What is the pricing model for this engagement?

FTE-based pricing was the most common answer (140 of 347 respondents, 40.35%), ahead of Fixed price (61 of 347 respondents, 17.58%) and Transaction or outcome based pricing (58 of 347 respondents, 16.71%). Cancellation was highest for Consumption based pricing (60.00% canceled, n = 10 engagements), with no difference across the tested answer levels (chi-square 2.97, p 0.412, Cramer’s V 0.20, n = 71 engagements). Customer satisfaction could not be compared across answer levels (fewer than two answer levels with at least 10 engagements). The engagement indicator mean was lowest for Consumption based pricing (mean 3.4, n = 10 engagements) and highest for Fixed price (mean 4.2, n = 13 engagements), with no difference under test (Welch F 1.31, p 0.290, omega squared −0.007, n = 71 engagements). Intent to leave was highest for Consumption based pricing (mean 7.7, n = 10 engagements), with no difference across the tested answer levels (Welch F 0.79, p 0.511, omega squared −0.015, n = 71 engagements). The answer mix did not differ after Holm control in any segment family (Holm p 0.161 or higher in every family).

The indicator had 33 written responses coded to it, from 33 of 850 respondents on 31 of 150 engagements. All 33 scored responses were negative. The strongest negative score, −10, was carried by 10 of the 33 scored responses (30.30%). Every response was a secondary response, written in answer to a question outside the indicator’s own survey section.

A categorical answer has no engagement mean. Each engagement took its respondents’ plurality answer, and tied engagements were left out. An answer was assigned to 107 of 150 engagements.

Distressed is not applicable, because the answer has no good or bad end on a scale.

All 33 responses were secondary responses. The secondary responses came from 33 respondents on 31 engagements, in answer to 3 questions outside the indicator’s own survey section (C135 with 16 responses, C029 with 14 responses and C055 with 3 responses). Among the Part 1 indicators with 30 or more scored responses, the secondary share of the indicator ranked 1 of 6 from the highest, tied with 4 other indicators. The median secondary share was 100.00% across the 6 indicators.

The indicator has no good or bad end. Under the coding rulebook’s rule for such indicators, the written score records the effect each response states on the engagement or on the respondent. The survey question is “What is the pricing model for this engagement?” The frequency, scope and consequence a response states set the strength of its score. A score of +10 marks a response that describes “this attribute of the engagement as helping the engagement at its best.” A score of −10 marks a response that describes “this attribute as harming the engagement at its worst . . . .”

Of the 33 scored responses, all were negative and 10 (30.30%) carried the strongest negative score. The responses had a mean written score of −6.5 (95% CI −7.3 to −5.8, n = 33 responses on 31 engagements).

Shares are of the answered respondents in each row, which is the base beside them. A row under 10 prints counts in place of shares.

Engagement families were tested on engagement answers, and respondent role and vendor type were tested on respondents. No difference held after Holm control for outsourcing model, institution type, process family, class of work, engagement status and lifecycle stage, respondent role and vendor type.

Scored responses came from 10 engagements in the Active, Build-Out group, 9 engagements in the Active, Transition/Cut-Over group, 4 engagements in the Active, Production group and 8 engagements in the Canceled group. A test of mean written scores across the groups, with each engagement counted once, showed no difference that reached significance at 0.05 (Kruskal-Wallis H 0.61, df 3, p = 0.894, n = 31 engagements). Groups under 10 responses are reported as counts only. Negative responses numbered 9 of 9 in the Active, Transition/Cut-Over group, 4 of 4 in the Active, Production group and 8 of 8 in the Canceled group.

Across respondent roles, no test was run, because fewer than two roles reached 30 scored responses. Roles under 10 responses are reported as counts only. Among portfolio directors, negative responses numbered 5 of 5.

The share of engagements canceled on the study date, measured at engagement level (base 150 engagements, 56 canceled). The association is a descriptive contrast, inflated because canceled engagements were rated after cancellation, and is not predictive.

The measure by the engagement’s assigned answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

Cancellation in the study set did not differ across answer levels (Monte Carlo, simulated, chi-square 2.97, p 0.412, n = 71 engagements).

The satisfaction score recorded against each engagement on a 100-point scale. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

The measure by the engagement’s assigned answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

The study set test could not be computed: fewer than two answer levels with at least 10 engagements.

The mean of an engagement’s 100 indicator ratings, measured at engagement level (base 150 engagements). Where this indicator is one of the 100, the mean is recomputed without it.

The measure by the engagement’s assigned answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

The measure in the study set did not differ across answer levels (Welch F 1.31, p 0.290, n = 71 engagements).

The respondent’s stated likelihood of leaving the current job within 12 months, rated 1 to 10 (C019). Measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

The measure by the engagement’s assigned answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

The measure in the study set did not differ across answer levels (Welch F 0.79, p 0.511, n = 71 engagements).

The cancellation and customer satisfaction contrasts compared engagements with a negative written response against the 119 other engagements, none of which had a scored written response coded to the indicator. The two contrasts therefore did not test written sentiment by themselves. Cancellation was recorded for 8 of the 31 engagements with a negative response (25.81%), against 48 of the 119 other engagements (40.34%). The difference did not reach significance at 0.05 (Fisher exact p = 0.151). Mean customer satisfaction was 65.83% on the 23 active engagements with a negative response, against 63.54% on the 71 other active engagements. The difference did not reach significance at 0.05 (Welch t 0.78, p = 0.440). The association of written sentiment itself with the study measures was tested by two correlations. The association of the engagement mean written score with cancellation did not reach significance at 0.05 (r = −0.11, p = 0.539, n = 31 engagements). The association of the respondent mean written score with intent to leave did not reach significance at 0.05 (rho = −0.14, p = 0.448, n = 33 respondents). None of the four outcome comparisons reached significance at 0.05. Both comparisons with cancellation were descriptive, because responses on canceled engagements were written after cancellation.

Steiger’s z compares two correlations, and a categorical answer yields an effect size across levels rather than a correlation, so the pairs are not tested.

Clustering reduced the 575 respondent answers to about 352 independent answers. Respondent level intervals therefore used the cluster bootstrap, and respondent level tests on intent to leave used cluster-robust errors.

The bound is the ruled |r| < 0.30 expressed as variance explained (V15-R22). Equivalent to zero: intent to leave.

Every computable check held. 4 checks could not be computed, as the table shows. A check holds when its test lands on the same side of 0.05 as the primary test.

Each of the four measures was computed from data this indicator does not contain, so each result is a finding rather than an arithmetic consequence.

Not applicable. A categorical item has no signed correlation, so it has no profile. Its per-level means carry the comparison.

The peer set comprised 101 indicators of the four Parts with 30 or more scored responses. The indicator was within the peer set. Its share of negative responses ranked 1 of 101 from the highest. Its mean written score (−6.5, n = 33 responses) ranked 15 of 101 from the most negative. The peer median of the mean written score was −4.6 across the 101 indicators.

The responses coded to the indicator were most often also coded to “Rate how well your employer is transforming its commercial model as agentic AI reduces the billable human effort it sells today” (C116, 18 responses). The second most frequent co-coded indicator was “Rate your employer’s ability to price agentic work profitably when billable hours no longer measure the value delivered” (C128, 17 responses). The most frequent theme of the coding rulebook in the same responses was “Raised it, nothing came back” (5 responses).

Consumption based pricing had the weakest figures on the three measures that could be compared, on the smallest tested group of engagements (60.00% canceled, n = 10 engagements). Every robustness check that could be computed on this item held (5 of 9 checks, 4 not computable). None of the three differences across pricing models held under test (chi-square 2.97, p 0.412, Cramer’s V 0.20, n = 71 engagements, Welch F 1.31, p 0.290, omega squared −0.007, n = 71 engagements, Welch F 0.79, p 0.511, omega squared −0.015, n = 71 engagements).

The indicator is a survey question that is not a rating, so the written responses were not set against a rating. Written responses were voluntary. They recorded what the respondents who wrote reported, and how common it was is measured by the answers.

The evidence is reported experience: what the delivery team said about its own engagement, gathered in one cross-section. The figures showed associations, not causes. Indicators about the financial institution were reported at second hand by vendor staff. Cancellation is a descriptive contrast, because canceled engagements were rated after they ended.

Tests across respondent role and vendor type were run at respondent level and treated respondents as independent. The section Clustering and Effective Sample Size states whether, and by how much, clustering within engagements reduced the information in respondent level figures.

Each engagement took its plurality answer, with ties left out (107 of 150 engagements). The tests left out Transaction or outcome based pricing, which fell under the size floor, and the I do not know answers (n = 8, n = 28 engagements).

Shares of written sentiment were shares of responses, not of the study set of respondents. The written score took five fixed values, so its mean and median were coarse. Responses clustered within engagements. Intervals for written sentiment used a bootstrap by engagement. Coding was performed by AI coders under a fixed rulebook. Agreement on whether a response was coded to the indicator rested on 6 responses second-coded under rulebook 0.5, with the coders agreeing on 6 of 6. Agreement on the score rested on 3 responses second-scored under rulebook 0.6, with the scores agreeing in 2 of 3. These bases of second-coded responses are too small for an indicator-level alpha.