Despite the practical relevance of many tourism research studies, organizations and policymakers often struggle to integrate them due to time constraints, language barriers, limited resources, and interaction challenges. Generative artificial intelligence (GenAI) offers new capabilities to overcome these barriers. We propose a GenAIenabled knowledge translation process with three stages: (i) research curation to identify and translate relevant literature; (ii) content creation to produce materials; and (iii) market research using synthetic guests to pre-test their effectiveness. We examine the capabilities, limitations, and ethical implications of GenAI at each stage, drawing on a systematic review of GenAI and tourism literature. To equip managers with the knowledge and tools needed to harness research-based insights effectively, we offer a toolkit comprising a handbook, a promptbook, and tailored GPT models. The toolkit enables tourism and hospitality practitioners to apply research findings in their decision-making and content strategies without direct stakeholder interaction.

We introduce the concept of invasive rapid innovations, technologies characterized by both their novelty and the speed of their development, which significantly affect daily routines, personal privacy, or bodily autonomy. The introduction of such innovations is typically accompanied by limited knowledge and heightened uncertainty. Consequently, individuals' assessments of the benefits, costs, and risks associated with adopting these innovations are often shaped by broader factors, including their trust in government, perceptions of the severity of the threat the innovations are designed to address, and their aversion to ambiguity. To capture these dynamics, we propose an integrative framework that examines these relationships and highlights the social environment as a key factor that can strongly override the influence of such determinants. We validate our framework through an empirical study (n = 916) focusing on vaccine uptake and the adoption of contact-tracing apps. Our findings suggest that policymakers, who often struggle to effectively communicate the benefits and costs of innovations, should leverage the power of social influence to enhance acceptance. For example, an individual's mistrust in government becomes less consequential when they perceive that their social environment favors the innovation.

Dieses Buch vermittelt die Grundlagen des Konsumentenverhaltens verständlich und in klarer Sprache. Warum ist der Einkaufswagen voller als geplant? Wieso will man unbedingt das neuste Smartphone? Die

Responsible innovation (RI) seeks to align innovation processes with societal values and ethical principles. Understanding how consumers perceive RI is crucial, as innovation failures often arise from mismatches between managerial intentions and consumer expectations. This paper develops and validates a novel scale to measure consumer perception of responsible innovation (CPRI) through a multistep process grounded in the AIRR framework, anticipation, inclusion, reflexivity, and responsiveness. First, an initial pool of 53 items was generated from multiple sources, including academic literature, existing scales, qualitative interviews, and AI-assisted item generation. Second, expert evaluations refined this item pool. Third, a quantitative prestudy (N = 242) reduced the scale to 20 items. Study 1 (N = 240) further refined the scale to 16 items and confirmed its factor structure. Study 2 (N = 326) confirmed the scale's robustness. Study 1 and Study 2 demonstrated the scale's predictive power for innovation adoption and how it is embedded within a nomological network. Study 3 (N = 288) further established that CPRI influences anticonsumption behaviors significantly, such as voluntary simplicity-based avoidance and boycotting. Study 4 (N = 214) employed an experimental design to show that the CPRI dimensions are sensitive to manipulations and that CPRI mediates the impact of RI activities. The findings demonstrate reliability and validity of the CPRI scale across diverse innovation contexts, including AI image generator, cultured meat, robotaxis, and emotional support robots.

Recent advances in chain-of-thought (CoT) reasoning in large language models are expected to improve the transparency and coherence of AI-generated recommendations, yet evidence on how CoT affects users' perceptions and downstream behavior in real decision contexts remains limited. To address this gap, we develop and evaluate a reasoning-enabled conversational agent in the domain of sustainable consumption, where uncertainty, value-laden trade-offs, and multi-criteria decisions make reasoning transparency especially salient. We employ a three-phase research design that combines model development with empirical evaluation. In Phase 1, we finetune a conversational agent using a multi-agent framework to produce high-quality CoT data, leveraging Group Relative Policy Optimization (GRPO) and Low-Rank Adaptation (LoRA). Phase 2 tests the agent in a between-subjects experiment (N = 417) comparing a CoT-enabled chatbot with a standard chatbot. Grounded in the Technology Acceptance Model, IS Success Model, Cognitive Load Theory, and Privacy-Calculus Theory, we examine how user-friendliness, usefulness, personalization, trust, transparency, cognitive load, inefficiency, and privacy concerns mediate CoT effects on perceived knowledge and behavioral intentions. Results show that CoT significantly increases perceived knowledge and behavioral intentions. Specifically, PLS-SEM supports our mediation model, demonstrating that CoT acts through promoters such as enhanced trust, perceived usefulness, and personalization, which outweigh inhibitors like cognitive load and privacy concerns. Phase 3 complements these findings through qualitative content analysis, indicating that CoT improves user experience by providing clearer, more helpful reasoning, whereas standard chatbots are often perceived as verbose, vague, or technically unreliable. Overall, the study provides empirical evidence on how reasoning-enabled conversational systems shape user perceptions and behavioral outcomes, offering actionable guidance for designing decision-support transparent AI in complex domains.

Misinformation poses a growing threat to firms, distorting consumer beliefs and damaging brand evaluations. A common corrective strategy involves attaching fact-checking labels to false claims, yet concerns persist that such corrections may backfire by strengthening familiarity with the misinformation. Across five studies (N = 4337), this article systematically compares the competing effects of repetition and correction on belief in corporate misinformation and brand evaluations. Repetition reliably increases belief in misinformation (illusory truth effect), while correction typically offsets this effect and even reverses it with strong, unambiguous labels. This research finds no evidence of a familiarity backfire effect: in none of the studies, repetition increases belief in the misinformation more than correction reduces it. While brand evaluations are less affected by repetition, they do decline following exposure to misinformation and are only partially restored by corrections. The article further examines how brand familiarity and the timing of assessment shape these effects. Corrections are effective both immediately and after a delay, and benefit unfamiliar brands more than familiar ones. Finally, corrections issued at first exposure, reaching new audiences, also reduce belief in misinformation without backfiring during future exposures. These findings inform managerial decisions on misinformation response and contribute to understanding how misinformation familiarity and correction compete in shaping consumer judgments.