Omnichannel Bot Communication, Brand Awareness, and Purchase Intention: A Descriptive S-O-R Analysis in the GraPARI KBP Service Context

Abstract

Digital transformation has increased the demand for fast, accessible, and personalized service communication, making omnichannel bot communication increasingly relevant to telecommunications customer service. This study aimed to describe response patterns related to omnichannel bot communication, brand awareness, and purchase intention among Telkomsel customers in the Kota Baru Parahyangan (KBP) service context and to organize these findings using the Stimulus–Organism–Response (S-O-R) framework. A cross-sectional descriptive quantitative survey was conducted in March 2026 using Google Forms distributed through WhatsApp. Respondents included Telkomsel customers who visited GraPARI KBP and customers from the surrounding KBP community. Non-probability recruitment resulted in 133 complete responses. Thirteen researcher-developed items were analyzed at the individual-item level using frequencies, percentages, means, and standard deviations. The findings showed that personalization had the largest observed mean among the stimulus items (M = 4.73), while ease of access recorded a lower mean (M = 3.20). Brand recall showed a highly concentrated response pattern (M = 4.96). Purchase-intention responses were less uniform, particularly for preferential/consideration intention (M = 3.11), while exploratory intention showed no response variation. The study provides context-specific empirical evidence by using S-O-R as a conceptual framework for organizing descriptive findings rather than testing causal relationships. The findings highlight the relevance of personalization and service communication while also emphasizing the need for validated multi-item measures and inferential research to examine relationships among the S-O-R domains.

Keywords: omnichannel bot communication; brand awareness; purchase intention; stimulus–organism–response; telecommunications service

Introduction

Digital transformation has changed the way organizations communicate with customers and manage customer relationships. The growing use of artificial intelligence, automation, and integrated communication channels has enabled service interactions to become faster, more accessible, and increasingly personalized. Digital communication is therefore no longer limited to delivering information but has become part of the customer experience across interconnected service touchpoints (Lemon & Verhoef, 2016; S. M. Rahman et al., 2022; Siebert et al., 2020). In an omnichannel environment, the integration of digital and physical touchpoints is particularly important for creating a more consistent customer journey (Chung et al., 2022; Silva et al., 2024). This development is particularly relevant to the telecommunications industry, where customer-service interactions involve a combination of technical assistance, product and service information, activation, account administration, and commercial communication. AI-

based technologies can support routine customer interactions, while human employees continue to play an important role in addressing more complex service needs (Davenport et al., 2020; M.-H. Huang & Rust, 2021; Larivière et al., 2017). Within this setting, bots integrated into omnichannel services can support initial customer responses, provide information, and facilitate access to services. The use of these technologies also provides an opportunity to examine how customers respond to different communication features and how these responses coexist with brand-related and purchase-related perceptions. Previous studies have examined various aspects of chatbot-based services, including service quality, satisfaction, trust, loyalty, and purchase-related outcomes (Ashfaq et al., 2020; Chen et al., 2022; Hsu & Lin, 2023; Kbaier et al., 2025; M. S. Rahman et al., 2024). Research on omnichannel services has similarly focused on customer experience, channel integration, engagement, and consumer behavior (Gahler et al., 2023; S. M. Rahman et al., 2022, 2025). These studies have contributed to understanding the relationships between digital service experiences and customer responses. However, descriptive evidence remains limited in telecommunications customer-service settings where omnichannel bot communication, brand awareness, and different aspects of purchase intention are examined together. This context is relevant because telecommunications service touchpoints combine servicerelated and commercial interactions, which may produce different customer-response patterns from those commonly observed in retail settings. The Stimulus–Organism– Response (S-O-R) framework developed by Mehrabian & Russell (1974) provides the conceptual basis for organizing these dimensions. In this study, omnichannel bot communication represents the stimulus domain, brand awareness represents the organism domain, and purchase intention represents the response domain. Omnichannel bot communication is examined through interactivity, response speed, ease of access, information quality, and personalization. Brand awareness is represented by brand recognition, brand recall, brand knowledge, and brand attention, while purchase intention includes transactional, preferential/consideration, exploratory, and referral intentions. The S-O-R framework is used to organize and interpret these domains within the study context rather than to establish causal relationships among them. Based on this framework, the study addresses four research questions: (RQ1) How are responses to the five omnichannel bot communication indicators distributed among the respondents? (RQ2) How are responses to the four brand awareness indicators distributed? (RQ3) How are responses to the four purchase intention indicators distributed? (RQ4) How can the observed response patterns across omnichannel bot communication, brand awareness, and purchase intention be organized and interpreted through the S-O-R framework? Novelty Statement. Previous studies have primarily examined relationships among omnichannel experience, customer engagement, chatbot service quality, and behavioral outcomes, particularly through relational or inferential approaches. This study provides a different perspective by applying the S-O-R framework to organize itemlevel descriptive evidence from a telecommunications customer-service setting in which service and commercial interactions occur within the same customer touchpoint. The novelty of the study therefore lies in its contextual and empirical application of S-O-R rather than in testing or extending the causal structure of the framework. Accordingly, this study aims to describe response patterns related to omnichannel bot communication, brand awareness, and purchase intention among Telkomsel customers in the Kota Baru Parahyangan (KBP) service context and to interpret

these findings using the S-O-R framework. By focusing on itemlevel responses, the study provides contextual evidence of how communication, brand-related, and purchase-related responses are reflected within a telecommunications customer-service environment, while providing a basis for further research using validated measurement instruments and inferential designs. The development of omnichannel service has evolved from multichannel and cross-channel arrangements toward more integrated customer journeys. Earlier studies distinguish these channel structures and emphasize the importance of coordination across customer touchpoints (Beck & Rygl, 2015; Piotrowicz & Cuthbertson, 2014; Verhoef et al., 2015). Customer-journey research further shows that service experiences develop across multiple interactions rather than through a single encounter (Følstad & Kvale, 2018). In Indonesia, the continued expansion of internet use also provides a relevant setting for examining digitally mediated customer-service communication (Asosiasi Penyelenggara Jasa Internet Indonesia, 2025). The dimensions examined in this study are also grounded in established brand and consumer-response literature. Brand awareness is closely related to consumers’ ability to recognize and recall a brand and to the knowledge structures associated with it (Aaker, 1991; Keller, 1993). Purchase-related evaluations, meanwhile, can be shaped by information about brands, products, and service encounters (Dodds et al., 1991). In omnichannel settings, technology acceptance and channel use have also been associated with purchase intention (Juaneda-Ayensa et al., 2016). These perspectives support examining brand awareness and purchase-related intentions as distinct response domains rather than assuming that favorable service evaluations automatically translate into purchase preference. Research on conversational agents likewise indicates that customers use chatbots for practical service and information needs, while perceptions of chatbot quality, trust, and the nature of human-like interaction can shape customer evaluations (Brandtzaeg & Følstad, 2017; D. Huang et al., 2024; Jin & Youn, 2021; Othayoth & Khanna, 2024; Suh & Yoon, 2019). Artificial intelligence in service can complement human service roles, but its value depends on the characteristics of the service encounter (M.-H. Huang & Rust, 2018). From an S-O-R perspective, prior studies have used environmental or informational stimuli to explain subsequent internal evaluations and behavioral intentions in digital contexts (Zhou et al., 2022; Zhu et al., 2020). In the present study, these streams of literature inform the selection and conceptual organization of the item-level indicators, while the empirical analysis remains descriptive.

Methods

Research Type and Design

This study employed a cross-sectional descriptive quantitative design. Data were collected at a single point in time in March 2026, with individual respondents serving as the unit of analysis. The findings were analyzed at the item level and organized according to the Stimulus–Organism–Response (S-O-R) framework. Omnichannel bot communication indicators (X1–X5) represented the stimulus domain, brand awareness indicators (Y1.1–Y1.4) represented the organism domain, and purchase intention indicators (Y2.1–Y2.4) represented the response domain. In line with the descriptive nature of the study, the S-O-R framework was used to structure the interpretation of the findings rather than to test relationships among the three domains. Therefore, the study did not estimate causal effects, mediation effects, or structural relationships

Population and Sample

The study involved Telkomsel customers within the Kota Baru Parahyangan (KBP) service context. Eligible respondents included Telkomsel customers who visited GraPARI KBP and Telkomsel customers from the surrounding KBP community who received the questionnaire link. Respondents were recruited using a non-probability sampling approach based on accessibility and relevance to the study context. A total of 133 respondents completed the questionnaire, and all complete responses were included in the analysis. Given the nonprobability sampling approach and the specific geographical and service context of the study, the findings are interpreted within the study sample and are not intended to represent the broader population of Telkomsel customers.

Research Location and Context

The study was conducted at GraPARI Kota Baru Parahyangan (KBP), West Bandung Regency, West Java. The location was selected purposively because GraPARI KBP serves as a telecommunications service point where customers access mobile and fixed-service information, service activation, technical assistance, and other customerservice and commercial support. Routine operational data from December 2025 to February 2026 were included to provide contextual information about customer activity at the study location. These data were obtained from GraPARI KBP operational records and were used solely to describe the service context rather than as variables in the primary analysis

Research Instrument and Measurement

Primary data were collected using a researcher-developed questionnaire consisting of 13 analytical items derived from the theoretical concepts and relevant literature reviewed in this study. The items were developed specifically for the research context rather than adopted from a previously validated scale. Responses were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Within the S-O-R framework, the stimulus domain comprised five indicators of omnichannel bot communication: interactivity (X1), response speed (X2), ease of access (X3), information quality (X4), and personalization (X5). The organism domain comprised four brand awareness indicators: brand recognition (Y1.1), brand recall (Y1.2), brand knowledge (Y1.3), and brand attention (Y1.4). The response domain comprised four purchase intention indicators: transactional intention (Y2.1), preferential/consideration intention (Y2.2), exploratory intention (Y2.3), and referral intention (Y2.4). Each indicator was represented by a single questionnaire item and was therefore analyzed individually rather than combined into a composite construct score. The instrument was not subjected to a formal pilot study or factor validation prior to the main survey. A post-hoc internalconsistency diagnostic produced Cronbach’s alpha coefficients of .093 for the stimulus domain, −.325 for the organism domain, and .212 for the response domain. These coefficients, together with the absence of variance in Y2.3, did not support treating the respective items as internally consistent composite scales. Accordingly, Cronbach’s alpha was not used as evidence of construct reliability, and the analysis and interpretation were retained at the item level. This approach also avoids implying psychometric properties that were not established by the present instrument. Particular attention was given to the interpretation of preferential/consideration intention (Y2.2). The original item stated, “Saya cenderung mempertimbangkan produk atau layanan Telkomsel sebagai pilihan ketika membutuhkan layanan telekomunikasi” (“I tend to consider Telkomsel products or services as an option when I need telecommunications services”). Accordingly, Y2.2 was

interpreted specifically as the respondent’s tendency to consider Telkomsel as an available option when telecommunications services are needed, rather than as an indication that Telkomsel is the respondent’s primary or preferred choice over competing providers.

Data Collection Procedure

Data were collected in March 2026 through an online questionnaire developed using Google Forms and distributed via WhatsApp. The questionnaire was completed by Telkomsel customers who visited GraPARI KBP and by Telkomsel customers living or conducting activities in the surrounding Kota Baru Parahyangan area. Before completing the questionnaire, respondents were informed that the survey aimed to identify their internet needs, service-related problems, and the types of services they required. They were also informed that completing the questionnaire would take approximately 2–3 minutes.

A total of 133 respondents completed the questionnaire voluntarily. All responses were complete and were included in the analysis, with no cases excluded due to missing data. Before analysis, the data were reviewed to ensure completeness and to examine the distribution of responses across the questionnaire items. The survey did not employ a documented technical mechanism to identify duplicate submissions; therefore, this aspect was considered when interpreting the response patterns and is acknowledged as a limitation of the study

Data Analysis

Data were processed and analyzed using Microsoft Excel. Before the analysis, the dataset was reviewed for completeness, response range, and consistency across the 13 questionnaire items. All 133 responses were complete, and all recorded values were within the five-point Likert scale. Descriptive analysis was conducted at the item level using frequencies, percentages, means, standard deviations, minimum values, and maximum values. All five response categories were retained in the frequency analysis, including categories with zero responses. Means and standard deviations were used to summarize the distribution of responses for each item. Because the questionnaire consisted of single-item indicators and several items showed limited response variation, these statistics were interpreted descriptively rather than used to classify responses into categories such as high or low. The S-O-R framework was used to organize the findings into stimulus, organism, and response domains. Comparisons across these domains were descriptive and were not intended to establish statistical associations, temporal relationships, mediation, or causal effects. An additional internal-consistency check was conducted to assess whether the items within each domain could reasonably be combined into composite measures. Cronbach’s alpha values were .093 for the stimulus domain, −.325 for the organism domain, and .212 for the response domain. These results did not support combining the items into composite scores. In addition, Y2.3 showed no response variation. Therefore, the final analysis was conducted and reported at the individualitem level, and no claims of construct reliability or causal relationships were made

Result and Discussion

Primary Questionnaire Findings

The primary findings are presented according to the research questions and the three domains of the S-O-R framework: stimulus, organism, and response. Table 1 summarizes the response distribution and descriptive statistics

for the 13 questionnaire items completed by 133 respondents. The results include frequencies and percentages for all five Likert-scale categories, together with the mean and standard deviation for each item. Across the 13 items, responses were concentrated within scores 3 to 5, with no respondents selecting scores 1 or 2.

Omnichannel Bot Communication (Stimulus)

Observed means varied across the five stimulus items. Personalization (X5) had the largest observed mean (M = 4.73, SD = 0.57), with 105 respondents (78.95%) selecting score 5. Interactivity (X1) recorded M = 4.15 (SD = 0.36), and information quality (X4) recorded M = 4.12 (SD = 0.33). Rawdata verification showed that response speed (X2) was not invariant as previously reported: three respondents selected score 3 and 130 selected score 4 (M = 3.98, SD = 0.15). Ease of access (X3) recorded M = 3.20 (SD = 0.40), with 107 respondents selecting score 3 and 26 selecting score 4. These are descriptive comparisons of single items and are not inferential rankings.

Brand Awareness (Organism)

Brand recall (Y1.2) showed the most concentrated score5 response in this domain: 128 respondents (96.24%) selected score 5 (M = 4.96, SD = 0.19). Brand recognition (Y1.1), brand knowledge (Y1.3), and brand attention (Y1.4) were concentrated mainly at score 4. These results describe high self-reported item responses within this sample; they do not establish that omnichannel bot communication increased brand awareness. The restricted dispersion also limits discrimination among respondents.

Purchase Intention (Response)

The four purchase intention items showed different response patterns. Transactional intention (Y2.1) and referral intention (Y2.4) each recorded a mean of 4.01 (SD = 0.09), with 132 respondents selecting score 4 and one selecting score 5. Both items showed identical response distributions in this sample, indicating limited ability to distinguish between the two dimensions at the item level. Exploratory intention (Y2.3) showed no response variation, as all 133 respondents selected score 4 (M = 4.00, SD = 0.00). This lack of variation limits the item’s ability to differentiate responses among participants. Preferential/consideration intention (Y2.2) showed a different pattern, with a mean of 3.11 (SD = 0.45). Most respondents (125; 93.98%) selected score 3, while one

respondent selected score 4 and seven selected score 5. Given the wording of the item, this distribution reflects predominantly neutral responses regarding the consideration of Telkomsel products or services when telecommunications services are needed. Therefore, the result is interpreted as a tendency toward neutral consideration rather than as evidence that respondents reject Telkomsel as their preferred choice.

Complementary Survey Findings

The questionnaire also included complementary questions concerning respondents’ service needs and interests. These findings were obtained from the same 133 respondents and are presented as additional descriptive information to provide further context for the primary findings. The percentages reported in Table 2 are based on the total sample (N = 133) and represent responses to the respective service-needs items. These findings were analyzed separately from the 13 S-O-R indicators, and no statistical relationships between the two sets of items were examined. Contextual Operational Evidence

Routine operational data from GraPARI KBP are presented to provide additional context regarding the service setting during the study period. Customer visits totaled 1,305 in December 2025, 1,295 in January 2026, and 1,097 in February 2026, with an average of 1,232.3 visits per month. GraPARI KBP operated from Monday to Saturday, from 08:00 to 17:00. These operational data are presented as contextual information and were not included as variables in the analysis of the research questions (Table 3).

The findings provide a descriptive account of omnichannel bot communication, brand awareness, and purchase intention among Telkomsel customers in the GraPARI KBP service context. The results are interpreted at the item level because the questionnaire indicators were not established as validated composite constructs. For RQ1, personalization showed the largest observed mean among the five omnichannel bot communication items, while ease of access showed a lower response pattern. Response speed was concentrated within a narrow range. The complementary service-needs findings also indicate respondents’ interest in chator WhatsApp-based support and personalized recommendations. These findings suggest that personalization and communication accessibility are relevant aspects of the customer experience in this setting. However, the complementary questions and the stimulus indicators were analyzed separately; therefore, no statistical

relationship between these responses can be inferred. For RQ2, brand recall showed the strongest concentration at score 5, while brand recognition, brand knowledge, and brand attention were concentrated mainly at score 4. This pattern indicates that respondents reported relatively favorable brand-related responses, particularly for brand recall. Nevertheless, the findings do not demonstrate that omnichannel bot communication increased brand awareness, as the study did not test relationships between the stimulus and organism domains. The limited variation observed across several brand-awareness items also restricts interpretation beyond the individual-item level. For RQ3, the purchase intention items showed less uniform response patterns. Transactional intention and referral intention were concentrated at score 4, while exploratory intention (Y2.3) showed no variation, with all respondents selecting the same response category. Preferential/consideration intention (Y2.2), in contrast, was concentrated at score 3. The wording of Y2.2 asked whether respondents would consider Telkomsel products or services as an option when telecommunications services were needed, rather than whether Telkomsel was their first choice compared with competing providers. The concentration of responses at the neutral category may therefore reflect differences in respondents’ consideration tendency, the wording of the item, or both. Similarly, the absence of variation in Y2.3 limits its ability to distinguish differences among respondents. For RQ4, the findings can be organized descriptively within the S-O-R framework, with omnichannel bot communication features representing the stimulus, brand-awareness responses representing the organism, and purchase-related responses representing the

response. In this study, however, S-O-R serves as a conceptual framework for organizing the observed response patterns rather than as a statistically tested causal model. The crosssectional descriptive design does not establish associations among the three domains, temporal ordering, mediation, or a stimulus–organism–response mechanism. The GraPARI setting also provides a specific context for interpreting these findings. Customer interactions at GraPARI involve not only product information and commercial communication but also technical assistance, account administration, and other service-related needs. This differs from retail environments in which customer journeys are primarily centered on product search and purchase. Such a mixed service context may help explain why respondents placed considerable emphasis on personalization and communication-related features while purchase-intention responses were not distributed uniformly. Previous omnichannel studies have emphasized the importance of integrated experiences across customer touchpoints (Chung et al., 2022; Gahler et al., 2023; S. M. Rahman et al., 2022; Silva et al., 2024), while chatbot research has highlighted the role of service quality and conversational experience in customer responses (Ashfaq et al., 2020; Chen et al., 2022; Hsu & Lin, 2023; M. S. Rahman et al., 2024). The present findings add evidence from a telecommunications service setting by showing that favorable responses to communication-related features do not necessarily correspond to uniform patterns across purchase-intention indicators. From a practical perspective, the findings provide a basis for further evaluation of a hybrid bot–human service approach. Respondents’ interest in chat-based support and personalized recommendations suggests that these features may be relevant when developing customer service at GraPARI KBP. However, the present study did not test the effectiveness of such an intervention. Further evaluation using serviceperformance indicators, customer behavioral data, or experimental designs would be required before conclusions regarding its effectiveness could be established. Several measurement limitations should be considered when interpreting the findings. The 13 questionnaire items were developed from the theoretical concepts and literature used in this study, with each dimension represented by a single item. The instrument was not formally pilot-tested or subjected to factor validation. Post-hoc internal-consistency analysis produced Cronbach’s alpha values of .093 for the stimulus domain, −.325 for the organism domain, and .212 for the response domain. These coefficients do not support combining the individual indicators into internally consistent composite scales; consequently, the analysis was retained at the item level. The response distributions also showed limited variation. None of the 13 analytical items received scores of 1 or 2, Y2.3 showed zero variance, and several items had small standard deviations. Y2.1 and Y2.4 also produced identical response patterns across the 133 cases. In addition, only 23 unique response patterns were identified across the 13 items, with one pattern appearing in 75 cases. Because the survey did not employ a documented technical mechanism for identifying duplicate submissions, identical response patterns cannot be interpreted as duplicate responses. They may instead reflect respondent homogeneity, response-set tendencies, restricted measurement sensitivity, or characteristics of the item wording. These limitations reinforce the need to interpret the findings as preliminary item-level evidence. Future research should refine and pilot the questionnaire, use multiple indicators for each construct, establish its psychometric properties, and apply an appropriate inferential design before examining relationships among the S-O-R domains.

Conclusion

This study examined the responses of 133 Telkomsel customers in the KBP service context to items measuring omnichannel bot communication, brand awareness, and purchase intention, with the S-O-R framework used to organize the findings. The results showed different response patterns across the three domains. Personalization recorded the largest observed mean among the stimulus items, brand recall showed the strongest concentration of favorable responses, while the purchase intention items were less consistent. In particular, preferential/consideration intention was concentrated at the neutral response, whereas exploratory intention showed no variation. These findings indicate that favorable responses to communication features and brand-related items do not necessarily correspond to stronger purchase intention and should not be interpreted as evidence of causal relationships among the S-O-R domains.

Academic Contribution. This study provides empirical evidence from a telecommunications customer-service setting, a context in which omnichannel communication serves both service and commercial purposes. It also demonstrates the use of the S-O-R framework as a conceptual structure for interpreting descriptive item-level findings without assuming causal relationships or model validation. In addition, the findings highlight the importance of measurement quality, particularly when single-item indicators produce highly concentrated response distributions.

Practical Implication. The findings indicate that personalization, chat-based support, and tailored information are relevant areas for further development in the GraPARI customer-service experience. These findings may inform the evaluation and refinement of omnichannel services, including the integration of bot-based and human-assisted communication. However, the effectiveness of such service interventions should be evaluated further using customer behavioral data, service-performance measures, or research designs capable of assessing changes over time.

Limitations and Future Research. This study was limited to a single service context and used non-probability recruitment and a researcher-developed questionnaire in which each dimension was represented by a single item. The instrument was not formally pilot-tested or subjected to construct validation, and several items showed restricted response variation. Future studies should refine the questionnaire, employ validated multi-item measures, conduct pilot and psychometric testing, and involve broader and more clearly defined samples. Once adequate measurement properties have been established, future research may examine relationships among omnichannel bot communication, brand awareness, and purchase intention using appropriate inferential, longitudinal, or experimental designs.

Acknowledgements

The authors would like to thank the GraPARI Kota Baru Parahyangan team, including the Team Leader, Frontliner Support, and Customer Service staff, for their operational support during the study. The authors also acknowledge PT Agrabudi Komunika and PT Telekomunikasi Selular (Telkomsel) for providing contextual operational data and supporting the research activities. The development of the questionnaire, data analysis, interpretation of the findings, and preparation of the manuscript were carried out independently by the authors.

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Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multichannel retailing. Journal of Retailing, 91(2), 174–181. https://doi.org/10.1016/j.jretai.2015.02.005

Zhou, S., Li, T., Yang, S., & Chen, Y. (2022). What drives consumers’ purchase intention of online paid knowledge? A stimulus-organism-response perspective. Electronic Commerce Research and Applications, 52, 101126. https://doi.org/10.1016/j.elerap.2022.101126

Zhu, L., Li, H., Wang, F.-K., He, W., & Tian, Z. (2020). How online reviews affect purchase intention: A new model based on the stimulus-organismresponse (S-O-R) framework. Aslib Journal of Information Management, 72(4), 463–488. https://doi.org/10.1108/AJIM-11-2019-0308

Declarations

Author Contributions

The authors were responsible for conceptualization, research design, instrument development, data collection and processing, analysis and interpretation, and preparation and revision of the manuscript. Both authors read and approved the final manuscript.

Funding

This was an independent study and received no specific funding, grant, or financial support from any institution, company, or external party.

Conflict of Interest

The authors declare no conflict of interest. PT Telekomunikasi Selular and PT Agrabudi Komunika provided contextual data and activity support but were not involved in questionnaire development, data analysis, interpretation of the findings, or manuscript preparation/review.

Generative Artificial Intelligence Use

Generative artificial intelligence was used only as a writingsupport tool to assist with language clarity, organization, and consistency. It was not used to generate, alter, or manipulate research data. The authors verified the manuscript and remain responsible for all scientific content, analyses, interpretations, and final decisions.

Ethics Approval and Informed Consent

Before completing the questionnaire, respondents were provided with information about the purpose of the survey and the estimated completion time of 2–3 minutes. Participation was voluntary, and respondents indicated their willingness to participate by completing and submitting the online questionnaire. No personally identifiable information is reported in this article, and the findings are presented only in aggregate form. The study was not submitted for formal review by an institutional ethics committee, and no ethics approval or exemption reference number was issued. The introductory information provided to respondents did not include a separate written consent form or detailed provisions regarding withdrawal procedures, data use, or researcher contact information. These limitations are acknowledged to ensure transparency regarding the ethical procedures applied in this study.

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Suh, C. J., & Yoon, J. O. (2019). The effects of perceived chatbot service quality on customer satisfaction and word of mouth. Journal of Service Management Society, 20(1), 201–222. https://doi.org/10.15706/jksms.2019.20.1.010

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Zhou, S., Li, T., Yang, S., & Chen, Y. (2022). What drives consumers’ purchase intention of online paid knowledge? A stimulus-organism-response perspective. Electronic Commerce Research and Applications, 52, 101126. https://doi.org/10.1016/j.elerap.2022.101126

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