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Information Technology (IT) evolution in the recent decades has been able to grow the mobility of the end-user. A contributing factor has impacted and improved the smartphone area. This, in turn, has changed end-users’ expectations and experiences in the recent years in terms of available services, which have made mobile applications (apps) to find their place in daily life. In line with the significant growth of smartphones, as the iPhone and Android phones, is also the development of mobile applications exploded. Mobile use has - with this - fundamentally changed and much of what customers used to do on the computer, they do now on their mobile. It makes of course requires that third-parties have a mobile presence and correct utilization, this new arena enormous business opportunities. Hence, as mobile devices become a more visible business tool, it becomes important for organizations to develop applications to meet customer needs and stay competitive. Third-party development is relevantly in the core of software development nowadays. The introduction of smartphones and tablet devices, the associated products, and services in the form of apps has significantly changed software developments practices for developers. The aim of this research formulated to study the development process and practices associated with third-parties and their effects on dealing with various mobile platforms and boundary resources (SKDs and APIs). Qualitative study and exploratory design are extensively conducted for data collection and analysis. Primary data is gathered based on experiences, opinions, and insights from the experts in the subject, and secondary data is gathered from literature, which constantly supports the primary data and ensures the research credibility. The main contribution and results of this study are the factors and development process that third-party developers take into consideration when developing apps for iOS and Android such as ecosystem, programing language, platform, programing model, tools, development process model, supply chain, and submission apps. The success of the digital ecosystems and platform knowledge adopted by Apple and Google has gained a lot of attention from developers in the last years concerning mobile application development. This, in turn, was the key factor of their success at the smartphones market and the portal for developers from all the world.

The growth of the gig economy has resulted in an increased interest in the relatively new phenomenon of multi-sided digital platforms. Previous research has naturally been focused on one of the key actors on these platforms, namely the gig workers, and how the gig economy has affected the labor market. However, multi-sided platforms entails complex dynamics and relationships between several actors, and the purpose of this study is to investigate what drives a business actor to join a multi-sided platform, what conditions it entails, as well as how it affects their business. To fulfill the purpose, a qualitative research design was conducted, in which semi-structured interviews with seven business actor representatives were included. Through theoretical perspectives on outsourcing, network externalities, and relationship management, this study is able to suggest that to handle the complexity, multi-sided platforms entail a certain degree of standardization that leads to increased transparency in the food delivery industry. In order to access the positive network effects that come from joining the platform, the business actors must adapt to the standardized conditions. Furthermore, they can differentiate to adapt or accept a change towards becoming commodity producers. The study indicates that there is a discrepancy between reality and theory on who outsources to whom when it comes to understanding a multi-sided platform in the gig economy.

This dissertation presents three essays that focus on various aspects pertaining to the management of online platforms, defined as "digital services that facilitate interactions between two or more distinct, but interdependent sets of users (whether firms or individuals) who interact through the service via the Internet" (OECD, 2019). The interactions benefit both the users and the platform. Managing online platforms involves developing strategies for one or more of three value adding functions: (a) lowering search costs for the parties connecting through the platform, (b) providing a technology infrastructure that facilitates transactions at scale by sharing both demand and supply side costs; and (c) locating other audiences or consumers for the output that results from the transaction. The platform manager must manage these value adding functions. Thus, one important management task is to recognize potential asymmetries in the economic and/or psychological motivations of the transacting parties connected through the platform. In this dissertation, I empirically examine these issues in greater detail. The first essay, "Incentivizing User-Generated Content—A Double-Edged Sword: Evidence from Field Data and a Controlled Experiment," addresses the conundrum faced by online platform managers interested in crowdsourcing user-generated content (UGC) in prosocial contexts. The dilemma stems from the fact that offering monetary incentives to stimulate UGC contributions also has a damping effect on peer approval, which is an important source of non-monetary recognition valued by UGC contributors in prosocial contexts. The second essay, "Matching and Making in Matchmaking Platforms: A Structural Analysis," examines matchmaking platforms, focusing specifically on the problem of misaligned incentives between the platform and the agents. Based on data from the Ultimate Fighting Championship (UFC) on fighter characteristics, and pay-per-view revenues associated with specific bouts, we identify the potential for conflicts of interest and examine strategies that may be used to mitigate such problems. The third essay, "Matching and Making in Matching Markets: A Managerial Decision Calculus," extends the empirical model and analytical work to a class of commonly encountered one-sided matching market problems. It provides the conceptual outline of a decision calculus that allows managers to explore the revenue and profitability implications of adaptive changes to the tier structures and matching algorithms. / Doctor of Philosophy / The 21st century has witnessed the rise of the platform economy. Consumers routinely interact with online platforms ways in their day to day activities. For instance, they interact with platforms such as Quora, StackOverflow, Uber, and Airbnb to name only a few. Such platforms address a variety of needs starting from providing users with answers to a variety of questions to matching them with a range of service providers (e.g., for travel and dining needs). However, the rapid growth of the platform economy has created a knowledge gap for both consumers and platforms. The three essays in this dissertation attempt to contribute to the literature in this area. The first essay, "Incentivizing User-Generated Content—A Double-Edged Sword: Evidence from Field Data and a Controlled Experiment," examines how crowdsourcing contests influence the quantity and quality of user-generated content (UGC). Analyzing data from the popular question and answer website Quora, we find that offering monetary incentives to stimulate UGC contributions increases contributions but also has a simultaneous damping effect on peer endorsement, which is an important source of non-monetary recognition for UGC contributors in prosocial contexts. The second essay, "Matching and Making in Matchmaking Platforms: A Structural Analysis," examines matchmaking platforms, focusing on the problem of misaligned incentives between the platform and the agents. Based on data from the Ultimate Fighting Championship (UFC) on fighter characteristics, and pay-per-view revenues associated with specific bouts, we identify the potential for conflicts of interest and examine strategies that may be used to mitigate such problems. The third essay, "Matching and Making in Matching Markets: A Managerial Decision Calculus," extends the empirical model and analytical work to a class of commonly encountered one-sided matching market problems. It provides the conceptual outline of a decision calculus that allows managers to explore the revenue and profitability implications of adaptive changes to the tier structures and matching algorithms.

In the digital age, with the accelerating pace of e-commerce, online platforms such as Amazon, Yelp, TripAdvisor, Facebook, Netflix, Uber and others have gained in prominence. Furthermore, in the wake of the COVID-19 pandemic, even businesses which were heretofore primarily brick-and-mortar have had to shift to a strong online presence in order to adapt and survive; which, while beneficial to all stakeholders, has resulted in dire challenges for the producers/service providers, platform owners, as well as consumers. In my first essay, I investigate the challenges faced by mobility as a service (MaaS) platforms such as Uber and Lyft for managing their demand and the pool of available drivers. On one hand, driver compensation issues in MaaS platforms is a highly discussed topic. On the other hand, the MaaS platforms are expanding to encompass several external businesses in search of profitability. In this chapter, I focus primarily on driver compensation issues in MaaS platforms when the platforms engage in external businesses. I find that in the majority of instances, the driver compensation reduces when the platforms get involved in external businesses; however, there are a few cases, where it leads to an increment in driver compensation, thus benefiting them. The second essay is on the impact of online reviews from digital platforms such as Yelp and TripAdvisor on business performance. Using a data set from Yelp, first, I study the interaction of average rating and number of reviews on business performance; second, how competition affects the interaction effect of the average rating and number of reviews on the focal business' performance. I find that the impact of the interaction of average rating and number of reviews on business performance is different at various levels of average ratings, and the inclusion of competition negatively influences the interaction effect of the average rating and number of reviews on the performance of the focal restaurant. In my third essay, I analyze how the interaction of supplier encroachment and consumer showrooming impacts an omnichannel retailer and her upstream manufacturer, who encroaches the downstream retailer's market with an online direct sales channel. I identify different scenarios in a covered market where either the retailer, or the manufacturer, or both will be better off. Taken together, these three essays provide valuable managerial insights for real world business problems, which will empower researchers in academia and industry managers, and help them improve their businesses and maximize their operational performance. / Business Administration/Marketing

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Since the development of modern technology, we are faced with more choices than ever. Our choices are based on the visual impression of a product or a service (Watzman, 1992). But what is it that makes us choose a specific product or service? This essay sheds light on the problems surrounding the development of technology that have led to a digitalized society where products are replaced by digital services. It also highlights a new professional role that has emerged from the digitalisation of society. Furthermore, the essay also enlightens on how a digital service's design might affect its users by examining how the music streaming services Spotify and Apple Music affect user's first impression of the service through a semiotic analysis. The results show that the design of a service is decisive for which target group the company is aimed at. And that the choice of functions maintains the interest of the users. In addition, the graphic design profession takes place through a development, where you may need to broaden your knowledge towards a digital competence. The essay opened up the question of whether there are more aspects that are based on the choice of service, such as the cost of the service and class differences.

Despite the growing expectation on corporate Learning & Development (L&D) units to transform practices and roles and take a more strategic role to better meet the needs of lifelong learning and up- and reskilling in organizations, many fail to use technology such as Massive Open Online Course (MOOC) platforms that could support the shift. The aim of this study is to provide a better understanding of why those in L&D who are using MOOC platforms do so and what impact it has on the role of L&D. Empirical data from in-depth interviews with six L&D professionals, with extensive experience from large, global companies, has been employed in this exploratory research. The analysis imply a situation in which MOOC platforms, when used in organizations where employee language, literacy and digital devices are not a limitation, can support in meeting the needs of lifelong learning and up- and reskilling in ways not possible with traditional L&D practices. The findings also indicate that while L&D units in large, global organizations can transform traditional practices and roles and contribute strategically, with the support of MOOC platforms, they struggle to make full use of the platforms due to lack of prioritization in the organization. This could inhibit L&D to fully take on a strategic role. Previous research has not explored the impact and possibilities the use of MOOC platforms can have specifically on the L&D role. The findings provide a novel contribution to L&D research as well as input to professionals in L&D or Human Resource, business leaders, organizational developers and MOOC platform providers in decisions connected to digitalization of corporate learning.

Usage of cloud-based storage systems gained a lot of prominence in fast few years. Every day millions of files are uploaded and downloaded from cloud storage. This data that cannot be handled by traditional databases and this is considered to be Big Data. New powerful platforms have been developed to store and organize big and unstructured data. These platforms are called Big Data systems. Some of the most popular big data platform are Mongo, Hadoop, and Cassandra. In this, we used Cassandra database management system because it is an open source platform that is developed in java. Cassandra has a masterless ring architecture. The data is replicated among all the nodes for fault tolerance. Unlike MySQL, Cassandra uses per-column basis technique to store data. Cassandra is a NoSQL database system, which can handle unstructured data. Most of Cassandra parameters are scalable and are easy to configure. Amazon provides cloud computing platform that helps a user to perform heavy computing tasks over remote hardware systems. This cloud computing platform is known as Amazon Web Services. AWS services also include database deployment and network management services, that have a non-complex user experience. In this document, a detailed explanation on Cassandra database deployment on AWS platform is explained followed by Cassandra performance tuning.    In this study impact on read and write performance with change Cassandra parameters when deployed on Elastic Cloud Computing platform are investigated. The performance evaluation of a three node Cassandra cluster is done. With the knowledge of configuration parameters a three node, Cassandra database is performance tuned and a draft model is proposed.             A cloud environment suitable for the experiment is created on AWS. A three node Cassandra database management system is deployed in cloud environment created. The performance of this three node architecture is evaluated and is tested with different configuration parameters. The configuration parameters are selected based on the Cassandra metrics behavior with the change in parameters. Selected parameters are changed and the performance difference is observed and analyzed. Using this analysis, a draft model is developed after performance tuning selected parameters. This draft model is tested with different workloads and compared with default Cassandra model. The change in the key cache memory and memTable parameters showed improvement in performance metrics. With increases of key cache size and save time period, read performance improved. This also showed effect on system metrics like increasing CPU load and disk through put, decreasing operation time and The change in memTable parameters showed the effect on write performance and disk space utilization. With increase in threshold value of memTable flush writer, disk through put increased and operation time decreased. The draft derived from performance evaluation has better write and read performance.

High-technology platform companies have emerged in large numbers in recent years, seeing great success and high valuations. In this paper, I examine whether platform companies have a significantly greater probability of achieving exceptional market value than non-platform companies. I do not find enough evidence to suggest that platform companies are significantly more likely than non-platform companies to achieve a high-value market capitalization within the 5 years after their initial public offering (IPO), conditional on their market value not being high-value at the time of IPO. However, I do find evidence to support that platform companies are significantly more likely to achieve a high-value market capitalization at the time of their IPO. Therefore, this study suggests that there is a significant positive effect of platform business models on market capitalization, which is incorporated into a company’s market value at the time of IPO, consistent with the efficient market hypothesis.