Abstract Event history analysis is closely related to a new understanding of causation as a “generative process.” Event history models allow the researcher (1) to relate the rate of change in future outcomes to changes in conditions in the past; (2) to use the transition rate as a local, time‐related description of how the dependent process in a causal system evolves in time; and (3) to use time‐dependent covariates in order to study the impact of parallel processes and time lags between causes and their effects as well as different temporal effect shapes. Depending on the precision of the time measurement of events, discrete‐time and continuous‐time event history methods can be distinguished.
Online fashion retailers often use lenient return policies to compensate for the lack of physical product experience, effectively turning customers' homes into fitting rooms. This convenience, however, contributes to high return rates. Digital nudges are widely used in e-commerce to influence decision-making. When these nudges promote environmentally friendly behavior, they are referred to as digital green nudges. Drawing on self-determination theory, this study examines how and whether digital green nudges influence return motivation. Within e-commerce loyalty programs, digital green nudging is often combined with other measures, such as gamification or financial incentives. Therefore, we analyze and compare how and whether combining digital green nudges with gamification or gamification and monetary incentives influences return motivation. Using structural equation modeling, we analyze data from a survey-based online experiment with US online shoppers (n=1949). Among other results, we show that these measures directly affect return motivation, indicating their role as extrinsic motivators. This study is the first to compare digital green nudges and their combinations with gamification and monetary incentives in the context of online returns. Overall, our results show that all three measures can help mitigate the challenges of lenient return policies. However, we observe side effects on intrinsic motivation that reduce purchase motivation.
PurposeEntrepreneurship Infrastructure Organizations (EIOs) include chambers of commerce, startup incubators, accelerators, and consulting companies. There are very limited studies on the activities and effectiveness of EIOs. The potential of digitizing EIOs activities has received particularly little attention. The purpose of this study is to investigate the potential of generative AI to improve the work of B2B-focused EIOs, both internally and in their relationships with external market actors. The paper also evaluates key constraints, including data security, regulatory restrictions, and workforce challenges.Methodology/approachThe study used a qualitative research design including semi-structured interviews with 23 professionals from 18 German EIOs that operate in B2B ecosystems. The data was analyzed using the Gioia methodology, allowing for the integration of existing theoretical constructs on generative AI in B2B markets with new insights from EIOs practice. Five propositions guided the study, focusing on the impact of generative AI on operational efficiency, expert roles, customer personalization, innovation culture, and the strategic positioning of EIOs in B2B networks.FindingsThe results show that generative AI tools improve EIOs efficiency by automating repetitive tasks (e.g. document creation, customer queries, or market analytics) and improved personalization in B2B service offerings. Contrary to the assumption that generative AI reduces the need for human expertise, this research shows that AI moves expert roles toward higher-value tasks, especially in strategic consulting and customer interactions. Successful adoption is more common in EIOs with strong innovation cultures, digital maturity, and B2B customer-centricity. However, challenges remain, such as data protection, gaps in staff AI skills, and the integration of AI with older systems.Research implicationsThis study extends our understanding of the digital transformation of B2B ecosystems by proposing a conceptual framework that represents how generative AI is changing organizational processes, human-machine interaction, and B2B value co-creation. It emphasizes that, in complex B2B environments, generative AI complements, rather than replaces, EIOs. It also links theories of collaborative intelligence, organizational readiness, and business model innovation in B2B contexts.Practical implicationsB2B-focused EIOs can use generative AI increasingly to optimize workflows, provide real-time information to companies and startups, and create scalable, personalized support services. The proposed four-step roadmap - awareness, pilot testing, full deployment, and long-term optimization - offers a strategic path for B2B EIOs to responsibly and effectively integrate AI. EIO management teams are encouraged to invest in digital skills development, create AI-friendly cultures, and ensure compliance with evolving legal frameworks such as the EU AI Act.Originality/value/contributionThis research is one of the first to explore generative AI in the context of B2B entrepreneurship infrastructure organizations. It provides empirical evidence and practical insights on how generative AI enables B2B service innovation, knowledge-intensive customer interactions, and improved strategic decision making. The results offer a balanced perspective on the additional role of generative AI in enabling EIOs future-proofing and strengthening their relevance in B2B digital ecosystems.
Generative Artificial Intelligence (GenAI) systems, such as large language models (LLMs), are increasingly permeating everyday tasks; yet, they sometimes present false or misleading information as fact – a phenomenon known as hallucination. As GenAI becomes more accessible to the public, it is essential to understand how users’ AI literacy shapes their reliance on imperfect advice from AI systems. Building on the concept of correspondence bias, the study examines how individuals with different levels of AI literacy respond to faulty GenAI advice. Evidence from an online programming experiment with 542 U.S. programmers shows that individuals with higher AI literacy rely less on GenAI advice, particularly when the advice is flawed. Correspondence bias provides a plausible explanatory mechanism for these findings and helps reconcile mixed results in prior research on AI literacy. Overall, the findings offer a more nuanced perspective on the benefits and risks of AI-literacy-driven mistrust. This informs education, integration, and evaluation initiatives for GenAI while cautioning against naive evaluation strategies.
The present study investigates the meaning and effect of leadership and management within the salesforce, particularly during innovation commercialisation. Employing a triad-perspective approach, we conducted 45 interviews with three individuals from fifteen companies, each holding distinct roles (Sales Executives; Internal Sales; Direct Sales Representatives). While most respondents describe a need for both disciplines (management and leadership) while selling existing products, there is a clear indication of more leadership during innovation commercialisation, especially when silent silo-thinking is present, or innovations are not groundbreaking. Promoting great managers to leadership positions is further described as a secure risk. The present study is the first known to bring the streams of "innovation commercialisation by the salesforce" and the role of "management or leadership in sales" together. The employed triadic approach brings additional value through more and systematic diversity of perspectives on perceiving and performing leadership, for instance, the internal perspective of the operative implementers.
