Digital Transformation: A Foundational Capability Building Block Perspective on Maturing the IT Capability

The enterprise-wide scope of an organisation's IT capability in sustainably leveraging technology for business value is well-researched, and the level of maturity of this capability is a key determinant of an organisation's success. IT capability maturity has become more critical as technological developments continue at an accelerated pace and as whole industries are being disrupted by digital developments. Maturity in terms of IT leadership, IT processes, IT infrastructure, and a myriad of other supporting organisation-wide capabilities is required. Since the 1980s, maturity models in the literature have focused on specialist niche areas, with few adopting a holistic perspective. Across these models, a lack of consensus is evident on the key capabilities that should be matured and on what the important sub elements or building blocks of these capabilities are. How does the organisation achieve an adequate level of maturity if the required capabilities are unclear? As one of the most holistic IT capability maturity models identified, this paper undertakes a systematic analysis of the 36 IT capabilities within IT Capability Maturity Framework (IT-CMF) and the 315 sub elements (Capability Building Blocks (CBBs)) that comprise these capabilities. This research aims to identify the common sub elements or building blocks inherent across the 36 capabilities, which we will refer to as Foundational Capability Building Blocks (FCBBs), and a high-level definition of these FCBBs abstracted from the relevant sub elements and discussed in terms of their recognised importance to effecting successful digital transformations. From an academic perspective, the research provides deeper insight on common themes that are pertinent to IT capability improvement. From an industry practitioner perspective, it breaks down the complexities of IT capability maturity with a focus on a manageable number of considerations. © 2022 Authors. All rights reserved

Interoperability Maturity Model: Orchestrator Tool for Platform Ecosystems

The orchestration of platform ecosystems is becoming increasingly complex due to the growing number of players, complementary services and technological innovations. Interoperability is an important prerequisite for convincing customer journeys as well as functional and quality-assured data exchange and offers increasing potential for automation, especially with the help of machine learning or artificial intelligence. The interoperability maturity model developed in this study can be used as a conceptual framework to measure the interoperability of current and future platform ecosystem components and complements. The model, developed as an artifact of design science research, was evaluated using an iterative approach with orchestrators of health data platforms and their ecosystem. The results suggest that it can contribute to achieving and sustaining integrated value chains with multiple actors and diverse technologies, and can be used to assess the interoperability of care chains (e.g., care scenarios such as diabetes or cardiac insufficiency) and guide future interoperability considerations

A Data Science Maturity Model Applied to Students' Modeling

Maturity models define a series of levels, each representing an increased complexity in information systems. Data Science appears in the Business Intelligence (BI) and Business Analytics (BA) literature. This work applies the _IABE maturity model, which includes two additional levels: Data Engineering (DE) at the bottom and Business Experimentation (BE) at the top. This study uses the _IABE model for students' modeling in the ModEst project. For this purpose, the Public Administration organism is the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Education Ministry. DGEEC provided vast data on two million students per year in the Portuguese school system, from pre-scholar to doctoral programs. This work presents the comprehensible _IABE maturity model to extract new knowledge from the DGEEC dataset. The method applied is _IABE, where after the DE level, wh-questions are formulated and answered with the most appropriate techniques at each maturity level. This work's novelty is applying the maturity model _IABE to a unique dataset for the first time. Wh-questions are stated at the BI level using data summarization; at the BA level, predictive models are performed, and counterfactual approaches are presented at the BE level. Doi: 10.28991/ESJ-2023-07-06-08 Full Text: PD

Maturity Models Architecture: A large systematic mapping

Maturity models are widespread in research and in particular, IT practitioner communities. However, theoretically sound, methodologically rigorous and empirically validated maturity models are quite rare.  This systematic mapping paper focuses on the challenges faced during the development of maturity models. More specifically, it explores the literature on maturity models and standard guidelines to develop maturity models, the challenges identified and solutions proposed.  Our systematic mapping  revealed over six hundred articles on maturity models. Extant literature reveals that researchers have primarily focused on developing new maturity models pertaining to domain-specific problems and/or new enterprise technologies. We find rampant re-use of the design structure of widely adopted models such as Nolan’s Stage of Growth Model, Crosby’s Grid, and Capability Maturity Model (CMM). We also identify three dominant views of maturity models and provide guidelines for various approaches to constructing maturity models with a standard vocabulary. We finally propose using process theories and configurational approaches to address the main theoretical criticisms with regard to maturity models and conclude with some recommendations for maturity model developers

Empirical Evaluation of the Influence of EMR Alignment to Care Processes on Data Completeness

Data completeness is an important dimension of data quality in electronic medical records (EMR). There are many constructs that influence data completeness in EMR. In this paper, we investigate three of these constructs: Clinical staff participation, EMR integration, and EMR alignment to care processes. We use these constructs from related studies as theoretical support to propose a conceptual model of factors influencing data completeness in EMR. The conceptual model is empirically validated using a survey with clinical staff participants. The results reveal that a high level of clinical staff’s participation influences the data completeness in EMR. Furthermore, the alignment of EMR to the care processes has an impact on the data completeness in EMR

Real-time big data processing for anomaly detection : a survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Lt

Hospital Management Maturity Models: Literature Review

Previous researches show that hospital organizations have initiated improvement programs and invested considerably in the orientation and management of processes, using maturity models to improve structures and learning. In this context, the objective of the present paper is to analyze previous researches related to hospital management maturity models, using the Morton (1994) organizational dimensions' analysis model, adapted for hospital organizations. The Web of Science, Scopus, Spell, Scielo and BDTD platforms were used for this study. We screened 305 identified papers, published from January 2005 till December 2019, using search descriptors: “Maturity Model” and “Hospital management". We identified Forty-one articles as eligible for information extraction and analysis. The surveys are classified into five organizational dimensions: Strategy, Structure, Decision Making, Technology, and People. We found a predominance of the technology management dimension in 25 studies, based on the organizational dimensions. The research is essentially related to information systems, supply management and quality management. Although there are different models of hospital management maturity, it was found that the models developed for hospital organizations are mostly related to their technical / operational areas, but in a fragmented way. The present study contributes to a comprehensive literature review of hospital maturity and management models

A Business Intelligence Maturity Model in Healthcare Based on the Combination of Delphi and DEMATEL-ANP Methods

The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care