Management Domain Research Articles

Graphical Specification of Sensitive Business Processes

This paper aims at developing a new BPMN extension, called “BPMN4SBP”, supporting the multi-dimensional modeling of Sensitive Business Processes (SBPs) (i.e., the knowledge, functional, organizational, behavioral, informational and intentional dimensions). It aims to explicitly integrate all the relevant issues and aspects relevant at the coupling of the business process modeling (BPM) domain and the knowledge management (KM) domain for improving the identification and management of crucial knowledge which are mobilized and created by these processes. This paper provides the analysis of requirements and relevant concepts for modeling SBP. Based on a core domain ontology, need for extension is identified and the valid BPMN4SBP extension is designed (according to the BPMN extension mechanism) by the construction of a conceptional domain model and the corresponding BPMN extension model. This extended BPMN4SBP meta-model is derived by applying model transformation rules and by adapting, also, the UML profile mechanism to BPMN. The applicability of the proposed BPMN extension is demonstrated by modeling an SBP in a real case study in the healthcare domain.

Work engagement interventions linked to employee learning and improvement

PurposeThe purpose of this paper is to identify influential workplace engagement interventions that were initiated by managers over several years as revealed in many studies. Also, an action learning guide is offered for an individual manager to use such interventions with the employee in shaping activities to assist employee learning and performance improvement which may result in greater workplace engagement.Design/methodology/approachThe first step was to examine the research literature on: the meaning of workplace engagement along with theory bases for it, the framework for stimulating employee engagement and information about action learning in the workplace. Second, was the identification of manager/organization interventions found to result in employee assessment of their own positive growth in workplace engagement? Third, we offer a practical, action learning approach.FindingsAn action guide is presented as an example of a method to assist a manager working with, in this case, a single employee. The guide is grounded on empirical research, mostly meta-analyses, and reflects positively assessed workplace engagement interventions.Originality/valueIn the performance management, human resources and training domains there is limited research and/or examples of practical guides for managers to use to link guidance with efforts to apply specific interventions to support employee workplace engagement. This paper provides the manager with a path to assist employees.

Sustainability of healthcare supply chain management: a panoramic approach through SLR and bibliometric

PurposeThe healthcare sector increasingly recognizes how critical sustainable supply chains are to lowering environmental impact, boosting productivity and satisfying public expectations for morally and responsibly provided healthcare. Consequently, the current study aims to thoroughly evaluate the literature on the sustainability of healthcare supply chain management.Design/methodology/approachThe paper uses a systematic literature review (SLR) technique and bibliometric review to examine the benefactions of different authors, nations and organizations to healthcare sustainability through bibliometric and network analyses.FindingsThe study concludes that the healthcare industry may advance sustainability on all levels by incorporating technology into the fundamentals of sustainability. Patient care is given priority in this proposed approach, which can also help healthcare executives create strategies that support efficient healthcare supply chains.Research limitations/implicationsThe research study can serve as a basis for future investigations into additional healthcare management domains, where integrating a sustainable supply chain can yield superior and observable results and bridge deficiencies in management protocols.Originality/valueUsing bibliometric visualization, this study shows the relevance of sustainability in the healthcare supply chain. By identifying its advantages, present-day circumstances, applications and prospective future research fields, the study took up the review and relevance of sustainability in many parts of the healthcare industry.

Interactions between Cognitive, Affective, and Respiratory Profiles in Chronic Respiratory Disorders: A Cluster Analysis Approach.

This study conducted at Leamna Pulmonology Hospital investigated the interrelations among cognitive, affective, and respiratory variables within a cohort of 100 patients diagnosed with chronic respiratory conditions, utilizing sophisticated machine learning-based clustering techniques. Spanning from October 2022 to February 2023, hospitalized individuals confirmed to have asthma or COPD underwent extensive evaluations using standardized instruments such as the mMRC scale, the CAT test, and spirometry. Complementary cognitive and affective assessments were performed employing the MMSE, MoCA, and the Hamilton Anxiety and Depression Scale, furnishing a holistic view of patient health statuses. The analysis delineated three distinct clusters: Moderate Cognitive Respiratory, Severe Cognitive Respiratory, and Stable Cognitive Respiratory, each characterized by unique profiles that underscore the necessity for tailored therapeutic strategies. These clusters exhibited significant correlations between the severity of respiratory symptoms and their effects on cognitive and affective conditions. The results highlight the benefits of an integrated treatment approach for COPD and asthma, which is personalized based on the intricate patterns identified through clustering. Such a strategy promises to enhance the management of these diseases, potentially elevating the quality of life and everyday functionality of the patients. These findings advocate for treatment customization according to the specific interplays among cognitive, affective, and respiratory dimensions, presenting substantial prospects for clinical advancement and pioneering new avenues for research in the domain of chronic respiratory disease management.

A Recommendation System for Prosumers Based on Large Language Models.

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home's comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers.

The COVID-19 pandemic's impact on user consumption: A shift towards very efficient load forecasting

The COVID-19 pandemic has prompted significant shifts in energy consumption patterns, necessitating a departure from traditional data analysis methods. Accurate energy forecasting models play a crucial role in various domains of planning and energy management. It is possible to predict the demand for electricity consumption using a variety of techniques. Fundamentally, this research comprises two main components. The first phase assesses the influence of the COVID-19 pandemic on user consumption patterns through time series analysis. The subsequent segment is dedicated to long-term load forecasting, employing both statistical methods termed as Holt-Winters and Prophet algorithms and an Artificial Neural Network approach known as Long Short-Term Memory. These approaches aim to predict electricity demand amid the diverse and intricate context of the COVID-19 pandemic. It utilizes both conventional time series forecasting methods and advanced Artificial Intelligence models to enhance the accuracy of load forecasting. Since rapid societal and economic shifts are unpredictable during a pandemic, methods based on Artificial Intelligence are becoming extremely important due to its high precision. Though, it is problematic to conduct load prediction with high accuracy because of influencing factors like climatic, socioeconomic, and seasonal aspects. Finally, using hourly-driven power consumption data from Houston, Texas, USA, the Long Short-Term Memory model outperforms the Holt-Winters and Prophet models in a test of generalizability and accuracy. It is possible to verify the accuracy of experimental findings by analyzing the resulting constraints and influential factors.

Availability of Human Resource Learning Content in Virtual Reality: Reasons and Implications

The rapidly growing domain of virtual reality (VR) has witnessed considerable traction in the realm of education. Notwithstanding, a discernible void persists in the accessibility of VR content specifically tailored for the domain of Human Resource Management (HRM). This paper endeavors to explain the underlying causes contributing to the scarcity of HR-related VR content. Furthermore, the paper focuses on the reasons and implications of not having sufficient HR content in the immersive space. The paper proposes an innovative solution in the form of a “VR-HR Training Needs Identification Framework”. This conceptual framework is designed to facilitate VR content developers in pinpointing the precise core requirements inherent to HR, thereby enhancing the development process.

Role Of Artificial Intelligence in Big Database Management

This exploration article digs into the significant effect of (artificial intelligence) AI, on big Data base management in the field of computer science by utilizing the broad abilities of artificial intelligence AI technologies, this study investigates different strategies to improve the oversight and administration of huge information bases. Through an exhaustive assessment of existing academic works, itemized contextual investigations, and master experiences, the article disentangles the mind-boggling combination of Ai’s integration and its important impacts on database management rehearses. Covering an extensive variety of artificial intelligence aspects, for example, AI, regular language handling, and profound realizing, this academic request plans to explain the complex job of computer-based intelligence in the domain of big database management. As a critical commitment to scholarly talk, this research article fills in as an original work, planning the groundbreaking excursion of Ai transformative into the center of data base management system, introducing another time of development and productivity in the computerized scene.

Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain

As the core competitiveness of the national industry, large-scale equipment such as ships, high-speed rail and nuclear power equipment, their production process involves in-depth personalization. It includes complex processes and long manufacturing cycles. In addition, the equipment’s supply chain management is extremely complex. Therefore, the development of a supply chain management knowledge graph is of significant strategic significance. It not only enhances the synergistic effect of the supply chain management but also upgrades the level of intelligent management. This paper proposes a distant supervision knowledge extraction and knowledge graph construction method in the supply chain management of large equipment manufacturing, which achieves digital and structured management and efficient use of supply chain management knowledge in the industry. This paper presents an approach to extract entity-relation knowledge using limited samples. We achieve this by establishing a distant supervision model. Furthermore, we introduce a fusion gate mechanism and integrate ontology information, thereby enhancing the model’s capability to effectively discern sentence-level semantics. Subsequently, we promptly modify the weights of input features using the gate mechanism to strengthen the model’s resilience and address the issue of vector noise diffusion. Finally, an inter-bag sentence attention mechanism is introduced to integrate different sentence bag information at the sentence bag level, which achieves more accurate entity-relation knowledge extraction. The experimental results prove that compared with the latest distant supervision method, the accuracy of relation extraction is improved by 2.8%, and the AUC value is increased by 3.9%, effectively improving the quality of knowledge graph in supply chain management.

Quality of Work Life Among Faculty Members at Jerash University From Their Point of View

Objectives: The study aims to identify the level of quality of work life among faculty members at Jerash University because it is important in motivating them to work in a good way and raising their motivation to work. The University competes with other Universities, so the majority of Universities in general and private universities in particular seek to attract the best academic cadres to create a university environment improve the educational environment, and increase the university’s productivity, as well as identify whether there is Statistically significant differences in the level of quality of work life among faculty members at Jerash University due to the variables: gender, academic rank, college, and experience in university teaching. Theoretical framework: This study focuses on the quality of work life among faculty members at Jerash University, Higher education institutions face enormous challenges and great competition between universities in general and private universities in particular. The concept of quality of work life is one of the modern concepts in the domain of management in general and in the domain of higher education institutions in particular and in light of the importance of educational institutions and their effective role in Developing the work and improving the performance of faculty members, which reflects positively on the student as a way out of the educational-learning process and competitiveness with other educational institutions. Method: Obtain survey data and use descriptive-analytical method and employing conceptual and analytical approach analysis of the literature articles and using a questionnaire as a tool for the study to collect data from a sample of (94) faculty members at Jerash University who were selected randomly Results: The results indicate that the level of quality of work life among faculty members at Jerash University from their point of view was moderate. There were no statistically significant differences in the degree of strategic leadership practice among academic leaders from the point of view of faculty members due to the variables of gender, academic rank, and experience. Conclusion: These results highlight the need for college leaders to diversify the methods of evaluating faculty members in the college. Training courses for academic leaders related to resolving conflicts and problems through scientific methods.

STRESS MANAGEMENT AND TEACHERS' PERFORMANCE IN THE TERESIAN DAUGHTERS OF MARY-MANAGED SCHOOLS

This study investigated the stress management and teachers’ performance in the Teresian Daughters of Mary-Managed Schools High School Department. The study used the non-experimental quantitative research design utilizing correlational method. The respondents were composed of 149 teachers in the Teresian Daughters of Mary-Managed Schools High School Department using a universal sampling. The statistical tools used were Mean, Pearson Product Moment Coefficient Correlation and Regression Analysis. The findings revealed that the teachers were able to always observe stress management. This suggests that they are able to control their stress by being respectful and responsible, managing and communicating existing and future work, managing the individual within the team and reasoning/ managing difficult situations. Meanwhile teacher’s performance is always observed by the teachers. This indicates that teacher is able to observe always the expected standard performance in terms of designing learning experiences for children, knowledge and understanding of subject matter, strategies for facilitating learning and interpersonal relationship. There is no significant relationship between the stress management and teacher’s performance in the Teresian Daughters of Mary- Managed Schools High School Department. None of the domains of the stress management that significantly influence of the teacher’s performance in the Teresian Daughters of Mary-Managed Schools High School Department. It is articulated in this study that that further study may be conducted on the other stress management indicators that may influence the teacher’s performance in the Teresian Daughters of Mary-Managed Schools High School Department. It was concluded that teachers who have always observed stress management do not have an impact to their performance. KEYWORDS: Stress management, Teacher’s performance, Teresian Daughters of Mary- Managed Schools.

Design of a bi-directional methodology for automated assessment of compliance to continuous application of clinical guidelines, and its evaluation in the type 2 diabetes domain.

We introduce a new approach for automated guideline-based-care quality assessment, the bidirectional knowledge-based assessment of compliance (BiKBAC) method, and the DiscovErr system, which implements it. Our methodology compares the guideline's Asbru-based formal representation, including its intentions, with the longitudinal medical record, using a top-down and bottom-up approach. Partial matches are resolved using fuzzy temporal logic. The system was evaluated in the type 2 Diabetes management domain, comparing it to three expert clinicians, including two diabetes experts. The system and the experts commented on the management of 10 patients, randomly selected from 2,000 diabetes patients. On average, each record spanned 5.23 years; the data included 1,584 medical transactions. The system provided 279 comments. The experts made 181 different unique comments. The completeness (recall) of the system was 91% when the gold standard was comments made by at least two of the three experts, and 98%, compared to comments made by all three experts. The experts also assessed all of the 114 medication-therapy-related comments, and a random 35% of the 165 tests-and-monitoring-related comments. The system's correctness (precision) was 81%, compared to comments judged as correct by both diabetes experts, and 91%, compared to comments judged as correct by one diabetes expert and at least as partially correct by the other. 89% of the comments were judged as important by both diabetes experts, 8% were judged as important by one expert, and 3% were judged as less important by both experts. Adding the validated system comments to the experts' comments, the completeness scores of the experts were 75%, 60%, and 55%; the expert correctness scores were respectively 99%, 91%, and 88%. Thus, the system could be ranked first in completeness and second in correctness. We conclude that systems such as DiscovErr can effectively assess the quality of continuous guideline-based care.

Vision for Emergency Management: Conceptualizing Mission-Critical Ecosystems

Emergency management is seldom viewed through the lens of enterprises/industries, which aim to safeguard their critical missions to deliver seamless services. This article advocates for an integrative perspective on mission-critical (eco)systems. In the digital transformation landscape, the traditional notions of "mission-critical systems," often associated with physical spaces like control facilities or hardware-software crucial for business operations, are challenged. Based on document reviews, this article argues for a holistic comprehension of an organization's mission-critical ecosystems, considering internal and external factors across physical, digital, and human aspects. The article uses renewable energy and e-health as cases. The overarching goal is to expand the research scope in emergency management by examining current concerns and current trends in emergency management domain, combining preparedness, resilience, and cybersecurity. This comprehensive approach aims to enhance our ability to navigate and respond effectively to emergencies in an increasingly complex and interconnected world.

Transfer Learning for Prognostics and Health Management: Advances, Challenges, and Opportunities

As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management (PHM) domain, transfer learning provides a fundamental solution to enhance generalization of data-driven methods. In this paper, we briefly discuss general idea and advances of various transfer learning techniques for PHM domain, including domain adaptation, domain generalization, federated learning, and knowledge driven transfer learning. Based on the observations from state of the art, we provide extensive discussions on possible challenges and opportunities of transfer learning for PHM domain to direct future development. Conflict of Interest Statement The authors declare no conflicts of interest.

Enhancing integrated resource management through remote sensing and GIS

Integrated Resource Management plays a crucial role in sustainable development by ensuring efficient allocation and utilization of natural resources. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as powerful tools for collecting, analyzing, and managing spatial data, enabling comprehensive and integrated decision-making processes. This review article uniquely focuses on Integrated Resource Management (IRM) and its role in sustainable development. It specifically examines the application of RS and GIS in IRM across various resource management domains. The article stands out for its comprehensive coverage of the benefits, challenges, and future directions of this integrated approach.

Maritime accident analysis and reduction technique for analysing maritime collision accidents

ABSTRACT The Human Error and Reduction Technique (HEART) is a robust method consisted of both qualitative and quantitative steps. The HEART method has been utilized to analysed the human reliability in various industries. In maritime safety research, understanding human error's role in accidents is crucial. This study introduces a novel methodology integrating the HEART method with the 4M framework and TOPSIS analysis to comprehensively assess human error's interplay with other accident factors. By categorizing generic tasks and error-producing conditions (EPC) into man, machine, media, and management domains, the methodology reveals intricate relationships among accident factors. Additionally, TOPSIS analysis facilitates quantitative evaluation, enhancing decision-making by assigning weights to identified factors. The methodology offers a systematic approach to identify critical areas for intervention, such as improving lookout tasks and communication. Application of this methodology can enhance maritime safety practices by providing actionable insights into accident prevention strategies. Through its holistic and quantitative approach, this study contributes to advancing maritime safety research and fostering a safer operational environment.

On the Feasibility of Forgetting in Data Streams

In today's digital age, it is becoming increasingly prevalent to retain digital footprints in the cloud indefinitely. Nonetheless, there is a valid argument that entities should have the authority to decide whether their personal data remains within a specific database or is expunged. Indeed, nations across the globe are increasingly enacting legislation to uphold the "Right To Be Forgotten" for individuals. Investigating computational challenges, including the formalization and implementation of this notion, is crucial due to its relevance in the domains of data privacy and management. This work introduces a new streaming model: the 'Right to be Forgotten Data Streaming Model' (RFDS model). The main feature of this model is that any element in the stream has the right to have its history removed from the stream. Formally, the input is a stream of updates of the form (a, Δ) where Δ ∈ {+, ⊥} and a is an element from a universe U. When the update Δ=+ occurs, the frequency of a, denoted as f a , is incremented to f a +1. When the update Δ=⊥, occurs, f a is set to 0. This feature, which represents the forget request, distinguishes the present model from existing data streaming models. This work systematically investigates computational challenges that arise while incorporating the notion of the right to be forgotten. Our initial considerations reveal that even estimating F 1 (sum of the frequencies of elements) of the stream is a non-trivial problem in this model. Based on the initial investigations, we focus on a modified model which we call α-RFDS where we limit the number of forget operations to be at most α fraction. In this modified model, we focus on estimating F 0 (number of distinct elements) and F 1 . We present algorithms and establish almost-matching lower bounds on the space complexity for these computational tasks.

Artificial Intelligence Capabilities for Demand Planning Process

Background: Technological advancements, particularly in Artificial Intelligence (AI), are revolutionizing operations management, especially in the domain of supply chain management. This paper delves into the application of AI in demand planning processes within the supply chain context. Drawing upon a comprehensive review of the existing literature, the main objective of this study is to analyze how AI is being applied and adopted in the demand planning process, identifying the resources needed to build the capacity of AI in the demand process, as well as the mechanisms and practices contributing to AI capability’s advancement and formation. Methodology: The approach was qualitative, and case studies of three different companies were conducted. Results: This study identified crucial resources necessary for fostering AI capabilities in demand planning. Our study extends the literature on AI capability in several ways. First, we identify the resources that are important in the formation of the capacity to implement AI in the context of demand planning. Conclusions: This study’s practical contributions underscore the multifaceted nature of AI implementation for demand planning, emphasizing the importance of resource allocation, human capital development, collaborative relationships, organizational alignment, and relational capital and AI.

Land management criteria for green building certification systems in Turkey.

The issue of energy holds significant implications for nations heavily reliant on it, such as Turkey. In recent times, legislative measures, including the Energy Efficiency Law and the Energy Performance Regulation of Buildings, have been enacted in Turkey. Despite the implementation of the Energy Performance Certificate for buildings, the absence of a national green building certification system remains conspicuous. To address this gap, an examination of globally recognized certification systems and relevant components concerning land management, pertinent to Turkey's context, was undertaken. This involved a comprehensive literature review to establish the foundation for survey inquiries. Subsequently, interviews were conducted with subject matter experts in Turkey to glean valuable insights and perspectives. Utilizing the Analytic Hierarchy Process (AHP) methodology, the responses from these experts were collated and analyzed to delineate the criteria and sub-criteria germane to land management. This study delves into the discourse surrounding the establishment of a green building certification system within Turkey, specifically within the domain of land management, a sector of paramount importance, particularly within the purview of survey engineering. Through a structured survey, the criteria pertinent to land management were identified and subjected to rigorous analysis. Furthermore, the sub-criteria within the land management category were delineated, and their respective weights were computed, thereby providing a comprehensive framework for the envisioned green building certification system in Turkey.

Democratising complex system modelling

In the contemporary context of an acute need for sustainability and swift response to imminent crises such as global warming, pandemics and economic system disruptions, the focus on responsible decision making, ethical risk assessment and mitigation at all organizational levels is an overarching goal. Our aim is to introduce a deterministic method for investigating the stability of complex systems, in order to find the most important elements of such systems and their impact on different scenarios. The novelty of the current approach lies in its compact format and intuitive nature, designed to accommodate a limited amount of computational resources. The proposed modelling method involves the mapping of complex systems from a diversity of disciplines (economic markets, resource management domain and the community impact of suburbanisation) onto a sequence of chemical reactions and involving a subsequent mathematical analysis. Mapping the results back onto the use cases shows that one can retrieve a considerable amount of detail, making the modelling strategy general enough to be adaptable and scalable while also detailed enough to provide valuable insights for practical scenarios.