*3.2. Application of AHP*

The AHP was presented by Thomas L. Saaty as a new approach to dealing with complex economic, technological, and sociopolitical problems, which often involve a great deal of uncertainty [42]. It is a structured technique for analyzing Multi-Criteria Decision Analysis (MCDM) problems according to a pairwise comparison scale [41]. To deal with complexity, our mind must model it by creating a structure and providing observations, measurements, and judgements and hopefully, of course, rigorous analysis to study the influences of the various factors included in the model [43,44].

The AHP is an MCDA method, used by Nassar et al. [45] to measure the relative importance among a set of criteria, and it is suitable for this research due to its ability to compare tangible (subjective) and intangible (objective) factors.

In this study, the Transparent Choice tool for the AHP [46] was employed as it concentrates all the procedure into one tool. It also incorporates a function to produce and disseminate the questionnaires, according to the imported criteria. The operators' performance criteria were classified into levels and sublevels, forming the hierarchy model in Figure 6. Even though the main function of the AHP is to guide the final decision to a certain alternative option, this research takes advantage of the AHP's criteria-weighting function to hierarchize them based on their final weighting score. Based on the same

procedure utilized by Petroutsatou et al. [47], this process leads to one alternative, which is the best scoring criterion.

**Figure 6.** AHP decision tree model.

The model was imported to the Transparent Choice AHP platform; the questionnaires were created and distributed among the experts, who evaluated each criterion according to the AHP's fundamental evaluation scale. The number of evaluators who participated was 13, with different kinds of expertise in the construction sector, as allocated in Table 5. Special emphasis was given to the quality of the evaluators; this was based mostly on their expertise and not on their quantity. This fact does not affect the quality of the results as the AHP is a method with no specific statistical sample but is one that relies explicitly on the Consistency Ratio (CR), because in making paired comparisons, just as in thinking, people do not have the intrinsic logical ability to always be consistent [48]. Furthermore, this study does not constitute a polling exercise, as conducted by Tsafarakis et al. [49], where they exploited the capabilities of the AHP to investigate the preferences of individuals on public transport innovations using the Maximum Difference Scaling method.



The academia group includes professors of construction equipment disciplines with a vast experience in field engineering operations. Two experienced project managers were selected, due to their extended field work. Their perspective is based mostly on the project's performance indicators, which are directly affected by the performance of the operators they supervise. Construction equipment operators were chosen based on their experience in operating heavy earthwork machinery. Finally, representatives of OEMs, such as Caterpillar and JCB, were selected, representing the group of construction equipment owners.

The questionnaires were distributed to the above evaluators with the use of the Transparent Choice AHP Software, through its online survey application. The aggregated AHP results are presented in Table 6.


**Table 6.** Transparent Choice aggregated criteria weights.

The "local" column illustrates the sub-criteria (level 3) weighting in the context of each main (level 2) criterion. The "global" column illustrates each criterion or sub-criterion weighting in the context of the overall decision (level 1). The rankings for each group of evaluators are presented in Table 7.

**Table 7.** Transparent Choice evaluators weighting results comparison.



**Table 7.** *Cont.*

An additional examination was conducted to identify each evaluator's profile with regard to the main criteria ranking coming from their perspective. Table 8 illustrates the total scores for each main criterion in order to give a comparable form and to help extract further results.

**Table 8.** Main criteria ranking comparison.


## **4. Results**

*4.1. Cumulative Evaluation*

The questionnaires were answered with a view to prioritizing the criteria affecting the construction equipment operators' performance. Figure 7 illustrates the cumulative results by percentage. The operator's competence is the most influencing factor, with an overall score of 41%, while construction equipment and task follow with a score of 24% and 15%, respectively. According to the above results, construction companies or contractors should carefully select experienced and trained personnel in order to efficiently complete any construction project. Investing in further training for their operators could also be an option to leverage the overall productivity of their construction projects.

**Figure 7.** Cumulative results.

Each group of evaluators presented a different perspective, resulting in a different scoring for each criterion, as shown in Figure 8.

**Figure 8.** Criteria scoring for each group of evaluators.

The academia group presented similar results to the cumulative ones. The operator's competence was the most important criterion for all the groups of evaluators, with a different percentage in every group. The academia group and the construction equipment operators, for example, formed similar profiles, evaluating "operator's competence" as the most important criteria, with a total score of 41% and 51%, respectively. Construction equipment owners gave a total score of 38% for the operator's competence, 24% for construction equipment, and 18% for relationships—interaction. Project managers, on the other hand, evaluated the operator's competence with a total score of 26% and equipment and task with a total score of 25% and 24%, respectively.

The above analysis allows the formulation of each evaluator's different approach when dealing with earthwork operations. The operators consider the equipment as an extension of themselves, one which is totally dependent on their own skills and operating attitude. Consequently, their ability to efficiently handle the equipment improves the project's progression and the equipment's productivity. Thus, their competence is the dominant factor, with a direct effect on their performance. The academia group agrees too. The project managers score "relationships—interaction" at the lowest level among the criteria. The equipment owners rated the equipment operator's competence with the highest score.

#### *4.2. Sub-Criteria Evaluation*

According to Saaty [41], to make a decision we need to know the problem, the need and purpose of the decision, the criteria of the decision, the sub-criteria, the stakeholders and groups affected, and the alternative actions to take. In this study, where the AHP is used to hierarchize the criteria by their weighting score, the sub-criteria are used to expand the pairwise comparisons at a more in-depth level. In that way, the analysis gets to the root of the decision-making problem and becomes more precise and understandable. Based on Table 4, these sub-criteria comparisons are visualized and analyzed in the following sections.
