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
Data Storytelling Facilitates Meaning-Making
The most richly and consistently supported theme for RQ1. All twelve participants, across every represented sector, described a common transformation: numbers alone were ambiguous, unmemorable, or distrusted, while numbers accompanied by context, cause, and consequence became meaningful and usable.
Research question
RQ1
Strength of evidence
Strong
Codes
Key findings
- P10 offered the clearest structural account of storytelling: “You have the starting point, you have what changed, you have the impact, and then you have the response.”
- P10 on why structure matters: “If I simply put a spreadsheet on the screen, people may look at it, but they may not understand what they’re supposed to take away from it.”
- P9: “if you show us that a certain supplier delay is going to stop a production activity tomorrow, people pay attention because now the data has meaning.”
- P12 contrasted a bare status with a meaningful one: “If I just report, ‘Part missing,’ that’s not very useful. But if the system shows that it’s needed for a specific operation… people understand the urgency better.”
- P5 described storytelling as functional transformation, not decoration: “it turns data from something people simply review into something they can actually use to improve their work.”
- P11 described the absence of narration as actively disengaging: “the data gets overwhelmingly boring at times because you’re just doing the same thing over and over and over again.”
Divergence, preserved — not smoothed
The dataset’s clearest divergent case: P4 stated plainly that storytelling was largely absent from his own routine reporting — “there’s really no storytelling goes into that part” — in direct contrast to P6’s and P7’s descriptions of storytelling as normalized practice. The presence of storytelling appeared to track organizational and leadership norms rather than data complexity.
Added scholarly interpretation
Why this theme matters
A narrative makes a number consequential: starting point → change → impact → response. The contribution is interpretive usefulness, not entertainment or a more persuasive presentation.
Proposed design implication · not tested
Organize a status update around what changed, why it matters for this role, and what response is possible; keep the original figures available for challenge.
Boundary of the claim
P4’s largely absent storytelling practice remains a contrast case. The accounts suggest perceived usefulness, not a measured improvement in performance.
In their words
“The data became much more meaningful when leadership explained the story behind the numbers.”— P5
“We somehow assume the person who reads it understands what those numbers mean.”— P4
“I realized that data doesn't always tell the entire story by itself.”— P9
“You have the starting point, you have what changed, you have the impact, and then you have the response.”— P10
“If I simply put a spreadsheet on the screen, people may look at it, but they may not understand what they're supposed to take away from it.”
“If I just report, 'Part missing,' that's not very useful. But if the system shows that it's needed for a specific operation or that the shortage is preventing us from moving forward, people understand the urgency better.”— P12
“If you show us that a certain supplier delay is going to stop a production activity tomorrow, people pay attention because now the data has meaning.”
“It turns data from something people simply review into something they can actually use to improve their work.”
“The data gets overwhelmingly boring at times because you're just doing the same thing over and over and over again.”— P11
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.