The study explores the hormonal influences on entrepreneurial opportunity persuasion decisions (OPD). It also analyzes how behavioural psychology influences the entrepreneurial opportunity recognition-refinement process (OPRR). The machine learning and structural equation modelling analyses of the data from 271 Indian entrepreneurs support the hypotheses of a direct effect of the physiological biomarker—Testosterone-Cortisol Ratio (TCR) and behavioural marker—communities of inquiry (COI) on OPD. Further, partial mediation indicated an indirect positive effect of COI on OPD, with OPRR moderating this relationship. Shedding new light on the enigmatic entrepreneurial opportunity behaviour from a biological and behavioural perspective improves the causal inference of external and socially-influenced COI and internally-triggered TCR. Finally, theoretical and methodological contributions are discussed along with implications that can guide future research on entrepreneurial opportunity behaviours.
This paper widens the focus on how artificial intelligence (AI) can foster the learning abilities of human actors, adopting a wider view with respect to a strict focus on tasks and activities. The interaction between AI and human learning has not been investigated in service research. Placing its theoretical roots in work by Huang and Rust [Huang MH, Rust RT (2021) Engaged to a robot? The role of AI in service. J. Service Res. 24(1):30–41.] in service research and on Bloom’s revised taxonomy in education studies [Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC (2001) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives (Longman, London).], this study offers an integrative framework for the ways AI enhances human learning abilities. Some cases in the context of COVID-19 offer insightful illustrations of the framework.
Purpose
Existing business model frameworks show weak conceptual unification, a paucity of measurement focus and limitations when applied in emerging economies. The study proposes a new business model framework – “Start-up Evaluation Calculus Using Research Evidence” (SECURE). The purpose of this study is to allow the measurement of the impact of business model design on start-up performance in emerging economies.
Design/methodology/approach
Data collected from 713 entrepreneurs in select cities of India, Oman and the United Arab Emirates is analyzed through structural equation modeling. The study uses measurement and structural models to examine the validity of measures and additionally tests the five hypothesized relationships proposed in the study.
Findings
The SECURE’s components comprising desirability, marketability, feasibility, scalability and viability showed validity and reliability. They synergistically demonstrated a statistically significant effect on a mix of financial and non-financial start-up performance outcomes. An alternative structural relationship that examined the impact of SECURE on only financial performance outcomes showed a weaker model fit. The findings indicate that a business model framework is useful when its ex ante measures show a positive causal effect on the desired performance outcomes.
Practical implications
The scores obtained by the SECURE framework serve as an evaluative tool that informs entrepreneurs and start-ups on the readiness of their proposed, incubated or existing start-ups.
Originality/value
Replacing subjective judgments with objective assessment criteria, SECURE is one of the first quantitative and performance-driven business model frameworks that contain measures from all functional domains of a start-up business. Start-ups can evaluate their business models against the SECURE model’s research-driven quantitative criteria and assess their impact on start-up performance.
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