Research brief
Open science: A review of its effectiveness and implications for service research
Yves Van Vaerenbergh, Simon Hazée & Thijs J. Zwienenberg · Journal of Service Research (2026)
In one sentence
Not all open science practices are equally effective—sharing materials, data and code, and peer-reviewed preregistration hold the most promise—and while service research already reports its methods well, it still lags on sample-size justification, replication and data sharing.
Why this matters
Science is meant to be transparent and trustworthy, but concerns about questionable research practices—from selectively reporting results to fabricating data—have shaken many disciplines. When a large project tried to repeat 100 well-known psychology experiments, only 36 produced the same results. In response, journals, funders and policymakers increasingly push for open science: preregistering studies, sharing data and code, publishing open access. Leading marketing journals now require replication packages, and EU-funded research must be openly accessible. Yet the evidence on which practices actually work is scattered, leaving researchers unsure which ones are worth their time.
What we did
We combined three steps, grouping practices into four families of transparency: in study design, in intentions, in data and analysis, and in publication.
Step 1
Review of the evidence
What meta-research across fields such as psychology, medicine and economics tells us about which practices work
Step 2
Audit of 517 studies
A preregistered analysis of all empirical papers (254) in the Journal of Service Research and Journal of Service Management, 2019–2023
Step 3
Practical roadmap
Short- and long-term recommendations for authors, reviewers and editors, with tools and a ready-to-use checklist
What we found
Some practices clearly pay off. Transparent study designs and shared materials make research easier to reproduce. Policies requiring data and code work: after Management Science introduced one, the share of reproducible articles rose from 55% to 95%. Shared data and open access also bring more citations and a wider readership.
Others work less well than assumed. Preregistration curbs questionable practices mainly when it is peer-reviewed before data collection (registered reports). Unreviewed preregistration may not prevent p-hacking and can even invite new problems, such as preregistering after the results are known. Statements that data are “available upon request” often fall short: fewer than half of such datasets can actually be obtained.
Service research reports its methods well. Most studies describe their analytical approach and their reliability and validity checks, and about two-thirds of articles have a freely accessible version.
But clear gaps remain. Few studies justify their sample size, share their full materials, analysis scripts or data, or include a replication, and preregistration is almost absent.
Open science in service research, by the numbers
Share of the 517 studies (or 254 articles) published in the two leading service journals, 2019–2023, that reported or provided each element:
Widely adopted
92%
describe their analytical method or model
82%
report reliability and/or validity checks
65%
of articles have a freely accessible version
56%
explain how their sample was selected
Rarely adopted
15%
make their materials fully available
13%
justify their sample size
10%
share analysis scripts or coding trees
<1%
are preregistered or include a data availability statement
Key takeaways for researchers, reviewers and editors
Authors: justify your sample size. Use a power analysis for quantitative studies, or state the type of saturation reached for qualitative ones.
Authors: share materials, data and scripts in a repository. Keep the paper readable and put the full details on a platform such as Dataverse or Zenodo, rather than promising data “upon request”.
Authors: label exploratory analyses clearly. Preregistration is not the only route to transparency; explicitly distinguishing what was hypothesised from what emerged later achieves much of its purpose.
Authors: make post-prints freely available. Institutional repositories or SSRN offer free “green” open access; tools such as Open Policy Finder show what each journal allows.
Reviewers: judge studies on their merits. Don’t treat an unreviewed preregistration as a stamp of quality or demand one, and do ask for missing sampling details.
Editors: nudge now, build structures over time. In the short term, encourage data and script sharing, require sample-size justifications and consider open science badges. In the longer term, require replication packages, offer registered reports as a submission format, and champion replication through special issues and dedicated methods reviewers.
Put it into practice
Our openness and transparency checklist lists 15 practices to verify before submitting—or when reviewing—a paper.
Keep in mind
Most evidence on the effectiveness of open science comes from fields outside service research, and some practices—such as replicating field studies under identical conditions—are harder to apply in our domain. Our audit covered two journals over five years and is descriptive, not a ranking against other fields. Because authors may follow good practices without reporting them, our figures are conservative. This paper is not meant to critique past research, but to open a conversation.
Citation: Van Vaerenbergh, Y., Hazée, S., & Zwienenberg, T. J. (2026). Open science: A review of its effectiveness and implications for service research. Journal of Service Research, 29(1), 22-44.
