大數據產業化之服務模式創新與成功關鍵因素探討

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Title:	大數據產業化之服務模式創新與成功關鍵因素探討
Other Titles:	Exploring the Service Model Innovation and Critical Success Factors in Big Data Industrialization
Authors:	張繡方 CHANG, HSIU-FANG
Contributors:	資訊管理學系陳宗義 CHEN, TSUNG-YI
Keywords:	客製化服務;交易市集;虛擬團隊;大數據;層級分析法 customized services;trading markets;virtual teams;Big data;analytic hierarchy process
Date:	2021
Issue Date:	2022-08-18 13:57:09 (UTC+8)
Abstract:	隨著資訊科技日益進步，企業對於大數據分析與資料的需求更勝已往。本研究主要探討大數據產業化之服務模式創新與影響運作成功的關鍵因素。首先，透過文獻的整理、歸納及釐清可能影響成功之因素，再與實際服務於相關產業之專家進行深度訪談。再針對大數據產業鏈及創新服務模式的影響關鍵因素，以問卷訪談的方式，向資訊專業人士進行意見彙整，透過AHP層級分析法分析後得知，在大數據產業化之服務模式創新部份，在本研究所提出之三種模式中，如何提供有資料而無人力及技術之企業，進行客製化大數據分析之媒合服務為多數專家認為之最佳模式，而這其中又以提供客製化服務者其本身之專業能力及相關經驗在層級權重中顯示最為重要。另在影響成功關鍵因素中，主要探討則以能否在大數據的引用過程中保有個人及企業的隱私及安全最為大家重視，尤其在資料如何去識別化，更是首重之考量因素。　　With the advancement of information technology, companies' demand for big data analysis and data has increased. This study mainly explored key factors for the success of service model innovation and operations in big data industrialization. First, this study compiled and summarized the literature, identified potential factors associated with such success, and conducted in-depth interviews with industry experts from related sectors. Focusing on the key factors affecting the big data industry chain and innovative service models, this study collected opinions from information professionals through a questionnaire survey. According to an analytic hierarchy process, the optimal innovative service model determined by the experts, among the three models proposed by this study, was to provide customized matchmaking services based on big data analysis to companies that have no human resources or technologies to play to the data they own. Professional competencies and relevant experience of the customized service providers were the most critical factors according to the hierarchical weight. Regarding the key factors associated with success, the experts attached the greatest importance to the maintenance of personal and business privacy and security during the use of big data, with data de-identification being of utmost concern.
Appears in Collections:	[Department of Information Management] Disserations and Theses

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