Skip to main content
Log in

Performance evaluation of Indian states in tourism using an integrated PROMETHEE-GAIA approach

  • Application Article
  • Published:
OPSEARCH Aims and scope Submit manuscript

Abstract

Tourism development has a unique responsibility in regional policy of almost all countries. It is a well known fact that tourism has a remarkable contribution in sustainable development, economic growth and social benefits for a country, if planned methodically. This is due to its unquestionable advantages and benefits for the local community with respect to economic, social and environmental aspects. Since few decades, it has become a driving area in Indian economic planning strategy to deal with tourism issues for effective utilization of its wide range of destination resources and also optimize the intensity of financial involvement for developing tourist infrastructure in a restraint economic province. This paper applies a simple methodology to quantify the tourism potential of Indian states using an integrated visual decision aid model of preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). Several social and physical attributes are considered to evaluate and rank the Indian states with respect to their performance in tourism. The adoption of this integrated visual decision aid model identifies Jammu and Kashmir, and Jharkhand as the best and the worst performing states respectively. In GAIA plane, the position of Jammu and Kashmir is the farthest from origin, followed by Madhya Pradesh. A close comparison between these two top performing states reveals that Jammu and Kashmir mainly outperforms Madhya Pradesh with respect to budget allocation, population density, pollution index and cost of living index criteria. The performance of Jharkhand is not at all the best with respect to even a single criterion. In GAIA plane, it is also observed that the considered beneficial and non-beneficial criteria form two different clusters, as expected. This performance evaluation problem is identified to be not at all a hard problem to solve.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ali, N.H., Sabri, I.A.A., Noor, N.M.M., Ismail, F.: Rating and ranking criteria for selected Islands using fuzzy analytic hierarchy process (FAHP). Int. J. Appl. Math. Inform. 6(1), 57–65 (2012)

    Google Scholar 

  2. Ardakani, M.K.: Ranking different factors influencing on development of tourism industry. Manag. Sci. Lett. 4(5), 917–920 (2014)

    Article  Google Scholar 

  3. Ashouri, P., Fariyadi, S.: Potential assessment of nature-based tourism destinations using MCA techniques (case study: Lavasan-e Koochak). J. Environ. Stud. 36(55), 1–3 (2010)

    Google Scholar 

  4. Brans, J.P., Vincke, P.: A preference ranking organization method: The PROMETHEE method for MCDM. Manag. Sci. 31(6), 647–656 (1985)

    Article  Google Scholar 

  5. Brans, J.P., Vincke, P., Mareschal, B.: How to select and how to rank project: The PROMETHEE method. Eur. J. Oper. Res. 24(2), 228–238 (1986)

    Article  Google Scholar 

  6. Brans, J.P., Mareschal, B.: The PROMCALC and GAIA decision-support system for multi criteria decision aid. Decis. Support. Syst. 12(4–5), 297–310 (1994)

    Article  Google Scholar 

  7. Enright, M.J., Newton, J.: Tourism destination competitiveness: A quantitative approach. Tour. Manag. 25(6), 777–788 (2004)

    Article  Google Scholar 

  8. Formica, S.: Destination Attractiveness as a Function of Supply and Demand Interaction. Doctoral Dissertation, Virginia Polytechnic Institute and State University, Virginia, USA (2000)

  9. Forsyth, P., Dwyer, L.: Tourism price competitiveness. In: Blanke, J., Chiesa, T. (eds.) The Travel and Tourism Competitiveness Report 2009, pp. 77–90. World Economic Forum, Geneva (2009)

    Google Scholar 

  10. Gooroochurn, N., Sugiyarto, G.: Competitiveness indicators in the travel and tourism industry. Tour. Econ. 11(1), 25–43 (2004)

    Article  Google Scholar 

  11. Hsu, T.-K., Tsai, Y.-F., Wu, H.-H.: The preference analysis for tourist choice of destination: a case study of Taiwan. Tour. Manag. 30(2), 288–297 (2009)

    Article  Google Scholar 

  12. http://www.imf.org/external/pubs/ft/weo/2013/02/weodata/index.aspx, World Economic Outlook Database, International Monetary Fund

  13. Kozak, M., Rimmington, M.: Measuring tourist destination competitiveness: conceptual considerations and empirical findings. Hosp. Manag. 18(3), 273–283 (1999)

    Article  Google Scholar 

  14. Liu, C.-H., Tzeng, G.-H., Lee, M.-H.: Improving tourism policy implementation: the use of hybrid MCDM models. Tour. Manag. 33(2), 413–426 (2012)

    Article  Google Scholar 

  15. Melián-González, A., García-Falcón, J.-M.: Competitive potential of tourism in destinations. Ann. Tour. Res. 30(3), 720–740 (2003)

    Article  Google Scholar 

  16. Michailidis, A., Chatzitheodoridis, F.: Scenarios analysis of tourism destinations. J. Soc. Sci. 2(2), 41–47 (2006)

    Google Scholar 

  17. Mohamad, D., Jamil, R.-M.: A preference analysis model for selecting tourist destinations based on motivational factors: A case study in Kedah, Malaysia. Procedia - Soc. Behav. Sci. 65(3), 20–25 (2012)

    Article  Google Scholar 

  18. Rao, R.V.: Decision Making in the Manufacturing Environment using Graph Theory and Fuzzy Multi Attribute Decision Making Methods. Springer, London (2007)

    Google Scholar 

  19. Riza, P., Asokan, R.: A comparative study of tourism industry in north-eastern states of India. IOSR J. Bus. Manag. 12(4), 56–62 (2013)

    Article  Google Scholar 

  20. Rodenburg, E.E.: The effects of scale in economic development: Tourism in Bali. Ann. Tour. Res. 7(2), 177–196 (1980)

    Article  Google Scholar 

  21. Sharma, K.K.: Tourism and Regional Development. Sarup & Sons, New Delhi (2004)

    Google Scholar 

  22. Singh, N., Ahuja, S., Nedelea, A.: Comparative analysis between centralized and state-wise tourism campaigns in India. J. Tour. 13(1), 14–20 (2011)

    Google Scholar 

  23. Uma Devi, R.: An evaluative study of tourism industry in Puducherry, U.T. of India. Int. J. Innov. Res. Dev. 2(6), 80–103 (2013)

    Google Scholar 

  24. Wu, W.-W.: Beyond travel and tourism competitiveness ranking using DEA, GST. ANN and Borda count. Expert Syst. Appl. 38(10), 12974–12982 (2011)

    Article  Google Scholar 

  25. Zhang, H., Gu, C.-L., Gu, L.-W., Zhang, Y.: The evaluation of tourism destination competitiveness by TOPSIS and information entropy: A case in the Yangtze River Delta of China. Tour. Manag. 32(2), 443–451 (2011)

    Article  Google Scholar 

  26. Zhou, Y., Maumbe, K., Deng, J., Selin, S.W.: Resource-based destination competitiveness evaluation using a hybrid analytic hierarchy process (AHP): The case study of West Virginia. Tour. Manag. Perspect. 15, 72–80 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shankar Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ranjan, R., Chatterjee, P. & Chakraborty, S. Performance evaluation of Indian states in tourism using an integrated PROMETHEE-GAIA approach. OPSEARCH 53, 63–84 (2016). https://doi.org/10.1007/s12597-015-0225-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12597-015-0225-6

Keywords

Navigation