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PurposeThis study introduces a novel approach to preventing construction quality problems by examining the complex interrelations among such issues. Recognizing the overlooked coupling between problems is essential, as it can exacerbate quality issues, triggering chain reactions that compromise project success. The research justifies its focus on these interrelations by highlighting the insufficiency of traditional quality management methods, which often fail to account for interconnected quality problems in the architecture, engineering and construction (AEC) industry.Design/methodology/approachAt the core of this research is the establishment of a knowledge base for construction quality issues, marking a pioneering effort to systematically organize unstructured textual data on construction quality problems and their interconnections. This base serves as a platform for the subsequent application of advanced analytical techniques. Specifically, the study leverages preprocessing, text similarity algorithms and association rule mining to dissect and illuminate the nuanced coupling relationships among construction quality issues, a facet not thoroughly explored in prior research.FindingsThe innovative analytical methodology employed here reveals significant insights into the dynamics of construction quality issue coupling. These insights not only deepen the understanding of these complex interactions but also guide the development of targeted intervention strategies. The practical applicability and effectiveness of the proposed approach are demonstrated using selected textual materials as experimental evidence. The findings show that understanding and addressing these couplings can significantly mitigate potential chain reactions of defects, thus enhancing overall project quality.Originality/valueThe originality of this study lies in its threefold contribution: the creation of a dedicated knowledge base for construction quality issues, the application of novel analytical methodologies to decipher coupling relationships and the extension of text analysis techniques to the realm of construction quality problem prevention. Together, these innovations open new avenues for research and practice in construction management, offering a robust framework for the systematic identification and mitigation of quality issues in construction projects.
This study aims to describe the current framework of the Italian agricultural sector and the changes that occurred in the decade between the two general censuses of agriculture of 2010 and 2020, and the EU Common Agricultural Policy (CAP) programming period 2014–2020. The General Census of Agriculture is an economic census carried out to fulfill international and EU legislation requirements, but also to meet national information needs. It consists in counting farms and identifying their characteristics. For this study, the official data of the 7th Italian General Census of Agriculture (GCA) of 2020 were collected, analyzed, and compared to those of the previous 6th GCA of 2010. Farms’ type of activities, structure, digitalization/computerization, innovation, and workforces’ characteristics were analyzed. Correlations between farms with investments in innovation and other variables like the age and the educational qualification of entrepreneurs and the farm’s size (agricultural used area) were calculated. Groups of similar Italian regions for types of farm and types of farming (segmenting the sector into subsets of regions that share common characteristics), and groups of similar farming characteristics in the entire agricultural sector, were highlighted. The results showed a notable positive correlation between farms’ investment in innovation and farms’ size, and a medium but positive correlation also with other two variables, the entrepreneur’s range of age and educational qualification. Results found groups of regions that are similar in terms of types of farm and farming types, highlighting that the agricultural sector in Italy is not homogeneous among all the regions of north, center, and south. Moreover, the discovered different groups of farming characteristics highlighted the Italian “farm profiles”, i.e., descriptions of key information about different specific types of farm. The overall analysis of all the results of this study provided the current situation of the Italian agricultural sector and discussion about its characteristics and changes during the last ten years. Based on our knowledge, this study is the first one with such a level of comprehensiveness. Findings are of high interest to academics in agriculture economics and policy maker, because they contribute to identifying the farms’ and territories’ strategic elements that require strengthening to foster economic and social development. Moreover these findings may provide food for thought on the effectiveness of the development strategy of the EU CAP 2023–2027 (through greening and digitization) at the regional and European levels, starting from the baseline situation of this country, which is certainly one, but which is among the most relevant ones in the European agri-food system and also globally.
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