abstract: Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms, this dissertation investigates whether and how IT-enabled monitoring systems can mitigate moral hazard and reshape the labor demand and supply by providing detailed information about workers’ effort. In the first chapter, I propose and demonstrate that monitoring records can substitute for reputation signals such that they attract more qualified inexperienced workers to enter the marketplace. Specifically, only the effort-related reputation information is substituted by monitoring but the capability-related reputation information. In line with this, monitoring can lower the entry barrier for inexperienced workers on platforms. In the second chapter, I investigate if there is home bias for local workers when employers make the hiring decisions. I further show the existence of home bias from employers and it is primarily driven by statistical inference instead of personal “taste”. In the last chapter, I examine if females tend to have a stronger avoidance of monitoring than males. With the combination of the observational data and experimental data, I find that there is a gender difference in avoidance of monitoring and the introduction of the monitoring system increases the gender wage gap due to genders differences in such willingness-to-pay for the avoidance of monitoring. These three studies jointly contribute to the literature on the online platforms, gig economy and agency theory by elucidating the critical role of IT-enabled monitoring. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2019

No description available.

On February 14, 2007, JetBlue Airways experienced a major blow to their well-respected reputation when ten planes were delayed with passengers stranded on board for up to eight hours each. Through intense coverage and negative reporting from the media, JetBlue launched a multi-pronged crisis communication response strategy to repair the damage. Using Situational Crisis Communications Theory (SCCT) as a framework, the research in this study demonstrated the importance of crisis communications planning, corporate apologia, corporate impression management, and image restoration within an organization. A discourse analysis was utilized to identify the types of messages delivered by the media, the crisis response strategies and tactics implemented by JetBlue, and stakeholder reactions to the JetBlue responses. Content from the messages were then placed in appropriate categories identifying the type of strategy and tactic utilized. Category definitions, examples of comments, and the identifying attributes were included to help support that JetBlue was successful in repairing and recovering their reputation

In this thesis, we use methods from econometrics to empirically measure and quantify how digital information influences industries and markets. Specifically, we focus on two important areas of marketing: online reputation management, and competition between online and offline markets. In the first part of the thesis, we study the impact of management review responses, a popular reputation management mechanism, on consumer ratings. To do so, we exploit a difference in managerial practice across two hotel review plat- forms, TripAdvisor and Expedia: while hotels regularly respond to their TripAdvisor reviews, they never do so on Expedia. Based on this observation we employ a “difference-in-differences” design to identify the causal impact of management responses on ratings, and show that responding hotels see an average increase of 0.1 stars. We then turn to analyze the mechanisms behind this increase in ratings and show that by responding to reviews, hotels attract consumers who are inherently more positive, and therefore more likely to leave good reviews. In the second part of the thesis, we study peer-to-peer markets and their impact on traditional industries. We do so by looking at Airbnb, a sharing economy pioneer offering short-term accommodation. We combine data from Airbnb and the Texas hotel industry, and estimate the impact of Airbnb’s entry into the Texas market on hotel room revenue. To identify Airbnb’s causal impact on hotel room revenue, we use a “difference-in-differences” empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across city-level markets. We estimate that in Austin, where Airbnb supply is highest, the impact on hotel revenue is roughly 8-10% for the most affected hotels. Further, we find that affected hotels have responded by reducing prices, an impact that benefits all consumers, not just participants in the sharing economy. The results presented in this thesis have practical implications for firms seeking to improve their operations and marketing strategies, platforms seeking to design better and efficient marketplaces, and consumers who are often not aware of important dynamics that can be helpful in their decision-making process.

Smart grid is a power supply system that uses digital communication technology to detect and react to local changes for power demand. Modern and future power supply system requires a distributed system for effective communication and management. Blockchain, a distributed technology, has been applied in many fields, e.g., cryptocurrency exchange, secure sharing of medical data, and personal identity security. Much research has been done on the application of blockchain to smart grid. While blockchain has many advantages, such as security and no interference from third parties, it also has inherent disadvantages, such as untrusted network environment, lacking data source privacy, and low network throughput.In this research, three systems are designed to tackle some of these problems in blockchain technology. In the first study, Information-Centric Blockchain Model, we focus on data privacy. In this model, the transactions created by nodes in the network are categorized into separate groups, such as billing transactions, power generation transactions, etc. In this model, all transactions are first encrypted by the corresponding pairs of asymmetric keys, which guarantees that only the intended receivers can see the data so that data confidentiality is preserved. Secondly, all transactions are sent on behalf of their groups, which hides the data sources to preserve the privacy. Our preliminary implementation verified the feasibility of the model, and our analysis demonstrates its effectiveness in securing data source privacy, increasing network throughput, and reducing storage usage. In the second study, we focus on increasing the network’s trustworthiness in an untrusted network environment. A reputation system is designed to evaluate all node’s behaviors. The reputation of a node is evaluated on its computing power, online time, defense ability, function, and service quality. The performance of a node will affect its reputation scores, and a node’s reputation scores will be used to assess its qualification, privileges, and job assignments. Our design is a relatively thorough, self-operated, and closed-loop system. Continuing evaluation of all node’s abilities and behaviors guarantees that only nodes with good scores are qualified to handle certain tasks. Thus, the reputation system helps enhance network security by preventing both internal and external attacks. Preliminary implementation and security analysis showed that the reputation model is feasible and enhances blockchain system’s security. In the third research, a countermeasure was designed for double spending. Double spending is one of the two most concerned security attacks in blockchain. In this study, one of the most reputable nodes was selected as detection node, which keeps checking for conflict transactions in two consecutive blocks. Upon a problematic transaction was discovered, two punishment transactions were created to punish the current attack behavior and to prevent it to happen in future. The experiment shows our design can detect the double spending effectively while using much less detection time and resources.

With the dramatic growth of vehicles around the world, Vehicular Ad-hoc Networks (VANETs) have been proposed as a solution to advance road safety, improve transportation efficiency, and satisfy road users. In the VANET environment, vehicles communicate with each other and with road infrastructure in an ad-hoc manner. This communication may be safety-related or non-safety-related and may often include vehicle information (e.g., location, direction, speed, and control), road conditions, and events. A key component in assessing the veracity of the information is the trustworthiness of the information source. Thus, trust evaluation is one of the main requirements of VANET design. In this work, we investigate performance improvements in the trust evaluation framework of VANETs. First, we propose a risk-based trust evaluation model (RTEAM) to estimate the risk of taking action or refraining from action regarding a reported event (in case of receiving conflicting messages about the event's existence). Some trust metrics such as direct trust, hop-based trust values, proximity to the event, and consequences of acting on a wrong decision are used to estimate the risk of the vehicle’s actions. Vehicles make individual decisions by seeking the action with the lowest risk. Second, we propose a fog-based reputation evaluation model (FREM) to support trust management framework. We promote fog computing as a new paradigm since it can provide several services to users in the edge layer. In our work, Fog supports the decision-making process in the reputation evaluation framework. Fog nodes play a key role in collecting vehicles' reputation records and cooperating with the roadside units (RSUs) to update these records. We propose the use of Digital Trustworthiness Cards (DTC), where the latest reputation evaluation of a vehicle automatically appears on its card. The benefits of the DTC are twofold: 1) the communication load on vehicles is reduced, and 2) historical trust records are established for each vehicle. We also take advantage of fog’s familiarity and greater knowledge of the vehicles that frequently visit its zones; with more intimate knowledge, fog can smartly employ vehicles to perform specific tasks based on their experiences. Further, we implement a strategy for establishing trust based on specific task categories. This permits a nuanced evaluation of the vehicle best suited for the task at hand and has the further benefit of preventing malicious vehicles from being naively trusted based on successful completion of unimportant or non-safety-related tasks. Finally, we expand the role of the fog in the decision-making process when vehicles need to ensure the existence of serious events. We propose a fog-based event validation model (FEVM) to validate the event’s existence through cooperation between vehicles and fog nodes. The vehicles are used as mobile fog nodes, which compute their confidence in events based on the available information. Fog nodes then validate the event after combining vehicles’ confidence values by applying the Extended Dempster-Shafer (EDS) theory of evidence. To test our proposed models, we conduct many experiments to investigate their performance and compare them with other existing models.

Background: With digitalization, the increased use of the World Wide Web and social media combined with the aware and engaged costumer, the conditions of reputation management are also evolving (Beal & Strauss, 2008; Jones et al., 2009). Startups’ circumstances are even more challenging, in terms of several limitations, increased risks and uncertainties (Bortolini et al. 2021). Thus, reputation management strategies need to develop and adapt to current changes. Purpose: The purpose of this study is to describe how startups currently are managing their online reputation in a digital age and what kind of challenges they are facing. Based on the discoveries of this study the research will later discuss how strategies can be adjusted and improved to support startups in establishing and protecting their image and brand in an exposed and vulnerable landscape. Method: To answer the research question, four semi-structured online interviews with different startups were conducted and secondary data was observed. These case studies cover startups operating in different industries and respondents with different roles and experiences of reputation management. Through this variation, the empirical data could achieve comprehensive and nuanced insights. Findings: The gathered empirical data revealed that startups face several challenges when managing their online reputation due to their unique context. In a small startup, everyone is involved in engaging with stakeholders, monitoring the public’s perception, as well as contributing to a strong image, and building brand equity. Because of startups’ limitations, priorities are creating personal long-term relationships, networking, and gaining trust and loyalty to build reputation.  Conclusion: Recommendations for startups to manage their reputation more efficiently is practicing transparency to avoid misunderstandings that could damage the reputation, spread a consistent message to the public to establish a strong image, as well as ensuring that their performance conforms with their promises and planted perceptions. Adopting automatic and preventative tools to monitor information without human resources should also be considered. However, there is a need for further research in this field to continue exploring more efficient reputation management strategies tailored to startups’ conditions in the digital age.

No description available.

No description available.

No description available.