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Case Study

Challenging an incumbent: continue, improve or attack?

New Taipei’s mayoral contest: how voters responded when Lee Shu-chuan and Su Chiao-hui positioned themselves toward Mayor Hou Yu-ih

SPI Political Knowledge Lake | Campaign message-testing case

Study note: This module was asked only of 231 New Taipei respondents in the Taiwan survey. The article reports directional results from those 231 people and includes 95% bootstrap intervals. The decision value lies in the relative gaps between options—not in treating a single percentage as candidate support, vote share or a causal persuasion effect.

A candidate challenging within an incumbent mayor’s political territory faces a basic choice: inherit the incumbent’s achievements, promise improvements, or attack unresolved problems. This study suggests that Lee Shu-chuan (李四川) and Su Chiao-hui (蘇巧慧) face different directional signals when discussing Hou Yu-ih (侯友宜).

The short answer: Lee has a relatively stronger opening in “recognise and continue”; Su’s strongest option is “retain and improve.” But the greater limitation for both is not the spread between four frames. It is the large share who said that none would affect them or that they could not judge. The strategic signal is to move the main contest back to each candidate’s own issues—not to search for a perfect line about Hou. Between almost half (46.8% for Lee) and more than half (55.8% for Su) said no way of discussing Hou would influence them. For those voters, the next test should focus on each candidate’s own offer. In the same wave, everyday policy messages that clearly named the beneficiary, benefit and proof attracted the most attention. Discussing the incumbent is defence; building your own issue territory is offence. SPI’s companion analysis uses 1,003 complete Taiwan responses and 12 policy messages to show how teams can test the issue and frame: Same issue, different frame.

Three action signals in the relative gaps

How the test worked

We asked 231 New Taipei respondents one single-choice question: if [candidate] discussed Hou Yu-ih during the campaign, which approach would be “most likely” to make you seriously consider supporting [candidate]? Respondents chose one of four approaches, “none of these would affect me,” or “don’t know / cannot judge.” The question was asked once for Lee Shu-chuan and once for Su Chiao-hui.

1

Recognise and continue

Publicly recognise what Hou did well and pledge to continue effective policies

2

Retain and improve

Keep effective policies while proposing the candidate’s own improvements and direction

3

Identify problems and replace

Name unresolved problems and propose a different solution

4

Do not raise him proactively

Avoid discussing Hou and speak only about the candidate’s own policy and capability

Two additional options—“none would affect me” and “don’t know”—produce the study’s third signal.

Lee: continuity over criticism

+6.9 pts

“Recognise and continue” drew 17.3%, 6.9 points above “identify problems and replace” at 10.4%, and 7.4 points above “do not raise him proactively.” (Option 1 exceeded option 3 by 6.9 points.)

Su: improvement over silence

+6.5 pts

“Retain and improve” drew 14.7%, 6.5 points above “do not raise him proactively” at 8.2%, and 5.2 points above “recognise and continue.” (Option 2 exceeded option 4 by 6.5 points.)

About half: no Hou frame moves them

46.8% / 55.8%

The combined “none would affect me” and “don’t know” shares were 46.8% for Lee and 55.8% for Su. These voters were not moved by how a candidate discussed Hou; the candidate’s own agenda is the remaining route.

How to read the result

  1. Read the relative gap before the standalone percentage. The module compares four approaches for the same candidate; the decision signal is how far apart they are.
  2. Directional, not a guarantee. This module has n=231.
  3. Do not infer cross-candidate causality. This was not a randomised persuasion experiment. It cannot show that one sentence will add a fixed amount of support.

Four approaches at a glance

Common scale: 0%–35%. The coloured bar is each candidate’s highest-scoring approach; grey bars are “none would affect me” and “don’t know.”

Lee Shu-chuan (李四川)

1 Recognise and continue17.3%
2 Retain and improve15.6%
3 Identify problems and replace10.4%
4 Do not raise him proactively10.0%
None would affect me26.4%
Don’t know20.3%

Su Chiao-hui (蘇巧慧)

2 Retain and improve14.7%
3 Identify problems and replace11.7%
1 Recognise and continue9.5%
4 Do not raise him proactively8.2%
None would affect me33.3%
Don’t know22.5%

Lee: inherit first, then define his own improvements

The two approaches that treated Hou’s administration positively ranked first: “recognise and continue” at 17.3% and “retain and improve” at 15.6%, totalling 32.9%. “Identify problems and replace” drew only 10.4%. Acceptance of the two continuity-oriented approaches combined was about three times the criticism frame; that relative gap is the decision signal. A continuity frame warrants priority testing for Lee, while attacking the incumbent city government is not his stronger route. Equally important, 46.8% said no approach would affect them or could not judge. Discussing Hou may help, but it cannot substitute for Lee’s own proposition.

The priority test is therefore not a clean break with Hou. It is to inherit recognised municipal achievements, then add Lee’s own improvements.

How Lee discusses HouShare
Recognise and continue17.3%
Retain and improve15.6%
Identify problems and replace10.4%
Do not raise him proactively10.0%
None would affect me26.4%
Don’t know / cannot judge20.3%

The 95% bootstrap interval for every option appears in the technical note below.

Su: do not just promise continuity—specify what she would improve

Su’s highest directional option was “retain and improve.” It exceeded “identify problems and replace” by 3.0 percentage points, “recognise and continue” by 5.2 points, and “do not raise him proactively” by 6.5 points.

This signal is closer to “acknowledge effective policy, then state the improvement clearly” than either blanket endorsement of the incumbent or purely negative attack.

How Su discusses HouShare
Retain and improve14.7%
Identify problems and replace11.7%
Recognise and continue9.5%
Do not raise him proactively8.2%
None would affect me33.3%
Don’t know / cannot judge22.5%

The 95% bootstrap interval for every option appears in the technical note below.

Hou’s municipal-performance rating: high scores exceeded low scores by 17.3 points

Among 216 valid ratings on a 0–10 municipal-performance scale, Hou’s mean was 5.95, with a 95% bootstrap interval of 5.59–6.28. Scores of 8–10 accounted for 26.4%, while scores of 0–2 accounted for 9.1%—a 17.3-point difference. The most common single score was 5, at 18.2%.

This is directional context on the incumbent from the current New Taipei sample. It is not a mayoral-approval poll and cannot be converted directly into support for any candidate.

What a campaign team could test next

Lee Shu-chuan | The position fits better than the current product shelf.
The continuity position is available to him: respondents accepted “inherit and continue” more than “criticise the incumbent.” The potential problem lies in the offer. His infrastructure-oriented messages did not sit among the most attractive tested items, while one everyday policy that matched the leading-message formula was not used as the central proposition.

Suggested next tests:

  1. Use a two-part structure: “I will continue what Mayor Hou got right; what I will improve faster is ___”—and test an everyday-life benefit in the blank, rather than an engineering project.
  2. Develop the senior-card policy into a lead everyday-life line, using the same structure to develop two more.
  3. Use infrastructure as evidence of delivery capability rather than the opening proposition.

Su Chiao-hui | Hou is a defensive question, not the main offensive territory.
Even Su’s best Hou frame had a small, uncertain lead, and more than half said no framing of Hou would move them. Her own everyday-policy messages occupied more of the leading group in the companion test.

Suggested next tests:

  1. Prepare a short, clear answer—what to retain, two things to improve, and the proof—then return immediately to her own agenda.
  2. Add funding and timing evidence to every everyday-policy promise, anticipating the obvious “where will the money come from?” challenge.
  3. Put candidate time and page space behind her own issues rather than making “how to discuss Hou” the main arena.

Shared interpretation | This is a contest between policy offers, not a referendum on trust in Hou.
The first side to rewrite its offer consistently as “beneficiary + benefit + proof” gains the better testable proposition for voters who remain unpersuaded. The next correct design is to test each side’s own message shelf and include the opponent’s strongest item as a defensive benchmark.

Technical note: interpretation and evidence

InterpretationNumerical basis
Lee: continuity over criticismOption 1, recognise and continue, 17.3%—6.9 points above option 3 at 10.4%, and 7.4 points above option 4 (n=231)
Hou’s record: mildly positive directional contextMean municipal-performance rating 5.95 (216 valid ratings); 26.4% rated 8–10 and 9.1% rated 0–2
Lee’s infrastructure messages were not leading itemsAcross the nationwide n=1,003, 12-message test, rail extension, industry corridor and cross-party governance fell in “do not lead with yet”
Lee’s senior-card policy was an exceptionThe only leading-group message corresponding to a public Lee policy (correspondence follows the questionnaire’s disclosed source)
Su’s best Hou frame was only a defensive patchOption 2, retain and improve, 14.7%—only 3.0 points above option 3 at 11.7%, with overlapping intervals
Do not overinvest in discussing Hou“None would affect me + don’t know”: 55.8% for Su and 46.8% for Lee
Leading-message structureThe leading items—night childcare, young-child healthcare, denture subsidy and youth entrepreneurship—each specify beneficiary + benefit + proof

95% bootstrap intervals for each option

Lee Shu-chuan: 1: 12.6%–22.1%; 2: 11.3%–20.3%; 3: 6.5%–14.3%; 4: 6.5%–13.9%; none would affect: 20.8%–32.0%; don’t know: 15.2%–26.0%.

Su Chiao-hui: 2: 10.4%–19.5%; 3: 7.4%–16.0%; 1: 6.1%–13.4%; 4: 4.8%–12.1%; none would affect: 27.3%–40.3%; don’t know: 17.3%–28.1%.

Study at a glance

Research organisation: ShareParty Insight (SPI); lead: Tatt Chen

Field dates: 17–21 August 2026

Method: SPI Taiwan online research panel, mobile self-completion; by design this module was asked only of New Taipei respondents

Sample: 231 New Taipei respondents within 1,003 complete nationwide responses; the other 772 were not asked this module

Analysis: Unweighted; 2,000 respondent-level bootstrap resamples; 95% option intervals above

Precision reference: approximately ±6.4 percentage points; the interpretation is directional

Full methodology (routing, interval method and all limitations)

Methodology and limitations

  • Responsible research organisation: ShareParty Insight (SPI); lead: Tatt Chen.
  • Field period: 17 August to 21 August 2026 09:31:21.
  • Design: The Taiwan survey contained 1,003 complete responses. By design, this module was asked only of the 231 New Taipei respondents, all of whom answered; the other 772 were not asked it. This case study analyses those 231 responses.
  • Analysis: Unweighted; 2,000 respondent-level bootstrap resamples, with 95% percentile intervals.
  • Sample reference: n=231. This is a sample-size reference, not a Taiwan-wide estimate or population-sampling error.
  • Evidence boundary: This is a directional reading centred on relative gaps between options. It must not be interpreted as support, vote share, a candidate-win forecast or a causal persuasion effect.
  • Only aggregate group statistics are released; no personal data are published.

About SPI

ShareParty Insight (SPI) combines survey research, consented mobile-use behaviour and strategy analysis to help teams decide which issue to contest, how to frame it, who should carry it and when the strategy needs to change.

Explore SPI Taiwan Political Intelligence, or contact sales@sharepartyinsight.com.

AI assistance disclosure: Generative AI assisted the first draft. ShareParty Insight is responsible for numerical verification, editing and publication decisions.

Further reading