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Toward Preprototype User Acceptance Testing of New Information Systems: Implications for Software Project Management
Abstract
Errors in requirements specifications have been identified as a major contributor to costly software project failures. It would be highly beneficial if information systems developers could verify requirements by predicting workplace acceptance of a new system based on user evaluations of its specifications measured during the earliest stages of the development project, ideally before building a working prototype. However, conventional wisdom among system developers asserts that prospective users must have direct hands-on experience with at least a working prototype of a new system before they can provide assessments that accurately reflect future usage behavior after workplace implementation. The present research demonstrates that this assumption is only partially true. Specifically, it is true that stable and predictive assessments of a system's perceived ease of use should be based on direct behavioral experience using the system. However, stable and behaviorally predictive measures of perceived usefulness can be captured from target users who have received information about a system's functionality, but have not had direct hands-on usage experience. This distinction is key because, compared to ease of use, usefulness is generally much more strongly linked to future usage intentions and behaviors in the workplace. Two longitudinal field experiments show that preprototype usefulness measures can closely approximate hands-on based usefulness measures, and are significantly pr…
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“Perceived ease of use has been theorized to be closely associated with individuals' self-efficacy beliefs and procedural knowledge, which requires handson experience and execution of skills (Davis et al., 1989;Venkatesh, 2000;Davis & Venkatesh, 2004).”
From Technology Acceptance Model 3 and a Research Agenda on Interventions (2008) · cites this paper as Davis & Venkatesh, 2004
Citations
Cites
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Self-citationUser Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Self-citation(1975) · PhilPapers (PhilPapers Foundation) · Cited by 36,737
(1980) · Medical Entomology and Zoology · Cited by 25,810
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
Self-citationA Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
Self-citationSelf-efficacy mechanism in human agency
Bandura, A. (1982) · American Psychologist · doi:10.1037/0003-066x.37.2.122 · Cited by 15,975
Understanding information technology usage: a test of competing models
Taylor, S., & Todd, P. A. (1995) · Information Systems Research · doi:10.1287/isre.6.2.144 · Cited by 8,815
Anderson, J. R. (2013) · doi:10.4324/9781315799438 · Cited by 6,761
Flagged first, then by citations
2 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- 10.1111/j.1540-5915.1996.tb01822.x · DOI · Paper page
- jones, 1996, Patterns of Software Systems Failure and Success
- 1994, Charting the Seas of Information Technology
- pfleeger, 1998, Software Engineering Theory and Practice
- markus, 1994, if we build it, they will come: designing information systems that people want to use, Sloan Manage Rev, 11
- mann, 2002, why software is so bad, Technol Rev, 33
Cited by
Sorted by citations
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
“…recent work has challenged the role of behavioral intention as the key predictor of technology use and introduced a new theoretical construct (i.e., habit) as another critical predictor of technology use (e.g., Davis and Venkatesh 2004; Kim and Malhotra 2005; Kim et al. 2005; Limayem et al. 2007).”
Self-citationTechnology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159
“Perceivedeaseofusehasbeentheorizedtobecloselyassociatedwith individuals’self-efficacybeliefsandproceduralknowledge,whichrequireshands-onexperienceandexecutionofskills(Davisetal.,1989;Venkatesh,2000;Davis &Venkatesh,2004).”
BackgroundMethodInfluentialSelf-citationA meta-analysis of the technology acceptance model
King, W. R., & He, J. (2006) · Information & Management · doi:10.1016/j.im.2006.05.003 · Cited by 2,906
Technology acceptance model: a literature review from 1986 to 2013
Marangunić, N., & Granić, A. (2015) · Universal Access in the Information Society · doi:10.1007/s10209-014-0348-1 · Cited by 2,458
“Usage measures: usage measures for operationalization of actual system usage like attitude toward technology [23], usage perception [37, 61, 77], and actual usage of technology [26].”
BackgroundDoes the technology acceptance model predict actual use? A systematic literature review
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010) · Information and Software Technology · doi:10.1016/j.infsof.2009.11.005 · Cited by 851
Alalwan, A. A. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.008 · Cited by 962
“…in terms of using smartphones and the attached apps or in terms of ordering food from restaurants, could be a very critical component in shaping customer intention and behaviour towards MFOAs (Alalwan, Dwivedi, Rana, & Algharabat, 2018; Davis & Venkatesh, 2004; Eriksson, Kerem, & Nilsson, 2008).”
BackgroundAlalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2017.08.026 · Cited by 576
“Also, Venkatesh et al. (2012) argued that while behavioural intention has been identified as the most significant factor predicting technology adoption, habit, according to Davis and Venkatesh (2004), has been recognised as an alternative determinant to technology usage.”
BackgroundSykes, T. A., Venkatesh, V., & Gosain, S. (2009) · MIS Quarterly · doi:10.2307/20650296 · Cited by 522
Self-citationUnderstanding information systems continuance: The case of Internet-based learning technologies
Limayem, M., & Cheung, C. M. K. (2008) · Information & Management · doi:10.1016/j.im.2008.02.005 · Cited by 546
“In IS, Davis and Venkatesh [11] found that users’ hands-on experience was the key driver of sustained usage.”
BackgroundManis, K. T., & Choi, D. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2018.10.021 · Cited by 494
Venkatesh, V., & Goyal (2010) · MIS Quarterly · doi:10.2307/20721428 · Cited by 512
Self-citationEmpirical investigation of e-learning acceptance and assimilation: A structural equation model
Al‐Gahtani, S. S. (2014) · Applied Computing and Informatics · doi:10.1016/j.aci.2014.09.001 · Cited by 551
“Prior individual information and perceptions – about how easy a system is to use from important referents – are unlikely to be stable as individuals need to form these stable perceptions based on their own general computer beliefs and hands-on experience with the sys-tem [21].”
BackgroundEl-Masri, M., & Tarhini, A. (2017) · Educational technology research and development · doi:10.1007/s11423-016-9508-8 · Cited by 440
“According to Davis and Venkatesh (2004), habit has been recognized as an alternative determinant of technology usage along with behavioural intention.”
BackgroundDrivers of Precision Agriculture Technologies Adoption: A Literature Review
Pierpaoli, E., Carli, G., Pignatti, E., & Canavari, M. (2013) · Procedia Technology · doi:10.1016/j.protcy.2013.11.010 · Cited by 463
Evaluating the Role of Trust in Consumer Adoption of Mobile Payment Systems: An Empirical Analysis
Chandra, S., Srivastava, S. C., & Theng, Y. (2010) · Communications of the Association for Information Systems · doi:10.17705/1cais.02729 · Cited by 462
“Numerous studies over the past decade have validated the significant relationship of PEOU with the AI [Davis et al., 1989; Venkatesh, 2000; Davis and Venkatesh, 2004].”
BackgroundResultResearch Note—Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005) · Information Systems Research · doi:10.1287/isre.1050.0070 · Cited by 381
The role of task-technology fit as users’ motivation to continue information system use
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009) · Computers in Human Behavior · doi:10.1016/j.chb.2009.02.006 · Cited by 382
“…indirectly supported through studies where the argument is that prior behavior (i.e., acceptance of functions) is the most important antecedent of future behavior (i.e., continued use of the functions) (Bagozzi & Kimmel, 1995; Conner & Armitage, 1998; Davis & Venkatesh, 2004; Norman & Smith, 1995).”
BackgroundZhang, P. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.1.11 · Cited by 331
Pookulangara, S., & Koesler, K. (2011) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2011.03.003 · Cited by 354
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015) · Journal of Financial Services Marketing · doi:10.1057/fsm.2015.5 · Cited by 281
Hossain, M. A., Quaresma, R., & Rahman, H. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.09.016 · Cited by 302
Predicting information technology usage in the car
Osswald, S., Wurhofer, D., Trösterer, S., Beck, E., & Tscheligi, M. (2012) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/2390256.2390264 · Cited by 261
“A preprototype acceptance evaluation has a ”greater informational value than their postprototype counterparts” [6] because it is much more flexible, costs less and allows for early modification of system attributes.”
BackgroundMethodInfluentialTeam Cognition: Development and Evolution in Software Project Teams
He, J., Butler, B. S., & King, W. R. (2007) · Journal of Management Information Systems · doi:10.2753/mis0742-1222240210 · Cited by 255
An empirical investigation on the adoption of cryptocurrencies among the people of mainland China
Shahzad, F., Xiu, G., Wang, J., & Shahbaz, M. (2018) · Technology in Society · doi:10.1016/j.techsoc.2018.05.006 · Cited by 264
Understanding the sustainability of a virtual community: model development and empirical test
Cheung, C. M. K., & Lee, M. (2009) · Journal of Information Science · doi:10.1177/0165551508099088 · Cited by 250
Behavioral software engineering: A definition and systematic literature review
Lenberg, P., FELDT, R. H., & Wallgren, L. G. (2015) · Journal of Systems and Software · doi:10.1016/j.jss.2015.04.084 · Cited by 257
Expectation confirmation: An examination of three competing models
Brown, S. A., Venkatesh, V., Kuruzovich, J., & Massey, A. P. (2007) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.09.008 · Cited by 244
“As noted earlier, the strengths of the context studied here tie to the extent to which information systems investments have grown and continue to grow in organizations but the failure rate is still alarmingly high (see Davis & Venkatesh, 2004).”
BackgroundSelf-citationHoque, M. R., Bao, Y., & Sorwar, G. (2016) · Informatics for Health and Social Care · doi:10.3109/17538157.2015.1075541 · Cited by 252
“Davis (41) himself argued that future acceptance research needs to address how other variables affect PEU, PU, and user acceptance.”
BackgroundMethodOnline drivers of consumer purchase of website airline tickets
Rodríguez, T. E., & Carvajal-Trujillo, E. (2013) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2013.06.018 · Cited by 222
Expectation Confirmation in Technology Use
Brown, S. A., Venkatesh, V., & Goyal, S. (2011) · Information Systems Research · doi:10.1287/isre.1110.0357 · Cited by 229
“…to much prior research that, perhaps for practical reasons, has typically examined use only for a short duration following the implementation (e.g., Davis and Venkatesh 2004), our work helps managers understand use after the shakedown phase has passed and at least a semblance of a steady state has…”
BackgroundInfluentialSelf-citationCannibalization or synergy? Consumers' channel selection in online–offline multichannel systems
Kollmann, T., Kuckertz, A., & Kayser, I. (2012) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2011.11.008 · Cited by 202
“However, the integration of electronic channels by traditional enterprises goes along with substantial costs and increased risks, given that the failure rate in terms of acceptance of innovative information systems remains high (Davis and Venkatesh, 2004).”
Soror, A. A., Hammer, B. I., Steelman, Z. R., Davis, F. D., & Limayem, M. (2015) · Information Systems Journal · doi:10.1111/isj.12065 · Cited by 220
“Habits play a signi fi cant role in automatically driving a wide array of behavioural responses such as technology use (Davis & Venkatesh, 2004; Kim & Malhotra, 2005; Kim et al ., 2005; Limayem et al ., 2007; Venkatesh et al ., 2012 ).”
BackgroundSelf-citationThe Development of Online Project Based Collaborative Learning Using ADDIE Model
Razali, S. N., & Shahbodin, F. (2015) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2015.06.392 · Cited by 216
Expectation Confirmation in Information Systems Research: A Test of Six Competing Models
Brown, S. A., Venkatesh, V., & Goyal, S. (2014) · MIS Quarterly · doi:10.2139/ssrn.3681718 · Cited by 199
Self-citationSprenger, D. A., & Schwaninger, A. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00243-4 · Cited by 232
Hoque, M. R. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0289-0 · Cited by 181
Abu-Dalbouh (2013) · Journal of Computer Science · doi:10.3844/jcssp.2013.763.770 · Cited by 177
“According to TAM individuals’ intention to use technology determines the actual use of the application and attitudes toward technology affect the intention (Davis et al ., 1989; Davis and Venkatesh, 2004; Venkatesh et al ., 2012).”
BackgroundTechnology acceptance model for the use of information technology in universities
Jan, A. U., & Contreras, V. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.11.009 · Cited by 152
“Davis and Venkatesh (2004) shows the rate of failure for information systems on a sample of 6700 projects in 500 companies, studied by Software Productivity Research, in which 24% of projects were canceled and 17% had over costs.”
BackgroundStudents’ satisfaction and continued intention toward e-learning: a theory-based study
Rajeh, M. T., Abduljabbar, F. H., Al-Qahtani, S. M., Waly, F. J., Alnaami, I., Aljurayyan, A. N., & Alzaman, N. S. (2021) · Medical Education Online · doi:10.1080/10872981.2021.1961348 · Cited by 147
“The willingness to adopt e-learning would be considered successfully implemented if students moved from initial adoption to continued usage intention [14,30].”
BackgroundMethodExploring the emotional antecedents and outcomes of technology acceptance
Lu, Y., Papagiannidis, S., & Alamanos, E. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.056 · Cited by 174
Feeling the Sense of Community in Social Networking Usage
Zhang, Z. (2009) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2009.2023455 · Cited by 144
“For this reason, a longitudinal study would be helpful to monitor the patterns of social connection development, and to examine the relationship between SOC and user behaviors over time [19].”
BackgroundRezaei, R., Safa, L., & Ganjkhanloo, M. M. (2020) · Global Ecology and Conservation · doi:10.1016/j.gecco.2020.e00941 · Cited by 140
IT projects success factors: a literature review
Iriarte, C., & Bayona-Oré, S. (2021) · International journal of information systems and project management · doi:10.12821/ijispm080203 · Cited by 174
“The perception of the stakeholders rather than being a global perspective is due to a perception of the achievement of their own objectives [34-36].”
BackgroundNadeem, M. A., Liu, Z., Pitafi, A. H., Younis, A., & Xu, Y. (2021) · SAGE Open · doi:10.1177/2158244021998704 · Cited by 161
“The study of Davis and Venkatesh (2004) revealed that the impact of the perceived ease of use on the perceived usefulness will be higher and stronger as the user will get a better understanding and capability to complete sophisticated objectives (i.e., the perceived usefulness) grounded on the…”
BackgroundInvestigating Factors Affecting Elderly's Intention to Use m-Health Services: An Empirical Study
Quaosar, G. M. A. A., Hoque, M. R., & Bao, Y. (2017) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2017.0111 · Cited by 133
“28 All items except the demographic profile were measured using a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5).”
Electronic Health Interventions for Patients With Breast Cancer: Systematic Review and Meta-Analyses
Singleton, A. C., Raeside, R., Hyun, K. K., Partridge, S. R., Tanna, G. L. D., Hafiz, N., Tu, Q., Tat-Ko, J., Sum, S. C. M., Sherman, K. A., et al. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.21.01171 · Cited by 141
Wouters, M. J. F., & Roijmans, D. (2010) · Contemporary Accounting Research · doi:10.1111/j.1911-3846.2010.01055.x · Cited by 127
Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour
Meschtscherjakov, A., Wilfinger, D., Scherndl, T., & Tscheligi, M. (2009) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/1620509.1620526 · Cited by 124
“An early assessment of UA in the development of ICTs can give advantages on both cost and effort [3].”
BackgroundMethodResultInfluentialIntention to use e-banking services in the Jordanian commercial banks
Yaseen, S. G., & Qirem, I. A. E. (2018) · International Journal of Bank Marketing · doi:10.1108/ijbm-05-2017-0082 · Cited by 132
E. Koh, C., R. Prybutok, V., D. Ryan, S., & Andy" Wu, Y. (2010) · Informing Science: The International Journal of an Emerging Transdiscipline · doi:10.28945/1326 · Cited by 120
Theories Informing eHealth Implementation: Systematic Review and Typology Classification
Heinsch, M., Wyllie, J., Carlson, J., Wells, H., Tickner, C., & Kay-Lambkin, F. (2021) · Journal of Medical Internet Research · doi:10.2196/18500 · Cited by 114
“Individual theories that occurred most frequently in the literature were the TAM by Davis and Venkatesh [76] (33 instances), UTAUT by Venkatesh [77] (32 instances), and Diffusion of Innovations Theory by Rogers [78] (16 instances).”
BackgroundGroen, B. A. C., Wouters, M. J. F., & Wilderom, C. P. M. (2012) · Management Accounting Research · doi:10.1016/j.mar.2012.01.001 · Cited by 110
Goal-oriented visualizations of activity tracking
Santos, J. L., Govaerts, S., Verbert, K., & Duval, E. (2012) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2330601.2330639 · Cited by 106
“Paper prototyping is an important first step in user interface design to get quick feedback [28] and minimize costs in the software design process [11].”
Smith, J. A. B., & Sivo, S. A. (2011) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2011.01223.x · Cited by 119
Durak, H. Y. (2018) · Journal of Computing in Higher Education · doi:10.1007/s12528-018-9200-6 · Cited by 103
“According to Davis and Venkatesh (2004), skills of using technology are accepted as an alternative determinant of behavioral intention and use of technology.”
BackgroundAntecedents of application service continuance: A synthesis of satisfaction and trust
Kim, J., Hong, S., Min, J., & Lee, H. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.01.142 · Cited by 106
Open strategy-making with crowds and communities: Comparing Wikimedia and Creative Commons
Dobusch, L., & Kapeller, J. (2017) · Long Range Planning · doi:10.1016/j.lrp.2017.08.005 · Cited by 108
Jariyapan, P., Mattayaphutron, S., Gillani, S. N., & Shafique, O. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.814087 · Cited by 102
“For instance, a high-level identity can provide a high quality report in the context of text processing software, whereas a low-level identity can be marking keys or using a particular software function (Davis and Venkatesh, 2004).”
BackgroundDesign characteristics of virtual learning environments: state of research
Mueller, D., & Strohmeier, S. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.06.017 · Cited by 110
Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2019) · Education and Information Technologies · doi:10.1007/s10639-019-09965-5 · Cited by 101
“According to Davis and Venkatesh (2004), “HT has been recognized as an alternative predictor of technology usage along with behavioral intention”.”
BackgroundDimensions of self-efficacy in the study of smart phone acceptance
Chen, K., Chen, V., & Yen, D. C. (2011) · Computer Standards & Interfaces · doi:10.1016/j.csi.2011.01.003 · Cited by 96
“, [15,56]) but our study focuses on acceptance of software and hardware as a whole.”
BackgroundCheung, C. M. K., & Limayem, M. (2005) · Journal of the Association for Information Systems · Cited by 92
The privacy paradox and its implications for learning analytics
Tsai, Y., Whitelock‐Wainwright, A., & Gašević, D. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375536 · Cited by 92
“As shown in this literature, the failure to meet service user expectations within the pre-implementation stages of a new technology may result in limited adoption due to the dissatisfaction that arises [6, 13].”
BackgroundEdo, O. C., Ang, D. S., Etu, E.-E., Tenebe, I. T., Edo, S., & Diekola, O. A. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100186 · Cited by 87
Palas, J. U., Sorwar, G., Hoque, M. R., & Achchuthan, S. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01917-3 · Cited by 103
“Effort Expectancy (EE) [41, 81, 120, 164] H2: EE has positive impact on the elderly’s intention to use mHealth [77, 88, 92]”
BackgroundPredicting older adults’ perceptions about a computer system designed for seniors
Mitzner, T. L., Rogers, W. A., Fisk, A. D., Boot, W. R., Charness, N. H., Czaja, S. J., & Sharit, J. (2014) · Universal Access in the Information Society · doi:10.1007/s10209-014-0383-y · Cited by 86
“Behavioral intention is a precursor to actual behavior and is significantly correlated with actual behavior [1, 19].”
Zhang, P., Carey, J., Te’eni, D., & Tremaine, M. (2005) · Communications of the Association for Information Systems · doi:10.17705/1cais.01529 · Cited by 82
“• go forward as planned, • modify or refine requirements to improve acceptability, or • abandon to avert major losses [ Davis and Venkatesh, 2004 ].”
BackgroundInfluentialZhang, Y. (2017) · International Conference on Machinery, Electronics and Control Simulation (MECS) · doi:10.2991/mecs-17.2017.52 · Cited by 81
Lippert, S. K. (2007) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2007.900792 · Cited by 80
“, [1], [4], [37], [98], [117], and [139]) have found perceived ease of use to be important in technology adoption.”
BackgroundBettiga, D., Lamberti, L., & Lettieri, E. (2019) · Health Care Management Science · doi:10.1007/s10729-019-09468-2 · Cited by 81
Herrenkind, B., Nastjuk, I., Brendel, B., Trang, S. T.-N., & Kolbe, L. M. (2019) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2019.07.023 · Cited by 86
Must evaluation methods be about usability? Devising and assessing the utility inspection method
Johannessen, G. H. J., & Hornbæk, K. (2012) · Behaviour and Information Technology · doi:10.1080/0144929x.2012.751708 · Cited by 78
“Some work in software engineering and information systems do work from related notions (e.g. intention to use, Davis and Venkatesh 2004), but, unfortunately, this literature is rarely related to usability evaluation and HCI (Sutcliffe et al. 2011).”
BackgroundWu, M.-C., & Kuo, F. (2008) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/1453794.1453801 · Cited by 79
“In accordance with self-perception theory, prior work has demonstrated that IS past usage has a significant impact on evaluations of usefulness and intention and acts as a principal predictor of future behavior (Davis & Venkatesh, 2004; Kim & Malhotra, 2005; Venkatesh et al., 2000).”
Factors Influencing Student Information Technology Adoption
VanDerSchaaf, H., Daim, T. U., & Başoğlu, N. (2021) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3053966 · Cited by 85
“…such as the influence gender and age on adoption [47], e-Government services tailored for senior citizens [48], preprototype user acceptance testing [49], e-commerce website adoption [50], consumer acceptance of personal technology-enabled services [51], consumers’ intention to purchase on social…”
BackgroundAlmarzouqi, A., Aburayya, A., & Salloum, S. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0272735 · Cited by 91
“Constructs on the adoption of an EMR system were measured using 3 items as advised by [70], while constructs related to behavioural intention were measured using 3 items as advised by [71] study.”
MethodEffects of characteristics of in-store retail technology on customer citizenship behavior
Gong, T., Wang, C., & Lee, K. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102488 · Cited by 78
MoHTAM: A Technology Acceptance Model for Mobile Health Applications
Mohamed, A. H. H. M., Tawfik, H., Al‐Jumeily, D., & Norton, L. (2011) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2011.79 · Cited by 70
“Davis et al [2] defined Perceived Usefulness as “The degree to which a person believes that using a particular system would enhance his or her job performance.”
BackgroundMethodResultInfluentialResatsch, F., Sandner, U., Leimeister, J. M., & Krcmar, H. (2008) · Electronic Markets · doi:10.1080/10196780802265728 · Cited by 68
“Their findings indicated that user acceptance could be assessed at this early stage of system development, that is, stimuli in the form of non-interactive pre-prototypes could yield good approximations of user perceptions (Davis and Venkatesh 2004).”
BackgroundMethodResultInfluentialAntecedents of internet acceptance and use as an information source by tourists
García, J. A. C., Frías, D. M., & Rodríguez, M. A. (2009) · Online Information Review · doi:10.1108/14684520910969952 · Cited by 71
“The TAM offers a rich research framework for the prediction of end-user IT acceptance (e.g. Venkatesh and Davis, 2000; Venkatesh et al., 2003; Davis and Venkatesh, 2004), and it has been applied successfully by different authors to explain internet usage (e.”
BackgroundPramatari, K. C., & Theotokis, A. (2009) · European Journal of Information Systems · doi:10.1057/ejis.2009.40 · Cited by 76
“In parallel, Davis & Venkatesh (2004), in their pre-prototype user acceptance framework, propose that user perceptions about a system can be predicted if we know user perceptions about the general system concept, which describes what the final system is designed to do, that is its intended…”
Managing user expectations on software projects: Lessons from the trenches
Petter, S. (2008) · International Journal of Project Management · doi:10.1016/j.ijproman.2008.05.014 · Cited by 68
“[18] determined that users develop their opinions and expectations during the prototyping stage and remained consistent even after they had an opportunity to use the system.”
BackgroundWasserman, E., & Migdal, R. (2019) · Online Learning · doi:10.24059/olj.v23i1.1299 · Cited by 91
Aloysius, J. A., Hoehle, H., Goodarzi, S., & Venkatesh, V. (2016) · Annals of Operations Research · doi:10.1007/s10479-016-2276-3 · Cited by 67
“Third, and related to the previous implication, we contextualize technology adoption (Davis and Venkatesh 2004) and privacy calculus (Dinev and Hart 2006) theories and found support for them in the context of emerging services and cutting-edge technologies.”
InfluentialSelf-citationCultural Influence on Adoption and Use of e-Health: Evidence in Bangladesh
Hoque, M. R., & Bao, Y. (2015) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2014.0128 · Cited by 63
“20 PU is defined as ‘‘ . the degree to which a person believes that using a particular technology will enhance his performance,’’ and PEU is defined as ‘‘ . the degree to which person believes that using a particular system would be free of effort.’’”
The Student Expectations of Learning Analytics Questionnaire
Whitelock‐Wainwright, A., Gašević, D., Tejeiro, R., Tsai, Y., & Bennett, K. (2019) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12366 · Cited by 63
“The importance of focusing on service user expectations has been demonstrated in both health services (Bowling et al., 2012; Thompson & Suñol, 1995) and technology adoption research (Brown et al., 2012; Brown et al., 2014; Davis & Venkatesh, 2004).”
InfluentialKowatsch, T., & Maaß, W. (2012) · Lecture notes in business information processing · doi:10.1007/978-3-642-33244-9_14 · Cited by 61
“Having its roots in the Information Systems discipline, TAM describes determinants of technology adoption and was published in various variations in the past [8,13,22].”
BackgroundYuan, Y., Tsai, S., Dai, C.-Y., Chen, H.-M., Chen, W.-F., Wu, C., Li, G., & Wang, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183994 · Cited by 60
Toral, S. L., Barrero, F., Torres, M. D. R. M., Vázquez, S. G., & Moreno, A. J. L. (2005) · IEEE Transactions on Education · doi:10.1109/te.2005.853074 · Cited by 59
“As shown in Table I, different groups of questions measuring several pedagogical dimensions have been selected: learning goal orientation, application-specific self-efficacy and enjoyment [22]; temporal dissociation, focused immersion, curiosity, playfulness, and willingness [23]; and user friendliness, usefulness, be…”
BackgroundToral, S. L., Barrero, F., & Torres, M. D. R. M. (2006) · Computers & Education · doi:10.1016/j.compedu.2005.12.003 · Cited by 58
Post‐adoption online shopping continuance
Liu, C., & Forsythe, S. M. (2010) · International Journal of Retail & Distribution Management · doi:10.1108/09590551011020110 · Cited by 58
Chou, H.-K., Lin, I.-C., Woung, L., & Tsai, M.-T. (2010) · Journal of Medical Systems · doi:10.1007/s10916-010-9630-9 · Cited by 58
“An e-learning program can truly be considered as successfully implemented when a significant number of students have moved from initial adoption to continuance usage [44, 45].”
BackgroundAgarwal, V., & Sahu, R. (2021) · Journal of Foodservice Business Research · doi:10.1080/15378020.2021.1951074 · Cited by 64
“Then this habit also starts influencing their future intentions as Davis and Venkatesh (2004) reported habit as an essential predictor of technology adoption behavior.”
BackgroundNguyen, G. D., & Hà, M.-T. (2021) · Cogent Business & Management · doi:10.1080/23311975.2021.1980248 · Cited by 69
“In our view of m-shopping, because users are adapting and experiencing with the emerged service, their experience and adaptation will lead from an initial phase of usage to the CI in the long-term, as reported by prior seminal works (Davis & Venkatesh, 2004; Mou et al., 2017).”
ResultBao, Y., Hoque, M. R., & Wang, S. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.01.003 · Cited by 55
Bile Hassan, I., Murad, M. A. A., El-Shekeil, I., & Liu, J. (2022) · Informatics · doi:10.3390/informatics9020031 · Cited by 55
Chen, T., Chen, J., Or, C. K., & Lo, F. P. (2022) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2022.103360 · Cited by 52
User involvement in e-government development projects
Følstad, A., Jørgensen, H. D., & Krogstie, J. (2004) · Nordic Conference on Human-Computer Interaction · doi:10.1145/1028014.1028047 · Cited by 52
Perceptions of Digital Assistants From Early Adopters Aged 55+
Koon, L. M., McGlynn, S. A., Blocker, K. A., & Rogers, W. A. (2020) · Ergonomics in Design: The Quarterly of Human Factors Applications · doi:10.1177/1064804619842501 · Cited by 51
“Prior research examining technology acceptance models for older adult populations has only focused on behavioral intention to adopt (e.g., Davis & Venkatesh, 2004) and initial perceptions of ease of use and usefulness (e.g., Mitzner et al., 2016).”
BackgroundDesign Thinking in Digital Innovation Projects—Exploring the Effects of Intangibility
Przybilla, L., Klinker, K., Lang, M., Schreieck, M., Wiesche, M., & Krcmar, H. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2020.3036818 · Cited by 65
“These observations are in line with findings that rudimentary, low-fidelity prototypes are sufficient to conduct initial tests of digital innovation [42].”
ResultEdwards, D., Subramanian, N., Chaudhuri, A., Morlacchi, P., & Zeng, W. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05307-4 · Cited by 59
Metadata
- Authors
- Davis, F. D., & Venkatesh, V.
- Affiliations
- University of Arkansas at Fayetteville (US)
- University of Maryland, College Park (US)
- ORCID
- Davis, F.D.
- Venue
- IEEE Transactions on Engineering Management
- Published
- 2004-02
- Volume
- 51
- Issue
- 1
- Pages
- 31-46
- ISSN
- 0018-9391 (ISSN-L)
- OpenAlex
- W2130427181
- Cited by
- 582
- References
- 78
- Citations by year
- 2026: 16 · 2025: 31 · 2024: 28 · 2023: 29 · 2022: 41 · 2021: 32 · 2020: 29 · 2019: 36 · 2018: 30 · 2017: 30 · 2016: 24 · 2015: 32 · 2014: 35 · 2013: 37 · 2012: 18
- FWCI
- 40.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Engineering Management (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- user acceptance testing, perceived usefulness, perceived ease of use, requirements verification, software project management, intention to use, information system development, requirements errors
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