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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.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.

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.

The purpose of this study is to examine the effects of banking innovations (INNs) on customer experience (EXP), satisfaction (SAT) and loyalty (LOY).

The author evaluated the data using a structural equation method-artificial neural network (SEM-ANN) method. The author’s results show the presence of relationship between INN, EXP, SAT and LOY. In this study, the node layers of ANNs add an input layer, hidden layers and an output layer. Each “node” acts as an artificial neuron that communicates with others. The ANN model takes the variables from the SEM analysis as input neurons.

The author observed the significant effects between INN, EXP, SAT and LOY using the normalised importance generated by the multilayer perceptron used in the feed-forward back propagation of the ANN methodology. In this study, the ANN model can predict LOY through service innovation, with a forecast accuracy of 77.6%.

By applying neural network modelling, this research helps us understand how service innovation affects customer behaviour. For the first time, the author examined service innovations' direct and indirect impact on loyalty through EXP and SAT. The author made a significant conceptual contribution by using a non-compensatory model of ANNs to circumvent the limitations of linear models.

This research aims to demonstrate the possible link between the sociodemographic profile of visitors motivated by the visit to flamenco shows and the city of Córdoba (Spain), and the preferences and sensations regarding these experiences.

The methodology used (multilayer perceptron) is based on the development of an artificial neural network.

The results show that the variables age and educational level are determining factors in the profile of the visitor. Also, as the level of income increases, so does the interest in flamenco, a fact that can be useful to determine the target audience for this type of shows.

Flamenco is an art that originated in the Andalusian region that arouses the interest of the visitor due to its music, way of singing and dance. Flamenco is a popular art that excites and awakens the senses of those who attend this dance, song and guitar show. Its recognition as Intangible Heritage of Humanity by United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2010, makes it a tourist product that motivates visitors to travel to the city of Córdoba (Spain), being also one of flamenco's places of origin. Córdoba has this art in its tourist offer so that the identity of the city has two aspects: patrimonial and immaterial, among the flamenco highlighted.

This study aims to investigate how gender diversity and remuneration of boards of directors’ influence earnings quality for Spanish-listed firms.

The sample includes 105 nonfinancial Spanish firms from 2013 to 2018, corresponding to an unbalanced panel of 491 firm-year observations. The primary empirical method uses a Tobit semiparametric estimator with firm- and industry-level fixed effects and an innovative set of measures for earnings quality developed by StarMine.

Results exhibit a positive correlation between increased gender diversity and a firm’s earnings quality, suggesting that a gender-balanced board of directors is associated with more transparent financial reporting and informative earnings. We also find a nonmonotonic, concave relationship between board remuneration and earnings quality. This indicates that beyond a certain point, excessive board compensation leads to more opportunistic manipulation of financial reporting with subsequent degradation of earnings quality.

This study only covers nonfinancial Spanish listed firms and is silent about how alternative board features’ influence earnings quality and their informativeness.

This study introduces measures of earnings quality developed by StarMine that have not been used in the empirical literature before as well as measures of board gender diversity applied to a suitable Tobit semiparametric estimator for fixed effects that improves the precision of results. In addition, while most of the literature focuses on Anglo-Saxon countries, this study discusses board gender diversity and board remuneration in the underexplored context of Spain. Moreover, the hand-collected data set comprising financial reports provides previously untested board features as well as a nonlinear relationship between remuneration and earnings quality that has not been thoroughly discussed before.

This study aims to propose and test a research model outlining the chain effects of social media marketing activities (SMMA) on brand equity, encompassing the potential mediators of self-congruity, consumer empowerment and brand experience.

An online survey was conducted, and 241 valid responses were acquired. The data was submitted to Partial Least Squares Structural Equation Modelling (PLS-SEM), complemented by the artificial neural network (ANN) analysis.

The results revealed that SMMA can foster the development of self-congruity and consumer empowerment. These two psychological responses represent the key drivers to reinforce the positive brand experience and ultimately lead to brand equity. The sequential mediating effect was confirmed. The ANN analysis offered further insights into the ranking of variable importance.

The present study presents a breakthrough by taking into account the roles of self-congruity, consumer empowerment, brand experience simultaneously and assesses their sequential mediating roles in the linkage between SMMA and brand equity.

Understanding the adoption of m-commerce in small and medium-sized enterprises (SMEs) is critical for their sustainable development. This study aims to investigate the adoption of m-commerce in Vietnamese SMEs, leading to the identification of the critical determinants and their relative importance for m-commerce adoption.

An integrated model is developed by combining the diffusion of innovation theory and the technology–organization–environment framework. Such a model is then tested and validated using structural equation modeling and artificial neural networks in analyzing the survey data.

The study indicates that perceived security is the most critical determinant for m-commerce adoption. It further shows that customer pressure, perceived compatibility, organizational innovativeness, perceived benefits, managers’ IT knowledge, government support and organizational readiness all play a critical role in the adoption of m-commerce in Vietnamese SMEs.

The findings of this study can lead to the formulation of better strategies and policies for promoting the adoption of m-commerce in Vietnamese SMEs. Such findings are also of practical significance for the diffusion of m-commerce in SMEs in other developing countries.

To the best of the authors’ knowledge, this is the first attempt to explore the adoption of m-commerce in Vietnamese SMEs using a hybrid approach. The application of this approach can lead to better understanding of the relative importance of the critical determinants for the adoption of m-commerce in Vietnamese SMEs.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.