Guides

Guides for marketing and business researchers

Practical guides on replication, research credibility, methods and publishing, each built on the published studies it cites.

Research ideas and questions

How to find a research gap, write research questions and formulate hypotheses that can be tested.

• Research gap in marketing: how to find one worth filling
  How to find a research gap in marketing: review agendas, untested limits of known regularities, publication maps and practice problems, plus a test of whether it matters.
• Hypothesis examples and how to write a testable hypothesis
  Testable and untestable hypothesis examples from marketing, how to write a competing hypothesis, and why hypotheses written after seeing the data are not tests.
• Research question examples for marketing and business
  Weak and strong research question examples for marketing and business, built from published research agendas, with four steps for narrowing a topic into a question.

Replication and generalization

When replications get published, how to design one, and how to build on the empirical regularities marketing already knows.

• Empirical generalizations in marketing: how to build on them
  What an empirical generalization is, where marketing's established ones are collected, and how a new study can extend, bound or update one instead of starting from zero.
• Designing a replication study: a step-by-step plan
  How to plan a replication: choose the finding, define the effect, record every deviation, power for a smaller effect, preregister, and set the success test in advance.
• Direct vs conceptual replication: what each result can tell you
  Direct replication repeats a study's methods; conceptual replication retests its idea with new ones. What success and failure of each mean, and how to choose.
• Publishing a replication in marketing: when editors say yes
  Which replications marketing journals publish, why an important original and a real extension matter, and which outlets and formats have printed replication papers.

Research credibility and open science

Preregistration, publication bias and the open science practices that make marketing findings easier to trust.

• Open science practices in marketing: what journals expect now
  Which open science practices leading marketing journals require, how to build a replication package, what to do with proprietary data, and which habits to adopt now.
• Preregistration in marketing research: what to register and when
  What preregistration protects against, what a marketing preregistration should contain, how to handle archival and predictive data, and what journals expect.
• Publication bias and inflated effects: how to read a literature
  Why published effect sizes run high, how much correction can shrink them in nudging and advertising research, and checks for judging a literature's average effect.

Methods and evidence

Causal claims, effect sizes, survey measurement and the use of language models in research, with the evidence behind each.

• Causal claims from observational data in marketing research
  When can archival, platform or survey data support a causal claim? What ad experiments that tested observational methods found, and how to word your results.
• Effect sizes and meta-analysis in marketing research
  How marketing reports effect sizes (elasticities, r, d), how to compare them with published benchmarks, and how a meta-analysis is built and should be read.
• LLM synthetic respondents in marketing research: evidence and limits
  Can large language models stand in for survey or experiment respondents? Where silicon samples match human data, where they fail, and the risk of AI-completed surveys.
• Survey measurement pitfalls in marketing research
  Common ways survey measures mislead in marketing: common method variance, narrow proxies, usage-driven attitudes, stated intentions, ratio indices and fake respondents.

Publishing and impact

What drives citations and how to read journal metrics without over-reading them.

• Journal metrics explained: impact factor, SJR, SNIP and h-index
  What the impact factor and other citation indicators measure, why they vary by field and database, and why a journal's score says little about one article.
• What drives citations of marketing articles
  Why some marketing articles get cited: quality, topic and author visibility matter more than titles or readability, and citations track quality only loosely.

Submitting and peer review

Choosing a journal, avoiding desk rejection and working with editors and reviewers.

• Cover letter to the editor: what to state for the desk screen
  What marketing journal editors screen for before review (fit, contribution, generalizability, relevance) and how to state each plainly in your cover letter.
• Desk rejection: why journals reject papers without review, and how to avoid it
  Why marketing and business journals desk reject papers, how often it happens at top journals, and seven checks to run before you submit.
• How to choose a journal for a marketing paper
  Shortlist journals for a marketing paper by fit, contribution type and audience, with editors' figures on desk rejection and what impact factors miss.
• Research gap vs contribution: why a gap is not enough
  A research gap says what has not been studied; a contribution says what the field now understands differently. How editors tell them apart and how to state yours.
• Responding to reviewers: planning a revision and the response letter
  How to handle a revise-and-resubmit: sort reviewer comments, keep your core idea, disagree with reasons and write a response letter the editor can follow.