Support for small businesses during a health crisis
Bibliographic record
Abstract
Purpose Crises threaten the operations of small businesses and endanger their survival; however, when the crisis is not attributable to the firm, consumers may rally around the business. This study aims to examine how attitudes toward helping others can create support for small businesses, which in turn can direct consumers to help businesses with increased financial support. It is hoped that this paper will inform how consumers will help firms pivot during crises. Design/methodology/approach A conceptual model was proposed which linked support for helping others to increased willingness to tip/amount tipped. The model was tested using structural equation modeling from two surveys given to customers of two small businesses, a coffee shop and an independent movie theater, respectively. Findings During a crisis, support for helping others has a positive impact on feelings of support for small businesses. Consumers direct their support to small businesses that they are interested in seeing survive and continue operations. They either tip more or tip when they otherwise would not have tipped. Practical implications Firms that pivot their operations because of a crisis imposed on them can still generate revenues. Consumers who have a self-interest in the continuing operations of the firm want to support it, and by pivoting their business model, the firm gives consumers the opportunity to give the firm and its employees more than they would have in the form of tips. Originality/value Prior work in crisis management has focused primarily on how firms recover and respond to a crisis of their doing. Overwhelmingly, consumers have been shown to punish firms during times of crisis. However, for a crisis that is imposed on the firm, consumers may rally behind the firm and respond by supporting it more than they are required to.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".