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

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

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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%.

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

New Car Registration

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Car Taxes in Finland

http://www.aut.fi/en/statistics/taxation_and_car_prices/price_formation_of_new_passenger_car

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)

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Innovation management

•  Knowledge creation fuels innovation (Takeuchi, 2013)

•  Tidd and Bessant (2009) introduce four types of innovation: process, product/service, positioning and paradigm innovations.

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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

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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%

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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.

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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

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.

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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.

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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.

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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

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Further research areas

•  Does strategic and innovative fit indicate smart social media use in a company?

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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

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