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Introduction 

Closely connected to the embryonic idea of a physical environment interwoven with a network of devices and systems, sensors and actuators, has been the advent of “big data” and “artificial intelligence”, which shift the focus from quantity to the quality of the information collected and the manner in which it is used (Marr, 2015; Chan & Daim, 2017). Within this context, new tools to leverage entrepreneurial activities and support decision-making emerge, highlining the need for a different perspective on firm resources and capabilities. The Resource-based View (RBV) (Barney, 1991) is a managerial framework used to determine the strategic resources and capabilities firms can exploit to achieve sustainable competitive advantages, thus supporting internal and external strategies. In this context, Problem Structuring Methods (PSM) and Multiple Criteria Decision Making/Analysis (MCDM/A) have attracted increasing attention over the past 20 years and may have a lot to contribute. This is reflected in a increasing growth in the number of published applications which use a formal approach to problem structuring in combination with an analytic method for multi-criteria analysis (Marttunnen et al., 2017). 

PSM include a broad group of general approaches aiming at structuring complex decision making problems. PSM have usually a participative and interactive character and may refer to problem situations for which classical decision making approaches have limited applicability (Rosenhead, 1996; Belton & Stewart, 2002; Mingers & Rosenhead 2004). Such situations are usually met in entrepreneurial ecosystems studies, where problem factors, constraints, and objective function are not established and agreed in advance. In parallel, MCDM/A has evolved as a major area in operations research and management science (OR/MS) aiming at developing and implementing systematic approaches to decision problems that require the consideration of multiple criteria, objectives, goals, and points of view (Keeney, 1992; Doumpos & Grigoroudis, 2013). MCDM/A can be used to provide a systemic approach in order to consider stakeholders’ preferences and support their decisions. The implementation of MCDM/A approaches can take into account the conflicting nature of criteria or stakeholder’s preferences when studying entrepreneurial ecosystems. Applied alone or combined with other approaches, PSM and MCDM/A methods constitute valuable tools for structuring and evaluating complex decision situations, and can allow for more informed, transparent and consistent decisions (Carayannis et al., 2016, 2018a and 2018b) 

Because MCDM/A approaches have grown exponentially over the past few decades, causing a change in the decision-making arena in general, the objective of this special issue is to bring together recent developments and methodological contributions within the context of entrepreneurship and RBV, as these themes pertain to innovation, management decision and strategic management. 

 

List of topic areas

We are interested in topics such as: 

• Advances in decision-making methods for entrepreneurship and RBV 
• Collaborative decision making for entrepreneurial ecosystem development 
• Decision support and strategic planning for entrepreneurship development 
• Information aggregation and use for resource-based management 
• MCDM/A applications for entrepreneurship and RBV 
• MCDM/A applications for policy making in entrepreneurial ecosystems 
• Mental models and group cognitive mapping for entrepreneurship development 
• Performance measurement system design and development 
• PSM for entrepreneurial ecosystem development 
• Soft systems for RBV and change management 
• Systems thinking and business dynamics for entrepreneurship development 
• Value-focused thinking for innovation and entrepreneurship 

 

Guest Editors

Elias G. Carayannis
George Washington University, USA 
[email protected]

Fernando A. F. Ferreira
University Institute of Lisbon, Portugal and University of Memphis, USA
[email protected]

 

Submission Information  

Submissions are made using ScholarOne Manuscripts. Registration and access are available by clicking the button below.

Submit your paper here!
Author guidelines must be strictly followed.
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.  
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal. 
For any queries, please feel free to contact the guest editors. 

 

Key Deadlines

Opening date for manuscript submissions: 01 September 2023  

Closing date for manuscripts submission: 31 December 2023 

 

References

Barney, J. (1991). Firm resources and sustained competitive advantage, Journal of Management, Vol. 17(1), 99-120. 
Belton, V. & Stewart, T. (2002), Multiple Criteria Decision Analysis: An Integrated Approach, Dordrecht: Kluwer Academic Publishers. 
Carayannis, E.; Ferreira, J.; Jalali, M. & Ferreira, F. (2018a), MCDA in knowledge-based economies: Methodological developments and real-world applications, Technological Forecasting and Social Change, Vol. 131, 1-3. 
Carayannis, E.; Goletsis, Y. & Grigoroudis, E. (2018b), Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework, Technological Forecasting and Social Change, Vol. 131, 4-17. 
Carayannis, E.; Grigoroudis, E. & Goletsis, Y. (2016), A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach, Expert Systems with Applications, Vol. 62, 63-80. 
Chan, L. & Daim, T. (2017), A research and development decision model for pharmaceutical industry: Case of China, R&D Management, Vol. 48(2), 223-242. 
Doumpos, M. & Grigoroudis, E. (2013), Multicriteria Decision Aid and Artificial Intelligence: Links, Theory, and Applications, Chichester: John Wiley & Sons. 
Keeney, R. (1992), Value-focused Thinking: A Path to Creative Decision Making, Harvard: University Press Harvard. 
Marr, B. (2015), Big data: Using SMART big data, analytics and metrics to make better decisions and improve performance, John Wiley & Sons: NY. 
Marttunen, M.; Lienert, J. & Belton, V. (2017), Structuring problems for multi-criteria decision analysis in practice: A literature review of method combinations, European Journal of Operational Research, Vol. 263(1), 1-17. 
Mingers, J. & Rosenhead, J. (2004). Problem structuring methods in action, European Journal of Operational Research, Vol. 152(3), 530-554. 
Rosenhead, J. (1996). What’s the Problem? An Introduction to Problem Structuring Methods, INFORMS Journal on Applied Analytics, Vol. 26(6), 117-131.