kanagawa*
Quebec
Catalonia
Lombardy
Karnataka
Queensland*
Shanghai
Rhône-Alpes
Baden-Württemberg
Flanders
Scotland
Nord-Pas-de-Calais
Oklahoma*
*: Candidate members
Qingdao
Kanagawa*
l 2
2. Raising awareness: The best way to predict the future is to invent it
Flanders DC encourages entrepreneurs and citizens to look
ahead and ?nd creative solutions today for tomorrow's problems.
Flanders DC has developed an idea-generation tool to encourage
people and organizations to take the ?rst step toward innovation. In
addition, Flanders DC runs a general awareness-raising campaign
entitled “Flanders’ Future”.
3. The Flanders DC Knowledge Centre: Academic support
The Flanders DC Knowledge Centre serves as a link between Flanders
DC and Vlerick Leuven Gent Management School. Each year, the Flanders
DC Knowledge Centre publishes several reports and develops various tools,
case studies and courses. All these projects focus on the role of creativity
in a business environment and identify obstacles to, and accelerators of
competitive growth.
The Creativity Talks ? brief monthly, interactive info sessions ? update you on these research
activities. See www.creativitytalks.be for a current calendar and subscription information.
ONDERZOEKSRAPPORT
HET INNOVATIEPROCES
IN GROTE BEDRIJVEN
EN KMO’S
Geert Devos, Mieke Van De Woestyne, Herman Van den Broeck
Februari 2007
Kenni spartner
Kennispartner
ISBN-NUMMER : 9789080712195
EAN : 9789080712195
HET INNOVATIEPROCES IN GROTE BEDRIJVEN EN KMO’S - Februari 2007
l 3
Research reports:
? De Vlaamse economie in 2015: Uitdagingen voor de toekomst, Koen De Backer en Leo
Sleuwaegen, September 2005, Published in Dutch
? Ondernemingscreativiteit als motor van groei voor Vlaamse steden en Brussel, Isabelle
De Voldere, Eva Janssens en Jonas Onkelinx, November 2005, Published in Dutch
? The Creative Economy: challenges and opportunities for the DC-regions, Isabelle De
Voldere, Eva Janssens, Jonas Onkelinx en Leo Sleuwaegen, April 2006, Published in English
? Spelers uit de televisiesector getuigen: een verkennende studie in de creatieve industrie,
Marc Buelens en Mieke Van De Woestyne, Juni 2006, Published in Dutch
? Mobiliseren, dynamiseren en enthousiasmeren van onze toekomstige zilvervloot, Thomas
Dewilde, Annick Vlaminckx, Ans De Vos en Dirk Buyens, Juni 2006, Published in Dutch
? Development of a regional competitiveness index, Harry Bowen, Wim Moesen and Leo
Sleuwaegen, September 2006, Published in English
? Innovation outside the lab: strategic innovation as the alternative, Marion Debruyne and
Marie Schoovaerts, November 2006, Published in English
? De creatieve industrie in Vlaanderen, Tine Maenhout, Isabelle De Voldere, Jonas Onkelinx en
Leo Sleuwaegen, December 2006, Published in Dutch
? Het innovatieproces in grote bedrijven en KMO’s, Geert Devos, Mieke Van De Woestyne en
Herman Van den Broeck, Februari 2007, Published in Dutch
? Creatief ondernemen in Vlaanderen, Tine Maenhout, Jonas Onkelinx en Hans Crijns, Maart
2007, Published in Dutch
? Hoe ondernemers in Vlaanderen opportuniteiten identi?ceren. Een rapport met tips
en tools voor de ondernemer in de praktijk, Eva Cools, Herman Van den Broeck, Sabine
Vermeulen, Hans Crijns, Deva Rangarajan, Mei 2007, published in Dutch
? Networking in multinational manufacturing companies, Ann Vereecke, July 2007, published
in English
? How entrepreneurial are our Flemish students, Hans Crijns and Sabine Vermeulen, November
2007, published in English
? Fashionate about Creativity, Isabelle De Voldere, Tine Maenhout en Marion Debruyne,
December 2007, published in Dutch
? Find the innovator. Identifying and understanding adopters of innovative consumer
technologies in Flanders, Marion De Bruyne and Bert Weijters, December 2007, published in
English
l 4
? De case Arteconomy, Eva Cools, Herman Van den Broeck en Tine Maenhout, December 2007,
published in Dutch
? Entrepreneurship and globalization, Italo Colantone and Leo Sleuwaegen, December 2007,
published in English
? HR Tools als stimulans voor creativiteit bij uw werknemers, Kristien Van Bruystegem, Vickie
Decocker, Koen Dewettinck, Xavier Baeten, December 2007, published in Dutch
? Internationalization of SMEs, Jonas Onkelinx, Leo Sleuwaegen, April 2008, published in
English
? HRM-uitdagingen voor groeiende ondernemingen, Mieke Van De Woestyne, Kristien Van
Bruystegem, Prof. Dr. Koen Dewettinck, Maart 2008, published in Dutch
? Sociaal Ondernemerschap in Vlaanderen, Hans Crijns, Frank Verzele, Sabine Vermeulen,
April 2008, published in Dutch
? Foreign direct investments. Trends and developments, Frederik De Witte, Isabelle De
Voldere, Leo Sleuwaegen, June 2008, published in English
Published research reports can be downloaded via the Vlerick Leuven Gent
Management School library catalogue or via www.?andersdc.be.
In addition to these research projects, the Flanders DC Knowledge
Centre has also developed the following tools and training sessions:
? Ondernemen.meerdan.ondernemen, an online learning platform
? Creativity Class for young high-potentials
? Flanders DC Fellows, inspiring role models in business creativity
? Creativity Talks, monthly seminars on business creativity and innovation
? Innovix, online innovation management game
? Flanders DC Academic Seminars: research seminars on business creativity and innovation
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
- Knowledge networks in industry-science relations (auteurs: Johan Bruneel, Bart Clarysse,
Annelies Maesen, Nathalie Morray and André Spithoven), December 2006
- De ondernemer in de praktijk. Een praktijkboek voor de Vlaamse ondernemer. (auteurs:
Herman Van den Broeck, Eva Cools, Hans Crijns, Sabine Vermeulen en Deva Rangarajan)
- Networking and innovation capacity of multinational companies in Flanders (auteurs: Ann
Vereecke and Evelyne Vanpoucke), December 2006
- Het innovatieproces in grote bedrijven en KMO’s (auteurs: Geert Devos, Mieke Van De
Woestyne en Herman Van den Broeck), Februari 2007
- De case Arteconomy (auteurs: Steven Mestdagh en Herman Van den Broeck), Februari 2007
- Creatief ondernemen (auteurs: Tine Maenhout, Jonas Onkelinx en Hans Crijns), Maart 2007
- De creativiteit en ondernemingsgezindheid in kaart gebracht via het online leerplatform
(auteurs: Veronique Warmoes en Herman Van den Broeck), April 2007
- Open innovation in Europe (auteurs: Els Van de Velde, Bart Clarysse and Wim Van Haverbeke),
July 2007
- How innovative are we really? (auteurs: Marion De Bruyne and Bert Weijters), September 2007
- Flanders’ attractiveness for foreign investment (auteurs: Harry Bowen, Juan Enrique Gutierrez
Chavez, Isabelle De Voldere and Leo Sleuwaegen), November 2008
Kennisverspreiding
- Flanders DC & Vacature Winter Academy (16 tot en met 19 Februari 2006)
- Rob Dew, Visiting professor from Auckland on Creative Problem Solving, September 2006
- Scholarships for the Master Class in Entrepreneurship and Innovation.
- Flanders DC Fellows: Creatieve en innovatieve ondernemers als rolmodel. Eerste lichting De-
cember 2006.
- Creativity Talks. Maandelijkse sessies over ondernemingscreativiteit en innovatie. September
2006 tot Mei 2007.
De partners en leden van de raad van bestuur van Flanders DC zijn:
| 5
Board of Directors
of Flanders DC
l 5
TABLE OF CONTENT
List of Tables ................................................................................................................................7
List of Figures ................................................................................................................................8
Introduction ................................................................................................................................9
1. Research setting: the market research industry .................................................................10
1.1 Industry background ........................................................................................................10
1.2 Online market research: a business model innovation? .....................................................10
2 Literature review and conceptual framework ......................................................................12
2.1 Conceptual framework .....................................................................................................12
2.2 Ability to respond .............................................................................................................13
2.2.1 The role of existing complementary assets .............................................................13
2.2.2 The role of dynamic capabilities .............................................................................14
2.2.3 Structural inertia ....................................................................................................16
2.3 Motivation to respond ......................................................................................................16
2.3.1 New business model attractiveness .......................................................................17
2.3.2 Degree of threat to core competence ....................................................................17
2.3.3 Competitive behavior .............................................................................................17
2.4 Role of embeddedness ....................................................................................................18
3 Methodology and data .........................................................................................................20
3.1 Dataset and measures .....................................................................................................20
3.1.1 Introduction ...........................................................................................................20
3.1.2 Measures of the dependent variable ......................................................................20
3.1.3 Measures of the incumbent’s ability to respond ......................................................22
3.1.3.1 Complementary assets .............................................................................22
3.1.3.2 Dynamic capabilities .................................................................................22
3.1.3.3 Structural inertia .......................................................................................23
3.1.4 Measures of the incumbent’s motivation to respond ..............................................23
3.1.4.1 Attractiveness of online market research...................................................23
3.1.4.2 Threat to core competence ......................................................................23
3.1.4.3 Competitive behavior ................................................................................23
3.1.5 Embeddedness .....................................................................................................23
3.2 Empirical model ...............................................................................................................24
4 Discussion of results ............................................................................................................25
4.1 Introduction ................................................................................................................25
4.2 Testing the conceptual framework ....................................................................................28
4.2.1 Correlations and descriptives .................................................................................28
4.2.2 Basic copy results .................................................................................................32
4.2.3 Full copy results .....................................................................................................34
4.2.4 Test for completeness ............................................................................................37
4.3 Hypotheses discussion ....................................................................................................38
l 6
4.3.1 Complementary assets ..........................................................................................38
4.3.2 Dynamic capabilities ..............................................................................................39
4.3.3 Structural inertia ....................................................................................................39
4.3.4 New business model attractiveness .......................................................................40
4.3.5 Degree of threat .....................................................................................................40
4.3.6 Competitive behavior .............................................................................................41
4.3.7 Embeddedness .....................................................................................................41
5 Discussion and managerial implications .............................................................................43
5.1 Two golden questions ......................................................................................................43
5.2 The ability to respond .......................................................................................................43
5.3 The motivation to respond................................................................................................43
5.4 Further research ...............................................................................................................44
6 Acknowledgements ..............................................................................................................45
References ..................................................................................................................................46
l 7
LIST OF TABLES
Table 01: ESOMAR offering variables ........................................................................................... 21
Table 02: Correlation matrix for probability tests ........................................................................... 30
Table 03: Descriptives for probability tests ................................................................................... 30
Table 04: Correlation matrix for timing tests ................................................................................. 31
Table 05: Descriptives for timing tests .......................................................................................... 31
Table 06: cases in timing analysis ................................................................................................ 32
Table 07: test results for basic copy (without Embeddedness) ..................................................... 33
Table 08: test results for basic copy (with Embeddedness) .......................................................... 34
Table 09: test results for full copy (without Embeddedness) ......................................................... 35
Table 10: test results for full copy (with Embeddedness) .............................................................. 36
Table 11: Test results for completeness (with Embeddedness) ..................................................... 37
Table 12: Overview tests for peripheral complementary assets..................................................... 39
l 8
Figure 1: Conceptual Framework ................................................................................................. 13
Figure 2: Adoption behavior in Belgium ........................................................................................ 25
Figure 3: Adoption behavior of online as basic market research tool ............................................ 26
Figure 4: Adoption behavior of online as specialism ..................................................................... 27
Figure 5: Adoption behavior of online as basic market research tool (in %) ................................... 27
Figure 6: Adoption behavior of online as specialism (in %) ............................................................ 28
LIST OF FIGURES
l 9
INTRODUCTION
Since Gary Hamel’s bestseller Leading the Revolution (2000) and Kim & Mauborgne’s Blue Ocean
Strategy (2005), and the ever-returning examples like Cirque du Soleil, Southwest Airlines, IBM’s
solutions orientation, Canon, Starbucks coffee, etc. a new type of innovation is getting mounting
attention as a viable source for creating new growth businesses. Companies are not only looking
into product innovation anymore, but also consider re-inventing their business model. The stream
of research that investigates this new type of innovation gave birth to several terms, among them
strategic innovation and business model innovation
1
.
A business model is a combination of the company’s core strategy, strategic resources, value
network, and customer interface that is put into practice (Hamel, 2000). A business model indicates
then among others a company’s target customers, value proposition and product/service attributes,
based on the different assets, capabilities, and competences possessed. A company’s existing
business model is however mostly based on the industry’s generally accepted de?nition or industry
recipe of “how to do business in the industry” (Markides, 1997).
Existing literature on business model innovations focused strongly on descriptions about what
business model innovation is, which players initiate a new business model, how to come up with
new business models, how to make them work, what barriers exist towards implementing a new
business model and ways of overcoming those barriers (Christensen, Johnson, and Rigby, 2002;
Kim and Mauborgne, 1997; Markides, 1997, 1998; Matthyssens et al., 2006). Debruyne and
Schoovaerts (2006) serves as a basis for this study and posits that strategic innovation consists
of 4 key elements, namely value innovation, new market creation, go-to-market innovation, and
competitive disruption. Debruyne and Schoovaerts (2006) also found that ?rms that operate in highly
competitive environments (where strategic innovation or business model innovation matters the
most) are less likely to be strategically innovative. So “a substantial effort is needed to educate ?rms
about the nature and potential of strategic innovation” to which we also want to contribute with this
study.
Taking into account this focus of previous research efforts, which is focused on the innovator, we
will focus on the incumbent dealing with business model innovation in its industry. Our quantitative
study tackles untapped or less developed research questions about how new business models
diffuse within industries, how incumbents
2
can react to new business models, and what the role is
of companies’ complementary assets to deal effectively with new business models. The research
questions that are speci?cally treated are:
-How do existing industry players, named incumbents, respond to the emergence of new business
models?
-How does the imitation of business models develop over time?
-To what extent do existing complementary assets of incumbents affect the response of incumbents
after the emergence of a new business model?
1
In this study we use Markides’ de?nition of business model innovations (2006), but in the existing literature business
model innovation is strongly related, even sometimes similar, to the terms strategic innovation, strategy innovation,
business concept innovation, and value innovation.
2
‘Incumbency’ re?ects whether a ?rm participated in the previous generation of products. (Chandy and Tellis, 2000)
l 10
1.1 Industry background
The market research industry, ‘invented’ in the US and introduced in Europe before the 2
nd
World War,
was a mature and stable growing industry back in the nineties. With the advent and breakthrough
of internet however, the market research industry also encountered a big change, namely the
introduction of online data collection. Suddenly, everybody who could get two PCs and a server
could start-up a small, online market research agency and attack vested incumbents, almost without
any barriers…at ?rst sight.
The use of internet in market research, what we call online market research, is considered to have
started around 1995. Before 1995 it was virtually nonexistent, but the worldwide spend on online
is estimated by Inside Research at US$3,6 Billion in 2007 and increasing to US$4,3 Billion in 2008
(ESOMAR, 2008). In the Netherlands one of the ?rst commercial uses of online market research was
in 1996 with NSS/Market Research that investigated which online payment methods were preferred
by internet users. It proved to be a very easy and quick way to conduct research, and it didn’t
take long for lots of entrepreneurs to set up their own online market research agency. In Belgium
Insites Consulting (founded in 1997) is considered to be the pioneer in the online market research
?eld. Previous to commercial online market research, online research was already used in academic
environments.
1.2 Online market research: a business model innovation?
Online market research has some characteristics that indicate it represents a business model
innovation. Over time incumbents develop an industry recipe about their industry through education
and experience (Markides, 1997). This industry recipe is a combination of culture, routines, and
unwritten rules of behavior that de?nes “the way business is done in a certain industry”. Business
model innovations break through this industry recipe.
Firstly, industry experts agree that online enlarges the total market size of market research by creating
a ‘new market’ of e.g. smaller companies that didn’t use to have suf?cient means to conduct market
research and that are now facing lower boundaries to order some market intelligence data and
advice by using often cheaper online market research.
Moreover, the online market research business model emphasizes different product/service attributes
to customers that indicate the character of a value innovation. Whereas online emphasizes speed, low
cost, more objective response, and ease-of-use, of?ine points e.g. to higher validity in sampling and
interviewing, the possibility to track qualitative information, and the experience of the interviewer.
However, the new, online market is not expected to overtake the whole market research market.
Industry players are convinced that online will take in its position in the spectrum of research
methods and techniques, but will not replace all existing methods to conduct market research.
Traditional methods are still recommended in certain situations, but a shift towards online is felt by
some methods like e.g. in-home face-to-face personal interviews that are very expensive.
1 RESEARCH SETTING: THE MARKET RESEARCH INDUSTRY
l 11
It is also considered to be dif?cult to compete both in traditional and online research methods. In
the period 1999-2000 online was a ‘hot’ topic in the industry, and some incumbents started up their
own online panel because the visible, direct costs associated with going online were low. The indirect
costs however of investing in appropriate software and in building up a panel community with less
biased response proved to be hard, which makes it dif?cult to build up a competitive advantage in
both ways of doing research. Some have stopped their attempts to ?ght in both markets and are
now outsourcing web panels to agencies with appropriate core competences.
Finally, the old business model (traditional of?ine methods of market research) is not necessarily
worse than the new business model of using internet in methods and techniques to perform market
research. Since the advent of online, there are large debates in the industry about the validity of
online market research, but online gets more and more accepted by the industry. The discussion
centers on the premise that online panels would have less validity because of the bias created by
online respondents who would be more incentive-driven than respondents from of?ine panels and
the observation that a small number of people completes a large number of online questionnaires.
Three main questions are important when deciding to undertake online market research instead
of traditional of?ine methods: population characteristics (who is online?), channel characteristics
(what are they doing there?), and method characteristics (how do you measure on the internet?).
These questions and the on-going discussions about validity indicate that online is expected to be a
research method next to of?ine, without making of?ine research obsolete.
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2.1 Conceptual framework
In an industry there are several viable choices of a business model and a particular business model
is then just one possible way to go to market (Moore, 2004). A company should come up with a
business model that is maximally aligned with its strengths and weaknesses, and its customer needs
(Markides, 1997). On the one hand this implies that if a new business model emerges, incumbents
should not blindly follow the new trend and automatically commit themselves to copy this new
business model. On the other hand, as strengths and weaknesses, as well as customer needs
evolve over time, the company’s decision towards copying a particular business model can also be
subjected to evolutions over time.
When confronted with a new business model in their industry, incumbents have many ways to
respond. Copying a new business model is just one way. A company could also decide to stick to
their current business model with heavy investments in this current business model to withstand the
new business model. A company could even totally ignore a business model innovation as this could
be perceived as not their business, because it targets different customers, pronounces a different
value proposition, and/or stresses different skills and competences. Incumbents can also come
up themselves with an innovation towards the new business model, and thus emphasizing again
different product/service attributes. A last response strategy could be focusing on and investing in
the traditional way of competing (Charitou and Markides, 2003; Markides, 2006).
In this study we focus on an incumbent’s copy response and we make the distinction between basic
and full copy of the new business model. We de?ne basic and full copy as two points on the axis of
completeness of the response, meaning that incumbents could only copy parts of the new business
model (basic) or replace their existing business model completely by the new business model (full
copy). Next to the completeness of the incumbent’s response, we also look into the timing and
probability of basic or full copy response.
We propose and test an overarching framework (Figure 1) that identi?es drivers and enablers for the
incumbent’s response to a new business model. The response decision is split up in three parts.
A ?rst response action is whether the incumbent does or does not adopt the new business model.
This is a simple yes/no question and speci?es the probability of either basic or full copy response.
A second part of the incumbent’s response behavior we investigate is when the incumbent takes
action, and expresses the timing of the response.
A third part of the response is the completeness. Incumbents can fully embrace the new business
model (full copy) or they can copy it only partly (basic copy).
In what follows we discuss this conceptual framework based on our literature study. We can identify
2 main drivers that have an impact on the response behavior of incumbents, namely the ?rm’s ability
to respond and the ?rm’s motivation to respond (Charitou and Markides, 2003). These 2 drivers are
reinforced by the industry embeddedness of the incumbent. We now discuss each of these building
blocks of the framework.
2 LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
l 13
Figure 1: Conceptual Framework
2.2 Ability to respond
The incumbent’s ability to respond (“Can I do it?”) consists of three main factors: its existing
complementary assets, its possession of dynamic capabilities, and its degree of structural inertia.
2.2.1 The role of existing complementary assets
Each company has certain strengths and weaknesses, relatively seen towards a particular market and
competitor(s). Those strengths and weaknesses make up the ‘resources’ of a ?rm and form together
with a ?rm’s skills and routines the basis for a potential competitive advantage (Wernerfelt, 1984).
Resources on their turn consist of tangible and intangible assets which are tied semi-permanently to
the ?rm (Barney, 1991).
We propose in this study that the complementarity of a ?rm’s assets with certain business model-
related activities of that ?rm, will be of major importance in deciding which business model to adopt
and when to adopt it. We propose this because companies will strive for maximum complementarity
between their resources and business model (Markides, 1997). Therefore the role of complementary
assets can not be underestimated and should be considered (Rothaermel and Hill, 2005; Dutta et
al., 2004; Helfat, 1997).
Teece (1986) untangles three sorts of complementary assets: generic, specialized and cospecialized
ones. Generic complementary assets are general purpose assets that do not need to be tailored to
the innovation or business model in question. They are commodity-type assets that can be transacted
for in the open market. Specialized complementary assets have a unilateral dependence with the
innovation, whereas cospecialized assets contain a bilateral dependence with the innovation. Both
specialized and cospecialized assets are built over a long time period.
Incumbent’s response
behavior:
-probability
-timing
-completeness
Motivation to respond:
-new business model attractiveness
-threat to core competence
-competitive behavior
Ability to respond:
-existing complementary assets
-possession of dynamic capabilities
-degree of structural inertia
Embeddedness in an industry
l 14
Early theory (and examples) on complementary assets focuses especially on the importance of
these assets to the commercialization of an innovation (Teece, 1986). Examples of (co)specialized
complementary assets in the commercialization of an innovation are extensive infrastructure of
switching networks in telecom industry, regulatory and legal expertise, large sales forces of detail
people in pharmaceutical industry, etc. The possession of complementary assets is considered to
play a role in the following situations:
-predicting who wins in case of innovation (Teece, 1986)
-assessing the industry and ?rm performance evolution following a new technology (Rothaermel and
Hill, 2005)
-predicting the probability of incumbents entering an emerging technological sub?eld (Mitchell,
1989).
We propose that complementary assets have interesting repercussions on the incumbent’s response
behavior to business model innovations. On the one hand, complementary assets increase entry
barriers for incumbents to copy a new business model, because an incumbent’s strengths and
weaknesses are not maximally aligned with the newly needed strategic resources to compete
in the new business model. On the other hand, complementary assets that are still useful in the
new business model buy the incumbent some time to decide whether or not to respond, because
newcomers will ?rst have to build those complementary assets needed.
Building on the theory of complementary assets and our propositions, we hypothesize:
H1: Complementary assets that an incumbent possesses towards a business model innovation
increase the probability that the incumbent copies the new business model, the later it does so, and
the more complete the copy is.
H2: The degree of complementarity of assets has a bigger in?uence than the number of complementary
assets on the probability, the timing, and the completeness with which an incumbent copies the new
business model.
To test for these hypotheses, we will use two variables: core complementary assets and peripheral
complementary assets. Core complementary assets are a small amount of assets that have a high
degree of complementarity with the business model innovation. Peripheral complementary assets
are a larger amount of assets that have a lower degree of complementarity with the new business
model. The measures of these two variables are presented in section 3.1.3.1.
2.2.2 The role of dynamic capabilities
Because business model innovations fundamentally reconceptualize what the business is all about by
breaking the industry’s recipe and by questioning the whole go-to-market approach of incumbents,
being responsive to this type of innovation demands a high ability to change or to adapt to change
from incumbents. The ability to continually integrate, build, and recon?gure internal and external
resources, competences, and capabilities to achieve new forms of competitive advantage and
congruence with rapidly changing business environments, has been de?ned as ‘dynamic capabilities’
(Teece et al., 1997).
Like with many of the constructs developed in the research ?eld originating from the resource-
based view (RBV) of the ?rm (e.g. resources, competences, and capabilities), conceptualization
l 15
and measurement is one of the most criticized aspects of the concept of dynamic capabilities. This
is re?ected in a lack of convincing quantitative support (Newbert, 2007). There are however some
efforts to universally de?ne these constructs and to make them more operational and measurable
(Ray and Ramakrishnan, 2006; Dutta et al., 2004; Armstrong and Shimizu, 2007).
Based on Teece (2007) we try to capture some key types of dynamic capabilities in this study to
partly explain response behavior of incumbents towards business model innovations. We suggest
market orientation, absorptive capacity, and entrepreneurial character of a company as key types of
dynamic capabilities.
Recognizing the potential of a business model innovation is not easy. A new business model can have
the strategy of creating a new market targeted towards non-customers, or of disrupting the existing
business model from the low end targeted towards ‘low-pro?t’ customers. These situations of attack
on non- or low-pro?t-customers increase the probability (the former even more than the latter) that
incumbents overlook or neglect the new business model. Therefore we hypothesize that the more
market oriented a company is, the better it knows its market and the faster it sees the new business
model. This entails a faster reaction towards the business model innovation. Market orientation is
even considered as part of kick-starting strategic innovation (Markides, 1997). We though want to
remark that a too ‘narrow’ market orientation could also negatively in?uence incumbent reaction.
That is because incumbents that focus too much on the market as de?ned in their industry recipe,
could overlook the new business model. Such a ‘narrow’ market orientation can thus postpone an
incumbent’s reaction and its probability of copying the business model innovation. A distinction can
be made between reactive and pro-active market orientation. A reactive market orientation focuses
on the manifest and expressed needs of customers, whereas a pro-active market orientation focuses
on the latent, and nascent needs of customers. The key difference between reactive and pro-active
market orientation is the forward-looking nature of pro-active market orientation. It is not about
responding to customer’s current needs, but about anticipating future needs.
A new business model is innovative in terms of the applied business model to serve a market. If a
company wants to adopt a new business model, the company’s industry recipe (cf. the assumption
of which business it thinks it is in) should change and this asks a particular capability to value,
assimilate, and apply new knowledge (cf. not necessarily technological knowledge). This is called
‘absorptive capacity’ (Rothaermel and Hill, 2005). Absorptive capacity could be a main determinant
of the organizational learning degree of a company. Researchers have commented extensively on
the tendency of decision-makers to rely on old frameworks to assess new information (Barr and
Huff, 1997). Change only happens at the point when new information impels a change in cognitive
frameworks (Kiesler and Sproull, 1982). However, cognitive frameworks are built up over time,
based on past experiences and established beliefs, and are deeply rooted within the organization.
Organizational cognition in?uences decision-making through its effect on the interpretation of new
information, but also through its effect as information ?lter. We propose a high absorptive capacity
as being a key type of dynamic capabilities. If an incumbent then has a high absorptive capacity, its
response would probably be more copy-oriented, more complete, and earlier.
Together with the absorptive capacity of an incumbent, we propose that also entrepreneurial behavior
of the incumbent plays a role as key type of dynamic capabilities. An incumbent having a high degree
of entrepreneurial behavior is proposed to have a more probable intention to copy the new business
model, to react more timely, and to be more complete in its response.
l 16
Building on the theory of dynamic capabilities and our propositions, we hypothesize:
H3: The higher the incumbent’s dynamic capabilities, the more likely, the quicker, and the more
complete it will respond to a business model innovation. A company’s market orientation, absorptive
capacity, and entrepreneurial character are key types of dynamic capabilities.
2.2.3 Structural inertia
Every organization develops over time certain routines and procedures to optimize working processes
that have a repetitive character. Even adapting to major changes can become a routine (cf. dynamic
capabilities) suggesting transformational experience of incumbents (King and Tucci, 2002). However,
establishing change within an organization and making it work is not easy especially in the case of
a major turnaround like a new business model. Questions like “Why should I change? What shall I
change into? What if I jump into a new position and it turns out to be a mistake?” are only a few ever-
returning issues that pop up as obstacles for business model innovation (Markides, 1998).
We propose structural inertia as being a major postponing and non-copying in?uence towards the
incumbent’s response in the advent of a business model innovation. The preferred state of the
organization is to remain inert. Organizational inertia has been de?ned as the propensity of a ?rm
to sustain the status quo and maintain the current course of action (Chandrashekaran et al., 1999).
Along with the increased interest for the sources of organizational dynamics, the notion of inertia
has received much attention from organizational theorists in the recent two decades. Scholars
adopting an inertial view to organizational and strategic change argue that organizations generally
resist change and it is their nature to preserve the status quo (Hannan and Freeman, 1984; Boeker,
1997). The emphasis of organization research on inertia has been primarily devoted to structural,
institutional and political barriers to change within the organization, often related to factors such as
the organization size and age (Kelly and Amburgey, 1991).
There are a few reasons why we observe this type of structural inertia which also explains why
business model innovators are mostly entrepreneurial start-ups or new market entrants, and not
incumbents (Markides, 1998). Firstly, the impact or return of projects in existing markets is ‘easier’
to assess than the impact of new ‘adventures’. Secondly, incumbents often seem paralyzed by their
current thinking and this further strengthens their traditional go-to-market approach (Matthyssens et
al., 2005). Breaking through this industry recipe is however a prerequisite to come to business model
innovation. A last component of structural inertia is the dif?culty of overcoming negative responses
internally that is driven by established product/service lines in large organizations and by the necessity
to achieve managerial consensus (Teece, 2007).
Based on the theory of structural inertia and our propositions, we hypothesize:
H4: The higher the structural inertia of an incumbent, the lower its probability of copying a new
business model, the later and the less complete it does so.
2.3 Motivation to respond
The incumbent’s motivation to respond to business model innovations is considered to be in?uenced
by 3 main factors, namely the attractiveness of the new business model, the threat it introduces to
the core competence(s) of incumbents, and the competitive behavior in the industry.
l 17
2.3.1 New business model attractiveness
The attractiveness of a market and pro?ts that are made by its market players attract other players. In
case of a business model innovation, the growth of the new market that causes as well substitution as
market expansion has a big in?uence on incumbent entry (Debruyne and Reibstein, 2005). When the
new business model is highly attractive, incumbents not only ‘feel’ that the new market substitutes
some pieces of the existing market, but they also ‘see’ that business model innovators are gaining
attractive pro?ts. This leads us to propose that the new business model attractiveness has a positive
in?uence on the incumbent’s adoption behavior towards business model innovations.
H5: A new business model’s attractiveness has a positive in?uence on the incumbent’s copy behavior
in terms of probability, timing and completeness.
2.3.2 Degree of threat to core competence
The substitution potential of a new business model leads us to the threat a business model innovation
has on incumbents and their core competences. This threat lies especially in the cannibalization
upon a ?rm’s existing markets served and products/services offered, and is more important for
incumbents whose core competence is directly linked to that part of the market that is ‘under attack’
of the new business model.
An incumbent’s response towards a new business model is thus expected to be in?uenced by the
cannibalization potential of the business model innovation (Debruyne and Reibstein, 2005). And
the greater the competitive threat, the less likely an incumbent will enter but the earlier it will do so
(Mitchell, 1989).
Based on the theory, we hypothesize:
H6: The greater the threat of a new business model towards the incumbent’s core competence(s),
the less likely an incumbent will copy the new business model but the earlier it will do so.
2.3.3 Competitive behavior
In their industry recipe of the business they are in, incumbents not only have a ?xed idea of target
markets, ideal customers, possible go-to-market approaches and a viable business model, but they
also develop an image of who their competitors are and who they are not. Each company de?nes
its key competitors and monitors their actions (in a structured or unstructured way) to know what’s
going on in the business. These competitive actions (or non actions) not only increase the incumbent’s
knowledge of the industry, but it also stimulates incumbent’s action (or non action).
In the advent of business model innovation there is on the one hand high uncertainty, because of
infringement on the very business logic of an entire industry. On the other hand there is high risk
aversion, because large, strategic key issues are at stake with the choice of a business model.
Therefore, industry players tend to show herd behavior or organizational imitation behavior towards
competitors. Herd behavior is an organizational behavior in which each response to a new practice
by a particular industry player makes the imitation of that response from another industry players
more likely (Burt, 1987). This process is called ‘contagion’ and serves as the dynamic behind
l 18
diffusion of new products (Bass, 1969; Rogers, 1962). Synonyms of herd behavior are bandwagon
or demonstration effect, and organizational imitation (Kennedy, 2002).
Organizational imitation behavior is considered to play an important role in the response towards
business model innovations by incumbents, especially among incumbents similar in size and
resources (Debruyne and Reibstein, 2005; Porac and Thomas, 1990; Haveman, 1993; Kraatz,
1998). We also propose that incumbent’s are in?uenced by the response behavior of market leaders,
and put this as standard in?uencer in our conceptual framework.
This imitation behavior is explained by the reduced uncertainty, and thus reduced risk, of incumbents
about the value of a particular business model because other incumbents also copy (or don’t copy)
it. The adoption of incumbent ?rms of the new business model may also create legitimacy for it.
However, the entry of other incumbents also changes the competition between the existing and new
markets with the possibility to accelerate the level of substitution between the old and the new, and
thus increasing the threat to the incumbent’s core competences (King and Tucci, 2002). Next to that,
complementary assets that are still useful in the new business model buy the incumbent some time
to decide whether or not to respond (section 2.2.1). But once other incumbents enter, the incumbent
will need to follow quickly not to lose its competitive advantage if the formers also possess those
complementary assets (Mitchell, 1989, 1991).
Based on the theory of organizational imitation behavior, we hypothesize:
H7: Incumbents are signi?cantly in?uenced by incumbent market leaders in responding towards a
new business model.
H8: Incumbents are not signi?cantly in?uenced by newcomers in responding towards a new
business model.
H9: Incumbents experience a bigger imitation behavior effect from incumbents that are similar in
size and/or resources.
H10: Incumbents experience a bigger imitation behavior effect from incumbents that have similar
complementary assets.
2.4 Role of embeddedness
The embeddedness of a company signi?es to what extent a company is anchored in an industry,
potentially re?ected in the strength of an incumbent’s ties with customers and suppliers, the strength
of an incumbent’s idea of the business (industry recipe), and the magnitude of an incumbent’s
inventory of path-dependent assets. An indicator of embeddedness is e.g. the period of time a
company has been around in a certain industry.
In our framework we suggest that embeddedness is more a moderator than a direct independent
variable. We thus propose that embeddedness changes the effect of existing complementary assets,
the effect of dynamic capabilities, the effect of structural inertia, the effect of a new business model’s
attractiveness, the effect of threat to core competence(s), and the effect of competitive behavior.
If an incumbent possesses path-dependent complementary assets, it is very dif?cult to make these
assets obsolete (redundant) in the event of an innovation that poses a threat to these assets (Teece,
l 19
2007). The more embedded an incumbent thus is in the industry, the stronger this path-dependency
and the stronger a threat to core competence(s) could play a role.
The longer an incumbent is present in an industry, the more engrained the dynamic (or non-dynamic)
character of the company is in the hearts of employees. Embeddedness thus renders the company’s
dynamic capabilities more extreme. Low dynamic capabilities become very low and high dynamic
capabilities become very high.
The time an incumbent has been around in an industry also impacts its degree of structural inertia.
The longer and the more an industry recipe has been con?rmed and has brought success, the more
that industry recipe is engrained in the minds and hearts of people as being ‘true’. So the bigger
an organization, the more time it consumes to convince the whole organization of another industry
recipe.
The existence of established assets and routines exacerbate problems of excessive risk aversion
(Teece, 2007). This increased risk aversion together with the increased attributed value of market
actions of long-time survivors over time, suggests a moderating effect of embeddedness on
organizational imitation behavior.
Based on the theory of industry embeddedness and our propositions, we hypothesize:
H11: Embeddedness has signi?cant effects as moderating variable and no or less signi?cant effects
as a direct independent variable.
l 20
3 METHODOLOGY AND DATA
3.1 Dataset and measures
3.1.1 Introduction
The data used for modeling the incumbent’s response behavior in this study is drawn from ESOMAR
3
directories. These global, yearly directories contain general information like e.g. year of foundation,
number of employees, turnover, etc. per market research agency that is member of ESOMAR. The
directories also disclose yearly information about a market research agency’s offering in terms of
methods/techniques used (for the period 1990-2005), operational ?elds of research (for the period
1990-2005), market sectors (for 2006-2007), research solutions presented (for 2006-2007), and
research services (for 2006-2007). We entered this hard-copy information in an electronic database
consisting of directory information of Belgium, the Netherlands, and the UK throughout the period
1990-2007. As such, we obtained a cross-country, longitudinal dataset.
Table 1 gives an overview of all relevant offerings presented in the ESOMAR directories together with
their appropriated values necessary for calculating some measures.
3.1.2 Measures of the dependent variable
We tested each time for two models, namely basic copy and full copy of online market research.
Incumbents in the period 1999-2005 (t = 1999, 2000,… 2005) were considered to offer a basic
copy of online market research if they offered internet research (1999-2005) in their operational ?elds
of research. Incumbents in the period 2006-2007 were considered to offer a basic copy of online
market research if they offered ‘online quantitative’ or ‘online focus groups’ as research services, or
if they offered ‘web panel’ as research solution.
Incumbents in the period 1999-2005 were considered to offer a full copy of online market research if
they offered internet research (1999-2005) in their operational ?elds of research when indicating that
internet research is their specialism. Incumbents in the period 2006-2007 were considered to offer a
full copy of online market research if they offered ‘web panel’ as research solution.
We want to remark that incumbents that offer a full copy of online market research are also included
in the analysis of incumbents that offer a basic copy of online market research.
3
ESOMAR is a worldwide organization for enabling better research into markets, consumers and societies. The or-
ganization currently has 4.400 individual members in more than 100 countries, and was founded in 1948. We highly
appreciate the willingness of ESOMAR to provide us all directories from the period 1990-2007.
l 21
Table 1: ESOMAR offering variables
Methods/Techniques Used
(1990-2005)
Operational Fields of Research
(1990-2005)
Research
Services
(2006-2007)
Market Sectors
(2006-2007)
Research Solutions
(2006-2007)
Personal ?eldwork and/or mail
surveys***
Advertising / Packaging Desk
Research**
Advertising/Public Relations* Advertising Research*
Personal Fieldwork*** Advertising Research* Agriculture Audience Research
Telephone ?eldwork*** Agricultural Research CAPI*** Automotive Brand Research***
Mail Surveys*** Automotive Research CATI*** Beverages Business-to-Business**
Panel and/or continuous survey
research*
Business/Industrial Research Mail*** Catering/Hospitality Children/Youth’s
Research**
Omnibus surveys* Business-to-Business Research** Online Charity/Non For Pro?t Consumer Research***
Qualitative/Psychological marketing
research
Child Studies** Mystery
Shoppers
Chemicals Concept Testing
Laboratory test facilities Consumer Marketing Research*** Focus Groups Confectionery Customer Satisfaction
Studies
Desk research, market analysis and/or
operations research**
Customer Satisfaction Recruiting Consultancy*** Data Mining**
Data processing, computer facilities** Financial/Corporate image** In-Depth
Interviews
Cosmetic/Hygiene Demographic Research
Datamining** Food&Drink Online Focus
Groups
Durables/Electrical Goods Employee Research**
Statistical analysis, interpretation of
survey data**
Industrial Research Detergents Ethnographic Research
Business and/or research consultancy International Marketing Research Energy/Utilities Image Studies
Educational services Internet Financial Services** International Studies
Market modelling Fragrance Industry Media Testing*
Segmentation/Typology Food New Product
Development
Media* Healthcare/Pharmaceutical*** Omnibus*
Medical/Pharmaceutical Research*** IT/Software/Hardware* Opinion Polling*
Packaging Research*** Legal/Lawyers Packaging/Design***
Personnel/Staff** Logistic/Mail/Transportation Panels*
Pricing/Promotions Media/Entertainment* Pricing Studies**
Pricing Research** Petrol/Oil/Gas Projective Techniques
Product Testing/NPD Public Sector/Government Product Testing
Promotions Research** Retail/Wholesale** Retail Audit
Social/Opinion* Telecommunication** Scenario Planning
Tracking/Brand Image*** Textile/Fashion/Clothing Segmentation Research
Travel/Tourism/Motorist Research Tobacco/Cigarettes Semiotic and Cultural
Analysis
Travel/Tourism Research Toys/Games Senior Citizen/Mid-Life
Wholesale/Retail** Travel/Tourism/Sport/Leisure Statistic Analysis**
Syndicated Research
Tracking Studies***
Usage&Attitude
Studies***
Web Panel
0 = not offered; 1 = basic offer; 2 = offered as specialization 0 = not offered; 2 = offered as specialization
*included in the measure of existing core complementary assets
**included in the measure of existing peripheral complementary assets
***included in the measure of threat to core competence(s)
l 22
3.1.3 Measures of the incumbent’s ability to respond
3.1.3.1 Complementary assets
The core complementary assets of an incumbent were calculated as the sum of variables in Table 1
marked with*. The peripheral complementary assets of an incumbent were calculated as the sum of
variables in Table 1 marked with **.
The selection of the different variables in our measures for complementary assets is based upon
qualitative interviews and desk research. We tried to balance the measures throughout the change
in data collection (1990-2005 versus 2006-2007) by looking for similar variables across periods and
by striving for a higher number of variables from the period 2006-2007 because those variables
have less chance of being indicated for a market research agency in the directories (cf. it are
specializations). Only for peripheral complementary assets is the number of variables from the period
1990-2005 larger than those from the period 2006-2007. This could however only impact our testing
of hypotheses 1 and 2.
Core complementary assets are suggested to have a higher degree of complementarity towards
the online market research business model than peripheral complementary assets. Peripheral
complementary assets are though larger in number than core complementary assets. We use core
complementary assets in our basic tests for ?t of our conceptual framework.
3.1.3.2 Dynamic capabilities
We measured an incumbent’s market orientation by the number of ‘key people’ or ‘managing
director(s)’ of that incumbent that were also presented as ‘ESOMAR member’. This measure indicates
the degree to which the market research agency is aware of the newest trends and industry news in
the market research world.
Absorptive capacity is measured by the degree to which an incumbent is being focused on
specializations in the product offering or markets served. We calculate for each year of the period
1999-2007 the number of specializations of each incumbent from the 14 ‘methods and techniques
used’ variables, the 29 ‘operational ?elds of research’ variables, the 29 ‘market sectors’ variables,
and the 33 ‘research solutions’ variables (see Table 1). We then rescale the variable ‘absorptive
capacity’ for the years 2006 and 2007 by multiplying it with 43/62 to balance for the number of
variables in our measure.
Entrepreneurial behavior is considered to be re?ected by the change of the operational ?elds of
research offering throughout the years (see Table 1). The variable ‘entrepreneurship’ is then calculated
as follows. For each 10-year-timeframe, starting with the period 1990-1999 till 1998-2007, we
calculated every incumbent’s standard deviation of its 29 ‘operational ?elds of research’ and made the
sum of those standard deviations. We then coded the variable entrepreneurship for each incumbent
for the period 1999-2007 as follows:
? the value for year 1999 = sum of standard deviations for timeframe 1990-1999
? the value for year 2000 = sum of standard deviations for timeframe 1991-2000
? etc.
l 23
3.1.3.3 Structural inertia
Structural inertia is measured by the number of ‘key people’ or ‘managing director(s)’ divided by the
number of ‘employees’.
3.1.4 Measures of the incumbent’s motivation to respond
3.1.4.1 Attractiveness of online market research
We measure the attractiveness of online market research for each year in the period 1999-2007
by dividing the global spend on online market research for each year by the number of newcomers
in our dataset in the period 1995-2007. The spend of online market research is estimated by
Inside Research at US$3,6 Billion in 2007, US$3,1 in 2006, and US$2,66 Billion in 2005 (ESOMAR,
2008). Because we missed data from 1999 till 2004, we assumed a linear market growth from
1999 onwards till 2005. So we started in 1999 with US$0,38 Billion, in 2000 with US$0,76 Billion,
etc.
3.1.4.2 Threat to core competence
The threat to core competence(s) was calculated by the sum of variables in Table 1 marked with***.
3.1.4.3 Competitive behavior
We use the cumulative basic copy of online market research by market leaders as basic competitive
behavior variable in our tests for ?t of our conceptual framework.
To test for imitation behavior among groups of incumbents with similar size and complementary
assets (cf. H9 and H10), we split our analyses each time for two groups. We then compare the
explanatory strength of our competitive behavior variable in the case of different groups based on
size and complementary assets versus the case of no formation of different groups (cf. standard test
for ?t of our conceptual framework).
To test for similar size, group 1 is the group with market research agencies that have a size in terms
of mean number of employees for the period 1999-2007 under the median value of all incumbents,
so the group with ‘smaller’ agencies. Group 2 is then the group with market research agencies that
have a size in terms of mean number of employees for the period 1999-2007 above the median
value of all incumbents, so the group with ‘bigger’ agencies.
To test for similar complementary assets, group 1 is the group with market research agencies that
have a value for the mean of their core complementary assets for the period 1999-2007 lower than
the appropriate median value of all mean values of market research agencies. Group 2 is then the
group with a mean value higher than the median value.
3.1.5 Embeddedness
We measure the incumbent’s embeddedness by its year of foundation.
l 24
3.2 Empirical model
Our conceptual framework (Figure 1) has a dependent variable, the incumbent’s response behavior
towards online market research, which is split up in three parts. We not only test for the probability
an incumbent will copy the online business model and for the timing of the response. We also test
the completeness of that response (basic or full copy).
The timing of the incumbent’s response is tested using a hazard model. The probability that an
incumbent copies the new business model and the completeness of its response are tested using
a binary logistic regression model. Time-varying covariates were averaged over the measurement
period to enter the logistic regression equation.
As all our hypotheses pertain to an incumbent’s response behavior, we only considered cases in
which (basic or full) copy of the online business model was possible and incumbents were involved.
The probability of offering online market research is estimated starting from 1999 when the ?rst
recording of online occurs in the dataset was. A company is considered an incumbent if it was
present in the market before 1995. Our total dataset contains 304 cases from three countries:
Belgium, the Netherlands, and the UK. Please notice that our dataset covers the period from 1990
till 2007.
l 25
Adoption Behavior Belgium
0
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30
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
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B-Basic B-Specialist B-Basic-Incumbent B-Specialist-Incumbent
4.1 Introduction
The ESOMAR dataset contains company information for the period 1990-2007. Information about
online adoption behavior starts from 1999 onwards. We distinguish among two types of adoption
behavior. On the one hand the ‘basic adoption’ referring to market research agencies that offer online
methods (e.g. online focus groups or online quantitative research methods) or internet research,
but not commercialized as a specialism of that agency. On the other hand there is a ‘specialist
adoption’ referring to market research agencies that offer online market research as one of their core
competences (e.g. internet research and web panels).
Figure 2 sets out the adoption behavior in Belgium in cumulative numbers of market research agencies
for the two types of adoption (basic vs specialist), and for two samples for the period 1999-2007. The
?rst sample is the total ESOMAR population in Belgium active in the period 1999-2007, consisting
of incumbents (founded before 1995) and startups (founded in or after 1995). The second sample is
the ESOMAR incumbents that are active in the period 1999-2007.
We are interested in the cumulative number of adopters of online, so online adopters disappearing
from the market are not taken into account as negative values for adoption. Also startups (agencies
that are founded in or later than 1995) that adopted online and that are later acquired by incumbents
are not taken into account as a separate adoption. These remarks remain valid throughout our whole
analysis.
Figure 2: Adoption behavior in Belgium
4 DISCUSSION OF RESULTS
Adoption Behavior Belgium
0
5
10
15
20
25
30
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
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B-Basic B-Specialist B-Basic-Incumbent B-Specialist-Incumbent
Adoption Behavior Belgium
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l 26
Figure 2 shows that there is a large increase in the number of agencies adopting online as a basic
offering in the period 1999-2001,and the period 2005-2006. The number of agencies adopting
online as a core competence stays throughout the whole period stable with in total less than 5
agencies that adopted online as their specialism.
Figure 3 and Figure 4 represent the evolution of adoption behavior for incumbents of Belgium, the
Netherlands and the UK for respectively a basic adoption, and a specialist adoption. There is a large
increase of the number of basic adopters in the UK in the period 1999-2001, followed by stability and
a small increase in 2005-2006. Belgium has a more stable growth over time just like the Netherlands,
but the latter has a larger number of adopters which is normal if we know that the Netherlands has
more market research agencies than Belgium. To make comparisons across countries possible,
we can correct for the number of market research agencies present in each country. Therefore we
introduce Figure 5 and Figure 6 where the cumulative number of adopters in a country is expressed
in relative terms to the total number of incumbent market research agencies in that country.
Figure 3: Adoption behavior of online as basic market research tool
The Netherlands seems good ground for the specialist adoption of online (Figure 4), and this becomes
very clear when we look at Figure 6. The Netherlands takes the lead in specialist adoption in relative
cumulative numbers of adopters with a large increase in 1999-2000. Belgium and the UK are lagging
behind, and in 2007 Belgium almost closes the gap with the UK.
Figure 5 indicates that Belgium, the Netherlands, and the UK are experiencing a similar basic adoption
evolution, but it is again clear that the Netherlands was the quickest to adopt relatively seen towards
Adoption behavior of online as basic market
research tool in an incumbent's o?er in Belgium,
Netherlands, and UK
0
20
40
60
80
100
1998 2000 2002 2004 2006 2008
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Adoption behavior of online as basic market
research tool in an incumbent’s offer in Belgium,
Netherlands, and UK
l 27
the number of players in each country. Figure 5 also shows that it is possible to have more than
100% of cumulative adoption due to consolidation of the market and bankruptcies.
Figure 4: Adoption behavior of online as specialism
Figure 5: Adoption behavior of online as basic market research tool (in %)
Adoption behavior of online as specialism in an
incumbent's market research tools ofer in
Belgium, Netherlands, and UK
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UK-Specialist-Incumbent
Adoption behavior of online as specialism in an
incumbent's market research tools ofer in
Belgium, Netherlands, and UK
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UK-Specialist-Incumbent
Adoption behavior of online as basic market research tool in an
incumbent's o?er in Belgium, Netherlands, and UK
0%
20%
40%
60%
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100%
120%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
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Adoption behavior of online as basic market research tool in an
incumbent's o?er in Belgium, Netherlands, and UK
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Adoption behavior of online as specialism in an
incumbent’s market research tools offer in
Belgium, Netherlands, and UK
Adoption behavior of online as basic market research tool in an
incumbent’s offer in Belgium, Netherlands, and UK
l 28
Figure 6: Adoption behavior of online as specialism (in %)
4.2 Testing the conceptual framework
4
We tested two models based on our conceptual framework. On the one hand we tested if the
probability and timing of a ‘basic copy’ is in?uenced by the determinants of an incumbent’s ability to
respond and its motivation to respond, with and without embeddedness as a moderating variable.
On the other hand we tested the probability and timing also for a ‘full copy’. Basic copy and full copy
are the two points on our axis of response completeness.
We ?rst present correlations and descriptive statistics of our framework variables. Thereafter, we
discuss the results of our framework for basic copy and then for full copy.
4.2.1 Correlations and descriptives
Table 2 and Table 4 represent the Pearson correlations (and the number of cases included in the
correlation calculation) between the variables of our empirical model.
Further investigation of Table 2 and Table 4 indicates that there is only one case of excessive
correlation (cut-off rate set at 0,400) between variables in our framework. The correlation between
the mean of existing complementary assets and the mean of absorptive capacity is 0,415. Therefore
4
We want to remind that all time-varying covariates were averaged over the measurement period to enter the logis-
tic regression equation used for probability testing. Thus the variables discussed in the timing tests are not time-
averaged whereas the variables in the probability tests are time-averaged (although we use in the tables the same
names).
Adoption behavior of online as specialism in an incumbent's
market research tools o?er in Belgium, Netherlands, and UK
0%
5%
10%
15%
20%
25%
30%
35%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
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B-Specialist-Incumbent N-Specialist-Incumbent UK-Specialist-Incumbent
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Adoption behavior of online as specialism in an
incumbent’s market research tools offer in
Belgium, Netherlands, and UK
l 29
we have not included absorptive capabity in our probability testing. We choose to exclude absorptive
capacity and not the mean of existing complementary assets, because the latter is the only variable
indicating complementary assets whereas the former is only one of three variables indicating dynamic
capabilities.
For our timing tests, there is no problem of correlation among independent variables.
We also want to remark that there is a high, but not problematic, correlation between the mean of
existing complementary assets and the mean of threat to core competence(s). This corresponds to
the dif?culty of making a clear distinction between those two variables, and will be further discussed
upon in section 5.
Table 3 and Table 5 are representing some key descriptive statistics for our incumbents, mentioning
number of cases (N), minimum value, maximum value, mean, and standard deviation.
We see that there is an overall probability among incumbents to offer a basic or full copy of online
market research during the period 1999-2007 of 74% (Table 3). For only a full copy of online market
research this percentage decreases till 17%.
Among the three variables indicating different types of dynamic capabilities, absorptive capacity
has the largest standard deviation, meaning that this variable makes the clearest distinction among
incumbents of how their degree of dynamic capabilities could be.
Table 3 and Table 5 also indicate that the ‘normal’ age of an incumbent is 30 years, because the
mean value of year of foundation is 1979.
Table 6 indicates that there are suf?cient cases available in our ESOMAR dataset to test our
framework for timing. There are 1647 cases covering the period 1999-2007 and countries Belgium,
the Netherlands, and the UK. Note however that the number of cases available in the analysis
(namely 1463), thus cases with no missing values, was not enough to test separately for countries.
Table 6 also shows that basic copy (52,7% of all cases) is more experienced than full copy (9,9% of
all cases) which is logical if we look at the probability means in Table 3. ‘Censored’ expresses the
number of cases that not yet experienced a copy action.
Dropped cases with missing values are kept low enough. In total there are 1647 cases in our sample
for testing for response timing. Note that the number of cases for probability testing is 304 (Table
3).
l 30
Table 2: Correlation matrix for probability tests
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Complementary
Assets
Pearson
Correlation
1 0,091 ,415 ,180 -,162 -,127 ,367 0,070
N 303 303 303 268 294 303 303 303
Market
Orientation
Pearson
Correlation
0,091 1 0,035 0,042 ,131 -0,077 0,041 0,098
N 303 304 304 268 295 304 303 304
Absorptive
Capacity
Pearson
Correlation
,415 0,035 1 ,234 -,170 -0,048 ,377 -,126
N 303 304 304 268 295 304 303 304
Entrepreneurial
behavior
Pearson
Correlation
,180 0,042 ,234 1 -0,100 -,171 ,145 0,004
N 268 268 268 268 262 268 268 268
Structural Inertia Pearson
Correlation
-,162 ,131 -,170 -0,100 1 0,105 -,149 -0,008
N 294 295 295 262 295 295 294 295
Attractiveness Pearson
Correlation
-,127 -0,077 -0,048 -,171 0,105 1 ,166 -,239
N 303 304 304 268 295 304 303 304
Threat Pearson
Correlation
,367 0,041 ,377 ,145 -,149 ,166 1 0,056
N 303 303 303 268 294 303 303 303
Competitive
Behavior
Pearson
Correlation
0,070 0,098 -,126 0,004 -0,008 -,239 0,056 1
N 303 304 304 268 295 304 303 304
Table 3: Descriptives for probability tests
N Minimum Maximum Mean Std. Deviation
Probability basic and
full copy
303 0 1 0,74 0,44
Probability full copy 303 0 1 0,17 0,38
Complementary
Assets
303 0 9,33 3,08 1,80
Market Orientation 304 0 3,78 0,96 0,57
Absorptive Capacity 304 0 38 8,63 5,15
Entrepreneurial
Behavior
268 0 17,50 4,32 3,20
Structural Inertia 295 0 1 0,13 0,16
Attractiveness 304 1,52 270 65,47 61,92
Threat 303 0 17 6,44 2,46
Competitive Behavior 304 1 3 2,15 0,43
Embeddedness 304 1923 1994 1979 13,32
l 31
Table 4: Correlation matrix for timing tests
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Complementary
Assets
Pearson
Correlation
1 ,109 ,343 ,164 -,148 -,091 ,275 ,066
N 1.639 1.639 1.639 1.529 1.570 1.639 1.639 1.639
Market
Orientation
Pearson
Correlation
,109 1 0,042 0,014 ,078 -0,034 0,024 ,103
N 1.639 1.644 1.644 1.533 1.574 1.644 1.639 1.644
Absorptive
Capacity
Pearson
Correlation
,343 0,042 1 ,222 -,119 -,083 ,288 -,103
N 1.639 1.644 1.645 1.534 1.574 1.645 1.639 1.645
Entrepreneurial
Behavior
Pearson
Correlation
,164 0,014 ,222 1 -,120 -,137 ,107 0,024
N 1.529 1.533 1.534 1.536 1.469 1.536 1.529 1.536
Structural Inertia Pearson
Correlation
-,148 ,078 -,119 -,120 1 ,098 -,115 -0,023
N 1.570 1.574 1.574 1.469 1.574 1.574 1.570 1.574
Attractiveness Pearson
Correlation
-,091 -0,034 -,083 -,137 ,098 1 ,259 -,193
N 1.639 1.644 1.645 1.536 1.574 1.647 1.639 1.647
Threat Pearson
Correlation
,275 0,024 ,288 ,107 -,115 ,259 1 ,076
N 1.639 1.639 1.639 1.529 1.570 1.639 1.639 1.639
Competitive
Behavior
Pearson
Correlation
,066 ,103 -,103 0,024 -0,023 -,193 ,076 1
N 1.639 1.644 1.645 1.536 1.574 1.647 1.639 1.647
Table 5: Descriptives for timing tests
N Minimum Maximum Mean Std.Deviation
Complementary Assets 1639 0 16 3,27 2,07
Market Orientation 1644 0 6 1,03 0,69
Absorptive Capacity 1645 0 58 8,69 6,30
Entrepreneurial Behavior 1536 0 19,14 4,53 3,34
Structural Inertia 1574 0 1 0,12 0,15
Attractiveness 1647 1,52 270 65,18 73,52
Threat 1639 0 22 6,70 2,78
Competitive Behavior 1647 1 3 2,20 0,52
Embeddedness 1647 1923 1994 1979 12,95
l 32
Table 6: cases in timing analysis
Basic copy Full copy
N Percent N Percent
Cases available in analysis Event 868 52,7 163 9,9
Censored 595 36,1 1.353 82,1
Cases dropped Cases with missing values 184 11,2 131 8
Total 1.647 100 1.647 100
4.2.2 Basic copy results
Table 7 shows the probability and timing test results of our conceptual framework without
embeddedness as a moderating independent variable. Table 8 presents the results for our framework
including embeddedness as a moderating independent variable. Both tables present Chi-square
values, signi?cance level of variables, coef?cient estimates per variable (B), and standard error per
variable (SE).
All test results indicate by the Chi-square value that our overall framework contributes signi?cantly at
level 0,001 to explaining timing and probability of the incumbent’s response of offering a basic copy
of online market research throughout the period 1999-2007 and across the countries Belgium, the
Netherlands, and the UK. Especially the ‘change from block 0’ is interesting, because it shows the
contribution of our model relative to the situation that all our variables would be zero.
The results in Table 7 and Table 8 also show that more variables are signi?cant in the estimation
of the timing than the probability of response. A possible explanation is the high overall probability
percentage, which enables us to discriminate less between those adopters and non-adopters. In
other words, if eventually almost all incumbents adopt the new business model, when they do so is
more insightful then if they do so.
There is a signi?cant positive impact of Complementary Assets, Absorptive Capacity, and
Entrepreneurial Behavior for the incumbent’s response timing, indicating that the higher the value for
these variables, the earlier an incumbent will offer a basic copy of online market research. Signi?cant
negative impacts are assessed for Market Orientation, Structural Inertia, Attractiveness, Threat, and
Competitive Behavior. Embeddedness has also a signi?cant moderating impact with Complementary
Assets, Market Orientation, Entrepreneurial Behavior, Structural Inertia, Threat, and Competitive
Behavior on an incumbent’s response timing. We note that the inclusion of Embeddedness as a
moderating independent variable has not much in?uence on the B-values for the direct independent
variables. Including Embeddedness-moderating variables also shows a small increase of 6,33% of
our framework’s overall contribution in explaining response timing behavior.
We see a signi?cant positive in?uence of Entrepreneurial Behavior, Attractiveness, Threat, and
Competitive Behavior to an incumbent’s probability of offering basic online market research. A
signi?cant negative impact on basic copy probability is noted for Structural Inertia. Embeddedness
has a signi?cant moderating impact with Attractiveness and Threat on basic copy probability. Including
Embeddedness as a moderating independent variable in the model increases overall contribution of
the model with 25,84%, but has an important impact on the signi?cance and attributed value of the
direct independent variables.
A further discussion of all variables and their impact follows in section 4.3.
l 33
Table 7
5
: test results for basic copy (without Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets 0,174 0,116 Complementary Assets** 0,039 0,016
Market Orientation 0,363 0,324 Market Orientation**** -0,203 0,051
Absorptive Capacity Not included Absorptive Capacity**** 0,043 0,006
Entrepreneurial Behavior* 0,092 0,055 Entrepreneurial Behavior**** 0,055 0,010
Structural Inertia** -2,964 1,193 Structural Inertia**** -1,591 0,327
Motivation Motivation
Attractiveness**** 0,018 0,004 Attractiveness**** -0,010 0,001
Threat ** 0,174 0,084 Threat **** -0,048 0,013
Competitive Behavior** 1,013 0,398 Competitive Behavior**** -1,113 0,082
Constant**** -3,814 1,073
Overall ?t Overall ?t
Chi Square**** 54,429 Chi Square**** 575,267
Change from block 0 Change from block 0
Chi Square**** 54,429 Chi Square**** 563,033
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
5
Signi?cance levels are calculated based on the Wald statistic. The Wald statistic is calculated per variable via the
division of the estimated coef?cient (B) by the standard error (SE).
l 34
Table 8: test results for basic copy (with Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets** 0,248 0,124 Complementary Assets*** 0,054 0,018
Market Orientation 0,389 0,328 Market Orientation**** -0,222 0,053
Absorptive Capacity Not included Absorptive Capacity**** 0,055 0,007
Entrepreneurial Behavior 0,072 0,058 Entrepreneurial Behavior**** 0,050 0,011
Structural Inertia** -3,201 1,317 Structural Inertia**** -1,689 0,329
Motivation Motivation
Attractiveness**** 0,020 0,004 Attractiveness**** -0,010 0,001
Threat** 0,207 0,091 Threat*** -0,040 0,013
Competitive Behavior** 1,335 0,459 Competitive Behavior**** -1,130 0,084
Embeddedness Embeddedness
Complementary Assets
x Embeddedness
-0,104 0,211 Complementary Assets x
Embeddedness**
0,061 0,029
Market Orientation
x Embeddedness
-0,055 0,184 Market Orientation x
Embeddedness**
-0,070 0,031
Absorptive Capacity
x Embeddedness
Not included Absorptive Capacity x
Embeddedness
0,042 0,031
Entrepreneurial Behavior
x Embeddedness
-0,070 0,186 Entrepreneurial Behavior x
Embeddedness**
-0,069 0,034
Structural Inertia
x Embeddedness
0,006 0,310 Structural Inertia x
Embeddedness***
0,170 0,055
Attractiveness
x Embeddedness*
-0,491 0,296 Attractiveness x Embeddedness 0,041 0,036
Threat
x Embeddedness**
0,617 0,249 Threat x Embeddedness** 0,084 0,038
Competitive Behavior
x Embeddedness
0,145 0,163 Competitive Behavior x
Embeddedness**
-0,086 0,035
Constant**** -4,777 1,217
Overall ?t Overall ?t
Chi
Square****
68,494 Chi
Square****
611,693
Change from block 0 Change from block 0
Chi Square**** 68,494 Chi Square**** 605,907
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
4.2.3 Full copy results
Table 9 and Table 10 give an overview of the test results for full copy behavior of incumbents towards
online market research.
The lower Chi-square values and the lower signi?cance levels of our variables indicate that the overall
contribution of our framework to explaining the timing and the probability of full copy behavior of
l 35
incumbents is much less than for basic copy behavior. The overall contribution of our framework is
again much bigger for explaining the timing than the probability of incumbents offering a full copy of
online market research.
Towards the incumbent’s response timing, there is a signi?cant positive impact of Complementary
Assets, Absorptive Capacity, and Entrepreneurial Behavior. A signi?cant negative impact is noted for
Structural Inertia, Attractiveness, Threat, and Competitive Behavior. These results are comparable
with the results obtained for basic copy behavior, but there is no signi?cant impact of Market
Orientation for full copy. We can also notice signi?cant effects of the moderating independent
variable Embeddedness with all variables except Market Orientation. Including Embeddedness as
moderating independent variable has a minor impact on variable values and their signi?cant levels.
The incumbent’s probability of offering a full copy of online market research is positively in?uenced
by Complementary Assets, Entrepreneurial Behavior, Attractiveness, and Competitive Behavior.
No signi?cant negative and moderating impact is observed, except for a negative constant. The
introduction of Embeddedness as a moderating independent variable is thus not adding value to our
framework. The Chi-square is however increasing, but this is merely because of the larger amount
of variables included in the model.
A further discussion of all variables and their impact follows in section 4.3.
Table 9: test results for full copy (without Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets** 0,261 0,107 Complementary Assets**** 0,181 0,039
Market Orientation 0,190 0,309 Market Orientation -0,037 0,106
Absorptive Capacity Not included Absorptive Capacity**** 0,076 0,009
Entrepreneurial Behavior*** 0,147 0,056 Entrepreneurial Behavior**** 0,137 0,023
Structural Inertia -1,584 1,728 Structural Inertia* -1,790 0,923
Motivation Motivation
Attractiveness* 0,008 0,004 Attractiveness**** -0,015 0,002
Threat 0,069 0,098 Threat **** -0,173 0,038
Competitive Behavior*** 1,304 0,420 Competitive Behavior** -0,415 0,179
Constant**** -6,930 1,363
Overall ?t Overall ?t
Chi Square**** 35,068 Chi Square**** 356,377
Change from block 0 Change from block 0
Chi Square**** 35,068 Chi Square**** 295,269
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
l 36
Table 10: test results for full copy (with Embeddedness)
Probability Timing
Variables B SE Variables B SE
Ability Ability
Complementary Assets** 0,247 0,117 Complementary Assets**** 0,242 0,049
Market Orientation 0,179 0,326 Market Orientation -0,172 0,130
Absorptive Capacity Not included Absorptive Capacity**** 0,086 0,014
Entrepreneurial Behavior** 0,141 0,057 Entrepreneurial Behavior**** 0,141 0,025
Structural Inertia -1,950 1,754 Structural Inertia** -1,869 0,951
Motivation Motivation
Attractiveness* 0,008 0,004 Attractiveness**** -0,016 0,002
Threat 0,062 0,100 Threat**** -0,149 0,042
Competitive Behavior*** 1,364 0,432 Competitive Behavior** -0,460 0,190
Embeddedness Embeddedness
Complementary Assets
x Embeddedness
0,112 0,160 Complementary Assets x
Embeddedness****
0,250 0,068
Market Orientation
x Embeddedness
-0,004 0,153 Market Orientation x
Embeddedness****
-0,181 0,053
Absorptive Capacity
x Embeddedness
Not included Absorptive Capacity x
Embeddedness
0,013 0,053
Entrepreneurial Behavior
x Embeddedness
0,059 0,168 Entrepreneurial Behavior x
Embeddedness**
0,134 0,068
Structural Inertia
x Embeddedness
0,267 0,325 Structural Inertia x
Embeddedness***
0,432 0,153
Attractiveness
x Embeddedness
-0,141 0,284 Attractiveness x Embeddedness* 0,223 0,130
Threat
x Embeddedness
-0,202 0,275 Threat x Embeddedness -0,089 0,103
Competitive Behavior
x Embeddedness
0,043 0,178 Competitive Behavior x
Embeddedness
-0,004 0,073
Constant**** -6,927 1,400
Overall ?t Overall ?t
Chi
Square****
37,139 Chi
Square****
437,575
Change from block 0 Change from block 0
Chi Square**** 37,139 Chi Square**** 331,645
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
l 37
4.2.4 Test for completeness
We also test for completeness of the incumbent’s copy behavior (cf. basic versus full copy of online
market research). We do this by again running a binary logistic regression analysis. We note that only
responding incumbents (either basic or full copy) are included in the completeness test.
A positive impact in our test means that a responding incumbent is more inclined to offer a full copy
of online market research.
Table 11 shows that our overall ?t of the empirical model for explaining the response completeness
of incumbents is the lowest compared to other tests using binary logistic regression. The number
of signi?cantly contributing covariates is thus also low with only two covariates signi?cant (at 0.05
level). The test indicates that Entrepreneurial Behavior and Competitive Behavior are signi?cantly and
positively in?uencing incumbents towards responding in a full copy way.
Table 11: Test results for completeness (with Embeddedness)
Completeness
Variables B SE
Ability
Complementary Assets 0,176 0,119
Market Orientation 0,160 0,319
Absorptive Capacity Not included
Entrepreneurial Behavior** 0,126 0,058
Structural Inertia -1,886 1,954
Motivation
Attractiveness 0,003 0,005
Threat 0,035 0,108
Competitive Behavior** 1,062 0,445
Embeddedness
Complementary Assets x Embeddedness 0,115 0,165
Market Orientation x Embeddedness 0,015 0,150
Absorptive Capacity x Embeddedness Not included
Entrepreneurial Behavior x Embeddedness 0,051 0,170
Structural Inertia x Embeddedness 0,405 0,357
Attractiveness x Embeddedness -0,009 0,296
Threat x Embeddedness -0,317 0,284
Competitive Behavior x Embeddedness 0,028 0,191
Constant**** -5,232 1,430
Overall ?t
Chi Square** 25,632
* Signi?cant at .1 level
** Signi?cant at .05 level
*** Signi?cant at .01 level
**** Signi?cant at .001 level
l 38
4.3 Hypotheses discussion
4.3.1 Complementary assets
H1: Complementary assets that an incumbent possesses towards a business model innovation
increase the probability that the incumbent copies the new business model, the later it does so, and
the more complete the copy is.
Our results show that complementary assets always have a positive impact on the probability of
offering a basic or full copy of online market research among incumbents. The second part of H1 is
not supported by our results. It appears that complementary assets have always a signi?cant positive
effect on the incumbent’s response timing, meaning that the bigger the value of complementary
assets, the earlier the incumbent will respond. When testing for completeness of the incumbent’s
response, there was no signi?cant effect found of complementary assets.
The results thus demonstrate that possessing complementary assets helps incumbents to deal with
new business models in their industry. Incumbents that have a wide range of complementary assets
not only have a higher likelihood to respond to a new business model, they also do it earlier than
others.
H2: The degree of complementarity of assets has a bigger in?uence than the number of complementary
assets on the probability, the timing, and the completeness with which an incumbent copies the new
business model.
To test for H2 we used the variable peripheral Complementary Assets (See section 3.1.3.1).
Peripheral complementary assets have less complementarity with online market research and are
wider in range than core complementary assets (See Table 1). When testing our framework with
peripheral Complementary Assets we excluded core Complementary Assets, Absorptive Capacity,
and their time-averaged measures. We also excluded time-averaged Threat from the analysis. These
exclusions were needed because of high correlation with peripheral Complementary Assets. We
always tested excluding the moderating independent variable Embeddedness. An overview of the
test results is given in Table 12.
From Table 12 we see that peripheral complementary assets have a highly positive and signi?cant
impact on an incumbent’s probability and timing. There is also indication, just like with core
complementary assets but to a lesser extent, that the bigger peripheral complementary assets are
the more probable an incumbent will copy the new business model in a more complete way. Overall,
we remark that complementary assets play a bigger role in the case of a full copy.
Table 12 also demonstrates that the probability effect of peripheral complementary assets, although
they are bigger in number, is lower compared to the effect of core complementary assets. This
is seen for basic and for full copy. It means that the complementarity rather than the number of
complementary assets plays a bigger role in the probability of copying a new business model.
With respect to timing, we see that the number of complementary assets is more important in the
advent of basic copy. The degree of complementarity is then more vital when deciding about a full
copy reaction.
l 39
We conclude that part 1 and 3 of H2 are supported. The trade-off however between number and
degree of complementarity of complementary assets is more ambiguous with respect to explaining
the timing of an incumbent’s response towards new business models.
Table 12: Overview tests for peripheral complementary assets
Probability Timing Completeness
Peripheral
Complementary
Assets (B)
Basic copy 0,169** 0,116****
Full copy 0,204*** 0,213**** 0,178**
Core Complementary
Assets (B)
Basic copy 0,276*** 0,080****
Full copy 0,285*** 0,281**** 0,231**
4.3.2 Dynamic capabilities
H3: The higher the incumbent’s dynamic capabilities, the more likely, the quicker, and the more
complete it will respond to a business model innovation. A company’s market orientation, absorptive
capacity, and entrepreneurial character are key types of dynamic capabilities.
The effect of the incumbent’s dynamic capabilities is supported by our results. However, only
entrepreneurial behavior has signi?cant positive effect on an incumbent’s probability to respond;
even bigger and more signi?cant in case of full copy. We also note that absorptive capacity was not
included in the ?rst analyses because of high correlation with complementary assets. If we include
absorptive capacity (and thus exclude complementary assets), we see however also no signi?cant
effects of absorptive capacity.
Results show that the impact of Absorptive Capacity and Entrepreneurial Behavior is positive and
highly signi?cantly supporting that higher dynamic capabilities lead to a quicker response of an
incumbent. The effect is even bigger in case of full copy. We also see that Market Orientation slows
down the incumbent’s response, supporting our remark of the danger of a too narrowly de?ned
market orientation of incumbents (See section 2.2.2).
In terms of completeness of the incumbent’s response, we notice a signi?cant, positive impact
of Entrepreneurial Behavior (B = 0,126**). This supports H3, because a positive impact in our
completeness test means that an incumbent is more inclined to adopt a full offering of online market
research.
The results indicate the importance for incumbents to possess dynamic capabilities to deal with
the changing environment created by a new business model in their industry. Entrepreneurship in
particular seems to enable incumbents to respond swiftly.
4.3.3 Structural inertia
H4: The higher the structural inertia of an incumbent, the lower its probability of copying a new
business model, the later and the less complete it does so.
Results show that the higher the structural inertia of an incumbent, the lower its probability of copying
a new business model is. Signi?cance is however only found in the case of a basic copy.
l 40
Our tests present a very strongly signi?cant negative effect of structural inertia towards the incumbent’s
response timing. This means that the bigger an incumbent’s structural inertia, the later it will respond
towards a business model innovation.
Our completeness analysis does not give a signi?cant result for structural inertia, but the indication
is that higher structural inertia drives incumbents towards lower response completeness. Another
indication for this lower completeness when having more structural inertia is that the negative impact
of structural inertia is bigger in the case of a full copy than for a basic copy.
We thus ?nd evidence that inertia hampers an incumbent’s response to new business models.
4.3.4 New business model attractiveness
H5: A new business model’s attractiveness has a positive in?uence on the incumbent’s copy behavior
in terms of probability, timing and completeness.
Our results show that the online market research’s attractiveness has a signi?cant positive impact on
an incumbent’s response behavior. The higher the attractiveness of a business model innovation, the
higher the probability that an incumbent will copy the new business model.
In terms of timing we don’t ?nd support for H5 in our data. There is a very signi?cant negative, but
again very small in?uence of the attractiveness of online market research on response timing of the
incumbent.
In our completeness test there is no signi?cant effect of market attractiveness at all.
4.3.5 Degree of threat
H6: The greater the threat of a new business model towards the incumbent’s core competence(s),
the less likely an incumbent will copy the new business model but the earlier it will do so.
Our test results don’t support H6, but the opposite.
The greater the threat of a business model innovation, the more likely an incumbent will copy the
online offering. This is especially true for a basic online market research offering. The threat of a new
business model also signi?cantly postpones a reaction of incumbents to copy the business model
innovation. These results contradict earlier ?ndings on the response of incumbents to innovation in
their industry (Mitchell, 1989).
A potential explanation for our ?ndings is that business model innovations represent a different type
of innovation than a technological innovation for incumbents to tackle. Our ?ndings demonstrate that
incumbents who are highly threatened by a business model innovation do respond to it, but respond
late. This result, combined with the non signi?cant effect of the attractiveness of the market, allow us
to speculate about the process that underlies these ?ndings. It appears that incumbents, in an effort
to minimize the effect of the new business model, act as late as possible. In fact, incumbents can, by
entering a new business model, provide it with additional legitimacy and accelerate its cannibalization
effect. Given this, it makes sense for incumbents to delay entry.
l 41
4.3.6 Competitive behavior
H7: Incumbents are signi?cantly in?uenced by incumbent market leaders in responding towards a
new business model.
H8: Incumbents are not signi?cantly in?uenced by newcomers in responding towards a new business
model.
We see in Table 7 till 10 a very big (minimum B = 1,013**) and signi?cantly positive effect of incumbent’s
copy probability being in?uenced by market leaders. The more market leaders copy (basic or full)
online market research, the more likely and more complete an incumbent will also copy online market
research, but the later it will do so.
If we test for the in?uence of copy behavior of newcomers we leave out Attractiveness because
of high correlations, but we can still apply Competitive Behavior, because of no high correlations
between market leaders’ and newcomers response behavior. From our results we see that there
is no effect of newcomers’ response behavior on the copy completeness of incumbents towards
online market research. On probability and timing of the incumbent’s response, there are very small
but highly signi?cant results. The more newcomers copy the online market research business model,
the more likely an incumbent will also offer online market research (B = 0,063**** for basic copy and
B = 0,031* for full copy). The more newcomers however copy the online market research business
model, the later incumbents will react (B = -0,032**** for basic copy and B = -0,046 for full copy).
H9: Incumbents experience a bigger imitation behavior effect among incumbents that are similar in
size and/or resources.
H10: Incumbents experience a bigger imitation behavior effect among incumbents that have similar
complementary assets.
Our results for probability tests show that there is a very small but signi?cantly positive imitation
behavior based on similarity in size (B = 0,017** for small agencies and B = 0,024** for larger
agencies) and complementary assets (B = 0,021*** for low complementary assets and B = 0,022**
for high complementary assets). This signi?cant effect occurs only in the advent of a basic copy
of online market research and is much smaller than the imitation effect based on market leaders’
behavior (cf. minimum B = 1,013).
In terms of timing we see a very small but signi?cantly negative imitation behavior effect for
incumbents similar in complementary assets especially in the advent of a full copy (B = -0,012** for
low complementary assets and B = -0,013** for high complementary assets). This effect is very weak
in comparison with the highly negative imitation effect based on market leaders’ copy response (cf
minimum B = -0,415**).
H9 and H10 are thus not supported by our dataset.
4.3.7 Embeddedness
H11: Embeddedness has signi?cant effects as moderating variable and no or less signi?cant effects
as a direct independent variable.
l 42
Table 7 and Table 10 indicate that there are signi?cant effects of Embeddedness as a moderating
independent variable especially for the timing of incumbents to respond towards online market
research. The biggest signi?cant effect (positive) is of Structural Inertia x Embeddedness meaning that
embeddedness decreases the negative effect of structural inertia. When testing for embeddedness
as a direct independent variable for probability, timing, and completeness of an incumbent’s reaction
towards online market research, we ?nd not any signi?cant effect.
The moderated effect of Threat on the probability of responding is very large for a basic copy (B
= 0,617**) when we include Embeddedness as a moderating independent variable. This means
that the more embedded or the older a market research agency is, the less a threat towards the
existing market offering triggers the agency to respond. The moderating effect however is not found
signi?cant for a full copy of online market research.
l 43
5 DISCUSSION AND MANAGERIAL IMPLICATIONS
5.1 Two golden questions
There are three basic strategic options for incumbents when confronted with an upcoming new
business model: no reaction, basic copy of the new business model, or full copy (cf. complete
imitation). The type of reaction and the appropriate timing is driven by two strategic questions: “Am
I able to do it?” and “Why should I do it?”
Companies should ask themselves those two questions before deciding if, how, and when to react.
5.2 The ability to respond
If incumbents want to be able to increase their responsiveness towards heavily changing environments
and deal effectively with new business models, they should monitor and manage their complementary
assets, dynamic capabilities, and structural inertia.
Incumbents should assess their complementary assets and manage them. Results indicate that
the possession of complementary assets helps incumbents to deal with new business models in
their industry. Incumbents that have a wide range of complementary assets not only have a higher
likelihood to respond to a new business model, they also do it earlier than others. However, it is not
always needed to have a high number of complementary assets. The degree of complementarity
of an incumbent’s assets towards a new business model has even more importance in terms of
response likelihood and completeness. Incumbents that have assets highly complementary with the
business model innovation respond more quickly than others in a complete way.
Important to note is that we could speculate (further research here is needed) that the degree of
complementarity and the number of complementary assets is key in the incumbent’s management
of complementary assets. Number and complementarity of assets could thus be seen as two axes
of a decision matrix used in the management of complementarity assets.
Results also show the importance of building up dynamic capabilities (especially entrepreneurial
behavior) and avoiding structural inertia to respond timely to changing business models. This pleads
for ?at and entrepreneurial organizations.
5.3 The motivation to respond
The motivation of incumbents to react is especially motivated by competitive behavior. However,
results also seem to let us speculate that an interaction between threat and market attractiveness
could be a defendable argument for incumbents to delay action. This interaction could be e.g. that
a copy creates increased legitimacy which could decrease threat, and that in turn could increase
attractiveness. This creates again increased copy, etc.
Organization imitation behavior is not new and certainly not gone. Results demonstrate that
incumbents are heavily watching incumbents with market leadership rather than incumbents similar
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in size and/or complementary assets. Newcomers are not ignored by incumbents, but don’t have
much motivation in?uence either.
Results however indicate that incumbents have, next to pointing the ?nger towards competitors,
other possibilities to motivate certain response types. These possibilities include indications of the
attractiveness and threat of a new business model. Apart from the monitoring of competitors, it is
therefore important for incumbents to investigate the attractiveness of a new business model and
the threat it poses.
5.4 Further research
Although we built up a unique dataset covering 17 years of industry knowledge and response
behavior, we acknowledge that there is still work to do in terms of cross-country analysis and in
terms of coupling performance-related data with the existing dataset. Especially the relationship of
response behavior with performance-related outcomes is highly interesting to further investigate.
Also further investigation and re?nement of the empirical model over more industries and countries
is needed to untangle the speci?c nature of the drivers behind strategic response behavior in the
advent of business model innovations. There is already clear indication (See section 4.3.5) that
different effects can be expected in the advent of technological innovation versus business model
innovation.
We also remark that entrepreneurial behavior appears to be the most robust type of dynamic
capabilities we de?ned in our conceptual framework. Further research and thorough conceptualization
and measurement of other types of dynamic capabilities like market orientation and absorptive
capacity is certainly needed. This research is not only to better understand the concept of dynamic
capabilities and the different types, but also to be able to measure and steer these types of dynamic
capabilities.
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6 ACKNOWLEDGEMENTS
We would like to express our sincerest thankfulness towards Flanders District of Creativity for the
?nancial support to make this research study possible.
Also ESOMAR helped us a lot in building up our longitudinal dataset covering 17 years of unique
industry data, and in understanding the market research industry by providing us with their market
and price studies.
A last word of gratitude towards Ludovic Depoortere (ROGIL ?eld research), Tim Rofessart and Stijn
De Rammelaere (PROFACTS), and Niels Schillewaert and Kristof Dewulf (INSITES Consulting) for
their qualitative insights and feedback.
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