Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships

The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships

A FRAMEWORK TO SUPPORT SERVICE-ORIENTED ARCHITECTURE INVESTMENT DECISION

Service-oriented architecture (SOA) is a system paradigm that structures business functions as loosely coupled services to enable business agility. SOA requires significant up-front investments, and in return, promises a vast array of benefits. Unfortunately, in contrast to the costs of the investment, monetary benefits associated with SOA are more difficult to measure. For one reason, benefits such as increased agility or improved flexibility are elusive in nature, making it harder to define metrics for their calculation. For another, SOA value is realized in long term under uncertainty, and traditional capital budgeting methods often fail to capture uncertainty when valuing investments. In this paper, we provide a decision framework to analyze the monetary impact of SOA investment in an organization. Combining traditional NPV analysis with option pricing models, our framework accounts for operational and strategic costs and benefits of SOA and proposes an extended investment value to support managerial investment decision

Essays on E-Service Management: IT Servitization Under SOA and CRM Domains

We are living in a world of service economy. Global markets have radically transformed from product-based industrial structures to service-based post-industrial ones over the past fifty years. IT has catalyzed a significant portion of this transformation. Advances in IT have not only alleviated the accessibility of existing service systems, but also enabled Servitization of products and commodities that were delivered through traditional mediums. Ironically, IT itself has been a commodity that has met its own share of Servitization. Hardware computing resources have been virtualized, whereas software and media content have been delivered through distributed networks. Causing a paradigm shift on how IT is delivered and used, Servitization of IT is expected to impose technical, economical and managerial challenges in various business domains of organizations. In this dissertation, I develop novel methods and policies to overcome such challenges. By conducting four closely related studies, I address common IT Servitization problems encountered in the service-oriented architecture and customer relationship management domains. Specifically, I make the following contributions: (1) in study one, I work on the efficient creation of software services out of legacy software by annotating source code components of an IT system with business semantics. The approach facilitates source code reuse in developing new web services. (2) In study two, I develop a financial valuation model for SOA investments. The model quantifies evident and elusive costs and benefits of SOA and supports managerial decision making regarding the investment. (3) In study three, I investigate the deployment of live-chat online service channels by evaluating the impact of priority-based admission control policies. I show that under imperfect profiling of customer types, reserve-type admission control policies may have negative consequences for the entire system. (4) In study four, I investigate the value of adding flexibility in live-chat contact centers. Contrary to the service management literature, I find out that cross-training of agents in large contact centers suffers from switching costs as well as capacity shifting inefficiencies. Methods, models and policies proposed in this dissertation are expected to contribute towards understanding the short-term applicability and long-term impact of service-orientation and IT Servitization in business organizations

Stability of Transaction Fees in Bitcoin: A Supply and Demand Perspective

Cryptocurrencies such as Bitcoin are breakthrough financial technologies that promise to revolutionize the digital economy. Unfortunately, their long-term adoption in the business world is imperiled by a lack of stability that manifests as dramatic swings in transaction fees and severe participant dissatisfaction. To date, there has been little academic effort to study how system participants react to volatility in fee movements. Our study addresses this research gap by conceptualizing the Bitcoin platform as a data space market and studying how market equilibrium forms between users who demand data space while trying to avoid transaction delays, and miners who supply data space while trying to maximize fee revenues. Our empirical analysis based on past bitcoin transactions reveals the existence of a relatively flat downward-sloping demand curve and a much steeper upward-sloping supply curve. Regarding users, the inelastic nature of demand signals the utility of Bitcoin as a niche platform for transactions that are otherwise difficult to conduct. This result challenges the belief that users may easily abandon Bitcoin technology given rising transaction costs. We also find that the use of bitcoins as a trading asset is associated with higher levels of tolerance to fees. Regarding miners, the comparatively elastic nature of supply indicates that higher fees stimulate mining by a larger magnitude than suppressing demand. This finding implies that, ceteris paribus, the Bitcoin system turns to self-regulate transaction fees in an efficient manner. Our work has implications for the management of congestion in blockchain-based systems and more broadly for the stability of cryptocurrency markets