Factors influencing user acceptance of public sector big open data

Weerakkody, Vishanth; Kapoor, Kawaljeet Kaur; Balta, Maria; Irani, Zahir; Dwivedi, Yogesh K.

doi:10.1080/09537287.2017.1336802

Cited by 55 publications

(57 citation statements)

References 89 publications

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“…e study of Wu & Chen [51] also proved the direct positive relationship of PU with a user's IU of massive open online courses. Many studies demonstrated the PU positive direct predictor of IU the BDA [4,43,49,52]. So, with the essence of the TAM model, we also propose that PU has an influence on IU of BDA in healthcare.…”

Section: Perceivedmentioning

confidence: 68%

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

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The big data analytics (BDA) has dragged tremendous attention in healthcare organizations. Healthcare organizations are investing substantial money and time in big data analytics and want to adopt it to get potential benefits. Thus, this study proposes a BDA adoption model in healthcare organizations to explore the critical factors that can influence its adoption process. The study extends the technology acceptance model (TAM) with the self-efficacy as an external factor and also includes gender and resistance to change (RTC) as moderators to strengthen the research model. The proposed research model has been tested on 283 valid responses which were collected through a structured survey, by applying structural equation modeling. Our results portray that self-efficacy is a strong predictor of intention to use BDA along with other TAM factors. Moreover, it is confirmed by the results that RTC dampens the positive relationship between intention to use and actual use of BDA in healthcare organizations. The outcomes revealed that male employees as compared to female employees are dominant towards the positive intention to use BDA. Furthermore, females create more RTC than males while adopting BDA in healthcare organizations. Theoretical and practical implications, limitations, and future research directions also underlined in this study.

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Section: Perceivedmentioning

confidence: 68%

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

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“…TAM model determinants are optimal for determining user perceptions about its adoption [54]. TAM has been adopted by many researchers to successfully check and manage new system adoptions [54,57,83,84]. Since the invention of TAM, an abundance of studies have produced different research models to effectively predict user attitudes and behavioral intentions towards technology adoption.…”

Section: Technology Acceptance Modelmentioning

confidence: 99%

“…PU is an essential variable for studies of the adoption intentions of innovation from the user's perspective [54]. The previous literature has provided evidence of a positive relationship with intention to use many times, which has been successfully measured in various fields [26,54,94], including the field of big data [21,23,57], but BDA not has not been a focus in healthcare. The user cannot shape a positive perception of usefulness until he or she feels any practical worth of BDA in healthcare organizations.…”

Section: Perceived Usefulnessmentioning

confidence: 99%

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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“…Fun and enjoyment is an influential driver for hackers to engage with open data in Swedish hackathons (Juell-Skielse et al, 2014). Instances of extrinsic motivation include expectancy towards job performance (Zuiderwijk et al, 2015) or other advantage (Weerakkody et al, 2017a), usefulness for doing the tasks at hand (Wirtz et al, 2018, Wirtz et al, 2017, Weerakkody et al, 2017b, and prospects of financial gains or future employment (Kuk and Davies, 2011).…”

Section: Individual Conditions and Factorsmentioning

confidence: 99%

Citizen engagement with open government data

Purwanto

Janssen

2020

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Purpose Citizen engagement is key to the success of many Open Government Data (OGD) initiatives. However, not much is known regarding how this type of engagement emerges. This study aims to investigate the necessary conditions for the emergence of citizen-led engagement with OGD and to identify which factors stimulate this type of engagement. Design/methodology/approach First, the authors created a systematic overview of the literature to develop a conceptual model of conditions and factors of OGD citizen engagement at the societal, organizational and individual level. Second, the authors used the conceptual model to systematically study citizens’ engagement in the case of a particular OGD initiative, namely, the digitization of presidential election results data in Indonesia in 2014. The authors used multiple information sources, including interviews and documents, to explore the conditions and factors of OGD citizen-led engagement in this case. Findings From the literature the authors identified five conditions for the emergence of OGD citizen-led engagement as follows: the availability of a legal and political framework that grants a mandate to open up government data, sufficient budgetary resources allocated for OGD provision, the availability of OGD feedback mechanisms, citizens’ perceived ease of engagement and motivated citizens. In the literature, the authors found six factors contributing to OGD engagement as follows: democratic culture, the availability of supporting institutional arrangements, the technical factors of OGD provision, the availability of citizens’ resources, the influence of social relationships and citizens’ perceived data quality. Some of these conditions and factors were found to be less important in the studied case, namely, citizens’ perceived ease of engagement and citizens’ perceived data quality. Moreover, the authors found several new conditions that were not mentioned in the studied literature, namely, citizens’ sense of urgency, competition among citizen-led OGD engagement initiatives, the diversity of citizens’ skills and capabilities and the intensive use of social media. The difference between the conditions and factors that played an important role in the case and those derived from the literature review might be because of the type of OGD engagement that the authors studied, namely, citizen-led engagement, without any government involvement. Research limitations/implications The findings are derived using a single case study approach. Future research can investigate multiple cases and compare the conditions and factors for citizen-led engagement with OGD in different contexts. Practical implications The conditions and factors for citizen-led engagement with OGD have been evaluated in practice and discussed with public managers and practitioners through interviews. Governmental organizations should prioritize and stimulate those conditions and factors that enhance OGD citizen engagement to create more value with OGD. Originality/value While some research on government-led engagement with OGD exists, there is hardly any research on citizen-led engagement with OGD. This study is the first to develop a conceptual model of necessary conditions and factors for citizen engagement with OGD. Furthermore, the authors applied the developed multilevel conceptual model to a case study and gathered empirical evidence of OGD engagement and its contributions to solving societal problems, rather than staying at the conceptual level. This research can be used to investigate citizen engagement with OGD in other cases and offers possibilities for systematic cross-case lesson-drawing.

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Factors influencing user acceptance of public sector big open data

Cited by 55 publications

References 89 publications

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

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

Citizen engagement with open government data

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