DOI: 10.7341/20181412 JEL codes: O31, Q55, R10/

Received 12 May 2017; Revised 18 November 2017, 28 November 2017; Accepted 15 December 2017

Anna Golejewska, Ph.D., Economics of European Integration Department, University of Gdańsk, ul. Armii Krajowej 119/121, 81-824 Sopot, Poland, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it..

Abstract

The article examines the innovativeness of enterprises in 69 Polish NUTS3 sub-regions in 2014. The analysis is based on unpublished regional data from the Polish Central Statistical Office covering the following variables: the share of enterprises which have incurred outlays for innovative activities, the share of enterprises implementing process or product innovations, the share of companies collaborating in the field of innovation, and the share of new or modernized products in total production sold in industrial companies. The analysis focuses on building rankings and cluster analysis of NUTS3 regions. As research methods, the author uses selected methods of multidimensional comparative analysis, principal component analysis and the hierarchical Ward’s method. The results show that there are substantial differences among NUTS3 sub-regions as regards innovativeness of enterprises. The low level of cooperation does not foster innovation. Innovation outputs of enterprises are also unsatisfactory. The highest variation is seen in the share of new or modernized products in total production sold in industrial companies. The final effect of the cluster analysis is the division of regions into 7 groups. In the case of units where innovation inputs are not reflected in innovation outputs, it would be useful to explore regional and local factors influencing those relations. Further research is still needed.

Keywords: innovation, enterprises, regional differences.

INTRODUCTION

In the majority of the EU countries, there is a noticeable outflow of enterprises from innovative activity. The same applies in Poland, particularly to service enterprises. In well-developed economies with accumulated innovation potential this trend does not entail such enormous risks as in less innovative countries. Despite the declining percentage of innovative firms in Poland, one can observe an increase in expenditure on innovation, but it still remains below the EU average. The level of cooperation between Polish enterprises seems to be quite favorable in comparison to other countries; nevertheless, innovation cooperation remains unsatisfactory (Zadura-Lichota, 2015, pp. 5-63).

The activity of companies determines innovativeness at a national, regional and local level. Researchers on innovation issues often underline the importance of regions in the innovation process. There exists extensive evidence that knowledge and innovation are concentrated in selected regions, sub-regions or cities (Simmie, 2003; Nowakowska, 2009; Siłka, 2012; Golejewska, 2013; Golejewska, 2012). A region, through its specific assets including knowledge, learning ability, organizational culture, infrastructure, etc., has an impact on the competitiveness of local businesses and their innovative activity. Local competitive advantages result from a concentration of highly specialized knowledge, the presence of public institutions, competition, trade partners and consumers (Pinto, 2009).

The aim of the paper is to examine disproportions in innovativeness between enterprises in 69 Polish NUTS3 sub-regions in 2014. To achieve the main objective of the paper, the following detailed objectives are expected to be met: 1) presentation of the literature review; 2) empirical research on the innovativeness of Polish firms covering the creation of rankings and cluster analysis, and finally 3) conclusions.

LITERATURE REVIEW

Innovation has been and continues to be an important topic of study for a number of different disciplines, including economics, business, engineering, science, and sociology. The importance and role of knowledge assets in determining competitiveness, productivity, and finally output growth is a frequent theme in the spatial and non-spatial literature (Harris, 2008, p. 16).

Technology consists of three key elements: knowledge, skills and artefacts. Technological innovation involves “the process of applying knowledge and skills to combine an existing set of artifacts into a novel combination that fill a market demand and thereby create value” (Wolfe, 2011, p. 44). A firm is a central actor for the effectuation of innovation and technological change. Innovativeness determines the standards and directions of development of an enterprise, and thus its development and competitive advantage. Through a process of competition, firms with new products make firms with old products redundant and firms with more efficient modes of production eliminate less-efficient producers from the market. Differences in total factory productivity account for roughly half the differences in income across countries and are generally associated with differences in technological progress (Hall & Jones, 1999). Firms introducing new products and new methods of production and distribution directly enhance economic growth (Bosma, Schutjens & Stam, 2011, p. 483).

A region may be regarded as an “innovation incubator” which provides appropriate conditions for the setting up and the development of innovative companies, as well as pro-innovation behavior among other important entities in a territory. Recent literature calls into question whether innovations emerge from the single inventor or even in whole internally within a firm or organization (Amara, Landry & Lamari, 2003; Wolfe, 2009; Johnson, 2011). Knowledge-based transformations should not be understood as the results of the actions of firms alone, “but as a structural characteristic of knowledge-based economies” (Leydesdorff, 2001, p. 4) and “a social process that depends on interaction and learning” (Hall, 2010, p. 10). The literature indicates various “territorial innovation systems” (Lagendijk, 1997; Moulaert & Mehmood, 2010). Their typology often includes industrial districts (focused on the growth dynamics of small and medium-sized enterprises), innovative milieu, regional innovation systems, clusters and learning regions (Lagendijk, 1997; Porter, 2000; De Propris & Crevoisier, 2011). The last ones may be treated as a general synthesis of the above-mentioned concepts (Moulaert & Mehmood, 2010).

Innovation is a complex and multidimensional activity that cannot be measured directly or with a single indicator. Measuring innovation has been studied extensively by scholars and practitioners. In the literature, even “innovation economics” exists - a sub-discipline that analyses the relationship between investments in innovation and their financial outcomes. Innovation indicators are split into four groups-generations, from less to more complex. The first group, focusing on a linear model of innovation, includes such indicators as R&D investment, research personnel, university graduates, etc. The second group is extended by output indicators. The third generation is focused on a wider set of innovation indicators and indexes based on surveys and the integration of publicly available data. The fourth generation is currently at an embryonic stage and includes knowledge, networks, risk, clusters, management techniques, etc. (Gamal, 2011, p. 10).

In the literature, two broad streams of research on the measurement of innovation are noticeable. The first one concentrates on innovation inputs, such as R&D intensity, and outputs, such as patents. Nevertheless, these measures merely concern a small part of all the possible innovation activities. Due to empirical evidence, the linkage between such measures and organizational innovativeness and economic growth is vague. An appropriate example is a research conducted by Booz (2005) based on 1000 top global innovation spenders which confirmed there was no significant relationship between R&D spending and nearly all measures of business success. The value of patents as indicators of innovation, at a micro level, is rather limited (Gittleman, 2008). The second stream is focused on the macro level. In the EU, countries’ innovation capabilities are measured through objective economic measures, such as the Oslo Manual (2005), the European Community Innovation, and the European Innovation Scoreboard (EIS) (Gamal, 2011, p. 9). Regional innovation performance, measured by the Regional Innovation Scoreboard, should be based on the same indicators as EIS. Nevertheless, for many of them regional data are not available and are calculated using only 18 of the 27 EIS indicators. Some indicators relating to entrepreneurial activity belong among others: R&D expenditure in the business sector as a percentage of GDP; SMEs innovating in-house as a percentage of SMEs; innovative SMEs collaborating with others as a percentage of SMEs; EPO patent applications per billion of regional GDP; SMEs introducing product or process innovations as a percentage of SMEs; SMEs introducing marketing or organizational innovations as a percentage of SMEs, sales of new-to-market and new-to-firm innovations as a percentage of total turnover etc. (Hollanders & Es-Sadki, 2017). In the paper, the author followed the approach of the Regional Innovation Scoreboard, in which input-type and output-type measures have been used simultaneously.

There have been and continue to be substantial differences among Polish regions as regards innovativeness (Kowalik, 2014; Golejewska, 2013; Górecka & Muszyńska, 2011; Nowakowska, 2009; Siłka, 2012). According to the findings presented in the Regional Innovation Scoreboard (2017), 7 out of 16 Polish regions have been classified as moderate innovators and none as innovation leader or strong innovator. Research results show that high innovation inputs do not often correspond with high innovation outputs (Golejewska, 2013). Polish regions are also internally diversified as regards innovativeness (Brodzicki & Golejewska, 2017). Disproportions between the best performing regions and the rest of the country are a big challenge for regional innovation policy.

RESEARCH METHODS

The group of analyzed regions consists of 69 units (out of 72 units according to the territorial breakdown of 1 January 2015). The analysis is based on unpublished regional data of the Polish Central Statistical Office covering the following variables: the share of enterprises which have incurred outlays for innovative activities, the share of enterprises implementing process or product innovations, the share of companies collaborating in the field of innovation, and the share of new or modernized products in total production sold by industrial companies. The fifth available variable - internal expenditure on research and development – has been omitted from the analysis due to a significant lack of data. The data cover industrial enterprises employing more than 49 people and have been extracted from innovation statements in the industry (PNT-02). The analysis was conducted for 2014, the most recent year for which data were available up to this point. Due to a lack of data, three NUT3 regions have been omitted: Bialski (PL311), Ciechanowski (PL12B) and Nowotarski (PL 219).

The empirical analysis starts with the creation and comparison of innovativeness rankings on the basis of the method of ranks and method of standardized values. Some of the differences between the ranks have been explained by the results of the principal component analysis. Finally, a cluster analysis employing the hierarchical Ward’s method was conducted. The applied method is effective in building homogenous clusters with the lowest inter-group variance (Grabiński, 2003, p. 110).

RESULTS AND DISCUSSION

Descriptive statistics of the analyzed variables are presented in Table 1. The highest coefficient of variation (75.4) was recorded for the share of sold production of new or substantively improved (modernized) goods in the sold value of industry, the lowest (19.1) for the share of enterprises which implemented process or product innovations. The results of the analysis of mean values indicate a very low level of innovation cooperation and sold production of new goods. This might suggest that there is still a mutual distrust between companies in Poland and also that they do not derive significant benefits from cooperation and implemented innovations.

Table 1. Descriptive statistics

Variable

N

Mean

Median

Min.

Max.

Lower quartile

Upper quartile

Standard deviation

Coefficient of variation

X1

69

28.9

28.2

15.2

44.7

23.8

34.0

6.9

23.9

X2

69

14.1

13.2

5.1

29.1

9.9

17.6

5.5

38.7

X3

69

35.7

36.2

19.6

48.4

30.5

40.8

6.8

19.1

X4

69

10.1

8.0

0.7

44.3

4.2

12.9

7.6

75.4

Note: X1: share of enterprises which have incurred outlays for innovative activities in 2014 (input),

X2: share of enterprises involved in innovation cooperation in 2012-2014 (input),

X3: share of enterprises which implemented process or product innovations in 2012-2014 (output),

X4: share of sold production of new or substantively improved (modernized) goods introduced in 2012-2014 in sold value of industry in 2014 (output).

Source: own elaboration based on CSO data.

The highest share of enterprises which incurred outlays for innovative activities was recorded in PL213: city of Kraków – 44.7%, PL523: Nyski – 43.8%, PL514: city of Wrocław and PL127: city of Warszawa – 42.7%. The lowest, less than 20% - in PL116: Sieradzki, PL417: Leszczyński, PL634: Gdański, PL637: Chojnicki, PL312: Chełmsko-Zamojski, PL616: Grudziądzki and PL12D: Ostrołęcki. Enterprises implementing innovations most frequently cooperated in PL213: city of Kraków, PL326 Tarnobrzeski, PL343 Białostocki and PL514: city of Wrocław (in all sub-regions at least 25%). The lowest share of cooperating enterprises was recorded for PL616: Grudziądzki, PL417: Leszczyński, PL636: Słupski, PL116: Sieradzki, PL345 Suwalski and PL312: Chełmsko-Zamojski. In all cases, the share did not exceed 7.5%. Most of the leaders in the share of innovative enterprises were placed highly in the ranking based on implemented process or product innovations. The highest share of sold production of new or substantively improved (modernized) goods was recorded in Trójmiejski sub-region (44.3%), the city of Łódź (31.7%) and Ostrołęcki sub-region (25%). The difference between the best and the worst performing sub-region was, in this case, the highest in comparison to other variables. The lowest shares amounted to 0.7% in Siedlecki and 1.9% in Przemyski sub-region. Rankings by selected variables are presented in Table 2.

Table 2. Rankings by selected variables (method of ranks)

Rank

X1

X2

X3

X4

Rank

X1

X2

X3

X4

Rank

X1

X2

X3

X4

Rank

X1

X2

X3

X4

1

PL213

PL213

PL523

PL633

36

PL432

PL619

PL117

PL432

2

PL523

PL326

PL343

PL113

37

PL12C

PL332

PL224

PL332

3

PL514

PL343

PL127

PL12D

38

PL228

PL637

PL344

PL617

4

PL127

PL514

PL514

PL225

39

PL424

PL634

PL411

PL619

5

PL415

PL21A

PL415

PL638

40

PL218

PL117

PL516

PL116

6

PL325

PL127

PL213

PL129

41

PL618

PL115

PL619

PL22A

7

PL315

PL424

PL21A

PL518

42

PL331

PL623

PL426

PL637

8

PL21A

PL523

PL315

PL418

43

PL414

PL344

PL432

PL224

9

PL343

PL325

PL113

PL22B

44

PL517

PL411

PL115

PL623

10

PL314

PL613

PL229

PL218

45

PL431

PL324

PL617

PL424

11

PL22B

PL12A

PL217

PL517

46

PL411

PL12E

PL114

PL417

12

PL324

PL415

PL326

PL616

47

PL224

PL622

PL431

PL426

13

PL113

PL22C

PL517

PL613

48

PL427

PL428

PL218

PL636

14

PL326

PL524

PL314

PL325

49

PL117

PL515

PL622

PL12A

15

PL229

PL314

PL129

PL214

50

PL12E

PL217

PL515

PL523

16

PL22C

PL22A

PL325

PL514

51

PL217

PL638

PL128

PL415

17

PL524

PL114

PL22A

PL127

52

PL621

PL416

PL427

PL217

18

PL129

PL22B

PL418

PL326

53

PL515

PL617

PL331

PL621

19

PL225

PL516

PL22C

PL515

54

PL623

PL12D

PL618

PL431

20

PL22A

PL517

PL225

PL516

55

PL617

PL224

PL227

PL634

21

PL613

PL225

PL428

PL114

56

PL227

PL618

PL621

PL331

22

PL633

PL633

PL613

PL227

57

PL128

PL426

PL414

PL115

23

PL518

PL113

PL623

PL428

58

PL345

PL12C

PL634

PL344

24

PL418

PL418

PL518

PL343

59

PL115

PL431

PL416

PL312

25

PL428

PL229

PL323

PL22C

60

PL638

PL128

PL12D

PL427

26

PL332

PL432

PL22B

PL229

61

PL636

PL621

PL636

PL128

27

PL516

PL227

PL633

PL213

62

PL416

PL218

PL616

PL315

28

PL619

PL129

PL324

PL414

63

PL12D

PL315

PL345

PL12C

29

PL12A

PL331

PL524

PL524

64

PL616

PL312

PL638

PL622

30

PL114

PL228

PL214

PL416

65

PL312

PL345

PL228

PL117

31

PL344

PL427

PL332

PL314

66

PL637

PL116

PL312

PL618

32

PL214

PL214

PL12E

PL323

67

PL634

PL636

PL637

PL345

33

PL323

PL323

PL12A

PL411

68

PL417

PL417

PL417

PL324

34

PL426

PL518

PL424

PL21A

69

PL116

PL616

PL116

PL12E

35

PL622

PL414

PL12C

PL228

         

Source: own elaboration based on CSO data (2014).

Ranking considering all variables, based on the method of ranks is presented in Table 3. The leaders among cities are Wrocław, Warszawa and Kraków. The top ten also includes Łódź and sub-regions of Podkarpackie (PL325, PL326), Małopolskie (PL 21A), Podlaskie (PL343), Opolskie (PL523) and Śląskie (PL 22B). Among the ten least innovative sub-regions, three represent the region of Pomorskie. These results differ from the results obtained by the method of standardized values (Table 4). This is particularly the case for such NUTS 3 sub-regions as Trójmiejski (PL633), Rzeszowski (PL325), Bytomski (PL228), Płocki (PL12C), Skierniewicki (PL117), Łomżyński (PL344) and the City of Poznań (PL415). In those cases, the differences amount to at least six places. In the case of Trójmiejski sub-region, a significant impact on the difference has the highest value of the share of sold production. Low values of this variable in Rzeszowski and Bytomski sub-region result in their lower position in the second ranking. In other cases, the sub-regions were classified higher in ranking based on the method of standardized values. In this case, it results mainly from a high value of the share of enterprises which implemented process or product innovations in those regions.

Table 3. Rankings of NUTS3 sub-regions (method of ranks)

Rank

NUT 3 region

Rank

NUTS 3 region

Rank

NUTS 3 region

1

City of Wrocław

24

Legnicko-Głogowski

47

Koszaliński

2

Capital City Warszawa

25

Krakowski

48

Starogardzki

3

City of Kraków

26

Łódzki

49

Częstochowski

4

Białostocki

27

Szczeciński

50

Inowrocławski

5

Rzeszowski

28

Warszawski Zachodni

51

Skierniewicki

6

Tarnobrzeski

29

Krośnieński

52

Szczecinecko-Pyrzycki

7

City of Łódź

30

City of Szczecin

53

Płocki

8

Oświęcimski

31

Sandomiersko-Jędrzejowski

54

Olsztyński

9

Nyski

32

Puławski

55

Siedlecki

10

Sosnowiecki

33

Zielonogórski

56

Piotrkowski

11

Bielski

34

Włocławski

57

Kaliski

12

Bydgosko-Toruński

35

Przemyski

58

Gorzowski

13

Warszawski Wschodni

36

Nowosądecki

59

Grudziądzki

14

Lubelski

37

Rybnicki

60

Chojnicki

15

Trójmiejski

38

Pilski

61

Świecki

16

City of Poznań

39

Koniński

62

Gdański

17

Tyski

40

Tarnowski

63

Elbląski

18

Poznański

41

Ełcki

64

Radomski

19

Gliwicki

42

Bytomski

65

Słupski

20

Wrocławski

43

Łomżyński

66

Sieradzki

21

Wałbrzyski

44

Jeleniogórski

67

Leszczyński

22

Opolski

45

Ostrołęcki

68

Suwalski

23

Katowicki

46

Kielecki

69

Chełmsko-Zamojski

Source: own elaboration based on CSO data (2014).

Table 4. Rankings of NUTS3 sub-regions (method of standardized values)

Rank

NUT 3 region

Rank

NUTS 3 region

Rank

NUTS 3 region

Rank

NUT 3 region

Rank

NUTS 3 region

Rank

NUTS 3 region

1

City of Kraków

24

Warszawski Zachodni

47

Starogardzki

2

City of Wrocław

25

Legnicko-Głogowski

48

Częstochowski

3

Capital City Warszawa

26

City of Szczecin

49

Ostrołęcki

4

Białostocki

27

Łódzki

50

Bytomski

5

Nyski

28

Krakowski

51

Kielecki

6

Trójmiejski

29

Szczeciński

52

Siedlecki

7

City of Łódź

30

Puławski

53

Olsztyński

8

Oświęcimski

31

Krośnieński

54

Szczecinecko-Pyrzycki

9

Tarnobrzeski

32

Sandomiersko-Jędrzejowski

55

Inowrocławski

10

City of Poznań

33

Przemyski

56

Piotrkowski

11

Rzeszowski

34

Włocławski

57

Gorzowski

12

Bielski

35

Zielonogórski

58

Świecki

13

Bydgosko-Toruński

36

Łomżyński

59

Kaliski

14

Warszawski Wschodni

37

Ełcki

60

Elbląski

15

Sosnowiecki

38

Tarnowski

61

Radomski

16

Tyski

39

Nowosądecki

62

Gdański

17

Lubelski

40

Pilski

63

Grudziądzki

18

Poznański

41

Rybnicki

64

Chojnicki

19

Gliwicki

42

Koniński

65

Słupski

20

Katowicki

43

Koszaliński

66

Suwalski

21

Opolski

44

Jeleniogórski

67

Chełmsko-Zamojski

22

Wrocławski

45

Skierniewicki

68

Leszczyński

23

Wałbrzyski

46

Płocki

69

Sieradzki

Source: own elaboration based on CSO data (2014).

Some of the aforementioned differences between scores might be explained by the results of principal components analysis (Górniak, 1998; Leech, Barrett & Morgan, 2005). In the analysis, the first component is a composition of variables x1, x2 and x3 and the second represents variable x4. According to the scree plot, the first component explains the total variance of the analyzed variables at 66.44%, the second at 22.78%. Generally, the differences between rankings result from implemented methods. In ranking, each variable has in principle the same meaning but after standardization what is very important is the dispersal of observations, which is the highest for the fourth variable and thus has a greater impact on the final results of this method. From Figure 1 it is clear that Component 1 is the most significant for PL213, PL314, PL127, PL343 and PL523 and the least significant for PL116, PL417 and PL312. Component 2 remains the most significant for PL633, PL113, PL12D, PL638 and PL225 and the least significant for PL523, PL415 and PL315 (see Figure 1).

Figure 1. Results of principal component analysis

Source: own elaboration based on CSO data (2014).

The next step was the cluster analysis conducted using the hierarchical Ward’s method. As a result, 69 sub-regions have been divided into 7 groups (see Table 5). The most numerous group consists of 16 NUTS3 sub-regions, the least numerous of 6 sub-regions. The results are presented graphically on the map (Figure 2).

The differences among groups were analyzed using mean values of the standardized variables (see Figure 3). The first group consists of 8 sub-regions located – apart from the capital region- in Małopolskie, Wielkopolskie, Dolnośląskie, Opolskie and in two Eastern regions: Podkarpackie and Podlaskie. It is characterized by the highest mean values of analyzed variables, apart from the value of sold production of new or substantively improved (modernized) goods which remains average. The second group comprises eight sub-regions located in Pomorskie (3 sub-regions), Wielkopolskie (2 sub-regions), Łódzkie and in two Eastern regions: Lubelskie and Podlaskie. In contrast to the previous group, the sub-regions have the lowest values of variables apart from sold production which remains low.

Table 5. Results of cluster analysis, Ward’s method

Group 1

Group 2

Group 3

Group 4

Group 5

Group 6

Group 7

PL514

PL213

PL21A

PL127

PL523

PL326

PL343

PL415

PL312

PL116

PL345

PL634

PL636

PL416

PL417

PL637

PL619

PL315

PL432

PL323

PL324

PL344

PL332

PL426

PL12C

PL516

PL517

PL613

PL314

PL114

PL214

PL12A

PL524

PL325

PL229

PL22A

PL22B

PL22C

PL424

PL428

PL515

PL616

PL218

PL12D

PL638

PL227

PL414

PL431

PL115

PL117

PL217

PL128

PL228

PL224

PL331

PL621

PL623

PL622

PL411

PL427

PL617

PL618

PL12E

PL518

PL113

PL129

PL633

PL225

PL418

Source: own elaboration based on CSO data (2014).

The third group consists of 9 NUTS3 sub-regions and is characterized by a high share of innovative enterprises in which the level of cooperation and implemented innovation remain respectively low and average while sold production is rather low.

The next group is much more numerous and consists of 15 sub-regions. It is heterogynous and it is characterized by a high share of innovative enterprises, highly involved in cooperation, a high share of enterprises which implemented innovations and an average share of sold production of new goods. The fifth group comprises 7 sub-regions with low values of all the indicators apart from high production sold. The most numerous group 6 is characterized by low values of all indicators and the lowest mean value of production sold. The last group is the least numerous one. It consists of 6 sub-regions, all located in different regions. It is characterized by the highest mean value of the share of sold production of new or substantively improved (modernized) goods.

Definite leaders of the first, best performing group are Kraków and Wrocław. In the weakest group 2, the highest innovation indicators have enterprises located in Słupski and Kaliski sub-region. In the third group, the most innovative are enterprises in Krośnieński and Puławski sub-region, and in the fourth group – enterprises located in Rzeszowski, Sosnowiecki and Bydgosko-Toruński sub-region. The leaders of the next group are enterprises of Nowosądecki and Rybnicki sub-region. The highest innovation indicators in the sixth group were recorded in Ełcki, Tarnowski and Pilski sub-region and finally, in the last group in the enterprises of Trójmiejski sub-region.

C:\Users\A0157~1.GOL\AppData\Local\Temp\mapka.png

Figure 2. Results of cluster analysis, Ward’s method, groups

Source: own elaboration based on CSO data (2014).

Figure 3. Mean values of variables by groups of regions

Source: own elaboration based on CSO data (2014).

CONCLUSION

Results of the analysis show that there are substantial disproportions in innovativeness between industrial enterprises located in different Polish NUTS 3 sub-regions. The greatest differences are visible in the share of sold production of new goods in the sold value of industry and in the level of innovation cooperation which remains unsatisfactory. The results confirm that there might still be a mutual distrust between companies in Poland as regards innovation activity and also that they might not derive significant benefits from cooperation and implemented innovations. The ranking scores show some differences mainly due to the high dispersal of observations for the share of sold production of new products. The scores confirm dominance at the forefront of major urban centers. Among the cities, the leaders are Wrocław, Warszawa and Kraków. It is noteworthy that not many of the Eastern sub-regions performed badly. The lowest innovation indicators have enterprises in Suwalski and Chełmsko-Zamojski sub-region. It is also interesting to note that three out of the ten worst performing sub-regions are located in the region of Pomorskie. Their low position results mainly from low innovation cooperation and a low share of sold production of new goods.

The final effect of the cluster analysis is the division of regions into 7 groups, of which the first one is characterized by the highest innovativeness of industrial enterprises and the second one by the lowest. The group in which low inputs translate into low outputs is group 6. The groups with high input variables are group 4 and group 7. In the latter group they translate into the highest share of sold production of new products. In group 5 low inputs correspond with the second highest value of the mentioned output indicator. Finally, group 3 consists of units in which the mean values of input and output variables are mixed: low or average. The groups of sub-regions are not “homogenous geographically” which means that Polish NUTS 2 regions are internally diverse as regards innovativeness of industrial enterprises. The only exception is Warmińsko-Mazurskie. As benchmarking, it could be interesting to identify sub-regions with high innovation outputs corresponding with lower or proportionate innovation inputs. In the case of units where inputs are not reflected in outputs, it would be useful to explore regional and local factors influencing those relations. It shall be a question for further study.

References

Amara, N., Landry, R., & Lamari M. (2003). Social capital, innovation, territory and public policy. Canadian Journal of Regional Science, 26(1), 87-120.

Booz, A. H. (2005). Relationship between R&D spending and sales growth, earnings, or shareholder returns. Retrieved from http://www.boozallen.com

Bosma, N., Schutjens, V., & Stam, E. (2011). Regional entrepreneurship. In P. Cooke, B. Asheim, R. Boschma, R. Martin, D. Schwartz & F. Toedling (Eds.), Handbook of Regional Innovation and Growth (pp. 482-494). Cheltenham: Edward Elgar Publishing.

Brodzicki, T., & Golejewska, A. (2017). Regional variation of innovation activity in Poland. The positive role of location in metropolitan areas affirmed. Working Papers of Economics of European Integration Division, 1, 1-26.

De Propris, L., & Crevoisier, O. (2011). From regional anchors to anchoring. In P. Cooke, B. Asheim, R. Boschma, R. Martin, D. Schwartz & F. Toedling (Eds.), Handbook of Regional Innovation and Growth. (pp. 167-180). Cheltenham: Edward Elgar Publishing.

European Commission. (2017). Regional Innovation Scoreboard 2017. Retrieved from http://ec.europa.eu/growth/industry/innovation/facts-figures/regional_pl

Gamal, D. (2011). How to measure organization innovativeness? An overview of innovation measurement frameworks and innovation audit/management tools. Retrieved from http://tiec.gov.eg/backend/reports/measuringorganizationinnovativeness.pdf

Gittelman, M. (2008). Comment: The value of European Patents. European Management Review, 5, 85-89.

Golejewska, A. (2013). Input-output innovativeness of Polish regions. Social Research, 4(33), 87-97.

Golejewska, A. (2012). Innowacyjność a konkurencyjność regionalna krajów Grupy Wyszehradzkiej w latach 1999-2008. Prace Komisji Geografii Przemysłu PTG, 20, 93-115.

Górecka, D., & Muszyńska, J. (2011). Analiza przestrzenna innowacyjności polskich regionów. Acta Universitatis Lodziensis Folia Oeconomica, 253, 55-70.

Górniak, J. (1998). Analiza czynnikowa i analiza głównych składowych. Retrieved from https://kb.osu.edu/dspace/bitstream/handle/1811/69494/ASK_1998_83_102.pdf

Grabiński, T. (2003). Analiza Taksonomiczna Krajów Europy w Ujęciu Regionalnym. Kraków: Wydawnictwo Akademii Ekonomicznej w Krakowie.

Hall, R. E., & Jones, Ch. I. (1999). Why do some countries produce so much more output per worker than others?. Quarterly Journal of Economics, 114(1), 83-116.

Harris, R. (2008). Models of regional growth: Past, present and future. SERC Discussion Paper, 2.

Hollanders, H., & Es-Sadki, M. (2017). Regional Innovation Scoreboard. Internal Market, Industry, Entrepreneurship and SMEs. European Union: Belgium.

Kowalik, J. (2014). Regional innovativeness strategies and their impact on innovativeness of provinces in Poland. A spatio-temporal analysis. Comparative Economic Research, 17(4), 121-135.

Johnson, S. (2011). Where Good Ideas Come From. New York: Riverhead Trade.

Lagendijk, A. (1997). Will the New Regionalism Survive? Tracing Dominant Concepts in Economic Geography. UK: University of Newcastle-Upon-Tyne.

Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). SPSS for Intermediate Statistics: Use and Interpretation. Mahwah, New Jersey: Lawrence Erlbaum Associates Publishers.

Leydesdorff, L. (2001). Knowledge-based innovation systems and the model of a triple helix of university-industry-government relations. Conference on new economic windows: New paradigms for the new millennium, September 14, Sacerno, Italy.

De Mel, S., McKenzie, D., & Woodruff, Ch. (2009). Innovative firms or innovative owners? determinants of innovation in micro, small, and medium enterprises. IZA Discussion Papers, 3962, 1-32.

Moulaert, F., & Mehmood, A. (2010). Analysing regional development and policy: A structuralist realist approach. Regional Studies, 44(1), 103-118.

Nowakowska, A. (Ed.). (2009). Zdolności Innowacyjne Polskich Regionów. Łódź: Wydawnictwo Uniwersytetu Łódzkiego.

OECD. (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data (3rd ed.). Paris: OECD.

Pinto, H. (2009). The diversity of innovation in the European Union: Mapping latent dimensions and regional profiles. European Planning Studies, 17(2), 303–326.

Porter, M. E. (2001). Clusters of Innovation: Regional Foundations of US Competitiveness. Washington DC: Council on Competitiveness.

Siłka, P. (2012). Potencjał innowacyjny wybranych miast Polski a ich rozwój gospodarczy. Prace Geograficzne, 236, 1-261.

Simmie, J. (2003). Innovation and urban regions as national and international nodes for the transfer and sharing of knowledge. Regional Studies, 37(6–7), 607–620.

Strahl, D. (1998). Taksonomia Struktur w Badaniach Regionalnych. Wrocław: Wydawnictwo Akademii Ekonomicznej.

Wolfe, D.A. (2009). 21st century cities in Canada: The geography of innovation. Retrieved from: http://www.mun.ca/harriscentre/Misc/21stCenturyCitiesinCanada_2009_web.pdf

Wolfe, D. (2011). Neo-Shumpeterian perspectives on innovation and growth. In P. Cooke (Ed.), Regional Innovation and Growth. Cheltenham: Edward Elgar Publishing.

Zadura-Lichota, P. (Ed.). (2015). Innowacyjna Przedsiębiorczość w Polsce. Odkryty i Ukryty Potencjał Polskiej Innowacyjności. Warszawa: PARP.

Biographical note

Anna Golejewska, Ph.D., is a member of the staff of the Chair of Economics of European Integration at the Faculty of Economics of Gdańsk University. She lectures on economy and regional policy, including the system of implementation of the EU structural funds in Poland. She is a member of the Team of Experts assessing projects co-financed by the EU structural funds. She has authored publications devoted to competitiveness and innovativeness issues. She is a member of the Regional Studies Association.