**1. Introduction**

Construction projects are currently prevailing in every aspect of human life, with the goal of improving the quality of people's lives. As clearly is defined in the European Commission (2012) Road Transport Report, their standards are strictly specified so that they will eventually correspond to the demanding reality. Their successful completion relies on successful project management, which must strongly emphasize the efficient utilization of labor, material, and equipment in order to deliver a successful project on time, within the budget, and as per the defined quality standards [1]. Under this framework, the productivity of construction projects was always an issue worth examining [2,3]. Productivity is used to denote a relationship between output and its associated input used in the production system [2]. It depends on a variety of factors, such as construction equipment, which represents a significant capital investment for companies in this sector [3]. Efforts to improve productivity have been made in recent decades, focusing on the most influential factors.

A project's productivity is directly affected by fleet management, which concerns the selection of suitable construction equipment for each task according to its requirements [4]. The fleet and asset management function is responsible for strategic decisions regarding fleet composition, fleet average age, capital expenditure, finance, tax, and return on investment. It uses the data developed in other functions, interfaces with the company strategic planning process, and develops the rates, estimates, budgets, benchmarks, and standards

**Citation:** Petroutsatou, K.; Ladopoulos, I.; Tsakelidou, K. Scientometric Analysis and AHP for Hierarchizing Criteria Affecting Construction Equipment Operators' Performance. *Sustainability* **2022**, *14*, 6836. https://doi.org/10.3390/ su14116836

Academic Editors: Srinath Perera, Albert P. C. Chan, Xiaohua Jin, Dilanthi Amaratunga, Makarand Hastak, Patrizia Lombardi, Sepani Senaratne and Anil Sawhney

Received: 14 April 2022 Accepted: 27 May 2022 Published: 2 June 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

needed to manage the whole process [4]. Nowadays, construction companies are facing multiple difficulties with how to properly and effectively manage their fleet of construction equipment. Fleet management is a feature that allows companies to avoid or minimize the risks associated with investing in equipment, efficiency, productivity, overall transportation costs, and impartial compliance in legislation [5]. On the other hand, low productivity means inefficiency of resources with the inevitable results of cost and time overruns [6,7].

Previous research on construction project productivity primarily focused on the efficiency of construction project delivery and focused on tangible input-output schema within the construction process [8]. Liberda et al. [9] managed to identify the most critical aspects in terms of human, external, and management issues that affect construction productivity. Ghoddousi and Hosseini [10] conducted a survey of the factors affecting the productivity of construction projects in Iran and concluded that the most important grounds affecting sub-contractor productivity include, in descending order: materials/tools, construction technology and method, planning, supervision system, reworks, weather, and jobsite condition. Hasan et al. [11] identified more than 46 articles from different sources concerning the factors affecting construction productivity within the last 30 years. They finally concluded that despite noticeable differences in the socio-economic conditions across both developed and developing countries, an overall reasonable consensus exists on a few of the significant factors impeding productivity.

As Hedman et al. [12] certify, the equipment operators are a crucial factor influencing the duration of the time loss, which refers to planned downtime, setup time, measurement and adjustment, equipment failure, etc. This perspective is strengthened by He et al. [13]; they studied a construction project's resilience (CPR) by measuring specific systemic indicators from the perspective of employee behavior, such as operators.

Moreover, the construction equipment operators' performance is related to their safety preconditions during earthwork, which rely upon the synergy of the work and their interactions with each other and with their supervisors [14].

The basis of this study is set on the criteria affecting the construction equipment operators' performance. Skills and aptitude are also significant factors that are considered to be critical for the performance of earthmoving equipment operators, but they co-exist with more quantitative factors, which are examined in this study. Several studies have highlighted the relationship between aptitude and employee performance. Aptitude is the potential to demonstrate the ability to perform a certain kind of work at a certain level [15]. This research contributes to the body of knowledge by combining those two abilities with other, still untapped, factors. It is agreed that the operators' performance is a mixture of tangible and intangible factors. It is described as their ability to complete their work, fulfilling certain standards, based on the goals or objectives set by their employers [12,13].

In an effort to highlight the effect of the power of equipment operators on the construction project productivity, this study dives deep into the human factors to extract the tangible (or subjective) and intangible (or objective) criteria related to the construction equipment operators' performance in the field. The definition of worker productivity is widely examined. Tangen [16] examined the ways in which the concepts of "productivity" and "performance" are dealt with in the literature, demonstrating that the terms used within these fields are often vaguely defined and poorly understood.

However, performance entails more. It includes their willingness and ability to communicate or collaborate, their promptness, and their demeanor at work. Consequently, the abovementioned factors have a significant impact on the overall performance of the construction project. The expectations and standards set by their supervisors can shape the operators' experience, can affect performance, and can certainly have an impact on their productivity and, ultimately, the project's success. In a nutshell, productivity concentrates on the output, i.e., what is produced, whereas performance is often activity-based and is quantitative or qualitative [17]. Maqsoom et al. [17] realized through their research that worker productivity is critical within construction projects as it is the measure of the rate at which work is performed, and more importantly, it helps to build knowledge on how

to motivate the workers to perform at high levels. Much earlier, Navon [18] measured indirect productivity parameters and converted them into sought indicators in order to comprehensively point out the importance of the operator's performance to the project's productivity.

In order to quantify the earthmoving equipment operators' performance factors, this research focuses on identifying and hierarchizing those factors. The necessary data concerning the performance criteria were investigated through: (i) scientometric analysis, (ii) structured interviews with construction equipment experts, and (iii) structured interviews with construction equipment operators. The findings of this research will be beneficial for contractors, project managers, and equipment operators as they reveal the key issues regarding the attitudes and behaviors that play an integral role in enhancing productivity in construction projects [19].

#### **2. Literature Review**

This paper conducts a two-step literature review by adopting an interpretivist philosophical approach and inductive reasoning to generate new theories on the phenomena under investigation. In the first step, a scientometric analysis was conducted, as described in Section 2.1, to reveal the necessity of connecting the operator's performance with the construction equipment's productivity. This analysis involves the application of the "science mapping" method, which acts as both a descriptive and a diagnostic tool for research policy purposes, processing immense reservoirs of bibliometric data [20–24].

The second step justifies the criteria selection by looking into the relevant past studies that were extracted by the previous step (Section 2.2). It collects a great amount of related literature from the place where the criteria concerning operator performance are extracted and presented in a comprehensive list. Most importantly, in this section, each selected factor has been scrutinized, with a view to justifying every sub-criterion.

The above process is deemed as necessary in order to form a final criteria and subcriteria list, as key constituents for the AHP decision tree, presented in Section 3.2.

### *2.1. Scientometric Analysis*

This study goes deep into the published literature to reveal the void regarding the research made on the criteria that affect the construction equipment operators' performance. A scientometric analysis is used to objectively map the scientific knowledge on this specific field and to identify the research themes and the corresponding challenges based on the scientometric results, with the use of the VOSviewer application [20]. In order to create those scientometric networks, a four-step process was followed, as described in Figure 1.

**Figure 1.** Flowchart of map creation in VOSViewer.

In step one, the research framework is defined, with the intention of recognizing and setting the desired goals. At this point, an initial investigation is conducted to seek the necessary research components by separating the relevant from the irrelevant.

During step two, the articles were retrieved which were closely related to the examined topic. Those articles were extracted by well-recognized bibliographic databases, such as Web of Science and Scopus, covering a period from 2001 to 2021. To identify the relevant publications, search terms were used (Table 1). Figure 2 illustrates the evolution of the research made from 2001, where an increase from 2016 and onwards has been observed. Step three includes a comprehensive relevance assessment of the extracted documents in order to finalize the publications to be inserted for scientometric mapping into VOSViewer and to comment upon the extracted maps.


**Table 1.** Search Terms in Web of Science and Scopus.

The asterisk (\*) suggests that it can be replaced by any word or phrase.

**Figure 2.** Total Number of Publications Related to Operator Productivity and AHP.

The fourth step of the scientometric mapping process includes the extraction of the selected literature in a recognized form for processing by the VOSViewer application.

Its final product is the production of a comprehensive network comprising the terms which coexist inside the overall publications, where their linkage strength, their appearances, and their relativity are visible, weighted, and clustered. Different clusters are represented by different automatically assigned colors and each color designates a specific research area. The terms inside each cluster are represented by circles, and their size reflects the number of publications in which they were found. The spacing between those circles indicates their relatedness, and their degree of relativity is indicated by the thickness of the curved lines connecting them. The degrees of relatedness between words are indicated by the curved lines.

This paper presents two types of visualization of terms by the VOSViewer network: (a) text data co-occurrence among the titles and their abstracts and (b) keyword cooccurrence. Their visualization networks are presented in Figures 3 and 4, and the produced clusters by subject are in Tables 2 and 3, respectively.

**Figure 3.** VOSViewer map based on title and abstract text data.




**Table 3.** Keyword Co-occurrence Clustering.

**Figure 4.** VOSViewer map based on keywords (network visualization).

2.1.1. Text Data Co-Occurrence among the Titles and Their Abstracts

In this scientometric network, "ahp" constitutes a heavily weighted subject in the scientific community, presenting a significant proximity with the "decision making" term, as the AHP is a specific decision-making method. A strong proximity also exists between the "construction equipment" and the "decision making" terms, a fact that supports the application of the MCDA methods to a variety of the utilization aspects of construction equipment. Nevertheless, the "operator" or "productivity" terms are absent inside the network, while terms such as "maintenance" and "equipment selection" are orbiting and directly linked with the main reference terms of the AHP and decision making. This approach indicates a void in the literature with regard to the discussed topic.

Further scrutinization of the map leads to further implied conclusions:

