Balancing creativity and risk intelligence in professional knowledge-intensive business services

The main strengths of professional knowledge-intensive business services (P-KIBS) are knowledge and creativity which needs to be fostered, maintained and supported. The process of managing P-KIBS companies deals with financial, operational and strategic risks. That is why it is reasonable to apply risk management techniques and frameworks in this context. A significant challenge hides in choosing reasonable ways of implementing risk management, which will not limit creative ability in organization, and furthermore will contribute to the process. This choice is related to a risk intelligent approach which becomes a justified way of finding the required balance. On a theoretical level the field of managing both creativity and risk intelligence as a balanced process remains understudied in particular within KIBS industry. For instance, there appears to be a wide range of separate models for innovation and risk management, but very little discussion in terms of trying to find the right balance between them. This study aims to shed light on the importance of well-managed combination of these concepts. The research purpose of the present study is to find out how the balance between creativity and risk intelligence can be managed in P-KIBS. The methodological approach utilized in the study is strictly conceptual without empirical aspects. The research purpose can be achieved through answering the following research supporting questions: 1. What are the characteristics and role of creativity as a component of innovation process in a P-KIBS company? 2. What are the characteristics and role of risk intelligence as an approach towards risk management process implementation in a P-KIBS company? 3. How can risk intelligence and creativity be balanced in P-KIBS? The main theoretical contribution of the study conceals in a proposed creativity and risk intelligence stage process framework. It is designed as an algorithm that can be applied on organizational canvas. It consists of several distinct stages specified by actors involved, their roles and implications. Additional stage-wise description provides detailed tasks for each of the enterprise levels, while combining strategies into one. The insights driven from the framework can be utilized by a vast range of specialists from strategists to risk managers, and from innovation managers to entrepreneurs. Any business that is designing and delivering knowledge service can potentially gain valuable thoughts and expand conceptual understanding from the present report. Risk intelligence in the current study is a unique way of emphasizing the role of creativity in professional knowledge-intensive industry and a worthy technique for making profound decisions towards risks.

Emotional intelligence and multicultural project leadership in Finnish-Chinese MNC context

The main goal of this thesis was to examine how the emotional intelligence skills and multicultural project leadership style of a project manager interrelate and affect the success of a project. The research methods used are literature review in theoretical part of the thesis and semi-structured interviews in empirical part of the thesis. This study is a single case study i.e. one case company was selected to be the secondary level of analysis. Within the case company, four project managers were selected as research units to form the primary level of analysis. Literature review formed the basis for the empirical research and the interview questions were derived from the literature. Findings from the interviews were mirrored against the literature review findings, based on which both conclusions and generalisations could be made. Thus, both deductive and inductive methods were utilised to get more complete picture about the research topic. In the first part of the literature review the general leadership theories and the project leadership terminology are introduced as a background for the concept of emotional intelligence and the integrated leadership model. Emotional intelligence and its interrelation to different leadership concepts are discussed during the literature review. Chinese cultural aspects affecting the way of making business, and the multicultural leadership styles of the Finnish project managers are introduced in the following part of the literature review. It was found that the most successfully used multicultural leadership styles in Finnish-Chinese context are synergistic and polycentric, and these require emotional intelligence skills. In the empirical part on this thesis the findings from the semi-structured interviews are introduced, discussed and analysed. Interviews were done in private meeting rooms, and they were recorded and transcripted to add reliability and validity. Although the sample was only four project managers, the results show that the sample is quite saturated as the responses to several questions followed the same pattern. It was found that Finnish project managers in the case company are democratic and take cultural differences into account in their project leadership. Both synergistic and polycentric leadership styles are used with Chinese team members. Emotional intelligence capabilities and the emphasis of those differ a bit depending on the interviewee. Though, the results show that EI skills and the multicultural project leadership style used in Chinese context are interrelated. The findings from the literature review and the empirical research in this thesis are similar. Though, there is need for further research as the sample was small, and this thesis is a single case study. It is recommendable to make a multi-company study with larger sample of project managers. Also multi-industry perspective is recommendable for further research.

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Evaluation of Location Intelligence exploitation in capital-intensive business

As technology has developed it has increased the number of data produced and collected from business environment. Over 80% of that data includes some sort of reference to geographical location. Individuals have used that information by utilizing Google Maps or different GPS devices, however such information has remained unexploited in business. This thesis will study the use and utilization of geographically referenced data in capital-intensive business by first providing theoretical insight into how data and data-driven management enables and enhances the business and how especially geographically referenced data adds value to the company and then examining empirical case evidence how geographical information can truly be exploited in capital-intensive business and what are the value adding elements of geographical information to the business. The study contains semi-structured interviews that are used to scan attitudes and beliefs of an organization towards the geographic information and to discover fields of applications for the use of geographic information system within the case company. Additionally geographical data is tested in order to illustrate how the data could be used in practice. Finally the outcome of the thesis provides understanding from which elements the added value of geographical information in business is consisted of and how such data can be utilized in the case company and in capital-intensive business.

Building a Roadmap for a Market Intelligence function in a Global B2B company

Tämän diplomityötutkimuksen tarkoituksena on luoda markkinaälyyn (MI) erikoistunut funktio suurelle, globaalisti toimivalle B2B-yritykselle. Tämän päivän muut-tuvilla markkinoilla, teollisuusyrityksen on oltava markkinalähtöinen selviytyäkseen. Markkinatiedon tehokas hyödyntäminen ei pelkästään luo tietoa markkinoista, vaan tuottaa kilpailukykyistä tietoa ja toimii strategisen päätöksenteon tukena pitkällä aikavälillä. Tämä tutkimus on kvalitatiivinen toimintatutkimus, joka sisältää kirjallisuuskat-sauksen, yritystapaustutkimuksen sekä syväanalyysin yrityksen MI-ympäristöstä. Kirjallisuuskatsaus pitää sisällään teoriaa liittyen markkinaälyyn useassa eri kon-tekstissa, asiakassuhteeseen, sekä prosessinmallintamiseen. Empiiriseen osaa seuraa tutkimusmenetelmäkappale, joka sisältää kaksivaiheisen tutkimuksen mukaan lu-kien 20 päällikkötason haastattelua sekä yhden laaja-alaisen työryhmätapaamisen. Työn tuloksena syntyy kolmivaiheinen tiekartta, jonka tarkoitus on toimia pohjana uuden MI-funktion rakentamiselle Case-yrityksessä. Tuloksen mukaan MI-funktio tulisi sijoittaa yrityksen asiakasrajapintaan sekä tukea yksiköiden välistä integraa-tiota. Markkinaälyn jakaminen yrityksen sisällä vaatii käytäntöjen, tarpeiden ja ta-voitteiden systemaattista viestintää eri organisaatiotasoille, jotta yritys voi edelleen saada asiakkaalta tarpeeseen vastaavaa tietoa. Viestintä yrityksen ja asiakkaan välil-lä on oltava molemminpuolista, jotta tulokset voisivat parantaa asiakassuhdetta. Kun asiakassuhde paranee, yritys voi oppia asiakkaalta arvokasta tietoa, markkinaälyä.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.