PEER-REVIEWED PAPER

ANALYSIS OF CLIENT-SATISFACTION FACTORS INCONSTRUCTION INDUSTRY

By Syed M. Ahmed1 and Roozbeh Kangari,2 Member, ASCE

ABSTRACT: What factors do clients perceive as being most important when dealing withcontractor organizations? How does the perception of clients in various industries differfrom each other? To answer these questions, and develop a client-satisfaction model, asurvey of 10I client companies was conducted. Data analysis by a one-way analysis of variancewas carried out to locate the important independent variables of the client-satisfaction model.Finally, to check if there was any interaction between the client groups (transportation,food, chemical and paper, utility, and other miscellaneous industries) and the model factors(time, cost, quality, client orientation, communication skills, and response to complaints),a two-way analysis of variance was performed. Through the analysis of data generated bythe survey, it is concluded that all the factors identified in the client-satisfaction model donot possess the same significance when it comes to satisfying clients. However, clients fromdifferent industrial sectors do not display significant differences in their perception of factorsleading to satisfaction. In addition, no interaction was detected between the model factorsand client groups.

INTRODUCTION

Ouality has become one of the important forces lead­ing to organizational success and company growth innational and international markets. Construction haslagged behind other industries in implementing totalquality management (TOM). The main reason is theperception that TOM is for manufacturing only. How­ever, that perception is rapidly changing.

One of the key elements of TOM is client satisfaction.TOM is a complete management philosophy that em­phasizes overall satisfaction through the continuous im­provement of products and processes. However, beforethe stage of satisfying the client is reached, a concertedeffort is needed to understand client requirements.

One of the industry's weaknesses in implementingthe TOM philosophy is its inability to accurately de­termine client requirements and successfully transformthese requirements into the completed facility. The con­struction industry has begun to understand the need forthe adoption of a new client-oriented philosophy into

'Asst. Dir.. Capital Development Authority. Parliament Ave.,Islamahad. Pakistan.

'Assoc. Prof.. Civ. Engrg. School. Georgia Inst. of Techno!., At­lanta. GA 30332-0355.

Note. Discussion open until September 1. 1995. To extend theclosing date one month. a written request must he filed with theASCE Manager of Journals. The manuscript for this paper was suh­mittcd for review and possihle puhlication on November 9,1993. Thispaper is part of the Journal of Management in Engineering. Vo!. 11.No.2. March/April. 1995. (OASCE. ISSN 0742-597X/95/0002-0036­0044/$2.00 + $.25 per page. Paper No. 7318.

36 JOURNAL OF MANAGEMENT IN ENGINEERING / MARCH/APRIL 1995

the construction and engineering environment. Accu­rately determining user requirements is a key factor inTOM. Therefore, construction companies must designand continuously improve user systems that focus ondiscovering, creating, improving, and delivering valueto the client.

Functional definitions for client, satisfaction, andperception are prerequisites to measuring client satis­faction. Client is defined as the one who pays the bills.The term satisfaction is defined as the result of somecomparison process in which expectations are comparedwith what is actually received (Czepiel 1985). Percep­tion in this study is defined as the client's impressionand feeling about a service process. Satisfaction andperceptions are intimately related in the service expe­rience. Clients are most likely to be satisfied when theirperception of the service matches or exceeds their ex­pectations. Clients perceive service in their own uniqueways, and the client's perceptions may differ from thecontractor's perceptions. Satisfaction is the client's cu­mulative memory of many positive experiences, but thosepositive experiences can be tarnished by just one badexperience (Austin and Peters 1985).

Although the construction industry has become awareof the importance of client satisfaction, it is also nec­essary to know how well the industry is meeting clientexpectations. There is a need to develop a mechanismto measure client satisfaction in place. The objective ofthis paper is to analyze the data collected from a surveyof clients to study the major factors influencing client

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satisfaction in the construction industry, and the levelof importance assigned to each client-satisfaction factor.

CLIENT SATISFACTION

The benefits of taking an aggressive approach to iden­tify what clients value and to concentrate on a few selectcapabilities are clearly highlighted by Sirkin and Stalk(1990). They concluded that, when armed with first­hand knowledge of what clients value, managers wereable to devise systems that uncovered the root causesof their quality and service problems, and implementpermanent changes to eliminate these problems.

Chase and Tansik (1983) developed a high-low c1ient­contact continuum as the basis of their contingency model.This model distinguishes high-contact services as morecomplex because the client is an uncertain variable. Thechallenge, they contend, is to match people-orientedempolyees with high-contact jobs, and to layout theservice facility to accommodate the client's needs andexpectations.

Albrecht and Zemke (1985) developed a modelsomewhat similar to the Chase and Tansik model, ba­sically labeling different points on the high-low c1ient­contact continuum. Primary service people are thosewho have direct, planned contact with clients. Theseemployees should possess quality interpersonal skills.Secondary service people may have incidental contract,and support people generally do not have any clientcontact. It should be mentioned that neither Chase andTansik (1983) nor Albrecht and Zemke (1985) mentionthat clients are exempt from possessing good interper­sonal skills. In high-contact situations, all parties shouldhave well-toned interpersonal skills, especially when thecontact is prolonged, as in service or industrial contacts.

However, there are very few established methods tomeasure client satisfaction in the construction industry.Ashley et al. (1987) investigated the determinants ofconstruction project success. The results of their pilotstudy show that successful projects emphasized plan­ning effort, project-manager goal commitment, project­team motivation, project-manager technical capabili­ties, scope and work definition, and control systems.Six criteria to measure success were budget, schedule,client satisfaction, functionality, contractor satisfaction,and project-manager/team satisfaction.

A study by Wilemon and Baker (1983), in the humanfactors of project management, shows that to attain highlevels of perceived success (including client satisfac­tion), effective coordination and relations patterns areextremely important. Moreover, success criteria sali­ence and consensus between the client and project teamare crucial.

Many construction companies claim they have diffi­culty in making an assessment of their quality. Manyof the design and construction firms evaluate client sat­isfaction periodically. However, those firms that havemeasured their service performance recently cannot tellwhether it has increased or declined ("TOM" 1993).

However, one of the reasons that construction com-

panies do not openly talk to their clients about qualityand service is that they can seldom expect to receivehonest and sincere input. Clients are frequently criticalof contractors. So contractors 10gicalIy tend to mindtheir own business within the terms of the contract.

In the construction industry, usually the client's re­quirements are to get the construction needs translatedinto a design that specifies technical characteristics, per­formance criteria, and conformance to specifications,and to get the facility built within the cost and time.

The writers' investigation shows that in addition tothe aforementioned major factors, the following threecharacteristics also play an important role in the overallsatisfaction of the client in the construction industry:(1) Client orientation; (2) communication skills; and(3) response to complaints.

Therefore, cost, time, and quality along with clientorientation, communication skills, and response tocomplaints form the basis of the proposed client-satis­faction model by the writers. Based on this model aclient-satisfaction questionnaire was developed to so­licit information from clients in five major sectors ofthe construction industry, as identified by the Engi­neering News Record ("Top owners" 1990, 1992): util­ity, transportation, food, chemical and paper, and othermiscelIaneous industries. The questionnaire was de­signed with the following two questions in mind: (1)What factors do clients perceive as being most impor­tant when dealing with contractor organizations? (2)how do the perceptions of clients in utility, transpor­tation, food, chemical and paper, and other miscella­neous industries differ from each other?

The folIowing section describes the sampling plan,the survey instrument, and the statistical tests employedin this study.

SAMPLING METHODOLOGY

The main objective of the sampling plan was to selecta representative group of clients in the constructionarena. The random variable for this study is client sat­isfaction, which is defined as the means of client re­sponses to the questionnaire items on a scale of 1-5.

For this study, a stratified random sample was chosenfrom a listing of the 700 top owners from data on publiccompanies compiled by McGraw-Hill's Compustat Ser­vices, Inc., a division of Standard & Poor's Corp ("Topowners" 1990,1992). Based on the value of plant, ware­house, and other building structures added, this sampleis drawn from a database of almost 3,000 companies.Some of the major industries covered were food andkindred products, paper and allied products, chemicaland allied products, primary metal industries, electronicand other electrical equipment, transportation andtransportation equipment, utilities (electric, gas, sani­tary services), and health services. Different industriesare grouped according to the U.S. Department of Com­merce Standard Industrial Classification (SIC) codes.

Since the targeted population was classified into sub­populations according to the SIC codes, the stratified

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sampling plan was adopted. Based on the size of thepopulation (700 top owners), a sample size of at least10% of the population was judged to be a representativemeasure of the population under study (Campbell 1987).A total of 359 questionnaires were mailed to five cli­ent groups (transportation, food, chemical and paper,utility, and other miscellaneous industries) accordingto their size in the population as described in Appen­dix I.

The survey yielded a direct measurement of the c1ient­satisfaction phenomena. Because this survey solicitedinformation of a fairly general nature, the respondentsdid not need a detailed memory or highly specializedknowledge. Also, each question in the survey was de­signed to limit biasing.

The questionnaire, as shown in Appendix II, wasbroken down into the following three sections to assistin data interpretation: (1) Demographic data; (2) seriesof statements believed to best represent each client­satisfaction factor in the model; and (3) additional com­ments that might have significant impact on the study.

DATA ANALYSIS

Overall, 101 of the 359 surveys mailed were com­pleted and returned-a response rate of 28.1 %, 10 outof the 359 mailed surveys were returned, "undeliverableas addressed." Table 1 summarizes the response ratesfrom each sample subgroup. Further analysis of thelevel of experience an decision-making among the re­spondents indicates that an overwhelming majority ofsurvey participants were highly placed in their respec­tive organizations, and had the relevant knowledge andexperience to accurately answer the questionnaire.

A correlation analysis based on all the 101 cases wasperformed to determine the intercorrelations of the c1ient­satisfaction factors (time, cost, quality, client orienta­tion, communication skills, and response to com­plaints). All the coefficients were found to be statisti-

TABLE 1. Sample Subgroup Response Rate

Surveys Surveys ResponseClient groups mailed returned rate

(1 ) (2) (3) (%)

Utility 95 30 31.6Transportation 43 16 37.2Food 52 17 32.7Chemical and paper 7f1, 19 24.4Miscellaneous 91 19 20.9

Total 359 101 2f1,.1

cally significant at ex = 0.05, n = 101. Table 2 showsthe correlation coefficients among the client-satisfactionfactors. The results indicate that relationship definitelyexists among the client-satisfaction factors. The strengthof the associations, however, are weak.

To further investigate the data, analysis of variance(ANOYA) was carried out to check whether all theclient-satisfaction factors were equally important to theclients, or whether certain factors needed to be droppedfrom the model. Finally, a two-way analysis of variancewas carried out to see if there was any interaction be­tween the client-satisfaction factors and client groups.

Preliminary data analysis indicated no significant dif­ference among client-satisfaction factors within each clientgroup. However, the mean responses relating to clientsatisfaction within each client group were significantlydifferent. Therefore, a one-way analysis of variance onclient groups was not conducted.

A preliminary analysis of data in Tables 3 and 4 in­dicates that the satisfaction factors have been perceivedto be of approximately equal importance by respondingclients. Whether a statistically significant difference ex­ists among the six factors can be determined only afterconducting the analysis of variance.

One-Way Analysis of Variance forClient-Satisfaction Factors

Table 3 represents the data for the variance analysis.The average value for the responses to each statementfor a particular client-satisfaction factor has been cal­culated. This value was then used to calculate the over­all mean and standard deviation for all 101 cases.

The real question addressed here is not whether thesix sample means differ from each other per se; instead,the question is whether the population's means beingestimated by the sample means differ from one another.

An important advantage of the analysis of varianceis that this method can be employed to evaluate dif­ferences among any number of sample means via asingle test; it does not require several tests on pairs ofsample means as would be the case if Z or T tests wereused. Table 5 shows the results of a one-way analysisof variance at a level of significance of 0.05. Tukey'sprocedure (Campbell 1987) was also employed for pair­wise comparisons of the client-satisfaction factors, asshown in Table 5. After reviewing this analysis, a de­termination can be made about the equal significanceof the size factors.

The 95% confidence intervals for means based on thepooled standard deviation is represented in Fig. 1. The

TABLE 2. Correlation Matrix

Response toClient-satisfaction factors Time Cient orientation Communication Cost complaints

(1 ) (2) (3) (4) (5) (6)

Client orientation 0.40 - - - -

Communication 0.48 0.52 - - -Cost 0.29 0.33 0.43 - -Response to complaints 0.47 0.41 0.47 0.40 -

Quality 0.45 0.39 0.46 0.52 0.49

38; JOURNAL OF MANAGEMENT IN ENGINEERING I MARCH/APRIL 1995

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TABLE 3 Statistical Analysis of Model Factors

StandardClient-satisfaction factor N Mean Median deviation Variance

(1) (2) (3) (4) (5) (6)

Time 101 3.97 4.00 0.48 0.23Client orientation 101 3.89 4.00 0.61 0.37Communication 101 4.02 4.00 0.50 0.25Cost 101 4.10 4.00 0.60 0.36Response to complaints 101 4.04 4.00 0.54 0.29Quality 101 4.17 4.16 0.54 0.29

TABLE 4. Low and High Values of Model Factors

Client-satisfaction factor Minimum Maximum Quartile 1 Quartile 3(1) (2) (3) (4) (5)

Time 2.66 5.00 3.66 4.32Client orientation 2.50 5.00 3.45 4.25Communication 3.00 5.00 3.66 4.33Cost 2.75 5.00 3.75 4.50Response to complaints 2.60 5.00 3.60 4.40Quality 2.66 5.00 3.83 4.66

TABLE 5. One-Way Analysis of Variance for Model Factors: oc = 0.05

Source of variation Sum of squares Degrees of freedom Mean square Observed F P value(1) (2) (3) (4) (5) (6)

Between satisfaction factors 4.75 5 0.95 3.19 0.007Within satisfaction factors 178 600 0.30 - -

Total 183 605 - - -

Client Satisfaction Factors Mean Responses3.90 4.05 4.20

1. Time2. Client orientation3. Communication4. Cost5. Response to complaints6. Quality

3.96843.89504.02444.10154.04234.1708

< ------------+--------- >< --------+---------->

< ----------+---------->< ----------+.---------- >

< ---------+--------- >< ----------+---------->

----T"j-----T"I-----"TI-----> Five-point3.90 4.05 4.20 Likert Scale

POOLED STDEV = 0.5454FIG. 1. Individual 95% Confidence Intervals for Means Based on Pooled Standard Deviation

TABLE 6. Intervals for Column-Level Mean Minus Row-Level Mean: Tukey's Analysis

Client-satisfaction Time Client orientation Communication Cost Response to complaints(1) (2) (3) (4) (5) (6)

Client orientation 0.15, 0.29 - - - -Communication -0.27,0.16 -0.35,0.09 - - -Cost -0.35,0.09 - 0.43, 0.01 - 0.30, 0.14 - -

Response to complaints -0.29,0.15 -0.37,0.07 -0.24,0.20 -0.16,0.28 -

Quality -0.42,0.02 -0.49, -0.06 -0.37,0.07 -0.29,0.15 -0.35,0.09

pooled standard deviation is equal to 0.54. Based onthe results shown in Table 5, we reject the null hy­pothesis. The P value of less than 0.05 signifies thisoccurrence. Thus, we conclude that the population'smeans are not all equal.

By failing to accept the null hypothesis, the conclu­sion drawn is that all client-satisfaction factors are notequally important to the clients. This variation is dueto the nature of the industry a client belongs to. How-

ever, the full extent of the interactions, if any, betweenthe client-satisfaction factors and client groups cannotbe determined by this procedure. However, Tukey's(Campbell 1987) pairwise comparisons can be employedto evaluate means that are significantly different fromeach other and those that are not. Table 6 illustratesthe intervals for column-level mean minus the row-levelmean.

By observing Fig. I, and Table 6 it can be seen that

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there are no significant differences between each of thepairs analyzed except between client orientation andquality. They are represented by levels 2 and 6, re­spectively, in Fig. 1. Only these two factors now haveoverlapping confidence intervals. Client orientation hasthe lowest mean score (3.89) and quality has the highestmean score (4.17). The rest of the mean scores lie inbetween these two extremes and are not significantlydifferent from each other. The second and sixth con­fidence intervals representing client orientation andquality (see Fig. 1), respectively, and clearly nonover­lapping, indicating significant difference in the meanscores of these two factors. Likewise, negative values( - 0.49 and - 0.(6) in the third column of Table 6indicates the significant difference in the two mean scores.The conclusion drawn is that all the client-satisfactionfactors are not perceived to be equally important to theclients. Specifically, two of the client-satisfaction fac­tors, client orientation and quality, show significant dif­ferences.

The same problem will now continue to be analyzed,but this time from an experimental perspective. Whetherthe mean responses to client-satisfaction factors differedas a group will also be investigated. By experimenta­tion, the investigator can play an active rather than apassive role by varying one or more factors whose ef­fects should be studied.

Two-Way Factorial Arrangement ofTreatment Levels

Factorial arrangement of treatment levels refers toan arrangement of experimental data such that eachlevel of one independent variable is paired with eachlevel of every other independent variable. In this study,the two independent variables are client groups andclient satisfaction factors. For example, timeliness (aclient-satisfaction factor) is paired with transportation,food, chemical and paper, utility, and other industries(client groups).

The main purpose for going through the two-wayvariance analysis is to check for the presence or absenceof any interaction between the client-satisfaction factorsand client groups. Interaction between variables playsa very important role in decision making and has to beinvestigated in detail. The two-way analysis provides aprocedure to check the behavior of the two independentvariables when taken into consideration simultane­ously. So far, the independent variable, client-satisfac­tion factors, has been analyzed separately via the one­way analysis of variance. The analysis concluded thatthe mean responses to the satisfaction factors by dif­ferent client groups were not all equal.

Table 7 contains the data used for the two-way anal­ysis with 16 observations per cell. The numbers in eachcell represent the mean scores of responses by a clientgroup corresponding with a particular client-satisfactionfactor. Sixteen observations per cell were used because

40 JOURNAL OF MANAGEMENT IN ENGINEERING / MARCH/APRIL 1995

this was the lowest number of responses received froma client group and was the limiting factor in this study.

MULTIPLE·REGRESSION ANALYSIS

A multiple-regression analysis was carried out withall the 101 responses to test the validity of the assump­tions regarding equal variances and normal distributionof the population. These assumptions have been vali­dated through Figs. 2 and 3. By plotting the residualsagainst the expected mean scores, a determination canbe made about the dispersion or variability of the data.Residual analysis, a capability of many multiple-regres­sion computer programs, examines the degree to whicha specified model satisfies the traditional assumptionsof multiple regression, and thereby suggests the inclu­sion of additional variables to improve the fit of themodel. A plot of the residuals, which are essentially theerrors of predicted (e;), were used for the analysis ofresiduals. As shown in Fig. 2, since the deviations fromthe mean are displaying constant variances, the meanshave equal variance. This satisfies the first requirementfor developing a client-satisfaction model as describedin the following section.

The s-curve of the plot, as shown in Fig. 3, betweenthe n-score and the residuals indicates that the residualsare normally distributed, and since the residuals arenormally distributed, the population can also be as­sumed to be normally distributed. The three nulI andalternate hypotheses tested are

For rows (client groups)

Ho : fLA = fLB = fLc = fLJ) = fLE (I)

Hu : At least one means is unequal (2)

For columns (client-satisfaction factors)

Ho : fLl = fLz = fL3 = fL4 = fLs = fL6 (3)

Ho : At least one means is unequal (4)

For interaction (client groups and satisfaction factors).On analyzing the results presented in Table 8, the

first null hypothesis is accepted, the second null hy­pothesis is rejected, and the final null hypothesis is alsoaccepted.

H o : Interaction (rc) = 0 for all cells (5)

H o : Interaction (rc) =1= for at least one celI (6)

The P value of 0.004, which is less than 0.05, signifiesthe rejection of the second null hypothesis. This testconcludes that the mean responses of the clients to thesix client-satisfaction factors do not significantly differas a group. The client-satisfaction factors are, however,not perceived to be equal by the clients when consideredjointly as one client group. The reasons for this couldbe the nature of the industry a client represents. Thereis also no evidence of a significant interaction effectbetween the client groups and client-satisfaction factors.

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TABLE 7 Mean Scores of Responses by Client Groups

Client Response to

Client group Time orientation Communication Cost complaints Quality

(1 ) (2) (3) (4) (5) (6) (7)

Utility 3.86 3.77 4.01 4.38 4.08 4.05

Transportation 4.01 3.91 3.97 3.92 4.10 4.25

Food 3.88 3.64 3.84 3.98 3.90 4.25

Chemical and paper 4.02 4.01 4.11 4.01 4.75 4.12

Miscellaneous 3.87 3.98 3.98 3.89 4.08 4.05

2.0+

gSlDS

0.0+

-2.0+

2 * 33* 2 4* * 3 2 * .24*42 5 4 2 *3

3 2 7*4 ** 2 * 53*3283 * 3 2 4* * * 2 48 * 2 2533 ·36 • • 26 3

3 + 359 447 3 7 8+2 3655 • 3 223 2 2 924 238 • 5 ·75323+ 38 43 • 2 457 25+ 3 4 282 •• 5 3 2 4* •

2 * 4*4 • 5 2 4 4644 ·3* 2 • 24 52 4 234 324 3 3 *63* 2*5 4 3 ·53 4 5 • 2*6* 2 •

• * 2 •• 3 • ••• • • • • •

• ••

------+---------+---------+-~----~-~---~--+---------+EXPECTED3.80 3.90 4.00 4.10 4.20 4.30

2.0+

0.0+

-2.0+

•

FIG. 2. Plot of Residuals against Expected Means

·26

·742+5+

++8+5++6+8

++++9++++2

+++++4++++2

5++84+446

2443.*•••

• *

----+---------+-------~---------+----~-~-----~---+--RESIDS-3.0 -2.0 -1.0 0.0

FIG. 3. Normal Probability Plot1.0 2.0

TABLE 8. Analysis of Variance for Model Factors and Client Groups: Multiple Regression

Degrees ofSource of variation Sum of squares freedom Mean square Observed F P value

(1 ) (2) (3) (4) (5) (6)

Between client groups 1.33 4 0.33 1.17 0.324Between satisfaction factors 4.94 5 0.99 3.47 0.004Interaction between clients and factors 4.63 20 0.23 0.81 0.699Error 128.13 450 0.29 - -

Total 139.02 479 - - -

JOURNAL OF MANAGEMENT IN ENGINEERING I MARCH/APRIL 1995/41

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TABLE 9. Model Variables for Each Client Group

CLIENT-SATISFACTION MODEL

TABLE 10. Model Variables for Each Client-Satisfaction Fac­tor

XI X 2 X} X 4 YIo 1 000

spect to cost (factor 4), the following values of the var­iables:

from Tables 9 and 10 would be substituted in the regres­sion (7) to get a mean score of 4.09. This indicates thatclients in the chemical and paper industries consider thecost factor very important (4.09 on a scale of 1-5) fortheir satisfaction. This value provides a close estimatewhen compared with the mean score of responses of4.01 as shown in Table 7, column 5. The fit index, orthe goodness of fit, measured by the sum of squaresdue to regression divided by the total sum of squares(r2

) in this case is 4.5%.

CONCLUSIONS

Data generated through the client-satisfaction surveywas analyzed and certain hypotheses were tested. Theresults indicated that a relationship definitely exists amongthe six client-satisfaction factors (time, cost, quality,client orientation, communication skills, and responseto complaints); however, the strength of the correla­tions are weak. The test results showed that the meanresponses of the clients to the six factors did not sig­nificantly differ as a group. This indicates that the sixfactors are considered important for client satisfaction.

Further investigation of the data by a one-way anal­ysis was carried out to check whether all six client­satisfaction factors were equally important to the clientgroups (transportation, food, chemical and paper, util­ity, and other miscellaneous industries), or whether cer­tain factors needed to be dropped from the client-sat­isfaction model. The analysis showed that by failing toaccept the null hypothesis, the conclusion drawn is thatall client-satisfaction factors are not perceived to beequally important by the clients. Specifically, two of theclient-satisfaction factors, client orientation and quality,showed significant differences.

In addition, a two-way analysis of variance was car­ried out to see if there was any interaction between theclient-satisfaction factors and client groups. Interactionbetween variables plays a very important role in deci­sion making and, thus, was investigated in detail. Thetwo-way analysis also provided a procedure to checkthe behavior of the two independent variables whentaken into consideration simultaneously. There was noevidence of a significant interaction effect between theclient-satisfaction factors and client groups. When thereis no interaction, the relationship among the columneffects (client-satisfaction factors) is the same regardlessof the row (client groups) being considered, and therelationship among the row effects (client groups) is thesame regardless of the column (client-satisfaction fac­tor) being considered. The two-way variance analysisconfirms the results of the one-way analysis that thesatisfaction factors were not perceived equally by theclient groups. The mean responses of the client groupsto the criterion variable client satisfaction did not differ

(7)

Z = 3.95 - O.109X I + O.0332X2 + O.0223X}

- O.0498X4 - O.0654YI + O.0496Y2

+ O.IIIY, + O.I13Y4 + 0.257Ys

A client-satisfaction model is developed based on themultiple-regression analysis between the mean scoresand two independent variables: client-satisfaction fac­tors and client groups. The regression equation is

When there is no interaction, the relationship amongthe column effects (client-satisfaction factors) is the sameregardless of the row (client groups) being considered,and the relationship among the row effects is the sameregardless of the column being considered. The two­way variance analysis confirms the result of the one­way analysis that the satisfaction factors were notperceived equally by the client groups. The means re­sponses of the client groups to the criterion variableclient satisfaction did not differ as a group when con­sidered in combination with the client-satisfaction fac­tors.

in which Z = mean score of the client satisfaction, avalue between 1 and 5 indicating the level of importanceof a given client-satisfaction factor to a client group;XI' X 2 , X}, and X 4 = four variables (0 or I) for eachclient group; Y" Y2 , Y}, Y 4 , and Ys = five variables(0 or I) for each client-satisfaction factor. These vari­ables are defined in Tables 9 and 10.

Clients 1-5 correspond with clients grouped in thetransportation, food, chemical and paper, utility, andother miscellaneous industries, in that order. Factors1-6 correspond with client-satisfaction factors: time,client orientation, communication, cost, response tocomplaints, and quality.

For example, to predict the mean response of a clientin the chemical and paper industry (client 3) with re-

Client group X, X2 X3 X.(1) (2) (3) (4) (5)

Client 1: Transportation 0 () 0 ()

Client 2: Food I 0 0 ()

Client 3: Chemical and paper 0 1 () ()

Client 4: Utility 0 0 1 ()

Client 5: Miscellaneous 0 0 0 1

Client-satisfaction factor Y, Y2 Y3 Y. Ys(1) (2) (3) (4) (5) (6)

Factor 1: Time () 0 () 0 ()

Factor 2: Client orientation I () () () ()

Factor 3: Communication () 1 () () ()

Factor 4: Cost 0 () I 0 ()

Factor 5: Response to complaints () () 0 1 ()

Factor 6: Quality 0 () () 0 1

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e. Assistant Project Directorf. Construction Supervisorg. Building Managerh. Other

as a group when considered in combination with c1ient­satisfaction factors.

Finally, the overall results are summarized in a c1ient­satisfaction model. The model is developed based onthe multiple-regression analysis between the mean scoresand two independent variables: client-satisfaction fac­tors and client groups. The model provides a Z score,which shows a client's level of satisfaction, on a scaleof 1-5, for a given model factor (time, cost, quality,client orientation, communication skills, or response tocomplaints) .

ACKNOWLEDGMENTS

The writers would like to thank the companies andindividuals who participated in this study. The writersare also grateful to the ASCE reviewers for their pro­ductive comments.

APPENDIX I. SAMPLE COMPOSITION

For this study the proportional-allocation procedure,which partitions the sample size among the strata pro­portional to the size of the strata was used. The majoradvantage of using proportional allocation is that it pro­vides a self-weighing sample, since the sampling fractionis the same in each stratum. For proportional allocation,the sample size n is allocated among the L strata so thatn = n t + nz + n1 ' .• + nL , in which each ni is givenby the following formula:

ni = n (~) i = 1, 2, 3, ... , L (8)

in which ni = sample size in each client group; N i =

number of elements in stratum i; and N = size of thepopulation (Mendenhall and Reinmuth 1982). The cal­culation results using this method show that n l = 95,nz = 43, n 1 = 52, n4 = 78, and ns = 91, which indicatesa total of 359 sample questionnaires.

APPENDIX II. CLIENT-SATISFACTIONQUESTIONNAIRE

This questionnaire was mailed to a stratified randomsample of 359 owners from the 700 top owners list pub­lished by McGraw-Hill's Compustat Services, Inc., adivision of Standard & Poor's Corp ("Top owners" 1990,1992). The questionnaire was attached to a cover letterthat requested the evaluation of each statement ac­cording to how owners think it would influence theirsatisfaction with the contractors. They were requestedto base their responses on their perceptions of clientsatisfaction when dealing with contractors.

The questionnaire was broken down into three sec­tions to assist in data interpretation. The first sectionof the survey was used to gather demographic data onthe survey respondents. Response to the demographicquestions were used to categorize the respondents ac­cording to title and tenure. The second section com­prised a series of statements believed to best representeach of the model factors of the client-satisfaction model.

Respondents were asked to evaluate each statement ona five-point Likert scale on how they perceived it toinfluence their satisfaction with the contractor's orga­nization. The third and final section of the survey askedthe respondents to note any additional comments theythought was not covered by the questionnaire, whichmight have significant impact on the study. The follow­ing section illustrates the client-satisfaction question­naire:

Section I

I. Your Name: _

2. What is your title:a. Project Directorb. Project Managere. Construction Managerd. Assistant Project Manager

3. How long have you been a member of. or worked for, this organization"a. 0-5 yearsb. 5-10 yearsc. 11-20 yearsd. More than 20 years

Section II

Please evaluate each statement according to how you think it would influenceyour satisfaction with the contraetor's/eonstructor's organization. Your re­sponses should be based on your perceptions of client satisfaction when dealingwith construction/building contractors.

Legend

I ~ No importance2 ~ Little importance3 ~ Average importance4 ~ Considerable importance5 ~ Extreme importance

Timeliness

I. When requests for work are submitted, pro-vide a reasonable estimate of work and whenwork will begin. 2 3 4 5

2. Give the small jobs high priority. 2 3 4 53. Plan and schedule jobs quickly. 2 3 4 54. Once a job is started, complete it quickly. 2 3 4 55. Respond immediately to work status in-

quiries. 2 3 4 56. Maintain a sense of urgency.

Client Orientation

7. Display a courteous and helpful attitude. 2 3 4 58. Empathize with my problem, and treat it

as an important request. 2 3 49. Completely explain policies. procedures, and

coordination requirements in advance. 2 3 410. Provide assistance and direction for com-

pleting paperwork. 2 3 4 5

Communications

II. Provide periodic listings of all my work or-ders and their status. 2 3 4

12. Explain the proposed job prior to startingit. 2 4

13. Provide notifications and explanations forwork delays. 2 3 4

14. Provide updates on work as il progresses. 2 3 415. Explain what was done to solve a particular

problem. 2 3 4 516. Follow up to make sure the job was done

satisfactorily. 2 4

Cost

17. Conduct value engineering to reduce cost. 2 3 4 518. Employ adequate cost-control measures to

stay within budget. 2 3 4 519. Reduce wastes to a minimum. 2 3 4 520. Have adequate financing armngements. 2 3 4 5

Response to Complaints

21. Simplify procedures to lodge complaints. 2 3 4 522. Offer personal attention to complaints. 2 3 4 5

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APPENDIX III. REFERENCES

Section 111Please add any additional factors that may influence your satisfaction as a clientor a client of construction services. Also feci free to add any comments youthink might he of hclp to this study.

Aaker. D. A" and Day, G. S. (1986). Marketing research, 3rd Ed.,John Wiley & Sons, Ltd., New York, N.Y., 306-312.

Albrecht, K., and Zemke. R. (1985). Service America. Dow Jones­Irwin, Homewood. Ill.

Ashley, D. B" Lurie, C. S" and Jaselskis, E. J. (1987). "Determi-

23. Offer rl..'asonahlc explanation for com­plaints.

24. Treat complaints on completed johs aspriorities.

25. Respond quickly to legitimate complaints.

Quality

26. Give top priority to the performance char­acteristics of the facility.

27. Give equal preference to the secondarycharacteristics or fl..'atures of the facility.

2X. Efforts should he made hy the contractorto meet or exceed all specifications or con­formance requirements.

2Y. Ensure the durahility of the completed fa­cility as an integral part of contractor func­tions.

311. Give importance to aesthetics. such as howa product fecls. sounds. and looks.

31. Perceive quality as an essential dimensionof overall client satisfaction.

2

22

2

2

2

2

2

2

3

33

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3

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3

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55

5

nants of construction project success." Proj. Mgmt. J., 18(2),69­79.

Austin, N., and Peters, T. J. (1985). A passion for excellence. WarnerBooks, New York, N.Y.

Bowen, D. E., and Schneider, B. (1985). "Boundary-spanning-roleemployees and the service encounter: some guidelines for man­agement and research." The service encounter, J. A. Czepiel et aI.,eds., Lexington Books, Lexington Mass., 128-129.

Campbell, S. K. (1987). Applied business statistics: texts, problems,and cases, Harper & Row, New York, N.Y.

Chase. R. B., and Tansik, D. A. (1983). "The customer contactmodel for organization design." Mgmt. Sci., 29(Sep.), 1037-1050.

Czepiel, J. A., et al. (1985). "Service encounters: an overview." Theservice encounter, Lexington Books, Lexington, Mass" 13.

Mendenhall, W., and Reinmuth, J. E. (1982). Statistics for manage­ment and economics, 2nd Ed., PWS Publishers, Boston, Mass.,703-724.

Sirkin, H., and Stalk Jr., G. (1990). "Fix the process, not the prob­lem." Harvard Business Rev., July-Aug.

"Top owners spend cautiously: industry takes careful stock of newconstruction needs for the future." (1990). Engrg. News Rec.,227(24),41-62.

"Top owners: owners powering up for recovery," (1992). Engrg.News Rec., 229(21), 22-38.

"TOM is underutilized according to poll." (1993). Engrg. News Rec.,Feb. I, 14.

Wilemon, D. 1.., and Baker, N. B. (1983). "Some major researchfindings regarding the human element in project management."Project management handbook, Cleland, D. 1., and King, W. R.,eds., Van Nostrand Reinhold Co., New York, N.Y., 623-641.

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