Hasil untuk "Management information systems"

Jotiar H. Mohammed Kochar, Nizar M. Ali. ALSulaifani

The current research aims to explore the impact of participative leadership represented by (decision-making participation, delegation of authority, human relations, and justice and equality) in human resource sustainability, defined by (human resource well-being, human resource development, human resource retention, and work-life Equilibrium). At the same time, testing the mediating role of the organizational Equilibrium variable represented by (contributions and inducements) in the influential relationship between the two variables. The study community comprised all employees working in private hospitals in the governorate, totaling approximately 650 individuals across 15 hospitals. Data were collected from a random sample of 306 medical staff members working in private hospitals in Duhok Governorate, Kurdistan Region, Iraq, using a questionnaire. The research employs a descriptive-analytical approach, and the results, analyzed using SPSS V.25, indicate a significant direct effect of both participative leadership and organizational Equilibrium on human resource sustainability. Furthermore, the findings confirm the potential of organizational Equilibrium as a partial mediator in the relationship between participative leadership and human resource sustainability. The study recommends that private hospital management enhance human resource sustainability by promoting participative leadership practices and leveraging the dimensions of organizational Equilibrium to achieve this goal..

Henrique Rodrigues Lelis, Daniel Jardim Pardini, Eloy Pereira Lemos Junior

Compliance programs have legal, administrative and technological attributes that help organizations find solutions related to strategy, management and organizational governance. In turn, blockchain has been described as a digital system with potential for use in numerous activities, as any activity whose function is to protect and transfer digital assets can be impacted by the system. However, there are criticisms and reservations regarding its adoption by organizations, especially regarding issues related to the regulatory framework, corporate governance and technological management. From this perspective, it becomes relevant to relate the attributes of compliance programs to the development of blockchain in the organizational dimension, which is the proposal of this research. The gap explored with this research is to describe the implications that the attributes of compliance programs can bring to the development of blockchain technology, in the context of organizations. To explore the topic, a panel of experts and a Delphi round were created to structure a survey that sought evidence that demonstrates the existence or not of contributions from compliance programs to the development of the blockchain. This article presents the results relating to the organizational dimension of the doctoral thesis “Attributes of Compliance Programs for the blockchain, in the context of the Dimensions of the State, Organization and Individual”, defended by the first author, in the Doctoral program in Information and Management Systems of Knowledge at Universidade Fumec, with UNIVERSIDADE FUMEC and FAPEMIG as funding institutions.

Louise Maple-Brown, Alex Brown, Peter Azzopardi et al.

Introduction Premature onset of type 2 diabetes and excess mortality are critical issues internationally, particularly in Indigenous populations. There is an urgent need for developmentally appropriate and culturally safe models of care. We describe the methods for the codesign, implementation and evaluation of enhanced models of care with Aboriginal and Torres Strait Islander youth living with type 2 diabetes across Northern Australia.Methods and analysis Our mixed-methods approach is informed by the principles of codesign. Across eight sites in four regions, the project brings together the lived experience of Aboriginal and Torres Strait Islander young people (aged 10–25) with type 2 diabetes, their families and communities, and health professionals providing diabetes care through a structured yet flexible codesign process. Participants will help identify and collaborate in the development of a range of multifaceted improvements to current models of care. These may include addressing needs identified in our formative work such as the development of screening and management guidelines, referral pathways, peer support networks, diabetes information resources and training for health professionals in youth type 2 diabetes management. The codesign process will adopt a range of methods including qualitative interviews, focus group discussions, art-based methods and healthcare systems assessments. A developmental evaluation approach will be used to create and refine the components and principles of enhanced models of care. We anticipate that this codesign study will produce new theoretical insights and practice frameworks, resources and approaches for age-appropriate, culturally safe models of care.Ethics and dissemination The study design was developed in collaboration with Aboriginal and Torres Strait Islander and non-Indigenous researchers, health professionals and health service managers and has received ethical approval across all sites. A range of outputs will be produced to disseminate findings to participants, other stakeholders and the scholarly community using creative and traditional formats.

Dimitrios G. Katehakis, Dimitrios Filippidis, Konstantinos Karamanis et al.

Digital innovation can significantly enhance public health services, environmental sustainability, and social welfare. To this end, the European Digital Innovation Hub (EDIH) initiative was funded by the European Commission and national governments aiming to facilitate the digital transformation on various domains (including health) via the setup of relevant ecosystems consisting of academic institutions, research centres, start-ups, small and medium-sized enterprises, larger companies, public organizations, technology transfer offices, innovation clusters, and financial institutions. The ongoing goal of the EDIHs initiative is to bridge the gap between high-tech research taking place in universities and research centres and its deployment in real-world conditions by fostering innovation ecosystems. In this context, the smartHEALTH EDIH started its operation in Greece in 2023, offering technical consultation services to companies and public sector organizations to accelerate digitalization in precision medicine and innovative e-health services by utilizing key technologies such as artificial intelligence, high-performance computing, cybersecurity, and others. During its first 20 months of operation, over 50 prospective recipients have applied for consulting services, mainly seeking “test-before-invest” services. This paper aims to provide insights regarding the smartHEALTH initiative, preliminary outcomes and lessons learned during this first period of operation. To this end, this paper outlines smartHEALTH’s approach to attracting recipients and providing expert guidance on utilizing state-of-the-art technologies for innovative services, product development, and process creation to accelerate digital transformation.

Zhihong WANG, Supeng LENG, Kai XIONG

Driven by the development of intelligent internet of things (IoT) technology, unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area, recruiting neighboring UAVs to form perception and computation task groups to collaboratively complete the perception, acquisition and processing of data.However, repetitive sensory data and imbalance in the supply and demand of computational resources between multiple tasks cause additional computational and communication overheads and increase the end-to-end processing latency.To address this challenge, a multi-task resource allocation approach combining bionics and multi-agent independent reinforcement learning was proposed, making collaborative resource allocation decisions based on local task information.The method represents the resource requirements of individual tasks as situational information concentrations and dynamically updates the heterogeneous resource requirements of each task by spreading the situational information across task groups.At the same time, it combines multi-agent independent reinforcement learning methods for intelligent decision making in order to collaboratively allocate the heterogeneous resources of each task.Simulation results show that this solution can not only effectively reduce the task execution time, but also significantly improve the computational resource utilization.

Mahmoud Masoud

E-scooters are gaining popularity for short-distance travel, but their recharging presents challenges. To reduce their downtime, we propose a Hybrid K-Means/Particle Swarm Optimisation (PSO) approach, optimizing charging routes using machine learning and meta-heuristics. The research in this paper attempts to determine if a combination of a meta-heuristic such as PSO and a machine learning algorithm for clustering such as K-Means, would be effective at solving the vehicle routing problem for e-scooters. We compared this method with other algorithms and found that Tabu Search excelled in over 95% of tests. While Hybrid K-Means/PSO led in only approximately 52% of scenarios, it was also the only one to provide an output that surpassed Tabu Search in one of the scenarios. The core difference in efficiency is due to traditional meta-heuristic methods providing routes that while optimal, may also travel from locations relatively far from each other, while Hybrid K-Means/PSO will provide routes between locations that are clustered and in local groups. This results in Hybrid K-Means/PSO being slightly less efficient but may be more practical for charging personnel as they can operate in designated areas close to each other rather than a more optimal route with nodes further apart. This research underscores the effectiveness of Tabu Search and the potential of our Hybrid K-Means/PSO approach for optimizing e-scooter charging routes.

Georgios I. Papayiannis

The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under the context of model uncertainty in the one-stage framework. In practice, uncertainty issues appear to several aspects of the problem, e.g. liability process characteristics, market conditions, inflation rates, inside information effects, etc. A framework relying on the notion of the Wasserstein barycenter is presented which is able to treat robustly this type of ambiguities by appropriate handling the various information sources (models) and appropriately reformulating the relevant decision making problem. The proposed framework can be applied to a number of different model settings leading to the selection of investment portfolios that remain robust to the various uncertainties appearing in the market. The paper is concluded with a numerical experiment for a static version of the ALM problem, employing standard modelling approaches, illustrating the capabilities of the proposed method with very satisfactory results in retrieving the true optimal strategy even in high noise cases.

Gregor Svindland, Alexander Voß

We introduce a decision-making framework tailored for the management of systemic risk in networks. This framework is constructed upon three fundamental components: (1) a set of acceptable network configurations, (2) a set of interventions aimed at risk mitigation, and (3) a cost function quantifying the expenses associated with these interventions. While our discussion primarily revolves around the management of systemic cyber risks in digital networks, we concurrently draw parallels to risk management of other complex systems where analogous approaches may be adequate.

Rashikala Weerawarna, Shah J Miah

The advent of Blockchain technology (BT) revolutionised the way remittance transactions are recorded. Banks and remittance organisations have shown a growing interest in exploring blockchain's potential advantages over traditional practices. This paper presents a data-driven predictive decision support approach as an innovative artefact designed for the blockchain-oriented remittance industry. Employing a theory-generating Design Science Research (DSR) approach, we have uncovered the emergence of predictive capabilities driven by transactional big data. The artefact integrates predictive analytics and Machine Learning (ML) to enable real-time remittance monitoring, empowering management decision-makers to address challenges in the uncertain digitised landscape of blockchain-oriented remittance companies. Bridging the gap between theory and practice, this research not only enhances the security of the remittance ecosystem but also lays the foundation for future predictive decision support solutions, extending the potential of predictive analytics to other domains. Additionally, the generated theory from the artifact's implementation enriches the DSR approach and fosters grounded and stakeholder theory development in the information systems domain.

Kieran A. Murphy, Dani S. Bassett

One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems.

Brett C. Ramirez, Steven J. Hoff, Morgan D. Hayes et al.

Swine heat production (HP) data are an essential element of numerous aspects affecting swine production sustainability, such as, housing environmental control design, energetics and thermoregulation modeling, as well as understanding of feed energy partitioning. Accurate HP values that reflect the continuous advances in growth, nutrition, health, and reproduction are needed to update outdated models and data; hence, this review of swine HP values is a critical contribution. This review updates the last previous review conducted in 2004, by reviewing literature from growing and breeding pigs from 2003 to 2020. In total, 33 references were identified that provided relevant HP data and from these references, 192 records were identified for pigs ranging in weight from 12.5 to 283 kg and exposed to temperatures between 12.0°C and 35.5°C. For growing pigs at thermoneutral conditions, a 4.7% average increase in HP was observed compared to HP data summarized from 1988 to 2004. Only five records were identified for gestating sows and the 43 records for lactating sows plus litter. This sow data shows high variability and inconsistent trends with temperature, most likely attributed to variation in experimental protocols, management, and limited reported information. There is still a lack of data on growing pigs greater than 105 kg, gilts and gestating sows housed in different systems (stall, pen, mixed, etc.), and latent HP values that reflect different housing systems. Further, there is a need to standardize reporting of HP values (with an example provided) across different disciplines to drive documentation of increased swine production efficiency, environmental control design, and energetics modeling.

Tarcisio Teixeira Alves Junior, Adalberto Grassi Carvalho, Lillian Maria Araújo Rezende Alvares et al.

Objetive. To describe and discuss preliminarily the informational flow related to the processes of information and knowledge management involved in the elaboration of models for granting quotas of scholarships from institutional development programs of CAPES. Method. The procedures involved in the management of information and knowledge related to the processes of creation, storage and use of information necessary for the elaboration of models of granting of scholarships by the institution were prospected and characterized through documentary research and in loco observations. Results. We verified the existence of different systems for the storage and availability of academic information and scholarship management, highlighting the need for greater integration between them, in order to add greater value to the large volume of data and information available to managers, in order to support the improvement of decision making.

Bandar Alamri, Katie Crowley, Ita Richardson

Identity and Access Management (IAM) systems are crucial for any information system, such as healthcare information systems. Health IoT (HIoT) applications are targeted by attackers due to the high-volume and sensitivity of health data. Thus, IAM systems for HIoT need to be built with high standards and based on reliable frameworks. Blockchain (BC) is an emerging technology widely used for developing decentralized IAM solutions. Although, the integration of BC in HIoT for proposing IAM solutions has gained recent attention, BC is an evolving technology and needs to be studied carefully before using it for IAM solutions in HIoT applications. A systematic literature review was conducted on the BC-based IAM systems in HIoT applications to investigate the security aspect. Twenty-four studies that satisfied the inclusion criteria and passed the quality assessment were included in this review. We studied BC-based solutions in HIoT applications to explore the IAM system architecture, security requirements and threats. We summarized the main components and technologies in typical BC-based IAM systems and the layered architecture of the BC-based IAM system in HIoT. Accordingly, the security threats and requirements were summarized. Our systematic review shows that there is a lack of a comprehensive security framework, risk assessments, and security and functional performance evaluation metrics in BC-based IAM in HIoT applications.

Hasan MM, Al Amin M, Moon ZK et al.

Md Mahedi Hasan,1 Md Al Amin,2,3 Zarin Khan Moon,4 Farhana Afrin2 1Faculty of Business Studies, Jashore University Science and Technology, Jashore, 7408, Bangladesh; 2Department of Marketing, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, Bangladesh; 3School of Business and Management, Queen Mary University of London, England, UK; 4Department of Accounting and Information Systems, Jashore University Science and Technology, Jashore, 7408, BangladeshCorrespondence: Md Al Amin, Department of Marketing, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh, Email alamin.bsmrstu21@gmail.comPurpose: Green banking, an ethical banking concept, concentrates on environmental protection and encourages social and environmental sustainability, perceived cognitive efforts, and subjective norms ensuring ecologically responsive banking services. Consequently, although there have been considerable green banking attempts in Bangladesh, it is yet unknown how environmental sustainability, perceived cognitive effort, and subjective norms affect usage behavior. The present research aims to uncover this gap, extending the Theory of Reasoned Action (TRA) to examine the determinants of the bankers’ green banking usage behavior during COVID-19.Methods: Data were collected from 366 bankers in Bangladesh using a purposive sampling technique and analyzed with structural equation modeling (SEM) using SMART PLS 3 software.Findings: The study found management support (0.291, t-statistics = 1.978, p 0.000), environmental sustainability (β = 0.278, t-statistics = 2.752, p < 0.001), perceived cognitive efforts (β = 0.401, t-statistics = 3.549, p < 0.000), and subjective norms (β = 0.309, t-statistics = 4.352, p < 0.000) influence bankers’ attitudes. Whereas environmental sustainability (β = 0.503, t-statistics = 3.726, p < 0.001), perceived cognitive efforts (β = 0.103, t-statistics = 2.020, p < 0.002), subjective norms (β = 0.281, t-statistics = 4.607, p < 0.000), and attitudes (= 0.602, t-statistics = 5.523, p 0.015) influence bankers’ green banking usage behavior. Finally, the mediating role of management supports, environmental sustainability, cognitive efforts and subjective norms on green banking usage behavior through attitudes was significant.Contribution/Conclusion: The study contributed to existing literature validating the proposed holistic framework applying TRA and three contemporary dimensions explaining bankers’ behavior toward green banking practice. Finally, the implementers should focus on green banking practices as green banking is one of the key strategies to protect the environment, assure social justice, and create economic success.Keywords: environmental sustainability, management supports, perceived cognitive efforts, green finance, sustainable banking

Jim Samuel, Rajiv Kashyap, Yana Samuel et al.

Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets and a rapidly growing array of information representations. Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information and consequently affect human performance. Extant research in cognitive fit, which preceded the big data and AI era, focused on the effects of aligning information representation and task on performance, without sufficient consideration to information facets and attendant cognitive challenges. Therefore, there is a compelling need to understand the interplay of these dominant information facets with information representations and tasks, and their influence on human performance. We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary for these complex information environments. To this end, we propose and test a novel *Adaptive Cognitive Fit* [ACF] framework that explains the influence of information facets and AI-augmented information representations on human performance. We draw on information processing theory and cognitive dissonance theory to advance the ACF framework and a set of propositions. We empirically validate the ACF propositions with an economic experiment that demonstrates the influence of information facets, and a machine learning simulation that establishes the viability of using AI to improve human performance.

Нух Таха Насіф

The transformation of the outdated electrical grid into the Smart Grid (SG), which provides a two-way information flow between the various SG components, creates many problems in designing and developing efficient SG communication infrastructures for connecting various SG components. In addition to the currently used core networks and protocols, new wired and wireless approaches are planned for various SG components and applications deployment. The proposed SG communications infrastructure will have many interconnected systems with a variety of capabilities and management to provide end-to-end services to users, as well as among intelligent devices.

Leonardo Christino, Martha D. Ferreira, Fernando V. Paulovich

Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users' visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder's line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations.

Jim Samuel

Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of big-data phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned- ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions. Keywords: Information; Big Data; Electronic Markets; Analytics; Behavior

Yang Li, Chunling Wang, Guoqing Li et al.

Insufficient flexibility in system operation caused by traditional "heat-set" operating modes of combined heat and power (CHP) units in winter heating periods is a key issue that limits renewable energy consumption. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, a novel optimal scheduling model based on chance-constrained programming (CCP), aiming at minimizing the lowest generation cost, is proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipments. In this model, due to the uncertainties of renewable generations including wind turbines and photovoltaic units, the probabilistic spinning reserves are supplied in the form of chance-constrained; from the perspective of user experience, a heating load model is built with consideration of heat comfort and inertia in buildings. To solve the model, a solution approach based on sequence operation theory (SOT) is developed, where the original CCP-based scheduling model is tackled into a solvable mixed-integer linear programming (MILP) formulation by converting a chance constraint into its deterministic equivalence class, and thereby is solved via the CPLEX solver. The simulation results on the modified IEEE 30-bus system demonstrate that the presented method manages to improve operational flexibility of the IES with uncertain renewable generations by comprehensively leveraging thermal inertia of buildings and different kinds of auxiliary equipments, which provides a fundamental way for promoting renewable energy consumption.

Yuxiang Lu, Zhuqing Jia, Syed A. Jafar

Double blind $T$-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index ($θ_1, θ_2$, resp.), to efficiently retrieve a message $W(θ_1,θ_2)$ labeled by the two indices, from a set of $N$ servers that store all messages $W(k_1,k_2), k_1\in\{1,2,\cdots,K_1\}, k_2\in\{1,2,\cdots,K_2\}$, such that the two users' indices are kept private from any set of up to $T_1,T_2$ colluding servers, respectively, as well as from each other. A DB-TPIR scheme based on cross-subspace alignment is proposed in this paper, and shown to be capacity-achieving in the asymptotic setting of large number of messages and bounded latency. The scheme is then extended to $M$-way blind $X$-secure $T$-private information retrieval (MB-XS-TPIR) with multiple ($M$) indices, each belonging to a different user, arbitrary privacy levels for each index ($T_1, T_2,\cdots, T_M$), and arbitrary level of security ($X$) of data storage, so that the message $W(θ_1,θ_2,\cdots, θ_M)$ can be efficiently retrieved while the stored data is held secure against collusion among up to $X$ colluding servers, the $m^{th}$ user's index is private against collusion among up to $T_m$ servers, and each user's index $θ_m$ is private from all other users. The general scheme relies on a tensor-product based extension of cross-subspace alignment and retrieves $1-(X+T_1+\cdots+T_M)/N$ bits of desired message per bit of download.