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Data models 3. Define the main indicators in each knowledge area with the university expert's collaboration.
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The exponential growth of data in digital environments has highlighted the emergence of developing analytics processes for data visualization and evaluation. The same happens at the university research level. Therefore, universities need to have specific analytical processes for research evaluation.
The aim of our study is to find the way to apply...
Citations
... Worldwide, many universities and colleges have implemented BIA solutions in their operations, with a varying degree of success. BI has been applied in HEIs, for instance, to ensure compliance [32], to analyze student learning [33,34], in current research information systems or CRIS [35], or in monitoring strategic goals [36]. However, for the most part, BI investment in the education sector is still lagging behind the industrial sector and larger enterprises, partially due to budget limitations [37]. ...
Business Intelligence and Analytics (BIA) systems play an essential role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions. However, many Higher Education (HE) institutions do not have accessible and usable models to guide them through the incremental development of BIA solutions to realize the full potential value of BIA. The situation is becoming ever more acute as HE operates today in a complex and dynamic environment brought forward by globalization and the rapid development of information technologies. This paper proposes a domain-specific BIA maturity model (MM) for HE–the HE-BIA Maturity Model. Following a design science approach, this paper details the design, development, and evaluation of two artifacts: the MM and the maturity assessment method. The evaluation phase comprised three case studies with universities from different countries and two workshops with practitioners from more than ten countries. HE institutions reported that the assessment with the HE-BIA model was (i) useful and adequate for their needs; (ii) and contributed to a better understanding of the current status of their BIA landscape, making it explicit that a BIA program is a technology endeavor as well as an organizational development.
... In the following years leading up to 2018 many operations have been carried out for this purpose, and the results for each stage were recorded. In this process, the biggest challenge was to interrelate research and teaching [7] UPN adopts a research management system based on the Plan Do Check Act (PDCA) cycle proposed by the American statistician WE Deming. This cycle is a wellestablished framework for process improvement, which focuses on the continuous learning and creation of knowledge and favors the effectiveness of the activities. ...
This study aims to evaluate the development and sustainability of a research management system in a private Peruvian university. The Research Management System (RMS) of Universidad Privada del Norte (UPN) was monitored from 2012-2017. The study adopts a non-experimental longitudinal design, which firstly inspects the research management status of the university throughout the year 2011. Based on this, the executed cycle-steps adopted between 2012 and 2017 were documented from the perspective of Deming’s cycle to finally analyze the research management status of the university during 2018 and, thus, assess the development and sustainability achieved during the process. The results indicate that the methodology adopted for the development of the research management system, that follows Deming’s cycle of continuous improvement, promotes the development and sustainability of the university. This is supported by monitoring the defined management indicators through the study timeline, whose rates indicate significant changes.
Ensuring sustainable development of scientific organizations is impossible without coordinating development strategies at all levels of management, taking into account the totality of factors and environmental conditions. The possibility of using probabilistic modeling tools in making management decisions to ensure sustainable development of scientific organizations is considered. A methodology for modeling the management decisions implementation results is proposed. An algorithm for adapting the decision trees structure to new data arriving in real time has been implemented.
In this paper we aim to analyse the adoption of Current Research Information Systems (CRIS) for Research Data Management (RDM). We show how CRISs hold a key role in facilitating the management and reporting of an
institution's research activities and outputs – not only do they offer extensive functionality for researchers and research administrators to effectively manage all aspects of their research information, but are also integrating more and more with specialized RDM tools, Institutional Repositories (IR), and other external systems.
This paper provides an overview of how CRISs have evolved and integrated to become a crucial part of the RDM chain, including the interoperability, registration, linking, and archiving of data.
In academic and industrial research, writing a project proposal is one of the essential but time-consuming activities. Nevertheless, most proposals end in rejection. Moreover, research funding is getting more competitive these days. Funding agencies are increasingly looking for more extensive and more interdisciplinary research proposals. To increase the funding success rate, this PhD project focuses on three open challenges: poor data quality, inefficient funding discovery, and ineffective collaborative team building. We envision a Predictive Analytics-based approach that involves analyzing research information and using statistical and machine learning models that can assure data quality, increase funding discovery efficiency and the effectiveness of collaboration building. Accordingly, the goal of this PhD project is to support decision-making process to maximize the funding success rates of universities.
