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Differential management of waste by constructionsectors: a case study in Michigan, USABen Dozie Ilozor aa LEED AP Associate Professor and Research Coordinator, School of Engineering Technology,Eastern Michigan University, USAPublished online: 22 Sep 2009.

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Construction Management and Economics

(

August 2009)

27

, 763–770

Construction Management and Economics

ISSN 0144-6193 print/ISSN 1466-433X online © 2009 Taylor & Francishttp://www.informaworld.com

DOI: 10.1080/01446190903117769

Differential management of waste by construction sectors: a case study in Michigan, USA

BEN DOZIE ILOZOR

*

LEED AP Associate Professor and Research Coordinator, School of Engineering Technology, Eastern Michigan University, USA

Taylor and Francis

Received 22 December 2008; accepted 15 June 2009

10.1080/01446190903117769

Waste is a great problem in the world of construction. If dealt with appropriately, there can be many benefits,including lower overall cost, faster production, a higher quality and more sustainable buildings. There aremany solutions available for minimizing waste during construction. However, a great amount of waste stillexists, whether in residential, commercial, industrial, infrastructural or other constructions. The purpose ofthis investigation is to ascertain key sources of waste, and whether generation varies with the type and size ofthe constructors. A sample of 30 general contractors was studied, and several null hypotheses on wastegeneration and minimization differences among sectors were tested using the Kruskal–Wallis H-test. Althoughsubtle shifts were observed in the aspects of waste behaviour that seemed predicated on construction sectorsand capital base, to some extent the proposition that the construction type and size can influence wastegeneration and minimization was validated. Based on this study, some solutions are provided as viable avenuesto managing and minimizing construction waste across sectors.

Keywords:

Construction sectors, waste management, differences.

Introduction and background

Tremendous resources continue to be invested inconstruction despite progressive growth in this sectorsince the 1950s. The total US construction sector wasprojected to be $1.18 trillion in 2007 according to theforecasters at the FMI Corp. (Grogan, 2006).Construction consumes up to 60% of raw materialsused in the US economy, and about 136 million tons ofbuilding-related construction and demolition (C&D)waste is generated each year, out of which only 20–30%is recycled (Swingle, 2006). On average, 8000 poundsof waste are generated in a single 2000 sq. ft. home(Sustainable Sources, 2008). The leading materialsthat are being wasted in all types of construction areconcrete, wood and drywall (Sandler, 2003).

According to Nebraska Energy Office (2008), mostconstruction waste currently goes into landfills, thusincreasing the burden on landfill loading and operation.Nationally, construction waste contributes a largeportion to the waste stream destined for the USlandfills. It is estimated that 2.5 to 4 tons—about 3 to5 pounds per square foot of waste is created during the

construction of a typical home. Very little reuse or recy-cling is currently practised. Construction waste consistsmainly of lumber and manufactured wood products(35%); drywall (15%); masonry materials (12%); andcardboard (10%). The remainder is taken up by a mixof roofing materials, metals, plaster, plastics, foam, insu-lation, textiles, glass and packaging. The Leadership inEnergy and Environmental Design (LEED) certifica-tion system encourages diverting waste by awardingpoints for diverting at least 50% of waste (LEED, USGreen Building Council, 2008).

Many current building practices encourage waste.Lean construction principles, promoted by several leanbuilding and construction groups, aim to reduce wastecaused by unpredictable workflow. They define wastein seven categories: defects, delays due to waiting forupstream activities to finish, over-processing, over-production, maintaining excess inventory, unnecessarytransport of materials and unnecessary movement ofpeople (Pinch, 2005). Other sources of waste includedesign changes, leftover material and poor weather(Faniran and Caban, 1998). Improper purchasing is apractice that leads to a great deal of waste. Purchasing

*

E-mail: bilozor@emich.edu

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personnel are usually very cost-conscious and buycheap equipment and materials that cause future waste(Germain, 1997). Buying high quality materials reducesrejects, which in turn reduces waste (SustainableSources, 2008). However, it is hard to convince uppermanagement to spend more money on these products.Also, it would be difficult to trace the problems causedby low quality products back to purchasing, so thepurchasing agents would not change their behaviour.

Previous research has proffered many solutions forminimizing waste. The Lean Construction Institute(LCI) seems to have done the most research on thissubject. The LCI groups believe in causing a desiredfuture rather than identifying the difference betweenplaned and actual. They encourage tools such as feed-back loops and post-occupancy evaluations to producebuildings efficiently (Ballard, 2000). One main leanprinciple is based on an innovation by Toyota. AToyota engineer, Ohno, insisted that workers stop theproduction line rather than release a defective partdownstream (Pinch, 2005). This same idea applies toconstruction—don’t release defective or incompletework into the process (Pinch, 2000). The lean princi-ples also focus on how one activity affects the next, andnot eliminating waste in individual activities. The leanprinciples are good ideas, but are hard to work with,since they do not address specific goals. If these princi-ples are established, there can be long-term benefits,but it is very difficult and overwhelming to try to changethe mindsets of everyone on the jobsite. Some specificsolutions have been offered by previous research suchas waste minimization by design (Osmani

et al

., 2008)and waste reduction by prefabrication (Jaillon

et al

.,2009). Using separate bins for waste (Lennon, 2006)makes the recycling process easier. Donating to organi-zations such as the Habitat for Humanity is a great wayto get rid of extra material with a tax deduction(Sustainable Sources, 2008). However, some construc-tion managers still find it troublesome to arrange for thematerials to be delivered or picked up. Furthermore, therecycling facilities and centres may be non-existent.Pre-cut materials and prefabricated systems minimizeonsite waste (Mueller, 2006). Just in time (JIT) deliverysystems (Sustainable Communities Research Group,2008) reduce the cost of storing materials, stolen mate-rials, and damage from weather, but constructionmanagers must be able to execute a well-planned sched-ule for this to work. The Sustainable CommunitiesResearch Group posits that the main tool for reducingwaste is to have a plan. Acknowledge waste by using awaste inventory, and look for areas where you canimprove. Designating someone on the staff for wastecontrol is also a great idea.

Gudenau and Ilozor (2007) maintain that all thesesolutions for waste minimization are good ideas, but

are useless if not put into action. Programmes likeLEED also encourage waste minimization, and build-ing effectively, but builders and owners must be awareof the programme and be persuaded to follow it. Ifbuilders are not aware of where the problem comesfrom, then they will not seek a solution. In some cases,not all stakeholders on a project site are aware of theproblem. For instance, upper management may beaware, but the subcontractors are not. Obviously, thisproblem has to be brought to light for all, and incen-tives must be provided for efforts at minimizing waste,and sanctions imposed for wasting.

Despite the many solutions available for minimizingwaste during construction, a great amount of waste haspersisted, whether in residential, commercial, indus-trial, infrastructural or other types of construction.None of the previous studies has focused on thesectoral differences in handling and managing waste,but it is expected that the perspective taken in this workwill contribute to better apprehending and approachingthe problem of waste in construction. The purpose ofthis investigation is therefore to ascertain the keysources of waste, and whether generation varies withthe type and size of the constructors. Based on thisstudy, some solutions are provided as viable avenues tomanaging and minimizing construction waste acrosssectors. It provides an awareness that can inform allconcerned persons across all types and sizes ofconstruction about waste problems, and help themmake informed choices with regard to practiceimprovements. While by analogy the solutions mayhold true beyond Michigan, it is not the intention ofthis work to generalize to populations beyond the studysetting due to infrastructural, locational and otherdifferences.

The research purpose is addressed in this paperunder the following subheadings:

●

Research design;

●

Data analysis, results and discussion;

●

Conclusion.

Research design

This section describes the methodology adopted incarrying out the study. Presented are the nature of thesample, with a mention of the questionnaire instru-ment, operational measures of the study variables andthe statistical methods used for analysing the data. It isacknowledged that the design of the study, the samplesize and statistical methodologies will necessarily limitthe accuracy of the results, and conclusions fromthem. However, further statistical tests are applied toascertain the reliability of the results.

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Methodology

A combination of resources was used to achieve thepurpose of this research project. Prior research formedits foundation. After gaining an understanding of thecurrent topic issues, a 28-question survey was devel-oped to conduct the pilot research. The questionswere aimed towards determining familiarity withmanaging construction waste, what people are doingabout it and how, and attitudes towards the topic, aswell as new ideas for this problem. General contrac-tors were selected as the participants from whom toseek data, since they are most likely dealing withconstruction waste in one form or another. Exhaustiveefforts were made to achieve a good base of surveysfor statistical analysis. Surveys were first circulated atthe Eastern Michigan University’s ConstructionManagement Job Fair, which is usually attended bymany construction companies. A large list of generalcontractors was obtained through Association ofGeneral Contractors (AGC), Washtenaw ContractorsAssociation, and Crain’s Business Top 25 GeneralContractors.

In order to make the data fairly representative, asystematic random selection was used to selectprospective respondents from the assembled list ofcontractors from these associations. Surveys were sentout via e-mail and fax to members of these associationsalong with a follow-up phone call to notify respondentsto complete the questionnaires. The initial feedbackwas not as large as expected, so secondary phone callswere made to remind people. When that feedbackwasn’t enough, it was decided that direct contactshould also be used. Local general contractors withprojects in the area were contacted personally on thejob to complete the survey. Even visiting jobsites andbusiness did not ensure 100% feedback; however,enough surveys were eventually obtained. Out of over100 possible participants, 30 surveys were returned,suggesting a 30% return rate. The number of datapoints required to establish a baseline comparison isarbitrary but should be at least 10; 20–30 points isbetter (AIHA, 2009). Besides Agresti (1996), morerecently, it has been generally recommended that, as acompromise between minimizing both the dispersionof the estimators and the experimental effort, a samplesize of 30 should be sufficient to ensure reliable estima-tions of the statistical parameters for a given physicalquantity (Gong

et al

., 2001). These authors suggestedthat the indentation toughness for a given materialshould be evaluated using at least 30 data points inorder to yield a complete description of its inherentscatter. Hence, the 30 data points used in this study areconsidered sufficient to conduct meaningful statisticalanalysis.

Main study issues

Besides demographic information, specific questionswere asked to explore the main study issues. A deter-mination of interrelationships would ideally requireinterval level of measurement. However, since many ofthe variables were based on respondents’ assessmentsand not on existing records, some ordinal rankingscales were developed and treated in a quantitativemanner by assigning ordered scores to the categories(Agresti, 1996). Some variables were measured usingratio scales since they have all the characteristics of aninterval scale in addition to having true zero points oforigin, and are independent of their units of measure-ment (Siegel, 1956). The interval/ratio, and ordinalmeasurements are however omitted here for reasons ofbrevity.

There were three sections of the questionnaire.Section A focused on the demographic information onthe respondents, and this includes gender, age, size orcapital base, construction type or focus, and job title ofthe respondent.

Section B aimed to obtain information on currentpractices, while Section C obtained general waste mini-mization views or behaviour.

The following null hypotheses were tested:

Hypothesis 1

: There will be no difference in the meanranking of rated waste behaviour among constructionfirms of varying capital bases or sizes.

Hypothesis 2

: There will be no difference in the meanranking of rated waste behaviour among predominantlyresidential, commercial, industrial, infrastructural orother construction sectors.

The Kruskal–Wallis H-test was used to validatethese hypotheses, testing whether there are differencesin the mean ranking of rated waste behaviour of vary-ing sizes and types of constructors. However, in orderto capture the correlation sense of the results, theexplanation of the chi-squared results is supported witha discussion of the Pearson product-moment andpartial correlations.

Data analysis, results and discussion

Both descriptive and inferential statistical analyseswere conducted. However, only Pearson product-moment and partial correlations, as well as theKruskal–Wallis test are presented here. Pearson prod-uct-moment correlation rather than Spearman rho orKendall correlation was preferred because of the para-metric proportion of the data—a key justification forchoice of statistical methods. The Kruskal–Wallis H-test for three or more unrelated samples was preferredover the Mann–Whitney U-test for two unrelated

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samples since more than two samples were involved.To follow are the descriptive statistics (frequencydistributions, histograms and pie charts, etc.) usedwhere deemed appropriate, and higher level inferen-tial statistical analysis (regression and path analysis)that will later be conducted on the data. The resultswere interpreted and discussed based on cross-evalua-tion of the data, and interdependency was assumedsince the direction of dependency was not determinedbeforehand.

The correlation coefficient,

ρ

, is calculated as:

where X and Y are two variables varying together—thatis, two variables dependent on each other, and

n

is thesample size.

Considering the type of data and measurements, aparametric statistical method such as Pearson product-moment correlation was deemed more appropriate tocorrelate the dependent and independent variables.The Kruskal–Wallis one-way analysis of variance testwas performed as another level of test to ascertain thevariation in the mean ranks of the data relative to theresponses from varying sizes and types of constructionfirms, and to give chi-square (

χ

2

) values of similardistribution.

For brevity, the relationships presented are the onesfound significant at the 0.05 level of significance. Inaddition, to shorten the discussions, a positive correla-tion between variables should indicate to readers thatan increase in the one variable (independent variable)is associated with an increase in the other variable(dependent variable) or vice versa. Conversely, a nega-tive correlation between variables should indicate thatan increase in one variable is associated with a decreasein the other. The correlations should be read in tandemwith the frequencies, whereby the correlation results(positive or negative) are deemed to relate more to thehigher frequencies.

Furthermore, where the reported probability level ishigher than 0.05 in the Kruskal–Wallis test, readersshould rely on the significance level of the correlationstatistics, as this suggests that there is no differencebetween the varying sizes and types of constructionfirms in relation to the variable in question. However,this test applies to variables measured at ordinal andnominal scales. Higher than normal probability levelsof significance are expected owing to the relativelysmall size of the sample population, the sometimeswide ranges between the mean ranks, the missing cases

and low frequencies. Disparate mean ranks are indicesof non-homogeneous sample population which was notthe case in this study. If disparate mean ranks are notthe result of chance variations such as are to beexpected among several random samples from the samepopulation, then the result may explain, for instance,the differing waste behaviours of varying sizes and typesof construction firms. Missing cases were omitted inthe Kruskal–Wallis test results.

The significance of the test results should be read inthe ways suggested by Coolican (1990), based on theprobability level, p:

significant: 0.05>p<0.01;highly significant: 0.01>p<0.001; andvery highly significant: 0.001>p.

All probabilities reported are based on two-tailed tests,as the study was exploratory and the outcome of eachcomparison had two possible directions—positive ornegative.

Relational analysis

Although the central tendency and dispersion in thedata were determined, they have not been presentedhere for reasons of brevity, but the variability observedwas sufficient to warrant further inferential statisticalanalysis using partial correlation.

Partial correlation was used as the multivariateanalytical technique to examine the actual relationshipsamong the key variables, in order to avert potentialmisleading aspects of other correlation methods, wherecorrelation between two variables may be caused byeach being separately correlated with a third variable(Leedy, 1993; Faniran

et al

., 1994). Partial correlationenables statistical controls which allow one or two testvariables to be held constant, while the relationshipsbetween two other variables are examined (Bryman andCramer, 1997). As partial correlation also computesPearson’s coefficient, r, for each of the pairs of the vari-ables (in this case, zero-order correlation coefficients),the sense of the parametric correlation of the variableswas gained as well.

A limitation of this study is in regard to the require-ment to specify conditions for partial correlation asapplicable to all multivariate analyses, which has notbeen fully met by the data. For instance, partial corre-lation requires that measurements be, at least, at inter-val/ratio levels. Some of the data are ordinal andnominal. However, Labovitz (1970) suggested thatalmost all ordinal variables can and should be treated asinterval variables. Furthermore, the capability of thistechnique in detecting interaction effects resulting frommoderated relationships is limited.

ρ =−

− −

Σ Σ Σ

Σ Σ Σ Σ

XYX Y

n

XXn

YYn

( )( )

(( )

)(( )

)22

22

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Sample result and discussion

Some results and brief discussion are as follows.

Size of construction firm

The positive and negative correlations of the construc-tor size are as shown in Table 1. A majority of therespondents (22 out of 27) earn from $750 000 to over$1 million per year, and 19 out of 30 focus on residen-tial (8) and commercial (11) sectors. A majority of therespondents (20 out of 28) attach consequences and/orrewards to waste management—that is, one can berewarded or penalized for managing or not managingwaste appropriately. Virtually all respondents (28 outof 30) indicated that they sometimes or never incorpo-rate salvaged materials in their buildings. Hence, basedon firm size by capital base, it would appear that thelarger establishments seldom incorporate salvagedmaterials in their buildings. This may be an interestingunique situation in the Michigan study setting. It maybe connected with the availability of recycling infra-structure, but given the centrality of Recycle AnnArbor, a building materials recycle centre within thestudy setting, this outcome is rather surprising. Hence,we seek for other indicators as to the reason for thisanomaly. A majority of the respondents (24 out of 29)indicated that owners sometimes do not or never voiceconcern about managing waste. It may be that ownershiring bigger construction firms are either not suffi-ciently informed about the options of recycling, or areless keen to explore recycling of materials, since theyare sure of their construction budget. If this is the case,then a lot of public education work is anticipated fromconstructors and construction educators in this aspect.

A plan in place for minimizing waste (waste manage-ment plan) is hoped to affect behaviour. However,when the existence of this plan was partialled out, thecorrelation of constructor size and construction firms’readiness to incorporate salvaged materials into theirbuildings remained relatively unchanged, suggesting arobust association (

ρ

=

−

0.649, p = 0.004, degrees offreedom (df) = 16). The same situation was observedin terms of the correlation of constructor size andowners voicing of concern about managing waste (

ρ

=

−

0.631, p = 0.005, df = 16). Furthermore, on partial-ling out this plan, constructor size became significantly

negatively associated with a lack of concern aboutwaste minimization (

ρ

=

−

0.514 up from

ρ

=

−

0.250,p = 0.029 up from p = 0.218, df = 16). Out of 29respondents, 23 gave reasons for their lack of concernabout waste minimization as being the cost involved (5/29), time-consuming coordination (10/29) and thedifficulty of convincing everyone on the jobsite of theneed to minimize waste (8/29). This observation pointsto the significance of a waste management plan in mini-mizing construction waste.

The initial observed significant correlations thatbecame insignificant on partialling out this plan can beexplained in several ways. Where the zero-order corre-lation coefficient has decreased relative to the coeffi-cient of determination, r

2

×

100, but not to the extentthat would suggest spurious or partially spurious rela-tionships, or the intervening effects of an in-place wastemanagement plan, the most definite outcome is thatthe intervening effect of this plan brings aboutdecreases in the correlations of the experimental vari-ables (Bryman and Cramer, 1997). In this case, thecontrol variable, in-place waste management plan,appears to constitute a somewhat causal variable prob-ably related to the independent variable, which largelyeliminated its effect on the dependent variable. Hence,multiple causation was suspected to be the situation.As there is only a very fine dividing line between multi-ple causation and intervening variable and/or spuriousrelationship situations, discounting the most likelypossibilities required resorting to a combination ofintuitive logic and theoretical reflections (Bryman andCramer, 1997). One approach employed was tosuspect a multiple causation situation, where thecontrol variable appears logically to cause variation inthe waste behaviour variable; or where it seems toprecede it in time order or sequence.

Where there have been increases in the correlationcoefficients, it suggests that, in many cases, the rela-tionships might have been moderated as a result of theinteraction effects of the in-place waste managementplan. In other words, the relationships might have heldtrue for some categories of the sample, but not forothers. Owing to the single values of partial correla-tion, it was difficult to detect in which category therelationships might be most attributable in testing formoderated relationships.

Table 1

Zero-order correlations of constructor size

Variable Coefficient,

ρ

Significance, p Respondents, N

Type or sector (residential, commercial, industrial, infrastructural or other) 0.387 0.046 27Consequences and/or rewards for waste management 0.420 0.036 25

Incorporation of salvaged materials into buildings

−

0.553 0.003 27Owners voicing of concern about managing waste

−

0.619 0.001 26

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Finally, where the relationship remained virtuallyunchanged or slightly changed, it suggests robustness orreplication in the data; that is, existence of true andgeneral relationships that cannot be easily explained (deVaus, 1995). In other words, the relationship holdstrue, irrespective of the influence of the control variable.

Based on the chi-squared values of the Kruskal–Wallis test, and the significance levels with respect to theconstruction firms incorporating salvaged materials intotheir buildings (p = 0.017, df = 3 and

χ

2

= 10.172), andowners voicing concern about managing waste p =0.026, df = 3 and

χ

2

= 9.277, null Hypothesis 1 wasrejected. That means there is indeed a difference in themean ranking of rated waste behaviour among construc-tion firms of varying capital bases especially in terms oftheir preparedness to incorporate recycled contents intotheir buildings, and their clients’ apathy to voicingconcern over management of waste. Understandably, itwould appear that the bigger a construction company,the less likely it is keen to include recycled contents inits buildings, and the less often its clients voice concernsfor managing waste. This result may indicate that theless well-off owners may be readily more open to therecycling option and less wasting than their wealthiercounterparts.

Constructor sector

The positive and negative correlations of the construc-tor sector are as shown in Table 2. A majority of therespondents (27 out of 30) waste no more than 10% ofthe total concrete cubic feet used.

When the existence of a waste management plan waspartialled out, the positive correlation coefficient ofconstructor sector and concrete waste increased signif-icantly (

ρ

= 0.539, p = 0.021, df = 16), while its corre-lation with recording of waste became positivelystatistically significant (

ρ

= 0.486 up from

ρ

= 0.229,p = 0.041 up from p = 0.234, df = 16), as was thecorrelation with inclusion of waste management in thebuilding contract (

ρ

= 0.492 up from

ρ

= 0.276, p =0.038 up from p = 0.55, df = 16). Out of 30 respon-dents, 21 sometimes or never record wastes from theirprojects, and 25 out 28 respondents sometimes ornever include waste management in their contract.While the significant negative association between

constructor sector and construction firms’ incorpora-tion of salvaged materials into their buildings remainedliterally unchanged on partialling out this plan (

ρ

=

−

0.506, p = 0.032, df = 16), the correlation with thepractice of designating someone for managing wastebecame significant negatively (

ρ

=

−

0.492 up from

ρ

=

−

0.185, p = 0.038 up from p = 0.327, df = 16), as wasits correlation with the frequency of discussion of wastemanagement during project meetings (

ρ

=

−

0.523up from

ρ

=

−

0.176, p = 0.026 up from p = 0.353, df= 16), subcontractors’ awareness of waste policies (

ρ

=

−

0.492 up from

ρ

⟩

=

−

0.098, p = 0.038 up from p =0.613, df = 16), and lack of concern about waste mini-mization (

ρ

=

−

0.512 up from

ρ

=

−

0.250, p = 0.030up from p = 0.218, df = 16).

It was found that 17 respondents designate someonefor managing waste, and 13 do not; 14 respondentsseldom or never discussed waste management in theirproject meetings; 18 respondents indicated that theirsubcontractors are aware of the waste policies, while 11said their subcontractors are not. Again, observationstresses the significance of a waste management plan inminimizing construction waste.

Based on the chi-squared values of the Kruskal–Wallis test, and the significance levels with respect tothe construction firms wasting steel (p = 0.029, df = 3and

χ

2

= 9.000), and subscription to lean constructionprinciples (p = 0.019, df = 3 and

χ

2

= 9.912), nullHypothesis 2 was not rejected at least in these aspects.It was found that 29 out of 30 respondents waste nomore than 10% of steel used in their constructions,despite the fact that 22 out of 26 respondents do notsubscribe to lean construction principles. That means,there is in these aspects no difference in the mean rank-ing of rated waste behaviour among construction firmsof varying sector focus in terms of their propensity towaste steel, regardless of whether or not they subscribeto lean construction principles. The implication is forwaste in steel to be minimal irrespective of wastebehaviour of the constructor. This finding validates theliterature that lauds steel’s amenability to reuse andrecycling.

Conclusion

Key conclusions are as follows. Based on the analysis ofthe results, it would appear that the larger constructionestablishments in Michigan seldom incorporatesalvaged materials in their buildings, despite the exist-ence of a noted recycling and reuse facility, RecycleAnn Arbor. Michigan’s wealthier owners also some-times do not, or never, voice concern about managingwaste to constructors. It may be that owners hiringbigger construction firms are either not sufficiently

Table 2

Zero-order correlations of constructor sector

VariableCoefficient,

ρ

Significance, p

Respondents, N

Concrete waste 0.368 0.045 30

Incorporation of salvaged materials into buildings

−

0.450 0.013 30

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informed about the options of recycling, or are lesskeen to explore recycling of materials, since they aresure of their construction budget. This finding contrib-utes to our knowledge of waste management behaviourof major constructors and their clients in Michigan,and has practical implications for Michigan’s construc-tors and construction educators seeking to expandpublic education and awareness about recycling andother construction waste minimization approaches.

Irrespective of any existing waste management plan,the bigger the capital base of a construction firm inMichigan, the less it is keen to include recycledcontents in its buildings. Such a company is also lesslikely to receive complaints about waste from its clients.The Kruskal–Wallis test results further confirmed thatthe construction firms do differ in terms of theirpreparedness to incorporate recycled contents intotheir buildings, and their clients’ indifference to voicingconcern over managing of waste. Owners hiring largerconstructors are likely to be better situated financially.

This finding defies common sense: one can easilyexpect that the bigger construction firms have moreresources to deal with waste in a positive and beneficialway but, surprisingly, this is not the case. It may be thatsuch attitude of the bigger constructors to recycling isemboldened by the indifference of their clients in termsof challenging wasteful construction behaviour. Thepractical implication of this finding is for Michiganclients who desire significant incorporation of recycledcontents in their buildings to avoid the larger construc-tion companies. This finding is also a lesson for thebigger construction establishments to do more in termsof incorporation of more recycled contents in theirconstructions. Again, the practical implication of thisfinding is for a re-education and/or raising of awarenessamong Michigan constructors and owners alike.

An important understanding gained from this studyis that, compared to other sectors, to a larger extent,commercial (followed closely by residential) construc-tions will waste concrete during construction withoutthe existence of a waste management plan. Furthermore,irrespective of the existence of a waste managementplan, the construction sectors’ attitude towards incor-porating salvaged materials into their buildings remainsthe same. Where no plan exists, it would appear that,compared to other sectors, to a larger extent, in commer-cial followed closely by residential constructions,nobody will be designated to manage waste, and theywould be less keen to discuss waste management duringtheir project meetings, or make their subcontractorsaware of any waste policies. As far as the Michiganindustry is concerned, both findings contribute to wastemanagement knowledge and efforts from the perspec-tive of implications for waste managing behaviourrelative to varying constructor sizes.

Another finding is that there are no differences in theattitudes of the varying construction sectors towardswasting steel, regardless of whether or not the firmsubscribes to lean construction or other waste manage-ment principles. Setting aside the limitations of thisstudy on account of its scope, at the basic knowledgecontribution level, it can initiate a rethink that wouldencourage construction stakeholders in Michigan tobecome conscious of construction waste, and to initiateefforts to eliminate, or at least mitigate, occurrence ontheir jobsites. As well as increasing the sample size,further analysis and interpretation of the results arerequired in further future explorations of this researchin order to achieve more definitive and extensiveconclusions.

This research certainly may not have provided anoverly exhaustive review and examination of all aspectsthat may be relevant to waste management practices inMichigan and beyond. However, it furnishes somecontexts in which this topic can be further fruitfullyinvestigated and brought to a decisive conclusion.Pertaining to concerted future efforts in theory, policyand practice, this study may provide useful catalyticstimuli.

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