A Risk and Benefits Behavioral Model to Assess Intentions to Adopt Big Data

Esteves, José; Curtò, J. de

doi:10.37380/jisib.v3i3.74

Cited by 50 publications

(36 citation statements)

References 12 publications

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“…Thus, "this research defines perceived usefulness at a degree where a student's beliefs perceived usefulness on social media use would enrich their teaching and learning". These results are consistent with [25,29,35,36,37,38], who argued that perceived usefulness on social media use was useful on teaching and learning. See Table 6.…”

Section: A Descriptive and Examination Of Factorssupporting

confidence: 89%

Social Media Use (SMU) for Teaching and Learning in Saudi Arabia

Alyoussef¹,

Alamri²,

Al-Rahmi³

2019

IJRTE

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Abstract: This research aims to explore the factors of teaching and learning measurement through using social media, incorporating the literature of social media adoption on resource sharing, collaborating and communicating for educational purpose. The current research explore factors that perceived usefulness (PU), perceived ease of use (PEU), attitude toward use social media use have certain influence on adoption of resource sharing, collaboration and communication for educational use. Therefore, resource sharing, collaboration and communication influence educational use, while educational use influences PU, PEU, SMU and attitude toward the use (AT) of SMU for teaching and learning. Both the processes of collecting and analyzing the data followed the quantitative method. The main tool of data collection was a questionnaire that has been distributed among 236 students using stratified random sampling technique. The view of the students and their implication of social media use for teaching and learning were solicited through the questionnaire. The Statistical Package for the Social Sciences (SPSS) was used as the main tool in the process of data examination. The results of this research were related to two main constructs: teaching and learning as well as educational use.According to the results, it appears that perceived usefulness, perceived ease of use, attitude toward use, and social media are considered powerful determinants of the former while resource sharing, collaboration and communication were significant indicators of the latter. Educational use, PU, PEU, AT succeeded in explaining 74.9% of SMU for teaching and learning.

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Section: A Descriptive and Examination Of Factorssupporting

confidence: 89%

Social Media Use (SMU) for Teaching and Learning in Saudi Arabia

Alyoussef¹,

Alamri²,

Al-Rahmi³

2019

IJRTE

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“…The prior literature has proven the existence of a significant direct relationship between the PEOU and a user's intentional behavior to adopt the system in various fields of study [54,93,94]; this relationship was also discussed by Esteves, Shin, and Weerakkody [21,23,39] when studying user intentional behaviors regarding the adoption of big data, although these studies were less focused on this study context. For example, measuring the relationship of PEOU with the intention to use BDA is essential for healthcare organizations.…”

Section: Perceived Ease Of Usementioning

confidence: 96%

“…The study proposes that TAM will provide superior understanding of BDA adoption, because BDA is a heavily technology-driven research area that is also user-oriented and innovation-focused [23]. Previous studies by Esteves, Rahman, and Shin [21,23,91] also concluded that TAM constructs (i.e., PEOU and PU) were significant predictors of BDA adoption/usage.…”

Section: Technology Acceptance Modelmentioning

confidence: 98%

“…According to Dishaw [19], the technology acceptance model (TAM) and task-technology fit (TTF) provide better outputs than either TAM or TTF alone in the adoption of information technology systems. The prior literature tries to explain BDA adoption through perceptions of technology, such as perceived ease of use and perceived usefulness [20][21][22][23][24]. However, emphasizing only the end user's perception of technology may not be sufficient.…”

mentioning

confidence: 99%

“…Despite the fame of BDA, insufficient empirical research has investigated factors that can influence BDA adoption in healthcare [21,49]. Empirical evidence from Pakistan's healthcare organizations represents a big gap in the literature from both dimensions (i.e., knowledge about BDA and adoption of BDA) [50].…”

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confidence: 99%

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Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

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IntroductionBig data analytics (BDA) is a course of action to examine large and complex data sets (i.e., big data) and select veiled information that can help organizations with efficient decision making [1]. The volume of data related to healthcare organizations has grown dramatically in past years and is expected to increase in coming years due to the use of innovative technologies [2]. Meanwhile, healthcare reimbursement methods are changing, and pay for performance is an emerging factor in the current healthcare environment. Recently, healthcare organizations have only focused on profit and have neglected to acquire the essential tools, infrastructure, and technologies for effective control of big data to ensure citizens' health care [3,4]. Big data incorporates features such as variety, velocity, and veracity. BDA techniques can be applied to the massive amount of prevailing patient-related medical information to analyze outcomes for improvement of the healthcare sector [5,6]. Using BDA in the healthcare sector will help inform each physician of the medical histories of individuals and the population and enable appropriate Abstract Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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