This study investigates the impact of entrepreneurial attitudes, subjective norms, and perceived behavioral controls on entrepreneurial intention among two distinct groups of entrepreneurs: opportunity-driven and necessity-driven. It also explores the relationship between entrepreneurial motivations, intentions, and the number of ventures undertaken by entrepreneurs. Structural equation modeling and survey data from 201 necessity-driven entrepreneurs and 204 opportunity-driven entrepreneurs were utilized. Drawing on the Theory of Planned Behavior, the findings indicate that attitudes do not positively correlate with entrepreneurial intention for both types of entrepreneurs. Subjective norms significantly influence entrepreneurial intention among entrepreneurs by necessity but not among entrepreneurs by opportunity. Perceived behavioral controls exhibit a partial correlation with entrepreneurial intention, which is not significant for entrepreneurs by opportunity. Both motivations and entrepreneurial intention positively relate to the number of ventures undertaken by entrepreneurs by necessity, whereas motivations alone determine venture creation among entrepreneurs by opportunity. Furthermore, a significant correlation exists between being a female entrepreneur by necessity and entrepreneurial intention, while this relationship is not significant for women entrepreneurs by opportunity. These insights contribute to the literature on entrepreneurial intention and carry important policy and managerial implications in developing countries.

Purpose: The purpose of this paper is to explore the microfactory model, the elements that enable it and its implications. The authors argue that microfactories reduce the risks and costs of innovation and that they can move various industries toward more local, adaptive and sustainable business ecosystems. Design/methodology/approach: This conceptual paper explores several processes and practices that are relatively new; hence, it uses online secondary sources (e.g. interviews with CEOs, videos, blogs and trade magazine articles) extensively. Findings: Given its versatility and high automation levels, the microfactory model can fill the gap between artisanal and mass production processes, boost the rate of innovation, and enable the local on-demand fabrication of customized products. Practical implications: Currently, manufacturers generally need to make large investments when launching a new product, despite high uncertainty about customer acceptance, thus risking considerable losses. The microfactory model offers a safer alternative by allowing a firm to develop and fabricate new products and test their acceptance in a local market before mass producing them. Microfactories also enable the local on-demand fabrication of highly customized products. Originality/value: This paper contributes to the discussion on the economic advantages and disadvantages of scale and scope, which have been insufficiently explored in the digital domain.

Recent literature suggests there is a natural connection between innovation and digital transformation, two key topics of interest in management and organization that have spawned large, independent and well-defined areas of study. While this connection might be analytically straightforward and notable examples are not hard to find, in everyday life, it materializes in a multiplicity of ways. A need emerges, then, to better understand the interaction between innovation and digital transformation so that it can be explored and exploited by actors in academia and the public, private and third sectors. In this article, we use a co-word analysis, a text mining technique that permits to systematically map the intellectual structure of a research field, to characterize the most notable dynamics of ‘innovation-driven digitalization’ and ‘digitalized innovation’— the two major dimensions of interaction between innovation and digital transformation. The text identifies the relevant themes, subthemes and concepts that appear in the literature, as well as their relationship and level of development. It, then, aggregates them in a taxonomy that, on the one hand, readily displays their connection and, on the other hand, (i) informs about current or potential controversies, (ii) gaps, (iii) lines for novel and further research, and (iv) alternatives to bridge to other areas of study.

Collaborative innovation addresses intricate, multifaceted problems of a systemic nature involving a multitude of actors with diverse and often unknown expectations. To facilitate this collaboration, innovation spaces—such as Makerspaces, Hackerspaces, Fablabs, among others—have emerged as arenas where networks of actors interconnect and solidify. Understanding the diverse nature of these innovation spaces poses a challenge, particularly in distinguishing their specific characteristics and assessing how each contributes to fostering innovative networks. This article aims to address the question: how can we classify innovation spaces based on the innovation networks they constitute? To tackle this query, we initially conducted an in-depth exploration of various innovation spaces through web content analysis, scrutinizing their individual value propositions. Subsequently, employing innovation network theory alongside domain analysis methodology, we proposed a taxonomy designed to classify the distinct types of innovation spaces under scrutiny. Our taxonomy reveals three types of spaces—learn-and-explore, partner-and impact, and transitory—as well as convergent and divergent spaces, showing the diversity and complexity of networks they constitute. The findings also show that while the majority of innovation spaces unite diverse actors to drive collaboration and innovation, many resulting networks have a medium level of formality and are project-centric. These networks tend to be synthetic in nature, seeking to apply existing knowledge, and represent exploration networks wherein the adaptability and diversity of initiatives foster learning, the acquisition of new knowledge, and the development of fresh capacities through interactions.

Purpose: This article explores a particular on-demand fabrication unit, the microfactory (MF). It identifies and contrasts several MFs and proposes a taxonomy. This research also explores online manufacturing platforms (OMP) that complement certain MFs.Design/methodology/approach: This research implements a multiple case study (71 cases in 21 countries), triangulating data available on the web with interviews, virtual/physical tours and experiential research.Findings: The results suggest that automation and openness are the main dimensions that differentiate the MFs. Using these dimensions, a taxonomy of MFs is created. MFs with relatively low automation and high openness tend to be innovation-driven microfactories (IDMFs). MFs with high automation and low openness levels tend to be customization-driven microfactories (CDMFs). And MFs with relatively low automation and low openness tend to be classic machine shops (MSs). There are two types of OMP: closed (COMPs) and multisided (MOMPs). MOMPs can be low-end or high-end.Practical implications: In a world where online platforms are becoming central to the reinvention of manufacturing, multisided online platforms and small fabricators will become strongly symbiotic.Originality/value: This paper offers a clearer conceptualization of MFs and OMPs, which may help to better understand the reality of local on-demand fabrication. Moreover, it explores a new type of experiential research, which tries to describe and interpret firms through transactional activities. Many details of a firm that are difficult to capture via interviews and netnography can be revealed this way.