THE IMPACT ON THE COMMON CITIZEN OF COLLABORATIVE GEORREFERENCING OF CRIMINAL ACTS, BASED ON THE TECHNOLOGY ACCEPTANCE MODEL

Abstract

Society’s participation in public information management by using collaborative tools has allowed to define efficient models for retrieving and managing data. Public and private institutions have received important feedback of their management with this kind of information. Nevertheless, a quantitative evaluation of the acceptance of the management of felony acts by using socially-enhanced information management systems has been not found in the literature. The objective of this paper is to show the acceptance of web application technologies of georeferencing felonies by citizens. A web application prototype has been implemented and its use has been validated with the participation of 122 college students who were victims of felonies. The quantification of the results was carried out by using the technology acceptance model. Results were interpreted after completing a Kendall’s Tau-b correlation. A significantly positive correlation among the technology acceptance criteria was found.

Downloads

Download data is not yet available.

Author Biographies

Hernán Naranjo-Avalos, Universidad Técnica de Ambato
Docente Investigador de la Universidad Técnica de Ambato
Félix Fernández-Peña, Universidad Técnica de Ambato
Docente Investigador de la Universidad Técnica de Ambato
Pilar Urrutia-Urrutia, Universidad Técnica de Ambato
Docente Investigador de la Universidad Técnica de Ambato
Orlando Cholota-Morocho, Universidad Tecnológica Indoamérica
Docente Universidad Tecnológica Indoamérica

References

Battin, P., y Markande, S. (2016). Location Based Reminder Android Application Using Google Maps API. International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). 649–652. doi: 10.1109/ICACDOT.2016.7877666

Curtis-Ham, S., y Walton, D. (2017). Mapping crime harm and priority locations in New Zealand: A comparison of spatial analysis methods. Applied Geography, 86, 245–254. doi: https://doi.org/10.1016/j.apgeog.2017.06.008

Dele-Ajayi, O., Strachan, R., Sanderson, J., y Pickard, A. (2017). A modified TAM for predicting acceptance of digital educational games by teachers. IEEE Global Engineering Education Conference, EDUCON, 961–968. doi:https://doi.org/10.1109/EDUCON.2017.7942965

Deraman, A. B., y Salman, F. A. (2019). Managing usability evaluation practices in agile development environments. International Journal of Electrical and Computer Engineering, 9(2), 1288–1297. doi:https://doi.org/10.11591/ijece.v9i2.pp.1288-1297

Eikelboom, A., Martini, E., Ruiz, L., St. Pierre, A. D., y Tejani, A. (2017). Public Crime Mapping in Canada: Interpreting RAIDS Online. Cartographica: The International Journal for Geographic Information and Geovisualization, 52(2), 108–115. doi: https://doi.org/10.3138/cart.52.2.5101

García-Albertos, P., Picornell, M., Salas-Olmedo, M. H., y Gutiérrez, J. (2019). Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis. Transportation Research Part A: Policy and Practice, 125, pp. 294-307. https://doi.org/10.1016/j.tra.2018.02.008

Gupta, D., y Ahlawat, A. (2019). Taxonomy of GUM and Usability Prediction Using GUM Multistage Fuzzy Expert System. International Arab Journal of Information Technology, 16(3), 357–363.

Ibrahim, R., y Shafiq, M. O. (2019). Detecting taxi movements using Random Swap clustering and sequential pattern mining. Journal of Big Data, 6(1). doi: https://doi.org/10.1186/s40537-019-0203-6

Islam, M. T., Hoque, M. R., y Sorwar, G. (2016). Understanding Customer Intention to Use E-Commerce In Bangladesh: An Application Of The Technology Acceptance Model (TAM). 19th International Conference on Computer and Information Technology, ICCIT 2016, 512–516. doi: 10.1109/ICCITECHN.2016.7860251.

Jakkhupan, W., y Klaypaksee, P. (2014). A web-based criminal record system using mobile device: A case study of Hat Yai municipality. Proceedings, APWiMob 2014: IEEE Asia Pacific Conference on Wireless and Mobile 2014, 243–246. doi: https://doi.org/10.1109/APWiMob.2014.6920295

Manis, K. T., y Choi, D. (2018). The virtual reality hardware acceptance model ( VR-HAM ): Extending and individuating the technology acceptance model ( TAM ) for virtual reality hardware. Journal of Business Research, (October), 100, pp. 503-513. doi: https://doi.org/10.1016/j.jbusres.2018.10.021

Meier, A., y Teran, L. (2014). eGovernment framework. EDemocracy & EGovernment (ICEDEG), 2014 First International Conference on eDemocracy & eGovernment (ICEDEG 2014), 9–11. doi: https://doi.org/10.1109/ICEDEG.2014.6819930

Nurwarsito, H., y Savitri, N. (2019). Development of Mobile Applications for Posyandu Administration Services Using Google Maps API Geolocation Tagging. 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, 168–173. doi: https://doi.org/10.1109/SIET.2018.8693170

Park, E., Baek, S., Ohm, J., y Chang, H. J. (2014). Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics, 31(1), 3–15. doi: https://doi.org/10.1016/j.tele.2013.07.001

Patil, K. (2017). Retail adoption of Internet of Things: Applying TAM model. International Conference on Computing, Analytics and Security Trends, CAST 2016, 404–409. doi: https://doi.org/10.1109/CAST.2016.7915003

Perea-Medina, B., Rosa-Jiménez, C., y Andrade, M. J. (2019). Potential of public transport in regionalisation of main cruise destinations in Mediterranean. Tourism Management, 74(April 2018), 382–391. doi: https://doi.org/10.1016/j.tourman.2019.04.016

Pérez, Y. F., Corona, C. C., y Estrada, A. F. (2019). Fuzzy Cognitive Maps for Evaluating Software Usability. Studies in Fuzziness and Soft Computing, 377, pp. 141-155. doi: https://doi.org/10.1007/978-3-030-10463-4

Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., y Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare : the moderating role of resistance to change. Journal of Big Data, 6(1),6. doi:https://doi.org/10.1186/s40537-019-0170-y

Singh, N., Sinha, N., y Liébana-cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India : Analysis of the effect of innovativeness , stress to use and social influence. International Journal of Information Management, 50, 191–205. doi: https://doi.org/10.1016/j.ijinfomgt.2019.05.022

Sunehra, D., Priya, P. L., y Bano, A. (2016). Children Location Monitoring on Google Maps Using GPS and GSM Technologies. Proceedings - 6th International Advanced Computing Conference IACC 2016, 711–715. doi: https://doi.org/10.1109/IACC.2016.137

Tamimi, H., y Bensefia, A. (2018). Software Usability Challenges for Native Arab Users Proceedings - 2018 3rd International Conference on System Reliability and Safety, ICSRS 2018, 8688826, pp. 6-12. doi:https://doi.org/10.1109/ICSRS.2018.8688826

Tan, Q. Q., Luo, H. C., Ren, Z. L., y Liu, Q. (2017). Research on earthquake emergency response technology based on Google Maps data. Proceedings of 2016 2nd International Conference on Cloud Computing and Internet of Things, CCIOT 2016, 85–88. doi: https://doi.org/10.1109/CCIOT.2016.7868308

Teo, T., y Huang, F. (2018). Investigating the influence of individually espoused cultural values on teachers ’ intentions to use educational technologies in Chinese universities values on teachers. Interactive Learning Environments, 27(5-6), pp. 813-829. doi: https://doi.org/10.1080/10494820.2018.1489856

Toppireddy, H., Saini, B., y Mahajan, G. (2018). Crime Prediction & Monitoring Framework Based on Spatial Analysis. Procedia Computer Science, 132(Iccids), 696–705. doi: https://doi.org/10.1016/j.procs.2018.05.075

Tsai, J., Cheng, M., Tsai, H., Hung, S., y Chen, Y. (2019). Acceptance and resistance of telehealth : The perspective of dual-factor concepts in technology adoption. International Journal of Information Management, 49, pp. 34-44. doi: https://doi.org/10.1016/j.ijinfomgt.2019.03.003

Vandeviver, C., y Bernasco, W. (2017). The geography of crime and crime control. Applied Geography, 86, 220–225. doi: https://doi.org/10.1016/j.apgeog.2017.08.012

Vijaya Rohini, D., y Isakki, P. (2016). Crime analysis and mapping through online newspapers: A survey. 2016 International Conference on Computing Technologies and Intelligent Data Engineering, ICCTIDE 2016, 1–4. doi: https://doi.org/10.1109/ICCTIDE.2016.7725331

Windarni, V. A., Sediyono, E., y Setiawan, A. (2017). Using GPS and Google maps for mapping digital land certificates. 2016 International Conference on Informatics and Computing, ICIC 2016, (Icic), 422–426. doi: https://doi.org/10.1109/IAC.2016.7905756

Youn, S.-Y., y Lee, K.-H. (2019). Proposing value‑based technology acceptance model_ testing on paid mobile media service. Fashion and Textiles, 6(1),13. doi: 10.1186/s40691-018-0163-z

Zhou, G., Lin, J., y Zheng, W. (2012). A web-based geographical information system for crime mapping and decision support. 2012 International Conference on Computational Problem-Solving, ICCP 2012, 147–150. doi: https://doi.org/10.1109/ICCPS.2012.6384228

Published
2019-09-26
How to Cite
Naranjo-Avalos, H., Fernández-Peña, F., Urrutia-Urrutia, P., & Cholota-Morocho, O. (2019). THE IMPACT ON THE COMMON CITIZEN OF COLLABORATIVE GEORREFERENCING OF CRIMINAL ACTS, BASED ON THE TECHNOLOGY ACCEPTANCE MODEL. Science Magazine Unemi, 12(31), 83-94. https://doi.org/10.29076/issn.2528-7737vol12iss31.2019pp83-94p
Section
Artículos Científicos