Transformation of human capital strategies in the tourism industry under the influence of Economy 4.0

  • Received February 28, 2021;
    Accepted May 18, 2021;
    Published May 26, 2021
  • Author(s)
  • DOI
  • Article Info
    Volume 19 2021, Issue #2, pp. 145-156
  • Cited by
    9 articles

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The digital transformation of society affects socio-economic relations in all spheres of life; however, the degree of this influence differs depending on countries and regions. Various industries and sectors of the economy are also affected by digitalization to one degree or another.
In this context, the purpose of the paper is to determine the dependence of human capital in the tourism industry on the digitalization level of the economy.
The following methods were used: data standardization, cluster analysis, analysis of variance, K means, and SWOT analysis.
The panel sample includes indicators from 61 countries for 2018. The analysis revealed distinctive features that allowed allocating the countries into clusters. Cluster 1 (14 cases): countries with average Human Capital Index (HCI) and World Digital Competitiveness (WDC) values, depending on tourism. Cluster 2 (13 cases): countries with slightly above average HCI and WDC values that are less dependent on tourism. Cluster 3 (15 cases): countries with HCI and WDC values below average, not particularly dependent on tourism. Cluster 4 (1 case): outliers. Cluster 5 (18 cases): countries with above average HCI and WDC that are tourism dependent. The calculation results made it possible to identify the cluster principles. The use of the identified distinctive features in the SWOT analysis allows formulating the key elements of human capital strategies in the tourism sector for each group of countries.

view full abstract hide full abstract
    • Figure 1. Distribution of countries by groups
    • Figure 2. Values of indicators in clusters
    • Figure 3. Mean and confidence intervals of variables in clusters
    • Table 1. Methods and data for the analyzed countries
    • Table 2. Analysis of variance
    • Table 3. Descriptive statistics for Cluster 1 (the cluster contains 14 cases)
    • Table 4. Descriptive statistics for Cluster 2 (the cluster contains 13 cases)
    • Table 5. Descriptive statistics for Cluster 3 (the cluster contains 15 cases)
    • Table 6. Descriptive statistics for Cluster 4 (the cluster contains 1 case)
    • Table 7. Descriptive statistics for Cluster 5 (the cluster contains 18 cases)
    • Table 8. SWOT analysis matrix of human resources and human capital strategies in tourism
    • Conceptualization
      Olena Stryzhak
    • Data curation
      Olena Stryzhak, Olena Akhmedova
    • Formal Analysis
      Olena Stryzhak
    • Investigation
      Olena Stryzhak, Olena Akhmedova, Nelli Leonenko, Inna Lopatchenko, Nataliia Hrabar
    • Methodology
      Olena Stryzhak
    • Project administration
      Olena Stryzhak, Olena Akhmedova
    • Software
      Olena Stryzhak
    • Supervision
      Olena Stryzhak
    • Visualization
      Olena Stryzhak
    • Writing – original draft
      Olena Stryzhak
    • Resources
      Olena Akhmedova, Nelli Leonenko, Inna Lopatchenko, Nataliia Hrabar
    • Validation
      Olena Akhmedova, Nelli Leonenko, Inna Lopatchenko, Nataliia Hrabar
    • Writing – review & editing
      Olena Akhmedova
    • Funding acquisition
      Nelli Leonenko, Inna Lopatchenko, Nataliia Hrabar