There is an increasing pressure on service firms to innovate and compete on new offerings. As our lives become more digitized through the ubiquitous connectivity by the usage of digital devices, companies are now able to collect vast amount of various data in real-time, and thus, know radically more about their customers. Companies could leverage on this growing body of data and developing relevant services based on customer demands accordingly. One industry compelled to benefit by utilizing customer information is the telecom industry due to fierce competition and a need of innovation in a saturated market. Hence, the purpose of this study is to investigate how telecom companies use customer information in their development process of new services by answering the research question: How do telecom companies use customer information within their New Service Development process? To illuminate this, a qualitative research was conducted on three Swedish telecom companies. The findings indicate that telecom companies possess a beneficial position since they are able to collect a vast amount of data about their customers due to the digital nature of their services. However, they struggle to efficiently integrate the data and seamlessly disseminate the obtained knowledge internally. Hence, leveraging customer information in new service development has not reached its full potential and how well it is incorporated is determined by the skills of key employees and their collaboration rather than deployed internal processes.

The aim of this paper is to present the possibilities of the usage of advanced analytical tools to optimize decision-making in personnel practice. The literature review part of the thesis deals with the so-called HR analytics, its development, possibilities of its usage, and the methodological framework on which it is based. The next part of the paper deals with the specific application of HR analytics in the field of employee retention according to the methodological framework of CRISP-DM. The last chapter describes in detail the phenomenon of employee turnover, its consequences, and possible explanatory variables. The empirical part of the paper is framed as a quantitative, applied research and deals with voluntary turnover of employees in a particular company-a large Czech bank. Firstly, the statistical-inference part of the research identifies several statistically significant predictors of employee turnover through binary logistic regression-unemployment rate, number of changed teams, time spent in the company, salary and total income, salary growth rate, team size, extraordinary bonus, and gender. Secondly, in the data-science part, several prediction models are compiled, one using binary logistic regression as well and another based on several machine learning techniques. The models are...

Background: Digital technologies are increasingly transforming traditional businesses, and their pervasive impact is leading to a radical restructuring of entire industries. While the significance of generating competitive advantages for businesses utilizing big data analytics is recognized, there is still a lack of consensus of big data analytics influencing strategic decision-making in organisations. As big data and big data analytics become increasingly common, understanding the factors influencing decision-making quality becomes of paramount importance for businesses. Purpose: This thesis investigates how big data and big data analytics affect the operational strategic decision-making processes in enterprises through the theoretical lens of the strategy-as-practice framework. Method: The study follows an abductive research approach by testing a theory (i.e., strategy-aspractice) through the use of a qualitative research design. A single case study of IKEA was conducted to generate the primary data for this thesis. Sampling is carried out internally at IKEA by first identifying the heads of the different departments within the data analysis and from there applying the snowball sampling technique, to increase the number of interviewees and to ensure the collection of enough data for coding. Findings: The findings show that big data analytics has a decisive influence on practitioners. At IKEA, data analysts have become an integral part of the operational strategic decision-making processes and discussions are driven by data and rigor rather than by gut and intuition. In terms of practices, it became apparent that big data analytics has led to a more performance-oriented use of strategic tools and enabling IKEA to make strategic decisions in real-time, which not only increases agility but also mitigates the risk of wrong decisions.

This thesis formulates and solves an economic decision problem of the acquisi- tion of costly information in data-driven decision making. The thesis assumes an agent predicting a random variable utilizing several costly explanatory vari- ables. Prior to the decision making, the agent learns about the relationship between the random variables utilizing its past realizations. During the deci- sion making, the agent decides what costly variables to acquire and predicts using the acquired variables. The agent's utility consists of the correctness of the prediction and the costs of the acquired variables. To solve the decision problem, the thesis divides the decision process into two parts: acquisition of variables and prediction using the acquired variables. For the prediction, the thesis presents a novel approach for training a single predictive model accepting any combination of acquired variables. For the acquisition, the thesis presents two novel methods using supervised machine learning models: a backward es- timation of the expected utility of each variable and a greedy acquisition of variables based on a myopic increase in the expected utility of variables. Next, the thesis formulates the decision problem as a Markov decision process which allows approximating the optimal acquisition via deep...

I takt med digitaliseringen blir datadrivet beslutsfattande det nya normala i många branscher. Konkurrensfördelarna är allmänt kända eftersom det hjälper företag att utvecklas. Denna fallstudie syftar till att belysa de möjligheter som datadriven optimering bidrar med för skidorter när det kommer till att förbättra tjänster och anpassa skidanläggningar för framtiden. Huvudfokuset är att studera rörelsemönster hos skidåkare med hjälp av processutvinningsverktyg och andra metoder för visualisering. Detta har lett till följande forskningsfrågor: Vilken information går att utvinna ur data från liftsystem? Hur skulle denna typ av information kunna skapa värde i en organisation? Tidigare studier inom detta forskningsområde visar på stora möjligheter med användning av datautvinning och uppmanar till fortsatt forskning. Studien bidrar till forskningen genom att studera specifika åldersgrupper vilket tidigare inte genomförts. Studien visar att det finns skillnader i rörelsemönster hos olika åldersgrupper av skidåkare, vilket i sin tur visar på potentiella optimeringsområden hos skidanläggningarna. Utöver att belysa potentiella förbättringsområden med hjälp av datadrivna beslut visar studien även på en markant förändring hos typen av skidåkare som besöker svenska skidorter 2021, vilket troligtvis berodde på att Alperna höll stängt under skidsäsongen. I framtiden kan studien spela en viktig roll för forskning gällande hur Covid-19 påverkade svenska skidorter. / Given the digitalization, data-driven decision making is becoming the new normal in many industries. The competitive advantages are widely known as it helps companies to evolve. This case study aims to highlight the possibilities data-driven optimization provides when it comes to improving services and adapting to the future for ski resorts. Our focus is skier movement patterns which we generated by analyzing ski lift transportation data with a process mining tool and other methods for visualizations. Hence, our research questions: What information can be extracted from lift usage data? In what way can this information create value in an organization? Previous studies done in the field demonstrate many possibilities with data mining and urges for continued research. The research provided by this study is a contribution to the field through the research done on specific age-groups as this has not previously been done. This study introduces findings based on differences in the movement patterns based on skier age groups which lead to possible areas of optimization. In addition to highlighting possible ways to improve decision making using data, this study shows a significant shift in the type of skier visiting the Swedish ski-resorts 2021, possibly due to The Alps being closed this season. In the future, this study could play an essential role in studying how Covid-19 impacted Swedish ski-resorts.

The competition in the manufacturing industry has never been higher. Owing to the technological changes and advancements in the market, readily available data is no longer a thing of the past. Numerous studies have discussed the impact of industry 4.0, digital transformation as well as better production planning methods in the manufacturing industry.  The Mass-Manufacturing industry, in specific, has gained efficiency levels in production that were previously unimaginable. Industry 4.0 has been discussed as the ‘next big thing’ in the manufacturing context. In fact, it is seen as a necessity for manufacturing companies to stay competitive. However, efficient production planning methodologies are a preliminary requirement in order to successfully adopt the new manufacturing paradigms. The Engineering-to-order (ETO) industry is still widely unexplored by the academia ETO industries, barely have any production planning methodologies to rely on owing to their complex production processes and high reliance on manual-labour. Regression techniques have repeatedly been used in the production planning context. Considering its statistical prowess, it is no surprise that even the newer machine-learning techniques are based on regression. Considering its success in the mass-manufacturing industry for production planning, is it possible that its usage in the ETO industry might lead to the same results? This thesis involves a case study that was performed at an electrical transformer manufacturing plant in Sweden. After understanding the several operations that are performed in the production process, regression techniques are employed to estimate man-hours. The results from the study reconfirm the statistical prowess of regression and show the possibility of using regression in order to estimate man-hours in the ETO industry. In addition, several factors that can affect successful adoption of this tool in the production planning context are discussed. It is hoped that this study will lay the foundation for better production planning methodologies for the ETO industries in the future which might subsequently result in more data-driven decision making rather than instincts.

Reading competency is related to individuals' success at school and in their careers. Students who experience significant problems with reading may be at risk of long-term academic and social problems. High-quality measures that determine student progress toward curricular goals are needed for early identification and interventions to improve reading abilities and ultimately prevent subsequent failure in reading. The purpose of this quantitative nonexperimental ex post facto research study was to determine whether a correlation existed amongst student achievement scores on the Fountas and Pinnell Reading Benchmark Assessment and reading comprehension scores on the Criterion Reference Competency Test (CRCT). The item response theory served as the conceptual framework for examining whether a relationship exists between Fountas and Pinnell Benchmark Instructional Reading Levels and the reading comprehension scores on the CRCT of students in Grades 3, 4, and 5 in the year 2013-2014. Archival data for 329 students in Grades 3-5 were collected and analyzed through Spearman's rank-order correlation. The results showed positive relationships between the scores. The findings promote positive social change by supporting the use of benchmark assessment data to identify at-risk reading students early.

Data-driven approaches to decision making are today applied far and wide. With origins in the field of judgment and decision making (JDM), data-driven decision making (DDDM) has become an emergent topic within I-O psychology, especially within the fields of people analytics and human resource analytics. In light of the current AI revolution, it is evident that the next steps in JDM research include data- driven approaches. The purpose of this Master’s thesis was to compile the research on data-driven decision making conducted across disciplines into a comprehensive overview. Main research questions: based on systematic reviews and scoping reviews about implementations of DDDM affecting individuals, groups, or organizations, what areas of research can be identified? How and to what extent are they linked? To address these questions, this thesis utilizes a scoping meta-review design and bibliometrics. After rigorous search and screening processes, the final sample consisted of n = 1,008 systematic and scoping reviews. The results indicated that there are research areas within the included reviews that are isolated to a varying extent. Based on a multiple correspondence analysis (MCA), five areas of research were identified: business intelligence; learning analytics/education; mHealth/telemedicine; general decision making/decision support; and clinical decision support/diagnosis/healthcare. As a scoping meta-review encompassing a large number of scientific fields and methodologies, this thesis contributes to the progression of DDDM research at large. The results highlight the scattered nature of current research practices within DDDM and identify an opportunity for scientific advancement through interdisciplinary research.

People analytics (PA) has experienced significant growth in recent years due to the increasing availability of employee data and the impact of digitalization on organizations. This data-driven approach utilizes inductive methods to predict various outcomes in the field of human resources. Nevertheless, concerns have emerged regarding the availability and reliability of the data used in PA. Surprisingly, the quality standards of these data-driven methods have not been evaluated in the PA literature, despite their widespread adoption. To address these gaps, nine research questions covering expertise areas, psychological constructs, patterns/trends, study types, data sources, reliability reporting, data-driven frameworks, prediction accuracy, and open science practices in PA were reviewed. A scoping review was conducted to extract relevant information from each piece of literature, while bibliometric analysis provides a structured analysis of trends, themes, and key contributors. A total of 3,103 records were identified from the Scopus (n = 449) and APA PsycINFO (n = 2,700) databases, with nine studies included in the review. Findings indicated a lack of consideration given to quality, reliability aspects, and open science practices within PA literature. The predominant emphasis of the research was on the evaluation of variables, particularly turnover intention. This study contributes to advancing the understanding of PA by emphasizing the importance of incorporating quality standards and open science practices to enhance the reliability and credibility of research findings. The classification of the PA literature and recommendations for future research directions are provided, highlighting the need for a hierarchy of knowledge in the field. / Scoping Review of People Analytics

Organizations are social systems established to make decisions. Modern organizational decision-making is complex and can easily overwhelm the capacity of individuals. Because of the complexity of multi-person decisions, there is a big risk for uncertainty in decision-making. In recent years, the rise of business intelligence has enabled organizations to base their decisions on data and minimize uncertainty in their decision-making. However, deployment of business intelligence systems is characterized by complexity, making many small and medium-sized organizations fail to use such a system effectively.This thesis aims to identify and describe variables that influence successful use of a business intelligence architecture to support small organizations in making data-based decisions, what small organizations need to become data-driven in decision-making, and what measures small organizations can take to use business intelligence systems efficiently. Eight semi-structured interviews were conducted with professionals from Vasaloppet, a small organization deploying a business intelligence system. The empirical data gathered have been analyzed with a thematic approach. The thematic analysis identified four themes’ Deficiencies in organizational governance, Deficiencies in data management, Perceived workload, and Degree of matching between processes, organization, and strategy. Findings in these themes and underlying codes within these themes revealed problem areas in organizational governance when making decisions. Respondents mentioned challenges with a lack of a decision model, clear business plan, and intra-organizational understanding. When it comes to becoming data-driven, respondents said deficiency of structure for communication, lack of access to data, lack of data in decision-making, general workload, deficiencies in project results, and deficiencies in degree of matching as problematic. Based on the results of this study, guidelines are presented for small organizations to become data-driven in their decision-making.Keywords: Data-driven decision-making, business intelligence, small organizations