An integrated partial least square and rough set approach for studying pilgrimage attitude towards cultural heritage of Odisha

  • D. P. AcharjyaEmail author
  • Biswajit Acharjya
Original Research


The growth of information and communication technology makes people neglect their cultural heritage due to various factors, and it leads to a lack of cultural heritage transformation from one generation to the next generation. It greatly impacts on pilgrimage attitude towards cultural heritage. Besides, the expansion of heritage places improves the economic worth of any nation. Further, pilgrimage attraction is a major concern, which in turn improves the business opportunities. In general, cultural heritage depends on the historical, aesthetic, and architectural value of a particular place. Apart from these factors, some other parameters are also associated with cultural heritage. Therefore, it is significant to understand the behavioral pattern of the pilgrimage and their likeliness. This paper makes a phenomenological approach to uncover subliminal values associated with the cultural heritage places of Odisha, India. The prime objective is to study the attitude of pilgrimage towards visiting cultural heritage places. The attitude of pilgrimage depends on different dimensions, such as historical, aesthetic, architectural, spiritual, environment, economic, and management. Looking into uncertainty and frequent changes in human behavior, we employ variance-based structural equation modeling using partial least square and rough set for analyzing the information system. Variance-based structural equation modeling using partial least square help us to identify the factors that are essential for the study, and then the rough set is used to generate the rules. It, in turn, study the attitude of pilgrimage towards cultural heritage place of Odisha.


Rough set Partial least square Historical value Aesthetic value Architectural value Cultural heritage Pilgrimage attitude 


Compliance with ethical standards

Conflict of interest

D. P. Acharjya declares that he has no conflict of interest. Biswajit Acharjya declares that he has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Acharjya DP (2014) Rough set on two universal sets and knowledge representation. In: Issac B, Israr N (eds) Case studies in intelligent computing: achievements and trends. CRC Press, Boca Raton, pp 79–107CrossRefGoogle Scholar
  2. Acharjya DP, Das TK (2017) A framework for attribute selection in marketing using rough computing and formal concept analysis. IIMB Manag Rev 29(2):122–135CrossRefGoogle Scholar
  3. Acharjya DP, Tripathy BK (2008) Rough sets on fuzzy approximation spaces and applications to distributed knowledge systems. Int J Artif Intell Soft Comput 1(1):1–14CrossRefGoogle Scholar
  4. Ahmad Y (2006) The scope and definitions of heritage: from tangible to intangible. Int J Herit Stud 12(3):292–300CrossRefGoogle Scholar
  5. Ahmed NSS, Acharjya DP, Sanyal S (2017) A framework for phishing attack identification using rough set and formal concept analysis. Int J Commun Netw Distrib Syst 18(2):186–212CrossRefGoogle Scholar
  6. Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103(3):411–423CrossRefGoogle Scholar
  7. Anitha A, Acharjya DP (2015) Neural network and rough set hybrid scheme for prediction of missing associations. Int J Bioinform Res Appl 11(6):503–524CrossRefGoogle Scholar
  8. Bagozzi RP, Fornell C (1982) Theoretical concepts, measurements, and meaning. Second Gener Multivar Anal 2(2):5–23Google Scholar
  9. Bisoyi N, Gupta H, Padhy NP, Chakrapani GJ (2019) Prediction of daily sediment discharge using a back propagation neural network training algorithm: a case study of the narmada river, india. Int J Sedim Res 34(2):125–135CrossRefGoogle Scholar
  10. Byrne BM (2001) Structural equation modeling with amos, eqs, and lisrel: comparative approaches to testing for the factorial validity of a measuring instrument. Int J Test 1(1):55–86CrossRefGoogle Scholar
  11. Davig T, Hall AS (2019) Recession forecasting using bayesian classification. Int J Forecast 35(3):848–867CrossRefGoogle Scholar
  12. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209zbMATHCrossRefGoogle Scholar
  13. Eklund P, Wray T, Goodall P, Lawson A (2012) Design, information organisation and the evaluation of the virtual museum of the pacific digital ecosystem. J Ambient Intell Humaniz Comput 3(4):265–280CrossRefGoogle Scholar
  14. Fornell C, Larcker DF (1981) Structural equation models with unobservable variables and measurement error. J Mark Res 18(3):382–388CrossRefGoogle Scholar
  15. Garcia I, Sebastia L, Onaindia E (2011) On the design of individual and group recommender systems for tourism. Expert Syst Appl 38(6):7683–7692CrossRefGoogle Scholar
  16. Gefen D, Straub D, Boudreau MC (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inform Syst 4(1):1–74Google Scholar
  17. Gholitabar S, Alipour H, Costa C (2018) An empirical investigation of architectural heritage management implications for tourism: the case of Portugal. Sustainability 10(1):1–32. CrossRefGoogle Scholar
  18. Greco S, Matarazzo B, Slowinski R (1999) Rough approximation of a preference relation by dominance relations. Eur J Oper Res 117(1):63–83zbMATHCrossRefGoogle Scholar
  19. Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (2006) Multivariate data analysis. Prentice Hall, Upper Saddle RiverGoogle Scholar
  20. Hair JF Jr, Hult GTM, Ringle C, Sarstedt M (2016) A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, Thousand OakszbMATHGoogle Scholar
  21. Hong M, An S, Akerkar R, Camacho D, Jung JJ (2019) Cross-cultural contextualisation for recommender systems. J Ambient Intell Humaniz Comput. CrossRefGoogle Scholar
  22. Huang R, Feng W, Fan M, Guo Q, Sun J (2017) Learning multi-path cnn for mural deterioration detection. J Ambient Intell Humaniz Comput. CrossRefGoogle Scholar
  23. Kar AK (2016) Bio inspired computing—a review of algorithms and scope of applications. Expert Syst Appl 59:20–32CrossRefGoogle Scholar
  24. KC C, Karuppannan S, Sivam A (2019) Assessing the values of living heritage sites in kathmandu valley: a community perspective. J Cult Herit Manag Sustain Dev 9(1):93–110CrossRefGoogle Scholar
  25. Keitumetse S (2009) The eco-tourism of cultural heritage management (ect-chm): linking heritage and environment in the Okavango delta regions of Botswana. Int J Herit Stud 15(2–3):223–244CrossRefGoogle Scholar
  26. Keitumetse SO (2011) Sustainable development and cultural heritage management in Botswana: towards sustainable communities. Sustain Dev 19(1):49–59CrossRefGoogle Scholar
  27. Kotsopoulos KI, Chourdaki P, Tsolis D, Antoniadis R, Pavlidis G, Assimakopoulos N (2019) An authoring platform for developing smart apps which elevate cultural heritage experiences: a system dynamics approach in gamification. J Ambient Intell Humaniz Comput. CrossRefGoogle Scholar
  28. Kumar NTG (2017) An analytical study of Odisha’s heritage prospectus in tourism development. Int J Res Appl Sci Eng Technol 5(10):2130–2135Google Scholar
  29. Levi D, Kocher S (2013) Perception of sacredness at heritage religious sites. Environ Behav 45(7):912–930CrossRefGoogle Scholar
  30. Liu G (2010) Rough set theory based on two universal sets and its applications. Knowl Based Syst 23(2):110–115CrossRefGoogle Scholar
  31. Long PT, Perdue RR, Allen L (1990) Rural resident tourism perceptions and attitudes by community level of tourism. J Travel Res 28(3):3–9CrossRefGoogle Scholar
  32. Lowenthal D (2005) Natural and cultural heritage. Int J Herit Stud 11(1):81–92CrossRefGoogle Scholar
  33. Maji S, Arora S (2019) Decision tree algorithms for prediction of heart disease. In: Fong S, Akashe S, Mahalle P (eds) Information and communication technology for competitive strategies (LNNS), vol 40. Springer, New York, pp 447–454CrossRefGoogle Scholar
  34. Meyer É, Grussenmeyer P, Perrin JP, Durand A, Drap P (2007) A web information system for the management and the dissemination of cultural heritage data. J Cult Herit 8(4):396–411CrossRefGoogle Scholar
  35. Munjeri D (2004) Tangible and intangible heritage: from difference to convergence. Mus Int 56(1–2):12–20CrossRefGoogle Scholar
  36. Navrud S, Ready RC (2002) Valuing cultural heritage: applying environmental valuation techniques to historic buildings, monuments and artifacts. Edward Elgar Publishing, CheltenhamCrossRefGoogle Scholar
  37. Ngamsomsuke W, Hwang T, Huang C (2011) Sustainable cultural heritage tourism indicators. Int Conf Soc Sci Humanity IPEDR 5:516–519Google Scholar
  38. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356zbMATHCrossRefGoogle Scholar
  39. Pawlak Z (1993) Rough sets: present state and the future. Found Comput Decis Sci 18(3–4):157–166MathSciNetzbMATHGoogle Scholar
  40. Plieninger T, Dijks S, Oteros-Rozas E, Bieling C (2013) Assessing, mapping, and quantifying cultural ecosystem services at community level. Land Use Policy 33:118–129CrossRefGoogle Scholar
  41. Rathi R, Acharjya DP (2018) A framework for prediction using rough set and real coded genetic algorithm. Arab J Sci Eng 43(8):4215–4227CrossRefGoogle Scholar
  42. Rössler M (2006) World heritage cultural landscapes: a unesco flagship programme 1992–2006. Landsc Res 31(4):333–353CrossRefGoogle Scholar
  43. Song H, Kim H et al (2018) Value-based profiles of visitors to a world heritage site: the case of suwon hwaseong fortress (in South Korea). Sustainability 11(1):1–19CrossRefGoogle Scholar
  44. Throsby D (2003) Determining the value of cultural goods: how much (or how little) does contingent valuation tell us? J Cult Econ 27(3–4):275–285CrossRefGoogle Scholar
  45. Timothy DJ, Boyd SW (2006) Heritage tourism in the 21st century: valued traditions and new perspectives. J Herit Tour 1(1):1–16CrossRefGoogle Scholar
  46. Tripathy BK, Acharjya DP, Cynthya V (2011) A framework for intelligent medical diagnosis using rough set with formal concept analysis. Int J Artif Intell Appl 2(2):45–66Google Scholar
  47. Wang YS, Wu MC, Wang HY (2009) Investigating the determinants and age and gender differences in the acceptance of mobile learning. Br J Educ Technol 40(1):92–118MathSciNetCrossRefGoogle Scholar
  48. Xu H, Cui Q, Ballantyne R, Packer J (2013) Effective environmental interpretation at chinese natural attractions: the need for an aesthetic approach. J Sustain Tour 21(1):117–133CrossRefGoogle Scholar
  49. Yao Y (2008) Probabilistic rough set approximations. Int J Approx Reason 49(2):255–271zbMATHCrossRefGoogle Scholar
  50. Yoon Y, Gursoy D, Chen JS (2001) Validating a tourism development theory with structural equation modeling. Tour Manag 22(4):363–372CrossRefGoogle Scholar
  51. Yu CP, Chancellor HC, Cole ST (2011) Measuring residents attitudes toward sustainable tourism: a reexamination of the sustainable tourism attitude scale. J Travel Res 50(1):57–63CrossRefGoogle Scholar
  52. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353zbMATHCrossRefGoogle Scholar
  53. Zhu W, Wang FY (2003) Reduction and axiomization of covering generalized rough sets. Inf Sci 152:217–230MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Computer Science and Engineering, VITVelloreIndia
  2. 2.VIT Business School, VITVelloreIndia

Personalised recommendations