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Modelling Internet Diffusion Across Tourism Sectors

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Applied Economics in the Digital Era

Abstract

This study investigates the Bass Diffusion model. Bass’ parameters of innovation (p) and imitation (q) help explain adoption, and the ratio of these parameters sheds insights on critical mass. This study compares the parameters p and q across 13 internet diffusion datasets in five tourism sectors across international, European and five national datasets. Information and communication technologies (ICTs) play an increasing role with tourists and tourism organisations. The data contain destination management organisations (Switzerland, Austria and Germany), tour operators (European and Swiss), accommodation providers (international chain hotels, Malaysian hotels, Swiss affiliated hotels and Swiss guest houses) and Swiss cable cars. This study also uses the Gamma/Shifted Gompertz model to incorporate heterogeneous adoption. Across the same datasets, tourism organisations showed heterogeneous adoption tendencies and the influence of critical mass. This exploratory research illustrates the usefulness of Bass’ parameters both as a foundation and to measure critical mass.

This chapter draws on a pedagogical explanation of Bass modelling of Swiss accommodation in (Scaglione, forthcoming), and a first draft of this paper presented at the Annual Conference of International Association of Scientific Experts in Tourism—AIEST 2012 held at Khon Kaen (Thailand).

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Author information

Authors and Affiliations

  1. Institute of Tourism, University of Applied Sciences and Arts Western Switzerland Valais, Sierre, Switzerland

    Miriam Scaglione

  2. University of Eastern Finland, Joensuu, Finland

    Jamie Murphy

Authors
  1. Miriam Scaglione
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  2. Jamie Murphy
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Corresponding author

Correspondence to Miriam Scaglione .

Editor information

Editors and Affiliations

  1. College of Engineering and Applied Science, University of Colorado Boulder, Boulder, CO, USA

    James Alleman

  2. Temple University, Wyncote, PA, USA

    Paul N. Rappoport

  3. Orange Business Services, Saint Denis, France

    Mohsen Hamoudia

Appendix

Appendix

Cumulative series, Observed, estimated (one-step ahead) and forecasted for the last 24 months using Bass model for all sectors under study.

See Figs. 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 6.10, 6.11, 6.12, 6.13, 6.14, 6.15, and 6.16.

Fig. 6.4
figure 4

(Source Authors’ computation)

Swiss Destination Managements Organizations (DMOs)

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Fig. 6.5
figure 5

(Source Authors’ computation)

Austrian DMOs

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Fig. 6.6
figure 6

(Source Authors’ computation)

German DMOs

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Fig. 6.7
figure 7

(Source Authors’ computation)

Swiss restaurants

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Fig. 6.8
figure 8

(Source Authors’ computation)

Swiss cable cars companies

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Fig. 6.9
figure 9

(Source Authors’ computation)

International hotel chains

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Fig. 6.10
figure 10

(Source Authors’ computation)

European tour operators (TOs)

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Fig. 6.11
figure 11

(Source Authors’ computation)

Malaysian hotels

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Fig. 6.12
figure 12

(Source Authors’ computation)

Swiss non-affiliated hotels

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Fig. 6.13
figure 13

(Source Authors’ computation)

Swiss affiliated hotels

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Fig. 6.14
figure 14

(Source Authors’ computation)

Swiss hotels

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Fig. 6.15
figure 15

(Source Authors’ computation)

Swiss gasthouses

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Fig. 6.16
figure 16

(Source Authors’ computation)

Swiss travel agencies

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Residual and forecasting errors statistics.

See Table 6.3.

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Scaglione, M., Murphy, J. (2020). Modelling Internet Diffusion Across Tourism Sectors. In: Alleman, J., Rappoport, P., Hamoudia, M. (eds) Applied Economics in the Digital Era. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-40601-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-40601-1_6

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