For manufacturing firms, having knowledge about customers is very important, in particular for the developers and designers of new products. A way in which software can help to build an information channel between the customers and the firm is through Customer Relationship Management (CRM) and Business Intelligence (BI) solutions. Customers’ data are captured into the Customer Relationship Management solution while Business Intelligence analyses them and provide clear processed information to the developers and designers of new products. In this study we have researched if this process occurs in the industry, if and how it can be improved and what advantages it could bring to manufacturing firms. We have carried out the data collection by interviewing experts in four companies, three software companies that provide Business Intelligence solutions and one manufacturing firm. We found out that those software solutions are not used to directly connect developers and designers to customers’ data, and that there are no specific technical obstacles that prevents this, if not managerial reasons rooted in everyday practice. We also uncovered facts that would help to make this process more efficient and make customers’ data even more relevant to development.

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

The budget has for decades been a cornerstone in most organizations management accounting and control. The budget has also faced criticism for being expensive, time-consuming, for quickly becoming obsolete and for impeding flexibility in organisations. In an increasingly dynamic world, companies need to become more adaptive and forward-looking. Researchers believe that budgeting is generally a data-driven activity. Hence, it becomes suitable for application of analytical technologies, such as Business Intelligence (BI). Although BI has been at the top of companies' agendas for a long time, research show that many organizations still struggle to harvest the benefits or reach the expectations with BI-systems. Thus, the purpose of this study has been to analyze and compare how business intelligence can be used for and influence budgeting among Swedish car dealers. To experience several perspectives on how BI affects budgeting in practice the study takes a qualitative approach as seven interviews with individuals at seven different car dealerships was conducted. From the existing body of literature and research, a framework to describe and analyze how BI can be used for budgeting was constructed. The study found that the use of BI for budgeting practice does not reach the potential that the literature and previous research illuminates. The conclusion is based on a minimal gathering of external and unstructured data, the modest use of predictive analysis and the non-existence occurrence of prescriptive analysis. Despite this, BI can still be used for and affect budgeting in several positive remarks. BI can gather and quickly provide a both comprehensive as well as deeply detailed overview of the business. Furthermore, BI seems to enable budget evaluation more frequently and in combination with budget follow-up and control. Something that potentially might mitigate the critique the budget has endured for impeding flexibility. However, the study identified tendencies that the follow-up of budgets seems more compatible with BI-systems than the actual making of budgets. Moreover, indications points toward a more reciprocal relationship between BI-use for budgeting, as the budgeting style and style of management control might affect the use of BI. / Budgeten har under årtionden varit en grundbult inom de flesta organisationers ekonomi- och verksamhetsstyrning. Budgeten har mött kritik för att vara dyr, tidskrävande, för att snabbt bli inaktuell samt för att förhindra flexibilitet i organisationer. I en alltmer dynamisk omvärld behöver företag bli mer adaptiva och framåtblickande. Forskare menar att budgetering generellt sett är en data driven aktivitet. Vilket gör den lämplig att applicera analytiska teknologier på, så som business intelligence (BI). Även om BI varit på toppen av företagens agenda under en lång tid visar forskning att många organisationer fortfarande inte lyckats skörda de fördelar eller uppnå de förväntningar som finns på BI-system. Syftet med denna studie har således varit att analysera och jämföra hur business intelligence kan användas för och påverka budgetering bland svenska bilåterförsäljare. För att erfara olika perspektiv på hur BI påverkar budgetering i praktiken tar studien en kvalitativ ansats. Sju personer intervjuades hos sju olika bilåterförsäljare. Utifrån den existerande litteraturen och forskning utarbetades en analysmodell i syfte att beskriva och analysera hur BI kan användas inom budgetering. Studien fann att användningen av BI i praktiken inte uppnår den potential som forskningen belyser. Slutsatsen grundar sig i en minimal användning av externa och ostrukturerade data. Samt en blygsam användning av prediktiva analyser och total avsaknad av preskriptiva analyser. Trots det kan BI användas för och påverka budgetering positivt i flera bemärkelser. BI kan samla in data och snabbt ge både en överblickande och en detaljerad bild över verksamheten. Vidare möjliggör BI att utvärdering av budgetar kan ske kontinuerligt i kombination med uppföljning. Något som potentiellt kan mildra den kritik budgeten utsått för att förhindra flexibilitet. Dessutom visade empirin tendenser på att uppföljning av budgetarna verkar vara mer förenligt med BI än själva byggande av budgetarna. Studien fann även det inte bara är BI som påverkar budgeteringen utan sättet att budgetera i sin tur kan påverka användningen av BI.

This research presents an exploratory study concerning organizations’ best practices of Web analytics for a successful performance and the factors influencing the companies’ successful adoption of Web analytics. A qualitative research methodology was used engaging a comprehension of Web analytics adoption using the Diffusion of Innovation theory (Rogers, 1995) and the theory building approach (Eisenhardt, 1989). Interviews with five companies from different industries were conducted. Findings suggest that for a successful performance, companies should consider: • Data for better decision making. • Web analytics barriers • Selecting the right KPIs and metrics based on the company’s goals. • Web analytics trends A mixed-method approach comprising other extensive methods of data collection should be conducted. Investigation of the use of specific metrics and KPIs within companies from different industries, as well as the strategies for working past the barriers that impede companies from adopting Web analytics should be considered.

Business Intelligence (BI) är en teknik som används av företag för att förbättra beslutsfattningen i den dagliga verksamheten. Bättre beslutsfattning kan betyda stora fördelar för de företagen som nyttjar BI-tekniken på rätt sätt. Att hitta korrekt tillvägagångssätt är däremot svårt, då organisationer bedriver sin verksamhet på olika sätt och inom olika branscher. Individer inom organisationer har ansvaret för implementeringen av tekniken, som kan begränsas av bristande kompetens. För en ny användare inom BI kan det vara svårt att veta vilken kompetens som behövs, något vi försöker klargöra i denna rapport. Det saknas däremot information om uppfattat behov av kompetens hos yrkesverksamma inom BI, därav vill vi även skapa en grund för denna kunskapsbrist. Tekniken förändras även över tid, en aktuell granskning av yrkesverksamma kan därför leda till klarhet angående behov av kompetens hos BI-användare. Studien genomfördes genom en kvalitativ ansats med semistrukturerade intervjuer där BI-användares åsikter om deras kompetens och ansedda behov av kompetens samlades in. De anonyma respondenterna var vid tidpunkten av rapportens genomförande yrkesverksamma på stora företag och myndigheter i Sverige. Intervjuerna transkriberades och analyserades genom en tematisk analys. Resultatet av studien visade på en trend av kunskap och kompetens hos yrkesverksamma samt deras individuella uppfattningar angående behov av kunskap och kompetens för att arbeta med BI. Kunskapen om tekniken hos företag som använder BI behöver spridas inom hela organisationen. De behoven av kunskap och kompetens som har kunnat identifierats i vår studie redovisas i resultatet. Vi tror att tydlighet angående BI och behov av kompetens kan gynna förståelsen för teknikens potential hos användare samt även öka teknikens användning. Studien bidrar med information ämnad för yrkesverksamma BI-användare, slutanvändare, ledning för organisationer, forskare inom BI eller nyfikna amatörer.

The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to rank the forklifts’ productivity from 1 – 15 using the Operating Equipment Effectiveness formula (OPEE). This ranking was compared to the industry standard for utilization to demonstrate how this approach would yield a better performance analysis and provide a more accurate tool for operations managers to manage their fleets of equipment than current methods. This analysis was shared with a fleet operations manager, and his feedback indicated there would be considerable value to analyzing his operations using this process. The results of this research identified key areas for improvement in equipment reliability and the need for additional operator training on the proper use of machines and provided insights into equipment operations in remote locations to managers who had not visited or evaluated those locations on-site.

Im digitalen Zeitalter sind Innovationskraft und eine effiziente Adaption digitaler Technologien für Unternehmen entscheidend, um sich Wettbewerbsvorteile zu sichern. Der Einsatz digitaler Technologien für Innovation verspricht in diesem Zusammenhang nicht nur Produktivitätsvorteile, sondern steigert auch die Kundenzufriedenheit und macht Unternehmen agiler und widerstandsfähiger gegenüber Krisen. Eine zentrale Rolle spielt dabei die Anwendung von Big Data Analytics, jedoch bestehen derzeit erhebliche Forschungsbedarfe, um genauer zu ergründen, wie Big Data Analytics systematisch in Innovationsökosystemen genutzt werden können. Zum einen herrscht ein Mangel an Forschung über die strategischen Beiträge von Big Data Analytics für Innovation, insbesondere im Kontext des Zusammenwirkens verschiedener Akteure. Zum anderen liegt der Fokus bestehender Forschungsarbeiten oft nur auf Teilaspekten der Anwendung von Big Data Analytics und vernachlässigt umfassendere Betrachtungen, aus einer Ökosystem-Perspektive heraus. Für die Praxis liegen die primären Hürden dabei häufig nicht in der Technologie selbst, sondern in deren Adaption innerhalb der wertschöpfenden Strukturen von Unternehmen. Diese Dissertation zielt darauf ab, diese Lücke zu schließen und untersucht die systematische Anwendung von Big Data Analytics in Innovationsökosystemen und nutzt dafür einen Design-Science-Research-Ansatz als übergeordnete Forschungsmethode. Im Dachbeitrag und in den Einzelbeiträgen des kumulativen Dissertationsvorhabens wird dafür gestaltungsorientierte Forschung angewendet, um theoretische Erkenntnisse direkt in die praktische Gestaltung und Entwicklung von Lösungen zu integrieren. Im Ergebnis liefert die Dissertation einen übergeordneten Ordnungsrahmen für die Anwendung von Big Data Analytics in Innovationsökosystemen, der die gesammelten Erkenntnisse aus dem Forschungsprojekt CODIFeY und den Einzelbeiträgen integriert. Damit trägt die Dissertation über den entwickelten Ordnungsrahmen und die IT-Artefakte der Einzelbeiträge dazu bei, ein besseres Verständnis für die strategische Nutzung digitaler Technologien zur Förderung von Innovation und Wettbewerbsvorteilen zu erreichen, was sowohl wissenschaftlich als auch praktisch einen Mehrwert bietet.:Danksagung i Einzelbeiträge iii Inhaltsverzeichnis iv Abkürzungsverzeichnis x Abbildungsverzeichnis xii Tabellenverzeichnis xiv Kurzzusammenfassung 1 Abstract 2 I. Dachbeitrag 3 1 Einleitung 3 1.1 Motivation 3 1.2 Problem- und Fragestellung 5 1.3 Zielstellung 8 1.4 Aufbau des Dachbeitrags 9 2 Forschungsansatz 11 2.1 Wissenschaftstheoretische Grundpositionierung 11 2.2 Forschungsmethode 12 2.2.1 Design Science Research als übergeordnetes Forschungsparadigma 12 2.2.2 Das Projekt Community-basierte Dienstleistungs-Innovation für e-Mobility 14 2.2.3 Aufbau des kumulativen Dissertationsvorhabens 17 3 Stand der Wissenschaft und Forschung 24 3.1 Big Data Analytics 24 3.2 Datengetriebene Innovation 25 3.3 Innovationsökosysteme aus der Perspektive der Service Dominant Logic 27 4 Gestaltung eines Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 30 4.1 Das Modell eines Innovationsökosystems aus Sicht der Service Dominant Logic 30 4.2 Ableitung der Dimensionen des Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 35 5 Eine Systematisierung von Anwendungsfällen von Big Data Analytics in Innovationsökosystemen 39 5.1 Big Data Analytics als Mittel für Innovation 39 5.2 Big Data Analytics als Ergebnis von Innovation 44 5.3 Demonstration & Evaluation des Ordnungsrahmens 50 6 Fazit 52 II. Research Papers of the Dissertation 55 Paper A – Capturing the Bigger Picture? Applying Text Analytics to Foster Open Innovation 55 A1 Introduction 57 A2 Background and Terminology 60 A2.1 Complexities of Sustainability-Oriented Innovation 60 A2.2 Open Innovation as an Instrument for Participation 62 A2.3 Sustainable-Oriented Innovation and Open Innovation 64 A2.4 Silent Stakeholders 67 A2.5 Research Focus: Text Analytics in Direct Search Methods for Sustainability-Oriented Innovation 69 A3 Action Research Study 72 A3.1 Description of the Action Research Cycle 72 A3.2 Diagnosing the Project Background 73 A3.3 Action Planning and Taking—Application of Text Analytics 77 A4 Results 82 A4.1 Findings from the Overall Discourse Analysis 82 A4.2 Findings from Zooming into Single Topics 84 A4.3 Applicability in the Innovation Process for the Label Development 85 A5 Discussion 87 A6 Implications and Conclusions 88 Paper B – What the Hack? – Towards a Taxonomy of Hackathons 92 B1 Introduction 93 B2 A Process-centric Perspective on Open Innovation and Hackathons 95 B3 Research Approach 97 B3.1 Taxonomy Development 97 B3.2 Literature Review 98 B4 A Taxonomy of Hackathons 101 B4.1 Overview of the Taxonomy 101 B4.2 Strategic Design Decisions 102 B4.3 Operational Design Decisions 104 B5 Discussion 107 B6 Conclusion 109 Paper C – Combining Open Innovation and Knowledge Management in Communities of Practice - An Analytics Driven Approach 110 C1 Introduction 111 C2 Foundations 113 C2.1 Knowledge Management and Innovation 113 C2.2 Communities of Practice 114 C2.3 Analytics domains 114 C3 Research Methodology 117 C4 Conceptual Framework for the Integration of Open Innovation and Knowledge Management 118 C4.1 Conceptual Data Model 119 C5 Implementation & Evaluation of a Pilot Project 122 C5.1 The Research Project CODIFeY 122 C5.2 Evaluation and Preliminary Findings 124 C6 Conclusions 126 Paper D – Entwicklung eines Analytics Framework für virtuelle Communities of Practice 127 D1 Einführung 128 D2 Grundlagen 130 D2.1 Communities of Practice 130 D2.2 Analytics 131 D2.3 Design eines Analytics Frameworks für Communities of Practice 132 D3 Demonstration und Evaluation im Projekt CODIFeY 136 D4 Fazit 138 Paper E – Teaching Data Driven Innovation – Facing a Challenge for Higher Education 139 E1 Introduction 140 E2 Foundations and Theoretical Underpinning 142 E2.1 Data Driven Innovation 142 E2.2 Teaching Data-Driven Innovation 142 E2.3 Pedagogical Approach 143 E3 Research Method 145 E3.1 General Morphological Analysis 145 E3.2 Data Collection and Empirical Analysis 146 E4 Design of the Morphological Box 148 E4.1 Teaching Method 148 E4.2 Course Setting 149 E4.3 Course Content 149 E4.4 Innovation Approach 150 E4.5 Morphological Box for Teaching Data Driven Innovation 151 E5 Teaching Cases 153 E5.1 Case A: Data Driven Value Generation for the Internet of Things 153 E5.2 Case B: Data Driven Innovation Project in the Field of E-mobility 154 E6 Conclusion 156 Paper F – Cross-Disciplinary Collaboration for Designing Data-Driven Products and Services 157 F1 Introduction 158 F2 Foundations and Theoretical Background 161 F2.1 Data Literacy as a Foundation for the Design of Data-Driven Product and Services 161 F2.2 Collaborative Processes and Knowledge Transfer 162 F2.3 Knowledge Boundaries 162 F2.4 Boundary Objects 163 F2.5 Boundary Objects for Collaboration Processes and Knowledge Integration 164 F3 Research Approach 166 F4 Design of the Data Vignette 169 F4.1 Thematic View 169 F4.2 Structural View 173 F5 Evaluation of the Artifact 178 F5.1 Artificial Evaluation Using the Guidelines of Modelling 178 F5.2 Application of the DV - A First Pilot 179 F6 Conclusion 182 Paper G – Towards the Development of a Typology of Big Data Analytics in Innovation Ecosystems 184 G1 Introduction 185 G2 Foundations 187 G2.1 The Role of Technology for Innovation Ecosystems 187 G2.2 Big Data Analytics in Innovation Ecosystems 188 G3 Research Approach 189 G4 Towards a Typology of Big Data Analytics in Innovation Ecosystems 190 G5 Further research 192 Paper H – Hackathons als Gestaltungswerkzeug für plattform-basierte digitale Ökosysteme 193 H1 Einleitung 194 H2 Grundlagen 196 H2.1 Plattform-basierte digitale Ökosysteme 196 H2.2 Hackathons als Gestaltungswerkzeug 197 H3 Forschungsmethode 199 H4 Hackathons für die Gestaltung plattform-basierter Ökosysteme 202 H4.1 Markt-orientierte Plattform-Hackathons 202 H4.2 Technologie-orientierte Plattform-Hackathons 204 H5 Fazit 206 Literaturverzeichnis xv Anhang li Anhang 1 li / In the digital age, the ability to innovate and the efficient adoption of digital technologies are crucial for companies to gain competitive advantages. The use of digital technologies for innovation promises not only productivity gains but also increases customer satisfaction and makes companies more agile and resilient to crises. The focus here is on the application of big data analytics, but there is currently still a considerable need for research to understand how big data analytics can be used systematically in innovation ecosystems. On the one hand, there is a lack of research on the strategic contributions of big data analytics to innovation, particularly in the context of the interaction of various actors. On the other hand, the focus of existing research often only addresses partial aspects of the application of big data analytics and neglects broader considerations from an ecosystem perspective. For practice, the primary hurdles often lie not in the technology itself but in its adaptation within the value-creating structures of companies. This dissertation aims to close this gap and examines the systematic application of big data analytics in innovation ecosystems, using a design science research approach as the overarching research method. In the summary and in the individual papers of the cumulative dissertation project, design-oriented research is used to integrate theoretical insights directly into the practical design and development of solutions. As a result, the dissertation provides an overarching framework for the application of big data analytics in innovation ecosystems, integrating the insights gathered from the CODIFeY research project and the individual contributions. The dissertation on the developed framework and the IT artifacts of the individual contributions contributes to a better understanding of the strategic use of digital technologies to promote innovation and competitive advantages, which offers added value both scientifically and practically.:Danksagung i Einzelbeiträge iii Inhaltsverzeichnis iv Abkürzungsverzeichnis x Abbildungsverzeichnis xii Tabellenverzeichnis xiv Kurzzusammenfassung 1 Abstract 2 I. Dachbeitrag 3 1 Einleitung 3 1.1 Motivation 3 1.2 Problem- und Fragestellung 5 1.3 Zielstellung 8 1.4 Aufbau des Dachbeitrags 9 2 Forschungsansatz 11 2.1 Wissenschaftstheoretische Grundpositionierung 11 2.2 Forschungsmethode 12 2.2.1 Design Science Research als übergeordnetes Forschungsparadigma 12 2.2.2 Das Projekt Community-basierte Dienstleistungs-Innovation für e-Mobility 14 2.2.3 Aufbau des kumulativen Dissertationsvorhabens 17 3 Stand der Wissenschaft und Forschung 24 3.1 Big Data Analytics 24 3.2 Datengetriebene Innovation 25 3.3 Innovationsökosysteme aus der Perspektive der Service Dominant Logic 27 4 Gestaltung eines Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 30 4.1 Das Modell eines Innovationsökosystems aus Sicht der Service Dominant Logic 30 4.2 Ableitung der Dimensionen des Ordnungsrahmens für die Anwendung von Big Data Analytics in Innovationsökosystemen 35 5 Eine Systematisierung von Anwendungsfällen von Big Data Analytics in Innovationsökosystemen 39 5.1 Big Data Analytics als Mittel für Innovation 39 5.2 Big Data Analytics als Ergebnis von Innovation 44 5.3 Demonstration & Evaluation des Ordnungsrahmens 50 6 Fazit 52 II. Research Papers of the Dissertation 55 Paper A – Capturing the Bigger Picture? Applying Text Analytics to Foster Open Innovation 55 A1 Introduction 57 A2 Background and Terminology 60 A2.1 Complexities of Sustainability-Oriented Innovation 60 A2.2 Open Innovation as an Instrument for Participation 62 A2.3 Sustainable-Oriented Innovation and Open Innovation 64 A2.4 Silent Stakeholders 67 A2.5 Research Focus: Text Analytics in Direct Search Methods for Sustainability-Oriented Innovation 69 A3 Action Research Study 72 A3.1 Description of the Action Research Cycle 72 A3.2 Diagnosing the Project Background 73 A3.3 Action Planning and Taking—Application of Text Analytics 77 A4 Results 82 A4.1 Findings from the Overall Discourse Analysis 82 A4.2 Findings from Zooming into Single Topics 84 A4.3 Applicability in the Innovation Process for the Label Development 85 A5 Discussion 87 A6 Implications and Conclusions 88 Paper B – What the Hack? – Towards a Taxonomy of Hackathons 92 B1 Introduction 93 B2 A Process-centric Perspective on Open Innovation and Hackathons 95 B3 Research Approach 97 B3.1 Taxonomy Development 97 B3.2 Literature Review 98 B4 A Taxonomy of Hackathons 101 B4.1 Overview of the Taxonomy 101 B4.2 Strategic Design Decisions 102 B4.3 Operational Design Decisions 104 B5 Discussion 107 B6 Conclusion 109 Paper C – Combining Open Innovation and Knowledge Management in Communities of Practice - An Analytics Driven Approach 110 C1 Introduction 111 C2 Foundations 113 C2.1 Knowledge Management and Innovation 113 C2.2 Communities of Practice 114 C2.3 Analytics domains 114 C3 Research Methodology 117 C4 Conceptual Framework for the Integration of Open Innovation and Knowledge Management 118 C4.1 Conceptual Data Model 119 C5 Implementation & Evaluation of a Pilot Project 122 C5.1 The Research Project CODIFeY 122 C5.2 Evaluation and Preliminary Findings 124 C6 Conclusions 126 Paper D – Entwicklung eines Analytics Framework für virtuelle Communities of Practice 127 D1 Einführung 128 D2 Grundlagen 130 D2.1 Communities of Practice 130 D2.2 Analytics 131 D2.3 Design eines Analytics Frameworks für Communities of Practice 132 D3 Demonstration und Evaluation im Projekt CODIFeY 136 D4 Fazit 138 Paper E – Teaching Data Driven Innovation – Facing a Challenge for Higher Education 139 E1 Introduction 140 E2 Foundations and Theoretical Underpinning 142 E2.1 Data Driven Innovation 142 E2.2 Teaching Data-Driven Innovation 142 E2.3 Pedagogical Approach 143 E3 Research Method 145 E3.1 General Morphological Analysis 145 E3.2 Data Collection and Empirical Analysis 146 E4 Design of the Morphological Box 148 E4.1 Teaching Method 148 E4.2 Course Setting 149 E4.3 Course Content 149 E4.4 Innovation Approach 150 E4.5 Morphological Box for Teaching Data Driven Innovation 151 E5 Teaching Cases 153 E5.1 Case A: Data Driven Value Generation for the Internet of Things 153 E5.2 Case B: Data Driven Innovation Project in the Field of E-mobility 154 E6 Conclusion 156 Paper F – Cross-Disciplinary Collaboration for Designing Data-Driven Products and Services 157 F1 Introduction 158 F2 Foundations and Theoretical Background 161 F2.1 Data Literacy as a Foundation for the Design of Data-Driven Product and Services 161 F2.2 Collaborative Processes and Knowledge Transfer 162 F2.3 Knowledge Boundaries 162 F2.4 Boundary Objects 163 F2.5 Boundary Objects for Collaboration Processes and Knowledge Integration 164 F3 Research Approach 166 F4 Design of the Data Vignette 169 F4.1 Thematic View 169 F4.2 Structural View 173 F5 Evaluation of the Artifact 178 F5.1 Artificial Evaluation Using the Guidelines of Modelling 178 F5.2 Application of the DV - A First Pilot 179 F6 Conclusion 182 Paper G – Towards the Development of a Typology of Big Data Analytics in Innovation Ecosystems 184 G1 Introduction 185 G2 Foundations 187 G2.1 The Role of Technology for Innovation Ecosystems 187 G2.2 Big Data Analytics in Innovation Ecosystems 188 G3 Research Approach 189 G4 Towards a Typology of Big Data Analytics in Innovation Ecosystems 190 G5 Further research 192 Paper H – Hackathons als Gestaltungswerkzeug für plattform-basierte digitale Ökosysteme 193 H1 Einleitung 194 H2 Grundlagen 196 H2.1 Plattform-basierte digitale Ökosysteme 196 H2.2 Hackathons als Gestaltungswerkzeug 197 H3 Forschungsmethode 199 H4 Hackathons für die Gestaltung plattform-basierter Ökosysteme 202 H4.1 Markt-orientierte Plattform-Hackathons 202 H4.2 Technologie-orientierte Plattform-Hackathons 204 H5 Fazit 206 Literaturverzeichnis xv Anhang li Anhang 1 li

In order to assess the continuing imbalance of top executives between genders, I examine the effects of gender diversity within top management teams on firm risk, performance, and executive compensation. Capitalizing on previous analysis, I apply three unique differentiators. First, I utilize current data from 2012 to 2017 from Compustat, CRSP, and ExecuComp. Second, I provide a unique subset view on a firm and individual performance of female CEOs to examine executive compensation. Third, my scope of analysis expands to S&P Composite 1500 companies. I use separate models to estimate the effect of gender diversity on firm risk by examining a firm’s beta and standard deviation of daily returns, on firm performance by examining a firm’s Tobin’sQ, and lastly on executive compensation by examining an executive’s natural logarithm of total compensation. My findings suggest gender diversity among executives appears to have an immaterial effect on a firm’s risk and performance. In turn, I also find that female executives continue to receive less compensation than their male colleagues. However, I find an average female CEO receives a higher level of compensation than an average male CEO. Lastly, I find as gender diversity increases among executives, specifically CEOs, the compensation differences between genders decreases.

Made available in DSpace on 2018-08-01T23:40:20Z (GMT). No. of bitstreams: 1 tese_10014_LARISSA SINCORÁ - DISSERTAÇÃO_VERSÃO FINAL_CAPA DURA.pdf: 2216917 bytes, checksum: 9fa69e4311823dccf2820e6e65e9abbf (MD5) Previous issue date: 2016-06-20 / CAPES / Esta dissertação foi desenvolvida com o intuito de avaliar o papel exercido pelas capabilidades analíticas organizacionais quando relacionadas à maturidade de gestão de processos de negócio e à resiliência organizacional. A motivação para o estudo, por sua vez, se insere num contexto no qual a sobrevivência e o crescimento das organizações estão ligados às suas capabilidades de efetivamente utilizar grandes volumes de dados provenientes de diferentes fontes para auxiliar em suas orientações estratégicas e operacionais, constituindo atualmente um fator crítico para o sucesso. Isto se evidencia porque diversas empresas, de diferentes segmentos de atuação, e de várias partes do mundo, têm adotado a abordagem analítica como um diferencial competitivo em suas operações com capacidade de influenciar as demais variáveis organizacionais. Por conseguinte, a partir da fundamentação teórica dos construtos, foi possível identificar seus domínios e formas de operacionalização, bem como as relações teóricas existentes, o que resultou no delineamento do modelo teórico da pesquisa e no questionário do tipo survey. Quanto ao percurso metodológico, a aplicação do questionário foi conduzida pelo IEL/FINDES (Instituto Euvaldo Lodi vinculado à Federação das Indústrias do Estado do Espírito Santo) a partir do envio de carta-convite aos informantes-chave da pesquisa, durante os meses de setembro a dezembro de 2015. Dessa maneira, a técnica de análise de dados empregada para avaliar as relações hipotetizadas e a qualidade do modelo teórico elaborado, consistiu-se na modelagem de equações estruturais, por meio do software Smart PLS-SEM 3.0, baseado no algoritmo dos mínimos quadrados parciais (PLS-SEM). Em seguida, após o tratamento dos dados, os resultados foram interpretados e discutidos, apontando para a existência de relações estatisticamente significativas e coerentes com o aporte teórico. Logo, foi possível concluir que as capabilidades analíticas organizacionais atuam como antecedentes de resiliência organizacional, bem como desempenham o papel de moderar a relação existente entre a maturidade de gestão de processos de negócio e a resiliência organizacional. Por fim, se teceu as considerações finais, contendo as limitações do estudo, as contribuições para a evolução dos temas pesquisados e as recomendações de futuras pesquisas em tópicos tangentes às respectivas temáticas investigadas. / This work was developed in order to evaluate the role played by organizational analytical capabilities as they relate to business processes management maturity and organizational resilience. The motivation for the study, in turn, is a part of a context in which the survival and growth of organizations are connected to their capabilities to effectively use large amounts of data from different sources to assist in their strategic and operational guidelines, being currently a critical success factor. It becomes clear because several companies from different segments and in various parts of the world have adopted the analytical approach as a operational competitive advantage, with the ability to influence other organizational variables. Therefore, through the theoretical foundations of the constructs were identified domains and forms of implementation, as well the existing theoretical relationships, which resulted in the delineation of the theoretical research model and the questionnaire used for the survey. As for the methodological approach, the questionnaire was conducted by IEL/FINDES (Institute Euvaldo Lodi linked to the Federation of Industries of the State Espírito Santo) from sending an invitation letter to key-informants of the research, during the months of September to December 2015. Thus, the data analysis technique used to evaluate the hypothesized relationships and the quality of the developed theoretical model consisted on structural equation modeling by using the software Smart PLS-SEM 3.0, based on the algorithm of partial least squares (PLS-SEM). Therefore, it was concluded that the organizational analytical capabilities acts as organizational resilience antecedents, as well plays the role of moderating the relationship between the business processes management maturity and organizational resilience. In addition, after data treatment, the results were analyzed and discussed, pointing to the existence of statistically significant and consistent relations with the theoretical framework. Finally, were made the final considerations, exposing the study's limitations, the contributions to the evolution of the researched topics, and future research recommendations on tangential topics related to the themes investigated.

Les mégadonnées font partie de ces sujets dont on entend parler sans trop savoir ce qu’ils signifient précisément. Souvent associés au domaine de l’intelligence artificielle, ces volumineux ensembles de données sont à la base d’un nombre croissant de modèles d’affaires axés sur la valorisation des données numériques que nous générons au quotidien. Le présent mémoire cherche à démontrer que cette exploitation des mégadonnées par les entreprises ne s’effectue pas dans un vide juridique. Les mégadonnées ne peuvent être considérées comme un objet de droit en l’absence d’une définition formelle. Une revue de la littérature multidisciplinaire à leur sujet, invite à les concevoir comme un actif informationnel doté de cinq caractéristiques principales, soit leur volume, leur vélocité, leur variété, leur valeur et leur véracité. L’analyse de ces caractéristiques permet au juriste d’atteindre une compréhension suffisante de ce phénomène afin de l’aborder sous le prisme du droit positif. Suivant un exercice de qualification juridique, les mégadonnées émergent à la fois comme un bien meuble incorporel et comme un ensemble de documents technologiques portant divers renseignements dont certains peuvent être qualifiés de renseignements personnels. Le cadre juridique applicable à l’exploitation des mégadonnées s’articule donc autour de la protection législative de la vie privée informationnelle qui s’incarne à travers les lois en matière de protection des renseignements personnels. Cet encadrement est complété par certaines règles relatives à la gestion documentaire et au droit à l’égalité. Une manière efficace de présenter cet encadrement juridique est selon le cycle de vie des renseignements personnels au sein des mégadonnées. Ainsi, il appert que les principes issus de l’approche personnaliste et minimaliste du droit québécois à la protection des renseignements personnels s’appliquent tant bien que mal à la collecte des données numériques ainsi qu’à leur traitement par les entreprises. / Big data is one of those topics we keep hearing about without knowing exactly what it means. Often associated with the field of artificial intelligence, these large datasets are the backbone of a growing number of business models that focus on leveraging the digital data we generate on a daily basis. This Master’s thesis seeks to demonstrate that this exploitation of big data by businesses is not happening in a legal vacuum. Big data cannot be considered as an object of rights in the absence of a formal definition. A review of the multidisciplinary literature on the subject invites us to conceive them as an information asset with five main characteristics: volume, velocity, variety, value and veracity. The study of these characteristics allows the jurist to reach a sufficient understanding of the phenomenon in order to approach it through the lens of positive law. Following a legal qualification exercise, big data emerges both as intangible movable property and as a set of technological documents carrying various types of information, some of which can be qualified as personal information. The legal framework governing the exploitation of big data is therefore built around the legislative protection of informational privacy, which is embodied in privacy laws. This framework is complemented by certain rules relating to document management and the right to equality. An effective way to present this legal framework is according to the life cycle of personal information within big data. Thus, it appears that the principles stemming from the personalist and minimalist approach of Quebec's data protection law apply, albeit not without struggle, to the collection of digital data as well as their processing by businesses.