It is crucial for entrepreneurs to identify and evaluate marketable business opportunities. The identification and exploration of opportunities leads to the discovery of new businesses. Hence, opportunity recognition is, and has always been considered, a focal point in the field of entrepreneurship. However, in entrepreneurship education research, little attention has been devoted to understanding the connection between entrepreneurship education and entrepreneurial opportunity recognition.

To strengthen the comprehension of how to improve opportunity recognition abilities by effective entrepreneurship education activities is therefore the objective of this research.

siehe Erasmus Projekt Beschrieb

The research focus lies in the intersection of the extensive theoretical and empirical studies in corporate finance and the nascent topics related to distributed ledger developments. Existing capital structure theories, such as the pecking order of financing preferences, have limited explanatory power on the choice of funding sources and shall be extended. Moreover, the traditional financing paradigm may even be offset by new financing technologies that reduce contracting costs and that offer a different governance profile. While the theoretical promises of these technologies for equity capital raising are high, financing with tokens has not become a mainstream option for funding entrepreneurial ventures yet, which leads to interesting questions for research and wider society. An interesting area is the digital representation of an investment product, recorded on a distributed ledger, and subject to regulation under securities laws. The Blockchain Act (2020) in Liechtenstein and the DLT law (2021) in Switzerland, for example, enable these developments and lead to an opportune timing to investigate the development of this digital financing as a possible approach to ease access to capital for SMEs.

The research focus lies in the intersection of the extensive theoretical and empirical studies in corporate finance and the nascent topics related to distributed ledger developments. Existing capital structure theories, such as the pecking order of financing preferences, have limited explanatory power on the choice of funding sources and shall be extended. Moreover, the traditional financing paradigm may even be offset by new financing technologies that reduce contracting costs and that offer a different governance profile. While the theoretical promises of these technologies for equity capital raising are high, financing with tokens has not become a mainstream option for funding entrepreneurial ventures yet, which leads to interesting questions for research and wider society. An interesting area is the digital representation of an investment product, recorded on a distributed ledger, and subject to regulation under securities laws. The Blockchain Act (2020) in Liechtenstein and the DLT law (2021) in Switzerland, for example, enable these developments and lead to an opportune timing to investigate the development of this digital financing as a possible approach to ease access to capital for SMEs.

Double Wood explores how timber resources in construction can be used "twice," treating primary and reclaimed wood as equally valuable materials. Using the dismantling of a traditional Bregenzerwald house, the project analyses the circular potential of reclaimed wood. In collaboration with regional craftsmen, engineers, and recycling firms, it develops a practical defect and quality framework, pre-sorting and quality pathways, and alternative, tolerance-robust construction techniques. The findings contribute to revisions of the Vorarlberg Timber Award regulations and point toward resource-efficient, regionally rooted building.

Significant changes in the consumer's purchasing behavior have revolutionized the development of recommender systems. B2C enterprises have been at the forefront in producing the most advanced algorithms and utilizing recommender systems to improve customer experience. On the other hand, the complex hierarchical structure of the consumer, multi-domain business models, and increasing communication channels have hindered B2B enterprises in capitalizing on these systems. The focus of the research will be
to use Machine Learning models to generate recommendation and the best practices to disseminate these recommendations to the customers. The document proposes a cumulative and paper-based dissertation project, "Domain Integrated Product Recommendations for a B2B Enterprise: A Unified Recommender System", with the Hilti
Chair of Data and Application Security at the University of Liechtenstein. The following sections of the proposal describe the need for the research, existing literature, core problem
statements, and quantitative research methodology to be used during the research course.
Digitalization over the past decade has dramatically changed the purchasing behavior of consumers. Amazon, selling just books in 1995 to racing towards a $2 trillion giant in 2021 (Jeff Bezos and Amazon.Com by Mark E. Parry :: SSRN), narrates the growth of e-commerce in the internet age. The last few months with the covid-19 pandemic have accelerated the ecommerce industry by 4-6 years, increasing the sales by 77% compared to the sales of the
past year(Nanasaheb Tayade, Khandeshwar and Amravati, 2021).

The growth in the e-commerce industry has fuelled competition, making more companies become part of the global market. An increase in choices for a consumer has become a challenge for enterprises to retain customers, and hence, in this context, a recommender system comes across as one of the efficient solutions.

Business-to-Consumer (B2C) companies have effectively developed and used recommender systems. From a novelty at an e-commerce site, these recommendation algorithms - suggesting products and services - have become serious business tools(Schafer, Konstan and
Riedl, 1999). Business to Business (B2B) companies have tried to capitalize on the same technology, but the intrinsic behavior of a business as a consumer has not made it easy.
Utilizing recommender systems for B2B companies with multiple business solutions and increasing communication channels is extremely difficult, making it a focus area of the dissertation.

Digitalization provides a challenging but unique opportunity to B2B companies operating with a direct sales model. The convergence of a massive physical sales infrastructure (people and stores) with an understanding of customer's digital footprint can generate better sales opportunities. A coherent consolidation of physical and digital will serve as the secondary area of research for the dissertation.

Significant changes in the consumer's purchasing behavior have revolutionized the development of recommender systems. B2C enterprises have been at the forefront in producing the most advanced algorithms and utilizing recommender systems to improve customer experience. On the other hand, the complex hierarchical structure of the consumer, multi-domain business models, and increasing communication channels have hindered B2B enterprises in capitalizing on these systems. The focus of the research will be
to use Machine Learning models to generate recommendation and the best practices to disseminate these recommendations to the customers. The document proposes a cumulative and paper-based dissertation project, "Domain Integrated Product Recommendations for a B2B Enterprise: A Unified Recommender System", with the Hilti
Chair of Data and Application Security at the University of Liechtenstein. The following sections of the proposal describe the need for the research, existing literature, core problem
statements, and quantitative research methodology to be used during the research course.
Digitalization over the past decade has dramatically changed the purchasing behavior of consumers. Amazon, selling just books in 1995 to racing towards a $2 trillion giant in 2021 (Jeff Bezos and Amazon.Com by Mark E. Parry :: SSRN), narrates the growth of e-commerce in the internet age. The last few months with the covid-19 pandemic have accelerated the ecommerce industry by 4-6 years, increasing the sales by 77% compared to the sales of the
past year(Nanasaheb Tayade, Khandeshwar and Amravati, 2021).

The growth in the e-commerce industry has fuelled competition, making more companies become part of the global market. An increase in choices for a consumer has become a challenge for enterprises to retain customers, and hence, in this context, a recommender system comes across as one of the efficient solutions.

Business-to-Consumer (B2C) companies have effectively developed and used recommender systems. From a novelty at an e-commerce site, these recommendation algorithms - suggesting products and services - have become serious business tools(Schafer, Konstan and
Riedl, 1999). Business to Business (B2B) companies have tried to capitalize on the same technology, but the intrinsic behavior of a business as a consumer has not made it easy.
Utilizing recommender systems for B2B companies with multiple business solutions and increasing communication channels is extremely difficult, making it a focus area of the dissertation.

Digitalization provides a challenging but unique opportunity to B2B companies operating with a direct sales model. The convergence of a massive physical sales infrastructure (people and stores) with an understanding of customer's digital footprint can generate better sales opportunities. A coherent consolidation of physical and digital will serve as the secondary area of research for the dissertation.

The aim of DOKONARA - doctoral colloquium in sustainable spatial planning - is to provide scientific debates and an interdisciplinary dialogue on ongoing dissertations, including their research questions, theoretical backgrounds and empirical designs. Submissions from PhD candidates working in sustainability dimensions of Regional Development and Planning, Urban Design, Architecture, Urban and Regional Planning, Landscape Planning, Regional Science, Geography, Sociology, Social and Cultural Anthropology and related field.

In addition DOKONARA offers an integrated definition of sustainability studies and planning studies, which
a) supports PhD candidates with their dissertations in the field sustainable spatial planning
b) helps PhD candidates understand what it means to work in the field of sustainable spatial planning in terms of cognitive abilities, values, and traits and skills that exposure to sustainability studies fosters
c) introduces candidates to important controversies and recent advances in the field
d) facilitates candidates ability to think critically about real-world problems and intellectual questions that span a range of disciplines and interdisciplinary fields
e) introduces an environment that enables Candidates to assess the quality of work in the field of sustainable planning work by others as well their own
f) integrates discussions concerning intellectual autobiographies, PhD candidates portfolios and service learning
g) presents material that is consistent with a constructivist and pragmatic approach to researching and h) prepares PhD candidates for future challenges

With this research project, we want to innovate the perspective how to evaluate currency carry trades (henceforth called carry trade) and implement this enhanced perspective into the asset management process.
A currency carry trade tries to profit from the different interest rate levels in various currencies. The naïve currency carry trade is funded (invested) in a low (high) yield currency. The currency selection criterion of the funding and investment currency is the interest rate level of the two currencies. Naïve carry trades have a pre-defined, fixed amount of money to invest and fund. Additionally, naïve carry trades have a non-negligible crash and downside risk.
In a first part of this project we aim to reduce the risks of carry trades by introducing optimized carry trades. The main difference between naïve carry trades and optimized carry trades is, that optimized carry trades relax the restriction of fixed funding and investment.
In a second part we discuss the measurement of the market risk for naïve carry trades and optimized carry trades regarding interest rate shocks. Therefore, the second research method derives an approach, such that the impact of an isolated, single interest rate shock is observable. The target of this research objective is to identify the main risk drivers of carry trades, such that an investor can run a more sophisticated risk management.
The last part of this research project addresses the diversification effect of carry trades. Therefore, the last research objective will have the aim to shift the mean-variance frontier significantly. We will investigate the diversification effect by the statistical and economical significance.
The overall aim of this research project is to show the economical importance of this enhanced and innovative understanding of carry trades and, especially, the advantages of optimized currency carry trade portfolios in asset management.

Distributed Ledger Technology (DLT) and particularly the Blockchain-Technology are one of the most discussed topics when it comes to digitalization and new technologies. Since developments are advancing rapidly and new business models are being developed continuously, it is particularly important to detect those topics that will continue to be of relevance in the future. In this context, it is essential to identify the issues and trends in order to analyse their compatibility with the current legal framework and to deal with the emerging legal challenges in good time.
The aim of this project is to identify the most relevant possibilities of application and potential legal consequences and risks emerging from these applications. The main focus will be on Liechtenstein contract, consumer and company law, as in these areas frictions with applicable law are most likely to be expected. The neighbouring jurisdictions are also included in the considerations.