Abstract:Purpose
The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry.
Design/methodology/approach
The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature.
Findings
A significant rise in construction big data research is identified with an increasing trend i… Show more
“…To enhance the clarity and enrich the information, the related study of HR analytics and strategic management was referred as a basis comparison of the results achieved [27]. Table 1 presents a comparative analysis of the performance metrics achieved by the proposed algorithms in this study compared to previous research:…”
This study considers the impact of big data analytic by a Resource-based view (RBV) framework on strategic HR Quality Management System (HR QMS). The study employed a mixed-method strategy to gather data of employees' performance metrics (quantitative) as well as HR professionals’ viewpoints (qualitative) through data analysis. Four machine learning algorithms, for instance Decision Trees, Random Forest, K-Means Clustering and Linear Regression were employed for the purpose of predicting and optimizing Human Resource outcomes. Study indicated the effectiveness of these algorithms in improving organizational productivity where Random Forest reached 89% correctness in predicting employee turnover and Linear Regression demonstrated a positive correlation (R-squared = 0.75) between the training hour and performance rating. Through a comparison with existing literature, the newness and relevance of the clinical data are stressed, going beyond well-known trends and into a cutting-edge analytical applications of big data analytics. The study symbolizes how big data analysis is capable of revolutionizing practices by emphasizing innovation, improving efficiencies, and learning decision making in the field of HR management.
“…To enhance the clarity and enrich the information, the related study of HR analytics and strategic management was referred as a basis comparison of the results achieved [27]. Table 1 presents a comparative analysis of the performance metrics achieved by the proposed algorithms in this study compared to previous research:…”
This study considers the impact of big data analytic by a Resource-based view (RBV) framework on strategic HR Quality Management System (HR QMS). The study employed a mixed-method strategy to gather data of employees' performance metrics (quantitative) as well as HR professionals’ viewpoints (qualitative) through data analysis. Four machine learning algorithms, for instance Decision Trees, Random Forest, K-Means Clustering and Linear Regression were employed for the purpose of predicting and optimizing Human Resource outcomes. Study indicated the effectiveness of these algorithms in improving organizational productivity where Random Forest reached 89% correctness in predicting employee turnover and Linear Regression demonstrated a positive correlation (R-squared = 0.75) between the training hour and performance rating. Through a comparison with existing literature, the newness and relevance of the clinical data are stressed, going beyond well-known trends and into a cutting-edge analytical applications of big data analytics. The study symbolizes how big data analysis is capable of revolutionizing practices by emphasizing innovation, improving efficiencies, and learning decision making in the field of HR management.
“…Until now, however, management methods in the traditional construction practice have been effective to some extent in addressing the said common problems. However, it looks that the efficiency of conventional project management methods remains inexpressive status while most of the problems remain the same (Madanayake, 2015).…”
Purpose
The construction industry is inefficient in terms of quality products, productivity and performance worldwide, including in Australia and New Zealand. The construction industry is becoming more innovative, competitive and complex; and more participants are involved in construction projects. There are new attempts to implement the Lean construction philosophy, integrated project delivery method and building information modelling (BIM) technology in construction industry to improve productivity and efficiency. This paper aims to identify Lean and BIM integration benefits in construction industry globally and in the New Zealand.
Design/methodology/approach
A systematic literature review and case studies were used to identify various benefits of the integrating Lean and BIM in construction industry. It focused on articles published between 1995 and 2021.
Findings
Lean and BIM benefits identified in the study were documented such as benefits over the traditional approach, critically increased efficiency and visualization, better building process, better building performance, mitigating risk and reduce cost. Also, several factors were identified as major benefits such as improved onsite collaboration, better coordination, improve onsite communication, increase productivity, mitigating risk, reducing waste and reduced cost. The study showed integrating Lean and BIM in construction management practice will help reduce several challenges which affect expected goals and customer anticipation. The research outcome ultimately will assist different stakeholders in applying Lean and BIM in construction management practice.
Originality/value
This study practically focused on using the integration of BIM and Lean principles to improve the construction industry productivity and performance.
“…Although natural language processing is a growing area within the construction industry [ 10 ], only a few studies have applied computational linguistic techniques to social media data sets [ 5 , 11 ]. A recent systematic review of the literature on big data studies within construction called for research on how social media big data analytics can be used to prevent threats such as safety issues, injury, or mental illness caused by work-related stress [ 12 ]. This study directly addresses this need.…”
Background
Construction and nursing are critical industries. Although both careers involve physically and mentally demanding work, the risks to workers during the COVID-19 pandemic are not well understood. Nurses (both younger and older) are more likely to experience the ill effects of burnout and stress than construction workers, likely due to accelerated work demands and increased pressure on nurses during the COVID-19 pandemic. In this study, we analyzed a large social media data set using advanced natural language processing techniques to explore indicators of the mental status of workers across both industries before and during the COVID-19 pandemic.
Objective
This social media analysis aims to fill a knowledge gap by comparing the tweets of younger and older construction workers and nurses to obtain insights into any potential risks to their mental health due to work health and safety issues.
Methods
We analyzed 1,505,638 tweets published on Twitter (subsequently rebranded as X) by younger and older (aged <45 vs >45 years) construction workers and nurses. The study period spanned 54 months, from January 2018 to June 2022, which equates to approximately 27 months before and 27 months after the World Health Organization declared COVID-19 a global pandemic on March 11, 2020. The tweets were analyzed using big data analytics and computational linguistic analyses.
Results
Text analyses revealed that nurses made greater use of hashtags and keywords (both monograms and bigrams) associated with burnout, health issues, and mental health compared to construction workers. The COVID-19 pandemic had a pronounced effect on nurses’ tweets, and this was especially noticeable in younger nurses. Tweets about health and well-being contained more first-person singular pronouns and affect words, and health-related tweets contained more affect words. Sentiment analyses revealed that, overall, nurses had a higher proportion of positive sentiment in their tweets than construction workers. However, this changed markedly during the COVID-19 pandemic. Since early 2020, sentiment switched, and negative sentiment dominated the tweets of nurses. No such crossover was observed in the tweets of construction workers.
Conclusions
The social media analysis revealed that younger nurses had language use patterns consistent with someone experiencing the ill effects of burnout and stress. Older construction workers had more negative sentiments than younger workers, who were more focused on communicating about social and recreational activities rather than work matters. More broadly, these findings demonstrate the utility of large data sets enabled by social media to understand the well-being of target populations, especially during times of rapid societal change.
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