Motivating Construction Productivity

Learning from Other Disciplines


Alexander Laufer

Israel Institute of Technology

G. Douglas Jenkins, Jr.

University of Texas at Austin


Construction productivity has been declining steadily over the last decade [22] and construction labor efficiency has been often cited as very poor. Since labor costs comprise between 25 and 40 percent of the total project cost, reduced labor costs present a great potential source of increased productivity. While a firm's productivity is influenced by production factors other than labor, such as equipment, materials, and management, these resources are inanimate unless they are transformed into productive uses by the human element. The quality of human performance in large part depends on human motivation, the focus of this article.

So far, very little has been done to raise construction labor motivation. This is reflected in the negligible number of studies focussing on this subject (See the recent review by Maloney [11] and also [7]), as well as in the meager number of construction companies employing any kind of motivational programs. Those studies that have been conducted on construction labor motivation were based on theories of motivation that are not well accepted by most comtemporary industrial and organizational researchers [11]. The few recent attempts of several construction companies to introduce motivational programs based on the recommendations of these studies [19] ]20] are likely, therefore, to have only limited success.

The main objective of this article is to present a different approach to motivating construction workers. The proposed approach is based primarily on research evidence, practices, and documented results from nonconstruction organizations. The massive experience that has accumulated in those industries is used discriminately to develop a set of guidelines for construction managers. Using a hypothetical case, this article demonstrates how construction companies can develop tailor-made productivity improvement programs.

Construction-Related Motivation Studies

In recent years, several attempts have been made to examine the applicability of work motivation theories to construction workers [1] [5] [15] [17] [21] [23], Most of the studies are not based upon empirical evidence, but are rather analyses of the unique characteristics of construction work in light of motivational theories. The studies limit themselves primarily to Maslow's [14] need theory and to Herz-berg's [6] two-factor theory. Although Maslow's and Herzberg's approaches have a surface logic, subsequent research evidence points out definite limitations. There is very little research support for the theoretical basis and predictability of either model. The trade-off for simplicity sacrifices true understanding of the complexity of work motivation. Nadler and Lawler [16] have concluded that running through these approaches and other similar models are a series of implicit but clearly erroneous assumptions, namely: (1) all employees are alike; (2) all situations are alike; and (3) there is one best way to motivate employees. With regard to the first assumption, the different theories present different ways of looking at people, but each assumes that all employees are basically similar in their makeup. For example, they assume employees all want economic gains, or all want a pleasant climate, or all aspire to be self-actualizing, etc. The second assumption shared by these models is that all managerial situations are alike, and that one managerial course of action for motivation is applicable in all situations. Out of these two assumptions emerges the third assumption, that there is “one best way” to motivate employees. When these “one best way” approaches are applied in the “correct” situation they are likely to work. All of them, however, are bound to fail in some situations. They are, therefore, not adequate managerial tools.

These limitations clearly explain why most behavioral researchers do not view Maslow's need theory and Herzberg's two-factor theory as final answers for motivating workers. Thus, most past research on construction labor motivation has been based on motivational theories that are not well accepted by most contemporary behavioral scientists. This, in conjunction with the fact that most studies of construction workers were not based upon empirical evidence, raises questions concerning the validity of some of their findings, and indicates an urgent need for new guidelines for managing labor motivation. Such a set of guidelines can be derived using the more widely accepted expectancy theory of work motivation.

Expectancy Theory Of Motivation

Expectancy theory [24] offers an alternative means for viewing and altering the motivation of construction workers. The theory emphasizes the necessity of analyzing how several personal and organizational variables interact to influence motivation. It recognizes that different people have different types of needs, desires, and goals, and that not everyone values the same rewards equally. Almost all the studies related to the use of expectancy theory in work motivation have confirmed the predictions of the theory [2] [16]. For these reasons, many behavioral scientists have concluded that expectancy theory represents the most comprehensive, valid, and useful approach to understanding work motivation. In the last decade this theory has become widely accepted in academic circles [2] [4] [10] and there are indications that practitioners are following suit. Yet, it has received little attention in the construction area [7] [12].

Expectancy theory argues that the motivational force to perform or expend effort is a multiplicative function of the expectancies that individuals have concerning future outcomes and the value they place on those outcomes [24], Lawler [9] divided the concept of expectancy into two specific types. First, in the mind of the individual, each behavior has associated with it an expectancy or belief about the probability of success. This expectancy reflects how difficult the individual believes the task to be and his subjective probability of successfully completing it. For example, a worker may have a strong belief (e.g., a chance of ninety to ten) that, if he tries, he can lay 600 bricks per day, but that he has only a fifty-fifty chance of laying 900 bricks per day. These expectancies are labeled effort-performance linkage (EimgP) beliefs.

Second, and again in the mind of the individual, certain outcomes are associated with every behavior. The individual expects that if he behaves in a particular way, particular things will happen to him. For example, he may believe that if he produces at only 50 percent of the company's standard, he will be fired and if he produces at 150 percent of the standard that he will receive a bonus or that management will raise job requirements. Similarly, he may believe that certain levels of performance will lead to approval or disapproval from his peers and/or his supervisor. These expectancies are labeled beliefs about performance-outcome linkages (PimgO). Any given performance level or behavior can be thought of as being linked to a number of outcomes that may differ in size (e.g., bonuses of $5 versus bonuses of $50) and type (e.g., increased job security versus disapproval by peers).

The final element in the expectancy model is the attractiveness or valence (V) of an outcome. Each outcome is more or less attractive to a specific individual and outcomes have different levels of attractiveness for different individuals. How attractive or unattractive an outcome is to an individual is determined by the particular set of values an individual holds and reflects many factors in the individual's life. For example, some individuals may find the opportunity for promotion attractive because it would provide more income, authority and a sense of power, and achievement. Others may not value this opportunity because it would mean they would have to leave their current work group, thus losing attractive opportunities for social interaction. Finally, it is important to note that outcomes fall into two major categories. Some outcomes are seen as occurring directly as a result of performing the task itself and are outcomes which the individual thus provides himself (i.e., feelings of accomplishment, achievement, competence, creativity, etc.). These are called intrinsic outcomes and always follow from effective performance. Other outcomes that are associated with performance like pay, promotion, praise, or acceptance are provided or mediated by external factors such as the organization, the supervisor, the work group, etc. These outcomes are called extrinsic outcomes.

In combination, then, an individual's motivation to behave in a particular way will be greatest when (a) he/she believes the behavior will lead to certain positive outcomes (strong beliefs about performance img outcome linkages), (b) the individual finds the outcomes to be attractive (strong positive valence levels), and (c) he or she believes that performance at a particular level is possible (strong beliefs about effort img performance linkages). Furthermore, given a number of alternative behaviors or alternative levels of behavior (e.g., 400, 600, or 800 bricks per day), an individual will aspire to that behavior or level of performance that has the greatest multiplicative combination of relevant EimgP beliefs, PimgO beliefs, and attractiveness of outcomes associated with it.

Stated in more formal notation, for a given behavior or level of performance,


This formulation is depicted schematically in a simplified form in Figure 1.

The theory identifies specific management actions for improving workers’ motivation. By affecting employee beliefs about effort leading to performance and performance leading to given outcomes, the manager can influence an individual's level of motivation. For example, by removing obstacles to effective performance (e.g., inadequate tools, equipment, and supplies), a supervisor probably increases beliefs that effort leads to performance. Similarly, by consistently rewarding effective performance, a supervisor can change beliefs that performance leads to that reward or outcome.

Hypothetical Case

In order for the reader to better understand how to use the expectancy model, a hypothetical case is presented. Suppose a construction project is suffering from low productivity, and the project manager suspects that this is partly due to low labor motivation. We will describe the development of a motivational program geared toward raising this construction project's labor productivity by increasing employee motivation. The following sections outline a way to measure motivation, how motivational problems can be diagnosed, and ways to devise programs to improve employee motivation. The presentation includes a description of the decision process that probably takes place in the mind of the individual worker.

Measuring Motivation. Expectancy theory argues that it is important to measure the attitudes individuals have in order to diagnose motivational problems. Such measurement helps the manager to understand why employees are motivated or not, what the strength of motivation is in different parts of the organization, and how effective different rewards are for motivation and peformance. One relatively inexpensive and reliable method of doing this is through standardized employee questionnaires (e.g., see reference [3]). A number of organizations already use such techniques, surveying employees’ perceptions and attitudes at regular intervals using either standardized surveys or surveys developed specifically for that organization. A sample of the questions that could be used to measure motivation in a construction organization is presented below. It is a modification of an instrument developed at the Survey Research Center of the University of Michigan [3] [16].


Figure 1 Expectancy Theory Relationships

The questionnaire includes three types of questions:

1. Questions assessing beliefs that effort leads to performance (shown in Table 1),

2. Questions assessing beliefs about performance leading to outcomes (shown in Table 2), and

3. Questions assessing the attractiveness or valence of various outcomes available to the employee (shown in Table 3).

Table 1 Assessment of Effort-Performance Expectancy

Below you will see a set of performance levels that might be associated with working hard.

Please indicate by checking the appropriate number to the right of each level how likely it is for you personally that working hard leads to a particular performance level on your job.

No Chance At All Fifty-Fifty Always
a) Working hard→Laying 400 Bricks [0] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10*]
b) Working hard→Laying 600 Bricks [0] [1] [2] [3] [4] [5] [6] [7] [8] [9*] [10]
c) Working hard→Laying 800 Bricks [0] [1] [2] [3] [4] [5] [6*] [7] [8] [9] [10]

Table 2 Assessment of Performance-Outcome Expectancy

Here are some things that could happen to people if they perform at different levels. How likely is it that each of these things would happen if you lay 600 bricks per day?

Not Likely
At All
Fifty-Fifty Certain
a) You will get a feeling that you have accomplished something worthwhile [0] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10*]
b) Your supervisor will praise you [0] [1*] [2] [3] [4] [5] [6] [7] [81 [9] [10]
c) You will get a bonus or pay increase [0*] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
d) You will have an opportunity to develop your skills and to learn new things [0*] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
e) You will be fired or laid off [0] [1] [2] [3*] [4] [5] [6] [7] [8] [9] [10]
f) The people you work with will be angry with you [0] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
How likely is it that each of these things would happen if you lay 800 bricks per day?
Not Likely
At All
Fifty-Fifty Certain
a) You will get a feeling that you have accomplished something worthwhile [0] [1] [2] [3] [4] [5] [6] [7] (8) [9] [10*]
b) Your supervisor will praise you [0] [1] [2] [3] [4*] [5] [6] [7] [8] [9] [10]
c) You will get a bonus or pay increase [0] [1*] [2] [3] [4] [5] [6] [7] [8] [9] [10]
d) You will have an opportunity to develop your skills and to learn new things [0] [1] [2*] [3] [4] [5] [6] [7] [8] [9] [10]
e) You will be fired or laid off [0] [1] [2*] [3] [4] [5] [6] [7] [8] [9] [10]
f) The people you work with will be angry with you [0] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10*]

Table 3 Assessment of Valence of Outcomes

Different people want different things from their work. Here is a list of things a person could have on his or her job. How desirable or undesirable is each of the following to you?
Makes No
a) The chances you have to accomplish something worthwhile [-5] [-4] [-3] [-2] [-1] [0] [1] [2] [3] [4*] [5]
b) The praise you get from your supervisor [-5] [-4] [-3] [-2] [-1] [0] [1] [2] [3*] [4] [5]
c) The amount of pay you get [-5] [-4] [-3] [-2] [-1] [0] [1] [2] [3] [4*] [5]
d) The opportunity to develop your skills and to learn new things [-5] [-4] [-3] [-2] [-1] [0] [1] [2*] [3] [4] [5]
e) That you will be fired or laid off [-5] [-4*] [-3] [-2] [-1] [0] [1] [2] [3] [4] [5]
f) The people you work with being angry with you [-5*] [-4] [-3] [-2] [-1] [0] [1] [2] [3] [4] [5]

In our hypothetical case, we use the results of this questionnaire to illustrate the calculations of motivation scores. According to expectancy theory, the work motivation scores are determined by multiplying the strength of the belief that effort leads to performance by the sum of the beliefs that performance leads to outcomes weighted by the attractiveness of the corresponding outcome.

Diagnosing Motivational Problems. For the hypothetical case we will restrict the analysis to one construction trade, bricklaying, and the responses to those of a single employee. Assume that the questionnaire was completed by a bricklayer, his answers denoted in Tables 1, 2, and 3 by asterisks. Examining the responses of our hypothetical bricklayer indicates his beliefs that effort leads to performance or that performance leads to valued outcomes are not in favor of high levels of motivation to perform well. The investigation of the motivational problems can be pursued very effectively by trying to predict the bricklayer's motivation to exert effort that would lead to various levels of performance. In this example, the bricklayer's motivation to exert effort at two levels of output (600 bricks per day and 800 bricks per day) is examined. Figure 2 presents the decision process that expectancy theory predicts takes place in the bricklayer's mind. The six outcomes included are those that were found in the questionnaire to be the most attractive to our hypothetical bricklayer.


Figure 2 Choosing Level of Effort Before Introduction of Motivational Program

The illustration of our hypothetical bricklayer's “motivational map” indicates that he is not motivated to produce at the higher rate since (1) he does not believe that he can produce at that rate, and (2) he does not expect that producing at that rate will lead to desired outcomes. The outcomes included in this example are provided by four different sources. The first source is the bricklayer himself, who senses feelings of accomplishment when he successfully produces at a given level. Apparently, due to low productivity on site, even accomplishing only 600 bricks per day would result in obtaining this intrinsic reward. The second source of outcomes is the supervisor. Figure 2 indicates that while there are more chances that the boss will praise him if he produces more, still, more often than not, even his high performance will not be recognized. Third, outcomes such as pay, learning and development, and dismissal are controlled by the organization. Figure 2 shows that these rewards are not tied to performance. The fourth source that provides outcomes is the work group. It is obvious that the work group has a norm of low performance and has a great influence on our bricklayer.

The expectancy model predicts that people will choose to behave in whatever way has the highest motivational force computed by the formula presented above. Figure 2 shows that the unfavorable beliefs that effort will lead to performance and the weak beliefs that performance will lead to valued outcomes, render the bricklayer's motivational force to try to lay 600 bricks a day higher than his motivational force to try to lay 800 bricks.

Improving Motivation. The expectancy model implies that the manager can influence the performance of his subordinates by influencing their beliefs about effort-performance and performance-outcome linkages. The question of what determines the beliefs that individuals hold about the relationships between effort and performance and between performance and outcomes has been discussed at length by Lawler [9] [18]. He concludes that the single most important determinant of an individual's belief about the effort-performance linkage is the objective situation. In addition, the person's feelings of self-esteem, past experiences in similar situations, and the communications from others (e.g., coworkers and supervisors) are also major inputs into the person's assessment of the effort-performance linkage. The person's perception of the performance-outcome linkages is influenced by many of the same things that influence the belief about the effort-performance linkage. In addition, the attractiveness of the outcomes, and the beliefs about who controls the outcomes (the person himself or others), will all have an impact on the person's beliefs about performance-outcome linkages. With regard to influencing the attractiveness of outcomes, research evidence indicates that the organization has little influence over how attractive various outcomes will be to its members [9],

Strengthening the beliefs that effort leads to performance for our hypothetical bricklayer will probably required further investigation. By using questionnaires, interviews, work sampling, or time-lapse photography, management can identify some of the specific sources of beliefs about a weak effort-performance linkage. Then, by removing performance obstacles (e.g., inadequate equipment of crew size), management can change the objective situation and probably change these beliefs. If the problem is the individual's poor ability, then introducing on- or off-site training should change beliefs about the effort-performance linkage and, that, in turn, could improve motivation. It should be noted parenthetically that in strengthening the beliefs in these ways, there is an additional impact on performance because performance is a function of ability as well as motivation [9], Thus, improving the individual's ability to perform will lead to improved performance because of higher ability and because of increased motivation.

The second avenue to improving motivation involves changing the beliefs that performance results in the worker obtaining valued outcomes. The expectancy model indicates that outcomes will motivate job performance only if they are attractive, are seen as tied to performance, and the performance is believed to be obtainable. The company, therefore, should link the employee's desired outcomes to the company's desired performances. This calls for management to determine what kinds of performance are required, how to measure performance, and how to link it to outcomes. Performance aspects frequently measured are: (1) quantity of production in terms of physical output or cost of output; (2) quality of the product; (3) rate of accidents; and (4) employee turnover and absenteeism rates. Research shows that when group performance is measurable, at least a portion of the reward should be structured around group performance rather than individual performance [8] [16]. Groups can be a powerful and potent source of desired outcomes for its members. Groups can provide or withhold acceptance, affection, needed information, assistance, etc. In construction, group members frequently have to cooperate with each other to produce a product and the individual's unique contribution is often difficult to determine. In addition, rewards based on group performance will provide a common goal for group members and promote cooperation among members. Thus, it is particularly important to tie rewards, at least partially, to group performance.

As discussed above, in this example, the company and the supervisor can influence directly the linkage with performance of only four of the six most important outcomes for our bricklayer: recognition, pay, self-development, and dismissal. However, indirectly, they may also affect the performance linkage with outcomes provided by the individual himself and by his peers. First, if management removes performance obstacles and rewards good performance with these four outcomes, most workers will feel that they can be highly productive, and that several positive outcomes associated with being a good producer are available. These perceptions will lead them to develop a desire for higher performance. There is a high probability that eventually the work group will translate this desire to a norm of higher performance, making available another set of valued outcomes, work group approval. This will be particularly likely if rewards are also linked to group performance.

Management can sometimes further positively affect group norms by involving the workers in making decisions about work practices. Research shows that workers’ participation can increase the degree to which group members feel they “own” their work practices, and therefore the likelihood that the group will develop a high norm of support for those practices [24] and the rewards associated with abiding by those norms. Under some conditions, participation contributes to increased work effectiveness by leading to higher quality decisions and greater understanding of the work process [8], thus strengthening effort-performance linkages. Finally, if norms that favor high performance (e.g., laying 800 bricks a day) prevail, the individual will no longer sense a feeling of accomplishment with an output of only 600 bricks a day, lowering his level of motivation to produce at that level in relation to his motivation to produce at the 800 bricks per day level.

If the management of our hypothetical construction project were to attack the low productivity problem from a motivational perspective as outlined above, then it will probably witness tangible gains in productivity. Assuming the program was successful and management administered a second questionnaire to our hypothetical worker, we should observe marked changes in beliefs about the model's variables. Questionnaire results might now portray our worker's “motivational map” to be that shown in Figure 3. (Note that there has been no change in the attractiveness of the various outcomes.)

This figure demonstrates that management was successful in influencing the bricklayer's motivation to exert effort, leading to the bricklayer to try to lay 800 bricks a day instead of 600 per day.

It is important to note that even with the implementation of the motivational program, satisfactory performance is not seen as always being followed by the desired rewards. This is true for several reasons. First, most performance measurement techniques are not capable of faultlessly tying rewards to performance [7]. Second, some rewards cannot be related closely to performance because the organization does not have full control over the administration of the rewards. For example, during periods of worker scarcity, the company cannot afford to fire even bad performers. In some situations, on the other hand, even good performers must be laid off (e.g., between the completion of one project and beginning of another). In some cases the assignment of the workers to projects is accomplished by a union, further diminishing the contractor's ability to tie job security to good performance. Third, some rewards are of limited availability. For example, promotions cannot always follow high levels of performance unless there are openings in senior positions to absorb all high performers.

Some comments regarding the feasibility and effectiveness of such a motivational program are in order. The hypothetical case focuses on a “typical” bricklayer, assuming that he represents the entire work force. Since different people have different types of valued outcomes, not all outcomes will be equally attractive to all workers. This implies that any motivational program that provides identical rewards to all workers will result in different levels of effectiveness across employees. Unfortunately, there has been relatively little research on the degree and determinants of outcome preferences among individuals. Still, a review of related studies reported in [7] indicates that people holding the same occupation in a particular environment could be quite homogeneous with respect to the attractiveness of at least some outcomes. The construction company may expect, therefore, that the motivational program will be effective in motivating many employees if it can identify those commonly desired outcomes and tie performance to their attainment.


Figure 3 Choosing Level of Effort After Introduction of Motivational Program

Besides employees’ preferences, the actual set of extrinsic rewards offered may be affected by organizational constraints. For example, a construction company that employs union workers who value pay may find it difficult to introduce productivity pay programs due to a union's objection. If management wishes to achieve the highest motivation level, it may try to compensate for the exclusion of pay by strengthening the beliefs about performance-outcome linkages for other outcomes already included in the program, or by offering additional, somewhat less attractive, rewards.

Most construction companies with existing motivational programs stress recognition and status symbols, rather than pay. This is true in union and nonunion companies alike. They provide the workers with rewards such as letters of commendation, special plaques, hard hats, and belt buckles. These companies reported that the rewards were received enthusiastically by the work force and the programs were very effective [20], Since the programs have not been in effect for a long period of time and have not been rigorously evaluated, the results cannot be viewed as conclusive. Intuitively, it seems likely that the attractiveness of additional belt buckles, letters of commendation, etc., will decline after the first ones are received.

Limitations of the Model

A conceptual model (such as the one diagrammed in Figure 1) provides the framework of ideas one needs for analyzing complicated problems. A model can help a manager to predict how well a proposed solution will work in a given situation. Managers sometimes experience two major problems in trying to implement changes without a guiding theory or conceptual model: (1) management ideas about the nature of the problem are rarely clear, making it difficult to pinpoint solutions; and (2) no follow-up programs are designed to check on the effectiveness of the changes in solving the problem.

It should be noted that the quantification shown in Figures 2 and 3 is for illustrative purposes only. Clearly, human behavior is much more complex than implied by our hypothetical case. Individuals frequently base their decisions on a less thorough and less rational process. People often fail to consider all alternatives and they choose a level of effort that produces a “satisfactory” set of outcomes rather than an optimal set of outcomes [13]. People are also limited in the amount of information they can handle at one time [13], making optimal choices even more difficult to achieve. Thus, in using this model to predict an individual's behavior, consideration should be limited to those factors that the person is actually using as a basis for decision. Furthermore, the precise mental calculus and metric may be somewhat different from that specified by the expectancy model. On the other hand, the model does provide enough information and is consistent enough with reality to present clear implications for managers who are concerned with the question of how to motivate the people who work for them.


The research evidence referenced in this article demonstrates that motivation can be positively influenced and managed. Research evidence and practices from non-construction settings are used to develop a different approach to motivating construction workers. With the employment of the motivational model and measurement tools like those presented in this article, construction companies can embark on tailor-made productivity improvement programs.

The major recommendations for construction managers are: (1) play an active and continuous role in managing workers’ motivation; (2) identify what outcomes your employees value; (3) determine what levels of performance the construction company desires; (4) develop adequate performance measurement techniques; (5) link desired outcomes to desired performance; and (6) involve the employees in a cooperative venture aimed at improving productivity.


1. Borcherding, J.D., An Exploratory Study of Attitudes that Affect Human Resources in Building and Industrial Construction, Stanford University, Stanford, CA, 1972

2. Campbell. J.P., & Pritchard, R.D., Motivation Theory in Industrial and Organizational Psychology, in M.D. Dunnette (Ed.), Handbook of Industrial and Organizational Psychology, Chicago, 111: Rand McNally, 1976.

3. Cammann, C., Fichman, M., Jenkins, G.D., Jr., & Klesh, J.R., Assessing the Attitudes and Perceptions of Organizational Members, in Seashore, S.E., Lawler, E.E., III, Mirvis, P.H., & Cammann, C. (Eds.), Assessing Organizational Change: A Guide to Methods, Measures, and Practices, New York: Wiley-Interscience, 1983.

4. Chung, K.H., Motivational Theories and Practices, Columbus, Ohio: Grid Publishing Co., 1977.

5. Hazeltine, C.S., Motivation of Construction Workers, Journal of the Construction Division, ASCE, 102, Proc. Paper 12376, 1976, 599-614.

6. Herzberg, F., Mausner, B., & Snyderman, B., The Motivation to Work, New York: John Wiley & Sons, 1959.

7. Laufer, A., & Jenkins, G.D., Jr., Motivating Construction Workers, Journal of the Construction Division, ASCE, 108, Proc. Paper 17508, 1982, 531-545.

8. Lawler, E.E., III, Pay and Organizational Effectiveness: A Psychological View, New York: McGraw-Hill, 1971.

9. Lawler, E.E., III, Motivation in Work Organizations, Brooks Cole, Monterey, CA: Brooks Cole, 1973.

10. Luthans, F., Organizational Behavior, New York: McGraw-Hill, 1977.

11. Maloney, W.F., Motivation in Construction: A Review, Journal of the Construction Division, ASCE, 107, Proc. Paper 16711, 1981, 641-647.

12. Maloney, W.F., & McFillen, J.M., Research Needs in Construction Worker Performance, Journal of Construction Engineering and Management, 109, 1983, 245-255.

13. March, J.G., & Simon, H.A., Organizations, New York: John Wiley & Sons, 1977.

14. Maslow, A.H., Motivation and Personality, New York: Harper, 1954.

15. Mason, A., Worker Motivation in Building, The Institute of Building, Occasional paper No. 19, Ascot, England, 1978.

16. Nadler, D.A., & Lawler, E.E., III, Motivation: A Diagnostic Approach, in J.R. Hackman, E.E. Lawler, III, and L.W. Porter (Eds.), Perspectives on Behavior in Organizations, New York: McGraw-Hill, 1977.

17. Neale, R.H., Motivation of Construction Workers: Theory and Practice, The Institute of Building, Site Management Information Service, No. 72, Ascot, England, 1979.

18. Porter, L.W., & Lawler, E.E., III, Managerial Attitudes and Performance, Homewood, IL: Irwin-Dorsey, 1968.

19. Productivity Gets Attention, Engineering News-Record, Sept. 18, 1980, p. 239.

20. Ruff, E.R., Proceedings of the Conference on Construction Productivity Improvements, The University of Texas at Austin, Texas, 1980.

21. Schrader, C.R., Motivation of Construction Craftsmen, Journal of Construction Division, ASCE, 98, Proc. Paper 9485, 1972, 257-273.

22. Stokes, K.H., Jr. An Examination of the Productivity Decline in the Construction Industry, Construction Productivity Frontier, Construction Productivity Center, IIT Center, Chicago, IL, April, 1980, 10-16.

23. Talbot, P. J., Financial Incentives-Do They Work? The Institute of Building, Occasional paper No. 10, Ascot, England. 1976.

24. Vroom, V.H., Work and Motivation, New York: John Wiley & Sons, 1964.

Dr. Alexander Laufer is currently a Senior Research Engineer at the Building Research Station, Technion, Israel Institute of Technology, Haifa, Israel.

Dr. G. Douglas Jenkins, Jr. is an Assistant Professor in the Department of Management, University of Texas at Austin, Austin, Texas.

PMI Members:

This is the time of the year when the membership will be billed for the 1984 annual dues.

Please note any changes of address, name and/or telephone number on your invoice stub to be returned in the attached envelope to Drexel Hill.

Your prompt payment and notice of changes will be greatly appreciated and will assure you that the 1984 PMI mailings will be correctly sent throughout the year.

1This research was conducted while Dr. Laufer was an Assistant Professor in the Civil Engineering Department, Texas A & M University, College Station, Texas.



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