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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.

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.

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.

Building information modelling (BIM) has been established in the literature as a successful platform that creates an intelligent virtual model for processing data from conceptual design through construction to operational stage of a facility. However, its adoption for facilities management (FM) provision in Nigeria has been slow due to inherent barriers. The aim of this paper is to (1) assess and categorise using factor analysis BIM for FM barriers and (2) model the barriers using stakeholders' personal/professional attributes.

Anchored on quantitative research design, 205 copies of structured questionnaire were distributed to key stakeholders and facilities managers in Nigeria's three strategic cities while 135 valid responses were received giving a response rate of 65.8%. Data collected were analysed using descriptive statistics while multiple regression analysis was used to model the barriers. Kruskal Wallis test was used to test the only hypothesis postulated for the study.

The study established lack of awareness of BIM for FM, poor supporting infrastructure for Internet services, and lack of education and training as the top three rated barriers militating against adoption of BIM for FM in Nigeria while corruption, widespread mistakes and errors and cultural issues were established as the three least rated barriers. Besides, findings also established eight underlying factors that explained 23 barrier factors used for the study which were subsequently used to develop eight regression models. In effect, gender, professional affiliation, organisation, experience, education, expertise, BIM for FM project type, and location were found to statistically predict the 8 extracted factors driving perceived barriers of BIM for FM adoption in Nigeria.

The study has provided a framework of barrier factors to help stakeholders identify specific barriers for which appropriate measures can be taken to ameliorate consequences of the perceived barriers. Meanwhile, an improved and rejuvenated advocacy on inherent benefits of BIM for facilities management by frontline stakeholders could potentially steer up interests and increased participation of stakeholders on BIM for FM.

The unique study developed the first ever regression model that links BIM for FM barriers to professional attributes of facilities management stakeholders in Nigeria.

This study aimed at identifying the prevalence of statistic anxiety among students in social sciences programs and analyzing the relationship between statistics anxiety and four key variables: attitude toward statistics, statistics self-efficacy, achievement in statistics and academic procrastination.

Method a total of 440 students from all statistics sections offered in the social sciences programs in a four-year public medium-sized university in UAE participated in this study. Students participated by responding to a package that has four instruments: Statistics Anxiety Scale, Attitudes Toward Statistics, Current Statistics Self-Efficacy and Procrastination Assessment Scale.

More than 27% of students have above average statistics anxiety. Significant correlations were observed between statistics anxiety and each of attitude toward statistics course (−0.34), academic procrastination (0.33) and achievement (−0.35). On the other hand, Attitudes Toward Statistics and statistics self-efficacy have non-significant correlations. The four variables together predict more than 32% of the total variance of statistics anxiety, and the best predictor was procrastination followed by course attitude.

Students from only one university participated in this study. Adding more universities and countries could increase the generalizability of the study.

Statistics anxiety is prevalent among students in social sciences programs. Statistics instructors must be ready to deal with reducing it. Several procedures have been suggested to reduce statistics anxiety but improving attitudes towards statistics courses is the key factor in this regard.

Many students from social sciences and humanities programs and colleges experience anxiety or tension related to statistics courses which are required in most higher education institution. This paper tried to understand which variables affecting this phenomenon and in which directions. At the end, statistics is an important subject for all students during their academic life as well as practical life after graduation.

Statistic anxiety is the feeling of worrying and tension that students experience when taking statistics courses, especially in social sciences programs. Studying statistic anxiety and the related variables is crucial because this anxiety negatively and significantly affects students’ achievement and learning.

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.

Gender disparity is a global phenomenon where females outnumber male participants. It has been observed that males are the early leaver from higher education, thus reflecting a severe concern about social instability. Malaysia is a prominent example where females outnumber males in higher education. In this context, this paper aims to examine the effect of individual, social and financial factors on the higher education self-efficacy of male and female students. It develops a comprehensive understanding of gender-based decision factors in pursuing higher education.

The hypothesis was formed based on a comprehensive literature review following the hypothetico-deductive positivist approach. These hypotheses were tested based on a sample of 250 respondents. A multiple regression analysis was deployed to test the relationship between the dependent variable and its predictors.

The results suggest that male and female students’ self-efficacy depends on five determinants, i.e. family influence, peer influence, career expectancy outcome, gender roles and institutional factors. Male students tend to be influenced more by these five determinants than females. Additionally, male students with better financial backgrounds are more likely to have higher self-efficacy, whereas gender roles negatively affect male and female students’ self-efficacy for higher education.

The breakout of COVID-19 resulted in the selection of limited students in Malaysia. Due to restricted movement orders, it was impossible to reach out to the students for data collection. Future research could include a broader area to include multiple other regions of Malaysia. For a broader aspect, the study could be conducted in other areas/countries where the problem of less male participation exists.

The relationship between higher education self-efficacy is assessed with social, financial and institutional factors for male and female students. It will enable the stakeholders and policymakers to make better decisions in increasing the self-efficacy of students to attain equity in higher education institutions.

The finding of this paper will assist in increasing male participation in higher education institutions to avoid any social instability.

This paper contributes to the literature in understanding the causes of gender gap reversal, focusing on Malaysian higher education institutions. It also provides empirical evidence to look at potential factors that affect the higher education self-efficacy of male and female students.

Safety, one of the basic human needs for existence, is a very important factor in achieving a successful urban space. A lack of its presence could make it challenging for residents of a place to live and function effectively. Therefore, this study aims to identify the urban design attributes that would potentially enhance the perceived sense of safety, mainly focusing on two case studies in El-Sherouk neighborhood in Cairo. The two selected case studies are considered car-oriented due to their reduced levels of safety.

This study was conducted through a set of data collection phases from field surveys and survey questionnaires that infer the influence of the surrounding urban environment on a specific target group. Statistical Package for the Social Sciences (SPSS) statistical analysis tool was used to analyze data collected from survey questionnaires.

Finally, by the end of this research, a set of urban design qualities vital for achieving the desired levels of safety were introduced. The findings of this study revealed key urban design qualities that can potentially contribute to enhancing the perceived sense of safety as they showed a strong positive correlation: (1) imageability, (2) transparency, (3) complexity and (4) human scale and enclosure. Moreover, multiple linear regression indicates that urban design qualities are strong predictors of perceived safety.

This study presents a holistic approach to studying the relationship between urban design and perceived safety by examining two case studies located in El-Sherouk City in Cairo, Egypt. While previous research has focused on one theory of safety design such as crime prevention, defensible space theory, eyes on the street or safer city centers, this article tries to fill in the gap in the literature by analyzing all aspects of urban design and its correlation to an enhanced perceived safety. In addition, most of the previous studies have tackled the safety aspects of old urban settlements. However, this study tackles a new urban settlement.

South Africa’s wool industry plays an important role in the agricultural sector. The wool industry provides a valuable source of income for farmers who practice sustainable farming practices. However, wool farmers face numerous challenges, such as wool contamination, dirty wool and producing good-quality wool. Good-quality wool is determined by fibre diameter, clean yield, vegetable matter and staple length. This study aims to address these challenges.

A multiple regression analysis of price (R/kg) of White wool and Merino wool was applied to four variables fibre diameter: vegetable matter, clean yield and staple length. The analysis was based on the data for the 2009–2019 data from Cape Wools auctions.

Fibre diameter, clean yield and staple length, with exception of vegetable matter, made a statistically significant contribution to the determination of wool price after all other independent variables were controlled for (p < 0.05). A one-unit (micron) increase in fibre diameter resulted in a 0.404-unit decrease in wool price (R/kg). A one-unit (mm) increase in staple length resulted in a 0.022-unit increase in wool price (R/kg). There was no statistically significant association between vegetable matter and wool price. A one-unit increase in clean yield was associated with a 0.111-unit increase in wool price (R/kg).

Since wool fleeces consist of the largest portion of wool shorn from sheep, it is important for wool farmers to focus on wool with low fibre diameter, high clean yield percentage, low percentage of vegetable matter content and good length of the wool.

Since wool fleeces consist of the largest portion of wool shorn from sheep, it is important for wool farmers to focus on wool with low fibre diameter, high clean yield percentage, low percentage of vegetable matter content and good length of the wool.

In a developing country such as South Africa, this study is important for the following reason. It is understanding the wool characteristics that have the most significance influence on the determination of wool price for Merino wool and White wool might effectively help the wool farmers to adapt their production systems to improve the wool characteristics that determine wool price.

This study identified a need for a study to be conducted on all wool classes.

Due to the recent disruptions caused by COVID-19, global supply chains are stress tested. The affected supply chains have interfered with market tonnage prices for the yield of perishable products like mangoes that are highly dependent on their quality. This research, through empirical findings, thus determines and comprehends the factors influencing mango quality (size).

A framework is developed for finding the potential factors of quality building on the previous literature and studies on the available topic. The data collection included face-to-face interviews comprising 240 farmers, hired managers and preharvest contractors in India's Jangaon, Rangareddy and Yadadri Bhuvanagiri districts of Telangana state. The data analysis is done using multiple regression, and the outcomes form the basis of the design of the experiments model.

The empirical insights support that the quality of mango is affected by factors such as the number of picking cycles, the cost of fertilizer, the variety of fertilizers used, the variety of pesticides used and pesticide application frequency. The direct implications are the benefit to farmers in improving mango quality and maximizing profit per yield cycle.

To the best of the authors’ knowledge, the first research that has specifically focused on holistically improving the quality(size) of mangoes.

The findings contribute to the perishable supply chain literature, specifically to the mango study, to comprehensively showcase the factors impacting the quality of mangoes and provide guidance to farmers regarding orchard practices.