An Open Data Repository for Engineering Design: Using Text Mining with Open Government Data

Giordano, Vito; Coli, Elena; Martini, Antonella

doi:10.1016/j.compind.2022.103738

Cited by 8 publications

(1 citation statement)

References 53 publications

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“…In another study, Chen (2020) extracted open data sources to characterise the collaboration patterns of academics in Taiwan. Also, Giordano et al (2022) (Saxena & Muhammad, 2018); ecosystem building (Reggi & Dawes, 2022); and protest movements (Meng et al, 2019).…”

Section: Academics and Studentsmentioning

confidence: 99%

Exploring Open Data Affordances for Entrepreneurial Opportunity Recognition

Mohamad

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<p><strong>Start-up entrepreneurs have an interest in open data in practice. However, open data for start-up entrepreneurship is a concept that has not been thoroughly investigated. Also, there is a lack of understanding of how start-up entrepreneurs utilise open data for entrepreneurial opportunity recognition. Hence, the research explores open data affordances for entrepreneurial opportunity recognition amongst start-up entrepreneurs in Malaysia. The research developed three theoretically based research questions from affordance theory. The first research question revolves around the perceived affordances of open data to find business opportunities. The second research question is about open data actualisation for entrepreneurial opportunity recognition. The third research question investigates facilitating conditions which enable entrepreneurs to actualise open data for entrepreneurial opportunity recognition. This qualitative research employed online semi-structured interviews supported with documentary evidence as the primary data source. Thirty online semi-structured interviews were conducted with twenty-four start-up entrepreneurs who were CEOs, founders, and co-founders. Six interviews were conducted to triangulate the data: three representatives from open data providers, a former president of a start-up association, and two representatives from start-up accelerator programmes. The findings indicate that start-up entrepreneurs have positive, negative, and mixed perceptions regarding open data affordances. Start-up entrepreneurs actualise open data for product building, creating values with existing products, and using open data for research and strategy. The findings highlight facilitating conditions required for opportunity recognition to emerge from open data. These facilitating conditions are in five areas: data quality, start-up readiness, the interdependence of stakeholders, open data enablers, and broader contexts. Several notable unexpected findings include discovering sandbox and quadruple helix programmes as emerging open data enablers. The study also contributes to the empirical evidence on a possible open data misconception among start-up entrepreneurs. This study developed a novel theoretical framework for the research topic. This research is significant to practitioners to improve open data initiatives. Researchers can apply or extend the theoretical framework in this research as a foundation for future studies.</strong></p>

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