Purpose: AI and big data have transformed customer behaviors and marketing strategies, with businesses using customer data for personalized services that improve efficiency and reduce workload. For customers, AI enhances convenience by automating tasks. Post-COVID, AI adoption accelerated, with 42% of businesses using chatbots that leverage natural language processing and machine learning. In banking, 51.4% of representatives acknowledge chatbots’ effectiveness, while retail implementation has increased 31% since 2021, now reaching 73% of global companies. Current research limitations include focus on functional rather than intelligent features, limited empirical research on personalization and psychological responses, insufficient research on empathy’s influence, minimal privacy concern research, and poor understanding of interaction factors. This study examines chatbot personalization’s impact on perceived empathy, how empathy affects customer citizenship behavior, and privacy concerns’ moderating effect. The research draws on CASA theory, AI Job Replacement Theory, AI Device Use Adoption model, and Information Boundary Theory. Based on this theoretical background, the study developed the following hypotheses: [H1] Perceived personalization of AI chatbot services will increase customer citizenship behavior.
[H2] Perceived empathy will mediate the effect of perceived personalization of AI chatbot services on customer citizenship behavior.
[H3] Privacy concerns will moderate the mediating pathway where perceived personalization of AI chatbot services affects customer citizenship behavior through perceived empathy.
Research design, data and methodology: This study surveyed Korean adults who had used AI chatbots, following Hair et al.’s (2010) guidelines recommending 5–15 responses per observed variable for structural equation modeling. With 14 variables, they targeted 140–210 responses. The online survey ran February 1–15, 2024, through a domestic panel agency, screening for those who had used AI chatbot customer service within the past year. From 234 participants, 24 were excluded for short response times or irrelevant answers, leaving 210 respondents. The sample was 64% male with an average age of 32.1 years, distributed across age groups: 30s (39%), 20s (31%), 40s (23%), and 50s (7%). For chatbot service frequency, 41.1% reported “less than 10 times,” 21.9% “20+ times,” 19.4% “less than 5 times,” 10.6% “less than 15 times,” and 7.0% “less than 20 times.” With 81.6% having used chatbots at least 5 times, the sample confirmed their widespread use. Measurement items used a 5-point scale, with perceived personalization measured by four items, perceived empathy by three items, customer citizenship behavior by four items, and privacy concerns by three items.
Results: Using Hayes’ PROCESS MACRO Model 4, the total effect of perceived personalization on perceived empathy was significant (b=0.671, p<0.001), supporting Hypothesis 1. Bootstrapping analysis (n=1,000) showed a significant indirect effect of perceived empathy (0.273, 95% CI=[0.186, 0.371]) and significant direct effect (95% CI=[0.282, 0.514]), confirming perceived empathy partially mediates the influence of perceived personalization on customer citizenship behavior, supporting Hypothesis 2 (show <Table 1>).
To verify whether privacy concerns moderate the indirect effect of perceived personalization of AI chatbot services on customer citizenship behavior through perceived empathy, Hayes' PROCESS MACRO Model 7 was used. The analysis showed a statistically significant interaction effect between perceived personalization and privacy concerns on perceived empathy (b=–0.160, p<0.001). This confirms that the influence of perceived personalization on perceived empathy varies depending on the level of privacy concerns (see <Table 2>).
A conditional effect analysis examined the moderating effect of privacy concerns, which were classified based on values one standard deviation above and below the mean (3.2 ±1SD). At low privacy concern levels (–1SD), the indirect effect was 0.022 (95% CI=[–0.058, 0.104]), slightly positive but not statistically significant. At mean level, the indirect effect turned negative at –0.070 (95% CI=[–0.104, –0.035]) and was statistically significant. At high privacy concern levels (+1SD), the indirect effect became more negative at –0.162 (95% CI=[–0.254, –0.074]) and remained significant. The moderated mediation index was –0.115 (95% CI=[–0.175, –0.058]), confirming that the indirect effect significantly varies with privacy concern levels. This demonstrates that increasing privacy concerns not only decreases but may reverse the effect of AI chatbot personalization on customer citizenship behavior through empathy (see <Table 3>).
Conclusions: This study examines AI chatbot personalization and empathy effects on customer behavior in omnichannel retail, offering four key theoretical insights: (1) chatbots foster emotional connections beyond technical integration, (2) empathy drives customer responses per CASA theory, (3) personalization influences customers through empathy per the AI Device Use Acceptance model, and (4) privacy concerns moderate personalized service effects. Practical implications include strengthening emotional elements in chatbot design, implementing phased personalization strategies based on relationship development, creating “digital personal shopper” experiences, maximizing ROI through customer citizenship behaviors, and introducing emotional recognition technologies at appropriate customer journey points. Limitations include treating empathy as single-dimensional and not fully considering control variables like usage frequency and demographics. The study contributes by expanding chatbots’ role from response tools to value co-creation facilitators, extending research from functional to social-psychological dimensions, and analyzing the personalization-privacy paradox in retail contexts.
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