汽車貨運業一直以來都為台灣的經濟帶來極大的貢獻，雖然之前相關的研究提供了這個產業許多價值，但是目前還沒有是針對汽車貨運公司主要所載的商品來做研究，如此的把所有汽車貨運公司一視同仁忽略了承載不同的商品有不同的競爭程度的事實，倒置這些研究所提出來的架構無法解釋許多這個產業裡發生的現象。 將會焦點放在承載鋼鐵的汽車貨運公司，這份研究延伸策略管理的相關文獻來了解這個產業的運作，以質性研究之案例研究法來了解是什麼因素影響了公司表現、公司行為，以及產業所衍生的問題。研究發現產業的狀態，公司所擁有的資源，與正式和非正式的制度錯中交雜的影像了產業間的行為與競爭程度，所以管理者應該避免過度專注於某一層面的策略，他們應該在擬定策略時考慮到策略管理中的產業基礎理論、資源基礎理論、與制度基礎理論。 / The trucking industry has been a major contributor to Taiwan’s economy for decades. Previous research concerning strategic management practiced in the industry, while insightful, did not pay enough attention to the specific commodity primarily carried by trucking companies. The generalization that all trucking companies carry the same commodity neglects the fact that competitive intensity varies considerably within the industry depending on the commodity carried. Consequently, many phenomenons could not be explained using frameworks from previous researches. This study concentrates on trucking companies primarily carrying steel and extends various disciplines from the strategic management literature to the trucking industry. Using the qualitative method of case study, this research attempts to understand the underlying drivers of firm performances and behaviors in addition to challenges faced by the industry. The findings suggest that industry conditions, firm resource portfolio, and formal and informal institutions collectively affect the behaviors and competition in the industry, therefore managers should avoid focusing on a single strategy and bear in mind the industry-based view, the resource-based view, and the institution-based view.

環境惡化與經濟下行的壓力，讓中國各級政府必須尋求一條有別於過去的集約、創新、綠色的發展道路。在這種背景下，類似新能源汽車等兼具節能環保、產業轉型的一批戰略性新興產業開始興起；而地方在承接與發展這些產業時，則傾向於將產業的發展同地方的城市化相結合，採行「以產業為動力，以新城為依託，以生態為源泉」的產城融合模式。 本文通過實地田野調查對上海市嘉定區發展新能源汽車產業的案例進行研究並認為：地方政府為了應對外部發展環境的變化，選擇「把握發展主動權」引入新能源汽車產業，但其實質為把握地方不變的關鍵性資源，即土地開發權。憑藉新能源汽車所帶來的產業不確定性與「綠色共識」下發展正當性的橋接，地方政府獲得了更大的行為自主性與容錯空間，得以透過產業基地與國際示範區的建立帶動地方整體的空間轉型，進而謀取短期的土地開發利益；但產業發展的長期目標卻在這樣的過程中被推遲乃至消解。經由以上案例，本研究將揭示「產城融合」「城」大於「產」之本質，並深入討論綠色發展成為主流論述之下中國地方發展模式的轉變。 / Under the pressure of environmental deterioration and economic downturn, Chinese government have to seek to find a more intensified, innovative and environmental friendly (green) path to development, which differs from the inherent one. As a result, strategic emerging industries like New Energy Vehicles (NEVs) that bear the target of energy saving, environmental protection and industrial transformation start rising. For local governments who take main responsibility of acting, the way how they implement is to combine the industrial development with local urbanization process, which could be called as City-Industry Integration Model. This paper research the development of New Energy Vehicles in Jiading District, Shanghai Municipality and argues that in order to adapt to the changes of external conditions, local government chooses to “grasp the developmental initiative” to introduce the NEVs industry. However, the essence of the action is to grasp the changeless local key resources, which is the land development rights. Rely on the industrial uncertainty and the bridging of developmental legitimacy under Green Consensus, more autonomy and fault-tolerant space has been obtained by local government. Moreover, local officials could implement spatial transformation through the construction of industrial base and international demonstration area, and then gain short-term land development interests, which is in the cost of long-standing goal of industry booming. Overall, this research would reveal the fact that urbanization occupies a larger proportion than industrial development in the City-Industry Integration Model, and discuss the transformation of local developmental model in China under the new mainstream discourse of sustainable development.

資訊科技(Information Technology, IT)對我們的生活與企業帶來極大的影響與改變。在企業中，資訊科技經常扮演不同的角色，這些不同的資訊科技角色(IT Role)可以自動化企業流程、支援決策制定、整合資源，甚至實現轉型與創新，對於企業的決策帶來不同層面的影響。而我們從近年來新興的資訊科技─大數據與人工智慧技術中，發現了不同於過去的新資訊科技角色。為了近一步了解這個新角色，本研究選擇人工智慧應用之一的自動駕駛車作為研究案例。本研究目的是探討自動駕駛車的資訊科技所扮演的新資訊科技角色；研究問題包含 (1) 自動駕駛車的資訊科技如何影響駕駛決策制定 (2) 在決策制定過程中，人與資訊科技分別扮演何種角色與職責。 本研究採用多個案研究法，分為兩個階段。首先，為解構資訊科技的決策制定流程，本研究依據決策理論與系統理論建構一研究架構。於文獻探討的章節中，本研究根據過往文獻與案例，提出四種企業常見的資訊科技角色─Automation、Supporter、Mentor與Enabler，並將研究架構應用於以上資訊科技角色以進行調整與驗證。接著，本研究選擇Google (Waymo)與Tesla作為自動駕駛車的研究個案，並將研究架構套用於兩個個案研發的自動駕駛車。由於不同的自動駕駛車研發理念與實現方式，Google與Tesla自動駕駛車的資訊科技分別扮演兩種不同的資訊科技角色─Autonomer與Smart Automation，本研究進一步比較所有資訊科技角色的研究架構結果，了解資訊科技角色的特性、影響與適用的決策類型。 自動駕駛的決策問題與環境與過去有極大的不同。為了實現安全的自動駕駛，資訊科技需要的資料類型更加多元，除了傳統數位類型資料，也需要收集周遭環境的3D影像等資料；另外，由於決策從過去的靜態問題轉移到動態與快速變化、擁有爆炸性資料與資訊的環境中，資訊科技需要更多的應變能力以制定更即時與適當的決策。由於資料、決策問題與環境的改變，企業對於資訊科技能力的需求也隨之改變，從自動駕駛車的個案中，本研究發現原本的資訊科技角色(Automation、Supporter、Mentor、Enabler)並不具備能應對如此動態與快速變化的決策問題與環境的能力，因而根據個案提出有能力實現動態即時決策制定的兩種新資訊科技角色。 使用人工智慧技術的Google無人駕駛車扮演著Autonomer的角色。資訊科技角色Autonomer能夠與外界進行互動，並且能夠不斷地追蹤、反饋與修正以實現自我成長；此外，面對各種駕駛決策情境，也能夠在無人為干預的情況下獨力完成駕駛決策的制定。資訊科技的學習能力是面對未知與難以預測的問題的最大優勢，而Autonomer的自我學習與決策制定能力也是與其他資訊科技角色最大的不同之處。使用大數據技術的Tesla自動駕駛車的Autopilot系統扮演著Smart Automation。資訊科技角色Smart Automation擁有更進步的資料收集與分析能力，能夠在動態與快速變化的環境中處理更為複雜的決策問題；此外，面對各種駕駛決策情境，Autopilot系統能在駕駛人保持監督的條件下進行自動駕駛以駕駛輔助的方式減輕駕駛人的負擔。最後，我們發現對於決策制定，資訊科技不僅能扮演一個完全獨立的角色，也能夠扮演一個與人互補的角色。大部分的人工智慧如同Google無人駕駛車做為一個Autonomer的角色，但同時更多企業目前使用的資訊科技屬於Supporter、Mentor與Smart Automation以支援或強化決策者的能力。 本研究探討在自動駕駛過程中不同資訊科技角色如何影響決策制定，以及駕駛人與資訊科技的角色與職責。並且從決策類型與資訊科技能力的角度，協助決策者與使用者全面地了解每個資訊科技角色的特性與適用的決策類型。此外，科技不斷在進步，本研究也提供一個了解各種資訊科技角色的基石，透過本研究的研究架構與方法，協助企業與決策者了解不同資訊科技對於決策的影響，本研究結果也能延伸應用於其他自動化、大數據與人工智慧相關領域，如無人工廠、吾人航空載具、工業4.0與金融科技(Fintech)。 / Information technology (IT) has brought great changes to people and business. In various applications, IT plays diverse roles that can automate business processes, support decision-making, integrate resources, and enable transformation and innovation and brings the impacts on different aspect of decision-making in enterprises. However, with the emerging technology of big data and artificial intelligence (AI), there is a new role for IT. To understand this role, we chose the autonomous car, an application of AI, as a study case. The objective of the research is to understand the new roles played by IT in the autonomous car. We focused on two questions: (1) how IT impacts decision-making in the autonomous car; and (2) what roles do IT and humans play during the decision-making process. This study applies a multiple case study in two phases. First, we built a conceptual framework, based on decision theory and system theory, to deconstruct the decision process of IT. To adjust and verify the framework, we applied it to actual cases and proposed IT roles of Automation, Supporter, Mentor and Enabler. Second, we applied the framework to the chosen autonomous car case studies, Google (Waymo) and Tesla, to explore the new role of IT in the autonomous car. Because of the different philosophies, there were two distinct roles played by IT in Google and Tesla’s autonomous cars, Autonomer and Smart Automation, respectively. We furthermore compared the frameworks of Google and Tesla, as well as the existing and new IT roles, explained the differences regarding the IT roles and decision types, and found out the applicable decision-making type of each IT roles.. Compared to the past, there were the great differences for the decision problems and environment of autonomous driving. To realize the safe autonomous driving, the data IT required became more diverse including non-text or non-digit data; besides, the decision-making also changed from static decision problems into dynamic and rapid decision environment with the explosive data and information that IT required more resilience to make decision. Due to the changes of the data, decision problems and environment, the demand for IT capability also changed. From the cases of the autonomous car, we found the original roles including Automation, Supporter, Mentor and Enabler was not enough – they did not possess the capability to make the dynamic and instantaneous decision. Therefore, we proposed two new IT roles – Smart Automation and Autonomer in this research that these two new IT roles which were applicable to the dynamic and instantaneous decision-making. The computer of the Google driverless car using AI technology acted as an Autonomer that was responsible for interacting with the surroundings and being self-growing with continuous tracking and adjustment; furthermore, under driving decision circumstances, this computer could assume the entire decision-making process without human intervention. The self-learning and decision-making ability of Autonomer is the characteristic most different from other IT roles; additionally, the learning ability was the greatest strength for dealing with unknown and unpredictable circumstances. The Autopilot system of the Tesla self-driving car, leveraging big data technology, acted as a Smart Automation that could process more complex decision problems in the dynamic environment with the advancement of data collection and analysis ability; furthermore, under the driving decision circumstances, the Autopilot system of the Tesla self-driving car could temporarily take over the driving control to decrease the driving burden and provide assistance to make driving easier. According to the research results, IT can not only play a totally independent role but also a complementary role. Most AI played the same IT role – Autonomer, such as the computer of the Google driverless car; meanwhile, much of the IT introduced by businesses acted as Supporter, Mentor and Smart Automation to assist and complement humans. This research provided a perspective for identifying how the different IT roles impact decision-making while driving an autonomous car and clarify the responsibility of humans and IT in the driving experience; moreover, from the perspective of decision problems and IT ability, it also provided a comprehensive and general understanding for realizing the characteristics of diverse IT roles and the applicable decision problems.

為了共同面對來自中國大陸廠商的價格競爭壓力，台灣自行車業的兩大領導廠商──巨大與美利達，以朝向高值化的共同目標籌組了競爭者間的合作體系。然而，競爭者雙方以合作為前提下，競合關係下的動態性以及廠商間的競爭性行動呈現何種面貌？又，如何應用策略矩陣分析法更細緻地分析，在競合關係中廠商間的競爭行為？ 本研究結合動態競爭的對偶層次分析及策略矩陣分析法，以策略點為分析基礎，更細緻地展開處於合作情境下的兩家競爭廠商，如何在歐洲自行車市場佈局競爭性行動。根據結構內容分析兩家廠商2006至2016 年間的新聞事件後發現，在合作的前提下，兩家廠商分別在競爭性行動的時間軸、地理涵蓋範圍、價值單元以及策略點皆進行具差異性的佈局，呈現多樣化的競爭內涵。 本研究除擴展競合情境下的動態競爭研究外，亦藉由策略矩陣分析法豐富化廠商間可分析的競爭行為，並探討資源條件不同的廠商間，其競爭行為如何佈局的實質細節。本研究對於廠商如何在競合關係中發起競爭性行動，提供了實務性的指引與參考。 / To present a united front against the stress of price-cutting competition from Mainland China, Taiwan’s two leaders in the bicycle industry, Giant and Merida, established an inter-firm cooperation system between competitors in order to achieve the common goal of delivering high-valued product. However, under the premise of cooperation agreement between two competing leaders, what is the dynamicity in co-opetition and the inter-firm competitive action under the real scenario? And, how can we apply the strategic matrix analysis to inter-firm rivalry on competitive behvior involved in co-opetition relationship? This study combines dyadic analysis from competitive dynamics with strategic matrix analysis to investigate the two research questions addressed above. By using the “strategic point” as the analytical basis, this study examines two competing leaders under cooperation agreement and extracts their competitive actions in European bicycle market with structured content analysis. Building on the results from the analysis of the news between 2006 and 2016, two competing leaders separately differentiate and conduct their competitive action deployments on four dimensions, which are time-axis, geographical coverage, value unit, and strategic point. Therefore, this study reveals the various contents of two competing leaders in the cooperation-competition. More than contributing to the research field of competitive dynamics in co-opetition, this study enriches the analyzability of inter-firm competitive behaviors by conducting strategic matrix analysis. It provides the details about how the firms compete with each other with different resource endowments. This study broadens our view of the competitive dynamics in co-opetition and introduces practical guidelines on initiating competitive action to the firms in co-opetition relationship.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22094号 / 情博第704号 / 新制||情||121(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 熊田 孝恒, 教授 黒橋 禎夫, 教授 楠見 孝, 准教授 河原 大輔 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18575号 / 工博第3936号 / 新制||工||1605(附属図書館) / 31475 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 中川 大, 教授 谷口 栄一, 准教授 松中 亮治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・論文博士 / 博士(経済学) / 乙第13059号 / 論経博第394号 / 新制||経||280(附属図書館) / 立命館大学大学院経営学研究科 / (主査)教授 塩地 洋, 教授 椙山 泰生, 准教授 田中 彰 / 学位規則第4条第2項該当 / Doctor of Economics / Kyoto University / DGAM

京都大学 / 0048 / 新制・論文博士 / 博士(工学) / 乙第13088号 / 論工博第4149号 / 新制||工||1675(附属図書館) / (主査)教授 藤井 聡, 教授 宇野 伸宏, 准教授 山田 忠史 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21758号 / 工博第4575号 / 新制||工||1713(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 椹木 哲夫, 教授 松原 厚, 教授 小森 雅晴 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

In the nearest future, the human driver is viewed as a reliable backup even for the fully automated road motor vehicles (cars). Indeed, the driver is assumed to swiftly take the control of the car in cases of suddenly occurring (i) challenging environmental conditions, (ii) complex unforeseen driving situations, or (iii) degradation of performance of the car. However, due to the cognitive overload in such a sudden, stressful takeover of the control, the driver would often experience the startle effect, which usually results in an unconscious, instinctive, yet incorrect response. An extreme case of startle is freezing, in which the driver might be incapable to respond to the sudden takeover of control at all. The possible approaches to alleviate the startle during the takeover of control (i.e., the automation startle) include an offset- (i.e., either early- or delayed-), gradual yielding the controls to the driver. In the cases considered above, however, these approaches are hardly applicable because of (i) the presumed unpredictability of the events that result in the need of takeover of control, and (ii) the severe time constraints of the latter. Conversely, the objective of our research is to propose an approach of minimizing the need of yielding the control to the driver in challenging environmental conditions by guaranteeing an adequate automated control in these conditions. Focusing on slippery roads as an instance of challenging conditions, and steering control as an instance of control, we aim at developing such an automated steering that controls the car adequately in various road surfaces featuring low friction coefficients without the need of driver’s intervention.In order to develop such an automated steering we employed an in-house evolutionary computation framework – XML-based genetic programming (XGP) – which offers a flexible, portable, and human readable representation of the evolved optimal steering functions. The trial runs of the evolved steering functions were performed in the Open Source Racing Car Simulator (TORCS), which features a realistic, yet computationally efficient simulation of the car and its environment. The obtained experimental results indicate that due to the challenging dynamics of the unstable car on slippery roads, neither the canonical (tuned) servo-control (as a variant of PD) nor the (tuned) PID-controller could control the car adequately on slippery roads. On the other hand, the controller, featuring a relaxed, arbitrary structure evolved by XGP outperforms both the servo- and PID controllers in that it results in a minimal deviation of the car from its intended trajectory in rainy, snowy, and icy road conditions. Moreover, the evolved steering that employs anticipated perceptions is even superior as it could anticipate the imminent understeering of the car at the entry of the turns and consequently – to compensate for such an understeering by proactively turning the steering wheels in advance – well before entering the turn. The obtained results suggest a human competitiveness of the evolved automated steering as it outperforms the commonly used alternative steering controllers proposed by human experts. The research could be viewed as a step towards the evolutionary development of automated steering of cars in challenging environmental conditions. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University