Smart Mask Consumer Behavior: TRA and TAM Hypotheses
This paper presents a hypothesis chapter examining consumer behavior toward smart masks — wearable IoT-enabled devices designed to detect airborne pathogens and monitor respiratory health in real time. Drawing on the Theory of Reasoned Action (TRA) and the Technology Acceptance Model (TAM), the paper develops seven hypotheses linking consumer evaluation of smart masks (across technology, design, and social acceptability dimensions) to consumer attitude and, ultimately, intention to use. The chapter surveys relevant literature on smart devices, personalization, perceived usefulness, social influence, and trust to establish the theoretical foundations for each hypothesis, situating the smart mask within the broader context of IoT wearables and health technology adoption.
- Theory of Reasoned Action and Consumer Behavior: TRA framework links beliefs, norms, and buying intent
- The Influence of Consumers' Evaluation on Attitude: Product evaluation shapes consumer attitude via smart technology
- Smart Mask Technology and Value-Added Features: IoT, AI, and filtration features of smart mask design
- Social Influence, Trust, and Hypothesis Development: Trust and social norms drive H3–H5 hypothesis formation
- The Influence of Attitude on Intention to Use: Positive attitude drives H6 intention to use
- Technology Acceptance Model: TAM explains perceived usefulness and ease of adoption
- The Influence of Technology Aspects on Intention to Use: Technology evaluation predicts H7 usage intention
✍️ How to write this paper — guide, tools & examples ▾
What makes this paper effective
- Grounds each hypothesis explicitly in a named theoretical framework (TRA or TAM), making the logical chain from theory to prediction transparent and academically rigorous.
- Moves systematically from general consumer behavior theory to a specific novel product (smart masks), bridging broad marketing literature and emerging health-tech research.
- Uses analogous smart-device examples — smart home systems, smartwatches — to build intuitive support for claims about smart mask adoption before stating each hypothesis.
Key academic technique demonstrated
The paper demonstrates hypothesis derivation through literature synthesis. Rather than stating hypotheses arbitrarily, it assembles supporting citations across consumer behavior, IoT, and wearable technology research, then crystallizes each body of evidence into a falsifiable, formally numbered hypothesis (H3–H7). This is a core skill in quantitative marketing research chapters.
Structure breakdown
The chapter opens with a theoretical overview of TRA, then pivots to the specific mechanism of consumer evaluation and how it shapes attitude across smart-device contexts. A dedicated section surveys smart mask technology in depth before articulating the attitude-related hypotheses (H3–H5). The chapter then addresses attitude's effect on intention (H6) before introducing TAM as a complementary framework, concluding with the technology-aspects-to-intention hypothesis (H7). The references section is extensive, reflecting graduate-level citation density.
Theory of Reasoned Action and Consumer Behavior
The Theory of Reasoned Action (TRA), formulated by Fishbein and Ajzen, is one of the key frameworks for understanding consumer behavior, intention, and planned purchase decisions for a specific product or service. The theory attempts to predict the behavioral intention of the buyer based on two factors: the attitudes that would lead to adopting the buying behavior, and the subjective norms that arise from the buyer's social influences (Hosseini et al., 2015; Myresten & Setterhall, 2015, p. 5). The factors that shape a buyer's social influences mainly include the beliefs held by surrounding people, which impact his or her decision-making process. There is an element of trust in other people's beliefs that affects the buyer's action, which is highly dependent on past behavior or experience (Chuchinprakarn, 2005).
The resulting behavior is shaped by attitudes modified by held beliefs, which in reality constitute subjective norms. When an individual is motivated to fulfill other people's expectations — particularly when those beliefs influence his or her mind — the resulting action is altered accordingly (Hosseini et al., 2015). Subjective norms or beliefs become positive when the person is motivated by others' positive expectations, and this motivation is soon reflected in affirmative actions (Hosseini et al., 2015). Conversely, negatively held subjective norms result in negative perceptions when the individual interprets others' expectations as unfavorable, taking the form of de-motivation.
Building on the theoretical foundation of TRA, it can be interpreted that the consumer evaluates information from peers about a product or service he or she intends to buy, so that the positive or negative attitudes of those peers can inform the final purchase decision (Haris et al., 2017). The buyer's intention is highly dependent on the previously held beliefs of peers and social networks, which shape rejection or acceptance of the product. In essence, the potential buyer perceives information obtained from peers with a degree of subjectivity that supports product evaluation (Haris et al., 2017). This evaluation process assists in forming a "yes" or "no" judgment about the product, ultimately leading to the final purchase decision.
References
Amer, A., Yahya, S. & Jani, S.H.M. (2014). Research on hyper-connectivity element and its utilization on consumer performance expectancy: A literature analysis. The SIT Transactions on Industrial and Business Management (IFBM), 2(3), 108–116.
Beldad, A.D. & Hegner, S.M. (2017). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users' willingness to continue using the fitness app: A structural equation modeling approach. International Journal of Human-Computer Interaction, 34(9), 882–893. https://doi.org/10.1080/10447318.2017.1403220
Chuchinprakarn, S. (2005). Application of the theory of reasoned action to online shopping. https://www.bu.ac.th/knowledgecenter/epaper/jan_june2005/supanat.pdf
De Sio, L., Ding, B., Focsan, M., Kogermann, K., Pascoul-Faria, P., Petronela, F., Mitchell, G., Zussman, E. & Peirini, F. (2020). Personalized reusable face masks with smart nano-assisted destruction of pathogens for Covid-19: A visionary road. Chemistry — A European Journal, 27(20), 6112–6130.
El-Atab, N., Mishra, R.B. & Hussain, M.M. (2021). Toward nanotechnology-enabled face masks against SARS-CoV-2 and pandemic respiratory diseases. Nanotechnology, 33. https://doi.org/10.1088/1361-6528/ac3578
Georgiev, A. & Schlogl, S. (2018, February). Smart home technology: An exploration of end-user perceptions [Paper presentation]. Smarter Lives, Innsbruck, Austria. https://www.researchgate.net/publication/327136969_Smart_Home_Technology_An_Exploration_of_End_User_Perceptions
Gultom, S., Dalle, J., Restu, Baharuddin, Hairudioar & Gultom, S. (2020). The influence of attitude and subjective norm on citizen's intention to use e-government services. Journal of Security and Sustainability Issues, 9(M), 173–187. )
Haris, A., Kefeli, Z., Ahamad, N. & Nurazira, S. (2017). Consumers' intention to purchase dates: Application of theory of reasoned action (TRA). Malaysian Journal of Consumer and Family Economics, 20, 1–15.
Henkels, B., Verleye, K. & Larivière, B. (2020). The smarter, the better?! Customer well-being, engagement, and perceptions in smart service systems. International Journal of Research in Marketing, Advance online publication. https://doi.org/10.1016/j.ijresmar.2020.09.006
Hosseini, Z., Gharlipour Gharghani, Z., Mansoori, A., Aghamolaei, T. & Mohammadi Nasrabadi, M. (2015). Application of the theory of reasoned action to promoting breakfast consumption. Medical Journal of the Islamic Republic of Iran, 29, 289.
Hussein, Z., Oon, S.W. & Fikry, A. (2017). Consumer attitude: Does it influence the intention to use mHealth? Procedia Computer Science, 105, 340–344. https://doi.org/10.1016/j.procs.2017.01.231
Hussein, Z. (2017). Leading to intention: The role of attitude in relation to technology acceptance model in e-learning. Procedia Computer Science, 105, 159–164. https://doi.org/10.1016/j.procs.2017.01.196
Hyysalo, J., Dasanayake, S., Hannu, J., Schuss, C., Rajanen, M., Leppanen, T., Doermann, D. & Sauvola, J. (2022). Smart mask — wearable IoT solution for improved protection and personal health. Internet of Things, 18. https://doi.org/10.1016/j.iot.2022.100511
Kalavakonda, R.R., Masna, N.V.R., Mandal, S. & Bhunia, S. (2021). A smart mask for active defense against airborne pathogens. Scientific Reports, 11. https://doi.org/10.1038/s41598-021-99150-x
Korneeva, E., Olinder, N. & Strielkowski, W. (2021). Consumer attitudes to the smart home technologies and the Internet of Things (IoT). Energies, 14.
Kumar, S., Tiwari, P. & Zymbler, M. (2019). Internet of Things is a revolutionary approach for future technology enhancement: A review. Journal of Big Data, 6. https://doi.org/10.1186/s40537-019-0268-2
Lorenz, G. & Buhtz, K. (2017, June 5–10). Social influence in technology adoption research: A literature review and research agenda [Paper presentation]. 25th European Conference on Information Systems (ECIS), Guimarães, Portugal.
Massoro, Z.Z. & Adewale, N.T. (2019). Influence of attitude, subjective norms and personal innovativeness on intention to use open access journals: A case of agricultural research institutes. Library Philosophy and Practice (e-Journal). https://www.researchgate.net/publication/333104047
Moura, I.V., de Almeida, L.B., da Silva, W.V., de Veiga, C.P. & Costa, F. (2020). Predictor factors of intention to use technological resources: A multi-group study about the approach of technology acceptance model. Sage Open. https://doi.org/10.1177/2158244020967942
Myresten, E. & Setterhall, M. (2015). Theory of reasoned action and the role of external factors in organic food purchase [Master's thesis, University]. Diva-Portal.org. https://www.diva-portal.org/smash/get/diva2:824923/FULLTEXT01.pdf
Neuhofer, B., Buhalis, D. & Ladkin, A. (2015). Smart technologies for personalized experiences: A case study in the hospitality domain. Electron Markets, 25, 243–254. https://doi.org/10.1007/s12525-015-0182-1
Otieno, O.C., Liyala, S., Odongo, B.C. & Abeka, S. (2016). Theory of reasoned action as an underpinning to technological innovation adoption studies. World Journal of Computer Application and Technology, 4(1), 1–17. https://doi.org/10.13189/wjcat.2016.040101
Raff, S., Wentzel, D. & Obwegesser, N. (2020). Smart products: Conceptual review, synthesis, and research directions. Journal of Product Innovation Management, 37(5), 379–404.
Riegger, A., Merfeld, K., Klein, J.F. & Henkel, S. (2022). Technology-enabled personalization: Impact of smart technology choice on consumer shopping behavior. Technology Forecasting and Social Change, 181. https://doi.org/10.1016/j.techfore.2022.121752
Scarpi, D., Pizzi, G. & Matta, S. (2022). Digital technologies and privacy: State of the art and research directions. Psychology and Marketing, Early View.
Siepmann, C. & Kowalczuk, P. (2021). Understanding continued smartwatch usage: The role of emotional as well as health and fitness factors. Electronic Markets, 31, 795–809. https://doi.org/10.1007/s12525-021-00458-3
Skoumpopoulou, D., Wong, A., Ng, P. & Lo, M.F. (2018). Factors that affect the acceptance of new technologies in the workplace: A cross-case analysis between two universities. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 14(3), 209–222.
Teo, T. & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26, 124–142. https://doi.org/10.1007/s12528-014-9080-3
Wang, J., Shahzad, F., Ahmad, Z., Abdullah, M. & Hassan, N.M. (2022). Trust and consumers' purchase intention in a social commerce platform: A meta-analytic approach. Sage Open. https://doi.org/10.1177/21582440221091262
Xu, W., Yu, Y. & Zhang, Q. (2018). An evaluation method of comprehensive product quality for customer satisfaction based on intuitionistic fuzzy number. Discrete Dynamics in Nature and Society, 2018.
Create your account
Always verify citation format against your institution’s current style guide requirements.