DSpace collection: 會議論文

Split Delivery Vehicle Routing Problem for Transportation – A Case Study

Wed, 25 Mar 2026 04:08:40 GMT

title: Split Delivery Vehicle Routing Problem for Transportation – A Case Study abstract: The Split Delivery Vehicle Routing Problem (SDVRP) is a modification of the classical Vehicle Routing Problem (VRP), which aims to establish optimal routes for a vehicle’s group serving customers with multiple delivery splits. In this study, we develop an exact solution MILP model for solving the SDVRP with a specific case study of a company operating a sugar factory in Bien Hoa, Dong Nai, Vietnam. This study contributes a MILP SDVRP model to help the company to construct a delivery system with the optimal delivery routes for their vehicle fleet to minimize the total traveled distance. The computational results prove that our proposed MILP is superior in solving a large number of customer problems and produces optimal solutions which is much better than the current system of the company. Moreover, the proposed MILP also outperforms an existing method in a publication for solving the same problem. The numerical results emphasize the contribution of our proposed MILP in both practical and academic aspects.

Big Data Analytics-Driven Supply Chain Traceability in the Coffee Industry: A Study in Vietnam

Wed, 25 Mar 2026 04:08:37 GMT

title: Big Data Analytics-Driven Supply Chain Traceability in the Coffee Industry: A Study in Vietnam abstract: The Vietnamese coffee industry has experienced significant growth, but its supply chain has struggled to achieve sustainability. Although some companies recognize the importance of sustainability, profitability remains their priority in business practices. Meanwhile, customers now focus on coffee selection for health reasons, which creates high demand for product traceability. This has led to a dilemma in the coffee industry since the current supply chain management system faces severe issues such as product counterfeiting, inadequate traceability, delays, and poor real-time information sharing. However, new technologies like Big Data Analytics (BDA) can help address these challenges by providing crucial features such as decentralization and transparency to share mass data for all supply chain stakeholders and trace the sustainable coffee supply. The study explores the drivers of e-traceability adoption through environmental, economic, social, and technological dimensions (Volume, Velocity, Variety, and Veracity). A multi-criteria decision-making framework was proposed, utilizing the best–worst method to calculate the weights of these criteria and the fuzzy TOPSIS method to rank the significant technology. The study identified social and technological factors as the most significant in adopting e-traceability supply chain. Sensitivity analysis was used to verify the research framework's validity and remove bias effects.

Hierarchical flexible project scheduling under multi- mode uncertain resources and durations

Wed, 25 Mar 2026 04:08:34 GMT

title: Hierarchical flexible project scheduling under multi- mode uncertain resources and durations abstract: In this chapter, a hierarchical flexible project scheduling (HFPS) algorithm is proposed to deal with the multi-objective stochastic multi-mode resource-constrained project scheduling (MO-SMRCPSP) problem. Besides utilizing the time-based robust measure (TRM) to cover the variation in activity durations, the HFPS method also takes into account multiple modes and both nonrenewable and renewable resources. The NSGA-II is used to generate the set of candidate project schedules that aim to minimize makespan and maximize TRM. To choose the best project schedule for implementation, the TOPSIS method evaluates these Pareto front solutions. The proposed HFPS approach is tested using the standard datasets from the "Project Scheduling Problems Library" (PSPLIB) to compare with exact solutions. The obtained project schedule performed within approximately 10% of the optimal solution.

Developing an Optimization Model for a Green Dual-Channel Closed-Loop Supply Chain Network

Wed, 25 Mar 2026 04:08:30 GMT

title: Developing an Optimization Model for a Green Dual-Channel Closed-Loop Supply Chain Network abstract: In response to the growing adoption of circular economy (CE) principles and heightened environmental concerns, businesses are increasingly integrating green practices into supply chain management to achieve both economic and environmental benefits. Despite this shift, a significant research gap remains in the optimization of green dual-channel closed-loop supply chain (G-DCCLSC) networks, especially when considering multi-product, multi-period scenarios with uncertain demand. This study addresses this gap by formulating a multi-objective optimization model aimed at reducing total costs and environmental impacts, specifically CO2 and particulate matter (PM) emissions. The research employs a multi-objective mixed integer linear programming (MO-MILP) model in conjunction with the non-dominated sorting genetic algorithm II (NSGA-II) to meet these dual objectives. Results indicate that the proposed model significantly enhances the balance between economic and environmental objectives compared to existing models that typically focus on single-product, single-period scenarios. The solutions derived offer a range of Pareto-optimal choices, effectively illustrating the trade-offs between costs and emissions. The study underscores the necessity of incorporating both forward and reverse logistics in supply chain design to achieve true sustainability. Through a comprehensive case study of an electrics business in Vietnam, the practical applicability of the model is demonstrated, providing robust insights for optimizing G-DCCLSC networks under realistic conditions. This study adds to the subject of sustainable supply chain management by offering an improved optimization framework that incorporates economic and environmental objectives.

Identifying the tasks leveraging sustainable product development project performance

Wed, 25 Mar 2026 04:08:27 GMT

title: Identifying the tasks leveraging sustainable product development project performance

Assessing the Importance of Indicators Related to Agriculture and Water Resources for Drought Prevention

Wed, 25 Mar 2026 04:08:18 GMT

title: Assessing the Importance of Indicators Related to Agriculture and Water Resources for Drought Prevention

Integration of Machine Learning Algorithms with Data Envelopment Analysis for Evaluating and Enhancing ESG Efficiency in Taiwan: A Case Study of Semiconductor Industry

Wed, 25 Mar 2026 04:08:14 GMT

title: Integration of Machine Learning Algorithms with Data Envelopment Analysis for Evaluating and Enhancing ESG Efficiency in Taiwan: A Case Study of Semiconductor Industry

Exclusive and inclusive talent management: Preferred fit with organization transformation strategy.

Thu, 19 Mar 2026 04:08:02 GMT

title: Exclusive and inclusive talent management: Preferred fit with organization transformation strategy. abstract: The fit between talent management and organizational strategy can contribute to organizational performance. This fit represents an important theoretical basis for talent management theory but also lacks empirical research. Two different talent management views exist that each brings different talent management practices: the exclusive talent system (ETS) and the inclusive talent system (ITS). Using Taiwan’s industrial transformation directions of servitization, greenization, and globalization, we apply the mediation model to explore the talent management system–ETS or ITS– that fits with organizational transformation. By investigating a manufacturing sector sample (n=164) and using structural equation modeling, the current study found that the servitization and globalization strategies are a fit with ETS, and greenization is a fit with ITS. This study contributes to the talent management theory by providing an empirical result. The fit model provided in this study can be used as a reference for enterprises’ policy formulation efforts.

Resilience developed as a result of bricolage: The prism of the social information processing theory

Thu, 19 Mar 2026 04:07:58 GMT

title: Resilience developed as a result of bricolage: The prism of the social information processing theory abstract: This study introduces the emerging concept of bricolage into the domain of organizational behavior and examines it in relation to the important workplace competence of psychological resilience. Most research on psychological resilience has emphasized promoting resilience via positive resources and traits. This study draws on the social information processing (SIP) theory to suggest that resource constraints may also encourage employees to engage in bricolage at work, thereby building psychological resilience and producing the effects of negative emotional control at work. Moreover, power distance in the team is considered key social information in the organization to investigate the moderating relationship between this cultural variable and psychological resilience. Data were collected from an aggregate of 63 teams of registered professional nurses, with approximately 323 valid questionnaires. The results indicated that performing bricolage at work under resource constraints helped the employees build their psychological resilience and further control negative emotions. Resultantly, the power distance culture in the team positively moderated the relationship between bricolage and psychological resilience. Therefore, the theoretical model of this study confirms that through the lens of the SIP theory, resource limitation may also represent a way to promote the generation of resilience.

Artificial Intelligence (AI) and Vocational Attitudes: The Role of AI-Related Event Strength in Career Optimism and Adaptability Using a Mixed-Methods Approach

Thu, 19 Mar 2026 04:07:55 GMT

title: Artificial Intelligence (AI) and Vocational Attitudes: The Role of AI-Related Event Strength in Career Optimism and Adaptability Using a Mixed-Methods Approach

運用鷹架理論於遊戲式課程學習

Tue, 17 Mar 2026 04:08:34 GMT

title: 運用鷹架理論於遊戲式課程學習

育兒階段夫妻工作家庭壓力下的危機與轉機

Thu, 23 Oct 2025 04:07:00 GMT

title: 育兒階段夫妻工作家庭壓力下的危機與轉機

工作家庭雙介面之要求、資源與衝突感受之性別差異

Thu, 23 Oct 2025 04:06:56 GMT

title: 工作家庭雙介面之要求、資源與衝突感受之性別差異 abstract: 本研究目的為探討工作要求、家庭要求、工作資源、與家庭資源等前因變項與職家衝突關連的性別差異。樣本為台灣264名全職工作者，資料分析主要以分群階層迴歸檢驗兩性於職家衝突（包括工作－家庭衝突、家庭－工作衝突）前因之差異。首先，T檢定顯示職家衝突感受均無性別差異。再者，工作負荷與家庭責任是男性與女性職家衝突的顯著預測因子。第三，組織家庭支持文化與主管理念性支持對於男性的雙向職家衝突有顯著預測力，但這兩項工作資源中，僅有「主管理念性支持」對女性工作－家庭衝突有顯著的預測力。第四，「來自配偶的家事協助」是男性工作－家庭衝突的顯著預測因子；然而，對女性來說，「來自父母的家事協助」才可有效降低工作－家庭衝突。是故，組織需了解兩性在職家衝突歷程中的差異，方能協助員工找出最佳因應方式，在職家兩者間取得最終的平衡。

家庭工作衝突歷程中自我效能的調節作用

Thu, 23 Oct 2025 04:06:53 GMT

title: 家庭工作衝突歷程中自我效能的調節作用 abstract: 職家衝突（work and family conflict）在國內外已累積了相當豐富的成果，本研究以過往較少探究的「家庭對工作衝突」（family-to-work conflict, FWC）為焦點，並納入工作與家庭場域的前因變項與後果變項，探討完整的「壓力源－FWC－壓力後果」反應歷程。此外，亦探討員工個人自我效能在其中所扮演的調節作用角色。本研究縱貫兩個時間點，以全台全職工作者為研究對象，總計回收279份有效問卷，採用階層迴歸（hierarchical regression）來驗證假設。研究結果顯示，工作負荷與家庭衝突為FWC的顯著預測因子；家庭滿意度則為FWC重要的後果變項。在調節作用方面，自我效能會影響家庭衝突與FWC、以及FWC與家庭滿意度之間的關聯。本研究亦探討管理意涵與未來研究建議。

What differentiates the succeeded from the strained: the moderating effects of self-efficacy.

Thu, 23 Oct 2025 04:06:49 GMT

title: What differentiates the succeeded from the strained: the moderating effects of self-efficacy.

Differential predictors of work-family conflict for Taiwanese male and female employees.

Thu, 23 Oct 2025 04:06:46 GMT

title: Differential predictors of work-family conflict for Taiwanese male and female employees.

The moderating effects of self-views in the relationships between leadership styles and subordinates’ work consequences.

Thu, 23 Oct 2025 04:06:42 GMT

title: The moderating effects of self-views in the relationships between leadership styles and subordinates’ work consequences. abstract: The aim of this research was to explore the relations among supervisors’ transformational leadership, transactional leadership, and subordinates’ work outcomes (including job satisfaction, organizational commitment, organizational citizenship behavior, and job performance). Using structured questionnaires, a diverse sample of 784 full-time employees drawn from a variety of organizations in Taiwan and mainland China was surveyed. Analyses revealed that transformational leadership was positively related to all four outcome variables in our study; whereas transactional leadership was positively related to job satisfaction and organizational commitment. More importantly, we found that the social-oriented self view enhanced the positive effect of transformational leadership on job performance but mitigated the positive effect of transformational leadership on job satisfaction. On the other hand, the individual-oriented self view mitigated the positive effect of transactional leadership on organizational citizen behavior. It is thus recommended that employee’s self views may be important contingent factors of leadership effectiveness.

Optimizing a sustainable production-inventory model by integrating low-carbon material selection and progressive carbon tax policies

Thu, 19 Jun 2025 04:05:38 GMT

title: Optimizing a sustainable production-inventory model by integrating low-carbon material selection and progressive carbon tax policies abstract: Amid the escalating challenges of climate change, governments and enterprises worldwide are intensifying efforts to achieve net-zero emissions. Among various carbon reduction strategies, progressive carbon tax policies have emerged as a key mechanism for driving sustainable industrial transformation. However, how these policies influence production and inventory decisions within supply chains remains an important research question. Therefore, this study develops a sustainable multi-stage supply chain production-inventory model that explicitly incorporates progressive carbon tax policies, low-carbon raw material selection, and carbon reduction technology investment. The proposed model considers a supply chain structure consisting of a single manufacturer and a retailer, capturing the dynamic interactions between production, inventory management, and carbon emission costs. Through mathematical modeling and numerical simulations, we analyze the effects of progressive carbon tax schemes on optimal decision-making and system performance. Further, sensitivity analysis is conducted to evaluate the impact of key parameters, such as tax rates, emission reduction investments, and material selection, on supply chain efficiency and total costs. By integrating progressive carbon tax policies into supply chain management, our research fills a critical gap in the literature and provides practical insights for both policymakers and enterprises with high carbon emissions. We expect the findings can help businesses develop cost-effective sustainability strategies while assisting governments in designing policies that balance economic growth with environmental responsibility.

影響大學生心理健康狀態類型之要素

Thu, 20 Mar 2025 04:07:30 GMT

title: 影響大學生心理健康狀態類型之要素

大學生壓力哪爆棚？！全國大專學生壓力調查研究初探

Thu, 20 Mar 2025 04:07:27 GMT

title: 大學生壓力哪爆棚？！全國大專學生壓力調查研究初探

理想與現實的差距：淡水新篇章教師社群與新創課程計畫前導行動的看見與反思

Thu, 20 Mar 2025 04:07:21 GMT

title: 理想與現實的差距：淡水新篇章教師社群與新創課程計畫前導行動的看見與反思

淡水新篇章：淡江大學議題導向跨領域敘事力教師團隊和興仁國小相遇的反思

Thu, 20 Mar 2025 04:07:14 GMT

title: 淡水新篇章：淡江大學議題導向跨領域敘事力教師團隊和興仁國小相遇的反思

生成式人工智慧於問題解決之應用：文獻回顧

Thu, 20 Mar 2025 04:07:11 GMT

title: 生成式人工智慧於問題解決之應用：文獻回顧

Evaluating Risk Factors Affecting Employee Overload in Healthcare Institutions Using Machine Learning Models: Predictions Based on Health Screening Indicators

Thu, 20 Mar 2025 04:07:08 GMT

title: Evaluating Risk Factors Affecting Employee Overload in Healthcare Institutions Using Machine Learning Models: Predictions Based on Health Screening Indicators abstract: Taiwan’s National Health Insurance (NHI) system only utilizes 6-7% of the nation's GDP, yet it provides high healthcare quality and highly affordable services. However, healthcare staff are under considerable pressure, further exacerbating the strained workforce. This study applies six machine learning algorithms to explore important risk factors related to the excessive workload (overload) of healthcare institution staff, highlighting the significance of understanding these factors. Workload overload refers to the physiological state caused by long-term high stress, a serious 21st-century health issue. In Taiwan, the standard for recognizing overwork is based on workload during the six months and before the onset of illness, as outlined by the "Guidelines for Preventing Diseases Triggered by Abnormal Workload" from the Occupational Safety and Health Administration (OSHA) of the Ministry of Labor, Taiwan. These guidelines serve as a reference for defining working hours and overwork for both employers and employees. The Ministry of Labor mandates annual health checkups for workers across various industries in Taiwan, which includes an assessment of employee overload using the "Employee Overwork Assessment Scale.".

Developing Explainable Feature Selection Scheme using Machine Learning and SHAP for Multi-Step Ahead Patient Queue Length Prediction in Outpatient Phlebotomy Units

Thu, 20 Mar 2025 04:07:05 GMT

title: Developing Explainable Feature Selection Scheme using Machine Learning and SHAP for Multi-Step Ahead Patient Queue Length Prediction in Outpatient Phlebotomy Units abstract: In large hospitals, outpatient phlebotomy units (OPUs) often face overcrowding during peak hours due to limited resources, leading to longer patient queues and compromised care quality. Factors such as service capacity, patient age, and wheelchair usage can significantly impact queue length. Accurately predicting queue lengths and identifying key influencing factors is essential for effective resource management. Machine learning (ML) methods are commonly used in clinical settings for their ability to handle complex feature interactions. However, ML methods provide limited explanations of how a key factors influencing the predictive outcome. These detailed insights could help managers to be more informed when planning resources allocations. Moreover, sufficient time is required to respond to and mobilize resources when managing a real-case scenario. Proactively forecasting future queue lengths can provide information to support managers in reacting effectively during real-time service operations. It is a challenging and complex task for managers to manage OPUs as wide variety of aspects are needed to be considered. To address the challenges, this study develops a feature selection scheme that incorporates SHapley Additive exPlanations (SHAP) to gain more detailed feature insights and direct strategy of multi-step ahead forecasting to predict future queue length at different time steps in OPUs. Using OPU data from a Taiwanese medical center (2017–2019) and three well-known ML methods of random forest (RF), least absolute shrinkage and selection operator regression (Lasso) and extreme gradient boosting (XGB) under the proposed scheme, RF emerged as the most accurate model across all horizons. Wheelchair usage was consistently the most influential feature, while elder patients became critical in three-step ahead forecasting. SHAP provided detailed insights into how these features affect queue length, supporting better resource planning and operational decision-making.

Dual-Channel Supply Chain Inventory Optimization Using Teaching-Learning-Based Algorithm for Carbon Efficiency

Thu, 20 Mar 2025 04:07:01 GMT

title: Dual-Channel Supply Chain Inventory Optimization Using Teaching-Learning-Based Algorithm for Carbon Efficiency abstract: The impact of global climate change and shifting consumption patterns has made managing multinational supply chain inventory crucial, especially in light of net-zero carbon emission goals. The adoption of dual-channel marketing models, combining online and physical channels, adds complexity to supply chain management. A key challenge for enterprises is balancing environmental sustainability with profitability, while facing global pressure to reduce carbon footprints. In dual-channel supply chains, the profits of manufacturers and retailers offering substitutable products are interdependent, further complicating inventory management and efforts to optimize profit alongside meeting carbon reduction targets. This study proposes sustainable production-inventory models for multinational supply chains with dual channels and multiple physical retailers, incorporating collaboration on carbon reduction investments among supply chain members. The model calculates the total profit and carbon emissions of manufacturers and retailers separately, and then optimizes selling prices, material supply, production, delivery, investment strategies, and replenishment strategies to maximize overall supply chain profit under a carbon cap-and-trade policy. Due to the complexity introduced by multiple physical retailers, traditional mixed-integer nonlinear programming models become difficult to solve as the number of retailers increases. Therefore, the study employs the Teaching-Learning-Based Optimization (TLBO) algorithm to find optimal solutions effectively. Numerical and sensitivity analyses validate and illustrate the proposed models, providing insights for managers to optimize production, shipping, ordering, investing, and pricing strategies across channels while responding to national carbon reduction policies. This research offers a comprehensive framework for balancing sustainability and profitability in modern supply chain management.

Predictive Modeling for Patient Queue Length in Blood Collection Centers during Peak Hours using Multi-step-ahead Forecasting and Machine Learning Models

Thu, 08 Aug 2024 04:05:46 GMT

title: Predictive Modeling for Patient Queue Length in Blood Collection Centers during Peak Hours using Multi-step-ahead Forecasting and Machine Learning Models abstract: Blood collection centers in hospitals experience congestion during peak hours, leading to long waiting times for patients. This study investigates the application of machine learning to predict patient queue lengths in blood collection centers, aiming to minimize wait times and improve patient satisfaction. Existing literature explores various approaches to address congestion, including call systems, quality improvement initiatives, and phlebotomy assistant systems. However, these methods primarily focus on improving service efficiency, neglecting the challenge of predicting patient arrival patterns. Traditional queue length forecasting methods like simple moving average (SMA) have limitations. This study proposes a multi-step forecasting approach using machine learning techniques to predict patient queue lengths during peak times. The research employs two frameworks, Direct and Hybrid, incorporating six machine learning algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Lasso Multiple Linear Regression (LaMLR), Multivariate Adaptive Regression Splines (MARS), Light Gradient Boosting Machine (LightGBM), and CatBoost. The study utilizes data from a medical center in Taiwan, covering a period of three years. Empirical results demonstrate that the Random Forest technique with the Direct framework achieves the most accurate predictions for one to four time steps ahead. For four-step-ahead forecasting, CatBoost with the Hybrid framework proves most effective. These findings suggest that machine learning offers a promising approach for predicting patient queue lengths in blood collection centers. This information can be valuable for staff scheduling, resource allocation, and implementing early congestion mitigation strategies, ultimately enhancing patient experience and healthcare service quality.

遊戲式學習於個人知識管理之應用

Wed, 20 Mar 2024 04:05:38 GMT

title: 遊戲式學習於個人知識管理之應用

「淡水新篇章」課程的實踐與反思:八位老師與學生在通識課程裡的相遇與共學

Tue, 19 Mar 2024 04:05:30 GMT

title: 「淡水新篇章」課程的實踐與反思:八位老師與學生在通識課程裡的相遇與共學

Applying particle swarm optimization algorithm to a multi-retailer supply chain inventory problem

Tue, 30 Jan 2024 04:05:29 GMT

title: Applying particle swarm optimization algorithm to a multi-retailer supply chain inventory problem abstract: With the growing emphasis on environmental, social, and governance (ESG), the concept of green supply chain inventory management has become crucial for sustainable business operations. Taking into account environmental impact and resource efficiency for the purpose of reducing carbon emissions, the integration of green policies into inventory and production decisions has evolved into a significant issue within current supply chain management. Especially different supply chain members are subjected to varying carbon reduction policies for multinational supply chains. Therefore, this study aims to examine the multi-stage production inventory problem within a multinational supply chain involving a single manufacturer and multiple retailers from different countries under a combination of carbon reduction policies where the manufacturer faces a carbon cap-and-trade policy while all the retailers are subjected to a carbon tax policy. First, the total profits and carbon emissions functions for both the manufacturer and retailers are separately established in three stages: material supply, finished product production and delivery, and order and sales. Subsequently, the optimal material supply, finished product production, delivery, and replenishment strategies for each supply chain member are determined to maximize the integrated total profit of the supply chain system. Next, the corresponding problem has been formulated as a nonlinear mixed integer optimization problem and solved by a particle swarm optimization algorithm. Sensitivity analysis to variation of the solver and parameter/parameter combination is further illustrated using several numerical example analyses. The main finding reveals that within a multinational supply chain system involving multi-retailers, an appreciation in the currency of the individual retailers' respective countries leads to decreases in their optimal order quantity and the manufacturer's optimal material purchase quantity. Furthermore, sensitivity analysis of retailer-related parameter combinations shows that the manufacturer's number of shipments is significantly influenced by changes in ordering cost, unit wholesale price, and exchange rate. Additionally, increased fixed cost parameters result in higher total carbon emissions, while heightened variable cost parameters lead to a reduction in total carbon emissions. Finally, the pursuit of net-zero carbon emissions is a crucial and publicly stated goal for enterprises, supply chains, and even governments. When it becomes necessary to make investments in carbon reduction, priority can be given to reducing the carbon emissions generated by the procurement, manufacturing, and delivery of finished products to achieve maximum benefits. In summary, the contributions of this study are well-positioned to provide valuable guidance to enterprises or supply chain decision-makers, especially those operating within a multinational framework. Its goal is to effectively balance carbon reduction and profitability within the context of global trends in carbon emission reduction. We anticipate that the findings will guide the enterprise or the supply chain toward sustainable development, aligning with the global trend of carbon emissions reduction.

基於粒子群演算法探討考慮碳排政策組合的多零售商跨國供應鏈生產存貨問題之研究

Wed, 13 Dec 2023 04:06:01 GMT

title: 基於粒子群演算法探討考慮碳排政策組合的多零售商跨國供應鏈生產存貨問題之研究

基於碳稅政策下最適維護與存貨模式之探討

Wed, 13 Dec 2023 04:05:57 GMT

title: 基於碳稅政策下最適維護與存貨模式之探討

網約車技術於自動即時派單系統

Fri, 28 Apr 2023 10:25:25 GMT

title: 網約車技術於自動即時派單系統

生產部門大數據分析及可視化看板建置之研究-以T公司為例

Fri, 28 Apr 2023 10:25:23 GMT

title: 生產部門大數據分析及可視化看板建置之研究-以T公司為例

基於機器學習與變數集成之整合式架構於具MetS之第三期CKD患者之預測與風險因子評估

Fri, 28 Apr 2023 10:25:20 GMT

title: 基於機器學習與變數集成之整合式架構於具MetS之第三期CKD患者之預測與風險因子評估 abstract: 代謝症候群(Metabolic syndrome, MetS)和慢性腎臟病(chronic kidney disease, CKD)的共病會導致許多疾病與併發症的發生。對於 CKD 而言，除了 Mets與常見的生理指標等風險因子外，人們的生活習慣也是需考慮的重要風險因子。機器學習技術已被廣泛用於找出重要的風險因子，若只使用單一技術建構分析模式與評估重要風險因子，可能使分析結果較無有效性與穩定性。集成技術相較於單一結果可以提升分析結果的穩健性，因此本研究將在考慮人口統計變數、血液檢查指標、人體量測指標與生活型態等相關風險因子下，基於六種機器學習與五種變數集成規則建構一個有效的整合式預測架構於具 MetS 之第三期 CKD 患者之預測與風險因子評估。實證結果顯示，集成規則都能夠獲得有效的辨別出較重要的風險因子，並且提供風險因子的變數重要性的排序資訊，提供具有參考價值的資訊於具Mets 的第三期 CKD 患者的風險評估。

台灣公部門與私部門資訊安全策略背後重要驅動力

Fri, 28 Apr 2023 10:25:17 GMT

title: 台灣公部門與私部門資訊安全策略背後重要驅動力

高績效員工特徵與行為對心理韌性之影響－以員工協助方案為調節變項

Fri, 28 Apr 2023 10:25:13 GMT

title: 高績效員工特徵與行為對心理韌性之影響－以員工協助方案為調節變項 abstract: 新冠肺炎 (COVID-19)的爆發，如給了企業毫無預警的突襲檢查，讓早已做好準備的組織擁有自我審視的機會，而對於毫無防備的組織帶來嚴厲的警告。然而，疫情帶來的影響，短時間內不會息止。組織中的關鍵人才是否皆已具備後疫情時代的新能力，能否全身心的專注於眼前工作當中，這些最為基本的人資議題，對組織影響甚大，領導者應當重視並且隨時關注。故本研究欲探討高績效員工特徵與行為、員工協助方案及心理韌性間之關聯性，並以全台灣之在職工作者為研究對象，發放 360 份問卷，有效問卷 310 份。研究結果發現，高績效員工特徵與行為會正向顯著影響心理韌性，員工協助方案在高績效員工特徵與行為對心理韌性之影響上具有正向顯著調節效果。綜言之，對身處後疫情時代的組織來說，該如何引入員工協助資源，培育和幫助高績效人才，以韌性戰勝環境帶給組織的打擊，為降低人才的流失率的重要關鍵。

The Interaction of National Culture and Organizational Culture on Cross-Strait Employee Work Performance with the Moderator of P-O Fit

Fri, 28 Apr 2023 10:25:10 GMT

title: The Interaction of National Culture and Organizational Culture on Cross-Strait Employee Work Performance with the Moderator of P-O Fit abstract: Following continual disputes in Cross-Strait relations in the past 60 years, questions regarding the development of businesses in Taiwan and China remain unanswered: How do national and organizational cultures interact in these businesses? How do such interactions affect employee behavior, such as work performance? Do the differences in culture complement or undermine the businesses? Or do national and organizational cultures not interact with each other? If we categorize employees according to their backgrounds—such as the social background of their place of birth—how will such interactions be affected by the interaction among four entities, namely Taiwanese residents, Chinese residents, Taiwanese companies, and Chinese companies? Person–organization fit is a variable that provides critical insights because it varies across employees and potentially moderates the interacting relationship between national and organizational cultures. Accordingly, this study adopted person–organization fit as a research variable to expediate the comprehensive observation and understanding of factors affecting work performance. Employees in Taiwanese and Chinese companies no matter are located in Taiwan and China were recruited through purposive sampling as the research participants, to whom a questionnaire survey was administered to explore the aforementioned questions. Statistical analyses on valid responses from the participants revealed significant differences between several dimensions of national and organizational cultures but also high correlations between other dimensions. Examining the four entities in pairs also revealed significant differences with regard to cultural issues and to their effects on work performance. Regarding the relationship between culture and work performance, team culture and adhocracy culture significantly and positively affected task performance. Hierarchy culture significantly and negatively affected task performance. Adhocracy culture exerted a significant and positive effect on contextual performance. In terms of person–organization fit, supplementary fit significantly and positively affected task performance; person–organization fit partially moderated the relationships of national and organizational cultures with work performance. Finally, managerial implications and recommendations for future research were proposed according to the study findings.

An Evaluation Model for Service Value Creation of Pre-need Contract

Fri, 28 Apr 2023 10:25:06 GMT

title: An Evaluation Model for Service Value Creation of Pre-need Contract abstract: The aim of this study is to construct an evaluation model of pre-need contract service value creation in Taiwan, and to analyze the model and weights of the criteria. A “decision criteria for pre-need contract service value creation questionnaire” was developed based on literature review and induction of main decision factors and criterion evaluation factors in the selection of contracts. 15 experts in the funeral service were enrolled, and the data was analyzed by fuzzy analytic hierarchical procedure (FAHP). Construction of a hierarchical structure and evaluation model according to the ranking of five dimensions and 21 criteria obtained. In this study for managers in the funeral enterprises to formulate the pre-need contract more relevant to customer needs. At the same time, it provides customers with an understanding of the features of pre-need contract to create better customer value.

壓力心態對組織工作績效之影響-以情緒勞務為中介角色

Thu, 02 Jun 2022 04:15:52 GMT

title: 壓力心態對組織工作績效之影響-以情緒勞務為中介角色