The technology acceptance scale: Its Bayesian psychometrics assessed in a factor analysis via Markov chain Monte Carlo models
暂无分享,去创建一个
[1] Neville A. Stanton,et al. Progressing Toward Airliners’ Reduced-Crew Operations: A Systematic Literature Review , 2019, The International Journal of Aerospace Psychology.
[2] František Milichovský. Financial Key Performance Indicators in Engineering Companies , 2015 .
[3] J. Loehlin. Latent variable models , 1987 .
[4] J. Krems,et al. Learning and development of trust, acceptance and the mental model of ACC. A longitudinal on-road study , 2015 .
[5] Zoubin Ghahramani,et al. General Latent Feature Models for Heterogeneous Datasets , 2017, J. Mach. Learn. Res..
[6] Jean-Paul Fox,et al. An Aggregate IRT Procedure for Exploratory Factor Analysis , 2015 .
[7] Viswanath Venkatesh,et al. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..
[8] Neville A. Stanton,et al. Exploring Bayesian analyses of a small-sample-size factorial design in human systems integration: the effects of pilot incapacitation , 2019, Human-Intelligent Systems Integration.
[9] E. Wagenmakers,et al. A default Bayesian hypothesis test for correlations and partial correlations , 2012, Psychonomic bulletin & review.
[10] Fred D. Davis,et al. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .
[11] Jan C Zoellick,et al. Assessing acceptance of electric automated vehicles after exposure in a realistic traffic environment , 2019, PloS one.
[12] S. Chib,et al. Understanding the Metropolis-Hastings Algorithm , 1995 .
[13] Henk A. L. Kiers,et al. Alternating least squares algorithms for simultaneous components analysis with equal component weight matrices in two or more populations , 1989 .
[14] Nai-Hua Chen,et al. Domestic Technology Adoption: Comparison of Innovation Adoption Models and Moderators , 2016 .
[15] Christian Schumacher,et al. Identifying relevant and irrelevant variables in sparse factor models , 2017 .
[16] Jungkun Park,et al. What Matters the Most? The Key Factors That Lead to a New Service Adoption , 2016 .
[17] Marika Hoedemaeker,et al. Behavioural adaptation to driving with an adaptive cruise control (ACC) , 1998 .
[18] T. Berge,et al. Generic global indentification in factor analysis , 1997 .
[19] Neal R Haddaway,et al. Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources , 2020, Research synthesis methods.
[20] M. Lee,et al. Statistical Evidence in Experimental Psychology , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.
[21] N. Lazar,et al. The ASA Statement on p-Values: Context, Process, and Purpose , 2016 .
[22] F. E. Ritter,et al. Learning Curve, The , 2001 .
[23] Richard Romano,et al. Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority , 2020, Hum. Factors.
[24] Wan-I Lee,et al. Assessing the effects of consumer involvement and service quality in a self‐service setting , 2011 .
[25] David B. Hitchcock,et al. A History of the Metropolis–Hastings Algorithm , 2003 .
[26] Fred D. Davis,et al. A critical assessment of potential measurement biases in the technology acceptance model: three experiments , 1996, Int. J. Hum. Comput. Stud..
[27] R. Schoot,et al. Small Sample Size Solutions , 2020 .
[28] J. Krems,et al. The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial information , 2013 .
[29] R. Gorsuch. Exploratory factor analysis: its role in item analysis. , 1997, Journal of personality assessment.
[30] Philip B. Stark,et al. Cargo‐cult statistics and scientific crisis , 2018, Significance.
[31] Rémi Piatek,et al. Maintaining (Locus of) Control? Data Combination for the Identification and Inference of Factor Structure Models , 2016 .
[32] Linda Ng Boyle,et al. Extending the Technology Acceptance Model to assess automation , 2011, Cognition, Technology & Work.
[33] Michael D. Lee,et al. Discussion points for Bayesian inference , 2020, Nature Human Behaviour.
[34] Iven Van Mechelen,et al. UvA-DARE ( Digital Academic Repository ) A structured overview of simultaneous component based data integration , 2009 .
[35] E L Hamaker,et al. A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models , 2016, Multivariate behavioral research.
[36] B. Efron. Why Isn't Everyone a Bayesian? , 1986 .
[37] Kim-Phuong L. Vu,et al. An Investigation of the Harbor Pilot Concept for Single Pilot Operations , 2015 .
[38] Tom Brijs,et al. Driving with intelligent speed adaptation: Final results of the Belgian ISA-trial , 2007 .
[39] Gordon B. Davis,et al. User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..
[40] S. D. Winter,et al. A Systematic Review of Bayesian Articles in Psychology: The Last 25 Years , 2017, Psychological methods.
[41] Karel Brookhuis,et al. Towards defining a unified concept for the acceptability of Intelligent Transport Systems (ITS): A conceptual analysis based on the case of Intelligent Speed Adaptation (ISA) , 2010 .
[42] David Lindley,et al. The future of statistics-A Bayesian 21st Century , 1974 .
[43] J. Heckman,et al. Bayesian Exploratory Factor Analysis , 2014, Journal of econometrics.
[44] A. Apte,et al. Tradeoffs among Attributes of Resources in Humanitarian Operations: Evidence from United States Navy , 2020 .
[45] Sarah Depaoli,et al. A Tutorial on Using The Wambs Checklist to Avoid The Misuse of Bayesian Statistics , 2020 .
[46] David J. Spiegelhalter,et al. The ASA's p‐value statement, one year on , 2017 .
[47] Steven N. Goodman,et al. Aligning statistical and scientific reasoning , 2016, Science.
[48] Joel Lachter,et al. Enhanced ground support: lessons from work on reduced crew operations , 2017, Cognition, Technology & Work.
[49] Sebastian Hergeth,et al. Self-report measures for the assessment of human–machine interfaces in automated driving , 2019, Cognition, Technology & Work.
[50] Bradley P. Carlin,et al. Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .
[51] P. Mair,et al. Modern Psychometrics with R , 2018 .
[52] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[53] Joachim Vandekerckhove,et al. Editorial: Bayesian methods for advancing psychological science , 2018, Psychonomic bulletin & review.
[54] W. Ledermann. On the rank of the reduced correlational matrix in multiple-factor analysis , 1937 .
[55] Cigdem Altin Gumussoy,et al. Acceptance of the virtual item auctioning system in online games: The role of intrinsic motivation, extrinsic motivation, and trust , 2016 .
[56] I. Ajzen,et al. A Bayesian analysis of attribution processes. , 1975 .
[57] Duco Veen,et al. The Importance of Collaboration in Bayesian Analyses with Small Samples , 2020 .
[58] Lesley Strawderman,et al. Modelling driver acceptance of driver support systems. , 2018, Accident; analysis and prevention.
[59] Patricia Delhomme,et al. Simulator Training With a Forward Collision Warning System , 2012, Hum. Factors.
[60] Yi Zhang,et al. Modeling and Analysis of Bus Satisfaction Based on improved structural equation model , 2019 .
[61] Neville A. Stanton,et al. Evaluating the reduced flight deck crew concept using cognitive work analysis and social network analysis: comparing normal and data-link outage scenarios , 2019, Cognition, Technology & Work.
[62] Ang Li,et al. An Evaluation Analysis on Three-Wheeled Personal Mobility Vehicles , 2016, Int. J. Intell. Transp. Syst. Res..
[63] Marco Steinhauser,et al. Adverse Behavioral Adaptation to Adaptive Forward Collision Warning Systems: An Investigation of Primary and Secondary Task Performance. , 2020, Accident; analysis and prevention.
[64] Samuel B. Green,et al. Incorporating Uncertainty Into Parallel Analysis for Choosing the Number of Factors via Bayesian Methods , 2020, Educational and psychological measurement.
[65] Neville A. Stanton,et al. A future airliner’s reduced-crew: modelling pilot incapacitation and homicide-suicide with systems theory , 2019, Human-Intelligent Systems Integration.
[66] A. E. Bayraktaroglu,et al. Predicting the Intention to Use a Web‐Based Learning System: Perceived Content Quality, Anxiety, Perceived System Quality, Image, and the Technology Acceptance Model , 2014 .
[67] Scott D. Brown,et al. A simple introduction to Markov Chain Monte–Carlo sampling , 2016, Psychonomic bulletin & review.
[68] James L. Szalma,et al. A Meta-Analysis of Factors Influencing the Development of Trust in Automation , 2016, Hum. Factors.
[69] Joel Lachter,et al. Single Pilot Operations in Domestic Commercial Aviation , 2018, Hum. Factors.
[70] Jessie Y. C. Chen,et al. A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction , 2011, Hum. Factors.
[71] Mark S. Young,et al. What price ergonomics? , 1999, Nature.
[72] Aki Vehtari,et al. Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.
[73] James L. Szalma,et al. On the Application of Motivation Theory to Human Factors/Ergonomics , 2014, Hum. Factors.
[74] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[75] I. Cantarino,et al. An urban sprawl index based on multivariate and Bayesian factor analysis with application at the municipality level in Valencia , 2018 .
[76] Fred D. Davis,et al. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.
[77] Ting Wang,et al. Bayesian latent variable models for the analysis of experimental psychology data , 2018, Psychonomic bulletin & review.
[78] Dick de Waard,et al. A simple procedure for the assessment of acceptance of advanced transport telematics , 1997 .
[79] Jinsong Chen,et al. A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step , 2021 .
[80] Marco Del Giudice,et al. Effective Dimensionality: A Tutorial , 2020 .
[81] kuo-Wei Lee,et al. Design and validation of a knowledge map system—the case of construction industry in Taiwan , 2017 .
[82] Carolyn Petersen,et al. Beyond TAM and UTAUT: Future directions for HIT implementation research , 2019, J. Biomed. Informatics.
[83] Fred D. Davis. A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .
[84] David A. Spade. Markov chain Monte Carlo methods: Theory and practice , 2020 .
[85] Natasha Merat,et al. User acceptance of automated shuttles in Berlin-Schöneberg: A questionnaire study , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.
[86] Fred D. Davis. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..
[87] Viswanath Venkatesh,et al. Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead , 2016, J. Assoc. Inf. Syst..
[88] Marco Steinhauser,et al. Adaptive forward collision warnings: The impact of imperfect technology on behavioral adaptation, warning effectiveness and acceptance. , 2019, Accident; analysis and prevention.
[89] Scott A. Sisson,et al. Reversible Jump MCMC , 2011 .
[90] Lesley Strawderman,et al. Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. , 2017, Accident; analysis and prevention.
[91] Sarah Depaoli,et al. Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist , 2017, Psychological methods.
[92] František Milichovský,et al. Evaluations of Financial Performance Indicators Based on Factor Analysis in Automotive , 2019, Periodica Polytechnica Social and Management Sciences.
[93] John K Kruschke,et al. Posterior predictive checks can and should be Bayesian: comment on Gelman and Shalizi, 'Philosophy and the practice of Bayesian statistics'. , 2013, The British journal of mathematical and statistical psychology.
[94] Henriette Wallén Warner,et al. Factors Influencing Drivers' Speeding Behaviour , 2006 .