Journal of business analytics Acceptance Rate - ResearchHelpDesk - Business analytics research focuses on developing new insights and a holistic understanding of an organisation’s business environment to help make timely and accurate decisions, and to survive, innovate and grow. Thus, business analytics draws on the full spectrum of descriptive/diagnostic, predictive and prescriptive analytics in order to make better (i.e., data-driven and evidence-based) decisions to create business value in the broadest sense. The mission of the Journal of Business Analytics Journal (JBA) is to serve the emerging and rapidly growing community of business analytics academics and practitioners. We aim to publish articles that use real-world data and cases to tackle problem situations in a creative and innovative manner. We solicit articles that address an interesting research problem, collect and/or repurpose multiple types of data sets, and develop and evaluate analytics methods and methodologies to help organisations apply business analytics in new and novel ways. Reports of research using qualitative or quantitative approaches are welcomed, as are interdisciplinary and mixed methods approaches. Topics may include: Applications of AI and machine learning methods in business analytics Network science and social network applications for business Social media analytics Statistics and econometrics in business analytics Use of novel data science techniques in business analytics Robotics and autonomous vehicles Methods and methodologies for business analytics development and deployment Organisational factors in business analytics Responsible use of business analytics and AI Ethical and social implications of business analytics and AI Bias and explainability in analytics and AI Our editorial philosophy is to publish papers that contribute to theory and practice. Journal of Business Analytics is indexed in: AIS eLibrary Australian Business Deans Council (ABDC) Journal Quality List British Library CLOCKSS Crossref Ei Compendex (Engineering Village) Google Scholar Microsoft Academic Portico SCImago Scopus Ulrich's Periodicals Directory

International Journal of Data Science and Analytics Acceptance Rate - ResearchHelpDesk - International Journal of Data Science and Analytics - Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.

Decision-structuring techniques used by academic coaching and decision analysis.

One way sensitivity analysis and justification of variability for each input variable included in standard decision analysis model.

Generated from raw data by MNE-BIDS (Appelhoff et al., 2019) and custom code to join to behavioural data, stimulus information, and metadata.

Notes on the Data

For full details on this dataset, see our preprint: (url here once out)

• An issue during recording meant that sub-05 completed the first block without data being saved. The experiment was restarted from the beginning for this participant. This participant was not included in our analyses, but the data are included in this dataset. They are also identified with the recording_restarted field in participants.tsv.

• A separate issue during recording meant that EEG data for some trials were lost for sub-01, though enough trials were recorded in total to meet our criteria for inclusion in the analysis. The raw data comprised two separate recordings. In this dataset, the two recordings are concatenated end-to-end into one file. The point at which the files are joined is marked with a boundary event. This participant is identified with the recording_interrupted field in participants.tsv.

• During the course of the experiment, we identified an issue with the wiring in one splitter box, which meant that voltages from channels FT7 and FC3 were swapped in the raw recorded data. We elected to keep the wiring as it was for the duration of the experiment, and then swapped the data from the two channels in the code that generated this BIDS dataset. This means that this issue has been corrected in this BIDS version of the data.

• "BAD" periods (MNE term) for key presses and break periods are included in the events files.

• Recording dates/times have been anonymised by shifting all recordings backwards in time by a constant number of days (same constant for all participants). This obscures information that may be used to identify participants, but preserves time-of-day information, and the relative times elapsed between different recordings.

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

Click "Files" on the left-hand side to see all files in this repository, as they were at the time of the submission of the revised manuscript "Developing a new national MDMA policy: Results of a multi-decision multi-criterion decision analysis (MD-MCDA)" to the Journal of Psychopharmacology.

This paper aims to evaluate the performance of logistic distribution centers (LDCs) by Quality Function Deployment (QFD) based on the Best-Worst Method (BWM). The originalities of this paper include: (1) exploring five constructs with 20 customer requirement attributes (CRAs) for LDCs’ operations based on the SERQUAL model, (2) using the QFD model to identify five primary divisions and 18 corresponding service technical requirements (STRs) for LDCs’ operations, (3) recognizing the top five STRs should be prioritized for the allocation of limited resources from House of Quality (HoQ), including cargo order (7.15%), value-added activities (6.68%), document preparation (6.31%), consolidating and assembling (6.10%), and document management (6.00%), (4) applying the Best-Worst Method (BWM) to estimate CRAs’ relative weight. The proposed research model can provide a methodological reference to the relevant literature in association with logistics operations and multiple-criteria decision analysis (MCDA).

PRISMA Checklist for systematic review and meta-analysis. (DOC)

pone.0246235.t004 - Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis

Transition matrix showing how many times each transition was used during the trial period.

The chance of a vaccine safety signal being detected in a given month is shown in Fig. 1. NA implies that the probability is zero. In the febrile seizures case, this occurred because the surveillance system is powered to detect the signal 2–3 months after the start of vaccination.Abbreviations: GBS, Guillain-Barre Syndrome; NA, not applicable.Multi-Criteria Decision Analysis Results.

BackgroundIncreasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix.Methods and findingsTwo independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched.ConclusionsThe measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Miscellaneous studies included in the meta-analysis.

Results from Markov cohort analysis.

Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transport by a continuous risk management matrix (CRMM) based on the fuzzy Best-Worst Method (BMW). Its originalities include developing CRMM to figure out the risk level of risk factors (RFs) for ferry transport and adopting fuzzy BWM to estimate the probability and severity weights vector of RFs. Empirical results show that twenty RFs for ferry navigation are divided into four zones corresponding to their risk values, including extreme-risk, high-risk, medium-risk, and low-risk areas. Particularly, results identify three extreme-risk RFs: inadequate evacuation and emergency response features, marine traffic congestion, and insufficient training on navigational regulations. The proposed research model can provide a methodological reference to the pertinent studies regarding risk management and multiple-criteria decision analysis (MCDA).

Descriptive analysis on decision variables (N = 1173).

Cost-effectiveness analysis.

Mixed effects linear regression analysis of inverse temperature parameter β2.