Hartmann, T. (2016), "Advancing ECAM knowledge: stepping stones and the shoulder of giants", Engineering, Construction and Architectural Management, Vol. 23 No. 3. https://doi.org/10.1108/ECAM-03-2016-0076Download as .RIS
Emerald Group Publishing Limited
Advancing ECAM knowledge: stepping stones and the shoulder of giants
Article Type: Editorial From: Engineering, Construction and Architectural Management, Volume 23, Issue 3.
As I write my second editorial for ECAM, it is a pleasure to see the journal growing. By now we have a healthy backlog of accepted papers and ECAM is becoming a truly interdisciplinary journal with contributions from a wide range of different disciplines. More importantly, we also see an increase of papers that address managerial issues that cut across disciplines, something we as editorial team explicitly ask for in the new scope of the journal. We also see a continuous improvement in the quality of submissions, a second aspect of the revised scope. Thorough and detailed comparisons of findings across different regions seem to slowly become common practice for papers published.
Moving forward, the journal is in a great position to further establish and strengthen the “science” of ECAM. A practice that requires the application of conceptually strong theoretical frameworks that allow for providing different viewpoints on ECAM practice. A practice that, at the same time, requires the development of coherent streams of publications. Streams that thoroughly dispute or support recent work and, in turn, slowly advance knowledge by establishing stepping stones for future scholars.
The papers collected in this issue illustrate this practice well. The first paper (Elbeltagia et al., 2016), for example, builds upon particle swarm optimization (Kennedy and Eberhart, 1995), a method that is well established in the field of computer science and has been used for many optimization problems across a wide range of disciplines. What makes this paper then interesting is how it thoroughly builds upon a stream of papers that were concerned with developing optimization models for construction schedules, a stream that begins in with Hegazy (1999) and ends with recent work by Abbasy et al. (2012). Similar, the second paper (Ma et al., 2016) of the journal uses the conceptual framework of conditional frontier, a well-developed concept within economic growth theory (Solow, 1956) to explore the convergence of construction labor productivity in Australia. Again this study builds upon a stream of construction labor productivity research from early work by Teicholz (Teicholz et al., 2001) to recent work by Langston (2014). Within this train of thought, the third paper (Lam and Oshodi, 2016) is interesting as it compares the applicability of two different well established methods – artificial neural networks (Bishop, 1995) and Box and Jenkins (1970) models – in this way contributing to the ongoing work on forecasting construction output (see, e.g. early work by Tang et al., 1990, or very recent work by Jiang and Liu, 2014).
In contrast to the first three paper of this issue that focus more or less on model building; the following four papers in this issue represent work that is more empirically, applying case study and interview-based methods. Despite this difference in research philosophy and style, all four of these papers still follow the practice of theory building introduced above. The fourth paper (Forsythe, 2016) uses the marketing concept of service quality to understand satisfaction of housing customers. The fifth paper (Vilventhan and Kalidinidi, 2016) uses the concept of cognitive mapping to understand construction delays. The sixth paper (Zhao et al., 2016), applies maturity modeling techniques to develop a knowledge based for risk-based enterprise management. And finally, the last paper in this issue (Chand and Loosemore, 2016) applies resilience and learning theories to the field of disaster management. All in all, these papers demonstrate that standing on the shoulder of giants to build stepping stones within a consistent stream of research is not only applicable to model building efforts, but also to more empirical research work.
Moving the journal forward it will be important that together as readers, authors, reviewers, and editors, we are able to increasingly establish such strong and consistent lines of research. Increasing the strength of such lines will help the journal to become more recognized as leading outlet for work in specific streams. Such recognition, in turn, will help to further strengthen the scientific claim of ECAM research as manifested in the journal’s scope. It is self-evident that establishing such strong lines requires that authors review and synthesize recent work within this and other related journals, such as Construction Management and Economics, the ASCE Journal of Construction Engineering and Management, or the Engineering Project Organization Journal. Our reviewers, in turn, should safeguard that authors provide concise reviews of work published in the last three to five years. Last but not least, we as editors need to not only stress the importance of this practice, but also more and more insist that papers that are sent to reviewers build upon a well synthesized review of recent work.
Abbasy, M.S., Zayed, T. and Elazouni, A. (2012), “Finance-based scheduling for multiple projects with multimode activities”, Construction Research Congress, ASCE, pp. 386-396
Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford University Press, Oxford
Box, G. and Jenkins, G. (1970), Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, CA
Chand, A.M. and Loosemore, M. (2016), “Hospital disaster management’s understanding of built environment impacts on healthcare services during extreme weather events”, Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 385-402
Elbeltagia, E., Ammar, M., Sanad, H. and Kassab, M. (2016), “Overall multiobjective optimization of construction projects scheduling using particle swarm”, Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 265-282
Forsythe, P.J. (2016), “Construction service quality and satisfaction for a targeted housing customer”, Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 323-348
Hegazy, T. (1999), “Optimization of resource allocation and leveling using genetic algorithms”, Journal of Construction Engineering and Management, Vol. 125 No. 3, pp. 167-175
Jiang, H. and Liu, C. (2014), “A panel vector error correction approach to forecasting demand in regional construction markets”, Construction Management and Economics, Vol. 32 No. 12, pp. 1205-1221
Kennedy, J. and Eberhart, R. (1995), “Particle swarm optimization”, Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948
Lam, K.C. and Oshodi, O.S. (2016), “Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model”, Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 302-322
Langston, C. (2014), “Construction efficiency: a tale of two developed countries”, Engineering, Construction and Architectural Management, Vol. 21 No. 3, pp. 320-335
Ma, L., Liu, C. and Mills, A. (2016), “Construction labor productivity convergence: a conditional frontier approach”, Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 283-301
Solow, R.M. (1956), “A contribution to the theory of economic growth”, The Quarterly Journal of Economics, Vol. 70 No. 1, pp. 65-94
Tang, J.C., Karasudhi, P. and Tachopiyagoon, P. (1990), “Thai construction industry: demand and projection”, Construction Management and Economics, Vol. 8 No. 3, pp. 249-257
Teicholz, P., Goodrum, P. and Haas, C. (2001), “Discussion: US construction labor productivity trends, 1970-1998”, Journal of Construction Engineering and Management, Vol. 127 No. 5, pp. 427-429
Vilventhan, A. and Kalidindi, S.N. (2016), “Interrelationships of factors causing delays in the relocation of utilities: a cognitive mapping approach”, Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 349-368
Zhao, X., Hwang, B.-G. and Low, S.P. (2016), “An enterprise risk management knowledge-based decision support system for construction firms”, Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 369-384