Currie Journal of Knowledge
An Application of the Technology Acceptance Model to Individual Protective Measures (IPMs) Against Viruses
How to cite: Haverila, M. & Husain, S. (2021). An Application of the Technology Acceptance Model to Individual Protective Measures (IPMs) Against Viruses. Cascade Journal of Knowledge, volume 2 (2), 7:51. https://doi.org/10.46290/cjok000013
Abstract: This presentation describes Technology Acceptance Model (TAM) when using individual protective measures (IPMs) against the spreading of viruses like COVID-19. The constructs in TAM are perceived usefulness, and ease of use, attitude towards the use of IPMs and the actual use as well as social influence, which were measured with relevant indicator variables. The statistical method used in the analysis was Partial Least Squares Structural Equation Modelling (PLS-SEM). IPMs include personal protective measures for everyday use (e.g., voluntary home isolation, respiratory etiquette, and hand hygiene); Personal protective measures for influenza pandemics (e.g., voluntary home quarantine, and use of face masks in community settings); and Environmental measures (e.g., routine cleaning of frequently touched surfaces). The results indicate that all relationships were significant also so that the effect sizes were large to medium with the exception of social influence -> perceived usefulness and social influence -> attitude towards usage.
Keywords: Technology Acceptance Model, Viruses, TAM

Learning outcomes:

Transcribed copy of screencast

Hello. This presentation will focus on the application of the technology acceptance model known as TAM. In regard to individual protective measures, IPMs, against the spreading of viruses. Technology is defined as the use of scientific knowledge for practical purposes. TAM is an (information systems) theory and explains how users accept and use technology. It was first developed by Fred Davis in 1985. It has also been used in the adoption of health informatics.

The structural model includes multiple latent constructs such as satisfaction, which are not directly observable. It has theory driven relationships between the constructs. Exogenous constructs explain other constructs, whereas endogenous are explained by other constructs and these are all measured by indicator variables.

TAM has three design features or external variables. The cognitive responses are perceived usefulness and perceived ease of use. The users’ motivation towards a technology will elicit either a negative or positive attitude towards the usage. Therefore, this would result in a positive or negative action of that specific behavioral response. There are key constructs which are perceived usefulness a particular system might enhance a person’s job performance for example.

Perceived ease of use is how easy a person can use a product or service. What is their attitude toward usage? Will it be a positive or negative feeling in terms of the targeted behavior? And how likely will an individual actually use a product or service and how a person is influenced by their social environment. Will this affect their behavior or not?

Attitudes expected predict actual usage. There is a function of two major beliefs: perceived usefulness and perceived ease of use. There is a causal effect on each other. And attitudes directly influence perceived usefulness and perceived ease of use.

It is noted within the Fishbein paradigm, design features fall into the category of external variables, it is actually not expected to have a direct effect on attitude or behavior. However, the impact comes only indirectly through perceived usefulness and perceived ease of use.

The COVID-19 pandemic impacted the world and people were now highly aware of the spread of viruses. IPMs were introduced. For example, personal protective measures, such as hand hygiene, and voluntary home isolation. Also, the use of face masks in community settings and environmental measures which were the routine cleaning of frequently touched surfaces.

In Fall 2020, a study identified how IPMs are used to protect and prevent the spreading viruses and how are people actually responding to these measures in place. There were 278 participants who responded. The sample included people from different socio-demographic variables such as gender, relationship status, and population size center. Also, the ethnicity and education level of the individual were taken into consideration and calculated.

And as we can see there is a correlation between all the constructs. Now to the responses to the questionnaire were as follows:

NPIs minimize the spreading viruses, improves one’s life in general and immediately it is also advantageous and protects one’s health.

NPIs are beneficial, complicated, and may take a little bit of effort. However, they are easy to use and easy to learn.

An individual’s attitude toward the NPIs were that it was good idea, sensible and valuable for one’s health.

Family and friends thought NPIs are useful, have advantages and are not surprised if one engages in its usage.

The behavioral intentions were positive as an individual were willing to introduce and plan to use NPIs.

Individuals have already used and already introduced to NPIs. And, as we can see the results indicate a positive correlation between all constructs.

Furthermore, these are the results with the hypothesis status was all Yes. For example, the path coefficient for attitude towards usage and behavioral intention, the effect size description was large with hypothesis supported. And between all the relationships, the effect sizes were large to medium, except for the social influence where the effect size was medium to small.

It is to be noted that the statistical significance is not sufficient when reporting the research findings.

There needs to be a significantly larger sample size to justify the merit in real world. It doesn’t reveal the real effect size, which is particularly important in health sciences.

Now, when it comes to the use of IPMs, attitude is essential to the behavioral intention. It has a large effect on actual usage. Perceived ease of use has a large effect on perceived usefulness and therefore this is crucial for IPMs and perceived usefulness has a medium effect on attitude towards the use of IPMs.

Now, attitudes essentially affect behavioral intention. Behavioral intention has a large effect on actual usage. Perceived ease of use has a large effect on perceived usefulness, it is crucial on how to use IPMs. Perceived usefulness has a medium effect on attitude towards the use, and social influence has a medium to small effect.

Now, the limitation was the sample as it could have been expanded to include a wider variety of individuals from different and unique demographics and socio-economic backgrounds.

In conclusion, established theories are used to produce robust structural and measurement models. TAM is an excellent model to identify use of individual protective measures against viruses like COVID-19.

Thank you kindly for watching this presentation.

Matti Haverila and Salma Husain

Thompson Rivers University, Canada
mhaverila@tru.ca

Dr. Matti Haverila is a professor of marketing at Thompson Rivers University. He is an author of three books, and has published about 100 academic papers. His research interests are brand communities, new product development, customer satisfaction and loyalty and wine marketing. Dr. Haverila also has managerial experience in Finland, U.K., the Bahamas and the United States in industries like software, consumer products and forest industry products. Matti graduated with MBA from University of Oregon, a M.Sc., a Certificate in Education from Tampere University of Applied Sciences and Ph.D. from Tampere University of Technology, Finland. Ms. Salma Hussain is an MBA student in the School of Business and Economics at Thompson Rivers University.

References:

Chen, M.-F. and Lin, N.-P. (2018). Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 28(2), 351–373.

Davis, F.D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results,” doctoral dissertation, MIT Sloan School of Management, Cambridge, MA.

Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

Rahimi, B., Nadri, H., Lotfnezhad, A. H., and Timpka, T. (2018). A systematic review of the Technology Acceptance Model in health informatics. Applied Clinical Informatics, 9(3), 604‐634.

Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the p value is not enough. Journal of Graduate Medical Education, 4(3), 279-282.

Wood W. (2000). Attitude change: Persuasion and social influence. Annual Review of Psychology, 51, 539–570.

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