Purpose
The purpose of this paper is to identify factors affecting performance of heritage conservation projects in terms of project management parameters of time, cost, and quality.
Design/methodology/approach
An exploratory study was conducted in India, wherein 41 conservation professionals were interviewed. The method adopted for the study was unstructured interviews, wherein the respondents were asked open-ended questions about the issues faced in such projects and factors affecting project performance.
Findings
The interview recordings and notes, made during the exploratory study, have been manually coded to identify the most frequently mentioned problems, group them into categories, and assess their relative importance.
Research limitations/implications
This paper has presented a broad overview of the challenges faced by heritage conservation projects in general. Further research is necessary to analyse if the challenges depend on factors like type of heritage, project delivery model, and stakeholders involved, and to develop mitigation strategies for these challenges.
Practical implications
The findings from this study can be used by practitioners to improve performance of heritage conservation projects in terms of time, cost, and quality.
Originality/value
The findings of the exploratory study help to better understand the reasons of poor performance of heritage conservation projects in terms of time, cost, and quality. The paper has identified major challenges of the sector, and assessed their relative importance, which can help in developing project management strategy for future projects.
The construction industry is the backbone of a nation’s economy. It is a matter of great concern that such an industry suffers from time and cost overruns, especially in these challenging times. Coupled with the overrun issues, the sector is often criticized for lacking adequate quality and quantity of structured secondary data. The emerging technologies in data science and machine intelligence present a unique opportunity to understand the sector better and aid in effective decision-making. To better understand the utility of such technologies, the Management Discussion and Analysis ssections of the annual reports of publicly listed top Indian construction contracting firms are analyzed to identify the presence of ‘strategy themes’ and further map them to the organizations considered. Natural Language Processing (NLP)-based topic modeling algorithms, namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), are used in this study to perform a qualitative content analysis to identify the latent themes. From a methodological standpoint, considering the context of this study, the NMF results are better in accuracy, precision, and recall compared with the LDA. The results show that while most construction contracting firms prioritized a ‘revenue-focused’ strategy to expand their order books, a smaller set of large-sized firms seem to prioritize process improvement to improve their execution productivity and therefore are ‘profit margin improvement focused’ or ‘lean-focussed’ in their approach. Although a proof-of-concept, this study unlocks the immense potential of unsupervised NLP-based topic-modeling tools to understand and infer from unstructured and freely available text data in the public domain to aid sectoral analysis and policymaking.
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