Privacy issues are a top priority in web design. However, websites’ evaluation methods do not consider legal and ethical issues. This article proposes a fuzzy logic–based methodology for evaluating websites’ compliance with legal and ethical principles. Using fuzzy Delphi and fuzzy numbers, the methodology develops the Fuzzy Legal and Ethical Compliance Index (FLECI) that addresses the inherited vagueness of the evaluation process and calculates websites’ conformity to legal and ethical guidelines. To illustrate the proposed methodology, this research collects data and then evaluates and classifies 100 websites with respect to their privacy policies using fuzzy equivalence. This article provides a foundation for the development of comprehensive website evaluation methods that include privacy and ethical issues in their evaluations. Future research can investigate the applicability of the proposed methodology and the fuzzy numbers calculated in this article in websites across industries and cultural activities.
EarpJBBaumerDL.Innovative web use to learn about consumer behavior and online privacy. Commun ACM2006; 46(4): 81–83.
2.
FurnellSMKarweniT.Security implications of electronic commerce: a survey of consumers and businesses. Internet Res1999; 9(5): 372–382.
3.
PalviaP.The role of trust in e-commerce relational exchange: a unified model. Inform Manage2009; 46(4): 213–220.
4.
YenY.Can perceived risks affect the relationship of switching costs and customer loyalty in e-commerce?Internet Res2010; 20(2): 210–224.
5.
BenamatiJHOzdemirZDSmithHJ.An empirical test of an antecedents – privacy concerns–outcomes model. J Inf Sci. Epub ahead of print 22 June 2016. DOI: 10.1177/0165551516653590.
6.
YaoMZRiceREWallisK.Predicting user concerns about online privacy. J Am Soc Inf Sci Tec2007; 58(5): 710–722.
7.
LiuCMarchewkaJTLuJ. Beyond concern: a privacy–trust–behavioral intention model of electronic commerce. Inform Manage2004; 42(1): 127–142.
8.
MalhotraNKKimSSAgarwalJ.Internet users’ information privacy concerns (IUIPC): the construct, the scale, and a causal model. Inform Syst Res2004; 15(4): 336–355.
9.
RanganathanCGanapathyS.Key dimensions of business-to-consumer web sites. Inform Manage2002; 39(6): 457–465.
10.
SmithHJMilbergSJBurkeSJ.Information privacy: measuring individuals’ concerns about organizational practices. MIS Quart1996; 20(2): 167–196.
11.
KangJ.Information privacy in cyberspace transactions. Stanford Law Rev1998; 50(4): 1193–1294.
12.
MasonRO.Four ethical issues of the information age. MIS Quart1986; 10(1): 4–12.
13.
ReayIDickSMillerJ.An analysis of privacy signals on the World Wide Web: past, present and future. Inform Sciences2009; 179(8): 1102–1115.
14.
LiaoChLiuChChenK.Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: an integrated model. Electron Commer R A2011; 10(6): 702–715.
15.
US Department of Health, Education and Welfare (HEW). Records, computers, and the rights of citizens: report of the secretary’s advisory committee on automated personal data systems (seminal). HEW, 1973.
16.
Organisation for Economic Co-operation and Development (OECD). Guidelines on the protection of privacy and transborder flows of personal data, privacy online: OECD guidance on policy and practice. Paris: OECD, 1980.
17.
Council of Europe. Convention for the protection of individuals with regard to automatic processing of personal data. Strasbourg: Council of Europe, 1981.
18.
Council of Europe. European convention of human rights. Strasbourg: Council of Europe, 1950.
19.
United Nations. The guidelines concerning computerized personal data files (Adopted by the General Assembly). Geneva: United Nations, 1990.
20.
The Information Infrastructure Task Force (IITF), Information Policy Committee. Privacy and the national information infrastructure: principles for providing and using personal information. United States: Privacy Working Group, 1995.
21.
Directive 95/46/EC. Directive on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Directive 95/46/EC of the European Parliament and of the Council, 24October1995, Official Journal of the European Communications, No. L. 281/31-95.
Directive 2002/58/EC. Directive on privacy and electronic communications, Directive 2002/58/EC of the European Parliament and of the Council, 12July2002, Official Journal of the European Communications, No. L. 201/37-47.
24.
Online Privacy Protection Act (OPPA). 108th congress of the United States of America (1st Session, H.R. 69), California State Law, 2003.
25.
VailMWEarpJBAntonAI.An empirical study of consumer perceptions and comprehension of web site privacy policies. IEEE T Eng Manage2008; 55(3): 442–453.
26.
BeldadAde JongMSteehouderM.Reading the least read? Indicators of users’ intention to consult privacy statements on municipal websites. Gov Inform Q2010; 27(3): 238–244.
27.
MarkelM.The rhetoric of misdirection in corporate privacy-policy statements. Tech Comm Q2005; 14(2): 197–214.
28.
YuXGuoSGuoJ. Rank B2C e-commerce websites in e-alliance based on AHP and fuzzy TOPSIS. Expert Syst Appl2011; 38(4): 3550–3557.
29.
ChouWChengY.A hybrid fuzzy MCDM approach for evaluating website quality of professional accounting firms. Expert Syst Appl2012; 39(3): 2783–2793.
30.
BüyüközkanGÇifçiG.A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst Appl2012; 39(3): 2341–2354.
31.
NgaiEWTWatFKT. A literature review and classification of electronic commerce research. Inform Manage2002; 39(5): 415–426.
32.
EndicottTA.Law is necessarily vague. Leg Theor2001; 7: 379–385.
33.
EndicottTA.Vagueness in Law. New York: Oxford University Press, 2001.
34.
SickerDCOhmPGrunwaldD.Legal issues surrounding monitoring during network research. In: Proceedings of the 7th ACM SIGCOMM conference on internet measurement, San Diego, CA, 24–26 October 2007, pp. 141–148. New York: ACM.
35.
WaldronJ.Vagueness in law and language: some philosophical issues. Calif Law Rev1994; 82: 509.
McNeillMFThroE.Fuzzy logic: a practical approach. San Diego, CA: Academic Press Professional, 1994.
38.
LinCTChiuHTsengYH.Agility evaluation using fuzzy logic. Int J Prod Econ2006; 101: 353–368.
39.
LinCTChenCT.A fuzzy-logic-based approach for new product Go/NoGo decision at the front end. IEEE T Syst Man Cy A2004; 34(1): 132–142.
40.
ChenLHChiouTW.A fuzzy credit-rating approach for commercial loans: a Taiwan case. Omega1999; 27(4): 407–419.
41.
TurbanEZhouDMaJ.A group decision support approach to evaluating journals. Inform Manage2004; 42(1): 31–44.
42.
ZhuanglinMChunfuSSheqiangM. Constructing road safety performance indicators using Fuzzy Delphi Method and Grey Delphi Method. Expert Syst Appl2011; 38(3): 1509–1514.
43.
KuoYFChenPC.Constructing performance appraisal indicators for mobility of the service industries using Fuzzy Delphi Method. Expert Syst Appl2008; 35(4): 1930–1939.
44.
KaufmannAGuptaMM.Fuzzy mathematical models in engineering and management science. Amsterdam: North Holland, 1988.
45.
ChenMYChenAP.Integrating option model and knowledge management performance measures: an empirical study. J Inf Sci2005; 31(5): 381–393.
46.
HsuYLLeeCHKrengVB.The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Syst Appl2010; 37(1): 419–425.
47.
KardarasDKKarakostasBMamakouXJ.Content presentation personalisation and media adaptation in tourism web sites using Fuzzy Delphi Method and Fuzzy Cognitive Maps. Expert Syst Appl2013; 40(6): 2331–2342.
48.
MikaeilROzcelikYYousefiR. Ranking the sawability of ornamental stone using Fuzzy Delphi and multi-criteria decision-making techniques. Int J Rock Mech Min2013; 58: 118–126.
49.
RossTJ.Fuzzy logic with engineering applications. 3rd ed.Chichester: Wiley-Blackwell, 2010.
50.
LuukkaP.Similarity classifier using similarities based on modified probabilistic equivalence relations. Knowl Based Syst2009; 22(1): 57–62.
SiegelmannHTSontagED. Analog computation via neural networks. Theor Comput Sci1994; 131(2): 331–360.
53.
ParkYSklanskyJ.Automated design of linear tree classifiers. Pattern Recogn1990; 23(12): 1393–1412.
54.
LiangGSChouTYHanTC.Cluster analysis based on fuzzy equivalence relation. Eur J Oper Res2005; 166: 160–171.
55.
SunPGGaoLHanSS.Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks. Inform Sciences2011; 181: 1060–1071.
56.
WangYL.A clustering method based on fuzzy equivalence relation for customer relationship management. Expert Syst Appl2010; 37: 6421–6429.
CaoYLiY.An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst Appl2007; 33(1): 230–240.
60.
ChanCCHChengCBHsuCH.Bargaining strategy formulation with CRM for an e-commerce agent. Electron Commer R A2008; 6(4): 490–498.
61.
ChenSH.Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Set Syst1985; 17: 113–129.
62.
ChangSLWangRCWangSY.Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. Int J Prod Econ2006; 100: 348–359.
63.
ChouTYChouSTTzengGH.Evaluating IT/IS investments: a fuzzy multi-criteria decision model approach. Eur J Oper Res2006; 173: 1026–1046.
64.
DikmenIBirgonulMTHanS.Using fuzzy risk assessment to rate cost overrun risk in international construction projects. Int J Proj Manag2007; 25: 494–505.
65.
LinCTChiuHChuPY.Agility index in the supply chain. Int J Prod Econ2006; 100: 285–299.
66.
YehCHDengHChangYH.Fuzzy multicriteria analysis for performance evaluation of bus companies. Eur J Oper Res2000; 126: 459–473.
67.
GuesgenHWJochenA.Imprecise reasoning in geographic information systems. Fuzzy Set Syst2000; 113: 121–131.
68.
LinCTChenYT.Bid/no-bid decision-making – a fuzzy linguistic approach. Int J Proj Manag2004; 22: 585–593.
69.
MurrayTJPipinoLLvan GigchJP. A pilot study of fuzzy set modification of Delphi. Hum Syst Manage1985; 5(1): 76–80.
