Can you see opportunity knocking? An examination of technology-based political skill on opportunity recognition in online communities for MTurk workers

Gangi, Paul Michael Di; McAllister, Charn P.; Howard, Jack L.; Thatcher, Jason Bennett; Ferris, Gerald R.

doi:10.1108/intr-03-2021-0175

Cited by 7 publications

(2 citation statements)

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“…In the 1980s, there were some applications, such as vehicle license plate image segmentation and vehicle detection. However, domestic scholars mainly focus on the research of fusion NN based on character pixel and feature extraction, positioning algorithm and edge synthesis and enhancement technology [5][6]. In recent years, some people have also taken fusion image preprocessing as an important link in the design of digital filters, but there is no unified standardization standard at present.…”

Section: Introductionmentioning

confidence: 99%

License Plate Recognition Technology Based on Neural Network

2020

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License plate recognition is an important part of intelligent transportation system. It plays an irreplaceable role in solving the problem of vehicle license plate issuance and improving the level of urban road traffic management. In this paper, the method of vehicle location and character segmentation is studied by using artificial NN algorithm. First, the BP Neural Network (NN) training method is used to complete the fusion model from the image extraction feature parameter set to the sample classification to the input mode selection and output mapping, the calculation and analysis of the rotation distance of the license plate area, and the recognition of the background color of the license plate. Then, the correctness and feasibility of this strategy are verified by the MATLAB simulation experiment results. The test results show that the learning time of the license plate number recognition technology based on the NN algorithm is between 20s and 30s, The recognition time is between 12s and 16s, and the correct recognition rate is above 90%. This shows that the algorithm is excellent in optimizing the license plate recognition technology.

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Section: Introductionmentioning

confidence: 99%

License Plate Recognition Technology Based on Neural Network

2020

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“…Another entrepreneur may identify an entrepreneurial opportunity through social networking (Urban, 2019). Also, an entrepreneur may discover an opportunity using digital technologies such as social media or open data sources (Di Gangi et al, 2022;Hosseininia et al, 2023;Kraus et al, 2023). With this background in mind, the central position of the study is to investigate how start-up entrepreneurs can actualise open data affordances to capture entrepreneurial opportunities.…”

Section: Entrepreneurial Opportunity Recognitionmentioning

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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