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Motivations and Inhibitions Behind the Adoption and Continuous Use of IoT Wearable Devices: Exploring and Comparing Three Major Frameworks

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Abstract

The recent growth of IoT and connected objects has given birth to a market characterized by innovative offerings and new consumer behaviors. In this framework, this paper considers the specific case of the adoption and continuous use of IoT wearable devices. The literature proposes three main theoretical models: Diffusion of Innovations theory (DOI), Theory of Planned Behavior (TPB), and Technology Acceptance Model (TAM). Through a qualitative exploratory research based on 51 in-depth interviews, we try to understand the motivations and inhibitions behind the adoption and continuous use of these new products. The findings of qualitative interviews allowed us to compare the main theoretical models available in the literature and to propose an enhanced framework adapted to the specific case of IoT wearable devices.

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Author information

Authors and Affiliations

  1. NEOMA Business School, Rouen Campus, Rouen, France

    Mourad Touzani

  2. EBS – Paris, European Business School Paris, Paris, France

    Ahmed Anis Charfi

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  1. Mourad Touzani
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  2. Ahmed Anis Charfi
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Correspondence to Ahmed Anis Charfi .

Editor information

Editors and Affiliations

  1. ESEN, University of Manouba, Manouba, Tunisia

    Rim Jallouli

  2. ESEN, University of Manouba, Manouba, Tunisia

    Mohamed Anis Bach Tobji

  3. Université de Sherbrooke, Sherbrooke, QC, Canada

    Deny Bélisle

  4. Université Laval, Quebec, QC, Canada

    Sehl Mellouli

  5. International University of Beirut, Mazraa, Lebanon

    Farid Abdallah

  6. American University of Beirut, Beirut, Lebanon

    Ibrahim Osman

Appendix 1: The Constructs Mentioned in DOI, TPB and TAM

Appendix 1: The Constructs Mentioned in DOI, TPB and TAM

Theory

Factor

Definition

DOI

Relative advantage

“The degree to which an innovation is perceived as better than the idea it supersedes” (Rogers 2010, p. 15)

Compatibility

“The degree to which an innovation is perceived as being consistent with the existing values, past experiences and needs of potential adopters” (Rogers 2010, p. 15)

Complexity

“The degree to which an innovation is perceived as difficult to understand and use” (Rogers 2010, p. 16)

Trialability

“The degree to which an innovation may be experimented with on a limited basis” (Rogers 2010, p. 16)

Observability

“The degree to which the results of an innovation are visible to others” (Rogers 2010, p. 16)

Adoption

“A decision to make full use of an innovation as the best course of action available” (Rogers 2010, p. 21)

TPB

Attitude toward behavior

“An individual’s positive or negative feelings (evaluative effect) about performing the target behavior” (Fishbein and Ajzen, 1975, p. 216)

Subjective norm

“The person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen 1975, p. 302)

Perceived behavioral control

“The perceived ease or difficulty of performing the behavior” (Ajzen 1991, p. 188), In the context of information systems research, “Perceptions of internal and external constraints on behavior” (Taylor and Todd 1995, p 149)

Behavioral intention to use

“The strength of one’s intention to perform a specified behavior” (Fishbein and Ajzen, 1975, p. 288)

TAM

Perceived usefulness

“The degree to which a person believes that using a particular system would enhance his or her job performance” (Davis 1989, p. 320)

Perceived ease of use

“The degree to which a person believes that using a particular system would be free of effort” (Davis 1989, p. 320)

Behavioral intention to use

“The strength of one’s intention to perform a specified behavior” (Fishbein and Ajzen, 1975, p. 288)

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Touzani, M., Charfi, A.A. (2019). Motivations and Inhibitions Behind the Adoption and Continuous Use of IoT Wearable Devices: Exploring and Comparing Three Major Frameworks. In: Jallouli, R., Bach Tobji, M., Bélisle, D., Mellouli, S., Abdallah, F., Osman, I. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2019. Lecture Notes in Business Information Processing, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-30874-2_26

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