모든 템플릿
Industry-Specific

Internal Knowledge Management Effectiveness Survey

Assesses whether employees can actually find, trust, and contribute to your organization's documentation, wikis, and knowledge base — with an AI follow-up that reconstructs a real moment someone got stuck instead of relying on abstract satisfaction ratings. Built for IT, L&D, and operations teams evaluating or improving a knowledge management system.

샘플 질문

템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.

질문 14개 · 약 7분
Q01
메시지

Thanks for taking a few minutes on this! We're looking at how well our documentation, wiki, and knowledge base actually work for you day to day. Your responses are completely confidential and anonymized. Your honest experience helps us fix what's broken. About 5-6 minutes.

Q02
객관식필수

In a typical week, how often do you search internal documentation or a knowledge base to do your job?

  • Daily or almost daily
  • A few times a week
  • About once a week
  • A few times a month
  • Rarely or never
Q03
의견 척도필수

When you need specific information to do your job, how easy is it to find something accurate?

척도: 17
최소:Very difficult최대:Very easy
Q04
객관식필수

Where do you most often go first when you need information you don't already know?

  • Company wiki or knowledge base
  • Shared drive or folder
  • Asking a teammate directly
  • Chat/Slack search
  • Email search
  • A manager or team lead
  • Trial and error / figuring it out myself
Q05
매트릭스필수

How much do you agree with each statement about our current documentation and knowledge base?

5개 행 × 5개 열
  • The information I find is accurate
  • The information I find is up to date
  • Content is easy to search and locate
  • It's clear who owns or maintains each document
  • New employees could get up to speed using it alone
: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q06
평점 척도필수

Overall, how satisfied are you with our current knowledge management tool(s)?

범위: 15
최소:Very dissatisfied최대:Very satisfied
Q07
객관식필수

In the last 3 months, how often have you added, corrected, or updated a document or wiki page yourself?

  • Never
  • Once
  • A few times
  • Regularly, it's part of my routine
Q08
최선·최악 선택형(MaxDiff)필수

Which of these improvements would help you most, and which would help you least?

  • Better search functionality
  • Clear ownership for each page
  • More consistent formatting/templates
  • Faster updates when things change
  • Easier way to flag outdated content
  • AI-assisted search or summaries
  • Better onboarding to the tool itself
  • Fewer duplicate or conflicting pages
세트별 최선·최악 선택최선:Most helpful최악:Least helpful
Q09
AI 인터뷰

Ask the respondent to walk through the most recent specific time they needed information and couldn't find it easily: what they were trying to do, where they looked first, how long it took, and what they ended up doing instead (asking a person, giving up, guessing). Probe for the actual business impact — delay, rework, wrong answer given to a customer — not just frustration. If they say finding information is easy, ask what makes their team's documentation better than others they've encountered.

Q10
객관식

What stops you from contributing more to shared documentation? Select all that apply.

  • Not enough time
  • Unclear where or how to add content
  • Not sure my input is 'official' enough
  • No recognition or incentive to do it
  • Past edits got overwritten or ignored
  • Don't know who to check with before publishing
Q11
의견 척도필수

How likely are you to recommend our current knowledge management tool to a new colleague joining your team?

척도: 010
최소:Not at all likely최대:Extremely likely
Q12
객관식

Which department or team are you part of?

  • Engineering / IT
  • Operations
  • Sales
  • Customer Support
  • Marketing
  • HR / People
  • Finance
  • Prefer not to say
Q13
객관식

How long have you been with the organization?

  • Less than 6 months
  • 6 months to 2 years
  • 2 to 5 years
  • More than 5 years
  • Prefer not to say
Q14
메시지

That's everything, thank you! Your answers, along with everyone else's, will directly shape how we prioritize fixes to our documentation and knowledge base this quarter.

포함된 기능

  • AI 후속 질문

    정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.

  • 주의력 확인 장치

    성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.

  • AI가 작성한 문안

    문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.

  • 자동 리포트

    응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.

다른 서비스와 비교

다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.

이 템플릿을 선택하는 이유

  • Includes an AI follow-up interview that asks respondents to walk through a real recent moment they got stuck finding information, instead of relying only on abstract satisfaction ratings
  • Combines quantitative measures (opinion scale on findability, rating on tool satisfaction, matrix agreement statements, max-diff prioritization of improvements) with qualitative depth from the AI interview
  • Directly measures contribution behavior (how often employees add or correct documentation) and the specific barriers stopping them from contributing more, not just consumption habits
  • Segments by department and tenure so IT, L&D, and operations teams can see whether pain points differ by team or how long someone has been onboarded onto the knowledge base

QuestionPro

Company - Knowledge Management Survey Template

This is a fielding-ready template covering satisfaction and usage of internal knowledge management systems, similar in topic scope to ours. It relies on standard static question types (ratings, multiple choice) rather than adaptive follow-up, so depth comes from the number of questions asked rather than dynamic probing. Good for benchmarking broad sentiment but not for surfacing specific stuck-moments or root causes in a respondent's own words.

잘하는 점

  • Purpose-built specifically for knowledge management topic, not a generic repurposed template
  • Backed by an established survey platform with broad template library and distribution tools
  • Likely covers standard KM satisfaction metrics familiar to HR/IT survey designers

아쉬운 점

  • Static question set with no adaptive AI follow-up to reconstruct a specific real incident where someone couldn't find information
  • No indication of per-response quality scoring or transparent prompt methodology
  • No voice AI interview or guided screen-share task option for observing actual search behavior

설문을 공개할 준비가 되셨나요?

이 템플릿을 편집기에서 열어 보세요. 첫 응답자가 보기 전에 모든 부분을 원하는 대로 바꿀 수 있습니다.

관련 템플릿

같은 카테고리의 다른 설문을 만나 보세요.

전체 보기
Industry-Specific

Workplace Diversity and Inclusion Climate Survey

Measures how included, respected, and fairly treated employees feel across belonging, fairness, and access to opportunity, plus whether they've witnessed or experienced exclusionary behavior. Built for HR and DEI teams benchmarking climate over time. The AI follow-up interview reconstructs the specific moment behind a respondent's inclusion score instead of leaving it as just a number.

템플릿 보기
Industry-Specific

Employee Performance Review & Growth Opportunities Survey

Measures how employees experience performance reviews, career conversations, and growth opportunities — with an AI follow-up interview that digs into the specific stretch assignments, mentorship moments, or blockers behind their advancement ratings. Built for HR and people leaders benchmarking review quality and career pathing across teams.

템플릿 보기
Industry-Specific

온라인 구매 습관 및 결정 요인 설문조사

사람들이 얼마나 자주 온라인에서 쇼핑하는지, 실제로 구매 또는 포기 결정을 이끄는 요인이 무엇인지, 그리고 결제·배송·반품 과정에서 어디에서 불편이 발생하는지를 측정합니다. AI 후속 인터뷰는 일반적인 만족도 평가에 의존하는 대신 실제 최근 구매(또는 구매 직전) 순간을 재구성하여, 이탈을 진단하는 이커머스 및 리테일 팀에게 유용합니다.

템플릿 보기
Industry-Specific

Organizational Concern for Employee Satisfaction Survey

Measures whether employees believe the organization genuinely cares about their satisfaction and wellbeing, based on behavioral signals like manager check-ins and follow-through on feedback rather than stated policy. An AI follow-up reconstructs a specific recent moment behind each respondent's score, surfacing concrete gaps between what leadership says and what employees actually experience.

템플릿 보기
Industry-Specific

Personal Style and Fashion Shopping Attitudes Survey

Explores how people relate to clothing, what actually drives a purchase decision, and where sustainability and price trade off in the real world. An AI follow-up interview digs into the story behind a recent purchase to separate stated values from actual behavior — built for fashion brands, retailers, and trend researchers.

템플릿 보기
Industry-Specific

Hardware Post-Installation Satisfaction Survey

Captures how customers experienced the installation of physical equipment — from scheduling and technician conduct to whether the hardware has performed as promised since setup. An AI follow-up interview digs into the specific moment satisfaction dropped (or held), turning vague complaints into fixable installation-process issues.

템플릿 보기