mïnd (meaning, institutions, collective intelligence, information diffusion, networks, and decision-making)
mïnd focuses on the mind-challenging activities required to effectively cope with complexity and generate innovation and creativity in an entrepreneurial setting.
Dr Anke PIEPENBRINK, Director for mïnd
mïnd – which stands for meaning, institutions, collective intelligence, information diffusion, networks, and decision-making – studies people and organisations as they face complexity. This means it looks at how they engage and perceive their environment, how they create, extract, and make meaning, how they organize, how they define problems, how they decide, how they communicate, how they collaborate, how they create, and what they mean by their acts and behavior, among others. Thus, mïnd welcomes all submissions dealing with the complexities arising when human mind is put into action. Our objective is to understand people and organisations in all aspects of business and management and help them make sense of and manage the complexity around them. In doing so, we will strive to tackle some very impactful and current topics in management, such as how to deal with grand challenges.
mïnd welcomes all kind of papers and funding submissions that explore the above mentioned aspects. As in any top management journal (such as Administrative Science Quarterly, Academy of Management Journal, or Strategic Management Journal), we are agnostic of the method utilized to advance a specific management theory or explore one of the phenomena within our scope, as they are only a tool toward the goal of scientific advancement. Quantitative and qualitative works are equally welcome. Same goes for more traditional (e.g., regression) and more contemporary (e.g., fsQCA or data science tools and methods) analyses.
mïnd offers its researchers access to two tools/facilities. The Integrated Learning Engine (ILE) is a tool developed within the Area with the objective of making vast amounts of data and capabilities to process it available to our faculty. Its current functionalities include Twitter data extraction, basic topic modeling with Latent Dirichlet Allocation (LDA), and sentiment analysis. The Area has also developed an Experimental Laboratory (the eLab) for carrying on experiments on human decision and behavior. Our plan is to continue to extend what we can do with these tools, so stay tuned for more.
As an Area of Excellence, the mïnd philosophy is to aim for research excellence (our motto is “we develop excellence”). Hence, while we are open to all work focusing on our domain, papers targeting a top journal – namely A and A*– will be strongly prioritized for acceptance in the Area and resource allocation. This includes papers at all stages of development, including manuscripts about to be submitted or already under review. We also welcome work that, although initially conceived for lower-ranked outlets, you would like to further develop to target a top journal, at least in its first submission. We will do our best to work with you to help you make this happen. Papers aiming at B-level journals will be also considered when instrumental to the Area development (e.g., funding applications)
mïnd includes the following five sub-areas:
- Meaning, Words, & Complexity works with online as well as offline overabundant data, in order to make sense of the large amount of text available to organizations and, in doing so, fully capture such environmental complexity, especially weak signals, shifts of meaning over time and coevolution of actors and their environment, thus help managers and employees to make sense, focus attention, and manage the complexity they operate in;
- Information Diffusion & Complexity investigates questions such as how information travels online, how value of information changes as it moves from one point to many, and what businesses can learn from that. Views reflected in this sub-area include content-based view (considering the content of the information such as sentiment level, media attachment, content enhancement, content size and others), user-based view (as in user interactions around the information), and time-based view;
- Collective Intelligence & Complexity aims to answer questions regarding collaborative efforts both online and offline. The sub-area covers a variety of collaborative efforts. Topics include, but are not limited to, crowdsourcing, collaborative innovation, group decision making, knowledge ecosystems, open source intelligence, and online collective intelligence;
- Networks, Institutions & Complexity focuses on the role networks and institutions – e.g., institutional logics – for individuals and organizations. In today’s complex world, such actors get an increasing number of constraints and opportunities from being part of multiple networks and being on the receiving end of multiple stakeholders’ expectations. In this context, we also look at topics that are affected and at the same affect networks and institutions (and logics), such as categorization processes. The intertwining of networks, institutional logics, and categories is barely explored, and thus contributions that will deal with such topics and how they affect individual and organizational outcomes are most welcome. A better understanding of the antecedents and consequences of such influences, the processes leading to their emergence and maintenance over time, and their role for individual and organizational outcomes in an increasingly complex world is critical in order to help managers understand and leverage complexity;
- Problems, Decisions & Complexity accepts topics such as problem formulation and its process (including identification), decision making in organizational or consumer research, exploration of the decision-making process of individuals (especially via neurophysiological approaches, e.g., eye tracking, facial emotion coding, etc.), the role of artifacts in individuals’ problem formulation and decision making, how to frame and tackle grand challenges, and so on.
For more information, please contact : Dr Anke PIEPENBRINKanke.email@example.com