Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms
Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research a...
Ausführliche Beschreibung
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| Autor*in: |
Al-Emran, Mostafa [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Education and information technologies - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996, 28(2022), 3 vom: 31. Aug., Seite 2727-2746 |
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| Übergeordnetes Werk: |
volume:28 ; year:2022 ; number:3 ; day:31 ; month:08 ; pages:2727-2746 |
| Links: |
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| DOI / URN: |
10.1007/s10639-022-11294-z |
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| Katalog-ID: |
SPR049616382 |
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| 520 | |a Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. | ||
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| 700 | 1 | |a Arpaci, Ibrahim |4 aut | |
| 700 | 1 | |a Al-Sharafi, Mohammed A. |4 aut | |
| 700 | 1 | |a Anthony Jnr., Bokolo |4 aut | |
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10.1007/s10639-022-11294-z doi (DE-627)SPR049616382 (SPR)s10639-022-11294-z-e DE-627 ger DE-627 rakwb eng Al-Emran, Mostafa verfasserin (orcid)0000-0002-5269-5380 aut Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. Smartwatches (dpeaa)DE-He213 Wearable technologies (dpeaa)DE-He213 TPB (dpeaa)DE-He213 PMT (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Al-Nuaimi, Maryam N. aut Arpaci, Ibrahim aut Al-Sharafi, Mohammed A. aut Anthony Jnr., Bokolo aut Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2022), 3 vom: 31. Aug., Seite 2727-2746 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2022 number:3 day:31 month:08 pages:2727-2746 https://dx.doi.org/10.1007/s10639-022-11294-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2022 3 31 08 2727-2746 |
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10.1007/s10639-022-11294-z doi (DE-627)SPR049616382 (SPR)s10639-022-11294-z-e DE-627 ger DE-627 rakwb eng Al-Emran, Mostafa verfasserin (orcid)0000-0002-5269-5380 aut Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. Smartwatches (dpeaa)DE-He213 Wearable technologies (dpeaa)DE-He213 TPB (dpeaa)DE-He213 PMT (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Al-Nuaimi, Maryam N. aut Arpaci, Ibrahim aut Al-Sharafi, Mohammed A. aut Anthony Jnr., Bokolo aut Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2022), 3 vom: 31. Aug., Seite 2727-2746 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2022 number:3 day:31 month:08 pages:2727-2746 https://dx.doi.org/10.1007/s10639-022-11294-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2022 3 31 08 2727-2746 |
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10.1007/s10639-022-11294-z doi (DE-627)SPR049616382 (SPR)s10639-022-11294-z-e DE-627 ger DE-627 rakwb eng Al-Emran, Mostafa verfasserin (orcid)0000-0002-5269-5380 aut Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. Smartwatches (dpeaa)DE-He213 Wearable technologies (dpeaa)DE-He213 TPB (dpeaa)DE-He213 PMT (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Al-Nuaimi, Maryam N. aut Arpaci, Ibrahim aut Al-Sharafi, Mohammed A. aut Anthony Jnr., Bokolo aut Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2022), 3 vom: 31. Aug., Seite 2727-2746 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2022 number:3 day:31 month:08 pages:2727-2746 https://dx.doi.org/10.1007/s10639-022-11294-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2022 3 31 08 2727-2746 |
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10.1007/s10639-022-11294-z doi (DE-627)SPR049616382 (SPR)s10639-022-11294-z-e DE-627 ger DE-627 rakwb eng Al-Emran, Mostafa verfasserin (orcid)0000-0002-5269-5380 aut Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. Smartwatches (dpeaa)DE-He213 Wearable technologies (dpeaa)DE-He213 TPB (dpeaa)DE-He213 PMT (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Al-Nuaimi, Maryam N. aut Arpaci, Ibrahim aut Al-Sharafi, Mohammed A. aut Anthony Jnr., Bokolo aut Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2022), 3 vom: 31. Aug., Seite 2727-2746 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2022 number:3 day:31 month:08 pages:2727-2746 https://dx.doi.org/10.1007/s10639-022-11294-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2022 3 31 08 2727-2746 |
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10.1007/s10639-022-11294-z doi (DE-627)SPR049616382 (SPR)s10639-022-11294-z-e DE-627 ger DE-627 rakwb eng Al-Emran, Mostafa verfasserin (orcid)0000-0002-5269-5380 aut Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. Smartwatches (dpeaa)DE-He213 Wearable technologies (dpeaa)DE-He213 TPB (dpeaa)DE-He213 PMT (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Al-Nuaimi, Maryam N. aut Arpaci, Ibrahim aut Al-Sharafi, Mohammed A. aut Anthony Jnr., Bokolo aut Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2022), 3 vom: 31. Aug., Seite 2727-2746 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2022 number:3 day:31 month:08 pages:2727-2746 https://dx.doi.org/10.1007/s10639-022-11294-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2022 3 31 08 2727-2746 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR049616382</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230323105036.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230323s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10639-022-11294-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049616382</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10639-022-11294-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Al-Emran, Mostafa</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5269-5380</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. 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Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms |
| abstract |
Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The evidence from these findings provides the policy-makers in higher educational institutions with a clear vision of the most effective policies and best practices to enhance the capacity and potential use of these wearables in educational activities. The theoretical contributions and practical implications were also discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms |
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Al-Nuaimi, Maryam N. Arpaci, Ibrahim Al-Sharafi, Mohammed A. Anthony Jnr., Bokolo |
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| score |
7.398368 |