Abstract
The age-of-acquisition effect suggests that things learned early in life, including brand names, are recognized faster and more accurately. This study confirms this effect but cautions that the managerial impact of age of acquisition is small. Brand exposure frequency and usage recency have a far greater effect on recognition than age of acquisition. The strongest age-of-acquisition effect is observed among individuals who are unfamiliar with the brand, suggesting that repetition, such as in advertising, is necessary. Respondents were slower to identify brands released before they turned 15, indicating that memory-based processes occur for early-learned brands, whereas late-learned brands relied more on processes that were not memory based, such as guessing.
MANAGEMENT SLANT
Early-learned (established) brands are recognized faster and more accurately than late-learned (newer) brands. The effect sizes, however, are tiny, with very limited practical value.
Other factors such as brand usage and exposure become crucial as consumers age, highlighting the need for continued exposure to help sustain any early-learned advantage.
Newer brands can reach recognition fluency comparable with that of established brands among their brand users and individuals who are exposed to their advertising, suggesting that new brands can become a part of individuals’ mental “brand lexicon” at any age.
Older consumers’ usage and exposure status for newer brands are especially influential, indicating that this cohort requires greater repetition and maintenance to sustain recognition fluency.
A faster response time does not necessarily indicate better memory structures or the ability to identify branding correctly. Thus, using time-to-respond measures in a branding context may be problematic.
INTRODUCTION
Brand names, in and of themselves, can elicit consumers’ associations that can lead to brand choice (Aaker and Keller, 1990), and known brands are more likely to be included in buyers’ consideration sets (Macdonald and Sharp, 2000). Considering the limited time consumers allocate to buying everyday goods, spending as little as five seconds in stores (Sorensen, Bogomolova, Anderson, et al., 2017) and 10 seconds in online shopping (Anesbury, Nenycz-Thiel, Dawes, and Kennedy, 2016), it becomes essential for marketers to understand the techniques and tactics that facilitate such rapid brand recognition. By understanding and leveraging these tactics and strategies, brands can enhance their likelihood of being noticed, recognized, and selected at the critical point of purchase.
One relevant concept studied extensively in psychological research is the age-of-acquisition effect, which suggests that items learned early in life are recognized with greater speed and accuracy than those learned later in life (Carroll and White, 1973; Pérez, 2007). In marketing, foundational research explored the influence of age of acquisition on brand names and proposed intriguing results regarding the age-of-acquisition effect on brand name recognition (Ellis, Holmes, and Wright, 2010). It reported that, for adults, brands learned early in life are recognized more quickly and more accurately than brands learned later in life. Although this is becoming the precedent in this space, the conclusions drawn were based on a single study that remains untested. Therefore, the current study aims to fill this gap, replicating the initial study with a larger sample and incorporating additional variables. This research explores the practical and theoretical implications of the age-of-acquisition effect on brand name recognition and seeks to provide actionable insights for marketing practitioners.
The accessibility of a concept is determined by the age it is learned and remains largely unchanged. Thus, the advantage enjoyed by early-learned brands can be permanent, and the disadvantage for late-learned or newly launched brands may be irreversible.
RESEARCH BACKGROUND
Bias toward Early-Learned Items—Including Brands
Words, objects, and faces learned early in life are identified more quickly and with fewer errors than those learned later in life (Carroll and White, 1973; Pérez, 2007). This phenomenon, called the “age-of-acquisition effect,” has been studied widely in the psychological research (Cortese and Khanna, 2007; Ellis and Morrison, 1998; Johnston and Barry, 2006; Pérez, 2007). Early studies in this area have focused on how age of acquisition assists in the process of retrieving words from the mental lexicon, such as “book” or “shoe” for early-acquired items and “syringe” or “tuning fork” for late-acquired items (e.g., Carroll and White, 1973). Further studies have reported age-of-acquisition effects in other tasks, such as determining whether images illustrate real or imaginary objects (Holmes and Ellis, 2006) or whether faces depict famous or unknown people (Richards and Ellis, 2009). A similar effect may be seen in developing music preferences (Davies, Page, Driesener, et al., 2022).
According to age-of-acquisition studies, the accessibility of a concept is determined by the age it is learned and remains largely unchanged (Morrison, Hirsh, Chappell, and Ellis, 2002). Thus, the advantage enjoyed by early-learned brands can be permanent, and the disadvantage for late-learned or newly launched brands may be irreversible. Memories consolidated and stored in long-term memory, however, fade over time without reinforcement (e.g., synaptic connections retract with time; Chang, Jo, and Lu, 2011). There is also the possibility of memories being displaced, in this instance, by competitor advertising: Retroactive interference makes it difficult to remember previously learned items after learning a new concept (Tulving and Craik, 2000).
Ellis et al. (2010) studied age-of-acquisition effects in the consumer context and showed that brands learned early in life (younger than five years old) are recognized more fluently than brands learned later in life. If two brands have similar exposure levels, early-learned brand names were recognized slightly faster (reaction time: 632 ms vs. 659 ms), t(19) = 2.93, p < .01; and more accurately (mean error rates: 6 vs. 11 percent; z = 2.71, p < .01) than late-learned brand names. From these results, it appears that brand exposure during childhood helps recognition later in life. Other experiments explored and confirmed that age-of-acquisition effects were present for related semantic information (category belonging) and long-established brands that had either survived or exited the marketplace.
Conversely, these findings imply that new brands (late-learned) are disadvantaged and may require an entire generation to reach the recognition fluency of established brands. This presents an opportunity for investigation of whether other controllable factors, such as advertising, could neutralize the predisposed age-of-acquisition advantage for early-learned, established brands. Greater insights into the role of marketing efforts in maintaining early memories and establishing new ones across generations of consumers will benefit brands of any age.
Nevertheless, the generalizability of the conclusions needs to be tested, as in Ellis et al.’s (2010) study, in which a small student sample was used (for further discussion, see Ashraf and Merunka, 2016). The current research replicates Ellis et al.’s (2010) study using a representative sample and extends it by including the two new variables of brand usage and brand exposure.
Ongoing Exposure and Reinforcement May Improve Recognition Performance
According to associative network theory (widely adopted in marketing; e.g., Teichert and Schontag, 2010) everything associated with a brand (e.g., feelings, images, and usage situations) is organized in memory as individual nodes that link to each other in a network of associations (Anderson, 1983). The richness of a brand’s representation in the network can affect memory fluency, including encoding and retrieving relevant information (Simmonds, Bellman, Kennedy, et al., 2020; Stocchi, Wright, and Driesener, 2016).
By being exposed to brands through advertising and incidental environmental exposure, a consumer’s memory associations for a brand are refreshed. Newer brands may need to work harder to form memories in consumers’ minds because they compete with brands with more established memory networks. Unfamiliar (or late-acquired) brands are more prone to competitive advertising interference (Kumar and Krishnan, 2004). Thus, established brands have advantages in advertising: Brand users generally have higher levels of advertisement recall (Vaughan, Beal, and Romaniuk, 2016), consumers’ memories are less affected by interference by competitors’ advertisements (Kent and Allen, 1994), and people tend to favor brands with established memories when confused (Braun-LaTour and LaTour, 2004).
Brands require ongoing exposure to maintain these networks (Vaughan, Beal, Corsi, and Sharp, 2021). Memories may fade if a brand is learned earlier in life but not reinforced. Conversely, if a brand is learned later in life, but an individual is more frequently exposed to it, this may counteract the advantage of an early-learned brand name. Similar recognition performance is seen for expert vocabulary (late-acquired, high-frequency) and early-acquired, low-frequency words (Stadthagen-Gonzalez, Bowers, and Damian, 2004). This differentiation in exposure/usage needs to be explored in the context of consumer research. This research tests the application of age-of-acquisition effects by inspecting known conditions influencing brand recognition.
Rather than just sensory exposure, brand experience is subjective, internalized, and related to one’s actual use of brands and products (Brakus, Schmitt, and Zarantonello, 2009). Direct brand usage builds memory links, which increases the consumer’s ability to retrieve brand-related information from the memory (Hoch, 2002). People who have ever used a brand tend to score higher on brand awareness and brand image associations than people who have never used a brand (Vaughan et al., 2021). This is because users interact with and build more diverse mental associations with the brand (Stocchi et al., 2017), although sometimes the effects of these wider networks can be unexpected (Stocchi, Wright, and Driesener, 2016).
Brand users are, therefore, more likely to experience (and remember) greater exposure frequency. The ease of retrieving information from memory is, however, not only influenced by exposure: It also is affected by the recency of exposure (Jones, 2007). Current brand users are more likely to have recent, direct experience with the brand. Consequently, brand usage is important to contextualize age-of-acquisition effects concerning brand name recognition performance. Hence, our hypotheses are as follows:
H1: Consumers will recognize early-learned brands faster than late-learned brands.
H2: Consumers will more accurately recognize early-learned brands than late-learned brands.
H3: Brand usage recency will positively influence brand recognition performance.
H4: Brand exposure frequency will positively influence brand recognition performance.
METHOD
Product Categories and Brand Selection
The research focuses on five categories of consumer packaged goods (beer, breakfast cereal, chocolate, hair care, and pet food) in Australia, with data obtained through a survey. These categories were selected because respondents are likely to interact with them in childhood (e.g., cereal) or adulthood (e.g., beer). Each category included brands launched across the twentieth century to accommodate younger and older consumers. Launch years were obtained from company websites.
The authors selected brands with varying market shares to increase the likely variation for a comparison of recognition performance. Brands were selected to ensure enough responses for brand exposure (seen/never seen) and usage (user/nonuser). Brands included were available in major retailers. There were 52 real brands in the survey and an equal number of fictitious brands included, as the task required respondents to determine whether brands were real or false. Fictitious brand names were generated to appear realistic. Consistent with the original study, real and fictional sets were matched for the number of words (M = 1.54 words).
Experimental Procedure
Respondents completed the experiment using a keyboard. They were instructed to identify real brand names quickly and accurately in an uninterrupted environment. The sample of n = 1,000 was sourced through a reputable commercial provider of panels for research purposes and was representative of the population by age (18–74 years), gender, and geography.
Each brand name (real or fictitious) was randomly presented one at a time in the center of the screen using a large clear font. Participants responded by pressing “F” if the brand was real or “J” if it was fictitious, so that respondents could focus on the screen rather than their fingers (Ingmarsson, Dinka, and Zhai, 2004). Each brand name was displayed one at a time and stayed on the screen until a response was made. The screen would blank for 500 ms before the next name was shown. Response latency was recorded in milliseconds, from the time when a stimulus was presented until a response was made. Only the response times (RTs) for correct answers (i.e., accurately identified real brands as “Yes, this is a brand”) were included in this section. Recognition accuracy is the proportion of responses that accurately identify a real brand stimulus as a real brand.
The experiment started with three real and fictitious brand names (not included in the analysis), followed by the 104 stimuli presented randomly for each respondent. The investigation concluded with questions about respondents’ prior brand exposure, usage experience, and relevant demographics.
Respondents indicated whether they had seen each brand in shops, on television, in newspaper or magazine advertisements, and so forth, in the past 12 months (brand exposure) and whether they had used it in the past 12 months (brand usage). Consistent with Ellis et al., (2010), the authors defined early-learned brands as those that existed when respondents were less than five years old.
Data Cleaning
Outliers (interquartile range, ±1.5× the upper or lower quartile) were removed through the following three phases: (1) individuals with extremely incorrect total responses (>36/52 incorrect); (2) total RTs unreasonably short (cutoff = 7 minutes); and (3) individual RTs to each real brand (shorter than 372 ms). After this process, 59,100 individual responses were used for the analyses.
RESULTS AND DISCUSSION
Brands Learned During Childhood Are Recognized Faster and More Accurately
The authors found that early-learned brand names (M = 746 ms, SD = 127 ms) were recognized significantly faster than late-learned brands (M = 760 ms, SD = 125 ms), t(21704) = −7.53, p < .01. This finding is consistent with prior studies of words/concepts (e.g., Pérez, 2007) and brand name recognition (Ellis et al., 2010). Hypothesis 1 is thus supported, but with a small effect size.
Brands learned early in life were recognized more accurately (76 percent) than brands learned later in life (74 percent). There is a significant association between age of acquisition and recognition accuracy, χ2(1, N = 29,550) = 10.86, p < .01, but with a small effect size (V = .02). The current findings are directionally consistent with those of Ellis et al. (2010), but with a smaller magnitude of effects (See Table 1). Additional analysis that included all responses revealed consistent findings. Early-learned brands were recognized faster and more accurately than late-learned ones (See Table A1 in the Appendix). Hypothesis 2 is thus supported.
Fictitious brands were rejected with 80 percent accuracy and a mean RT for correct responses of 768 ms.
Usage Recency Does Not Meaningfully Influence Response Time
Users of a brand were quicker to correctly identify it as real (M = 743 ms, SD = 123) than brand nonusers (M = 762 ms, SD = 129), t(21704) = 10.98, p < .05. A one-way analysis of variance (ANOVA) showed that brand usage recency has a significant effect on RT, F(5, 21706) = 34.73, p < .01. The effect size, however, was small for the actual difference in mean scores between the groups (η2 = .001), indicating that usage recency does not meaningfully act RT (See Table A2 in the Appendix for detailed results for RT by usage recency).
An additional ANOVA was performed to examine whether different levels of brand usage influence brand recognition by excluding brand nonusers from the analysis. A significant effect was found for the level of exposure frequency on RT, F(4, 12175) = 13.81, p < .01, with a small effect size (η2 = .005). A similar pattern was found across the categories tested. Although there was some variation in the mean RT for different recency levels, the small effect sizes suggest that usage recency levels have a very small influence on recognition RT (η2 = .02 or less in all categories).
A series of ANOVAs showed no significant interaction effects (p > .05) between age of acquisition and brand exposure and usage. Detailed results are presented in the Appendices (See Figures A1, A2, and A3 [and Tables A6, A7, and A8] in the Appendix for illustrations of the interactions across these variables on RT; the statistical results are reported).
Brand Usage Recency Influences Recognition Accuracy to a Certain Extent
Brand users were 1.6 times more likely to identify a brand name accurately than nonusers (94 percent vs. 58 percent). A chi-square test for independence indicated a large effect size and significant association between usage recency and recognition accuracy, χ2(5, N = 29,045) = 5,663.36, p < .01, V = .44. Hypothesis 3 is thus supported: The more recently a brand was last used, the higher the recognition accuracy. Note that those respondents who reported never using the brand may still have seen it (e.g., they may have seen it on shelves or in advertising but never directly used the product). (See Table A3 in the Appendix for details on brand usage recency and recognition accuracy.)
An additional test that excluded nonusers showed a significant but small effect between usage levels and recognition accuracy, χ2(4, N = 12,598) = 43.73, p < .01; V = .06. Consistent results were found in each category, suggesting that brand usage recency (e.g., in the past month to over a year) has a real but limited practical influence on recognition accuracy among brand users (V ≤ .11 in all categories). Consumers who last used a brand over a year ago can recognize a brand with an accuracy similar to that of recent users.
Brand Exposure Does Not Meaningfully Influence Response Time
Claimed brand exposure was associated with faster recognition reaction time (M = 751 ms, SD = 125) than no claimed exposure (M = 765 ms, SD = 146); t(21704) = 3.14, p < .05.
Brands with a claimed higher exposure frequency were recognized faster. A one-way ANOVA showed a significant but small effect of claimed exposure frequency on RT, F(5, 22123) = 8.94, p < .01; η2 = .002 (See Table A4 in the Appendix for detailed results on brand exposure levels on RT).
An additional test investigating brand exposure (ever vs. never) on RT yielded a significant but small effect, F(4, 21311) = 8.901, p < .01, η2 = .002. All categories showed consistent results, reinforcing that brand exposure frequency and exposure (never vs. ever exposed) have a real but very small effect on RT (η2 < .01 in all categories).
Higher Recognition Accuracy for Frequently Encountered Brands
As expected, brand exposure frequency positively affects accuracy. Notably, brands that were reported to have been seen at least once a year were recognized with high accuracy (>90 percent). A chi-square test for independence indicated a statistically significant association between exposure frequency and recognition accuracy, χ2(5, N = 29550) = 14,896.25, p < .01. Hypothesis 4 is thus supported (See Table A5 in the Appendix for details on the recognition accuracy results of brand exposure levels).
An additional test performed by excluding those that had never seen the brand resulted in a statistically significant association between exposure levels and recognition accuracy, χ2(4, N =23,703) = 559.51, p < .01, with a moderate effect size at V = .15. Similar findings were found in each category, meaning that brand exposure frequency (from daily to over a year) systematically increases recognition accuracy (effect sizes ranged from V = .11 to V = .19).
Using a binary logistic regression to examine the relationship between brand name acquisition (early/late), brand exposure (never or hardly seen/seen recently), brand usage (nonuser/user), and brand name recognition accuracy (incorrect/correct), the authors found that brand exposure and usage were significant predictors (p < 0.01). Brand exposure has a slightly larger influence on recognition than usage (odds of accurate recognition increased by 2.7 times vs. 1.5 times).
The aforementioned analyses applied the same cutoff as Ellis et al. (2010): the age of four as a threshold for considering a brand to be learned early in life, which is a limitation. The following section addresses this shortcoming.
ADDITIONAL ANALYSIS
Identification Accuracy Declined with Respondent Age when the Brand Was Launched
The first stage was to investigate the accuracy of identifying brands by age of acquisition. If a brand existed before a respondent was born, they had a roughly 80 percent likelihood of correctly identifying a brand as real. There is a downward trend for correct identifications. When respondents were 50 years of age at the brand launch, this likelihood declined to 50 percent (See Figure 1). An age-of-acquisition effect is visible.
Incorrect Responses Are Systematically Quicker
The next stage was to investigate the speed of response. To account for the variability in the responses of individuals, each individual had their RTs for each response standardized by subtracting their mean RT and dividing by the standard deviation. This was then compared with the relative age when a brand was launched. Correctly identified real brands had a higher (slower) standardized RT. By contrast, incorrect responses were faster (See Figure 2). One explanation is that recognition or recall of a brand requires memory access, a slower process than finding no trace in memory or guessing (for more discussion of the differences in recognition, recall, and guessing, see Gardiner, Ramponi, and Richardson-Klavehn., 1998; Wixted and Mickes, 2010). Correct responses were faster when respondents were exposed to brands launched when they were 15–20 years old, although respondent accuracy increased with younger age. When the brand is older than the respondent (See Figure 1), the slope was effectively flat, but when the respondent considers brands they do not use and that were launched after their birth, the slope changed. This slope change appears to happen during the first few years of the respondent’s life. The speed and accuracy of response appeared to be moderately correlated (r = .47), but this requires further investigation.
Brand Usage Has the Greatest Influence on Recognition, Especially for Older Consumers/Newer Brands
Applying the same approach, the authors found a clear pattern in brand usage (user versus nonuser) and exposure (last seen 12 months versus never/longer) on brand name recognition. The results were consistent with the previous analysis, but the presentation described in the following text provides greater insights into the relationship between recognition performance and the age of respondents at brand launch.
Regardless of the respondent’s age when the brand was launched, accuracy was consistently higher for brands that the respondents had used (See Figure 3) and had some exposure to within a year (See Figure A4 in the Appendix).
Respondents’ age at brand launch, however, played an important role in recognition accuracy for brands that were not used/not seen recently. Where the brand was older than the respondent, the slope was effectively flat, but the slope changed for nonusers or the unexposed when the brand was launched after the respondent’s birth. This is particularly obvious among older consumers with newer brands, suggesting that it is especially vital for newer brands to reach this cohort (e.g., through advertising) to stay competitive. This would be moderated, however, by the relative importance of this age group to category sales.
Brand users’ standardized RTs were slightly slower (M = .24, SD = .91) than for nonusers (M = .04, SD = 1.0); See Figure 4. Similarly, respondents responded slightly more slowly to recently seen brands (M = .19, SD = .94) than to brands never/hardly seen (M = .03, SD = .10); See Figure A5 in the Appendix.
CONCLUSIONS AND IMPLICATIONS FOR PRACTICE
Despite statistically significant differences and the differences in accuracy found in recognition performance between early- and late-acquired brands, the current study repeatedly found small effect sizes. This suggests that childhood brand exposure has limited practical influence on recognition performance and only partially contributes to the fluency of brand name recognition in adulthood. Other factors, such as brand exposure and usage experience, have far greater implications for consumer memory and marketing practice. Childhood exposure can positively influence consumer memory, but other factors become increasingly crucial as consumers enter adulthood, especially in a competitive environment. This is good news for practitioners who cannot change their brand’s history (e.g., launch date) or influence consumers’ personal history with their brand. Industry professionals know the significance of consistent brand communication, advertising campaigns, and positive customer experiences (through usage) in fostering brand recognition and repeat patronage. The findings remind brand owners to allocate resources strategically and invest in initiatives that generate maximum brand exposure (e.g., high-reach continuous advertising) and reinforce positive experiences (e.g., stay relevant and deliver value to consumers throughout their lifetime).
Among the conditions tested, brand exposure (seen versus never seen) had the largest effect on recognition accuracy (i.e., knowing the brand) compared with usage (used versus never used) or childhood brand exposure. The levels of claimed exposure frequency have real effects, but the magnitude of its impact on recognition is limited. Brands seen less than once a year can still positively impact memory, but this is a short-term effect.
These findings have implications for brands at all stages. Although established brands learned at a younger age may have some mental advantage (e.g., Bronnenberg, Dubé, and Gentzkow, 2012; Lambert-Pandraud and Laurent, 2020), this is limited to individuals who do not use/have not seen the brand for a long time. (Although not tested in this study, these respondents may be category nonusers and, hence, less susceptible to competitive interference.) It is important to note that this advantage is not an impenetrable barrier for new or competing brands. Continuous brand reinforcement (e.g., from advertising or usage) is needed throughout an individual’s lifetime to sustain this early-learned mental advantage. Apart from the obvious of keeping the same brand name, one way to extend this early-learned mental advantage is having consistent branding (over decades). Consumers’ brand memories extend beyond the brand name (Vieceli and Shaw, 2010; Ward, Yang, Romaniuk, and Beal, 2020), and so, the disciplined use of a brand’s attributes, such as distinctive assets (e.g., logo, color, shapes, characters) across all touchpoints can leverage consumers’ brand-related memories to capture attention. In practice, this means mandating the likes of a brand’s messaging and image while limiting changes to the logo, pack, or name.
For new brands, there is potential to achieve recognition fluency similar to established brands. The findings emphasize the importance of marketing efforts to help newer brands become a part of individuals’ mental “brand lexicon” across all ages. That said, older consumers’ usage and exposure status are especially influential for newer brands, indicating that this cohort requires greater repetition and maintenance to sustain recognition fluency. Older consumers (e.g., those older than 55 years) can recognize and recall as many brands as consumers younger than 40 years (Mecredy, Wright, Feetham, and Stern, 2023) and exhibit brand choices similar to those of their younger counterparts (e.g., purchasing newer brands; Phua, Kennedy, Trinh, et al., 2020). Brand managers can influence whom they reach, how often, when, and in what ways, such as by providing free trials or samples to encourage usage.
The additional analyses imply that a faster response time does not necessarily indicate more memory structures or the ability to identify branding correctly. This finding raises questions about the validity and usefulness of using time-of-response measures to evaluate branding success. The number of influences on speed-of-response measures (e.g., respondent age, time of day; see Zhou, Ferguson, Matthews, et al., 2011) mean that these are unsuitable when investigating branding clarity or quality: There is more value in correctly identifying a brand or brand assets than in a marginally faster but incorrect brand identification. The ultimate goal of branding is to establish a strong and accurate association between the brand and its desired attributes in consumers’ minds. Thus, measuring the accuracy and clarity of brand identification should take precedence over the speed of response.
Measures of consumers’ memories should also consider the context of brand usage and exposure, as the age-of-acquisition effect seems only apparent for non-brand users or those who have not seen the brand recently. The findings suggest that the effect of the mere exposure or availability of a brand may be more prominent for non-brand users or individuals who have not recently encountered the brand. This supports that targeting efforts should be tailored to reach broadly (including non- and light buyers). This implication is consistent with the growing evidence on the importance of light buyers in sustaining long-term brand growth (e.g., Dawes, Graham, Trinh, and Sharp, 2022; Graham and Kennedy, 2022; Trinh, Dawes, and Sharp, 2023).
THEORETICAL IMPLICATIONS AND FUTURE RESEARCH
The finding that recent brand exposure and usage have a far greater influence on consumers’ memories than the age of acquisition highlights some important insights about the age-of-acquisition theory and its practical value in the context of consumer memory. Although this study technically replicated the initial study by Ellis et al., with statistically significant results, the differences are minimal, response times in the order of 10 to 15 ms. This research cautions against overemphasizing small effect sizes that could lead to an incomplete understanding. In today’s world, where sample sizes are increasingly large (Kennedy, Scriven, and Nenycz-Thiel, 2014) and devices are increasingly accurate in measuring timed differences, attention needs to be given to the practical importance of such findings.
The current findings suggest that age-of-acquisition theory has limited utility in brand recognition in adulthood, as the impact of childhood brand exposure diminishes as individuals enter adulthood. Factors such as recent exposure and usage experience become more crucial in memory processes in adulthood. The emphasis on recent brand exposure and usage experiences highlights the dynamic nature of memory and its susceptibility to change over time. The research emphasizes the cumulative impact of brand exposure and usage on recognition fluency, reinforcing that repeated exposure and positive experiences are crucial in strengthening brand memory. This insight aligns with the broader theoretical understanding of memory consolidation and retention, highlighting the importance of reinforcement and ongoing interactions with a brand (e.g., Romaniuk and Gaillard, 2007).
Moreover, this study expands understanding of memory and recognition by exploring the interplay between memory-based processes (associated with early-learned brands) and non-memory-based processes (guessing) for late-learned brands. This finding highlights the complexity of memory and recognition as measures and suggests that different cognitive processes may be involved depending on the timing of brand exposure.
More specifically, in future research into consumer learning, these findings call into question the use of the age of five years as a threshold for considering a brand as early- or late-learned, with these results giving some initial evidence to support late adolescence as a potential tipping point in the formation of branded networks of memory. Future research in brand recognition and memory theories can benefit from further exploration of the role of recent brand use.
This research is an important reminder for practitioners and academics in today’s data-rich world. With such abundant data and user-friendly access, it is crucial to avoid getting overly focused on small and potentially insignificant findings. This article emphasizes the need for a balanced and thoughtful approach to data analysis and interpretation, encouraging a broader perspective to avoid losing sight of the big picture.
ABOUT THE AUTHORS
Peilin Phua is a lecturer at UniSA Business and a senior marketing scientist at the Ehrenberg-Bass Institute for Marketing Science, University of South Australia. Her research, which focuses on consumer behavior and advertising, has been published in the Journal of Retailing and Consumer Services and Journal of Advertising Research.
Bill Page is a senior marketing scientist at the Ehrenberg-Bass Institute, University of South Australia. His research focuses on understanding habitual behavior through innovative techniques, including shopper marketing, retailing, and music consumption.
Giang Trinh is an associate professor at UniSA Business and a senior marketing scientist at the Ehrenberg-Bass Institute. His research expertise lies in quantitative method development and applications in the areas of consumer purchasing behavior, brand competition, market structure, price promotion, advertising and sales relationship, and new product launches. Trinh’s work can be found in Marketing Letters, European Journal of Marketing, Journal of Business Research, Journal of Retailing and Consumer Services, International Business Review, Journal of Marketing Management, Journal of Business and Industrial Marketing, International Journal of Market Research, Journal of Product and Brand Management, Journal of Consumer Behaviour, and Australasian Marketing Journal.
Nicole Hartnett is a senior marketing scientist at the Ehrenberg-Bass Institute for Marketing Science, University of South Australia. She has a keen interest in advertising creativity and effectiveness, considering measurement approaches, and managerial decision making. Hartnett’s work can be found in the Journal of Advertising Research, Journal of Advertising, and European Journal of Marketing.
Rachel Kennedy is a research professor, director, and co-founder of the Ehrenberg-Bass Institute. Her research is focused on advertising and media knowledge to help grow brands. Kennedy is on a number of journal editorial boards, and her work can be found in the Journal of Advertising Research, Journal of Advertising, Journal of Business Research, and Journal of Retailing and Consumer Services, among others.
APPENDIX
- Received February 6, 2023.
- Received (in revised form) July 17, 2023.
- Accepted July 19, 2023.
- Copyright © 2023 ARF. All rights reserved.