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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.

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.

Given the importance of interdisciplinary research in addressing wicked problems, this paper aims to explore the development of and prospects for interdisciplinary research through evidence gained from academic accountants in Australia.

Extant literature is complemented with interviews of accounting academics in Australia to reveal the challenges and opportunities facing interdisciplinary researchers and reimagine prospects for the future.

Evidence indicates that accounting academics hold diverse views toward interdisciplinarity. There is also confusion between multidisciplinarity and interdisciplinarity in the journals in which academic accountants publish. Further, there is mixed messaging among Deans, disciplinary leaders and emerging scholars about the importance of interdisciplinary research to, on the one hand, publish track records and, on the other, secure grants from government and industry. Finally, there are differing perceptions about the disciplines to be encouraged or accepted in the cross-fertilisation of ideas.

This paper is novel in gathering first-hand data about the opportunities, challenges and tensions accounting academics face in collaborating with others in interdisciplinary research. It confirms a discouraging pressure for emerging scholars between the academic research outputs required to publish in journals, prepare reports for industry and secure research funding, with little guidance for how these tensions might be managed.

Ethical principles and practices frequently support the position that people with disability are vulnerable. Vulnerability in research traditionally infers a need for protection from harm and raises questions over the person’s capacity to consent and engage. In addition, vulnerability in ethics infers a state of permanency and one that is all-encompassing for everyone within the vulnerable groups. This construction of vulnerability in effect legitimises the exclusion of people with disability from research or monitors and restricts how people with disability can engage in research. This results in an implicitly ableist environment for research. In this chapter, which has been led by researchers with disability, we argue that there is a critical need to move beyond a popularised social construction of vulnerability which serves to perpetuate barriers to including people with disability in research. Like all terms, the traditional and popular construction of vulnerability is open to reclaiming and reframing. Under this reconstruction, what is traditionally viewed as a limiting vulnerability can be owned, openly disclosed and accommodated. Following a pandemic-inspired ‘new normal’ that supports flexible workplace practices, and in accordance with UNCRPD goals of inclusive employment and reducing disability inequity, we argue that the pathway for people with disability as career researchers needs an ethical review and overhaul. We provide readers with a practical roadmap to advance a more inclusive academy for researchers with disability.

Aerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.

The nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.

Ratios of low- and high-fidelity training samples to degrees of freedom of n_LF/n_DOFs = 50 and n_HF/n_DOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.

The application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.

The purpose of this paper is to examine prominent issues and contributions from extant research and explore the literature on the services provided by Knowledge-Intensive Public Organizations (KIPOs) and its pursuit to achieve the United Nations (UN) 2030 Sustainable Development Goals (SDGs) (hereafter referred to as the UN 2030 SDGs agenda) amidst the challenges represented by COVID-19 pandemic. It emphasizes the crucial role of accounting in dealing with techniques and social and moral practices concerned with the sustainable utilization of resources. This paper also provides an overview of the other papers presented in this JPBAFM Special Issue and draws from their findings to scope out future impactful research opportunities in this area.

The design consists of a review and examination of the prior relevant literature and the other papers published in this JPBAFM Special Issue.

The paper identifies and summarizes three key research themes in the extant literature: the growth in the types of KIPO; the rise in the research approaches to study the provision of public services by KIPO in pursuit of the UN 2030 SDG agenda and the consequent call for developments in the accounting field; and unintended consequences during COVID-19 pandemic. It draws upon work within these research themes to set out four broad areas for future impactful research.

The value of this paper rests with collating and synthesizing several important research themes on the nature and unintended consequences of the UN 2030 SDG agenda, and the challenges represented by COVID-19 pandemic in the governance, management and accounting for KIPO and in prompting future extensions of this work through setting out areas for further innovative research within the field.

The research examined in this paper and the future research avenues proposed are highly relevant to the health sector, the judiciary, museums, research centers and the UN. The focus on accounting and accountability towards a broader spectrum of stakeholders calls for new avenues of study in the accounting field. They also offer important insights into matters of management, accounting, accountability, sustainability accounting and control more generally.

The research examined in this paper and the future research avenues proposed are highly relevant to the health sector, the judiciary, museums, universities, research centers and the UN. They also offer important insights into matters of management, accounting, accountability, sustainability accounting and control more generally.

This paper adds to vibrant existing streams of research in the area of KIPO by bringing together authors from different areas of accounting research for this JPBAFM Special Issue. In scoping out an agenda for impactful research approaches used to study the provision of public services by KIPO, this paper also draws attention to underexplored issues pertaining to extents such as the “lived experience” of personnel in the KIPO and envisioning what a future system of governance, management and accounting of SDG might look like.

Online health communities (OHCs) have emerged on the Internet, substantially changing the conventional healthcare delivery model. Despite this emergence, the lack of patient participation and contribution always limits the success and sustainability of OHCs. Previous studies have disclosed that patients’ value co-creation behavior (VCB) helps organizations sustain OHCs. However, how the recent surge in artificial intelligence (AI) tools, such as social support chatbots (SSCs), drives patients’ VCB is still unknown. Therefore, this study examines the complex mechanism behind patients’ VCB to establish sustainable OHCs.

Using value co-creation and social support theories, the author develops a moderated mediation model and analyzes survey data from 338 respondents using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods.

Results demonstrate that perceived social support (PSS) from SSCs positively affects VCB directly and indirectly via patient learning (PL). This indirect effect is stronger when patient ability/readiness (PAR) is high. ANN findings highlight the model’s robustness and the significant role of PAR in VCB.

This study’s integrated framework offers unique insights into key drivers of patients’ VCB in OHCs. The findings indicate that PSS from SSCs enhances PL and VCB, with PAR influencing the strength of these relationships. Understanding these dynamics can inform user-centric interventions to promote effective learning and collaboration in OHCs.

In this study, it is aimed to develop cooling models for the efficient use of batteries and to show how important the busbar material is. Batteries, which are indispensable energy sources of electric aircraft, automobiles and portable devices, may eventually run out. Battery life decreases over time; the most critical factor is temperature. The temperature of batteries should not exceed the safe operating temperature of 313 K and it is recommended to have a balanced temperature distribution through the battery.

In this study, the effect on the battery temperature caused by using different busbar materials to connect batteries together was investigated. Gold, copper and titanium were chosen as the different busbar material. The Air velocities used were 1 m/s and 2 m/s, the air inlet temperatures were 295 and 300 K and the discharge rates 1.0–1.5–2.0–2.5C were chosen for cooling the batteries.

The best busbar material was identified as copper. Because these studies are long-term studies, it is also suggested to estimate the data obtained with ANN (Artificial Neural Networks). The purpose of ANN is to enable the solution of many different complex problems by creating systems that do not require human intelligence. Four different program (BR-LM-CGP-SCG) were used to estimate the data obtained with ANN. It was found that the most reliable algorithm was BR18. The R2 size of the BR18 algorithm in the test phase was 0.999552, the CoV size was 0.007697 and the RMSE size was 0.005076.

When the literature is considered, the cooling part of the battery modules has been taken into consideration during the temperature observation of the battery modules, but busbar materials connecting the batteries have always been ignored. In this study, various busbar materials were used and it was noticed how the temperature of the battery model changed under the same working conditions. These studies are very time-consuming and costly studies. Therefore, an estimation of the data obtained with artificial neural networks (ANN) was also evaluated.

This research explores how contextual elements and significant events influence the changing storylines within a company's directors' reports spanning a period of six decades. These elements and events encompass the internal dynamics of the family that owns the company, industry-specific advancements, political and socioeconomic climates, and explicit guidelines related to corporate reporting.

This research employs a case study methodology to analyse the directors' reports of Barilla, a prominent Italian food manufacturer, within the theoretical framework of historical institutionalism. A systematic content analysis is conducted on sixty directors' reports published between 1961 and 2021. The study also identifies and examines significant contextual events within this six-decade period, which are linked to four key institutional factors.

Based on the research findings, the directors' reports exhibited notable fluctuations throughout the studied timeframe in reaction to shifts in the institutional setting. Our investigation highlights that each institutional element experienced crucial pivotal moments, and given their interconnected nature, modifications in one factor impacted the others. It was noted that these pivotal moments resulted in alterations in the directors' reports' content across various thematic areas. Additionally, despite Barilla being a multinational company, it was found that national events within Italy had a more pronounced influence on the evolving narratives than global events.

Previous research on directors' reports or chairman's statements has primarily focused on the influence of macro-level institutional factors on the narratives. In contrast, our study considers both macro-level and micro-level institutions, specifically examining the internal events within a family business and how they shape the content of directors' reports. Our study is also distinctive in its analysis of specific critical junctures and their interactions with the investigated institutional factors. Additionally, to the best of our knowledge, few existing studies span a timeframe of sixty years, particularly concerning an Italian company.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.