Strategies for the emergence of a dominant design for heat storage systems

ABSTRACT

An important component of sustainable home energy systems is the self-sufficient generation and usage of energy. Although sustainable solutions to both generation and usage of energy in homes have been extensively studied in the past, the storage of energy has only scarcely been studied. This paper focuses specifically on thermal energy storage. Three competing designs are currently available: sensible, latent and thermochemical heat storage systems. The question is which will become the dominant design. Relevant antecedents for design dominance are explored and applied to this case in order to determine their weights. Furthermore, it is assessed which of these three alternatives will have the highest chance of achieving market dominance. Technological characteristics are most important and latent heat storage technology has the highest likelihood of achieving design dominance. The paper contributes to ongoing research that attempts to assign weights to factors for technology dominance in different arenas.

KEYWORDS:

• Dominant designs
• strategy
• heat energy storage
• best worst method

Acknowledgements

The author would like to thank Igor Djordjevski, Wouter van der Hilst Karrewij, and Kaan Kuguoglu for their significant contributions to data collection and analysis and to various parts of the paper including the abstract, introduction, literature survey, technology analysis, methodology, results, discussion and conclusion. Additionally, the author would like to thank the anonymous reviewers for their insightful comments on various parts of the manuscript. Furthermore, the author would like to thank all experts who took the time to participate in the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 The local average weights are the average weights for the subcriteria (factors) within a category while the global average weights are the overall weights of factors. So the global average weight of a sub-criterion is obtained by multiplying the local average weight of the sub-criterion by the weight of the criterion to which it belongs. For instance the global average weight of ‘‘financial strength’’ (.01) is obtained by multiplying the weight of ‘‘Characteristics of technology supporter’’ (0.10) by the local average weight of ‘‘financial strength’’ (0.10).

2 Average performance score.

3 Average performance score multiplied by average global weight.

Additional information

Notes on contributors

Geerten Van de Kaa

Geerten van de Kaa is Associate Professor of Standardization and Business Strategy at Delft University of Technology. He holds a PhD from Rotterdam School of Management, Erasmus University. His research focuses on a better understanding of the standardization process in order to enable complex innovations and solve societal and business challenges. He has published in high ranking international journals including Organization Studies, IEEE Transactions on Engineering Management, Technovation, and Technological Forecasting and Social Change. He is editor-in-chief of the International Journal of Standardization Research.