In today's data-driven era, ubiquitous concern about environmental issues pushes more startups to engage in business model innovation that promotes environmentally friendly technologies. The goal of these startups is to create technology-based products and services that enhance environmental sustainability. In this context, artificial intelligence promises to be a key instrument to create, capture, and deliver value. However, the existing literature lacks a deep understanding of how startups using AI innovate their business models to achieve a positive environmental impact. Therefore, this paper investigates how green technology startups utilize AI from a business model innovation perspective for environmental sustainability. We conduct a qualitative, exploratory multiple-case study using the Eisenhardt methodology, based on interview data analyzed using qualitative content analysis. We derive five predominant manifestations for AI-driven business model innovation and identify archetypical connections between business model dimensions. Further, we establish three overarching archetypical associations among the cases. In doing so, we contribute to theory and practice by providing a deeper account of how green technology startups attempt to maximize their positive environmental impact through AI. The results of this study also highlight how business model innovation driven by AI can support society in securing a more environmentally sustainable future.

The recent surge in artificial intelligence (AI) adoption by small and medium-sized enterprises (SMEs) has garnered significant research attention. However, the existing literature reveals a fragmented landscape that hinders our understanding and application of insights about AI use in SMEs. We address this through a systematic literature review, wherein we analyze 102 peer-reviewed articles on AI adoption in SMEs and categorize states and trends into eight clusters—(1) compatibility, (2) AI readiness, (3) knowledge, (4) resources, (5) culture, (6) competition, (7) regulation, and (8) ecosystem—according to the technology–organization–environment model. Our research reveals valuable insights but also identifies significant gaps in existing literature, notably the oversight of trends identification as a pivotal driver and the neglect of legal requirements. Our study clarifies the AI implementation within SMEs, offering a holistic and theoretically grounded perspective to empower researchers and practitioners to facilitate more effective AI adoption and application within the SME sector.

Climate-related issues have become increasingly relevant, as reflected in current political and academic discourse. This development is also reflected in investors' capital allocation decisions and their demand for climate-related information. Considering the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD), we first investigate the climate-related disclosure quality of listed German firms. We use self-constructed scoring models based on the TCFD recommendations to measure disclosure quality. Second, we use regression analysis to investigate whether corporate governance can explain climate-related disclosure quality. The results indicate that disclosure quality is heavily dispersed across firms, with risk disclosure being better than disclosure of opportunities. Corporate governance factors exert distinct but mostly weak influence on climate-related disclosure quality and that institutional ownership promotes climate-related disclosure quality. We show several implications for research and practice and highlight the relevance for firms to implement a comprehensive approach to communicating climate-related issues.

Past research showed how multinational enterprises (MNEs) create specific organizational structures to manage tensions in collaborations with competitors (i.e., coopetition). In this study, we explore how MNEs design their organizational structure to approach tensions specifically in the formation phase of coopetition. Formation is the first and arguably most difficult step in coopetition when tensions are particularly high. Based on an in-depth case study in the agrochemical industry, we find that MNEs create dedicated Coopetition Formation Teams (CFTs), moving within and between their firms to tackle a mix of four paradoxical tension types: performing tensions (conflicting goals), belonging tensions (incompatible values and beliefs), organizing tensions (dysfunctional processes), and learning tensions (conflicts between prior and new knowledge). Separated from the rest of their organization and equipped with unique capabilities to manage conflicts, CFTs combine the separation and integration principles to dynamically address these tensions. However, when paradoxical tensions persist and start to exacerbate, CFTs rely on conciliation by top management as a critical third principle to resolve conflicts. This study is the first to analyze the formation of coopetition between MNEs, proposing an integrated framework that connects the organizational design, the four paradoxical tension types, and the three principles to manage them.

We suggest a new component efficient solution for monotonic TU games with a coalition structure, the conditional Shapley value. In contrast to other such solutions, it satisfies the null player property. Nevertheless, it accounts for the players’ outside options in productive components of coalition structures. For all monotonic games, there exist coalition structures that are stable under the conditional Shapley value. For voting games, such stable coalition structures support Gamson’s theory of coalition formation (Gamson, 1961).

This study sheds light on the relationships between digital transformation, business model, and process efficiency capabilities, and new product development (NPD) performance by employing a sequential explanatory approach, combining quantitative and qualitative research methodologies, utilizing structural equation modeling based on 430 questionnaire respondents, and a multiple case study design using four cases. The derived framework highlights that digital transformation does not directly lead to NPD performance but that organizations use idiosyncratic higher order (e.g., business model and process efficiency) capabilities that mediate this relationship to strategically cope with change. When assessing, reconfiguring, and integrating, organizations tap into internal and external value, individual, technological, and organizational (VITO) dimensions to transform operational capabilities and resources. Thus, higher order capabilities enable organizations to leverage firm-external opportunities to adjust intrafirm operational capabilities, resources, and competencies, emphasizing a complex hierarchical and contextual interplay. The contributions of the study are twofold: 1) we provide statistical evidence that the business model and process efficiency capabilities are coping mechanisms to master digital transformation and 2) the successful orchestration of VITO dimensions is essential for assessing, reconfiguring, and integrating resources, competencies, and operational capabilities to derive NPD performance.

This study explores the concept of massive and rapid business scaling (MRBS) in the context of digital start-ups by identifying 20 factors clustered into seven core drivers. Through inductive qualitative research, the study builds on 53 semi-structured interviews with founders, executives, and advisors, leading to the development of a framework that uncovers seven core drivers of MRBS contributing to the scaling process. These core drivers are as follows: 1) scanning the environment and recognizing opportunities, 2) iteratively adjusting the business model with an asset-light structure, 3) achieving operational excellence through digitization, 4) building an efficient and entrepreneurial workforce combined with leadership and vision, 5) leveraging internal resources to strengthen positioning and expand the market, 6) attracting capital to facilitate growth realization, and 7) cultivating organizational agility and a transformation culture. While core drivers one to five imply a processual nature, the sixth and seventh core drivers serve as a foundation for MRBS. Moreover, this study outlines several areas of tension within the process of MRBS. Therefore, the study provides valuable insights for scholars and practitioners.

Discussion in the scholarly literature about partnerships between entrepreneurs and universities for the creation of technological spinouts, and for helping universities to extract more value from their technology-related intellectual property (IP), is lively. However, the literature exhibits a gap in understanding how business schools may participate in the process of technology commercialization by facilitating the creation of intellectual property rights. In this conceptual paper, we seek to fill this gap in three ways. First, we offer some novel conceptual insights by studying the partnership between technical universities and entrepreneurs using a multi-level approach, incorporating a phenomenological research method, through the lenses of several established theoretical perspectives from the domains of economics, social science, and management: the division of labor, motivation, the nature of the firm, organization, and IP. Second, we develop a working hypothesis focused on learning reinforcement through multiple organizational levels that predicts how business schools may play a prominent role in technology commercialization, together with the theoretical conditions under which they may do so. Third, we offer an IP management model under which business schools, as such, may create and appropriate financial value by generating innovation-related IP that may be transferred to enterprises. Our research reveals a misalignment between promising approaches to university-based technological innovation suggested by normative theory and typical approaches associated with extant practice; and it also highlights a strategic issue, which is that the performance of most universities in the domain of technology transfer is disappointing. We suggest a way to address this misalignment, and this strategic issue, which is through the establishment of what we label as "Technology Innovation Laboratories" in business schools-analogous to technical laboratories usually associated with technical universities-that could generate various types of product- or service-related IP. This type of intellectual property-typically different from invention IP, and which we label here as "business IP"-could be exchanged for equity in spinouts or royalties from licensing, similar to the manner in which the invention IP of technical universities is usually commercialized.

Research in artificial intelligence and business model innovation is flourishing. Nevertheless, the current discussion lacks an overarching understanding of, and thus has not sufficiently addressed, the interface between artificial intelligence-enabled business model innovation and the critical role of top management. Although a paradigm shift affecting top management is already occurring, extant management literature is limited, especially in terms of primary research. Accordingly, this study explores how top management can encourage and facilitate artificial intelligence-enabled business model innovation. We utilized an inductive approach and conducted semistructured interviews with 47 practitioners to develop a grounded theory. The developed framework consists of five top management competencies and eight top management roles. Overall, our study contributes to research in business model innovation theory, revealing that top management requires a specific skill set to carry out their roles and fulfill expectations.

Transferring knowledge and technology from academia to industry is usually understood as a process. While previous research focuses on phenomena along the process and its outcomes, the starting point of the process—the initiation of a transfer activity—remains unstudied. We conceptualize this initiation as a simultaneous recognition of a transfer opportunity and the choice of a transfer channel and provide first empirical insights into the initiation of the transfer process. We use survey data from 1149 scientists from the German state of Thuringia and their activities in science–industry collaboration, intellectual property rights, and spin-off creation. We employ seemingly unrelated regressions to account for selection and multiple channel choices in our econometric approach. Our results show a positive relationship between scientists’ different kinds of prior knowledge and the probability of recognizing a transfer opportunity. Contrary to our expectations, scientific impact reduces the likelihood of recognizing a transfer opportunity. For the choice of the transfer channel, the results show a positive relationship between choosing the spin-off channel and risk willingness, as well as basic research. Applied research increases the likelihood of choosing intellectual property rights as a channel. Furthermore, role models are positively associated with these two channels.