The Trust Tax — Measuring the Economic Cost of Low Trust in Software Teams[Research Title]

[asya-psy]

Why?

Trust at work is one of the most consistently studied predictors of team performance in organisational psychology. Meta-analyses confirm that intrateam trust correlates positively with team performance (De Jong et al., 2016; ρ = .30), with the relationship strengthening under high task interdependence — a condition that characterises QA work by design. QA engineers sit at the intersection of every team boundary: they report defects to developers, flag risks to product owners, and raise quality concerns to leadership. Every one of those interactions is a trust transaction.

Yet despite the strength of the academic evidence, most organisations have no practical, data-anchored way to measure what low trust actually costs them in QA terms. This study addresses that gap by focusing on two observable, extractable outcomes that are directly shaped by trust dynamics:
- Defect Escape Rate — the proportion of defects that reach production unreported, driven by the suppression of bad news in low-trust environments
- Handoff Ping-Pong Rate — the frequency with which tickets bounce between developers and QA rather than being resolved through direct collaboration. Both metrics are routinely generated by teams already using Jira or equivalent tooling, and both have a defensible causal link to trust that distinguishes them from general performance noise.

The study draws on Transaction Cost Economics (TCE), adapted for intra-organisational dynamics: when trust is low, teams compensate with monitoring overhead, defensive controls, and "process armor" — each of which constitutes a hidden economic cost. These costs are categorised as:

• Monitoring/oversight costs — status requests, approval chains, excess check-ins
• Coordination costs — handoffs, rework, ticket ping-pong, unclear ownership
• Safeguarding costs — formal sign-offs, excessive documentation as blame insurance
• Opportunity costs — slower decisions, reduced experimentation

A key distinction underpins the methodology: not all friction signals distrust. Some process controls exist for regulatory, structural, or system-reliability reasons. The study will develop heuristics to differentiate trust-driven friction (defensive controls) from legitimate structural practices.

We´d like to find out, do software teams with higher team-level trust exhibit lower transaction costs, operationalised as measurable friction in everyday work tools, and better quality delivery outcomes, when controlling for relevant team and contextual factors?

_Sic: this is the final research proposal for a topic that had already been discussed in the Beyond Quality community, but it was removed because my previous account was flagged as suspicious.

Recovered discussion archive is available here, thanks to @sharovatov_

Expected results

1. A Trust Tax Index — a practical method for teams and leaders to measure friction and convert it into hours and cost
2. A conference talk and research paper aimed at both practitioner and academic audiences
3. Practical guidance: evidence-based actions teams can take to reduce trust-driven friction

Collaboration

Before the main study, we need 1–2 teams willing to participate in a pilot. The pilot will allow us to:

• Test the trust survey instrument and assess response quality
• Inspect the distribution, stability, and outlier profile of friction metrics over the prior 6 months
• Validate the distinction between trust-driven friction and legitimate process controls
• Refine metric definitions (especially Handoff Ping-Pong resolution classification)

What participation involves for a pilot team:

• Completing a short trust survey (~10 items, ~10 minutes)
• Granting read access to anonymised Jira/GitHub/Slack aggregate data for the past 6 months
• A brief interview or async Q&A to contextualise the data

All data will be handled at the team level. No individual identification will be published.
If your team meets the admission criteria and is open to contributing to practitioner-facing organisational research, please express interest in this thread or reach out directly.

Target artifact

research, talk

Background and additional information

References

De Jong, B., Dirks, K., & Gillespie, N. (2016). Journal of Applied Psychology, 101(8), 1134–1150.
Feitosa, J., Grossman, R., Kramer, W. S., & Salas, E. (2020). Journal of Organizational Behavior, 41(5), 479–501.
Jarvenpaa, S. L., et al. — Trust scale, as reviewed in Feitosa et al. (2020).
Edmondson, A. C. (2004). Psychological safety, trust, and learning in organizations.
Bromiley, P. & Cummings, L. L. — Trust and Transaction Cost Economics.

[asya-psy]

@sharovatov here is the survey file

There are some things that still look like something to clear out:

• Shared definition of "production defect"
• Bounce threshold — the "3+ transitions = a bounce" rule was discussed but never formally agreed.
• Participant anonymisation / ID protocol — not discussed; needed before any data is collected.

@sharovatov please have a look at the files and question, after this I'll create a form to collect data from our pilot teams