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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.

We analyse the role of subject matter experts' experience in establishing performance benchmarks for ambiguous and unstructured police tasks.

Participants included 156 students in the final week of their training to become commissioned officers of a police force (78.8% male, aged 21 to 54); 55.8% had previous experience as police officers, with 5–39 years of service (expert group). Participants completed an online questionnaire providing demographic data and responded to three written vignettes presenting critical high-ambiguity, time-pressure, and life-threatening situations.

Having prior police experience or being familiar with the situations presented in the vignettes did not impact the decisions made in two of the three vignettes. In the vignette where differences appeared, there was no clearly preferred option among the experts. Experts provided shorter and less elaborate justifications for their decisions compared to novices.

Overall experience and personal familiarity with situations do not appear to be sufficient conditions for identifying someone as an expert in this type of tasks. Results are discussed in relation to the difference between knowing what one should do and what one does due to stress and the moral or “sacred” values prevalent in police forces.

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 whistleblowing case study engages students in discussions about when and how to disclose differences of opinion on a revenue recognition matter with higher-ups in an organization. Factors to consider include the morality of whistleblowing, confidentiality obligations, the rules of conduct in the American Institute of Certified Public Accountants (AICPA) Code, Sarbanes–Oxley Act (SOX), Dodd–Frank, and the US Supreme Court ruling in Digital Realty, Inc. v. Somers that addresses when to report matters to the Securities and Exchange Commission (SEC). Case questions are designed to promote students’ critical thinking skills, ethical reasoning skills, and decision-making. A flowchart of AICPA ethics rule 2.130.020 (Subordination of Judgment) provides the framework for making decisions when differences exist in financial reporting. The case provides learning objectives, implementation guidance, and teaching notes. The case was used in an accounting ethics course taught at the undergraduate senior level but can also be used in auditing, fraud examination, and advanced financial reporting courses.

The objective of this research conducted in the city of Cuenca is to elaborate on the management platform model of the smart tourist destination (STD). The chapter provides the components to develop such a system as a pilot plan. To maximize the impact of the proposed approach and enhance the decision-making capacities of local actors, the metrics that the Inter-American Development Bank (IDB) developed for the so-called intermediate cities in the study “Cuenca Sustainable City” are used. The platform (www.smarturcuenca.com) considers the transcendence of the current reality of the tourism sector, which is open to multidisciplinary work with other sectors to promote the rational use of cultural and natural resources through the Internet of Things (IoT).

Occupational safety issues among employees remains a contemporary and omnipresent concern. In developing countries, safety-related problems are amplified, resulting in higher incidences of serious accidents and occupational diseases. This study aims to evaluate employees’ knowledge and attitudes toward occupational health and safety, and how these influence overall occupational health and safety compliance. Ghana’s oil and gas industry provides the contextual backdrop for this research, given it is characterized by high rates of injury.

A positivist and deductive research strategy was used to quantitatively analyze both primary and secondary data sources. A structured survey was administered to industry employees, and multiple linear regression was used to establish the effects of employee’s knowledge and attitude toward occupational health hazards on overall health and safety compliance.

The findings indicate that most employees had both a high level of knowledge and positive attitude toward mitigating occupational health hazards. Moreover, the study reveals that most employees complied with occupational health safety practices. However, the study also reveals that the effect of employees’ knowledge and attitude toward occupational health hazards does not translate into deployment of comprehensive safety practices. Interestingly, female employees were found to be more knowledgeable and compliant with occupational health and safety practices than their male counterparts.

Premised upon the findings, the study recommends: implementation of relevant education and training programs encompassing the proper usage of machinery and equipment, tailored hazard safety training appropriate to specific employee job requirements, effective dissemination of risk information and governance initiatives that enforce strict adherence to correct safety procedures.

The study uniquely examines the influence of employee’s knowledge of health and safety to overall compliance within the oil and gas industry. Cumulatively, the study’s findings and recommendations contribute to improving the occupational health and safety outcomes within the industry.

This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.

This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).

In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).

In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.

It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.

The purpose of this paper is to create a model that connects drivers between organizational innovation and business model innovation (BMI) to guide empirical research and the design of innovation management strategies.

The model was designed based on the results of a systematic literature review over the past 25 years that provides common predictor variables to build bridges between these two types of innovations.

It is a conceptual relationship between organizational innovation and BMI based on processes, new structures and customer relationship management. Moreover, there are five bridges from common predictors: strategy, top management, exploratory learning, technological innovation and environmental complexity.

The relationships between organizational innovation and BMI have been neglected in the literature. The model fills this gap by proposing hypotheses for empirical research and critical variables and relationships to steer organizational and business model innovation.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The purpose of this study is to investigate the effects of employee live streamers on consumers' purchase behaviors and brand image, as well as to understand the mediating roles of friendship and self-congruity.

A framework was proposed to explain the influence of employee live streamers' qualities on consumers' behaviors and brand image through the mediators of friendship and self-congruity. Primary data was collected from 225 valid survey responses in China, and the PLS-SEM analysis was employed to test the statistical significance of the hypothesized relationships.

The study found that four qualities of employee live streamers – trustworthiness, attractiveness, responsiveness and expertise – had significant effects on consumers' purchase behaviors and brand image through the mediators of self-congruity and friendship.

This research provides valuable insights into the varying roles of employee live streamers in consumers' decision-making and brand image formation. It offers a theoretical basis for scholars to understand the factors of PSI (parasocial interaction) between consumers and an employee streamer, contributing to the growing body of literature on live streaming and consumer behavior.

本研究旨在调查员工直播主对消费者购买行为和品牌形象的影响, 以及了解友谊和自我一致性在其中的中介作用。

本研究提出了一个框架, 以解释员工直播主的特质通过友谊和自我一致性的中介对消费者行为和品牌形象的影响。在中国收集了225份有效的调查问卷数据, 并采用PLS-SEM分析来测试假设关系的统计显著性。

研究发现员工直播主的四种特质 - 可信度、吸引力、反应能力和专业性 - 通过自我一致性和友谊的中介对消费者的购买行为和品牌形象产生了显著影响。

本研究深入探讨了员工直播主在消费者决策和品牌形象塑造中的不同作用, 为学者们理解消费者与员工直播主之间PSI（伪社交互动）的因素提供了理论基础, 为直播和消费者行为领域的日益增长的文献体系做出了贡献。