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This study aims to show what type of directors founders (or entrepreneurs) first appoint to the board and how these appointments differ across experienced and novice entrepreneurs.

The sample consists of the human capital of board members in 443 new ventures in the computer software and information technology industries between 2000 and 2014. The hypotheses were tested using tobit regression.

The findings in this study reveal that compared to novice entrepreneurs, experienced entrepreneurs tend to appoint early boards with greater human capital (entrepreneurial, technical/scientific and industry-specific) and with greater functional diversity. In contrast, novice entrepreneurs tend to appoint early boards with greater finance and director experience.

The value of this research lies in filling the gap in the current literature by comparing the board appointment/selection behavior of novice and experienced entrepreneurs, which is relatively underexplored.

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.