We live in times where major crisis can occur almost instantly and with limited possibility for firms to predict them. Events resulting from climate change, overpopulation, ethical/religious/political/social conflicts, poverty, terrorism, technology and business failures only to mention a few may instantly propel systemic and global crises. Over the last 12 years for example the world has seen two major crises causing massive disruptions on a global scale: the world’s financial crisis and the Covid-19 crisis.

Firms need to develop a capability to cope with such crises: Organizational resilience is an organization’s ability to absorb strain and preserve or improve functioning, despite the presence of adversity. For this purpose, organizations need to comprehend complex situations, to cope with unexpected situations, and to control unwanted variability.

While understanding the factors that influence organizations’ resilience is of major relevance, however the corresponding empirical investigation is challenging due to a lack of data. What is needed are objective and high-quality data over time spans of many years. In order to serve investors and shareholders many institutions evaluate certain aspects of enterprises in terms of indices reaching from A as “Ardour Global Alternative Energy Index“ to Was “Wilder Hill New Energy Global Innovation Index“ and many more. Therefore, the question arises whether these indices would be suitable to study the question at hand. The objective of this master thesis would be to study indices from different institutions on a global scale and to extract single or segments of indices that can help to understand, explain and predict organizational resilience.

The master thesis research is based on a research cooperation between WU Vienna (Prof. Dr. Christopher Lettl) and TU Berlin (Prof. Dr. Jan Kratzer). When you are interested in this topic please shortly draft on 2-3 pages an outline/essay along the lines given here and send this per email

If you are interested, please contact:

We live in times where major crisis can occur almost instantly and with limited possibility for firms to predict them. Events resulting from climate change, overpopulation, ethical/religious/political/social conflicts, poverty, terrorism, technology and business failures only to mention a few may instantly propel systemic and global crises. Over the last 12 years for example the world has seen two major crises causing massive disruptions on a global scale: the world’s financial crisis and the Covid-19 crisis.

Firms need to develop a capability to cope with such crises: Organizational resilience is an organization’s ability to absorb strain and preserve or improve functioning, despite the presence of adversity. For this purpose, organizations need to comprehend complex situations, to cope with unexpected situations, and to control unwanted variability.

While understanding the factors that influence organizations’ resilience is of major relevance, however the corresponding empirical investigation is challenging due to a lack of data. What is needed are objective and high-quality data over time spans of many years. Corporations and larger enterprises are by legal terms obliged to reveal data for its shareholders on an annual basis, so-called annual reports. Therefore, the question arises whether data in annual reports would be suitable to study the question at hand. The objective of this master thesis would be to study annual reports on a global scale and to extract patterns that can help to understand, explain and predict organizational resilience.

The master thesis research is based on a research cooperation between WU Vienna (Prof. Dr. Christopher Lettl) and TU Berlin (Prof. Dr. Jan Kratzer). When you are interested in this topic please shortly draft on 2-3 pages an outline/essay along the lines given here and send this per email

If you are intersted, please contact:

Background

Creativity and innovation go hand in hand. Understanding the dynamics of highly creative industries such as the music sector, can help the understanding of innovation dynamics in less creative fields as well.

Even though the abundance of data resulting from the digitalization of the music industry, offers a great opportunity to study these creative dynamics and to answer innovation and creativity related research questions, identifying, fetching, and analyzing the right data is not a trivial task.

Projects in this field will be decidedly quantitative and oriented to the identification and combination of data sources that will be subsequently explored and analyzed to answer innovation and creativity related questions regarding:

• Spatial analysis of music-similarity

• Identification of causal effects from industry/policy shocks on artists and labels

• Nature and relevance of creativity on performance outcomes (e.g. analysis of antecedents and consequents of remixing music)

By working on this topic, you will:

1. ­Have the chance to engage in an “applied” M.Sc. dissertation project in which you can develop and showcase your data science skills.

2. Be providing a very valuable contribution to (hopefully) future published studies by IfSTO researchers.

 

Goal

Familiarizing with:

• Associating a research question on creativity and innovation to data analysis

• Data collection via data dumps, APIs, and web scraping

• Data cleaning

• Data analysis

Potential methods

Exclusively quantitative, most likely involving the collection of your own data via web-scraping, API or public data-dump queries.

Please note that, due to their data intensive nature, projects in this field require a certain familiarity with programming languages (i.e. Python or R) before initiating the project. While the topic lends itself well to expanding one’s programming and analysis competences (e.g. accessing new data sources, scraping an unfamiliar website, applying a new analysis technique – e.g. panel-data regression, spatial econometrics, ML-classification), it is not suitable for novices (i.e. no prior experience outside of online-courses or introductory workshops)

If you are interested, please contact:

Social information has been shown to spread rapidly through networks, e.g. via contagion, social influence or social learning processes, which can lead to positive (e.g. spread of evacuation messages) as well as negative outcomes (e.g. spread of fake news) on an individual and collective level. However, previous research showed that biased information spreads faster than unbiased information through networks. Therefore, it is of central interest to explore factors that influence the spread of biased information in social networks to improve individual and collective decision-making.

I am looking for students, who are interested in conducting quantitative research (mostly in form of surveys/experiments, but other methodology is possible as well). The aim is to find out, how we can correct individual and collective misinformation the most effective- and efficiently. The results will be incorporated into my research.

Some examples for questions that we can work on are: What proportion of a social network needs to be targeted with debiasing information to reduce the spread of misinformation? What influence do the characteristics of individuals in the network have (e.g. confidence)? How does group deliberation influence the spread of biases through the network?

If you are interested, please contact:

The flow of knowledge within or across a firm’s boundaries is a crucial aspect to foster innovation.
Covid-19 has changed human interactions and imposed new practices to communicate (i.e., virtual meetings) that have altered knowledge flows within and between organizations. However, the implications of the new communication practices on the effectiveness and efficiency of knowledge flows are not yet clear, nor are the consequences of these practices for the firms’ innovating activity. Has the volume of knowledge exchanged within and between organizations increased or decreased with the new communications practices? Has the exchanged knowledge gained or lost value under the new practices? Have the new practices affected the firms’ innovating activity? If so, for all the firms in the same way?

Since a part of the new communication practices is expected to remain in place in the post-pandemic era, answering these questions and understanding how firms (un)successfully enhance their innovating activity under these practices is urgent and compelling.

If you are interested, please contact:

Corporate Venture Capitalists sharing knowledge with start-ups: good or bad? For whom?

A Corporate Venture Capitalist (CVC) can offer more than just financial support to a start-up. For example, a CVC’s complementary resources are often a valuable asset that the new firm can leverage to scale up and be successful.

Recent research has started to look at the role of a CVC’s knowledge in steering the innovations of the start-up firm that can use this knowledge, as well as at the benefits in terms of innovation that a CVC receives from sharing its knowledge with a start-up. Indeed, since the knowledge shared can be analyzed by several perspectives such as the attributes of the knowledge exchanged, the mode with which it is exchanged, or the amount of knowledge exchanged, the implications of sharing knowledge between these two entities are not straight-forward and deserve investigation.
Taking different perspectives, we will delve into understanding how several aspects related to the sharing of knowledge between a CVC and a start-up affect both parties' innovating activity.

If you are interested, please contact:

I am looking for students, who are interested in conducting quantitative research (mostly in form of surveys/experiments, but other methodology is possible as well). The aim is to find out, under which conditions crowds act wise or mad, what impact crowd characteristics, social influence and cognitive biases have. The results will be incorporated into my research.

The wisdom of crowds in form of forecasts, decisions and evaluations is used in a variety of contexts ranging from sports, over politics to business applications. The underlying logic of crowd decisions is that crowds can be on average more accurate than individuals and even experts. However, there remain many open questions: How can we reduce herding behavior in crowd decisions? What influence do the characteristics of a crowd have? Do individual cognitive biases aggregate on a collective level? These are examples for overarching questions, we can research together in context of a bachelor thesis.

If you are interested please contact:

Online communities have evolved into valuable forms of organizing and diverse knowledge exchange (see e.g. Wasko and Faraj, 2005, Puranam et al, 2014). Research on user behavior in OCs span fields from open source software communities (von Krogh et al, 2003, 2012; Lerner and Tirole, 2003) to firm led communities in consumer goods (Hienerth et al, 2014) and music production software (Jeppesen and Frederiksen, 2006). A central question which continues to motivate research, is around drivers of user contribution to the knowledge within online communities (Wasko and Faraj, 2005; Wiertz and de Ruyter, 2007; Seo et al, 2020).

I am looking for students interested in fostering understanding around user behavior in online forms of organizing and what community owners or managers can do to incentivize, or potentially dis-incentivize, contributions. While there a number of seminal articles that have investigated possible interventions and their consequences from various angles (e.g. Chen et al, 2018; Gallus, 2017; Lerner and Tirole, 2003), the topic remains of both theoretical and practical relevance.

In particular, questions around the impact of sanctions and monitoring activities, motivations of users to become active or lurk as well as potentially diverging effects due to member type or community type could form the subject of inquiry for a Bachelor thesis.

Literature reviews:

• What kinds / types of members can be found in online communities

• What are the benefits and challenges of using online (open) innovation communities for companies terms of:

  • searching for new ideas

  • co-creating / developing new ideas

  • diffusing innovation

• What kinds of online communities exist and how do behavioral antecedents/motivations for contributions differ

• Why do lurkers lurk and what are ways to de-lurk them

• How do punitive incentives (down votes, banning, etc.) affect user behaviour in online communities

  • Negative effects of deviant/asocial/negative behavior online

  • Desired behavior

• Principal-agent problems within traditional, hierarchical organizations vs new forms of organizing (online)

• Voice/Audio based communities (Clubhouse) vs "traditional" online communities (Reddit; StackExchange)

Emprirical (Surveys / interviews ):

• How do challenges of organizing online communities change over time (and how is this different / similar to traditional organizational challenges)

• How do online community owners incentivize online community owners to participate

• Why do members contribute to communities they contribute to

• Why do (don't) online community/crowdsourcing members collect non-monetary rewards?

• How and why do community owners implement negative incentives (downvoting, threat of banning) ?

• Does theory on negative incentives (downvoting, threat of banning) match experience of community owners or does the interpretation of effects of negative incentives diverge from theory

Empirical (econometric / quantitative data analysis)

• Does theory on negative incentives (downvoting, threat of banning) match experience of community owners or does the interpretation of effects of negative incentives diverge from theory

  • empirical - archival data - stack overflow data is available.

• Beatstars community research - would require scarping of the community:

  • How are tags used and how does it influence plays?

  • What happens after receiving badge?

• David, P. A., & Rullani, F. (2008). Dynamics of innovation in an "open source" collaboration environment: lurking, laboring, and launching FLOSS projects on SourceForge. Industrial and Corporate Change, 17(4), 647–710. https://doi.org/10.1093/icc/dtn026

• Faraj, S., Jarvenpaa, S. L., & Majchrzak, A. (2011). Knowledge Collaboration in Online Communities. Organization Science, 22(5), 1224–1239. https://doi.org/10.1287/orsc.1100.0614

• Faraj, S., von Krogh, G., Monteiro, E., & Lakhani, K. R. (2016). Special Section Introduction - Online Community as Space for Knowledge Flows. Information Systems Research, 27(4), 668–684.

• Frey, B. S., & Gallus, J. (2017). TOWARDS AN ECONOMICS OF AWARDS. Journal of Economic Surveys, 31(1), 190–200. https://doi.org/10.1111/joes.12127

• Goes, P. B., Guo, C., & Lin, M. (2016). Do Incentive Hierarchies Induce User Effort? Evidence from an Online Knowledge Exchange. Information Systems Research, 27(3), 497–516. https://doi.org/10.1287/isre.2016.0635

• Jeppesen, L. B., & Frederiksen, L. (2006). Why Do Users Contribute to Firm-Hosted User Communities? The Case of Computer-Controlled Music Instruments. Organization Science, 17(1), 45–63. https://doi.org/10.1287/orsc.1050.0156

• Lerner, J., & Tirole, J. (2002). Some simple economics of open source. The Journal of Industrial Economics, 50(2), 197–234. https://doi.org/10.1111/1467-6451.00174

• Puranam, P., Alexy, O., & Reitzig, M. (2014). What’s “New” About New Forms of Organizing? Academy of Management Review, 39(2), 162–180. https://doi.org/10.5465/amr.2011.0436

If you are interested please contact:

Non-monetary incentive, e.g. awards, medals, badges etc., have been used over centuries in contexts ranging from governments to private enterprises and from education to the arts (Gallus and Frey, 2016; Gemser et al, 2008). Similar to monetary forms of incentives, the motive behind offering non-monetary incentives is to reward and encourage behavior or accomplishments that are deemed desirable by the bestowing entity (Gallus and Frey, 2017), like extraordinary achievement in an academic field (Nobel Price, John B. Clark Medal) or for achieving the highest sales in a given month (salesperson of the month).

Research has offered insights into differences in award type, award mechanism and tournament design (Charness et al, 2014; Gallus, 2017; Robinson et al, 2018), on varying outcomes (Park and Lee, 2015; Gemser et al, 2008) and within different contexts (Borjas and Doran, 2015; Chan et al, 2014; Gubler et al, 2016; Kosfeld and Neckermann, 2011; Neckermann and Frey, 2013). However there is still much to learn about how non-monetary incentives are used and why they do (don’t) work.

Questions include: What types of non-monetary forms of rewards are used in organizations? What are moderating factors (like rewarder identity; reward meaning) of relevance for effect of non-monetary rewards?  How do punitive incentives ( punishment or sanctions ) affect organizational member performance? Questions can be answered in literature reviews as well as via empirical (qualitative and quantitative) methods and results will directly impact the institutes research

Literature Reviews:

• What types of non-monetary forms of rewards are used in organizations?

  • are there differences to be expected between monetary and non-monetary forms of punishment/sanctions

  • effect on various performance dimensions

• The role of the incentive giver on performance of recipients and non-recipients?

• How do punitive incentives ( punishment or sanctions ) affect organizational member performance?

  • What are forms of punishment/sanctions in organizations - monetary and non-monetary

  • performance in terms of different forms of performance - extra-role, core tasks, creativity

Empirical (interviews / surveys)

• What are awards used for in the workplace/State of awards in organizations

If you are interested please contact

For innovation, it is not sufficient to generate new ideas, but also to recognize and select the best ideas. However, the extant research on novelty evaluation and creativity assessment points to a negative bias toward novel ideas in the context of idea selection (e.g., Blair and Mumford, 2007; Chan et al., 2018; Mueller et al., 2012). To date, only little research has examined individual-level differences that influence why individuals shy away from novel ideas.

In order to investigate this topic further and to better understand the underlying mechanisms that influence the bias against novelty, I am looking for students who are interested in writing their Bachelor Theses in this field. The topic can be examined from different angles, among others: (1) How are ideas selected in companies? (2) Do individuals with different backgrounds (e.g. industry knowledge) evaluate novel ideas differently? (3) Which situational factors have an impact on individual decision-making? (4) How to reduce the novelty bias?

Literature

• Afuah, A. & Tucci, C. (2012). Crowdsourcing as a solution to distant search. Academy of Management Review, 37: 355-375.

• Criscuolo, P., Dahlander, L., Grohsjean, T. & Salter, A. (2017). Evaluating Novelty: The Role of Panels in the Selection of R&D Projects. Academy of Management Journal, 60: 433–460.

• Haas, M., Criscuolo, P. & George, G. (2015). Which problems to solve? Online knowledge sharing and attention allocation in organizations. Academy of Management Journal, 58: 680–711.

• Ocasio, W. (1997): Towards an attention-based view of the firm. Strategic Management Journal, 18: 187-206.

• Piezunka, H. & Dahlander, L. (2015). Distant search, narrow attention: How crowding alters organizations’ filtering of suggestions in crowdsourcing. Academy of Management Journal, 58: 856-880.

• Holmqvist, K., Nyström, N., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (Eds.) (2011). Eye tracking: a comprehensive guide to methods and measures, Oxford, UK: Oxford University Press.

• Mueller, J. S., Melwani, S., Goncalo, J. (2012). The bias against creativity: Why people desire yet reject creative ideas. Psychological Science: 21(1): 13-17.

• Mueller, J. S., Melwani, S., Loewenstein, J. & Deal, J.J. (2018). Reframing the Decision-Makers' Dilemma: Towards a Social Context Model of Creative Idea Recognition. Academy of Management Journal, 61: 94-110

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