The situations and problems that police officers face are more complex in today’s society, due in part to the increase of technology and growing complexity of globalization. Accordingly, to solve these problems and deal with the complexities, law enforcement organizations develop and apply new techniques and methods such as geographic information systems (GIS). However, the successful implementation of a new technology does not just depend on providing perfect technical support, but effective and active interaction between the user and system. For this reason, research examining user acceptance of GIS technologies provides a valuable source to investors and designers to predict …
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The situations and problems that police officers face are more complex in today’s society, due in part to the increase of technology and growing complexity of globalization. Accordingly, to solve these problems and deal with the complexities, law enforcement organizations develop and apply new techniques and methods such as geographic information systems (GIS). However, the successful implementation of a new technology does not just depend on providing perfect technical support, but effective and active interaction between the user and system. For this reason, research examining user acceptance of GIS technologies provides a valuable source to investors and designers to predict whether the results of the technology will meet user expectations; understanding the factors that influence user acceptance is vitally important to make the system more usable and preferable. This study attempts to explain Turkish National Police officers’ beliefs about and behaviors toward GIS applications by using the technology acceptance models. It contributes to the technology acceptance literature by testing the proposed model in a rarely studied organization: law enforcement. Regarding methodology, I distributed a survey questionnaire in Turkey; the unit of analysis was the law enforcement officers in the Turkish National Police (TNP). In order to analyze the data derived from the survey instrument, structural equation modeling (SEM), a multivariate statistical technique, was used to analyze the quantitative data by utilizing the AMOS 16.0 software. The analysis resulted in good model fit, and 6 of the 7 hypotheses were supported.
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