Enabling Crowdsourcing in the Framework of User-centred SDIs for Information Management of Geographical Volunteer Content

Imran, Muhammad

doi:10.1109/infoman.2019.8714670

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…It provides the technical interoperability to the seamless transfer of data between different platforms in an SDI setting (Welch et al, 2016). Besides, semantic interoperability associates agreed-on meaning to data concepts and terminologies by thesauruses and shared ontologies (Imran, 2019).…”

Section: Identifying Barriers and Research Hypothesesmentioning

confidence: 99%

Factors influencing integrated information management: Spatial data infrastructure in Pakistan

Ali

Imran

Jabeen

et al. 2021

Information Development

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Spatial data is one of the core components in all information retrieval processes for decision-making. Spatial data acquisition consumes enormous monetary resources and time. The Integrated Geospatial Information Framework (IGIF) provides a basis and guide for developing, integrating, strengthening, and maximizing geospatial information management and related resources in all countries. To this, governments all over the world are establishing national spatial data infrastructures (SDIs). However, such initiatives face a considerable amount of resistance as organizations often do not want to share their data assets. The present study investigates these barriers in the establishment of national SDI in Pakistan. The constraints studied through the IGIF pathways and past studies were adapted via a pilot study and conceptualized in a hypothesized model. We collected primary data via the administration of 520 questionnaire surveys to 280 public and private organizations. Partial least squares structural equation modeling (PLS-SEM) was applied to statistically confirm the conceptual model of the barriers to disseminating spatial data. The results indicate institutional barriers from the absence of national data policy, lack of specified roles of stakeholders, poor inter-organizational coordination, missing data-sharing policy, and weak organizational partnerships, with coefficients 0.26, 1.555, 1.305, 8.288, and 0.136, respectively, at the p < 0.001 significance level. The PLS-SEM R2 0.65 indicates a good explanatory power of the model. The methodology developed in the present study will allow devising more sustainable policies for spatial data management and dissemination in Pakistan and beyond.

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Section: Identifying Barriers and Research Hypothesesmentioning

confidence: 99%