Improving the Measurement of Aging in Consumer Behavior Research – Lessons from Demography

• Published in: Advances in Consumer Research Vol. 47; pp. 947 - 948
• Main Authors: Zniva, Robert, Weber, Daniela
• Format: Conference Proceeding
• Language: English
• Published: Urbana Association for Consumer Research 01.01.2019
• Subjects: Age; Aging; Consumer behavior; United States > US
• ISSN: 0098-9258

Recent contributions that review the influence of aging on consumer behavior discuss the usefulness of specific age dimensions other than chronological age for theory development. The motivation of this paper is to add to the ongoing debate by shedding light on measuring aging processes in consumer behavior. The intention of this approach is twofold: first, the paper aims to summarize how age-related changes were assessed. Second, the authors introduce a well-established concept from demography, the characteristic age approach (Ryder 1975), to the field of consumer behavior research, to facilitate and enrich research on the influence of specific aspects of the aging process. Based on the methodology of Zniva and Weitzl (2016) the authors conducted a rigorous literature search using two databases and 21 keywords related to aging. 145 articles dealing with older consumers and published in quality peer-reviewed journals from 1980 to 2017 were selected. To analyze these studies a typical distinction made in social sciences and gerontology to classify age and aging (e.g. В irren and Cunningham 1985; Settersten and Mayer 1997) was used. According to this framework aging processes are usually grouped into: (1) biological aging, (2) psychological aging, and (3) social aging. Because of the strong interdependence between biological, psychological, and social aging a fourth category was added to describe (4) holistic approaches to the measurement of aging. Furthermore, for completeness sake, the authors included (5) chronological age. The five aspects of aging were used as a classification scheme for analysis. Independent variables of all 145 quantitative studies were screened for measures of aging processes or chronological age and afterwards categorized into the five categories. As a first result, the usage of chronological age was assessed. Virtually all identified quantitative studies (144 out of 145) used chronological age to some extent. Biological age measures were used in 13% of the identified studies. Biological age measures can be divided into three subgroups: (1) measures assessing a self-reported generic health status, (2) measures of self-reported health-related activities, and (3) self-reported biological/health events. Measures of psychological age were applied in 21% of the analyzed articles and can be divided into two subgroups, namely measures of self-perceived age and measures of memory and cognition. Social age measures were applied in 8% of the identified articles. Studies typically measured (1) retirement status, (2) grandparenthood and (3) widowhood and household composition. Holistic measurement approaches were used by 7% of the identified articles. They can be divided into (1) studies evaluating the impact of life events on older consumer behavior and (2) studies using a mix of different approaches. Life events were typically measured using indices based on the self-re - ported experience of transitional events (e.g. from Chiriboga 1989). Other holistic measures combined a multitude of different aspects of aging. The majority of studies used the gerontographics approach (Moschis 1992). A more recently introduced holistic approach is the concept of old-age orientations (Mathur et al. 2017). In summary, results of the review show that chronological age is still the most used measure. Alternative measures of aging differed widely within aging dimensions. Furthermore, a mix of selfdeveloped measures and measures derived from previously extent literature could be observed. The most dominant measurement approach identified was a psychological approach. Studies measuring self-perceived age used predominantly so-called "cognitive age" developed by Barak and Schiffman (1981). Finally, alternative measures of aging in consumer behavior research were often of subjective nature (based on individual awareness or experience). Only two out of a 145 studies used objective measures to assess performance of cognitive aging. Based on the results of the literature review the authors believe that a certain complexity and subjectivity of alternative measures of aging explain the substantial use of chronological age in consumer behavior research. The parsimony of the chronological age measure, which integrates all dimensions of aging into one number, commonly understood in everyday life, is a very strong argument irrespective of its weak explanatory power compared to alternative approaches. To integrate the catholicity of chronological age with more rigid, complex and objective aging measures, the authors propose a concept from demography, the so-called "characteristic ages" (Sanderson and Sherbov 2014). To illustrate the advantages of the characteristic age approach the authors present an empirical example. Using data from the 2016 wave of the Health and Retirement Study (National Institute on Aging 2007), characteristic-based ages across older adults (aged 50 years or older) in the United States are compared. Distinguishing two subgroups (e.g. those with high versus those with low out-of-home food consumption), the example highlights disparities in psychological and biological aging. Biological age was measured using the objective performance measure of hand grip strength. Psychological aging, in turn, was measured with an objective memory test. Findings show that older adults of the high out-of-home food consumption subgroup are significantly about 10 years younger cognitively than their counterparts with low out-of-home food consumption. Thus, men and women aged 70 years of the high out-of-home food consumption have the same psychological age as their counterparts from the low out-of-home consumption group aged only 60 years. The interpretation of the results shows the advantages of the characteristic age approach. The approach converts relatively complex measures into a chronological age number eliminating the hassle of having to explain it any further. Furthermore, the approach facilitates the comparison of measures of aging across different subgroups and would also allow for the evaluation of the measurement properties of different subjective and objective age measures within different aging dimensions. Given the presented review and data, the authors hope to encourage more research using the characteristic age approach in consumer behavior research.