Comparison of Listening Experiences by Podcast Styles: Monologue versus Dialogue

Comparison of Listening Experiences by Podcast Styles: Monologue versus Dialogue | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of Listening Experiences by Podcast Styles: Monologue versus Dialogue Jimpei Hitsuwari, Takayoshi Hayashi, Haruki Moriya, Yoichi Ohtake This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6217086/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the effects of podcast format (monologue versus dialogue) on listener experience among Japanese participants (N = 296). Despite the growing popularity of dialogue-based podcasts, research comparing podcast formats remains limited. Participants were randomly assigned to listen to either a monologue or dialogue version of an AI-generated podcast discussing psychological research. We hypothesized that dialogue-based podcasts would yield higher impression ratings and better comprehension and that individual differences would moderate format preferences. Contrary to expectations, no significant differences were found in the overall evaluations, including enjoyment, interest, and immersion, between formats. Comprehension results revealed a single advantage of the monologue format: participants showed higher accuracy and confidence in identifying key information. Agreeableness and the need for complexity and novelty positively predicted several evaluation metrics, regardless of the format, while no significant interactions between podcast format and personality traits were observed. Free-response analysis showed predominantly positive reactions to both formats. These findings suggest that podcast format may have less impact on listener experience than assumed, and that content quality and listener traits may be more influential factors. This study contributes to the understanding of digital media consumption, with implications for podcast creators and educational content developers. Psychology Media Studies podcasts dialogue monologue listening comprehension media psychology Figures Figure 1 Introduction Research on Podcasts Podcasts have grown in popularity (Chan-Olmsted & Wang, 2022; Hayashi, 2024; Kelly et al., 2022). A survey targeting 20 countries found that 35% of respondents listened to podcasts, up from 29% in 2018 (Newman et al., 2024). In particular, 44% of people in the United States and Spain listen to podcasts, making them the highest among the 20 countries, while Japan had the lowest at 26%, meaning one in four people there listen to podcasts (Newman et al., 2024). Meanwhile, research on podcasts has also increased (Fig. 1). The areas of research are diverse, including those related to politics and journalism (Bird, 2025; Tranová & Veneti, 2024), advertising and marketing (Bezbaruah & Brahmbhatt, 2023; Haygood, 2007), education, including language learning, such as English, and specialized fields, such as healthcare (Kelly et al., 2022; Rosell-Aguilar, 2007). In psychology, although still relatively few compared to other fields, the number of studies of podcasts is gradually increasing (Fig. 1). Rahimi and Katal (2012) demonstrated that individuals with Metacognitive Listening Strategies, such as advanced problem-solving skills (e.g., making inferences while listening), tend to listen to English-learning podcasts more frequently. Wolenski and Pettit (2023) showed that people who received psychosocial treatment used podcasts more often to obtain information than those who did not. Furthermore, another study indicated that individuals who listened to mental health-themed podcasts within the past 12 months demonstrated greater mental health knowledge (Carrotte et al., 2023). Several psychological scales related to podcast usage and motivation have been developed, with information gathering and entertainment being characteristic aspects of the podcast experience (Chou et al., 2023; McNamara & Min, 2024). Dialogue Podcast Among these podcasts, dialogue-format personality podcasts are attracting attention. For example, in the UK, nine of the top 10 news podcasts in 2023 were dialogue-format podcasts (Newman et al., 2023). In Japan, among the five Japan Podcast Awards held so far, the grand prizes for the 1st, 4th, and 5th awards were all won by two-person channels (JAPAN PODCAST AWARDS, 2024). Recently, Google introduced the Audio Overview feature in NotebookLM, which enables the automatic conversion of documents into a two-person dialogue format (not a monologue) (Google, 2024). This feature transforms uploaded documents, slides, and figures into conversations between two AI hosts, providing information in a more accessible format. Despite the popularity of podcast research and two-person personalities, few studies have compared two-person personality podcasts with solo narrative podcasts. Below, we compare these two formats with reference to dialogue and monologue discourse research. Monologue versus Dialogue Extending beyond podcasts, referencing previous research on listening and learning allows us to identify the characteristics and respective advantages of dialogue and monologue formats. Tolins et al. (2018) revealed that dialogue formats include more perspectives—the number of variations in ways of perceiving and explaining things presented in conversation—than monologue formats, and that the number of these perspectives showed a positive correlation with subsequent correct answer rates on puzzle tasks. In another study (Branigan et al., 2011), three conditions were prepared: dialogue (a conversation between a speaker and listener), half-dialogue (a condition in which only the speaker's utterances were presented after the listener's utterances were removed from the dialogue condition), and monologues. The results showed that dialogue and half-dialogue conditions led to higher objective comprehension than monologues. Additionally, attempts to generate dialogue-format content from non-dialogue data have emerged due to the advantage of easy comprehension through concise exchanges (Iwahashi & Inaba, 2022). There are also philosophical and pedagogical arguments for the advantages of monologues, as they allow recipients freedom of interpretation and do not necessarily impose the meaning intended by the sender (Kvernbekk, 2012). Read (2002) conducted English listening tests in both monologue and three-person dialogue formats with non-native English speakers and found that test scores were higher for the monologue format. While some of these studies have demonstrated the advantages of the monologue format, overall, they have highlighted the significant effectiveness of the dialogue format. The reasons dialogues lead to higher comprehension are not so much due to the amount of information but rather the diversity of perspectives (Fox Tree, 1999; Tolins et al., 2018), refinement of explanations through feedback (Fox Tree, 1999), and the adjustment of mutual understanding between speaker and listener to form common perspectives (Branigan et al., 2011). Podcasts have entertainment aspects, such as enjoyment and favorability, in addition to educational aspects, such as comprehension. Here, too, the advantages of the dialogue format become apparent. Compared to monologues, dialogues create more lively and attractive radio commercials, increase favorability toward products, enhance purchase motivation, and elevate the status of speakers in radio commercials (Van Meurs et al., 2019). Spoelders and Claes (2006) noted that one reason dialogue captures audience attention is the psychological expectation of answers to questions raised during conversation. While studies on podcasts and radio emphasizing the enjoyment of monologues are scarce, research on speech indicates that a speaker's passion, sincerity, and empathy improve audience engagement (Garil et al., 2024), suggesting that even solo personalities (monologues) can effectively convey personal thoughts with emotion. Personality Traits Moderating Podcast Experiences In this study, we also examine how personality traits influence the listening experiences of monologue and dialogue podcasts. While few psychological studies have examined podcast episodes and personality traits, Tobin and Guadagno (2022) revealed that openness, curiosity, and the need for cognition were positively associated with podcast listening habits. Furthermore, in detailed analyses, they found that highly extraverted individuals actively engage in social activities related to podcasts (such as discussing podcasts with others) and that highly agreeable individuals tend to develop a strong affinity for podcast hosts (Tobin and Guadagno, 2022). Additionally, highly extraverted individuals are known to participate in many conversations (Mehr et al., 2006), highly open individuals have a greater tendency to ask questions, and highly agreeable individuals avoid aggressive statements and strive to maintain good relationships with others (de Vries et al., 2011). These personality traits may moderate the evaluation of podcast conversation styles, namely monologues and dialogues. Aims and Hypotheses This study aims to investigate the impact of podcast format—dialogue-based versus monologue-based—on listeners' evaluations, comprehension, and individual preferences. By exploring these variables, this study seeks to contribute to the growing body of research on podcast media and identify the factors influencing listener engagement and understanding. As preregistered, the specific hypotheses are as follows: H1. Dialogue-based podcasts will result in higher impression ratings compared to monologue-based podcasts. Impression ratings will include factors such as enjoyment, interest, and immersion. H2. Dialogue-based podcasts will lead to higher comprehension scores and greater confidence in understanding content compared to monologue-based podcasts. H3. Individual differences will moderate preferences for dialogue-based versus monologue-based podcasts. Specifically, participants with higher levels of extraversion, agreeableness, openness, and need for complexity and novelty will provide higher ratings (e.g., enjoyment, interest, and immersion) for dialogue-based podcasts. Method Ethical Considerations and Open Science Declaration This study adhered to the ethical guidelines established by the Experimental Psychology Unit, Faculty of Humanities and Social Sciences, Helmut Schmidt University. The study was approved by our general ethical vote, ensuring compliance with the ethical standards for research involving human participants. Before participating, all participants were provided with detailed information about the stud. They gave their informed consent before proceeding with the study. In line with Open Science practices, the study design, hypotheses, and data analysis plan were registered on the Open Science Framework (OSF; https://osf.io/s7gup/?view_only=cadba4ab5d5645969493e7b630b984e8). Additionally, the raw data, analysis scripts, questionnaires used in the study, and their English translations will also be made publicly available on the OSF upon the completion of the study (https://osf.io/7dycn/?view_only=b379718ca53843d8958713d48e14e4e2). Participants A total of 296 participants were recruited using CrowdWorks (https://crowdworks.jp/), a Japanese online platform for survey participation and freelance work. The eligibility criteria required participants to be at least 18 years old and fluent in Japanese, as the study materials were presented in Japanese. Participants were randomly assigned to one of the two podcast conditions: monologue-based or dialogue-based. The monologue-based condition included 152 participants (M age = 43.3, SD = 10.5; 75 women and 77 men). The dialogue-based condition comprised 144 participants (M age = 43.0, SD = 10.2; 64 women, 79 men; one no answer). To determine the required sample size, an a priori power analysis was conducted using G*Power (version 3.1.9.7; Faul et al., 2007). The analysis was based on a two-tailed independent t-test with a medium effect size ( d = 0.5), a Bonferroni-adjusted alpha error probability of 0.00625 (for eight dependent variables), and α power of 0.95. The results indicated that a minimum of 278 participants (139 per group) was required to achieve sufficient statistical power. Participants received 150 yen as compensation for completing the study. Materials Questionnaire . The questionnaire consisted of the following components: 1. Podcast Evaluation Participants evaluated the podcast’s content and format using a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). The evaluation items included the following: a. Enjoyment: "Listening to this podcast was enjoyable." b. Liking: "I liked this podcast." c. Interest: "I was interested in the content of this podcast." d. Clarity: "The content of this podcast was easy to understand." e. Comprehension: "I understood the content of this podcast." f. Immersion: "I was immersed in this podcast." g. Repeat Intention: "I would like to listen to other episodes of this podcast introducing different research papers." h. Trust: "I trust the podcast host(s)." 2. Podcast Listening Habits Information on participants’ podcast listening habits was collected using questions adapted from Tobin and Guadagno (2022). These included the frequency of listening (e.g., daily, weekly), total listening duration per week, number of different podcasts listened to per month, and proportions of preferred formats (e.g., single host, multiple hosts). Participants were also asked about their listening environments (e.g., multitasking at home or on the go) and the devices used (e.g., smartphones, computers). 3. Psychological Traits and Demographic Information Psychological traits were assessed using two measures. The Big Five personality traits were evaluated using the Ten-Item Personality Inventory (TIPI; Gosling et al., 2003; Oshio et al., 2012), which measures extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. The need for complexity and novelty was assessed using a subscale of the Multidimensional Attitudes Toward Ambiguity Scale (MAAS; Lauriola et al., 2016; Hitsuwari & Nomura, 2021). Both measures used a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). Basic demographic information, including age and gender, was also collected. 4. Podcast Content Comprehension Check Participants’ comprehension of the podcast content was assessed through multiple-choice questions. These included identifying the AI model used in the study, determining which haiku was rated as the most beautiful, and recognizing the future research directions mentioned in the podcast. Participants also rated their confidence in each response on a 7-point Likert scale (1 = Not confident at all, 7 = Very confident). AI Podcast . The scripts for the AI podcasts used in this study were created using ChatGPT 4o (OpenAI, 2024). The scripts were constructed by having ChatGPT read the paper "Discover Beauty of Haiku Created Through Co-Creation with AI" (Hitsuwari et al., 2024; see Hitsuwari et al., 2023 for English version) and present its content. This study compared three conditions: human-made haiku, AI-generated haiku, and human-AI co-created haiku (AI-generated haiku selected by humans), demonstrating that the human-AI co-created haiku received the highest aesthetic evaluations. Two types of podcasts were prepared—monologue and dialogue formats—each designed to provide a different listening experience. First, the dialogue script was created, adopting a style in which a general audience host and a psychology expert (the first author of both this paper and the paper that was fed to ChatGPT) explored the paper's content through dialogue. In this format, the host played the role of eliciting information from the guest, and when complex concepts or terminology appeared, the host asked additional questions to encourage explanations in simpler terms. For the monologue format, a script was prepared in which the same content was spoken continuously by a single psychology expert. The two prepared scripts were converted into audio using VoiceVox (https://voicevox.hiroshiba.jp/), software that enables AI voice synthesis. The expert's voice was the same across both podcasts. Male voices were used throughout, including for the general audience host. After the voice conversion, minor adjustments were made to the scripts to ensure that both podcasts were under 5 minutes and approximately the same length (monologue 4:39; dialogue 4:57). They were subsequently reconverted to audio for use as stimuli. These scripts, the ChatGPT prompts used for script creation, and the audio files (all in Japanese) were uploaded to the OSF. Procedure In this study, participants listened to a podcast after providing consent to participate in the research, then completed a questionnaire. Participants were randomly assigned to either a monologue or dialogue format podcast condition. After listening, participants freely described their impressions of the podcast, taking at least 30 seconds to do so. They then answered questions assessing their evaluation of the content, listening habits, and psychological characteristics. Podcast evaluation items included Enjoyment, Liking, Interest, Clarity, Comprehension, Immersion, Repeat Intention, and Trust in the personality. Information on the participants' podcast listening habits—such as frequency, duration, devices used, and listening environments—was also collected. Furthermore, to measure participants' psychological characteristics, questionnaires on the Big Five personality traits and the need for novelty and complexity were administered. Finally, multiple-choice questions on the AI models, haiku evaluation results, and other study-related aspects were included to assess content comprehension, along with confidence ratings for their answers. For Hypothesis 1 (differences in podcast format evaluations), independent samples t-tests were used to compare the eight evaluation variables, with effect sizes calculated using Cohen's d. For Hypothesis 2 (the relationship between podcast format and test performance/confidence), t-tests were used to compare total test scores, while χ² tests were used to compare correct answer rates for each question. Additionally, t-tests were conducted to compare confidence levels in responses for each question. For Hypothesis 3 (interaction with individual characteristics), multiple regression analysis was performed. Podcast evaluation served as the dependent variable, while experimental conditions (monologue/dialogue), individual characteristics (Big Five, Need for Complexity and Novelty), and the interaction terms between condition and individual characteristics were set as independent variables. All continuous variables were centered. To control for Type I errors from multiple comparisons, Bonferroni correction was applied, with the significance level set at α = 0.00625 (0.05/8 variables). For exploratory analysis of the open-ended response data, sentiment analysis (positive/negative/neutral) was conducted using Bidirectional Encoder Representations from Transformers (BERT; https://huggingface.co/tohoku-nlp/bert-base-japanese-v3). χ² tests were used to compare the distribution of sentiment categories between conditions. For negative comments, instances were also categorized by content type. All statistical analyses were performed using Python (Version 3.8.5; Python Software Foundation, 2020), with a significance level of α = 0.05 unless otherwise specified. Results Regarding Hypothesis 1, we compared the eight evaluation measures between the monologue and dialogue format podcasts using t-tests (Table 1 ). The results of the independent samples t-tests showed no statistically significant differences between the two formats for any dependent variable, starting with Enjoyment ( t = -0.62, p = .54, d = .07). Overall, effect sizes (Cohen's d ) were small for all variables (| d | ≤ .13), suggesting that the podcast format (monologue or dialogue) did not have a substantial impact on listeners' evaluations. These results do not support our initial hypothesis that dialogue format podcasts would receive higher ratings. Table 1 Mean Values, Standard Deviations, and t-test Results for Each Variable in Monologue and Dialogue Podcasts Monologue Dialogue t test Variables Mean SD Mean SD t value p value d Enjoyment 4.86 1.38 4.96 1.48 − .62 .54 .07 Liking 4.53 1.22 4.49 1.36 .27 .79 − .03 Interest 5.24 1.38 5.24 1.43 .00 1.00 .00 Clarity 5.26 1.22 5.16 1.39 .64 .52 − .07 Comprehension 5.48 1.13 5.45 1.12 .22 .83 − .03 Immersion 4.83 1.28 4.86 1.38 − .21 .83 .02 Repeat Intention 4.64 1.37 4.71 1.52 − .42 .68 .05 Trust 4.82 1.08 4.97 1.20 -1.13 .26 .13 For Hypothesis 2 (test performance and confidence in answers), the analysis of mean differences in total scores (one point per question) revealed no significant difference between monologue and dialogue conditions ( M monologue = 1.93, SD = 0.88; M dialogue = 1.81, SD = 0.94; t = 1.15, p = 0.25, d = -0.13). In the analysis of correct answer rates for each question, the question about the most beautiful haiku showed a significantly higher correct answer rate in the monologue condition than in the dialogue condition (correct rate monologue = 86.18%; correct rate dialogue = 70.83%; χ² (1) = 9.50, p < 0.01). For the questions about the machine learning model (correct rate monologue = 56.58%; correct rate dialogue = 60.42%; χ² (1) = 0.30, p = 0.58) and future research directions (correct rate monologue = 50.66%; correct rate dialogue = 50.00%; χ² (1) = 0.0, p = 1.0), no significant differences were found between monologue and dialogue conditions. In the analysis of confidence ratings for each question, participants in the monologue condition showed significantly higher confidence for the question about the most beautiful haiku, similar to the correct answer rate results ( M monologue = 5.58, SD = 1.57; M dialogue = 4.92, SD = 1.93; t = 3.20, p < 0.01, d = -0.37). For questions about the machine learning model ( M monologue = 3.02, SD = 1.92; M dialogue = 3.05, SD = 1.97; t = -0.13, p = 0.90, d = 0.01) and future research directions ( M monologue = 4.28, SD = 1.93; M dialogue = 4.50, SD = 1.94; t = -0.99, p = 0.32, d = 0.12), no significant differences were found between monologue and dialogue conditions. For Hypothesis 3 (interaction with individual characteristics), multiple regression analyses were conducted using the Bonferroni-corrected significance level ( α = 0.00625) (Supplementary Table 1). The results revealed that Agreeableness showed consistent positive effects on multiple dependent variables. Specifically, Agreeableness had statistically significant effects on Liking ( β = 0.27, SE = 0.09, t = 2.92, p = 0.004), Interest ( β = 0.29, SE = 0.10, t = 2.94, p = 0.004), Immersion ( β = 0.39, SE = 0.10, t = 4.05, p < 0.001), and Trust ( β = 0.31, SE = 0.08, t = 3.72, p < 0.001). Additionally, Need for Complexity and Novelty showed significant positive effects on Enjoyment ( β = 0.34, SE = 0.12, t = 2.82, p = 0.005) and Repeat Intention ( β = 0.41, SE = 0.12, t = 3.32, p = 0.001). Regarding the hypothesized interactions between the experimental conditions and individual characteristics, no significant interactions were found for any of the variables. Finally, an exploratory analysis was conducted on the open-ended responses (natural language) regarding participants’ impressions of the podcasts. Using Tohoku University's BERT model, sentiment analysis (positive/negative/neutral) was performed for each condition (Table 2 ). Overall, positive comments were the most frequent (63.51%), followed by neutral (21.62%) and negative comments (14.86%). There was no significant difference between the monologue and dialogue conditions ( χ² (2) = 2.03, p = .36). Additionally, content analysis of the negative comments revealed 17 instances related to "anxiety/challenges about AI evolution," 16 instances of "dissatisfaction with audio," eight instances of "difficulty understanding content," and three instances of "doubts about content/lack of interest." Table 2 Occurrence Rates of Positive and Negative Words in Comments on Monologue and Dialogue Podcasts Monologue Dialogue Sum Count Ratio Count Ratio Count Ratio Positive 102 67.11 86 59.72 188 63.51 Neutral 31 20.39 33 22.92 64 21.62 Negative 19 12.50 25 17.36 44 14.86 Sum 152 100 144 100 296 100 Discussion This study examined how monologue and dialogue format podcasts affect listeners' evaluations, comprehension, and personal preferences. Our initial hypothesis predicted that dialogue format podcasts would receive higher evaluations and comprehension than monologue format podcasts; however, the results did not support this hypothesis. First, no statistically significant differences occurred in any dependent variable between podcast formats (monologue/dialogue). This finding is inconsistent with previous research demonstrating the superiority of the dialogue format (Van Meurs et al., 2019 ; Tolins et al., 2018 ). The main reason for this inconsistency could be that our study used stimuli with nearly identical information content and number of perspectives (variations in how things are perceived and explained within the conversation). Previous research has suggested that the advantage of the dialogue format stems from the diversity of perspectives and refinement of explanations through feedback (Fox Tree, 1999 ) rather than the amount of information. In this study, the same content was presented in both monologue and dialogue formats, meaning that the information and perspectives conveyed were substantially identical, which may have prevented differences between formats from emerging. Furthermore, as Branigan et al. ( 2011 ) suggested, the effectiveness of the dialogue format arises from the process by which speakers and listeners adjust their understanding of each other and form common perspectives. The dialogue podcast used in this study was based on a prepared script that may have differed from naturally occurring conversations. Next, regarding comprehension, while no significant difference was observed in total test scores between monologue and dialogue formats, the monologue format showed significantly higher correct answer rates and confidence for the question about the most beautiful haiku. This may be because, in a monologue, the speaker can consistently emphasize important information. This result partially aligns with Read's (2002) research, which found higher listening test scores for the monologue format among non-native English speakers (noting that the dialogue condition in that study involved three-person discussions). Furthermore, as Kvernbekk ( 2012 ) noted, monologues can effectively direct listeners' attention to specific points by systematically presenting information. Particularly for relatively clear information such as the aesthetic evaluation of a haiku, direct transmission by a solo voice may have been more advantageous for information retention than exchanges in dialogue. However, because a difference was observed in only one question, this advantage of the monologue format can be considered limited. Furthermore, regarding personality traits, while no interaction was observed between podcast format and personality traits, Agreeableness and Need for Complexity and Novelty showed positive effects on multiple evaluation items. The finding that participants high in Agreeableness gave more favorable evaluations of podcasts is consistent with Tobin and Guadagno's (2022) observation that highly agreeable individuals tend to develop a strong affinity with podcast hosts. Additionally, the association between Need for Complexity and Novelty, and both enjoyment and repeat intention aligns with the discovery of curiosity, which predicts podcast listening (Tobin & Guadagno, 2022 ). These results suggest that listeners’ personality traits may play a more significant role in podcast evaluations than the formats. Limitations and Future Direction This study has several limitations. First, the podcasts used in the experiment were based on AI-generated scripts. Although this approach has the advantage of controlling the content and emotional expression between conditions, it may not fully reproduce the characteristics of natural conversations. In particular, as Spoelders and Claes ( 2006 ) noted, the appeal of dialogue lies in the anticipation of answers to questions raised during conversations; however, in script-based conversations, this natural process of question generation and resolution may be restricted. Second, the length of the podcasts used in the experiment (approximately 5 minutes) was shorter than that of typical podcasts and may have been insufficient for format effects to become apparent. With longer content, the advantages of the dialogue format may become more evident. Based on these limitations, future research should employ experimental designs using podcasts that include more natural dialogue and content covering diverse topics. It would also be valuable to examine how the effects of format differ depending on podcast listening contexts (e.g., listening in multitasking environments and focused listening) and questionnaire items not addressed in this study (e.g., intimacy with hosts). Conclusion The results of this study suggest that the impact of podcast format (monologue or dialogue) on listener evaluations and comprehension may not be as significant as suggested by previous research. Additionally, no interaction was found between format and listener personality traits, providing no evidence that listeners with specific personality traits prefer particular formats. Rather, listener personality traits themselves and content substance appear to be factors that have a greater influence on podcast experiences. For educational content, the monologue format may be partially effective for conveying specific information, while overall, the content substance and host personality may be more important than the format itself. In future research on podcasts, examining the interaction of more complex factors—not just format, but also content type, listener characteristics, and listening environment—will be key to deepening our understanding of podcast media. References Bezbaruah, S., & Brahmbhatt, K. (2023). Are podcast advertisements effective? An emerging economy perspective. 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Proceedings of the 96th SIG-SLUD Workshop, The Japanese Society for Artificial Intelligence , 96 , 51–56. https://doi.org/10.11517/jsaislud.96.0_10 (in Japanese with English abstract) JAPAN PODCAST AWARDS. (2024). JAPAN PODCAST AWARDS. Retrieved December 18, 2024, from https://www.japanpodcastawards.com/index.html (in Japanese) Kelly, J. M., Perseghin, A., Dow, A. W., Trivedi, S. P., Rodman, A., & Berk, J. (2022). Learning through listening: a scoping review of podcast use in medical education. Academic Medicine, 97 (7), 1079–1085. https://doi.org/10.1097/ACM.0000000000004565 Kvernbekk, T. (2012). Revisiting dialogues and monologues. Educational Philosophy and Theory, 44 (9), 966–978. https://doi.org/10.1111/j.1469-5812.2010.00695.x Lauriola, M., Foschi, R., Mosca, O., & Weller, J. (2016). Attitude toward ambiguity: empirically robust factors in self-report personality scales. Assessment, 23 (3), 353–373. https://doi.org/10.1177/1073191115577188 McNamara, S. W., & Min, S. D. (2024). Understanding why educational professionals engage with podcasts: Educational Podcasts Motivational Scale development and validation. British Journal of Educational Technology, 55 (4), 1728–1746. https://doi.org/10.1111/bjet.13428 Mehl, M. R., Gosling, S. D., & Pennebaker, J. W. (2006). Personality in its natural habitat: Manifestations and implicit folk theories of personality in daily life. Journal of Personality and Social Psychology, 90 (5), 862–877. https://doi.org/10.1037/0022-3514.90.5.862 Newman, N., Fletcher, R., Eddy, K., Robertson, C. T., & Nielsen, R. K. (2023). Digital News Report 2023. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/ Newman, N., Fletcher, R., Eddy, K., Robertson, C. T., & Nielsen, R. K. (2024). Digital News Report 2024. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/ OpenAI. (2024). Hello GPT-4o . OpenAI. Retrieved March 5 2025 from https://openai.com/index/hello-gpt-4o/ Oshio, A., Shingo, A. B. E., & Cutrone, P. (2012). Development, reliability, and validity of the Japanese version of Ten Item Personality Inventory (TIPI-J). Japanese Journal of Personality, 21 (1), 40–52. https://doi.org/10.2132/personality.21.40 (in Japanese with English abstract) Python Software Foundation. (2020). Python (Version 3.8.5) [Computer software]. Retrieved March 1, 2025, from https://www.python.org Rahimi, M., & Katal, M. (2012). The role of metacognitive listening strategies awareness and podcast-use readiness in using podcasting for learning English as a foreign language. Computers in Human Behavior, 28 (4), 1153–1161. https://doi.org/10.1016/j.chb.2012.01.025 Read, J. (2002). The use of interactive input in EAP listening assessment. Journal of English for Academic Purposes, 1 (2), 105–119. https://doi.org/10.1016/S1475-1585(02)00018-8 Rosell-Aguilar, F. (2007). Top of the pods—In search of a podcasting “podagogy” for language learning. Computer Assisted language learning, 20 (5), 471–492. https://doi.org/10.1080/09588220701746047 Spoelders, S., & Claes, R. (2006). Creative advertisements for the Cinderella medium: The case of Flanders, Belgium. Journal of Radio Studies, 13 (1), 68–88. https://doi.org/10.1207/s15506843jrs1301_5 Tobin, S. J., & Guadagno, R. E. (2022). Why people listen: Motivations and outcomes of podcast listening. Plos one, 17 (4), e0265806. https://doi.org/10.1371/journal.pone.0265806 Tolins, J., Zeamer, C., & Fox Tree, J. E. (2018). Overhearing dialogues and monologues: How does entrainment lead to more comprehensible referring expressions?. Discourse Processes, 55 (7), 545-565. https://doi.org/10.1080/0163853X.2017.1279516 Tranová, K. L., & Veneti, A. (2024). The use of podcasting in political marketing: The case of the Czech Republic. Journal of Political Marketing, 23 (4), 305–322. https://doi.org/10.1080/15377857.2021.2024479 Van Meurs, F., Hendriks, B., & Köksal, D. (2019). The effect of monologues and dialogues in radio commercials. Tijdschrift voor Taalbeheersing, 41 (1), 189–201. https://doi.org/10.5117/TVT2019.1.013.MEUR Wolenski, R., & Pettit, J. W. (2023). Social media usage is associated with lower knowledge about anxiety and indiscriminate use of anxiety coping strategies. Psychology of Popular Media, 13 (2), 190–200. https://doi.org/10.1037/ppm0000456 Additional Declarations The authors declare no competing interests. Supplementary Files supplementaryfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6217086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428171798,"identity":"bd650497-5f14-41de-a49e-642d9db78956","order_by":0,"name":"Jimpei Hitsuwari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACA2Yog429B0QdSGBgSCCkBaqHjecMsVoYYNZI5BCpxZyd//Bn3ja7fD7Jt4c/8zDcyWNgTz6AV4tlMzObNG9bsmWbdF6aNA/Ds2IGnmf4rTE4zMzGzLuN2YBNOseMmfff4cQGiRwDQlqYP/NuqzdgkzxjDHQYSEv+B0JaGKR5tx02YJPgMZCGaMnBqwOkxUxy7r/jBmw8OWaSc4Ba2nieEXDY+YOPP7w5U20g337G+MMboJZ+9uQH+K3BAGwkqh8Fo2AUjIJRgAUAAO6kPqVOj9ESAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0061-5318","institution":"Helmut Schmidt University","correspondingAuthor":true,"prefix":"","firstName":"Jimpei","middleName":"","lastName":"Hitsuwari","suffix":""},{"id":428171799,"identity":"1f1b2bf3-4ca4-446a-8494-922515cd1209","order_by":1,"name":"Takayoshi Hayashi","email":"","orcid":"","institution":"Research Community Aimaito","correspondingAuthor":false,"prefix":"","firstName":"Takayoshi","middleName":"","lastName":"Hayashi","suffix":""},{"id":428171800,"identity":"e455e76a-64f7-4711-a888-3c7000645ba3","order_by":2,"name":"Haruki Moriya","email":"","orcid":"","institution":"Research Community Aimaito","correspondingAuthor":false,"prefix":"","firstName":"Haruki","middleName":"","lastName":"Moriya","suffix":""},{"id":428171801,"identity":"3fb7d68b-46e3-497c-a767-dd059953064f","order_by":3,"name":"Yoichi Ohtake","email":"","orcid":"","institution":"Research Community Aimaito","correspondingAuthor":false,"prefix":"","firstName":"Yoichi","middleName":"","lastName":"Ohtake","suffix":""}],"badges":[],"createdAt":"2025-03-13 06:41:12","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6217086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6217086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78692826,"identity":"915cf53c-af07-4ed9-a2b2-640851caeea9","added_by":"auto","created_at":"2025-03-17 16:30:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEvolution of the Number of Published Papers Using Relevant Keywords from 2000 to 2024\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. We counted 26,052 papers that matched a search for \"Podcast\" and 437 papers that matched a search for \"Podcast AND Psychology\" in the Lens database (tabulated in December 2024).\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6217086/v1/4e4b345ca36b49a3f0b98e24.jpg"},{"id":78692833,"identity":"58ff2017-5954-4bdf-8582-8b86d7cdc8d0","added_by":"auto","created_at":"2025-03-17 16:30:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":732400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6217086/v1/3d96334f-0974-49bf-8fa5-1ef3d7604946.pdf"},{"id":78691944,"identity":"026029be-cb96-458a-a7ce-63088cbda5fb","added_by":"auto","created_at":"2025-03-17 16:22:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33384,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6217086/v1/ce8e05e8de985bf636aca945.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComparison of Listening Experiences by Podcast Styles: Monologue versus Dialogue\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\"\u003e\n \u003ch2\u003eResearch on Podcasts\u003c/h2\u003e\n \u003cp\u003ePodcasts have grown in popularity (Chan-Olmsted \u0026amp; Wang, 2022; Hayashi, 2024; Kelly et al., 2022). A survey targeting 20 countries found that 35% of respondents listened to podcasts, up from 29% in 2018 (Newman et al., 2024). In particular, 44% of people in the United States and Spain listen to podcasts, making them the highest among the 20 countries, while Japan had the lowest at 26%, meaning one in four people there listen to podcasts (Newman et al., 2024). Meanwhile, research on podcasts has also increased (Fig. 1). The areas of research are diverse, including those related to politics and journalism (Bird, 2025; Tranová \u0026amp; Veneti, 2024), advertising and marketing (Bezbaruah \u0026amp; Brahmbhatt, 2023; Haygood, 2007), education, including language learning, such as English, and specialized fields, such as healthcare (Kelly et al., 2022; Rosell-Aguilar, 2007).\u003c/p\u003e\n \u003cp\u003eIn psychology, although still relatively few compared to other fields, the number of studies of podcasts is gradually increasing (Fig.\u0026nbsp;1). Rahimi and Katal (2012) demonstrated that individuals with Metacognitive Listening Strategies, such as advanced problem-solving skills (e.g., making inferences while listening), tend to listen to English-learning podcasts more frequently. Wolenski and Pettit (2023) showed that people who received psychosocial treatment used podcasts more often to obtain information than those who did not. Furthermore, another study indicated that individuals who listened to mental health-themed podcasts within the past 12 months demonstrated greater mental health knowledge (Carrotte et al., 2023). Several psychological scales related to podcast usage and motivation have been developed, with information gathering and entertainment being characteristic aspects of the podcast experience (Chou et al., 2023; McNamara \u0026amp; Min, 2024).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDialogue Podcast\u003c/h3\u003e\n\u003cp\u003eAmong these podcasts, dialogue-format personality podcasts are attracting attention. For example, in the UK, nine of the top 10 news podcasts in 2023 were dialogue-format podcasts (Newman et al., 2023). In Japan, among the five Japan Podcast Awards held so far, the grand prizes for the 1st, 4th, and 5th awards were all won by two-person channels (JAPAN PODCAST AWARDS, 2024). Recently, Google introduced the Audio Overview feature in NotebookLM, which enables the automatic conversion of documents into a two-person dialogue format (not a monologue) (Google, 2024). This feature transforms uploaded documents, slides, and figures into conversations between two AI hosts, providing information in a more accessible format. Despite the popularity of podcast research and two-person personalities, few studies have compared two-person personality podcasts with solo narrative podcasts. Below, we compare these two formats with reference to dialogue and monologue discourse research.\u003c/p\u003e\n\u003ch3\u003eMonologue versus Dialogue\u003c/h3\u003e\n\u003cp\u003eExtending beyond podcasts, referencing previous research on listening and learning allows us to identify the characteristics and respective advantages of dialogue and monologue formats. Tolins et al. (2018) revealed that dialogue formats include more perspectives—the number of variations in ways of perceiving and explaining things presented in conversation—than monologue formats, and that the number of these perspectives showed a positive correlation with subsequent correct answer rates on puzzle tasks. In another study (Branigan et al., 2011), three conditions were prepared: dialogue (a conversation between a speaker and listener), half-dialogue (a condition in which only the speaker's utterances were presented after the listener's utterances were removed from the dialogue condition), and monologues. The results showed that dialogue and half-dialogue conditions led to higher objective comprehension than monologues. Additionally, attempts to generate dialogue-format content from non-dialogue data have emerged due to the advantage of easy comprehension through concise exchanges (Iwahashi \u0026amp; Inaba, 2022).\u003c/p\u003e\n\u003cp\u003eThere are also philosophical and pedagogical arguments for the advantages of monologues, as they allow recipients freedom of interpretation and do not necessarily impose the meaning intended by the sender (Kvernbekk, 2012). Read (2002) conducted English listening tests in both monologue and three-person dialogue formats with non-native English speakers and found that test scores were higher for the monologue format. While some of these studies have demonstrated the advantages of the monologue format, overall, they have highlighted the significant effectiveness of the dialogue format. The reasons dialogues lead to higher comprehension are not so much due to the amount of information but rather the diversity of perspectives (Fox Tree, 1999; Tolins et al., 2018), refinement of explanations through feedback (Fox Tree, 1999), and the adjustment of mutual understanding between speaker and listener to form common perspectives (Branigan et al., 2011).\u003c/p\u003e\n\u003cp\u003ePodcasts have entertainment aspects, such as enjoyment and favorability, in addition to educational aspects, such as comprehension. Here, too, the advantages of the dialogue format become apparent. Compared to monologues, dialogues create more lively and attractive radio commercials, increase favorability toward products, enhance purchase motivation, and elevate the status of speakers in radio commercials (Van Meurs et al., 2019). Spoelders and Claes (2006) noted that one reason dialogue captures audience attention is the psychological expectation of answers to questions raised during conversation. While studies on podcasts and radio emphasizing the enjoyment of monologues are scarce, research on speech indicates that a speaker's passion, sincerity, and empathy improve audience engagement (Garil et al., 2024), suggesting that even solo personalities (monologues) can effectively convey personal thoughts with emotion.\u003c/p\u003e\n\u003ch3\u003ePersonality Traits Moderating Podcast Experiences\u003c/h3\u003e\n\u003cp\u003eIn this study, we also examine how personality traits influence the listening experiences of monologue and dialogue podcasts. While few psychological studies have examined podcast episodes and personality traits, Tobin and Guadagno (2022) revealed that openness, curiosity, and the need for cognition were positively associated with podcast listening habits. Furthermore, in detailed analyses, they found that highly extraverted individuals actively engage in social activities related to podcasts (such as discussing podcasts with others) and that highly agreeable individuals tend to develop a strong affinity for podcast hosts (Tobin and Guadagno, 2022). Additionally, highly extraverted individuals are known to participate in many conversations (Mehr et al., 2006), highly open individuals have a greater tendency to ask questions, and highly agreeable individuals avoid aggressive statements and strive to maintain good relationships with others (de Vries et al., 2011). These personality traits may moderate the evaluation of podcast conversation styles, namely monologues and dialogues.\u003c/p\u003e\n\u003ch3\u003eAims and Hypotheses\u003c/h3\u003e\n\u003cp\u003eThis study aims to investigate the impact of podcast format—dialogue-based versus monologue-based—on listeners' evaluations, comprehension, and individual preferences. By exploring these variables, this study seeks to contribute to the growing body of research on podcast media and identify the factors influencing listener engagement and understanding. As preregistered, the specific hypotheses are as follows:\u003c/p\u003e\n\u003cp\u003eH1. Dialogue-based podcasts will result in higher impression ratings compared to monologue-based podcasts. Impression ratings will include factors such as enjoyment, interest, and immersion.\u003c/p\u003e\n\u003cp\u003eH2. Dialogue-based podcasts will lead to higher comprehension scores and greater confidence in understanding content compared to monologue-based podcasts.\u003c/p\u003e\n\u003cp\u003eH3. Individual differences will moderate preferences for dialogue-based versus monologue-based podcasts. Specifically, participants with higher levels of extraversion, agreeableness, openness, and need for complexity and novelty will provide higher ratings (e.g., enjoyment, interest, and immersion) for dialogue-based podcasts.\u003c/p\u003e"},{"header":"Method","content":"\u003ch2\u003eEthical Considerations and Open Science Declaration\u003c/h2\u003e\u003cp\u003eThis study adhered to the ethical guidelines established by the Experimental Psychology Unit, Faculty of Humanities and Social Sciences, Helmut Schmidt University. The study was approved by our general ethical vote, ensuring compliance with the ethical standards for research involving human participants. Before participating, all participants were provided with detailed information about the stud. They gave their informed consent before proceeding with the study. In line with Open Science practices, the study design, hypotheses, and data analysis plan were registered on the Open Science Framework (OSF; https://osf.io/s7gup/?view_only=cadba4ab5d5645969493e7b630b984e8). Additionally, the raw data, analysis scripts, questionnaires used in the study, and their English translations will also be made publicly available on the OSF upon the completion of the study (https://osf.io/7dycn/?view_only=b379718ca53843d8958713d48e14e4e2).\u003c/p\u003e\u003ch3\u003eParticipants\u003c/h3\u003e\u003cp\u003eA total of 296 participants were recruited using CrowdWorks (https://crowdworks.jp/), a Japanese online platform for survey participation and freelance work. The eligibility criteria required participants to be at least 18 years old and fluent in Japanese, as the study materials were presented in Japanese. Participants were randomly assigned to one of the two podcast conditions: monologue-based or dialogue-based. The monologue-based condition included 152 participants (M\u003csub\u003eage\u003c/sub\u003e = 43.3, SD = 10.5; 75 women and 77 men). The dialogue-based condition comprised 144 participants (M\u003csub\u003eage\u003c/sub\u003e = 43.0, SD = 10.2; 64 women, 79 men; one no answer). To determine the required sample size, an a priori power analysis was conducted using G*Power (version 3.1.9.7; Faul et al., 2007). The analysis was based on a two-tailed independent t-test with a medium effect size (\u003cem\u003ed\u003c/em\u003e = 0.5), a Bonferroni-adjusted alpha error probability of 0.00625 (for eight dependent variables), and α power of 0.95. The results indicated that a minimum of 278 participants (139 per group) was required to achieve sufficient statistical power. Participants received 150 yen as compensation for completing the study.\u003c/p\u003e\u003ch2\u003eMaterials\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eQuestionnaire\u003c/strong\u003e. The questionnaire consisted of the following components:\u003c/p\u003e\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Podcast Evaluation\u003c/p\u003e\u003cp\u003eParticipants evaluated the podcast’s content and format using a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). The evaluation items included the following:\u003c/p\u003e\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Enjoyment: \"Listening to this podcast was enjoyable.\"\u003c/p\u003e\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Liking: \"I liked this podcast.\"\u003c/p\u003e\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Interest: \"I was interested in the content of this podcast.\"\u003c/p\u003e\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Clarity: \"The content of this podcast was easy to understand.\"\u003c/p\u003e\u003cp\u003ee.\u0026nbsp; \u0026nbsp;Comprehension: \"I understood the content of this podcast.\"\u003c/p\u003e\u003cp\u003ef.\u0026nbsp; \u0026nbsp;\u0026nbsp;Immersion: \"I was immersed in this podcast.\"\u003c/p\u003e\u003cp\u003eg.\u0026nbsp; \u0026nbsp;Repeat Intention: \"I would like to listen to other episodes of this podcast introducing different research papers.\"\u003c/p\u003e\u003cp\u003eh.\u0026nbsp; \u0026nbsp;Trust: \"I trust the podcast host(s).\"\u003c/p\u003e\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Podcast Listening Habits\u003c/p\u003e\u003cp\u003eInformation on participants’ podcast listening habits was collected using questions adapted from Tobin and Guadagno (2022). These included the frequency of listening (e.g., daily, weekly), total listening duration per week, number of different podcasts listened to per month, and proportions of preferred formats (e.g., single host, multiple hosts). Participants were also asked about their listening environments (e.g., multitasking at home or on the go) and the devices used (e.g., smartphones, computers).\u003c/p\u003e\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Psychological Traits and Demographic Information\u003c/p\u003e\u003cp\u003ePsychological traits were assessed using two measures. The Big Five personality traits were evaluated using the Ten-Item Personality Inventory (TIPI; Gosling et al., 2003; Oshio et al., 2012), which measures extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. The need for complexity and novelty was assessed using a subscale of the Multidimensional Attitudes Toward Ambiguity Scale (MAAS; Lauriola et al., 2016; Hitsuwari \u0026amp; Nomura, 2021). Both measures used a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). Basic demographic information, including age and gender, was also collected.\u003c/p\u003e\u003cp\u003e4.\u0026nbsp; \u0026nbsp;Podcast Content Comprehension Check\u003c/p\u003e\u003cp\u003eParticipants’ comprehension of the podcast content was assessed through multiple-choice questions. These included identifying the AI model used in the study, determining which haiku was rated as the most beautiful, and recognizing the future research directions mentioned in the podcast. Participants also rated their confidence in each response on a 7-point Likert scale (1 = Not confident at all, 7 = Very confident).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAI Podcast\u003c/em\u003e\u003c/strong\u003e. The scripts for the AI podcasts used in this study were created using ChatGPT 4o (OpenAI, 2024). The scripts were constructed by having ChatGPT read the paper \"Discover Beauty of Haiku Created Through Co-Creation with AI\" (Hitsuwari et al., 2024; see Hitsuwari et al., 2023 for English version) and present its content. This study compared three conditions: human-made haiku, AI-generated haiku, and human-AI co-created haiku (AI-generated haiku selected by humans), demonstrating that the human-AI co-created haiku received the highest aesthetic evaluations. Two types of podcasts were prepared—monologue and dialogue formats—each designed to provide a different listening experience. First, the dialogue script was created, adopting a style in which a general audience host and a psychology expert (the first author of both this paper and the paper that was fed to ChatGPT) explored the paper's content through dialogue. In this format, the host played the role of eliciting information from the guest, and when complex concepts or terminology appeared, the host asked additional questions to encourage explanations in simpler terms. For the monologue format, a script was prepared in which the same content was spoken continuously by a single psychology expert.\u003c/p\u003e\u003cp\u003eThe two prepared scripts were converted into audio using VoiceVox (https://voicevox.hiroshiba.jp/), software that enables AI voice synthesis. The expert's voice was the same across both podcasts. Male voices were used throughout, including for the general audience host. After the voice conversion, minor adjustments were made to the scripts to ensure that both podcasts were under 5 minutes and approximately the same length (monologue 4:39; dialogue 4:57). They were subsequently reconverted to audio for use as stimuli. These scripts, the ChatGPT prompts used for script creation, and the audio files (all in Japanese) were uploaded to the OSF.\u003c/p\u003e\u003ch3\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/h3\u003e\u003cp\u003eIn this study, participants listened to a podcast after providing consent to participate in the research, then completed a questionnaire. Participants were randomly assigned to either a monologue or dialogue format podcast condition. After listening, participants freely described their impressions of the podcast, taking at least 30 seconds to do so. They then answered questions assessing their evaluation of the content, listening habits, and psychological characteristics. Podcast evaluation items included Enjoyment, Liking, Interest, Clarity, Comprehension, Immersion, Repeat Intention, and Trust in the personality. Information on the participants' podcast listening habits—such as frequency, duration, devices used, and listening environments—was also collected. Furthermore, to measure participants' psychological characteristics, questionnaires on the Big Five personality traits and the need for novelty and complexity were administered. Finally, multiple-choice questions on the AI models, haiku evaluation results, and other study-related aspects were included to assess content comprehension, along with confidence ratings for their answers.\u003c/p\u003e\u003cp\u003eFor Hypothesis 1 (differences in podcast format evaluations), independent samples t-tests were used to compare the eight evaluation variables, with effect sizes calculated using Cohen's d. For Hypothesis 2 (the relationship between podcast format and test performance/confidence), t-tests were used to compare total test scores, while χ² tests were used to compare correct answer rates for each question. Additionally, t-tests were conducted to compare confidence levels in responses for each question. For Hypothesis 3 (interaction with individual characteristics), multiple regression analysis was performed. Podcast evaluation served as the dependent variable, while experimental conditions (monologue/dialogue), individual characteristics (Big Five, Need for Complexity and Novelty), and the interaction terms between condition and individual characteristics were set as independent variables. All continuous variables were centered. To control for Type I errors from multiple comparisons, Bonferroni correction was applied, with the significance level set at α = 0.00625 (0.05/8 variables). For exploratory analysis of the open-ended response data, sentiment analysis (positive/negative/neutral) was conducted using Bidirectional Encoder Representations from Transformers (BERT; https://huggingface.co/tohoku-nlp/bert-base-japanese-v3). χ² tests were used to compare the distribution of sentiment categories between conditions. For negative comments, instances were also categorized by content type. All statistical analyses were performed using Python (Version 3.8.5; Python Software Foundation, 2020), with a significance level of α = 0.05 unless otherwise specified.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eRegarding Hypothesis 1, we compared the eight evaluation measures between the monologue and dialogue format podcasts using t-tests (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results of the independent samples t-tests showed no statistically significant differences between the two formats for any dependent variable, starting with Enjoyment (\u003cem\u003et\u003c/em\u003e = -0.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.54, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07). Overall, effect sizes (Cohen's \u003cem\u003ed\u003c/em\u003e) were small for all variables (|\u003cem\u003ed\u003c/em\u003e| \u0026le; .13), suggesting that the podcast format (monologue or dialogue) did not have a substantial impact on listeners' evaluations. These results do not support our initial hypothesis that dialogue format podcasts would receive higher ratings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMean Values, Standard Deviations, and t-test Results for Each Variable in Monologue and Dialogue Podcasts\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMonologue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eDialogue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003et test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnjoyment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmersion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeat Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Hypothesis 2 (test performance and confidence in answers), the analysis of mean differences in total scores (one point per question) revealed no significant difference between monologue and dialogue conditions (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emonologue\u003c/em\u003e\u003c/sub\u003e = 1.93, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.88; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edialogue\u003c/em\u003e\u003c/sub\u003e = 1.81, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.94; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003ed\u003c/em\u003e = -0.13). In the analysis of correct answer rates for each question, the question about the most beautiful haiku showed a significantly higher correct answer rate in the monologue condition than in the dialogue condition (correct rate\u003csub\u003e\u003cem\u003emonologue\u003c/em\u003e\u003c/sub\u003e = 86.18%; correct rate\u003csub\u003e\u003cem\u003edialogue\u003c/em\u003e\u003c/sub\u003e = 70.83%; \u003cem\u003eχ\u0026sup2;\u003c/em\u003e(1)\u0026thinsp;=\u0026thinsp;9.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). For the questions about the machine learning model (correct rate\u003csub\u003e\u003cem\u003emonologue\u003c/em\u003e\u003c/sub\u003e = 56.58%; correct rate\u003csub\u003e\u003cem\u003edialogue\u003c/em\u003e\u003c/sub\u003e = 60.42%; \u003cem\u003eχ\u0026sup2;\u003c/em\u003e(1)\u0026thinsp;=\u0026thinsp;0.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.58) and future research directions (correct rate\u003csub\u003e\u003cem\u003emonologue\u003c/em\u003e\u003c/sub\u003e = 50.66%; correct rate\u003csub\u003e\u003cem\u003edialogue\u003c/em\u003e\u003c/sub\u003e = 50.00%; \u003cem\u003eχ\u0026sup2;\u003c/em\u003e(1)\u0026thinsp;=\u0026thinsp;0.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0), no significant differences were found between monologue and dialogue conditions.\u003c/p\u003e \u003cp\u003eIn the analysis of confidence ratings for each question, participants in the monologue condition showed significantly higher confidence for the question about the most beautiful haiku, similar to the correct answer rate results (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emonologue\u003c/em\u003e\u003c/sub\u003e = 5.58, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.57; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edialogue\u003c/em\u003e\u003c/sub\u003e = 4.92, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.93; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003ed\u003c/em\u003e = -0.37). For questions about the machine learning model (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emonologue\u003c/em\u003e\u003c/sub\u003e = 3.02, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.92; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edialogue\u003c/em\u003e\u003c/sub\u003e = 3.05, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.97; \u003cem\u003et\u003c/em\u003e = -0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and future research directions (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emonologue\u003c/em\u003e\u003c/sub\u003e = 4.28, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.93; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edialogue\u003c/em\u003e\u003c/sub\u003e = 4.50, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.94; \u003cem\u003et\u003c/em\u003e = -0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12), no significant differences were found between monologue and dialogue conditions.\u003c/p\u003e \u003cp\u003eFor Hypothesis 3 (interaction with individual characteristics), multiple regression analyses were conducted using the Bonferroni-corrected significance level (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00625) (Supplementary Table\u0026nbsp;1). The results revealed that Agreeableness showed consistent positive effects on multiple dependent variables. Specifically, Agreeableness had statistically significant effects on Liking (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), Interest (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), Immersion (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Trust (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, Need for Complexity and Novelty showed significant positive effects on Enjoyment (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and Repeat Intention (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Regarding the hypothesized interactions between the experimental conditions and individual characteristics, no significant interactions were found for any of the variables.\u003c/p\u003e \u003cp\u003e Finally, an exploratory analysis was conducted on the open-ended responses (natural language) regarding participants\u0026rsquo; impressions of the podcasts. Using Tohoku University's BERT model, sentiment analysis (positive/negative/neutral) was performed for each condition (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, positive comments were the most frequent (63.51%), followed by neutral (21.62%) and negative comments (14.86%). There was no significant difference between the monologue and dialogue conditions (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e(2)\u0026thinsp;=\u0026thinsp;2.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.36). Additionally, content analysis of the negative comments revealed 17 instances related to \"anxiety/challenges about AI evolution,\" 16 instances of \"dissatisfaction with audio,\" eight instances of \"difficulty understanding content,\" and three instances of \"doubts about content/lack of interest.\"\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eOccurrence Rates of Positive and Negative Words in Comments on Monologue and Dialogue Podcasts\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMonologue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eDialogue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined how monologue and dialogue format podcasts affect listeners' evaluations, comprehension, and personal preferences. Our initial hypothesis predicted that dialogue format podcasts would receive higher evaluations and comprehension than monologue format podcasts; however, the results did not support this hypothesis.\u003c/p\u003e \u003cp\u003eFirst, no statistically significant differences occurred in any dependent variable between podcast formats (monologue/dialogue). This finding is inconsistent with previous research demonstrating the superiority of the dialogue format (Van Meurs et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tolins et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The main reason for this inconsistency could be that our study used stimuli with nearly identical information content and number of perspectives (variations in how things are perceived and explained within the conversation). Previous research has suggested that the advantage of the dialogue format stems from the diversity of perspectives and refinement of explanations through feedback (Fox Tree, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) rather than the amount of information. In this study, the same content was presented in both monologue and dialogue formats, meaning that the information and perspectives conveyed were substantially identical, which may have prevented differences between formats from emerging. Furthermore, as Branigan et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) suggested, the effectiveness of the dialogue format arises from the process by which speakers and listeners adjust their understanding of each other and form common perspectives. The dialogue podcast used in this study was based on a prepared script that may have differed from naturally occurring conversations.\u003c/p\u003e \u003cp\u003eNext, regarding comprehension, while no significant difference was observed in total test scores between monologue and dialogue formats, the monologue format showed significantly higher correct answer rates and confidence for the question about the most beautiful haiku. This may be because, in a monologue, the speaker can consistently emphasize important information. This result partially aligns with Read's (2002) research, which found higher listening test scores for the monologue format among non-native English speakers (noting that the dialogue condition in that study involved three-person discussions). Furthermore, as Kvernbekk (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) noted, monologues can effectively direct listeners' attention to specific points by systematically presenting information. Particularly for relatively clear information such as the aesthetic evaluation of a haiku, direct transmission by a solo voice may have been more advantageous for information retention than exchanges in dialogue. However, because a difference was observed in only one question, this advantage of the monologue format can be considered limited.\u003c/p\u003e \u003cp\u003eFurthermore, regarding personality traits, while no interaction was observed between podcast format and personality traits, Agreeableness and Need for Complexity and Novelty showed positive effects on multiple evaluation items. The finding that participants high in Agreeableness gave more favorable evaluations of podcasts is consistent with Tobin and Guadagno's (2022) observation that highly agreeable individuals tend to develop a strong affinity with podcast hosts. Additionally, the association between Need for Complexity and Novelty, and both enjoyment and repeat intention aligns with the discovery of curiosity, which predicts podcast listening (Tobin \u0026amp; Guadagno, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results suggest that listeners\u0026rsquo; personality traits may play a more significant role in podcast evaluations than the formats.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Direction\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the podcasts used in the experiment were based on AI-generated scripts. Although this approach has the advantage of controlling the content and emotional expression between conditions, it may not fully reproduce the characteristics of natural conversations. In particular, as Spoelders and Claes (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) noted, the appeal of dialogue lies in the anticipation of answers to questions raised during conversations; however, in script-based conversations, this natural process of question generation and resolution may be restricted. Second, the length of the podcasts used in the experiment (approximately 5 minutes) was shorter than that of typical podcasts and may have been insufficient for format effects to become apparent. With longer content, the advantages of the dialogue format may become more evident. Based on these limitations, future research should employ experimental designs using podcasts that include more natural dialogue and content covering diverse topics. It would also be valuable to examine how the effects of format differ depending on podcast listening contexts (e.g., listening in multitasking environments and focused listening) and questionnaire items not addressed in this study (e.g., intimacy with hosts).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study suggest that the impact of podcast format (monologue or dialogue) on listener evaluations and comprehension may not be as significant as suggested by previous research. Additionally, no interaction was found between format and listener personality traits, providing no evidence that listeners with specific personality traits prefer particular formats. Rather, listener personality traits themselves and content substance appear to be factors that have a greater influence on podcast experiences. For educational content, the monologue format may be partially effective for conveying specific information, while overall, the content substance and host personality may be more important than the format itself. In future research on podcasts, examining the interaction of more complex factors\u0026mdash;not just format, but also content type, listener characteristics, and listening environment\u0026mdash;will be key to deepening our understanding of podcast media.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBezbaruah, S., \u0026amp; Brahmbhatt, K. (2023). Are podcast advertisements effective? An emerging economy perspective. \u003cem\u003eJournal of International Consumer Marketing, 35\u003c/em\u003e(2), 215\u0026ndash;233. https://doi.org/10.1080/08961530.2022.2085222 \u003c/li\u003e\n\u003cli\u003eBird, D. (2025). Democratic Podcasting: Mediating Subjectivity in Constructive Audio Journalism Practice. \u003cem\u003eJournalism Practice\u003c/em\u003e, 1\u0026ndash;21. https://doi.org/10.1080/17512786.2024.2448762 \u003c/li\u003e\n\u003cli\u003eBranigan, H. P., Catchpole, C. M., \u0026amp; Pickering, M. J. (2011). What makes dialogues easy to understand? \u003cem\u003eLanguage and Cognitive Processes, 26\u003c/em\u003e(10), 1667\u0026ndash;1686. https://doi.org/10.1080/01690965.2010.524765\u003c/li\u003e\n\u003cli\u003eCarrotte, E. R., Blanchard, M., Groot, C., Hopgood, F., \u0026amp; Phillips, L. (2023). Podcasts, mental health, and stigma: Exploring motivations, behaviors, and attitudes among listeners. \u003cem\u003eCommunication Studies, 74\u003c/em\u003e(3), 200\u0026ndash;216. https://doi.org/10.1080/10510974.2023.2196433\u003c/li\u003e\n\u003cli\u003eChan-Olmsted, S., \u0026amp; Wang, R. (2022). 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Dialogue-style content generation with multi-source training. \u003cem\u003eProceedings of the 96th SIG-SLUD Workshop, The Japanese Society for Artificial Intelligence\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e, 51\u0026ndash;56. https://doi.org/10.11517/jsaislud.96.0_10 (in Japanese with English abstract)\u003c/li\u003e\n\u003cli\u003eJAPAN PODCAST AWARDS. (2024). JAPAN PODCAST AWARDS. Retrieved December 18, 2024, from https://www.japanpodcastawards.com/index.html (in Japanese)\u003c/li\u003e\n\u003cli\u003eKelly, J. M., Perseghin, A., Dow, A. W., Trivedi, S. P., Rodman, A., \u0026amp; Berk, J. (2022). Learning through listening: a scoping review of podcast use in medical education. \u003cem\u003eAcademic Medicine, 97\u003c/em\u003e(7), 1079\u0026ndash;1085. https://doi.org/10.1097/ACM.0000000000004565 \u003c/li\u003e\n\u003cli\u003eKvernbekk, T. (2012). 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The effect of monologues and dialogues in radio commercials.\u003cem\u003e Tijdschrift voor Taalbeheersing, 41\u003c/em\u003e(1), 189\u0026ndash;201. https://doi.org/10.5117/TVT2019.1.013.MEUR \u003c/li\u003e\n\u003cli\u003eWolenski, R., \u0026amp; Pettit, J. W. (2023). Social media usage is associated with lower knowledge about anxiety and indiscriminate use of anxiety coping strategies. \u003cem\u003ePsychology of Popular Media, 13\u003c/em\u003e(2), 190\u0026ndash;200. https://doi.org/10.1037/ppm0000456\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"94c00f96-af1b-4ad6-94e6-44fb2e9188af","identifier":"10.13039/501100001691","name":"Japan Society for the Promotion of Science","awardNumber":"Overseas Research Fellowship","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Helmut Schmidt University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"podcasts, dialogue, monologue, listening comprehension, media psychology","lastPublishedDoi":"10.21203/rs.3.rs-6217086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6217086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e This study investigates the effects of podcast format (monologue versus dialogue) on listener experience among Japanese participants (N\u0026thinsp;=\u0026thinsp;296). Despite the growing popularity of dialogue-based podcasts, research comparing podcast formats remains limited. Participants were randomly assigned to listen to either a monologue or dialogue version of an AI-generated podcast discussing psychological research. We hypothesized that dialogue-based podcasts would yield higher impression ratings and better comprehension and that individual differences would moderate format preferences. Contrary to expectations, no significant differences were found in the overall evaluations, including enjoyment, interest, and immersion, between formats. Comprehension results revealed a single advantage of the monologue format: participants showed higher accuracy and confidence in identifying key information. Agreeableness and the need for complexity and novelty positively predicted several evaluation metrics, regardless of the format, while no significant interactions between podcast format and personality traits were observed. Free-response analysis showed predominantly positive reactions to both formats. These findings suggest that podcast format may have less impact on listener experience than assumed, and that content quality and listener traits may be more influential factors. This study contributes to the understanding of digital media consumption, with implications for podcast creators and educational content developers.\u003c/p\u003e","manuscriptTitle":"Comparison of Listening Experiences by Podcast Styles: Monologue versus Dialogue","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 16:22:36","doi":"10.21203/rs.3.rs-6217086/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"849de03f-b31b-452c-8df1-d9575fb16dbd","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45618140,"name":"Psychology"},{"id":45618141,"name":"Media Studies"}],"tags":[],"updatedAt":"2025-03-17T16:22:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 16:22:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6217086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6217086","identity":"rs-6217086","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}