107年科技管理學刊第二十三卷第三期 民國一○七年九月
Volume 23, Number 3 September 2018(若需下載全完請登入會員)
標題Article：服務創新保護模式探討-以統合觀點分析
Appropriability Modes in Service Sectors: Towards a Synthesis Approach
作者Aurthor：陳旻男、薄榮薇、張䕒心 Min-Nan Chen,Rung-Wei, Stacy Po,Chia-Hsin Chang
中文摘要Chinese Abstract：
本研究旨在探討台灣服務創新保護模式，採用服務創新理論中統合觀 點與公司具體性為理論基礎，同時考量正式化與非正式化創新保護機制， 以發展服務創新保護模式。本研究選擇第二次台灣產業創新活動調查 （2004-2006）中 1,769 服務業為實證對象，透過二階段集群分析方法，整 合階層與非階層統計方式，提出四項台灣服務創新保護模式：（1）無明顯 保護模式、（2）非正式化保護為基礎模式、（3）正式化保護為基礎模式、（4） 混合保護為基礎模式。本研究認為服務業選擇創新保護模式是基於公司屬 性與創新活動特性的不同，存在公司具體化差異，不同服務業中的企業可 能存在相似的創新行為，選擇相同的保護模式；最後本研究針對這四項創 新保護模式提出具體管理意涵與政策建議。
關鍵字：創新保護、統合觀點、公司具體性
English Abstract：
This study developed appropriablity modes in Taiwanese service firms. Derived
from the synthesis approach and firm-specificity to study service innovations,
we took both formal and informal appropribility forms into account. Based on
the dataset of 1,769 service firms from Taiwan Innovation Survey (2004-2006),
we conducted a two-step cluster analysis technique, combining the hierarchical
and non-hierarchical methods to characterize the appropriability modes. The four
distinct appropriability modes were identified as follows: (1) non-appropriability
mode, (2) informal-oriented mode, (3) formal-oriented mode, and (4) joint
use-oriented mode. The study revealed that appropriability modes in service
sectors differ from ones in manufacturing that characterize the firm-specific
features to conduct their protection patterns. Different service firms may conduct
the homogeneous appropriability modes even if they were categorized into the
different sectors. We concluded that the firm-specificity plays an impotanct role
in conducting appropriabilie modes and innovation protection patterns. Finally,
several management and policy implications for these appropriability modes
were provided.
Keywords: Appropriability, Synthesis approach, Firm-specificity
標題Article：創新服務設計運用於臺南老屋再造之研究
Applying the Service Innovation Design Theories in Reusing Old House at Tainan
作者Aurthor：劉哲宏、洪榮臨、劉馨玫 Che-Hung Liu,Rong Lin Horng,Sin-Mei Liu
中文摘要Chinese Abstract：
本研究以服務設計角度探討提供顧客價值之服務發展，並參考資策會 創新應用服務研究所發展之系統化服務研發方法－「服務體驗工程方法」 (Service Experience Engineering, S.E.E.)之服務塑模程序，透過系統化之研究 步驟進行服務設計流程，並運用服務設計工具及方法協助研究者將顧客行 為、體驗與需求結構化，萃取出發展創新服務之契機。並採單一個案質性 研究，探討老屋再造經營者，如何透過服務設計發覺核心價值並創造競爭 優勢。 透過適當的服務設計模型、方法與工具之輔助，本研究將服務設計過 程中的分析結果與想法具象化，考量服務機能發展完整度及服務缺失改善 效益，本研究針對產品服務模型中尚未滿足顧客需求的「點餐流程」及「出 餐流程」進行服務流程的討論與改良，並藉由服務藍圖找出服務潛在失效 點，並提出實務建議作為個案經營者服務決策之參考。
關鍵字：服務創新、服務設計、老屋再造、服務體驗工程方法
English Abstract：
This study discussed how to provide customer value based on the service design
perspective. This study referred to the service modeling of the Service
Experience Engineering that came from The Innovative DigiTech-Enabled
Applications & Services Institute. The systematic service processes of this
model offered tools and methods assisted researchers to identify customers’
behaviors and needs. Then we developed the research results into innovative and
value-added services. Applying study case to investigate how the owners of
reusing old house building competitive advantages through service design.
To conclude, this study had chosen the suitable models, methods and tools for
target case, which made the processes of service design concretely. Considering
the completion of service function and the efficiency, the process which not
meeting customers’ needs as ordering and serving those worked in progress of
service design. After that, the potential failures points of service were discovered
from the service blueprint and the practical advice had been put forward by this
study.
Keywords: Service Innovation, Service Design, Reusing Old House, Service Experience Engineering
標題Article：深度學習於智慧零售預測模型之研究: 以便利商店時效性商品為例
A Deep Learning Approach of Forecasting Model in Smart Retail ：A Case Study of Time-Critical Goods at Convenience Store
作者Aurthor：歐宗殷、洪志洋、林哲瑋 Tsung-Yin Ou,Chih-Young Hung,Che-Wei Lin
中文摘要Chinese Abstract：
近年來智慧零售的議題甚囂塵上，許多零售業者整合新興技術亟欲打造新型態的虛 實整合零售業，但無論應用或整合了那些資訊技術，其真正用意都是希望能打造一個以 大數據重新驅動消費者體驗的新零售模式，以先進科技和資料分析技術改變企業的營運 流程與內涵。在便利商店中，時效性商品的銷售佔比重日漸攀升，店鋪管理者雖可透過 POS (Point of Sales)系統得知歷史銷售數據和訂購建議進行下單採購，然而時效性商品 常因外在環境影響，使得每日的需求有所變動，若能提高商品銷售的預測準確度將可提 高營收並降低成本，本研究以便利商店時效性商品為研究對象，嘗試用新的方法來建構 更為精準的預測模型。 本研究整合多元數據集包括歷史銷售數據、店內促銷活動資訊、外部環境變數等資 料，採用深度學習方法建立便利商店時效性商品銷售預測模式，以個案公司過去兩年於 12 家分店的資料，每家分店各 54 項的銷售數據作為建立預測模式的資料，結果顯示， 使用隨機森林進行特徵變數篩選，以 CNN 與 RNN 建立銷售預測模型，並採用 MAE 和 MSE 指標進行誤差評比，準確度明顯優於 ARIMA、MLF、RBF、SVM 之預測結果， 顯見深度學習對於預測時效性商品的適用能力確實較為優異。
關鍵字：智慧零售、便利商店、時效性商品、隨機森林、深度學習
English Abstract：
In recent years, smart retailing has become a hot topic in convenience store
industry. Many retailers are integrating in emerging technologies to create a new
type of retail business. The intention of those applications or integrations by
information technology is to create a new retail model with big data by customer
experience. Advanced technology and data analysis technology are adapted to
this kind of new retail model to change the operation and process in convenience
stores. In convenience stores industry, the percentage of Time-Critical Goods is
growing up. Managers can place orders through historical sales data and
ordering advice by POS system currently. However, the demand of Time-Critical
Goods is impacted due to external environment changing. It will increase profits
and decrease unnecessary expenses if the accuracy of forecasting of the sales be
improved. The purpose of this study is to use advanced methods to construct
more accurate forecasting model for sales of Time-Critical Goods in
convenience stores.
Mmultivariate datasets including historical sales data, in-store promotion,
external environmental variables etc., are integrated in this research. Research
team use deep learning methods to establish forecasting model of sales of
Time-Critical Goods in convenient store. The data comes from 12 branch stores
of case company and each 54 items of sales data is used for models establishing.
After using (i) random forests for feature variable screening and (ii) sales
forecasting model by CNN and RNN, and (iii) MAE and MSE for error analysis,
the accuracy of forecasting is better than the sales forecast model constructed by
ARIMA、MLF、RBF and SVM methods. The result showed that deep learning
methodology is suitable for forecasting of sales of Time-Critical Goods in
convenient store.
Keywords: Smart Retail、Convenience Store、Time-Critical Goods、Random Forest、Deep Learning