Signaling theory predicts that communication, such as by CEOs, might complement observable environmental engagement by a firm, by reducing information asymmetry. Yet it remains unclear whether firms with stronger environmental engagement communicate more about these efforts and the potential implications for firm value. To analyze 14,523 firm-quarter earnings call Q A sessions held by 364 publicly listed firms between 2007 and 2022, the current research measures CEO communication about firm environmental engagement and the communicative intent behind such communication by combining MSCI Environmental Pillar Scores, Compustat data, and a fine-tuned DeBERTa model. Firms with stronger environmental engagement communicate less, rather than more, about their efforts during earnings call Q A sessions. Yet CEO communication about firm environmental engagement can be positively associated with firm value, depending on the communicative intent the CEO indicates, such that assertive and directive speech maintain this positive association, but expressive and commissive speech acts are negatively associated with firm value.
Consumers often show distrust in dynamic pricing, seeing it as a fundamentally biased process that facilitates profit-seeking by human price setters. Presumably, using artificial intelligence (AI)—an ostensibly more rational, less biased agent—might lead consumers to more readily accept the strategy. Across five studies, we show that consumer responses to AI and human price setters in dynamic pricing are more nuanced than prior work would suggest. All else equal, perceived price fairness and downstream consumer responses (e.g., purchase intentions and brand evaluations) are more favorable when prices are set by AI rather than a human. However, AI’s advantage is not universal. It diminishes when consumer evaluations are shaped by price setter characteristics (e.g., perceived competence), consumer characteristics (e.g., attitudes toward AI and religiosity), and attributed motives underlying pricing decisions (e.g., prosocial motives). Together, these findings advance understanding of when and why consumers respond differently to AI and human price setters in dynamic pricing.
Influencer marketing on short-video platforms has become tremendously popular. Managers hire a portfolio of influencers to promote their products. On algorithm-driven platforms, where content distribution relies on similarity and audience interest, the configuration of these influencer portfolios dictates campaign exposure and effectiveness. This research examines how portfolio focus (the concentration of influencers across content domains) and portfolio size interact to shape campaign performance. Using interviews with managers, panel data from a leading short-video platform, content analysis of consumer comments, and an online experiment, we show that the effectiveness of influencer portfolio focus depends on influencer portfolio size. Concentrated portfolios become more effective as campaign size increases. Experimental results identify increased advertising fatigue and reduced brand credibility as key mechanisms underlying the downside of portfolio focus. The findings advance influencer marketing theory by introducing a portfolio-level perspective and offer actionable guidance for influencer marketing campaigns in algorithmic media environments.
Consumers frequently encounter multigenerational products labeled with numerical sequences (e.g., A1→A2→A3 vs. B10→B20→B30). Although such naming structures may appear superficial, they systematically shape consumers’ upgrade decisions. We define the numerical gap as the difference between numerical values across product generations and demonstrate that large (vs. small) numerical gaps increase upgrade preferences. This effect arises because large (vs. small) numerical gaps lead consumers to infer stronger innovation momentum. However, this inference depends on context: it weakens or reverses when large numerical gaps conflict with consumers’ expectations about how innovation unfolds, when innovation is less central to the product category, or when the implied innovation is incongruent with the brand’s positioning. By highlighting how peripheral numerical structures convey innovation signals, this research advances our understanding of how consumers interpret multigenerational brand names and offers guidance to firms designing naming strategies that encourage product upgrades.
Drawing on visual complexity theory and the arousal properties of color, this paper challenges the prevailing assumption that only warm (longer wavelength) or highly saturated colors can induce arousal in consumers. Instead, we propose a novel pathway: multicolor logos with cool hues can elicit arousal through triggering curiosity. Across a total of eight experiments (seven preregistered with a total of 1,724 participants recruited), of which six experiments are reported in the main text, including a behavioral lab study employing biometric measures (eye tracking, galvanic skin response, pupil dilation), and three studies utilizing real product choices, we consistently find that multicolor cool logos are more arousing than single cool color designs. This heightened, curiosity-driven arousal subsequently impacts marketing outcomes, including willingness to pay and choice. These findings offer marketers alternative strategies for inducing arousal in brand communications and provide actionable guidelines for effectively designing logos.
