arbiter
JournalsPricingAccount

Mining Consumer Mindset Metrics With User-Generated Content

Raoul V. Kübler et al.

Schmalenbach Journal of Business Research2025https://doi.org/10.1007/s41471-025-00219-4article
AJG 2
Weight
0.46

What the paper says

In the wake of digital transformation, marketers gained access to large amounts of user-generated content and data in which consumers specifically mention and discuss brands, products, and services. This data offers rich information potential and may ultimately provide marketers with the ability to use this data pool to approximate survey-based consumer mindset metrics that mirror consumer attitudes alongside the different levels of the decision-making process. We argue that leveraging this potential may ultimately help marketers overcome common limitations of survey-based metrics and enable companies to observe and track mindset metrics that have been so far inaccessible due to financial and other constraints. To this end, we propose a four-step process that first identifies the key aspects of a mindset metric based on the existing body of developed constructs, then pinpoints potential data sources, and subsequently chooses an adequate data transformation tool.

4 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1007/s41471-025-00219-4

Or copy a formatted citation

@article{raoul2025,
  title        = {{Mining Consumer Mindset Metrics With User-Generated Content}},
  author       = {Raoul V. Kübler et al.},
  journal      = {Schmalenbach Journal of Business Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s41471-025-00219-4},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Mining Consumer Mindset Metrics With User-Generated Content

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.