NIVELES DE ILUMINACIÓN

A descriptive analysis was first conducted. The instrument for bidders’ assessment was tested with an exploratory factor analysis (EFA) using the data from Experiment 1, and it was then improved upon

and validated with a confirmatory factor analysis (CFA) using the data from Experiment 2. 5.5.1 Descriptive analysis

There were a total 298 undergraduate students who participated in the experiments. They were recruited from the business school of a large Canadian university. The students were taking an introductory course on management information systems. The experiment was part of their course work on e-procurement and was worth 6 percent of each student’s total mark, with both participation and performance being considered. The descriptive analysis are shown in Table 5-2.

Table 5-2. Descriptive analysis of the two experiments Experiment 1 Experiment 2 Treatment B Treatment BDwin Treatment BDwin Treatment BDall Number of auctions 26 21 17 13 Number of bidders 98 77 66 45 Age (25 or younger, %) 96 92 91 93 Gender (female, %) 45 49 48 43

Knowledge (novice-1, expert-7) 2.92 3.30 2.90 3.34 Experience with system (low or no, %) 77 84 79 84 Task complexity (easy-1, difficult-7) 3.88 4.08 3.82 3.85

There were 179 participants in Experiment 1; among these participants four were removed from the data set as their auctions were terminated accidently. There were 26 auctions for Treatment B and 21 auctions for Treatment BDwin. On average, each auction involved 3.72 bidders. In Experiment 2,

there were 119 participants and eight were excluded for the reason stated above. There were 30 auctions in total: 17 auctions for Treatment BDwin and 13 auctions for Treatment BDall. On average,

each auction involved 3.70 bidders. No significant differences were found between the treatments in terms of the number of bidders in each auction.

In both experiments, most of the participants were between 20 and 25 years old as they were undergraduate students. About 45 percent of the participants were female; gender does not differ across the treatments. The participants perceived their knowledge about auctions to be lower than average, and over 77 percent of the participants had low or no past experience in using an auction system. On average, the participants perceived the task would be relatively difficult. An ANOVA test

showed no significant difference in their perceived task complexity between the treatments. 5.5.2 Instrument testing and factor analysis

The participants’ responses to the post-questionnaire were used to examine their assessment of the auction process, outcomes, and system. All questions used a 7-point Likert scale that ranged from “Strongly disagree” to “Strongly agree.” The instrument contains nine questions. Table 5-3 lists the items for each type of assessment used in Experiment 1 and Experiment 2.

Table 5-3. Instrument for bidders’ assessment

Factors Items

In Experiment 1

Assessment of

process AP1. It was easy to keep track of the process. AP2. The organization of process was useful. AP3. This process was stimulating.

Assessment of

outcomes AO1. I am satisfied with the results that I achieved. AO2. I am satisfied with the results as compared to my expectations. AO3. The outcome is better for Milika than it is for the provider. Assessment of

system AS1. The system was helpful in achieving my objectives. AS2. The system was helpful in improving my performance. AS3. The system was helpful in managing the transaction.

In Experiment 2

Assessment of

process AP1. It was easy to keep track of the process. AP2. The organization of process in phases and steps was useful. AP3. This process was stimulating.

Assessment of

outcomes AO1. I am satisfied with the results that I achieved. AO2. I am satisfied with the results as compared to my expectations. AO3. I think I obtained the best results for the company that I represent. Assessment of

system AS1. The system was helpful in achieving my objectives. AS2. The system was helpful in improving my performance. AS3. The system was helpful in managing the process.

An EFA was conducted with the data from Experiment 1 in order to obtain fewer measures that could be aggregated from the items and could be used to compare bidders’ assessment. A correlation analysis indicated that the items were correlated. Thus, the maximum likelihood analysis was used with the Oblimin rotation method to identify the factors.

The results of EFA are shown in Table 5-4. The three factors extracted over 79 percent variance, and the factor loadings for all items were above 0.62. The eigenvalue for one factor was below one as suggested for EFA while it was able to explain over 10 percent of the variance. The result indicates that three factors may exist, corresponding to bidders’ assessment of process, outcomes, and system.

Table 5-4. EFA for bidders’ assessment Item Process Outcomes System

AP1 0.84 0.06 0.03 AP2 0.62 0.03 0.29 AP3 0.80 0.14 0.09 AO1 0.02 0.87 0.03 AO2 0.04 0.87 0.01 AS1 0.16 0.20 0.75 AS2 0.08 0.15 0.80 AS3 0.13 0.04 0.66 Eigenvalue 4.3 1.3 0.8 Explained variance 53.5% 16.1% 10.3%

One item for assessment of outcomes (AO3) did not load on any of these three factors and it was excluded from the final factor model. After reviewing the item, its wording was changed as there was concern that participants might become confused in evaluating their outcomes in comparison with the buyer’s outcomes. Minor wording changes were also made to items AP2 and AS3 to improve their clarity and consistency with other items. The revised items are shown in italic in Table 5-3, and the instrument was then used in Experiment 2.

To validate the instrument, a CFA was conducted with EQS 6.1. The dataset contained 90 bidders that participated in Experiment 2 and completed the questionnaire. A robust analysis was conducted, which is not restricted by the normality and sample size of the dataset. Figure 5-2 shows the factor model and CFA results.

The factor model provided a good fit for the data. The result of chi-square test statistics is χ2_{=49.79 and the probability value is significant (p=0.01). This is acceptable as the sample size is} relatively small and the model is not complex, which is consistent with a relative fit index independent from sample size (IFI=0.98) (Bollen 1990). Moreover, both CFI and NNFI are above 0.95, and RMSEA is located between zero and one (CFI=0.98; NNFI=0.96; RMSEA=0.07). These noncentrality-based indices meet the suggested cut-off criteria and indicate a valid factor model (Hu and Bentler 1999).

Figure 5-2. CFA for bidders’ assessment

The values of Cronbach’s α for all factors are above 0.82 (AP=0.82, AO=0.88, AS=0.85), exceeding the recommended cut-off criteria (α>0.70) and indicating an adequate internal consistency reliability of the instrument (Nunnally and Berstein 1994). Moreover, all factor loadings are above 0.75, which shows significant improvement from the EFA result and a good convergent validity. The lowest average variance extracted (AVE) for the three factors is 0.73 for assessment of process, which satisfies the reliability criteria for all single factors (AVE>0.50). It also exceeds the shared variances between the factors, except the assessment of process and system (0.77). Thus, the discriminant validity is partially satisfied (Fornell and Larcker 1981). Note that the correlations between the three types of assessment are all above 0.5, which indicates that when bidders evaluate certain aspects of the auctions they may also consider other aspects. In particular, a high correlation exists between assessment of process and system (0.88), which failed the discriminant validity for these two factors. This may be due to that fact that the auction process is governed by the rules that

are implemented in the system and thus the bidders might not distinguish their experience and feeling in the process from using the system. Further testing may be required with a larger sample. Considering the exploratory nature of this study is in the field, the three factors were all used to assess the process, outcomes and system in the auctions.

A weighted sum for each factor was calculated using the factor loadings and then used to compare the bidders’ assessment in subsequent analysis (Section 5.6). As the same dataset should not be used to conduct both EFA and CFA analysis, the calculation of the weighted sum for Experiment 1 was based on the factor loadings obtained from EFA (Table 5-4), and the calculation for Experiment 2 was based on the factor loadings from CFA (Figure 5-2).