There is a paucity of theory-driven research investigating the factors influencing consumer acceptance of cashless gambling technology. Thus, the purpose of this study is to explore whether the six constructs in the Unified Theory of Acceptance and Use of Technology 2 (i.e., performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and price-value) significantly influence intention to use cashless gambling technology. This work further examined important determinants of performance expectancy and effort expectancy, based on the Technology Acceptance Model 3. Survey responses were collected from U.S. consumers who had gambled at a land-based casino in the United States, and Australian consumers who had gambled at a club/casino in Australia in the preceding 12 months. The data were analyzed via confirmatory factor analysis and structural equation modeling. We found that social influence and price-value significantly influenced the intention to use cashless gambling technology. Performance expectancy was found to be an important predictor of behavioral intention. The results identified effort expectancy and hedonic motivation as key determinants of performance expectancy. Facilitating conditions and hedonic motivation were found to be significant determinants of effort expectancy. Our findings reveal new avenues of exploration in the emerging field of cashless technology within the hospitality/gaming sector. Finally, this study provides recommendations for hospitality/gaming operators and vendors seeking to enhance customer perceptions and acceptance of cashless technology.
AbrahamsonE.RosenkopfL. (1993). Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. Academy of Management Review, 18(3), 487–517.
2.
AgarwalR.KarahannaE. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694.
BagozziR. P.YiY. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.
7.
BentlerP. M. (2006). EQS 6 structural equations program manual. Multivariate Software.
8.
BervellB.UmarI. N. (2017). Validation of the UTAUT model: Re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research. EURASIA Journal of Mathematics, Science and Technology Education, 13(10), 6471–6490.
BobbittL. M.DabholkarP. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service. International Journal of Service Industry Management, 12(5), 423–450.
11.
BrunerG. C.KumarA. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58(5), 553–558.
12.
ByrneB. M. (2006). Structural equation modeling with EQS: Basic concepts applications and programming. Lawrence Erlbaum.
CollierJ.BarnesD. (2015). Self-service delight: Exploring the hedonic aspects of self-service. Journal of Business Research, 68(5), 986–993.
15.
DabholkarP. (1996). Consumer evaluations of new technology-based self-service options: An investigation of alternative models of service quality. International Journal of Research in Marketing, 13(1), 29–51.
16.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
17.
DavisF. D.BagozziR. P.WarshawP. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
18.
DeutschM.GerardH. (1955). A study of normative and informational social influences upon individual judgment. Journal of Abnormal and Social Psychology, 51(3), 629–636.
19.
DoddsW.MonroeK.GrewalD. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307–319.
Escobar-RodríguezT.Carvajal-TrujilloE. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70–88.
FlanaginA. J.MetzgerM. J.PureR.MarkovA.HartsellE. (2014). Mitigating risk in ecommerce transactions: Perceptions of information credibility and the role of user-generated ratings in product quality and purchase intention. Electronic Commerce Research, 14(1), 1–23.
24.
FornellC.LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
HairJ. F.BlackW. C.BabinB. J.AndersonR. E.TathamR. L. (2006). Multivariate data analysis. Pearson Prentice Hall.
29.
HuangC.KaoY. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 2015, 1–23.
30.
HuangR.DeggsD.JaborK.MachtmesK. (2011). Faculty online technology adoption: The role of management support and organizational climate. Online Journal of Distance Learning Administration, 14(2), 1–11. https://www.westga.edu/~distance/ojdla/summer142/huang_142.html
KhalilzadehJ.OzturkA.BilgihanA. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474.
33.
Koenig-LewisN.MarquetM.PalmerA.ZhaoA. L. (2015). Enjoyment and social influence: Predicting mobile payment adoption. The Service Industries Journal, 35(10), 537–554.
34.
LuJ.YaoJ. E.YuC. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268.
35.
MadiganR.LouwT.WilbrinkM.SchiebenA.MeratN. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology and Behaviour, 50, 55–64.
NordhoffS.MadiganR.Van AremB.MeratN.HappeeR. (2020). Interrelationships among predictors of automated vehicle acceptance: A structural equation modelling approach. Theoretical Issues in Ergonomics Science, 22(4), 383–408.
39.
NysveenH.PedersenP. E.ThorbjornsenH. (2005). Explaining intention to use mobile chat services: Moderating effects of gender. Journal of Consumer Marketing, 22(5), 247–256.
40.
OzturkA. B.BilgihanA.NusairK.OkumusF. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36(6), 1350–1359.
RintoulA.ThomasA. (2017). Pre-commitment systems for electronic gambling machines: Preventing harm and improving consumer protection (AGRC Discussion Paper No. 9). Australian Gambling Research Centre, Australian Institute of Family Studies.
43.
RosenbaumM. S.WongI. A. (2015). If you install it, will they use it? Understanding why hospitality customers take “technological pauses” from self-service technology. Journal of Business Research, 68(9), 1862–1868.
44.
SatorraA.BentlerP. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In von EyeA.CloggC. C. (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–1419). Sage.
VenkateshV. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
51.
VenkateshV.BalaH. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
52.
VenkateshV.DavisF. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
53.
VenkateshV.MorrisM. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.
54.
VenkateshV.MorrisM. G.DavisG. B.DavisF. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
55.
VenkateshV.ThongJ. Y. L.XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
56.
WheatonB.MuthenB.AlwinD. F.SummersG. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8(1), 84–136.
57.
XuX.LiQ.PengL.HsiaT. L.HuangC. J.WuJ. H. (2017). The impact of informational incentives and social influence on consumer behavior during Alibaba’s online shopping carnival. Computers in Human Behavior, 76, 245–254.
58.
YangK. (2010). The effects of technology self-efficacy and innovativeness on consumer mobile data service adoption between American and Korean consumers. Journal of International Consumer Marketing, 22(2), 117–127.
59.
ZeithamlV. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
