Restricted accessResearch articleFirst published online 2025-6
Using Business Simulation Games to Explore the Effects of Instructional and Technological Support on Students’ Psychological Needs,Cognitive Load,and Learning Achievement
Virtual-reality business simulation games (VRBSGs) have garnered attention in management education. However, investigating the design of efficient VRBSGs from an integrated perspective of social cognitive, self-determination, and cognitive load theories is crucial but under-addressed. Therefore, this study developed a VRBSG learning system to enhance students’ learning of retailing management in pet nutrition. Specifically, this study investigated the impact of technological and instructional support and how the VRBSG can affect students' psychological needs and cognitive load, improving their learning achievement. A total of 214 undergraduate students were recruited as research participants, and the data collected were analyzed using the partial least squares method. The results showed that the learning achievement of the experimental group playing the VRBSG improved significantly. Surprisingly, technological and instructional support did not significantly influence learning anxiety. The VRBSG design considered pet nutrition knowledge, the prompt of the VRBSG mechanism, and students’ successful feelings about the learning materials and content. This design can provide students with good feelings in a situated learning environment. These findings have significant implications for the use of VRBSGs in management education, particularly in the field of retailing management.
AiolfiS. (2023). How shopping habits change with artificial intelligence: Smart speakers’ usage intention. International Journal of Retail & Distribution Management, 51(9/10), 1288–1312. https://doi.org/10.1108/IJRDM-11-2022-0441
2.
AlbusP.VogtA.SeufertT. (2021). Signaling in virtual reality influences learning outcome and cognitive load. Computers & Education, 166, Article 104154. https://doi.org/10.1016/j.compedu.2021.104154
3.
AlexiouA.SchippersM. C. (2018). Digital game elements, user experience and learning: A conceptual framework. Education and Information Technologies, 23(6), 2545–2567. https://doi.org/10.1007/s10639-018-9730-6
4.
BagozziR. P. (1992). The self-regulation of attitudes, intentions, and behavior. Social Psychology Quarterly, 55(2), 178–204. https://doi.org/10.2307/2786945
5.
BaldwinJ.LeeJ.TateA. D.OkitondoC. D.JohnsenK.SchmidtM. D.RathbunS.NovotnyE.AhnS. J. (2023). Mediating social support through sensor-based technologies for children’s health behavior change. Journal of Computer-Mediated Communication, 28(5), zmad011. https://doi.org/10.1093/jcmc/zmad011
BarzN.BenickM.Dörrenbächer-UlrichL.PerelsF. (2024). The effect of digital game-based learning interventions on cognitive, metacognitive, and affective-motivational learning outcomes in school: A meta-analysis. Review of Educational Research, 94(2), 193–227. https://doi.org/10.3102/00346543231167795
8.
BehlA.JayawardenaN.IshizakaA.GuptaM.ShankarA. (2022). Gamification and gigification: A multidimensional theoretical approach. Journal of Business Research, 139, 1378–1393. https://doi.org/10.1016/j.jbusres.2021.09.023
9.
BuilI.CatalánS.MartínezE. (2019). Encouraging intrinsic motivation in management training: The use of business simulation games. International Journal of Management in Education, 17(2), 162–171. https://doi.org/10.1016/j.ijme.2019.02.002
10.
BurgersC.EdenA.Van EngelenburgM. D.BuninghS. (2015). How feedback boosts motivation and play in a brain-training game. Computers in Human Behavior, 48, 94–103. https://doi.org/10.1016/j.chb.2015.01.038
11.
CaiY.ShiW. (2022). The influence of the community climate on users' knowledge-sharing intention: The social cognitive theory perspective. Behaviour & Information Technology, 41(2), 307–323. https://doi.org/10.1080/0144929X.2020.1808704
12.
ChangC.-C.LiangC.ChouP.-N.LinG.-Y. (2017). Is game-based learning better in flow experience and various types of cognitive load than non-game-based learning? Perspective from multimedia and media richness. Computers in Human Behavior, 71, 218–227. https://doi.org/10.1016/j.chb.2017.01.031
13.
ChangC. C.YangS. T. (2023). Interactive effects of scaffolding digital game-based learning and cognitive style on adult learners’ emotion, cognitive load and learning performance. International Journal of Educational Technology in Higher Education, 20(1), 16. https://doi.org/10.1186/s41239-023-00385-7
14.
ChenH.LiH.PirkkalainenH. (2024). How extended reality influences e-commerce consumers: A literature review. Electronic Commerce Research and Applications, 65, Article 101404. https://doi.org/10.1016/j.elerap.2024.101404
15.
CheonJ.GrantM. M. (2012). The effects of metaphorical interface on germane cognitive load in web-based instruction. Educational Technology Research & Development, 60(3), 399–420. https://doi.org/10.1007/s11423-012-9236-7
16.
DahalanF.AliasN.ShaharomM. S. N. (2024). Gamification and game based learning for vocational education and training: A systematic literature review. Education and Information Technologies, 29(2), 1–39. https://doi.org/10.1007/s10639-022-11548-w
17.
DeciE. L.KoestnerR.RyanR. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–700. https://doi.org/10.1037/0033-2909.125.6.627
18.
DijkstraT. K.HenselerJ. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10–23. https://doi.org/10.1016/j.csda.2014.07.008
19.
EvansP.VansteenkisteM.ParkerP.Kingsford-SmithA.ZhouS. (2024). Cognitive load theory and its relationships with motivation: A self-determination theory perspective. Educational Psychology Review, 36(1), 7–25. https://doi.org/10.1007/s10648-023-09841-2
20.
FornellC.LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
21.
GaoW.LiuY.LiuZ.LiJ. (2018). How does presence influence purchase intention in online shopping markets? An explanation based on self-determination theory. Behaviour & Information Technology, 37(8), 786–799. https://doi.org/10.1080/0144929X.2018.1484514
22.
GoodhueD. L.ThompsonR. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689
23.
GottfriedA. E. (1985). Academic intrinsic motivation in elementary and junior high school students. Journal of Educational Psychology, 77(6), 631–645. https://doi.org/10.1037/0022-0663.77.6.631
24.
GrundA.FriesS.NücklesM.RenklA.RoelleJ. (2024). When is learning “effortful”? Scrutinizing the concept of mental effort in cognitively oriented research from a motivational perspective. Educational Psychology Review, 36(1), 11. https://doi.org/10.1007/s10648-024-09852-7
25.
GuoX. R.LiuS. Y.GongS. Y.CaoY.WangJ.FangY. (2024). Promoting math learning in educational games with virtual companions providing learning supports. Education and Information Technologies, 29(16), 22341–22370. https://doi.org/10.1007/s10639-024-12741-9
26.
HairJ. F.HultG. T. M.RingleC. M.SarstedtM. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
27.
HawlitschekA.JoeckelS. (2017). Increasing the effectiveness of digital educational games: The effects of a learning instruction on students’ learning, motivation and cognitive load. Computers in Human Behavior, 72, 79–86. https://doi.org/10.1016/j.chb.2017.01.040
28.
HeL.LiC. (2023). Continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory. Frontiers in Psychology, 14, Article 1185851. https://doi.org/10.3389/fpsyg.2023.1185851
29.
HeinV.EmeljanovasA.RiesF.ValantineI.EklerJ. H.LópezP. G. (2018). The perception of the autonomy supportive behaviour as a predictor of perceived effort and physical self-esteem among school students from four nations. Montenegrin Journal of Sports Science and Medicine, 7(1), 21–30. https://doi.org/10.26773/mjssm.180303
30.
HenselerJ. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1), 107–120. https://doi.org/10.1007/s00180-009-0164-x
31.
HenselerJ.RingleC. M.SarstedtM. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
32.
HongH. J.WilkinsonG.RochaC. M. (2023). The relationship between basic needs satisfaction, self-determined motivation, and burnout in Korean esports players. Journal of Gambling Studies, 39(1), 323–338. https://doi.org/10.1007/s10899-022-10132-8
33.
HongJ. C.HwangM. Y.TaiK. H.TsaiC. R. (2017). An exploration of students’ science learning interest related to their cognitive anxiety, cognitive load, self-confidence and learning progress using inquiry-based learning with an iPad. Research in Science Education, 47(6), 1193–1212. https://doi.org/10.1007/s11165-016-9541-y
34.
HsiehP. J.LinW. S. (2020). Understanding the performance impact of the epidemic prevention cloud: An integrative model of the task-technology fit and status quo bias. Behaviour & Information Technology, 39(8), 899–916. https://doi.org/10.1080/0144929X.2019.1624826
35.
HuL. T.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
36.
HuangX.MayerR. E. (2016). Benefits of adding anxiety-reducing features to a computer-based multimedia lesson on statistics. Computers in Human Behavior, 63, 293–303. https://doi.org/10.1016/j.chb.2016.05.034
37.
HuangY.WangS. (2023). How to motivate student engagement in emergency online learning? Evidence from the COVID-19 situation. Higher Education, 85(5), 1101–1123. https://doi.org/10.1007/s10734-022-00880-2
38.
HuangY. M.SilitongaL. M.MurtiA. T.WuT. T. (2023). Learner engagement in a business simulation game: Impact on higher-order thinking skills. Journal of Educational Computing Research, 61(1), 96–126. https://doi.org/10.1177/07356331221106918
39.
HwangG. J.LaiC. L. (2017). Facilitating and bridging out-of-class and in-class learning: An interactive e-book-based flipped learning approach for math courses. Journal of Educational Technology & Society, 20(1), 184–197.
40.
JiangJ.FryerL. K. (2024). The effect of virtual reality learning on students' motivation: A scoping review. Journal of Computer Assisted Learning, 40(1), 360–373. https://doi.org/10.1111/jcal.12885
41.
JungertT.HubbardK.DedicH.RosenfieldS. (2019). Systemizing and the gender gap: Examining academic achievement and perseverance in STEM. European Journal of Psychology of Education, 34(2), 479–500. https://doi.org/10.1007/s10212-018-0390-0
42.
KhanG. F.SarstedtM.ShiauW. L.HairJ. F.RingleC. M.FritzeM. P. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM) an analysis based on social network approaches. Internet Research, 29(3), 407–429. https://doi.org/10.1108/IntR-12-2017-0509
43.
KhattibH.AltD. (2024). A quasi-experimental study on the advantages of digital gamification using CoSpaces Edu application in science education. Education and Information Technologies, 29(15), 19963–19986. https://doi.org/10.1007/s10639-024-12635-w
44.
KingP. E.SchrodtP.WeiselJ. J. (2009). The instructional feedback orientation scale: Conceptualizing and validating a new measure for assessing perceptions of instructional feedback. Communication Education, 58(2), 235–261. https://doi.org/10.1080/03634520802515705
45.
KockN. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
46.
KrathJ.SchürmannL.Von KorfleschH. F. (2021). Revealing the theoretical basis of gamification: A systematic review and analysis of theory in research on gamification, serious games and game-based learning. Computers in Human Behavior, 125, Article 106963. https://doi.org/10.1016/j.chb.2021.106963
47.
KrishenA. S.BerezanO.AgarwalS.KachrooP. (2016). The generation of virtual needs: Recipes for satisfaction in social media networking. Journal of Business Research, 69(11), 5248–5254. https://doi.org/10.1016/j.jbusres.2016.04.120
48.
LambillotteL.MagrofuocoN.PoncinI.VanderdoncktJ. (2022). Enhancing playful customer experience with personalization.Journal of Retailing and Consumer Services, 68, Article 103017. https://doi.org/10.1016/j.jretconser.2022.103017
49.
LandersR. N.AuerE. M.CollmusA. B.ArmstrongM. B. (2018). Gamification science, its history and future: Definitions and a research agenda. Simulation & Gaming, 49(3), 315–337. https://doi.org/10.1177/1046878118774385
50.
LazaridesR.BuchholzJ. (2019). Student-perceived teaching quality: How is it related to different achievement emotions in mathematics classrooms?Learning and Instruction, 61, 45–59. https://doi.org/10.1016/j.learninstruc.2019.01.001
51.
LeeH. W.ChaY. M. (2022). The effects of digital written feedback on paper-based tests for college students. The Asia-Pacific Education Researcher, 31(4), 489–497. https://doi.org/10.1007/s40299-021-00592-8
52.
LeppinkJ.van den HeuvelA. (2015). The evolution of cognitive load theory and its application to medical education. Perspectives on Medical Education, 4(3), 119–127. https://doi.org/10.1007/S40037-015-0192-X
53.
LinH. C. S.YuS. J.SunJ. C. Y.JongM. S. Y. (2021). Engaging university students in a library guide through wearable spherical video-based virtual reality: Effects on situational interest and cognitive load. Interactive Learning Environments, 29(8), 1272–1287. https://doi.org/10.1080/10494820.2019.1624579
54.
LiuD.SanthanamR.WebsterJ. (2017). Toward meaningful engagement: A framework for design and research of gamified information systems. MIS Quarterly, 41(4), 1011–1034. https://doi.org/10.25300/misq/2017/41.4.01
55.
LiuY. C.WangW. T.HuangW. H. (2023). The effects of game quality and cognitive loads on students’ learning performance in mobile game-based learning contexts: The case of system analysis education. Education and Information Technologies, 28(12), 16285–16310. https://doi.org/10.1007/s10639-023-11856-9
56.
LuK.PangF.ShadievR. (2023). How to deepen college students' approach to using technologies in TOIBL? Examining the mediating influence of deep approaches to using technologies between learning factors and higher-order thinking skills. Journal of Computer Assisted Learning, 39(1), 182–193. https://doi.org/10.1111/jcal.12738
57.
MaY.CaoY.LiL.ZhangJ.ClementA. P. (2021). Following the flow: Exploring the impact of mobile technology environment on user’s virtual experience and behavioral response. Journal of Theoretical and Applied Electronic Commerce Research, 16(2), 170–187. https://doi.org/10.4067/S0718-18762021000200112
58.
MaierU.WolfN.RandlerC. (2016). Effects of a computer-assisted formative assessment intervention based on multiple-tier diagnostic items and different feedback types. Computers & Education, 95, 85–98. https://doi.org/10.1016/j.compedu.2015.12.002
59.
MaoP.CaiZ.WangZ.HaoX.FanX.SunX. (2024). The effects of dynamic and static feedback under tasks with different difficulty levels in digital game-based learning. The Internet and Higher Education, 60, Article 100923. https://doi.org/10.1016/j.iheduc.2023.100923
60.
MarshH. W.HocevarD. (1985). Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups. Psychological Bulletin, 97(3), 562–582. https://doi.org/10.1037/0033-2909.97.3.562
61.
MayerR. E. (Ed.), (2005). The Cambridge handbook of multimedia learning. Cambridge University Press.
62.
MayerR. E. (2024). The past, present, and future of the cognitive theory of multimedia learning. Educational Psychology Review, 36(1), 8. https://doi.org/10.1007/s10648-023-09842-1
63.
MeuterM. L.OstromA. L.BitnerM. J.RoundtreeR. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899–906. https://doi.org/10.1016/S0148-2963(01)00276-4
64.
MillerG. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
65.
Nadi-RavandiS.BatooliZ. (2022). Gamification in education: A scientometric, content and co-occurrence analysis of systematic review and meta-analysis articles. Education and Information Technologies, 27(7), 10207–10238. https://doi.org/10.1007/s10639-022-11048-x
66.
NtoumanisN. (2001). A self-determination approach to the understanding of motivation in physical education. British Journal of Educational Psychology, 71(Pt 2), 225–242. https://doi.org/10.1348/000709901158497
67.
NücklesM.RoelleJ.Glogger-FreyI.WaldeyerJ.RenklA. (2020). The self-regulation-view in writing-to-learn: Using journal writing to optimize cognitive load in self-regulated learning. Educational Psychology Review, 32(4), 1089–1126. https://doi.org/10.1007/s10648-020-09541-1
68.
OberT. M.LuY.BlacklockC. B.LiuC.ChengY. (2023). Development and validation of a cognitive load measure for general educational settings. Journal of Psychoeducational Assessment, 41(6), 689–708. https://doi.org/10.1177/07342829231169171
69.
PaasF.TuovinenJ. E.van MerriënboerJ. J. G.Aubteen DarabiA. (2005). A motivational perspective on the relation between mental effort and performance: Optimizing learner involvement in instruction. Educational Technology Research & Development, 53(3), 25–34. https://doi.org/10.1007/bf02504795
70.
PetkoD.SchmidR.CantieniA. (2020). Pacing in serious games: Exploring the effects of presentation speed on cognitive load, engagement and learning gains. Simulation & Gaming, 51(2), 258–279. https://doi.org/10.1177/1046878120902502
71.
PetterS.StraubD.RaiA. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656. https://doi.org/10.2307/25148814
72.
PodsakoffP. M.MacKenzieS. B.LeeJ. Y.PodsakoffN. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
73.
RadiantiJ.MajchrzakT. A.FrommJ.WohlgenanntI. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, Article 103778. https://doi.org/10.1016/j.compedu.2019.103778
74.
RatmonoD.SariR. C.WarsonoS.UbaidillahM.WibowoL. M. (2024). Virtual reality and perceived learning effectiveness in accounting studies: The mediating role of task-technology fit. Cogent Business & Management, 11(1), Article 2316890. https://doi.org/10.1080/23311975.2024.2316890
75.
RyanR. M.DeciE. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
76.
RyanR. M.DeciE. L. (2019). Brick by brick: The origins, development, and future of self-determination theory. Advances in Motivation Science, 6, 111–156. https://doi.org/10.1016/bs.adms.2019.01.001
77.
SailerM.SailerM. (2021). Gamification of in-class activities in flipped classroom lectures. British Journal of Educational Technology, 52(1), 75–90. https://doi.org/10.1111/bjet.12948
78.
SariR. C.PranestiA.SolikhatunI.NurbaitiN.YuniartiN. (2024). Cognitive overload in immersive virtual reality in education: More presence but less learnt?Education and Information Technologies, 29(10), 12887–12909. https://doi.org/10.1007/s10639-023-12379-z
79.
SarstedtM.RingleC. M.HairJ. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer International Publishing.
80.
SchneiderS.NebelS.BeegeM.ReyG. D. (2018). The autonomy-enhancing effects of choice on cognitive load, motivation and learning with digital media. Learning and Instruction, 58, 161–172. https://doi.org/10.1016/j.learninstruc.2018.06.006
81.
ScorgieD.FengZ.PaesD.ParisiF.YiuT. W.LovreglioR. (2024). Virtual reality for safety training: A systematic literature review and meta-analysis. Safety Science, 171, Article 106372. https://doi.org/10.1016/j.ssci.2023.106372
82.
ShanshanS.WenfeiL. (2022). Understanding the impact of quality elements on MOOCs continuance intention. Education and Information Technologies, 27(8), 10949–10976. https://doi.org/10.1007/s10639-022-11063-y
83.
ShiY.HallerA.ReesonA.LiX.LiC. (2024). Investigating the effects of nudges to promote knowledge-sharing behaviours on mooc forums: A mixed method design. Behaviour & Information Technology, 1–26. https://doi.org/10.1080/0144929X.2024.2316287
84.
StandageM.DudaJ. L.NtoumanisN. (2006). Students' motivational processes and their relationship to teacher ratings in school physical education: A self-determination theory approach. Research Quarterly for Exercise & Sport, 77(1), 100–110. https://doi.org/10.1080/02701367.2006.10599336
85.
StoneM. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B, 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
86.
SuC. H. (2016). The effects of students’ motivation, cognitive load and learning anxiety in gamification software engineering education: A structural equation modeling study. Multimedia Tools and Applications, 75(16), 10013–10036. https://doi.org/10.1007/s11042-015-2799-7
87.
SunJ. C. Y.YuS. J. (2019). Personalized wearable guides or audio guides: An evaluation of personalized museum guides for improving learning achievement and cognitive load. International Journal of Human-Computer Interaction, 35(4–5), 404–414. https://doi.org/10.1080/10447318.2018.1543078
88.
SupriadiN.JamaluddinZ. W.SuhermanS. (2024). The role of learning anxiety and mathematical reasoning as predictor of promoting learning motivation: The mediating role of mathematical problem solving. Thinking Skills and Creativity, 52, Article 101497. https://doi.org/10.1016/j.tsc.2024.101497
SwellerJ.Van MerrienboerJ. J.PaasF. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/a:1022193728205
91.
TanW. K. (2018). Gamification in aquarium context: Intention to play game that imparts knowledge and promotes marine animal conservation. Information Technology & People, 31(6), 1070–1090. https://doi.org/10.1108/ITP-02-2017-0054
92.
TuY. F.HwangG. J.ChenJ. C. C.LaiC. (2021). University students’ attitudes towards ubiquitous library-supported learning: An empirical investigation in the context of the Line@ Library. The Electronic Library, 39(1), 186–207. https://doi.org/10.1108/EL-03-2020-0076
93.
TzafilkouK.PerifanouM.EconomidesA. A. (2021). Negative emotions, cognitive load, acceptance, and self-perceived learning outcome in emergency remote education during COVID-19. Education and Information Technologies, 26(6), 7497–7521. https://doi.org/10.1007/s10639-021-10604-1
94.
UnalA.TopuF. B. (2021). Effects of teaching a computer programming language via hybrid interface on anxiety, cognitive load level and achievement of high school students. Education and Information Technologies, 26(5), 5291–5309. https://doi.org/10.1007/s10639-021-10536-w
95.
Van den BroeckA.VansteenkisteM.De WitteH.SoenensB.LensW. (2010). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the work‐related basic need satisfaction scale. Journal of Occupational and Organizational Psychology, 83(4), 981–1002. https://doi.org/10.1348/096317909X481382
96.
Van der KleijF. M.LipnevichA. A. (2021). Student perceptions of assessment feedback: A critical scoping review and call for research. Educational Assessment, Evaluation and Accountability, 33(2), 345–373. https://doi.org/10.1007/s11092-020-09331-x
97.
WangW. S.LinC. J.LeeH. Y.HuangY. M.WuT. T. (2024). Integrating feedback mechanisms and ChatGPT for VR-based experiential learning: Impacts on reflective thinking and AIoT physical hands-on tasks. Interactive Learning Environments, 1–18. https://doi.org/10.1080/10494820.2024.2375644
98.
WangW. T.LinY. L. (2021). An integrated theoretical investigation of healthcare students’ perceived compatibility using online learning systems on their learning performance. Journal of Internet Technology, 22(1), 143–155.
99.
WangW. T.LinY. L.LuH. E. (2023). Exploring the effect of improved learning performance: A mobile augmented reality learning system. Education and Information Technologies, 28(6), 7509–7541. https://doi.org/10.1007/s10639-022-11487-6
100.
WeiC. L.WangY. M.LinH. H.WangY. S.HuangJ. L. (2022). Developing and validating a business simulation systems success model in the context of management education. International Journal of Management in Education, 20(2), Article 100634. https://doi.org/10.1016/j.ijme.2022.100634
101.
WuS. (2024). Application of multimedia technology to innovative vocational education on learning satisfaction in China. Plos One, 19(2), Article e0298861. https://doi.org/10.1371/journal.pone.0298861
102.
XiangY.ChaeS. W. (2022). Influence of perceived interactivity on continuous use intentions on the Danmaku video sharing platform: Belongingness perspective. International Journal of Human-Computer Interaction, 38(6), 573–593. https://doi.org/10.1080/10447318.2021.1952803
103.
XiaoY.HewK. F. (2024). Personalized gamification versus one-size-fits-all gamification in fully online learning: Effects on student motivational, behavioral and cognitive outcomes. Learning and Individual Differences, 113, Article 102470. https://doi.org/10.1016/j.lindif.2024.102470
104.
XuW.ZhangN.WangM. (2024). The impact of interaction on continuous use in online learning platforms: A metaverse perspective. Internet Research, 34(1), 79–106. https://doi.org/10.1108/INTR-08-2022-0600
105.
YangL.LiR.ZhouY. (2024). Research trends of game‐based language learning in K-12 education: A systematic review of SSCI articles during 2009–2022. Journal of Computer Assisted Learning, 40(3), 1218–1230. https://doi.org/10.1111/jcal.12944
106.
YoungJ. Q.BoscardinC. K.van DijkS. M.AbdullahR.IrbyD. M.SewellJ. L.Ten CateO.O’SullivanP. S. (2016). Performance of a cognitive load inventory during simulated handoffs: Evidence for validity. SAGE Open Medicine, 4, Article 2050312116682254. https://doi.org/10.1177/2050312116682254
107.
YuY. C. (2016). Teaching with a dual-channel classroom feedback system in the digital classroom environment. IEEE Transactions on Learning Technologies, 10(3), 391–402. https://doi.org/10.1109/TLT.2016.2598167
108.
ZhangH.LuY.GuptaS.ZhaoL. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information & Management, 51(8), 1017–1030. https://doi.org/10.1016/j.im.2014.07.005
109.
ZhangY.MaY.LiangC. (2024). Understanding older adults’ continuance intention toward wearable health technologies: An empowerment perspective. Behaviour & Information Technology, 1–18. https://doi.org/10.1080/0144929X.2024.2350674
110.
ZhangY.TianY.YaoL.DuanC.SunX.NiuG. (2023). Teaching presence promotes learner affective engagement: The roles of cognitive load and need for cognition. Teaching and Teacher Education, 129, Article 104167. https://doi.org/10.1016/j.tate.2023.104167
111.
ZhengL.LiY. (2024). Customer journey design in omnichannel retailing: Examining the effect of autonomy-competence-relatedness in brand relationship building.Journal of Retailingand Consumer Services, 78, Article 103776. https://doi.org/10.1016/j.jretconser.2024.103776
