Never too old to learn: the transformational power of technology Open Access

The world’s ageing population has resulted in one of the most significant social transformations of this century, with implications for all service sectors (Bianchi, 2021; He et al., 2016). The editors of several special issues of the Journal of Services Marketing have recently encouraged service-focused scholars and practitioners to dedicate more research to the service implications of ageing consumers. Alexander (2019) suggests that service organisations will increasingly need to consider how they can fulfil the growing needs for social support and meaningful experiences in different consumption settings. Kuppelwieser and Klaus (2021) argue that in ageing societies globally, we must have a better understanding of age and age-related needs, as they are essential for creating new theory development, gaining practical insights, and understanding social implications. This is because of the fact that age beliefs are mental maps of how we expect older people to behave based on age. Sadly, however, across societies, we are particularly susceptible when it comes to negative age beliefs (Levy, 2022).

Rosenbaum et al. (2022) further urge future research to investigate how older consumers choose to embrace technology and learn how to use it. Indeed, a recent study found that the magnitude of the effect of technology use on brain function is similar to or stronger than other known protective factors, such as physical activity or maintaining a healthy blood pressure (Benge and Scullin, 2025). Research on this topic is therefore very timely, considering that the global population aged 60 and over is predicted to expand to over two billion people by 2040 (Angus and Westbrook, 2022). It is therefore essential for service organisations to understand how this cohort acquires and uses technologies and digital services and how they choose to let them assist in their daily lives and personal experiences. Kuppelwieser and Klaus (2020) summarise the importance of changing the ways in which services marketing research thinks about age. As they put it, “age is a construct and not an individual’s attribute or a sample’s characteristic” (p. 3). So how do we approach age in services marketing? Some researchers have called for adapted services that focus on the specific needs of older consumers (Charness and Boot, 2009), such as by increasing visual stimuli and emotional appeals for consumers as they age (Guido et al., 2020). Wu et al. (2020) expand on the concept of emotions by conducting two experimental studies focused on the necessity of human interaction. Their findings reveal that older consumers normally prefer services provided by humans rather than those delivered by technology or machines.

However, other researchers question why we treat age as a reason for different and adapted services. For instance, Bae et al. (2021) question why older consumers do not adopt innovative services and find that it is not inability, but rather self-awareness of age-related changes, that decreases their tendency to adopt novel products and services. The same article raises the important question of why we create a negatively connotated picture of ageing and allow this to reflect on the individual. Similarly, Bateson (2020) warns that stereotyping people because of their chronological age not only infects society but also has a negative effect on the self-image and attitudes of individuals. He warns that service marketing research runs the risk of associating older age as something negative.

Confirming this negative effect, Bae et al. (2021) show that awareness of age-related change leads to a decreased tendency in older consumers to adopt new and innovative products and services. The same study also found that when sensing that they are negatively viewed as older people, consumers limit their interaction with innovations to avoid being seen as incompetent. Researchers therefore urge us to think more inclusively about service design (Charness and Boot, 2009; Czaja et al., 2013), as well as innovative and novel services, and to create service experiences that are “age neutral” (Walker, 2011). Our study responds directly to these calls for research. Specifically, it aims to understand how older adults acquire digital experience and confidence using age-neutral technologies and how they choose to adopt these technologies in their daily lives. Older adults in Western cultures choose to predominantly live in their own homes and tend to stay there as long as possible (Broadbent et al., 2009; Scopelliti et al., 2005). The ultimate purpose of this study is to investigate how robotic technologies can be a transformative service for these people at home and how they can alter people’s perceptions of their age and learning abilities.

The article is organised as follows. First, the literature review centres on two main themes: the relationship between service consumption and age and the learning abilities of consumers as they age. Next, we introduce the research design, participant selection and data-collection method. Subsequently, we present the main findings and engage in an in-depth discussion of them. The article concludes with the key theoretical and practical implications as well as future research directions.

Although ageing involves far more than a simple number of years, articles in the services literature have traditionally focused on chronological age, as they “define” the attitudes and behaviours of older consumers. Chronological age is a simple count from the moment we were born until the current date (Kuppelwieser and Klaus, 2020). Chronological age is proven to explain several possible changes that occur in people’s psychological, social and societal functioning over their life cycle. However, what makes ageing a much more complex phenomenon is its cognitive aspect. Cognitive age, simply put, reflects individuals’ perceptions of how old they “feel” (Barak and Schiffman, 1981), which can be very different from how old they “are”. Cognitive age is also described as our state of mind (Schiffman and Sherman, 1991) and has long been known to affect how we feel and behave (Mathur and Moschis, 2005). Indeed, Guido et al. (2018) find that cognitive age is not an objective concept but a context-dependent construct that varies according to physical environment, social context, interaction with different product categories, and which goals older adults pursue. More recently, Segel-Karpas et al. (2022) further suggest that individuals’ perceptions of age are influenced by their physical and mental health status. Feeling younger than one’s chronological age is a mechanism that can help to cope with ageing. Research indicates that the ability to adopt a younger identity is highly beneficial to ageing and is associated with well-being and mental flourishing (Keyes and Westerhof, 2012).

The social science literature has long postulated that cognitive age has a strong association with chronological age. However, it is less clear whether this association is because of individuals’ active perceptions or simply passively assumed by society through the social role affixed to individuals (Ihira et al., 2015). Previous research has found that cognitive age is a metric that is influenced by events. This means that the emergence of particular events, such as a decline in one’s health, the loss of a spouse or some other loss scenario, can dampen someone’s ageing perspective and raise their cognitive age (Mathur and Moschis, 2005; Ward, 1977). Instead of the commonly examined “loss scenarios” (Van Auken and Barry, 2009), this study seeks to discover whether it is possible to create a “gain scenario” for someone’s cognitive age by examining whether independent interactions with robotic technologies alter people’s cognitive age in a positive way.

According to cognitive reserve theory, exposure to complex mental activities leads to better cognitive well-being in older age (Stern et al., 2023). They define reserve as “a property of the brain that allows for cognitive performance that is better than expected given the degree of life-course-related brain changes” (Stern et al., 2023, p. 3). In the long term, this would mean that if cognitive age can be positively influenced, older adults should evidence better health and an absence of loss, or at least a more positive adjustment to loss. In the long term, if cognitive age can be positively influenced, older adults should demonstrate better health outcomes. This could result in either a reduced experience of age-related losses or a more positive adjustment to such losses.

In modern society, consumers are encouraged to take proactive roles in services generally and specifically to co-create their own well-being (Chen et al., 2020). However, to reach beyond passive service consumption and become effective co-creators, they need to have more than a basic form of literacy about the service concerned (Pham et al., 2022). Thus, in recent years, scholars have called for more insights into understanding how consumers can optimise service co-creation, thereby contributing to their well-being (Bieler et al., 2022; Landry and Furrer, 2023). Rosenbaum et al. (2007) called for a transformative paradigm, stressing that services can impact consumers’ well-being. Transformative service research (TSR) refers to the integration of consumer and service research. It centres on creating uplifting changes and enhancements for consumers, communities and the service ecosystem (Anderson et al., 2011; Ge and Schleimer, 2023). Value co-creation behaviours have been found to be especially helpful for older consumers to cope better with age-related changes, to improve service inclusion and to enhance their well-being (Pera et al., 2020; Previte and Robertson, 2019).

Despite increasing concerns about how to improve the well-being of older consumers within TSR (Feng et al., 2019; Plaud and Guillemot, 2014), scant research exists on how older consumers can improve their well-being by co-creating value when consuming technology-related services (Krueger et al., 2018). This is especially important because it causes a digital divide – particularly for older adults, who often lack formal training and experience in learning how to use and interact with technology. They also experience lower self-efficacy and have higher levels of frustration and perceived benefits of new technology (Li and Luximon, 2018). Developing technologically innovative solutions with rather than for older adults requires a degree of visioning and understanding of co-creation (Battersby et al., 2017). The purpose of co-creation is to explore the needs for a product or service by enabling users to state these needs (Tabeau et al., 2024). In this study, we were interested in how consumers interact with new technologies independently rather than within a structured, researcher-led environment. Specifically, we wanted to provide the consumer with the opportunity to independently co-create their own experiences with the technologies and ultimately decide whether they had a need for the products.

In sum, based on existing literature, this study is interested in how the adoption of robot technology affects the learning capability of older adults. Leading into the research question, we build on cognitive reserve theory. In specific, in line with the technological reserve hypothesis – which argues that engagement with digital technologies promotes positive cognitive outcomes (Benge and Scullin, 2025) – we examine how interacting with robot technologies alters consumers’ cognitive age and how this, in turn, influences their learning abilities. We therefore propose the following overarching research question and two sub-questions (see Figure 1):

In this study, we define cognitive age, similar to Catterall and Maclaran (2001), as an individual’s attitude towards chronological ageing. We therefore use a methodological and empirical approach that focuses on robotic technologies that can be considered “age neutral” in terms of their proposed benefits. We identified three specific commercially available robotic technologies intended to assist consumers at home. These technologies were designed specifically to help with the three most common needs at home: (1) cleaning; (2) communication with family, friends and the community; and (3) companionship. These are basic needs that people of all ages have if they are to remain living at home; however, older adults living at home are at greater risk of not having these needs fulfilled because of social isolation and a lack of accessibility.

This study is designed to obtain an in-depth understanding of the perceptions and factors affecting the perceptions of older consumers in relation to robotic technologies. To assess any potential changes in the attitudes of these participants, a qualitative method approach was chosen, involving focus group discussions at two points in time across three separate trials. Focus group discussions are traditionally defined as a group of people brought together to participate in the discussion of an area of interest (Dick, 1999). There are different ways to obtain qualitative data from groups; in this study, we have chosen focus group discussion, which is designed to create a safe environment that encourages all members of the group to discuss the area of interest with one another (Boddy, 2005). This type of discussion encourages open discussion of opinions and perceptions, with participants free to agree or disagree, ask each other questions and generally discuss the topic in an open and (usually) friendly manner (Boddy, 2005). Our aim for the discussion was to gain both a breadth and depth of communication among participants.

The research team conducted three pairs of focus groups, three pre-home focus groups and three post-home focus groups during the research trials for this study, which occurred throughout 2020 and 2021. The research team also took the role of discussion moderator in these groups. In focus group discussions, the moderator intervenes only to keep the topic of discussion in the area of interest.

Several studies find that cognitive age varies according to physical environment, social context, interaction with different product categories, and which goals older adults pursue (Amatulli et al., 2018; Guido et al., 2018). In specific, Guido et al. (2018) grouped product categories and product consumption into different contexts and found that they evoke different age feelings. In particular, they specified contexts according to “hedonic” (they used examples such as purchasing sportswear and sports equipment) and “utilitarian” elements (they used examples such as purchasing food and technology). In our study, we decided to use robot technology that was built for either hedonic or utilitarian or both elements. For example, one robot (Paro) is made to be a companion, and hence has mostly hedonic consumption elements. The second robot (Roomba) has been designed for cleaning purposes, so it has first and foremost a utilitarian function. The third chosen robot (Temi) has both hedonic and utilitarian elements.

Furthermore, the research team chose robotic technologies that were all commercially available and affordable to address common needs consumers may have at home. As part of choosing the robots, the research team focused on technologies that were easy to understand and use and safe to handle for adults. Table 1 illustrates the key functions and features of each robot.

Substantial literature exists on ageing. In an extensive literature review, Zniva and Weitzl (2016) classify studies into three groups:

1. research comparing the consumer responses of chronologically older and younger consumers;

2. studies defining older consumer groups and investigating chronological or non-chronological age influences within the group; and

3. research that solely defines chronologically older consumer groups without comparing them and without researching any age influence within them.

Our study falls into the second group, as we identify one specific participant group from one culturally and linguistically diverse (CALD) background. Previous research has also found that participants feel comfortable when they share some common backgrounds to facilitate a smooth interaction (Eckhardt, 2004). Therefore, in collaboration with a local community service provider, the research team recruited participants aged 65 years or older from a Japanese cultural background who reside in their own homes on the Gold Coast, Queensland, Australia. This coastal setting was selected because the Australian Bureau of Statistics (ABS) population projections show that older adults continue to be concentrated in areas along the Australian coastline (Department of Health, 2008). Participants generally perceive focus group settings to be less threatening because of less fear of being judged by the interviewer (Nuttavuthisit, 2019). Focus groups are also a suitable method for obtaining data on perceptions from this specific sample, as people from Japanese cultural backgrounds are known to feel more comfortable voicing their opinions in a group setting (Chen and Lamberti, 2015). Finally, unlike online surveys and tablet- or computer-based data collection, focus groups do not systematically exclude older adults who have not crossed the digital divide (Salathé et al., 2012).

The data collection was conducted during the peak of the COVID-19 pandemic, throughout 2020 and 2021. We conducted six focus groups as part of a series of research trials under strict social distancing precautions. We had to adjust the planned research activities in line with the changing instructions from the Australian government to ensure the safety of the participants at all times. In total, 26 participants took part in the focus groups, ranging from 65 years to 89 years old. In total, there were seven female and 19 male participants in the sample. Of these participants, 11 owned a pet during the study period, while 15 did not.

The first stage of data collection comprised the pre-home focus groups in a local community centre. All participants were familiar with this venue, as this community organisation runs regular community activities for older adults from this group. The purpose of the pre-home focus groups was to explain the project and introduce each of the robots to the participants. All three robots were presented to the participants and their main features were explained. Out of the three robots, participants had not seen or interacted with Temi or Paro prior to the study. However, several participants had heard about Roomba prior to the study and two had owned an earlier version of this robot. The discussion in the pre-home focus groups, therefore, focused on gaining the participants’ initial thoughts and impressions of the robots and creating a group discussion about these among the participants. The focus group length was around 90 min.

To avoid any form of manipulation, we took three key measures, including: First, we avoided leading questions to prevent bias; the research team created questions that were open-ended, socially appropriate, and free from jargon or leading language that might influence responses. Second, the research team comprised skilled moderators who facilitated discussions without imposing their own views and conducted the moderation in a neutral manner. Third, we recruited participants who were all relevant to the research topic, and there were no power dynamics among them to influence any responses or interactions. As a result, the study’s focus groups provided an environment where participants felt safe and empowered to speak and interact with the technologies. All sessions were conducted in English and translated into the Japanese language simultaneously by the research team when needed to allow participants to speak and voice their opinions in the language they felt most comfortable using. From the start, all participants were reminded that the research team was interested in hearing their views and perceptions, irrespective of whether they were positive or negative about the robotic technologies. Each robot was introduced and demonstrated to all participants. After that, participants were given time to interact freely with the robots. Participants were asked about their initial thoughts on the robots, their functions, potential benefits and usefulness in their households. After the pre-home focus group, each participant was delivered all three robots, which they independently spent time with during a seven-day home trial.

After the home trials finished, the research team hosted post-home focus group sessions with all participants. The purpose of these sessions was to have participants, as a group, share their experiences of living and interacting with the robots at home and to evaluate whether and how their attitudes towards themselves and the technologies had changed. As outlined by Morgan (1996), focus groups are more than the sum of separate individual interviews. Therefore, the research team was able to gain a more fine-grained understanding of how participants perceived the robots after having interacted with them at home. While the focus group followed a guided set of questions for each robot, it also allowed for a more open discussion among all participants in relation to their experiences living and interacting with the robots. All participants took an active part in these discussions where they exchanged their experiences with and behaviours towards the robots during the home trial. The participants were stimulated to compare and reflect on each robot’s perceived benefits and encouraged to suggest whether and what improvements were needed in relation to each robot to maximise service experience. Finally, the participants were asked to reflect on the learning they acquired throughout the study.

All focus group discussion sessions were audio-recorded, transcribed verbatim and translated from Japanese into English where needed. Using previous thematic analysis methods (Braun and Clarke, 2013; Westberg et al., 2021), transcripts were consecutively examined by the research team using thematic analysis to identify patterns of meaning emerging from the data to provide insight into the research questions. The two authors used an inductive analytic process and coded all transcripts independently. To minimise any personal beliefs or bias, the researchers used the recordings as well as the translated transcriptions in addition to their own notes (Hadi and Closs, 2016).

Next, the researchers followed a rigorous process of data familiarisation and development and revision of themes to identify key underlying topics (Westberg et al., 2021). As the first step, familiarisation with the data meant reading and rereading the transcripts of all focus groups to gain a rich understanding of each participant and all groups. After that, data from each transcript were systematically reduced to capture all relevant content in relation to the research questions. All narrative data were examined for underlying similarities and grouped according to each robot and relevance to the research questions. Subsequently, themes were developed and systematically reviewed to identify patterns of opinions that were shared or different among participants and participant groups.

In this study, we explored the interrelatedness of older adults’ perceptions of their own age in light of interacting with innovative technologies. The most intriguing finding is that the involvement in the research project changed how the participants saw themselves and how they interacted with their surroundings. Interestingly, participants’ perceptions and behaviours changed towards all three robots. Participants expressed significantly higher levels of confidence in understanding the robots’ benefits as well as successfully interacting with them. They generally expressed a much more positive attitude towards the potential benefits of owning the robots as well as a more confident attitude about their own abilities in interacting meaningfully with the innovations. The results below are structured according to the participants’ experiences with each of the robots. In specific, we highlight how the participants’ cognitive age changed from the pre-home to the post-home focus groups and how this change differed depending on each robot. In addition, the findings below reveal how the changed perceived cognitive age affected their learning capability in light of their interactions with each robot.

Participants developed a closer, and in many cases even emotional, connection with Paro, the companion robot. This is interesting, especially considering that there were several participants in the pre-home focus groups who initially saw Paro’s perceived benefits to be very limited and showed a restricted view towards robotic technologies in general. For example, three male participants in the pre-home focus groups expressed their doubts about whether Paro would be useful in their homes, as they felt too young to consider Paro beneficial. They were reluctant to touch this robot and expressed that they were unsure about whether they had sufficient interest or time to interact with Paro, and they expressed that they were still “too young and too physically active to need such a robot”. Another participant suggested that, rather than him, his “100-year-old mother was very lonely and would love Paro”. A third participant thought Paro would be more suitable for dementia patients. Interestingly, female participants did not share this concern – in fact, all female participants were quickly drawn to Paro across all pre-home focus groups and wanted to hold and hug this robot.

Across all three post-home focus groups, participants compared Paro with a “real pet” after having had the robot at their homes. This is particularly relevant because Paro was only referred to as a “pet” by a few participants in the pre-home focus groups. In all post-home focus groups, Paro was thought to be a real option as a substitute for their pets after living with the robot for only a short period of time, especially for those who owned pets (42% of participants owned at least one pet during the study). The comments about why Paro would be a suitable companion were similar across the three post-home trials, with participants referring to its pet-like responses to touch and sound, movements and convenience. Across all post-home focus groups, and irrespective of gender, participants became very familiar with this robot’s features and its ability to learn from and with the participant. They commented on Paro’s ability to learn like a pet, respond well to voice and touch, and feel like a pet when held. One participant said that “Paro learned to have more expressions after a while”. Most participants also agreed that Paro was less expensive and more convenient than a pet. Participants across all post-home focus groups developed an emotional connection with Paro; some voiced that this connection was quite close. One participant stated, “the longer I was there, the more it connected with me”. Interestingly, it was the three male participants – who in the pre-home focus groups expressed no interest in this robot – who particularly developed a loving relationship with the Paro. Paro was even given a name by the male participants in several households and treated like a real companion. One participant commented that she thought this robot was too self-sufficient and that she would prefer a more “needy” robot. Interestingly, many participants wanted Paro to have a more advanced talking function (the current version of this robot can only make a seal-like sound).

Temi was the robot that generated the most initial concern in terms of its perceived benefits and the participants’ ability to successfully interact with it. Some participants in the pre-home focus groups were impressed by Temi’s functions, such as multiple language options, communication functions and sound functions. Temi was compared with the telecommunication application Skype when talking with family members not living together and with an “Apple iPad on wheels” in terms of its display and music-playing functions. However, other participants shared their initial concerns about Temi when first introduced to this robot. They pointed out their worries about controlling Temi. They were also unsure of its purpose for them. Participants generally raised their concern about its suitability for older adults, as Temi was perceived to be more suitable for “younger” consumers because of its multiple, complex functionality, as one participant put it, “due to my age, I find it hard to use new technologies like this one”.

Surprisingly, across all post-home focus groups, participants no longer felt too old to successfully use and interact with Temi. Several participants mentioned that they were positively surprised at their own abilities to use the robot competently and experienced empowerment of technology engagement. Participants were not only able to engage with the robot’s obvious functions but also started to explore this robot’s functions independently. The majority of participants enjoyed the music function of Temi the most. For example, one participant mentioned how she really enjoyed “sending Temi to my husband’s bed in the morning to wake him up with his favourite classical music”. Another participant also stated that “I would be interested in uncovering more functions of Temi if I would be able to have Temi live with me for a longer period”.

However, several participants also raised concerns about Temi’s current functions. They confirmed its limited usefulness in multi-storey houses (as Temi is not portable for older adults because of its weight and size) and felt Temi’s movements needed to be improved to make its operation smoother across surfaces and in confined spaces. The participants across all post-home focus groups also agreed that the voice recognition needed to be improved, as they had to often give commands more than once before Temi responded. Several participants asked for a more detailed manual so they could learn to uncover more features of this robot, such as “to be able to talk to families” and wanted “to learn more about this robot’s applications”. Several participants mentioned that they were happy about Temi’s Japanese language functions, and most participants highlighted the multitude of other communication and music functions they enjoyed while interacting with this robot. Participants across all post-home focus groups agreed that Temi could increase their learning capability around other technologies: One participant highlighted that “Temi would help me ease into computers as I am not very technology savvy”. Most participants across all post-home focus groups also agreed that they would like Temi if this robot were to become available as part of a home care package, as they were curious to interact with Temi further.

Participants were generally impressed with Roomba’s functions across all three pre-home focus groups. They all agreed on the seemingly impressive cleaning power of this robot and said they would be interested in trying Roomba in their homes. Several participants voiced their initial concern about being able to successfully use the mobile application on their mobile phones to control this robot successfully (although all participants in this study owned a smartphone). A few participants also raised their concern about Roomba potentially interfering with their pets at home (especially those participants who owned cats) and Roomba potentially damaging their carpets. This concern was based on participants mentioning their worries about this robot independently cleaning the floors with one setting only rather than handheld vacuum devices, which allow for different cleaning settings depending on the floor condition.

Similar to the other two robots, participants across the post-home focus groups generally had a positive perception of Roomba and were generally impressed to learn about the robot’s functions when it came to cleaning, as one participant stated, “I am amazed by the intelligence of the Roomba”. All participants were able to successfully use the mobile application without any difficulties and swiftly learned how to direct the robot to clean their homes. One participant stated how much he enjoyed “taking the time to create a cleaning map” for the robot. Several participants also highlighted that they really enjoyed “having the freedom to do what I wanted while Roomba was cleaning my home” and thought of it as being a very “useful” domestic assistance at home. In addition, in the post-home focus groups, participants praised Roomba’s ability to clean pet hair off the floor and its thorough cleaning power compared with traditional, handheld vacuum cleaners. However, they also raised some concerns. Some participants voiced their concern that the robot got stuck trying to go from the carpet onto flat surfaces in their homes. In addition, participants also suggested making the dustbin larger, increasing the size of the rollers to manoeuvre on and off carpets, and increasing the cleaning speed.

In summary, our findings revealed an interesting shift in the participants’ 1) self-awareness of their perceived cognitive age through engaging with the new technologies and 2) increased learning capability. Overall, we found that participants in the pre-home focus groups were providing limited comments on their initial perceptions of the robots and were generally quite shy in expressing their opinions and interacting with the robots. They expressed a sense of intimidation when it came to using the robots, as our project provided their first opportunity to interact with the robots, and they were worried that they would understand and use them successfully. These attitudes shifted profoundly for all participants after living with the robots at home for a limited period of time. We witnessed a change in the perceived cognitive age where participants’ felt age seemed to be in a dynamic state that allowed them to feel open to engaging with and learning from all three robots.

The projected exponential growth of the older adult population globally has become a critical concern for service providers across all economies. As a consequence, it is essential for service organisations to understand how older adults acquire and use technologies and digital services successfully and how they choose to let them assist in their daily lives. In this study, we set out to find answers to our main research question: how can adopting robotic technologies affect the learning capability of older consumers? Informed by the key findings and in light of the study’s two sub-research questions, the following discussion is structured into two main sections.

Benge and Scullin pointed out in their recently published meta-analysis that “understanding technology’s influence on cognition over time is inherently challenging” (Benge and Scullin, 2025, p. 2). The literature in services marketing has focused on the acceptance of technology by older consumers, and the results to date are also mixed. While some research claims there is a declining acceptance of technology as people age (Berg, 2016; Wildenbos et al., 2018), other studies have been unable to confirm this decline (Bae et al., 2021; Rogers and Fisk, 2010; Carrigan and Szmigin, 2000).

Guido et al. (2018) found that the difference between chronological age and cognitive age is higher for hedonic product consumption than when they consumed utilitarian products. Our findings further revealed that the ways of how respondents perceive the products may change through consumption of them. For instance, whilst Temi was viewed as initially utilitarian in the pre-home focus groups (as a complex communication and monitoring tool), through interacting with this robot, respondents started to see more hedonic elements in this robot. Also, Roomba, which was predominantly seen as utilitarian, through consumption of the product, it evoked feelings of freedom and happiness as respondents watched the robot work whilst they could enjoy doing things they love.

As a result, our findings respond to Sub-RQ1 and confirm the technological reserve hypothesis (Benge and Scullin, 2025) as they suggest that adopting technology impacts cognitive age. In specific, our findings highlight that older people not only have the ability to accept technologies, but technology has a much bigger influence on older consumers’ felt age than previously explored.

According to a recent editorial in the Journal of Services Marketing (Russell-Bennett et al., 2024), the volume of research on consumer “vulnerability” has dramatically increased in service marketing research over the last few years. However, the same authors warn that this use of language is “disturbing at best and harmful at worst” (Russell-Bennet et al., p. 2). This is especially the case when people become older, as they are often stigmatised as becoming “too old” and “too vulnerable” to be exposed to new technologies. However, by labelling these consumers, we fail to accurately assess or recognise their capabilities, which in turn can be disempowering and reduce the person’s autonomy, well-being, self-confidence and possibly even dignity (Kabadayi et al., 2023).

Rather than being designed for a specific age group, such as older adults, the robots used in this study were designed to be used by consumers of all ages. While our participants initially expressed doubts about some of the benefits because of their perceptions of their own cognitive age, our findings indicate that the advantages of technology play a more significant role in its adoption than perceived age. We thus echo the recent Journal of Services Marketing Editorial (Russell-Bennett et al., 2024) to stop referring to older adults as “vulnerable” consumers and rather empower consumers of all ages to become confident and competent in interacting with and using new technologies. Our study demonstrates that consumers’ growing mindset and their curiosity about engaging with technology outweigh their fear of being stigmatised. In fact, we have shown that consumers can move themselves out of any stigmatised status to a new sphere of cognitive empowerment and freedom.

As a consequence, we suggest including older adults in the co-design of future robots. They are important future consumers who are not only able to successfully interact with new technologies but also understand and have the competency to explore improvements of technologies to suit their needs. We propose that older consumers may have more time and possibly more patience to co-create than younger consumers, as our study shows that even when the service interaction was not perfect and there were technology limitations/glitches, participants never gave up trying to learn how to successfully interact with the robotic technologies.

Recent literature has recognised the dynamic and fluctuating nature of cognitive age. However, studies have done so mainly in relation to life events and daily experiences. For example, several studies have found that subjective age correlates with daily stress and depressive symptoms and affect (Bodner et al., 2021; Segel-Karpas et al., 2022; Shrira et al., 2018). Our findings confirm that cognitive age is not a constant; it can change through events and interactions with stimuli, such as the robotic technologies in this study. Some of our participants felt too young for Paro at the start, but they experienced a change in their perceived cognitive age after living with Paro and accepted that Paro had clear benefits for them and their well-being. In another example, Temi was initially thought to be too challenging and more suitable for a younger age group based on the perceptions of most of our participants in light of their cognitive age. Once again, this perception shifted in the post-home focus groups, as all participants no longer viewed their cognitive age as too old after living and interacting with this robot.

The existing literature views cognitive age as a metric that is culture-free; thus, it is influenced by life events (Van Auken et al., 2006). Our study confirms this, as the interaction with the robots altered participants’ perceptions of their cognitive age. In fact, our study suggests that cognitive age is a fluid dimension of one’s self-awareness that can be altered through events and interactions with new stimuli. The findings suggest that participants found their own perceived age to be less relevant once they were interacting with the robots: discussing the benefits of the robots became more prominent in their minds rather than what age the robots may or may not be useful for. This is in line with several neuroscience studies, which confirm that neuroplasticity, the brain’s ability to stay flexible and sprout new neural connections, continues throughout the ageing process (Levy, 2022; World Health Organization, 2021). We, therefore, suggest that feeling “too young” or “too old” toward engaging with a technology may cause consumers not to feel the need to engage, which in turn prohibits their learning ability. Based on our findings, we therefore argue that it is not about necessarily increasing or decreasing the age consumers feel but about creating a “felt” age that creates a desire to learn.

The service literature often uses cross-sectional data and assumes that one’s perceived cognitive age parallels the chronological age of the person at that point in time. Our study, however, shows that cognitive age needs to be captured in relation to different stimuli at any given point in time and at different points in time. It is therefore multidimensional, context-dependent and dynamic in nature. Responding to Sub-RQ 2, our findings suggest that cognitive age can be seen as a dynamic capability that drives the ability to learn.

In sum, our study reveals that the impact of adopting new technology on our learning capability is mediated by cognitive age. Our findings suggest that the competency older adults gain through the adoption of new technologies can lead to a transformational learning capability. This capability even allows us to learn different things at the same time (our participants in this study had successful learning experiences with three completely different robotic technologies). This transformational learning capability is therefore the ability to exponentially improve one’s learning capacity by adapting to new circumstances and stimuli through cognitive age, which is consistent with cognitive reserve theory.

Our study makes several important theoretical contributions. First, we suggest that cognitive age has more than one dimension, as consumers can feel too young for certain things and too old for others at the same time, making it a multidimensional dynamic capability that is unique to every person. It should therefore not be treated as a static construct. Second, we suggest that cognitive age is also not only fluid but can purposefully be altered through new stimuli and interactions, such as through adopting new technologies. Therefore, we confirm that the relationship between cognitive age and chronological age is complex, and we propose that it constantly changes depending on what stimuli consumers are exposed to. As Levy pointed out (2022, p. 62), “age beliefs don’t exist in a vacuum […] they affect how we, as a culture and individuals, design, structure, and experience old age. This is why their effects ripple out in such significant ways, changing not just how we remember, but how we behave”. Our study confirms this, as our findings suggest that technology does not just have the possibility of making life easier but can alter how older adults see themselves and how they interact in their environments. Cognitive age is thus much more than a number or how old one feels; it is about being in a dynamic state where you are more – or less – open to your environment. Figure 2 graphically depicts this relationship.

We find that the impact of technology is therefore much more profound than the obvious benefits of a service interaction. Our findings suggest that the changed behaviour through interaction can occur in a much shorter time frame than identified by previous studies. For example, over a two-month study in a care facility, Wada and Shibata (2007) found that residents developed personal relationships with Paro. Our results suggest that consumers’ preferences may change much quicker, which may be because of the home setting and independent rather than guided interactions with the robots. Therefore, rather than more research in laboratory and clinical settings and in line with the vision of research on robotic–human interaction (Charness and Boot, 2009; Kidd and Breazeal, 2008), we also call for additional future research to investigate how robots can contribute to our daily lives.

Our study suggests that technologies can empower consumers well beyond the stated benefits of service experience. Therefore, as people age, their learning abilities are not declining linearly. Instead, our learning ability is, in fact, a transformative capability that can be altered – and increased, as shown in our study – through appropriate stimuli such as new technologies (see Figure 3). Our results suggest that it is not necessarily about how old one feels after interacting with technology (such as feeling “younger” or “older” than prior to the interaction) but it is more about being at an optimal “feel” age that prompts a desire to learn.

Even though one of the robot technologies, Paro, was initially designed to assist older people with dementia, our findings add to a growing body of research that finds that this robot can also be beneficial for people of all ages that don’t suffer from this condition (González-González et al., 2021; Moerman et al., 2019). It is about rising above stereotyping, as it limits how we are open to interact with new stimuli and ultimately about our ability to learn. We thus suggest that technology can have long-term positive benefits on cognitive health and ultimately the overall well-being of older consumers.

Our research also has important practical implications. In an extensive literature review, Zniva and Weitzl (2016) summarised research findings of more than 120 studies, which collectively show that cognitive age affects cognitive responses toward new products and services and impacts behavioural responses in consumer behaviour. According to previous studies, acceptance is defined as robotic technology being willingly incorporated into older consumers’ life (Heerink et al., 2006; Kidd and Breazeal, 2008). Broadbent et al. (2009, p. 320) further argue that for acceptance to occur, there are three basic requirements, including “a motivation for using the robot, sufficient ease of use, and comfort with the robot physically, cognitively and emotionally”. Adding to this body of research, our study shows that consumers not only accept new technology, but that they are capable of changing their perceptions and behaviours toward it.

Our study thus plays a pivotal role in marketing research. The “silver market” often emphasises age (Brusov et al., 2021). However, when promoting technology to consumers, it is more important to emphasise the benefits of the technologies rather than the often limited and tailored benefits of those designed just for “older” consumers. We therefore suggest that communicating age as part of new products and services to consumers is less beneficial and may, in fact, limit the potential adoption of these products by older adults. Our study finds that older adults are not just curious about technology but capable of successfully interacting and even co-creating service experiences with robotic technologies. Interacting with technologies stimulates learning and reduces declining cognitive abilities. As a consequence, it provides confidence that enables older adults to be technologically proficient and thus interact more independently and successfully in our increasingly digital and technology-driven society.

Human ageing is complex, as it involves many different changes. Hence, no framework can capture the process of ageing in its entirety (Pannhorst and Dost, 2022). Therefore, the existing literature explores ageing through a broad range of research that seeks to explore specific aspects of the ageing process (Crimmins and Beltrán-Sánchez, 2011; Yoon et al., 2009). Despite calls for more research on age-related factors – chronological, biological, psychological and social age – research on older consumers is still dominated by investigations using chronological age as a constant, parsimonious measure (Zniva and Weitzl, 2016). Based on our study’s findings, we propose that future research should treat cognitive age as a multidimensional construct that is dynamic in nature. We thus call for more studies on examining the impact of cognitive age on service adoption and co-creation. Also, our study only examined one cultural group of older consumers located in one geographic location. Echoing Levy (2022), we thus urge future research to engage with additional CALD groups across different locations to gain a more comprehensive understanding of how technology alters their cognitive age and ultimately learning capability.

In sum, this study was one of the first to design research that allowed older consumers to independently interact with multiple robots in a real-life setting. Overall, with the fast developments of new robotic technologies, we call for more transformative research on the adoption of these technologies by consumers using different research designs (different lengths of trial periods) and methodological tools (such as standardised cognitive tasks and structured observation of learning behaviour).

Funding: This study was supported by Commonwealth Home Support Programme Innovation Fund, No. GO1759.