This study investigated a cultural portal measurement model as it relates to Korean user needs. With the conceptual framework that a Web portal is a complex Web service application that has the characteristics of both the information system and service agent, and additionally that users’ reactions to technology can have an impact on the service quality, a measurement model of cultural portal user needs was created by adapting data quality, service quality, and technology adoption models. The analyses indicated that the factor structure is the same regardless of users’ gender, educational background, role when using cultural portals, and experience with cultural portals. It was, however, found that the factor structure is not the same across age groups, providing evidence of generational differences and suggesting that additional efforts to create inclusive cultural portals are needed.
AjzenI (1985) From intentions to actions: A theory of planned behavior. In: KuhlJ.BeckmannJ. (eds) Action control: From cognition to behavior. Berlin: Springer pp. 11–39.
2.
AjzenI (2011) The theory of planned behaviour: research and reflections. Psychology and Health26: 1113–1127.
AlamSLCampbellJ (2012) Crowdsourcing motivations in a non-for-profit GLAM context: The Australian newspapers digitization program. In: Proceedings of the 23rd Australasian Conference on Information Systems (ed JLamp), Geelong, Australia, pp. 1–11. Australia: ACIS.
5.
ArmbrechtJ (2014) Developing a scale for measuring the perceived value of cultural institutions. Cultural Trends. Epub ahead of print 6 May 2014. DOI: 10.1080/09548963.2014.912041.
6.
BatiniCCappielloCFrancalanciCMaurinoA (2009) Methodologies for data quality assessment and improvement. ACM Computing Surveys41(3): 16:1–16:52.
7.
BentlerPM (1990) Comparative fit indices in structure models. Psychological Bulletin107(2): 238–246.
8.
BertossiLRizzoloFJiangL (2011) Data quality is context dependent. In: Lecture notes in business information processing (eds. MCastellanosUDayalVMarkl), Berlin, pp. 52–67. Berlin: Springer.
9.
BradyMKCroninJ (2001) Some new thoughts on conceptualizing perceived service quality: a hierarchical approach. Journal of Marketing65: 34–49.
10.
ByrneBM (2010) Structural Equation Modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge.
11.
CappielloCFrancalanciCPerniciB (2004) Data quality assessment from the user’s perspective. In: Proceedings of the 2004 International Workshop on Information Quality in Information Systems (ed. ACM) Paris, France, 18 June, pp. 68–73. New York: ACM.
12.
ChenFF (2007) Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling14 (3): 464–504.
ConcordiaCGradmannSSiebingaS (2010) Not just another portal, not just another digital library: A portrait of Europeana as an application program interface. IFLA Journal36(1): 61–69.
15.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly13: 319–340.
16.
DavisFDBaggoziRPWarshawPR (1989) User acceptance of computer technology: A comparison of two theoretical models. Management Science35 (8): 982–1003.
17.
GrönroosC (1984) A service quality model and its marketing implications. European Journal of Marketing18(4): 36–64
18.
HanS (2010) The transformation and implication of Naver’s structure in portal industry of Korea. Korean Journal of Journalism & Communication Studies54 (1): 107–127.
19.
HelsperE (2010) Gendered Internet use across generations and life stages. Communication Research37 (3): 352–347.
20.
HuLBentlerPM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling6 (1): 1–55.
21.
ISO/IEC: ISO/IEC FDIS 25012 (2008) Software Engineering – Software product quality requirements and evaluation (SQuaRE) – Data quality model.
22.
KarahannaEAgarwalRAngstCM (2006) Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly30 (4): 781–804.
23.
KingWRHeJ (2006) A meta-analysis of the technology acceptance model. Information & Management34: 740–755.
24.
KlineRB (2010) Principles and Practice of Structural Equation Modeling (3rd ed.). New York: Guilford Press.
25.
KnightSABurnJM (2005) Developing a framework for assessing information quality on the World Wide Web. Informing Science Journal8: 159–172.
26.
KoHRobertsMSChoC (2006) Cross-cultural differences in motivations and perceived interactivity: A comparative study of American and Korean Internet users. Journal of Current Issues and Research in Advertising28(2): 93–105.
27.
LeeMKOCheungCMKChenZ (2005) Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information and Management42(8): 1095–1104.
28.
LegrisPInghamJColleretteP (2003) Why do people use information technology? A critical review of the technology acceptance model. Information and Management40: 191–204.
29.
LeitePGonçalvesJTeixeiraPRochaÁ (2014) Towards a model for the measurement of data quality in websites. New Review of Hypermedia and Multimedia2014: 1–16.
30.
MarcumDB (2014) Archives, libraries, museums: Coming back together?Information & Culture: A Journal of History49 (1): 74–89.
31.
MontagnierPWirthmannA (2011) Digital Divide: From computer access to online activities – A micro data analysis. Report for OECD Digital Economy Papers. No. 189, Paris: OECD Publishing.
32.
MartínezJAMartínezL (2010) Some insights on conceptualizing and measuring service quality. Journal of Retailing and Consumer Services17: 29–42.
33.
MoonJWKimYG (2001) Extending the TAM for a World-Wide-Web context. Information & Management38 (4): 217–230.
34.
MoragaMACaleroCPiattiniM (2004) A first proposal of a portal quality model. In: Proceedings of the IADIS International Conference on e-society 2004 (eds IsaíasPKommersPMcPhersonM) Ávila, Spain, 16-19 July 2004, pp. 630–638. Lisbon: IADIS Press.
35.
MoragaCMoragaMACaleroCCaroA (2009) SQuaREaligned data quality model for web portals. In: Proceedings of the 9th International Conference on Quality Software (ed BChoi) Jeju, Korea, 24-25 August 2009, pp. 117–122. New York: IEEE.
36.
MunnellyGHampsonCLawlessSConlanO (2014) The CULTURA portal. In: Communications in Computer and Information Science: Vol.416. Theory and Practice of Digital Libraries – TPDL 2013 Selected Workshops (eds. LBolikowskiVCasarosaPGoodaleNHoussosPManghiJSchirrwagen). Valletta, Malta, 22-26 September 2014, pp. 155–158Switzerland: Springer International Publishing.
37.
NamJ (2014) Understanding the motivation to use web portals. Computers in Human Behavior36: 263–273.
OliverRL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research17: 460–469.
41.
OomenJArroyoL (2011) Crowdsourcing in the cultural heritage domain: opportunities and challenges. In: Proceedings of the 5th International Conference on Communities & Technologies (eds. JKjeldskovJPaay) Brisbane, Australia, 29 June – 2 July 2011, pp. 138–149. New York: ACM.
42.
Paneva-MarinovaD (2012) Personal work space and content analysis functionality in a cultural heritage digital library. Digital Presentation and Preservation of Cultural and Scientific HeritageII:78–84.
43.
PapaWHPapaMJ (1992) Communication network patterns and the re-inventing of new technology. Journal of Business Communication29: 41–61.
44.
ParasuramanABerryLLZiethamlVA (1991) Refinement and reassessment of the SERVQUAL scale. Journal of Retailing67 (4): 420–450.
45.
ParasuramanAZeithamlVABerryLL (1985) A conceptual model of service quality and its implications for future research. Journal of Marketing49 (4): 41–50.
46.
ParasuramanAZeithamlVABerryLL (1988) SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing64 (1): 12–40.
47.
ParasuramanAZeithamlVAMalhotraA (2005) E-S-QUAL: a multiple-item scale for assessing electronic service quality. Journal of Service Research7 (X): 213–233.
48.
PrakashAMohantyRP (2012) Understanding service quality. Production Planning & Control24:1050–1065.
49.
Pruulmann-VengerfeldtPAljasA (2009) Digital cultural heritage – Challenging museums, archives and users. Journal of Ethnology and Folkloristics3 (1): 109–128.
50.
PurdayJ (2009) Think culture: Europeana.eu from concept to construction. Bibliothek33 (2): 170–180.
51.
RiceRERogersEM (1980) Reinvention in the innovation process. Science Communication1: 499–514.
52.
RigdonEE (1996) CFA versus RMSEA: A comparison of two fit indexes for structural equation modeling. Structural Equation Modeling3 (4): 369–379.
53.
RochaÁ (2012) Framework for a global quality evaluation of a website. Online Information Review36(3): 374 – 382.
54.
SalvadorPRochaÁ (2014) An assessment of content quality in websites of basic and secondary Portuguese schools. In RochaÁCorreiaAMTanFBStroetmannKA. (eds.) New Perspectives in Information Systems and Technologies. Switzerland: Springer International Publishing, pp. 71–82.
55.
SethNDeshmukhSG (2005) Service quality models: A review. International Journal of Quality & Reliability Management22: 913–949.
StrongDLeeYWangRY (1997) Data quality in context. Communications of the ACM40 (5): 103–110.
58.
TayiGKBallouDP (1998) Examining data quality. Communications of the ACM41 (2): 54–57.
59.
ThompsonMSGreenSB (2006) Evaluating between-group differences in latent variable means. In HancockGRMuellerRO (eds.) Structural Equation Modeling: A second course. Greenwich: Information Age Publishing, pp.171–196.
60.
Van der HeijdenH (2003) Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & Management40 (6): 541–549.
61.
VandenbergRJ (2002) Toward a further understanding and improvement in measuring invariance methods and procedures. Organizational Research Methods5 (2): 267–286.
62.
VanderboschBHigginsCJ (1996) Information acquisition and mental models: An investigation into the relationship between behavior and learning. Information Systems Research7: 189–214.
63.
VenkateshVDavisFDMorrisMG (2007) Dead or alive? The development, trajectory and future of technology adoption research. Journal of the Association of Information Systems8: 268–286.
64.
WangRYKonHBMadnickSE (1993) Data quality requirements analysis and modeling. In: Proceedings of the Ninth International Conference of Data Engineering (ed. IEEE) Vienna, Austria, 19-23 April 1993, pp. 670–677. New York: IEEE.
65.
WilliamsKChatterjeeSRossiM (2008) Design of emerging digital services: a taxonomy. European Journal of Information Systems17: 505–517.
66.
WuADLiZZumboBD (2007) Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factory analysis: A demonstration with TIMSS data. Practical Assessment Research & Evaluation12 (article 3). Available at: http://pareonline.net/getvn.asp?v=12&n=3(accessed 20 October 2014)
67.
YarrowAClubbBDraperJL (2008) Public libraries, archives and museums: Trends in collaboration and cooperation. Report for the IFLA. Report no. 108. The Hague: IFLA.
68.
YousafzaiSYFoxallGRPallisterJG (2007) Technology acceptance: A meta-analysis of the TAM: Part 1. Journal of Modeling in Management2: 251–280.
