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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.

The authors contrast Beckett and Professional Sports Authenticator (PSA) baseball card valuations. Also, the authors contrast the Bill James statistics for winshares (WIN) and reference.com statistics for wins above replacement (WAR).

This study examines the impact of analytics on Topps 1957 baseball card values.

The authors' examination of variables that influence Topps 1957 baseball card values yields similar results for mint and very good rated cards over the early period (1982), pre-strike (1989), post-strike (1998) and recent (2009) periods. In single variable and multiple regressions, Baseball Hall of Fame (HOF) membership and New York Yankee (NYY) nostalgia coefficient are significant at the 5% level or higher for mint and very good rated cards over all reported periods. The Brooklyn Dodger (BD) parameter is significant at the 5% level or higher in single variable regressions for all reported periods and for 1982 and 1989 for multiple regressions. Reflecting a lack of nostalgia, the New York Giant card coefficients are statistically insignificant over all periods. Also, the authors see a lack of negative bias for Black-player cards. The authors observe that Black-player card coefficients are positive and sometimes statistically significant. This indicates a positive relationship between Black-player cards and prices.

This is the first study to examine the impact of WINS and WAR analytics on baseball card values.

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.

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 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 purpose of this study is to examine the level of Shariah governance disclosure among Islamic banks in Malaysia and the Gulf Cooperation Council (GCC) countries (i.e. Kuwait, Bahrain, United Arab Emirates, Qatar, Oman and Saudi Arabia). On top of that, the effect of Shariah governance disclosure on Islamic banks financial performance is investigated.

Data underwent quantitative content analysis and a mean comparison of the Shariah governance disclosure mechanisms as well as multiple regression analysis. Shariah governance information is obtained from the Islamic banks' official websites and the Bursa Malaysia Exchange.

The results of the content analysis revealed that the level of Shariah governance disclosure among Malaysian Islamic banks has been more pronounced than in the GCC countries. Additionally, the multiple regression analysis results specified that of the five Shariah governance disclosure mechanisms, the Shariah committee emerged as the strongest determinant in the financial performance of the Islamic banks, followed by transparency and disclosure.

Islamic banks should emphasise publishing Shariah governance information in annual reports to reflect superior accounting practices as assessed by certified Shariah auditors with an effective monitoring system.

The empirical findings are vital for serving as a guideline for Islamic banks in Malaysia and the GCC countries to disclose their practice of Shariah governance and gain empirical insights into its effect on firms’ financial performance. Following that, Islamic banks would improve their accounting practices while adhering to Shariah principles, strengthen internal controls and boost their brand reputation.

This study assesses performance measurement (PM) system implementation efforts across various organizational contexts and investigates which factors are critical to achieving implementation success (IS).

An empirical field study was conducted to refine a framework of PM system IS that consists of 5 dimensions of success and 29 factors. A survey questionnaire was used to investigate actual organizational practice and exploratory factor analysis was conducted to refine constructs corresponding to potential factors and dimensions of IS. The resulting variables were then investigated using multiple regression analysis to identify critical success factors for implementing PM systems.

The survey was completed by representatives from 124 organizations and the exploratory factor analysis results indicated that there are three underlying dimensions of IS (i.e. Use of the System, PM System Performance, and Improved Results and Processes) and 12 factors. Of the factors, nine can be considered critical success factors having a significant relationship with at least one dimension of IS: Leader Support, Design and Implementation Approach, Reward System Alignment, Organizational Acceptance, Organizational Culture and Climate, Easy to Define Environment, IT Infrastructure Capabilities, PM System Design Quality, and PM Participation and Training.

The results show that there are distinct dimensions of IS and, although some factors are associated with all dimensions, most are more closely related to only one dimension. This suggests that different strategies should be utilized based on the types of challenges experienced during implementation.

As competition in the industry intensifies, companies must use market-oriented approaches to gain competitive superiority; one of the approaches that can lead to the success of companies in the competitive market is to undertake social co-creation with the help of customers. Although the use of social media for the development of social interactions has expanded, very little attention has been paid to how the concept of social co-creation is formed on social media by users. Therefore, this study aims to investigate the effect of personality traits and website quality on social co-creation, with the mediating role of trust in tourism websites.

This research, in terms of purpose, is practical, and in terms of information collection, it is a descriptive survey. The research statistical population is all users of active tourism sites in Iran. The sampling method is non-probability and available sampling. The questionnaire was designed based on the Likert scale and was distributed electronically among the statistical sample. After collecting and reviewing the questionnaires, 203 were used for analysis. The data analysis method in this study is hierarchical multiple regression.

The results indicated that personality traits and website quality are correlated with trust and social co-creation. The dimensions of website quality, including quality of information, quality of system and quality of service on tourism websites, have considerable and positive effects on trust. Also, all dimensions of the personality traits, except extraversion and neuroticism, have a considerable and positive effect on trust. Moreover, the correlation between trust and social co-creation is positive.

According to the review of the digital marketing literature, some researchers examined the influential factors in co-creation, but there is little research about how the interaction of these three concepts (personality traits, website quality and trust) enhances co-creation. This study contributes to the existing literature with empirical evidence of how personality traits and website quality influence co-creation by mediating the role of trust.

This paper aims to analyze the potential variables explaining the compliance value added tax (VAT) gap, which basically represents an estimate of the unpaid VAT in the economy. A major component of compliance VAT Gap is represented by tax fraud; there exist other causes too, like insolvencies, bankruptcies, optimizations practices and maladministration. The objective of our paper is to revisit the main determinants of the VAT compliance gap for the European Union (EU)-27 member states. Using econometric modeling, our study identifies the relationship between the VAT gap and various determinants of it.

Our work focuses on the shadow economy, final consumption, VAT revenues, standard VAT rates, differences between the standard and reduced rates, economic prosperity, press freedom, political stability and others, as determinants of European VAT compliance gaps, for the 2005–2020 time interval. The methods include panel data analysis through simple and multiple regression modeling, the combinatorial approach, fixed and random effects.

Our study validates the direct impact of shadow economy and the indirect impact of VAT revenues, economic prosperity and press freedom, upon VAT compliance gaps. Upon subsampling of EU member states within old and new ones, our results estimate a larger positive impact of shadow economy upon old member states, compared to new ones.

The policy implications include leverage effects of governments acting upon a reduction in shadow economy phenomena and boosts of economic development, political stability and press freedom, in order to attain the contraction of compliance VAT gaps.

Our paper sheds light in a poorly explored scientific area, that of the determinants of VAT gap, especially in relationship with financial and economic crime phenomena.