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Abstract

As chatbots gradually replace human employees on the front lines, how to optimize their services to promote value co-creation between the older adults and robots has become a key topic to tap the potential of the elderly market. Grounded in social presence theory, this research explores how chatbot language form affects older adults’ value co-creation intention. Through three experiments, the results revealed that the use of dialect increases cognitive effort, social presence, and enhances older adults’ value co-creation intention. In addition, the research also shows that this language effect varies in different situations. For the older adults with a high need for interaction, the use of dialects can effectively improve their value co-creation intention. However, for older adults with a low need for interaction, the effect was not significant. The study provides insights into how to flexibly use language strategies, such as dialects, based on the varying needs of different senior groups. It aims to encourage greater participation from older adults in value co-creation and further develop the senior market.

Plain Language Summary

Chatbots using Local Dialects help Seniors Engage Better with Technology

With the global senior population growing rapidly, designing inclusive technology helps this group stay connected and engaged in the digital world. Chatbots should offer dialect options in regions where seniors commonly speak them, especially in high-interaction settings (e.g., travel planning). This reduces tech barriers and makes seniors feel understood, encouraging their active participation.

Keywords

Chatbots dialect need for interaction value co-create social presence

Introduction

Chatbots have been widely applied in customer service departments (Zhang et al., 2024). However, these designs are generally oriented by efficiency, mainly aimed at general consumers or young groups (Lu et al., 2024), and insufficient attention is paid to the adaptability of older adults. However, with the continuous aging of the global population, the population aged 60 and above is expected to reach 1.4 billion by 2030 (World Health Organization, 2021). Compared with young people, older adults have weak ease of use and low willingness to use robots, which makes it difficult for service robots to achieve inclusiveness in the elderly tourism market (Jang et al., 2024). In academic research, robots have been widely discussed in helping tolder adults cope with social vulnerability, improving the quality of life (Tan et al., 2021). Existing research focuses on care for older adults and health promotion (Nomura et al., 2021), while applied research in non-medical and non-care scenarios such as tourism, advertising and entertainment is still scarce. How to make older adults and robots actively interact through design optimization is still a problem that has not been fully explored.

Language is a natural form of social communication, and it is also an important carrier of emotional expression and cultural identity (Hu et al., 2023). In human-computer interaction, language choice is not only a technical problem, but also deeply shapes the user’s perception and behavioral intention. Traditionally, chatbots have primarily communicated with users using standard language. Standard language, known for its grammatical precision and universality, caters to diverse user need (Giebels et al., 2017). However, as conversational AI is increasingly integrated into daily life, scholars are beginning to realize that purely standardized language may not have emotional resonance or social connection with users. Therefore, recent studies have explored how language clues such as politeness, humor or dialect affect the user experience when interacting with robots (Shams et al., 2024). Among these language clues, dialect is a particularly meaningful form of personalization.

As a regional language variant rooted in local culture, dialects convey familiarity, sense of belonging and authenticity (Zhang et al., 2024). While prior research has confirmed that dialect-speaking chatbots can boost social presence and emotional intimacy, most studies have focused on young or ordinary consumers, thus neglecting the specific needs and responses of the older adults (Li et al., 2024). The language habits and social motivations of this group are very different from those of young users. Due to the relatively short time of popularizing Mandarin, many older adults people mainly use local dialects to communicate in the first half of their lives, and dialects have become an indispensable part of their growth. Some senior people may not fully understand or use standard language. For them, dialect not only represents a habitual expression, but also reflects a sense of identity and comfort. Therefore, the use of dialect-speaking chatbots may help reduce the unfamiliarity of the older adults with technology and reduce the psychological distance between humans and artificial intelligence (Ng et al., 2024). However, empirical evidence is still limited on whether dialect-based communication can really promote the joint creation of value between older adults and chatbots. In response to this gap, this study raises the first research question: Can dialects enhance the value and creativity of older adults compared with standard languages?

For older adults, their memory ability and processing speed tend to decrease with age. According to cognitive load theory, the cognitive resources that individuals can use to deal with social and emotional clues are limited (Lieder & Griffiths, 2020). In human-computer interaction, any complexity of the interface, language or conversation process may bring a significant cognitive load, thus reducing their using intention (Mirhoseini et al., 2021). However, existing literature mainly regards the use of dialects as an emotional clue to enhance emotional connection, and rarely pays attention to its potential cognitive consequences (Martin & Jenkins, 2024). While standard language is widely understood, it may require additional cognitive processing for older adults who primarily use regional dialects in daily life. Dialects, by contrast, align more closely with their phonological and cultural schemas, enabling more direct understanding and reducing mental strain (Xiong et al., 2025).

Social presence describes how strongly an individual senses the social traits of another party during interactions, significantly shaping the human-AI interaction experience (J. E. Park et al., 2024). According to social presence theory, it is a key factor affecting trust, recognition (Janson, 2023) and value co-creation intention (Song et al., 2024). When language understanding itself occupies a large amount of cognitive resources, individuals’ perception of these social clues will be inhibited, thus weakening the experience of social presence (Rzepka et al., 2022). On the contrary, when cognitive efforts are reduced, individuals can allocate more cognitive resources to perceiving social clues and interpersonal warmth, thus enhancing social presence. Enhanced social presence boosts older adults’ acceptance of chatbots and motivates them to actively participate in interactions and value co-creation (Tosun et al., 2025). Accordingly, this study further proposes a serial mediating mechanism in which cognitive load and social presence explain the relationship between language form and older adults’ value co-creation intention.

Older adults often experience increased loneliness due to changes in social roles and shrinking social networks, and they tend to have greater needs for emotional support and social interaction (Wang et al., 2022). However, existing studies often view older adults as a whole and compare them with the adult group (Shiomi & Sumioka, 2025). In fact, some older adults have lower need for interaction due to stable social support. This difference in need for interaction directly influences the experience and behavioral performance of older adults when interacting with chatbots. Individuals with high need for interaction tend to seek human-like engagement to fulfill their social needs (Jo Bitner et al., 1997). Since social presence fosters the feeling of interaction with others (van Doorn et al., 2017), individuals become more receptive to chatbots that convey social presence (Flavián et al., 2024). In such cases, enhanced social presence will significantly augment their value co-creation intention. On the contrary, when individuals’ need for interaction is low, this effect may be weakened.

Through three experiments, the contributions of this research are divided into three main aspects. Firstly, previous research has mainly focused on older adults’ care and health companionship scenarios, such as medical counseling and psychological comfort. This study extends the exploration of chatbot dialects to the context of older adults’ tourism consumption, revealing their potential role in promoting value co-creation among older adults. Secondly, previous studies have mostly explained the interaction between older adults and intelligent machines from the perspective of emotional attachment. This study further emphasizes the cognitive effort mechanism triggered by dialects and reveals how the psychological input generated by older adults in understanding and processing dialect language promotes their value. Thirdly, this study explores the moderating role of individual differences in need for interaction on the above relationship, and points out that older adults with high need for interaction can benefit more from dialect communication. It advances the theoretical understanding of AI-driven customer service and affords practical strategies for creating more efficient, user-focused chatbot systems, which can promote broader acceptance and adoption of AI among older adults.

Theoretical Foundation and Literature Review

Cognitive Load Theory

Cognitive load refers to the cognitive demands imposed by a specific task on an individual’s cognitive system, encompassing mental load, mental effort, and performance (Martínez-Molés et al., 2024). It reflects the task’s requirement for cognitive capacity, the amount of cognitive resources available for temporary storage and information processing. In contrast, cognitive effort denotes the proportion of cognitive resources that an individual actually allocates and utilizes during task performance, reflecting the extent to which one engages in regulating and mobilizing cognitive resources to achieve optimal performance (Kahai, 2025). Cognitive load theory believes that the cognitive ability of individuals to process information is limited (Burmeister et al., 2022). When the task involves high cognitive load, individuals need to mobilize more psychological resources to maintain cognitive control, which is often accompanied by stronger psychological consumption and lower experience pleasure (Nettelhorst et al., 2017). On the contrary, when the task characteristics match the individual’s existing knowledge structure or language habits, the cognitive effort required is lower, and the individual can complete understanding and interaction with higher fluency. In this case, individuals will experience more relaxed information processing, smoother communication rhythm and more positive emotional response (Barta et al., 2023).

In the communication between humans and chatbots, language will have an impact on cognitive efforts. Standard or formal language styles may require cognition for older adults, especially when they are different from the language patterns used in daily life (Białek et al., 2020). However, dialect contains familiar phonetic and semantic clues, which are consistent with the user’s habitual language and cognitive patterns, thus reducing the mental labor required to understand and respond to chatbots. This reduction in cognitive efforts promotes smoother communication (Zong et al., 2025), enabling users to focus on the social and emotional aspects of interaction rather than basic understanding. Therefore, this study takes cognitive effort as a key psychological mechanism that connects the language form of chatbots with the willingness of older adults to create values together.

Social Presence Theory

Social presence is an individual’s perception of the real presence and social characteristics of others in social interaction, which is an important psychological factor affecting the effectiveness of interaction (Short et al., 1976). It can strengthen social ties in the process of interaction and bring more favorable results, such as enhancing trust and dependence (Yeboah & Afrifa-Yamoah, 2024), promoting emotional connection (J. Kim et al., 2022), and shaping decision-making (Jin et al., 2024; Vazquez et al., 2023).

When AI shows human-like characteristics, it may also be regarded as a social entity. For example, body, face and language may affect social response (J. Kim et al., 2022). In language, dialects are closely related to the daily life of individuals and carry rich cultural meanings (Elfenbein et al., 2007). As a key anthropomorphic feature, it can deepen the social and cultural understanding in interaction and strengthen social presence.

Dialect

A dialect is a language variant used by a specific region or social group that is different from the standard language (Laver, 1994; Sikorski, 2005). Its definition lies not only in the difference in language form, but also in its uniqueness in culture, history and social context (Joseph et al., 2020; Regan, 2020). China is a multi-ethnic and multilingual country with a wide variety of dialects. Chinese dialects include official dialects and nine other sub-dialects (Li et al., 2024). Since these dialects are geographically distant in distribution, they also show diversity in language elements such as phonetics, vocabulary and grammar (Liu et al., 2020).

Dialect is not only a tool for daily communication, but also carries rich cultural connotations and historical memories. Since China began to promote Mandarin in 1956 (Deng, 1999), dialects have remained the main means of communication in some families, especially older adults. As a cultural marker, dialects help maintain ethnic group identity and enhance the cultural identity and social cohesion of local residents (Bell et al., 2016; Papapavlou & Sophocleous, 2009). It remains crucial in maintaining intergenerational and neighborhood relationships. In addition, in business activities, using local dialects can build trust and familiarity, which helps to increase the willingness to buy products (He et al., 2022) and the marketing of handmade products (Mapes, 2020).

Hypotheses Development

The Impact of Language Form on Value Co-Creation Intention

Chatbots tend to be programs that utilize conventional languages in the conventional service environment. In the case of dialects, chatbots may surpass the expectation of users and instill a sense of surprise and emotional fulfillment in them. The expectation confirmation theory presumes that aspects outside of the expectations on the service experience can be considered to create increased satisfaction and intent to persist in participation (Wong et al., 2024). In the case of the older adults, dialects create a source of unforeseen warmth and familiarity. This feeling of familiarity stimulates consumers to form a desire to act in an interrelational way (Uslu & Caber, 2022).

In the meantime, dialects do not represent only a tool of communication. It bears with it culture and profundity of feeling (Sun et al., 2024). To most older adults, it is a reflection of their native language or their youthful one, full of personal memories and cultural affinities (Li et al., 2024). The theory of emotional resonance shows that any strong emotional associations will have a positive impact on cognitive and behavioral reactions and advance increased involvement (Zhang et al., 2024). Applying dialects may result in evoking the memory of family, community and past experiences, and enhance the strong emotional bond between the older adults and chatbots (Desnickaja, 1973). This emotional tie increases chatbots beyond an effective tool to a partner one should trust, which further facilitates value co-creation intention. Based on this, we propose:

H1. Compared to standard language, dialect enhances senior tourists’ value co-creation intention.

The Mediating Role of Social Presence

Language clues are one of the core factors that shape the sense of presence in society. Dialect is a language resource with both emotional and cultural indicatives. It not only conveys information, but also stimulates emotional resonance and identity (Falck et al., 2012). Therefore, when chatbots communicate with older adults in dialect, users often perceive a higher degree of social presence, as dialect activates long-stored social memories and a sense of cultural belonging. In addition, the perspective of anthropomorphism further reveals the mechanism between dialect and social presence. Research shows that forms of expression with personality characteristics or local colors in language will enhance the humanized perception of technical entities, thus strengthening users’ experience of its social presence (T. Kim et al., 2020; J. Kim et al., 2022). For the older adults, dialects are not only a medium of communication but also social cues that evoke a sense of familiarity and identity, which makes them more likely to view chatbots as social participants with personality and intentions. Therefore, we propose:

H2a. Compared to standard language, dialect leads to higher social presence.

Social presence provides customers with a strong sense of social experience (Breazeal, 2003). Thus, users who perceive a high social presence tend to view chatbots as trustworthy concrete social actors and ignore their artificial nature (McLean et al., 2020). This amplification in trust helps facilitate collaborative interactions with chatbots, which inspires a higher value co-creation intention. In addition, social presence advances the sense of realism and immersion of interaction, and makes users feel more pleased and more satisfied when communicating with chatbots (Huang et al., 2023). This improved quality of interaction may amplify senior tourists’ value co-creation intention (Luo et al., 2019). Researches have shown that the sense of social presence can positively predict a variety of interactive behavior intentions, such as usage intention (Dinh & Park, 2024; Fu et al., 2023) and donation intention (G. Park et al., 2023). Additionally, research in areas such as social TV (J. Kim et al., 2019), media marketing (T. Kim et al., 2020), and charitable fundraising (Lee et al., 2023) highlights that it has a universal motivating effect in social interaction decision-making. Therefore, we suggest:

H2b. Social presence mediates the relationship between language form and value co-creation intentions.

The Mediating Role of Cognitive Effort

When individuals process new information, relying on existing patterns or scripts can significantly reduce the cognitive load. As the cognitive mother tongue of many older adults, dialects are very familiar in terms of phonetic rhyme, vocabulary and expression, so they are more fluent in auditory perception and understanding, thus reducing the need for additional processing. This familiarity enables them to quickly extract existing knowledge structures and reduce the occupation of new cognitive resources when understanding and responding to chatbot information (Darejeh et al., 2021). Compared with the second language, the first language usually requires less cognitive effort (Mazzaggio et al., 2021). In addition, the natural, smooth and emotional resonance of language will improve the subjective processing fluency, which will bring psychological pleasure and sense of security. Dialects are usually closely related to family, childhood and local culture, and can stimulate emotional responses of kindness, relaxation and trust. This emotional comfort experience further reduces the psychological tension and alertness of individuals in interaction, thus indirectly reducing the perceived cognitive load. Therefore, we suggest:

H3a. Compared to standard language, dialect leads to lower cognitive effort.

The reduction of cognitive efforts not only optimizes the understanding process, but also may have an important impact on the social perception of interaction (Yang et al., 2020). Excessive cognitive load will inhibit social processing, making it difficult for individuals to focus on other social clues of others. In the interaction with chatbots, this means that it is easier for older adults to perceive the “human-like” existence of chatbots and experience a more natural and pleasant interactive atmosphere. This enhanced social presence further enables older adults to participate more comfortably and enhances their value co-creation intention (Uslu & Tosun, 2024). Therefore, we propose:

H3b. Cognitive effort and social presence serially mediates the relationship between language form and value co-creation intentions.

The Moderating Role of Need for Interaction

Need for interaction (NFI) refers to the need of interpersonal interaction with customers in service contexts (Dabholkar & Bagozzi, 2002). Senior tourists’ need for interaction varies based on task complexity, individual social tendencies, and the characteristics of the current situation (Xie et al., 2020). Traditionally, the need for interaction has been associated with real human interactions. However, the social presence created by chatbots can imitate this effect, making users feel that they are interacting with others, thus meeting their social needs (Flavián et al., 2024). Individuals with a high NFI are usually driven by emotional and social factors. They actively seek social participation, making it easier for them to accept the contact and emotional support that chatbots can provide (Morosan & DeFranco, 2019; Urumutta Hewage et al., 2024). When these users perceive a strong sense of social presence, it will encourage them to participate more deeply in value co-creation. In contrast, individuals with low NFI are more inclined to be task-oriented and cognitively driven. Their main goal is to complete the interaction efficiently, so the extra social clues provided by chatbots will bring unnecessary cognitive burdens. Social presence has a limited impact on value co-creation intention (Sarmah et al., 2017). From the perspective of motivation-situational fit, when social presence matches the individual’s interaction needs, it creates emotional consistency, thus amplifying the participation of co-creation. Based on this, we propose:

H4. Need for interaction moderates the relationship between social presence and value co-creation intentions. When need for interaction is high, social presence enhances value co-creation intention; when need for interaction is low, social presence does not increase value co-creation intention.

Building upon the four hypotheses outlined earlier, this research proposes a conceptual model (see Figure 1).

Figure 1.

The conceptual model.

Study Overview

This study tests the proposed hypotheses through three experiments, systematically exploring the impact of chatbot language form on senior tourists’ value co-creation intentions, with cognitive effort and social presence as mediating factors and the need for interaction as a moderating variable. Study 1 aimed to verify the direct effect of dialect (Cantonese) and standard language (Mandarin) as chatbot language form on senior tourists’ value co-creation intentions. The experimental design simulated a hotel interaction scenario between senior tourists and chatbots, focusing on the main effect of language form. Considering that the anthropomorphic features of chatbots may have an additional impact on users’ value co-creation intentions (Solakis et al., 2022), Study 2 retained the language type variable but altered the chatbot’s anthropomorphic appearance. The experimental context was set in a scenic spot service scenario to enhance the validity of the experiment. Study 2 further explored the mediating effect of cognitive effort and social presence. Study 3 focused on investigating the moderating role of need for interaction.

To ensure the ecological validity of the research and the representativeness of the sample, the research team randomly distributed questionnaires in several typical service scenarios in Guangzhou that have introduced AI technology (including scenic spots, hotels, and restaurants, etc.). The survey subjects were older adults aged 55 and above who could communicate in Cantonese on a daily basis. All questionnaires are guided and filled in by researchers on the spot, and the validity is verified immediately after recovery. For the cases of mid-term withdrawal or failure to pass the attention test, 2, 3, 5, 8, and 13 people were eliminated in pre-experiment 1, pre-experiment 2, research 1, research 2, and research 3 respectively, and the final effective sample sizes were 60, 60, 128, 200, and 240 respectively. Through experimental methods, this study comprehensively reveals the mechanism by which chatbot language form affect the value co-creation intention of older adults, and provides necessary theoretical insights and practical suggestions for the design of service-oriented chatbots.

This study involved older adult participants and was conducted in accordance with established ethical standards for research involving human subjects. Prior to participation, all participants received a written informed consent statement explaining the study purpose, procedures, expected duration, voluntary nature of participation, potential risks and benefits, and data confidentiality protections. Consent was obtained before participants proceeded to the questionnaire. No physical risks were involved, and psychological risks were limited due to the non-sensitive nature of the questions.

Study1

Pretest

Participants and Procedure

Before the formal experiment commenced, a pretest was conducted to select appropriate experimental materials. Experimental materials use virtual scenes to avoid interference from real destinations. As a dialect that is very different from Mandarin in pronunciation and expression, Cantonese has great influence in the world. There are about 60 million to 120 million native speakers of Cantonese (Chen et al., 2023). Therefore, Cantonese is used as the experimental language in this experiment.

Researchers recruited older adults aged 55 and above in Guangdong. Participants are guided into a hotel where robots provide services to guests. The chatbot will welcome guests, ask for reservation details, confirm room availability and provide assistance. Among them, the dialect group and the standard language group use Cantonese and Mandarin respectively to convey the same message. The total length of the two videos is 32 s (see Supplemental Appendix C). After watching the video, the participants judged the language form (Zhang et al., 2024) and completed the demographic information questions. All specific demographic data can be found in Supplemental Appendix A.

Results

Independent-sample t-tests results revealed a significant difference between the two groups regarding language form (M dialect = 6.17, M standard language = 1.83, t = 21.813, p < .01). The language form manipulation was successful.

Main Experiment

Participants and Procedure

The experimental process is basically the same as pretest1. After watching the video, participants were asked to evaluate the value and jointly create intentions and language forms, and complete the demographic information problem. In order to ensure the quality of the data, “What color is the robot?” is embedded in the questionnaire. Wait for the attention check items. At the end of the experiment, the participants were told that all the scenes were fictional.

Measures

To measure value co-creation intention (Cronbach’s α = .884), a three-item scale was employed (Im & Qu, 2017; Vermehren et al., 2023). All specific scale items can be found in Supplemental Appendix B, all of which were measured using a seven-point Likert scale.

Result

Manipulation Check

The independent-samples t-test analysis confirmed the manipulation’s effectiveness, with participants exposed to the dialect condition scoring significantly higher than those in the standard language condition (M dialect = 6.25, M standard language = 1.58, t = 42.994, p < .01).

Results for value co-creation intention. One-way ANOVA revealed a significant difference in value co-creation intention across language conditions, with participants in the dialect group reporting higher value co-creation intentions than those in the standard language group (M dialect = 4.36, SD = 0.67, M standard language = 3.98, SD = 0.53, F(1, 126) = 12.721, p < .01; see Figure 2). Therefore, H1 was supported.

Figure 2.

The Influence of language form on value co-creation intention.

Study 2

Pretest

Participants and Procedure

To strengthen the robustness of the findings, Study 2 adjusted the anthropomorphic characteristics of the chatbot and changed the setting to the destination scene, while ensuring that all other experimental conditions were consistent with Study 1. Both videos are 38 s long (see Supplemental Appendix C). In the video, chatbot Xiao Meng introduced the unique features of A scenic spot, offering guests a choice of two routes: nature walk Route A and cultural experience Route B. The older adults were recruited and randomly divided into two groups. After viewing the videos, participants evaluated the language form and provided demographic details.

Results and Discussion

Independent-sample t-tests results revealed a significant difference between the two groups regarding language form (M dialect = 6.03, M standard language = 1.80, t = 18.067, p < .01), validating the effectiveness of the language manipulation.

Main Experiment

Participants and Procedure

A total of 200 senior participants were randomly allocated into two groups, using a procedure similar to that of Study 1. The administered questionnaire encompassed assessments of value co-creation intention, social presence, cognitive effort, a language form manipulation check, and demographic details. Cognitive effort was assessed with three items from Benke et al. (2022). Social presence was measured utilizing a 4-item scale adapted from McLean et al. (2021). All scales demonstrated satisfactory reliability: visit intention (Cronbach’s α = .812), cognitive effort (Cronbach’s α = .816) and social presence (Cronbach’s α = .854).

Result

Manipulation Check

The independent-samples t-test analysis demonstrated a statistically significant difference between the two groups, with the dialect group scoring significantly higher than the standard language group (M dialect = 6.14, M standard language = 1.83, t = 48.510, p < .01).

Results for Cognitive Effort, Social Presence and Value Co-Creation Intention

To test H1 and H2, a one-way ANOVA was conducted. Results showed significant differences in cognitive effort (M dialect = 3.61, SD = 1.27, M standard language = 4.27, SD = 1.05, F(1, 198) = 16.213, p < .01), social presence (M dialect = 4.71, SD = 0.59, M standard language = 4.33, SD = 0.48, F(1, 198) = 24.365, p < .01) and value co-creation intention (M dialect = 4.55, SD = 0.58, M standard language = 4.13, SD = 0.51, F(1, 198) = 29.997, p < .01). Therefore, H1, H2a, H3a were supported.

Results of the Mediation Analysis with Cognitive Effort and Social Presence

Mediation analysis was conducted using PROCESS Model 6 with bootstrap samples and a confidence interval, controlling for participants’ education level. The results demonstrated that social presence significantly mediated the relationship between language form and value co-creation intention (β = .062, SE = 0.023, 95% CI [ 0.0183, 0.1094]), confirming H2b. Second, the analysis revealed a serial mediation process, where cognitive effort and social presence sequentially mediated the relationship between language form and value co-creation intention (β = .158, SE = 0.043, 95% CI [ 0.0792, 0.2462], see Figure 3). Therefore, H3b was supported.

Figure 3.

Mediating effect in Study 2.

Study3

Participants and Procedure

To explore the moderating effect of need for interaction, participants were randomly assigned to one of four experimental conditions in a 2 (language form: dialect vs. standard language) × 2 (need for interaction: high vs. low) between-subjects design, with 60 senior participants in each condition. The experimental procedure closely followed that of Study 2, with the addition of measures to assess need for interaction within the questionnaire. Need for interaction was measured using a four-item scale adopted by Xie et al. (2020). All scales demonstrated satisfactory reliability: need for interaction (α = .891), cognitive effort (α = .833), social presence (α = .906), and value co-creation intention (α = .871).

Result

Manipulation Check

The independent-samples t-test analysis revealed a statistically significant difference in language form between groups, with participants in the dialect group scoring significantly higher than those in the standard language group (M dialect = 6.24, M standard language = 2.00, t = 48.776, p < .01). Additionally, the need for interaction was determined by median split based on participants’ responses (Xie et al., 2020), categorizing them into low need for interaction and high need for interaction groups (M high NFI = 5.26, M low NFI = 3.98, t = 25.279, p < .01).

Results for Cognitive Effort, Social Presence and Value Co-creation Intention

Hypotheses H1 and H2 were tested using a one-way ANOVA. Results showed significant differences in cognitive effort (M dialect = 3.33, SD = 1.40, M standard language = 4.37, SD = 1.19, F(1, 238) = 38.242, p < .01), social presence (M dialect = 4.87, SD = 0.53, M standard language = 4.28, SD = 0.57, F(1, 238) = 69.464, p < .01) and value co-creation intention (M dialect = 4.61, SD = 0.67, M standard language = 4.07, SD = 0.58, F(1, 238) = 43.952, p < .01). Therefore, H1, H2a, H3a were supported.

Moderated Mediation Analysis

A moderated mediation analysis was performed using PROCESS Model 87 with 5,000 bootstrap samples, controlling for participants’ education level. The analysis uncovered a significant moderated mediation effect (β = .299, SE = 0.059, 95% CI [0.1913, 0.4229], see Figure 4). For participants with a high need for interaction, the serial mediating role of cognitive effort and social presence was significant (β = .260, SE = 0.052, 95% CI [0.1662, 0.3721]). Conversely, for those with a low need for interaction, the serial mediating role of cognitive effort and social presence was not significant (β = −.040, SE = 0.028, 95% CI [−0.1010, 0.0109]). These findings highlight the critical role of need for interaction, validating H4.

Figure 4.

Moderating effect of need for interaction between social presence and value co-creation intention.

Conclusion

Grounded in cognitive load theory and social presence theory, this study proposed and empirically validated a model explaining how chatbot language form influences value co-creation intentions. These studies provide convergent evidence that dialect can meaningfully shape older adults’ interactions with chatbots through both cognitive and social pathways. Specifically, Study 1 confirmed that chatbots using dialects positively enhance value co-creation intentions. Study 2 further explored the specific pathways of this influence, revealing that dialect affects value co-creation intention by decreasing cognitive effort and increasing social presence. Study 3 investigated how the need for interaction moderates this relationship. Consistent with prior studies, when need for interaction was high, the enhanced social presence induced by dialect led to stronger cooperative intentions. However, when need for interaction was low, the pathway from social presence to co-creation weakened. This highlights that while dialect can enhance social presence, its influence on value co-creation behaviors may be context-dependent, particularly in settings where interaction is less frequent or essential. Taken together, the evidence from this study shows a coherent pattern: dialect consistently enhances value co-creation intentions (Studies 1–3), primarily through increased social presence and decreased cognitive effort (Study 2), with this effect moderated by interaction motivation (Study 3).

Theoretical Implications

First, this study supplements the existing literature at the level of research object and situation. Previous robot research on the older adults often focused on care, health monitoring or auxiliary service scenarios, focusing on functional acceptance and usability (such as the effect evaluation of nursing robots) (Ahtinen et al., 2025; Maneeprom et al., 2019). In contrast, this study shifts the focus to tourism service settings, examining chatbots as the frontline agents interacting with senior tourist. It also brings language, a previously neglected interactive element, into the framework of value co-creation with older adults.

Second, previous studies have been conducted to explain the positive response of dialects from the perspective of emotion or ability, emphasizing the mechanism of anthropomorphism, emotional resonance or trust (Kühne et al., 2024; Li et al., 2024). This research expands social presence theory from the path of emotional attachment to the path of cognitive processing, providing a new perspective for understanding the role of language in technical interaction. It further examines the mediating role of cognitive effort and finds that, compared with the standard language, dialects significantly reduce the cognitive effort of older adults, thereby enhancing value co-creation through increased social presence. Unlike the traditional cognitive load theory, this article focuses on cognitive efforts, which focuses on the process of individuals actively mobilizing psychological resources in the face of external task needs. It expands the cognitive load theory from the perspective of task complexity to the perspective of active adjustment, and reveals how language characteristics can optimize the interactive experience by reducing effort input.

Third, this study provides new insights from the perspective of individual differences. Previous research on technology acceptance often relies on intergenerational comparisons (e.g., older versus younger adults) to examine age effects (Polak et al., 2018; Rossato et al., 2021), with less attention to heterogeneity within the senior population. This study identifies the need for interaction as a key moderating variable. It reveals that older adults with different motivational orientations respond differently to the same language strategy. Specifically, those with high interaction needs are more likely to translate enhanced social presence into actual co-creation behavior. In contrast, low-interaction individuals are more task-oriented, meaning additional social cues have a limited effect on them. The discovery not only enriches the theoretical discussion on user heterogeneity, but also provides a theoretical basis for user stratification and personalized strategies in service design.

Managerial Implications

Firstly, the choice of language form is a key strategy to improve user experience and value co-creation intention. This study shows that for the older adults who speak Cantonese, adding Cantonese elements to chatbots can improve their participation. Therefore, service-oriented chatbots can give priority to the development of dialect-based interactive functions according to the language preferences of the target older adults’ market. Especially in areas where dialects are widely used and Mandarin level is low, dialect robots can effectively narrow the psychological distance between older adults and technology. However, potential challenges such as voice recognition errors, regional variations, and mixed dialects should also be carefully considered. Successful language communication is the prerequisite for successful service. Therefore, technicians need to continuously optimize the voice database, adaptive recognition algorithm and provide user feedback mechanism to ensure communication accuracy.

Second, this paper finds that cognitive effort and social presence are key factors in promoting the interaction between older adults and chatbots. Therefore, technicians can focus on improving the human-like capabilities of chatbots by incorporating dialect recognition, emotional tone modulation, and personalized interaction design, which enhance users’ social presence. At the same time, it is necessary to reduce cognitive efforts. Although the study shows that dialect chatbots can benefit the older adults who speak dialects, managers should be cautious in areas or groups where dialects are not common or less popular.

Third, the influence of social presence is moderated by the customer’s need for interaction. The destination should adjust the interaction depth and language form of the chatbot according to different usage scenarios. In scenarios with high need for interaction, social elements such as emotional expressions can be enhanced to improve communication quality. In scenarios with low need for interaction, designers should focus on creating simple and efficient communication flows that quickly address tourists’ needs while minimizing unnecessary social engagement to enhance overall service efficiency.

Limitations and Future Research

First, the study focuses on Cantonese-speaking participants, as Cantonese’s wide usage and strong cultural identity provide a suitable context for investigating the emotional and cognitive effects of dialect use. However, dialects have different meanings in different situations in different regions. In some places, dialects are discriminated against, which is related to low social status. Future research could expand to other dialect regions (e.g., Wu, Minnan) or cross-linguistic and cross-cultural settings (e.g., Indonesia, India) to test the universality and boundary conditions of the proposed model.

Second, there are methodological limitations. To reveal the causal pathways between language form and value co-creation intention, the study employed a highly controlled experimental design conducted in virtual scenarios. While this approach ensures internal validity, it may overlook the nuanced experiences and emotional responses of older adults in real-world interactions, limiting ecological validity. Future research could use longitudinal and field-based approaches to validate the robustness of the observed effects. Studies with real robots in different service settings (e.g., scenic spots and hotels) could examine whether the positive effects remain consistent and sustainable over time across various situations. Additionally, integrating dialect into chatbot systems faces practical challenges, such as speech recognition accuracy and semantic understanding flexibility. Future research could adopt qualitative methods to capture the psychological and emotional meanings of dialects through individual narratives and interactional contexts. This approach would allow a more comprehensive understanding of how language forms exert their influence.

Third, although this study identifies how dialects affect the value co-creation intention, it does not delve deeply into the underlying mechanisms behind the reduction in cognitive effort. For instance, it does not empirically distinguish whether the reduction stems from fluency due to familiarity or fluency due to comfort. Additionally, individual difference variables such as language attachment, technology acceptance, and education level are not systematically included in the current model. These factors may influence older adults’ cognitive and emotional reactions to dialect-based chatbot interactions. Future research could incorporate these variables to clarify the relative contributions of familiarity, comfort, and user characteristics. This would lead to a more nuanced understanding of how language form affects older adults’ engagement in human-robot interaction.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440261423574 – Supplemental material for To Talk the Talk: Enhancing Older Adults’ Value Co-Creation Intention Through Dialect Use in Chatbots

Supplemental material, sj-docx-1-sgo-10.1177_21582440261423574 for To Talk the Talk: Enhancing Older Adults’ Value Co-Creation Intention Through Dialect Use in Chatbots by Han Chen, Shuaifang Liu, Yiyan Wang, Nuo Dong, Woody Kim and Jun (Justin) Li in SAGE Open

Footnotes

Acknowledgements

We wish to thank anonymous referees for their valuable comments and suggestions.

ORCID iD

Jun (Justin) Li

Ethical Considerations

Approval was obtained from the Ethics Committee of South China Normal University. No human or animals were harmed/used in the study. All participants willingly participate in the study to fill the questionnaire and their confidentiality was maintained during and after the data collection.

Consent to Participate

Written informed consent was obtained from all participants.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Social Science Foundation of China (Grant Number: 24BGL143), the Jiangsu Provincial College Student Innovation and Entrepreneurship Training Program (Grant Number X202510323078), and the Huaiyin Normal University College Student Innovation and Entrepreneurship Project (2025).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data will be made available upon request sent to the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

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