From TAM to AVRTS: development and validation of the attitudes toward Virtual Reality Technology Scale | Virtual Reality Skip to main content

Advertisement

Log in

From TAM to AVRTS: development and validation of the attitudes toward Virtual Reality Technology Scale

  • Original Paper
  • Published:
Virtual Reality Aims and scope Submit manuscript

Abstract

To address a deficiency of scales measuring attitudes toward virtual reality technology, the Attitudes toward Virtual Reality Technology Scale (AVRTS) was developed following a three-step procedure. Items were generated based on literature and in a focus group pilot study (n = 20). Using factor analysis with maximum likelihood extraction and direct oblimin rotation, the initial scale was created after administration of these items (n = 314) and further refined after a second survey (n = 473). In the final solution, a total of 22 items clustered into a three-factor solution with the factors being “ease of use” (alpha = .858), “usefulness” (alpha = .857), and “enjoyment” (alpha = .919) and overall reliability of .910. Construct validity was established by correlating the AVRTS with the Technology Readiness Index and an access barrier scale, and internal validity was established by correlating the scale with its sub-scales.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. This modification took place via a focus group, and reliability and factor analysis, similar to the process described for the AVRTS. Due to space limitations, details are not presented here. It should be noted that after the focus groups, the access barrier items used included an item about VR causing motion sickness but just as in study 1 for the AVRTS, the item was not robust and was thus not included in the final list of items used.

References

  • Ajzen I, Fishbein M (1977) Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychol Bull 84:888–918. https://doi.org/10.1037/0033-2909.84.5.888

    Article  Google Scholar 

  • Biocca F, Delaney B (1995) Immersive virtual reality technology. In: Biocca F, Levy MR (eds) Communication in the age of virtual reality. Erlbaum, Hillsdale, pp 57–124

    Google Scholar 

  • Bowman ND, Oliver MB, Rogers R, Sherrick B, Woolley J, Chung MY (2016) In control or in their shoes? How character attachment differentially influences video game enjoyment and appreciation. J Gaming Virtual Worlds 8:83–99

    Article  Google Scholar 

  • Bracken C, Skalski P (2010) Telepresence in Everyday Life. In: Bracken C, Skalski P (eds) Immersed in media: telepresence in everyday life. Routledge, New York, pp 5–8

    Chapter  Google Scholar 

  • Chau PY, Hu PJH (2002) Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf Manag 39:297–311

    Article  Google Scholar 

  • Comfrey AL, Lee HB (1992) A first course in factor analysis. Erlbaum, Hillsdale

    Google Scholar 

  • Costello AB, Osborne JW (2003) Exploring best practices in factor analysis: Four mistakes applied researchers make. In: Trabajo presentado en la Annual Meeting of the American Educational Research Association (AERA), Chicago, IL

  • Davis F (1985) A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Dissertation, Massachusetts Institute of Technology

  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340

    Article  Google Scholar 

  • Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35:982–1003

    Article  Google Scholar 

  • Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ (1999) Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 4:272–299

    Article  Google Scholar 

  • Fishbein M, Ajzen I (1975) Belief, attitude, intention and behavior. Addison-Wesley, Boston

    Google Scholar 

  • Fox J, Bailenson JN (2009) Virtual self-modeling: the effects of vicarious reinforcement and identification on exercise behaviors. Media Psychol 12:1–25

    Article  Google Scholar 

  • Fox J, Arena D, Bailenson JN (2009) Virtual reality. A survival guide for the social scientist. J Media Psychol 21:95–113

    Article  Google Scholar 

  • Gidley S (2017) Virtual reality: Tourism firms use VR to attract visitors. BBC. http://www.bbc.com/news/uk-wales-41635746. Accessed 22 Oct 2017

  • Gorsuch RL (1983) Factor analysis, 2nd edn. Hillsdale, Erlbaum

    MATH  Google Scholar 

  • Hatcher L (1994) A step-by-step approach to using the SAS® system for factor analysis and structural equation modeling. SAS Institute Inc, Cary

    Google Scholar 

  • Hinking TR, Tracey JB, Enz CA (1997) Scale construction: Developing reliable and valid measurement instruments. Cornell University, School of Hotel Administration. https://scholarship.sha.cornell.edu/articles/613/. Accessed Feb 4 2019

  • Hu PJ, Lin C, Chen H (2005) User acceptance of intelligence and security informatics technology: a study of COPLINK. J Am Soc Inform Sci Technol 56:235–244

    Article  Google Scholar 

  • Jetter J, Eimecke J, Rese A (2018) Augmented reality tools for industrial applications: What are potential key performance indicators and who benefits? Comput Hum Behav 87:18–33

    Article  Google Scholar 

  • Küçük S, Yilmaz RM, Baydaş Ö, Göktaş Y (2014) Augmented reality applications attitude scale in secondary schools: validity and reliability study. Educ Sci 39:383–392

    Google Scholar 

  • Lanier J (1992) Virtual reality: the promise of the future. Interact Learn Int 8:275–279

    Google Scholar 

  • Liaw SS, Huang HM (2003) An investigation of user attitudes toward search engines as an information retrieval tool. Comput Hum Behav 19:751–765

    Article  Google Scholar 

  • Lin JSC, Hsieh PL (2007) The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Comput Hum Behav 23:1597–1615

    Article  Google Scholar 

  • Lin JHT, Wu DY, Tao CC (2018) So scary, yet so fun: the role of self-efficacy in enjoyment of a virtual reality horror game. New Media Soc 20:3223–3242

    Article  Google Scholar 

  • Mathieson K, Peacock E, Chin WW (2001) Extending the technology acceptance model: the influence of perceived user resources. ACM SigMIS Database 32:86–112

    Article  Google Scholar 

  • McGloin R, Embacher K (2018) “Just like riding a bike”: a model matching approach to predicting the enjoyment of a cycling exergame experience. Media Psychol 21:486–505

    Article  Google Scholar 

  • Moon JW, Kim YG (2001) Extending the TAM for a World-Wide-Web context. Inf Manag 38:217–230

    Article  Google Scholar 

  • Mun YY, Hwang Y (2003) Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. Int J Hum Comput Stud 59:431–449

    Article  Google Scholar 

  • Nunnally JC (1978) Psychometric theory, 2nd edn. McGraw Hill, New York

    Google Scholar 

  • Osborne JW, Costello AB (2004) Sample size and subject to item ratio in principal components analysis. Practical Assessment, Research & Evaluation 9. http://PAREonline.net/getvn.asp?v=9&n=11. Accessed 4 Feb 2019

  • Osso VR (2019) http://ossovr.com/. Accessed 4 Feb 2019

  • Parasuraman A (2000) Technology readiness index (TRI). A multiple-item scale to measure readiness to embrace new technologies. J Serv Res 2:307–320. https://doi.org/10.1177/109467050024001

    Article  Google Scholar 

  • Porter CE, Donthu N (2006) Using the technology acceptance model to explain how attitudes determine Internet usage: the role of perceived access barriers and demographics. J Bus Res 59:999–1007

    Article  Google Scholar 

  • Pullen JP (2016) Virtual Reality at CES Moved Me to Tears. http://time.com/4172998/virtual-reality-oculus-rift-htc-vive-ces/. Accessed 4 Feb 2019

  • Raney AA, Smith J, Baker K (2006) Adolescents and the appeal of video games. In: Vorderer P, Bryant J (eds) Playing video games: Motives, responses, and consequences. Erlbaum, Mahwah, pp 165–179

    Google Scholar 

  • Rese A, Baier D, Geyer-Schulz A, Schreiber S (2017) How augmented reality apps are accepted by consumers: a comparative analysis using scales and opinions. Technol Forecast Soc Chang 124:306–319

    Article  Google Scholar 

  • Seibert J, Shafer DM (2018) Control mapping in virtual reality: effects on spatial presence and controller naturalness. Virtual Real 22:79–88

    Article  Google Scholar 

  • Steuer J (1992) Defining virtual reality: dimensions determining telepresence. J Commun 42:73–93

    Article  Google Scholar 

  • Tamborini R, Bowman ND, Eden A, Grizzard M, Organ A (2010) Defining media enjoyment as the satisfaction of intrinsic needs. J Commun 60:758–777. https://doi.org/10.1111/j.1460-2466.2010.01513.x

    Article  Google Scholar 

  • Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46:186–204

    Article  Google Scholar 

  • Walczuch R, Lemmink J, Streukens S (2007) The effect of service employees’ technology readiness on technology acceptance. Inf Manag 44:206–215

    Article  Google Scholar 

  • Wang CC, Lo SK, Fang W (2008) Extending the technology acceptance model to mobile telecommunication innovation: the existence of network externalities. J Consumer Behav 7:101–110

    Article  Google Scholar 

  • Webster A (2017) Playstation VR surpasses 1 million units sold. https://www.theverge.com/2017/6/5/15719382/playstation-vr-sony-sales-one-million Accessed 4 Feb 2019

  • Wiederhold BK, Riva G, Gutiérrez-Maldonado J (2016) Virtual reality in the assessment and treatment of weight-related disorders. Cyberpsychol Behav Soc Netw 19:67–73

    Article  Google Scholar 

  • Wojciechowski R, Cellary W (2013) Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Comput Educ 68:570–585

    Article  Google Scholar 

  • Yi Y, Tung LL, Wu Z (2003) Incorporating technology readiness (TR) into TAM: are individual traits important to understand technology acceptance? DIGIT 2003 proceedings, 2. https://aisel.aisnet.org/digit2003/2/. Accessed 4 Feb 2019

  • Zhang XY, Bie BJ, McLemore D, Conlin L, Bissell K, Parrott S, Lowrey P (2017) Active video game play in African American children: the effect of gender and BMI on exertion and enjoyment. Howard J Commun 28:280–296

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Rachel Secharan for her contributions during the item generation stage of the project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ulla Bunz.

Ethics declarations

Conflict of interest

All authors have declared that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

List of 12 items deleted to reduce the original 37 items to the 25-item scale presented in Table 1 under “Study 1.”

  • I would enjoy using VR at home (Item 4).

  • I find VR useful (Item 8).

  • Using VR makes me feel sick (reverse coded; item 12).

  • I believe VR is a user-friendly tool (Item 13).

  • Interacting with VR requires a lot of my mental effort (reverse coded; item 14).

  • I believe using VR can help me (Item 15).

  • I find it easy to recover from errors while using VR (Item 16).

  • Using VR allows me to do things that would otherwise be impossible (Item 20).

  • Interacting with VR is frustrating (reverse coded; item 28).

  • VR is intuitive to use (Item 30).

  • VR is instinctual to use (Item 34).

  • I believe that VR is helpful (Item 35).

Appendix 2

Items used for the access barrier scale, modified from three subscales presented by Porter and Donhu (2006, p. 1003).

Items adapted from the Perceived access barriers sub-scale:

  • I do not want to spend the money to buy a VR system for personal use.

  • I cannot afford to buy a VR system for personal use.

Items adapted from the Attitude toward Internet usage sub-scale:

  • I am positive toward VR.*

  • It makes sense to use VR.*

  • People should adopt VR.*

Items adapted from the Internet usage sub-scale:

  • I use VR quite often for personal use.*

  • I spend a lot of time on VR per personal use.*

  • I have been using VR for personal use for a long time now.*

Additional items added:

  • I do not have regular access to free VR technology.

  • I do not know much about VR.

  • I want to adopt VR.*

  • I have no reason to use VR.

All items on a 1–5 Likert-type scale range from strongly disagree to strongly agree. * denotes reverse-coded items.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bunz, U., Seibert, J. & Hendrickse, J. From TAM to AVRTS: development and validation of the attitudes toward Virtual Reality Technology Scale. Virtual Reality 25, 31–41 (2021). https://doi.org/10.1007/s10055-020-00437-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10055-020-00437-7

Keywords

Navigation