Rate the maturity of the customer’s data governance over the data this engagement uses

Ratings of the maturity of the financial institution’s data governance over the data the engagement used (mean 3.6, median 3.0, n = 797 respondents) were associated with all four study measures. Engagements with higher ratings were canceled less often (r = −0.56, 95% CI −0.66 to −0.44, p < 0.001, n = 150 engagements). Higher ratings came with higher customer satisfaction on active engagements (r = 0.67, 95% CI 0.54 to 0.77, p < 0.001, n = 94 active engagements). Ratings rose with the engagement indicator mean, computed with this item left out (r = 0.76, 95% CI 0.68 to 0.82, p < 0.001, n = 150 engagements). Respondents who gave higher ratings reported lower intent to leave (r = −0.60, cluster bootstrap 95% CI −0.67 to −0.53, p < 0.001, n = 750 respondents). Ratings differed after Holm control by engagement status and lifecycle stage (Welch F 25.22, Holm p < 0.001, omega squared 0.296), by respondent role (Welch F 7.37, Holm p 0.006, omega squared 0.015), and by vendor type (Welch F 5.69, Holm p < 0.001, omega squared 0.028).

The indicator had 172 written responses coded to it, from 155 of 850 respondents on 99 of 150 engagements. Negative responses numbered 152 of the 172 scored responses (88.37%). The strongest negative score, −10, was carried by 106 of the 172 scored responses (61.63%). Secondary responses, written in answer to questions outside the indicator’s own survey section, numbered 111 of the 172 responses (64.53%). Respondents who wrote rated the indicator lower than respondents who did not (mean 3.0, n = 145 respondents, against mean 3.7, n = 652 respondents), a difference whose interval excluded zero.

The study set mean was 3.6 on 797 answering respondents, with a median of 3.0. 401 answers, 50.31% of the 797, were at the distressed end. A distressed engagement mean was recorded in 60 of 150 engagements.

Of the 172 responses, 61 (35.47%) were primary and 111 (64.53%) were secondary. The secondary responses came from 102 respondents on 70 engagements, in answer to 7 questions outside the indicator’s own survey section (most often C202 with 49 responses, C029 with 39 responses and C204 with 11 responses). Both a primary and a secondary response came from 8 respondents. Among the Part 2 indicators with 30 or more scored responses, the secondary share of the indicator ranked 13 of 71 from the highest. The median secondary share was 18.92% across the 71 indicators.

Negative responses made up 103 of the 111 secondary responses (92.79%), against 49 of the 61 primary responses (80.33%). The difference reached significance at 0.05 (Fisher exact p = 0.023, n = 172 responses). The share of negative responses was larger among secondary responses than among primary responses.

Of the 172 scored responses, 152 (88.37%) were negative, 106 (61.63%) carried the strongest negative score and 20 (11.63%) were positive. The responses had a mean written score of −6.9 (95% CI −7.7 to −6.0, n = 172 responses on 99 engagements).

Respondents who rated the indicator 3 or below wrote 104 of the 144 negative responses with a rating. Written score and rating showed a strong positive correlation that reached significance at 0.05 (Spearman rho 0.56, 95% CI 0.44 to 0.67, p < 0.001, n = 162 responses on 94 engagements). Its interval excluded zero. Where a written score diverges from the same respondent’s rating, the rating is the measure of the indicator.

Respondents who wrote about the indicator rated it 0.7 points lower than respondents who did not (mean 3.0, n = 145 respondents, against mean 3.7, n = 652 respondents, difference 95% CI −1.0 to −0.4, cluster bootstrap by engagement). The interval of the difference excluded zero. Ratings of 3 or below came from 92 of the 145 respondents who wrote and gave a rating (63.45%), against 309 of the 652 respondents who did not write and gave a rating (47.39%). Respondents who wrote formed a self-selected group and not a random sample of the answering base, because written responses were voluntary.

Figures are respondent level. Means are on the goodness view. A level under 30 prints no interval and a level under 10 prints counts only.

Engagement families were tested on engagement means, and respondent role and vendor type were tested on respondents. A distressed p marked exact is a Fisher exact test for a two by two table, and one marked simulated is a Monte Carlo test run until its p is stable to the third decimal, both used where an expected count is under 5. Engagement status and lifecycle stage differed after Holm control (Welch F 25.22, Holm p < 0.001, tested on engagement means). In Descriptive Statistics by Segment, Active, Build-Out had the highest mean, 4.4 (n = 160 respondents), and Canceled the lowest, 2.5 (n = 305 respondents). Respondent role differed after Holm control (Welch F 7.37, Holm p 0.006, tested on respondents). In Descriptive Statistics by Segment, Portfolio Director had the highest mean, 4.8 (n = 31 respondents), and Individual Contributor the lowest, 3.5 (n = 614 respondents). Vendor type differed after Holm control (Welch F 5.69, Holm p < 0.001, tested on respondents). In Descriptive Statistics by Segment, Consulting firm had the highest mean, 4.1 (n = 106 respondents), and BPO specialist the lowest, 3.1 (n = 185 respondents). No difference held after Holm control for outsourcing model, institution type, process family and class of work.

Scored responses came from 18 engagements in the Active, Build-Out group, 28 engagements in the Active, Transition/Cut-Over group, 10 engagements in the Active, Production group and 43 engagements in the Canceled group. A test of mean written scores across the groups, with each engagement counted once, showed a difference that reached significance at 0.05 (Kruskal-Wallis H 13.00, df 3, p = 0.005, n = 99 engagements). Among the groups with 10 or more responses, the lowest mean written score came from the Canceled group (−8.5, n = 80 responses). The highest came from the Active, Transition/Cut-Over group (−5.0, n = 48 responses).

The role test compared individual contributors and engagement managers, the roles with 30 or more scored responses. A test of mean written scores between the two roles, with each respondent counted once, showed no difference that reached significance at 0.05 (Kruskal-Wallis H 1.55, df 1, p = 0.213, n = 147 respondents). The roles compared had mean written scores of −7.3 for individual contributors (n = 134 responses) and −5.7 for engagement managers (n = 30 responses). Roles under 10 responses are reported as counts only. Among portfolio directors, negative responses numbered 6 of 8.

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 study set r was −0.56 (95% CI −0.66 to −0.44, Holm p < 0.001, n = 150 engagements). Of the 20 tested segment levels, 11 held after Holm control.

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 study set r was 0.67 (95% CI 0.54 to 0.77, Holm p < 0.001, n = 94 engagements). Of the 16 tested segment levels, 13 held after Holm control.

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 study set r was 0.76 (95% CI 0.68 to 0.82, Holm p < 0.001, n = 150 engagements). Of the 24 tested segment levels, 23 held after Holm control.

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 study set r was −0.60 (95% CI −0.65 to −0.56, Holm p < 0.001, n = 750 respondents). Of the 37 tested segment levels, 36 held after Holm control.

The cancellation and customer satisfaction contrasts compared engagements with a negative written response against all other engagements. The comparison group comprised 62 other engagements, of which 51 had no scored written response coded to the indicator and 11 had only scored responses that were not negative. The two contrasts therefore did not test written sentiment by themselves. Cancellation was recorded for 42 of the 88 engagements with a negative response (47.73%), against 14 of the 62 other engagements (22.58%). The difference reached significance at 0.05 (Fisher exact p = 0.002). Mean customer satisfaction was 62.98% on the 46 active engagements with a negative response, against 65.17% on the 48 other active engagements. The difference did not reach significance at 0.05 (Welch t −0.91, p = 0.366). 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 reached significance at 0.05 (r = −0.34, p < 0.001, n = 99 engagements). The association of the respondent mean written score with intent to leave reached significance at 0.05 (rho = −0.39, p < 0.001, n = 142 respondents). Of the four outcome comparisons, three reached significance at 0.05: the cancellation contrast, the correlation of the engagement mean written score with cancellation and the correlation of the respondent mean written score with intent to leave. Both comparisons with cancellation were descriptive, because responses on canceled engagements were written after cancellation.

Pairs were compared on the engagement level form of every measure, each turned so that a higher value was the better outcome, on the 150 engagements where the indicator and all four measures existed. Of the 6 pairs, 5 differed after Holm control.

Clustering reduced the 797 respondent answers to about 218 independent answers. Respondent level intervals therefore used the cluster bootstrap, and respondent level tests on intent to leave used cluster-robust errors.

No measure showed a tested null. No result was inconclusive. The detection floor column is the study floor at 150 engagements. Intent to leave was tested on its effective n.

Each change was computed before rounding, so it can differ by 0.01 from the difference of the two printed values.

Every check held. A check holds when its test lands on the same side of 0.05 as the primary test and, where the primary test is below 0.05, its r keeps the primary sign. Thin-base rule: engagements in any level under 30 engagements of outsourcing model or process family removed. Institution type is not used, because every one of its levels is under 30.

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. This indicator is one of the 100 components, so the engagement indicator mean was recomputed without it, and the correction changed r by 0.00, from 0.76 to 0.76.

Each deviation was computed before rounding, so it can differ by 0.01 from the difference of the two printed values.

The comparison ran on 150 engagements. The indicator departed from the study’s typical pattern on engagement indicator mean.

The peer set comprised 71 indicators of Part 2 with 30 or more scored responses. The indicator was within the peer set. Its share of negative responses ranked 16 of 71 from the highest. Its mean written score (−6.9, n = 172 responses) ranked 6 of 71 from the most negative. The peer median of the mean written score was −4.7 across the 71 indicators.

The responses coded to the indicator were most often also coded to “Rate the quality of the customer’s data across the systems this engagement depends on” (C094, 62 responses). The second most frequent co-coded indicator was “I have altered client-provided data” (C199, 49 responses). The most frequent theme of the coding rulebook in the same responses was “No validation on the financial institution’s side” (50 responses).

Distressed answers made up more than half of the study set (401 of 797 answers, 50.31%). Active, Build-Out had the highest mean by lifecycle stage (mean 4.4, n = 160 respondents), and Canceled had the lowest (mean 2.5, n = 305 respondents). Portfolio Director respondents gave the highest ratings by role (mean 4.8, n = 31 respondents), and Individual Contributor respondents gave the lowest (mean 3.5, n = 614 respondents). The associations held under every robustness check run on this item (22 of 22 checks).

The written responses corroborated the rating at respondent level, with a strong positive correlation between written score and rating. Of the 62 responses also coded to C094, 37 were secondary responses to the indicator. Written responses were voluntary. They recorded what the respondents who wrote reported, and how common it was is measured by the ratings.

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.

Customer Management Executive answers were at anecdote strength (n = 2). Answers clustered within engagements, which reduced the effective number of independent answers (ICC 0.62, 797 answers worth about 218 independent answers).

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 17 responses second-coded under rulebook 0.5, with the coders agreeing on 14 of 17. Agreement on the score rested on 14 responses second-scored under rulebook 0.5, with the scores agreeing in 14 of 14. These bases of second-coded responses are too small for an indicator-level alpha.