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

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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Correspondence to Jonas Repschlaeger.

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Responsible Editor: Ricardo Colomo-Palacios

Appendices

Appendix A—survey design

Table 4

Table 4

Appendix B—survey results

Appendix B1

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

Appendix B2

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

Appendix B3

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

Appendix B4

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

Appendix B5

Table 9 Final factor analysis: rotated component matrix

Appendix B6

Table 10 Reliability analysis: internal consistency of the factor scores

Appendix B7

Table 11 Increase of the heterogeneity coefficient of the Ward algorithm

Appendix B8

Table 12 Cluster analysis: agglomeration schedule of the Ward Algorithm

Appendix B9

Fig. 4
figurea

Cluster analysis: dendrogram of the Ward algorithm

Appendix B10

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

Appendix B11

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

Appendix B12

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

Appendix B13

Fig. 5
figureb

Customer segment characteristics: cloud computing relevance

Appendix B14

Fig. 6
figurec

Customer segment characteristics: outsourcing degree

Appendix B15

Fig. 7
figured

Customer segment characteristics: cloud computing adoption degree

Appendix B16

Table 16 Discriminant analysis: classification results

Appendix B17

Table 17 Discriminant analysis: Eigenvalues

Appendix B18

Table 18 Discriminant analysis: Wilk’s Lambda

Appendix B19

Fig. 8
figuree

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

Appendix B20

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

Appendix B21

Fig. 9
figuref

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

Appendix B22

Fig. 10
figureg

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

Appendix B23

Fig. 11
figureh

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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Keywords

  • Cloud computing
  • Cloud adoption
  • Customer preferences
  • Start-up companies
  • Customer segmentation
  • Provider properties

JEL

  • 033