This study explores the overlapping of platform technology and cooperative processes in logistics and how this can be of interest to make the industry more environmentally sustainable. It examines the literature regarding platform technology, horizontal collaboration, and environmental sustainability in logistics. Data was collected through interviews with experts in the logistics industry, representatives from logistics companies and a software solutions provider, to obtain a nuanced image from the industry. A framework was created to provide answers to how digital platforms can better be utilized for cooperation in logistics to improve environmental sustainability. In order to fulfill this, incentives and challenges for small- and medium-sized enterprises to cooperate through platform technology were identified. The incentives that were found were classified into driving and pushing. The driving incentives refer to evident benefits for small- and medium-sized enterprises of joining cooperative logistics platforms, while pushing incentives are based on external factors and current issues that potentially could be solved through a cooperative platform. The identified challenges to cooperating through digital platforms are related to cooperative processes, technology and digitalization, and environmental sustainability. These were analyzed from a systems perspective to understand how these affect the problem on several levels in the industry of logistics. Together, the incentives and challenges form the final framework and illustrate how platforms for cooperation can be realized in the logistics industry. / Denna studie undersöker överlappningen av digitala plattformar och samarbetsprocesser inom  logistik och hur detta kan vara av intresse för att göra industrin mer miljömässigt hållbar. Studien utforskar forskningslitteraturen om digitala plattformar, horisontellt samarbete och miljömässig hållbarhet inom logistik. Data samlades in genom intervjuer utförda med experter inom logistikindustrin och representanter från en mjukvarulösningsleverantör och från små- och medelstora företag inom logistik, för att få en nyanserad bild från branschen. Ett ramverk skapades för att ge svar på hur digitala plattformar kan användas bättre för samarbete inom logistik för att förbättra miljömässig hållbarhet. För att uppfylla detta så identifierades incitament och utmaningar för små- och medelstora företag för samarbete genom plattformsteknik. De incitament som hittades klassificerades som drivande och pressande. De drivande incitamenten syftar till de uppenbara fördelar för små- och medelstora företag att ansluta sig till kooperativa logistikplattformar, medan de drivande incitamenten baseras på externa faktorer och problem som potentiellt kan lösas genom en samarbetsplattform. Utmaningarna för att samarbeta via digitala plattformar identifierades relaterat till samarbetsprocesser, teknik och digitalisering, samt miljömässig hållbarhet. Dessa utmaningar identifierades sedan ur ett systemperspektiv för att förstå hur de påverkar problemet på flera nivåer inom logistikbranschen. Tillsammans utgör incitamenten och utmaningarna det slutgiltiga ramverket och illustrerar hur plattformar för samarbete kan förverkligas mer inom logistikindustrin.

This paper examines how streaming services, with a particular focus on Spotify, have influenced music consumption through the implementation of algorithmically and humanly curated playlists and recommendation features. By placing the role of streaming services in a historical context, this paper explores streaming services as an extension of previous gatekeeping functions in the music industry. The paper examines how curated playlists and recommendations affect music consumption and the individual listener’s ability to discover new music. The result suggest that streaming services’ use of algorithmically and humanly curated playlists and recommendation functions constitute gatekeeping mechanisms. Due to the monopoly status of streaming services in society, this has an impact on what music is listened to and disseminated in society. Streaming services have also contributed to an increasingly more personalised music curation that differs from the governing mechanisms that previously controlled popular music. This partly contradicts the stated ambitions of streaming services to provide listeners with a means of discovering new music. The controlling functions of streaming services have in turn affected music creators who distribute their music on the platform. In what follows, it is explained how music creators have become increasingly aware of algorithmic features, leading to an adaptation of both the musical content and the music packaging to benefit from algorithmic advantages.

This research project is a quantitative enquiry focused specifically on the value consumption of digital business platforms (DBP's) in relation to how informal sector traders (IST's) are using them to buy, sell and market their product and services in their daily trade functions. This study has adopted the approach of classifying the social media platforms, internet banking platforms and mobile payments platforms, as a single collective and defined under the banner of Digital Business Platforms. The advantage to this approach, is that it provides a multi-dimensional view of the perceived value-axis based on positive and negative experiences of using a variety of digital platforms including messaging, payments, and banking applications on their mobile phones in their daily trading functions. This study examines the positive experiences of perceived value (PV), collectively in the form of social value (SV), emotional value (EV) and functional value (FV). On the opposite end of the value spectrum, are the negative experiences of perceived risk in the form of price risk (PR) and technical barriers (TB) for the same group of informal sector traders. Understanding the relational impacts of these positive and negative experiences of value consumption, may lead to an understanding of their influence in the perceived usefulness (PU) and the perceived ease of use (PEoU) of DBP's. The results of these relationships will shed some light towards the behavioural intention (BI) to adopt DBP's and their actual usage in the long term. The analysis yielded the following results: for positive experiences, emotional value had the least impact on both perceived ease of use and usefulness and functional value was higher than social value. This means that IST's place more value on the utility of DBP's, than the emotional and social values. Regarding the negative experiences, price risk had the most significant impact on the willingness to adopt DBP's by ISTs.

Platforms have become one of the most important business logics in the last twenty years and have enabled unprecedented rapid growth for various companies. This the-sis begins with an overview of the state of research on platforms, business models, servitization and recurring revenue models. The third chapter then presents the meth-ods used in the individual studies. Subsequently, the fourth, fifth, and sixth chapters of the thesis present the results of the empirical research from the three field studies. Field Study I (chapter four) reveals three business model patterns of IoT platforms that could be identified in the machinery and equipment industry. In field study II (chapter five) the results from field study I are then taken into account and with the help of a qualitative approach are investigated deeper. As a consequence of this re-search, a business model continuum emerged along which companies with IoT plat-forms move as they increasingly integrate services into their portfolio of offerings and evolve the business model from a linear to a platform-oriented logic. Finally, field study III (chapter six) takes an in-depth look at the issue of recurring revenues. Com-panies with a platform logic transform their business model in the area of the profit equation in such a way that they generate predominantly recurring revenues in order to construct a more resilient business model that is secure against crises. In addition to a summary of the results, this dissertation outlines the theoretical and managerial implications obtained during the empirical work and concludes with future research and opportunities of this work.:Academic Curriculum Vitae of the author II Bibliographic description III Foreword IV Table of contents VI List of abbreviations IX List of tables X List of figures XI 1. Motivation 1 1.1 Emergence of platforms 1 1.2 Managerial importance 6 1.3 Theoretical importance 15 1.4 Research gap and question 17 1.5 Structure of the thesis 19 2. Research approach 24 2.1 Research process 24 2.2 Research methodologies 25 2.3 Reference on publications 26 3. Theoretical Background 28 3.1 Platforms 29 3.1.1 Platform definition, concepts, and typologies 37 3.1.2 Foundational elements of platforms 41 3.2 Business models 45 3.2.1 Definition 55 3.2.2 Business model concepts 56 3.2.3 Holistic view 57 3.2.4 Component view 59 3.2.5 Business model innovation 61 3.2.6 Platforms as business models 63 3.3 Servitization 65 3.3.1 Servitization definition and concepts 73 3.3.2 Digital servitization 76 3.4 Types of recurring revenues – Capturing value within service- and platform-oriented business models 78 3.4.1 Definition, characteristics, types and (dis-)advantages of recurring revenues 79 3.4.2 Approaches to the integration of recurring revenues in companies 82 3.4.3 The Hockey Stick - revenue growth model 83 4. Field Study I: Clustering business model innovations in product-oriented companies enabled by a platform approach 86 4.1 Empirical context, research approach and question 86 4.2 Quantitative survey to identify patterns of business model innovations 87 4.2.1 Research method 87 4.2.2 Results of business model innovation patterns 94 4.3 Discussion 97 4.3.1 Theoretical contributions 97 4.3.2 Managerial contributions 100 5. Field Study II: Exploring different development stages of platform-oriented business models 102 5.1 Empirical context, research approach and question 102 5.2 Case studies to gain deeper insights into the business model innovation patterns 103 5.2.1 Case study method 103 5.2.2 Case studies for the three BMI patterns 105 5.3 Discussion 122 5.3.1 Theoretical contributions 122 5.3.2 Practical contributions 126 6. Field Study III: There is more than one Hockey Stick - Growth trajectories for businesses with recurring revenues models 127 6.1 Empirical context, research approach and question 127 6.2 Growth trajectories for recurring revenue models 130 6.2.1 Empirical evidence for the Hockey Stick (Step 1) 130 6.2.2 Empirical evidence for growth trajectories of recurring revenue (Step 2) 134 6.2.3 Discriminating factors of recurring revenue growth trajectories (Step 3) 136 6.2.4 Growth trajectories 139 6.3 Contributions 148 7. Conclusion 151 7.1 Summary of the results 152 7.1.1 Summary of Field Study I 153 7.1.2 Summary of Field Study II 155 7.1.3 Summary of Field Study III 158 7.2 Implications for research 160 7.2.1 Field Study I 162 7.2.2 Field Study II 164 7.2.3 Field Study III 165 7.2.4 Overarching theoretical implications 166 7.3 Implications for management practice 168 7.4 Future research opportunities 171 Bibliography 175 Appendix 209 Resume of the author in table form 240 Declaration of academic integrity 245 Declaration of origin 245

IoT (Internet of Things) systems implement event-driven architectures that are deployed on an ever-increasing scale as more and more devices (things) become connected to the internet. Consequently, IoT cloud platforms are becoming increasingly distributed and complex as they adapt to handle larger amounts of user requests and device data. The complexity of such systems makes it close to impossible to predict how they will handle failures that inevitably occur once they are put into production. Chaos engineering, the practice of deliberately injecting faults in production, has successfully been used by many software companies as a means to build confidence in that their complex systems are reliable for the end-users. Nevertheless, its applications in the scope of IoT systems remain largely unexplored in research. Modern IoT cloud platforms are built cloud native with containerized microservices, container orchestration, and other cloud native technologies, much like any other distributed cloud computing system. We therefore investigate cloud native chaos engineering technology and its applications in IoT cloud platforms. We also introduce a framework for getting started with using cloud native chaos engineering to verify and improve the resilience of IoT systems and evaluate it through a case study at a commercial home appliance manufacturer. The evaluation successfully reveals unknown system behavior and results in the discovery of potential resilience improvements for the case study IoT system. The evaluation also shows three ways to measure the resilience of IoT cloud platforms with respect to perturbations, these are: (1) success rate of user requests, (2) system health, and (3) event traffic. / IoT(Sakernas Internet)-system implementerar händelsedrivna arkitekturer som driftsätts i allt större skala i och med att allt fler enheter (saker) blir anslutna till internet. IoT-molnplattformar blir därmed alltmer distribuerade och komplexa i takt med att de anpassas till att hantera större mängder användarförfrågningar och enhetsdata. Komplexiteten hos sådana system gör det nära omöjligt att förutsäga hur de hanterar problem som oundvikligen inträffar när de väl körs i produktionsmiljö. Kaosteknik, att avsiktligt injicera fel medans ett system körs i produktionsmiljön, har framgångsrikt använts av många mjukvaruföretag som ett sätt att bygga förtroende för att deras komplexa system är tillförlitliga för slutanvändarna. Trots det är dess tillämpningar inom ramen för IoT-system i stort sett outforskade inom dataforskning. Moderna IoT-molnplattformar byggs molnäkta med containeriserade mikrotjänster, containerorkestering, och andra molnäkta teknologier, precis som andra distribuerade molntjänstsystem. Vi undersöker därför molnäkta kaosteknik och dess tillämpningar i IoT-molnplattformar. Vi introducerar även ett ramverk för att komma igång med att använda molnäkta kaosteknik för att verifiera och förbättra motståndskraften hos IoT-system och utvärderar det genom en fallstudie hos en kommersiell tillverkare av hushållsapparater. Utvärderingen lyckas avslöja okänt systembeteende och resulterar i upptäckten av potentiella motståndskraftsförbättringar för IoT-systemet i fallstudien. Utvärderingen visar också tre sätt att mäta motståndskraften hos IoT-molnplattformar med hänsyn till störningar, dessa är: (1) andel framgångsrika användarförfrågningar, (2) systemhälsa och (3) händelsetrafik.

The methodologies for English language teaching and the roles os ESP and EIL are discussed. Additionally, the role of culture and learning theories for prefessional training are presented. This framework is applied to two pilot programs and the results are debated. Consequently, a new framework is designed that includes new methodologies and places a greater importance on the role of EIL.

Wood construction platforms are a common method for inexpensive, temporary soil stabilization under heavy machinery; however, platforms are not typically thought of as an engineered product. Review of literature has shown that only one design method is currently available and is specific to one type of platform configuration. The purpose of this thesis is to develop a design method that is simple, versatile and accurate. The proposed design method was intentionally developed so that the designer would have input in multiple areas of the design. Instrumentation allowed for increased insight into the mechanical behavior of the platforms. The objective of this research is to use measured strain, load, and deflection in conjunction with fundamental engineering mechanics principles to predict a single platform’s mechanical behavior on the ground. Results from this method compare favorably with the only other design guide available and improves the knowledge base by developing design guidance for any type of wood construction platform.

A framework for the quantitative analysis of performance, security and database management within a network system (e.g. a cloud computing platform) is presented within this research. Our study provides a methodology for modelling and quantitatively generating significant metrics needed in the evaluation of a network system. To narrow this research, a study is carried-out into the quantitative modelling and analysis of performance, security and database management trade-offs in cloud computing platforms, based on Stochastic Activity Networks (SANs) and combined metrics. Cloud computing is an innovative distributed computing archetypal based on the infrastructure of the internet providing computational power, application, storage and infrastructure services. Security mechanisms such as: batch rekeying, intrusion detection, encryption/decryption or security protocols come at the expense of performance and computing resources consumption. Furthermore, database management processing also has an adverse effect on performance especially in the presence of big data. Stochastic Activity Networks (SANs) that offer synchronisation, timeliness and parallelism are proposed for the modelling and quantitative evaluations of ‘optimal’ trade-offs involving performance, security and database management. Performance modelling and analysis of computer network systems has mostly been considered of utmost importance. Quantification of performance for a while has been assessed using stochastic models with a rising interest in the quantification of security stochastic modelling being applied to security problems. Quantitative techniques that includes analytical valuations founded on queuing theory, discrete-event simulations and correlated approximations have been utilised in the examination of performance. Security suffers from the point that no interpretations can be made in an optimal case. The most consequential security metrics are in analogy with reliability metrics. The express rate at which data grows increases the prominence for research into the design and development of cloud computing models that manages the workload intensity and are suitable for data exploration. Handling big data especially within cloud computing is a resource consuming, time-demanding and challenging task that necessitates titanic computational infrastructures to endorse successful data exploration. We present an improved Security State Transition Diagram (SSTD) by adding a new security state (Failed/Freeze state). The presence of this new security state signifies a security position of the computing network system were the implemented security countermeasures cannot handle the security attacks and the system fails completely. In a more sophisticated security system, when the security countermeasure(s) cannot in any form categorise the security attack, the network system is moved to the Failed/Freeze security state. At this security state, the network system can only resume operation when restored by the system administrator. In this study, we propose a cloud computing system model, defined security countermeasures and evaluated the optimisation problems for the trade-offs between performance, security and database management using SANs formalism. We designed, modelled and implemented dependency within our presented security system, developing interaction within the security countermeasures using our proposed Security Group Communication System (SGCS). The choice of Petri-Nets enables the understanding and capturing of specified metrics at different stages of the proposed cloud computing model. In this thesis, an overview of cloud computing including its classification and services is presented in conjunction with a review of existing works of literature. Subsequently, a methodology is proposed for the quantitative analysis of our proposed cloud computing model of performance-security-database trade-offs using Möbius simulator. Additionally, numerical experiments with relevant interpretations are presented and appropriate interpretations are made. We identified that there are system parameters that can be used to optimise the presented abstract combined metrics but they are optimal for neither performance or security or database management independently. Founded on the proposed quantitative simulation model framework, reliable numerical experiments were observed and indicated scope for further extensions of this work. For example, the use of Machine Learning (ML) or Artificial Intelligence (AI) in the predictive and prevention aspects of the security systems.

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As the workplace becomes increasingly digital, tasks have shifted from being physically demanding to instead digitally processing knowledge and information. Employees are therefore regarded as knowledge workers and for them to succeed in the digital workplace, they place high demands on IT systems that encourage communication, collaboration and document management. To meet the knowledge workers needs, it has become increasingly common to implement collaboration platforms. However, organizations often implement collaboration platforms without further thought about how they should be used. At the same time, there is no knowledge of previous research that shows what factors make employees use collaboration platforms. The purpose of the thesis is therefore to investigate which factors lead to IT accustomed knowledge workers in the IT consulting industry using the collaboration platform in the digital workplace. By examining knowledge workers' perceptions of their use, we hope to be able to find answers to the factors that contribute to the use of collaboration platforms. The thesis applies a qualitative approach through semi-structured interviews on ten IT accustomed knowledge workers divided into two organizations in the IT consulting industry. To consider socio-technical aspects of use, empiricism has been analyzed with a conceptual model that integrates Theory of planned behavior and Technology acceptance model. Our results show that attitude, subjective norm, perceived behavioral control, perceived usefulness and perceived ease-of-use play an influential role in the use of collaboration platforms. At the same time, subjective norms and perceived usefulness are proposed to be the factors that have the strongest impact on use. / I takt med digitaliseringen av arbetsplatsen har arbetsuppgifter övergått till att istället digitalt bearbeta kunskap och information. Anställda betraktas därför som kunskapsarbetare och för att de ska lyckas i sitt arbete ställer de höga krav på IT-system som uppmuntrar kommunikation, samarbete och dokumenthantering. För att tillgodose detta behov har det blivit allt vanligare att implementera samarbetsplattformar. Organisationer implementerar ofta samarbetsplattformar utan vidare eftertanke kring hur de ska användas. Samtidigt finns det ingen kännedom om tidigare forskning som visar vilka faktorer som får anställda att använda samarbetsplattformar. Syftet med uppsatsen är därför att undersöka vilka faktorer som leder till att IT-vana kunskapsarbetare inom IT-konsultbranschen använder samarbetsplattformen inom den digitala arbetsplatsen. Genom att undersöka kunskapsarbetares uppfattning om deras användande hoppas vi kunna finna svar på vilka faktorer som bidrar till användande av samarbetsplattformar. Uppsatsen tillämpar kvalitativa semistrukturerade intervjuer på tio IT-vana kunskapsarbetare uppdelat på två organisationer inom IT-konsultbranschen. För att ta hänsyn till socio-tekniska aspekter vid användande har empirin analyserats med en konceptuell modell som integrerat Theory of planned behavior och Technology acceptance model. Våra resultat visar att attityd, subjektiva normen, upplevd beteendekontroll, upplevd användbarhet och upplevd användarvänlighet har en påverkande roll för användandet av samarbetsplattformar. Samtidigt föreslås subjektiva normer och upplevd användbarhet vara de faktorer som har starkast påverkan på användande.