Citizens as consumers: Profiling e-government services' users in Egypt via data mining techniques
暂无分享,去创建一个
[1] Andrew Hunter,et al. Feature Selection Using Probabilistic Neural Networks , 2000, Neural Computing & Applications.
[2] L J Eaves,et al. Common Disease Analysis Using Multivariate Adaptive Regression Splines (MARS): Genetic Analysis Workshop 12 Simulated Sequence Data , 2001, Genetic epidemiology.
[3] Hee Seok Song,et al. Detecting the change of customer behavior based on decision tree analysis , 2005, Expert Syst. J. Knowl. Eng..
[4] Jaeki Song,et al. Exploring Decision Rules for Sellers in Business-to-Consumer(B2C) Internet Auctions , 2008, Int. J. E Bus. Res..
[5] P. Corona,et al. Site quality evaluation by classification tree: an application to cork quality in Sardinia , 2005, European Journal of Forest Research.
[6] I. Kabashima,et al. Casual cynics or disillusioned democrats? Political alienation in Japan , 2000 .
[7] Janine Dermody,et al. Segmenting Youth Voting Behaviour through Trusting-Distrusting Relationships: A Conceptual Approach , 2004 .
[8] Kuan-Yu Chen,et al. A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan , 2007, Expert Syst. Appl..
[9] Martin Wetzels,et al. A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects , 2007, Inf. Manag..
[10] Aixia Yan. Application of self-organizing maps in compounds pattern recognition and combinatorial library design. , 2006, Combinatorial chemistry & high throughput screening.
[11] Fred D. Davis,et al. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.
[12] R. Brislin. The wording and translation of research instruments. , 1986 .
[13] Graham Pervan,et al. ICT and OTs: A model of information and communication technology acceptance and utilisation by occupational therapists , 2007, Int. J. Medical Informatics.
[14] Tao Zhou,et al. Exploring Chinese users' acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory , 2009, Comput. Hum. Behav..
[15] Martin Natter,et al. Segmentation-based competitive analysis with MULTICLUS and topology representing networks , 2000, Comput. Oper. Res..
[16] K. Héberger,et al. Supervised pattern recognition in food analysis. , 2007, Journal of chromatography. A.
[17] Niranjan V. Raman,et al. Factors affecting consumers’ “Webad” visits , 1998 .
[18] Chen Ding,et al. User modeling for personalized Web search with self-organizing map , 2007, J. Assoc. Inf. Sci. Technol..
[19] Michael Shmoish,et al. Analysis of cognitive performance in schizophrenia patients and healthy individuals with unsupervised clustering models , 2008, Psychiatry Research.
[20] James N. Danziger,et al. THE IMPACTS OF INFORMATION TECHNOLOGY ON PUBLIC ADMINISTRATION: AN ANALYSIS OF EMPIRICAL RESEARCH FROM THE “GOLDEN AGE” OF TRANSFORMATION[1] , 2002 .
[21] L. G. Tornatzky,et al. Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings , 1982, IEEE Transactions on Engineering Management.
[22] Lei-da Chen,et al. Technology Adaptation in E-Commerce: Key Determinants of Virtual Stores Acceptance , 2004 .
[23] Ingoo Han,et al. Effect of trust on customer acceptance of Internet banking , 2002, Electron. Commer. Res. Appl..
[24] Anne-Françoise Audrain-Pontevia,et al. Kohonen Self-Organizing Maps: A Neural Approach for Studying the Links between Attributes and Overall Satisfaction in a Services Context , 2006 .
[25] John Day,et al. The Impact of Perceived Innovation Characteristics on Intention to Use Groupware , 2002, Inf. Resour. Manag. J..
[26] Arnold Depickere,et al. Comparing smart card adoption in Singapore and Australian universities , 2003, Int. J. Hum. Comput. Stud..
[27] Ismail Hmeidi,et al. Performance of KNN and SVM classifiers on full word Arabic articles , 2008, Adv. Eng. Informatics.
[28] Jesús Muñoz,et al. Comparison of statistical methods commonly used in predictive modelling , 2004 .
[29] Jen-Her Wu,et al. What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model , 2005, Inf. Manag..
[30] R. Rosen,et al. The Female Sexual Function Index (FSFI): Cross-Validation and Development of Clinical Cutoff Scores , 2005, Journal of sex & marital therapy.
[31] T. Kohonen. Self-organized formation of topographically correct feature maps , 1982 .
[32] Guoqing Chen,et al. Extended Information Technology Initial Acceptance Model and Its Empirical Test , 2007 .
[33] Paul A. Pavlou,et al. Encouraging Citizen Adoption of eGovernment by Building Trust , 2002, Electron. Mark..
[34] Mun Y. Yi,et al. Understanding information technology acceptance by individual professionals: Toward an integrative view , 2006, Inf. Manag..
[35] Abdulhussain E. Mahdi. Perceptual non-intrusive speech quality assessment using a self-organizing map , 2006, J. Enterp. Inf. Manag..
[36] Ajith Abraham,et al. Analysis of hybrid soft and hard computing techniques for forex monitoring systems , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).
[37] C. W. Lim,et al. Predicting the effects of physician-directed promotion on prescription yield and sales uptake using neural networks , 2005 .
[38] Yuehjen E. Shao,et al. Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines , 2004, Expert Syst. Appl..
[39] Thomas G. Calderon,et al. A roadmap for future neural networks research in auditing and risk assessment , 2002, Int. J. Account. Inf. Syst..
[40] Ignacio Olmeda,et al. Self-organizing maps could improve the classification of Spanish mutual funds , 2006, Eur. J. Oper. Res..
[41] Vadlamani Ravi,et al. Soft computing system for bank performance prediction , 2008, Appl. Soft Comput..
[42] Xin Jin,et al. Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks , 2001, IEEE Trans. Neural Networks.
[43] Nissan Levin,et al. Applying neural computing to target marketing , 1997 .
[44] Thomas S. Shively,et al. A semiparametric stochastic spline model as a managerial tool for potential insolvency , 2000 .
[45] Prodromos D. Chatzoglou,et al. Using a modified technology acceptance model in hospitals , 2009, Int. J. Medical Informatics.
[46] Paul Jen-Hwa Hu,et al. Examining a Model of Information Technology Acceptance by Individual Professionals: An Exploratory Study , 2002, J. Manag. Inf. Syst..
[47] C. Craig,et al. International Marketing Research , 1983 .
[48] Vijayakumar Chinnadurai,et al. Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps , 2007, Comput. Medical Imaging Graph..
[49] Olli Silvén,et al. Wood Inspection With Non-Supervised Clustering , 2000 .
[50] Donald R. Lehmann,et al. The Long-Term Impact of Promotion and Advertising on Consumer Brand Choice , 1997 .
[51] Benjamin Koetz,et al. Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data , 2008 .
[52] R. Hecht-Nielsen,et al. Theory of the Back Propagation Neural Network , 1989 .
[53] Ivan Katchanovski,et al. Cyberdemocracy or Potemkin E-Villages? Electronic Governments in OECD and Post-Communist Countries , 2005 .
[54] Kweku-Muata Osei-Bryson,et al. Analyzing the impact of information technology investments using regression and data mining techniques , 2006, J. Enterp. Inf. Manag..
[55] Mevlut Ture,et al. Comparing classification techniques for predicting essential hypertension , 2005, Expert Syst. Appl..
[56] João Cesar M. Mota,et al. Anomaly detection in mobile communication networks using the self-organizing map , 2007, J. Intell. Fuzzy Syst..
[57] Shouhong Wang. The unpredictability of standard back propagation neural networks in classification applications , 1995 .
[58] Lalit M. Patnaik,et al. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network , 2006, Biomed. Signal Process. Control..
[59] An-Sing Chen,et al. Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index , 2001, Comput. Oper. Res..
[60] Marcello Braglia,et al. The classification and regression tree approach to pump failure rate analysis , 2003, Reliab. Eng. Syst. Saf..
[61] Susan P. Worner,et al. Modelling global insect pest species assemblages to determine risk of invasion , 2006 .
[62] F. N. Ford,et al. Intraorganizational Versus Interorganizational Uses and Benefits of Electronic Mail , 2002, Inf. Resour. Manag. J..
[63] Sally Mckechnie,et al. Integrating intelligent systems into marketing to support market segmentation decisions , 2006, Intell. Syst. Account. Finance Manag..
[64] David C. G. Brown,et al. Electronic government and public administration , 2005 .
[65] Jatinder N. D. Gupta,et al. Neural networks in business: techniques and applications for the operations researcher , 2000, Comput. Oper. Res..
[66] Jijie Wang,et al. Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT , 2007, Inf. Manag..
[67] Daniela V. Dimitrova,et al. Profiling the Adopters of E-Government Information and Services , 2006 .
[68] Carolyn A. Lin,et al. Adoption of e-government in three Latin American countries: Argentina, Brazil and Mexico , 2008 .
[69] A Mauro,et al. Depression is the main determinant of quality of life in multiple sclerosis: A classification-regression (CART) study , 2006, Disability and rehabilitation.
[70] Guowang Xu,et al. Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data , 2002 .
[71] Juha Vesanto,et al. SOM-based data visualization methods , 1999, Intell. Data Anal..
[72] France Bélanger,et al. Trust and Risk in eGovernment Adoption , 2008, AMCIS.
[73] A. J. Englande,et al. Prediction of swimmability in a brackish water body , 2006 .
[74] Gordon C. Bruner,et al. Explaining consumer acceptance of handheld Internet devices , 2005 .
[75] Sovan Lek,et al. Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .
[76] Kristof Coussement,et al. Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin , 2022 .
[77] C. M. Tam,et al. Selection of vertical formwork system by probabilistic neural networks models , 2005 .
[78] M Hajmeer,et al. A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. , 2002, Journal of microbiological methods.
[79] C. May,et al. Interaction between States and Citizens in the Age of the Internet: “e-Government” in the United States, Britain, and the European Union , 2003 .
[80] Mariette Awad,et al. Dynamic classification for video stream using support vector machine , 2008, Appl. Soft Comput..
[81] Zhen-Ping Lo,et al. Analysis of the convergence properties of topology preserving neural networks , 1993, IEEE Trans. Neural Networks.
[82] J. Friedman. Multivariate adaptive regression splines , 1990 .
[83] H. Raghav Rao,et al. On risk, convenience, and Internet shopping behavior , 2000, CACM.
[84] Jeffrey A. Clark,et al. Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment , 2001, Intell. Syst. Account. Finance Manag..
[85] Vallabh Sambamurthy,et al. Research Report: The Evolving Relationship Between General and Specific Computer Self-Efficacy - An Empirical Assessment , 2000, Inf. Syst. Res..
[86] I. Ajzen,et al. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .
[87] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[88] Marjorie B. Platt,et al. Probabilistic Neural Networks in Bankruptcy Prediction , 1999 .
[89] Frederick Kaefer,et al. Determining the appropriate amount of data for classifying consumers for direct marketing purposes , 2003 .
[90] Li-yan Han,et al. Option Pricing Model with Fuzzy Measures under Knightian Uncertainty , 2007 .
[91] Peter M. Chisnall,et al. Marketing Research , 1992 .
[92] G. Hofstede,et al. Cultures and Organizations: Software of the Mind , 1991 .
[93] Ingoo Han,et al. The impact of the online and offline features on the user acceptance of Internet shopping malls , 2004, Electron. Commer. Res. Appl..
[94] Kurt Hornik,et al. Support Vector Machines in R , 2006 .
[95] Kurt Hornik,et al. Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .
[96] Marijana Zekic-Susac,et al. Modelling small-business credit scoring by using logistic regression, neural networks and decision trees , 2005, Intell. Syst. Account. Finance Manag..
[97] S. Chadwick. Communicating Trust in E-Commerce Interactions , 2001 .
[98] J. Hair. Multivariate data analysis , 1972 .
[99] Leonid Churilov,et al. Towards fair ranking of Olympics achievements: the case of Sydney 2000 , 2006, Comput. Oper. Res..
[100] Dave Feldman,et al. Mortgage Default: Classification Trees Analysis , 2004 .
[101] Tino Fenech,et al. Web retailing adoption: exploring the nature of internet users Web retailing behaviour , 2003 .
[102] Teuvo Kohonen,et al. Self-Organizing Maps , 2010 .
[103] David Enke,et al. The use of data mining and neural networks for forecasting stock market returns , 2005, Expert Syst. Appl..
[104] Lei-da Chen,et al. Enticing online consumers: an extended technology acceptance perspective , 2002, Inf. Manag..
[105] Chenn-Jung Huang,et al. Application of Probabilistic Neural Networks to the Class Prediction of Leukemia and Embryonal Tumor of Central Nervous System , 2004, Neural Processing Letters.
[106] Y Vander Heyden,et al. Evaluation of chromatographic descriptors for the prediction of gastro-intestinal absorption of drugs. , 2007, Journal of chromatography. A.
[107] Ajith Abraham,et al. Modeling intrusion detection system using hybrid intelligent systems , 2007, J. Netw. Comput. Appl..
[108] D. Gilbert,et al. Barriers and benefits in the adoption of e‐government , 2004 .
[109] Donald F. Specht,et al. Probabilistic neural networks , 1990, Neural Networks.
[110] Donald A. Singer,et al. Use of a Probabilistic Neural Network to Reduce Costs of Selecting Construction Rock , 2003 .
[111] Melody Y. Kiang,et al. An Evaluation of Self-Organizing Map Networks as a Robust Alternative to Factor Analysis in Data Mining Applications , 2001, Inf. Syst. Res..
[112] C. Robertson,et al. The relationship between Arab values and work beliefs: An exploratory examination , 2002 .
[113] Eugene Clark,et al. Managing the transformation to e‐government: An Australian perspective , 2003 .
[114] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[115] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[116] Taghi M. Khoshgoftaar,et al. CLUSTERING-BASED NETWORK INTRUSION DETECTION , 2007 .
[117] Bo Yu,et al. A comparative study for content-based dynamic spam classification using four machine learning algorithms , 2008, Knowl. Based Syst..
[118] Xiaoyun Zhang,et al. Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis , 2008 .
[119] Richard A. Berk,et al. Statistical Difficulties in Determining the Role of Race in Capital Cases: A Re-analysis of Data from the State of Maryland* , 2004 .
[120] Hung-Pin Shih,et al. An empirical study on predicting user acceptance of e-shopping on the Web , 2004, Inf. Manag..
[121] Fred D. Davis. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..
[122] Tian-Shyug Lee,et al. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..
[123] Su-Chao Chang,et al. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry , 2008, Int. J. Medical Informatics.
[124] Ramesh Sharda,et al. Neural Networks for the MS/OR Analyst: An Application Bibliography , 1994 .
[125] José Ramón Gil-García,et al. Understanding the complexity of electronic government: Implications from the digital divide literature , 2005, Gov. Inf. Q..
[126] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[127] Lai Lai Tung,et al. Adoption of electronic government services among business organizations in Singapore , 2005, J. Strateg. Inf. Syst..
[128] Zetian Fu,et al. An Innovation Adoption Study of Online E-Payment in Chinese Companies , 2006, J. Electron. Commer. Organ..
[129] Jack Duffy,et al. Applications of Artificial Intelligence Systems in the Analysis of Epidemiological Data , 2006, European Journal of Epidemiology.
[130] Fred D. Davis,et al. Investigating Determinants of Software Developers' Intentions to Follow Methodologies , 2003, J. Manag. Inf. Syst..
[131] R. J. Kuo,et al. Integration of self-organizing feature map and K-means algorithm for market segmentation , 2002, Comput. Oper. Res..
[132] Paul A. Pavlou,et al. Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003, Int. J. Electron. Commer..
[133] Detmar W. Straub,et al. Diffusing the Internet in the Arab world: the role of social norms and technological culturation , 2003, IEEE Trans. Engineering Management.
[134] B T Fan,et al. Study of probabilistic neural networks to classify the active compounds in medicinal plants. , 2005, Journal of Pharmaceutical and Biomedical Analysis.
[135] Dimitris K. Tasoulis,et al. Studying the performance of artificial neural networks on problems related to cryptography , 2006 .
[136] Rachel Silcock. What is E-government , 2001 .
[137] Xiugang Li,et al. Predicting motor vehicle crashes using Support Vector Machine models. , 2008, Accident; analysis and prevention.
[138] Vojislav Kecman,et al. Support Vector Machines – An Introduction , 2005 .
[139] Scott B. Wilson. Algorithm architectures for patient dependent seizure detection , 2006, Clinical Neurophysiology.
[140] Yuming Zhou,et al. Predicting object-oriented software maintainability using multivariate adaptive regression splines , 2007, J. Syst. Softw..
[141] R. Laczniak,et al. Consumer adoption of the Internet: The case of apparel shopping , 2003 .
[142] Helena Thuneberg,et al. Contributions of data mining for psycho‐educational research: what self‐organizing maps tell us about the well‐being of gifted learners , 2006 .
[143] W. Qualls,et al. Towards a theoretical model of technology adoption in hospitality organizations , 2007 .
[144] John A. Campbell,et al. Communicative Practices in Online Communication: A Case of Agreeing to Disagree , 2006, J. Organ. Comput. Electron. Commer..
[145] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[146] William R. King,et al. A meta-analysis of the technology acceptance model , 2006, Inf. Manag..
[147] Marvine Hamner,et al. Expanding the Technology Acceptance Model to examine Personal Computing Technology utilization in government agencies in developing countries , 2009, Gov. Inf. Q..
[148] H. Raghav Rao,et al. A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents , 2008, Decis. Support Syst..
[149] F. Gubina,et al. Allocation of the load profiles to consumers using probabilistic neural networks , 2005, IEEE Transactions on Power Systems.
[150] Parag C. Pendharkar,et al. Inductive Regression Tree and Genetic Programming Techniques for Learning User Web Search Preferences , 2006, J. Organ. Comput. Electron. Commer..
[151] Hian Chye Koh,et al. Going concern prediction using data mining techniques , 2004 .
[152] Matthew J. Kotchen,et al. Random effects analysis , 2003 .
[153] David Gefen,et al. Managing User Trust in B2C e-Services , 2003 .
[154] Thomas G. Calderon,et al. Determinants of client‐initiated and auditor‐initiated auditor changes , 2007 .
[155] Ibrahim H. Osman,et al. Self-organizing feature maps for the vehicle routing problem with backhauls , 2006, J. Sched..