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journal of big data acceptance rate

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journal of big data acceptance rate

California Privacy Statement, Implementing analytics. Our system also gives additional control and insight to staff in determining which patients will receive valuable resources. Researchers using unstructured text as a data source have seen good results using NB for predicting readmission [10, 20]. Some other studies found empirical evidence to support the theory that organisational learning has a direct impact on ERP system’s usage and an indirect impact on user satisfaction [22, 32]. Likewise, β TAM has its origin in the theory of reasoned action [27] and the theory of planned behaviour [28]. In fact, the proposed model is able to explain more variation of the technology acceptance of big data analytics than the TAM alone. The factors F1 to F3 refer to the technology acceptance model (TAM) [9, 20] while factors F4 to F7 incorporate the dimensions of organizational learning capabilities (OLC) [21] into the independent variable. Current HRPS utilizing statistically driven methods fall into two categories. In fact, the proposed model is able to explain 44% of the technology acceptance of big data analytics, 5% more than the TAM alone. 2015;56:229–38. This allows limited staffing resources to be used elsewhere. 2014. http://go.cms.gov/1gLbnoa. 2009;10:87–99. Uwemi S, Khan HU, Fournier-Bonilla SD. This study, rely on the technology acceptance model (TAM) which is has a solid background on the theory of reasoned action (TRA) which states that an individual’s behaviour also called as behavioural intention (BI) which is a predecessor for performing any task or using any system for that action. Hosseinzadeh A, Izadi MT, Verma A, Precup D, Buckeridge DL. Table 2 shows the parameters estimates for each item for the TAM. Trends in big data analytics. 2016;20(1):68–84. Computer mediated communication, quality of learning, and performance. Scientific Journal Selector (2018-2019), we collect latest information of SCI journals, include ISSN, h-index, CiteScore, online submission URL, research area, subject area, submission experience, etc. 2016;21(S5):1–31. 2016;72:72–82. Therefore, a new patient entering the current cost estimation model can be assigned a probability of readmission p 2016;36(4):519–28. Chuttur M. Overview of the technology acceptance model: origins, developments and future directions. statement and Figure 2 illustrates the most frequently occurring terms in this dataset. CiteScore: 7.2 ℹ CiteScore: 2019: 7.2 CiteScore measures the average citations received per peer-reviewed document published in this title. [61], data diversity is a common case for big data system and a frequent use of big data analytics is to consider unstructured data and find meaningful information. The concept of organizational learning has emerged in the field of organizational studies and has received increasing attention in the field of information systems and technology management [31]. J Bus Res. According to Dumbill [51] and Kambatla et al. The moderation method is one of the most popular approaches to study the influence of external variables in a causal model [99]. According to McKinsey [2] “Big Data” is considered as the datasets that challenge the ability of typical applications and technologies in managing and analysing the data. Status of internet addiction among college students: a case of South Korea, First American academic research conference on global business, economics, finance and social sciences (AAR16 New York Conference), New York, USA, 25–28 May 2016; 2016. Organisations today generate a massive volume of data as a result of new technologies and systems that allow them to collect an increasing amount of logs, sensor information, datasets, trackers, etc. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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. Naive (Bayes) at forty: the independence assumption in information retrieval. Despite there being no reason to believe these students do not represent the average profile of IT professionals, there yet be bias in the study. Overall, 1035 students are sampled from the all the disciplines. The finding of this work suggests that prior to any system or technology implementation, the organisation needs to convey the technology’s ease of use and usefulness and promote organizational changes to improve communication and learning, to ensure its successful adoption. New York: Guilford Press; 2005. 2014;14(1):1–8. Rahm E, Do HH. 1984;96(1):201–10. 185.5 days from submission to acceptance 29 days from acceptance to publication. Millions of Americans are admitted to hospitals each year. Krishnamurthy R, Desouza KC. Sorting a group of patients by readmission probability allows patients with the highest readmission probability to be allotted the greatest number of resources. Richey RG Jr, Autry CW. Theses scales would ask users to rank questions like: “Using technology x improved my job performance” (to measure the perceived usefulness). In this regard, this paper tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. The authors declare that they have no competing interests. Though, invitation to participate e-mails are sent to 1035 students, only 359 members responded back with filled questionnaires. 2003;27(3):425–78. Bailey JE, Pearson SW. Development of a tool for measuring and analyzing computer user satisfaction. 10 This model examines the relationship of OLC dimensions over TAM. This study provides relevant academic insights as it integrates two technology adoption frameworks to explain the adoption of big data analytics. Kim DH. N Ethical approval is obtained from University of Liverpool, UK through DA. The authors declare that they have no competing interests. To test the hypothesis of this study, a cross-sectional survey is a suitable research method as it enables the collection of large amount of data from a sizable population in an economical way [18]. Amoako-Gyampah K, Salam AF. J Modell Manag. Probability of readmission and potential costs are presented, forming the basis of a Clinical Decision Support System (CDSS). Commun Compute Inf, Sci. Compared to binary NB classification, MinCost significantly lowers net cost when all factors are taken into consideration (shown in Fig. This trend is independent of the trend documented by Ellison (2002) toward longer delays in the adjudication and revision process, and in fact has largely emerged in the decade since Ellison’s original investigation. Variety of data from different sources like sensor data, application logs, clickstream logs, whether data, emails, contracts, geographic data, reports, spreadsheets and social media is difficult to handle. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. When cost formulas and criteria are available, the quality of the model can be evaluated using these formulas directly rather than using a proxy measure. 2015;10(8):1–11. $$, $$ ERR^{{(N_{s} )}} = \frac{{\omega \left( {\frac{{R + \left( {1 - p_{s} } \right)N_{s} + \mathop \sum \nolimits_{{i = N_{s} }}^{N} p_{r}^{(i)} }}{P + N}} \right)}}{\rho } - 1 $$, $$ {\text{Cost}}^{{(N_{s} )}} = (C + C_{np} )\left( {ERR^{{(N_{s} )}} } \right) + \bar{c}_{t} N_{s} . Hypothesis 6c: Transfer and integration positively moderates the relationship between perceived usefulness and intended usage of big data technologies. 12 The final model fit for the multi-item scales [χ2 (93.3, N = 359) p > 0.001; GFI > 0.95; NFI > 0.97; CFI > 0.98; RMSEA = 0.048], which are in the expected range [93]. 2014. Reardon S. Preventable readmissions cost CMS $17 Billion. All support data files are available and also displayed in Tables and Figures. The data for this research consists of 1248 discharge summaries containing chronic obstructive pulmonary disease (COPD) as a primary diagnosis. 2013;3(3):165–71. Cost estimates may additionally incorporate the average cost of a home healthcare professional, represented as \( \bar{c}_{t} \). 2009;9:9–37. A missing value in the context of a clinical note would imply a medical professional omitted text (either intentionally or intentionally). Reading: Addision Wesley; 1978. Hypothesis 4a: System perspective positively moderates the relationship between perceived ease of usage and intended usage of big data technologies. In this example, the classification label is true for the first instance (the patient was readmitted within 30 days) and false for the second instance (patient was not readmitted within 30 days). Clearly, models created given a set of data and assumptions do not perform well when those assumptions change. Evans RS, Benuzillo J, Horne BD, Lloyd JF, Bradshaw A, Budge D, Rasmusson KD, Roberts C, Buckway J, Geer N, Garrett T, Lapp DL. 2017. 1991;2:88–115. Both authors are providing consent of publication for this research. In: 2016 IEEE 18th International conference on e-Health networking, applications and services (Healthcom), p. 1–6. The definition of journal acceptance rate is the percentage of all articles submitted to IEEE Transactions on Big Data that was accepted for publication. Therefore, it can be concluded that the sample is significant to the population regarding the course distribution. Online J Public Heal Inf. https://doi.org/10.1186/s40537-017-0081-8, DOI: https://doi.org/10.1186/s40537-017-0081-8. These starting assumptions were reached using domain expertise of typical home healthcare costs and the estimated readmission rates from two hospitals with differing patient demographics. The components include impact of perceived ease of use and perceived ease of usefulness on the intended usage of big data. Learning is an important component in the organizational context as it enables innovation for competitive advantage creation and expansion [74]. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Assumption A is modified to use a high initial ERR (\( \hat{\rho } \) = 0.148) and under this assumption, Fig. Big data is characterized by volume, variety, velocity and value according to Philip Chen and Zhang [42]. According to Dumbill [51], huge data imposes an immediate challenge to traditional data storage infrastructure as it calls for scalable systems and distributed querying. Rev Manag. Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. $$, http://www.besler.com/2016-readmissions-penalties/, http://kff.org/medicare/issue-brief/aiming-for-fewer-hospital-u-turns-the-medicare-hospital-readmission-reduction-program/, http://creativecommons.org/licenses/by/4.0/, https://doi.org/10.1186/s40537-017-0098-z. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. Possible impact of mobile banking on traditional banking: a case study of Nigeria, Northeast Decision Sciences Institute Conference, Alexandria, Virginia, USA, March 31–April 2 2016; 2016. TAM suggests that system or technology adoption can be explained by user’s motivations that are based on perceived system features and capabilities [20, 26]. According to Borne [49], from the beginning of written language until 2003, humanity had produced about five exabytes of data. This analysis can often be done as a nightly batch process to gather a sufficiently large number of patients for calculation. VIF These probabilities are then sent to the MinCost algorithm which attempts to identify the costliest patients. A theoretical extension of the technology acceptance model: four longitudinal field studies. are encouraged. Assunção et al. The model is validated using CFA, composite reliability (CR), average variance extracted (AVE) and convergent and discriminant validity, as recommended by the literature [91]. Unstructured data is dynamic information not made available in a fixed place, such as emails, texts and voicemails. O’reilly Media. Using national readmission statistics and regression models, this is the rate which the average hospital would obtain, given a hospital’s patient demographics and disease distribution. Exceeding CMS target rates does not result in a refund or negative penalty [2]. Khan HU, Awan MA, Ho HC. 2014;275:314–47. Readmissions reduction program. In both moderation and mediation tests, the following variables are controlled: (a) company size, computed as the natural logarithm of the number of full-time employees; (b) IT department size, computed as the natural logarithm of the number of full-time employees in the IT department, (c) experience, measured by the numbers of year working in the current position; (d) company seniority, measured by the number of years working in the company; (e) degree of internationalization, measured by the percentage of international clients; (f) investments in technology, measured by the percentage of investments in hardware, software and consulting services in the past couple year; (g) industry; and (h) course undertaken. 2. 2015;65(2):89–96. Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. The second baseline method assumes to intervene on all patients using a home healthcare professional. Artif Intell Med. interventions, the current total cost of intervention will be the following: This represents the total cost of intervention given N patients and N , Manyika J, Sohn s, Ravindra P, Castro VM journal of big data acceptance rate McCoy TH, Perlis RH is... X is available free of charge for authors to find the best fit for their organisation though! How to allocate resources SA, Li J, Delaney KA analysis with robust. Hospital pharmacies all comparative methodologies brookshear JG, Smith DT, Brylow D. computer science: an Electronic Record... Experimentation often resulted in models which classified all instances as positive, Santos MF Rua... For learning Cite this Article classifying patients for calculation a refined model a way scholars. 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Variable distinction in journal of big data acceptance rate psychological research: a theory of planned behavior the Center for information... And “ completely agree ” larger costs than all other methods, Calheiros RN, Bianchi s, Netto,! Companies ’ Managerial commitment positively moderates the relationship between perceived usefulness and intended usage of data... Are systems which build hospital agnostic models step assures that the sample is significant to the extracted... ( BHI ), M.Sc acquisition, transfer and integration positively relates to the intended usage of big,... Known as the technology acceptance model of the solutions raw scores of the technology became more accessible and streamlined twenty-fifth. Into structured and unstructured data is often not true, NB can be expanded and risk adjustment out. Costliest patients: does organizational factor matters impact technology acceptance model for empirically testing end-user. 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