Artificial Intelligence (AI) brings about new business models (Brynjolfsson & Mcafee, 2017; Burström et al., 2021; Iansiti & Lakhani, 2021), including new business models capturing economic value while creating social and environmental value (Snihur & Eisenhardt, 2022), which prior work conceptualizes as business models for sustainability (Lüdeke-Freund et al., 2024). Prior work within the business model view of strategy (Lanzolla & Markides, 2021) highlights that heterogeneity in value capture stems from fit among a configuration of business model elements and other strategic elements (Aversa et al., 2015; Desyllas et al., 2022; Leppänen et al., 2023; Snihur & Eisenhardt, 2022), on top of positioning (Porter, 1985) and resources (Barney, 1991) and capabilities, especially when there is low heterogeneity in resources and low barriers to entry in a given market (Lanzolla & Markides, 2021). Yet, current conceptualizations fail to explain value capture stemming from stakeholder-centric AI-enabled business models (Snihur & Eisenhardt, 2022).

We aim to cover this unexplored boundary condition by (i) integrating received theoretical perspectives in strategy and sustainability and (ii) mobilizing seperately two relevant concepts: sustainability and responsibility, following a recent call for research emphasizing their separate paradigmatic origins (Bansal & Song, 2017). Against this backdrop, we ask: “When and how do AI-enabled business models influence value capture, and what role does a focus on sustainability or responsibility-related issues play?”.

Drawing from five theoretical perspectives -ecosystem view (Adner, 2017), operations management (Fine, 2000), organizational capabilities view (Kemp, 2023), institutional theory (Durand et al., 2019; Meyer & Rowan, 1977) and the model of sustainable entrepreneurship (Cohen & Winn, 2007)-, we develop a model of strategic choices driving value capture in the AI era. These choices either pertain to business model elements -value proposition or value network (Bohnsack et al., 2021)- or to other strategic factors (Lanzolla & Markides, 2021).

While our model points to bundles of strategic choices conducive to value capture, we refrain from making hypotheses and develop theory grounded in empirical findings by mobilizing an exploratory methodology: qualitative comparative analysis (Greckhamer et al., 2018; Park et al., 2020). We applied our analysis to a unique cross-sectional dataset of 3,521 firms created based on raw data exported from Crunchbase in 2024.

We inductively identify five configurations of strategic choices that are conducive to economic value capture in an AI era: (i) Sustainable AI Software as a Service, (ii) Sustainable AI-enabled Value fueled by Sensing at the Physical Edge, (iii) Responsible AI Software as a Service, (iv) Responsible AI-enabled Platform, (v) AI Chip-Related Resources, Capabilities and Positioning. We discuss when the business model matters the most in predicting value capture based on the configuration at play.

We thereby contribute to the business model literature by exploring a novel boundary condition (Busse et al., 2017) and articulating central theoretical constructs and relationships related to value capture in that context. We contribute to practice by producing a framework of strategic choices that enables managers to succeed in operating a profitable firm in the AI era while simultaneously considering key issues related to sustainability and responsibility.

References

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Aversa, P., Furnari, S., & Haefliger, S. (2015). Business model configurations and performance: A qualitative comparative analysis in Formula One racing, 2005–2013. Industrial and Corporate Change, 24(3), 655–676. https://doi.org/10.1093/icc/dtv012

Bansal, P., & Song, H.-C. (2017). Similar But Not the Same: Differentiating Corporate Sustainability from Corporate Responsibility. Academy of Management Annals, 11(1), 105–149. https://doi.org/10.5465/annals.2015.0095

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Busse, C., Kach, A. P., & Wagne, S. M. (2017). Boundary Conditions: What They Are, How to Explore Them, Why We Need Them, and When to Consider Them. Organizational Research Methods, 20(4), 574–609. https://doi.org/10.1177/1094428116641191

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Durand, R., Hawn, O., & Ioannou, I. (2019). Willing and Able: A General Model of Organizational Responses to Normative Pressures. Academy of Management Review, 44(2), 299–320. https://doi.org/10.5465/amr.2016.0107

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The rapid development of financial technology, driven by increased internet usage and accelerated after the 2008 financial crisis, has transformed the financial sector. Open banking as a recent innovation shows promise in enhancing financial inclusion by providing greater access to financial services in particular for individuals and small businesses. It fosters competition and reduces prices through easy comparison of services from various providers. This is particularly beneficial for marginalized communities with limited access to traditional financial services. However, it is crucial to address risks including data privacy, online fraud, and cybersecurity. We utilized a difference-in-differences regression to assess the impact of open banking on financial inclusion across various countries. We found that there is evidence that open banking positively impacts financial inclusion. Accordingly, we provided adequate recommendations to policymakers.

This discussion contribution outlines an idea for an exploratory research project on the use of drones in municipal home care services. It outlines three concepts that can be further broken down to various service models with different aims and requirements.

This study investigates how Corporate Venture Capital (CVC) investments in green technologies can improve the environmental performance (E) of ESG metrics of firms with historically poor ESG records. By integrating data from the Thomson VentureXpert database, Refinitiv ESG, and patents filed at the European Patent Office (EPO), we employ panel data analyses to explore the impact of CVC investments on sustainable practices. Our research focuses on how green investments can help firms improve ESG performance alongside traditional firm outcomes, offering new insights into how strategic CVC investments contribute to a company's ESG (E) performance, addressing both competitive pressures and increasing demands for corporate responsibility.

Voluntary Carbon Markets (VCMs) have grown as a significant increasingly popular avenue for organizations to offset carbon emissions through projects like reforestation and agroforestry (Lee, Kim, & Kim, 2018). These markets are particularly relevant for smallholder farmers in the Global South, who can benefit from engaging in carbon-sequestering activities while receiving financial compensation for their contributions (Cammarata, Scuderi, Timpanaro, & Cascone, 2024). It is without doubt that the iInvolvement of smallholder farmers in VCMs therefore holds tremendous opportunities for integrating agricultural practices with carbon offset mechanisms Click or tap here to enter text.. Farmers involved in VCMs can be engaged in projects that provide both direct payments and non-monetary benefits, such as enhanced resilience to climate change through improved farming practices.

Despite these promising developments, significant challenges persist in understanding how to optimize achieve ‘fair’ compensation mechanisms and ensure transparency, especially for smallholder farmers (Howard et al., 2015, 2016; Narloch et al., 2013; Wongpiyabovorn et al., 2023). The complexity of VCM value chain governance, involving multiple stakeholders such as project developers, certification bodies, and buyers, often creates barriers that hinder the equitable distribution of benefits. Previous work on fairtrade and environmental certification has that by participating in these activities, smallholders and ecosystem service providers can capture added value, creating a positive ‘green premium’ that directly benefits them financially.

Our study is motivated by the need to explore how whether VCMs provide a green window of opportunity for smallholders in the Global South? compensation mechanisms -across the entire VCM suplly chain- can be better streamlined and transparency enhanced. This research ultimately aims to explore ways to make VCMs more effective for smallholder farmers, ensuring that financial and non-financial benefits are accessible and sustainable. Understanding these dynamics is crucial to fostering market credibility and increasing the attractiveness of VCM participation for smallholders.

Our study will adopt a qualitative approach, using two case studies (Eisenhardt, 2007) to examine the compensation structures within VCMs: the Jinotega & Matagalpa project in Nicaragua, which focuses on agroforestry practices integrated with coffee farming, and the Quilombolas Social Carbon Project in Brazil, centered on forest conservation and community-led carbon offset initiatives. To date, we have already conducted 13 semi-structured interviews (Table 1) with key stakeholders in project supply, exploring their experiences and perspectives on compensation, monetary and nonmonetary benefits, and transparency. The study aims to generate insights that can inform best practices for structuring VCMs to benefit smallholder farmers more effectively.

This research draws on theories of global value chains (Fitter & Kaplinsky, 2001; Gereffi et al., 2005) and voluntary carbon markets, providing a lens through which the complex interactions among VCM stakeholders can be understood. The global value chain (GVC) perspective enables us to analyze the distribution of benefits from carbon credits, focusing on transparency and governance structures that shape these outcomes. Our study aims to make two key contributions: (i) to deepen the understanding of mechanisms that enhance net benefits for smallholder farmers, whether through direct financial compensation, improved livelihoods, or sustainable agricultural practices, and (ii) to identify optimal governance structures that can maximize equity and effectiveness in the voluntary carbon market.

REFERENCES

Cammarata, M., Scuderi, A., Timpanaro, G., & Cascone, G. (2024). Factors influencing farmers' intention to participate in the voluntary carbon market: An extended theory of planned behavior. Journal of Environmental Management, 369, 122367.

Eisenhardt, K. M. (2007). Theory building from cases: opportunities and challenges. 50, 25-32.

Lee, D.-H., Kim, D.-h., & Kim, S.-i. (2018). Characteristics of forest carbon credit transactions in the voluntary carbon market. Climate Policy, 18(2), 235-245.

Fitter, R., & Kaplinsky, R. (2001). Who Gains from Product Rents as the Coffee Market Becomes More Differentiated? A Value-chain Analysis. IDS Bulletin, 32(3), 69–82. https://doi.org/10.1111/J.1759-5436.2001.MP32003008.X

Gereffi, G., Humphrey, J., & Sturgeon, T. (2005). The governance of global value chains. Review of International Political Economy, 12(1), 78–104. https://doi.org/10.1080/09692290500049805

Howard, R. J., Tallontire, A. M., Stringer, L. C., & Marchant, R. A. (2016). Which “fairness”, for whom, and why? An empirical analysis of plural notions of fairness in Fairtrade Carbon Projects, using Q methodology. Environmental Science & Policy, 56, 100–109. https://doi.org/10.1016/J.ENVSCI.2015.11.009

Howard, R. J., Tallontire, A., Stringer, L., & Marchant, R. (2015). Unraveling the Notion of “Fair Carbon”: Key Challenges for Standards Development. World Development, 70, 343–356. https://doi.org/10.1016/j.worlddev.2015.02.008

Kent, R., & Hannay, R. (2020). Explaining “Carbon” in Community Sequestration Projects: a Key Element in the Creation of Local Carbon Knowledges. Environmental Communication, 14(3), 364–377. https://doi.org/10.1080/17524032.2019.1673459

Narloch, U., Pascual, U., & Drucker, A. G. (2013). How to achieve fairness in payments for ecosystem services? Insights from agrobiodiversity conservation auctions. Land Use Policy, 35, 107–118. https://doi.org/10.1016/j.landusepol.2013.05.002

Wongpiyabovorn, O., Plastina, A., & Crespi, J. M. (2023). Challenges to voluntary Ag carbon markets. Applied Economic Perspectives and Policy, 45(2), 1154–1167. https://doi.org/10.1002/AEPP.13254