DSpace community: 企業管理研究所

心理韌性、工作不安全感、職涯決策自我效能與職涯適應力關係之研究;心理韌性、工作不安全感、職涯決策自我效能與職涯適應力關係之研究

Tue, 20 Oct 2026 07:33:32 GMT

title: 心理韌性、工作不安全感、職涯決策自我效能與職涯適應力關係之研究;心理韌性、工作不安全感、職涯決策自我效能與職涯適應力關係之研究 abstract: 近年來受到 AI 發展、數位轉型、組織重整與就業型態改變等因素影響，工作者面臨的職涯不確定性逐漸提高，進而引發潛在的工作不安全感，並影響其職涯適應能力。本研究旨在探討員工的心理韌性、工作不安全感、職涯決策自我效能與職涯適應力之間的關係，以階層迴歸分析進行假設驗證。此外，透過獨立樣本t檢定與單因子變異數分析ANOVA進行補充分析，以探索不同世代與產業別之差異。研究結果顯示，心理韌性對工作不安全感具有顯著負向關係，顯示心理韌性強者較能降低面對工作不穩定時所產生的焦慮與不安。同時，心理韌性對職涯決策自我效能與職涯適應力皆具有顯著正向關係，顯示心理韌性有助於提升個體的職涯決策信心與面對職涯變動的適應能力。此外，工作不安全感對職涯適應力具有顯著負向關係，而職涯決策自我效能則對職涯適應力具有顯著正向關係，代表威脅知覺會削弱個體的職涯適應能力，而決策信心則能促進其職涯適應表現。依據研究結果，本研究提出管理意涵與未來研究之建議。;Driven by AI and digital trends， rapidly changing work environments elevate employees’ job insecurity and hinder their career adaptability. This study examines the relationships between psychological resilience， job insecurity， career decision-making self-efficacy， and career adaptability. Data were collected from 286 individuals with work experience. Hierarchical regression was used for hypothesis testing， while t-tests and ANOVA were conducted as supplementary analyses to explore generational and industry differences. It is found that resilience is negatively related to employees’ job insecurity and positively related to career decision-making self-efficacy as well as career adaptability. Additionally， Gen Z demonstrates significantly lower level of resilience compare to the older cohorts. Drawn on the findings， managerial implications and suggestions for future research are thus discussed.

中壢產業園區標竿企業多元公平共融的基準分析：生技醫療業與食品製造業為例;A Benchmarking Analysis of Diversity， Equity， and Inclusion (DEI) among Leading Enterprises in Zhongli Industrial Park: A Case Study of the Biotechnology， Healthcare， and Food Manufacturing Industries

Tue, 20 Oct 2026 07:32:47 GMT

title: 中壢產業園區標竿企業多元公平共融的基準分析：生技醫療業與食品製造業為例;A Benchmarking Analysis of Diversity， Equity， and Inclusion (DEI) among Leading Enterprises in Zhongli Industrial Park: A Case Study of the Biotechnology， Healthcare， and Food Manufacturing Industries abstract: 多元、公平與共融已從國際倡議內化為台灣企業永續揭露之常規。綜觀既有 DEI 文獻，多數聚焦單一構面或單一產業，鮮少以一套結構完整、分級明確之國際基準，對不同產業之標竿企業進行系統性且可相互比較的實地評分，形成本研究之研究缺口。基於此，本研究之目的有二：其一，運用 GDEIB 評估中壢產業園區生技醫療業標竿企業 DEI；其二，以同一基準評估食品製造業標竿企業之 DEI 現況。研究方法上，本研究以 Global Diversity， Equity & Inclusion Benchmark 為評估架構。選定中壢產業園區生技醫療業與食品製造業各五家標竿企業，樣本員工規模自 138 人至 5，400 人，橫跨新創興櫃公司、中型民生製造業與大型上市集團及跨國集團在台子公司。資料來源為 2024 至 2026 年間所發布之公開次級資料，包含永續報告書、年報、官方網站、招募專頁及媒體報導，將 GDEIB 基準操作化為 0 至 4 分之符合程度量表，自等級二起依得分率逐級判定，最終以完全滿足之基礎等級加計本級得分率計算各類別分數。案例分析發現，兩產業呈現截然不同之分布型態。生技醫療業呈現一枝獨秀之集中化格局。相對地，食品製造業呈現齊頭並進之扁平化格局，五家企業平均分數介於 1.54 至 1.73 分之間，尚未出現具明顯標竿意義之領先者，且各企業強弱項互補。惟兩產業共同呈現外部延伸不足之瓶頸，外部群組於兩產業均為得分最低或偏低之群組，多數企業徘徊於 1.5 分附近，顯示將 DEI 承諾由組織內部延伸至供應鏈、社區與顧客，係台灣企業不分產業別於現階段共同面臨之挑戰。;Diversity， Equity， and Inclusion has evolved from an international initiative into a standard component of corporate sustainability disclosure in Taiwan. A review of the existing DEI literature reveals that most studies focus on a single dimension or a single industry， with few employing a structurally complete， tiered international benchmark to conduct systematic and comparable field-based assessments of benchmark firms across different industries. This gap motivates the present study， which has two objectives: first， to apply the Global Diversity， Equity & Inclusion Benchmark to assess the DEI practices of benchmark firms in the biotech and medical industry within the Zhongli Industrial Park; and second， to apply the same benchmark to assess the DEI status of benchmark firms in the food manufacturing industry. Methodologically， this study adopts the GDEIB as its evaluation framework. Five benchmark firms each from the biotech/medical and food manufacturing industries in the Zhongli Industrial Park were selected， with employee sizes ranging from 138 to 5，400， spanning emerging listed startups， mid-sized consumer-goods manufacturers， and Taiwan-based subsidiaries of large listed conglomerates and multinational corporations. Data were drawn from publicly available secondary sources published between 2024 and 2026， including sustainability reports， annual reports， official websites， recruitment pages， and media coverage. The GDEIB was operationalized into a 0-to-4 scale of compliance， with scores from Level 2 onward determined by achievement rate; the final score for each category was calculated as the fully satisfied base level plus the achievement rate within the current level. The case analysis reveals markedly different distribution patterns between the two industries. The biotech/medical industry exhibits a concentrated pattern led by a single standout firm. In contrast， the food manufacturing industry displays a flat， evenly matched pattern， with the five firms′ average scores ranging narrowly between 1.54 and 1.73， no firm yet emerging as a clear benchmark leader， and firms exhibiting complementary strengths and weaknesses across categories. Nevertheless， both industries share a common bottleneck of "insufficient external extension": the External group consistently ranks among the lowest- or relatively low-scoring categories in both industries， with most firms hovering around a score of 1.5. This finding suggests that extending DEI commitments from internal organizational practices to the supply chain， community， and customers remains a shared challenge for Taiwanese enterprises at the current stage， irrespective of industry.

歐盟《人工智慧法案》風險分類與義務規範之研究與個案分析;歐盟《人工智慧法案》風險分類與義務規範之研究與個案分析

Tue, 20 Oct 2026 07:32:34 GMT

title: 歐盟《人工智慧法案》風險分類與義務規範之研究與個案分析;歐盟《人工智慧法案》風險分類與義務規範之研究與個案分析 abstract: 近年來，人工智慧（Artificial Intelligence）技術快速發展，其衍生之潛在風險促使各國加速推動監理法制化。其中，歐盟《人工智慧法案》（EU AI Act）作為全球最具代表性的人工智慧監管框架，具備高度法規複雜性與細緻的義務架構。然而，企業在實務導入與遵循過程中，常因法規條文艱澀、適用條件連動盤根錯節，導致難以直觀地解讀法律要求。官方雖提供初步之合規檢核工具，難以讓企業綜觀整體法規全貌，亦不便於內部合規之交叉比對。為解決上述實務挑戰，本研究系統性分析歐盟《人工智慧法案》之規範架構與條文邏輯，並歸納一套具備實務操作性的人工智慧義務合規查核工具。該工具之判斷機制以三大核心模組為基礎：首先，透過「使用者角色判定（Your Role）」釐清組織於人工智慧供應鏈中的法定身分及其可能產生之動態角色轉換；其次，藉由「風險等級評估（Risk Level Assessment）」依據人工智慧系統之用途與應用情境進行風險分類；最後，根據前述判定結果，歸納並呈現企業應遵循之「合規義務（Your Obligations）」。考量企業面臨跨國法規遵循之需求，本研究亦進一步將歐盟《人工智慧法案》與臺灣《人工智慧基本法》及相關公部門治理指引進行比較分析，以建立兼具國際與本土視角之合規參考架構。最後，本研究將所歸納之合規判斷機制應用於實際企業個案進行實證分析，以驗證其可行性與實務效益。期望透過本研究協助企業更有效率地掌握國際與本土人工智慧治理規範，降低重複性合規評估成本，並強化負責任人工智慧治理之落實。;In recent years， the rapid advancement of Artificial Intelligence (AI) technology and its potential risks have prompted countries to accelerate the development of supervisory systems and regulatory frameworks. Among these， the EU Artificial Intelligence Act (EU AI Act) stands out as one of the world′s most representative AI regulatory frameworks， characterized by its high legal complexity and detailed structure of obligations. However， during practical implementation and compliance， enterprises often struggle to intuitively interpret legal requirements due to the dense nature of the regulations and the intricate， interdependent conditions of their applicability. While official compliance checker tools are available， they are insufficient for enterprises to grasp the full regulatory picture and are inconvenient for internal compliance cross-referencing. To address these practical challenges， this study systematically analyzes the regulatory structure and logical provisions of the EU AI Act and develops a practical AI compliance assessment tool. The assessment mechanism is built upon three core modules: first， "Your Role，" which clarifies an organization’s legal status within the AI supply chain and potential dynamic role transitions; second， "Risk Level Assessment，" which classifies risks based on the purpose and application scenarios of the AI system; and finally， "Your Obligations，" which synthesizes and presents the specific compliance duties that an enterprise must adhere to based on the preceding assessments. Furthermore， considering the need for cross-border regulatory compliance， this study conducts a comparative analysis between the EU AI Act and Taiwan’s draft Artificial Intelligence Basic Act， along with relevant public sector governance guidelines， to establish a compliance framework with both international and local perspectives. Finally， this research applies the constructed compliance assessment mechanism to practical corporate case studies to verify its feasibility and practical benefits. It is expected that this research will assist enterprises in more efficiently mastering both international and domestic AI governance norms， reducing the costs of redundant compliance assessments， and strengthening the implementation of responsible AI governance.

電子零組件與電機機械產業的多元、公平、共融評比：以中壢產業園區的企業為例;電子零組件與電機機械產業的多元、公平、共融評比：以中壢產業園區的企業為例

Tue, 20 Oct 2026 07:32:18 GMT

title: 電子零組件與電機機械產業的多元、公平、共融評比：以中壢產業園區的企業為例;電子零組件與電機機械產業的多元、公平、共融評比：以中壢產業園區的企業為例 abstract: 「人」是電子零組件業與電機機械業之核心。二產業雖以技術與資本密集著稱，其產出終究倚賴性別、國籍、年齡與職位各異之勞動者。在高工時且人力高度多元之職場中，DEI能否內化為企業文化而非僅止於書面政策，決定多元人力能否獲得公平對待與發展，並攸關產業之長期競爭力。惟既有台灣電子業之DEI研究，多以總體資料衡量薪資差距或以上市公司資料檢驗董事會多元，，少以以國標準化化準化對別別企業行逐項、、可跨企業比較之評比。有鑑於此，本研究以2024 GDEIB之四大群組、十五、類別與275、準化，針對中壢產業園區電子零組件業與電機機械業代表性企業之公開揭露資訊，依符合程度行逐五等級成熟度評定與跨產業比較。研究發現，企業之DEI成熟度普遍落於等級一與等級二之間，跨越等級三者僅屬單點突破。二產業呈現趨同而非分歧之樣態：均可分為領先、穩健與起步三層次，並共同呈現內重外輕、有願景而以結構，，且就已揭露董事行修內容之企業而言，，領導層欠缺DEI專屬培訓。企業對高工時、移工公平、職場健康與責任採購等核心勞動議題之回應，，多止於規遵循之之動動層次企企業間落差較之資源條件，更可由經營者承諾與制度化程度解釋。據此提出分階段改善建議，並建立依國標準化項企業評定之DEI成熟度紀錄，填補文獻缺口。關;People are the core of the electronic components and electrical machinery industries. Though both are technology- and capital-intensive， their output ultimately rests on workers differing in gender， nationality， age， and rank. In workplaces marked by long working hours and a highly diverse workforce， whether DEI (Diversity， Equity， and Inclusion) is embedded in corporate culture rather than confined to written policy determines whether such a workforce is treated fairly and allowed to develop， and bears directly on the industries′ long-term competitiveness. Yet existing DEI research on Taiwan′s electronics industry has largely used aggregate data to measure wage gaps or listed-company data to examine board diversity， and few studies have applied a standardized international benchmark to assess individual firms item by item in a cross-firm comparable manner. This study therefore employs the 2024 GDEIB (Global Diversity， Equity & Inclusion Benchmarks)， comprising four groups， fifteen categories， and 275 benchmarks， to assess the publicly disclosed information of representative firms from the electronic components and electrical machinery industries in the Zhongli Industrial Park， assigning a five-level maturity rating by compliance percentage and conducting cross-industry comparison. The findings show that firms′ DEI maturity generally falls between Levels 1 and 2， with breakthroughs to Level 3 appearing only as isolated strengths. The two industries prove convergent rather than divergent: firms in both fall into leading， steady， and emerging tiers， and both display internal-heavy but external-light action， a vision-rich but structure-poor profile， and among firms disclosing their directors′ training content， an absence of dedicated DEI training for leadership. Their responses to core labor issues， namely long working hours， fair treatment of migrant workers， occupational health and safety， and responsible sourcing， remain largely passive and compliance-oriented. The gaps between firms are better explained by managerial commitment and institutionalization than by resource conditions. On this basis， the study offers phased recommendations and contributes an internationally benchmarked， firm-by-firm record of DEI maturity that fills a gap in the existing literature.