Scientia et Technica Año XXIX, Vol. 29, No. 01, enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701 y ISSN-e: 2344-7214

Factors Affecting Labor Productivity in the Global

Construction Industry: A Critical Review,

Classification and Ranking

Factores que afectan la Productividad Laboral en la Industria Global de la Construcción: Una

Revisión Crítica, Clasificación y Ranking

R.A. Ardila-Cubillos

; M.Y. Durán-Prada

, K.Y. Vides-Martínez , G. Mejía-Aguilar

doi.org/10.22517/23447214.25546

Scientific and Technological Research Paper

Abstract — The construction industry plays a fundamental role

in economic development and job creation, but it faces challenges

of labor productivity that have been exacerbated due to the recent

COVID-19 pandemic. Labor productivity is a critical component

of success, as it influences the duration, costs, and efficiency of

projects. Understanding the nature of factors affecting labor

productivity is essential to finding solutions. This study examined

the literature to identify key factors influencing labor productivity,

departing from conventional analytical approaches. Through

mixed-methods analysis, qualitative approaches of systematic

reviews identified influential factors based on 97 documents.

Subsequently, they were classified using a combination of

statistical analysis and hierarchical clustering methods that

encompassed both internal and external factors. The Importance

Value Index allowed for the classification of the 30 most critical

factors, analyzing three possible ranking scenarios. The study

found that the interest in researching the topic remains relevant

and has evolved over time. In recent years, greater attention has

been paid to labor, management, work environment, and technical

aspects. The results indicate that internal project factors, such as

scheduling, planning, technical considerations, and resource

management, are more predictable and controllable than external

factors. Effective resource management and a comprehensive

approach are essential for optimizing construction productivity.

Project-level factors, as well as materials, tools, and equipment,

play an important role. By synthesizing existing knowledge and

identifying and classifying key productivity factors, this study

offers valuable insights to construction professionals,

policymakers, and researchers seeking to improve labor

productivity and optimize project outcomes.

Index Terms — Construction productivity; Hierarchical

clustering; Importance Value Index; Sectoral advancement;

Systematic review.

This manuscript was submitted on January 31, 2024, accepted on march 21,

2024 and published on April 1, 2024. This work was supported by Civil

Engineering School of University Industrial of Santander UIS, Colombia.

Ray Andrés Ardila Cubillos is a Magister in Civil Engineering, associate

professor and researcher in University Industrial of Santander UIS, Colombia

(email: ray2208070@correo.uis.edu.co).

Manuel Yesid Durán Padra is a Civil Engineer candidate in University

Industrial of Santander UIS, Colombia (email:

manuel2184611@correo.uis.edu.co).

Resumen — La industria de la construcción desempeña un papel

fundamental en el desarrollo económico y la generación de empleo,

pero enfrenta desafíos de productividad laboral que se han

intensificado debido a la reciente pandemia de COVID-19. La

productividad laboral es un componente crítico de éxito, ya que

influye en la duración, los costos y la eficiencia de los proyectos.

Comprender la naturaleza de los factores que afectan la

productividad laboral es esencial para encontrar soluciones. Este

estudio examinó la literatura para identificar factores clave que

influyen en la productividad laboral, apartándose de los enfoques

analíticos convencionales. A través de un análisis de métodos

mixtos, los enfoques cualitativos de revisiones sistemáticas

identificaron factores influyentes basados en 97 documentos.

Posteriormente, se clasificaron mediante una combinación de

análisis estadístico y métodos de agrupación jerárquica que

abarcaron tanto factores internos como externos. El Índice de Valor

de Importancia permitió la clasificación de los 30 factores más

críticos, analizando tres posibles escenarios de clasificación. El

estudio encontró que el interés en investigar el tema sigue siendo

relevante y ha evolucionado con el tiempo. En los últimos años, se

ha prestado mayor atención a la mano de obra, la gestión, el entorno

de trabajo y los aspectos técnicos. Los resultados indican que los

factores internos del proyecto, como la programación, la

planificación, las consideraciones técnicas y la gestión de recursos,

son más predecibles y controlables que los factores externos. Una

gestión eficaz de los recursos y un enfoque integral son

fundamentales para optimizar la productividad de la construcción.

Los factores a nivel de proyecto, así como los materiales,

herramientas y equipos, desempeñan un papel importante. Al

sintetizar el conocimiento existente e identificar y clasificar los

factores clave de productividad, este estudio ofrece perspectivas

valiosas para profesionales de la construcción, responsables

políticos e investigadores que buscan mejorar la productividad

laboral y optimizar los resultados de los proyectos.

Palabras clave — Agrupamiento jerárquico; Avance sectorial;

Índice de valor de importancia; Productividad en la construcción;

Revisión sistemática.

Karen Yuset Vides Martinez is a Civil Engineer candidate in University

Industrial of Santander UIS, Colombia (email:

karen2184609@correo.uis.edu.co).

Guillermo Mejía Aguilar is a Ph.D. in Construction Engineering, titular

professor and researcher in University Industrial of Santander UIS, Colombia

(email: gmejia@uis.edu.co).

Scientia et Technica Año XXIX, Vol. 29, No. 1, Mes enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701

INTRODUCTION

he construction industry has a crucial role in providing

vital infrastructure, transportation routes, and essential

housing for human development and social well-being.

Additionally, it significantly contributes to the Gross Domestic

Product (GDP) [1] and generates large-scale employment,

offering job opportunities at various levels and sectors [2],

Therefore, its performance is not only crucial for economic

development but also for social stability and sustainable

progress on both a global and regional scales.

However, the trend of recent decades indicates a slower

growth in productivity in the construction industry (1.0%)

compared to the global economy (2.7%) and other industrial

sectors (3.6%) [3], Additionally, the post-COVID-19 era has

brought new challenges, including labor shortages, debt,

inflation, and energy transition, among others. The latest report

from the McKinsey Global Institute (MGI) suggests that

increasing historical productivity rates in the industry could

mitigate these challenges [4].

Therefore, the study of labor productivity behavior is

relevant for both the industry and academia; hence, we will

conduct a background section featuring the most prominent

works, tracing their development throughout the decades as a

fundamental reference.

Background

Since the mid-1980s, there have been research inquiries

regarding labor productivity in the construction industry,

decade, Jarkas employs the RII method in a representative

sample of contractors to identify and classify factors belonging

to these classification groups: management; technological;

labor-related; and external [12].

In the 2020s, two relevant research studies stand out. On one

hand, Agrawal et al. adopt a novel approach by directly

capturing the perception of construction workers, as opposed to

managers or supervisors. They utilize the Relative Importance

Index (RII) method to assess the significance of various factors

[13]. On the other hand, Van Tam et al., through a

comprehensive review of previous studies, identify critical

factors influencing labor productivity in construction. These

factors are categorized into six main groups, covering

manpower, management, work conditions, project, and

external factors [14]. Both studies employed structured surveys

targeting project managers and contractors, utilizing methods

such as RII and descriptive statistics for data analysis.

Theoretical framework

Construction Labor Productivity (CLP)

A commonly used generic definition is the relationship

between outputs and inputs, but for the purposes of this

research, which is situated partly within the project context or

even at the granular level of site activity, and on the other hand,

focused on labor resources, the partial factor productivity

proposed by Thomas & Daily [15], Horner et al. [16], and

Jarkas [17] will be employed, as shown in the equation (1):

Output

focusing on its estimation. For instance, Neil & Knack

proposed a method emphasizing labor productivity units in

𝐶𝐿𝑃 =

(1)

𝐿𝑎𝑏𝑜𝑟 𝑡𝑖𝑚𝑒

man-hours and established a productivity index, with a

reference value of 1.0 denoting standard performance [5]. On a

different note, Brown explored the relationship between

demotivating factors and low labor productivity in construction

[6].

In the early 1990s, Thomas proposed a novel approach to

modeling labor productivity in construction. Taking into

account real factors observed on construction sites, he

classified project, site, management, and motivational

expectation factors. This classification elucidates why a team

endeavors to perform and how this effort correlates with

productivity [7]. Additionally, in collaboration with Sanders,

they established a methodology for identifying and quantifying

factors affecting masonry activity, employing standardized

statistical techniques for data collection [8]

In the mid-2000s, Abdul Kadir et al. highlighted the concern

of emerging countries in the Southeast Asian region regarding

the identification of factors affecting construction labor

productivity. To address this issue, they utilized surveys as a

data collection instrument and incorporated the perception of

importance using the Relative Importance Index (RII) method

[9]. A similar approach was proposed by Enshassi in the

Western Asian region [10].

During the 2010s decade, the study conducted by Shehata &

El-Gohary stands out, with its main finding being the absence

of a standard definition of productivity. This study provides a

guide for the necessary steps to improve labor productivity in

construction, as well as the use of benchmarks [11]. By mid-

CLP : Construction Labor Productivity.

Output : Installed units.

Labor time : Time units per member/crew.

Research issue

The research problem arises from answering the question:

What factors contribute to the low growth of labor productivity

in the global construction industry? This question serves as the

focal point of the study and will direct the research towards

identifying critical elements contributing to this gap in

Construction Labor Productivity.

Research objectives

The main objective of this study is to conduct a rigorous

literature review to identify the critical factors affecting labor

productivity in construction, considering their importance and

frequency. Additionally, the aim is to group these factors by

classifying them according to common attributes. Achieving

these objectives will allow us to gain a deeper understanding of

the challenges facing the construction industry and provide

valuable insights for the development of effective strategies to

promote growth and sustainability in the sector.

Methodological approach

The methodological approach adopted in this mixed-method

study integrates quantitative analysis with qualitative

evaluation of factor groups [18], aiming to provide a

comprehensive and accurate understanding of the drivers and

Scientia et Technica Año XXIX, Vol. 29, No. 01, enero-marzo de 2024. Universidad Tecnológica de Pereira

Study design

Document collection

Data visualization and analysis

Data interpretation

Structuring, and publication

obstacles to productivity in the construction industry, with the

future goal of developing strategic solutions tailored to each

context.

Contribution

In contrast to surveys with Likert scales commonly

employed in prior studies within specific contexts, this

investigation relies on a systematic literature review and

utilizes the "Importance Value Index" (IVI) to analyze the

relative contribution of factors in a global context.

Furthermore, it stands out for its comprehensive collection of

variables, encompassing data collection instruments, analytical

methods, research findings, and the inclusion of diverse

industry sectors. This holistic perspective enhances our

understanding of patterns and trends related to the variable

under examination. Ultimately, the research introduces an

innovative classification of factors, incorporating insights from

the systematic review and contributions from academics and

industry professionals, thereby adding distinctive value to the

existing body of knowledge in the field.

Document structure and development

Regarding the document structure, the methodology to be

implemented is initially described, followed by the research

results and key findings. These findings comprise a

characterization of the documents and the classification of

factor groups. Finally, a discussion and conclusions section is

presented, summarizing key points and highlighting the

practical and theoretical implications of the research.

II.

METHODOLOGY AND METHODS

Study design

The study is descriptive-analytical, of a cross-sectional

nature, with a mixed-method approach [18]. It involves the

collection of documents from previous research in scientific

literature databases related to factors influencing Labor

Productivity in Construction. These documents are

subsequently processed and statistically analyzed to identify

patterns and trends, allowing for the establishment of

similarities, differences, common characteristics, and

singularities regarding the behavior of factors influencing labor

productivity in the construction industry, from a global

perspective Fig 1.

Fig 1. Research methodology flowchart.

Document collection

This phase involves a systematic review of available

literature in scientific databases, using the Boolean equation:

(TITLE-ABS-KEY-AUTH (factors) AND TITLE-ABS-KEY

("labor productivity") AND TITLE-ABS-KEY ("construction

project")), conducted on 28/09/2022. Fig. 2 represents the

initial sample of 191 documents, outlining the three proposed

literature review phases, the processes, the three exclusion

criteria, and the acquisition of the final sample of 97

documents.

From the final sample, 52 documents provide ranked lists of

factors, while the remaining 47, although presenting groups of

factors and a series of attributes to consider, do not assign them

an importance rating. A total of 267 factors were identified,

with a total count of 1547 occurrences within the documents.

PROCESS PHASE OUTCOME

Fig 2. Scientific Literature Review Flowchart.

Data visualization and analysis

Within the dataset obtained, each identified factor

constitutes a subset of data. Three possible scenarios are

presented:

•

All Ranked Data Scenario:

In this scenario, all data within the subset are ranked.

Consequently, it is possible to calculate the relative importance

value index for each factor.

•

Mixed Ranked and Unranked Data Scenario:

The second scenario involves a mixture of ranked and

unranked data. Some factors may have rankings, while others

do not.

•

No Ranking, Only Frequency of Occurrence Scenario:

The third scenario is where none of the data within the subset

have rankings. In this case, only the frequency of occurrence is

evident for each factor.

Results identified

through database

Scopus: 162

Web of Science: 29

Phase 1

(Literature search)

N=191

Duplicates removed

N= 18

Phase 2

(Screening duplicates)

N=173

Criteria 1

The context was not

Labor Productivity in

Construction Industry

N= 72

Criteria 2

The document was not

in English

N= 3

Phase 3

(Elegibility evaluation

through title and abstract)

N=97

Criteria 3

The document did not

have relevant data

N= 1

Scientia et Technica Año XXIX, Vol. 29, No. 1, Mes enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701

Cleaning and

Preparing

Data

INPUT

DATA

Preprocess

Text

Vectorization

Distance

Matrix

Classification

Groups

Identified in

the Literature

Review

Hierarchical

Clustering

Insights and

directions

from

academic

experts, and

industry

professionals

New Classification Groups

Data Preprocessing

Since there is a desire to understand the behavior of an entire

dataset and achieve representativeness in the global ranking of

factors, the decision is made to preprocess the data. This

process involves the adoption of two data treatment techniques.

The first technique, named "Outlier Removal Using Standard

Deviation," is employed to detect and address values that

deviate significantly from the mean of all data subsets. This

enhances the overall data quality, ensuring more accurate and

reliable results [19].

In the development of this first technique, a range of two

standard deviations to the left (Lower limit) and to the right of

the mean (Upper limit) will be considered. Within a normal

distribution, this range encompasses 95.5% of the data, as

illustrated in Fig 3 and expressed in equations (2) and (3).

frequency, importance, and density, among others. In statistical

analysis, frequency represents the occurrence or presence of a

specific entity in a dataset, while importance may denote the

relevance or impact of the entity in the analysis.

By considering these attributes in the calculation of the IVI,

the relative importance of entities and their contribution to the

structure and relationships in the dataset under study can be

analyzed.

While its formulation is rooted in a general statistical

context, its use has been extended to the study of species

populations, as seen in the works of Cottam and Curtis in 1956,

Cox in 1967, and Mueller-Dombois and Ellenberg in 1974.

For our study, with 267 factors or data subgroups, the

subtotal of the sum-products of frequencies by their respective

importance in each factor, divided by the total sum-products of

all factors, yields the "Importance Value Index" (IVI) for each

factor. Equation (4) illustrates the relationship for obtaining the

"Importance Value Index".

𝐼𝑉𝐼 =

𝑛

𝑖=1

𝑖

∗ 𝑤

𝑖

(4)

In this formula:

𝑛

𝑖=1

𝑖

∗ 𝑊

𝑖

IVI : Importance Value Index

N : Number of subsets in study

f : Frequency of factors for subsets

w : Importance value of factors

Fig 3. Normal Distribution with Two Standard Deviation Range.

Upper limit : X

+ 2S (2)

Lower limit : X

- 2S (3)

In this formula

: Mean

S : Standard Deviation

The second applied technique is "Imputation of Missing

Values," implemented in the subsets of the second scenario to

assign the median value of the subset's data to those missing

attributes. This value is obtained after applying the first

technique, and by employing this neutral value, the frequency

of occurrence is considered within the analysis.

Finally, a validation of the data subset trend towards a central

value is performed by calculating the "coefficient of variation"

before and after the "Data Preprocessing." This validates an

improvement in this metric of relative dispersion.

Data analysis

On one hand, for the characterization of the documents in the

collected sample, descriptive statistics and frequency analysis

will be employed. On the other hand, to rank the factors

influencing labor productivity in the construction industry, the

"Importance Value Index" (IVI) method will be utilized.

The "Importance Value Index" (IVI) is a method used to

assess the relative importance of entities within a dataset,

considering the relative contribution of key attributes, such as

Factors Classification

The classification of collected factors is carried out through

a combination of approaches. On one hand, a statistical analysis

of frequencies obtained from the sample documents is

employed to discern significant patterns and trends. On the

other hand, "Hierarchical Clustering" methods with "Ward"

linkage are implemented using the workflow depicted in Fig 4

to generate a dendrogram constructed from a distance matrix.

Fig 4. Workflow of the New Classification of Factor Groups.

∑

Scientia et Technica Año XXIX, Vol. 29, No. 01, enero-marzo de 2024. Universidad Tecnológica de Pereira

Finally, the refinement of the classification is conducted

through the consideration of valuable insights and directions

from educators, academic experts, and industry professionals

with extensive experience. This input provides a critical and

informed perspective.

This multidimensional methodological strategy translates

into a holistic understanding of the key elements involved,

thereby laying the groundwork for more robust interpretations

and conclusions in the study's context.

III.

RESULTS

In order to understand trends and patterns, descriptive

statistics provide information of the frequency of scientific

publications from 1985 to 2022 Fig 5.

approach that integrates machine learning and artificial

intelligence for factor clustering and importance quantification.

This is further enhanced by utilizing both Likert scale surveys

and advanced quantitative techniques [24], [23] for factor

ranking. By synthesizing these different methods, our approach

not only leverages the strengths of each, but also minimizes

their limitations, ensuring more reliable and valid results

through methodological triangulation.

Sample Geographical Location

A broad geographic distribution has been observed among

the analyzed documents, with India (11.3%), the United States

(8.2%), and Canada (7.2%) standing out as the countries with

the highest participation, emphasizing the diversity of national

contexts under investigation, as depicted in Fig 6. The

prominent position of these countries in the realm of research

on labor productivity in construction may be attributed, in part,

to their high level of industrialization and economic

development. Additionally, the presence of educational and

research institutions, coupled with the availability of financial

and technological resources, drives the production and

dissemination of studies in this field.

Fig 5. Frequency of scientific publications related to Factors affecting Labor

Productivity in Construction Industry from 1985 to 2022.

In the first interval (1980s and late 1990s), there is observed

a commencement of research with minimal activity indicating

low interest, possibly influenced by global economic

challenges and challenges within the construction industry.

From the late 1990s through the 2000s, a gradual increase in

research is noted, driven by economic recovery and increased

investment in construction projects. Between the late 2000s and

early 2010s, research interest continues to rise due to

technological advancements in construction, environmental

concerns, and sustainable construction regulations. The

shortage of skilled workers also contributed. Finally, from 2013

to 2022, a substantial increase in research is recorded,

attributable to the economic recovery post-2008 crisis,

technological advancements such as Building Information

Modeling (BIM), environmental concerns, and the COVID-19

pandemic. This keeps the topic of labor productivity in

construction as relevant.

Researchers have used a range of methods to explore the

factors that influence labor productivity in the construction

industry, from traditional expert opinion and literature reviews

to objective, quantitative analysis. These conventional

approaches,

while rich in empirical insights, often carry the risk

of subjective bias, creating a need for more data-driven

methods. Acknowledging the geographical diversity of existing

studies and prone to bias ([20]; [21]; [22]; [23]), this study

provides a global perspective by employing a mixed-methods

Fig 6. Country-wise distribution of scientific publications related to Factors

Influencing Construction labor productivity from 1985 to 2022.

In contrast to the traditional continental division, we have

opted for the geographic classification proposed by the United

Nations (UN) to segment regions based on their location. This

classification system consists of 7 regions grouped not only by

geographical proximity but also by convergence in sustainable

development goals [25], as illustrated in Fig 7.

Fig 7. Distribution of Publications by Regions of Development Goals.

Adapted from United Nations Statistics Division (2019), Regional Groups

Report, https://unstats.un.org/sdgs/report/2019/regional-groups/".

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Scientia et Technica Año XXIX, Vol. 29, No. 1, Mes enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701

Taking this into account, the notable representation of

Western and Central Asia (32.6%) in the research corpus

highlights the importance and dynamism of these areas in the

context of the construction industry. Factors such as sustained

economic growth, the expansion of infrastructure, and large-

scale development projects, as well as the need to address

specific challenges related to construction, have generated

significant interest in labor productivity in these regions. The

concentration of studies reflects the necessity to develop

solutions and strategies tailored to the particular socio-

economic and cultural contexts of Western and Central Asia.

Additionally, it is noteworthy that the region with the highest

presence in research coincides with countries possessing

significant reserves of oil and gas, along with a marked

infrastructure development plan. This factor may have

influenced interest and investment in optimizing productivity

in the construction industry, given the close relationship

between the construction sector and the expansion of energy

infrastructure.

On the other hand, a significant presence of global review

studies (8.2%) is observed, indicating an interest in assessing

trends and patterns on a worldwide scale. However, the

relatively lower representation of other regions, such as Latin

America and the Caribbean (3.4%) and Sub-Saharan Africa

(3.4%), suggests a need for more research and analysis in these

contexts. The possible reason behind this disparity could be

related to differences in the availability of financial and

technological resources, as well as the lower prioritization of

research in the construction domain in these regions.

This geographic diversity underscores the importance of

addressing labor productivity in the construction industry from

a global and contextualized perspective, taking into account the

specific particularities and challenges faced by each region.

Types of Collected Documents

Scientific articles predominate, representing 72.16% of the

total sample, emphasizing their central role in communicating

findings on labor productivity in construction. Conference

papers have a significant presence at 21.65%, highlighting their

relevance as platforms for disseminating ongoing research or

preliminary results in this field. Review articles constitute

5.15%, indicating the potential for synthesis and critical

analysis of existing literature. Book chapters are less common,

accounting for 1.03%. These figures provide a clear insight into

the composition of available literature, underscoring the

importance of scientific articles and conference papers as

primary sources in research on labor productivity in the

construction industry, as evidenced in Fig 8.

Fig 8. Types of Documents Found in the Literature Review.

Data collection (Inputs)

Various methods and instruments used for data collection

have been identified, as depicted in

Fig 9. Particularly noteworthy is the literature review as the

predominant source of information collection, contributing

significantly with 38.05%, including review articles [26] [27]

[28]. This comprehensive literature review has provided a

contextual foundation for the theoretical framework of the

study. Surveys have also played a relevant role, representing

28.29% of the collected data, including notable instances [29]

[30] [31] [32] [33]. These findings underscore the importance

of gathering perceptions and opinions of respondents in the

development of research in this area. Additionally, direct on-

site observations, accounting for 11.22%, stand out,

exemplified by [34] [35] [36], along with interviews (9.27%)

featuring [37] [38] [39] [40] [41] [42] [43] [44], providing

perspectives through direct interaction with professionals and

key stakeholders in the field of study. Historical records [45]

[46] [47] [48] and previous research [49] [50] [51] [52] have

contributed 2.44% and 5.37%, respectively, enriching the

database with a temporal and comparative perspective. These

results emphasize the relevance and diversity of sources and

approaches employed in constructing a robust and contextually

relevant information base that underpins the examined

research.

Fig 9. Methods and Instruments Used for Data Collection.

REVIEW 5,15%

CONFERENCE

PAPER

21.65%

BOOK CHAPTER

1.03%

ARTICLE

72,16%

…

38.05%

28.29%

11.22%

9.27%

5.37%

2.44%

1.46%

0.49%

Literature review

Survey

Direct…

Interview

Previous research

Historical records

Questionnaire

Experience of…

Digitized…

Primary data…

On-site data…

Historical records

Contractor database

On-site data…

Focus Group…

Scientia et Technica Año XXIX, Vol. 29, No. 01, enero-marzo de 2024. Universidad Tecnológica de Pereira

Tools & Techniques (Analysis)

The analysis of tools, techniques, and methods reveals trends

in the study of labor productivity in construction, as observed

in Fig 10. In the 2000s, statistical techniques such as correlation

analysis [41] and logistic regression [53] [54] were employed,

representing 11.18% and 6.21% of studies [55] [56]. Since

2010, advanced models such as Agent-Based Modeling (ABM)

(1.86%) [57] [58] [46], System Dynamics (SD) (4.35%) [59]

[44] [60] [61] [58] [40] [62] [49] and Neural Networks (ANN)

(3.11%) [63] [64] [43] [65] [50] have emerged. The Relative

Importance Index (RII) constituted 20.50% in recent years,

with notable instances. [66] [67] [68] [69] [29] [70] [71] [72]

[30]; similarly, methods such as Fuzzy Logic [73] [74] [75] and

Genetic Algorithms [38] [75], sporadically used in the 2000s

[76], resurfaced, reflecting interest in these advanced tools. The

research shows a preference for sophisticated modeling

approaches and comparative analysis.

25.00%

[91] [92] [42] [65] [93] [94] [95] [74] [31] [28] [96] [97] [98]

[70] [71] [68] [69] [45] [66] [99] .

Furthermore, a marked interest is evident in evaluating the

impact of project changes and quantifying productivity loss at

4.95% [32] [60] [93], suggesting increased sophistication in the

methodologies employed. These findings reflect a trend

towards a nuanced and quantitative understanding of labor

productivity in the construction industry in recent years.

Analyzing the distribution of approaches, it stands out that

the identification of factors, their ranking, and classification

represent approximately 70% of the utilized methodologies

(26.58%, 23.87%, 18.47%), followed by modeling,

encompassing around 14.41%. This analysis underscores the

growing importance of quantitative and detailed approaches to

understand and enhance labor productivity in the construction

industry, emphasizing the need for integrated approaches

considering both qualitative and quantitative factors in the

pursuit of effective solutions.

20.00%

15.00%

20.50%

30.00%

25.00%

20.00%

10.00%

15.00%

5.00%

0.00%

10.00%

5.00%

0.00%

Fig 10. Tools, Techniques and Methods for Data Analysis of Factors affecting

Labor Productivity in Construction.

Research Outcomes (Outputs)

Regarding the research results, a notable evolution in

addressing labor productivity aspects in the construction

industry is observed over different periods, as depicted in Fig

11.

In the 1980s, the focus was on establishing quantitative

relationships between labor productivity and on-site labor costs

[77]. Starting from the year 2000, with the adoption of more

sophisticated techniques and tools, a shift towards formulating

models to record, measure, and predict labor productivity is

evident [41] [78] [79] [80] [81] [64] [75] [44] [82] [57] [83]

[47] [73] [50] [62] [84], indicating a growing interest in

creating quantitative frameworks to understand and optimize

labor efficiency in this sector.

The identification, classification, and evaluation of factors

influencing labor productivity also gain prominence,

highlighting a more analytical and detailed approach in

research guided by the insights of professionals and industry

stakeholders, including [85] [86] [87] [33] [59] [88] [89] [90]

Fig 11. Outcomes of research on factors influencing labor productivity in

construction as identified in the analyzed documents.

Sectors of the Construction Industry

Diversity is evident in terms of industry sectors represented

in the analyzed documents. Primarily, "Building Construction"

in general [100] [63] [101] [88] [89] [82] [92] [57] [60] [40]

[102] [37] [30] [69] [67] [103], emerges as the most

prominently addressed sector, representing 22,58% of the total.

Following in significance are infrastructure projects,

highlighted by [87] [104] [105] [97] [68], encompassing the

construction of bridges, treatment plants, tunnels, hydroelectric

projects, and roadways, contributing 16.13% of the total.

The "Residential" sector claims importance with 12.90%

[63] [11] [64] [42] [106] [65] [107] [39] [108] [109] [66] [84],

followed by a notable interest in industrial [76] and commercial

[110] [11] [74], contributing together with 16.13%. It is

relevant to highlight the significant presence of reinforced

concrete construction with 5.38% on one hand [101] [63] , and

projects related to electrical, mechanical, and metal sheeting

[53] [54], contributing together with 10.75%.

11.18%

6.21%

3.73%

4.35%

1.24%

1.24% 1.86% 1.86% 1.86%

2.48%

3.11%

26.58%

23.87%

18.47%

14.41%

4.95%

1.35%

0.90%

Logistic regression

Factor analysis

Comparative analysis

Progress tracking method

Reliability Test Method

Structural Modeling

Mathematical methods

Critical Path Method

Agent-Based Modelling (ABM)

Cross-validation

Genetic algorithm optimization

Work sampling method

Average Mean

Neural network model

Fuzzy logic algorithm

System Dynamic

Spearman rank correlation

Regression analysis

Stadistical analysis

Relative importance index…

Identify factors affecting

Construction Labour…

Rank factors affecting

Construction Labour…

Classification factors affecting

Construction Labour…

Model of Construction Labor

Productivity (CLP)

Assessment on factor

affecting Construction Labor…

Evaluate Construction Labor

Productivity (CLP)

calculate loss of Construction

labor productivity (CLP)

Identify productivity practices

on Construction Labour…

Quantifies the impact of

schedule compression on…

Framework to identify the

means for improving…

Scientia et Technica Año XXIX, Vol. 29, No. 1, Mes enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701

The construction of high-rise buildings [42] [107] [50] [46]

also holds significant representation, contributing 5.38%.

Educational projects [63] [64] [50] and those related to social

facilities [109], both with a 3.23% share. "Housing" projects

[44] [32] and construction in the oil and gas industry [77] [43]

each contribute 2.15%. Additionally, it was observed that

construction consultancy [31] and high-complexity projects

[83], each with a 1.08%, complete the observed sectoral

distribution.

The evolution in project selection over time shows a

significant trend. Between 1985 and 2010, the focus was on

large-scale infrastructure such as bridges, treatment plants, and

hydroelectric projects. However, from 2010, there was a shift

towards the construction of residential buildings, possibly in

response to demographic needs and urban development.

From 2018, a return to increased investment in infrastructure

projects was observed, especially in bridges, treatment plants,

and hydroelectric projects, accompanied by an increase in the

construction of reinforced concrete projects. This variability in

project allocation over the decades reflects the adaptability of

the construction industry to respond to changing environmental

demands. Fig 12.

25.00%

structural reinforcement, including fixing reinforcements and

steel [113] [86][57] [65] [38], along with excavation tasks,

exhibit considerable relevance [79] [32] [60] [38] [45],

contributing 9.43% each. In addition, crucial aspects such as

pipe installation [76], addressing both welding and assembly,

and the dismantling of main beams, each contribute 7.55%.

These results underline the breadth of areas of interest in

research on labor productivity in the construction industry. To

understand the underlying reasons for these findings, various

possibilities can be considered. The emphasis on masonry may

be related to its fundamental role in the initial phase of most

construction projects and its high demand for labor. On the

other hand, the prominence of works related to concrete,

formwork, reinforcement, and structure can be attributed to the

inclination of the documents toward vertical and residential

construction in the obtained sample. These results also suggest

a concern for the efficiency and functionality of building

systems.

25.00%

20.75%

20.00%

22.58%

15.00%

10.00%

5.00%

0.00%

5.00%

0.00%

Fig 13. Construction activities and processes found in the consulted

documents.

Fig 12. Sectors of construction industry represented in the consulted

documents.

Construction Activities and Processes

The analyzed documents adopt a micro-level approach,

focusing on basic construction activities [111], especially

regarding labor productivity in the construction industry, as

observed in Fig 13. Among the various construction processes

examined, masonry [79] [11] [52] [108] [47] [38] [46] [66],

encompassing the use of both clay bricks and sand-cement

blocks, stands out as the most significant activity, representing

20.75% of the total. Next, works related to concrete [44] [43]

[57] [32] [61] [112] [35] [38], and formwork installation

emerge as crucial elements [80] [63] [101] [65] [50],

contributing 16.98% and 13.21%, respectively. Similarly,

Identified Factor groups

In the analysis of consulted documents, various key

categories were identified. "Management Factors" [11] [33]

[88] [104] [89] [91] [12] [92] [32] [106] [114] [31] [108] [40]

[115] [71] [72] [30] [66] and "Labor" [11] [33] [88] [89] [12]

[32] [114] [74] [31] [40] [116] [30] [117] [106] [71] [72] stand

out with 11.5%, emphasizing their importance in construction

projects. They are followed by "External Factors" with 7.3%

[90] [12] [32] [117] [74] [83] [31] [40] [71] [69], focusing on

proactive management to mitigate impacts. Also relevant are

"Project-Level Factors" [76] [63] [75] [91] [65] [117] [50] [71]

[30] [69] and "Materials, Tools, and Equipment" [88] [104]

[117] [74] [40] [115] [66] with 6.1% and 4.8%, respectively.

These results highlight the need for a comprehensive approach

and effective resource management to optimize productivity in

16.13%

12.90%

10.75%

9.68%

6.45%

5.38% 5.38%

3.23%

2.15% 2.15%

1.08%

16.98%

13.21%

9.43% 9.43%

7.55%

5.66%

1.89% 1.89%

1.89%

Consultants companies

Very complex projects

Social amenities Projects

Housing projects

Oil & Gas construction

Educational Construction

High-rise construction

Reinforced concrete construction…

Industrial Construction

Commercial Construction

Electrical, Mechanical, Sheet metal…

Residential construction

Infraestruture (Bridges, Treatment…

Building construction

Marble finishing works for…

Carpentry

Electrical

Dismantling of the main…

Pile drilling

Plastering

Structural construction

Pipes (Welding, Rigging)

Excavation

Reinforcement (Rebar…

Formwork installation

Concreting

Masonry work (Brick,…

Scientia et Technica Año XXIX, Vol. 29, No. 01, enero-marzo de 2024. Universidad Tecnológica de Pereira

Exte

nal

factor

6,2%

Inte

nal

factor

86,8%

construction in concordance with the clusters found by others

authors [22]; [118].

When observing the evolution over time, key patterns

emerge. In the Initial Period (2005-2008), the focus was on

"Input Factors" and "Output Factors" [76] [28], indicating early

attention to resource and result management. A "Stability

Period" (2009-2016) maintained "Contextual Factors" [119]

[61], "External Factors," and "Management Factors," showing

sustained attention to contexts and management. From 2017,

there was an increase in attention to "Labor" and

"Management," focusing on their development. There also

emerged a focus on "Work Environment and Technical

Factors" (2019-2022), evidenced by the relevance of

"Technical Factors" [33] [12] [32] [74] [31] [40] [115] [72] and

"Working Environment/Condition Factors" [91] [66] [71] [69]

, indicating a growing interest in labor and technical conditions.

Categories such as "Labor," "Management Factors," and

"External Factors" maintained a constant presence, suggesting

their ongoing influence on labor productivity. These findings

provide a comprehensive view of the evolution of factors in the

construction industry, useful for understanding trends and

formulating strategies. However, these categories differ from

other study that have used relative important index [120].

Fig 14.

14.0%

12.0%

11.5%

10.0%

8.0%

6.0%

4.0%

2.0%

0.0%

to the planning and execution within the project's scope. This

includes activities ranging from scheduling and planning to

technical and design considerations, as well as crucial aspects

of human and material resource management. In comparison to

external factors, these elements are more predictable and

controllable [121].

Fig 15. Primary classification level. (Global)

On the other hand, external factors (16.2%) encompass a variety of elements

operating beyond the direct control of the construction company. This includes

environmental factors such as weather conditions and natural disasters, as well

as socio-economic, political, and regulatory variables that can significantly

impact project execution. Due to their external nature and diverse sources of

origin, these factors tend to be more challenging to predict and manage

effectively.

Fig 16 presents the proposed groups in the new classification within the

global context. The light grey shade corresponds to frequency analysis, while

the darker represents the Importance Value Index (IVI), considering the total

factors (267) and instances (1547).

Fig 14. Classification of identified factors influencing labor productivity in

construction, as found in the documents.

Innovative Classification of Factor Groups Influencing

Labor Productivity in the Construction Industry

After identifying the diverse factor groups present in the

sample, a novel classification is proposed based on the

methodology outlined in Fig 4, This classification addresses the

impact of these factors on the productivity of the construction

industry. It is structured according to the nature and order of the

factors, taking into consideration specific characteristics and

attributes, which are then consolidated into related categories.

The primary classification level highlights two main

categories: internal factors and external factors, as depicted in

Fig 15. Internal factors (86.8%) encompass elements intrinsic

Fig 16. Revised classification of factors impacting labor productivity in

construction within a global.

The Fig 17 illustrates the new classification of factor groups

affecting construction labor productivity

7.3%

6.1%

4.2%

4.8%

1.8%

2.4%

3.0%

23.3%

19.0%

14.2%

12.8%

6.1%

8.4% 8.5%

2.7%

5.1%

Supervision Factors

Work Factors

Socio/Psychological Factors

Contextual factors

Site Factors

Technological Factors

Health and Safety Factors

Micro/Micro level/Activity…

Physical Factors

Working…

Motivational Factors

Organizational Factors

Technical Factors

Financial and Economical…

Material/Tools/Equipment…

Project level factors

External Factors

Labour/Labor/Workforce/…

Management Factors

Industry and public

environmental factors

Environmental factors

Safety and health

factors

Planning factors

Company factors

Technical and design

factors

Human resources

management factors

Manpower and work

factors

Management factors

Scientia et Technica Año XXIX, Vol. 29, No. 01, enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701 y ISSN-e: 2344-7214

Fig 17. Innovative Classification of Factor Groups Influencing Labor Productivity in the Construction Industry

Top 30 most important factors on a Global Scale

TABLE I.

RANKING TOP 30 MOST IMPORTANT FACTORS INFLUENCING LABOR PRODUCTIVITY ON A GLOBAL SCALE.

Rank

Factor

I.V.I.

Classification I

Classification II

Availability of materials on site

0,0335

Internal factors

Management factors

level of skill labor

0,0330

Internal factors

Manpower and work factors

Site weather conditions

0,0281

External factors

Environmental factors

Rework

0,0268

Internal factors

Planning factors

levels of motivation and commitment of the workforce on construction

0,0244

Internal factors

Human resources management factors

Availability of tools and equipment on site

0,0236

Internal factors

Management factors

Delay in salaries payment

0,0234

External factors

Company factors

Financial incentive and rewards program/scheme

0,0232

External factors

Company factors

crew labor construction experience

0,0230

Internal factors

Manpower and work factors

Project planning

0,0195

Internal factors

Planning factors

Construction method and Building technique

0,0181

Internal factors

Technical and design factors

Supervisor experience

0,0179

Internal factors

Management factors

Communication between site management and labor and feedback

0,0165

Internal factors

Management factors

Turnover and labor absenteeism

0,0156

Internal factors

Manpower and work factors

Wages and economic conditions of workers

0,0156

Internal factors

Human resources management factors

Construction management team skills

0,0147

Internal factors

Management factors

Work planning and scheduling

0,0137

Internal factors

Planning factors

Working overtime

0,0126

Internal factors

Manpower and work factors

Availability of labour

0,0121

Internal factors

Manpower and work factors

Occupational Health & Safety conditions on site

0,0120

Internal factors

Safety and health factors

Change order

0,0119

Internal factors

Technical and design factors

Crew size and composition

0,0115

Internal factors

Manpower and work factors

Temperature

0,0113

External factors

Environmental factors

Physical fatigue

0,0110

Internal factors

Human resources management factors

Financial status of stakeholders

0,0106

External factors

Company factors

Design changes in the drawings

0,0105

Internal factors

Technical and design factors

Inspection and control delays

0,0103

Internal factors

Management factors

Overcrowding on the site

0,0098

Internal factors

Manpower and work factors

Accidents

0,0088

Internal factors

Safety and health factors

Incomplete drawings

0,0085

Internal factors

Technical and design factors

Environmental Factors

External Factors

Industry and Public Environmental Factors

Company Factors

Factors affecting

Construction

Labor Productivity

Classification

Planning Factors

Technical and Design Factors

Internal Factors

Management Factors

Human Resourses Factors

Safety and Health Factors

Manpower, Labor and Work Factors

Greater control over conditions

and causes

Less control over

conditions and causes

Scientia et Technica Año XXIX, Vol. 29, No. 01, enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701 y ISSN-e: 2344-7214

TABLE presents the ranking obtained using the "Importance

Value Index" (IVI) method in the "Global" context. It illustrates

the top 30 most influential factors, all contributing to labor

productivity within the construction industry. Notably, this

ranking deviates from previous studies where material

availability, although significant, does not consistently appear

among the top five factors [22]; [118]; [23].

Fig 18, the internal and external factor composition is

presented, this time applied to the top 30 most relevant factors.

On the other hand, Fig 19 illustrates the distribution of these

critical factors concerning the new classification of groups, with

the darker blue shade derived from applying IVI and the lighter

hue considering only the frequencies. To further emphasize the

order of importance in the new factor classification, Fig. 20

exclusively displays those relevant to IVI.

Fig 20. Distribution of 30 critical factors concerning the new classification of

groups (Only IVI).

Fig 18. Internal and external factor composition is presented, this time applied

to the top 30 most relevant factors.

26.0%

Fig 19. Distribution of 30 critical factors concerning the new classification of

groups.

IV.

CONCLUSIONS

In terms of geographical distribution and development, it is

evident that the countries with the highest participation in research

on the subject, according to the analyzed sample, are India (11.3%),

the United States (8.2%), and Canada (7.2%). This diversity in

national contexts underscores the need to tailor solutions and

strategies to specific socio-economic and cultural nuances,

including sustained economic growth, large-scale infrastructure

expansion, and addressing specific challenges related to

construction.

At the regional level, "Northern Africa and Western Asia"

(32.6%), "Central Asia and Southern Asia" (24.7%), "Europe

and Northern America" (19.1%), and "Eastern Asia and

Southeastern Asia" (14.6%) stand out as regions with the

highest research activity in the field. This concentration reflects

the necessity of developing strategies adapted to diverse

contexts.

Regarding information collection tools, "Literature review"

(38.05%) stands out as the most relevant method, followed by

"Survey" (28.29%), "Interview" (9.27%), and "Questionnaire"

(1.46%). "Direct observations on site" (11.22%) illustrate the

interaction between the academic and practical realms.

"Historical records" (2.44%), "Contractor databases" (0.49%),

and "Experience of previous projects" are also employed,

highlighting retrospective and comparative on-site approaches.

The combination of these methods suggests a comprehensive

approach in research, emphasizing theory, practical experience,

and comparative analysis.

In terms of analysis methods, the "Relative Importance Index

method" (20.50%) stands out, supported by statistical methods

such as "Statistical analysis" (11.18%) and "Regression

23.3%

20.0%

16.5%

17.4%

6.7%

7.9%

6.7%

13.3%

10.0% 10.0%

10.0%

10.7%

8.3%

5.4%

5.0%

2.9%

0.0%

Externa

factors

16.7%

Internal

factors

83.3%

23.3%

20.0%

13.3%

10.0% 10.0% 10.0%

6.7% 6.7%

Industry and public environmental

factors

Environmental factors

Safety and health factors

Human resources management

factors

Company factors

Planning factors

Technical and design factors

Management factors

Manpower and work factors

Safety and health factors

Environmental factors

Planning factors

Human resources management factors

Company factors

Technical and design factors

Management factors

Manpower and work factors

Scientia et Technica Año XXIX, Vol. 29, No. 1, Mes enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701

analysis" (6.21%). Innovative methods like "System Dynamic"

(4.35%) and "Fuzzy logic algorithm" (3.73%) indicate a search

for advanced approaches to model productivity dynamics. It is

crucial to note that the practical implementation of these

methods may require resources and specialized expertise.

The prevalence of vertical construction is highlighted,

representing a significant proportion in various subsectors.

Despite the minority presence of horizontal projects,

"Infrastructure" (16.13%) emphasizes its critical importance in

the industry. This might reflect the need to develop essential

infrastructures.

In the first-order classification of factors, internal factors

predominate (86.8%), indicating their relative importance in the

perception of industry stakeholders. Although external factors

are a minority, they represent a considerable percentage (16.2-

16.7%), underscoring the importance of balanced management.

In the second-order classification, "Management factors"

(23.3%) leads, highlighting the importance attributed to project

management. In the ranking of the top 30 factors, "Manpower

and work factors" (23.3%) stands out, followed by

"Management factors" (20.0%), emphasizing the relevance of

manpower and management.

Fig 19 contrasts both scenarios, showing consistency in the

proportion of internal and external factors in the two analyses.

At the individual factor level, resource management stands

out, with "Availability of materials on site" ranking first and

"Availability of tools and equipment on site" ranking sixth.

"Level of skill labor," along with factors related to manpower,

highlights the importance of training and skills. "Site weather

conditions" stands out as a relevant environmental factor.

These findings suggest specific strategies, such as training for

the management team, technical training programs, the

development of climate risk matrices, and effective human

resource management to optimize labor productivity in

construction projects globally.

Following this global-level research, it is recommended to

conduct a study at the level of Sustainable Development Goal

Regions (SDG-R), grouping diverse perceptions and influential

factors on labor productivity within each region.

Furthermore, it is advised to incorporate new tools of

Artificial Intelligence (AI), particularly Machine Learning,

alongside programming, in the processing and analysis of data

for future research endeavors. This integration can enhance the

depth and efficiency of data analysis, providing valuable

insights into the complex dynamics influencing labor

productivity in the construction industry.

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Ray Andrés Ardila Cubillos

Graduated as a Civil Engineer from the

Industrial University of Santander

(UIS) in 2003. In 2012, he earned a

specialization in State Contracting

from the Externado University of

Colombia. In 2023, he obtained his

M.Sc. degree in Civil Engineering

from UIS, standing out for his active

participation in the Research Group on

Construction Materials and Structures (INME). Currently, he

plays key roles as a professor, researcher, and specialized

consultant in Construction Project Management, both in the

private and public sectors.

ORCID: https://orcid.org/0009-0003-2521-7265

Manuel Yesid Durán Prada was born

on December 31, 2000, in Piedecuesta,

Santander. He is currently a Civil

Engineering student at the Industrial

University of Santander and is a

member of the research seedbed in

Modeling and Construction

Management.

ORCID: https://orcid.org/0009-0008-5605-3845

Scientia et Technica Año XXIX, Vol. 29, No. 1, Mes enero-marzo de 2024. Universidad Tecnológica de Pereira. ISSN 0122-1701

Karen Yuset Vides Martinez was

born on December 12, 2000, in

Barrancabermeja, Santander. She is

currently a Civil Engineering student

at the Industrial University of

Santander and is a member of the

research seedbed in Modeling and

Construction Management.

ORCID: http://orcid.org/0009-0002-0321-0268

Guillermo Mejía Aguilar Civil

Engineer with a degree from the

University of Cauca, Colombia, and a

Ph.D. in Construction Project

Management from the University of

Alabama, USA, and a former Fulbright

Scholar. I currently hold the position of

Full Professor affiliated with the

School of Civil Engineering at the

Industrial University of Santander,

Bucaramanga, Colombia. I serve as an Associate Researcher

within the Colombian Ministry of Science system.

ORCID: https://orcid.org/0000-0002-3829-7730