Rate how completely the engagement has mapped every third and fourth party its agents depend on, down the full chain

The mean rating was 4.6 and the median 5.0 (n = 791 respondents). Ratings of 3 or below, the distressed range, made up 31.86% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.64, 95% CI −0.72 to −0.53, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.71, 95% CI 0.59 to 0.80, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.87, 95% CI 0.83 to 0.91, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.68, cluster bootstrap 95% CI −0.73 to −0.62, p < 0.001 with standard errors clustered by engagement, n = 741 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 38.40, Holm p < 0.001, omega squared 0.394), respondent role (Welch F 7.25, Holm p = 0.008, omega squared 0.018) and vendor type (Welch F 3.37, Holm p = 0.030, omega squared 0.015).

All 44 written responses came from C198, the comment field on the third-party ecosystem. Thirty-five were negative and eight positive, and twenty-six carried the strongest negative score of −10. Most respondents said there was no list of what the agents depended on, or that the list stopped at the direct providers, and several had asked for one without an answer. Engagements with a negative written response were canceled more often, a more negative engagement mean written score came with cancellation, and respondents whose written responses were more negative reported higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 252 of the 791 answers (31.86%). The engagement mean fell in the distressed range in 36 of 150 engagements.

The primary responses numbered 44, all written in answer to C198, “Add any comments on the third-party ecosystem that the ratings in this section do not capture.” No response from any other question addressed the indicator, so every response was primary.

Eighteen responses said no list existed of what the agents depended on, or that no one could say what sat behind a feed or service. Several respondents said they could not rate the section because they had never been shown who the providers were. Four said they had asked for the list, in one case in the first month and twice in another, and received no answer. Nine responses said the direct providers were known but the chain beneath them was not, so that a security review or a contract stopped at the first layer. The gap surfaced through failure: a license expired on an unlisted plug-in and the whole nightly run failed unnoticed until morning, a throttling vendor turned out not to be on any list, and a screening feed changed format without ever having been mapped as a dependency. Three respondents contrasted the engagement with an earlier one that kept a list of every feed and whom to call.

Seven responses described a completed map. One respondent said the mapping took two weekends of analysts' time, went unthanked, and let them sleep properly. Another engagement had mapped its chain to the fourth party, and another had a list of sub-processors down to those its own vendor used.

Of the 44 responses, 35 (79.55%, 95% CI 65.91% to 91.30%) were negative and 26 (59.09%) carried the strongest negative score of −10. Positive responses numbered 8 (18.18%), and 1 was neutral. The mean written score was −5.9 (95% CI −7.8 to −3.9, n = 44 responses), and the median was −10.0.

Of the 44 responses, 43 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.79, 95% CI 0.64 to 0.90, p < 0.001, n = 43 responses). The written responses corroborated the rating. Of the 34 negative responses with a rating, 22 (64.71%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 43, with a mean rating of 3.9. The 748 respondents who rated the indicator without writing about it had a mean of 4.7. The difference, −0.8 (95% CI −1.7 to 0.0), included zero. Ratings in the distressed range made up 51.16% of the ratings from respondents who wrote (22 of 43) and 30.75% of the ratings from respondents who did not (230 of 748).

Every figure in the segment tables is at respondent level. Segments with fewer than 30 respondents carry no interval. Segments with fewer than 10 report counts only.

Segment families recorded at engagement level were tested on engagement means. Respondent role and vendor type were tested on respondents. Where an expected count fell below 5, the test of the distressed share used Fisher's exact test for a two-by-two table, marked exact, or a Monte Carlo test, marked simulated. After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 38.40, Holm p < 0.001, omega squared 0.394). Active engagements in Transition/Cut-Over rated highest (mean 5.6, n = 231 respondents) and canceled engagements lowest (mean 3.1, n = 302 respondents). Ratings also differed by respondent role (Welch F 7.25, Holm p = 0.008, omega squared 0.018). Portfolio directors rated highest (mean 6.0, n = 31 respondents) and individual contributors lowest (mean 4.5, n = 608 respondents). Ratings also differed by vendor type (Welch F 3.37, Holm p = 0.030, omega squared 0.015). Boutique or specialist providers rated highest (mean 5.2, n = 84 respondents) and BPO specialists lowest (mean 4.2, n = 186 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

Written sentiment differed by engagement status and lifecycle stage across the 37 engagements with a written response (Kruskal-Wallis H 12.48, df 3, p = 0.006). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −8.9, 22 responses) and responses from active engagements in Transition/Cut-Over the least negative (mean −4.2, 12 responses). Responses from active engagements in Build-Out (8) and active engagements in Production (2) are reported as counts only.

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 27, engagement managers wrote 12 and portfolio directors wrote 5 responses, reported as counts only.

Cancellation rate is the share of engagements canceled by the study date, measured at engagement level (150 engagements, 56 canceled). Canceled engagements were rated after cancellation. The association is therefore a descriptive contrast and not a forecast of cancellation.

Across the study set, higher ratings came with less cancellation (r = −0.64, 95% CI −0.72 to −0.53, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twelve of the 20 segment levels tested.

Customer satisfaction is the percentage recorded against each engagement. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

Across the active engagements, higher ratings came with higher customer satisfaction (r = 0.71, 95% CI 0.59 to 0.80, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in thirteen of the 16 segment levels tested.

The engagement indicator mean is the mean of an engagement's ratings across the rated indicators, measured at engagement level (150 engagements). The indicator is one of the rated indicators. Each comparison therefore uses the mean recomputed without it.

Higher ratings came with a higher engagement indicator mean (r = 0.87, 95% CI 0.83 to 0.91, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twenty-four of the 24 segment levels tested.

Intent to leave is the answer to C019, “How likely are you to leave your current job in the next 12 months?”, rated 1 to 10. It is measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

Respondents who gave higher ratings reported lower intent to leave (r = −0.68, 95% CI −0.72 to −0.64, Holm p < 0.001, n = 741 respondents). The association held after Holm correction in thirty-six of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 120 other engagements, 113 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 30 engagements with a negative response, 18 (60.00%) were canceled, against 38 of the 120 other engagements (31.67%). The difference reached significance (Fisher exact p = 0.006). Customer satisfaction among active engagements did not differ between the two groups (mean 66.58% on 12 engagements against 63.73% on 82, Welch t 0.69, p = 0.503).

Two correlations test written sentiment directly. Across the 37 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.52, p < 0.001). Across 42 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.62, p < 0.001). Of the four comparisons, three reached significance at 0.05.

The four associations were compared in pairs on their engagement-level forms, each oriented so that a higher value is the better outcome, across the 150 engagements with all four measures. After Holm correction, five of the six pairs differed. The association with the engagement indicator mean was the strongest and the association with cancellation the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.984).

Answers clustered within engagements (ICC 0.66). Clustering reduced the 791 respondent answers to the equivalent of about 206 independent answers. Respondent-level intervals therefore used the cluster bootstrap. Respondent-level tests of intent to leave used standard errors that allow for clustering by engagement.

No association was equivalent to zero. Each of the four differed from zero. The detection floor shown is the study floor at 150 engagements. Intent to leave was tested on its effective sample size.

Each change was computed before rounding and may differ by 0.01 from the difference between the two printed values.

All 22 checks held. A check holds when its test falls on the same side of 0.05 as the primary test. Where the primary test falls below 0.05, the check must also keep the sign of the primary correlation. The thin-base check removes the engagements in any level of outsourcing model or process family with fewer than 30 engagements. Institution type is not used for that check, because every one of its levels has fewer than 30 engagements.

Each of the four measures was computed from data the indicator does not contain. Each result is therefore a finding and not an arithmetic consequence. The indicator is one of the rated indicators, and the engagement indicator mean was recomputed without it. The correction changed r by 0.00, from 0.88 to 0.87.

Each deviation was computed before rounding and may differ by 0.01 from the difference between the two printed values.

The comparison covered 150 engagements. The indicator followed the study's typical pattern on all four measures, and no deviation held after Holm correction.

Among the 71 Part 2 indicators with 30 or more written responses, the indicator's share of negative responses, 79.55%, ranked 39th from the highest, against a median of 80.82%. Its mean written score, −5.9, ranked 16th from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C181, “Rate the quality of advance notice third-party providers give before changes that can affect the agents on this engagement.”, in 13 of the 44 responses (29.55%). C188, “Rate the quality of the data arriving from third-party feeds, judged by whether agents can use it without human cleanup.” (9 responses), C178, “Rate how well the agents on this engagement are protected from breaking silently when a third party changes something without warning.” (8 responses), C187, “Rate the stability of the third-party APIs and data structures the agents depend on, from one provider release to the next.” (8 responses) and C185, “Rate how well the engagement absorbs model version deprecations and retirements forced by model providers.” (7 responses) followed.

The most frequent theme was “Detection only by failure”, in 10 responses (22.73%). The second was “Comparison to a prior engagement”, in 9 responses.

About a third of all ratings of how completely the engagement mapped its third and fourth parties fell in the distressed range (252 of 791 answers, 31.86%). The rating was associated with all four study measures, and every association held under each of the 22 robustness checks. Canceled engagements rated far below the three active stages (mean 3.1, n = 302 respondents).

The written evidence identifies the dependency inventory as the precondition for most of the other third-party controls. Without it, no one could be notified of a change, monitor a feed, review its security or plan its fallback, and one respondent whose team built one put its cost at two weekends of analysts' time. Written sentiment was associated with cancellation in both engagement-level tests and with intent to leave.

The evidence is the reported experience of the vendor's delivery teams, collected in a single cross-section. The associations reported here are not causal.

Tests by respondent role and by vendor type treated respondents as independent. The section on clustering reports how far clustering within engagements reduced the information in respondent-level figures.

Customer management executives gave 2 answers, a base of anecdote strength.

Shares of written sentiment are shares of responses and not of the study set of respondents. The written score takes five fixed values, so its mean and median are coarse. Responses clustered within engagements (44 responses on 37 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.