AI · Empirical machine-learning modeling of cross-platform pricing and signaling relationships.

Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings

Journal of Business Research · 2022

Tatjana Christen, Manuel Hess, Dietmar Grichnik, Joakim Wincent

Summary

Platform choice and seller history are non-core signals that influence perceived customer value and pricing beyond core product attributes; machine learning can help SMEs predict cross-platform prices.

For your marketing

Possible founder application

Suggests testing reputation, platform/channel, history, and timing signals—not just feature lists—when building data-driven pricing models for software marketplaces or channel sales.

Reported result

What the study found

Research question

What the authors investigated

How do platform choice, seller history, and time-based price discrimination signal perceived value and help predict prices across platforms?

Design

Empirical machine-learning modeling of cross-platform pricing and signaling relationships.

Sample and setting

Accommodation-industry online platforms, including SMEs selling across platforms; the fetched abstract does not report observation count.

Scope and transfer

Limitations to keep in view

The empirical setting is accommodation platforms rather than developer tools or B2B SaaS, and the abstract does not report sample size; transfer to enterprise software requires validation.

Research sources

Fetched ScienceDirect publisher metadata and abstract establish DOI, authors, date, setting, machine-learning design, and findings. Firecrawl research job 01a1436c-f86b-77df-af1c-9a2f87e3535e returned this citation and metadata.

Study strength

3.5/5 Abstract-level evidence is useful for framing questions; verify full methods before transfer.

How this was assessed

The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.

Design transparency · 2/2

Based on the reported study design in the retrieved source.

Sample and setting · 1/1

The sample context is stated; a numerical count is preserved only when reported.

Outcome clarity · 1/1

The finding is summarized from publisher or article metadata.

Access for checking · 0.5/1

Only abstract or bibliographic access was confirmed.

Double-blind? The retrieved publisher record does not establish whether participants or outcome assessors were blinded.