Bank Type

Asset management was the most common institution type among the engagements (24 of 150 engagements, 16.00%), with Wealth management and private banking close behind (23 of 150 engagements, 15.33%). Cancellation ranged from Payments and merchant acquiring at the low end (23.08% canceled, n = 13 engagements) to Asset management at the high end (54.17% canceled, n = 24 engagements), with no difference under test (chi-square 5.71, p 0.693, Cramer’s V 0.20, n = 141 engagements). Customer satisfaction on active engagements was highest for Asset management (mean 69.5, n = 11 engagements) and lowest for Investment banking (mean 56.4, n = 10 engagements), with no difference under test (Welch F 2.39, p 0.097, omega squared 0.078, n = 46 engagements). The engagement indicator mean was lowest for Investment banking (mean 3.4, n = 19 engagements) and highest for Commercial and business banking (mean 4.5, n = 13 engagements), with no difference under test (Welch F 1.03, p 0.425, omega squared −0.007, n = 141 engagements). Intent to leave was highest for Investment banking (mean 7.7, n = 19 engagements) and lowest for Retail and consumer banking (mean 5.8, n = 12 engagements), with no difference under test (Welch F 0.83, p 0.583, omega squared −0.010, n = 141 engagements). The institution type mix differed after Holm control by process family (chi-square 60.77, Holm p 0.017, Cramer’s V 0.35) and by engagement status and lifecycle stage (chi-square 49.85, Holm p 0.015, Cramer’s V 0.33).

The indicator had 6 written responses coded to it, from 6 of 850 respondents on 6 of 150 engagements. Negative responses numbered 4 of the 6 scored responses. No response carried the strongest negative score, −10. Every response was a secondary response.

The value was recorded once per engagement, so every share counts out of 150 engagements.

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

All 6 responses were secondary responses. The secondary responses came from 6 respondents on 6 engagements, in answer to 2 questions (C135 with 4 responses and C114 with 2 responses).

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 engagement field is “ENG:Bank Type.” 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 . . . .”

The distribution was reported as counts only, because fewer than 10 responses carried a score. Of the 6 scored responses, 4 were negative, none carried the strongest negative score and none was positive.

Shares are of the answered engagements 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. Process family differed in the answer mix after Holm control (chi-square 60.77, Holm p 0.017, Cramer’s V 0.35). Engagement status and lifecycle stage differed in the answer mix after Holm control (chi-square 49.85, Holm p 0.015, Cramer’s V 0.33). No difference held after Holm control for outsourcing model and class of work.

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 recorded 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 5.71, p 0.693, n = 141 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 recorded 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 2.39, p 0.097, n = 46 engagements). The clean estimate ran inside active engagements only.

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 recorded 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 1.03, p 0.425, n = 141 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 recorded 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 0.83, p 0.583, n = 141 engagements).

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.

Not applicable. The field was recorded once per engagement, so every respondent in an engagement shared one value.

The bound is the ruled |r| < 0.30 expressed as variance explained (V15-R22). Equivalent to zero: cancellation rate, engagement indicator mean and intent to leave.

Every check held. 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 field has no signed correlation, so it has no profile. Its per-level means carry the comparison.

The indicator’s twin, C031, records the same fact under the coding rulebook’s twin rule. The twin was coded in all 6 responses coded to the indicator. The twin is left out of the counts that follow. The responses coded to the indicator were most often also coded to “Rate the adequacy of your employer’s investment in training its workforce” (C123, 3 responses). The second-highest count was shared by 5 indicators at 2 responses each. The same responses carried 7 themes of the coding rulebook, each in 1 response.

Investment banking engagements had the lowest engagement indicator mean (mean 3.4, n = 19 engagements) and the highest intent to leave (mean 7.7, n = 19 engagements). Neither gap held under test, and no other contrast across institution types held either. Every robustness check run on this field held (5 of 5 checks).

The indicator is an engagement field, 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 recorded values. The analysis of the written responses rested on 6 responses, a base of anecdote strength.

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.

The customer satisfaction test rested on the institution types with enough active engagements (Welch F 2.39, p 0.097, omega squared 0.078, n = 46 engagements). Private equity and private capital entered no measure test because of its small base (9 of 150 engagements, 6.00%).

Fewer than 10 responses carried a score, so written sentiment was reported as counts, with no statistic. Coding was performed by AI coders under a fixed rulebook. Agreement on whether a response was coded to the indicator rested on 1 response second-coded under rulebook 0.5, with the coders agreeing on 1 of 1. No response coded to the indicator was second-scored under rulebook 0.6, so agreement on the score could not be assessed. This base of second-coded responses is too small for an indicator-level alpha.