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Unternehmerisches Wachstum in der Care-Ökonomie

September 2011 until June 2016 (completed) FFF-Funding Project

Project Description

Erarbeiten eines theoretischen Überblicks zum Ansatz der Care-Ökonomie in Bezug auf Fürsorgeleistungen für abhängige Personen, insbesondere ältere pflegebedürftige Menschen

Systematisieren und erfassen des Marktes für profitorientierte Fürsorgeleistungen in Liechtenstein unter Anwendung des 5-Sektorenmodells der Gesamtwirtschaft

Identifizieren von potenziellen Barrieren, kritischen Schlüsselfaktoren und strategischem Verhalten bei der Gründung und zur Realisierung von Wachstum in profitorientierten Care­Unternehmen

Entwickeln von generischen Unternehmerischen Vorgehensmodellen für profitorientierte Care­Unternehmer/innen unter Berücksichtigung von Qualität und Geschlechtergerechtigkeit

Ableiten von Handlungsempfehlungen an Gründer/innen, Unternehmer/innen und politische Willensträger/innen

Keywords

Entrepreneurship Company growths

Participating Institutions

Forschungsförderungsfonds der Universität Liechtenstein / Sponsor Van Riemsdijk Chair in Entrepreneurship / Project Lead Regierung des Fürstentums Liechtenstein / Sponsor Wirtschaftsuniversität Wien / Partner

Project Participants

Employee
Dr. Barbara Fuchs
- Project Manager
Project Manager

Entrepreneurial Failure and the Search for Success

May 2020 until October 2021 (completed) FFF-Funding Project

Project Description

Failure is an integral part of business life. Organizations are regularly confronted with various failure experiences that range from minor accidents, project failures, and performance problems in general to scandals that threaten organizational reputation and survival (Dahlin, Chuang, & Roulet, 2018; Eggers & Suh, 2019; Haunschild, Polidoro Jr, & Chandler, 2015; Madsen & Desai, 2010; Shepherd, Patzelt, & Wolfe, 2011). Especially firms facing high technological and market uncertainty, such as entrepreneurial and innovative ventures, frequently experience failure (Cardon, Stevens, & Potter, 2011; Mantere, Aula, Schildt, & Vaara, 2013; Yamakawa, Peng, & Deeds, 2015).
Failure or, more broadly, negative performance feedback is widely regarded as an important predictor of organizational behavior, triggering search and organizational change (e.g., Desai, 2016; Gavetti, Greve, Levinthal, & Ocasio, 2012; Greve, 2003; Kuusela, Keil, & Maula, 2017; Maslach, 2016). Drawing on Cyert and March's (1963) behavioral theory of the firm, studies specifically suggest that failure to meet an aspiration level (i.e., the lowest level of performance deemed acceptable by decision makers) can lead organizations to increase their R&D intensity (Chen & Miller, 2007), launch innovations (Joseph & Gaba, 2015), undertake acquisitions (Iyer & Miller, 2008), invest in information systems (Salge, Kohli, & Barrett, 2015), implement strategy changes (Greve, 1998), and engage in risk-taking behavior more broadly (Audia & Greve, 2006). The process through which decision makers respond to failures by searching for and implementing alternative solutions that restore performance has been referred to as "problemistic search" (e.g., Cyert & March, 1963; Greve, 2003; Posen, Keil, Kim, & Meissner, 2018). In this project, we seek to provide new insights into the process of search strategies by examining entrepreneurs' behavioral responses to failures by leveraging high data availability and transparency on digital crowdfunding plattforms, where failure to reach funding goals is the norm rather than the exception.

Participating Institutions

Forschungsförderungsfonds der Universität Liechtenstein / Sponsor Chair of Entrepreneurship and Technology / Project Lead

Project Participants

Employee
Dr. Ferdinand Thies
- Principal Investigator
Principal Investigator

Publications

Entrepreneurial Decision Making in Business Model Innovation Projects

January 2010 (completed) KTT

Project Description

Against the background of intense change and quickly advancing technology, the innovation of one's business model becomes increasingly crucial. During this 2 day workshop, representatives of up to six companies will be taught how decision making can successfully be applied in the context of business model innovation. On the one hand business model innovation helps adapting and keeping up to a changing environment, on the other hand it helps utilizing existing core competencies.

Keywords

Decision Making Business Modell Innovation

Project Participants

Employee
PD Dr. habil. Christian Marxt
- Principal Investigator
Principal Investigator

Unlearning and Forgetting in Organizations

February 2015 until March 2018 (completed) PhD-Thesis

Project Description

The field of organizational studies offers plenty of research and scholarly debate on organizational learning. However, research efforts examining questions as to how and why organizations lose knowledge are still scarce. The doctoral thesis aims at clarifying, understanding, and empirically testing the construct of organizational forgetting and unlearning. More precisely, the project seeks to get a better understanding and discover why and how organizations forget and unlearn. It is important to differentiate between existing knowledge stocks and newly acquired knowledge (e.g. innovations) as well as deliberate unlearning and unintentional modes of forgetting. Clarifying these differences and understanding why and how organizations forget and unlearn are main research questions within the boundaries of this project. Furthermore, the dissertation project investigates the effect of organizational forgetting and unlearning on organizational performance. Empirical results should provide insight into the nature of organizational forgetting and unlearning as well as its effect on organizational performance.

Keywords

Organizational forgetting Organizational unlearning

Participating Institutions

Chair in International Management / Doctoral instiution

Project Participants

Employee
PD Dr. habil. Stefan Güldenberg
- Supervisor
Supervisor
Employee
Dr. Adrian Klammer
- PhD-Student
PhD-Student
Employee
Prof. Dr. Marco Furtner MBA
- Co-Supervisor
Professor - Entrepreneurship and Leadership Academic Director MSc EIL - Liechtenstein Business School
Co-Supervisor
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Prof. Dr. Pablo Martin de Holan
- Co-Supervisor
Co-Supervisor

Understanding Saving in Europe

September 2019 until August 2022 (completed) Erasmus

Project Description

Given the changing demographic and the continuous low interest rate environment, long-term decisions by individuals are becoming increasingly important. Saving optimally over the life cycle requires knowledge and competences many people have never acquired, yet everyone has made these decisions for themselves. Apart from basic financial knowledge, such as the time value of money or the interaction between risk and return, life-cycle savings and investment decisions require additional concepts and considerations, such as human capital, decisions on housing (rent or buy), inflation and how to protect one`s assets from it, or the impact of certain risks. The project "Understanding Saving in Europe" (USAVE) develops online courses that enable participants from two different target groups to acquire the necessary knowledge and skills in the field of life-cycle-based saving and investing. One of the courses is aimed at the general public and does not require specific previous knowledge. The other online course is aimed at students of finance, economics or related disciplines at universities. Both courses can be taken free of charge whenever the participants find the time, and at their own pace. Both courses are complemented by online software applications that allow calculations to be performed without having to spend much time understanding the formulas in the background (course 1, aimed at the general public) or coding (course 2, aimed at students). The main objectives of both courses are to improve the participants' understanding of long-term personal financial decisions and to equip them with the skills needed to create and periodically revisit/improve their own financial planning over the entire life cycle. The second course (aimed at students) also deepens and discusses the theoretical principles, methods and formulas behind the concepts and online applications.

Participating Institutions

Chair in Finance / Project Lead Libera Universita di Bolzano / Partner
Alguru / Partner

Project Participants

Employee
Prof. Dr. Michael Hanke
- Project Collaborator
Professor - Finance Dean - Liechtenstein Business School
Project Collaborator
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Employee
Dr. rer. oec. Lukas Salcher
- Project Collaborator
Postdoc - Financial Economics
Project Collaborator
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Employee
Assoz. Prof. Dr. Sebastian Stöckl
- Principal Investigator
Associate Professor - Financial Economics Academic Director - Liechtenstein Undergraduate and Graduate School Official Representative AACSB - University of Liechtenstein
Principal Investigator
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Understanding Pensions in Europe

September 2016 until August 2019 (completed) Erasmus

Project Description

The aim of this project is to develop online courses and software for individual pension planning targeted at two different audiences:
a) Students in higher education programs.
b) European citizens (the general public).
These courses will provide people across Europe with a better understanding of European pension systems in general, the way these systems are being adapted in response to changing demographics, and how this affects both society in general and their personal situation in particular. They will be enabled to make informed decisions about their own financial planning for retirement. The pension planning software will accompany the courses and will allow its users to assess the impact of various choices on their own prospective financial situation in retirement. Its intention is to enrich the online courses and provide general insights (it cannot, of course, take national tax or social security aspects into account, so it cannot and should not aim at replacing financial planners).

Keywords

Pensionskassen

Participating Institutions

Chair in Finance / Project Lead Freie Universität Bozen / Partner Schantz A/S / Partner

Project Participants

Employee
Prof. Dr. Michael Hanke
- Principal Investigator
Professor - Finance Dean - Liechtenstein Business School
Principal Investigator
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Employee
Assoz. Prof. Dr. Sebastian Stöckl
- Project Coordinator
Associate Professor - Financial Economics Academic Director - Liechtenstein Undergraduate and Graduate School Official Representative AACSB - University of Liechtenstein
Project Coordinator
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Employee
Prof. Dr. Martin Angerer
- Project Collaborator
Professor - Innovative and Digital Finance Academic Director MSc IF - Liechtenstein Business School
Project Collaborator
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Understanding and Explaining Organizational Dynamics Using Digital Trace Data

December 2023 until July 2025 (completed) PhD-Thesis

Project Description

The pervasive use of digital trace data in information systems research presents significant opportunities for exploring processes, changes, and temporal dynamics. Past research has leveraged the vast amount of available data to investigate process-related phenomena such as organizational change and broader organizational dynamics; however, a comprehensive understanding of how organizational dynamics intersect with and rely on digital trace data remains elusive. This dissertation project addresses this gap by employing digital trace data and computational techniques to analyze them, such as process mining, to elucidate how organizational change influences both process dynamics and the organization itself. To complement the quantitative data, qualitative data is incorporated, acknowledging the often limited nature of digital trace data which typically lacks context.

In summary, this research project seeks to make a contribution to process research by generating practical implications that will aid in the digital trace data analysis application for achieving process improvements.

Participating Institutions

Information Systems & Business Process Management / Doctoral instiution

Project Participants

Employee
Dr. rer. oec. Sophie Hartl
- PhD-Student
PhD-Student
Employee
Prof. Dr. Jan vom Brocke
- Supervisor
Visiting Professor - Information Systems and Process Science
Supervisor
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Prof. Dr. Martin Matzner
- Co-Supervisor
Co-Supervisor

Uncovering Local Effects in the Cross-Section of Stock returns via Unsupervised Machine Learning

August 2023 until June 2024 (completed) FFF-Funding Project

Project Description

Characteristic-based factor models have emerged as a workhorse tool in empirical finance. These factor models map expected stock returns via loadings/exposures to common risk factors. Risk factors are built using characteristic sorted portfolios aggregated to so-called “market anomalies”. Extensive research has been conducted to uncover an arsenal of new market anom-alies. For example, Hou et al. (2020) examine a dataset of 452 market anomalies. Market anom-alies are typically constructed using absolute stock characteristic measures like the market capi-talization/size or book-to-market ratio of single stocks. This project combines financial research with data science and machine learning literature to create local/relative measures to construct market anomalies. I propose applying modern unsupervised machine learning tools to uncover local stock return patterns related to underlying characteristics. Early results of the investigation revealed that one example local measure, namely: ‘local factor loading uncertainty’ is priced in the cross-section of stock returns. This result stands in alignment with the findings of Armstrong et al. (2013) and Hollstein et al. (2020). I strive to utilize this striking finding and deduct implica-tions for asset pricing research. In the finalization of this project, in addition to a scientific pa-per, I plan to provide a unique software toolbox in Python that implements visual interpreta-tions of the findings.

Participating Institutions

Finance / Project Lead

Project Participants

Employee
Dr. rer. oec. Merlin Bartel
- Principal Investigator
Postdoc - Finance
Principal Investigator
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Uncovering Local Effects in the Cross-Section of Stock returns via Unsu-pervised Machine Learning

July 2023 until May 2024 FFF-Funding Project

Project Description

Characteristic-based factor models have emerged as a workhorse tool in empirical finance. These factor models map expected stock returns via loadings/exposures to common risk factors. Risk factors are built using characteristic sorted portfolios aggregated to so-called "market anomalies". Extensive research has been conducted to uncover an arsenal of new market anomalies. For example, Hou et al. (2020) examine a dataset of 452 market anomalies. Market anomalies are typically constructed using absolute stock characteristic measures like the market capitalization/size or book-to-market ratio of single stocks. This project combines financial research with data science and machine learning literature to create local/relative measures to construct market anomalies. I propose applying modern unsupervised machine learning tools to uncover local stock return patterns related to underlying characteristics. Early results of the investigation revealed that one example local measure, namely: 'local factor loading uncertainty' is priced in the cross-section of stock returns. This result stands in alignment with the findings of Armstrong et al. (2013) and Hollstein et al. (2020). I strive to utilize this striking finding and deduct implications for asset pricing research. In the finalization of this project, in addition to a scientific paper, I plan to provide a unique software toolbox in Python that implements visual interpretations of the findings.

Participating Institutions

Project Participants

Employee
Dr. rer. oec. Merlin Bartel
- Principal Investigator
Postdoc - Finance
Principal Investigator
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