Introducing concepts: stairs of acceptance and project speciiic reputation score. Exploring public acceptance in three Finnish construction projects via large dataset media-analytics

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Abstract

The opposition to a deployed technology in large construction projects can grow step by step when transferred from a global level to local project delivery. Large construction projects with specific technology implementations put pressure on local public acceptance and community involvement. This pressure is transferred to project management, how to deal with the issue of stakeholder acceptance before, during, and after project execution. Hence, understanding public acceptance and project-specific reputation can prove beneficial. Utilized mostly in the company Market Intelligence function(MI), modem large dataset media analytics enables mining technology-related sentiments on global, regional, or local project levels. This paper measures the media sentiment towards three large Finnish construction projects. The specific interest is to investigate which stakeholder groups are visible through the editorial and social media and how these can be classified according to the level of required information or participation level. The aim is to gain a numerical value for project reputation, a concept belonging to the marketing field of studies. Relevant technology deployment indications are provided, and a stairs of acceptance concept is conceptualized to reject the project-specific public acceptance. Specific needs to increase efforts at a local project level are indicated. The means to counteract local resistance can involve the mode of project execution or social marketing. The new algorithm-based method for measuring public acceptance and the introduced stairs of acceptance concept may bring project-level benefits by providing the added focus for increasing public acceptance.

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APA

Nuortimo, K., & Hãrkôíien, J. (2023). Introducing concepts: stairs of acceptance and project speciiic reputation score. Exploring public acceptance in three Finnish construction projects via large dataset media-analytics. Journal of Intelligence Studies in Business, 12(2), 6–25. https://doi.org/10.37380/JISIB.V12I2.951

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