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The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

The current estimation practice in construction projects greatly needs upgrading, as there has been no improvement in the cost overrun issue over the past 70 years. The purpose of this research was to develop a new multiple regression analysis (MRA)-based model to forecast the final cost of road projects at the pre-design stage using data from 43 projects in New Zealand (NZ).

The research used the case study of 43 completed road projects in NZ. Document analysis was conducted to collect data, and statistical tests were used for model development and analysis.

Eight models were developed, and all models achieved the required F statistics and met the regression assumptions. The models’ mean absolute percentage error (MAPE) was between 21.25% and 22.77%. The model with the lowest MAPE comprised the road length and width, number of bridges, pavement area, cut and fill area, preliminary cost and cost indices change.

The model is based on road projects in NZ. However, it was designed to be able to adapt to other contexts. The findings suggest that the model can be used to improve traditional conceptual estimating methods. Past project data is often stored by the project team but rarely used for analysing and forecasting purposes. This research emphasises that past data can be effectively used to predict the project cost at the pre-design stage with limited information.

No research was conducted to adopt cost modelling techniques into the conceptual estimation practice in the NZ construction industry.

Introduction: The Stock Exchange is an economic indicator of sustainability in the global market over an extended period. The Indian economy has observed a downfall in foreign currency in quarter 2 of 2022, as per the reports of the International Monitory Fund. The central banks of many countries have been facing crises because of a piercing decline in their reserves, which is additionally affecting their sustainable performance. The Indian economy is one of the most potentially sound economies emerging as a global leader, and this study is an attempt to understand the economy’s vulnerability to foreign factors.

Purpose: The research explores the impact of the US Dollar, EURO and Japanese Yen on Bombay Stock Exchange and the National Stock Exchange Index.

Methodology: Four variables have been considered for the conduct of the study: Sensex, Nifty, inflation and foreign exchange. Sensex and Nifty have been taken as dependent variables, while foreign exchange and inflation have been taken as independent variables.

The regression analysis has been performed using Microsoft Excel: The variables used for the study are monthly values from January 2011 to December 2020. The specific period is selected to avoid the impact of COVID-19 on the stock market, avoiding biases in the results.

Findings: All the variables are affecting the performance of each other up to a certain level.

Practical Implication: The research chapter will help the investor understand the relationship between many variables and their impact on the stock market, which will assist them in gaining higher profits.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

This study aims to investigate the growth of Indonesian Islamic banks and explores organisational growth determinants from different perspectives, namely, organisational climate, intellectual capital (IC) and organisational service orientation. The study also attempts to develop a model to measure the growth of Islamic banks and uncovers the root causes of the stagnancy in Indonesian Islamic banking.

The study used survey questionnaires distributed to Islamic bank managers, who were considered representative experts in the field of Islamic banking. The data collected were analysed using the Statistical Package for Social Sciences (SPSS Version 21.0), and two analyses were performed with different strategies to build the regression model, namely, multiple linear regression and automatic linear regression.

The study found that IC significantly affected Islamic banks’ growth in Indonesia; however, organisational climate and service orientation did not predict such growth. Concerning service orientation as a mediating model, climate or IC had no indirect effect on growth.

This study’s results contribute to fill the gap by analysing the growth of Islamic banks. Hence, the study results will be especially practical and helpful for Islamic bank managers and policymakers to help develop mechanisms for Islamic banks in Indonesia.

By combining the aspects of organisational climate, IC and service orientation from earlier studies and categorising them by organisational growth, together with a comprehensive literature review, the study proposes a model specific to Islamic banks. It also offers new insight and discussion for determining organisational growth in Indonesian Islamic banks.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

With the growing concern of various sectors of society regarding environmental issues and the promotion of sustainable development, green technology innovation is generally considered to be conducive to the long-term development of enterprises. However, because of the existence of agency problems, managers may have shortsighted behaviors. Then how will managers' shortsighted behaviors affect enterprises' green technology innovation?

This paper uses machine learning-based text analysis methods to construct a manager myopia index based on the data from A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2020. We examine the impact of manager myopia on green technology innovation in companies.

Our study finds that manager myopia significantly inhibits green technology innovation in companies. However, when multiple large shareholders coexist and the proportion of institutional investors' holdings is high, it can alleviate the inhibitory effect of manager myopia on green innovation. Heterogeneity tests show that the impact of manager myopia on green technology innovation is relatively significant in non-state-owned and manufacturing companies, as well as in the electricity industry. Robustness tests demonstrate that our conclusions remain valid after using propensity score matching to eliminate endogeneity problems.

From the perspective of corporate governance, this paper incorporates managers' shortsightedness, multiple large shareholders and institutional investors' shareholding ratios into the same logical framework, analyzes their internal mechanisms, helps improve corporate governance, enhances green innovation capabilities and has strong implications for the implementation of national innovation-driven development strategies and the achievement of “carbon peak” and “carbon neutrality” targets.

The fluctuation of construction fatalities is influenced by both urbanization and economic levels. This study aims to understand the impact of Chinese construction economy development on construction accidents, providing valuable insights for enhancing construction safety and promoting sustainable development in construction.

The Kuznets curve model, multiple linear regression model, and data envelopment analysis (DEA) model are employed to process data sets spanning from 1992 to 2021 for examining the relationship between construction fatalities and the construction economy in China.

Significant correlations have been found between construction fatalities and the construction economy in China. Over the past three decades, as the total output value of construction increased, there have been upward, downward, and downward trends in per capita construction area, the mortality rate per million square meters, and the mortality rate per ten thousand persons respectively. However, it is worth noting that since 2015, there has been a slight upward trend in the fitted U-shaped curve depicting the relationship between the mortality rate per ten thousand persons and the construction economy. This specific trend necessitates the attention of construction safety policymakers. The growth of the construction economy is found to exhibit negative, positive, and positive correlations with the number of construction fatalities, construction area, and the number of employed persons respectively. The changing trends observed in the Kuznets curve model analysis align with the evaluation results obtained from the DEA-based model.

The research offers effective means to identify superior and inferior performance in macro construction safety, providing valuable references for construction safety policymakers to design effective safety strategies and enhance work safety conditions.

Grounded on brand equity theory and theory of patronage behavior, this study aims to investigate the moderating effect of consumer involvement and shopping situations in the relationship between consumer-based retailer equity (CBRE) and retail patronage behavior.

The data is collected through a self-administered survey of 338 shoppers in the three biggest shopping centers in Pakistan. Moreover, the data is analyzed through multi-nominal (multiple) regression and interactions analysis.

Results revealed a significant effect of CBRE on patronage behavior and confirmed shopping purpose as a boundary condition in the CBRE-retail patronage behavior relationship. However, the study surprisingly reported that this relationship is not moderated by consumers’ involvement.

Considering our focus on CBRE-based retail patronage behavior, the authors contribute to extant marketing/retailing literature that also yields ample openings for further research. The study offers valuable implications for retailers, especially for evaluating consumers’ behaviors.

This study assists retail-brand managers in best comprehending the CBRE-based patronage behavior paves the way for managers to increase retail patronage behavior.

Regardless of the growing comprehension of consumer-based brand equity and patronage behavior in marketing, more needs to be acknowledged about the relationship between CBRE/retail patronage behavior and related variables, as thus examined in this research.

Basado en la teoría del valor de marca y la teoría del comportamiento de patrocinio, este estudio investiga el efecto moderador de la implicación del consumidor y las situaciones de compra en la relación entre el valor del minorista basado en el consumidor (CBRE) y el comportamiento de patrocinio minorista.

Los datos se recogen mediante una encuesta autoadministrada a 338 compradores en los tres mayores centros comerciales de Pakistán. Además, los datos se analizan mediante regresión multinominal (múltiple) y análisis de interacciones.

Los resultados revelaron un efecto significativo del CBRE en el comportamiento de patrocinio y confirmaron el propósito de compra como una condición límite en la relación CBRE-comportamiento de patrocinio minorista. Sin embargo, el estudio informó sorprendentemente de que esta relación no está moderada por la implicación de los consumidores.

Teniendo en cuenta que nos centramos en el comportamiento de patrocinio minorista basado en el CBRE, contribuimos a la literatura existente sobre marketing/minoristas que también ofrece amplias posibilidades para futuras investigaciones. El estudio ofrece valiosas implicaciones para los minoristas, especialmente para evaluar los comportamientos de los consumidores.

El presente estudio ayuda a los gestores de marcas minoristas a comprender mejor el comportamiento de patrocinio basado en la CBRE y allana el camino para que los gestores aumenten el comportamiento de patrocinio minorista.

A pesar de la creciente comprensión de la equidad de marca basada en el consumidor y el comportamiento de patrocinio en marketing, es necesario reconocer más sobre la relación entre el comportamiento de patrocinio basado en la CBRE y las variables relacionadas, como se examinó en esta investigación.

本研究以品牌资产理论和顾客行为理论为基础, 探讨了消费者参与和购物情境在基于消费者的零售商资产（CBRE）与零售顾客行为之间关系中的调节作用。

数据是通过对巴基斯坦三大购物中心的 338 名购物者进行自填式调查收集的。此外, 还通过多项式（多元）回归和交互分析对数据进行了分析。

结果表明, CBRE 对顾客光顾行为有显著影响, 并证实购物目的是 CBRE 与零售顾客光顾行为关系的边界条件。然而, 令人惊讶的是, 研究报告称这种关系并没有受到消费者参与度的调节。

考虑到我们对基于 CBRE 的零售顾客行为的关注, 我们为现有的市场营销/零售文献做出了贡献, 同时也为进一步研究提供了广阔的空间。本研究为零售商提供了宝贵的启示, 尤其是在评估消费者行为方面。

本研究有助于零售品牌管理者更好地理解基于 CBRE 的顾客行为, 为管理者提高零售顾客行为铺平了道路。

尽管市场营销中对基于消费者的品牌资产和顾客行为的理解不断加深, 但仍需进一步认识 CBRE/零售顾客行为与相关变量之间的关系, 正如本研究中所探讨的那样。