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Front. Eng    2018, Vol. 5 Issue (2) : 167-181    https://doi.org/10.15302/J-FEM-2018056
RESEARCH ARTICLE
Exploring adoption of augmented reality smart glasses: Applications in the medical industry
Nuri BASOGLU1, Muge GOKEN1, Marina DABIC2, Dilek OZDEMIR GUNGOR3, Tugrul U. DAIM4()
1. Izmir Institute of Technology, Izmir 35433, Turkey
2. Nottingham Trent University, Nottingham, UK; University of Zagreb, Zagreb 10000, Croatia
3. Katib Celebi University, Izmir 35620, Turkey
4. Portland State University, Portland, OR 97201, USA
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Abstract

This study explores the use of augmented reality smart glasses (ARSGs) by physicians and their adoption of these products in the Turkish medical industry. Google Glass was used as a demonstrative example for the introduction of ARSGs. We proposed an exploratory model based on the technology acceptance model by Davis. Exogenous factors in the model were defined by performing semi-structured in-depth interviews, along with the use of an expert panel in addition to the technology adoption literature. The framework was tested by means of a field study, data was collected via an Internet survey, and path analysis was used. The results indicate that there were a number of factors to be considered in order to understand ARSG adoption by physicians. Usefulness was influenced by ease of use, compatibility, ease of reminding, and speech recognition, while ease of use was affected by ease of learning, ease of medical education, external influence, and privacy. Privacy was the only negative factor that reduced the perceived ease of use, and was found to indirectly create a negative attitude. Compatibility emerged as the most significant external factor for usefulness. Developers of ARSGs should pay attention to healthcare-specific requirements for improved utilization and more extensive adoption of ARSGs in healthcare settings. In particular, they should focus on how to increase the compatibility of ARSGs. Further research needs to be conducted to explain the adoption intention of physicians.
Keywords technology adoption      augmented reality smart glasses (ARSGs)      healthcare     
Corresponding Author(s): Tugrul U. DAIM   
Just Accepted Date: 15 January 2018   Online First Date: 26 February 2018    Issue Date: 28 June 2018
 Cite this article:   
Nuri BASOGLU,Muge GOKEN,Marina DABIC, et al. Exploring adoption of augmented reality smart glasses: Applications in the medical industry[J]. Front. Eng, 2018, 5(2): 167-181.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2018056
https://academic.hep.com.cn/fem/EN/Y2018/V5/I2/167
Study Sample/Study description Purpose Results
Elder and Vakaloudis (2015) Researched technical characteristics and application areas of 26 different ARSGs. To define elements of uniform characteristics. Usability in professional domain needs to be improved.
Social obstacles need to be overcome for ARSG adoption.
Rauschnabel and Ro (2016) Analyzed data collected from 201 randomly selected respondents. Regression analysis was used. To test an exploratory model based on TAM and proposed for ARSG acceptance by consumers. “Functional benefits” seems to be a determining factor of adoption. Privacy is not as effective as it is discussed.
Mitrasinovic et al. (2015) Literature review was conducted by using “smart glasses,” “healthcare,” “evaluation,” “privacy,” and “development” as keywords. To define application areas of ARSGs in healthcare settings. ARSGs are used in hands-free documentation, telemedicine, electronic health record retrieval and creation, rapid diagnostic test analyses, education, and broadcasting.
Rauschnabel et al. (2015) Data collected from 146 German students was analyzed by applying covariance-based structural equation modeling (SEM). To explore and define the role of personality in ARSG usage. “Social conformity” and “functional benefits” affect the intention to adopt ARSGs positively.
“Personality” is a significant moderating effect.
Basoglu et al. (2017) A web-survey was conducted. Collected data was analyzed with conjoint-analysis and regression. To identify influences of “product characteristics” and “user intention characteristics.” Product features influencing adoption are standalone device, interaction, price, display resolution, and field view.
Enjoyment and external influence are significant user intention characteristics.
Weiz et al. (2016) A web survey was conducted. Data collected from 111 completed questionnaires was analyzed with PLS-SEM. To evaluate the effect of “subjective norms” on ARSG adoption. Subjective norms indirectly affect “actual system use” through “perceived usefulness.”
Stock et al. (2016) 109 questionnaires were collected online and data was analyzed with PLS-SEM. To explore the effect of perceived health risk on ARSG adoption. Perceived health risks have an indirect negative effect, while perceived enjoyment has a direct positive effect on intention to use.
Nambu et al. (2016) Developed a question-answering module and tested it in the field. To examine the applicability of a work support system using ARSGs. Workers can perform their job autonomously with the assistance of the module.
Kawai et al. (2015) Proposed a game-based evacuation drill (GBED) and compared its usefulness with tablet-based GBED. To study the usefulness of ARSG-based GBED. Problems in system usability, such as view angle, screen size, and low accuracy of tsunami simulation, must be addressed to improve the system.
Sedarati and Baktash (2017) Developed a conceptual model for adoption of ARSGs in tourism industry based on systems dynamics. To investigate ARSG adoption in tourism industry. Social factors are influential in the intention to use ARSGs in tourism.
The masculinity/femininity of culture plays a moderating role.
Jung and Han (2014) Literature review was conducted. To explore existing implementations of AR in urban tourism and potential improvement areas. AR applications have the potential to enhance the experience of tourists and visitors; however, this area requires further investigation for application development.
Quint and Loch (2015) A scenario for documentation of maintenance processes was developed. To examine the usability of ARSGs in maintenance process documentation. Feasibility of ARSG usage in recording and playing instructional videos for maintenance processes.
Tab.1  Prior research on adoption and applications of ARSGs
Hypothesis Key factor Dependent factor Reference(s)
H1, H2, H3, H4 Main factors of TAM Rogers and Shoemaker (1971), Goodhue and Thompson (1995), Venkatesh et al. (2003), Aggelidis and Chatzoglou (2009), Holden and Karsh (2010)
H5 Compatibility Perceived usefulness Elder and Vakaloudis (2015), Mitrasinovic et al. (2015)
H6 Ease of reminding Perceived usefulness Mathad and Karnam (2014)
H7 Speech recognition Perceived usefulness Armstrong et al. (2014), Gregg (2014a)
H8 Ease of learning Perceived ease of use Galletta and Dunn (2014), Gefen and Straub (2000)
H9 Ease of medical education Perceived ease of use Armstrong et al. (2014), Moshtaghi et al. (2015)
H10 External influence Perceived ease of use Roesler (2015), Bhattacherjee (2000), Nisbet et al. (2002)
H11 Patient privacy Perceived ease of use Monroy et al. (2014)
Tab.2  Key factors and relations
Range Frequency Percentage/% Cumulative percentage/%
Gender
??Female 18 25.4 25.4
??Male 53 74.6 100.0
Age
??24 or younger 5 7.0 7.0
??25–29 12 16.9 23.9
??30–34 13 18.3 42.3
??35–39 11 15.5 57.7
??40–44 12 16.9 74.6
??45–49 9 12.7 87.3
??50–54 3 4.2 91.5
??55 or older 6 8.5 100.0
Education
??Medicine student 5 7.0 7.0
??Undergraduate degree 14 19.7 26.8
??Graduate degree 14 19.7 46.5
??Ph.D 38 53.5 100.0
Expertise
??Surgeon 22 31.0 31.0
??Internal specialist 3 4.2 35.2
??Pediatrician 1 1.4 36.6
??Other 45 63.4 100.0
Tab.3  Profile of participants
Variable Unstandardized coefficient Standardized coefficient T Significance Hypothesis
B Standard error Beta
Dependent variable: Intention; R 2=0.069
??(Constant) 1.591 0.850 1.873 0.065
??Attitude 0.421 0.186 0.263 2.263 0.027 H1
Dependent variable: Attitude; R 2=0.699
??(Constant) 0.482 0.338 1.426 0.158
??Perceived usefulness 0.805 0.087 0.764 9.249 0.000 H2
??Perceived ease of use 0.116 0.084 0.114 1.382 0.172 H3
Dependent variable: Perceived usefulness; R 2=0.625
??(Constant) –0.302 0.469 –0.643 0.522
??Perceived ease of use 0.425 0.085 0.428 5.000 0.000 H4
??Compatibility 0.362 0.093 0.349 3.896 0.000 H5
??Ease of reminding 0.152 0.068 0.196 2.233 0.029 H6
??Speech recognition 0.129 0.072 0.160 1.795 0.078 H7
Dependent variable: Perceived ease of use; R 2=0.533
??(Constant) 3.656 0.601 6.087 0.000
??Ease of learning 0.298 0.054 0.474 5.527 0.000 H8
??Ease of medical education 0.271 0.080 0.295 3.382 0.001 H9
??External influence 0.247 0.064 0.341 3.884 0.000 H10
??Privacy –0.211 0.095 –0.188 –2.183 0.033 H11
Tab.4  Results of regression analyses and hypotheses testing
Fig.1  Research framework and regression results
Dependent variable Variable Standardized beta Standard deviation t Significance
Intention Perceived usefulness 0.201 0.100 1.998 0.047
Perceived ease of use 0.117 0.056 2.089 0.038
Compatibility 0.066 0.042 1.565 0.119
Ease of reminding 0.047 0.031 1.536 0.126
Speech recognition 0.027 0.024 1.133 0.258
Ease of learning 0.055 0.030 1.863 0.063
Ease of medical education 0.034 0.020 1.725 0.085
External influence 0.040 0.021 1.852 0.065
Privacy –0.022 0.017 1.281 0.201
Attitude Perceived ease of use 0.329 0.084 3.926 0.000
Compatibility 0.247 0.080 3.084 0.002
Ease of reminding 0.178 0.084 2.132 0.034
Speech recognition 0.104 0.071 1.453 0.147
Ease of learning 0.210 0.063 3.308 0.001
Ease of medical education 0.131 0.045 2.903 0.004
External influence 0.151 0.055 2.753 0.006
Privacy –0.083 0.046 1.792 0.074
Perceived usefulness Ease of learning 0.204 0.053 3.823 0.000
Ease of medical education 0.127 0.042 2.997 0.003
External influence 0.147 0.055 2.648 0.009
Privacy –0.081 0.045 1.789 0.075
Tab.5  Indirect effects
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