How Speaking versus Writing to Conversational Agents Shapes Consumers’ Choice and Choice Satisfaction
Open accessAbstract
The use of conversational agents (e.g., chatbots) to simplify or aid consumers’ purchase decisions is on the rise. In designing those conversational agents, a key question for companies is whether and when it is advisable to enable voice-based rather than text-based interactions. Addressing this question, this study finds that matching consumers’ communication modality with product type (speaking about hedonic products; writing about utilitarian products) shapes consumers’ choice and increases choice satisfaction. Specifically, speaking fosters a feeling-based verbalizing focus, while writing triggers a reason-based focus. When this focus matches consumers’ mindset in evaluating the product type, preference fluency increases, thereby enhancing choice satisfaction. Accordingly, the authors provide insights into managing interactions with conversational agents more effectively to aid decision-making processes and increase choice satisfaction. Finally, they show that communication modality can serve as a strategic tool for low-equity brands to better compete with high-equity brands.
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Communication modality between text and voice affects cognitive focus on enhanced levels regarding choice satisfaction.
“The study of Schindler et al. [8] showed that communication modality between text and voice affects cognitive focus on enhanced levels regarding choice satisfaction”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as Schindler et al. [8]
The Academic implications section says speaking and writing induce different feeling- or reason-based focuses and can increase choice satisfaction. But the increase depends on the communication modality matching the product type; the citing sentence omits that condition. (LLM, conf=0.85)
Experimental — accuracy not yet measuredModality and interface cues determine whether autonomy is perceived as supportive or intrusive.
“In a similar vein, modality and interface cues determine whether autonomy is perceived as supportive or intrusive (e.g., the effects of voice versus text on cognition and satisfaction; assistant initiation patterns affecting reactance) [8,24,52].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Introduction and Academic implications sections report that speaking versus writing affects consumers’ focus on feelings or reasons, choice, and choice satisfaction. They support the parenthetical example about modality, cognition, and satisfaction, but do not establish that interface cues determine whether autonomy feels supportive or intrusive. The paper also does not report effects of assistant initiation on reactance. Page numbers were not supplied. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCo-assistant designs can maintain satisfaction and choice certainty when users recognize performance advantages without a reduction in governance, and modality/interaction selections can influence whether autonomy is perceived as supportive or intrusive.
“Conversely, co-assistant designs can maintain satisfaction and choice certainty when users recognize performance advantages without a reduction in governance [24], and modality/interaction selections can additionally influence whether autonomy is perceived as supportive or intrusive [8].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA significant body of literature examines anthropomorphism, modality, warmth/competence cues, and initiation patterns, frequently demonstrating impacts on satisfaction, social presence, or reactance in controlled environments.
“A significant body of literature examines anthropomorphism, modality, warmth/competence cues, and initiation patterns, frequently demonstrating impacts on satisfaction, social presence, or reactance in controlled environments [8,24].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Academic implications section (page unavailable) reports that matching speaking or writing with product type increases choice satisfaction, and the Empirical overview describes five studies of communication modality. The paper does not establish the sentence’s other attributed topics—anthropomorphism, warmth/competence cues, or initiation patterns—or effects on social presence or reactance. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA lot of the strongest causal evidence in the corpus comes from experiments instead of natural settings, leaving the real-world persistence of effects under-identified.
“This suggestion fits with the fact that a lot of the strongest causal evidence in the corpus comes from experiments instead of natural settings, leaving the real-world persistence of effects under-identified [8,40].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Limitations and future research section says the main studies were lab and online experiments and calls for field studies to test whether their findings hold in more natural settings. It also reports an initial offline field study, so the work is not exclusively experimental. The paper does not assess the corpus-wide strength of causal evidence or the persistence of effects over time. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Numerous studies exhibit robust internal validity while functioning within simulated purchasing contexts.
“Numerous studies exhibit robust internal validity while functioning within simulated purchasing contexts [7,8,24,63].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Empirical overview describes five studies, and Limitations and future research identifies them as lab and online experiments designed to mirror real-world interactions rather than field studies (page not provided). The paper describes controlled conditions and calls its behavioral outcomes strong evidence, but it does not assess the internal validity of numerous studies generally or explicitly establish that its own internal validity is robust. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPanels are often not random samples, and the range of topics they cover can be very limited, such as one product category, one platform, or a few task types.
“Panels are often not random samples, and the range of topics they cover can be very limited (for example, just one product category, one platform, or a few types of tasks) [7,8,41].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Method section describes a Prolific sample and a tablet-choice task, and Limitations and future research explicitly says Study 5 was restricted to a hedonic product context. These passages support an example of a narrowly scoped study, but the paper does not establish that panels are often nonrandom or generally cover only a limited range of topics; its studies use several choice contexts. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDesign decisions and interface modalities function through psychological pathways that ultimately determine a user's willingness to rely on the agent.
“Additionally, it is consistent with research demonstrating that design decisions and interface modalities function through psychological pathways that ultimately determine a user's willingness to rely on the agent [8,24].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Introduction and Academic implications sections report that speaking versus writing to a conversational agent affects consumers’ decision-making through verbalizing focus and preference fluency. The outcomes studied are product choice, intention to choose a product, and choice satisfaction—not willingness to rely on the agent. Page numbers were not available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBrand choice/choice share effects are context-dependent; effects are stronger when modality matches product type and when brand equity is low.
“Context-dependent; comes”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Study 5 findings report a stronger positive effect of matching communication modality to product type on intention to choose a low-equity brand, specifically when the alternatives differ in brand equity. The Method section shows that participants rated their intention to choose; it does not establish an effect on observed brand-choice share. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLab experiments often manipulate assistant design or autonomy but measure intentions or simulated choice instead of actual checkout/payment.
“For instance, lab experiments often manipulate the design of the assistant or give them more freedom, but they measure the results as intentions or simulated choice instead of actual checkout/payment [7,8,24,38,43,52,54,63].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
High-autonomy cues can reduce intention via powerlessness/threat-to-freedom, unless urgency/scarcity raises benefit salience and attenuates resistance.
“High-autonomy cues can reduce intention via powerless-raises benefit salience and attenuates resistance. ness/threat-to-freedom, unless urgency/scarcity”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Mediation and Manipulation check passages examine speaking versus writing to conversational agents, brand equity, and intention to choose. Searches for autonomy, powerlessness or threat to freedom, and urgency or scarcity found no passage addressing the claimed mechanism or exception. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPurchase intention/adoption intention is mostly positive via utility/trust, but can decrease under high autonomy.
“Mostly positive via utility/trust, but can decrease under high autonomy”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Method and Study 5 passages measure intention to choose a product, but the reported effects concern speaking versus writing and brand equity. Searches for utility, trust, autonomy, and adoption intention found no passage in our copy supporting the claimed pathway or a decrease under high autonomy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe utility chain dominates: quality/usefulness leads to attitude/satisfaction, which leads to intention.
“Utility chain dominates: quality/usefulness → at-titude/satisfaction → intention.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [8]
The Mediation passages (page not provided) link communication modality to choice satisfaction through preference fluency, and to intention to choose through verbalizing focus. Searches for quality or usefulness leading to attitude or satisfaction, then to intention, found no passage establishing that chain or saying it dominates. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect
Longoni, C., & Cian, L. (2022) · Journal of Marketing · doi:10.1177/0022242920957347 · Cited by 831 · see citing works
Choice goal attainment and decision and consumption satisfaction
Heitmann, M., Lehmann, D. R., & Herrmann, A. (2007) · Journal of Marketing Research · doi:10.1509/jmkr.44.2.234 · Cited by 371
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00704-3 · Cited by 204 · cited 1× · see citing works
“…“natural language computer programs designed to approximate human speech (written or oral) and interact with people via a digital interface” (Thomaz et al., 2020, p. 49) and are increasingly being deployed by many companies to simplify or aid consumers’ purchase decisions (e.g., Castelo et…”
Flagged first, then by citations
Research works printed but not linked: 53
- Akinnaso, F. N. (1982). On the differences between spoken and written language. Language and Speech, 25(2), 97-125.
- Altenberg, B. (1984). Causal linking in spoken and written English. Studia Linguistica, 38(1), 20-69.
- Berger, J., & Iyengar, R. (2013). Communication channels and word of mouth: How the medium shapes the message. Journal of Con- sumer Research, 40(3), 567-579.
- Berger, J., Rocklage, M. D., & Packard, G. (2022). Expression modali- ties: How speaking versus writing shapes word of mouth. Journal of Consumer Research, 49(3), 389-408.
- Berger, J., & Schwartz, E. M. (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48(5), 869-880.
- Berry, C., Burton, S., & Howlett, E. (2017). It's only natural: The medi- ating impact of consumers' attribute inferences on the relation- ships between product claims, perceived product healthfulness, and purchase intentions. Journal of the Academy of Marketing Science, 45(5), 698-719.
- Biswas, D., Hartmann, P., Eisend, M., Szocs, C., Jochims, B., Apao- laza, V., Hermann, E., López, C. M., & Borges, A. (2023). Caf- feine's effects on consumer spending. Journal of Marketing, 87(2), 149-167.
- Camacho, C. J., Higgins, E. T., & Luger, L. (2003). Moral value trans- fer from regulatory fit: What feels right is right and what feels wrong is wrong. Journal of Personality and Social Psychology, 84(3), 498-510.
- Castelo, N., Boegershausen, J., Hildebrand, C., & Henkel, A. P. (2023). Understanding and improving consumer reactions to service bots. Journal of Consumer Research. https:// doi. org/ 10. 1093/ jcr/ ucad0 · DOI
- Chafe, W. L. (1985). Linguistic differences produced by differences between speaking and writing. In D. R. Olson, N. Torrance, & A. Hildyard (Eds.), Literacy, language, and learning: The nature and consequences of reading and writing (pp. 105-123). Cambridge Univ. Press.
- Chafe, W. L., & Tannen, D. (1987). The relation between written and spoken language. Annual Review of Anthropology, 16, 383-407.
- Dhar, R., & Wertenbroch, K. (2000). Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research, 37(1), 60-71.
- Ding, Y., & Zhang, Y. (2020). Hiding gifts behind the veil of vouchers: On the effect of gift vouchers versus direct gifts in conditional promotions. Journal of Marketing Research, 57(4), 739-754.
- Erdem, T., & Swait, J. (1998). Brand equity as a signaling phenom- enon. Journal of Consumer Psychology, 7(2), 131-157.
- Fondacaro, R. A., & Higgins, E. T. (1985). Cognitive consequences of communication mode: A social psychological perspective. In D. R. Olson, N. Torrance, & A. Hildyard (Eds.), Literacy, language, and learning: The nature and consequences of reading and writ- ing (pp. 73-104). Cambridge Univ. Press.
- Fotheringham, D., & Wiles, M. A. (2023). The effect of implement- ing chatbot customer service on stock returns: An event study analysis. Journal of the Academy of Marketing Science, 51(4), 802-822.
- Giebelhausen, M., Chun, H. H., Cronin, J. J., & Hult, G. T. M. (2016). Adjusting the warm-glow thermostat: How incentivizing partici- pation in voluntary green programs moderates their impact on service satisfaction. Journal of Marketing, 80(4), 56-71.
- Guha, A., Bressgott, T., Grewal, D., Mahr, D., Wetzels, M., & Sch- weiger, E. (2023). How artificiality and intelligence affect voice assistant evaluations. Journal of the Academy of Marketing Sci- ence, 51(4), 843-866.
- Hammond, K. R., Hamm, R. M., Grassia, J., & Pearson, T. (1987). Direct comparison of the efficacy of intuitive and analytical cogni- tion in expert judgment. IEEE Transactions on Systems, Man, and Cybernetics, 17(5), 753-770.
- Hayes, A. F. (2018). Introduction to mediation, moderation, and condi- tional process analysis: A regression-based approach (methodol- ogy in the social sciences). Guilford Press.
- Heinberg, M., Katsikeas, C. S., Ozkaya, H. E., & Taube, M. (2020). How nostalgic brand positioning shapes brand equity: Differences between emerging and developed markets. Journal of the Acad- emy of Marketing Science, 48(5), 869-890.
- Higgins, E. T. (2000). Making a good decision: Value from fit. Ameri- can Psychologist, 55(11), 1217-1230.
- Hildebrand, C., & Bergner, A. (2021). Conversational robo advisors as surrogates of trust: Onboarding experience, firm perception, and consumer financial decision making. Journal of the Academy of Marketing Science, 49(4), 659-676.
- Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consump- tion: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92-101.
- Ho-Dac, N. N., Carson, S. J., & Moore, W. L. (2013). The effects of positive and negative online customer reviews: Do brand strength and category maturity matter? Journal of Marketing, 77(4), 37-53.
- Hoeffler, S., & Keller, K. L. (2003). The marketing advantages of strong brands. Journal of Brand Management, 10(6), 421-445.
- Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51(1), 57-71.
- Hulland, J., Baumgartner, H., & Smith, K. M. (2018). Marketing survey research best practices: Evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46(1), 92-108.
- Hulland, J., & Houston, M. (2021). The importance of behavioral outcomes. Journal of the Academy of Marketing Science, 49(3), 437-440.
- Humphreys, A., Isaac, M. S., & Wang, R. J.-H. (2021). Construal matching in online search: Applying text analysis to illuminate the consumer decision journey. Journal of Marketing Research, 58(6), 1101-1119.
- Huyghe, E., Verstraeten, J., Geuens, M., & van Kerckhove, A. (2017). Clicks as a healthy alternative to bricks: How online grocery shopping reduces vice purchases. Journal of Marketing Research, 54(1), 61-74.
- Lee, A. Y., & Aaker, J. L. (2004). Bringing the frame into focus: The influence of regulatory fit on processing fluency and persuasion. Journal of Personality and Social Psychology, 86(2), 205-218.
- Lee, A. Y., Keller, P. A., & Sternthal, B. (2010). Value from regula- tory construal fit: The persuasive impact of fit between con- sumer goals and message concreteness. Journal of Consumer Research, 36(5), 735-747.
- Mafael, A., Raithel, S., & Hock, S. J. (2022). Managing customer satisfaction after a product recall: The joint role of remedy, brand equity, and severity. Journal of the Academy of Marketing Science, 50(1), 174-194.
- Melzner, J., Bonezzi, A., & Meyvis, T. (2023). Information dis- closure in the era of voice technology. Journal of Marketing, 87(4), 491-509.
- Noble, S. M., & Mende, M. (2023). The future of artificial intelli- gence and robotics in the retail and service sector: Sketching the field of consumer-robot-experiences. Journal of the Academy of Marketing Science, 51(4), 747-756.
- Novemsky, N., Dhar, R., Schwarz, N., & Simonson, I. (2007). Prefer- ence fluency in choice. Journal of Marketing Research, 44(3), 347-356.
- Pezzuti, T., & Leonhardt, J. M. (2023). What's not to like? Negations in brand messages increase consumer engagement. Journal of the Academy of Marketing Science, 51(3), 675-694.
- Pham, M. T. (1998). Representativeness, relevance, and the use of feelings in decision making. Journal of Consumer Research, 25(2), 144-159.
- Pratt, A. B., Robinson, S. G., Voorhees, C. M., Wang, J., & Giebel- hausen, M. D. (2023). Unintended effects of price promotions: Forgoing competitors' price promotions strengthens incumbent brand loyalty. Journal of the Academy of Marketing Science, 51(5), 1143-1164.
- Rocklage, M. D., Rucker, D. D., & Nordgren, L. F. (2018). The Evalu- ative Lexicon 2.0: The measurement of emotionality, extremity, and valence in language. Behavior Research Methods, 50(4), 1327-1344.
- Roggeveen, A. L., Grewal, D., Townsend, C., & Krishnan, R. (2015). The impact of dynamic presentation format on consumer prefer- ences for hedonic products and services. Journal of Marketing, 79(6), 34-49.
- Rzepka, C., Berger, B., & Hess, T. (2022). Voice assistant vs. chatbot -examining the fit between conversational agents' interaction modalities and information search tasks. Information Systems Frontiers, 24(3), 839-856.
- Savary, J., Goldsmith, K., & Dhar, R. (2015). Giving against the odds: When tempting alternatives increase willingness to donate. Jour- nal of Marketing Research, 52(1), 27-38.
- Schmitt, B. H., & Zhang, S. (1998). Language structure and categoriza- tion: A study of classifiers in consumer cognition, judgment, and choice. Journal of Consumer Research, 25(2), 108-122.
- Sela, A., & Berger, J. (2012). How attribute quantity influences option choice. Journal of Marketing Research, 49(6), 942-953.
- Shen, H., & Sengupta, J. (2018). Word of mouth versus word of mouse: Speaking about a brand connects you to it more than writing does. Journal of Consumer Research, 36(1), 595-614.
- Slotegraaf, R. J., & Pauwels, K. (2008). The impact of brand equity and innovation on the long-term effectiveness of promotions. Journal of Marketing Research, 45(3), 293-306.
- Tannen, D. (1985). Relative focus on involvement in oral and written discourse. In D. R. Olson, N. Torrance, & A. Hildyard (Eds.), Literacy, language, and learning: the Nature and consequences of reading and writing (pp. 124-147). Cambridge Univ. Press.
- White, K., Macdonnell, R., & Dahl, D. W. (2011). It's the mind-set that matters: The role of construal level and message framing in influ- encing consumer efficacy and conservation behaviors. Journal of Marketing Research, 48(3), 472-485.
- Yoo, B., Donthu, N., & Lee, S. (2000). An examination of selected marketing mix elements and brand equity. Journal of the Academy of Marketing Science, 28(2), 195-211.
- Zhang, S., & Fitzsimons, G. J. (1999). Choice-process satisfaction: The influence of attribute alignability and option limitation. Organiza- tional Behavior and Human Decision Processes, 77(3), 192-214.
- Zierau, N., Hildebrand, C., Bergner, A., Busquet, F., Schmitt, A., & Leimeister, J. M. (2023). Voice bots on the frontline: Voice-based interfaces enhance flow-like consumer experiences & boost ser- vice outcomes. Journal of the Academy of Marketing Science, 51(4), 823-842.
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Anthropomorphism in artificial intelligence: a game-changer for brand marketing
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- Authors
- Schindler, D., Maiberger, T., Koschate-Fischer, N., & Hoyer, W. D.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Darmstadt University of Applied Sciences (DE)
- The University of Texas at Austin (US)
- Venue
- Journal of the Academy of Marketing Science
- Published
- 2023-11-30
- Volume
- 52
- Issue
- 3
- Pages
- 634-652
- ISSN
- 0092-0703 (ISSN-L)
- 1552-7824
- OpenAlex
- W4389200521
- Cited by
- 48
- References
- 3
- Citations by year
- 2026: 20 · 2025: 21 · 2024: 7
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of the Academy of Marketing Science (publishedVersion, cc-by)
- Econstor (Econstor) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Friedrich-Alexander-Universität Erlangen-Nürnberg
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- chatbots, voice interaction, text-based interaction, communication modality, purchase decision support, decision satisfaction, hedonic products, utilitarian products, brand equity, consumer choice
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