The hospitality industry confronts continuous challenges from external environments, such as the COVID pandemic, the proliferation of short-term rentals, and the disruptive innovations of Generative AI. For businesses, understanding these external conditions and adapting strategies accordingly is crucial yet challenging, especially considering environmental uncertainties. Therefore, this dissertation investigates the effectiveness of different strategies in navigating market, competitive, and technological uncertainties, through a big-data analytical approach. It incorporates three studies, each focusing on one specific strategy and its varying outcomes under environmental changes. These studies employ machine learning algorithms to quantify strategies and utilize econometric models to infer the causal relationships between strategies and their outcomes. The first study examines how standardization affects short-term rental unit survival across two market conditions: pre-COVID growth and during-COVID decline. The results indicate that the risks arising from standardization are heightened under market decline. In addition, the effectiveness of standardization varies with design attributes to which the strategy is applied. Standardizing functional design boosts unit survival in the growing market but leads to a higher failure rate during the decline. Aesthetic standardization, on the other hand, negatively impacts survival in both conditions, with a stronger effect in the declining market. The second study identifies the impacts of differentiation on unit performance in the short-term rental context in two competitive environments: local versus city-level. The findings suggest that the effectiveness of differentiation increases with competitive pressure. At the local level where firms face localized competition, differentiation enhances unit performance. Conversely, in city-level environments where direct competition diminishes, it yields negative outcomes. Moreover, competition intensity, as reflected by the number of competitors and the degree of market concentration, is found to amplify the benefits of and mitigate the drawbacks of differentiation. The third study explores if adopting Generative AI to hotel online review response can improve customer feedback, under varying technological settings. It finds that simulated AI adoption improves customer perceptions when Generative AI models operate at high temperatures, while models with low temperatures lead to negative outcomes. The findings further underscore the importance of task-technology fit, revealing that Generative AI's effectiveness varies with review valence. Specifically, high-temperature settings for positive reviews generate significant benefits, whereas low-temperature settings lead to adverse effects. Conversely, for negative reviews, AI adoption demonstrates more stable outcomes across temperature settings, indicating balanced benefits of both low and high temperatures. In short, this dissertation identifies that the effectiveness of standardization, differentiation, and AI adoption strategies is contingent on environmental conditions. It underscores the importance of strategic adaptation in navigating contemporary challenges. / Doctor of Philosophy / It is difficult to operate hospitality businesses because this industry faces constant challenges from ever-changing external conditions, including the COVID pandemic, the rise of short-term rental platforms, and the breakthroughs in technology like Generative AI. It is important but challenging for hotels and short-term rentals to understand these conditions and plan their operations accordingly. Thus, this dissertation aims to help business operators to understand how to deal with different external changes. It carries on a series of studies based on big data, using various analytical tools. This dissertation is composed of three studies. The first one finds that, generally, it is risker for short-term rental hosts to make one property similar to his/her other properties when the whole market declines. There are differences identified between functionality and aesthetics. Keeping the functionalities, such as WIFI and coffeemaker, consistent among multiple properties will make the property more likely to survive when the market grows but it increases the likelihood of failure when the market demand decreases. When deciding property aesthetics, like color or layout, it is risky to have properties similar to each other, no matter if the market demand grows or drops. The second study concludes that short-term rental hosts should decide the product design relative to their competitors from different scopes of areas. They are suggested to make their properties' interior design style different from their nearby competitors to gain high revenues, especially when there are more neighboring supplies managed by a large number of hosts. On the contrary, it is more beneficial to follow the general trend of properties located in the same city when deciding one property's aesthetic style. The third study guides hotels to apply Generative AI like ChatGPT to generate response to customer online reviews. It found that, to reply to online reviews with four- or five-star ratings, hotels should not use the default GPT model to increase the quality of customer communication. Instead, they need to use the professional OpenAI API and set the parameter called temperature to 2. However, when hotels reply to online reviews with lower star ratings, like one or two, there is no big difference between low and high temperatures (0 to 2). They can simply use the default model. In general, there are no one-size-for-all solutions to deal with external challenges. Hospitality operators are highly recommended to adjust their operations to fit different conditions.

In B2B organizations, the adoption of digital marketing strategies has increased, leading to the collection of large amounts of data, big data. This has enabled the use of big data analytics, BDA, to uncover valuable insights for digital marketing purpose. Yet, there is limited research on how the B2B organizations integrate and utilize BDA in their digital marketing strategies, especially in the international context. This study aimed to address this research gap by examining how international B2B organizations integrate and utilize BDA in their digital marketing strategy, employing a dynamic capabilities perspective. The methodology of qualitative case study was applied, focusing on two established Swedish B2B organizations with an international presence. Empirical data was collected through semi-structured interviews and complemented with document analysis. Through abductive approach and hermeneutic interpretation, the findings show that despite the need for internal structural improvements, international B2B organizations are actively integrating BDA into their digital marketing strategies. By developing new routines and skills, these organizations can navigate the challenges posed by BDA while harnessing its benefits. Additionally, a framework comprising 10 practices in which international B2B organizations leverage BDA is proposed.

This study aspired to gain a more a nuanced understanding of the emerging analytics technologies and the vital capabilities that ultimately drive evidence-based decision making. Big data technology is widely discussed by varying groups in society and believed to revolutionize corporate decision making. In spite of big data's promising possibilities only a trivial fraction of firms deploying big data analytics (BDA) have gained significant benefits from their initiatives. Trying to explain this inability we leaned back on prior IT literature suggesting that IT resources can only be successfully deployed when combined with organizational capabilities. We identified key theoretical components at an organizational, relational, and human level. The data collection included 20 interviews with decision makers and data scientist from four analytical leaders. Early on we distinguished the companies into two categories based on their empirical characteristics. The terms “Born analyticals” and “Big intelligent firms” were coined. The analysis concluded that social, non-technical elements play a crucial role in building BDA abilities. These capabilities differ among companies but can still enable BDA in different ways, indicating that organizations´ history and context seem to influence how firms deploy capabilities. Some capabilities have proven to be more important than others. The individual mindset towards data is seemingly the most determining capability in building BDA ability. Varying mindsets foster different BDA-environments in which other capabilities behave accordingly. Born analyticals seemed to display an environment benefitting evidence based decisions.

Background: Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges. The study objective was high performance establishment of interactive BDA platform of hospital system. Methods: A Hadoop/MapReduce framework formed the BDA platform with HBase (NoSQL database) using hospital-specific metadata and file ingestion. Query performance tested with Apache tools in Hadoop’s ecosystem. Results: At optimized iteration, Hadoop distributed file system (HDFS) ingestion required three seconds but HBase required four to twelve hours to complete the Reducer of MapReduce. HBase bulkloads took a week for one billion (10TB) and over two months for three billion (30TB). Simple and complex query results showed about two seconds for one and three billion, respectively. Interpretations: BDA platform of HBase distributed by Hadoop successfully under high performance at large volumes representing the Province’s entire data. Inconsistencies of MapReduce limited operational efficiencies. Importance of the Hadoop/MapReduce on representation of health informatics is further discussed. / Graduate / 0566 / 0769 / 0984 / dillon.chrimes@viha.ca

The goal of this thesis was to investigate the factors that influence the adoption of big data analytics by public sector employees based on the adapted Unified Theory of Acceptance and Use of Technology (UTAUT) model. A mixed method of survey and interviews were used to collect data from employees of a Canadian provincial government ministry. The results show that performance expectancy and facilitating conditions have significant positive effects on the adoption intention of big data analytics, while effort expectancy has a significant negative effect on the adoption intention of big data analytics. The result shows that social influence does not have a significant effect on adoption intention. In terms of moderating variables, the results show that gender moderates the effects of effort expectancy, social influence and facilitating condition; data experience moderates the effects of performance expectancy, effort expectancy and facilitating condition; and leadership moderates the effect of social influence. The moderation effects of age on performance expectancy, effort expectancy is significant for only employees in the 40 to 49 age group while the moderation effects of age on social influence is significant for employees that are 40 years and more. Based on the results, implications for public sector organizations planning to implement big data analytics were discussed and suggestions for further research were made. This research contributes to existing studies on the user adoption of big data analytics.

Eine Zeitreihe ist eine zeitlich geordnete Folge von Datenpunkten. Zeitreihen werden typischerweise über Sensormessungen oder Experimente erfasst. Sensoren sind so preiswert geworden, dass sie praktisch allgegenwärtig sind. Während dadurch die Menge an Zeitreihen regelrecht explodiert, lag der Schwerpunkt der Forschung in den letzten Jahrzehnten auf der Analyse von (a) vorgefilterten und (b) kleinen Zeitreihendatensätzen. Die Analyse realer Zeitreihendatensätze wirft zwei Probleme auf: Erstens setzen aktuelle Ähnlichkeitsmodelle eine Vorfilterung der Zeitreihen voraus. Das beinhaltet die Extraktion charakteristischer Teilsequenzen und das Entfernen von Rauschen. Diese Vorverarbeitung muss durch einen Spezialisten erfolgen. Sie kann zeit- und kostenintensiver als die anschließende Analyse und für große Datensätze unrentabel werden. Zweitens führte die Verbesserung der Genauigkeit aktueller Ähnlichkeitsmodelle zu einem unverhältnismäßig hohen Anstieg der Komplexität (quadratisch bis biquadratisch). Diese Dissertation behandelt beide Probleme. Es wird eine symbolische Zeitreihenrepräsentation vorgestellt. Darauf aufbauend werden drei verschiedene Ähnlichkeitsmodelle eingeführt. Diese erweitern den aktuellen Stand der Forschung insbesondere dadurch, dass sie vorverarbeitungsfrei, unempfindlich gegenüber Rauschen und skalierbar sind. Anhand von 91 realen Datensätzen und Benchmarkdatensätzen wird zusätzlich gezeigt, dass die hier eingeführten Modelle auf den meisten Datenätzen die höchste Genauigkeit im Vergleich zu 15 aktuellen Ähnlichkeitsmodellen liefern. Sie sind teilweise drei Größenordnungen schneller und benötigen kaum Vorfilterung. / A time series is a collection of values sequentially recorded from sensors or live observations over time. Sensors for recording time series have become cheap and omnipresent. While data volumes explode, research in the field of time series data analytics has focused on the availability of (a) pre-processed and (b) moderately sized time series datasets in the last decades. The analysis of real world datasets raises two major problems: Firstly, state-of-the-art similarity models require the time series to be pre-processed. Pre-processing aims at extracting approximately aligned characteristic subsequences and reducing noise. It is typically performed by a domain expert, may be more time consuming than the data mining part itself, and simply does not scale to large data volumes. Secondly, time series research has been driven by accuracy metrics and not by reasonable execution times for large data volumes. This results in quadratic to biquadratic computational complexities of state-of-the-art similarity models. This dissertation addresses both issues by introducing a symbolic time series representation and three different similarity models. These contribute to state of the art by being pre-processing-free, noise-robust, and scalable. Our experimental evaluation on 91 real-world and benchmark datasets shows that our methods provide higher accuracy for most datasets when compared to 15 state-of-the-art similarity models. Meanwhile they are up to three orders of magnitude faster, require less pre-processing for noise or alignment, or scale to large data volumes.

Netzwerk Theorie hat sich als besonders zweckdienlich in der Darstellung von Systemen herausgestellt. Jedoch fehlen in der Netzwerkdarstellung von Systemen noch immer essentielle Bausteine um diese generell zur Datenanalyse heranzuziehen zu können. Allen voran fehlt es an einer expliziten Assoziation von Informationen mit den Knoten und Kanten eines Netzwerks und einer schlüssigen Darstellung von Gruppen von Knoten und deren Relationen auf verschiedenen Skalen. Das Hauptaugenmerk dieser Dissertation ist der Einbindung dieser Bausteine in eine verallgemeinerte Rahmenstruktur gewidmet. Diese Rahmenstruktur - Deep Graphs - ist in der Lage als Bindeglied zwischen einer vereinheitlichten und generalisierten Netzwerkdarstellung von Systemen und den Methoden der Statistik und des maschinellen Lernens zu fungieren (Software: https://github.com/deepgraph/deepgraph). Anwendungen meiner Rahmenstruktur werden dargestellt. Ich konstruiere einen Regenfall Deep Graph und analysiere raumzeitliche Extrem-Regenfallcluster. Auf Grundlage dieses Graphs liefere ich einen statistischen Beleg, dass die Größenverteilung dieser Cluster einem exponentiell gedämpften Potenzgesetz folgt. Mit Hilfe eines generativen Sturm-Modells zeige ich, dass die exponentielle Dämpfung der beobachteten Größenverteilung durch das Vorhandensein von Landmasse auf unserem Planeten zustande kommen könnte. Dann verknüpfe ich zwei hochauflösende Satelliten-Produkte um raumzeitliche Cluster von Feuer-betroffenen Gebieten im brasilianischen Amazonas zu identifizieren und deren Brandeigenschaften zu charakterisieren. Zuletzt untersuche ich den Einfluss von weißem Rauschen und der globalen Kopplungsstärke auf die maximale Synchronisierbarkeit von Oszillatoren-Netzwerken für eine Vielzahl von Oszillatoren-Modellen, welche durch ein breites Spektrum an Netzwerktopologien gekoppelt sind. Ich finde ein allgemeingültiges sigmoidales Skalierungsverhalten, und validiere dieses mit einem geeignetem Regressionsmodell. / Network theory has proven to be a powerful instrument in the representation of complex systems. Yet, even in its latest and most general form (i.e., multilayer networks), it is still lacking essential qualities to serve as a general data analysis framework. These include, most importantly, an explicit association of information with the nodes and edges of a network, and a conclusive representation of groups of nodes and their respective interrelations on different scales. The implementation of these qualities into a generalized framework is the primary contribution of this dissertation. By doing so, I show how my framework - deep graphs - is capable of acting as a go-between, joining a unified and generalized network representation of systems with the tools and methods developed in statistics and machine learning. A software package accompanies this dissertation, see https://github.com/deepgraph/deepgraph. A number of applications of my framework are demonstrated. I construct a rainfall deep graph and conduct an analysis of spatio-temporal extreme rainfall clusters. Based on the constructed deep graph, I provide statistical evidence that the size distribution of these clusters is best approximated by an exponentially truncated powerlaw. By means of a generative storm-track model, I argue that the exponential truncation of the observed distribution could be caused by the presence of land masses. Then, I combine two high-resolution satellite products to identify spatio-temporal clusters of fire-affected areas in the Brazilian Amazon and characterize their land use specific burning conditions. Finally, I investigate the effects of white noise and global coupling strength on the maximum degree of synchronization for a variety of oscillator models coupled according to a broad spectrum of network topologies. I find a general sigmoidal scaling and validate it with a suitable regression model.

Big data is currently a buzzword in both academia and industry, with the term being used todescribe a broad domain of concepts, ranging from extracting data from outside sources, storingand managing it, to processing such data with analytical techniques and tools.This thesis work thus aims to provide a review of current big data analytics concepts in an attemptto highlight big data analytics’ importance to decision making.Due to the rapid increase in interest in big data and its importance to academia, industry, andsociety, solutions to handling data and extracting knowledge from datasets need to be developedand provided with some urgency to allow decision makers to gain valuable insights from the variedand rapidly changing data they now have access to. Many companies are using big data analyticsto analyse the massive quantities of data they have, with the results influencing their decisionmaking. Many studies have shown the benefits of using big data in various sectors, and in thisthesis work, various big data analytical techniques and tools are discussed to allow analysis of theapplication of big data analytics in several different domains.

<p>The topic of the research in this dissertation is the application of machine<br />learning in solving problems characteristic to biosystems, with special<br />emphasis on agriculture. Firstly, an innovative regression algorithm based on<br />big data was presented, that was used for yield prediction. The predictions<br />were then used as an input for the improved portfolio optimisation algorithm,<br />so that appropriate soybean varieties could be selected for fields with<br />distinctive parameters. Lastly, a multi-objective optimisation problem was set<br />up and solved using a novel method for categorical evolutionary algorithm<br />based on NSGA-III.</p> / <p>Предмет истраживања докторске дисертације је примена машинског учења у решавању проблема карактеристичних за биосистемe са нагласком на пољопривреду. Најпре је представљен иновативни алгоритам за регресију који је примењен на великој количини података како би се са предиковали приноси. На основу предикција одабране су одговарајуће сорте соје за њиве са одређеним карактеристикама унапређеним алгоритмом оптимизације портфолија. Напослетку је постављен оптимизациони проблем одређивања сетвене структуре са вишеструким функцијама циља који је решен иновативном методом, категоричким еволутивним алгоритмом заснованом на NSGA-III алгоритму.</p> / <p>Predmet istraživanja doktorske disertacije je primena mašinskog učenja u rešavanju problema karakterističnih za biosisteme sa naglaskom na poljoprivredu. Najpre je predstavljen inovativni algoritam za regresiju koji je primenjen na velikoj količini podataka kako bi se sa predikovali prinosi. Na osnovu predikcija odabrane su odgovarajuće sorte soje za njive sa određenim karakteristikama unapređenim algoritmom optimizacije portfolija. Naposletku je postavljen optimizacioni problem određivanja setvene strukture sa višestrukim funkcijama cilja koji je rešen inovativnom metodom, kategoričkim evolutivnim algoritmom zasnovanom na NSGA-III algoritmu.</p>

<div>This dissertation focuses on developing a series of mathematical models to understand the role of urban transportation system, urban mobility and information dissemination in the spreading process of infectious diseases within metropolitan areas. Urban transportation system serves as the catalyst of disease contagion since it provides the mobility for bringing people to participate in intensive urban activities and has high passenger volume and long commuting time which facilitates the spread of contagious diseases. In light of significant needs in understanding the connection between disease contagion and the urban transportation systems, both macroscopic and microscopic models are developed and the dissertation consists of three main parts. </div><div></div><div>The first part of the dissertation aims to model the macroscopic level of disease spreading within urban transportation system based on compartment models. Nonlinear dynamic systems are developed to model the spread of infectious disease with various travel modes, compare models with and without contagion during travel, understand how urban transportation system may facilitate or impede epidemics, and devise control strategies for mitigating epidemics at the network level. The hybrid automata is also introduced to account for systems with different levels of control and with uncertain initial epidemic size, and reachability analysis is used to over-approximate the disease trajectories of the nonlinear systems. The 2003 Beijing SARS data are used to validate the effectiveness of the model. In addition, comprehensive numerical experiments are conducted to understand the importance of modeling travel contagion during urban disease outbreaks and develop control strategies for regulating the entry of urban transportation system to reduce the epidemic size. </div><div></div><div>The second part of the dissertation develops a data-driven framework to investigate the disease spreading dynamics at individual level. In particular, the contact network generation algorithm is developed to reproduce individuals' contact pattern based on smart card transaction data of metro systems from three major cities in China. Disease dynamics are connected with contact network structures based on individual based mean field and origin-destination pair based mean field approaches. The results suggest that the vulnerability of contact networks solely depends on the risk exposure of the most dangerous individual, however, the overall degree distribution of the contact network determines the difficulties in controlling the disease from spreading. Moreover, the generation model is proposed to depict how individuals get into contact and their contact duration, based on their travel characteristics. The metro data are used to validate the correctness of the generation model, provide insights on monitoring the risk level of transportation systems, and evaluate possible control strategies to mitigate the impacts due to infectious diseases. </div><div></div><div>Finally, the third part of the dissertation focuses on the role played by information in urban travel, and develops a multiplex network model to investigate the co-evolution of disease dynamics and information dissemination. The model considers that individuals may obtain information on the state of diseases by observing the disease symptoms from the people they met during travel and from centralized information sources such as news agencies and social medias. As a consequence, the multiplex networks model is developed with one layer capturing information percolation and the other layer modeling the disease dynamics, and the dynamics on one layer depends on the dynamics of the other layer. The multiplex network model is found to have three stable states and their corresponding threshold values are analytically derived. In the end, numerical experiments are conducted to investigate the effectiveness of local and global information in reducing the size of disease outbreaks and the synchronization between disease and information dynamics is discussed. </div><div></div>