فیلترهای جستجو:
فیلتری انتخاب نشده است.
نمایش ۱ تا ۲۰ مورد از کل ۲٬۹۱۰ مورد.
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This research delves into the complex relationship between dynamic managerial capabilities (DMCs) and innovation performance, examining the moderating effects of activity type, export level, and firm size. Employing rigorous ANOVA methodologies, the study elucidates the nuanced and conditional impacts of managerial actions on innovation outcomes. A significant aspect of this investigation is the classification of managers based on their specific DMC components, a novel contribution to the field that enhances our understanding of how different managerial competencies shape innovation. The results indicate substantial interactions between DMCs and the outlined contextual factors. Notably, Type 1 managers, distinguished by their superior managerial human capital, expansive social networks, and propensity for intuitive decision-making, demonstrate a robust positive effect on innovation across varied activities. In contrast, Type 9 managers, who possess limited managerial human capital yet maintain extensive social networks, display performance variability contingent on operational contexts. In export-centric firms, both Type 1 and Type 5 managers emerge as key drivers of innovation, adeptly maneuvering the complexities of international markets through their strategic acumen and flexibility. Moreover, the effectiveness of DMCs is significantly modulated by firm size, with micro and small enterprises deriving optimal benefits from a multifaceted managerial skill set, whereas larger corporations exhibit a greater reliance on established systemic processes. This research lays the groundwork for subsequent inquiries into the strategic deployment of DMCs in diverse organizational scenarios and offers critical insights for enhancing innovation-led development.
Assessing the Impact of Nanotechnology on the Well-being of Human Life(مقاله علمی وزارت علوم)
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Nanotechnology, at the intersection of science and engineering, holds immense promise for revolutionizing various aspects of our lives. This study employs the Partial Least Squares Structural Equation Modelling (SEM AMOS) algorithm to comprehensively analyze the effects of nanotechnology on the future living standards of society. We examine how nanotechnology innovations in fields such as medicine, energy, materials, and electronics are poised to reshape our quality of life. Through an extensive review of existing literature and empirical data, we provide insights into the potential benefits and challenges associated with the widespread adoption of nanotechnology. Our findings suggest that nanotechnology has the potential to significantly enhance future living standards by enabling advancements in healthcare, sustainable energy solutions, and cutting-edge materials. However, we also identify key considerations, including ethical and safety concerns that must be addressed to ensure the responsible development and utilization of nanotechnology. This research contributes to a better understanding of the multifaceted impacts of nanotechnology on society, offering valuable insights for policymakers, researchers, and stakeholders seeking to harness its transformative potential for the benefit of humanity.
Blended Learning in Higher Education: Key Challenges(مقاله علمی وزارت علوم)
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Blended learning is a pedagogical approach that combines face-to-face instruction with online activities and has garnered significant interest in recent years. Despite the potential benefits of blended learning, it faces several critical issues that may dampen stakeholders' enthusiasm within universities. Consequently, the current study explores the key challenges of blended learning in higher education institutions. The data for this research were collected through a qualitative approach, using a descriptive phenomenological method and semi-structured interviews with experts in the field. The findings were then analyzed using Colaizzi’s (1978) seven-step method. During our research, interviews were conducted with 14 experts in the field through purposeful sampling. From these comprehensive discussions, a total of 112 significant statements were identified. These statements were then meticulously categorized into ten initial themes, providing a structured overview of the data. Further analysis allowed us to distill the challenges associated with the blended learning approach in higher education into three critical factors. The validity of the qualitative data, based on the four criteria of Lincoln and Guba (1985)—credibility, transferability, dependability, and confirmability—was examined through re-coding by two independent coders and confirmed. The findings reveal that the challenges associated with implementing blended learning in higher education can be categorized into personal, organizational, and support-related factors. Each of these dimensions encompasses various critical elements. Notably, these research outcomes hold significant potential for facilitating the successful adoption of a pragmatic blended teaching and learning approach within higher education.
تاثیر مدیریت دانش بر عملکرد تجاری با تاکید بر نقش کیفیت اطلاعات حسابداری (مورد مطالعه: موسسات مالی پذیرفته شده در بازار سرمایه تهران)(مقاله علمی وزارت علوم)
منبع:
مدیریت دانش سازمانی سال ۷ بهار ۱۴۰۴ شماره ۲۸
53 - 77
حوزههای تخصصی:
هدف: امروزه عملکرد موسسات مالی برای بسیاری از سرمایه گذاران و استفاده کنندگان از اطلاعات مالی جهت اخذ تصمیمات، مهم می باشد. لذا ضرورت دارد، عملکرد موسسات مالی در بازار سرمایه مورد بررسی قرار گیرند. هدف از مطالعه حاضر، بررسی تاثیر مدیریت دانش بر عملکرد تجاری با تاکید بر نقش کیفیت اطلاعات حسابداری می باشد. روش پژوهش: جامعه آماری در این پژوهش، 19 موسسه مالی پذیرفته شده در بازار سرمایه تهران می باشند، که به صورت هدفمند انتخاب شدند. در واقع تعداد جامعه آماری با تعداد نمونه برای هر متغیر برابر با تعداد 19 عدد می باشد. پژوهش حاضر، از نظر هدف کاربردی، از بعد فرآیند، کمی، از نظر شیوه گردآوری و تحلیل اطلاعات، توصیفی- همبستگی و از بعد منطقی، استقرایی می باشد. این پژوهش از لحاظ روش گردآوری داده ها، به صورت کتابخانه ای و میدانی انجام شده است. در این پژوهش داده های موردنیاز برای آزمون فرضیات از پرسشنامه های استاندارد Al-Dmour et al(2023) و هم چنین صورت های مالی موسسات عضو جامعه و نمونه آماری استخراج گردید. و روایی پرسشنامه ها از طریق روایی همگرا (AVE) و روایی واگرا (ماتریس فورنل و لارکر) آزمون شدند، که مقادیر روایی همگرا برای مدیریت دانش (495/0) و کیفیت اطلاعات حسابداری (481/0) بالای 4/0 بوده و مورد تایید قرار گرفتند. و پایایی پرسشنامه ها از طریق (ضریب الفای کرونباخ و پایایی ترکیبی) آزمون شدند، که مقادیر آنها برای مدیریت دانش (863/0 و 889/0) و کیفیت اطلاعات حسابداری (744/0 و 764/0) بالای 7/0 بوده و مورد تایید قرار گرفتند. همچنین برای سنجش مدیریت دانش از سه شاخص کسب دانش، استفاده دانش و ادغام دانش، برای سنجش کیفیت اطلاعات حسابداری از چهار شاخص قابلیت مقایسه، مربوط بودن، قابل فهم بودن و ارائه منصفانه و برای سنجش عملکرد تجاری از دو شاخص نرخ بازده دارایی-ها و نرخ بازده حقوق صاحبان سهام استفاده شده است. تجزیه و تحلیل داده ها از روش های توصیفی و مدل سازی معادلات ساختاری روش حداقل مربعات جزئی با استفاده از نرم افزار SEM-PLS2023 و SPSS24 انجام شدند. یافته ها: نتایج پژوهش نشان دادند که بین مدیریت دانش و عملکرد تجاری با ضریب مسیر به میزان (741/0، 578/0) تاثیر مثبت و معناداری وجود دارد، و همچنین کیفیت اطلاعات حسابداری با ضریب مسیر به میزان (461/0، 594/0) تاثیر مثبت و معناداری بر رابطه بین مدیریت دانش و عملکرد تجاری دارد و رابطه بین آنها را تعدیل می کند. نتیجه گیری: با توجه به نتایج، مدیران بازاریابی به دلیل استفاده از انواع مختلف دانش و مدیریت آن سبب بهبود عملکرد موسسات مالی شده اند. همچنین مدیران مالی اطلاعات باکیفیتی را به شیوه ای منظم ارائه می دهند که این اطلاعات جهت اخذ تصمیمات مهم یاری داده و منجر به بهبود عملکرد مالی آن ها می شود. اصالت/ارزش: این تحقیق اولین پژوهشی است، که تاثیر متغیر کیفیت اطلاعات حسابداری به عنوان متغیر تعدیلگر بر رابطه بین مدیریت دانش و عملکرد موسسات مالی در بازار سرمایه تهران مورد سنجش قرار می دهد. همچنین این موضوع به عنوان یک دستاورد علمی می تواند اطلاعات سودمندی را در اختیار تحلیل گران مالی و استفاده کنندگان صورت های مالی قرار می دهد.
A Governance Framework for Digital Transformation in Banking: Unveiling Archetypes through Latent Class Analysis(مقاله علمی وزارت علوم)
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The banking industry is undergoing a significant transformation driven by digital technologies, evolving customer behaviors, and increasing regulatory pressures. To remain competitive, banks must adopt governance frameworks that integrate digital innovations to enhance operational efficiency and improve environmental, social, and governance (ESG) performance. This study identifies governance framework archetypes essential to digital transformation in banks through a comprehensive research methodology, including a literature review of digital governance frameworks, a comparative analysis of 11 leading digital banks worldwide, and latent class analysis to uncover key archetypes. Our findings reveal nine distinct governance archetypes, categorized into three dimensions: structural, including Centralized, Semi-centralized, and Open Innovation-oriented banks; dedicated processes, comprising Continuous Improvement, Vanguard, and Fast Follower banks; and relational mechanisms, featuring Self-empowering, Explorer, and Relationship-oriented banks. This classification advances the understanding of governance approaches that effectively support banks in their digital transformation journeys. The implications of these archetypes are substantial, offering a framework for banks to align their strategies with digital transformation initiatives. By adopting these governance structures, banks can better navigate the complexities of the digital landscape, foster innovation, and ultimately enhance their service offerings while addressing the evolving demands of customers. This research contributes to the growing body of knowledge on digital governance in banking and provides guidance for financial institutions striving to succeed in an increasingly digital world.
A Robust Deep Learning Framework: Ensemble of YOLOv8 and EfficientNet(مقاله علمی وزارت علوم)
منبع:
Journal of Information Technology Management , Volume ۱۷, Special Issue on SI: Intelligent Security and Management, ۲۰۲۵
32 - 44
حوزههای تخصصی:
This research work aims to present a robust deep learning framework by devising a deep learning-based ensemble method of YOLOv8 and EfficientNet. The suggested model is evaluated on the dataset collected from Kaggle, comprising 10,000 high-definition images of stems, leaves, and cut fruits of banana and papaya. These images are captured under different lighting conditions and thus expanded to 80,000 images. Authors have proposed an ensemble model comprising YoloV8 and EfficientNet as base deep learning models to enhance prediction and classification performance. Here, authors combine the merits of both models, i.e., speed of YoloV8 and the accuracy of EfficientNet, by putting a majority voting method in place. The final forecast is determined by majority voting, and EfficientNet is given higher significance in the situation of a tie owing to its enhanced accuracy. The proposed model presents a robust solution for agricultural disease management and demonstrates significant improvements in the detection of diseases in papaya and banana, opening avenues for its widespread employment in real life.
Knowledge Management Foundations and Their Mediating Effects on Innovation and Performance: A Case Study of a Vocational Higher Education Institution in Indonesia(مقاله علمی وزارت علوم)
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Knowledge Management (KM) is essential in various sectors, including vocational higher education. A literature review revealed that adopting KM can improve innovation and organizational performance in Higher Education Institutions (HEIs). However, some HEIs in developing countries have yet to realize the benefits of KM, and its implementation still needs improvement. The KM process is affected by several issues, including organizational barriers, knowledge hoarding, a lack of a knowledge-sharing culture, ineffective KM mechanisms, and resistance to technology. Nevertheless, only a few researchers have investigated the antecedents of KM, particularly in vocational higher education institutions. This study aims to evaluate the influence of KM foundation factors on KM processes, innovation, and organizational performance in a vocational HEI, specifically the School of Meteorology, Climatology, and Geophysics (STMKG) in Indonesia. It also examines the mediating effects of KM processes and innovation and provides recommendations for improving STMKG's KM foundation. An explanatory research approach was applied in this study, incorporating both qualitative and quantitative techniques. The results show that Organizational Structure (OS) and Technological Factors (IT) significantly influence KM processes. Innovation (IN) is also a significant mediator between KM processes and Organizational Performance (OP). The practical implication of this study is that it provides recommendations for crucial KM factors based on Importance-Performance Analysis (IPA) for policymakers to improve the foundation of KM at STMKG. Additionally, the study contributes to the academic field by providing insights for further research on KM development in vocational higher education institutions.
Intention to Adopt Next-Level Technology in Food and Beverage Manufacturing SMEs in Bangladesh: UTAUT Model and Business Continuity Theory(مقاله علمی وزارت علوم)
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The performance of SMEs will keep the wheel of the country’s economy turning. Upgrading the technologies currently used in SMEs will make the manufacturing process of food and beverage (F&B) industries more efficient. These firms are still using traditional equipment and machinery in their F&B manufacturing process; however, it is required to adopt the next level of technologies for maximum productivity, which they cannot afford. Thus, the study focuses on the intention to adopt upgraded technologies in F&B SMEs in developing countries. A total of 230 F&B SME owners and top management were surveyed in Bangladesh. The collected data were analyzed using PLS-SEM with SmartPLS software. The results showed that disaster preparedness and business continuity had significant effects on the intention to adopt; similarly, performance expectancy, effort expectancy, and social influence were also significant in the intention to adopt the next level of IR technologies. The moderating role of government support and policy had mixed effects on intention. The study is significant because disaster preparedness and business continuity plans are utilized in technology adoption and intention scenarios. To investigate this intention, the study adopted two perspectives: the influence and role of disasters and hazards on SMEs through disaster preparedness and business continuity plans, and secondly, the Unified Theory of Acceptance and Use of Technology (UTAUT) for measuring the intention. The study contributed to these two theories in a new context with constructs. The study will also contribute to policymakers in developing constructive policies and providing effective financial and non-financial support for F&B manufacturing SMEs.
Understanding Millennial Adoption of E-Recruitment Platform: A Technology Acceptance Model (TAM) Method(مقاله علمی وزارت علوم)
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This study aimed to analyze the factors that influenced millennial use of E-recruitment (online recruitment), through the Technology Acceptance Model (TAM). In this regard, TAM was extended by incorporating trust and privacy in the context of millennial technology adoption. A cross-sectional quantitative survey was carried out, utilizing purposive sampling of millennials who used the E-recruitment platform. The data were obtained from 270 respondents and analyzed with Partial-Least Squares Structural Equation Modeling (PLS-SEM) to explore the relationships between the factors. The results showed that perceived ease of use and trust had a significant influence on millennials’ intention to use E-recruitment services. Meanwhile, perceived usefulness and privacy did not have a significant influence. Intention to use E-recruitment services was a strong predictor of actual use. This study provided a practical understanding for human resource professionals and organizations that aim to improve E-recruitment strategies. It was emphasized that ease of use and building trust were important criteria used for promoting adoption among millennials. Future studies are recommended to use diverse samples and investigate the impact of technostress and cultural factors on E-recruitment adoption. In addition, it is necessary to evaluate employers’ perspectives, which can provide a more in-depth understanding of technology adoption in E-recruitment.
Generative AI-Driven Hyper Personalized Wearable Healthcare Devices: A New Paradigm for Adaptive Health Monitoring(مقاله علمی وزارت علوم)
منبع:
Journal of Information Technology Management , Volume ۱۷, Special Issue on SI: Intelligent Security and Management, ۲۰۲۵
130 - 154
حوزههای تخصصی:
This study aims to present a novel generative AI-driven system for hyper-personalized health monitoring. Dynamic data processing, predictive modeling, and flexible learning improve real-time health evaluations. By combining weighted feature aggregation, iterative least squares estimation, and selective feature extraction, the suggested strategy makes predictions that are more accurate while using less computer power. Abnormality detection methods like adaptive thresholding and Kalman filtering provide accurate health monitoring. Attention, gradient-based optimization, and sequence learning improve health trend forecasts as the model improves. Generative AI-driven wearables outperform conventional and AI-based alternatives in many key performance tests. These evaluations include prediction accuracy (94%), real-time monitoring efficiency (93%), adaptability (92%), data integration quality (95%), and system reaction time (90 ms). These devices are safer (96%), have longer battery life (32 hours), and are simpler, more comfortable, and scalable. The results suggest that creative AI can transform personal healthcare into something more adaptable, safe, and affordable. Generative AI-powered smart gadgets are the most sophisticated means to monitor health in real time and deliver individualized, data-driven medical treatment. Future research will concentrate on improving prediction models and developing AI-driven modification approaches to make them more effective in additional healthcare scenarios.
A Sustainable eHealth Program to Enhance the Healthcare Sector(مقاله علمی وزارت علوم)
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This study aims to assess the efficiency of the eHealth program by conducting a comparative study between public and private healthcare settings in Bangladesh and Malaysia. A detailed literature review gathered from leading databases, articles, and case studies was conducted to achieve the study’s objective. After evaluating the literature and utilizing reducing methods the factors were organized and categorized into the four SWOT groups. The study exhibits that the government of Bangladesh has taken multiple approaches to efficient usage of their resources and providing primary healthcare services at grassroots levels. However, compared to a developing country like Malaysia it is far lag and struggling with multiple challenges like inadequate ICT infrastructure interoperability issues, low adoption of services, privacy and trust issues inadequate data security lack of policies and regulations, and insufficient funding along with multiple threats like resistance to change, cultural influence, and native environmental issues. Nonetheless, these challenges can be addressed through the development of adequate infrastructure, technology, financial support, and human resource capabilities. This will help to achieve the goal of universal health coverage and digital healthcare. This paper identified and explored numerous eHealth concerns and institutionalized favorable policy recommendations to establish an effective and successful eHealth system and boost the healthcare sectors of Bangladesh and similar emerging economies. Being the first to incorporate SWOT analysis and eHealthcare programs, the suggested approaches can have a significant positive impact on the implementation of an effective and sustainable eHealth system.
Enhancing Generative AI Usage for Employees: Key Drivers and Barriers(مقاله علمی وزارت علوم)
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This study examines the use of AI tools within work environments, particularly Generative AI (Gen-AI). Its objective is to comprehend the factors affecting employees' adoption and utilization of such tools. The research applies the Technology, Organization, and Environment (TOE) framework to pinpoint potential factors and formulate hypotheses regarding their influence on employees' Gen-AI usage frequency. A quantitative research approach was conducted among a sample of 316 American employees. Results suggest that employees' perceived Gen-AI intelligence and warmth positively impact their usage through the mediation of performance expectancy. Effort expectancy only mediates the relationship between perceived Gen-AI intelligence and Gen-AI employee usage. Findings also show that the perceived severity of Gen-AI has a negative influence on employees’ usage and that an organization's absorptive capacity of Gen-AI does not influence employees’ usage. Critical drivers for Gen-AI utilization encompass technological proficiency, peer influence, and regulatory backing. These outcomes underscore the significance of nurturing a corporate culture that encourages innovation and adherence to regulations to successfully integrate Gen-AI in workplaces.
The Role of Internet Usage in Shaping Psychological Well-Being: A Comparative Study of Internet-Addicted and Non-Addicted Undergraduates(مقاله علمی وزارت علوم)
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This study explores the psychological impact of Internet addiction, comparing the functioning of Internet-addicted individuals to their non-addicted counterparts. A survey of 175 undergraduates from a Malaysian public university assessed their perceptions of 18 statements reflecting key facets of psychological well-being, including autonomy, environmental mastery, positive relationships, personal growth, purpose in life, and self-acceptance. Significant differences were observed in 14 out of 18 statements, with small to medium effect sizes, indicating meaningful disparities between the two groups. The findings suggest that Internet-addicted individuals are more likely to experience challenges in psychological well-being, often turning to the Internet as a coping mechanism for dissatisfaction or unhappiness. This study underscores the need for targeted interventions in the realm of information technology to promote healthier Internet use and address its psychological effects.
Optimizing HRM Practices and Decision-Making Quality through Big Data Quality Components(مقاله علمی وزارت علوم)
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This research aims to examine the impact of Big Data Quality (BDQ) components, including completeness, accuracy, format, and currency, on Big Data-driven Human Resources (BDHRP) management practices and Decision-Making Quality (DMQ) from the viewpoint of HR managers. It also seeks to identify the most impactful components among completeness, accuracy, format, and currency in the context of BDHRP and DMQ. A survey of HR professionals in 108 French organizations deploying Big Data Analytics systems revealed positive relationships between BDQ, BDHRP, and DMQ. Statistical analyses conducted with the Partial Least Squares Structural Equation Modeling (PLSEM) method showed a positive relationship between BDQ components and BDHRP, with currency and accuracy emerging as the most influential factors. Additionally, a strong positive relationship was found between BDQ components and DMQ, with currency and accuracy leading the way. The research also found a significant connection between BDHRP and DMQ, further underscoring the importance of effective HRM practices in enhancing decision-making quality. These findings contribute significantly to understanding the crucial role played by big data quality in BDHRP and decision-making, highlighting the potential for organizations to improve outcomes by focusing on currency and accuracy-related concerns. In practical terms, this research offers insights that can guide data quality practices, resource allocation, and strategic decision-making within organizations.
Reevaluating the DeLone and McLean Model for EHR Success and Knowledge-Sharing in a Saudi Public Medical Complex(مقاله علمی وزارت علوم)
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This paper investigates the success of the EHR system and its impacts on the knowledge-sharing process by healthcare professionals in a public medical complex in Saudi Arabia. It aims to reexamine and evaluate the usefulness of the updated DeLone & McLean IS success model considering IS in the healthcare context. The study is based on a quantitative methodology conducted at a public medical complex in Saudi Arabia. The data analysis was performed by combining descriptive and exploratory analysis. SPSS was used to test the constructs' validity and scale reliability. Additionally, using AMOS to test the model fit and examine the direct and indirect relationships among dependent and independent variables, structural equation modeling was performed. The results support the role of EHR use in enhancing knowledge-sharing practices within the medical care complex. The findings show that EHR users appreciate knowledge transfer and collaboration between medical staff. The findings suggest that the EHR’s characteristics of information quality, system quality, and service quality promote medical care knowledge-sharing through system satisfaction and use. This study helps medical staff and health decision-makers to understand the EHR benefits and the medical digital innovations that may contribute to improving the work conditions at care organizations. It shows that healthcare organizations can identify various benefits from the use of the EHR**, ** especially in terms of knowledge management and sharing to propose better medical services. The paper contributes to the existing empirical literature by demonstrating the confirmation of the D&M Model as a relevant instrument for IS success within the healthcare sector.
The Influence of Social Media on Public Health Protection against the COVID-19 Pandemic through Public Health Awareness and Changes in Behavior: An Integrated Model(مقاله علمی وزارت علوم)
حوزههای تخصصی:
In the case of COVID-19, researchers have found a statistically significant improvement in public health protection against COVID-19 with social media platforms. Multiple studies suggest that evidence of social media's influence on health knowledge, behaviors, and outcomes indicates its potential benefits in meeting the needs of individuals and populations. Therefore, this study aims to investigate the influence of social media on public health protections against COVID-19 in terms of public health awareness and behavioral changes. It adopted a quantitative research design to identify the relationship and used questionnaires to collect the data. Data was collected throughout Malaysia using convenience sampling. The results underscore the potential of social media as a valuable tool for disseminating crucial health information and promoting positive behavioral changes during public health crises like the COVID-19 pandemic.
Hybrid EEG-Based Eye State Classification Using LSTM, Neural Networks, and Multivariate Analysis(مقاله علمی وزارت علوم)
منبع:
Journal of Information Technology Management , Volume ۱۷, Special Issue on SI: Intelligent Security and Management, ۲۰۲۵
16 - 31
حوزههای تخصصی:
This paper focuses on a new hybrid machine learning model for classifying eye states from EEG signals by integrating traditional techniques with deep learning methods. Our Hybrid LSTM-KNN architecture employs KNN for classification and uses LSTM networks to extract features temporally. In addition, we perform extensive feature engineering, including statistical Z-test and IQR filtering, dimensionality reduction using PCA, and multivariate analysis to further model the performance. Moreover, an SVM-based unsupervised clustering approach is proposed to partition the EEG feature space, followed by ensemble learning in each cluster to improve accuracy and robustness. Using the EEG Eye State Dataset for the first assessment, the Hybrid LSTM-KNN model recorded an accuracy of 87.2% without PCA. Further improvements through statistical filtering outperformed initial expectations, achieving a 6% rise in performance to 89.1% after outlier removal, 89.1% with Z-test (σ = 3), and 88.3% with IQR (1.5x). After applying PCA along with ensemble learning post clustering, the final model exceeded expectations with an accuracy and F1 score of 96.8%, surpassing Ensemble Cluster-KNN and traditional models based on Ensemble Cluster-KNN, Logistic Regression, SVM, and Random Forest. The outcome demonstrates the robustness and noise-resilience of the model’s performance in practical real-time brain-computer interface and cognitive monitoring systems.
A Comparison of Prerequisite and Post-requisite Microlearning Approaches with Traditional Training for Developing Professional Competence in Human Resources(مقاله علمی وزارت علوم)
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The microlearning approach is increasingly adopted in organizational training because it delivers educational content in concise and easily digestible segments. This approach is widely viewed as both engaging and effective. However, empirical evidence regarding its effectiveness remains limited, particularly when microlearning is a prerequisite or a post-requisite to conventional training courses. To address this gap, the present study evaluated the effectiveness of prerequisite and post-requisite microlearning formats compared to traditional training methods in enhancing employees’ professional competencies. This study employed a quasi-experimental post-test design with a control group. The target population comprised employees of a state-owned bank in Tehran Province. From this population, 90 individuals were selected through convenience sampling and randomly assigned to three groups: a prerequisite microlearning group (experimental), a post-requisite microlearning group (experimental), and a traditional training group (control). All groups received a training course titled Problem-Solving in the Banking Industry. For the experimental groups, the course was delivered using a blended approach that combined microlearning with face-to-face instruction. In contrast, the control group received the training exclusively through face-to-face sessions. Data collection and analysis were conducted over three weeks using the Kirkpatrick Evaluation Model. The findings revealed that the experimental groups reported significantly higher levels of reaction (p = 0.017) and learning (p = 0.001) compared to the control group. However, no significant difference in behavioral change was observed among the groups (p = 0.115). These results suggest that while microlearning can enhance learner reactions and learning outcomes, it may not be sufficient to drive behavioral change in the workplace.
Exploring Overlooked Features of Online Touchpoints in Multitouch Attribution Models: A Qualitative Study(مقاله علمی وزارت علوم)
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The challenge of allocating marketing budgets across multiple online channels is a significant issue for practitioners and continues to be a compelling area of research within the academic community. Many practitioners attribute credit to touchpoints in analyzing online users’ journeys based on intuition or by comparing existing models. Touchpoints are the interaction moments between companies and customers. Marketers monitor all data related to touchpoints throughout the customer journey and attempt to assess the impact of each advertising channel. Understanding each touchpoint is crucial for making decisions about budget allocations and setting inventory prices. Numerous studies have been conducted to categorize and analyze touchpoints. However, a detailed and comprehensive study on this topic is lacking. In this study, nine semi-structured interviews were conducted with experts and academics in the field, leading to the identification of 35 distinct touchpoint features. The features were extracted using MAXQDA software and a thematic analysis methodology. These features have been organized into five main categories: Time (9 features), Technology (6 features), Marketing (7 features), Visits (7 features), and Events (6 features). Utilizing these features allows for detailed monitoring of online user behavior, and by integrating them into attribution models, it becomes possible to make accurate predictions about conversions.
The Effect of Time-related IS Project Names on Project Escalation(مقاله علمی وزارت علوم)
حوزههای تخصصی:
Many Information Systems (IS) projects experience serious problems that prevent them from meeting schedule, budget, and functionality targets. Managers often escalate their commitment to such projects, plowing ahead with them instead of hitting the pause button to address issues, a process known as project escalation. Due to the increasing pressure to develop systems and bring products to market faster, making decisions that involve time are more important than ever. While time has been studied in the IS domain, the effect of emphasizing time in a project’s name on IS project escalation decisions is not known. In this study, we explore whether a project name that explicitly refers to time can influence escalation decisions, and we examine the underlying mechanism through which that occurs. Sixty-two practitioners participated in a 2x1 factorial design experiment in which the project name was manipulated, but all other project information was identical. We theorize that emphasizing time in the project name can cause selective perception, drawing attention towards the schedule of the project and away from other aspects. Such selective perception can increase the likelihood of escalation of commitment to the schedule of the project when facing quality issues that require deviating from this schedule. We hypothesize that this effect of selective perception on escalation can be both direct and indirect. The results from the experiment support our research model.