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Article
Publication date: 29 March 2021

Nikiforos T. Laopodis

This paper aims to investigate the impact of global macro and other risk factors of the New York Stock Exchange (NYSE)- and National Association of Securities Dealers Automated…

1374

Abstract

Purpose

This paper aims to investigate the impact of global macro and other risk factors of the New York Stock Exchange (NYSE)- and National Association of Securities Dealers Automated Quotation (NASDAQ)-listed shipping companies’ stock returns from January 2001 to December 2019.

Design/methodology/approach

The methodological design includes multi-factor regressions for individual companies, augmented versions of these regressions to examine the likely impact of additional factors and finally panel regressions to assess the impact risk factors on all companies simultaneously. Estimations are done via ordinary least squares and the generalized method of moments.

Findings

Multi-factor model results showed that some of the US-specific and global macro risk factors surfaced as statistically significant for most of the companies and appeared to exhibit a consistent pattern in the way they affected shipping stocks. Thus, these companies’ exposures emanate mostly from the general US market’s movements and to a lesser extent from other firm-specific factors. Second, from the results of panel specifications, this study observes that domestic risk factors such as unemployment, inflation rates and industrial production growth emerged as significant for the NYSE-listed companies. As regard, the NASDAQ-listed ones, it was found that Libor and the G20 inflation rate were also affecting their stock returns.

Research limitations/implications

Companies examined are listed only in the US’s NYSE and NASDAQ. Hence, companies listed elsewhere were excluded. It may be concluded that these US exchange-listed companies abide mostly by domestic fundamentals and to some extent to selected global factors.

Practical implications

The significance of the findings in this study pertains to global investors and shipping companies’ managers alike. Specifically, given the differential sensitivities of the shipping companies to various risk factors (and the global business cycle, in general), it is possible to view the shipping companies’ stocks as a separate, alternate asset class in a global, well-diversified portfolio. Thus, such a broader portfolio would permit investors to earn positive returns and reduce overall risk. Managers of shipping companies would also benefit from the findings in this study in the sense that they should better understand the varying exposures of their companies to changing global and domestic macro conditions and successfully navigate their companies through business cycles.

Originality/value

Research on the global shipping industry has lagged behind and was mainly concentrated on the investigation of the sources of shipping finance and capital structure of shipping companies, investment and valuation, corporate governance and risk measurement and management. Empirical research on the potential micro and macro determinants of the stock returns of shipping companies, however, is scant. This paper fills the gap in the literature of identifying and evaluating the various macroeconomic, US and international risk, factors that affect shipping companies’ stock returns in a highly financially integrated world.

Details

Maritime Business Review, vol. 7 no. 2
Type: Research Article
ISSN: 2397-3757

Keywords

Open Access
Article
Publication date: 31 October 2018

Assad Mehmood, Kashif Zia, Arshad Muhammad and Dinesh Kumar Saini

Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental…

Abstract

Purpose

Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.

Design/methodology/approach

In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.

Findings

For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.

Originality/value

This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

Details

International Journal of Crowd Science, vol. 2 no. 2
Type: Research Article
ISSN: 2398-7294

Keywords

Open Access
Article
Publication date: 11 March 2022

Edmund Baffoe-Twum, Eric Asa and Bright Awuku

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations…

Abstract

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Details

Emerald Open Research, vol. 1 no. 5
Type: Research Article
ISSN: 2631-3952

Keywords

Open Access
Article
Publication date: 18 August 2022

Oluseye Olugboyega, Abimbola Windapo, Clinton Aigbavboa and Godwin Ehis Oseghale

Because BIM adoption is still afflicted by various types of hurdles, a complete BIM implementation model is required to provide the necessary methods for driving BIM adoption. As…

1186

Abstract

Purpose

Because BIM adoption is still afflicted by various types of hurdles, a complete BIM implementation model is required to provide the necessary methods for driving BIM adoption. As a result, this study looked into the parts of the BIM implementation model that had the most impact on increasing the percentage of BIM adoption in South Africa.

Design/methodology/approach

This study developed a four-wheel model of BIM implementation based on implementation process theory, which includes BIM inspiration, BIM capacity development, BIM use and BIM commitment. To assess BIM capacity development, two sub-constructs (BIM learning process and BIM learning methodologies) were used. Two sub-constructs were used to assess BIM utilisation (efficient BIM application and effective BIM application). The sub-constructs employed to quantify BIM motivation were organisational competitiveness, societal conformity and contractual obligations. Incentives, investments and obligations were used to assess BIM commitment. The model was validated using four assumptions and maximum likelihood estimation – structural equation modelling (MLE-SEM).

Findings

The MLE-SEM results demonstrated unequivocally that all of the constructions are critical components of the BIM deployment paradigm in the South African construction industry. BIM motivation, as characterised by organisational competitiveness and social compliance, has the greatest impact. The findings on BIM motivation also revealed that the desire for technological sophistication, competitiveness and social acceptance by clients are encouraging construction organisations and professionals to embrace BIM adoption.

Research limitations/implications

This study's findings have contributed to the increasing body of literature on BIM deployment. The study has significant implications for achieving BIM implementation in underdeveloped nations where BIM deployment is either non-existent or in its early stages. The theoretical component of the study serves as the foundation for further analysis of BIM deployment.

Practical implications

This research is important for identifying BIM goals, developing a BIM implementation framework, allocating resources for BIM implementation and defining key performance indicators for BIM implementation. The BIM implementation aspects outlined in this study will be effective in lowering BIM adoption hurdles.

Originality/value

This study makes a unique contribution to BIM research by providing theoretical and empirical analysis into the elements of the BIM implementation model in a developing country. The study offers an excellent opportunity to further our understanding of BIM application in underdeveloped nations.

Details

Frontiers in Engineering and Built Environment, vol. 3 no. 1
Type: Research Article
ISSN: 2634-2499

Keywords

Open Access
Article
Publication date: 23 January 2024

Luís Jacques de Sousa, João Poças Martins, Luís Sanhudo and João Santos Baptista

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Construction Innovation , vol. 24 no. 7
Type: Research Article
ISSN: 1471-4175

Keywords

Open Access
Article
Publication date: 5 August 2022

Muhammad Saadullah, Zhipeng Zhang and Hao Hu

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology…

Abstract

Purpose

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework.

Design/methodology/approach

The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level.

Findings

MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only.

Originality/value

This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

Details

Smart and Resilient Transportation, vol. 4 no. 2
Type: Research Article
ISSN: 2632-0487

Keywords

Open Access
Article
Publication date: 9 November 2023

Abdulmohsen S. Almohsen, Naif M. Alsanabani, Abdullah M. Alsugair and Khalid S. Al-Gahtani

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the…

Abstract

Purpose

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

Design/methodology/approach

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

Findings

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

Practical implications

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

Originality/value

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

Details

Engineering, Construction and Architectural Management, vol. 31 no. 13
Type: Research Article
ISSN: 0969-9988

Keywords

Open Access
Article
Publication date: 5 March 2020

Fredrik Brunes, Cecilia Hermansson, Han-Suck Song and Mats Wilhelmsson

This paper aims to analyze how nearby property prices are affected by new construction projects in Stockholm. If there is an impact on property prices, the authors endeavor to…

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Abstract

Purpose

This paper aims to analyze how nearby property prices are affected by new construction projects in Stockholm. If there is an impact on property prices, the authors endeavor to investigate whether the effects vary among different areas within the municipality, for different groups of inhabitants and for different types of housing (i.e. public versus private housing).

Design/methodology/approach

The authors use a difference-in-difference specification in a hedonic model, and the sample consists of more than 90,000 observations over the period 2005-2013.

Findings

The results are robust and indicate that house prices in nearby areas increase following the completion of infill development. The results also indicate that infill development has a positive spillover effect on nearby dwelling prices only in areas with lower incomes, more public housing units and more inhabitants born abroad.

Originality/value

It provides an analysis on how nearby property prices are affected by new construction projects by creating a restricted control area, so as to make the treatment group and the control group more homogeneous. Thus, it mitigates any potential problems with spatial dependency, which can cause biased standard errors.

Details

Journal of European Real Estate Research , vol. 13 no. 1
Type: Research Article
ISSN: 1753-9269

Keywords

Open Access
Article
Publication date: 27 April 2020

Murat Gunduz and Hesham Ahmed Elsherbeny

This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the…

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Abstract

Purpose

This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.

Design/methodology/approach

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.

Findings

The data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.

Originality/value

The contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.

Details

Engineering, Construction and Architectural Management, vol. 27 no. 6
Type: Research Article
ISSN: 0969-9988

Keywords

Open Access
Article
Publication date: 22 May 2023

Edmund Baffoe-Twum, Eric Asa and Bright Awuku

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the…

Abstract

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

Details

Emerald Open Research, vol. 1 no. 5
Type: Research Article
ISSN: 2631-3952

Keywords

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