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This study addresses current research gaps by integrating resilience literature with crisis management theories, focussing on SMEs. Specifically, the authors examine how the entrepreneurial decision-making process, via the interplay of causation and effectuation logic, impacts a firm's ability to respond to unpredictable events. This paper aims to present an investigation that seeks to unearth the potentially complex interplay between causation and effectuation logic in fostering organisational resilience, particularly in the face of unprecedented disruptions such as the COVID-19 pandemic.

This study includes the responses of 80 Italian entrepreneurs operating in the hospitality sector. The paper deployed a joint analysis through a partial least squares structural equation modelling technique (PLS-SEM) and a necessary condition analysis (NCA) to assess how the decision-making logics impact the entrepreneurs' decision when reacting to the pandemic.

The findings show that how entrepreneurs make decisions influence how they react to the crisis. Causation was found as a direct cause of resilience and preparedness, and effectuation was a direct cause of resilience and agility. Moreover, causation indirectly caused resilience through preparedness, and effectuation indirectly caused resilience through agility. Finally, both preparedness and agility are direct causes of resilience.

This research generated insights into why and how some SMEs respond more effectively to uncertainty than others. It provides actionable strategies that business owners and managers can employ to enhance their ability to withstand and recover from crises.

This study's originality and novelty lie in its empirical investigation of the roles of causation and effectuation logic in entrepreneurial decision-making and, consequently, their influence on SME resilience. Focused on the Italian hospitality sector, it provides unique insights into resilience strategies under severe, real-world conditions, contributing to theoretical development and practical applications in crisis management.

By using the lens of effectuation and causation, this paper aims at exploring how the team is formed in high-tech academic spinoffs, by looking specifically at decisional heuristics in an academic context. Indeed, the team composition is a critical issue for the subsequent growth of the academic new venture: on the one hand, the scientific-centred knowledge of the academic entrepreneurs is one of the main elements that lay the foundation for the new venture; on the other hand, it has been widely recognized that the lack of market-related knowledge and experience often has detrimental effects on performance. Decisive is then to explore how team decisions pertaining to the team formation process are taken.

A qualitative methodology based on multiple case studies is adopted under an abductive approach.

Results shed light on how decisions pertaining to team formation are taken by academic entrepreneurs and with what effects on team composition, a fundamental element to foster the growth of academic new ventures. Specifically, this study derives some propositions about the adoption of effectuation and causation in the team formation process, their occurrence and sequence in the light of the scientific context in which academic new ventures spin out and the effects on the team composition.

This study contributes to the debate on academic entrepreneurs’ decisional heuristic and the use of effectuation or causation in the early stages of an academic new venture, by focusing on the team formation process. This study specifically considers three temporal micro-phases – the selection of founders before inception, the appointment of top management teams, and the integration of early employees after the inception – and whether the academic context influences the decisional heuristics. Managerial implications are also derived.

Large-scale data analytics have raised a number of ethical concerns. Many of these were introduced in a seminal paper by boyd and Crawford and have been developed since by others (boyd & Crawford, 2012; Lagoze, 2014; Martin, 2015; Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016). One such concern which is frequently recognised but under-analysed is the focus on correlation of data rather than on the causative relationship between data and results. Advocates of this approach dismiss the need for an understanding of causation, holding instead that the correlation of data is sufficient to meet our needs. In crude terms, this position holds that we no longer need to know why X+Y=Z. Merely acknowledging that the pattern exists is enough.

In this chapter, the author explores the ethical implications and challenges surrounding a focus on correlation over causation. In particular, the author focusses on questions of legitimacy of data collection, the embedding of persistent bias, and the implications of future predictions. Such concerns are vital for understanding the ethical implications of, for example, the collection and use of ‘big data’ or the covert access to ‘secondary’ information ostensibly ‘publicly available’. The author’s conclusion is that by failing to consider causation, the short-term benefits of speed and cost may be countered by ethically problematic scenarios in both the short and long term.

Schizophrenia is a devastating illness with a chronic and relapsing course. While Western countries may endorse, biological and psychosocial causes more commonly than supernatural causes, non-western cultures like Nigeria in contrast, tend to endorse supernatural causes. Belief in supernatural causes has been reported to have consequences for treatment seeking behavior. This study aimed to examine the causes of schizophrenia reported by family members of outpatients with schizophrenia in a neuropsychiatric hospital in Midwestern Nigeria. In this study, we recruited a convenient sample of 200 consecutive caregivers of patients visiting the outpatient department of the Psychiatric Hospital, Benin City, Nigeria. These primary caregivers were unpaid relatives who provided support to patients. The patients were service users who fulfilled the diagnostic criteria of the International Classification of Disease [ICD-10; World Health Organization 1993] for schizophrenia and had been on treatment for at least two years. Majority (72.0%) of caregivers endorsed supernatural causes as most important in the etiology of schizophrenia, while 28.0% endorsed natural causes. Every participant without formal education endorsed supernatural attribution. In our study, it was evident that participants embraced multiple causal attributions for schizophrenia.

The present paper aims to highlight how manufacturing expansions under conditions of increasing returns, which involve the growth of intermediate goods specializations, support advanced service employment. In addition, the increasing use of manufacturing products in services highlights additional, new service sector employment opportunities.

This paper investigates (1) the manufacturing and service interactions and (2) the investment behaviour in manufacturing using Auto-Regressive Distributed lags (ARDL) and Vector Autoregressive (VAR) models. The models allow for different specifications to study whether investment behaviour in manufacturing supports dynamic manufacturing and service interactions.

The results underpin how Kaldorian manufacturing as an engine of growth is still relevant in Indian growth and is key to achieving higher advanced employment, export-orientation and services and manufacturing nexus outcomes. What matters, though, is that manufacturing investments are to be guided mainly by intermediate goods specializations. The slowdown of these specializations, explaining the slowdown of manufacturing investment, is therefore, a concern.

A reinterpretation of manufacturing as an engine of growth in which primacy is given to investment behaviour in technical progress functions that can support the growth of specializations in manufacturing and such specialized service employment.

This paper aims to investigate the entrepreneurial behaviours exhibited by commercial smallholder farmers in Zimbabwe, focusing on their socio-economic characteristics, and considers their implication for outcomes of livelihood resilience in a resource-constrained and turbulent rural context.

The study used survey data collected from 430 smallholder farmers in Masvingo province, Zimbabwe. Using a two-step cluster analysis, the study constructed a typology of farmers based on their entrepreneurial behaviour and socio-economic characteristics.

The results revealed that commercial smallholder farmers are heterogeneous in terms of their entrepreneurial behaviours. Four clusters were identified: non-entrepreneurial, goal-driven, means-driven and ambidextrous. Beyond their entrepreneurial behaviours, these clusters significantly differ in the socio-economic characterises (gender, age, education levels, farm size, proximity to the market and social connection) and farm performance (seasonal sales per hectare and farm income per hectare).

The typology framework relating farmers’ entrepreneurial behaviours to their socio-economic characteristics and business performance is important to tailor and therefore improve the effectiveness of farmer entrepreneurship programmes and policies. In particular, tailoring farmer entrepreneurship education is crucial to distribute land, finance and market resources in purposive ways to promote a combination of smallholder farmers’ effectual and causal behaviours at an early stage of their farm ventures.

Researchers still know little about which farmers’ behaviours are entrepreneurial and how these behaviours manifest in action during their commercial farm activities. This research leverages effectuation and causation theory to unveil previously overlooked distinctions on farmers’ entrepreneurial behaviours, thereby enhancing a more grounded understanding of farmer entrepreneurship in a resource-constrained context.

The paper aims to explore the relationship between the congruence of mental models held by the members of entrepreneurial teams operating in an emerging economy (Poland) and entrepreneurial outcomes (performance and satisfaction).

The data obtained from 18 nascent and 20 established entrepreneurial teams was analysed to answer hypotheses. The research was quantitative and was conducted using an online questionnaire. Data was collected from each of the teams at two stages. Members of entrepreneurial teams were surveyed independently, which allowed measuring the congruence of their mental models pertaining to running a venture.

Findings reveal that team members’ mental model congruence is significantly related to financial performance and members’ satisfaction in the case of established entrepreneurial teams. However, in the case of nascent teams, there is no relationship between analysed variables.

Implications for theory and practice are offered with a special emphasis on entrepreneurship education. The concept of team mental model congruence is proposed to be included in training of nascent entrepreneurial teams, experienced companies and students.

The concept of team mental models investigated by the authors has been underexplored in entrepreneurship research. Results indicate that at least in some entrepreneurial teams, team mental models’ congruence is related to obtained outcomes. The paper proposes that principles of effectuation and causation can serve as the lens through which the mental model pertaining to running a venture can be analysed. It allows expanding studies on the congruence of team mental models in entrepreneurial teams beyond the strategic consensus.

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.