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Cloud computing adoption: an empirical study of customer preferences among start-up companies

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Abstract

Cloud computing represents a paradigm shift to utmost scalable and flexible IT services. However, research related to preferences of certain customers concerning cloud services is scarce. Especially start-up companies with their limited capacities to implement and operate IT infrastructure and their great demand for scalable and affordable IT resources are predestined as customers of cloud based services. In this study, we apply a multi-method approach to investigate customer preferences among start-up companies. Based on a literature review and a market analysis of cloud service models, we propose a set of cloud provider characteristics. These properties were examined among 108 start-up companies and analyzed in three steps using factor analysis to define customer preferences, cluster analysis to identify customer segments and discriminant analysis to validate the identified clusters. The results show that start-ups can be basically divided in five clusters each with certain requirements on cloud provider characteristics.

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Author information

Authors and Affiliations

  1. Technische Universität Berlin, Straße des 17.Juni 135, Sekr. H93, 10623, Berlin, Germany

    Jonas Repschlaeger, Koray Erek & Ruediger Zarnekow

Authors
  1. Jonas Repschlaeger
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  2. Koray Erek
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  3. Ruediger Zarnekow
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Corresponding author

Correspondence to Jonas Repschlaeger.

Additional information

Responsible Editor: Ricardo Colomo-Palacios

Appendices

Appendix A—survey design

Table 4

Table 4
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Appendix B—survey results

Appendix B1

Table 5 Final factor analysis: correlation matrix of the final set of provider properties (item codes)
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Appendix B2

Table 6 Final factor analysis: KMO and Bartlett’s test
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Appendix B3

Table 7 Final factor analysis: extraction of factors and total variance explanation
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Appendix B4

Table 8 Final factor analysis: communalities of initial and extracted factor solution
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Appendix B5

Table 9 Final factor analysis: rotated component matrix
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Appendix B6

Table 10 Reliability analysis: internal consistency of the factor scores
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Appendix B7

Table 11 Increase of the heterogeneity coefficient of the Ward algorithm
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Appendix B8

Table 12 Cluster analysis: agglomeration schedule of the Ward Algorithm
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Appendix B9

Fig. 4
figure a

Cluster analysis: dendrogram of the Ward algorithm

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Appendix B10

Table 13 Cluster analysis: mean values of the 5-cluster solution using the Ward algorithm
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Appendix B11

Table 14 Cluster analysis: mean values of the 5-cluster solution using k-means algorithm
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Appendix B12

Table 15 Cluster analysis: distances between final cluster centers using k-means algorithm
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Appendix B13

Fig. 5
figure b

Customer segment characteristics: cloud computing relevance

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Appendix B14

Fig. 6
figure c

Customer segment characteristics: outsourcing degree

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Appendix B15

Fig. 7
figure d

Customer segment characteristics: cloud computing adoption degree

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Appendix B16

Table 16 Discriminant analysis: classification results
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Appendix B17

Table 17 Discriminant analysis: Eigenvalues
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Appendix B18

Table 18 Discriminant analysis: Wilk’s Lambda
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Appendix B19

Fig. 8
figure e

Analysis of variance: mean values of the 12 factors among the five cluster solutions

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Appendix B20

Table 19 Analysis of variance of the 12 factors (ANOVA)
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Appendix B21

Fig. 9
figure f

Analysis of variance: mean factor scores for factors 1–4

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Appendix B22

Fig. 10
figure g

Analysis of variance: mean factor scores for factors 5–8

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Appendix B23

Fig. 11
figure h

Analysis of variance: mean factor scores for factors 9–12

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Repschlaeger, J., Erek, K. & Zarnekow, R. Cloud computing adoption: an empirical study of customer preferences among start-up companies. Electron Markets 23, 115–148 (2013). https://doi.org/10.1007/s12525-012-0119-x

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