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
- Kadlecová, J. Body-hacking: On the Relationship between People and Material Entities in the Practice of Technological Body Modifications. Hist. Sociol. 2020, 12, 49–63. [Google Scholar] [CrossRef]
- Olarte-Pascual, C.; Pelegrín-Borondo, J.; Reinares-Lara, E.; Arias-Oliva, M. From wearable to insideable: Is ethical judgment key to the acceptance of human capacity-enhancing intelligent technologies? Comput. Hum. Behav. 2021, 114, 106559. [Google Scholar] [CrossRef]
- Arias-Oliva, M.; Pelegrín-Borondo, J.; Murata, K.; Gauttier, S. Conventional vs. disruptive products: A wearables and insideables acceptance analysis. Technol. Anal. Strateg. Manag. 2021, 35, 1663–1675. [Google Scholar] [CrossRef]
- Heffernan, K.J.; Vetere, F.; Chang, S. You put what, where? Hobbyist use of insertable devices. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose, CA, USA, 7–12 May 2016; pp. 1798–1809. [Google Scholar]
- Pelegrin-Borondo, J.; Reinares-Lara, E.; Olarte-Pascual, C. Assessing the acceptance of technological implants (the cyborg): Evidences and challenges. Comput. Hum. Behav. 2017, 70, 104–112. [Google Scholar] [CrossRef]
- Warwick, K. The cyborg revolution. Nanoethics 2014, 8, 263–273. [Google Scholar] [CrossRef]
- Giger, J.C.; Gaspar, R. A look into future risks: A psychosocial theoretical framework for investigating the intention to practice body hacking. Hum. Behav. Emerg. Technol. 2019, 1, 306–316. [Google Scholar] [CrossRef]
- Heffernan, K.J.; Vetere, F.; Chang, S. Socio-technical context for insertable devices. Front. Psychol. 2022, 13, 991345. [Google Scholar] [CrossRef]
- Richard, R.; Andrieu, B. The Cybathlon experience: Beyond transhumanism to capability hybridization. J. Phil. Sport 2019, 46, 49–62. [Google Scholar] [CrossRef]
- Warwick, K. Superhuman enhancements via implants: Beyond the human mind. Philosophies 2020, 5, 14. [Google Scholar] [CrossRef]
- Warwick, K. Cyborg morals, cyborg values, cyborg ethics. Ethics Inf. Technol. 2003, 5, 131–137. [Google Scholar] [CrossRef]
- Jones, C.; Hemphill, D. Philosophical issues in high-tech sport and leisure. World Leis. J. 2007, 49, 199–206. [Google Scholar] [CrossRef]
- Garry, T.; Harwood, T. Cyborgs as frontline service employees: A research agenda. J. Serv. Theory Pract. 2019, 29, 415–437. [Google Scholar] [CrossRef]
- Howe, P.D. Cyborg and supercrip: The Paralympics technology and the (dis) empowerment of disabled athletes. Sociology 2011, 45, 868–882. [Google Scholar] [CrossRef]
- Dhandapani, J.P. Cyborg technology: A quiet revolution. Pondicherry J. Nurs. 2019, 12, 96–99. [Google Scholar] [CrossRef]
- Komkaite, A.; Lavrinovica, L.; Vraka, M.; Skov, M.B. Underneath the skin: An analysis of youtube videos to understand insertable device interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 4–9 May 2019; pp. 1–12. [Google Scholar] [CrossRef]
- Heffernan, K.J.; Vetere, F.; Chang, S. Insertables: Beyond cyborgs and augmentation to convenience and amenity. In Technology-Augmented Perception and Cognition; Springer: Berlin/Heidelberg, Germany, 2021; pp. 185–227. [Google Scholar] [CrossRef]
- Heffernan, K.J.; Vetere, F.; Chang, S. Insertables: I’ve got it under my skin. Interactions 2015, 23, 52–56. [Google Scholar] [CrossRef]
- Ramoğlu, M. Cyborg-Computer Interaction: Designing New Senses. Des. J. 2019, 22, 1215–1225. [Google Scholar] [CrossRef]
- Gauttier, S. ‘I’ve got you under my skin’–The role of ethical consideration in the (non-) acceptance of insideables in the workplace. Technol. Soc. 2019, 56, 93–108. [Google Scholar] [CrossRef]
- Fox, S. Cyborgs, robots and society: Implications for the future of society from human enhancement with in-the-body technologies. Technologies 2018, 6, 50. [Google Scholar] [CrossRef]
- Olivares, L. Hacking the body and posthumanist transbecoming: 10,000 generations later as the mestizaje of speculative cyborg feminism and significant otherness. NanoEthics 2014, 8, 287–297. [Google Scholar] [CrossRef]
- Chiu, W.; Oh, G.E.; Cho, H. Factors influencing consumers’ adoption of wearable technology: A systematic review and meta-analysis. Int. J. Inf. Technol. Decis. Mak. 2021, 20, 933–958. [Google Scholar] [CrossRef]
- Chaudhry, B.M.; Shafeie, S.; Mohamed, M. Theoretical Models for Acceptance of Human Implantable Technologies: A Narrative Review. Informatics 2023, 10, 69. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
- Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
- Reidenbach, R.E.; Robin, D.P. Toward the development of a multidimensional scale for improving evaluations of Business Ethics. J. Bus. Ethics 1990, 9, 639–653. [Google Scholar] [CrossRef]
- Pelegrín-Borondo, J.; Arias-Oliva, M.; Murata, K.; Souto-Romero, M. Does ethical judgment determine the decision to become a cyborg? Influence of Ethical Judgment on the Cyborg Market. J. Bus. Ethics 2020, 161, 5–17. [Google Scholar] [CrossRef]
- Reinares-Lara, E.; Olarte-Pascual, C.; Pelegrín-Borondo, J.; Pino, G. Nanoimplants that enhance human capabilities: A cognitive-affective approach to assess individuals’ acceptance of this controversial technology. Psychol. Mark. 2016, 33, 704–712. [Google Scholar] [CrossRef]
- Gangadharbatla, H. Biohacking: An exploratory study to understand the factors influencing the adoption of embedded technologies within the human body. Heliyon 2020, 6, e03931. [Google Scholar] [CrossRef] [PubMed]
- Shafeie, S.; Chaudhry, B.M.; Mohamed, M. Modeling subcutaneous microchip implant acceptance in the general population: A cross-sectional survey about concerns and expectations. Informatics 2022, 9, 24. [Google Scholar] [CrossRef]
- Toker, K.; Afacan Fındıklı, M.; Gözübol, Z.İ.; Görener, A. To be a cyborg or not: Exploring the mechanisms between digital literacy and neural implant acceptance. Kybernetes, 2023; ahead-of-print. [Google Scholar] [CrossRef]
- Sabogal-Alfaro, G.; Mejía-Perdigón, M.A.; Cataldo, A.; Carvajal, K. Determinants of the intention to use non-medical insertable digital devices: The case of Chile and Colombia. Telemat. Inform. 2021, 60, 101576. [Google Scholar] [CrossRef]
- Pelegrín-Borondo, J.; Reinares-Lara, E.; Olarte-Pascual, C.; Garcia-Sierra, M. Assessing the moderating effect of the end user in consumer behavior: The acceptance of technological implants to increase innate human capacities. Front. Psychol. 2016, 7, 132. [Google Scholar] [CrossRef] [PubMed]
- Reinares-Lara, E.; Olarte-Pascual, C.; Pelegrín-Borondo, J. Do you want to be a cyborg? The moderating effect of ethics on neural implant acceptance. Comput. Hum. Behav. 2018, 85, 43–53. [Google Scholar] [CrossRef]
- Murata, K.; Arias-Oliva, M.; Pelegrín-Borondo, J. Cross-cultural study about cyborg market acceptance: Japan versus Spain. Eur. Res. Manag. Bus. Econ. 2019, 25, 129–137. [Google Scholar] [CrossRef]
- Andres-Sanchez, J.; Arias-Oliva, M.; Pelegrin-Borondo, J. The influence of ethical judgements on acceptance and non-acceptance of wearables and insideables: Fuzzy set qualitative comparative analysis. Technol. Soc. 2021, 67, 101689. [Google Scholar] [CrossRef]
- Ahadzadeh, A.S.; Ong, F.S.; Deng, R.; Ali, R.S. Unravelling the Relationship between Competitiveness Trait and Intention to Use Memory Implants: The Moderating Roles of Moral Equity, Egoism, and Utilitarianism. Int. J. Hum.–Comput. Interact. 2023. [Google Scholar] [CrossRef]
- Van der Heijden, H. User acceptance of hedonic information systems. MIS Q. 2004, 28, 695–704. [Google Scholar] [CrossRef]
- Richter, N.F.; Schubring, S.; Hauff, S.; Ringle, C.M.; Sarstedt, M. When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA. Ind. Manag. Data Syst. 2020, 120, 2243–2267. [Google Scholar] [CrossRef]
- Dul, J. Necessary condition analysis (NCA) logic and methodology of “necessary but not sufficient” causality. Organ. Res. Methods 2016, 19, 10–52. [Google Scholar] [CrossRef]
- Dul, J.; Van der Laan, E.; Kuik, R. A statistical significance test for necessary condition analysis. Organ. Res. Methods 2020, 23, 385–395. [Google Scholar] [CrossRef]
- Dul, J.; Hauff, S.; Bouncken, R.B. Necessary condition analysis (NCA): Review of research topics and guidelines for good practice. Rev. Manag. Sci. 2023, 17, 683–714. [Google Scholar] [CrossRef]
- Lin, C.P.; Bhattacherjee, A. Extending technology usage models to interactive hedonic technologies: A theoretical model and empirical test. Inf. Syst. J. 2010, 20, 163–181. [Google Scholar] [CrossRef]
- Lowry, P.B.; Gaskin, J.; Twyman, N.; Hammer, B.; Roberts, T. Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM). J. Assoc. Inf. Syst. 2012, 14, 617–671. [Google Scholar] [CrossRef]
- Ciasullo, M.V.; Troisi, O.; Maione, G. User Acceptance of Hedonic Information System: A Structural Equation Model to Understand why Some People Prefer Apple Products. In Proceedings of the 21st International Conference, Paris, France, 30–31 August 2018; Excellence in Services Le Cnam. Available online: https://sites.les.univr.it/eisic/wp-content/uploads/2018/11/14-Ciasullo-Troisi-Maione.pdf (accessed on 20 December 2023).
- Akdim, K.; Casaló, L.V.; Flavián, C. The role of utilitarian and hedonic aspects in the continuance intention to use social mobile apps. J. Retail. Consum. Serv. 2022, 66, 102888. [Google Scholar] [CrossRef]
- Ajzen, I. Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. J. Appl. Soc. Psychol. 2002, 32, 665–683. [Google Scholar] [CrossRef]
- Ashraf, S.; Saleem, S.; Ahmed, T.; Aslam, Z.; Shuaeeb, M. Iris and foot based sustainable biometric identification approach. In Proceedings of the 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, 17–19 September 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Karagianni, R.; Terzidou, A. Cyborg art and the rise of a specific vocabulary: Which are the challenges for curators In the expression of a new post-human identity? In Proceedings of the International Conference on Digital Culture & AudioVisual Challenges, Corfu, Greece, 1–2 June 2018; pp. 34–40. Available online: https://www.ceur-ws.org/Vol-2811/Paper06.pdf (accessed on 20 November 2023).
- Duarte, B.N. Entangled agencies: New individual practices of human-technology hybridism through body hacking. NanoEthics 2014, 8, 275–285. [Google Scholar] [CrossRef]
- Maras, M.H.; Miranda, M.D. Augmented body surveillance: Human microchip implantations and the omnipresent threat of function creep. Technol. Soc. 2023, 74, 102295. [Google Scholar] [CrossRef]
- Arnold, M.J.; Reynolds, K.E. Hedonic shopping motivations. J. Retail. 2003, 79, 77–95. [Google Scholar] [CrossRef]
- Baart, R. In Conversation with Cyborg Choreographer Moon Ribas. Next Nature. 2021. Available online: https://nextnature.net/magazine/story/2021/interview-moon-ribas (accessed on 20 November 2023).
- Davies, S. First Person: Neil Harbisson. Financial Times. 2012. Available online: https://www.ft.com/content/50efc98a-e66a-11e1-ac5f-00144feab49a (accessed on 10 November 2023).
- Wang, Z.; Scheepers, H. Understanding the intrinsic motivations of user acceptance of hedonic information systems: Towards a unified research model. Commun. Assoc. Inf. Syst. 2012, 30, 17. [Google Scholar] [CrossRef]
- Gaobotse, G.; Mbunge, E.; Batani, J.; Muchemwa, B. Non-invasive smart implants in healthcare: Redefining healthcare services delivery through sensors and emerging digital health technologies. Sens. Int. 2022, 3, 100156. [Google Scholar] [CrossRef]
- Bello-Wilches, C.B. Transhumanización: Mitad humano y mitad robot. Rev. Fe Y Lib. 2021, 4, 9–24. [Google Scholar] [CrossRef]
- Birnbaum, Z.P. Regulating the Cyberpunk Reality: Private Body Modification the Dangers of ‘Body Hacking’. J. Bus. Technol. Law 2021, 16, 119. Available online: https://digitalcommons.law.umaryland.edu/jbtl (accessed on 10 November 2023).
- Venkatesh, V.; Bala, H. Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 2008, 39, 273–315. [Google Scholar] [CrossRef]
- Akçayır, M.; Dündar, H.; Akçayır, G. What makes you a digital native? Is it enough to be born after 1980? Comput. Hum. Behav. 2016, 60, 435–440. [Google Scholar] [CrossRef]
- Bishop, L.; van Maris, A.; Dogramadzi, S.; Zook, N. Social robots: The influence of human and robot characteristics on acceptance. Paladyn J. Behav. Robot. 2019, 10, 346–358. [Google Scholar] [CrossRef]
- Behr, D.; Sha, M. Introduction: Translation of questionnaires in cross-national and cross-cultural research. The Int. J. Transl. Interp. Res. 2018, 10, 1–4. [Google Scholar] [CrossRef]
- Conroy, R.M. The RCSI Sample Size Handbook. A Rough Guide. 2016, pp. 59–61. Available online: https://www.researchgate.net/publication/324571619_The_RCSI_Sample_size_handbook (accessed on 10 November 2023).
- Leung, S.O. A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. J. Soc. Serv. Res. 2011, 37, 412–421. [Google Scholar] [CrossRef]
- Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
- Dash, G.; Paul, J. CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technol. Forecast. Soc. Chang. 2021, 173, 121092. [Google Scholar] [CrossRef]
- Fornell, C.G.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Henseler, J.; Ringle, C.M.; Sarstedt, M. A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
- Sharma, P.N.; Liengaard, B.D.; Hair, J.F.; Sarstedt, M.; Ringle, C.M. Predictive Model Assessment and Selection in Composite-based Modeling Using PLS-SEM: Extensions and Guidelines for Using CVPAT. Eur. J. Mark. 2023, 57, 1662–1677. [Google Scholar] [CrossRef]
- Ringle, C.M.; Wende, S.; Becker, J.-M. SmartPLS 4; SmartPLS GmbH: Oststeinbek, Germany, 2022; Available online: https://www.smartpls.com (accessed on 10 November 2023).
- Royal Society. iHuman: Blurring Lines between Mind and Machine; Royal Society: London, UK, 2019; Available online: https://royalsociety.org/-/media/policy/projects/ihuman/report-neural-interfaces.pdf (accessed on 10 November 2023).
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 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
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
APA Stylede Andrés-Sánchez, J., Arias-Oliva, M., Souto-Romero, M., & Gené-Albesa, J. (2024). Assessing the Acceptance of Cyborg Technology with a Hedonic Technology Acceptance Model. Computers, 13(3), 82. https://doi.org/10.3390/computers13030082