Introduction

Purpose

Artificial intelligence (AI) is increasingly used in product and industrial design, with marketers often highlighting its role in product descriptions. However, there is limited understanding of how consumers respond to AI-designed products. This study examines the impact of AI awareness on consumer behavior, particularly impatience, and its implications for marketing strategies.

Importance

  • Academic Contributions: Expands on literature on psychological constructs in the context of AI
  • Industry Impact: Offers trategies to mitigate negative pereceptions of AI
  • Practical Relevance: Equips businesses with insights to design and market AI-created products

Analytics Objective

Examine the Mediating Role of Self-Importance Threat

  • To test whether awareness of AI involvement in product design influences willingness to pay for expedited services (H2), and whether this effect is mediated by self-importance threat.

Literature Review

Existence of Well-Established Consumer Behavior Theory/Models Relevant to Project

  • Consider Maslow's Hierarchy of Needs. Each individual has needs ranging from self-atualization to physoilogical needs.
  • In the context of AI, self-actualization and esteem needs are threatened due to AI’s imposing stance on multiple industries, specifically those requring design skills as demonstrated in our project.
  • Additionally, consumer behavior is affected due to the impending willingness or likelihood for an individual to support, buy, or use a product that has been designed or marketed using AI.
  • The Black Box Model supports this idea of consumer behavior being adversely affected by AI, where we can not know what happens in the mind of the buyer.
  • We hope to uncover some of the rationale for consumer willingness and consumption through participation in our survey.

Significance of Topic

  • Artificial intelligence (AI) has made significant inroads into product and industrial design in recent years. Not only are more and more products designed by AI, but marketers also frequently highlight such information in the product descriptions and their advertising communication materials.

  • Our research expands the scope of the AI literature by showing that the awareness of AI creation has far-reaching consequences on consumer behavior by systematically altering the level of patience consumers have in their subsequent decisions. We further show that heightened feelings of threat to human’s self-importance account for this AI-impatience effect.

Methods

Data and Sampling

  • Research Design: Descriptive research using surveys​
  • Population: US consumer​
  • Sampling Frame: 200 -300 respondents, two rounds of data collection​
  • Sampling Method: Prolific recruit platform, random sampling ​
  • Data Collection: Primary data collection with online experiment using Qualtrics​
  • Sample Characteristics: Demographic characteristics such as gender, age, education level​
  • Data Wrangling: Excel and SPSS (.sav file) data will be exported from Qualtrics

Data Wrangling

Data was acquired from Qualtrics, which is where the surveys were conducted. Data cleaned through SPSS to remove any incomplete surveys or missing values.

Completed Surveys Avg Time to Complete Avg of Self-Importance Threat in Relation to Product Development Model (1 - None, 5 - Very Much)
93 Surveys 433 Seconds 2.41

Measures

Independent Variables

  • AI-Created Product vs. Human-Created Product: Type: Nominal (1 = AI-created, 0 = Human-created) Measurement: Participants are randomly assigned to view either an AI-designed product description or a human-designed product description. This manipulation is adapted from prior research on product design cues (e.g., Granulo, Fuchs, and Puntoni, 2020).

  • AI Framing Condition (Facilitative vs. Dominant): Type: Nominal (1 = AI as a facilitator, 0 = AI as dominant) Measurement: In conditions where AI involvement is highlighted, the description will vary to emphasize either a collaborative (facilitative) role or a replacement (dominant) role. This framing is informed by attribution theory and research on perceived threat (e.g., Stephen & Wedel, 2020).

Dependent Variables

Consumer Impatience - Operationalization: - Study 1: Measured as a binary choice—selecting a smaller-sooner reward versus a larger-later reward. - Study 1/2: Measured using a 1–7 Likert scale where higher scores indicate greater impatience (e.g., a higher willingness to pay for expedited shipping).

  • Rationale: This dual approach allows us to capture both discrete decision-making and more graded impatience levels, as seen in prior literature (e.g., Zhong and DeVoe, 2010; Dai and Fishbach, 2013).

Analytics Methods

  • H1: Awareness of AI creation in product design increases consumer impatience. Method: Binary logistic regression (for choice-based impatience) and one-way ANOVA (for Likert-scale impatience scores). Justification: Logistic regression is used for binary outcome variables (choosing smaller-sooner vs. larger-later rewards), while ANOVA compares mean impatience scores across conditions.

  • H2: Self-importance threat mediates the AI-impatience effect. Method: Mediation analysis using Hayes’ PROCESS macro in SPSS (Model 4). Justification: PROCESS mediation analysis allows us to determine whether the effect of AI-designed products on impatience operates indirectly through self-importance threat.

  • H3: The role of AI (dominant vs. facilitative) moderates the AI-impatience effect. Method: Two-way ANOVA and moderation analysis using PROCESS macro (Model 1). Justification: Two-way ANOVA assesses the interaction between AI framing (dominant vs. facilitative) and AI design awareness on consumer impatience, while PROCESS moderation analysis confirms whether the strength of the effect depends on AI framing.

References

  • Allard, Thomas and Katherine White (2015), “Cross-Domain Effects of Guilt on Desire for Self-Improvement Products,” Journal of Consumer Research, 42 (3), 401–19.
  • Van den Bergh, Bram, Siegfried Dewitte, and Luk Warlop (2008), “Bikinis Instigate Generalized Impatience in Intertemporal Choice,” Journal of Consumer Research, 35 (1), 85–97.
  • Chatterjee, Promothesh, Caglar Irmak, and Randall L. Rose (2013), “The Endowment Effect as Self-Enhancement in Response to Threat,” Journal of Consumer Research, 40 (3), 460–76.
  • Chen, Haipeng (Allan), Sharon Ng, and Akshay R. Rao (2005), “Cultural differences in consumer impatience,” Journal of Marketing Research, 42 (3), 291–301.
  • Collins, Rebecca L. (1996), “For better or worse: The impact of upward social comparison on self-evaluations,” Psychological Bulletin, 119 (1), 51–69.
  • Dai, Xianchi and Ayelet Fishbach (2013), “When waiting to choose increases patience,” Organizational Behavior and Human Decision Processes, 121 (2), 256–66.
  • Daly, Martin and Margo Wilson (2005), “Carpe diem: Adaptation and devaluing the future,” Quarterly Review of Biology, 80 (1), 55–60.
  • Dasgupta, Partha and Eric Maskin (2005), “Uncertainty and Hyperbolic Discounting,” The American Economic Review, 95 (4), 1290–99.

Singelyn Graduate School of Business