京都大学 / 新制・課程博士 / 博士(経済学) / 甲第24375号 / 経博第662号 / 新制||経||302(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慶彦, 教授 江上 雅彦, 講師 柳 貴英 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DFAM

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This master thesis uses applied mathematicalstatistics to analyse purchase behaviour based on customer data of the Swedishbrand Indiska. The aim of the study is to build a model that can helppredicting the sales quantities of different product classes and identify whichfactors are the most significant in the different models and furthermore, tocreate an algorithm that can provide suggested product combinations in thepurchasing process. Generalized linear models with a Negative binomial distributionare applied to retrieve the predicted sales quantity. Moreover, conditionalprobability is used in the algorithm which results in a product recommendationengine based on the calculated conditional probability that the suggestedcombinations are purchased.From the findings, it can be concluded that all variables considered in themodels; original price, purchase month, colour, cluster, purchase country andchannel are significant for the predicted outcome of the sales quantity foreach product class. Furthermore, by using conditional probability andhistorical sales data, an algorithm can be constructed which createsrecommendations of product combinations of either one or two products that canbe bought together with an initial product that a customer shows interest in. / Matematisk statistik tillämpas i denna masteruppsats för att analysera köpbeteende baserat på kunddata från det svenska varumärket Indiska. Syftet med studien är att bygga modeller som kan hjälpa till att förutsäga försäljningskvantiteter för olika produktklasser och identifiera vilka faktorer som är mest signifikanta i de olika modellerna och därtill att skapa en algoritm som ger förslag på rekommenderade produktkombinationer i köpprocessen. Generaliserade linjära modeller med en negativ binomialfördelning utvecklades för att beräkna den förutspådda försäljningskvantiteten för de olika produktklasserna. Dessutom används betingad sannolikhet i algoritmen som resulterar i en produktrekommendationsmotor som baseras på den betingade sannolikheten att de föreslagna produktkombinationerna är inköpta.Från resultaten kan slutsatsen dras att alla variabler som beaktas i modellerna; originalpris, inköpsmånad, produktfärg, kluster, inköpsland och kanal är signifikanta för det predikterade resultatet av försäljningskvantiteten för varje produktklass. Vidare är det möjligt att, med hjälp av betingad sannolikhet och historisk försäljningsdata, konstruera en algoritm som skapar rekommendationer av produktkombinationer av en eller två produkter som kan köpas tillsammans med en produkt som en kund visar intresse för.

The purpose of this thesis is to research how consumer recommendations can be used in terms of marketing and sales purposes in the Fast Moving Consumer Goods sector. Due to technological changes and a shift from traditional to digital marketing, the way of communicating to the audience has changed. Consumer recommendations, micro influencers and social media platforms are a part of digital marketing that is growing progressively. The paradigm of the thesis is interpretivism with following a qualitative research method, inductive reasoning and a case study in focus. The findings show that digital marketing, thanks to aspects as social media and e-commerce, has been growing, however has not completely taken over the marketing field. Influencers are getting a bigger role as marketing tools with the shift of power evolving. Due to these reasons is why and how consumer recommendations have become such a big trend nowadays.

With a rapid growth in smartwatch and smartwatch audio technologies, there is a lack of knowledge regarding user needs for smartwatch audio experiences and how those needs can be satisfied through user-centered design. Previous smartwatch user behavior studies suggest that audio app usage is not a primary use case for the smartwatch. However, audio applications are increasingly incorporated into smartwatches, which leads to the question of the apps’ purpose, validity, overlooked contexts and use cases. This thesis aims to understand what kind of audio experience(s) a user-centered design process might generate for the smartwatch. The design process generated insights from smartwatch users of audio applications, that were used as design guidelines for Context Awareness, Micro-interactions, and Device Ecosystem. The resulting prototype HeartBeats considers Context Awareness with heart rate music recommendations, Micro-interactions with one-handed song skipping and Quickplay music, and Device Ecosystem with speaker access and phone battery support. / Med en snabb teknisk utveckling av smartklockor och tillhörande ljudteknik finns det en kunskapsbrist om användarbehov och hur dessa kan tillfredsställas genom användarcentrerad design. Tidigare forskning om smartklocksanvändares beteenden tyder på att ljudapplikationer inte är ett huvudsakligt användningsområde för smartklockor. Ljudapplikationer implementeras dock allt mer i smartklockor, vilket leder till frågan om vilket värde de ger och om användningsområden möjligen har förbisetts. Den här uppsatsen syftar till att förstå vilka sorts ljudupplevelser en användarcentrerad designprocess skulle resultera i för smartklockor. Designprocessen resulterade i insikter om smartklocksanvändares beteenden med ljudapplikationer, vilket användes som designriktlinjer för kontextmedvetenhet, mikrointeraktioner och ekosystem av enheter. Den resulterande prototypen HeartBeats nyttjar kontextmedvetenhetgenom att rekommendera musik med användarens hjärtrytm i åtanke, mikrointeraktioner med en gest för att byta låt och snabbstart av musik, samt ekosystem av enheter genom snabb åtkomst till klockhögtalare och stöd för att spara telefonbatteri.

This report investigates if it is possible to use the Indian Buffet Process (IBP), a stochastic process that defines a probability distribution, as part of a recommendation system. The report focuses on recommendation systems where one type of object, for instance movies, is recommended to another type of object, for instance users.         A concept of performing link prediction with IBP is presented, along with a method for performing inference. Three papers that are related to the subject are presented and their results are analyzed together with additional experiments on an implementation of the IBP.        The report arrives at the conclusion that it is possible to use IBP in a recommendation system when recommending one object to another. In order to use IBP priors in a recommendation system which include real-life datasets, the paper suggests the use of a coupled version of the IBP model and if possible perform inference with a parallel Gibbs sampling. / Denna rapport undersöker om det är möjligt att använda Indian Buffet Process (IBP), en stokatisk process som definierar en sannolikhetsfördelning, som en del av ett rekommendationssystem. Rapporten fokuserar på rekommendationssystem där en sorts objekt, exempelvis filmer, rekommenderas till en annan sorts objekt, exempelvis användare.         Ett sätt att förutse länkar, link prediction, mellan olika objekt med hjälp av IBP presenteras tillsammans med en metod för att dra statistiska slutsatser, inference. Tre rapporter som är relaterade till ämnet presenteras och deras resultat analyseras tillsammans med ytterligare experiment på en implementation av IBP.        Rapporten drar slutsatsen att det är möjligt att använda IBP i ett rekommendationssystem då systemet rekommenderar ett objekt till ett annat objekt. Rapporten föreslår en kopplad version av IBP för att kunna använda IBP i ett rekommendationssystem som arbetar på riktigt data samt att inference ska utföras med en parallell Gibbs sampling.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24934号 / 情博第845号 / 新制||情||141(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 伊藤 孝行, 教授 田島 敬史 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The study's primary objective is to investigate the obstacles associated with agile implementation in ICT public services and the importance of government facilitation and removal of these obstacles. It employs a case study strategy which includes data collection and analysis methods. The case study's purpose is to investigate the challenges of implementing the agile methodology in Kuwait's public services, and the findings are presented at the end. Thus the study is formulated around the following research questions: “What are the challenges of implementing agile methodology into the Kuwait ICT public services?” and “What are the recommendations for the government side to facilitate adopting agile transformation?”. In order to gain a better understanding, the study uses the appropriate approach for producing thematic analysis results in a table of themes, categories, and codes covering ICT managers and decision-makers in the public and private sectors who participated in semi-structured interviews. The conclusions are based on the data collected and analyzed, and the study provides evidence to support the final recommendations. The study concludes that implementing Agile methodology in Kuwait's public services faces several challenges, including cultural or regulation factors. The study provides recommendations for the Kuwaiti government to facilitate adopting Agile transformation, including developing a clear vision, providing training and support, and creating a culture of trust and collaboration. The findings also suggest that the Kuwaiti government can adopt an agile methodology to provide public services in the IT domain with faster response and feedback while changing the bureaucratic culture and ancient rules to be more adaptive and reasonable.

Effects of marketing mix tools, such as price promotions and digital advertising, are dynamic and long-term. Yet marketing literature has mainly focused on their immediate and short-term effects. Marketing Mix changes consumers' state, such as expectations and beliefs, and the changed state affects consumers' subsequent behaviors and responses to marketing stimuli and treatment, which may cause unintended consequences and generate important marketing opportunities as well. In Essay 1, we uncover the perils of tensile discounts (e.g., maximum discounts such as "up to 60% off") by examining their effects on store visits and purchases. Through a series of studies and using a multi-method approach including empirical data analysis, quasi-field experiment, numerical simulations, and controlled lab studies, we find that despite the initial efficacy in shaping consumer discount expectations and stimulating store visits, a maximum discount (vs. a minimum discount such as "starting at 20% off" or a range discount such as "20% off to 60% off") could reduce consumer purchases in many conditions. There is thus a need to balance the outcomes of the two stages and choose the optimal tensile discount under different discount distributions to maximize store sales. In Essay 2, we investigate how digital platforms leverage curation source (algorithm or human) advertising to promote depth or breadth selling and best sequence these two types of advertising to further improve advertising effects. Through a survey study and a field experiment, we find that consumers have a lay belief about the competitive advantages of algorithms and humans that algorithms (humans) generate personalized (novel) recommendations, and importantly, they consider and purchase products similar (new) to their previous preferences when receiving such advertising. Moreover, the complementarity between depth and breadth selling can be best leveraged in sequential advertising by the initial breadth selling amplifying the subsequent depth selling effectiveness. Thus, a carefully-sequenced hybrid advertising can help cultivate consumers’ ever-renewing interests and consumption. / Business Administration/Marketing