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Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.

The purpose of this paper is to study what extent readers’ socio-demographic characteristics, literary preferences and search behavior predict success in fiction search in library catalogs.

In total, 80 readers searched for interesting novels in four differing search tasks. Their search actions were recorded with a Morae Recorder. Pre- and post-questionnaires elicited information about their background, literary preferences and search experience. Readers’ literary preferences were grouped into four orientations by a factor analysis. Linear regression analysis was applied for predicting search success as measured by books’ interest scores.

Most literary orientations contributed to search success, but in differing search tasks. The role of result examination was greater compared to querying in contributing search success almost in each task. The proportion of variance explained in books’ interest scores varied between 5 (open-ended browsing) and 50 percent (analogy search).

The distribution of participants was biased toward females, and the results are aggregated within search session, both reducing the variation of the phenomenon observed.

This study is one of the first to explore how readers’ literary preferences and searching are associated with finding interesting novels, i.e. search success, in library catalogs. The results expand and support the findings in Mikkonen and Vakkari (2017) concerning associations between reader characteristics and fiction search success.

The implementation of mobile health (mHealth) in developing countries seems to be stuck in a pattern of successive pilot studies that struggle for mainstream implementation. This study addresses the research question: what existing health-related structures, properties and practices are presented by rural areas of developing countries that might inhibit the implementation of mHealth initiatives?

This study was conducted using a socio-material approach, based on an exploratory case study in West Africa. Interviews and participant observation were used to gather data. A thematic analysis identified important social and material agencies, practices and imbrications which may limit the effectiveness of mHealth apps in the region.

Findings show that, while urban healthcare is highly structured, best practice-led, rural healthcare relies on peer-based knowledge sharing, and community support. This has implications for the enacted materiality of mobile technologies. While urban actors see mHealth as a tool for automation and the enforcement of responsible healthcare best practice, rural actors see mHealth as a tool for greater interconnectivity and independent, decentralised care.

This study has two significant limitations. First, the study focussed on a region where technology-enabled guideline-driven treatment is the main mHealth concern. Second, consistent with the exploratory nature of this study, the qualitative methodology and the single-case design, the study makes no claim to statistical generalisability.

To the authors' knowledge, this is the first study to adopt a socio-material view that considers existing structures and practices that may influence the widespread adoption and assimilation of a new mHealth app. This helps identify contextual challenges that are limiting the potential of mHealth to improve outcomes in rural areas of developing countries.