The big country in the Far East appeared in the past as if only big corporates could do business there. This point of view changed immensely over the past 10 years. Nowadays, numerous co-working spaces, maker spaces, and incubators present a fertile ground for young ventures. The situation around the Chinese startup scene changed dramatically. With the current government’s Five-Year-Plan, it becomes obvious that the political change is in favor of those young individuals who bring jobs and new perspectives for the country. Obviously, the target is not to promote western startups to settle in China primarily but to give Chinese startups the inspiration through the foreign spirit. So the research question goes as follows: What aspects of a manufacturing project will influence the success of a foreign entrepreneur in China? The methodology for this study is widely based on literature review and semistructured interviews with entrepreneurs, consultancy agencies, and nongovernmental organizations. Whereas the focus lays on foreign startups whose goal is to set up manufacturing of a consumer product in China.

Choosing a data collection and analytics tool is no easy task. There are numerous tools available and no tool fits all. The choice of the tool depends on various factors and especially what type of organization and product the tool will be used for. Startups generally have less resources than established companies and the set of tools they can select therefore is limited. Therefore, the study has focused on finding and determining which tool is best suited for a startup. The study has chosen five criterias to evaluate a selected number of tools; setup, features, usability, privacy and cost. The criterias have been selected based on interviews, research and theoretical background. The study’s practical work has been performed at a startup in order to get real-world insight in how the tools operate and function. The tools evaluated are; Mixpanel, Amazon Pinpoint, and Google Sheets. The tools were selected based on answers from questionnaires, interviews and research. The result of the study shows that out of the three tools evaluated, Mixpanel is regarded as the most optimal tool for startups. Mixpanel is cost-effective, easy to set up, offers multiple features and is overall a user-friendly tool. It especially allows the company to have a transparent and strong privacy aspect. Allowing full control of the data collected and analyzed. However, what the study also concluded is that no specific tool is always best for everyone and the organizations should always understand their needs in order to pick the most suitable tool. / Att välja ett datainsamlings- och analysverktyg är ingen lätt uppgift. Det finns många verktyg men inget verktyg passar alla. Val av verktyget beror på olika faktorer särskilt vilken typ av organisation och produkt, verktyget ska användas till. Nystartade företag har i regel mindre resurser än etablerade företag vilket gör att verktyg de kan välja är begränsad. Därför har studien fokuserat på att hitta och avgöra vilket verktyg är bäst lämpat för en startup. Studien har valt fem kriterier för att utvärdera ett utvalt antal verktyg; installation, funktioner, användbarhet, integritet och kostnad. Kriterierna har valts ut utifrån intervjuer, forskning och teoretisk bakgrund. Studiens praktiska arbete har utförts vid en startup för att få verklig insikt i hur verktygen fungerar. De verktyg som utvärderas är; Mixpanel, Amazon Pinpoint, och Google Kalkylark. Verktygen valdes ut utifrån svar från enkäter, intervjuer och forskning. Resultatet av studien visar att av de tre utvärderade verktygen så anses Mixpanel vara det mest optimala verktyget för startups. Mixpanel är kostnadseffektivt, lätt att installera, erbjuder många funktioner och är generellt användarvänligt verktyg. Det tillåter företaget att ha transparent och stark integritet. Utöver det, tillåter Mixpanel full kontroll över data som samlas i och analyseras. Däremot, slutsatsen som kan också dras av studien är att inget specifikt verktyg alltid är bäst för alla och organisationerna bör alltid första sina behov för att välja det mest lämpliga verktyget.

Cryogenic air separation units (ASUs) are the most widely used technology for industrialscale production of large amounts of high-purity air components. These are highly energyintensive processes, which have motivated the development of demand response strategies to adapt their operation in response to the increased volatility of the energy market. The startup of ASUs warrants particular consideration within this context. ASUs are tightly integrated, thermally and materially, and have slow dynamics. These result in startup times on the order of hours to a day, during which electricity is consumed with limited revenue generation. In the current environment of electricity price deregulation, it may be economically advantageous for ASUs to shut down during periods of high electricity pricing, increasing the occurrences of startups. This presents a promising research opportunity, especially because ASU startup has received relatively little attention in the literature. This thesis investigates the optimal startup of ASUs using dynamic optimization. First, this thesis focuses on startup model development for the multiproduct ASU. Startup model development requires accounting for discontinuities present at startup. Four main discontinuities are considered: stage liquid flow discontinuity, stage vapor flow discontinuities, flow liquid out of sumps and reboilers, and opening and closing valves. Other types of discontinuities accounted for include the change in the number of phases of streams. These discontinuities are approximated with smoothing formulations, using mostly hyperbolic tangent functions, to allow application of gradient-based optimization. The modeling approach was assessed through three case studies: dynamic simulation of a successful startup, dynamic simulation of a failed startup, and dynamic optimization using a least-squares minimization formulation. Following startup model development, this thesis investigates the development of a framework for optimizing ASU startups using readily interpretable metrics of time and economics. For economics, cumulative profit over the startup horizon is considered. Two events are tracked for the definition of time metrics: time taken to obtain product purities and time to obtain steady-state product flows. Novel approaches are proposed for quantifying these time metrics, which are used as objective functions and in formulating constraints. The / Thesis / Doctor of Philosophy (PhD)

Artificial Intelligence has recently attracted attention due to its rapid advancement in various industries such as the healthcare and finance industry. The intersection between AI and entrepreneurship is still being studied, and this study explores the impact of AI-tools on startups, with a focus on Swedish startups. The study explores the utilization of AI- tools to optimize their operations or capture new opportunities. It also examines their alignment to The Lean Startup Development, designed to help entrepreneurs to navigate through challenges they face when launching a product or a service. The primary contribution is of qualitative nature, using semi-structured interviews with individuals from startups implementing AI-technologies. Interpretation of the data is done through thematic analysis, which involves identifying themes and core categories.  The startups use the AI-tools for strategic internal planning and operations. The findings suggest that the AI- tools are commonly used to minimize costs, automate certain tasks, saving time to focus more on complex tasks and thereby enhancing efficiency which gradually leads to strengthened competitiveness. Interestingly, the participating startups show a consideration for ethical risks making more careful decisions on the information provided by the AI-tools.

Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction. To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential and likelihood of success. This thesis explores whether online data about a company, particularly general company data, previous funding events, published news articles, internet presence, and social media activity can be used to identify fast-growing startups. Data collected from Crunchbase, the Google Search API, and Twitter was used to predict whether a company will raise a round of funding within a fixed time horizon. A total of ten machine learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 at predicting funding 1-5 years into the future. The final objective was to predict whether a startup that had already raised an angel or seed round would raise another investment within a one-year horizon. The CatBoost model with a 0.75 cutoff achieved precision and F0.1 scores of 0.790 and 0.774, beating the results of previous work in this field.

Influencer marketing has in recent years developed as a new concept for both companies and individuals. Social media has presented a new way for companies to market themselves. The emerge has produced influencers and nowadays influencers need to be divided into different levels according to the number of followers. Companies have proven that they easily look blindly at the number of followers, which has meant that the emergence of micro influencers has not received the publicity it deserves. The purpose with this degree project is therefore to investigate micro influencers and see the brand awareness they create. The authors will apply the conducted research on startup companies.  The research was conducted through a qualitative study were semi-structured interviews created the empirical findings chapter. The data were later analyzed and discussed by the authors in the analyze part of the degree project. The analyze was compared with an early conducted literature review to find similarities and differences by the literature review.  The conclusion of this degree project is that micro influencer is a useful marketing method for startups. If micro influencers are used correctly, which means that the company has analyzed its target group, it helps startups to create brand awareness for a favorable price

The brand is something every company must deal with whether they like it or not, and as a company grows it develops and works as a way to build recognition, trust, customer loyalty and employee branding (Jones, 2022). In the fast-paced startup environment, the resources are limited, and focus has to be split between different tasks and the most urgent problems are the ones to be dealt with - leaving the rest to be, where the unmeasurable and intangible focus areas such as branding often get deprioritized. The purpose of this study is to investigate the brand development process amongst startups, and to investigate how this aligns with the developed brand building framework for startups. The methods used are of qualitative nature, including 3 exploratory interviews, a literature review, framework development, 8 semi-structured interviews and thematic analysis of the collected data. The thesis resulted in insights on the brand understanding in startups, as well as findings concerning thresholds for startups to invest in their brand work. A varying level of understanding of brand between the startups could be seen, both in regards of what brand includes, but also which building blocks are necessary and how the brand can elevate the business. This paper is expected to contribute both toward a deeper theoretical understanding of branding frameworks for startups as well as working as a practical guide. In practice, this paper will guide startups in how to properly build their brand and keep a balanced approach to brand and how to properly build the brand and brand foundation.

Entrepreneurs formulate and implement strategies to tackle different knowledge problems over the entrepreneurial journey. This dissertation systematically disentangled and compared the value of two strategic decision logics that have been tested little in the extant entrepreneurship literature: the strategic decision logic that leverages speed advantages versus the logic that leverages comprehensive advantages. Using a pseudo-NK simulation method first in the management and entrepreneurship fields, the comparative effectiveness of new product development strategies that leverage speed (e.g., the lean method [paper one] and a modular strategy [paper two]) versus comprehensiveness (e.g., a more comprehensive method [paper one] and a parallel strategy [paper two]) was tested in the first and second essays of the dissertation. In the third essay, I systematically reviewed, analyzed, and disentangled two existing corporate strategy constructs of decision speed and decision comprehensiveness by investigating different ways these decision strategies could be effective within entrepreneurship. I also used a multilevel meta-analysis to synthesize and test the contingent role of decision speed and decision comprehensiveness in different entrepreneurial decision contexts. That is, I found that the value of these two entrepreneurial strategies would be contingent upon the external and internal conditions of ventures. Overall, this dissertation shows that strategies that leverage comprehensiveness might even be more effective in entrepreneurship than the strategies that leverage speed advantages, depending on the internal and external decision contexts. / Doctor of Philosophy / Entrepreneurs venture into the unknown to capture abnormal profits. Specifically, entrepreneurs make efforts to develop novel products/services, the market value of which is yet unknown. Thus, a rapid decision-making strategy is typically considered a good decision strategy because it allows entrepreneurs to test the potential new products first and then use the real-time feedback and information that come directly from the market (where there is a high level of 'unknowingness') after testing the products to update those that better satisfy market demands. However, such entrepreneurial strategies that leverage quick decisions do not necessarily capture profits because early adopters from which entrepreneurs collect feedback do not necessarily represent mainstream customers. Rather, it is possible that the market feedback obtained through quick product experimentations fails to indicate actual market demands. Hence, more comprehensive strategies can be a good alternative to rapid decision strategies (e.g., lean startups) for entrepreneurs. Specifically, entrepreneurs can use more comprehensive product designs (paper one), employ systematic approaches (e.g., advanced technologies such as artificial intelligence) to create multiple potential product candidates (paper three), and experiment with multiple products simultaneously (paper two) for new product development. However, this dissertation shows that the effectiveness of comprehensive strategies compared with the entrepreneurial strategies that leverage speed is contingent upon the internal contexts (e.g., available resources and venture lifecycles) and external conditions (e.g., market features).

<p dir="ltr">Force and moment measurements are valuable tools for evaluating designs in a wind tunnel environment. In fact, this type of research has been conducted ever since the earliest wind tunnels were in use. Load measurement techniques are complicated by hypersonic wind tunnel designs, which often have much shorter test times due to the immense stagnation pressures that are used. Previous research had been conducted once before in the Boeing/AFOSR Mach 6 Quiet Tunnel (BAM6QT) at Purdue University using a six-component moment balance. This initial testing utilized a balance with maximum load limits which far exceeded the loads experienced within the BAM6QT. Because of this, much of the data collected was imprecise. </p><p dir="ltr">Testing was conducted in the BAM6QT using three different balances - a five-component foil, five-component semiconductor, and six-component semiconductor balance. Data were taken for a variety of geometry configurations over a range of total pressures. All data were taken at 0 degree angle of attack. The two geometries used most commonly were the 1 inch diameter blunt nose-tip, 7 degree half-angle, 1.75 inch base diameter cone, with either a 20 degree or 30 degree curved ramp. An additional sharp nose-tip configuration was also used. Results for multiple load components were calculated during each run and compared between each balance type. Results were compared to the surface panel method results of CBAERO, which uses either modified Newtonian theory or the tangent cone method to compute loads. </p><p dir="ltr">Results between each balance type were similar and generally in good agreement. The semiconductor balance designs showed considerably less noise than the foil design. Results of CBAERO matched well with the balance data, with a baseline comparison of the plain blunt cone showing a maximum difference of 12% for the modified Newtonian theory. The more complicated ramp geometries, which exhibited regions of flow detachment, agreed surprisingly well with CBAERO results, despite the more complicated flow phenomena, which was unexpected. The best agreement was generally seen in the cases where the large 30 degree ramp was used, while the sharp nose-tip configuration produced the worst agreement. Overall, CBAERO proved valuable as an approximate method for determining the general magnitude of loads. The sting, used to mount the model in the wind tunnel, was found to drive the oscillation frequency of the model-sting system. The longer sting and less stiff balance used on the six-component system likely contributed to lower oscillation frequencies which affected the results for the pitching moment and normal force. The relationship between startup and running loads was also investigated and a startup-to-running load ratio of 5 to 20 was determined, depending on the load component and geometry.</p>

Transient viscosity growth measurements at the startup of shear flow were performed on long glass fiber-filled polypropylene. Samples were prepared with fibers pre-oriented either in 1-direction, 3-direction or random in 1-3 plane, where the 1-direction is the direction of shear motion, the 2-direction is perpendicular to the shear plane and the 3-direction is the neutral direction. A sliding plate rheometer incorporating a shear stress transducer was constructed in the lab. It was shown that this device works well for the tested materials including a Newtonian oil, a low density polyethylene (LDPE) and short glass fiber-filled polypropylene. The transient viscosity growth behavior for long glass fiber suspensions was subsequently investigated. The results suggested that both, fiber length and fiber concentration have pronounced effect on the steady state suspension viscosity. It was also observed that the transient behavior of the pre-oriented samples was highly dependent on the initial orientation state of the fibers. / Master of Science