Measuring marketing effectiveness is critical for marketers as the pharmaceutical industry is under great pressure for cost control. Pharmaceutical marketers need to optimally allocate these resources and ensure that they achieve the highest possible return on investment for the firm. Pharmaceutical manufacturers utilize a variety of marketing vehicles to promote their products to physicians and consumers. At the physician level, effects of detailing are typically identified to be positive. Direct- to-consumer advertising does impact the choice probability, but the impact of promotions aimed directly at physicians is significantly higher. Measuring value of marketing activities is important for a company to achieve a profit margin and best allocate its resources. To define and deliver quantitative measurements that justify how investment in specific marketing programs are paying off, marketers need metrics to show that their programs work. Then, selected metrics should be meaningful and related to financial performance. There are a few metrics regularly used by marketers such as brand awareness, market share, consumer attitudes toward brand, purchase intention, return on investment, lifetime value of an activity, and brand equity. The paper uses a case study to review and evaluate the effectiveness of a marketing plan for a new launch product. Specifically, return on investment (ROI) for patient programs and lifetime value of activity (LVA) for physician education programs were calculated in the case study. A company is able to increase sales profit by reallocating resources to activities with higher ROI and LVA. To conclude, marketers need to identify meaningful metrics, set up a tracking process, and regularly follow up all relevant marketing activities. The process of measuring marketing effectiveness through the tracking process will help companies to understand how the marketing activities work and whether these programs deliver profitable value growth. The follow up action to fine-tune budget plans can then optimize return of marketing investment and maximize profitability.

2017年英國《經濟學人》雜誌曾提出，「世界上最寶貴的資源不再是石油，而是數據」。隨著物聯網時代來臨，工業應用領域也開始整合各種技術而掀起新一波工業革命。因為大量自動化及數據化，除了升級自動化設備、整合網通系統，監控設備產生的大數據，透過工業電腦進行分析，經由人工智能判斷邏輯產生條件，再由設備自主處理各種生產問題。除去大量勞動，專注於大數據自動化處理，即能生產更優質的產品，並且優化流程，降低企業成本。 自動化流程機器人(Robotic Process Automation)能自動的管理並執行企業大量耗費時間與人力的業務流程，可用於客戶服務、人力管理、供應鏈管理、採購、會計等範疇。物聯網(IoT)時代下的機器人自動化流程加入了認知運算等新興技術，更能進一步提升企業效率並降低成本。自動化流程機器人(Robotic Process Automation)儼然成下一個新的生產力革命。 市場研究機構IDC預測，2017年全球在認知和人工智慧系統支出將達到125億美元，和2016年相比成長達59.3%。Google母公司Alphabet公開測試無人駕駛汽車、阿里宣佈投資千億成立達摩院、百度機器人入駐肯德基等等。人工智慧(Artificial Intelligence)將顛覆商業思維、改寫商業模式。在2020年，人工智慧(Artificial Intelligence)將成為市場上真正的「主流」技術思維。IDC並且認為亞洲將在2020年成為全球第二大認知與人工智慧輸出區域。 本文探討自動化流程機器人與人工智慧之間的關聯，以及流程優化後對企業所產生的影響與變革．並且針對個案的自動化解決方案所達到的效益與後續發展進行評估與檢討，藉以提升自動化解決方案，協助企業在未來挑戰的競爭環境中創造最佳化優勢． / “The Economist” stated in 2017 that “the world’s most precious resource is no longer oil but data”. With the advent of the Internet of Things, industrial applications have begun to integrate various technologies and set off a new wave of industrial revolution. Because of a large amount of automation and data, in addition to upgrading automation soluitons, integrating netcom systems, and monitoring the big data generated by the solutions, analysis is performed through industrial computers, and conditions are generated through the logic judgment of artificial intelligence, and then the solutions autonomously handles various processes. It can produce better products, optimize the process and reduce business costs to focus on automation of big data and to save a lot of labor hiring. Robotic Process Automation can automate the management and execution of a large number of business processes that consume time and manpower, and can be used in areas such as customer service, manpower management, supply chain management, procurement, finance and accounting. The robotic automation process in the Internet of Things (IoT) era has added emerging technologies such as cognitive computing to further enhance the efficiency of enterprises and to reduce costs. Robotic Process Automation becomes the next new productivity revolution. In 2017, marketing research firm, IDC, predicts that global spendings on cognitive and artificial intelligence systems will reach US$12.5 billion, which represents a growth of 59.3% compared to 2016. Google, the parent company of Alphabet, publicly tests driverless cars, Ali announced that it has invested 100 billion to establish Daruma House, Baidu Robots has settled in Kentucky. Artificial Intelligence will disrupt business thinking and rewrite business models. In 2020, Artificial Intelligence will become the real "mainstream" technical thinking in the market. IDC also believes that Asia will become the world’s second largest cognitive and artificial intelligence output region in 2020. The article discusses the relationships between robotic process automation and artificial intelligence, and also the impact and changes after implementing the solutions. It has also evaluated and reviewed the effectiveness and following development of the automated solutions, so as to enhance the values of automation solutions and to help companies create optimal advantages in the future challenging and competitive environment.

本文提出一個新的混合整數線性規劃模型建立投資組合。這個模型所採用的風險函數為最大損失的絕對值，而不是一般常用的損失變異數。在給定的報酬水準下，模型尋找在觀測期間中最小的最大損失的投資組合，即為大中取小的原則。模型也同時考慮實務上常遇見之情況，如：交易成本、最小交易單位、固定交易費用比率、資產總類數等限制。因此，模型內需使用整數變數及二元變數，導致模型的計算求解過程變得比不含整數變數及二元變數的模型困難許多。我們以固定整數變數的啟發式演算法增進求解的效率，並以台灣股票市場的資料做為實證計算的對象。 / A new mixed integer linear program (MILP) for selecting portfolio based on historical return is proposed. This model uses the downside risk rather than the variance as a risk measure. The portfolio is chosen that minimizes the maximum downside risk over all past observation periods to reach a given return level. That is a mini-max principle. The model incorporates the practical characteristics such as transaction costs, minimum transaction units, fixed proportional transaction rates, and cardinality constraint. For this reason a set of integer variables and binary variables are introduced. The introduction, however, increases the computational complexity in model solution. Due to the difficulty of the MILP problem, a heuristic algorithm has been developed for the solution. The computational results are presented by applying the model to the Taiwan stock market.

醫療技術的進步以及生活品質的提升，預計人類平均壽命將持續延長，以臺灣為例，男、女性平均壽命將從2011年的75.98歲、82.65歲，增加到2060年的82.0歲、88.0歲（資料來源：行政院經濟建設委員會2012年推估）。壽命延長意謂更長的退休生活，世界各國在21世紀均面對需求日殷的老年生活照顧，包括退休金制度以及老人醫療等，這些社會福利及保險勢必增加國家財務負擔，因此壽命是否繼續延長或存有極限成為大家關心的議題。近年來，不少研究透過死亡壓縮(Mortality Compression)連結壽命議題，亦即探討死亡年齡是否將集中至更窄的範圍，但因為資料及研究方法的限制，死亡壓縮是否成立仍無定論。 本研究以統計方法、分配假設、資料品質，三個面向來探討死亡壓縮與延壽之間的關係。本研究提出三種數值優化方法：加權最小平方法(Weighted Least Squares；WLS)、非線性極值法(Nonlinear-Maximization；NM)及最大概似估計法(Maximal Likelihood Estimation；MLE)，透過電腦模擬衡量方法優劣，與過去常見的方法比較（Kannisto的SD(M+)），探討何者具有較小的均方誤差(Mean Squared Error；MSE)。其次若死亡年齡之真實死亡分配為t分配時，探討以常態假設代入計算所產生的偏誤；最後則是套入各國實際死亡資料，使用上述較佳的估計方法，檢視死亡壓縮是否存在。 研究結果顯示，NM具有不偏性質且具有較小的均方誤差，過去研究常用的SD(M+)反而有明顯偏誤，且隨著觀察值越多變異數反而增加。而若真實死亡分配若為t分配時，以原先利用常態假設所計算的年金險保費皆有低估的情形，分配的重要性可見一斑，進而探討在實務上常態分配之假設，發現與仍與實際情形有明顯之差異，不論是NM及SD(M+)在死亡壓縮的探討下，皆受到資料的限制而有待商榷。 / Due to the advance in medical technology and the change of life style, the human life expectancy has been increasing since the end of the Second World War II and it is expected to continue the pace of increment. Longer life expectancy also means a longer life after retirement. People living in the 21st century are faced with growing demand for the retirement life, such as the pension funds and medical needs to the individuals, as well as the social welfare and insurance for the elderly to the government. Thus, the issue whether the lifespan has a limit receives a lot of attention. In particular, many studies focus on the topic of mortality compression, which means that the expectancy of lifespan has a limit and variance of lifespan converge. However, due to the availability of elderly data, there is still no consensus if the mortality compression is true. In this study, we propose estimation methods to estimate modal age and variance of the age-at-death. Three types of methods are involved: weighted least squares (WLS) method, nonlinear maximization (NM) method, and maximum likelihood estimation (MLE) method, and they are compared to the method proposed by Kannisto, namely SD(M+), in 2000. We found that the NM method has a smaller MSE, and we cannot decide the mortality compression is true based on the data from Human Mortality Database. We also applied the normality and t distribution assumption to the age-at-death and compute the pure premiums for annuity products. We found that normality distribution would produce larger premiums than using the empirical mortality rates. Similarity, the bankruptcy probability would be higher if the t distribution is used.

在這個新媒體服務竄起的時代，許多中小型商家卻難以充分利用新媒體在顧客生命週期(customer life cycle)管理的過程中提升顧客參與行為。而在所有新媒體當中，搜尋引擎又被認為是獲取和發展新顧客最有效的方式之一。因此本研究在搜尋引擎的基礎上提出了一個藉由合作營銷活動最大化顧客參與的新架構。 我們利用在顧客參與平台上建構反向鏈結以及長尾關鍵字服務來實現我們的架構。通過針對中小型商家和顧客的控制實驗，我們驗證了該架構的可用性和效果。我們發現兩種服務對於增進網路能見度以及搜尋精準度都有明顯的提升。而高網路能見度及高搜尋精準度可以幫助中小型商家提升顧客參與行為，不論是在顧客參與平台或是其官方網站。因此本研究認為中小型商家可以利用顧客參與平台上的服務來建立合作營銷活動，以促進顧客參與行為。在這個過程中，不僅有利於顧客對於品牌態度的建立也有助於其轉變為中小型商家忠實顧客的可能性。 / Facing the fast-changing trend of service economy upon new media, most small and medium enterprises (SMEs) don’t have the capability to utilize new media to stimulate customer engagement behavior (CEB) through customer life cycle (acquisition, development, and retention). In all the new media, search engine is the most helpful way on acquiring and developing new customers, thus we propose a new framework based on search engine to maximizing the CEB through brand alliance-based campaign (which is a popular marketing strategy for SMEs to acquire new customers). According to our framework, the study implements two search engine services including inlink building service and long tail keyword service on engagement site. With conducting controlled experiments toward SMEs and customers, we testify our system by SMEs and the effects of services toward the customers. We find that inlink building service and long tail keyword service increase both on high search targetability and web visibility for customers. With high web visibility and search targeatbiliy, CEB can be stimulated on engagement site and also target sites of SMEs. Thus we conclude that SMEs can use brand alliance-based campaign with our services as a trigger to stimulate CEB. With increment on engagement behavior, customer’s brand attitudes then increase and in the end become loyal customers to the SMEs.

死亡壓縮(Mortality Compression)意指死亡年齡更集中，是最近廣受注意的研究議題，和生存曲線矩形化（Rectangularization）關係密切，以統計分佈的角度描述，則是死亡年齡會逐漸退化到某個特定年齡。換言之，如果死亡壓縮和壽命有上限兩者都成立，以統計術語而言，代表壽命的期望值有上限、變異數會收斂，可藉由死亡年齡分配探討壽命變化。 本文希望以統計方法與資料品質等兩個面向探討死亡壓縮與延壽之間的關係。除了過去使用的無母數方法，如檢視各年度生命表上死亡分佈的最短區間（25%、50%及75%）與死亡人數最多的年齡（Modal Age）的變化，探討死亡壓縮與壽命是否有延長；另一方面，也將對死亡曲線作參數設定，觀察死亡年齡分佈的標準差變化。由於過往的研究多使用的生命表資料，本研究將比較使用生命表資料（死亡資料經過修勻）或原始死亡人數資料對結果的影響。 本研究藉由電腦模擬比較各種估計標準差方法的差異，包括Kannisto (2000) 提出的SD(M+)法與本文考量的非線性極值法(Nonlinear-Maximization)，衡量何者具有較小的均方誤差，並探討錯誤設定分配偵誤的敏感度；另外，本文可討論使用經過修勻的死亡率及原始死亡率對於估計結果的影響。除了電腦模擬，本研究也套入實際死亡資料（如臺灣、美國、…等國資料，資料來源：Human Mortality Database），檢視死亡壓縮是否存在。 / Mortality compression is one of the popular research issues in longevity risk. It means that the age-at-death would concentrate on a narrower range, and it is also related to the concept of rectangularization of survival curve. In terms of statistical distribution, mortality compression indicates that the age-at-death degenerates to a certain age, and it can be used to study changes of lifespan. If the lifespan has a limit, or mortality compression does exist, this suggests that the life expectancy has a limit and the variance of age-at-death would converge. In the study, we evaluate the mortality compression using the statistical methods and considering the issue of data quality. In addition to the nonparametric methods used in the previous studies, such as shortest confidence interval on the distribution of age-at-death and the modal age, we consider optimization methods for estimating the standard deviation of age-at-death distribution. In specific, we compare the SD(M+) proposed by Kannisto (2000) and the method of Nonlinear-Maximization, and check which method has a smaller MSE (Mean Squared Error). For the issue of data quality, we compare the estimation results of using mortality rates from life table data with those using the raw data. In addition to computer simulation, we consider the sensitivity analysis of age-at-death distribution, to evaluate the estimation method. Furthermore, based on the data from Human Mortality Database, we apply the method of Nonlinear-Maximization to life table data (i.e., graduated mortality rates) and raw data, and check if there are significant differences. The estimation results of empirical study are also used to evaluate if there is mortality compression and if there is a longevity limit.

知識管理是現代最重要的課題之一，企業轉型也在許多實務中證明對組織的成長與獲利很有幫助。然而因為轉型而創新的知識要如何管理，又是如何以知識螺旋的方式融入企業，進而幫助企業創新服務營運內容，則較少為人討論。本研究就是以此為研究動機，發展出的論文。藉由發生在Z公司轉型的過程中知識轉移發展的個案故事，來分析與印證企業在「知識管理」，與「企業轉型」兩方面的變化，如何與「知識螺旋」理論，交互影響的演變過程。 為了探討「知識轉換」的個案故事，本論文分別整理多篇「全球化」，與「知識螺旋」兩方面的重要理論文獻，設計出研究架構。本研究將個案公司企業知識的轉換過程，區分為三大階段; 而每一個歷程包含部份「共同化」、「外化」、「結合」與「內化」因子。企業知識管理上的重點之一，便是在於經理人們，如何能夠有效地調和這個「知識變換螺旋」過程，並將組織的策略資源與知識能力，藉由共同化、內化、外化與結合、轉化與學習而提昇。 經由個案資料與理論的分析與比較，主要的研究發現與結論如下：(1).在商業模式的轉型中，導入外部新資源與新知識，可讓其更迅速轉型並找到新策略。Z公司是企業轉型的最佳典範； (2). Z公司重新定位於「科技服務事業」，與轉型為「全球整合型企業」的創新商業模式，讓公司重新站上高峰；(3)個案A公司利用差異化分析，導入Z公司的經驗，建構新的能力，成就自己成為一個「全球整合型企業」。 最後，由個案故事的分析，本研究發現在「知識轉換」的螺旋中，即使是幫別的公司輔導轉型，也會對於因此學習到或是培養的新知識，經過知識的螺旋，再度轉換成企業本身的新核心能力。 關鍵字：知識螺旋、全球化、企業流程優化、委外加工、模組化、全球整合型企業、轉型、共享式服務中心 / Knowledge management is one of the most important topics in recent years. Business transformation has proved to be useful in the growth of the organization and profit. However, only few studies had focused on how to manage the innovative knowledge and how it is merged into a business through knowledge spiral and thereby helping to expand the business opportunities. This research is based on the case study of the knowledge conversion during a business transformation at company Z and analyzed the mutual effect and its relation with the knowledge spiral theory. The research structure is based on the summary of several key literatures on globalization and knowledge spiral to better understand the knowledge conversion cases. This research has divided the business transformation and knowledge conversion process in company Z into three major phases, and each phase is analyzed by four modules of knowledge transformation model, Socialization, Externalization, Combination and Internalization. One of the key points in business knowledge management is how the managers could cope with the knowledge conversion spiral process and improve the strategic resources with knowledge through the processes of socialization, externalization, internalization and combination. This research concludes that a company could introduce new external resources and knowledge during the business transformation in order to speed up the transformation process and craft a new strategy. Also, company Z repositioned itself as a service science company and then transformed with an innovative business model into a globally integrated enterprise to reclaim its glory. Finally, company A utilized gap analysis tool and introduced the transformation experiences by company Z to establish its own new expertise and become a globally integrated enterprise as well. This research also discovers that a company could also obtain new core comptetence through knowledge spiral by servicing other companies. Keywords: The Knowledge Spiral Theory, Globalization, Business Process Re-engineer, Outsource, Component Business Model, Globally Integrated Enterprise, Business Transformation, Shared Service Center