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The transfer and commercialisation of nanotechnology: a comparative analysis of university and company researchers

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Abstract

Nanotechnology has been proposed as the next general purpose technology and engine for growth for the 21th century. Increasing public R&D investments are foremost reflected in the growth of scientific publications, while nanotechnology still is in an uncertain phase of development with various directions of commercialization pending. This paper focuses on the challenges, modes and outcomes of nanotechnology as an emerging science-based field in Finland. The paper contributes by interrogating how challenges and modes of nanotechnology transfer differ across universities and companies and determine outcomes broadly defined. It uses survey data covering university and company researchers in the Finnish nanotechnology community. The results show significant differences in the perceptions of researchers across these organisations, and highlight specific challenges and modes as determinants of outcomes. The specificities of nanotechnology are also assessed.

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Notes

  1. This assessment is set against the benchmark of the 46% respondents which reported less involvement in nanotechnology even though the keyword search algorithm define them as part of the nanotechnology community. The educational background of these respondents is similar although there is a relatively larger share with a background in biosciences and biology (see Palmberg et al. 2007).

  2. The marginal effects are difficult to interpret strictly in a logistic regression. However, the sign changes across the four individual outcome values of the dependent variable give indication of the robustness of the estimations.

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Acknowledgements

I wish to thank the Finnish Funding Agency for Technology and Innovation (Tekes) and the Technology Industries of Finland Centennial Foundation for funding. This paper relates to the ongoing project “Nanotechnology and the renewal of Finnish industries (NANOREF)”. I also wish to thank Mika Pajarinen for help with the data, Tuomo Nikulainen, Petri Rouvinen, Cees van Der Beers, Daniel Ljungberg and two anonymous peer reviewers for valuable comments. All the usual disclaimers apply.

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Authors and Affiliations

  1. Etlatieto Ltd/ETLA, the Research Institute of the Finnish Economy, Lönnrotsgatan 4 B, Helsingfors, 00120, Finland

    Christopher Palmberg

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  1. Christopher Palmberg
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Correspondence to Christopher Palmberg.

Appendices

Appendix 1

Factor analysis

Factor

Variance

Difference

Proportion

Cumulative

Variable

Factor 1

Factor 2

Uniqueness

Outcomes of technology transfer: university researchers Factor analysis, orthogonal varimax rotation (Obs. = 306)a

Factor 1

1.14535

.31447

.7139

.7139

Identification of new research quetsions

.6424

.1818

.5543

Factor 2

.83089

 

.5179

1.2319

Identification of commercial opportunities

.6949

.3968

.3596

     

Patenting of research results

.4339

.5942

.4587

     

Licensing of research results

.248

.5361

.6512

Outcomes of technology transfer: company researchers Factor analysis, orthogonal varimax rotation (Obs. = 84)b

Factor 1

1.18885

.06003

.6369

.6369

Identification of new product ideas

.5604

.4074

.52

Factor 2

1.12882

 

.6047

1.2416

Patenting of research results

.3262

.6647

.4518

     

Licensing of research results

.197

.6385

.5535

     

Development of existing products/processes

.5174

.1742

.7019

     

Development of new products/processes

.6796

.2882

.4551

  1. aLR test: χ2(6) = 363.80, p = .000
  2. bLR test: χ2(10) = 114.19, p = .000

Appendix 2

Full logistic regressions for O_IDEA

 

(1) Coef.

(2) Coef.

(3) Coef.

(4) Coef.

(5) Coef.

(6) Coef.

Dependent variable O_IDEA

C_PASS

−.424**

−.554***

−.253

−.239

−.256

−.336

C_BASIC

−.599***

−.558***

−.195

−.201*

−.223*

−.206

C_COMOPP

.314**

.280*

.251

.254

.314*

.335*

C_COMM

.074

.109

−.053

−.040

−.030

−.005

C_IPR

.308**

.377**

.363**

.368**

.391**

.428**

C_NOMARK

−.108

−.086

−.018

−.045

−.061

−.106

C_NOPROD

.313**

.273*

.171

.158

.153

.117

BASIC*COMP

 

−.078

−.409

−.430

−.341

−.351

COMOPP*COMP

 

.328

−.048

−.059

−.212

−.174

IPR*COMP

 

−.460

−.544

−.518

−.510

−.564

NOMARK*COMP

 

−.232

−.143

−.157

−.137

−.140

NOPROD*COMP

 

.022

−.012

−.060

.031

.116

COMP

 

1.628

2.596*

2.733*

2.544*

2.583

M_CONF

  

.680***

.660***

.630***

.626***

M_RDINDIR

  

.207

.206

.198

.171

M_RDDIR

  

.994***

.974***

.960***

.960***

M_PUBPROG

  

.386***

.347**

.374***

.354**

M_JOINEMP

  

−.277

−.255

−.219

−.242

APPLI

   

.162

.155

.114

NNI

   

.034

.010

−.052

E_MULT

    

.678

.347

E_PHYSIC

    

−.007

−.513

E_CHEM

    

.117

−.154

E_BIO

    

−.212

−.229

E_ENG

    

−.296

−.897*

AGE_DEG

    

.001

−.001

ELECTRONICS

     

.621*

ENGINEERING

     

.613*

FOODSTUFFS

     

.128

PULP & PAPER

     

.499

PHARMA

     

.140

CHEMICALS

     

−.042

N

331

331

331

331

331

331

χ2

57.547***

61.024***

141.534***

144.378***

149.174***

167.224***

Pseudo R 2

.073

.087

.261

.264

.270

.281

Log pseudolikelihood

−399.548

−393.720

−318.546

−317.193

−314.964

−310.127

Block Wald tests (χ2 stat.)

 

10.907*

93.328***

2.759

4.027

9.852

  1. Statistical significance: * p < .1, ** p < .05, *** p < .01

Appendix 3

Full logistic regression for O_PATLIC

 

(1) Coef.

(2) Coef.

(3) Coef.

(4) Coef.

(5) Coef.

(6) Coef.

Dependent variable O _ PATLIC

C_PASS

−.238

−.386**

−.152

−.154

−.202

−.175

C_BASIC

−.577***

−.567***

−.265**

−.269**

−.225

−.262*

C_COMOPP

.317**

.413**

.442**

.469***

.614***

.625***

C_COMM

.152

.202

.148

.155

.091

.101

C_IPR

.299**

.380**

.342**

.328**

.242

.261

C_NOMARK

.002

−.136

−.069

−.097

−.240

−.217

C_NOPROD

.066

−.016

−.214

−.226

−.122

−.126

BASIC_COMP

 

.092

−.146

−.150

−.162

−.113

COMOPP_COMP

 

−.277

−.690*

−.708*

−.884**

−1.013***

IPR_COMP

 

−.423

−.389

−.350

−.313

−.279

NOMARK_COMP

 

.532

.658*

.632*

.722*

.709*

NOPROD_COMP

 

.294

.456

.430

.297

.390

COMP

 

−.060

.052

.127

.680

.586

M_CONF

  

.119

.095

.031

.024

M_RDINDIR

  

.265*

.268*

.205

.219

M_RDDIR

  

.634***

.616***

.657***

.705***

M_PUBPROG

  

.319**

.281*

.415***

.424***

M_JOINEMP

  

.008

.031

.079

.075

APPLI

   

.188

.375**

.344**

NNI

   

.011

.078

.077

E_MULT

    

−1.061*

−1.080*

E_PHYSIC

    

−2.070***

−2.096***

E_CHEM

    

−1.391***

−1.484***

E_BIO

    

−1.705***

−1.705***

E_ENG

    

−2.506***

−2.444***

AGE_DEG

    

.012

.016

ELECTRONICS

     

.315

ENGINEERING

     

−.410

FOODSTUFFS

     

−.090

PULP&PAPER

     

−.082

PHARMA

     

.026

CHEMICALS

     

.237

N

331

331

331

331

331

331

χ2

48.182***

54.212***

116.594***

119.938***

145.619***

148.896***

Pseudo R 2

.063

.075

.163

.166

.221

.225

Log pseudolikelihood

−343.610

−339.323

−307.016

−305.766

−285.781

−284.245

Block Wald tests (χ2 stat.)

 

10.277

62.221***

1.914

40.110***

3.754

  1. Statistical significance: * p < .1, ** p < .05, *** p < .01

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Palmberg, C. The transfer and commercialisation of nanotechnology: a comparative analysis of university and company researchers. J Technol Transfer 33, 631–652 (2008). https://doi.org/10.1007/s10961-007-9059-6

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