Influencer and other “buying” roles in the decision‐making process of retirement housing purchasers
Bibliographic record
Abstract
Purpose The purpose of this paper is to show that the role of children in persuading their parents to move is known to be significant, but there has been extensive debate about the roles of other people involved in the process. It is necessary to investigate who plays these roles, for they will be legitimate targets for informative promotion or help from not‐for‐profit agencies – the public sector and voluntary/charitable organisations – wanting to improve the older person's decision making when choosing from a range of housing options. Design/methodology/approach The paper shows that questionnaires were delivered to every property in nine retirement housing schemes, chosen at random, in the West Midlands region of the UK. Approximately 200 were completed. Semi‐structured interviews were undertaken with 20 of the respondents. Findings The findings in this paper demonstrate that almost all initiators were from within the family. The spouse and adult children were the most important influencers, but children had the greatest impact because almost all respondents had children, whereas only a quarter were married. Respondents alone made the decision to purchase in three‐quarters of instances. Originality/value The paper shows that not‐for‐profit agencies, when providing information and offering advice about retirement housing, need to target the potential purchaser, the spouse, adult children and other relatives. Other influencers can almost be ignored. Such action will improve the decision‐making process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".