整合科技接受模型
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The unified theory of acceptance and use of technology (UTAUT) is a technology acceptance model formulated by Venkatesh and others in "User acceptance of information technology: Toward a unified view".[1] The UTAUT aims to explain user intentions to use an information system and subsequent usage behavior. The theory holds that there are four key constructs: 1) performance expectancy, 2) effort expectancy, 3) social influence, and 4) enabling conditions.
The unified theory of acceptance and use of technology (UTAUT) is a technology acceptance model formulated by Venkatesh and others in "User acceptance of information technology: Toward a unified view". The UTAUT aims to explain user intentions to use an information system and subsequent usage behavior. The theory holds that there are four key constructs:
1) performance expectancy,
2) effort expectancy,
3) social influence, and
4) enabling conditions.
技术接受与使用统一理论(UTAUT)是 Venkatesh 等人在《信息技术的用户接受: 走向统一观点》中提出的一种技术接受模型。UTAUT 旨在解释用户使用信息系统的意图和随后的使用行为。该理论认为,有四个关键构成: 1)绩效预期,2)努力预期,3)社会影响力,和4)扶持条件。
The first three are direct determinants of usage intention and behavior, and the fourth is a direct determinant of user behavior. Gender, age, experience, and voluntariness of use are posited to moderate the impact of the four key constructs on usage intention and behavior. The theory was developed through a review and consolidation of the constructs of eight models that earlier research had employed to explain information systems usage behaviour (theory of reasoned action, technology acceptance model, motivational model, theory of planned behavior, a combined theory of planned behavior/technology acceptance model, model of personal computer use, diffusion of innovations theory, and social cognitive theory). Subsequent validation by Venkatesh et al. (2003) of UTAUT in a longitudinal study found it to account for 70% of the variance in Behavioural Intention to Use (BI) and about 50% in actual use.[1]
The first three are direct determinants of usage intention and behavior, and the fourth is a direct determinant of user behavior. Gender, age, experience, and voluntariness of use are posited to moderate the impact of the four key constructs on usage intention and behavior. The theory was developed through a review and consolidation of the constructs of eight models that earlier research had employed to explain information systems usage behaviour (theory of reasoned action, technology acceptance model, motivational model, theory of planned behavior, a combined theory of planned behavior/technology acceptance model, model of personal computer use, diffusion of innovations theory, and social cognitive theory). Subsequent validation by Venkatesh et al. (2003) of UTAUT in a longitudinal study found it to account for 70% of the variance in Behavioural Intention to Use (BI) and about 50% in actual use.
前三个因素是使用意图和行为的直接决定因素,第四个因素是使用者行为的直接决定因素。性别、年龄、经验和使用的自愿性被假定为调节四个关键结构对使用意图和行为的影响。该理论是通过对早期研究用来解释信息系统使用行为的8个模型(理性行为理论、技术接受模型、动机模型、计划行为理论、计划行为/技术接受模型组合理论、个人电脑使用模型、创新产品渗透理论理论和社会认知理论)的回顾和整合而发展起来的。随后由 Venkatesh 等人进行验证。(2003年) UTAUT 的一项追踪研究发现,在行为意图使用(BI)中,70% 的差异是由它造成的,而在实际使用中,大约50% 是由它造成的。
Application
- Koivumäki et al. applied UTAUT to study the perceptions of 243 individuals in northern Finland toward mobile services and technology and found that time spent using the devices did not affect consumer perceptions, but familiarity with the devices and user skills did have an impact.[2]
- Eckhardt et al. applied UTAUT to study social influence of workplace referent groups (superiors, colleagues) on intention to adopt technology in 152 German companies and found significant impact of social influence from workplace referents on information technology adoption.[3]
- Curtis et al. applied UTAUT to the adoption of social media by 409 United States nonprofit organizations. UTAUT had not been previously applied to the use of social media in public relations. They found that organizations with defined public relations departments are more likely to adopt social media technologies and use them to achieve their organizational goals. Women considered social media to be beneficial, and men exhibited more confidence in actively utilizing social media.[4]
- Verhoeven et al. applied UTAUT to study computer use frequency in 714 university freshmen in Belgium and found that UTAUT was also useful in explaining varying frequencies of computer use and differences in information and communication technology skills in secondary school and in the university.[5]
- Welch et al. applied UTAUT to study factors contributing to Mobile learning adoption among 118 museum staff in England. UTAUT had not been previously applied to the use of just-in-time knowledge interventions to development technological knowledge within the museum sector. They found that UTAUT was useful in explaining the determinants of mobile learning adoption.[6]
- Koivumäki et al. applied UTAUT to study the perceptions of 243 individuals in northern Finland toward mobile services and technology and found that time spent using the devices did not affect consumer perceptions, but familiarity with the devices and user skills did have an impact.
- Eckhardt et al. applied UTAUT to study social influence of workplace referent groups (superiors, colleagues) on intention to adopt technology in 152 German companies and found significant impact of social influence from workplace referents on information technology adoption.
- Curtis et al. applied UTAUT to the adoption of social media by 409 United States nonprofit organizations. UTAUT had not been previously applied to the use of social media in public relations. They found that organizations with defined public relations departments are more likely to adopt social media technologies and use them to achieve their organizational goals. Women considered social media to be beneficial, and men exhibited more confidence in actively utilizing social media.
- Verhoeven et al. applied UTAUT to study computer use frequency in 714 university freshmen in Belgium and found that UTAUT was also useful in explaining varying frequencies of computer use and differences in information and communication technology skills in secondary school and in the university.
- Welch et al. applied UTAUT to study factors contributing to Mobile learning adoption among 118 museum staff in England. UTAUT had not been previously applied to the use of just-in-time knowledge interventions to development technological knowledge within the museum sector. They found that UTAUT was useful in explaining the determinants of mobile learning adoption.
= 应用 = =
- Koivumäki et al。应用 UTAUT 研究了芬兰北部243个人对移动服务和技术的看法,发现使用移动设备的时间不会影响消费者的看法,但是对移动设备的熟悉程度和用户技能确实有影响。
- Eckhardt et al.应用 UTAUT 软件对152家德国公司的工作场所参照群体(上级、同事)对技术采用意愿的社会影响进行了研究,发现工作场所参照群体对信息技术采用的社会影响显著。
- Curtis et al.将 UTAUT 应用于409个美国非营利组织对社会媒体的采用。UTAUT 以前没有应用于公共关系中社交媒体的使用。他们发现,拥有明确的公关部门的组织更有可能采用社交媒体技术,并利用它们来实现组织目标。女性认为社交媒体是有益的,而男性在积极利用社交媒体方面表现出更大的信心。
- Verhoeven et al.应用 UTAUT 对比利时714名大学新生的计算机使用频率进行了研究,发现 UTAUT 还有助于解释中学和大学计算机使用频率的不同以及信息和通信技术技能的差异。韦尔奇等人
- 。应用 UTAUT 对英国118名博物馆工作人员采用手机学习的因素进行了研究。UTAUT 以前没有应用于利用即时知识干预来发展博物馆部门的技术知识。他们发现 UTAUT 有助于解释移动学习采纳的决定因素。
Extension of the theory
- Lin and Anol postulated an extended model of UTAUT, including the influence of online social support on network information technology usage. They surveyed 317 undergraduate students in Taiwan regarding their online social support in using instant messaging and found that social influence plays an important role in affecting online social support.[7]
- Sykes et al. proposed a model of acceptance with peer support (MAPS), integrating prior research on individual adoption with research on social networks in organizations. They conducted a 3-month-long study of 87 employees in one organization and found that studying social network constructs can aid in understanding new information system use.[8]
- Wang, Wu, and Wang added two constructs (perceived playfulness and self-management of learning) to the UTAUT in their study of determinants of acceptance of mobile learning in 370 individuals in Taiwan and found that they were significant determinants of behavioral intention to use mobile learning in all respondents.[9]
- Hewitt et al. extended the UTAUT to study the acceptance of autonomous vehicles. Two separate surveys of 57 and 187 individuals in the USA showed that users were less accepting of high autonomy levels and displayed significantly lower intention to use highly autonomous vehicles.[10]
- Wang and Wang extended the UTAUT in their study of 343 individuals in Taiwan to determine gender differences in mobile Internet acceptance. They added three constructs – perceived playfulness, perceived value, and palm-sized computer self-efficacy to UTAUT and chose behavioral intention as a dependent variable. They omitted use behavior, facilitating conditions, and experience. .l. Also, since the devices were used in a voluntary context, and they found that most adopters were ages 20–35, they omitted voluntariness and age. Perceived value had a significant influence on adoption intention, and palm-sized computer self-efficacy played a critical role in predicting mobile Internet acceptance. Perceived playfulness, however, did not have a strong influence on behavioral intention, but this may have been due to service or network communication quality issues during the study.[11]
- Cheng-Min Chao developed and empirically tested a model to predict the factors affecting students' behavioral intentions toward using mobile learning (m-learning). The study applied the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators. The study collected data from 1562 respondents to conduct a cross-sectional study and employed a research model based on multiple technology acceptance theories.[12]
- Lin and Anol postulated an extended model of UTAUT, including the influence of online social support on network information technology usage. They surveyed 317 undergraduate students in Taiwan regarding their online social support in using instant messaging and found that social influence plays an important role in affecting online social support.
- Sykes et al. proposed a model of acceptance with peer support (MAPS), integrating prior research on individual adoption with research on social networks in organizations. They conducted a 3-month-long study of 87 employees in one organization and found that studying social network constructs can aid in understanding new information system use.
- Wang, Wu, and Wang added two constructs (perceived playfulness and self-management of learning) to the UTAUT in their study of determinants of acceptance of mobile learning in 370 individuals in Taiwan and found that they were significant determinants of behavioral intention to use mobile learning in all respondents.
- Hewitt et al. extended the UTAUT to study the acceptance of autonomous vehicles. Two separate surveys of 57 and 187 individuals in the USA showed that users were less accepting of high autonomy levels and displayed significantly lower intention to use highly autonomous vehicles.
- Wang and Wang extended the UTAUT in their study of 343 individuals in Taiwan to determine gender differences in mobile Internet acceptance. They added three constructs – perceived playfulness, perceived value, and palm-sized computer self-efficacy to UTAUT and chose behavioral intention as a dependent variable. They omitted use behavior, facilitating conditions, and experience. .l. Also, since the devices were used in a voluntary context, and they found that most adopters were ages 20–35, they omitted voluntariness and age. Perceived value had a significant influence on adoption intention, and palm-sized computer self-efficacy played a critical role in predicting mobile Internet acceptance. Perceived playfulness, however, did not have a strong influence on behavioral intention, but this may have been due to service or network communication quality issues during the study.
- Cheng-Min Chao developed and empirically tested a model to predict the factors affecting students' behavioral intentions toward using mobile learning (m-learning). The study applied the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators. The study collected data from 1562 respondents to conduct a cross-sectional study and employed a research model based on multiple technology acceptance theories.
= 扩展的理论 =
- Lin 和 Anol 假设了 UTAUT 的扩展模型,包括在线社交支持对网络信息技术使用的影响。他们调查了317名台湾大学生在使用即时通讯时的网络社会支持情况,发现社会影响在影响网络社会支持方面起着重要作用。
- Sykes et al.提出了一种基于同伴支持的接受模型(MAPS) ,该模型将个体接受的先验研究与组织中社会网络的研究相结合。他们对一家公司的87名员工进行了为期3个月的研究,发现学习社交网络结构有助于理解新信息系统的使用。
- Wang,Wu 和 Wang 在他们对台湾370名个体接受移动学习的决定因素的研究中,在 UTAUT 中添加了两个结构(感知的游戏性和学习的自我管理) ,发现它们是所有受访者使用移动学习的行为意图的重要决定因素。
- Hewitt et al.将 UTAUT 扩展到研究自动驾驶汽车的接受程度。在美国对57名和187名个人分别进行的两项调查显示,使用者较少接受高度自动驾驶的水平,显示出使用高度自动驾驶汽车的意愿明显较低。
- Wang 和 Wang 扩展了 UTAUT 对台湾343人的研究,以确定移动互联网接受度的性别差异。他们在 UTAUT 中加入了三个构想——玩乐感知、价值感知和手掌大小的计算机自我效能感,并选择行为意图作为因变量。他们忽略了使用行为,促进条件和经验。。升。此外,由于这些设备是在自愿的情况下使用的,他们发现大多数采用者的年龄在20-35岁之间,他们忽略了自愿性和年龄。感知价值对移动互联网接受意愿有显著影响,手掌大小的计算机自我效能感对移动互联网接受有显著预测作用。然而,感知的玩乐性对行为意图没有很强的影响,但这可能是由于研究过程中的服务或网络交流质量问题。
- 郑敏超开发了一个预测影响学生使用移动学习行为意向的因素的模型,并进行了实证测试。本研究采用扩展统一技术接受与使用理论(UTAUT)模型,加入知觉享受、移动自我效能感、满意度、信任和知觉风险调节因子。该研究收集了1562名受访者的数据,进行了横向研究分析,并采用了基于多种技术接受理论的研究模型。
Criticism
- Bagozzi critiqued the model and its subsequent extensions, stating "UTAUT is a well-meaning and thoughtful presentation," but that it presents a model with 41 independent variables for predicting intentions and at least 8 independent variables for predicting behavior," and that it contributed to the study of technology adoption "reaching a stage of chaos." He proposed instead a unified theory that coheres the "many splinters of knowledge" to explain decision making.[13]
- Van Raaij and Schepers criticized the UTAUT as being less parsimonious than the previous Technology Acceptance Model and TAM2 because its high R2 is only achieved when moderating key relationships with up to four variables. They also called the grouping and labeling of items and constructs problematic because a variety of disparate items were combined to reflect a single psychometric construct.[14]
- Li suggested that using moderators to artificially achieve high R2 in UTAUT is unnecessary and also impractical for understanding organizational technology adoption, and demonstrated that good predictive power can be achieved even with simple models when proper initial screening procedures are applied. The results provide insights for organizational research design under practical business settings.[15]
- Bagozzi critiqued the model and its subsequent extensions, stating "UTAUT is a well-meaning and thoughtful presentation," but that it presents a model with 41 independent variables for predicting intentions and at least 8 independent variables for predicting behavior," and that it contributed to the study of technology adoption "reaching a stage of chaos." He proposed instead a unified theory that coheres the "many splinters of knowledge" to explain decision making.
- Van Raaij and Schepers criticized the UTAUT as being less parsimonious than the previous Technology Acceptance Model and TAM2 because its high R2 is only achieved when moderating key relationships with up to four variables. They also called the grouping and labeling of items and constructs problematic because a variety of disparate items were combined to reflect a single psychometric construct.
- Li suggested that using moderators to artificially achieve high R2 in UTAUT is unnecessary and also impractical for understanding organizational technology adoption, and demonstrated that good predictive power can be achieved even with simple models when proper initial screening procedures are applied. The results provide insights for organizational research design under practical business settings.Li, Jerry (2020), "Blockchain technology adoption: Examining the Fundamental Drivers", Proceedings of the 2nd International Conference on Management Science and Industrial Engineering, ACM Publication, April 2020, pp. 253–260.
Bagozzi 批评了这个模型及其后续的扩展,他说“ UTAUT 是一个善意的、深思熟虑的展示”,但是它提出了一个模型,其中包含了41个用于预测意图的独立变量和至少8个用于预测行为的独立变量,并且它有助于研究技术采用“达到一个混乱的阶段”相反,他提出了一个统一的理论,将“许多知识的碎片”连接起来,以解释决策过程。
- Van Raaij 和 Schepers 批评 UTAUT 不如以前的技术接受模型和 TAM2简约,因为它的高 R < sup > 2 只有在调节多达四个变量的关键关系时才能实现。他们还称项目和结构的分组和标签是有问题的,因为各种不同的项目组合在一起反映了一个单一的心理测量结构。
- 李,在 UTAUT 使用调节器人为地达到高 R < sup > 2 是不必要的,而且对于理解组织技术的采用也是不切实际的,并且证明了即使使用简单的模型,在适当的初步筛选程序下也可以达到良好的预测能力。这些结果为实际商业环境下的组织研究设计提供了见解。 Li,Jerry (2020) ,“区块链技术采用: 检验基本驱动因素”,《第二届国际管理科学与工业工程会议论文集》 ,ACM 出版社,2020年4月,pp。253–260.
See also
- Lazy user model
= 参见同样 =
- 惰性用户模型
References
- ↑ 1.0 1.1 Venkatesh, Viswanath; Morris, Michael G.; Davis, Gordon B.; Davis, Fred D. (2003). "User Acceptance of Information Technology: Toward a Unified View". MIS Quarterly. 27 (3): 425–478. doi:10.2307/30036540. JSTOR 30036540. S2CID 14435677.
- ↑ Koivimäki, T.; Ristola, A.; Kesti, M. (2007). "The perceptions towards mobile services: An empirical analysis of the role of use facilitators". Personal & Ubiquitous Computing. 12 (1): 67–75. doi:10.1007/s00779-006-0128-x. S2CID 6089360.
- ↑ Eckhardt, A.; Laumer, S.; Weitzel, T. (2009). "Who influences whom? Analyzing workplace referents' social influence on IT adoption and non-adoption". Journal of Information Technology. 24 (1): 11–24. doi:10.1057/jit.2008.31. S2CID 42420244.
- ↑ Curtis, L.; Edwards, C.; Fraser, K. L.; Gudelsky, S.; Holmquist, J.; Thornton, K.; Sweetser, K. D. (2010). "Adoption of social media for public relations by nonprofit organizations". Public Relations Review. 36 (1): 90–92. doi:10.1016/j.pubrev.2009.10.003.
- ↑ Verhoeven, J. C.; Heerwegh, D.; De Wit, K. (2010). "Information and communication technologies in the life of university freshmen: An analysis of change". Computers & Education. 55 (1): 53–66. doi:10.1016/j.compedu.2009.12.002.
- ↑ Welch, Ruel; Alade, Temitope; Nichol, Lynn (2020). "USING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) MODEL TO DETERMINE FACTORS AFFECTING MOBILE LEARNING ADOPTION IN THE WORKPLACE: A STUDY OF THE SCIENCE MUSEUM GROUP" (PDF). International Journal on Computer Science and Information Systems. 15 (1): 85–98. Retrieved 4 June 2021.
- ↑ Lin, C.-P.; Anol, B. (2008). "Learning online social support: An investigation of network information technology". CyberPsychology & Behavior. 11 (3): 268–272. doi:10.1089/cpb.2007.0057. PMID 18537495.
- ↑ Sykes, T. A.; Venkatesh, V.; Gosain, S. (2009). "Model of acceptance with peer support: A social network perspective to understand employees' system use". MIS Quarterly. 33 (2): 371–393. doi:10.2307/20650296. JSTOR 20650296.
- ↑ Wang, Y.-S.; Wu, M.-C.; Wang, H.-Y. (2009). "Investigating the determinants and age and gender differences in the acceptance of mobile learning". British Journal of Educational Technology. 40 (1): 92–118. doi:10.1111/j.1467-8535.2007.00809.x.
- ↑ Hewitt, Charlie; Politis, Ioannis; Amanatidis, Theocharis; Sarkar, Advait (2019-03-17). "Assessing public perception of self-driving cars: the autonomous vehicle acceptance model". Proceedings of the 24th International Conference on Intelligent User Interfaces (in English). Marina del Ray California: ACM: 518–527. doi:10.1145/3301275.3302268. ISBN 978-1-4503-6272-6. S2CID 67773581.
- ↑ Wang, H.-W.; Wang, S.-H. (2010). "User acceptance of mobile Internet based on the Unified Theory of Acceptance and Use of Technology: Investigating the determinants and gender differences". Social Behavior & Personality. 38 (3): 415–426. doi:10.2224/sbp.2010.38.3.415.
- ↑ Chao, Cheng-Min (2019). "Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model". Frontiers in Psychology (in English). 10: 1652. doi:10.3389/fpsyg.2019.01652. ISSN 1664-1078. PMC 6646805. PMID 31379679.
- ↑ Bagozzi, R.P. (2007). "The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift". Journal of the Association for Information Systems. 8 (4): 244–254. doi:10.17705/1jais.00122.
- ↑ van Raaij, E. M.; Schepers, J. J. L. (2008). "The acceptance and use of a virtual learning environment in China". Computers & Education. 50 (3): 838–852. doi:10.1016/j.compedu.2006.09.001.
- ↑ Li, Jerry (2020), "Blockchain technology adoption: Examining the Fundamental Drivers", Proceedings of the 2nd International Conference on Management Science and Industrial Engineering, ACM Publication, April 2020, pp. 253–260. doi:10.1145/3396743.3396750
Category:Product management Category:Technological change
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