Our re­search

In our research, we investigate the behaviour of human and machine market participants in digital markets and its effects from an economic perspective. A connecting element of our diverse research is the existence of a (digital) platform through which market participants (e.g. customers) are brought together with other or similar market participants (e.g. sellers or other customers) and through which they can interact with each other. The market participants influence the platform (e.g. by contributing content in the form of product reviews) and are in turn influenced by the platform (e.g. through the design of the rating system).

Building on these basic ideas, we address the following topics in particular in our research. We use a variety of methods to generate findings for science and practice, which we regularly present for discussion at relevant international conferences and workshops and document in the form of publications in conference proceedings or scientific (top) journals. Our research work is carried out - often as part of research projects funded by the EU, the federal government, the state or industry - in a wide variety of domains, such as

  • Software markets (including app markets)
  • retail
  • Hospitality industry
  • Tertiary education
  • Arts and entertainment

The wide range of application contexts requires an interdisciplinary exchange with various scientists from the fields of computer science, economics and cultural studies. As part of research-based transfer, we also aim to generate innovations - often under the organisational umbrella of the Software Innovation Campus Paderborn (SICP) - in collaborative settings together with companies or as part of start-up support.

Re­search top­ics

Elec­tron­ic Word of Mouth

Electronic Word of Mouth (eWOM), the digital form of word-of-mouth advertising, has established itself as an essential component of online commerce. Whether on platforms such as Ebay, Amazon, Yelp or via social media, eWOM enables customers to access a wide range of reviews and experiences from other users. These online reviews, consisting of star ratings and text comments, not only serve as an important decision-making tool for consumers, but also significantly influence the competitiveness and market position of companies. Our research is dedicated to analysing online reviews to understand how eWOM drives buyer behaviour and what strategies sellers and platform operators can use to positively impact their online reputation. In doing so, we investigate how the response behaviour of sellers to reviews or the design of the review platforms themselves can influence the generation and impact of eWOM. The aim is to decipher the mechanisms behind eWOM and develop effective approaches for managing digital reputations.

  • What influence does the variance of valuations have on demand and optimal pricing?
  • What influence do market characteristics such as market size have on the distribution of reviews?
  • Do online valuations have an influence on the pricing behaviour of market participants?
  • What impact does the provision of review templates have on the review text?
  • How does review behaviour change when the reviewer is incentivised by the platform or the seller?
  • How do sales of software products change if reviews are deleted every time a new version is updated?

To research these questions, we use our expertise in the field of automated web crawling, analytical modelling, observational data analysis and online experiments.

  • Zimmermann, S., Herrmann, P., Kundisch, D. Nault, B. 2018. Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand. Information Systems Research, 29 (4), 984-1002.
  • Gutt, D., Neumann, J., Zimmermann, S., Kundisch, D., Chen, J. 2019. Design of Review Systems - A Strategic Instrument to shape Online Reviewing Behaviour and Economic Outcomes. Journal of Strategic Information Systems, 28 (2), 104-117.
  • Gutt, D., Herrmann, P., Rahman, M. 2019. Crowd-Driven Competitive Intelligence: Understanding the Relationship between Local Market Competition and Online Rating Distributions. Information Systems Research, 30 (3), 980-994.
  • Gutt, D., Neumann, J., Jabr, W., Kundisch, D. 2019. The App Updating Conundrum: Implications of Platform's Rating Resetting on Developers' Behaviour, in: Proceedings of the 40th International Conference on Information Systems (ICIS), Munich, Germany.
  • Poniatowski, M., Neumann, J., Görzen, T., Kundisch, D. 2019. Organising Their Thoughts - How Online Review Templates Affect the Review Text, in: Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm, Sweden.

Plat­form eco­nomy

Digital platforms such as Uber, Airbnb and Netflix have revolutionised the way we travel, live and entertain ourselves. But how have they managed to change traditional industries so fundamentally? And what does this mean for the future of business and society? Our research dives into the world of the platform economy, a dynamic field that explores how digital platforms are reshaping markets, competing with each other and being influenced by laws. We explore how these platforms not only compete with each other, but also challenge traditional providers and the role regulation plays in ensuring a fair and innovative market environment. Our work provides insights into the complex interactions between digital platforms and the wider economy, with the aim of understanding how they enrich our everyday lives while redefining professional markets.

  • What strategies are there for introducing and establishing a digital platform in the platform economy?
  • What effect does the market entry of a delivery service platform have on the local restaurant market?
  • How does user activity on a platform change after a competing platform has entered the market?
  • How does pricing behaviour in the sharing economy change after legal restrictions on the use of the sharing economy platform?

To research these questions, we use our expertise in the field of automated web crawling, observational data analysis and the implementation of online experiments.

  • Poniatowski, M., Lüttenberg, H., Beverungen, D., Kundisch, D., 2022. Three Layers of Abstraction - A Conceptual Framework for Theorising digital Multi-Sided Platforms. Information Systems and e-Business Management, Special Issue on Platform Business Models and Platform Strategies, 20 (2), 257-283.
  • Beverungen, D., Kundisch, D., Wünderlich, N., 2021. Transforming into a Platform Provider: Strategic Options for Industrial Smart Service Providers. Journal of Service Management, 32 (4), 507-532.
  • Karl, H., Kundisch, D., Meyer auf der Heide, F., Wehrheim, H. 2020. A Case for a New IT Ecosystem: On-The-Fly Computing. Business & Information Systems Engineering, Research Note, 62 (6), 467-481.
  • Stummer, C., Kundisch, D., Decker, R. 2018. Platform Launch Strategies. Business & Information Systems Engineering, Catchword, 60 (2), 167-173.

Crowd­sourcing

Crowdsourcing enables companies to make flexible use of labour without having to hire permanent staff. On platforms such as Amazon Mechanical Turk, people, known as the "crowd", perform a wide range of tasks, from simple image classification to the development of new business models. This method offers a cost-effective solution to multiple challenges by allowing clients to outsource tasks and benefit from the collective intelligence and creativity of a global labour force. Our research focuses on the mechanisms behind crowdsourcing: How can tasks be designed in such a way that they are both appealing and efficient for the crowd? By investigating incentive structures and design elements, we aim to maximise the quality of work results while minimising costs. The self-motivation of crowdworkers and a cleverly designed task and reward structure play a decisive role in achieving high-quality results.

  • How should the design of an evaluation task for a crowd be organised?
  • Which incentive mechanism (e.g. fixed payment or quality-dependent payment) of an idea generation task leads to the highest possible quality results?
  • Can a crowd assess the quality of innovative ideas just as well as experts?
  • Does the display of previous evaluations influence the evaluation behaviour of the crowd?

To research these questions, we use our expertise in the field of conducting online experiments.

  • Görzen, T. 2020. "What's the Point of the Task?" Exploring the Influence of Task Meaning on Creativity in Crowdsourcing. To appear in: International Journal of Innovation Management.
  • Görzen, T., Laux, F. 2018. Extracting the Wisdom from the Crowd: A Comparison of Approaches to Aggregating Collective Intelligence, in: Tagungsband der Multikonferenz Wirtschaftsinformatik 2018 (MKWI), Lüneburg, Germany.
  • Görzen, T., Kundisch, D. 2017. When in Doubt Follow the Crowd: How Idea Quality Moderates the Effect of an Anchor on Idea Evaluation in: Proceedings of the 38th International Conference on Information Systems (ICIS), Seoul, South Korea.
  • Görzen, T. 2017. "What is it Good for - Absolutely Nothing?" Exploring the Influence of Task Meaning on Creativity in Crowdsourcing, in: Proceedings of the 38th International Conference on Information Systems (ICIS), Research-in-Progress, Seoul, South Korea.
  • Görzen, T., Kundisch, D. 2016. Can the Crowd Substitute Experts in Evaluation of Creative Ideas? An Experimental Study Using Business Models, in: Proceedings of the 22nd Americas' Conference on Information Systems (AMCIS), San Diego, USA.

Busi­ness mod­el in­nov­a­tion

Business model innovation is key to the success of companies such as Airbnb, Uber, Netflix and Apple, which have revolutionised the way we live, travel, consume entertainment and use technology. These companies have proven that by developing new business models that change the value proposition to customers and optimise operational delivery, both competitiveness can be strengthened and customer value increased. Digital platforms play a central role in these innovations by acting as intermediaries between buyers and sellers, creating new market dynamics without directly offering the products or services. In our research, we focus on providing scientifically sound insights into the mechanisms and success factors of business model innovations. The aim is to enable founders and decision-makers to effectively change and adapt their business models to new technologies, customer segments and global challenges in order to achieve significant improvements in the quality of results.

  • Which theories can help to improve tools/methods for generating business model ideas?
  • How can tools/methods for generating business model ideas be empirically evaluated?
  • How can companies be supported in the implementation of business model innovations with regard to idea generation and evaluation of idea quality?
  • What dependencies exist within and between business models and how can such business model dependencies be depicted and analysed?

To answer these and other questions, we research IT-based tools and methods, which we (further) develop on the basis of theory and whose effectiveness we evaluate quantitatively and empirically in experiments. Our research is based on our expertise at the interface of business model innovation, creativity research and software development.

  • Vorbohle, C., Szopinski, D., Kundisch, D. 2020. Business Model Dependencies: Towards conceptualizing dependencies for extending modeling languages for business models, in: Proceedings of the 10th International Symposium on Business Modeling and Software Design (BMSD), Potsdam, Germany, Lecture Notes in Business Information Processing (LNBIP), Vol. 391, Springer.
  • Szopinski, D., Schoormann, T., John, T., Knackstedt, R., Kundisch, D. 2020. Software tools for business model innovation: Current state and future challenges. Electronic Markets, 30 (3), 469-494.
  • Szopinski, D., John, T., Kundisch, D. 2019. Digital Tools for Teaching Business Model Innovation in Information Systems: A newly developed didactic approach comprising video-based peer feedback, contribution at: Workshop on Information Technology and Systems (WITS) 2019, Teaching Innovation, Munich, Germany.
  • John, T., Kundisch, D., Szopinski, D. 2017. Visual Languages for Modeling Business Models: A Critical Review and Future Research Directions, in: Proceedings of the 38th International Conference on Information Systems (ICIS), Seoul, South Korea.
  • John, T. 2016. Supporting Business Model Idea Generation Through Machine-generated Ideas: A Design Theory, in: Proceedings of the 37th International Conference on Information Systems (ICIS), Dublin, Ireland.

Gami­fic­a­tion

Gamification uses playful elements in non-game contexts to increase motivation and bring about changes in behaviour. This technique, which is successfully used in fitness apps such as Runtastic or Freeletics, attracts millions of users by utilising leaderboards, progress indicators and virtual badges. These elements encourage user activity and interaction on digital platforms, be it through answering questions in online communities or reaching new experience levels, which in turn fosters community dynamics and platform growth. Our research focuses on understanding, through empirical studies, how gamification can be used most effectively to intensify platform use and achieve positive behavioural change. We are investigating how gamified incentives can increase user participation and engagement, with benefits for both the users themselves and platform operators.

  • What impact does obtaining a virtual badge have on the endeavour to achieve the next badge?
  • How do different representations of progress bars affect user motivation?
  • How does the contribution behaviour of users change when they are about to achieve a virtual badge?
  • For which types of users and tasks does the competitive nature of leaderboards have a motivating effect?
  • Under what conditions does gamification have negative effects?

To research these questions, we use our expertise in the field of automated web crawling, observational data analysis and conducting experiments.

  • Klingsieck, K., John, T., Kundisch, D., 2022. Procrastination in the Looking Glass of Self-Awareness: Can Gamified Self-Monitoring Reduce Academic Procrastination? To appear in: die hochschullehre.
  • Gutt, D., von Rechenberg, T., Kundisch, D. 2020 Goal Achievement, Subsequent User Effort and the Moderating Role of Goal Difficulty, Journal of Business Research, 106, 277-287.
  • Kundisch, D., von Rechenberg, T. 2017. Does the Framing of Progress Towards Virtual Rewards Matter? Empirical Evidence from an Online Community. Business & Information Systems Engineering, 59 (4), 207-222.
  • Feldotto, M., John, T., Kundisch, D., Hemsen, P., Klingsieck, K., Skopalik, A. 2017. Making Gamification Easy for the Professor: Decoupling Game and Content with the StudyNow Mobile App, in: Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology (DESRIST), Karlsruhe, Germany.
  • von Rechenberg, T., Gutt, D., Kundisch, D. 2016. Goals as Reference Points: Empirical Evidence from a Virtual Reward System. Decision Analysis, 13 (2), 153-171.
  • Mutter, T., Kundisch, D. 2014 Don't take away my Status! - Evidence from the Restructuring of a Virtual Reward System. Computer Networks, 75 (Part B), 477-490.

Re­search meth­ods

Ana­lyt­ic­al mod­el­ling

Digitalisation is changing the rules of the game in markets, especially for digital goods such as apps, music or video games, whose marginal costs are almost zero. This influences not only pricing, but also how product information shapes consumer decisions. We explore these changing market dynamics through analytical modelling based on game theory and industrial economics. Our models make it possible to theoretically capture economic behaviour and make predictions about optimal pricing strategies and their effects on social welfare. These theoretical foundations in turn serve as the basis for empirical studies, which are supported by data analyses and field experiments in order to further sharpen and adapt our understanding of the digital economy.

  • What influence does the variance of ratings have on demand and optimal pricing?
  • How does pricing behaviour in the sharing economy change after receiving quality signals (e.g. new badges to identify outstanding providers)?
  • How do online reviews influence the market when consumers have an inherent need for variety?
  • Zimmermann, S., Herrmann, P., Kundisch, D. Nault, B. 2018. Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand. Information Systems Research, 29 (4), 984-1002.
  • Neumann, J. 2018. The Economics of Online Reviews in Markets with Variety-Seeking Consumers, contribution at: INFORMS Conference on Information Systems and Technology (CIST), Phoenix, Arizona, USA.
  • Neumann, J., Gutt, D. 2017. A Homeowner's Guide to Airbnb: Theory and Empirical Evidence for Optimal Pricing Conditional on Online Ratings, in: Proceedings of the 24th European Conference on Information Systems (ECIS), Guimarães, Portugal.
  • Kundisch, D., Mittal, N., Nault, B. 2014. Using Income Accounting as the Theoretical Basis for Measuring IT Productivity. Information Systems Research, Research Commentary, 25 (3), 449-467. European Research Paper of the Year Award 2015 Nominee
  • Kundisch, D. 2003 Market Efficiency in the Financial Services Industry: Buyer Search Behaviour on an Electronic Commodity Market, Electronic Markets, 13 (1), 80-93.

On­line and field ex­per­i­ments

Online and field experiments are crucial tools for companies in the digital economy to make informed decisions. Whether it's measuring the effectiveness of ads on Google or Facebook or analysing the impact of small customer gifts on reviews, experimental approaches offer clear insights. By deliberately manipulating variables and measuring the effects in a controlled environment, they make it possible to identify causal relationships between actions and outcomes. Field experiments take place in the participants' real-life environment, giving the results high external validity, while online experiments are conducted on the internet and extend the reach and flexibility of the research. Our research uses both types of experiments to test theoretical predictions and generate practical insights for companies in digital markets.

  • Amazon Mechanical Turk
  • Prolific
  • Figure Eight
  • How does the design of evaluation templates affect the evaluation text?
  • How does the evaluation of creative ideas differ between experts and the crowd?
  • How does displaying an anchor affect the evaluation of ideas in the crowd?
  • What impact does online advertising have on sales in stationary retail?
  • How does the contribution behaviour in an online community change after a change in the virtual points system?
  • Görzen, T. 2021. "What's the Point of the Task?" Exploring the Influence of Task Meaning on Creativity in Crowdsourcing. International Journal of Innovation Management, 25 (1).
  • Gutt, D., von Rechenberg, T., Kundisch, D. 2020 Goal Achievement, Subsequent User Effort and the Moderating Role of Goal Difficulty, Journal of Business Research, 106, 277-287.
  • Poniatowski, M., Neumann, J., Görzen, T., Kundisch, D. 2019. Organising Their Thoughts - How Online Review Templates Affect the Review Text, in: Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm, Sweden.
  • Schlangenotto, D., Kundisch, D., Wünderlich, N. 2018. Is Paid Search Overrated? When Bricks-and-Mortar-Only Retailers Should Not Use Paid Search. Electronic Markets, 28 (4), 407-421.
  • Schlangenotto, D., Kundisch, D. 2016. Read this Paper! A Field Experiment on the Role of a Call-To-Action in Paid Search, in: Proceedings of the 24th European Conference on Information Systems (ECIS), Istanbul, Turkey.

Ob­ser­va­tion data ana­lys­is

In the age of big data, a large amount of data is being generated on the internet. By using digital markets, customers and sellers leave behind a large amount of data, both consciously and unconsciously. This ranges from unconsciously generated click-stream data to purchases and consciously published online reviews. These different data sources can be combined to create comprehensive data sets for scientific studies. By suitably analysing this observational data econometrically, we can generate scientific findings on correlations and causal relationships in digital markets. For example, we use events such as legislative changes or changes to the design of websites as natural experiments without actively influencing the object of investigation as scientists.

  • Several thousand pay-per-bid auctions on a major German auction website
  • Several tens of thousands of apps in a leading app store for smartphones, including online ratings, sales ranks and release notes
  • Several million online reviews of a leading review platform for local restaurants, hotels, etc.
  • Several million answers to questions from a German leisure Q&A community
  • Tens of thousands of accommodations from a leading sharing economy platform for short-term accommodation rentals
  • How does the use of automatic bidding agents change bidding behaviour?
  • What impact does an app update have on sales figures?
  • How does the contribution behaviour in a Q&A community change after a change in the virtual incentive structure (badges, points, etc.)?
  • What effect does the market entry of a delivery service platform have on the local restaurant market?
  • How does pricing behaviour in the sharing economy change after legal restrictions on the use of the sharing economy platform?
  • Gutt, D., von Rechenberg, T., Kundisch, D. 2020 Goal Achievement, Subsequent User Effort and the Moderating Role of Goal Difficulty, Journal of Business Research, 106, 277-287.
  • Gutt, D., Neumann, J., Jabr, W., Kundisch, D. 2019. The App Updating Conundrum: Implications of Platform's Rating Resetting on Developers' Behaviour, in: Proceedings of the 40th International Conference on Information Systems (ICIS), Munich, Germany.
  • Kundisch, D., von Rechenberg, T. 2017. Does the Framing of Progress Towards Virtual Rewards Matter? Empirical Evidence from an Online Community. Business & Information Systems Engineering, 59 (4), 207-222.
  • Neumann, J., Gutt, D., Kundisch, D. 2017. The Travelling Reviewer Problem - Exploring the Relationship between Offline Locations and Online Rating Behavior, in: Proceedings of the 38th International Conference on Information Systems (ICIS), Seoul, South Korea.
  • Herrmann, P., Kundisch, D., Rahman, M. 2015. Beating Irrationality: Does Delegating to IT Alleviate the Sunk Cost Effect? Management Science, 61 (4), 831-850.

Tax­onom­ies

Categorising objects into groups helps us to understand them better and not be overwhelmed by their multitude. Taxonomies are tools that describe and classify existing or future objects in a domain, enabling both scientists and practitioners to analyse them. Taxonomies show what these objects and information have in common and how they differ. This is of great importance in business informatics as it helps to understand complex data and systems, to make assumptions and to recognise where research is still needed. This makes the development and evaluation of taxonomies a renowned methodology in business informatics, bringing structure and organisation to the analysis and understanding of IT artefacts and related phenomena.

  • What are the basic characteristics of dependencies between business models and within individual business models?
  • What are characteristic functions of software tools for the development of business models?
  • What are the dimensions and characteristics of the business models of cultural event platforms and what are their archetypal business model patterns?
  • Kundisch, D., Muntermann, J., Oberländer, A. M., Rau, D., Röglinger, M., Schoormann, T. &Szopinski, D. (2021) An Update for Taxonomy Designers: Methodological Guidance from Information Systems Research," Business & Information Systems Engineering, doi: 10.1007/s12599-021-00723-x.
  • Szopinski, D., Schoormann, T., John, T., Knackstedt, R. & Kundisch, D. (2020a). Software Tools for Business Model Innovation: Current State and Future Challenges, Electronic Markets (30:3), pp. 469-494, doi: 10.1007/s12525-018-0326-1.
  • Szopinski, D., Schoormann, T. & Kundisch, D. (2019). Because your Taxonomy is Worth it: Towards a Framework for Taxonomy Evaluation, Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm.
  • Szopinski, D., Schoormann, T. & Kundisch, D. (2020b). Visualize Different: Towards Researching the Fit Between Taxonomy Visualisations and Taxonomy Tasks, Proceedings of Internationale Tagung Wirtschaftsinformatik 2020, Potsdam.
  • Szopinski, D., Schoormann, T. & Kundisch, D. (2020c). Criteria as a Prelude for Guiding Taxonomy Evaluation, Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS), Maui, Hawaii.