Abschlussarbeiten

Basic research question/Objective:

• What is the current state of literature about innovation maturity models?
• How can new ventures increase new venture performance and therefore the innovation success rate?
• Is there a connection to the original maturity model literature in IS (e.g. in regards of validation)?

Motivation/Puzzle:

From prior literature we know the innovation process suffers from high uncertainty (Rhaiem and Amara 2021). To deal with uncertainty and to improve the rate of new ventures’ success, scholars developed Innovation Maturity Models (IMM) as a structured approach to the innovation process. They allow enable entrepreneurs to make more informed, data-based decisions during the innovation process, and thus, increase new venture performance while reducing uncertainty in the innovation process.

Maturity models (MM) in general are a commonly used tool for the evaluation of processes based on quantitative and qualitative data and take the view of either a performance or lifecycle perspective. Literature is categorized in model development, model application, and model validation (Wendler 2012).

Recent unstructured investigation and scarce theoretical literature on IMM mainly focus on development, where scholars assume that existing MM from other disciplines can be adapted to application to the innovation process. Hence, there is a need for a systematical comparison of existing innovation maturity models regarding structures, contents, underlying theories, assumptions, and processes. The results may be useful as quality indicators, which have to be prevalent when developing innovation maturity models. Furthermore, there is a call for investigations to compare MMs from different fields regarding structures, contents, underlying theories, assumptions, and processes to identify quality indicators for MM (Wendler 2012).

Methodology/Tools:

Literature Review

Contribution:

• Create a structured literature review about innovation maturity models (similar to Wendler (2012)) and point out the connection to IS maturity models and business model innovation.
• Point out the connection between IMM and new venture performance and innovation process success rates.

Language:

English

Supervisor:

Lukas Julius

Literature:

• Demir, Ferhat (2018): A strategic management maturity model for innovation. In Technology innovation management review 8 (11).
• Enkel, Ellen; Bell, John; Hogenkamp, Hannah (2011): Open innovation maturity framework. In International Journal of Innovation Management 15 (06), pp. 1161–1189.
• Essmann, Heinz; Du Preez, N. (2009): An innovation capability maturity model–development and initial application. In International Journal of Industrial and Manufacturing Engineering 3 (5), pp. 382–393.
• Müller-Prothmann, T.; Stein, A. (Eds.) (2011): I²MM–Integrated innovation maturity model for lean assessment of innovation capability. XXII ISPIM Conference.

Additional literature:

• Wendler, R. (2012). The maturity of maturity model research: A systematic mapping study. Information and software technology, 54(12), 1317-1339.
• Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing maturity models for IT management: A procedure model and its application. Business & Information Systems Engineering, 1, 213-222.
• De Bruin, T., Rosemann, M., Freeze, R., & Kaulkarni, U. (2005). Understanding the main phases of developing a maturity assessment model. In Australasian Conference on Information Systems (ACIS) (pp. 8-19). Australasian Chapter of the Association for Information Systems.

Basic research question/Objective:

What is the current state of literature about and related to data ecosystems and how can it be structured?

• RQ 1: What do we know about interorganizational data exchange?
• RQ 2: What triggered the emergence of data ecosystems and what is new about this type of data exchange?

Motivation/Puzzle:

Data has changed from being a purely incidental result of value generation processes to a strategic resource and, in some cases, a standalone product (Jarke et al., 2018). Such data-driven innovation and economic value creation is increasingly occurring in cross-industry, socio-technical networks – so-called data ecosystems or data ecosystem – rather than in traditional value chains or by a single firm (Hein et al. 2019; Oliveira and Lóscio 2018).

According to some authors, ecosystem engagement is no longer a choice for enterprises in today’s world, but rather a requirement (Llewellyn & Erkko 2015, Selander et al. 2013). This is also reinforced by McKinsey, which predicts that ecosystems would contribute 30% of global GDP by 2025 (Lorenz 2018). While data ecosystems are becoming more important, many organizations continue to pursue individual strategies and so do not take advantage of data ecosystem services, which entail data sharing and consequently collaborations (Kaiser et al. 2019; Prieelle et al. 2020).

Methodology/Tool:

Relevant data is identified by employing a systematic literature review, which is further examined and synthesized. The data should be acquired from the research of leading business information systems journals (cf. for example the Jourqual ranking; Basket of eight).

Contribution:

This thesis contributes by enhancing the knowledge about the evolution of data ecosystems and how companies can accelerate data ecosystems for joint value creation. Thus, it consolidates and critically reflects the current state of research in this stream.

Language:

English

Supervisor:

Franziska Wagner

Literature:

• Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, xiii-xxiii.
• Gelhaar, J., & Otto, B. (2020, June). Challenges in the Emergence of Data Ecosystems. In PACIS.
• Azkan, C., Möller, F., Ebel, M., Iqbal, T., Otto, B., & Poeppelbuss, J. (2022). Hunting the Treasure: Modeling Data Ecosystem Value Co-Creation.
• Lee, Y., Madnick, S. E., Wang, R. Y., Wang, F., & Zhang, H. (2014). A cubic framework for the chief data officer: Succeeding in a world of big data. MIS Quarterly Executive. (13:1)
• Further insights:
  https://www.economist.com/briefing/2017/05/06/data-is-giving-rise-to-a-new-economy

Basic research question/Objective:

• How can theories from entrepreneurial decision-making be used to develop business models?
• What entrepreneurial decisions and actions are relevant for designing a BM?

Motivation/Puzzle:

From prior literature, we know that the process of innovation, encompassing the business model innovation (BMI) process, suffer from high uncertainty (Rhaiem and Amara 2021). In general, BMI may refer to the design of novel BMs for newly formed organizations. Similar to Lehmann et al. (2022), who analyzed tensions arising when designing digital market offerings from a design research perspective, BM can be seen as artifacts that function as interface between the inner and outer environments of new ventures and that need to deal with similar tensions during the design process. Examples for uncertainties from the outer environment could be uncertain future market conditions (e.g., political, environmental, economic, legal, competition, customer demand, … (Brillinger et al., 2020)) and dynamics (Massa & Tucci, 2013). Examples for the inner environment might be, on the one hand side, computational complexity which arises because of the large number of logically possible combinations between BM components, activities and/or choices (Massa & Tucci, 2013). On the other hand, dynamic complexity which arises because of the non-linear interdependencies—including delays and feedback loops—between BM components, activities and/or choices (Massa & Tucci, 2013).

Therefore, BMs cannot be fully planned ex ante. Rather they take shape through a discovery-driven process that suffers from internal and external uncertainties (Brillinger et al., 2020; Massa & Tucci, 2013). During this process entrepreneurs have to make decisions under these uncertainties.

The literature of entrepreneurial decision-making encompasses multiple theories, such as behavioral decision making, effectuation vs causation (Futterer et al., 2018), resource-based view or hypotheses-based decision making (Camuffo et al., 2020) and their effect on new venture performance, and success rates of startup. So far, little research has been conducted what theories are applicable in what way during the process of business model development. In order to deal with the uncertainties during this process, we need to understand, how to make decisions when developing BM to increase the potential success of the newly designed BM. A better understanding of the application of different theories will help entrepreneurs to make more informed decisions and enable better predictions (Camuffo et al., 2020), and thus, increase new venture performance while reducing uncertainty in the innovation process.

Therefore, this study lines up with research focusing on elements and processes of BMI (Schneider & Speith, 2013), such as BMI for continuous reaction to changes in the environment, BMI as an evolutionary process, BMI as an on-going learning process and as a discovery-driven, trial-and-error-based process rather than an analytical approach and the role of leadership and decision-making within the process of conducting BMI.

Methodology/tools:

Literature Review

Contribution:

• Categorize findings on the applicability of theories from the field of entrepreneurial decision-making on business model development.
• Identify and introduce possible entrepreneurial decision-making theories for the application during the business model development process.
• Point out promising research gaps & promote a structured approach for future research.

Language:

English

Supervisor:

Lukas Julius

Literatur:

• Camuffo, A., Cordova, A., Gambardella, A., & Spina, C. (2020). A scientific approach to entrepreneurial decision making: Evidence from a randomized control trial. Management Science, 66(2), 564-586.
• Shepherd, D. A., Williams, T. A., & Patzelt, H. (2015). Thinking about entrepreneurial decision making: Review and research agenda. Journal of management, 41(1), 11-46.
• Velu, C., & Stiles, P. (2013). Managing decision-making and cannibalization for parallel business models. Long Range Planning, 46(6), 443-458.
• Qin, Q., Liang, F., Li, L., & Wei, Y. M. (2017). Selection of energy performance contracting business models: A behavioral decision-making approach. Renewable and Sustainable Energy Reviews, 72, 422-433.

Additional Literature:

• Brillinger, A. S., Els, C., Schäfer, B., & Bender, B. (2020). Business model risk and uncertainty factors: Toward building and maintaining profitable and sustainable business models. Business Horizons, 63(1), 121-130.
• Futterer, F., Schmidt, J., & Heidenreich, S. (2018). Effectuation or causation as the key to corporate venture success? Investigating effects of entrepreneurial behaviors on business model innovation and venture performance. Long Range Planning, 51(1), 64-81.
• Lehmann, J., Recker, J., Yoo, Y., & Rosenkranz, C. (2022). Designing digital market offerings: how digital ventures navigate the tension between generative digital technology and the current environment. MIS Quarterly, 46(3).
  Massa, L., & Tucci, C. L. (2013). Business model innovation. The Oxford handbook of innovation management, 20(18), 420-441.
• Sarasvathy, S. D. (2001, August). Effectual reasoning in entrepreneurial decision making existence and bounds. In Academy of management proceedings (Vol. 2001, No. 1, pp. D1-D6). Briarcliff Manor, NY 10510: Academy of Management.
• Schneider, S., & Spieth, P. (2013). Business model innovation: Towards an integrated future research agenda. International Journal of Innovation Management, 17(01), 1340001.

Basic research question/Objective:

• What kind of changes do digital innovations trigger in organizations? How do organizations handle them?

Motivation/Puzzle:

Driven by technological progress and the need to innovate, we can witness a growing dependence on information technologies at the societal, organizational as well as individual level (Kotlarsky et al., 2020). Organizations face critical challenges of organizing and competing for success in a fast-changing business environment (Henfridsson and Yoo, 2014). To remain competitive, incumbent firms recognize an increasing necessity of digital innovations, which are not only crucial to technology companies but also to functional units across all industries (Tumbas et al., 2018). Due to their generative and convergent nature (Yoo et al., 2010) digital innovations have radically changed the kind and structure of products and services (Nambisan et al. (2017), enabling new possibilities for creating and transforming value, experiences, relationships, and organizational forms (Yoo et al., 2012; Tumbas et al., 2018). To effectively tackle the imminent challenges of digital innovation and promptly adapt to disruptive changes, firms are required to reassess their existing organizational logic and corporate IT utilization to keep up with their networked character. This revision is essential for facilitating the adoption of new structures and processes that alleviate the growing tension between established business models and the evolving demands of digital innovation (Urbach et al., 2017, Yoo et al., 2012). So far, we know a lot about the idiosyncrasies of digital innovation (e.g., Yoo et al., 2012) and their consequences, for example, the rise of tensions (e.g., Svahn et al., 2017). However, it is still unclear what kind of changes digital innovation trigger and how companies, especially traditional ones, handle them.

Methodology/Tools:

Relevant data is identified by employing a systematic literature review, which is further examined and synthesized to provide an overview of what changes digital innovations trigger in organizations. Therefore, the data should be acquired from research of leading business information systems journals (cf. for example the Jourqual ranking; Basket of eleven)

Contribution:

This thesis contributes by enhancing the knowledge of the changes and challenges brought by digital innovation. Thus, it consolidates and critically reflects the current state of research in this stream.

Language:

English

Supervisor:

Isabel Bienfuß

Literatur:

• Oberländer, A. M., Röglinger, M., & Rosemann, M. (2021). Digital opportunities for incumbents–A resource-centric perspective. The Journal of Strategic Information Systems, 30(3), 101670.
• Faik, I., Thompson, M., & Walsham, G. (2019). Designing for ICT-enabled openness in bureaucratic organizations: Problematizing, shifting, and augmenting boundary work. Journal of the Association for Information Systems, 20(6), 7.
• Yoo, Y., Boland Jr, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for innovation in the digitized world. Organization science, 23(5), 1398-1408.
• Hund, A., Wagner, H. T., Beimborn, D., & Weitzel, T. (2021). Digital innovation: Review and novel perspective. The Journal of Strategic Information Systems, 30(4), 101695.

Basic research question/Objective:

• What is the current state of literature about and related to sustainability in digital resilience and how can it be structured?

Motivation/Puzzle:

The socio-technical tension in resilience research has been an omnipresent topic since the COVID-19 pandemic, which is gaining relevance precisely because of its diverse interactions (Weber et al., 2021). Particularly through the use of information technologies can the resilience of complex and vulnerable systems be addressed (Schemmer et al., 2021). Currently systematic literature reviews address the overall topic of digital resilience (Weber et al. 2021; Bhamra et al., 2011). In order to better address the advancing climate change, a comprehensive understanding of the sustainable components of digital resilience is needed. In this paper, central aspects of the research streams are systematically elaborated.

Methodology/Tools:

Relevant data is identified by employing a systematic literature review, which is further examined and synthesized based on the method of Webster & Watson, (2002). The data should be acquired from the research of leading business information systems journals (cf. for example the Jourqual ranking; AIS Basket of eleven).

Contribution:

This work contributes to the body of knowledge on the components of digital resilience related to sustainability. Thus, it consolidates and critically reflects the current state of research in this area. Hereby, research fields can be identified.

Language:

English

Supervisor:

Simon Riedle

Literatur:

• Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: The concept, a literature review and future directions. International journal of production research, 49(18), 5375-5393.
• Schemmer, M., Heinz, D., Baier, L., Vössing, M., & Kühl, N. (2021, June). Conceptualizing digital resilience for aI-based information systems. In ECIS.
• Weber, M., Hacker, J., & vom Brocke, J. (2021). Resilience in Information Systems Research-A Literature Review from a Socio-Technical and Temporal Perspective. In ICIS.
• Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly Executive, xiii-xxiii.

Basic research question/Objective:

• What are the components of a taxonomy for digital resilience?

Motivation/Puzzle:

Digital resilience is a way to make prevailing systems more resilient through the use of digital technologies (Oliveira, 2023). In order to address given challenges (climate change, disrupted supply chains, etc.) of businesses and societies, systematic approaches need to be created that make systems more resilient. The scientific community is specifically called upon to engage with resilience research and the creation of new understanding (Rai, 2020). In order to be able to take on this creative role, a fundamental understanding of what is meant by digital resilience is required. In particular, what research streams it includes. There are studies that have worked out the basic connections (Weber et al., 2021), that have worked out the resilience attributes (Russpatrick et al., 2023) and that have dealt with the components of shocks (Olivera, 2023). Especially the lack of a clear conceptualization is often criticized (Heeks & Ospina, 2019). A taxonomy can address the illustration of dependencies if it is carried out systematically and scientifically.

Methodology/Tools:

Relevant data is identified by employing a systematic literature review, which is further examined and synthesized. The data should be acquired from the research of leading business information systems journals (cf. for example the Jourqual ranking; Basket of eleven). Afterwards the gathered insights are structured based on Nickerson et al. (2013).

Contribution:

This thesis contributes by enhancing the knowledge about the components and dependencies of digital resilience. Thus, it consolidates and critically reflects the current state of research in this stream.

Language:

English

Supervisor:

Simon Riedle

Literatur:

• Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: The concept, a literature review and future directions. International journal of production research, 49(18), 5375-5393.
• Heeks, R., & Ospina, A. V. (2019). Conceptualising the link between information systems and resilience: A developing country field study. Information Systems Journal, 29(1), 70-96.
• Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3), 336-359.
• Oliveira, M. (2023). What is the role of information and communication technologies (ICT) in building resilience aspects in case of disaster?. In ECIS. 251.
• Rai, A. (2020). Editor’s comments: The COVID-19 pandemic: Building resilience with IS research. Management Information Systems Quarterly, 44(2), iii-vii.
• Russpatrick, S., Amarakoon, P., & Hedberg, C. (2023). Bounce forward resilience attributes: Information system strengthening in response to crisis. In ECIS. 322.
• Weber, M., Hacker, J., & vom Brocke, J. (2021). Resilience in Information Systems Research-A Literature Review from a Socio-Technical and Temporal Perspective. In ICIS.

Basic research question/Objective:

What factors influence the acceptance of AI-driven smart health services for the elderly?

Motivation/Puzzle:

In recent decades human life expectancy has increased rapidly due to advances in medical technology and progress in personal hygiene and nutrition. Further, with declining birth rates the older age group is continuing to represent a greater portion of the population in many countries. Consequently, supporting this rapidly growing elderly population is an emerging social challenge (Ji & Kim, 2022). Due to the significant financial impact of accommodating an elderly population with care institutions and their associated feeling of isolation, aging in place as well as ambient assisted living seem to be promising alternatives (Gochoo et al., 2021). With the rise of AI and IoT-technology and associated possibilities in terms of smart home applications an environment can be created for the elderly that enhances their quality of life, safety and health. That can be achieved through the provision of data driven services that utilize sensor data of the environment as well as personal data from the customer to make suggestions on a healthy lifestyle or detect patterns of misbehavior and thereby enable fast medical intervention (Liu & Tao, 2022). Thus, the question arises, how AI-driven services affect the acceptance of health applications in use cases such as ambient assisted living and aging in place.

Methodology/Tools:

The thesis can be based on the literature or on qualitative interviews with experts. Relevant data will be identified through a systematic literature review, which will be further examined and synthesized using the Webster & Watson (2002) method. Data should be sourced from research in leading business information systems journals (e.g. Jourqual ranking; Basket of eleven).

Contribution:

This thesis contributes to the body of knowledge on the acceptance of smart health technology from a perspective of the elderly. It consolidates and critically reflects the current state of research in this area. In particular, the emphasis is set on the specific AI-characteristics such as anthropomorphism & personalization and their impact on the acceptance of smart health services.

Language:

English

Supervisor:

Alexander Zieglmeier

Literatur:

• Gochoo, M., Alnajjar, F., Tan, T. H., & Khalid, S. (2021). Towards privacy-preserved aging in place: a systematic review. Sensors, 21(9), 3082.
• Guerra, K., & Johnson, V. L. (2023). AI healthcare adoption: a privacy calculus model incorporating emotions and techno-social factors.
• Ji, Y. A., & Kim, H. S. (2022). Scoping review of the literature on smart healthcare for older adults. Yonsei medical journal, 63(Suppl), S14.
• Kang, H. J., Han, J., & Kwon, G. H. (2022). The acceptance behavior of smart home health care services in South Korea: an integrated model of UTAUT and TTF. International Journal of Environmental Research and Public Health, 19(20), 13279.
• Klossner, S., Ghanbari, H., Rossi, M., & Sarv, L. (2023). Personalization-Privacy Paradox in Using Mobile Health Services.
• Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127, 107026.
• Ye, T., Xue, J., He, M., Gu, J., Lin, H., Xu, B., & Cheng, Y. (2019). Psychosocial factors affecting artificial intelligence adoption in health care in China: Cross-sectional study. Journal of medical Internet research, 21(10), e14316.
• Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly Executive, xiii-xxiii.

Basic research question/Objective:

What factors influence the value of data? Which approaches are utilized to determine this value?

Motivation/Puzzle:

In today's digital landscape, where data has emerged as a critical asset for businesses, it is imperative to recognize and comprehend its intrinsic value. Understanding the valuation of data marks a crucial milestone in harnessing its potential as an asset (Parvinen et al., 2020). This comprehension enables companies to optimize their data utilization and tailor their strategies accordingly. It's essential to differentiate between valuation and pricing. Valuation entails assessing the objective value of an asset through various factors like comparable transactions, future cash flows, or market trends. This process aids in comprehending the genuine value of an asset, facilitating informed decisions regarding its acquisition or divestiture. Conversely, pricing involves determining the cost of a product or service offered in the market, considering factors like competition, demand, supply, or cost (Koller et al., 2015). Thus, the question arises, which factors influence the value of data, among which contexts and which approaches are utilized to determine the value.

Methodology/Tools:

Relevant data will be identified through a systematic literature review, which will be further examined and synthesized using the Webster & Watson (2002) method. Data should be sourced from research in leading business information systems journals (e.g. Jourqual ranking; Basket of eleven).

Contribution:

This thesis contributes to the body of knowledge on determining the value of data. It consolidates and critically reflects the current state of research in this area. In particular, the emphasis is set on the diverse factors that drive the value of data as well as the approaches utilized to determine the value.

Language:

English

Supervisor:

Alexander Zieglmeier

Literatur:

• Bendechache, M., Limaye, N., and Brennan, R. 2020. “Towards an Automatic Data Value Analysis Method for Relational Databases,” in Proceedings of the 22nd International Conference on Enterprise Information Systems, Prague, Czech Republic: SciTePress
• Bodendorf, F., Dehmel, K., and Franke, J. 2022. “Scientific Approaches and Methodology to Determine the Value of Data as an Asset and Use Case in the Automotive Industry,” in Proceedings of the 55th Hawaii International Conference on System Sciences, Hawaii, USA.
• Fast, V., Schnurr, D., and Wohlfarth, M. 2021. “Data-Driven Competitive Advantages in Digital Markets: An Overview of Data Value and Facilitating Factors,” in Innovation Through Information Systems (Vol. 48), F. Ahlemann, R. Schütte, and S. Stieglitz (eds.), Cham: Springer International Publishing.
• Koller, T., Goedhart, M., and Wessels, D. 2015. Valuation: Measuring and Managing the Value of Companies, John Wiley & Sons.
• Parvinen, P., Pöyry, E., Gustafsson, R., Laitila, M., Rossi, M., Laitila, M., Futurice, Rossi, M., and Aalto University. 2020. “Advancing Data Monetization and the Creation of Data-Based Business Models,” Communications of the Association for Information Systems (47:1), pp. 25–49.
• Si, Y., Qin, S., Su, J., and Wang, M. 2020. “Research on Factors Influencing the Value of Data Products and Pricing Models,” in Proceedings of the 4th International Conference on Computer Science and Application Engineering, Sanya China: ACM, October 20, pp. 1–5.
• Wagner, A., Wessels, N., Buxmann, P., and Krasnova, H. 2018. “Putting a Price Tag on Personal Information – A Literature Review,” in Proceedings of the 51st Hawaii International Conference on System Sciences.
• Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly Executive, xiii-xxiii.

Basic research question/Objective:

• What are the characteristics of data ecosystems and how can they affect system resilience?

Motivation/Puzzle:

The socio-technical tension in resilience research has been a pervasive theme since the COVID-19 pandemic and is gaining relevance precisely because of its multiple interactions (Weber et al., 2021). In particular, the use of information technologies can address the resilience of complex and vulnerable systems (Schemmer et al., 2021). It becomes apparent that the concept of system is also understood very differently. For example, Heeks and Ospina (2019) define "systems of all kinds", whereby it can be assumed that systems behave differently and therefore these differences must be addressed in the work. Systematic literature reviews are currently being conducted on the topic of digital resilience (Weber et al., 2021; Bhamra et al., 2011). To better address, for example, advancing climate change, a comprehensive understanding of data ecosystems and their role in resilient systems is required. It is known that digital resilience is a way to make dominant systems more resilient using digital technologies (Oliveira, 2023). In particular, the role of data needs to be emphasized. For example, it enables systems to respond to shocks in a more scalable way (Oliveira, 2023; Boh et al., 2023; Heeks & Ospina, 2019). Data platforms in particular can contribute to this, for example through configurability (Russpatrick et al., 2023).

Methodology/Tools:

The thesis can be based on the literature or on qualitative interviews with experts. Conducting expert interviews is intended for a Master's thesis. Relevant data will be identified through a systematic literature review, which will be further examined and synthesized using the Webster & Watson (2002) method. Data should be sourced from research in leading business information systems journals (e.g., Jourqual ranking; Basket of eight). Alternatively, grounded theory can be used to gain qualitative insights into the resilience characteristics of data ecosystems.

Contribution:

This thesis contributes to the body of knowledge on the components of digital resilience in the context of sustainability. It consolidates and critically reflects the current state of research in this area. In particular, the environment of data ecosystems requires the identification of resilience characteristics that need to be considered in the implementation.

Language:

English

Supervisor:

Simon Riedle

Literature:

• Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: The concept, a literature review and future directions. International journal of production research, 49(18), 5375-5393.
• Heeks, R., & Ospina, A. V. (2019). Conceptualising the link between information systems and resilience: A developing country field study. Information Systems Journal, 29(1), 70-96.
• Oliveira, M. (2023). What is the role of information and communication technologies (ICT) in building resilience aspects in case of disaster?. In ECIS. 251.
• Russpatrick, S., Amarakoon, P., & Hedberg, C. (2023). Bounce forward resilience attributes: Information system strengthening in response to crisis. In ECIS. 322.
• Schemmer, M., Heinz, D., Baier, L., Vössing, M., & Kühl, N. (2021, June). Conceptualizing digital resilience for aI-based information systems. In ECIS.
• Weber, M., Hacker, J., & vom Brocke, J. (2021). Resilience in Information Systems Research-A Literature Review from a Socio-Technical and Temporal Perspective. In ICIS.
• Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly Executive, xiii-xxiii.

Basic research question/Objective:

• What is the impact of IT governance deviance, and how can firms deal with the resulting consequences?

Motivation/Puzzle:

Research emphasizes that traditional IT governance mechanisms may no longer be suitable to effectively support digital innovation due to their static nature (Gregory et al., 2018; Vaia et al., 2022; Vejseli et al., 2018). As a consequence, it is more common for business functions to try to circumvent these constraints by using digital solutions that are not approved by the IT function (Gregory et al., 2018). Thereby, they aim to enhance flexibility, speed, and the ability to adapt digital technologies to their everyday needs.

While previous research has mainly focused on the negative outcomes of bypassing IT governance policies (e.g., Furstenau et al., 2017), there are exceptions that demonstrate its positive impact in allowing business functions to overcome constraints when working on innovative ideas (e.g., Koch et al., 2021; Magnusson et al., 2020).

As research is still puzzled about whether deviance from IT is mainly negative or can also be positive, further research is needed to clarify this question. Furthermore, as companies increasingly face the challenge to deal with IT governance bypassing, a better understanding of how to manage it appears to be valuable.

Methodology/Tool:

In-depth literature review & survey

Contribution:

This thesis contributes by enhancing the knowledge of IT governance deviance and how it can be managed. Thus, it consolidates and critically reflects the current state of research in the context of digital innovation.

Language:

English

Supervisor:

Isabel Bienfuß

Literature:

• Gregory, R. W., Kaganer, E., Henfridsson, O., & Ruch, T. J. (2018). IT consumerization and the transformation of IT governance. Mis Quarterly, 42(4), 1225-1253.
• Koch, H., Chipidza, W., & Kayworth, T. R. (2021). Realizing value from shadow analytics: A case study. The Journal of Strategic Information Systems, 30(2), 101668.
• Vaia, G., Arkhipova, D., & DeLone, W. (2022). Digital governance mechanisms and principles that enable agile responses in dynamic competitive environments. European Journal of Information Systems, 31(6), 662-680.

Basic research question/Objective:

• What are uncertainties new ventures phase when designing digital business models?
• What business model elements are relevant when designing digital business models?
• How do new ventures deal with uncertainty when designing digital business models?

Motivation/Puzzle:

BMI may refer to the design of novel BMs for newly formed organizations, or the reconfiguration of existing BMs (Massa & Tucci, 2013). BMs cannot be fully planned ex ante. Rather they take shape through a discovery-driven process that suffers from uncertainties (Brillinger et al., 2020; Massa & Tucci, 2013). Same holds true for digital BMs. Similar to Lehmann et al. (2022), who analyzed tensions arising when designing digital market offerings from a design research perspective, digital BM can be seen as artifacts that function as interface between the inner and outer environments of digital ventures and that need to deal with similar tensions during the design process.

To design, develop, and implement digital business model innovation remains important for new ventures to profit from digital technologies (Trischler & Li-Ying, 2023). This paper aims to increase the understanding what BM elements are relevant when designing digital BMs under uncertainties. Therefore, this study lines up with research focusing on elements and processes of BMI (Schneider & Speith, 2013), such as BMI for continuous reaction to changes in the environment, BMI as an evolutionary process, BMI as an on-going learning process and as a discovery-driven, trial-and-error-based process rather than an analytical approach and the role of leadership and decision-making within the process of conducting BMI.

Methodology/Tools:

Qualitative empirical case study or design science research

Contribution:

• Identifying with what kind of uncertainties new ventures have to deal with when designing digital business models.
• Identifying digital BM core elements and the process of their identification, design, and evaluation.
• Performing field experiments and thereby collecting and observing actual entrepreneurial behavior when creating digital business model.

Language:

English

Supervisor:

Lukas Julius

Literatur:

• Lehmann, J., Recker, J., Yoo, Y., & Rosenkranz, C. (2022). Designing digital market offerings: how digital ventures navigate the tension between generative digital technology and the current environment. MIS Quarterly, 46(3).
• Massa, L., & Tucci, C. L. (2013). Business model innovation. The Oxford handbook of innovation management, 20(18), 420-441.
• Veit, D., Clemons, E., Benlian, A., Buxmann, P., Hess, T., Kundisch, D., Leimeister, JM, Loos, P & Spann, M. (2014). Business models. Business & Information Systems Engineering, 6(1), 45-53.
• Trischler, M. F. G., & Li-Ying, J. (2023). Digital business model innovation: toward construct clarity and future research directions. Review of Managerial Science, 17(1), 3-32.

Additional Literature:

• Rhaiem, Khalil/Amara, Nabil (2021). Learning from innovation failures: a systematic review of the literature and research agenda. Review of Managerial Science 15, 189–234.
• Schneider, S., & Spieth, P. (2013). Business model innovation: Towards an integrated future research agenda. International Journal of Innovation Management, 17(01), 1340001.

Basic research question/Objective:

• How do CSOs promote the sustainability transformation of their companies?

Motivation/Puzzle:

As sustainability has become one of the most important megatrends of the 20th century, companies are increasingly emphasizing responsible behavior toward the environment and society in addition to financial aims. As interest in sustainability has grown, the new role of Chief Sustainability Manager has been created and has increasingly changed in recent years. In the past, CSOs acted like secretive PR managers whose main task was to tell an appealing story about the company's sustainability initiatives to the company's numerous stakeholders and whose implicit goal was to ward off reputational risks. They had almost no involvement in setting corporate strategy or communicating with shareholders. That changed as investor interest in sustainability information increased, marking the time when sustainability moved into the boardroom across the board. With investment capital at stake, these issues became part of the mainstream of corporate governance and strategy.

Recent regulatory initiatives such as the EU Non-Financial Reporting Directive and the UK Companies Act require companies to create transparency regarding sustainability by publishing non-financial information (Bini et al., 2023). These developments translate into CSOs becoming even more relevant to the success of organizations and changing the understanding of the role CSOs play for organizations. However, these developments could also result in sustainability being seen only as an accounting task rather than as an approach to fundamentally change the system (Elkington, 2018).

To fully realize the potential of CSOs for business success, this thesis aims to understand and conceptualize the future role of CSOs. The research objectives unfold as follows:

RO1: The thesis aims to conceptualize the future role of CSOs.

Methodology/Tools:

Relevant data is identified by employing secondary data (e.g., sustainability reports, press releases, presentations, YouTube videos) and empirical data from interviews with sustainability experts. The results should be synthesized according to the method of triangulation.

Software for qualitative analysis of company and interview data, e.g., Nvivo, MAXQDA (free trial available).

Contribution:

An empirical investigation of the future role of CSOs in sustainable value creation, expanding the knowledge base of the increasingly growing field of sustainability research and providing a new perspective on the role of CSOs for corporations.

Language:

English

Supervisor:

Franziska Wagner

Literatur:

• Wang, T., Fu, Y., Rui, O., & De Castro, J. (2023). Catch Up with the Good and Stay Away from the Bad: CEO Decisions on the Appointment of Chief Sustainability Officers. Journal of Management Studies.
• Peters, G. F., Romi, A. M., & Sanchez, J. M. (2019). The influence of corporate sustainability officers on performance. Journal of Business Ethics, 159, 1065-1087.
• Kanashiro, P., & Rivera, J. (2019). Do chief sustainability officers make companies greener? The moderating role of regulatory pressures. Journal of business ethics, 155, 687-701.
• Fu, R., Tang, Y., & Chen, G. (2020). Chief sustainability officers and corporate social (Ir) responsibility. Strategic Management Journal, 41(4), 656-680.
• Farri, E., Cervini, P., and Rosani, G. (2023). The 8 Responsibilities of Chief Sustainability Officers. Harvard Business Review.
• Singh, A., & Hess, T. (2017). How chief digital officers promote the digital transformation of their companies. MIS Quarterly Executive, 16(1).
• Bogner, A., Littig, B., & Menz, W. (Eds.). (2009). Interviewing experts. Springer

Richtlinie Forschungsarbeit (PDF, 600 KB)

Forschungsarbeiten Formvorlage (DOCX, 48 KB)

Handreichung ChatGPT (PDF, 22.905 KB)

Empfehlungen zum Umgang mit generativen und anderen KI-Tools (PDF, 86 KB)