Examining the effects of source selection method on procurement outcomes

Timothy G. Hawkins (Department of Marketing, Western Kentucky University, Bowling Green, Kentucky, USA)

ISSN: 2399-6439

Article publication date: 3 July 2017

2788

Abstract

Purpose

The purpose of this research is to explore the effects of supplier selection method on key procurement outcomes such as procurement lead time (PLT), supplier performance and buyer team size.

Design/methodology/approach

Data were collected from a sample of 124 archival contract records from the US Department of Defense. A multiple regression model and multivariate analysis of covariance/analysis of covariance models were used to test the effects of source selection method on pertinent procurement outcomes.

Findings

The trade-off (TO) source selection method increases PLT, as does the number of evaluation factors and the number of proposals received. Substantially larger sourcing teams are also associated with the TO source selection method. Nonetheless, the TO method results in better supplier performance.

Practical implications

TO source selections yield superior supplier performance than low-bidder methods. However, they are costly in terms of time and personnel. Any assessment of supplier value should consider not only the price premium for higher performance but also the transaction costs associated with the TO method.

Originality/value

Very little research addresses a buying team’s evaluation of supplier-offered value ex ante and whether that value assessment materializes into actual value-added supplier performance. Low bidder tactics are pervasive, but price (i.e. sacrifice) is only one component of value. Benefits from superior supplier performance may yield greater overall value. If value is critical to the buyer, a TO source selection method – versus a low-bidder approach – is the appropriate tool because of higher supplier performance ex post.

Citation

Landale, K.A.F., Rendon, R.G. and Hawkins, T.G. (2017), "Examining the effects of source selection method on procurement outcomes", Journal of Defense Analytics and Logistics, Vol. 1 No. 1, pp. 47-68. https://doi.org/10.1108/JDAL-05-2017-0006

Publisher

:

Emerald Publishing Limited

Copyright © 2017, In accordance with section 105 of the US Copyright Act, this work has been produced by a US government employee and shall be considered a public domain work, as copyright protection is not available.

Introduction

One of the most hotly contested debates in defense procurement has been the choice of contractor selection method and the procurement outcomes resulting from that choice (Kashiwagi and Byfield, 2002; Lohfeld, 2015). The low-price, technically acceptable (LPTA) method has been pitted against the full trade-off (TO) method, with the LPTA method taking the brunt of the criticism. While Department of Defense (DoD) policies encourage contracting officers to:

[…] select the appropriate source selection process […] to match the specific requirement, meet Warfighter needs, and deliver a contracted solution that will provide the required performance levels at the lowest cost (Kendall, 2015, p. 3).

Stakeholders have differing views about how the choice of contractor selection method affects procurement outcomes.

Different buying situations call for different source selection methods (De Boer et al., 2001). Whether to use an LPTA or a TO source selection method is not always clear; it is often a judgment call (Nichols and Totman, 2013). One source selection method is to specify minimum levels of performance and minimum qualifications, then to select the lowest-priced offer (Ellram, 1995). The LPTA approach resembles a competitive bid (i.e. “low-bidder”) method and assumes well-defined requirements that fully meet the internal customer’s needs (Gransberg and Ellicott, 1997). The LPTA approach, therefore, is suited to more definable and stable requirements of moderate or low risk. The LPTA method is quite useful for the purchase of non-critical goods and services, for which the goal is to satisfy the need while transacting in the most efficient manner (Kraljic, 1983).

Government acquisition officials attest that the LPTA selection method offers a faster time-to-contract, as the technical acceptability criteria is binary and the evaluation of price – the most important factor in LPTA contractor selections – is objective (GAO, 2014). As it is more objective, the LPTA method is also less likely to be protested – a weighty consideration of government acquisition teams (Hawkins et al., 2016). The sellers’ perspective, however, is that the LPTA method stifles innovation, because price is more important than, say, an innovative approach that may ultimately better serve the government (Calisti, 2015). Critics argue that the LPTA method often results in the selection of a contractor that has undercut the cost of the procurement (Walenta, 2015). They argue that the contractor has essentially achieved “buy-in” by proposing an unreasonably low price that will later have to be adjusted (i.e. increased) via contract modification to fulfill the terms and conditions of the contract. In an LPTA source selection method, suppliers are motivated to minimize price and to meet – but not exceed – minimum qualifications and performance levels. As buyers often fail to adequately define requirements (Hawkins et al., 2015), the method, then, can result in receiving less performance than truly needed. Opponents also feel that LPTA contractor selections yield inferior products and services (Weckstein and Delgado, 2012). Proponents, however, suggest this is not the case, and that by providing clear technical acceptability criteria, the government can avoid receiving inferior products and services (Nichols and Totman, 2013).

A separate source selection method is to specify minimum qualification and performance thresholds, and then to permit a TO analysis , thus enabling the buyer to pay a higher price for qualifications or performance levels that are higher than the minimum requirements (Carter and Choi, 2008). The TO method fits situations that are rich in uncertainty, risk and high stakes (Rendon and Snider, 2008). The TO contractor selection method is perceived to take more time because of the subjective nature of the proposal evaluation process and the increased likelihood of receiving a bid protest (Hawkins et al., 2016). Internal customers and contractors seem to prefer this approach, as it allows customers to retain control over selecting the contractor that represents the best value – that by ranking the evaluation factors in terms of importance, they have the option of tailoring the evaluation to fully meet their needs. Contractors also seem to prefer this method, as it allows them to provide innovative solutions without the burden of competing mainly on price. By the scaled rating of past performance (i.e. not just pass/fail), the TO method also allows contractors to compete on their record of past success, on their reputations, and, consequently, on risk to the buyer. Proponents of the TO method argue that it results in higher-quality products and services because contractors are not “squeezed” on price (Gransberg and Ellicott, 1997). Opponents argue that the TO method does not necessarily produce better contractual outcomes, particularly given the anecdotal belief that TO procurements takes longer to award (Bunting, 2014).

Measures

The key consequences of source selection method (i.e. the dependent variables) are meant to provide answers about how long the sourcing process took, how well the supplier performed and the cost of the transaction. Accordingly, the dependent variables include

• PLT;

• contractor performance assessment reporting system (CPARS) ratings as a measure of contractor performance; and

• buyer team size as a measure of transaction cost.

PLT was measured by the number of days from the issuance of the request for proposal to the contract award. CPARS data were collected and used for the following performance categories:

• cost control;

• quality;

• schedule; and

CPARS measures each category using the following Likert-style scale: 1 = unsatisfactory, 2 = marginal, 3 = satisfactory, 4 = very good and 5 = exceptional. Buyer team size was measured by the number of people who participated on the source selection team.

Independent variables included source selection method, number of offers, a number of evaluation criteria and value of the contract. Consistent with prior research (Bajari et al., 2009), the contract value was included as a proxy for complexity. As complexity is expected to affect the key procurement outcomes investigated in this research (lead time, buying team size and supplier performance), it is modeled as a control variable. Source selection method was a binary variable with LPTA coded as zero and TO as one. The number of offers represented a count of the number of proposals received. The number of evaluation criteria was measured as counts of criteria used to discriminate between proposals. Contract value was measured in US dollars.

Assumption testing

Several assumptions were tested before proceeding. The assumptions for each analysis are described below.

Multiple regression.

We test the relationship between source selection method and PLT (H1) using multiple regression. Prior to analysis, we searched for outliers and influential observations and found none. PLT, number of offers and value received a logarithmic transformation to achieve normality of the residuals. There were no issues with linearity, multicollinearity [all variance inflation factors (VIF) < 2] or autocorrelation. The Breusch–Pagan test showed heteroscedastic errors, but Cameron and Trivedi’s test did not. Heteroscedastic errors can result in biased t-statistics. To be safe, we confirmed our results using a robust estimation technique that estimates standard errors using Huber–White sandwich estimators. With this technique, the coefficient estimates remain exactly the same as in ordinary least squares (OLS) regression; however, the standard errors produced by this method are robust to heteroscedasticity because they are derived from the empirical data rather than the model, thus correcting the bias in the t-statistics. Despite using robust standard errors, results were not changed.

Multivariate analysis of covariance/analysis of covariance

We look for source selection method induced differences in supplier performance (H2) and transaction cost (H3) using multivariate analysis of covariance (MANCOVA) and analysis of covariance (ANCOVA). Prior to the MANCOVA and ANCOVAs, we searched for multivariate outliers and influential observations and found none. The CPARS and buying team size data achieved multivariate normality for each source selection method. We also considered the normality of the covariates, as covariates are useful in reducing error, but not if they are non-normal and thus reduce power (Tabachnick and Fidell, 2007). As before, number of offers and value received a logarithmic transformation to achieve normality. There were no issues with linearity, multicollinearity (VIFs < 2.01), homogeneity of regression or homogeneity of variance/covariance matrices (Box’s M χ2(3) = 1.63, p = 0.6517). All observations were independent, and because we used an average the four CPARS ratings, the dependent variables are both continuous in nature.

All further analyses and statistical output use the transformed variables; however, the written results back-transform the variables into their original form for a better understanding of the effects. We use the untransformed variable nomenclature in the text for ease of reading.

Results

Descriptive statistics

The contracts ranged widely in terms of PLT, dollar value and number of offers received. Most source selections involved a small number of non-price evaluation criteria. A wide variety of types of goods and services were included (Appendix). Basic descriptive statistics for variables and demographics are shown in Table II.

Multiple regression

H1 was tested using multiple linear regression. Table III displays parameter estimates, significance levels and the explanatory power of the model. The model is given as:

ln(Y)= b0+ b1×ln(X1)+ b2X2 + b3X3 + b4×ln(X4)+ε

where:

Y = PLT

X1 = number of offers

X2 = number of evaluation criteria

X3 = source selection method

X4 = value

As seen in Table III, all four predictors show significant path estimates, with source selection method showing the strongest effect on PLT, but only achieving marginal significance. Additionally, a respectable amount of variance in PLT (38 per cent) was explained by the four independent variables. Given these results, H1 was supported.

Multivariate analysis of covariance and analysis of covariance

Because our intent was to analyze differences in contract outcomes (CPARS ratings and buying team size) based on contractor selection methodology (LPTA or TO), a group comparison statistical methodology was necessary. We seek to find whether there are differences in contract outcomes (CPARS ratings and buying team size) by group while controlling for the effect (s) of influential covariates (i.e. the number of offers, number of evaluation criteria and contract dollar value). To do this, we performed a MANCOVA with CPARS ratings and buying team size as the DVs. Source selection method and dollar value significantly affect the DVs [Wilks’ Λ = 0.453, F(8, 106) = 6.44, p < 0.001]. Wilks’ Λ shows that over 50 per cent of the variance in the outcome variables is explained by source selection method and the covariates. We performed individual ANCOVAs on each of the DVs to better understand these results.

Table IV provides the results of the ANCOVA with CPARS ratings as the DV. Clearly, TO source selections result in more favorable supplier performance (mean CPARS rating for LPTA = 3.64, mean CPARS rating for TO = 4.15), supporting H2. Sample-based means are provided for each of the covariates. The amount of variance in CPARS ratings explained by source selection method was a modest 7 per cent (Adjusted R2 = 0.070, n = 69), suggesting other variables might help explain CPARS ratings.

Table V provides the results of the ANCOVA with buying team size as the DV. TO source selections significantly increase the size of the team needed to properly manage the source selection process (mean buying team size for LPTA = 3.69, mean buying team size for TO = 11.22), supporting H3. Again, sample-based means are provided for the covariates. Taken together, source selection method and the covariates accounted for approximately 53 per cent of the variance in buying team size (Adjusted R2 = 0.525, n = 116). Table VI summarizes the results of the tests of hypotheses.

Discussion

Two fundamental approaches to the evaluation of competing suppliers’ proposals include a LPTA method and a TO method. Each method has advantages and disadvantages; but, heretofore, research has not explored the links between source selection methods and key procurement outcomes. Exploring the differences is important because buyers may not enable the co-creation of the most value if an inappropriate source selection method is used. Additionally, exigencies could nudge buyers toward emphasizing price such as the:

Managerial implications

Price still does not contain all of the information that a buyer will need to make a purchasing decision for many industrial goods and services. Value determinations will need to consider non-price factors as well. Indicators of value explored herein included PLT, supplier performance and transaction costs.

The multiple regression showed that average PLT was approximately 36 per cent longer[1] for the TO supplier selection method than for source selections using an LPTA approach. This quantified difference in lead times should be useful for those buying activities deciding on the appropriate source selection method and developing commensurate procurement milestones.

Additionally, while buying teams want flexibility to pay a greater price for increased performance, they should also consider the transaction costs and possible inventory costs associated with prolonging the proposal evaluations. Understanding suppliers’ offers of greater benefits than the minimum requirements consumes time, which drives labor costs. As greater cycle time can increase pipeline inventory, and considering that longer planning cycles increase uncertainty, and thus forecast error, the safety stock could increase. Specifically, PLT can affect the amount of inventory in at least two meaningful ways – cycle stock and safety stock (Emmett and Granville, 2007). Total lead time – the time from the identification of a customer’s need to the delivery of the product to the customer – includes PLT in cases in which the item or material of it must be sourced from a supplier. The longer the total lead time, the greater the amount of cycle stock that is in transit between and in-work among the entities in the supply chain. PLT can also affect forecast accuracy, which, in turn, has an effect on safety stock (Emmett and Granville, 2007). The longer the total lead time, the longer the forecasting planning horizon. As the planning horizon increases, forecast error increases. As forecast error increases, uncertainty (i.e. variability of demand during lead time) increases. As safety stock is determined by the variability, more safety stock inventory will be required to buffer against that increased uncertainty. Thus, during source selection, these transaction costs should be weighed against any surplus benefits offered by suppliers – in addition to any associated increase in offered price.

Turning back to information processing theory, buyers using the TO source selection method should seek innovative ways to either:

• increase the information processing capacity of evaluating non-price factors; or

• reduce the amount of information needed.

Speeding up the TO evaluation process will give buyers less of a reason to select an inappropriate source selection method for the sake of awarding a contract sooner (e.g. LPTA), a common occurrence in government contracting (Hawkins et al., 2015). The following four ideas could prove instrumental. First, buyers could define predetermined performance thresholds for each evaluation factor and their associated evaluation ratings. Examples include the DoD’s new value-adjusted technically evaluated price (VATEP) source selection method (Lohfeld, 2016), and the quality-infused price (QIP) method (Finkenstadt and Hawkins, 2016) for more subjective services and for past performance evaluations. The QIP method can save lead time by mitigating the amount of required information in the offeror’s past performance volume of their proposal and by effectively crowd sourcing past performance ratings (Finkenstadt and Hawkins, 2016). This idea is particularly germane to services, in which defining precise needs is difficult and requires substantial amounts of information. For services, supplier reputation serves as a useful proxy for the buyer’s uncertainty of capability and reliability, thereby reducing the amount of information needed. Second, buyers could require that offerors, in their proposals, quantify the costs and benefits of any components of their offer claimed to surpass the minimum requirements. Third, acquisition teams could use a secured information technology system to: receive proposals, evaluate proposals as a virtual team, meet virtually to discuss evaluations and present results to the reviewers and to the source selection authority. Fourth, agencies could establish a permanent source selection organization that helps conduct source selections for the enterprise.

While source selection method affects PLT, the number of evaluation factors is also quite impactful. Specifically, the more factors that are included in the request for proposals, the more time it takes to evaluate them properly and award a contract. The results show an increase of one evaluation factor increases PLT by 28 per cent. While it may be common sense that more evaluation factors require more assessment time, this research quantifies the effect. This finding raises a question about how many evaluation factors are optimal. In other words: Is there a tipping point at which the number of evaluation factors included in the supplier selection significantly affects PLT? Finding the answer to this question would help sourcing teams plan their procurements accordingly, or at least plan for the extra time required to assess many evaluation factors. Regardless, this result demonstrates the importance of including only the most important evaluation criteria in a request for proposal to be as efficient as possible yet still be able to distinguish between offers of value in a meaningful way.

Further, the number of offers received also affects PLT. More offers require more time to properly evaluate before awarding a contract. The results show that a 10 per cent increase in the number of offers results in a 1.9 per cent increase in PLT. This increase in PLT appears to be small. However, consider that one more proposal added to a set of ten received would add six days of PLT (1.9 per cent of 290 days of average PLT). Prior research shows that an average source selection costs $238,045 in transaction costs (Hawkins et al., 2017) or$821 per day – nearly $5,000 of added transaction costs. Across the tens of thousands of formal source selections conducted each year by the US Government, a seemingly small adjustment makes a big impact. Turning to supplier performance, this research offers additional insight. The supplier performance scores (e.g. CPARS) were, on average, 14 per cent higher for TO supplier selections when compared to LPTA supplier selections. Thus, sourcing teams, in allowing TOs of higher prices for higher performance, are indeed reaping the anticipated benefits, and this research reveals the quantified extent of improved performance. It also means that suppliers are delivering on promises of additional value. Therefore, in situations in which supplier performance is paramount, sourcing teams should consider, following diligent proposal scrutiny, paying a premium sacrifice (i.e. price) for extra benefits (i.e. supplier performance). It also means that a low-bidder source selection method should not be considered the same as a TO method, and buying teams can expect different results. In terms of information processing theory, with better performance comes less defects and less administrative burden documenting, communicating and rectifying failures. Hence, the amount of information to be processed is reduced. The findings suggest that the added value is not cost-free. With regard to transaction costs, more people are typically assigned to evaluate proposals and to assess value using the TO method than when using the LPTA method. On average, there was approximately three times the number of people for purchases using TO source selections than purchases using LPTA source selections. Clearly, TO source selections result in higher transaction costs, and those costs should be considered in conjunction with PLT and supplier performance when deciding which source selection method will provide the most value for the organization. Study limitations and areas for further research This study is not without limitations. First, the archival data, while rich and more objective than survey data, were only available from a sample of buying locations. And, within those locations, only a subset of complete contract files were sampled because of time and resource constraints. Second, the data were collected in the context of a not-for-profit buyer. Because of a need for public sector accountability and transparency, sourcing processes in this sector are heavily regulated and could consume more time than do for-profit sector counterparts – even though for-profit and not-for-profit sector sourcing have been found to be more similar than dissimilar (Hawkins et al., 2011). Third, while managerial implications to inventory costs associated with longer PLTs are drawn, the sample consisted of services, as well as products. Nonetheless, not all services are purely intangible; often they involve goods that must be inventoried. Additionally, several services in the sample are highly tangible and behave more like products than services (e.g. equipment repair that entail spare parts inventory). Contracts for maintenance services, logistics and products (i.e. those likely involving inventory) accounted for only 43 per cent of the sample. Future research should examine whether the additional transaction costs of evaluating proposals and assessing net value are worth the improvements in supplier performance. Clearly, a more rigorous evaluation that considers evaluation factors other than low price will consume more time and effort, but the question remains whether those transaction costs are worth it in terms of yielding surplus value. Additionally, this research examined PLT and supplier performance as key indicators of buyer value. Future research could explore the effects of source selection method on other aspects of value such as competitive advantage and financial performance. Future research could also seek to confirm whether the findings herein also pertain to a for-profit context. Finally, the literature shows that procurement research heretofore has largely ignored value as a key dependent variable. There may be great opportunity for enhancing our understanding of procurement phenomenon and buyer–supplier relations by prioritizing the investigation of value. Table I. Relevant literature Reference Method Theory Source selection method effects on buyer or supplier performance Value as the selection outcome TO of price and non-price criteria for added value Key findings Bajari et al. (2009) Logistic regression n = 4,087 construction projects N/A No No No More complex projects are more likely to be awarded by negotiation than by auction. An increase in work volume is related to negotiations (vs competitive bidding). Negotiated projects tend to be awarded to larger, more experienced suppliers Goldberg (1977) Conceptual N/A No Yes No N/A Ittner et al. (1999) Regression and recursive partitioning, survey, n = 249 N/A Yes No No Use of supplier partnership practices without the appropriate supplier selection and monitoring practices results in lower buyer profit and quality, examines only non-price selection criteria, treats selection and monitoring as one variable Kannan and Tan (2006) SEM, n = 527 Relational exchange Yes No No The greater the importance of select non-price evaluation criteria, the greater the success of the buyer-supplier relationship (comprising higher product quality, lower cost, less product development time and more cooperation) Kaufmann et al. (2014) Regression, n = 54 sourcing teams Dual process theory Yes No No Distinguished financial from non-financial supplier performance The use of rational supplier selection procedures increases cost performance but not quality, delivery or innovativeness. When sourcing team members use their experience-based intuition, the decision results in satisfactory supplier performance in terms of quality, delivery and innovativeness Koufteros et al. (2012) SEM, survey, n = 157 Resource-based view Yes Yes No Supplier selection using criteria of new product development, quality and cost directly affect the buyer’s performance in those respective domains Meschnig and Kaufmann (2015) Regression, survey, n = 88 Group decision-making theory Yes No No Consensus on objectives for supplier selection among sourcing team members is positively related to the selection of higher performing suppliers Nair et al. (2015) SEM, survey, n = 244 Organization theory Yes No No The importance of supplier selection criteria (strategic and operational measures) are positively related to supplier performance (strategic and operational measures). Supplier performance, in turn, affects buyer performance Skoumpopoulou et al. (2014) Single case study N/A No Yes No Price is the main deciding factor in selecting a supplier Reviews three supplier selection methods: the categorical method (de Boer, Labro and Morlacchi, 2001), the cost-ratio method (Thompson, 1990) and the weight-point methods (Arsan and Shank, 2011) van der Rhee et al. (2009) Discrete choice analysis, survey, n = 200 N/A No No No Explores TOs of cost, delivery, flexibility and service given a minimum level of quality (resembling LPTA). Flexibility is the most important selection criterion for commodities and cost is second Vonderembse and Tracey (1999) Correlations, t-tests survey, n = 268 N/A Yes No No The product quality and product performance dimensions of supplier selection criteria are positively correlated with manufacturing performance Table II. Descriptive statistics Variable Observation Mean SD Minimum Maximum Grouping variable PLT (days) 124 290.13 251.97 8 1019 55 147.16 112.76 8 482 LPTA SS 69 404.09 273.92 21 1019 TO SS CPARS (average rating) 69 4.00 0.78 2.5 5 20 3.64 0.67 3 5 LPTA SS 49 4.15 0.79 2.5 5 TO SS Buying team size 116 8.03 8.22 1 54 49 3.69 2.27 1 11 LPTA SS 67 11.22 9.45 2 54 TO SS Contract dollar value 124$26,400,000 $58,500,000$27,819 $410,000,000 55$2,419,850 $3,346,636$27,819 $20,000,000 LPTA SS 69$45,600,000 $73,100,000$36,000 $410,000,000 TO SS Number of evaluation factors 124 2.62 0.81 1 5 55 2.13 0.55 1 3 LPTA SS 69 3.01 0.78 2 5 TO SS Number of offers 124 4.45 4.52 1 23 55 4.00 4.57 1 23 LPTA SS 69 4.81 4.47 1 22 TO SS Notes: The italised rows denote overall mean values – including LPTA SS and Tradeoff SS sub-groups; whereas, non-italised rows denote only means of the sub-groups (i.e., LPTA SS or Tradeoff SS) Table III. Regression results DV: PLT Unstandardized coefficient Standardized coefficient^ t P > |t| Significance Intercept 1.714 2.87 0.005 Explanatory Variables: Number of offers 0.197 0.110 2.10 0.038 ** Number of evaluation criteria 0.243 0.223 2.08 0.040 ** Source selection method 0.310 0.281 1.67 0.098 * Control: Lnvalue 0.163 0.263 3.47 0.001 *** Adjusted R2 0.38 Prob > F 20.22 0.000 *** Notes: Significance level ***< 0.01; **< 0.05; *< 0.10; ^Results based on untransformed variables Table IV. ANCOVA results – supplier performance One-way analysis of CPARS ratings by source selection method Source Group means LPTA TO df SS MS F p Source selection method 3.64 4.15 1 2.47 2.47 4.33 0.041* Valuea^$26,300,000 $79,900,000 1 0.41 0.41 0.71 0.402 Residual 66 37.75 0.57 Total 68 41.86 0.62 Notes: Significance level *< 0.05; aMeans are based on observations used in this ANCOVA (n = 69), and do not match the means for the entire data set (Table II, n = 124); ^Means shown are untransformed, while the logarithmically-transformed variable (lnvalue) was used for the ANCOVA Table V. ANCOVA results – transaction costs One-way analysis of buying team size by source selection method Source Buying team size Group mean LPTA TO df SS MS F p Source selection method 3.69 11.22 1 3.371 3.371 9.860 0.002*** Valuea^$2,337,155 \$54,900,000 1 11.489 11.489 33.610 0.000***
Number of evaluation factors* 2.12 3.04 1 0.105 0.105 0.310 0.581
Number of offersa^ 3.73 4.94 1 2.687 2.687 7.860 0.006***
Residual 111 37.946 0.342
Total 115 82.764 0.720
Notes:

Significance level

***< 0.01;

**< 0.05;

*< 0.10;

aMeans are based on observations used in this ANCOVA (n = 116), and do not match the means for the entire data set (Table II, n = 124);

^Means are untransformed, while the logarithmically-transformed variables (lnvalue and lnnumoffers) were used for the ANCOVA

Table VI.

Results of hypotheses

Hypothesis Description Results
1 A TO source selection method will have longer PLT than will an LPTA source selection method Supported
2 Supplier performance will be higher when the supplier was selected via a TO, rather than an LPTA, source selection method Supported
3 The size of the buying team will be greater for source selections using the TO method versus the LPTA method Supported

Table AI.

Types of goods and services

* PSC Description Count (%) Cumulative
1265 Fire control transmitting and receiving equipment 1 0.81 0.81
1356 Torpedo explosive components 1 0.81 1.61
1510 Aircraft, fixed wing 1 0.81 2.42
1680 Miscellaneous aircraft accessories components 2 1.61 4.03
1903 Destroyers 1 0.81 4.84
1905 Sub-chasers 1 0.81 5.65
1915 Cargo and tanker vessels 3 2.42 8.06
1940 Small craft 1 0.81 8.87
2010 Ship and boat propulsion components 2 1.61 10.48
2020 Rigging and rigging gear 1 0.81 11.29
2090 Miscellaneous ship and marine equipment 5 4.03 15.32
2330 Trailers 1 0.81 16.13
4140 Fans, air circulators and blower equipment 1 0.81 16.94
4920 Aircraft maintenance and repair shop equipment 1 0.81 17.74
5340 Hardware, commercial 1 0.81 18.55
5821 Radio and TV equipment, airborne 1 0.81 19.35
6930 Operational training devices 2 1.61 20.97
7030 Software 5 4.03 25.00
7035 Computer support equipment 1 0.81 25.81
7050 Computer components 1 0.81 26.61
8470 Armor, personal 1 0.81 27.42
AC63 Research and development (R&D): electronics and communications equipment – advanced development 1 0.81 28.23
AC64 R&D: electronics and communications equipment – engineering development 1 0.81 29.03
AC65 R&D: electronics and communications equipment – operational system development 1 0.81 29.84
AC66 R&D: electronics and communications equipment – management and support 1 0.81 30.65
AD24 R&D: defense services – engineering development 1 0.81 31.45
AD91 R&D: other defense – basic research 1 0.81 32.26
AD94 R&D: other defense – engineering development 1 0.81 33.06
AJ11 R&D: physical sciences – basic research 1 0.81 33.87
D301 Automatic data processing facility management 1 0.81 34.68
D308 Automatic data processing programming services 1 0.81 35.48
D310 Automatic data processing backup and security services 1 0.81 36.29
D399 Other computer services 8 6.45 42.74
G001 Care of remains and/or funeral services 1 0.81 43.55
G002 Chaplain services 1 0.81 44.35
J015 Maintenance and repair of aircraft 3 2.42 46.77
J019 Maintenance and repair of ships, small craft, pontoons and floating docks 5 4.03 50.81
J020 Maintenance and repair of ship and marine equipment 1 0.81 51.61
J058 Maintenance and repair of communication, detection and coherent radiation equipment 1 0.81 52.42
J059 Maintenance and repair of electrical and electronic equipment 4 3.23 55.65
J063 Maintenance and repair of alarm, signal and security detection systems 1 0.81 56.45
J069 Maintenance and repair of training aids and devices 1 0.81 57.26
J070 Maintenance and repair of automatic data processing equipment, software, supplies and support equipment 2 1.61 58.87
L018 Technical representative services – space vehicles 1 0.81 59.68
L019 Technical representative services – ships, small craft, pontoons and floating docks 1 0.81 60.48
L2020 Technical representative services – ship and marine equipment 1 0.81 61.29
M127 Operation of government-owned-electronic and communications systems facilities 2 1.61 63.71
M181 Operation of government-owned-R&D GOCO facilities 1 0.81 64.52
N058 Installation of communication, detection and coherent radiation equipment 1 0.81 65.32
N059 Installation of electrical and electronic equipment 1 0.81 66.13
Q503 Medical service – Dentistry 1 0.81 66.94
Q999 Other medical services 2 1.61 68.55
R408 Program management/support services 5 4.03 72.58
R414 Systems engineering services 1 0.81 73.39
R425 Engineering and technical services 8 6.45 79.84
R426 Communications services 2 1.61 81.45
R699 Other administrative support services 2 1.61 83.06
R706 Logistics support services 1 0.81 83.87
R799 Other management support services 1 0.81 84.68
S201 Custodial, janitorial services 1 0.81 85.48
S208 Landscaping/grounds – keeping services 1 0.81 86.29
S209 Laundry and dry cleaning services 1 0.81 87.10
S216 Facilities operations support services 1 0.81 87.90
U005 Tuition, registration and membership fees 5 4.03 91.94
U008 Training/curriculum development 1 0.81 92.74
U009 Education services 1 0.81 93.55
U013 Education/training – combat 1 0.81 94.35
V121 Air charter for things 1 0.81 95.16
V227 Navigational aid and pilotage services 1 0.81 95.97
V999 Other transportation, travel and relocation services 2 1.61 97.58
W084 Lease or rental of clothing, individual equipment 1 0.81 98.39
Y239 Construction of electric power generation-Other 1 0.81 99.19
Z299 Maintenance, repair or alteration of all other non-building facilities 1 0.81 100.00
TOTAL 124
Note:

*PSC = Product or service code

Note

1.

Log-transformed variables are interpreted in the text. In this case, 36 per cent is the expected increase in PLT given a one-unit increase in source selection method, i.e. TO source selection (e.310).

Table AI

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