Giampaolo Viglia

We demonstrate that adding eco-friendly components to offerings improves sustainability judgments, whereas reducing or removing such components—despite potential sustainability gains—receives little recognition. We call this asymmetry the sustainable additive bias . Across nine studies, including a field and a lab experiment, we demonstrate the existence of this bias and uncover its underlying mechanism and boundary conditions. We show that companies are judged more sustainable when they add, rather than subtract, eco-components. This asymmetry is specific to eco-friendly components: consumers recognize the sustainability benefits of removing traditional, less environmentally friendly components. The bias arises because additive changes signal greater environmental effort on the part of the company and is attenuated by prototypicality—that is, the extent to which a component represents a typical example of sustainability. Overall, our findings highlight how environmental cues can distort sustainability judgments and favor additive changes that may be counterproductive to genuine sustainability. We describe the implications of this bias and actions to reduce it.

1. Ecological validity refers to the extent to which a research design reflects the conditions, stimuli, interactions, and behaviors of the real-world setting to which its findings apply (Holleman ...

Luxury brands are shifting the way they communicate with stakeholders. Traditionally, they relied on highly curated imagery, exclusive events, and selective celebrities to convey prestige and exclu...

Purpose: Brands are exploring innovative strategies to engage their target audience online by leveraging human and AI-generated metahuman influencers. This study examines the impact of endorsements from human and AI-generated metahuman influencers on online brand engagement, comparing each to a brand-only condition. Additionally, it assesses whether this impact is direct, indirect or moderated by influencer–brand fit. Design/methodology/approach: We deploy two experiments with two real brands. In the first experiment (n = 199), the comparison was between a human influencer endorsement condition and a brand-only condition. In the second experiment (n = 297), we introduced an additional metahuman influencer endorsement condition, enabling us to examine responses to both human and metahuman influencer endorsements relative to the brand-only baseline. Findings: The findings reveal that brand endorsements by human influencers significantly enhance online brand engagement compared to the brand-only condition, primarily through attachment transfer. While metahuman influencers can also drive engagement, the moderation effect – where the fit between the influencer and the brand strengthens attachment transfer – applies exclusively to human influencers. Originality/value: This study is among the first to examine the effects of brand endorsements by human and AI-generated metahuman influencers, each compared to a brand-only control, on online brand engagement. It uses attachment transfer and brand fit as key mechanisms to uncover the dynamics of this relationship. The findings provide valuable insights into the role of social media influencers in brand marketing communications, thus contributing to a deeper understanding of their influence in today’s dynamic digital marketing landscape.

Purpose: This study investigates how quantitative studies have conceptualized and measured the roles of mentoring and role models in entrepreneurial intention and behavior. The two research questions are (1) why do current approaches fall short in identifying which form of support more effectively influences entrepreneurial behavior among women? (2) What could be an alternative approach? Design/methodology/approach: The research employs a literature review on women's entrepreneurship, mentoring and role modeling to highlight the limits of existing quantitative approaches and propose a more experimental approach. Data were collected from the Web of Science Primary Database, focusing on articles published between 2001 and 2024. As a large majority of research on women entrepreneurship has been qualitative, the final analysis includes 15 empirical quantitative studies that discuss mentoring or role models in the context of women's entrepreneurial intention and behavior. Building on the distinction between the specific needs characterizing the motivational versus volitional phases (Delanoë-Gueguen and Fayolle, 2018), we analyzed the data using Dolnicar et al.'s (2024) methodological classification to highlight the limitations of most existing empirical studies in bridging the intention–behavior gap. The in-depth analysis of the methodology is preceded by a qualitative analysis adopting a feminist lens (Ahl, 2006; Calás et al., 2009) to uncover the underlying norms, the assumption regarding the role of women entrepreneurship and the conceptual framing of the mentors. Findings: By integrating feminist critique with methodological scrutiny, this study advances understanding of mentoring in entrepreneurship research. Our analysis shows that most studies adopt a deficit-based view, portraying women as needing to overcome individual shortcomings to fit existing support systems. Such framing neglects the imperative to reconfigure these systems around women entrepreneurs' experiences. We call for a methodological shift – especially experimental designs –to examine alternative support forms. Accordingly, we propose a mentee-centered mentoring model that recognizes mentors' dual role as guides and role models while addressing the specific challenges women face in moving from intention to action. Originality/value: This study makes two contributions to entrepreneurship research. Theoretically, it calls for a shift from role models to mentee-centered mentoring to bridge the gap between women's entrepreneurial intention and action, positioning mentoring as a driver of structural change. Methodologically, it stresses the value of experiments that capture real behavior to produce findings applicable to practice.

Despite the exponential growth of innovations that leverage artificial intelligence (AI), the understanding of how different firm stakeholders engage with these innovations lags behind, exposing an important gap in the literature. Bridging this gap, this interdisciplinary Special Issue explores the growing role that AI‐leveraging innovations play in cultivating stakeholders' engagement, as elucidated in nine articles featured in this Issue. To theoretically ground the articles, we develop an organizing framework that integrates the investment model's notions of intrinsic and extrinsic (resource) investments and resource investment‐based stakeholder engagement . We conceptualize stakeholders' (a) intrinsic engagement as their (e.g., cognitive/emotional) resource investment in their interactions with AI‐leveraging innovations, and (b) extrinsic engagement as their resource investment in maintaining or growing the result(s) of their prior interactions with these innovations, taking a more relational perspective. The framework also assesses the predicted differential effect of thinking (vs. feeling) AI‐leveraging innovations on the association of stakeholders' (a) intrinsic (extrinsic) engagement with AI‐leveraging innovations and their perceived innovation value, and (b) their perceived innovation value and trust in the innovation, as formalized in a set of propositions. We conclude by linking the featured articles to the propositions, and outlining research priorities in this emerging interdisciplinary topic area.