Assessing the Acceptance of Cyborg Technology with a Hedonic Technology Acceptance Model
Abstract
:1. Introduction
- RO1: To examine the ability of the proposed hedonic TAM to explain the adoption of insideables. This analysis allows us to determine the intensity and level of significance with which the latent variables generate behavioral intentions.
- RO2: To establish the threshold that must be reached in each of the input latent variables, if it exists, to ensure a certain degree of behavioral intention. In other words, while reaching that threshold in the analyzed input variable does not guarantee behavioral intention, having the score achieved fall below the threshold ensures rejection of the insertable tech.
2. Theoretical Framework
2.1. Previous Considerations
2.2. Hypothesis Development
2.2.1. Perceived Usefulness
2.2.2. Perceived Enjoyment
2.2.3. Perceived Ease of Use
2.2.4. Subjective Norm
Latent Variable | Theoretical Ground | Studies |
---|---|---|
Perceived usefulness | TAM | [29,30,31] |
UTAUT | [3,33] | |
CAN | [5,34,35] | |
MES | Refs. [2,28] find that egoism and utilitarianism influence positively behavioral intention and [38] reports that utilitarianism moderates positively the impact of perfectionism on intention to use. | |
Perceived enjoyment/positive and negative emotions | TAM | [29,32,60] |
UTAUT | [3,33] | |
CAN | [5,34,35] | |
Perceived ease of use | TAM | [31,33] |
UTAUT | [33] | |
CAN | [5,35] | |
Social norm | TAM | [31] |
UTAUT | [3,33] | |
CAN | [5,34,35] | |
MES | Refs. [2,28,36] find that moral equity and relativism influence positively behavioral intention. Ahadzadeh et al. [38] find that a moral equity against insertables moderates perfectionism in terms of intention to use. |
3. Materials and Methods
3.1. Materials
3.2. Measurement of Variables
3.3. Data Analysis
- Scale reliability was evaluated through an assessment of internal consistency and discriminant capacity. Internal consistency was measured using Cronbach’s alpha (C-α), convergent reliability (CR), average variance extracted (AVE), factor loadings, and confirmatory factor analysis (CFA). To finalize the determination of the items listed in Table 3 for consideration, we ensured that the outer model indicators did not exhibit excessive variance inflation factors (VIFs) in the latent variables they are part of, which we set at >5.
- 2.
- We fitted the paths of the model displayed in Figure 1 by running consistent partial least-squares percentile bootstrapping with 5,000 subsamples with replacement. At this stage, we calculated the net impact of these factors and evaluated their statistical significance. We also examined goodness-of-fit indicators such as R2, standardized root mean square error (SRMR), and normed fit index (NFI). Additionally, we ensured that the inner model did not exhibit multicollinearity issues by analyzing the VIF measure of the input variables.
- 3.
- We evaluated the predictive capability of the model using Stone and Greisser’s Q2 and a cross-validated predictive ability test (CVPAT) [70].
- We measured the constructs by using the latent variable scores used in the regressions performed by using PLS-SEM.
- We analyzed the scatter plots of bivariate representations of the explained variable (behavioral intention, Y-axis) with respect to the explanatory factors (X-axis). These methods allow for the identification of outliers in the sample and their removal. In any case, we considered the values of the difference between Y and X that were 3.5 times the standard deviation of the difference in these variables to be outliers. The scatter plots allowed us to visualize the ceiling envelopment-free disposal hull (CE-FDH) and ceiling regression-free disposal hull (CR-FDH), which were obtained by smoothing CE-FDH.
- After removing outliers (if they existed), we determined the size of the necessity effect (d). These effects can be classified as small (0 < d < 0.1), medium (0.1 ≤ d < 0.3), large (0.3 ≤ d < 0.5), or very large (≥0.5). We also stated statistical significance. According to [43], values of d > 0.1 with a p value <0.05 are considered relevant for practical purposes. We obtained two estimates of d: that from CE-FDH, whose value can be considered optimistic, and that obtained from CR-FDH.
- We presented bottleneck tables to enable bottleneck analysis. Bottleneck analysis involves an analysis of necessity in terms of degree: “level a of input Xa is necessary for level BIa of BI” [43].
4. Results
4.1. Descriptive Statistics and Results of PLS-SEM Analysis
4.2. Results of Necessity Condition Analysis
5. Discussion
5.1. General Considerations
5.2. Theoretical and Practical Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sex | Do You Use Wearables? (Smartwatches, Smartglasses, etc.) |
---|---|
Female: 827 (52.91%) | Yes 1260 (80.61%) |
Male: 736 (47.09%) | No 303 (19.39%) |
Age | Do you use insideables? (maybe for medical or nonmedical reasons) |
≥18 and ≤20: 790 (50.54%) | Yes: 1420 (90.85%) |
≥21 and ≤23: 544 (34.80%) | No: 143 (9.15%) |
≥24: 229 (14.65%) | |
Mean = 21.05; SD = 2.63 | |
The response comes from… | |
Chile: 244 (15.61%) | Mexico: 241 (15.42%) |
China: 229 (14.65%) | Spain: 286 (18.30%) |
Denmark: 210 (13.44%) | The USA: 53 (3.39%) |
Japan: 300 (19.19%) |
Latent Variable | Items | Source |
---|---|---|
Behavioral intention (BI) | BI1: I will intend to employ insertable techs. BI2: I think that I will adopt insertable tech | Venkatesh and Davis [26] and Van der Heijden [39] |
Perceived usefulness (PU) | PU1: Insideables allows daily more comfortable. PU2: Insideables will increase the likelihood of achieving my goals. PU3: Insideables will help me complete my tasks more quickly. PU4: Using insideables will boost my productivity. | Venkatesh and Davis [26] and Van der Heijden [39] |
Perceived enjoyment (PENJ) | PENJ1: Insertable technologies are enjoyable. PENJ2: Insertable technologies are pleasant. PENJ3: Insertable technologies are amazing. | Van der Heijden [39] |
Perceived ease of use (PEoU) | PEoU1: The use of insideables will be comfortable for me. PEoU 2: How to use insideables is clear and understandable. PEoU 3: Learning the use insideables will be straightforward for me. PEoU 4: Being an expert in insertable tech will be easy for me. | Van der Heijden [39] |
Social norm (SN) | SN1: People who are important to me will think I should use insideables. SN2: People who influence me will think I should use insideables. SN3: People whose opinions I value will prefer that I use insideables. | Venkatesh and Davis [26] |
Latent Variable | Item | Mean | SD | Loading | C-α | CR | AVE |
---|---|---|---|---|---|---|---|
Behavioral intention | BI1 | 3.66 | 3.13 | 0.979 | 0.96 | 0.96 | 0.92 |
(BI) | BI2 | 3.70 | 3.09 | 0.98 | |||
Perceived usefulness | PU1 | 4.71 | 3.12 | 0.928 | 0.94 | 0.94 | 0.84 |
(PU) | PU2 | 4.44 | 3.09 | 0.953 | |||
PU4 | 4.92 | 3.10 | 0.936 | ||||
Perceived enjoyment | PENJ2 | 4.07 | 2.99 | 0.969 | 0.96 | 0.96 | 0.88 |
(PENJ) | PENJ3 | 4.32 | 3.08 | 0.966 | |||
Perceived ease of use | PEoU1 | 4.91 | 3.00 | 0.941 | 0.94 | 0.94 | 0.83 |
(PEoU) | PEoU2 | 4.61 | 2.97 | 0.947 | |||
PEoU4 | 4.57 | 2.96 | 0.933 | ||||
Subjective norm | SN1 | 3.15 | 2.89 | 0.974 | 0.97 | 0.97 | 0.91 |
(SN) | SN3 | 3.16 | 2.84 | 0.975 |
BI | PU | PENJ | PEoU | SN | |
---|---|---|---|---|---|
BI | 0.96 | 0.83 | 0.81 | 0.68 | 0.76 |
PU | 0.82 | 0.91 | 0.80 | 0.69 | 0.68 |
PENJ | 0.81 | 0.80 | 0.94 | 0.74 | 0.73 |
PEoU | 0.68 | 0.68 | 0.74 | 0.92 | 0.63 |
SN | 0.76 | 0.68 | 0.73 | 0.62 | 0.95 |
Relation | β | VIF | SD | p Value | Decision |
---|---|---|---|---|---|
PU-> BI | 0.380 | 2.591 | 0.030 | <0.001 | Supported |
PENJ-> BI | 0.260 | 3.151 | 0.035 | <0.001 | Supported |
PEoU-> BI | 0.061 | 2.199 | 0.023 | 0.008 | Supported |
PEoU-> PU | 0.421 | 1.567 | 0.028 | <0.001 | Supported |
PEoU-> PENJ | 0.704 | 1 | 0.017 | <0.001 | Supported |
SN-> BI | 0.266 | 2.156 | 0.028 | <0.001 | Supported |
SN-> PU | 0.395 | 1.567 | 0.026 | <0.001 | Supported |
CVPAT: Indicator Average | CVPAT: Linear Model | ||||
---|---|---|---|---|---|
Q² | ALD | p Value | ALD | p Value | |
BI | 59.6% | −5.539 | <0.001 | 0.083 | 0.031 |
PU | 48.42% | −4.507 | <0.001 | 0.057 | 0.018 |
PENJ | 46.23% | −4.266 | <0.001 | 1.041 | <0.001 |
Overall | −4.733 | <0.001 | 0.346 | <0.001 |
CE-FDH | CR-FDH | |||
---|---|---|---|---|
Size Effect | p Value | Size Effect | p Value | |
PU | 0.232 | <0.001 | 0.211 | <0.001 |
PENJ | 0.172 | <0.001 | 0.152 | <0.001 |
PEoU | 0.177 | <0.001 | 0.150 | <0.001 |
SN | 0.020 | <0.001 | 0.014 | <0.001 |
CE-FDH | CR-FDH | |||||||
---|---|---|---|---|---|---|---|---|
BI | PU | PENJ | PEoU | SN | PU | PENJ | PEoU | SN |
0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
30 | 14.75 | 0.00 | 0.00 | 0.00 | 12.87 | 0.00 | 0.00 | 0.00 |
40 | 19.36 | 0.00 | 12.61 | 0.00 | 15.47 | 0.00 | 10.66 | 0.00 |
50 | 23.78 | 26.51 | 17.74 | 0.00 | 21.31 | 18.26 | 15.92 | 0.00 |
60 | 25.47 | 34.70 | 19.17 | 0.00 | 25.73 | 26.06 | 19.17 | 0.00 |
70 | 36.06 | 34.70 | 19.17 | 0.00 | 34.18 | 34.11 | 24.56 | 0.00 |
80 | 36.06 | 34.70 | 36.00 | 0.00 | 40.03 | 39.31 | 30.08 | 0.00 |
90 | 55.88 | 47.50 | 36.00 | 28.59 | 55.30 | 47.50 | 35.61 | 28.27 |
100 | 75.24 | 47.50 | 46.33 | 35.93 | 62.96 | 64.07 | 42.63 | 38.92 |
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de Andrés-Sánchez, J.; Arias-Oliva, M.; Souto-Romero, M.; Gené-Albesa, J. Assessing the Acceptance of Cyborg Technology with a Hedonic Technology Acceptance Model. Computers 2024, 13, 82. https://doi.org/10.3390/computers13030082
de Andrés-Sánchez J, Arias-Oliva M, Souto-Romero M, Gené-Albesa J. Assessing the Acceptance of Cyborg Technology with a Hedonic Technology Acceptance Model. Computers. 2024; 13(3):82. https://doi.org/10.3390/computers13030082
Chicago/Turabian Stylede Andrés-Sánchez, Jorge, Mario Arias-Oliva, Mar Souto-Romero, and Jaume Gené-Albesa. 2024. "Assessing the Acceptance of Cyborg Technology with a Hedonic Technology Acceptance Model" Computers 13, no. 3: 82. https://doi.org/10.3390/computers13030082