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Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

We present a critical literature review debating Brazilian research on social and environmental accounting (SEA). The aim of this study is to understand the role of politics in the construction of hegemonies in SEA research in Brazil. In particular, we examine the role of hegemony in relation to the co-option of SEA literature and sustainability in the Brazilian context by the logic of development for economic growth in emerging economies. The methodological approach adopts a post-structural perspective that reflects Laclau and Mouffe’s discourse theory. The study employs a hermeneutical, rhetorical approach to understand and classify 352 Brazilian research articles on SEA. We employ Brown and Fraser’s (2006) categorizations of SEA literature to help in our analysis: the business case, the stakeholder–accountability approach, and the critical case. We argue that the business case is prominent in Brazilian studies. Second-stage analysis suggests that the major themes under discussion include measurement, consulting, and descriptive approach. We argue that these themes illustrate the degree of influence of the hegemonic politics relevant to emerging economics, as these themes predominantly concern economic growth and a capitalist context. This paper discusses trends and practices in the Brazilian literature on SEA and argues that the focus means that SEA avoids critical debates of the role of capitalist logics in an emerging economy concerning sustainability. We urge the Brazilian academy to understand the implications of its reifying agenda and engage, counter-hegemonically, in a social and political agenda beyond the hegemonic support of a particular set of capitalist interests.

This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members.

As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation.

The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association.

This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus.

The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA.

Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs.

This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.

The development of the Amazon region depends on the organisation and improvement of production chains able to benefit forest species and animals. The purpose of this paper is to map and categorise the studies developed on the Arapaima gigas, a commercialisable fish native to the Amazon, responding to the following research questions: first, which links of the production chain have most of the studies on the pisciculture of the Amazon region? Second, is environmental performance being approached in studies on production chains in the Amazon region? To reach the objective, the authors used the systematic literature review (SLR) method. The authors analysed a sample of 121 articles published in 95 journals between 1981 and 2018. The research contains bibliometric and contents analyses. The main conclusions include the identification of various possibilities of studies throughout the different production chains in the Amazon region; the multidisciplinarity of research on a single species in the Amazon region; the importance of empirical studies in the construction of knowledge about the natural behaviour of the species; the need for integration and sharing of knowledge to create an efficient and competitive production chain. As a limitation, this study encompasses a broad spectrum of issues in the literature, therefore, it was only possible to offer a general overview of these issues. At the same time, this broad and intentional approach presents a comprehensive framing of the themes and phenomena that occur at each link of the production chain of Amazon fish farming.

This research consists in an SLR with organised, transparent and replicable procedures as recommended in the literature (Littell et al., 2008). The SLR is suited to the mapping of areas where there is a high level of uncertainty and new studies are necessary (Petticrew and Roberts, 2006). This research method is especially useful when dealing with a large volume of information (Tranfield et al., 2003). The use of SLR limits researcher bias by trying to evaluate and select relevant studies on the study theme (Petticrew and Roberts, 2006).

There are a number of possibilities for studies of the different production chains in the Amazon region; the results of mapping the production chains help to prioritise “what” should be researched in the Amazon region to promote more effective impacts for all stakeholders; research on pisciculture in the Amazon region can be used as a diagnostic tool for public policy formulators; the development of corporate environmental management is intrinsically linked to the process of analysis and understanding of the operations and costs that arise in different links of the production chain.

As a limitation, this study encompasses a broad spectrum of issues in the literature, therefore, it was only possible to offer a general overview of these issues.

In terms of practical implications, it is possible to note that the dispersion of themes found in this study confirms the plurality of the richness of the Amazon and suggests that research institutions should be able to commit to the drafting of integrated planning of science, as well as compilation of the results reached. It is also important to highlight Brazil’s role in the leadership of research in the Amazon region compared to other countries.

The aim of the paper was twofold: to supply a focussed review of the literature on the production chain of a species in Amazon pisciculture and to identify a research agenda capable of overcoming the gaps that impede the development of this chain. More specifically, this study reviewed the available research on the chain in question to analyse the links that have the largest volume of studies and to orient future research.

The purpose of this paper is to understand the decision process of enterprise software acquisition. The research aims to focus on identifying significant influences on enterprise software acquisition decisions.

As a research model and theoretical background, the organizational buying model (OBB) is proposed for the acquisition of enterprise systems. Influences on enterprise software acquisition decision processes were found by an empirical study carried out from a practitioner's perspective. The study collected data via a mail survey administered to information systems (IS) professionals involved in the acquisition of enterprise software (ES). The survey questionnaire was developed based on a previous research project and a literature review. Organizational buying behavior (OBB) models in the literature served as the basis for the influences included in the survey instrument. Factor analysis was carried out on the survey data to identify the most significant factors/influences.

The following five factors emerged as significant influences on the acquisition decision process of enterprise software: ES strategy and performance; BPR and adaptability; management commitment and user buy‐in; single vendor integrated solution; and consultants, team‐location, and vendor's financing. These factors are discussed and managerial implications are extracted. Conclusions are derived from the study findings and guidelines for further research are suggested.

The present study provides a starting point for further research in understanding a more comprehensive list of influences on enterprise software acquisition. A bigger sample from more industries is required to examine whether the significance of the influences remains stable.

Using OBB models has proven to be useful for organizations in making effective decisions on enterprise software acquisition.

Buoyed by the increasing demand for improved productivity and environmentally conscious manufacturing, research in the area of lean production and green manufacturing has experienced significant growth since Dües et al. (2013). Taking the latter as the point of reference, a review of recent developments in the complementary and conflicting areas between lean production and green manufacturing that has been missing is presented.

A systematic search was done to identify articles on lean production and green manufacturing from Scopus, Web of Science and Google Scholar. The population-intervention-outcome format was used to develop and answer the research questions. ATLAS.ti 22 was used to analyse 141 qualifying papers and identify the research themes.

Lean production and green manufacturing have strong synergy, and when integrated, they tend to deliver superior organisational performance than their individual implementations. This is consistent with the pre-2013 results, and other areas of synergy and divergence were also identified.

The study considers only papers published in the manufacturing sector after Dües et al. (2013). A review of lean production and green manufacturing in integrated product-service systems may also be relevant, especially due to the continuing trend since its introduction.

Any new adopter of lean production should consider implementing it simultaneously with green manufacturing.

This study establishes the persistence of the pre-2013 patterns of synergy and divergence between lean production and green manufacturing, and identifies new considerations for their joint implementation.

This study aims to analyze the entrepreneurial intention (EI) of Colombian researchers using machine learning (ML) techniques, considering their academic activity, contexts and social norms (SN).

Unsupervised classification techniques were applied, including principal component analysis, hierarchical clustering with the Ward method and a logistic model to evaluate the classification. This was done to group researchers according to their characteristics and EI.

The methodology used allowed the identification of three groups of academics with distinct characteristics, of which two showed a high presence of EI. The results indicate that EI is influenced by the connection with the private sector (consulting, intellectual property and applied research) and by the lack of institutional support from universities. Regarding SN, only the preference for entrepreneurial activity over being an employee and the social appreciation of entrepreneurial dedication were identified as predictors of EI.

The use of ML techniques to study the EI of researchers is uncommon. This study highlights the ability of the methodology used to identify differences between two groups of academics with similar characteristics but different levels of EI. One group was identified that, despite rejecting values associated with entrepreneurs, has a high predisposition to develop a career as an entrepreneur. This provides valuable information for designing policies that promote EI among Colombian researchers.