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Article

The Contribution of Workers’ Attributes on Sustainability of Construction Project Realization Goals—Survey on the Impact on Productivity in Croatia

by
Držislav Vidaković
*,
Marijana Hadzima-Nyarko
and
Saša Marenjak
Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, Croatia
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(23), 9946; https://doi.org/10.3390/su12239946
Submission received: 30 October 2020 / Revised: 20 November 2020 / Accepted: 24 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue Sustainability Issues in Civil Engineering and Management)
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Abstract

:
The work of workers is still dominant in the realization of most construction projects and therefore has a great impact on the productivity of contractors and, thereby, on the sustainability of realization of the goals of contractors and clients. This article provides an overview of the results of more than 100 studies from the previous decade on the impacts of factors related to the personal attributes of workers on the productivity and performance of construction projects. Based on the reviewed literature and semi-structured interviews with experienced professionals in the construction industry, the relation between worker characteristics and other influences on labor productivity is defined and a list of worker characteristics suitable for research in Croatia is compiled. A survey of 262 workers, engineers, and managers from construction industries of Croatia’s eastern region, Slavonia and Baranja, showed that the worker characteristics that most influence labor productivity are experience and dexterity. It was also noticed, with a high correlation between the different groups of respondents, that physical strength and morale and morality of the workers were rated as highly influential, while education level was rated as the least influential. The research results are useful because they indicate the possibility of significantly improving the productivity of construction contractors by selecting workers with appropriate physical abilities and cognitive and non-cognitive skills, as well as developing a suitable system for training and motivation of employees.

1. Introduction

Construction contractors must have good productivity to operate profitably, which means efficiently converting resources into marketable products [1]. The productivity of contractors has multiple impacts on their own business and the construction industry as a whole, as well as on other industries and the national economy [2]. At the construction projects level, improving the productivity of construction contractors reduces time and realization costs, allows for more competitive bids in job tenders, and creates the space to increase quality [3]. Productivity is positively correlated with the percentage of plan execution, i.e., the measure of changes in the work schedule [4], or, the sustainability of the initially set goals. Extending the realization deadline reduces the profit of construction contractors, and delays the possibility for clients to use the results of their investment and thus reduces their profitability [5]. Therefore, the subject of a large amount of research is the productivity of construction contractors and the factors that affect it.
Construction is a labor-intensive industry and there is, usually, a large share of labor in project realization, although this largely depends on the type of project (the different types of work it consists of) and the construction contractor (available technology). The execution of most construction operations and processes is based on the performance of working groups and the productivity of contractors is mostly related to this [6]. Therefore, the term productivity of construction contractors usually refers to labor productivity. Labor productivity in construction is usually defined as the ratio of output to inputs in production [7], i.e., the amount of work realized in a unit time [3,8] or its reciprocal value [1,9,10]. Labor productivity is one of the most frequently used performance indicators of the success of realization of a construction project [11]. Contractor productivity mainly depends on human effort and worker performance [12] and, according to research provided by Nasir et al. [13], construction contractors consider human resource management to be a high priority with respect to assessing project performance in Canada. Figure 1 highlights the characteristics of workers as one of the main groups that influences the achievement of project performance assessment goals, which are in the domain of the contractor.
The productivity of construction contractors is a dynamic category dependent on many factors, in connection with which certain corrective measures can be taken in a timely manner. In order to be able to manage productivity comprehensively and successfully, it is necessary to determine which characteristics and how these characteristics of workers significantly affect labor productivity.

2. Literature Review: Previous Research on the Influence of Labor Force Characteristics on Labor Productivity and the Performance of Construction Projects

2.1. The Influence of Labor Force on Achieving the Goals of Realization of Construction Projects

Most research on the impact on construction performance or time (delay) and cost of realization of construction projects consider some of the attributes of the labor force as important influencing factors. Based on past research papers, Soekiman et al. [14] compiled a list of 113 factors affecting construction labor productivity. Of the total number of factors, 18 factors are included in the group “labor”, and some of them are related to the personal attributes of workers. Zidane and Anderson [15] found out that, in 105 studies (1988–2017) from 46 countries worldwide, “poor labor productivity and shortening of skills” is 8th in the top ten universal delay factors (that factor was cited in 45% of studies). Asmi et al. [16] reviewed 12 studies (1996–2009) and found that “labor productivity” is the 5th to 7th factor of delay according to the frequency of appearance (in 42% of studies). Hesham et al. [17] reviewed 14 articles on research of causes of delay (2002–2016) and found that 50% of them include “unqualified workers” and 36% of them include “poor productivity of workers”. Abebe [18] reviewed 24 surveys of causes of construction delays in 17 African countries (2010–2020) and found out that “low productivity of labor” and “shortage of skilled labor” were second ranked factors (each in 54% of surveys), and “unqualified/inadequate experienced labor” was the sixth ranked factor (in 38% of studies).
In these studies, questionnaires are mostly used for data collection and evaluation. The assessment of impact factors is usually from 1 to 5, and the rank can be determined according to mean score (MS), relative importance index (RII), average index (AI), important weight (IW), severity index (SI), frequency index (FI), important index (IMP.I), etc. (The same factors are sometimes called differently in different studies).
Table 1 highlights the labor attributes, which in surveys in 41 different countries and parts of countries published in the last decade, are ranked as factors that affect the performance of construction projects (P), i.e., their delay (time overrun—T) and cost (C), with minimal uniformity of originally used terms. In order to better identify the significance and ratio of the magnitudes of individual factors, Table 1 contains, for each survey, the data of the respondents (project manager—Pm, project designer—Pd, clients—Cl, consultants—Cns, and contractors—Cnt), on the total number of evaluated factors (in relation to contractors) and the rank since they have different perceptions of the magnitude of the influence of individual factors.
In 65.5% of the research in Table 1, “labor productivity”, which results from several attributes of workers, is included among the influential factors. In 56.4% of the research, the impact of skills/qualifications of the workforce was evaluated (in seven studies “experience” is also stated in addition to “skills”), and in 20.0% “morale/morality” (independently or with motivation). The established rank of the importance of labor productivity and the attributes of workers differ significantly, so, for example, “labor productivity” in the reviewed studies is ranked in the range of 1st to 48th place.
Looking at the results of the studies, it can be seen that the impact of “labor productivity” on the performance of construction projects is greater in cases where:
  • there are no other problems that interfere with the performance of works, or where there is good organization of performance (no shortages of materials, equipment, equipment failures, etc.),
  • there are no problems with working conditions (extremely negative environmental impacts),
  • there are no changes to the project during execution,
  • there are no problems with the delay of payment for work performed, and
  • there are no other problems with the investor or consultants.

2.2. Characteristics of Workers Affecting the Construction Productivity

Inconsistency of labor productivity is a severe problem in many countries. Therefore, labor productivity and factors affecting productivity has become one of the most regular topics for researchers [1,2,71,72,73,74,75,76].
Borcherding [77] states that selection of workers is one of the factors that most influences productivity in large construction projects. Based on surveys of leading construction companies in Singapore, Alum and Lim [78] identified that hiring suitable workers is among the three most important factors affecting productivity. A survey by Mojahed and Aghazadeh [79] reveals that the skills and experiences of the workforce are the most influential factors for construction productivity on wastewater treatment plant projects in southern United States. Huang et al. [80] reviewed the literature and found that skilled labor availability is the most common influencing factor for productivity in U.S. construction. According to research by Inuwa [81] in Nigeria, adequate and competent workforce is a top-ranked measure for enhancing contractors’ project planning.
In studies of the impact on productivity and performance of construction projects, factors are often divided into groups called “people”, “workforce”, “labor characteristics”, etc. These groups include a number of attributes of workers and various other influences related to human resources (there is no uniformity in their definition). A comparative presentation of the relationship between the determined values of direct and indirect influences of the attributes of construction workers on labor productivity according to four research studies is given in Table 2 (research [76,82] shows only skill aspect factors).
In a study of negative factors impacting productivity on construction sites in Croatia 35 years ago, Oštarijaš [85] singled out 9 basic factors, among which are poor characteristics of workforce—insufficient training of workers, alcohol consumption on construction sites, and smoking and rest at unforeseen times. According to the survey in the Czech Republic [86], the productivity of small construction companies is mostly influenced by training and experience of the workforce, while in medium and large companies it is the 5th–8th factor (out of 17 factors).
Based on the scoring of results of 47 studies in 28 countries during the period 1983–2019, Hamza et al. [87] found that “worker efficiency/skills training” and “effectiveness/experience” of workers are 4th and 5th ranked factors according to construction labor productivity, while “personal problems” are 32nd–33rd ranked factors (out of 34 analyzed factors). Although the included studies examined the effects of different factors, some of them did not consider the characteristics of the workers at all.
In Table 3, the characteristics of different types of construction workers (craftsmen, auxiliary workers on site, equipment operators, and others) have been evaluated in research on the impact on construction productivity in the period from 2012 to 2020 in 44 different countries, regions within countries, and groups of countries on five continents. Part of the conducted research defines as an influential factor one or more characteristics of workers, while part of the research considers as an influential factor the absence of some of these characteristics (e.g., lack of skills, lack of training or inadequate training, poor health, etc.).
Table 4 provides data on research that examined the impact of worker characteristics (listed in Table 3) on productivity and their impact rank, as well as the size of the impact index, i.e., indicator.
It can be seen that the studies involving factors affecting productivity often include those attributes of labor that have been considered in other studies (in Table 1) and that affect performance and delays and cost overrun in construction projects as to the goals of realization of construction projects. Table 1, Table 2 and Table 3 and Table 5 confirm the finding of previous literature reviews [141] that research differs in the structure of the study (surveys) and in evaluation of the importance of influencing factors.
According to 55 studies included in Table 4, “skills” and “experience” were the most frequently evaluated characteristics of workers and were rated as the most important in terms of impact on productivity. The number of considerations and the assessed importance for the most significant characteristics in the studies from the Table 4 are presented in Figure 2. The importance was determined by the score for the ten first-ranked factors in each study (so the first-ranked received 10 points and the tenth-ranked received 1 point) divided by the maximum possible score, i.e., 55 × 10 (e.g., for “skills” 190/550 × 100 = 36.2%). Frequency percentage was calculated as the ratio of the number of studies in which a certain characteristic is evaluated (regardless of rank) and the total number of studies reviewed (e.g., for “experience” 34/55 × 100 = 61.8%, for “skills” 32/55 = 58.2%, etc.).
The Mechanical Contractors Association of America has published over the years a number of factors that affect labor productivity in the U.S., and since the 1970s their list of “factors affecting productivity” includes “morale” [142]. In the analyzed studies, morale is considered more often in studies of impact on time and performance than in studies of the impact on productivity. Horner and Witehead [82] define morale as a separate factor influencing labor productivity, and do not show how much more morale influences worker skills compared with the influences of other worker characteristics (listed in Table 2). According to [82], morale is rated in terms of good humor, cooperative attitude, pride in work, good timekeeping, and complaints, all equally weighted. Such an interpretation is closely related to management action and employee motivation. In addition to denoting the level of enthusiasm, morale (in Croatia) is also defined as a set of social norms associated with behavior according to the principles of honesty and integrity, i.e., morality. A number of studies on impacts on labor productivity do not directly mention worker morality, but moral-related traits (e.g., integrity, theft, and general behavior).
Quite a number of research papers have identified the importance of some other factors that are closely related to the personal attributes of workers as influential on the performance of construction projects and influences on labor productivity. However, these factors are strongly influenced by company management, social environment, and the labor market in various ways. These factors, and corresponding research that evaluated their impact, are listed in Table 5.
Absence from work and late arrival, early departure, and frequent unnecessary breaks outside the planned schedule significantly depend on discipline, work habits, competition and morale of workers, as well as on supervision and motivation (reward or punishment) by the management of contractor.
Many studies have identified motivation as a factor related to the workforce with a significant impact on productivity. However, it has not been considered in this study because, although it depends on the needs and preferences of individuals [163,164], apart from intrinsic motivation, motivation of workers primarily depends on competent management. (Thus, for example, the same worker may be motivated and productive in one company and not in another).

3. Investigation of the Influence Attributes of Construction Workers on Productivity in Croatia

3.1. Objectives, Scope and Methodology of the Research

The main objectives of this research include the following:
  • To identify the influence of construction worker attributes on contractors’ labor productivity in Eastern Croatia (i.e., Slavonia and Baranja region).
  • To determine the relationship between worker attributes and other factors that affect productivity.
  • To quantify the influence of the possession and absence of important characteristics of workers on labor productivity in Slavonia and Baranja.
  • To test the relevance of the assessed importance of worker characteristics by comparing the rankings of the characteristics between two different groups of respondents.
  • To identify general actions that can be taken to influence the considered attributes of workers in order to increase contractor productivity.
The research was performed through the steps shown in Figure 3.

3.2. Defining the Attributes of Workers to Examine the Influence on Labor Productivity

Based on the literature review, a list of worker characteristics that affect productivity was compiled. This list was further reviewed and updated through semi-structured interviews with seven civil engineers and one technician, who had more than 25 years of experience working with construction workers. Positive and negative impacts on the productivity of construction contractors resulting from a possession of higher level of labor characteristics or lack of labor characteristics (along with the action of other factors) selected for the survey were defined. This is shown in Table 6.
For the analysis of various influential characteristics, an Ishikawa chart was compiled with a focus on the influence of the labor on the productivity of construction contractors (Figure 4).
The consulted experts in Croatian practices emphasized the importance of independence in the work of construction workers, with additional desirable properties being the willingness to take initiative and responsibility. This is enabled by technical skills that arise from professional knowledge, i.e., training and experience.
For this research, workers’ attributes that were comparable to previous surveys and that were not interdependent were selected. Physical strength was taken as one of the examined characteristics because a recent analysis by Karthik and Rao [165] showed a significant correlation between physical strength and productivity of construction labor. However, the age and health of the workers were not taken into consideration, even though physical strength is dependent on these parameters. Fatigue is related to the physical characteristics of workers, but it largely depends on the technology and organization of work (rest schedule, etc.), and therefore has not been examined in the survey. Some of the workers’ attributes mentioned in the literature (surveys included in Table 1, Table 2, Table 3 and Table 4) were not included in this research in Croatia for the reasons listed in Table 7.
After the pilot survey of 20 construction workers and engineers, 3 characteristics of the workers whose influence was assessed to be the least were excluded from further research—“creativity”, “adaptability” (to new conditions/situations at work), and “affinity for teamwork”. Furthermore, these three characteristics cannot be compared with the results of the research in Table 2. “Affinity for teamwork” and “adaptability” on average had values similar to some of the left out characteristics, so they can be taken into consideration again in future research.
Through the survey, it was possible for the respondents to suggest characteristics that were not listed, but that they considered to be important for labor productivity. None of the proposals were accepted since they were not justifiable. This fact, together with the high marks obtained for the impact of the examined characteristics, shows that the examination covered all important attributes of workers.

3.3. Ranking the Influence of Workers’ Characteristics by Survey

Data collection was performed using survey questionnaires that were given directly to the respondents (face to face). In the survey, grades 1 to 5 (Likert scale) were used to assess in parallel the positive impact of personal characteristics of workers on labor productivity and the negative impact in the case that these characteristics are weak or absent. (This is because some characteristics can have a very positive effect on productivity, but if their absence can be well compensated, they do not necessarily have the same negative effect). The collected responses were based on respondents’ knowledge and experience and were not related to any particular building project.
The magnitude and rank of the impact were determined by the RII method, according to the equation [8,30,78,133]:
RII   =   5 n 1 + 4 n 2 + 3 n 3 + 2 n 4 + n 5 5   ( n 1 + n 2 + n 3 + n 4 + n 5 )
where:
n1—the number of respondents who answered “very strong influence” (rated with 5),
n2—the number of respondents who answered “strong influence” (rated with 4),
n3—the number of respondents who answered “medium influence” (rated with 3),
n4—the number of respondents who answered “weak influence” (rated with 2), and
n5—the number of respondents who answered “very weak influence” (rated with 1).
Impact assessments were collected by examining 290 participants in the implementation of construction projects, employed in 60 companies for construction and supervision of construction projects in Slavonia and Baranja. A total of 28 completed questionnaires were rejected due to incorrectly completed questionnaires and inadequate profession and very few years of practice. Finally, a total of 262 completed questionnaires were analyzed.
Considering the 12,300 employees in the construction industry in Slavonia and Baranja (about 13% of construction workers in the whole of Croatia) [166], 262 answered questionnaires provide a confidence interval of 6%.
Correlation and descriptive research require at least 30 respondents in each group [167], so for the analysis, the answers of the respondents were divided according to the work they perform:
-
Group A—supervising engineers and management of construction sites and construction companies and crafts,
  • A1—supervising engineers (authorized supervising and members of the supervisory team), and
  • A2—construction site management (headman and other engineers and construction technicians), management and owners of construction companies and crafts.
-
Group B—workers at the construction site,
  • B1—gang foreman and workers of various construction occupations (masons, carpenters, reinforcement workers, roofers, painters, and installers),
  • B2—site mechanization operators and drivers in construction companies, and
  • B3—auxiliary workers on the construction site (only with 5 and more years of practice in construction).
Figure 5 shows the percentages of respondents according to the jobs they were engaged in and their average years of experience in construction practice. The ratio of respondents from group A and group B was 37% to 63%. Supervising engineers had the most years of work in construction practice on average, and auxiliary workers the least.

3.4. Results of Surveys

The survey results for all groups of respondents are processed in Table 8. The magnitudes of the positive and negative effects of the examined characteristics of workers on productivity according to the assessments of all respondents are shown in Figure 6.
According to RII values, the positive impact on productivity of “experience in construction” and “dexterity” is very high (RII > 0.80). All other assessed influences were high (0.60 ≤ RII < 0.80). The order of magnitude of the positive impact of good worker traits on labor productivity is similar to the order of magnitude of the negative impact on productivity if those traits are lacking. Spearman’s rank correlation coefficient between positive and negative influence is rs’ = 0.8, (a very good correlation between the rank of positive and negative action is when rs’ > 0.76 [168].) On average, according to all respondents, the positive impact of possession of all assessed characteristics is greater than the negative impact due to their absence (from 16.4% to 40.2%). The most pronounced difference is the magnitude of the positive and negative impact of the worker’s experience, which shows that respondents believe that the lack of experience can be compensated in some way (such as including an experienced worker next to inexperienced workers). The smallest difference between a positive impact and a negative impact is related to workers morale/morality (since the lack of this characteristic cannot be compensated).
Table 8 shows that all groups of respondents also rank the first two characteristics according to the positive impact on productivity, and only supervising engineers do not rate school education as having the weakest impact. The degree of agreement in ranking the impact of worker characteristics by different groups of respondents was also expressed using Spearman’s rank correlation. All groups of workers (B1, B2, and B3) also ranked all characteristics of workers according to the positive impact (rs’ = 1.0). When evaluating the positive impact of the characteristics of workers from group A and group B (the same order was obtained for the whole group A as for group A2 because there were three times more respondents in group A2 than in A1), rs’ = 0.9, which means that there was an excellent correlation, i.e., agreement. The rank of negative influence due to the absence of evaluated characteristics from group A (same for A2) and B was rs’ = 0.55, which means that there was a medium-strong and positive relationship (rs’ > 0.5 [168]) between the grades of these two groups of respondents. Due to this agreement in ranking by different groups of respondents, the results of this study can be considered reliable.
The overall assessment of the consistency of the opinions of the respondents of all groups was determined using the Kendall coefficient. A maximum of 1 means complete disagreement, and 0 means that there is a complete match [168]. A value of 0.11 for this coefficient was obtained for the answers on the positive impact of worker traits, and 0.05 for the negative impacts of the lack of traits. These values indicate a high level of agreement of all respondents on the impact on labor productivity.

4. Discussion on Research Results and Improvement of Important Worker Attributes

4.1. Comparison of Research Results in Croatia and in Previous Research

In the Croatian regions of Slavonia and Baranja, the “experience” of construction workers was considered the most important factor for labor productivity, similar to what was obtained in the studies of Horner and Witehead [82] in the UK, Tammy et al. [76] in Malaysia, and Jain et al. [84] in India. A study by Lim [83] in Singapore showed that “good habits and work practices” have a second-ranked impact. In this research, “sense of responsibility”, which is related to employee morale, was rated the most important for improving productivity, and the positive impact of morale/morality was ranked fourth (very close to the third ranked impact) in research in Slavonia and Baranja. In this research, the impact of “physical strength” was rated one rank lower than in the research of Horner and Witehead [82]. However, in the UK, “dexterity” was rated the least important for workers’ skills and labor productivity, and in Slavonia and Baranja its positive impact was ranked as second, while for the lack of worker skills, respondents considered it to have the most negative impact. It can be assumed that the reason for this is the greater need for improvisation due to the presence of poorer equipment on construction sites in Croatia than equipment in the UK.
From the workers’ characteristics in Slavonia and Baranja, “school education” has the least impact on labor productivity (the positive impact of experience is 44% higher), as in the research of Tammy et al. [76] (in which experience was rated close to 30% more important) and Horner and Witehead [82] (in which experience was rated 60% more important). Tammy et al. (2019) have especially emphasized the greater importance of “learning and training at work” rather than “education level” [76]. The lack of skills in Slavonia and Baranja was rated 39% more negative for productivity than the lack of school education.

4.2. The Possibility of Improving Contractor Productivity through Labor Force Attributes

The results of this research in Croatia indicate the priorities of certain workers’ characteristics that need to be addressed in order to increase productivity. In order to identify possible ways to improve productivity through the labor force attributes, it is necessary to determine which attributes directly and indirectly affect productivity and which factors affect these attributes. The ability of workers consists of mental and physical activities and knowledge [169]. Figure 7 defines the flow of action and impact on the ability and willingness to work of workers, both of which, together, determine labor productivity [170].
The employers cannot significantly influence some attributes of workers, but they should certainly take them into account when hiring and choosing workers for a particular job. In addition, it is possible for contractor management to act through:
-
Leadership, appropriate supervision of workers, organization of work on the construction site (conditions for work and rest), incentive programs and development of the organization’s culture;
-
On-the-job training (with incentives to acquire new competencies) and an internal mentoring system for under-trained workers; and
-
Recognizing and retaining existing quality, experienced workers, and attracting young, quality workers.
The employer often does not have much choice when hiring construction workers, but it is important to recognize those workers who have desirable characteristics while working with employees and try to encourage them to transfer those characteristics to other workers. A continuous and consistent business policy with personalized measures is needed to maintain employee satisfaction, at least to the extent of satisfying their extrinsic (hygienic) motivational factors (i.e., avoiding dissatisfaction that would prevent workers from working within their capabilities or seeking another employer [171]).
In addition to technical skills, workers need to consider their cognitive (intelligence) and non-cognitive skills (such as personal traits and emotional stability [172]), which affect the willingness to work and thus can have a major indirect impact on labor productivity. (Non-cognitive skills can influence the use of cognitive skills [173].)
While the level of training and exercise, and especially experience, cannot be improved quickly, motivation of the worker can usually be raised very quickly. However, measures for external motivation are usually of temporary duration, i.e., until the individual’s needs are met and thus the motivational cycle is completed [174]. To be effective, the way of motivating should be adapted to the psychological characteristics (preferences) of employees and their needs, because the key motivators of one compared to another worker may differ [175]. In addition, motivation is significantly influenced by work-related perceptions and the work environment [176].
Previous research around the world shows the magnitude and frequency of the impact of skills and training of workers, and the importance of training on labor productivity has been confirmed by this research in Croatia. The globally emphasized problem of lack of training (Table 5) and satisfactorily skilled workers grows with the aging of the existing labor (which is very pronounced in Croatia [177], but also in other countries [80]).
All of the above-mentioned points to the importance of investing in the training of workers, both through education and compulsory work. The need for in-service training is emphasized by the deficit of trained construction workers and the frequent arrival in the construction industry of unskilled workers or workers from other professions who need to be retrained. Due to the introduction of new work technologies, lifelong education of workers is needed.
There seems to be a consensus on the lack of skills among construction workers [178], but there is no universal answer as to how best to improve their skills [170]. By achieving increased productivity and reductions in turnover, absenteeism, and rework due to mistakes, investments in employee training return to the employer multiple times [117,178,179].
The significantly weaker impact of school education of workers than professional experience on productivity determined by this research indicates the weaknesses of the school system of education of craft workers in Croatia. The slightest negative impact of school education deficiencies on productivity suggests that workers’ skills can be successfully improved with experience and through on-the-job training.
Short training programs or daily courses and seminars in educational institutions with the support of the local construction industry, professional societies, and state authorities are recommended [180].
Fayek et al. [181], Hewage and Ruwanpura [182], Enshassi et al. [183], and Ntuli and Allopi [178] highlight internal mentoring and skills acquisition with internal supervision as the best way to train workers. In addition to basic knowledge, through work in practice, workers also acquire job competencies such as workplace culture, work norms, and values in the organization in which they work. Unskilled workers working on a construction site with good craftsmen can acquire the technical skills of skilled workers in 5 to 10 years [117] and the ability to work independently. As a rule, the acquisition of more skills increases workers’ incomes, so they are more satisfied with their work [80] and more motivated to work.
Some of the desirable traits are acquired over time or are lost, and some are immanent to the individual (e.g., dexterity). As a rule, as age increases, so does experience, and physical strength decreases. Although the health of workers has not been included in the survey in Croatia, attention should be paid to the health of workers because it is associated with strength, fatigue, and absenteeism. This is primarily possible through working conditions (appropriate equipment, duration of work) and rests (timely, duration in accordance with the type of work and in good conditions) during the work shift.

5. Conclusions

Labor is one of the most important resources of construction contractors. Increasing its productivity means achieving better utilization of labor, which is increasingly scarce and relatively expensive.
Numerous research papers show the impact of different attributes of workers on the productivity of contractors and on the performance of construction projects, but the approach to examining the impact of these characteristics is very diverse (partly justified by regional specifics) and often lacking in previous analysis of interrelationships. Studies dealing with labor productivity do not look into enough detail with respect to differences in worker abilities.
The research conducted in this paper in Slavonia and Baranja defined the direct and indirect influence of important workers’ characteristics on productivity. The survey found that “experience in construction” and “dexterity” have a very strong positive impact on productivity, and “strength”, “morale” and “school education” have a strong impact. In the absence of these characteristics, all except the “school education” have a strong negative impact. This means that none of these characteristics should be ignored and neglected.
This research provides information that can be used in the implementation of measures to properly address issues related to poor productivity on construction projects, as well as the sustainability of the set goals for their realization. Furthermore, the obtained results can be useful for future research on the issue of human resources in construction, in a wider area than the region in which the survey was conducted.
There are several directions for future research. One possible direction would be to monitor the achieved productivity of workers with different characteristics in order to quantify the impact of characteristics on productivity, and to see whether and how much productivity increases with the improvement of worker characteristics. In doing so, one would need to be cautious with regard to the number of other possible factors impacting productivity. Another direction of further research would be to examine the interdependence of individual characteristics of workers, compared to this study where characteristics that were not related were deliberately chosen. In this study, the importance of the researched characteristics of workers was confirmed through their rankings in previous research. However, it is also possible to survey the frequency of deficiencies of these characteristics in the labor force in Croatia.
The workers’ characteristics that significantly affect productivity should be kept in mind when managing human resources in the company and on the construction site. Formal education, age, and years of experience are easy to determine, but both cognitive and non-cognitive skills of workers need to be considered, which can also be of influence. The importance of attracting and retaining workers in the company with desirable characteristics and programs for training workers in addition to work and the culture of knowledge transfer in the company were emphasized.
The whole country benefits from increased productivity in construction, so it is justified for the government to encourage the attraction of young labor in construction occupations and to raise the effectiveness of their education.

Author Contributions

Conceptualization, D.V.; methodology, D.V. and S.M.; validation, D.V. and M.H.-N.; formal analysis, D.V.; investigation, D.V.; writing—original draft preparation, D.V. and M.H.-N.; writing—review and editing, D.V. and M.H.-N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. The chain of influence on productivity during the implementation of construction projects.
Figure 1. The chain of influence on productivity during the implementation of construction projects.
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Figure 2. Frequency and importance of attributes of labor determined in all studies from Table 4.
Figure 2. Frequency and importance of attributes of labor determined in all studies from Table 4.
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Figure 3. Flow chart of research in Croatian regions Slavonia and Baranja.
Figure 3. Flow chart of research in Croatian regions Slavonia and Baranja.
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Figure 4. Workforce on Ishikawa chart of effects on contractor’s productivity.
Figure 4. Workforce on Ishikawa chart of effects on contractor’s productivity.
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Figure 5. The percentages of groups of respondents and the average years of experience in construction.
Figure 5. The percentages of groups of respondents and the average years of experience in construction.
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Figure 6. Characteristics of workers ranked by the magnitude of the impact on labor productivity according to the opinion of all respondents.
Figure 6. Characteristics of workers ranked by the magnitude of the impact on labor productivity according to the opinion of all respondents.
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Figure 7. The order-related effects on labor productivity.
Figure 7. The order-related effects on labor productivity.
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Table
Table 1. Influence of worker characteristics on the performance and goals of construction projects according to 55 chronologically arranged research papers. Construction projects—P, time overrun—T, cost—C, project manager—Pm, project designer—Pd, clients—Cl, consultants—Cns, contractors—Cnt.
Table 1. Influence of worker characteristics on the performance and goals of construction projects according to 55 chronologically arranged research papers. Construction projects—P, time overrun—T, cost—C, project manager—Pm, project designer—Pd, clients—Cl, consultants—Cns, contractors—Cnt.
Reference & Area of ResearchNumber and Structure of RespondentsNo. of FactorsAffect onRank Factor (Related to Labor) Which Affects Realization of the Construction Project
[19], Malaysia15 construction experts in Cl, Cns, and Cnt59C28–30. Labor productivity
[20] Central regions of Malaysia140 Cnt (46%), Cns (38%), and Cl (16%)8T&C6–7. Labor resource
[21], UK44 Cns (43%), Cnt (30%), and Cl (27%)45T18./22./23. Low skill of manpower—according to Cl/-according to Cnt/-according to Cns
[21], Libya72 Cl (39%), Cnt (33%), and Cns (28%)45T2./10. Low skill of manpower—to Cl/-to Cns and Cnt
[22], West. Australia32 Cns (53%), Cnt (34%), and Cl (13%)48T1. Shortage of skills
[23], Afghanistan60 Cl, Cns, and Cnt83T4. Unqualified workforce—according to Cl
[24], Malawi45 managers and engineers in Cnt (44%), Cl—Malawi Roads (29%), and Cns (27%)72T26. Low productivity of labor
37. Unqualified workforce
[25], Egypt2500 managers and engineers in 400 Cnt and Cns99T19. Unqualified/inadequately experienced labor
32. Low productivity of labor
85. Low motivation and morale
[26], Turkey64 managers, engineers, site supervisor, technician, and architects83T7–8. Unqualified/Inexperienced workers
31. Low worker productivity
52. Low worker motivation and morale
[27], Albania26 Pd (50%), and managers & engineers (50%) in Cns, Cnt, and Cl27C17–18. Labor productivity
[28], Jordan30 engineers in Cl—Ministry of public works and Cnt37T8–10. Presence of unskilled labors
[29], Jordan145 Cns (37%), Cl (32%), and Cnt (31%)45T25. Presence of unskilled labors
[30], Kuwait162 senior managers in Cnt45C19. Low labor productivity
[31], Egypt33 Cl, Cns, and Cnt organizations43T4. Low productivity of labors
10. Unqualified workforce
[32], Kampala, Uganda52 participants in the realization of construction projects27T12. Unqualified/inadequate experience
13. Low motivation and morale of labor
14. Low productivity of labor
[33], Hanoi, Vietnam51 graduate and postgraduate respondents in Cnt25T11–12. Workers’ awareness
13–14. Low skilled workers
[34], Tamil Nadu, India60 engineers in private sector (50%) and in government (50%)54C3./4. Unqualified/inadequate experienced
labor—District engineers/-Private engineers
[35], Tamil Nadu, India100 engineers in the private sector (50%) and in the government sector (50%)40T-Low productivity of labor (All outside top ten)
-Unqualified/inadequately experienced labor
-Low motivation and morale of labor
[36], Sri Lanka58 building construction projects44T7. Low productivity of labors
[37], Jordan106 Cl (36%), Cnt (34%) and Cns (30%)49C18. Low productivity of labor
[38], Iraq134 Cnt (34%), Cns (34%), and Cl (32%)65T15. Poor manpower productivity—to Cnt
[39], Botswana39 Pm and team member and others from Depart. of Build. and Engineer. Services77T&C17. Low productivity of labor
30–31. Unqualified/inadequate skill labor
35. Low motivation and morale of labor
[40], Slovenia62 Cl, Cnt and engineers, Pd and managers12T10. Unqualified workforce
[41], Poland927 Cl18T2. Quality of workforce
[42] Mecca, S. Arabia28 Cnt (75%), Cns (14%), and Pm (11%)81T1–2. Low productivity of labor
6. Unqualified workforce
[43], Qatar179 Cnt & Subcontractors (46%), Cl (30%), Cns (17%), and others42T1./7. Low productivity of labor—to FI/-RII
5./8. Unqualified workforce—to FI/-RII
[44], Kuwait22 engineers in Cnt, Cns and Cl40T28. Low productivity levels of general labor or qualified trade personnel
[16], Jakarta, Indonesia88 Cnt (64%), Cns (18%), Developer (9%), and Cl (9%)57T9. Labor productivity
[45], South Gujarat, India80 site supervisor, project engineer and Cnt54C20–21. Unskilled labor
[46], Jhansi area, IndiaNumber and type of respondents unknown58T3. Thefts on site
7. Low productivity of labors
[47], IndiaCnt, Cns, and public Cl
(number of respondents unknown)		24T&C5. Unskilled operators
7. Low productivity of labor
[48], IndiaCns and Cnt (number of respondents unknown)78T17. Low productivity of labor
23. Unskillful equipment operator
[49], Afghanistan30 related ministries30T18. Low productivity of labors
20. Low motivation and morale of labor
[50], Ethiopia51 Cnt (35%), Cl (35%), and Cns (30%)88T8. Low productivity of labor
9. Unqualified/inadequately experienced labor
[51], Western Ghana99 construction professionals34T17–18. Unqualified workforce
[52], Lagos Alure, Ondo state, Nigeria60 engineers, quantity surveyors and architects in Cl (42%), Cns (33%), and Cnt (25%)30T13. Labor productivity
[53] Lagos Mega-city, Nigeria86 architects, engineers, builders, and quantity surveyors in Cnt32T7. Labor productivity
30. Low motivation and morale
[54], Ethiopia77 experienced participants (type of respondents unknown)52T12. Unqualified/inexperienced workers
37. Low productivity
39. Less motivation and morale
52. Discipline problem
[55], South Africa75 Cns (37%), Cnt (36%), and Cl (27%)48T18/25. Low productivity of labors
[56], Pakistan96 practitioners involved in construction industry42T16. Poor skill labor
[57], Afghanistan51 Cnt (45%), Cl (35%), and Cns (20%)69C-Low productivity of labor (outside top 10)
[58], Yemen22 Pm in Cnt (54%), Cns (32%), and Cl (14%)55P3. Low labor productivity
[59], Iran175 Cl (36%), Cnt (33%), and Cns (31%)78T7–9. Unqualified workforce
18–19. Low productivity of labors
[60], Edirne, Turkey34 architects, builders, civil engineers, and other in Cnt and Cl54T8. Unskilled and/or unqualified labors
[61], Egypt67 Cnt (40%), Cns (33%), and Cl (27%)26T1. Labor productivity
3. Motivation and morale of labor
[62], UAE194 managers and engineersUnknownC1. Poor productivity
[63], Morocco330 Cnt (57%) and Cns (43%)49T10. Unskilled workforce
19. Low productivity of workers
[64], Bangkok & Vicinity, Thailand345 Pm, engineers, and executives in construction companies57P20. Workers’ Moral
31. Labor productivity
36. Teamwork
[65], Central Sulawesi, Indonesia38 section chiefs, work unit heads, commitment officers, assistants, and field supervisor coordinators36T-Low labor productivity (all outside top 3 factors)
-Low mentality and morality
[17], Jordan113 Cl (54%), Cns (27%), and Cnt (19%)56T43. Unqualified workers
48. Poor productivity of workers
[66], Shan-Ewadh, India40 Cnt, engineers, different departments81T&C3. Unskilled labors/labor strikes
[67], South Korea33 BIM Cns (33%), Cl (18%), Cnt (15%), architects (15%), managers (12%), and others34T15. Low labor productivity
[68], South western part of Nigeria78 engineers (46%), quantity surveyor (23%), architects (19%), and builders (12%)12C4./7–8. Low productivity of labor—Tendering
methods selective/-Competitive open
[69] Abuja, Lagos & Portharcourt, Nigeria129 Cnt & Subcontractors (47%), Site-engineers (19%), Cns (17%), Pm (9%), and Cl (8%)20C10. Low skilled manpower
[70] Portugal94 Cnt (56%), Cns (32%), and Cl (12%)47T10. Poor labor productivity and shortage of skills
Table
Table 2. Comparison of the extent of influence of workers’ attributes researched in four previous papers (the mentioned ratios of magnitude influence were calculated from the data in published studies).
Table 2. Comparison of the extent of influence of workers’ attributes researched in four previous papers (the mentioned ratios of magnitude influence were calculated from the data in published studies).
ResearchWorker AttributesExperience (and knowledge *)Good Habits and Work PracticesLearning at WorkTraining (at Work*)Education LevelAgeStrengthDexterityPossibility of Independent WorkSense of Responsibility & Willingness to Accept the ResponsibilityWillingness to Improve Work AttitudesInitiativePride and Interest in WorkCommitment to the Performance of TaskPersonal IssuesMisunderstanding Among WorkersCo-operation with ManagementAbsenteeism
[82], UK1.0 0.63 0.750.50
[83], Singapore 0.95 0.86 1.00.940.840.90 0.92
[76], Malaysia *1.0 0.890.820.77 0.940.93 0.97
[84], India1.0 0.83 0.500.65 0.96
* Only skill aspect factors (the influence of the group of factors “communication aspect” and “work culture aspect” was evaluated especially and “good interpersonal relationship between the workers” was ranked first, while “sharing knowledge and work information” and “attitude and behavior of people at the construction site” was ranked second).
Table
Table 3. Attributes of construction workers that affect productivity evaluated in previous research.
Table 3. Attributes of construction workers that affect productivity evaluated in previous research.
Labor (Worker/Craftsmen) Characteristics as Factors Influencing ProductivityLabel
Skills (level)F1
Quality of work/performance of laborF2
Occupational education level/training (technical knowledge)F3
Experience (level)F4
AgeF5
GenderF6
HealthF7
Strength and physical (fitness/ability)F8
Physical fatigueF9
AlcoholismF10
Addiction/drug abuse/similar problemsF11
Personal issues (family problems, poor economic condition, etc.)F12
LoyaltyF13
Job satisfactionF14
Workers’ pride in their workF15
HabitsF16
DisciplineF17
LazinessF18
CommitmentF19
InitiativeF20
Team spirit among workersF21
Labor moraleF22
Worker’s integrityF23
Psychophysiological abilityF24
Ability to adapt to changes and new environmentF25
Sense of observance of regulationsF26
Table
Table 4. Ranking of the influence of the attributes of construction workers on productivity in the implementation of different types of construction projects in 55 chronologically arranged research. Construction projects—P, time overrun—T, cost—C, project manager—Pm, project designer—Pd, clients—Cl, consultants—Cns, contractors—Cnt.
Table 4. Ranking of the influence of the attributes of construction workers on productivity in the implementation of different types of construction projects in 55 chronologically arranged research. Construction projects—P, time overrun—T, cost—C, project manager—Pm, project designer—Pd, clients—Cl, consultants—Cns, contractors—Cnt.
Reference & Area of ResearchNumber and Structure of RespondentsNo. of FactorsInfluence of Labor Force Characteristics on Construction Productivity
[88], USA28 project engineer (39%), Pm (14%), architect (11%), and others (36%)4012. F4 (RII2 = 488.75); 30. F10 (RII2 = 425.50); 34. F5 (RII2 = 408.25); 39. F13 (RII2 = 373.75); 40. F12 (RII2 = 368.00)
[89], Iran82 biggest construction companies313. F1 and F4
[90], Malaysia37 managers, engineers, and others in Cnt53. F1 and F3 (MS = 3.95)
[91], Lagos, Nigeria37 architects, builders, civil engineers from const. comp.161. F4 (MS = 4.38)
14. F5 (MS = 3.25)
[92] South Gujarat, India51 Cnt273. F1
7. F9
[8], Kerale, India185 site engineers/supervisors (49%), craftsmen (32%), and Pm (19%)4413. F4 (SI = 0.32); 23. F21 (SI = 0.30)
[93], Chambal Region, India37 professionals on the project from management to execution level455. F9 (RII = 72.43%)
9. F1 (RII = 69.72%)
[94], Spanish376 different employees of construction companies355. F1 and F4 (RII = 83.16%)
11. F25 (RII = 80.84%); 17. F23 (RII = 75.00%)
[95], New Zealand37 Cnt & subcontractors (61%), and Cns (39%)566. F1 and F4 (MS = 3.83)
14. F19/Level of motivation (MS = 3.60); 41. F7 (MS = 2.79)
[96], Trinidad & Tobago30 Cnt425. F1 (RII = 87.5%)
30. F9 (RII = 71.67%)
[97], GCC16 Cnt (81%), Cns (13%), and Cl (6%)408–13. F4 (RII = 38%)
8–13. F12 (RII = 38%); 14–20. F13 (RII = 31%)
[98], Saudi Arabia41 Cnt321. F4 (RII = 75.1%)
13. F13 (RII = 63.5%); 26. F12 (RII = 52.2%)
[99], Gaza Strip, Palestine110 craftsmen (at 25 sites)205–6. F4 (R = 0.70)
[100], Egypt55 Cnt, Cns, managers, and engineers272. F1 (RII = 86.91%)
12. F9 (76.00%)
[101], South Africa62 managers, site engineers, quantity surveyors, and architects in Cnt (60%), Cnt (21%), architect. (11%), and proj. management firm (8%)17816–18. F1—equipment operator (MS = 4.23); 41–46. F17 (MS = 3.98); 61–69. F10 (MS = 3.85); 84–88. F9 (MS = 3.74);
102–103. F5 (MS = 3.60)
[102], Zimbabwe43 Cns (51%) and Cnt (49%)405. F1 (MS = 4.14)
6. F4 (MS = 4.05)
[103], South-South Zone of Nigeria1043 craftsmen (60%) and project supervisors/engineers (40%)151. F15 (MS = 3.55)
2. F1 (MS = 3.26)
5. F12 (MS = 3.09)
6. F4 (MS = 2.98)
7. F7 (MS = 2.95)
12. F13 (MS = 2.59); 13. F5 (MS = 2.56); 14. F10 & F11(MS = 2.49)
[104], Nairobi County, Kenya99 foremen, contactors, cost estimators, const. managers, and others in contractors company121. F3/F1 (frequency of responses to get weighted total for responses =138)
[105], Saudi Arabia *36 professionals (from top managers to technicians and foremen) in 3 off-site Cnt435. F4 (II = 0, 63.89%)
12. F12 (II = 55.56%); 20–24. F13 (II = 52.22); 38. F5 (41.67%)
[106], Iraq83 employees in 9 Cnt & subcontractors (73%) and in Governmental Technical Department (27%)211. F4 (RII = 95.18%)
7. F12 (RII = 73.49%)
13. F5 (RII = 70.48%); 21. F13 (RII = 62.35%)
[107], Pakistan100 engineers in construction company411. F4 (RII = 0.89)
7–8. F5 (RII = 0.81)
15–18. F13 (RII = 0.74); 19–20. F7 (RII = 0.72)
[108], Karachi, Pakistan134 Cnt (41.2%), Cns (38.2%), and Cl (20.6%)373. F1 (RII = 0.79)
12. F17 (RII = 0.73); 17. F9 (RII = 0.71); 21. F10 & F11(RII = 0.69)
[109], Turkey126 craftsmen employed in 4 construction projects3721–22. F3 (MS = 3.53)
[110], Egypt227 Pm, engineers, construct. managers, foremen, site supervisors, quantity surveyors and architects in construction companies414. F17 (RII = 82.63%)
7. F4 and F1 (RII = 81.96%)
13. F12 (RII = 80.4%); 24. F9 (RII = 74.5%); 25. F5 (RII = 74.3%)
[111], Bale Zone in Oromia, Ethiopia **83 Pm, engineers, mason, foreman, superintendent and others in 9 road construction projects611. F4 (RII = 0.91)
3. F1 (RII = 0.84)
6. F16 (RII = 0.89)
23. F13 (RII = 0.71); 24. F23 (RII = 0.70); 25. F12 (RII = 0.68); 33. F25 (RII = 0.64); 50. F5 (RII = 0.57)
[112], Jordan150 Cnt (33.3%), Cns (33.3%), and Cl (33.3%)3723. F12 (RII = 61.37%)
32. F20 (RII = 54.86%)
[113], Madurai, India77 Pm, engineers and labors723. F10 (MS = 4.17)
20. F4 (MS = 3.71); 56. F12 (MS = 3.13); 66. F13 (MS = 2.99); 72. F5 (MS = 2.78)
[114], Gwalior, India23 Pm, project coordinators, site engineers, and Cl268. F4 (RII = 0.62)
-F13;—F12 (outside top 10)
[115], Tamil Nadu, India108 Pm, site engineer, architect and other at different level from the various constr. industries541. F1 and F4 (RII = 86.48%)
3. F7 (RII = 85.98%)
-F22/F19;—F5 (outside top 10)
[116], India35 Pm (40%), engineers (34%), Cnt (23%), and Cns (3%)258. F1 (Importance according to AHP = 0.045)
23. F4 (Importance according to AHP = 0.013)
[117] India ***(number and type of respondents unknown)489–11. F1 (RII = 0.65)
18–19. F7 (RII = 0.59); 20. F5 (RII = 0.58); 30–31. F8 (RII = 0.56)
[74], India140 employees in construction sector from top management level to operational level248. F1 (RII = 0.741)
10. F1 and F2 (RII = 0.739)
[118] Peshawar Khyber Pakhtunkhwa, Pakistan100 Cl, Cns, Cnt, and civil engineers233. F1 (RII = 0.78)
12. F3 (RII = 0.64); 16. F9 (RII = 0.58)
[119], Cambodia ***73 Cnt, project engineers, and Pm3621. F3 (RII = 0.687); 23. F7 (RII = 0.654)
[120], Singapore32 Pm, quantity surveyors and team members in Cns (38%), developers (37%), and Cnt (25%)262. F4 (MS = 3.84)/1. in green building projects—(MS = 4.44)
5. F1 (MS = 4.06)/5. in green building projects—(MS = 4.22)
[121], Singapore65 managers141. F1 (MS = 4.36)
[122], Klang Valley, Malaysia ****170 Cnt193. F4 (IMP.I = 60.0%)
18. F12 (IMP.I = 14.4%)
[123], Saudi Arabia1454 employees at Cnt & subcontractor on the 25 construction projects315. F3 and F4 (MS = 4.02)
[124], Croatia157 civil engineers172. F14 (RII = 4.17)
7. F17 (RII = 3.55)
[125], Egypt *****50 Cl, Cns, and Cnt8617. F4, F1 and F2 (II = 0.78); 52–55. F18 (II = 0.65)
[126], India108 Pm and engineer, architect and other on project from management level to operational level
191. F1 and F4 (RII = 86.48%)
3. F7 (RII =85.98%)
-F5;—F19 (outside top 10)
[127], India50 (type of respondents unknown)4327–30. F7 (RII = 0.69); 31–32. F16 and F11 (RII = 0.67); 35–38. F5 (RII = 0.65); 42–43. F12 (RII = 0,61)
[128], Hanoi, Vietnam185 (type of respondents unknown)431. F4 (RII = 4.31)
2–3. F17 (RII = 4.28)
9. F8 (RII = 4.20)
19. F26 (RII = 3.81); 24–26. F24 (RII = 3.74); 32–33. F5(RII = 3.52);
40. F6 (RII = 3.22); 43. F3 (RII = 3.08)
[129], Alberta, Canada ******35 tradespeople (as foreman, laborer, equipment operator and welder—57%) and Pm experts (43%)142./3. F4—equipment operator (score = 90.22%)—
Tradespeople/(score = 91.58%)—Project management
5./9. F1—crew (score = 85.86%)—Project management/
(score = 79.21%)—Tradespeople
6–8. F1—equipment operator (score = 81.17%)—Project manager
7. F4—Crew (score = 82.69)—Tradespeople
[130], Poland142 craftsmen (54%) and engineers, managers, directors, construction company owners, and others (46%)172. F7 (MS = 4.27)
3. F9 (MS = 4.18)
5. F5 (MS = 3.83)
17. F8 (MS =3.09)
[131], Iran and Lithuania (for all) *******4 members of the project management team184. F3 (fuzzy importance measures = 0.45%)
10. F4 (fuzzy importance measures = 0.03%)
[132], Iran63 Cl, Cns, and Cnt601. F9 (Effect rate = 0.135)
4. F1 (Effect rate = 0.108)
-F4 (outside top 12)
[133], Yemen91 architectural and structural engineers
on construction projects		521. F4 and F1 (RII = 88.6%)
28. F8 (RII = 73.6%); 35. F5 (RII = 69.6%); 52. F3 (RII = 58.4%)
[134], India ********(number and type of respondents unknown)202–3. F3 i.e., F1 (RII = 0.75)
2–3. F2—regarding the F5, wages, worker effort, work
environment at the time supervision (RII = 0.75)
[135], Maharashtra, India302 construction workmen291. F12 (RII = 55.4%)
13. F9 (RII = 47.6%); 16. F13 (RII = 47.1%); 27. F1 (RII = 39.0%)
[136], Ahmedabad, Vadodara and Gandhinagar, India111 Pm and site engineers724. F1 (RII = 0.78)
[137], Khulna city, Bangladesh100 Cl, engineers, Cnt, and subcontractors152. F1 (RII = 0.81)
[138], Libya76 Cns, Cnt, managers, and engineers302. F4 and F1 (RII = 0.87)
18. F9 (RII = 0.65)
[139], South Africa58 Cnt (that operate on building projects)334. F4 (RII = 0.67)
[140], Vietnam56 Pm453. F17 (RII = 0.79)
12. F4 and F1 (RII = 0.76); 17–18. F5 (RII = 0.74); 17–18. F8 (RII = 0.74); 33. F3 (RII = 0.71); 39. F12 (RII = 0.69)
Labor Productivity in: * off-site construction; ** road construction projects; *** residential building projects; **** non-residential projects; ***** pre-stressed concrete bridges; ****** equipment-intensive activities; ******* high-rise building; ******** precast bridge construction.
Table
Table 5. Factors associated with labor that are under the dominant influence of management and other factors.
Table 5. Factors associated with labor that are under the dominant influence of management and other factors.
Influenced FactorsStudies on Impacts on Project Performance and Contractor Productivity
Shortage of skilled and/or experienced labor[7,16,21,37,52,53,62,70,80,92,93,96,100,101,104,105,112,117,119,125,136,143,144,145,146,147,148,149,150,151,152,153,154,155]
Lack of training or level of training[62,63,64,95,96,102,105,114,115,117,121,128,131,132,134,137,148,156,157,158]
Absenteeism/late arrival, early quit, and frequent unscheduled breaks[8,14,16,19,25,34,45,53,65,84,88,91,95,98,99,102,103,104,105,107,108,110,111,112,113,115,119,122,123,126,127,129,130,133,135,145,159]
Lack of respect and personal relationsbetween workmates[17,25,35,48,57,59,98,99,105,106,111,112,113,115,116,122,123,124,132,160,161,162]
Table
Table 6. Worker attributes and positive and negative effects on labor productivity in case the attributes are better or worse.
Table 6. Worker attributes and positive and negative effects on labor productivity in case the attributes are better or worse.
Positive Effects on ProductivityLow LevelConsidered Workers’ CharacteristicsHigh LevelNegative Effects on Productivity
Physical durability—faster realization of work tasks←Strength, dexterity, and Education→Physical fatigue—slow work,
Good skills—flexibility (a wider range of action), quality performance, faster realization of work tasks, independent work action, working in a safe way←Education and experience, dexterity, creativity→Lack of skills—small area of operation, operating errors (rework), slow work, non-independence in work, injuries
Adaptability to new conditions at work←Adaptability to new conditions and situations (creativity and dexterity)→Inadaptability to new conditions at work
Good work habits and discipline, honesty, integrity, sense of responsibility, compliance with orders←Morale and morality→Absenteeism/late arrival, early quit and frequent unscheduled breaks, theft, negligence, alcoholism
Good interpersonal relationships, work atmosphere and cooperation←Affinity for teamwork (morale and morality)→Bad relations with coworkers, weak cooperation
Table
Table 7. Workers’ attributes not covered by this research.
Table 7. Workers’ attributes not covered by this research.
Characteristics Omitted from the SurveyReason for Omission
Loyalty, relations between workmates and superiors, diligence, obedience, job satisfaction, discipline and work habits, sense of responsibility, willingness to learn, willingness to improve work attitudes, willingness to perform overtime and shift workUnder the strong influence of other factors (largely depends on the company, or the actions of management)
Intrinsic motivation, personal issues, love of job, proactivity, sense of responsibility, ability to adapt to changes and new environmentsIt is difficult to assess the impact of such testing
Health, alcoholism, the sense of observance of regulationsIt must be within the regulations
Carefulness, diligence, resourcefulness, honesty, religionNot comparable with the results of other research
Table
Table 8. The rank of importance of workers’ characteristics by groups of respondents.
Table 8. The rank of importance of workers’ characteristics by groups of respondents.
Attributes of WorkersA1A2B1B2B3
Presence of attributeRIIRankRIIRankRIIRankRIIRankRIIRank
Experience in construction0.9451.0.9181.0.9151.0.9751.0.8881.
School education0.6544.0.6625.0.6245.0.7385.0.6245.
Dexterity0.8362.0.9032.0.8722.0.8872.0.8242.
Physical strength0.6095.0.7294.0.8313.0.8623.0.7823.
Morale/morality0.7183.0.8633.0.6984.0.7504.0.7594.
Absence of attribute
Experience in construction0.6821.0.6463.0.6642.0.7003.0.6772.
School education0.4815.0.5235.0.4915.0.6255.0.5065.
Dexterity0.6552.0.6772.0.7441.0.7372.0.6881.
Physical strength0.5364.0.6004.0.6723.0.7751.0.6294.
Morale/morality0.6003.0.7141.0.6154.0.6374.0.6763.
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Vidaković, D.; Hadzima-Nyarko, M.; Marenjak, S. The Contribution of Workers’ Attributes on Sustainability of Construction Project Realization Goals—Survey on the Impact on Productivity in Croatia. Sustainability 2020, 12, 9946. https://doi.org/10.3390/su12239946

AMA Style

Vidaković D, Hadzima-Nyarko M, Marenjak S. The Contribution of Workers’ Attributes on Sustainability of Construction Project Realization Goals—Survey on the Impact on Productivity in Croatia. Sustainability. 2020; 12(23):9946. https://doi.org/10.3390/su12239946

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Vidaković, Držislav, Marijana Hadzima-Nyarko, and Saša Marenjak. 2020. "The Contribution of Workers’ Attributes on Sustainability of Construction Project Realization Goals—Survey on the Impact on Productivity in Croatia" Sustainability 12, no. 23: 9946. https://doi.org/10.3390/su12239946

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