Analysis of the Article
Engineering Management Journal, September 2007
The article “Twinning Motivation, Productivity and Management Strategy in Construction Projects” by Hemanta Doloi is dedicated to the research of the motivational factors effectively influencing employees’ output and help to increase productivity in the field of Australian construction industry. The research was conducted in this area particularly, because although there is a considerable number of works dedicated to the employees’ influence in construction productivity and the overall study of the motivational factors, there were none such found that would describe the incentive programs enhancing positive motivation methods in the construction industry. But at the same time, according to the statistical data, the construction area is one of the most important for the Australian economy, while between 1991 and 2000 the heavy construction industry showed the worst results in terms of productivity, as it decreased by 8%. Thus, it becomes evident that it is necessary to look for effective ways of productivity improvement in construction so as the whole country’s economy could benefit from it.
What is the purpose/objective/thesis of the article?
The author of the article defined the following objectives for the study:
1. Identification of the relative importance of negative and positive motivational attributes that influence productivity in construction projects, as they are perceived by construction workers.
2. Understanding the latent properties of the motivational attributes that were identified in the construction industry so as to benchmark the most critical factors.
3. Benchmarking the framework that will incorporate the critical attributes so as to devise appropriate motivational and incentive schemes to achieve higher productivity in the industry of construction.
Brief summary of the article
The article provides rather deep insight into the motivational factors that influence work productivity in the Australian construction industry. In order to meet the objectives defined for the study, it was necessary to take several steps. First of all, a list of attributes ranked by their importance for workers was developed. Analysis of the results helps to better understand what steps have to be taken to achieve the necessary levels of performance. Then, with the help of factor analysis a set of four factors was defined – basic work environment and employment contracts; personal status and motivation; gender and comfort; and impact of education. Attributes adherent to each of the factors were analyzed. The most critical factor was then defined that should be paid the most attention when developing incentives programs in the construction field of the Australian economy.
The main study limitation was a small number of respondents – there were only 100 surveys analyzed, which means that the results cannot be absolutely representative of the whole industry. Still, it is possible to employ the results in real-world circumstances, as they show the weaknesses of today’s programs and have effective recommendations for development of the future ones. Although actual incentives schemes weren’t developed in the course of the study, the outcome can be further analyzed and the result can be still used in this relation.
What was the source of data that the author used to support the thesis?
Because of the fact that the study was focused on the construction stage of projects, it required a considerable amount of documented data on completed projects. However, there are no studies on the influence on construction productivity that worker motivational factors have. Due to this reason, the author chose questionnaire survey approach to establish and analyze the impact of such factors on projects productivity.
The main sources for motivational attributes identification were literature survey and interviews with selected professionals currently working in the industry. The data that was used in the research was gathered through several survey techniques, such as paper-based, electronic, telephone, group meetings and personal interviews. The main analysis was based on the answers of 100 respondents, which is a relatively small number from the statistical standpoint. The majority of respondents (47%) were representatives of small firms, where the number of employees doesn’t exceed 80. The smallest group of people were from middle-sized firms (24%) and 29% worked in large firms with more than 200 employees in staff. In my point of view, such small number of people involved in the research imposes considerable limitation to the study representativeness, but still it is possible to see certain trends in this field, which is also valuable, if we take into account the lack of data in this area.
Was the data gathered and reduced in a realistic and scientifically sound method?
I believe that the data for this study was gathered and reduced with the use of the most appropriate and scientifically sound methods.
In order to select the most appropriate alternative and elicit the consistent subjective judgment from decision makers in the selection process, the holistic analysis was required. Computerized decision support systems make the selection process more effective. For the purposes of meeting the objectives of the study, a list of 25 project attributes in four categories was drawn up, which are related to status of workers and their working environment. I believe that the number of attributes in the list is sufficient due to rather fragmented nature of the construction industry in Australia. To better understand and analyze the influence of these factors on the project productivity, two methods were used – multicriteria decision-making approach based on Analytical Hierarchy Process and statistical one on the basis of multivariate regression and factor analysis. To test the measurement model, the following validity and reliability tests were performed: Composite Factor Reliability, Cronbach’s alpha and Average Variance Extracted. All the tests showed good reliability level of the questionnaire sample.
The survey was developed at the early stage of the study and included two parts – with general personal information request and evaluative part with 25 attributes presented. Respondents had to evaluate the attributes by 5-point Likert scale, which I think is a good solution, as it allows to clearly see the way, in which workers perceive each motivational factor, creating an overall picture of the results. The survey was used for both multicriteria and statistical analysis.
For the purposes of ranking the defined attributes, the mean scores were calculated. But as this method was considered to be not representative of the overall rankings, the author found relative importance indices so as to define the relative ranking of these factors. In my point of view, such ranking method allows to properly assess the results and see the actual picture of the problem.
Another method that was used in the course of the research was factor analysis. It served to reduce data and provide its summarization. With its help it is also convenient to see the relationships between many correlated, but not related variables in terms of separate underlying variants. I think that this method of data reduction is appropriate in this study, as it allows to interpret data in a concise and effective manner.
As it is stated in the study, factor analysis consists of two steps: component analysis of the elements’ principal components and factor rotation. For the purposes of the first step, eigenvectors of the original variables’ matrices are used to transform the data into orthogonal variables. As linear elements are orthogonal, there is no multicollinearity or independence in the transformed data. The second step – factor rotation – is dedicated to rotating the identified principal components about the axis of the original variables. In this way, orthogonality is preserved but at the same time new transformation matrix is formed along with each rotation. The second stage can be performed in various ways, the most preferred of them being the method of maximum variance.
In this study factor analysis was performed on 25 project attributes. The factors that were extracted with the use of the principal component analysis are hard to interpret and understand, as they are orthogonal and contain many overlapping attributes. At this stage the majority of variables resulted in high loadings to one important factor and smaller to the other ones. I believe that the author did the right thing to overcome this problem – factor rotation was applied to discriminate between the factors. For rotation the varimax method was chosen, as in this way it becomes possible to maximize factor loading onto each of the factors, as a result of which the factors clusters become more interpretable. Threshold for the factor loadings was chosen as 0.522, as it is the most appropriate for the comparatively small sample size of 100, which was used in this research.
Thus, with the help of the factor analysis attributes were grouped by various factors, but in order to define the most critical of them, stepwise regression technique was applied. In it factors served as independent variables, while productivity as dependent one.
Did the data support the purpose/objective/thesis?
As it was stated earlier, the study had the following objectives: 1)
identification of the relative importance of negative and positive motivational attributes that influence productivity in construction projects, as they are perceived by construction workers; 2) understanding the latent properties of the motivational attributes that were identified in the construction industry so as to benchmark the most critical factors; 3) benchmarking the framework that will incorporate the critical attributes so as to devise appropriate motivational and incentive schemes to achieve higher productivity in the industry of construction.
The first objective received a considerable amount of attention in this article. As it was already described, in order to identify the importance of the motivational factors, survey results were analyzed with the help of the descriptive statistics method. As a result, job security attribute turned out to be the most significant one for the employees. The author assumed that it may be conditioned by the fact that the Australian construction area has limited industrial growth and privatized labor market. The second most important factor was work appreciation and rewards, while work environment was the third in the ranking list. This result should be paid special attention, in my view, as today the majority of work places have standardized environment, which is usually not analyzed from the point of view that it can influence productivity. The next factors that are significant for construction workers were found to be employer’s recognition and prospect of promotion. These two factors particularly show that workers in the Australian construction field aim at higher performance, and it is necessary to organize proper environment for the realization of this aim. Geographical position was also found to be important for workers, which means that they tend to perform better if they don’t have to spend a great amount of time on their travelling to work. I believe that this result should be paid special attention, as today the majority of construction firms are located in large cities, while the country is comparatively big and requires workers’ participation in all its parts.
Contract of employment is another significant attribute, which means that offering long-term contracts is an effective way of attracting professionals and achieving better performance. In this way workers also get the necessary sense of belongingness. Incentive schemes were ranked eighth, which shows that traditional employers’ approach that the more bonuses they offer, the better productivity becomes, is not as significant and effective as it was considered to be. Thus, at this stage the first objective was completed, as the author showed the most important motivational factors in the workers’ point of view and found justification for them.
In the course of factor analysis the second objective was met, as the most critical factor was defined on the basis of analysis of the latent properties of the identified attributes. In particular, there were extracted four factors: basic work environment and employment contracts; personal status and motivation; gender and comfort; and impact of education. The first factor has the greatest number of adherent attributes – 12, in which factor loadings range between 0,837 and 0.632. The interpretation that the author offered shows that it is highly important to develop effective incentive programs for workers with hard-working attitudes and high morale, as in this way productivity can be increased significantly. Financial security and work appreciation by employers are also very important motivational attributes. It was interesting to see that along with the fact that workers consider job security to be of major importance to them, penalty clauses in the employment contracts don’t have any negative effect on the work productivity.
In the factor called personal status and motivation there were defined 10 attributes ranging from 0.839 to 0.559 in factor loading. Age group was found to be the most critical among aspects in this factor. Marital status, as well as social status of the profession, was also considered important. Although it is known that pressured environment of work usually creates unrest, proper fringe benefits and schemes of incentives can counterbalance the possible negative outcomes and bring positive benefits on the whole.
For the third factor – gender and comfort – only two attributes were defined. Gender has factor loading of 0.879, which means that it is absolutely critical for the productivity purposes. Incentive programs were also identified as critical ones. The last factor, which is impact of education, included only one attribute – education level, which was found to be not critical for productivity in the Australian construction industry.
On the basis of the factor analysis results, the most critical factor was defined with the help of stepwise regression technique. It was found that it is basis work environment and employment contract, which has the most critical attributes united that are important especially for the purposes of productivity increase. Thus, the author was able to identify the most critical factors for the efficiency increase in the construction area.
The third objective, which was dedicated to the development of effective incentives program, didn’t get much attention from the author. In the conclusion to the article it was stated that in order to identify the constituents of such programs, it was necessary to conduct further research. At the same time, it was stated that the found factors may be not representative of the whole industry, as there was a limited number of case studies analyzed and sample size was rather small. Still, the results of the study can be useful for organizations in development of incentives programs, as general tendencies are well-described and can be effectively made use of.
How would you improve upon the article?
I enjoyed reading the article, as it was rather concise and the research described in it was interesting for me. I agree with the methods chosen for the purposes of this study and don’t have any critical comments on them. Still, if I had an opportunity to improve the article, I would have taken larger sample, as in my view, the target category of people is rather large and diverse, which allows to involve more people in the research so as to make it more representative.
Among the other points that I would like to improve is the fact that although it was mentioned in the methodology section that there were interviews conducted in the course of the research, their results are not present. I understand that it can be conditioned partly by the limited number of words required for the article submission, but still I think that it would have been interesting for people to consider these results as well, at least briefly. I think so, because usually researches benefit from the combination of such quantitative and qualitative techniques, as interviews allow to clarify all the necessary moments, providing deeper insight into the problem’s core. In this way it would also have been possible to interpret results in a more accurate way.
The last thing that I would like to change is the amount of attention to the third objective, as I consider it to be very important, while having practically no coverage in the article. It is clear that the incentives programs can be developed on the basis of the research results, but I think that it will be absolutely appropriate for the author to develop at least one example of such program so as to see all the study results in action, so to say. On the whole, I consider the article to be of major importance for the construction industry HRM, as it allows to improve the workplace and develop such programs that there will be no problems with productivity in this field of Australian economy.