Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459507
Tarek Othmani, S. Boubaker, F. Rehimi, S. Alimi
Achieving sustainable transportation poses a variety of challenges and difficulties, such as economic, social, and environmental dimensions. These challenges slow down the transition towards a transportation system that minimizes environmental impact and fuel consumption. The focus has shifted towards increasing efficiency and promoting greener alternatives by integrating innovative solutions like intelligent transportation systems, communication technologies, and other approaches to address these challenges and pursue sustainable and energy-efficient transportation. This study proposes a novel approach that integrates Python, SUMO (Simulation of Urban MObility), Vehicle-to-Infrastructure (V2I) communication, and Fuzzy Logic (FL) to estimate the optimal speed for vehicles based on vehicle velocity, road speed limits, and ambient temperature. In the simulation scenarios, we considered different temperature changes to evaluate the effectiveness of the proposed approach. By using SUMO to retrieve the road speed limit through V2I communication and incorporating it into fuzzy logic, we could estimate the optimal speed for vehicles in real time. The simulation results showed a significant reduction in energy consumption and pollutant emissions compared to the unequipped vehicles with V2I and Fuzzy Logic Systems. Specifically, the study found that adopting this approach led to an average reduction in fuel consumption and CO2 emissions of around 9%. These findings highlight the potential of integrating V2I communication and Fuzzy Logic to achieve more sustainable and efficient transportation energy use.
{"title":"Intelligent Speed Advisory System for Optimal Energy Efficiency Based on Ambient Temperature Leveraging Communication Technology and Fuzzy Logic","authors":"Tarek Othmani, S. Boubaker, F. Rehimi, S. Alimi","doi":"10.1109/ICETSIS61505.2024.10459507","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459507","url":null,"abstract":"Achieving sustainable transportation poses a variety of challenges and difficulties, such as economic, social, and environmental dimensions. These challenges slow down the transition towards a transportation system that minimizes environmental impact and fuel consumption. The focus has shifted towards increasing efficiency and promoting greener alternatives by integrating innovative solutions like intelligent transportation systems, communication technologies, and other approaches to address these challenges and pursue sustainable and energy-efficient transportation. This study proposes a novel approach that integrates Python, SUMO (Simulation of Urban MObility), Vehicle-to-Infrastructure (V2I) communication, and Fuzzy Logic (FL) to estimate the optimal speed for vehicles based on vehicle velocity, road speed limits, and ambient temperature. In the simulation scenarios, we considered different temperature changes to evaluate the effectiveness of the proposed approach. By using SUMO to retrieve the road speed limit through V2I communication and incorporating it into fuzzy logic, we could estimate the optimal speed for vehicles in real time. The simulation results showed a significant reduction in energy consumption and pollutant emissions compared to the unequipped vehicles with V2I and Fuzzy Logic Systems. Specifically, the study found that adopting this approach led to an average reduction in fuel consumption and CO2 emissions of around 9%. These findings highlight the potential of integrating V2I communication and Fuzzy Logic to achieve more sustainable and efficient transportation energy use.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"108 5-6","pages":"1250-1254"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of the proposed investigation is to examine the relationship between spirituality in the workplace and its numerous components, including spiritual orientation, compassion, meaningful work, and value alignment, and their impact on individuals' psychological well-being. To address this purpose, 402 full-time academicians from Bihar, India's state institutions were surveyed using standardized questionnaires. Study revealed strong evidence of a favourable link between spirituality in the workplace and psychological well-being. Workplace spirituality factors such as meaningful work, spiritual orientation, compassion, and value alignment were found to be substantially predicting various measures of psychological well-being in a stepwise linear regression analysis, except of environmental mastery. This indicates that companies should create a spiritual workplace for their employees and provide them with meaningful work in order to boost their health and happiness. The study's limitations and potential applications are discussed.
{"title":"Understanding Role of Workplace Spirituality in Predicting Psychological Well-being among Faculties of Higher Education Institutes","authors":"Shalu Kumari, Amjad Ali, Shabana Azmi, Zafrul Allam","doi":"10.1109/ICETSIS61505.2024.10459465","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459465","url":null,"abstract":"The purpose of the proposed investigation is to examine the relationship between spirituality in the workplace and its numerous components, including spiritual orientation, compassion, meaningful work, and value alignment, and their impact on individuals' psychological well-being. To address this purpose, 402 full-time academicians from Bihar, India's state institutions were surveyed using standardized questionnaires. Study revealed strong evidence of a favourable link between spirituality in the workplace and psychological well-being. Workplace spirituality factors such as meaningful work, spiritual orientation, compassion, and value alignment were found to be substantially predicting various measures of psychological well-being in a stepwise linear regression analysis, except of environmental mastery. This indicates that companies should create a spiritual workplace for their employees and provide them with meaningful work in order to boost their health and happiness. The study's limitations and potential applications are discussed.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"385 4","pages":"381-385"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/icetsis61505.2024.10459577
{"title":"Other reviewers","authors":"","doi":"10.1109/icetsis61505.2024.10459577","DOIUrl":"https://doi.org/10.1109/icetsis61505.2024.10459577","url":null,"abstract":"","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"407 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459383
A. Al-Alawi, Fatema Ahmed AlBinAli
Disengagement and turnover of employees are significant costs to organizations worldwide. In many organizations, it isn't easy to foster continuous engagement among employees. Analytically-driven performance management aims to capture and analyze workplace data with advanced analytical techniques to develop a sustainable solution. This systematic literature review (SLR) examines and analyzes frameworks proposed for optimizing engagement and retention through performance analytics. Among the forty initial papers screened, twenty-four highly relevant sources were selected and analyzed. Human resources (HR) related key themes included bias issues, text analysis of reviews, personalized HR management, talent assessments, augmenting HR work with Artificial Intelligence (AI), and integration challenges. According to the findings, a reliable emphasis was placed on the balance of human and machine perspectives. While analytics and algorithms offer insightful information, human judgment is needed to contextualize this data. If datadriven methods are the only ones used, complicated personal aspects that influence experience may be overlooked. Consequently, a human-machine strategy working together is crucial. Furthermore, effective integration requires both strategy alignment and cultural preparedness. Longitudinal evaluations and more real-world case studies help close gaps in the literature. Analytics with human-centric frameworks can maximize engagement and performance management.
{"title":"Unveiling the Retention Puzzle for Optimizing Employee Engagement and Loyalty Through Analytics-Driven Performance Management: A Systematic Literature Review","authors":"A. Al-Alawi, Fatema Ahmed AlBinAli","doi":"10.1109/ICETSIS61505.2024.10459383","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459383","url":null,"abstract":"Disengagement and turnover of employees are significant costs to organizations worldwide. In many organizations, it isn't easy to foster continuous engagement among employees. Analytically-driven performance management aims to capture and analyze workplace data with advanced analytical techniques to develop a sustainable solution. This systematic literature review (SLR) examines and analyzes frameworks proposed for optimizing engagement and retention through performance analytics. Among the forty initial papers screened, twenty-four highly relevant sources were selected and analyzed. Human resources (HR) related key themes included bias issues, text analysis of reviews, personalized HR management, talent assessments, augmenting HR work with Artificial Intelligence (AI), and integration challenges. According to the findings, a reliable emphasis was placed on the balance of human and machine perspectives. While analytics and algorithms offer insightful information, human judgment is needed to contextualize this data. If datadriven methods are the only ones used, complicated personal aspects that influence experience may be overlooked. Consequently, a human-machine strategy working together is crucial. Furthermore, effective integration requires both strategy alignment and cultural preparedness. Longitudinal evaluations and more real-world case studies help close gaps in the literature. Analytics with human-centric frameworks can maximize engagement and performance management.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"210 2","pages":"292-296"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459390
Navneet Tiwari, Jinesh Thakkar, Om Bansode, Hanmant Magar
This research paper addresses the escalating risk of fraud signatures in banking transactions. It introduces a Signature Forgery Detection System that utilizes offline verification and diverse geometric measures to discern genuine from forged signatures. With the prevalence of signature-based identity verification in financial transactions and the absence of foolproof systems, the proposed system aims to enhance the security of banking by efficiently detecting and preventing signature forgery.
{"title":"Signature Forgery and Veracity Detection using Machine Learning","authors":"Navneet Tiwari, Jinesh Thakkar, Om Bansode, Hanmant Magar","doi":"10.1109/ICETSIS61505.2024.10459390","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459390","url":null,"abstract":"This research paper addresses the escalating risk of fraud signatures in banking transactions. It introduces a Signature Forgery Detection System that utilizes offline verification and diverse geometric measures to discern genuine from forged signatures. With the prevalence of signature-based identity verification in financial transactions and the absence of foolproof systems, the proposed system aims to enhance the security of banking by efficiently detecting and preventing signature forgery.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"193 3","pages":"1756-1759"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459710
Mohana Hari Mohan, Muhammad Ehsan Rana
In an era where telehealthcare is becoming increasingly pivotal, this paper presents an extensive exploration of cloud computing as the key to unlocking its full potential. The research pivots around the unprecedented challenges and opportunities brought forth by the COVID-19 pandemic, showcasing cloud computing as a transformative force in telehealthcare. It meticulously dissects the critical issues of scalability, data security, and real-time analytics, offering robust solutions through cloud technology. This study extends beyond theoretical analysis, providing a detailed comparative assessment of leading cloud service providers such as Amazon Web Services, Microsoft Azure, and Google Cloud, and their instrumental roles in redefining healthcare delivery. Through a series of compelling case studies, the paper vividly illustrates the real-world impact of cloud computing in telehealthcare, underpinned by both quantitative and qualitative evaluations. Furthermore, it navigates the complex landscape of technical, economic, and user-centric considerations, culminating in strategic policy recommendations. This paper not only charts a new course in telehealthcare but also serves as a beacon for future research and implementation in the field, positioning cloud computing as the cornerstone of modern medical innovation.
{"title":"Revolutionizing Telehealthcare: Cloud Computing as the Catalyst for a New Medical Frontier","authors":"Mohana Hari Mohan, Muhammad Ehsan Rana","doi":"10.1109/ICETSIS61505.2024.10459710","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459710","url":null,"abstract":"In an era where telehealthcare is becoming increasingly pivotal, this paper presents an extensive exploration of cloud computing as the key to unlocking its full potential. The research pivots around the unprecedented challenges and opportunities brought forth by the COVID-19 pandemic, showcasing cloud computing as a transformative force in telehealthcare. It meticulously dissects the critical issues of scalability, data security, and real-time analytics, offering robust solutions through cloud technology. This study extends beyond theoretical analysis, providing a detailed comparative assessment of leading cloud service providers such as Amazon Web Services, Microsoft Azure, and Google Cloud, and their instrumental roles in redefining healthcare delivery. Through a series of compelling case studies, the paper vividly illustrates the real-world impact of cloud computing in telehealthcare, underpinned by both quantitative and qualitative evaluations. Furthermore, it navigates the complex landscape of technical, economic, and user-centric considerations, culminating in strategic policy recommendations. This paper not only charts a new course in telehealthcare but also serves as a beacon for future research and implementation in the field, positioning cloud computing as the cornerstone of modern medical innovation.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"104 5-6","pages":"1333-1341"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459615
A. Muttar, Ayda Isa Al Saadoon, M. Abdeldayem, S. Aldulaimi
This study aimed to examine the impact of digital skills of human resources, which was measured through (digital literacy, communication and cooperation, solving technical problems) on the job performance which measured in this study through (quality, efficiency, achievement) of the employees in the public schools in the Kingdom of Bahrain. The study further used the descriptive research method by using within the questionnaire instrument to collect the data which was formulated based on previous studies with a five-point Likert scale. The study analyzed the data and tested the research hypotheses by using the Statistical Packages for Social Sciences SPSS through some tests include one-way analysis of variance, regression analysis, reliability, and differences between groups. The results found a statistically significant impact of the digital skills of human resources in its dimensions (digital literacy, communication and cooperation, and solving technical problems) on the job performance in the Bahraini public schools. Also, the results revealed differences among the sample's perceptions about the digital skills for human resources and job performance due to the gender variable in favor of females, and a difference in the sample's perceptions due to the age, educational qualification and job title variable, while the years of experience variable came with no differences between the groups.
{"title":"Unleashing the Power of Digital Skills in Human Resources: Exploring the Relationship between Digital Transformation and Job Performance in the Government of Bahrain","authors":"A. Muttar, Ayda Isa Al Saadoon, M. Abdeldayem, S. Aldulaimi","doi":"10.1109/ICETSIS61505.2024.10459615","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459615","url":null,"abstract":"This study aimed to examine the impact of digital skills of human resources, which was measured through (digital literacy, communication and cooperation, solving technical problems) on the job performance which measured in this study through (quality, efficiency, achievement) of the employees in the public schools in the Kingdom of Bahrain. The study further used the descriptive research method by using within the questionnaire instrument to collect the data which was formulated based on previous studies with a five-point Likert scale. The study analyzed the data and tested the research hypotheses by using the Statistical Packages for Social Sciences SPSS through some tests include one-way analysis of variance, regression analysis, reliability, and differences between groups. The results found a statistically significant impact of the digital skills of human resources in its dimensions (digital literacy, communication and cooperation, and solving technical problems) on the job performance in the Bahraini public schools. Also, the results revealed differences among the sample's perceptions about the digital skills for human resources and job performance due to the gender variable in favor of females, and a difference in the sample's perceptions due to the age, educational qualification and job title variable, while the years of experience variable came with no differences between the groups.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"413 8","pages":"73-77"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459590
Priya Chanda, Sukanta Ghosh
Human capital is a paramount asset within any organization, evolving into distinct facets that fortify its competitive edge amid a perpetually shifting market landscape. Securing high-quality candidates necessitates minimizing human intervention and validating candidate credentials during recruitment. Moreover, gauging employee performance and anticipating attrition prove pivotal in effective human resource management. This study endeavors to introduce an innovative human resource management system employing machine learning and blockchain. The objective is to create an intelligent system that reduces human subjectivity and time in candidate selection while forecasting employee performance and attrition. Leveraging unsupervised learning algorithms and natural language processing, the system conducts skill assessment and resumes categorization after the extraction of raw data via object character recognition. Candidate validation relies on comparing blockchain-stored records. Supervised machine learning classification predicts employee performance and attrition with high precision, generating standardized scores based on multiple attributes aligned with specific e-competence frameworks, aiming to foster workplace productivity while minimizing financial losses.
{"title":"Optimizing Workforce Efficiency Using an Artificial Intelligence Approach: A Next-Gen HR Management System","authors":"Priya Chanda, Sukanta Ghosh","doi":"10.1109/ICETSIS61505.2024.10459590","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459590","url":null,"abstract":"Human capital is a paramount asset within any organization, evolving into distinct facets that fortify its competitive edge amid a perpetually shifting market landscape. Securing high-quality candidates necessitates minimizing human intervention and validating candidate credentials during recruitment. Moreover, gauging employee performance and anticipating attrition prove pivotal in effective human resource management. This study endeavors to introduce an innovative human resource management system employing machine learning and blockchain. The objective is to create an intelligent system that reduces human subjectivity and time in candidate selection while forecasting employee performance and attrition. Leveraging unsupervised learning algorithms and natural language processing, the system conducts skill assessment and resumes categorization after the extraction of raw data via object character recognition. Candidate validation relies on comparing blockchain-stored records. Supervised machine learning classification predicts employee performance and attrition with high precision, generating standardized scores based on multiple attributes aligned with specific e-competence frameworks, aiming to foster workplace productivity while minimizing financial losses.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"410 17","pages":"1416-1421"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459669
Meroua Sahraoui, Fouad Maliki, M. Bennekrouf
Production planning and control research has long emphasized scheduling strategies to address fluctuating demand and resource limitations. The Lot Sizing and Scheduling Problem (LSP) remains a significant challenge due to its complexity. In multi-line production systems, finding the right amount of each product to create each time is the difficult task of lot sizing. Getting lot sizing right is crucial because it directly affects both inventory levels and customer satisfaction. This work proposes a novel model for optimizing production planning in multi-line workshops, implemented using the CPLEX solver. The model aims to maximize gains by determining the optimal production batches.
{"title":"A Multi-Line Production System Lot Sizing Problem (Desing and Resolution)","authors":"Meroua Sahraoui, Fouad Maliki, M. Bennekrouf","doi":"10.1109/ICETSIS61505.2024.10459669","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459669","url":null,"abstract":"Production planning and control research has long emphasized scheduling strategies to address fluctuating demand and resource limitations. The Lot Sizing and Scheduling Problem (LSP) remains a significant challenge due to its complexity. In multi-line production systems, finding the right amount of each product to create each time is the difficult task of lot sizing. Getting lot sizing right is crucial because it directly affects both inventory levels and customer satisfaction. This work proposes a novel model for optimizing production planning in multi-line workshops, implemented using the CPLEX solver. The model aims to maximize gains by determining the optimal production batches.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"260 4","pages":"1317-1322"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1109/ICETSIS61505.2024.10459498
M. Alaghbari, A. Ateeq, Mohammed Alzoraiki, Marwan Milhem, B. Beshr
This research investigates the significant impact of technology on the transformation of Human Resource Management (HRM), with a specific emphasis on the modernization of conventional HR procedures. The integration of technology into Human Resource Management (HRM) is essential in the contemporary business landscape to augment organizational efficiency and effectiveness. This research provides a critical evaluation of the influence of digital breakthroughs, such as Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and cloud computing, on human resources (HR) operations. The use of these technologies is significantly influencing diverse human resources strategies, namely in the areas of recruiting, performance management, and employee engagement. This is achieved via the incorporation of predictive analytics and datadriven approaches, which facilitate informed decision-making processes. The study emphasizes the impact of these technology instruments on enhancing operational effectiveness and enabling HR practitioners to transition from administrative responsibilities to strategic positions in workforce planning, talent management, and organizational growth. This research explores the ramifications of technological advancements on workers and employers, specifically focusing on the difficulties and advantages associated with incorporating technology into human resource management. Through a comprehensive examination of contemporary scholarly literature and empirical investigations, this research tries to establish a connection between theoretical constructs and their tangible application. This resource is very beneficial for professionals in the field of human resources, as well as researchers and organizational leaders. It assists them in effectively navigating and managing the intricate challenges of modern human resource management within a technologically sophisticated environment.
{"title":"Integrating Technology in Human Resource Management: Innovations and Advancements for the Modern Workplace","authors":"M. Alaghbari, A. Ateeq, Mohammed Alzoraiki, Marwan Milhem, B. Beshr","doi":"10.1109/ICETSIS61505.2024.10459498","DOIUrl":"https://doi.org/10.1109/ICETSIS61505.2024.10459498","url":null,"abstract":"This research investigates the significant impact of technology on the transformation of Human Resource Management (HRM), with a specific emphasis on the modernization of conventional HR procedures. The integration of technology into Human Resource Management (HRM) is essential in the contemporary business landscape to augment organizational efficiency and effectiveness. This research provides a critical evaluation of the influence of digital breakthroughs, such as Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and cloud computing, on human resources (HR) operations. The use of these technologies is significantly influencing diverse human resources strategies, namely in the areas of recruiting, performance management, and employee engagement. This is achieved via the incorporation of predictive analytics and datadriven approaches, which facilitate informed decision-making processes. The study emphasizes the impact of these technology instruments on enhancing operational effectiveness and enabling HR practitioners to transition from administrative responsibilities to strategic positions in workforce planning, talent management, and organizational growth. This research explores the ramifications of technological advancements on workers and employers, specifically focusing on the difficulties and advantages associated with incorporating technology into human resource management. Through a comprehensive examination of contemporary scholarly literature and empirical investigations, this research tries to establish a connection between theoretical constructs and their tangible application. This resource is very beneficial for professionals in the field of human resources, as well as researchers and organizational leaders. It assists them in effectively navigating and managing the intricate challenges of modern human resource management within a technologically sophisticated environment.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":"260 2","pages":"307-311"},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：