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Future of Work: A review of reviews

In recent years, we have seen a tremendous growth of research dedicated to the future of work, covering topics like the transition to remote work, emerging forms of online labour, as well as (techno) stress and well-being. An effective transfer of scientific findings into practice is challenged by the unprecedented volume of research studies published each year. In this context, integrated overviews of existing research are essential to properly inform managerial stakeholders on the key areas and topics investigated by academia. Such work can also offer useful guidance and shape the agendas of researchers. The goal of this thesis is to provide an overview of prior research related to the future of work, focusing on prior review papers exclusively. For example, the reviews of Marsh et al. (2022), Mitchell et al. (2022), and Gol2019 cover relevant facets. More generally, the objective would be to use available guidelines (Thomson et al. 2010), search for review papers on digital work, complete a structured selection process (screen), and synthesize the key findings adopting a concept-centric perspective (see Webster and Watson 2000). Relevant implications for practice and research conclude the thesis.

Method: Literature review (qualitative)


  • Gol, E. S., Stein, M. K., & Avital, M. (2019). Crowdwork platform governance toward organizational value creation. The Journal of Strategic Information Systems, 28(2), 175-195.

  • Marsh, E., Vallejos, E. P., & Spence, A. (2022). The digital workplace and its dark side: An integrative review. Computers in Human Behavior, 128, 107118.

  • Mitchell, R., Shen, Y., & Snell, L. (2022). The future of work: a systematic literature review. Accounting & Finance, 62(2), 2667-2686.

  • Thomson, D., Russell, K., Becker, L., Klassen, T., & Hartling, L. (2010). The evolution of a new publication type: steps and challenges of producing overviews of reviews. Research synthesis methods, 1(3‐4), 198-211.

  • Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, xiii-xxiii.

Online labour markets: Key constructs, items, and nomological network

Online labour markets enable clients to hire individual workers, often for short-time contracts and tasks ranging from programming to translation services, and online marketing campaigns. Global demand for professional services on online labour markets is growing, but the experience of clients is not always satisfactory. In fact, clients fail to find suitable candidates for less than 75% of jobs in projects exceeding 1,000$ (Snir and Hitt, 2003). Prior research has invested considerable efforts to identify constructs explaining selection decisions and auction success (Wagner and Prester, 2021). Examples for explanatory constructs are worker experience, ratings, and cultural similarity. Yet, we lack an overview of how these constructs are measured (their items) and how they are related to each other in a nomological network. Developing a more systematic overview of the key constructs can draw inspiration from similar studies (e.g., Clark et al., 2007, Zhang and Venkatesh, 2017) and methodological works (e.g., Larsen et al. 2016; Larsen et al. 2020). Such a contribution is important to ensure nomological validity in future studies, to facilitate better compatibility of individual studies, and to achieve higher predictive accuracy, potentially helping clients to utilize online labour markets more effectively.

Method: literature synthesis (qualitative and/or quantitative)


  • Clark Jr, T. D., Jones, M. C., & Armstrong, C. P. (2007). The dynamic structure of management support systems: theory development, research focus, and direction. MIS Quarterly, 31(3), 579-615.

  • Larsen, K. R., & Bong, C. H. (2016). A tool for addressing construct identity in literature reviews and meta-analyses. Mis Quarterly, 40(3), 529-552.

  • Larsen, K., Gefen, D., Petter, S., & Eargle, D. (2020). Creating Construct Distance Maps with Machine Learning: Stargazing Trust. In: Proceedings of the Americas Conference on Information Systems.

  • Prester, J., & Wagner, G. (2021). Contracting Decisions on Digital Markets for Knowledge Work Services: A Qualitative Systematic Review. In: Proceedings of the International Conference on Information Systems.

  • Snir, E. M., & Hitt, L. M. (2003). Costly bidding in online markets for IT services. Management Science, 49(11), 1504-1520.

  • Zhang, X., & Venkatesh, V. (2017). A nomological network of knowledge management system use: Antecedents and consequences. MIS quarterly, 41(4), 1275-1306.

Data science: Design and evaluation of a machine learning classifier for information retrieval

To present users with the most relevant results in information retrieval and collaborative filtering settings, it is imperative to go beyond pure network structure and consider qualitative and (semi-) structured data (Herlocker et al. 2004). For example, a social media-post or newspaper article will be considered more relevant if it matches a users context and the topics s/he is interested in. This notion of relevance may not only be inferred from the original document, but also from others who refer to it (e.g., in the form of mentions and reviews). The objective of this thesis is to leverage data from referring documents to predict which of the original documents are relevant to a user.

The thesis will focus on the context of academic citation networks in which researchers are challenged to evaluate thousands of papers (original documents) to select those relevant to their work (see Prester et al. 2021). An existing data set will be provided (based on Wagner et al. 2021), which contains the network structure, the referring documents, as well as labels of relevance. The referring documents are in TEI formats (generated from PDFs), which allows for an efficient access of referring sections (contents, citations, and context). These structural and semi-structured data elements should be used to develop features as well as to implement and evaluate a machine learning classifier. Ultimately, such classifiers could help to offer more efficient alternatives to what is commonly known as snowballing, citation, or backward searches (Choong et al., 2014, Webster and Watson, 2001).

Methods: Machine learning (feature engineering, model development, evaluation)

Prerequisites: Programming experience (ideally with Python/Jupyter notebooks, git)


  • Choong, M. K., Galgani, F., Dunn, A. G., & Tsafnat, G. (2014). Automatic evidence retrieval for systematic reviews. Journal of Nedical Internet Research, 16(10), e3369.

  • Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.

  • Prester, J., Wagner, G., Schryen, G., & Hassan, N. R. (2021). Classifying the ideational impact of information systems review articles: A content-enriched deep learning approach. Decision Support Systems, 140, 113432.

  • Wagner, G., Prester, J., & Paré, G. (2021). Exploring the boundaries and processes of digital platforms for knowledge work: A review of information systems research. The Journal of Strategic Information Systems, 30(4), 101694.

  • Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii-xxiii.

Literature reviews: Conceptual models and tools for inductive and theoretical work

Literature reviews in the Information Systems as well as the Management and Organizational Disciplines often adopt inductive approaches for synthesis and theory development. This means that authors approach prior reseach with an open mind and let concepts and associations emerge from the analysis (see Wolfswinkel et al., 2013). This differs from deductive approaches in which a specific schema is selected at the beginning and applied to the literature. Inductive work is considered to be particularly valuable because it allows authors to draw new insights from extant work. Yet, it is also considered particularly challenging to complete inductive reviews from a conceptual and practical perspective. Conceptually, a well-known guideline suggests to develop a concept matrix (Webster and Watson, 2001), but less than 10% of theoretical reviews in Information Systems strictly follow this approach. Similarly, there is a lack of guidelines on appropriate tools and how they can support inductive reviews.

The goal of this thesis is to offer conceptual and practical guidance for inductive literature reviews. Conceptually, this could involve an analysis of inductive review papers and current guidelines, including Webster and Watson’s concept matrix, Glaser and Strauß’s Grounded Theory, or Luhmann’s method. Practically, it may be helpful to review available tools, such as MAXDQA, Atlas.TI, Obsidian, or Zettlr, and to simulate inductive work (in line with example review papers). This work should have implications for future inductive and concept-centric reviews, for instance in the form of archetype patterns, methodological propositions, or guidelines for tools.

Method: Literature review and analysis, tool evaluation


  • Bandara, W., Furtmueller, E., Gorbacheva, E., Miskon, S., & Beekhuyzen, J. (2015). Achieving rigor in literature reviews: Insights from qualitative data analysis and tool-support. Communications of the Association for Information systems, 37(1), 8.

  • Glaser, B. G., & Strauss, A. L. (2008). Grounded theory: strategien qualitativer forschung. Huber.

  • Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii-xxiii.

  • Wolfswinkel, J. F., Furtmueller, E., & Wilderom, C. P. (2013). Using grounded theory as a method for rigorously reviewing literature. European Journal of Information Systems, 22(1), 45-55.