'Challenging the culture-free hypothesis of cognitive age among older consumers: Evidence from a cross-national survey'
Bibliographic record
Abstract
The current ageing of the world’s population is probably the most profound demographic change in the history of humankind. It is a pervasive and truly global phenomenon, without precedent or parallel, largely irreversible, and with the young populations of the past unlikely to occur again. Indeed, at the world level, the number of older persons will exceed the number of children by 2047, which has already occurred in many developed regions. The profundity of this demographic change will impact on economic growth, labor markets, pensions, health care, housing, migration, politics, and of course consumption (United Nations 2007). If the second half of the 20th century focused on the young, the 21st century will have to focus on the mature.Despite the growing importance of the 50 population and its perception as an attractive market segment, older consumers are still routinely neglected by many marketing and advertising practitioners (Niemela-Nyrhinen 2007; Simcock and Sudbury 2006; Uncles and Lee 2006) and what is known about their consumer behavior still lags far behind what is known about other important segments (Williams et al. 2010; Yoon et al. 2005). This is particularly true of research conducted outside the USA, where there is a marked lack of a coherent body of knowledge pertaining to senior consumers which can guide international marketing decisions.Self-perceived or cognitive age has emerged as a key variable in studying older people and their consumer behavior (Psychology & Marketing 2001; Wilkes 1992). The relatively sparse number of studies that have investigated this type of age identity in cross-national settings have concluded that cognitive age is “culture-free” (cf. also Barak 2009; Van Auken and Barry 2009; Van Auken, Barry, and Bagozzi 2006). Using data from an empirical study in four different countries, we challenge this view of cognitive age as culture free and thus aim to make a contribution to knowledge on older consumers on an international scale.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".