This paper surveys various Quantum Computing Platforms and assesses their potential use and added value for Data Envelopment Analysis. Following a brief overview of Quantum Computing in Business Analytics with particular focus on Data Envelopement Analysis, we consider Quantum Computing platforms developed by various industries and open source and study their potential in solving various Data Envelopment Analysis case studies in Banking and Finance. The objective of the paper is to assess the potential of Quantum Computing in Business Analytics and Data Envelopment Analysis.

This paper proposes a metaverse framework for Data Envelopment Analysis (DEA) for banks and financial institutions. Following a brief state-of-the-art review of the metaverse and Data Envelopment Analysis for banks and financial institutions, we propose a new framework for a greater value from the application of Data DEA for banks and financial institutions within the metaverse. The proposed framework is based on a three-tier architecture. We apply the proposed framework to a well-established case study by Osman et al (2008) involving the application of DEA for banks and financial institutions. We re-engineered the considered case study to fit within a real estate metaverse environment to gain a greater value from the visual impact, perception and immersive navigation. We conclude with a research potential and limitations and future work to adapt the DEA basic model to the metaverse.

No efforts have been made yet to develop a comprehensive social inclusion index for all countries. We propose an international social inclusion index based on eight dimensions. Using this index, we analyze the data available on 16 Organization for Economic Co-operation and Development (OECD) countries. This task was accomplished using Shannon Entropy (SE) for weight determination and the Weighted Aggregated Sum Product Assessment (WASPAS) technique for country ranking. The results indicate that the top five countries that correctly employed the concept of social inclusion in their societies in 2014 and 2015 are Norway, followed by Ireland, France, Spain, and Sweden. However, the least-performing nation is Latvia in both studied years. Additionally, the findings emphasize that enrolling children in school, accessing broadband connection, fighting corruption in the public sector, being employed, attaining high GDP growth, decreasing homicide rate, and managing non-made imperfections that may lead to injuries or mortalities are vital elements for the creation of a more inclusive society.

The efficiency of banks has a critical role in development of sound financial systems of countries. Data Envelopment Analysis (DEA) has witnessed an increase in popularity for modeling the performance efficiency of banks. Such efficiency depends on the appropriate selection of input and output variables. In literature, no agreement exists on the selection of relevant variables. The disagreement has been an on-going debate among academic experts, and no diagnostic tools exist to identify variable misspecifications. A cognitive analytics management framework is proposed using three processes to address misspecifications. The cognitive process conducts an extensive review to identify the most common set of variables. The analytics process integrates a random forest method; a simulation method with a DEA measurement feedback; and Shannon Entropy to select the best DEA model and its relevant variables. Finally, a management process discusses the managerial insights to manage performance and impacts. A sample of data is collected on 303 top-world banks for the periods 2013 to 2015 from 49 countries. The experimental simulation results identified the best DEA model along with its associated variables, and addressed the misclassification of the total deposits. The paper concludes with the limitations and future research directions.

While the connection between the electronic government development index (EGDI) and sustainable development goals (SDGs) is well established, global efforts to provide appropriate means for achieving SDGs remain insufficient. This study addresses this issue by introducing a hybrid framework using Shannon entropy to derive data-driven weights for aggregation, technique for order preference by similarity to ideal solution for relative ranking, and data envelopment analysis for generating performance efficiency and effectiveness indices, benchmarks for setting targets, and analytical insights for smarter decisions and managerial actions. The hybrid methods are validated on the 2014, 2016 and 2018 EGDI datasets that cover 193 countries. Our findings illustrate a significant change in the weights of performance indices, leading to a change in the ranks of over 90% of the countries, and a decrease in the SDG performance trend due to less efficient utilization of resources, despite an increase in the effectiveness of outcomes.

Electronic government services (e-services) involve the delivery of information and services to stakeholders via the Internet, Internet of Things and other traditional modes. Despite their beneficial values, the overall level of usage (take-up) remains relatively low compared to traditional modes. They are also challenging to evaluate due to behavioral, economical, political, and technical aspects. The literature lacks a methodology framework to guide the government transformation application to improve both internal processes of e-services and institutional transformation to advance relationships with stakeholders. This paper proposes a cognitive analytics management (CAM) framework to implement such transformations. The ambition is to increase users' take-up rate and satisfaction, and create sustainable shared values through provision of improved e-services. The CAM framework uses cognition to understand and frame the transformation challenge into analytics terms. Analytics insights for improvements are generated using Data Envelopment Analysis (DEA). A classification and regression tree is then applied to DEA results to identify characteristics of satisfaction to advance relationships. The importance of senior management is highlighted for setting strategic goals and providing various executive supports. The CAM application for the transforming Turkish e-services is validated on a large sample data using online survey. The results are discussed; the outcomes and impacts are reported in terms of estimated savings of more than fifteen billion dollars over a ten-year period and increased usage of improved new e-services. We conclude with future research. (C) 2019 The Authors. Published by Elsevier B.V.

The adaptive layout for operating theatre (ALOT) problem in hospitals seeks to determine the ‘most efficient’ layout placement of a set of health-care operating facilities, corridors and elevators in a designated area subject to a set of constraints on professional standards. Such standards include regulations on: hygiene, safety and security of stakeholders (doctors, medical staff, patients and visitors); movements of technologies; and specifications of operating rooms (functions, orientations, space sizes, and desired closeness). Existing ALOT layouts are mostly generated from designs based on experiential judgments of experts. Due to the lack of scientific rigor and huge impact of layout design on the efficiency and effectiveness of an operating theater, the paper proposes mixed integer linear programming models to find optimal layouts under three different design variants: ALOT with multiple sections; ALOT with multiple rows and ALOT with multiple floors. Each variant has different demands for personnel, patients, and technologies over a planning horizon. Operating facilities can exchange functions at rearrangement costs from one period to another to meet the changing demands. The general objective consists of two sub-objectives: the first sub-objective is to minimize the total sum of the rearrangement and travel costs whereas the second sub-objective is to maximize the total sum of desired closeness among facilities. Computational experiences are presented on a set of quasi-real data instances for a hospital in France. They demonstrate the effectiveness of the formulations in providing optimal layouts for realistic-sized instances. Conclusion and future research directions are presented.

While research interest on product and service evaluation from unstructured text reviews is increasing, investigating the effectiveness of predictive analytical models in this context is still under-explored. With the advancement in machine learning research, an opportunity exists to bridge this gap using a model-based product and service evaluation. We propose in this article ReviewModus, a text mining and processing framework that (1) relies on the model structure and its corresponding assessment questions to train a machine learning algorithm to predict the classification of reviews around the model dimensions; (2) predicts the sentiments within the reviews based on external review training datasets; and (3) transforms the extracted measures from the reviews for further analysis. Our approach is evaluated in the context of 11 e-government services where the performance of the framework is compared to the manual processing of unstructured reviews crosschecked by three independent evaluators. Our study shows promising classification results with a micro-average F-score reaching 85.16%, and a high sentiment prediction correlation (71.44%) with the manually performed sentiment assessment. (C) 2019 Elsevier Inc. All rights reserved.

Big data provenance as a systematic and comprehensive approach to transparency is attracting increasing scholarly attention. Our paper contributes to this stream of research by proposing a systemat...

The surge and value of unstructured text is attracting substantial research and industry attention. Subsequently we are witnessing novel techniques and algorithms that are performing increasingly sophisticated text mining tasks. However the majority of such techniques are opaque, making it hard to trace the provenance of the analytical task on hand. We propose Catalyst, a framework to automatically transform, enrich and expose text into a linked graph-based layer to enable more transparent processing and access to the text elements. In brief, Catalyst extracts text dependencies, performs sentiment analysis, detects semantic relatedness, and links the text elements into a semantic triple-store that enables an easy access to the text entities through direct query functionalities. We plan to evaluate the performance of Catalyst by processing a dataset of user reviews around the dimensions of an evaluation model deployed in the context of e-government services.

Operating Theater Layout Problem (OTLP) has a great impact on the productivity and the efficiency of the health process. While solving OTLP, Real-life Operating Theater (OT) sizes are larger than exact methods capacity, this lead to explore other methods as heuristics, metaheuristics or parallel treatment looking for approximate solutions. In this paper we developed a novel approach using a Multi-Agent (MA) Decision Making System (DMS) based on Mixed Integer Linear Programming (MILP) for large-sized OTLP with objective of minimizing total traveling costs. The DMS generates exact solutions in reasonable time and gives the final OT layout in a graphic interface.

In this chapter, the current world's challenges, shortcomings of strategic performance measurement, and management methodologies are analyzed to introduce the new Cognitive Analytics Management (CAM) framework. CAM uses five components (SAMAS): Shared values to stakeholders; cognitive Analytics to generate applied insights; Mission, vision, and goals to develop corporate strategy; Activities to create—innovative models, products, and services—using various organizations' supporting Structure from people, process, regulation, and technology to develop smart organizations. The efficient frontier data envelopment analysis is proposed to generate performance measures and derive insights from best practices. The input-efficiency and output-effectiveness performances are used to prioritize scenarios. Insights from the best scenarios are used to improve inefficient ones, to manage performance, and to boost productivity growth. CAM also introduces a new cognitive leadership concept to help making informed decisions in a smart competitive world while alleviating societal challenges. Finally, CAM implementation roadmap, supporting literature, and new research innovations are provided.

Citizens’ satisfaction is acknowledged as one of the most significant influences for e-government adoption and diffusion. This study examines the impact of information quality, system quality, trust , and cost on user satisfaction of e-government services. Using a survey, this study collected 1518 valid responses from e-government service adopters across the United Kingdom. Our empirical outcomes show the five factors identified in this study have a significant impact on U.K. citizens’ satisfaction with e-government services.

Evaluating users' satisfaction of e-Government services has been addressed by numerous studies. These mainly looked at e-Government users as citizens who are nationals and comply with the local culture of the governments providing these services. However, the GCC region has a particular culture stemming from the social structure and working environment. The expatriate population from different backgrounds form a significant portion of e-Government users. Therefore, the aim of this study is to explore users' satisfaction towards the electronic services provided by governments in the GCC region represented by the State of Qatar. In order to examine the suitability of the e-Government service portal, a cross-sectional survey targeting the users of three common e-Government services in Qatar evaluated users' satisfaction based on the four dimensions of the COBRA framework (Osman et al. 2014): Cost, opportunity, benefit and risk. SEM analysis demonstrated a good model fit and supported the hypotheses related to the effect of risk and benefit on users' satisfaction.

The main objectives of the chapter are to evaluate the impact of the tsunami of big data, business analytics, and technology on the delivery and diffusion of knowledge around the world through the use of Internet-of-things and to design future academic education and training programs. Global and local trends are analyzed to evaluate the impact of the digital tsunami on the delivery and diffusion of knowledge; to identify the shortage of critical skills, drivers of challenges, hot skills in demand, and salaries in big data/business analytics; to highlight obstacles to make informed decisions. CAM education framework is proposed to design customized higher education and training programs to meet current shortage and future generation with the relevant and rigorous skills to boost productivity growth and to impact society and professional domains in the digital economy. Finally, new ideas on how governments, academic institutions, technology companies, and professional employers can work together to reform the traditional education value chain and integrate the “massive open online courses” to achieve mass diffusion of knowledge, to transform people from loyalty to parties, clergies, and dictatorships to society's loyalty, and to develop a culture of shared-value in a move towards a smarter and fairer planet in the 21st century.

The multi-objective Operating Theater Layout (OTL) problem consists of a given facilities of different areas to be placed in different sections of an operating theater department. The OTL problem seeks to find the layout of facilities in a hospital department in compliance with international health care accreditation standards, and to meet the internal constraints on the organizational layout. The main objectives are to minimize the total traveling costs among facilities and to maximize the desirable adjacencies among them. The paper presents a mathematical formulation to determine the optimal OTL. A Particle Swarm Optimization (PSO) algorithm is also proposed to solve approximately this NP-Hard OTL problem. The PSO algorithm employs a constructive heuristic to generate initial feasible solutions to explore by the PSO search concept to find an effective solution in an efficient computation time. The PSO approach is implemented to solve a real-life OTL instance available at Roanne hospital in France. The obtained solution was validated and approved by a senior official responsible of the OTL at the hospital.

The study sought to assess the state of consumer orientation within the health sector in Tamale Teaching Hospital. The sample size of 220 respondents was used for the study. Findings of the study indicated that the level of consumer orientation at the hospital was generally good. The technical aspects of healthcare delivery were also satisfactory. At the interpersonal level, patients were treated with courtesy. However, a number of inadequacies in the health delivery system of the hospital showed that customer satisfaction still had challenges.

With the daily increase of the amount of published information, research in the area of text analytics is gaining more visibility. Text processing for improving analytics is being studied from different angles. In the literature, text dependencies have been employed to perform various tasks. This includes for example the identification of semantic relations and sentiment analysis. We observe that while text dependencies can boost text analytics, managing and preserving such dependencies in text documents that spread across various corpora and contexts is a challenging task. We present in this paper our work on linking text dependencies using the Resource Description Framework (RDF) specification, following the Stanford typed dependencies representation. We contribute to the field by providing analysts the means to query, extract, and reuse text dependencies for analytical purposes. We highlight how this additional layer can be used in the context of feedback analysis by applying a selection of queries passed to a triple-store containing the generated text dependencies graphs.