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Nowadays companies are in a dynamic environment leading to competition in finding new revenue streams to strengthen their positions in their markets by using new technologies to provide capabilitiesto organize resources whilst taking into account changes that can occur in their environment. Therefore, decision making is inevitable to combat uncertainties where taking the optimal action by leveraging concepts and technologies that support decision making such as Business Intelligence (BI)tools and systems could determine a company’s future. Companies can optimize their decision making with BI features like Data-Driven Alerts that sends messages when fluctuations occur within a supervised threshold that reflects the state of business operations. The purpose of this research was to conduct an empirical study on how Swedish companies and enterprises located in different industries apply BI tools and with Data-driven Alerts features for decision making whereby we further studied the characteristics of Data-driven Alerts in terms of usability from the perspectives of different industry professionals through the thematic lens of the Technology acceptance model (TAM) in a qualitative approach. We conducted interviews with professionals from diverse organizations where we applied the Thematic Coding technique on empirical results for further analysis. We found out that by allowing possibilities for users to analyze data in their own preferences for decisions, it will provide managers and leaders with sufficient information needed to empower strategic and tactical decision-making. Despite the emergence of state-of-the-art predictive analytics technologies such as Machine Learning and AI, the literature clearly states that these processes are technical and complex to be comprehended by the decision maker. At the end of the day, prescriptive analytics will end up providing descriptive options being presented to the end user as we move towards automated decision making. This we see as an opportunity for reporting tools and data-driven alerts to be in contemporary symbiotic relationship with advanced analytics in decision making contexts to improve its outcome, quality and user friendliness.

Big data analytics (BDA) has become an increasingly popular topic over the years amongst academics and practitioners alike. Big data, which is an important part of BDA, was originally defined with three Vs, being volume, velocity and variety. In later years more Vs have surfaced to better accommodate the current need. The analytics of BDA consists of different methods of analysing gathered data. Analysing data can provide insights to organisations which in turn can give organisations competitive advantage and enhance their businesses. Looking into the necessary resources needed to build big data analytic capabilities (BDAC), this thesis sought out to find how Swedish organisations enable and use BDA in their businesses. This thesis also investigated whether BDA could lead to performance enhancement and competitive advantage to organisations. A theoretical framework based on previous studies was adapted and used in order to help answer the thesis purpose. A qualitative study was deemed the most suitable for this study using semi-structured interviews. Previous studies in this field pointed to the fact that organisations may not be aware of how or why to use or enable BDA. According to current literature, different resources are needed to work in conjunction with each other in order to create BDAC and enable BDA to be utilized. Several different studies discuss challenges such as the culture of the organisation, human skills, and the need for top management to support BDA initiatives to succeed. The findings from the interviews in this study indicated that in a Swedish context the different resources, such as data, technical skills, and data driven culture, amongst others, are being used to enable BDA. Furthermore, the result showed that business process improvements are a first staple in organisations use of benefiting from BDA. This is because of the ease and security in calculating profits and effect from such an investment. Depending on how far an organisation have come in their transformation process they may also innovate and/or create products or services from insights made possible from BDA. / Big data analytics (BDA) har blivit ett populärt ämne under de senaste åren hos akademiker och utövare. Big data, som är en viktig del av BDA, var först definierad med tre Vs, volym, hastighet och varietet. På senare år har flertalet V framkommit för att bättre uttrycka det nuvarande behovet. Analysdelen i BDA består av olika metoder av analysering av data. Dataanalysering som görs kan ge insikter till organisationer, som i sin tur kan ge organisationer konkurrensfördelar och förbättra deras företag. Genom att definiera de resurser som krävs för att bygga big data analytic capabilities (BDAC), så försökte denna avhandling att visa hur svenska organisationer möjliggör och använder BDA i sina företag. Avhandlingen försökte också härleda om BDA kan leda till prestandaförbättringar och konkurrensfördelar för organisationer. Ett teoretiskt ramverk, baserat på tidigare studier, anpassades och användes för att hjälpa till att svara på avhandlingens syfte. En kvalitativ studie utsågs vara den mest passande ansatsen, tillsammans med semi-strukturerade intervjuer. Tidigare studier inom området visade på att organisationer kanske inte helt är medvetna om hur eller varför BDA möjliggörs eller kan användas. Enligt den nuvarande litteraturen så behöver olika resurser arbeta tillsammans med varandra för att skapa BDAC och möjliggöra att BDA kan utnyttjas till fullo. Flera olika studier diskuterade utmaningar såsom kulturen inom organisationen, kompetens hos anställda och att ledningen behöver stödja BDA initiativ för att lyckas. Fynden från studiens intervjuer indikerade, i ett svenskt sammanhang, att olika resurser såsom data, tekniska färdigheter och datadriven kultur bland annat, används för att möjliggöra BDA. Fortsättningsvis påvisade resultatet att affärsprocessförbättring är en första stapel i användandet av fördelarna från BDA. Anledningen till det är för att det är lättare och säkrare med beräkning av förtjänst och effekt från en sådan investering. Beroende på hur långt en organisation har kommit i deras transformationsprocess kan de också innovera och/eller skapa produkter eller tjänster som möjliggjorts av insikter från BDA.

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京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24735号 / 情博第823号 / 新制||情||138(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 吉川 正俊 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

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This publication-based dissertation examines human-related success factors for the implementation and application of data analytics tools and methods within the decision-making process of organizations. Generated insights on human-related factors are outlined and described in six chapters. First, a general introduction to the subject is provided and the research is positioned within a broader overall context. Additionally, the first section comprises a summary of the research papers included, along with publication information. Chapter 2 presents a systematic literature review summarizing the capabilities of Big Data analytics (BDA) with regard to firm performance. Five key capability clusters have been identified to categorize all relevant human-related capabilities across existing research to date. Chapter 3 presents an empirical research paper examining the relevant managerial aspects that must be considered when shifting from intuitive to analytics-based decision-making. Introducing a six-factor framework, the chapter outlines the findings of an indepth single case study of a German manufacturing organization that has already implemented analytical methods and tools within its decision processes. Chapter 4 contains the second empirical paper, which outlines the crucial role that executives play within the process of a firm’s digital transformation toward the application of analytics. Based on conducted interviews, four managerial archetypes are identified, with detailed descriptions of their characteristics, capabilities, and contribution to transformation. Chapter 5 introduces a teaching case study that sheds light on best practices relevant to the application of analytics. This case study describes the most critical factors for success in the use of an AI tool using an example from Wilo, a leading German manufacturer of pumps and pump systems. Finally, Chapter 6 summarizes the findings of this publication-based dissertation, outlines its contributions to academia and practice, and presents its limitations and potential avenues for future research.

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