Chapter Four · Findings
The thematic framework
Six themes, thirty codes, and participants’ own words — as reported in Chapter Four.
AI-generated conceptual illustration · Not research evidence or a real study site
Narrative Visualization Enhances Interpretation
Dashboards were the single most frequently named artifact in the dataset. Their usefulness was described as contingent on an iterative, user-informed design process — and on fit between the visual form, the data’s structure, and the audience’s role — rather than on the sophistication of the tool.
Research question
RQ2
Strength of evidence
Strong
Codes
Key findings
- P5: “Visuals reduce connective load because they allow you to recognize the patterns much faster than reading tables full of numbers.”
- P9’s test of a good dashboard: “What happened? Why does it matter? And what do I need to do?”
- Chart choices were treated as substantive analytic decisions, not cosmetic ones — P4: “some data are not suitable when you look at it from the bar charts.”
- P3’s most distinctive example: geographic, map-based visualization — “when you start putting stuff on a map, right? It sounds so simple, but… you show them something they did not know.”
- P1 described redesigning a customer dashboard in direct response to user friction; P2 described an underused, low-confidence dashboard as “almost like a waste” — adoption versus failure tracked process and co-design, not tool choice.
- P12: “A number by itself doesn’t always catch my attention. But if something is highlighted or there is an indicator showing that action is needed, I notice it much faster.”
Divergence, preserved — not smoothed
P8 offered the most categorical rejection of visual reporting in the dataset: “That shit don’t help… Nobody cares about that on the floor,” and “It confuses you. A lot of people don’t know how to read bars or graphs.” P9 — structurally similar frontline role, but with routine system access — described dashboards as directly useful. Fit, not frontline status, is what the evidence differentiates.
Added scholarly interpretation
Why this theme matters
A visual display is an argument about what deserves attention. Its value depends on data structure, explanatory context, and audience fit—not on visual sophistication alone.
Proposed design implication · not tested
Test whether the intended reader can identify what happened, why it matters, and what to do. Offer a narrated or non-chart alternative where visual literacy or access is limited.
Boundary of the claim
P8 and P9 disagree sharply about dashboard usefulness. Do not erase that difference or treat visual reporting as universally beneficial; the cause of the contrast remains unresolved.
In their words
“Visuals reduce connective load because they allow you to recognize the patterns much faster than reading tables full of numbers.”— P5
“If I see a dashboard showing overdue deliveries, for example, I can understand the situation much faster than if somebody gives me a spreadsheet with hundreds of rows.”— P9
“I think a good dashboard should answer three questions: What happened? Why does it matter? And what do I need to do?”
“The same information, the same metric, the same dashboard, the same report is and will always be interpreted in different ways by different level of people.”— P7
“When you start putting stuff on a map, right? It sounds so simple, but… you show them something they did not know.”— P3
“A number by itself doesn't always catch my attention. But if something is highlighted or there is an indicator showing that action is needed, I notice it much faster.”— P12
“The dashboard tells us what is happening, but the people on the floor provide the context.”
“It confuses you. A lot of people don't know how to read bars or graphs.”— P8
Answers to the research questions
How do frontline employees and managers perceive the role of data storytelling in enhancing employee engagement within supply chain and operational environments?
Participants perceived data storytelling — the addition of context, cause, and consequence to raw operational figures — as associated with increased engagement, while raw or unexplained data was associated with confusion and, in one severe case, costly organizational error. The perception was strongly but not universally shared: P4 described an organizational culture in which storytelling was largely absent.
Drawn from themes 02, 05, 01, 03
How does narrative visualization influence employees’ trust, understanding, and interpretation of operational data?
Narrative visualization’s influence depended on the fit between visual form and data structure, the presence of accompanying explanation, and audience-specific tailoring. Transparency about data limitations was the most consistent trust-building practice; its absence — particularly deliberate omission — was the most serious threat to trust. P8’s categorical rejection of visual formats, set against P9’s affirmatively positive account, showed that fit, not frontline status, is what the evidence differentiates.
Drawn from themes 04, 02, 03, 05
Strength of evidence reflects the consistency, richness, and specificity of participant evidence across the twelve interviews, not participant count alone. Findings are participant perceptions, not statistically generalizable or causal claims.