Cluster analysis of Finnish car retail and service business operations strategy and innovation management capabilities. EurOMA 2016 presentation.

1. Cluster analysis of Finnish car
retail and service business
operations strategy and
innovation management
capabilities
Olli Rouvari, Pasi L. Porkka, Heli Aramo-Immonen*
heli.aramo-immonen@tut.fi
Tampere University of Technology, Pori Unit
Mikko Huhtala
Autoalan Keskusliitto ry, Finnish Central Organization for Motor Trades and Repairs

2. RQ
• This research was conducted in order to
explore the
• strategic management of operations and
• innovation capability in the Finnish car
retail and service business
• The primary goal of the data analysis was to
find out whether there existed clusters among
the respondents, which could help separate
organizations with a good level of strategic
management from those with a lower level
19/06/16 2

3. Research area
• Access to managers was facilitated via the Finnish
Central Organization for Motor Trades and Repairs and
covered all member companies (147 companies).
• This study gave a good overview of this industry in
Finland.
• Of these companies,
– 70 % had a turnover of between 5-50 M Eur and
– 27% had a turnover of more than 50 M Eur.
• We obtained responses from 37 company managers at a
response rate of 25.2%.
19/06/16 3

4. 19/06/16 4http://www.aut.fi/en/statistics/automobile_sector_in_finland/
employed_persons_by_automobile_sector
New Car Registration

5. 19/06/16 5
Car Taxes in Finland
http://www.aut.fi/en/statistics/taxation_and_car_prices/
price_formation_of_new_passenger_car

6. Strategic management
• Competitive strategy (Porter, 1985)
• Resource-based view (RBV) (Penrose, 1959;
Barney, 1991; Conner, 1991)
• Knowledge-based view (KBV) (e.g. Kaplan
and Norton, 1992; Teece, 2002; Sveiby,
2001; Kong, 2008)
• Operative strategy analysis SWOT (Weirich,
1982)
19/06/16 6

7. Innovation management
• Knowledge creation fuels innovation
(Takeuchi, 2013)
• Tidd and Bessant (2009) introduce four types
of innovation: process, product/service,
positioning and paradigm innovations.
19/06/16 7

8. Methodology
• Survey questionnaire of 110 questions
• Conducted on the car retail and service
business in Finland
• Among 147 CEOs and top managers.
• Obtained responses from 37 company
managers
• Response rate of 25.2%
• Statistical analysis methods
19/06/16 8

9. Methods
• Cluster analysis with all 24 variables revealed no
significant clustering among the data.
→ reduction of variables with factory analysis
• Exploratory factor analysis (EFA) was used for data
reduction
– The Kaiser-Meyr-Olkin (KMO) measure was 0.603.
– We used Oblimin rotation with Kaiser Normalization
– Scree test for deciding the number of factors
– Five factors, with total variance explained 71,12%
19/06/16 9

10. Methods
• Next we calculated values for each factor for each
respondent with rotated factor loadings greater than 0.5
• We employed these five factors as variables and
performed a cluster analysis.
19/06/16 10
Cluster
#
7
Clusters
6
Clusters
5
Clusters
4
Clusters
3
Clusters
1 1 1 1 1 1
2 1 1 1 1 1
3 1 1 1 4 35
4 3 3 3 31
5 2 2 31
6 10 29
7 19
N = 37 37 37 37 37

11. Result
• The values in the cluster with 19 respondents
were significantly higher in most statements
and included differentiating factors.
• Therefore, one can identify the factors that
the companies in the lower cluster should
improve.
• This distinction into two major clusters with
the use of 24 strategic statements also
applied to 40 innovation statements.
19/06/16 11

12. Result
• When the answers to the latter were
clustered accordingly, the differences
between the clusters were statistically
significant.
• This implies that there is a clear
connection or correlation between
strategic management and innovation
management in the companies involved.
19/06/16 12

13. 19/06/16 13
KMO and Bartlett's Test Structure Matrix
KMO Measure of Sampling Adequacy .603 Factor
Bartlett´s test Appr. Chi-Square 688.354 1 2 3 4 5
of Sphericity df .276 V3 .962
Sig. .000 V4 .794 .500
V2 .664 .538
V1 .605
Total Variance Explained V5 .596 .528
Factor
Initial Eigenvalues V15
Total
Var.
%
Cum.
%
V16 .986
1 8.118 33.823 33.823 V17 .641 .601
2 3.514 14.641 48.465 V19 .534
3 2.268 9.450 57.915 V22 .926
4 1.788 7.451 65.366 V20 .755
5 1.524 6.350 71.715 V21 .737
V23 .731 -.613
V18 .563 .510
Factor Correlation Matrix V24
Factor 1 2 3 4 5 V12 -.907
1 1.000 .027 .174 -.266 .279 V13 -.905
2 .027 1.000 .212 .030 .149 V14 .563 -.730
3 .174 .212 1.000 -.278 .147 V11 -.713
4 -.266 .030 -.278 1.000 -.186 V7 -.546
5 .279 .149 .147 -.186 1.000 V9 .723
Extraction Method: Maximum Likelihood V8 -.600 .700
Rotation: Oblimin with Kaiser Normalization V10 .648
V6
Extraction: Maximum Likelihood
Rotation: Oblimin with Kaiser Norm.

14. 19/06/16 14

15. Conclusions
• The strategy was not communicated to all
employees
• Attempts among managers to gain
commitment from employees were not
efficient
• Collaboration between companies would
allow joint resource allocation, which would
enable companies to focus on their core
competencies
19/06/16 15

16. Further research areas
• Does strategic and innovative fit indicate
smart social media use in a company?
19/06/16 16
http://www.slideshare.net/
jjussila/does-strategic-and-
innovative-fit-indicate-
smart-social-media-use-in-
a-company?
qid=5c401802-6083-4997-
bf2a-58ca015446c7&v=&b
=&from_search=5
IFKAD 2016

17. Contact!
Heli Aramo-Immonen *
heli.aramo-immonen@tut.fi
Tampere University of Technology
Pasi L. Porkka, Olli Rouvari
Tampere University of Technology, Pori Unit
Mikko Huhtala
Autoalan Keskusliitto ry, Finnish Central Organization for Motor Trades and Repairs