A Decision Support System for Website Selection for Internet Based Advertising and Promotions

  • Arpan Kumar Kar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 298)


With the onset of the internet era, the focus shifted in marketing from Word of Mouth to the Word of Web. However, the size of the web increasing in leaps and bounds. A major challenge is to identify suitable websites for promotion of marketing campaigns. The operational issues in addressing the challenge can be classified into two parts. The first is to identify the critical factors for evaluating websites. The second is to evaluate the websites against these evaluation factors, and select them from a large pool of websites. Creating a schema for selecting such websites from a large list is a major challenge. This paper proposes a decision support system to select suitable partner websites from a list, by evaluating them against a set of context specific website-quality evaluation criteria. The integrated methodology for decision support uses Delphi, Analytic Hierarchy Process and Cuckoo Search.


Decision support system Website selection Internet marketing Analytic hierarchy process Cuckoo search Delphi Marketing analytics 


  1. 1.
    Agarwal R, Venkatesh V (2002) Assessing a firm’s web presence: a heuristic evaluation procedure for the measurement of usability. Inf Syst Res 13(2):168–186CrossRefGoogle Scholar
  2. 2.
    Loiacono ET, Chen D, Goodhue DL (2002) WebQual revisited: predicting the intent to reuse a web site. In American conference on information systems, 301–309 Google Scholar
  3. 3.
    Barnes SJ, Vidgen R (2001) An evaluation of cyber-bookshops: the WebQual method. Int J Electron Commer 6:11–30Google Scholar
  4. 4.
    Devaraj S, Fan M, Kohli R (2002) Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Inf Syst Res 13:316–333CrossRefGoogle Scholar
  5. 5.
    Liu C, Arnett KP (2000) Exploring the factors associated with web site success in the context of electronic commerce. Inf Manage 38:23–33CrossRefGoogle Scholar
  6. 6.
    Webb HW, Webb LA (2004) SiteQual: an integrated measure of web site quality. J Enterp Inf Manage 17:430–440Google Scholar
  7. 7.
    Palmer JW (2002) Web site usability, design, and performance metrics. Inf Syst Res 13(2):151–167CrossRefGoogle Scholar
  8. 8.
    Wu F, Mahajan V, Balasubramanian S (2003) An analysis of e-business adoption and its impact on business performance. J Acad Mark Sci 31:425–447CrossRefGoogle Scholar
  9. 9.
    Eyrich N, Padman ML, Sweetser KD (2008) PR practitioners’ use of social media tools and communication technology. Public Relat Rev 34(4):412–414CrossRefGoogle Scholar
  10. 10.
    Hsu CC, Sandford BA (2007) The Delphi technique: making sense of consensus. Pract Assess, Res Eval 12(10):1–8Google Scholar
  11. 11.
    Saaty TL (1980) The Analytic Hierarchy Process. McGraw Hill International, New YorkMATHGoogle Scholar
  12. 12.
    Aguarón J, Moreno-Jiménez JM (2003) The geometric consistency index: approximated thresholds. Eur J Oper Res 147(1):137–145CrossRefMATHGoogle Scholar
  13. 13.
    Kar AK (2013) Revisiting the supplier selection problem: an integrated approach for group decision support. Expert systems with applications, (in Press)Google Scholar
  14. 14.
    Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: IEEE world congress on nature and biologically inspired computing, 210–214Google Scholar
  15. 15.
    Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos, Solitons Fractals 44(9):710–718CrossRefGoogle Scholar
  16. 16.
    Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Indian Institute of Management RohtakRohtakIndia

Personalised recommendations