Cluster analysis of finnish car retail and service business operations strategy and innovation...

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