Satisfaction and continuous use intention of e-learning service in Brazilian public organizations

We examined two public organizations that offer e-learning service for your employees.A structural model of satisfaction and continuous use intention was tested.Performance and technology readiness high influence each other.Performance exceeds expectancy and results in high satisfaction. The aim of this paper is to investigate the constructs of Technology Readiness Index (TRI) and the Decomposed Expectancy Disconfirmation Theory (DEDT) as determinants of satisfaction and continuous use intention in e-learning services applied in public organizations. The research was conducted by online survey in a sample of 343 employees of two public organizations in Brazil who have had e-learning experience. The results showed that quality, quality disconfirmation, value and value disconfirmation positively impact on satisfaction, as well as disconfirmation usability, innovativeness and optimism. Likewise, satisfaction proved to be decisive for the purpose of continuous use intention. In addition, technological readiness and performance are strongly related. The main contribution of this study is the delivery of an assessment tool for performance oriented to training courses at distance and applied in public organizations.

[1]  Christy M. K. Cheung,et al.  Antecedents and Consequences of User Satisfaction with an e-Learning Portal , 2011 .

[2]  T. Teo.  Development and validation of the E-learning Acceptance Measure (ElAM) , 2010, Internet High. Educ..

[3]  José Esteves.  Addressing the Justification of Enterprise Systems Benefits: A Desires and Expectancy Disconfirmation Model , 2005, AMCIS.

[4]  Erik M. van Raaij,et al.  The acceptance and use of a virtual learning environment in China , 2008, Comput. Educ..

[5]  Edgar Erdfelder,et al.  A short tutorial of GPower , 2007 .

[6]  Mirjana Kocaleva,et al.  Research on UTAUT Application in Higher Education Institutions , 2014 .

[7]  John R. Weeks,et al.  Towards a model and methodology for assessing student learning outcomes and satisfaction , 2010 .

[8]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[9]  B. Darab,et al.  An eclectic model for assessing e-learning readiness in the Iranian universities , 2011, Comput. Educ..

[10]  Szu-Yuan Sun,et al.  Usability, quality, value and e-learning continuance decisions , 2005, Comput. Educ..

[11]  Oye Nathaniel David,et al.  The Impact of UTAUT Model and ICT Theoretical Framework on University Academic Staff: Focus on Adamawa State University, Nigeria. , 2012, BIOINFORMATICS 2012.

[12]  Oliver James,et al.  Evaluating the Expectations Disconfirmation and Expectations Anchoring Approaches to Citizen Satisfaction with Local Public Services , 2007 .

[13]  G. V. Ryzin.  Testing the Expectancy Disconfirmation Model of Citizen Satisfaction with Local Government , 2006 .

[14]  Luis Roque Klering,et al.  Desenvolvimento de um Ambiente Virtual de Aprendizagem à Luz do Enfoque Sistêmico , 2011 .

[15]  Osama Harfoushi,et al.  E-Training Acceptance Factors in Business Organizations , 2011, Int. J. Emerg. Technol. Learn..

[16]  Shu-Sheng Liaw,et al.  Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system , 2008, Comput. Educ..

[17]  Vincent Cho,et al.  Continued Usage of E-Learning Communication Tools: A Study from the Learners' Perspective in Hong Kong , 2008 .

[18]  Anol Bhattacherjee,et al.  An empirical analysis of the antecedents of electronic commerce service continuance , 2001, Decis. Support Syst..

[19]  John W. Langford,et al.  To click or not to click: E-learning for the public sector , 2003 .

[20]  Kallol Kumar Bagchi,et al.  Using SERVQUAL to assess the quality of e-learning experience , 2011, Comput. Hum. Behav..

[21]  Ming-Chi Lee,et al.  Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model , 2010, Comput. Educ..

[22]  R. Oliver.  A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

[23]  Eric T. G. Wang,et al.  Understanding Knowledge Sharing in Virtual Communities: An Integration of Expectancy Disconfirmation and Justice Theories , 2011, Online Inf. Rev..

[24]  Bertil P. Marques,et al.  Applying the UTAUT model in Engineering Higher Education: Teacher's technology adoption , 2011, 6th Iberian Conference on Information Systems and Technologies (CISTI 2011).

[25]  David C. Bojanic.  Consumer perceptions of price, value and satisfaction in the hotel industry: an exploratory study. , 1996 .

[26]  Rohit Verma,et al.  Technology Readiness, Learning Goals, and eLearning: Searching for Synergy , 2007 .

[27]  Michele Biasutti,et al.  The student experience of a collaborative e-learning university module , 2011, Comput. Educ..

[28]  J. Arbaugh,et al.  A Structural Equation Model of Predictors for Effective Online Learning , 2005 .

[29]  Kan-Min Lin,et al.  e-Learning continuance intention: Moderating effects of user e-learning experience , 2011, Comput. Educ..

[30]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[31]  S. Mulaik,et al.  EVALUATION OF GOODNESS-OF-FIT INDICES FOR STRUCTURAL EQUATION MODELS , 1989 .

[32]  Binshan Lin,et al.  Influence of online learning skills in cyberspace , 2010, Internet Res..

[33]  Lai Ming Ling,et al.  PROFESSIONAL STUDENTS’ TECHNOLOGY READINESS, PRIOR COMPUTING EXPERIENCE AND ACCEPTANCE OF AN E-LEARNING SYSTEM , 2007 .

[34]  Anastasios A. Economides,et al.  Continuance acceptance of computer based assessment through the integration of user's expectations and perceptions , 2013, Comput. Educ..

[35]  Hsiao-hui Hsu,et al.  The Acceptance of Moodle: An Empirical Study Based on UTAUT , 2012 .

[36]  Lin-Chung Woung,et al.  Engagement in E-Learning Opportunities: An Empirical Study on Patient Education using Expectation Confirmation Theory , 2012, Journal of Medical Systems.

[37]  Anol Bhattacherjee,et al.  Explaining information technology usage: A test of competing models☆ , 2008 .

[38]  Issham Ismail,et al.  Accessing innovativeness of distance learners toward their readiness in embracing technology , 2011 .

[39]  Arthur Money,et al.  Fundamentos de Métodos de Pesquisa em Administração , 2005 .

[40]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[41]  Esmail Salehi-Sangari,et al.  Success of Government E-Service Delivery: Does Satisfaction Matter? , 2010, EGOV.

[42]  D. Bolliger.  Key Factors for Determining Student Satisfaction in Online Courses , 2004 .

[43]  J. E. Swan,et al.  Consumer perceptions of interpersonal equity and satisfaction in transactions: A field survey approach. , 1989 .

[44]  Timothy Teo,et al.  Using structural equation modelling (SEM) in educational technology research: Issues and guidelines , 2010, Br. J. Educ. Technol..

[45]  Vaclav Snasel,et al.  Consumers’ Acceptance and Use of Information and Communications Technology: A UTAUT and Flow Based Theoretical Model , 2013 .

[46]  David C. Yen,et al.  Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model , 2007, Comput. Hum. Behav..

[47]  Yi-Chuan Hsieh,et al.  Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems , 2011, J. Educ. Technol. Soc..

[48]  P. Nicholson.  A History of E-Learning , 2007 .

[49]  Silvana Trimi,et al.  The Impact of CMS Quality on the Outcomes of E‐learning Systems in Higher Education: An Empirical Study , 2012 .

[50]  Robert K. Yin,et al.  Estudo de caso: planejamento e métodos , 2005 .

[51]  Ming Ling Lai.  Professional students’ technology readiness, prior computing experience and acceptance of an e-learning system / Lai Ming Ling and Chong Moi Moi , 2007 .

[52]  Chechen Liao,et al.  Applying the Expectancy Disconfirmation and Regret Theories to Online Consumer Behavior , 2011, Cyberpsychology Behav. Soc. Netw..

[53]  Alberto Albuquerque Gomes.  Yin, Robert K. Estudo de caso - Planejamento e metodos. Porto Alegre, Bookman, 2005 , 2008 .

[54]  Jian-Liang Chen,et al.  The effects of education compatibility and technological expectancy on e-learning acceptance , 2011, Comput. Educ..

[55]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[56]  Tzyh-Lih Hsia,et al.  A study of student satisfaction in a blended e-learning system environment , 2010, Comput. Educ..

[57]  Rajiv Kohli,et al.  Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics , 2002, Inf. Syst. Res..

[58]  Richard A. Armstrong,et al.  Sample size estimation and statistical power analyses , 2010 .

[59]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[60]  Hui-Tzu Min,et al.  Understanding continuance intention of knowledge creation using extended expectation–confirmation theory: an empirical study of Taiwan and China online communities , 2010, Behav. Inf. Technol..

[61]  Xin Lai,et al.  A Monte Carlo Study of the Effects of Common Method Variance on Significance Testing and Parameter Bias in Hierarchical Linear Modeling , 2013 .

[62]  M. Summak,et al.  Technology readiness of primary school teachers: A case study in Turkey , 2010 .

[63]  J. Marôco.  Análise de Equações Estruturais: Fundamentos Teóricos, Software e Aplicações , 2010 .

[64]  A. Parasuraman,et al.  Technology Readiness Index (Tri) , 2000 .

[65]  Nihat Erdoğmuş,et al.  An Investigation of the Effects of Technology Readiness on Technology Acceptance in e-HRM , 2011 .

[66]  Paul Juinn Bing Tan.  Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan , 2013 .

[67]  Chao-Min Chiu,et al.  Understanding e-learning continuance intention: An extension of the Technology Acceptance Model , 2006, Int. J. Hum. Comput. Stud..

[68]  Ming Ling Lai,et al.  Technology readiness, internet self‐efficacy and computing experience of professional accounting students , 2008 .

[69]  Cees J. Gelderman,et al.  Choosing self-service technologies or interpersonal services—The impact of situational factors and technology-related attitudes , 2011 .

[70]  Richard A. Spreng,et al.  A Reexamination of the Determinants of Consumer Satisfaction , 1996 .

[71]  Anatália Saraiva Martins Ramos,et al.  Continued usage of e-learning: Expectations and performance , 2013 .

[72]  Ching-Jen Chen,et al.  Validating the Satisfaction and Continuance Intention of E-learning Systems: Combining TAM and IS Success Models , 2012, Int. J. Distance Educ. Technol..

[73]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[74]  Donyaprueth Krairit,et al.  Satisfaction in E-Learning: The Context of Supplementary Instruction. , 2006 .

[75]  Brigitte Maier,et al.  Students' expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction , 2010, Comput. Educ..

[76]  Jiun-Sheng Chris Lin,et al.  The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies , 2007, Comput. Hum. Behav..

[77]  Eduardo Salas,et al.  Optimizing e‐learning: Research‐based guidelines for learner‐controlled training , 2004 .

[78]  Chad W. Autry,et al.  Assessing interfirm collaboration/technology investment tradeoffs: The effects of technological readiness and organizational learning , 2009 .