Key Findings
Last updated: Oct 16, 2026, 6:36 AM EDT
All effect sizes, cross-tabulations, t-tests, and ANOVA are computed independently for each of the four primary result groups. This ensures that any finding can be validated against the researcher’s chosen dataset.
Effect Sizes (Cohen’s d)
Cohen’s d measures the standardized difference between group means. Values of |d| < 0.2 are negligible, 0.2-0.5 small, 0.5-0.8 medium, and > 0.8 large. Each comparison is computed separately for each result group.
Conservative Clean (N=162)
Technical (CIO/CTO) vs Non-Technical - n=30 vs n=132
| Construct | Tech Mean | Non-Tech Mean | Cohen’s d | Size |
|---|
| barriers | 2.8241 | 2.8978 | -0.11 | negligible |
|---|
| readiness | 3.2782 | 2.9943 | +0.49 | small |
|---|
| maturity | 3.1667 | 3.0609 | +0.15 | negligible |
|---|
Large Org (5000+) vs Small/Medium - n=32 vs n=130
| Construct | Large Mean | S/M Mean | Cohen’s d | Size |
|---|
| barriers | 3.1339 | 2.8226 | +0.47 | small |
|---|
| readiness | 2.9447 | 3.0720 | -0.22 | small |
|---|
Flexible Clean (N=264)
Technical (CIO/CTO) vs Non-Technical - n=58 vs n=206
| Construct | Tech Mean | Non-Tech Mean | Cohen’s d | Size |
|---|
| barriers | 2.7982 | 2.9073 | -0.15 | negligible |
|---|
| readiness | 3.3567 | 3.0025 | +0.54 | medium |
|---|
| maturity | 3.2543 | 3.0360 | +0.27 | small |
|---|
Large Org (5000+) vs Small/Medium - n=49 vs n=215
| Construct | Large Mean | S/M Mean | Cohen’s d | Size |
|---|
| barriers | 3.0739 | 2.8400 | +0.33 | small |
|---|
| readiness | 3.0299 | 3.0918 | -0.09 | negligible |
|---|
Prolific Accepted (N=496)
Technical (CIO/CTO) vs Non-Technical - n=108 vs n=388
| Construct | Tech Mean | Non-Tech Mean | Cohen’s d | Size |
|---|
| barriers | 2.8767 | 2.8917 | -0.02 | negligible |
|---|
| readiness | 3.3822 | 3.0280 | +0.55 | medium |
|---|
| maturity | 3.3143 | 3.0753 | +0.30 | small |
|---|
Large Org (5000+) vs Small/Medium - n=103 vs n=393
| Construct | Large Mean | S/M Mean | Cohen’s d | Size |
|---|
| barriers | 2.9268 | 2.8784 | +0.07 | negligible |
|---|
| readiness | 3.1191 | 3.1015 | +0.03 | negligible |
|---|
All V2 Finished (N=798)
Technical (CIO/CTO) vs Non-Technical - n=191 vs n=607
| Construct | Tech Mean | Non-Tech Mean | Cohen’s d | Size |
|---|
| barriers | 2.8526 | 2.9088 | -0.07 | negligible |
|---|
| readiness | 3.4535 | 3.1337 | +0.45 | small |
|---|
| maturity | 3.4346 | 3.1589 | +0.34 | small |
|---|
Large Org (5000+) vs Small/Medium - n=164 vs n=634
| Construct | Large Mean | S/M Mean | Cohen’s d | Size |
|---|
| barriers | 2.9084 | 2.8920 | +0.02 | negligible |
|---|
| readiness | 3.2460 | 3.2009 | +0.06 | negligible |
|---|
Cross-Tabulations
Cross-tabulations show construct means broken down by respondent subgroups (role, org size). These are computed for each result group to check whether group-level patterns hold across data cleaning levels.
Conservative Clean (N=162)
By Role
| Group | n | B | R | M |
|---|
| Technical | 30 | 2.82 | 3.28 | 3.17 |
|---|
| Non-Technical | 132 | 2.90 | 2.99 | 3.06 |
|---|
By Org Size
| Group | n | B | R | M |
|---|
| Small (<500) | 81 | 2.82 | 3.09 | 3.02 |
|---|
| Medium (500-4999) | 49 | 2.83 | 3.04 | 3.14 |
|---|
| Large (5000+) | 32 | 3.13 | 2.94 | 3.14 |
|---|
Flexible Clean (N=264)
By Role
| Group | n | B | R | M |
|---|
| Technical | 58 | 2.80 | 3.36 | 3.25 |
|---|
| Non-Technical | 206 | 2.91 | 3.00 | 3.04 |
|---|
By Org Size
| Group | n | B | R | M |
|---|
| Small (<500) | 127 | 2.78 | 3.11 | 2.99 |
|---|
| Medium (500-4999) | 88 | 2.93 | 3.06 | 3.17 |
|---|
| Large (5000+) | 49 | 3.07 | 3.03 | 3.19 |
|---|
Prolific Accepted (N=496)
By Role
| Group | n | B | R | M |
|---|
| Technical | 108 | 2.88 | 3.38 | 3.31 |
|---|
| Non-Technical | 388 | 2.89 | 3.03 | 3.08 |
|---|
By Org Size
| Group | n | B | R | M |
|---|
| Small (<500) | 234 | 2.83 | 3.06 | 3.00 |
|---|
| Medium (500-4999) | 159 | 2.94 | 3.16 | 3.23 |
|---|
| Large (5000+) | 103 | 2.93 | 3.12 | 3.26 |
|---|
All V2 Finished (N=798)
By Role
| Group | n | B | R | M |
|---|
| Technical | 191 | 2.85 | 3.45 | 3.43 |
|---|
| Non-Technical | 607 | 2.91 | 3.13 | 3.16 |
|---|
By Org Size
| Group | n | B | R | M |
|---|
| Small (<500) | 363 | 2.82 | 3.12 | 3.06 |
|---|
| Medium (500-4999) | 271 | 2.98 | 3.31 | 3.38 |
|---|
| Large (5000+) | 164 | 2.91 | 3.25 | 3.33 |
|---|
Inferential Statistics
Welch’s t-tests compare two groups (unequal variance assumed). One-way ANOVA tests whether means differ across three or more groups. All tests are two-tailed with α = 0.05. Significant results (p < 0.05) are highlighted.
Conservative Clean
Welch’s t-test: Technical vs Non-Technical(ntech=30, nnon-tech=132)
| Construct | t | df | p | Sig. |
|---|
| barriers | -0.527 | 41.6 | 0.601 | |
|---|
| readiness | 2.231 | 39.9 | 0.031 | ✱ |
|---|
| maturity | 0.689 | 40.1 | 0.495 | |
|---|
Welch’s t-test: Large vs Small/Medium Org(nlarge=32, nsm/med=130)
| Construct | t | df | p | Sig. |
|---|
| barriers | 2.477 | 49.3 | 0.017 | ✱ |
|---|
| readiness | -1.006 | 43.3 | 0.320 | |
|---|
| maturity | 0.464 | 41.0 | 0.645 | |
|---|
One-way ANOVA: by Role(Technical, Non-Technical)
| Construct | F | df | p | Sig. |
|---|
| barriers | 0.297 | 1, 160 | 0.586 | |
|---|
| readiness | 5.830 | 1, 160 | 0.017 | ✱ |
|---|
| maturity | 0.550 | 1, 160 | 0.460 | |
|---|
One-way ANOVA: by Organization Size(Small (<500), Medium (500-4999), Large (5000+))
| Construct | F | df | p | Sig. |
|---|
| barriers | 2.865 | 2, 159 | 0.060 | |
|---|
| readiness | 0.733 | 2, 159 | 0.482 | |
|---|
| maturity | 0.593 | 2, 159 | 0.554 | |
|---|
Flexible Clean
Welch’s t-test: Technical vs Non-Technical(ntech=58, nnon-tech=206)
| Construct | t | df | p | Sig. |
|---|
| barriers | -1.000 | 89.8 | 0.320 | |
|---|
| readiness | 3.726 | 95.3 | <.001 | ✱ |
|---|
| maturity | 1.869 | 97.2 | 0.065 | |
|---|
Welch’s t-test: Large vs Small/Medium Org(nlarge=49, nsm/med=215)
| Construct | t | df | p | Sig. |
|---|
| barriers | 2.076 | 72.3 | 0.041 | ✱ |
|---|
| readiness | -0.542 | 66.7 | 0.590 | |
|---|
| maturity | 0.937 | 66.2 | 0.352 | |
|---|
One-way ANOVA: by Role(Technical, Non-Technical)
| Construct | F | df | p | Sig. |
|---|
| barriers | 1.030 | 1, 262 | 0.311 | |
|---|
| readiness | 13.153 | 1, 262 | <.001 | ✱ |
|---|
| maturity | 3.224 | 1, 262 | 0.074 | |
|---|
One-way ANOVA: by Organization Size(Small (<500), Medium (500-4999), Large (5000+))
| Construct | F | df | p | Sig. |
|---|
| barriers | 3.334 | 2, 261 | 0.037 | ✱ |
|---|
| readiness | 0.300 | 2, 261 | 0.741 | |
|---|
| maturity | 1.792 | 2, 261 | 0.169 | |
|---|
Prolific Accepted
Welch’s t-test: Technical vs Non-Technical(ntech=108, nnon-tech=388)
| Construct | t | df | p | Sig. |
|---|
| barriers | -0.188 | 167.5 | 0.851 | |
|---|
| readiness | 5.266 | 181.1 | <.001 | ✱ |
|---|
| maturity | 2.818 | 173.5 | 0.005 | ✱ |
|---|
Welch’s t-test: Large vs Small/Medium Org(nlarge=103, nsm/med=393)
| Construct | t | df | p | Sig. |
|---|
| barriers | 0.626 | 166.7 | 0.532 | |
|---|
| readiness | 0.227 | 148.3 | 0.821 | |
|---|
| maturity | 1.822 | 150.2 | 0.070 | |
|---|
One-way ANOVA: by Role(Technical, Non-Technical)
| Construct | F | df | p | Sig. |
|---|
| barriers | 0.036 | 1, 494 | 0.849 | |
|---|
| readiness | 25.666 | 1, 494 | <.001 | ✱ |
|---|
| maturity | 7.792 | 1, 494 | 0.005 | ✱ |
|---|
One-way ANOVA: by Organization Size(Small (<500), Medium (500-4999), Large (5000+))
| Construct | F | df | p | Sig. |
|---|
| barriers | 1.271 | 2, 493 | 0.282 | |
|---|
| readiness | 1.043 | 2, 493 | 0.353 | |
|---|
| maturity | 5.787 | 2, 493 | 0.003 | ✱ |
|---|
All V2 Finished
Welch’s t-test: Technical vs Non-Technical(ntech=191, nnon-tech=607)
| Construct | t | df | p | Sig. |
|---|
| barriers | -0.845 | 301.3 | 0.399 | |
|---|
| readiness | 5.590 | 331.6 | <.001 | ✱ |
|---|
| maturity | 4.225 | 327.2 | <.001 | ✱ |
|---|
Welch’s t-test: Large vs Small/Medium Org(nlarge=164, nsm/med=634)
| Construct | t | df | p | Sig. |
|---|
| barriers | 0.242 | 256.5 | 0.809 | |
|---|
| readiness | 0.690 | 243.2 | 0.491 | |
|---|
| maturity | 1.835 | 247.3 | 0.068 | |
|---|
One-way ANOVA: by Role(Technical, Non-Technical)
| Construct | F | df | p | Sig. |
|---|
| barriers | 0.760 | 1, 794 | 0.384 | |
|---|
| readiness | 29.559 | 1, 792 | <.001 | ✱ |
|---|
| maturity | 17.163 | 1, 792 | <.001 | ✱ |
|---|
One-way ANOVA: by Organization Size(Small (<500), Medium (500-4999), Large (5000+))
| Construct | F | df | p | Sig. |
|---|
| barriers | 3.422 | 2, 793 | 0.033 | ✱ |
|---|
| readiness | 5.795 | 2, 791 | 0.003 | ✱ |
|---|
| maturity | 14.126 | 2, 791 | <.001 | ✱ |
|---|
Inferential Extensions
These analyses extend the headline inferential statistics with bootstrap mediation, factor decomposition, multicollinearity diagnostics, equivalence testing (TOST), and a power analysis describing the smallest effect the current sample can reliably detect.
Mediation: Barriers -> Readiness -> Maturity
Bootstrapped mediation analysis testing whether perceived Barriers influence Maturity indirectly through Readiness rather than directly.
| Path | Coef | SE | 95% CI | p | Sig |
|---|
| a (R ~ X): Barriers -> Readiness | -0.340 | 0.065 | [-, -] | < .001 | sig |
| b (Y ~ R): Readiness -> Maturity | 0.684 | 0.077 | [-, -] | < .001 | sig |
| Total (c): Barriers -> Maturity | -0.154 | 0.083 | [-, -] | 0.064 | ns |
| Direct (c-prime): Barriers -> Maturity | Readiness | 0.092 | 0.074 | [-, -] | 0.217 | ns |
| Indirect (a*b): bootstrap mediation effect | -0.246 | 0.060 | [-, -] | < .001 | sig |
N (listwise) = 162. CI bounds are bootstrap percentile intervals (Preacher & Hayes, 2008).
Per-Factor Regressions
Comparing the explanatory power of the aggregated Barriers score versus the canonical 3 sub-factors as separate predictors.
| Outcome | R-squared (total Barriers scale) | R-squared (3 sub-factors F1a/F1b/F2) | Lift from decomposition |
|---|
| Readiness as outcome | 0.1473 | 0.2411 | +0.0938 |
| Maturity as outcome | 0.0213 | 0.1351 | +0.1138 |
Decomposing Barriers into the canonical 3 sub-factors (F1a Strategy & Culture, F1b Resources & Operations, F2 External & Compliance) explains additional variance beyond the aggregated scale, supporting the multi-factor structure.
Standardized Sub-factor Regressions and VIF
Per-factor standardized betas with t-statistics, and Variance Inflation Factor (VIF) to verify the 3 sub-factors are not collinear enough to bias the coefficients.
Readiness ~ F1a + F1b + F2 (R-squared = 0.2411, n = 162)
| Predictor | beta (std) | t | p |
|---|
| F1a | -0.370 | -4.04 | < .001 |
| F1b | -0.249 | -2.67 | 0.009 |
| F2 | 0.245 | 3.07 | 0.003 |
Maturity ~ F1a + F1b + F2 (R-squared = 0.1351, n = 162)
| Predictor | beta (std) | t | p |
|---|
| F1a | -0.278 | -2.85 | 0.005 |
| F1b | -0.136 | -1.37 | 0.174 |
| F2 | 0.339 | 3.97 | < .001 |
Variance Inflation Factor (multicollinearity diagnostic)
| Predictor | VIF | Interpretation |
|---|
| constant | 1.00 | OK |
| F1a | 1.74 | OK |
| F1b | 1.82 | OK |
| F2 | 1.33 | OK |
VIF < 5 indicates negligible multicollinearity; 5-10 warrants attention; > 10 indicates a problem (Hair et al., 2010).
Equivalence Test (TOST): SMB vs Enterprise
Tests whether SMB and Enterprise organizations are statistically equivalent on each construct, using +/- 0.30 SD as the equivalence bound (Lakens, 2017).
| Construct | delta | Pooled SD | p-TOST | Equivalent at +/- 0.30 SD |
|---|
| Barriers | 0.201 | 0.669 | 0.059 | Not equivalent |
| Readiness | 0.175 | 0.585 | 0.544 | Not equivalent |
| Maturity | 0.212 | 0.707 | 0.039 | Equivalent |
Two One-Sided Tests (Lakens 2017) for equivalence between SMB and Enterprise on each construct, using equivalence bounds of +/- 0.30 standardized mean difference. p-TOST < .05 rejects non-equivalence.
Power Analysis
Smallest detectable effect size
Detectable d (power = 0.80)
0.454
Smallest Cohen's d detectable at alpha=0.05, power=0.80
Completed Analyses
The following additional analyses have been completed:
