http://dspace.cityu.edu.hk:80/handle/2031/763 2013-05-01T03:06:36Z http://dspace.cityu.edu.hk:80/handle/2031/6491 Title: Case studies of mobile commerce in Hong Kong and selected Middle Eastern markets : roles and effects of inter-firm information systems on organization form choice Authors: Yeh, Pak Fai Colin ( 葉百輝) Abstract: Mobile penetration has been growing at an exponential rate over the past decade, Mobile services have become an integral part of peoples' daily lives beyond basic communication needs. Consequently, mobile commerce (m-commerce) has become an important phenomenon that is demanding more understanding from both academic scholars and practitioners. Amongst the key stakeholders in the value chain, mobile network operators (MNOs) were the first to lead in the development of m-commerce. NTT-DoCoMo's i-mode was also an early successful example of m-commerce in Japan, representing the case of MNO Portals. Recently, device manufacturers have also been active in launching mobile application stores (App Stores) following the global success of Apple's App Store. Both MNO Portals and App Stroes possess an m-commerce platform (mCom Platform) which is central to both forms of m-commerce deployments. With the research objective of providing researchers and practitioners with further understanding of current m-commerce implementations, a key focus has been placed on the roles and effects of the mCom platform (i.e., the inter-firm IS infrastructure between related m-commerce value chain players). This thesis is conducted within a conceptual model of Organizational Form Choice prediction based on combined theoretical framework of Transaction Cost Economics and Agency Theory. Thus, this thesis proposes two approaches: (1) to adopt a positivist approach deployed to test the predictive conceptual framework of Organization Form Choice prediction based on combined Transaction Cost Economics and Agency Theory, focusing on MNO Portals' and App Stores' implementations within the m-commerce scenarios, and (2) to apply an evaluative and analytical framework based on RBV to investigate the mCom platform as IS resource to provide further insights of current m-commerce scenarios in the market. In particular, to the best knowledge of the researcher of this thesis, no academic work has been conducted on m-commerce using the combined TCE and agency theory framework. This thesis thus contributes to the acamdemic literature by providing aditional theoretical insights with empirical evidence. In summary, this thesis aims to address the following gaps. It first provides further industry insights into MNO Portals and App Stores based on well-developed theories; second, it highlights the importance and impact of the mCom platform as an inter-firm IS resource; third, it applies the combined TCE and agency theory framework to m-commerce; fourth, it tests the theoretical framework with empirical case studies; and finally it provides comparative market insights on the advanced mobile market versus the developing mobile market, with a special focus on the MNO Portals and App Stores phenomenon. Notes: CityU Call Number: HF5548.34 .Y44 2010; ix, 222 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2010.; Includes bibliographical references (leaves 197-216) 2010-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6490 Title: Reliability analysis of technical trading rules in security market based on conditional random field model Authors: Yang, Zonghang ( 楊宗杭) Abstract: Conditional random field model is a powerful model for dealing with time series with hidden states and is becoming more and more widely used in various fields. This model generalizes the hidden Markov chain model by releasing the independence assumptions between observations and obtains more accurate estimation results. Although conditional random field model has been successfully applied in both academics and industry, it has rarely been used for analyzing financial time series. In financial industry, there are mainly two types of skills for investment analysis: fundamental analysis, and technical analysis. Although these two kinds of analysis are equally important to security market participants, technical analysis does not receive enough attention from academics. Technical analysis is deemed to be a pseudoscience such as astrology and alchemy by the researchers of mainstream finance. One of the most important reasons is that technical analysis is contradictory with efficient market hypothesis, which is the theoretical foundation of modern finance. Also, the subjective nature of technical analysis makes it unfalsifiable, which constricts its applications. There is positive evidence of the profitability of technical analysis resulting from continued empirical studies testing the effectiveness of technical analysis in different security markets. The rapid development of computer science makes it possible to process large-scale calculations allowing increased interest in technical analysis. Some researchers have used behavioral economics to explain extra profits of technical analysis and several have used statistics and machine learning techniques to investigate the profitability of technical analysis. However, a uniform framework for technical analysis is still not available. Even the definitions of technical analysis in different studies are not consistent. In this thesis, we concentrate on technical analysis. Given trading signal sequence indicated by technical trading rules, we apply conditional random field to analyze this time series. Considering the issues mentioned above technical trading signals may be unreliable. The reliability analysis is important for the application of technical analysis. The research objective of this thesis is to apply conditional random field model to improve the reliability of technical analysis. Technical analysis depends on trading signals generated by technical trading rules, and these signals form a time series which is the target for our research. To achieve this objective the following problems should be solved. 1. How to scientifically measure the reliability of technical analysis? 2. What are the quantitative characteristics of trading signals for conditional random model? 3. How to use conditional random model to improve the reliability? For solving these research questions, we firstly redefine technical analysis scientifically, and then develop a framework based on conditional random field model for reliability of technical analysis. The technical analysis is defined quantitatively. The framework for reliability analysis is based on conditional random field model and also employs statistical methods such as two tailed t-test. Heng Seng Index stocks in Hong Kong stock market were taken as examples for experiments, and the famous simple moving average rule and Bollinger Bands rule were tested. Compared with the trading strategy without technical trading rules, i.e., buy-and-hold strategy, it has been found that most simple moving average rules cannot bring significantly larger returns while a Bollinger Bands rule is effective to indicate good trading chances. Notes: CityU Call Number: HG4529 .Y38 2011; vii, 83 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves [79]-83) 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6489 Title: Mining and analyzing customer opinions/sentiments of Web 2.0 for business applications Authors: Xu, Kaiquan ( 许开全) Abstract: With the explosive growth in the amount of user-generated review data in the era of Web 2.0 (social networking sites, blogs, mini-blogs, discussion forums, online shopping websites), there is a pressing need to develop effective methods and tools to automatically extract valuable business intelligence from these user opinion data. In addition, the large numbers of user reviews often contain information about competitors and have become a new source for mining competitive intelligence. Many companies have begun to use social networking sites (SNS) as an important channel and platform to do online marketing and reputation management. Thus, analyzing users' sentiments on SNS has become key for these business applications. Semantically annotating opinion data is an effective way to mine valuable information from the large number of customer opinions. Although supervised machine learning approaches have been explored for semantically annotating user opinions to facilitate market intelligence generation, such approaches often require numerous manually labelled training examples to produce accurate semantic annotations. In this study, we propose an active learning approach that can train a state-of-the-art, large-margin classifier with substantially fewer labelled training examples, and yet produce accurate semantic annotations of user opinions. In particular, our active learning method is underpinned by a novel query function that can efficiently locate the most informative unlabeled examples such that a large-margin classifier can learn the optimal parameter values based on them. Rigorous evaluation involving a benchmark test and an empirical test with real-world opinion data extracted from Amazon.com reveals that the proposed active learning method can train effective classifiers with far fewer training examples and yet achieve similar performance to a typical state-of-the-art classifier without active learning. For effectively mining competitive intelligence from customer opinions of Web 2.0, we propose a novel graphical model to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help companies discover potential risks and further design new products and marketing strategies. Our experiments on a corpus of Amazon customer reviews show that our proposed method can extract comparative relations more accurately than the benchmark methods. To analyze users' sentiments on SNS, a "sentiment community" is proposed as a tool. The sentiment communities with different polarities on SNS usually represent groups of users with certain preferences in common. Thus, discovering sentiment communities is very useful for enterprises to do customer segmentation and target marketing. A novel method based on an optimization technique is proposed for discovering users' sentiment communities, and comprehensive experimental evaluations are executed to demonstrate the method's effectiveness. In summary, this dissertation covers the topics of semantically annotating opinion data, extracting comparative opinions, and analyzing users' sentiments on SNS. This work opens the door to analyzing the rich consumer-generated data of Web 2.0 and SNS for enterprises to use in business applications. Notes: CityU Call Number: QA76.9.D343 X8 2011; x, 134 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 125-134) 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6488 Title: Achieving knowledge sharing outcomes through the activation of social capital in virtural communities : evidence from Chinese online social network communities Authors: Tian, Wen ( 田雯) Abstract: A key challenge that managers and administrators face today is how knowledge sharing can be achieved efficiently and effectively in the changing form of organizations, e.g., Online Social Network (OSN) communities. With the prevalence of Social Network Services (SNS), the way individuals mutually exchange their tacit and explicit knowledge and jointly create new knowledge varies significantly from that of past decades. There is increasing evidence that such a process is inherently a social and situated process that is strongly impacted by characteristics of social capital. However, traditionally, research on the link between social capital and knowledge sharing has taken a relatively static and single dimensional approach, thus failing to capture the rapidly changing dynamics of social capital initiation, maintenance and activation, especially brought about by Information and Communication Technologies (ICT) in an OSN environment. This consequently changes how individuals' knowledge sharing behaviors change. However, relatively little is known about explicit knowledge sharing in Chinese OSN communities, given the unique social and cultural characteristics of this environment. Guanxi is a strong orientation embedded in social relationships in Chinese culture, which has been considered a necessary condition to do business successfully in China, and is viewed in this thesis as a source of social capital. This thesis analyzes whether the SNS-supported: 1) structural, 2) relational, and 3) cognitive dimensions of guanxi capital development affect individuals’ perceived guanxi capital accumulation and activation. Two types of knowledge sharing outcomes are then measured in OSN communities: 1) outward knowledge sharing, i.e., knowledge contribution quality, quantity and diversity (supply-side outcomes) and 2) inward knowledge sharing, i.e., knowledge sourcing adoption (demand-side outcomes). By adopting an integrative perspective, the individual properties, network properties, and knowledge sharing properties are considered simultaneously to explore the mechanisms by which knowledge sharing outcomes occur. The research setting included individuals from several Chinese OSN communities. Three studies are included in this thesis: Study 1 uses focus group interviews and discussions mainly for the purpose of generating variables and developing measures, Study 2 employs survey method to empirically test the hypotheses, and Study 3 uses the case study method to triangulate the findings in Study 2 and elaborate on the issues that emerged beyond Study 2. The results demonstrate a strong predicting power of perceived guanxi capital activation on individuals’ knowledge sharing outcomes in Chinese OSN communities. This establishes the theoretical link between social capital theory and guanxi capital, and justifies investigating knowledge sharing behavior from an integrative view. Both theoretical and practical implications are achieved. Limitations and directions for future studies are discussed, and conclusions are drawn. Notes: CityU Call Number: HD30.2 .T497 2011; ix, 273 leaves 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 220-235) 2011-01-01T00:00:00Z