JNACSISSN:2582-3817

An Empirical Study of Smart Cities Framework Adoption for Sustainable Living in Province Balochistan of Pakistan

Abstract

Information and Communications Technology (ICTs) are being used to build smart cities capable of reacting to the city's multiple changes and obstacles, such as Smart living, competent governance, Smart climate, intelligent people, smart tractability, and wonderful economics. For a better understanding of smart cities, several academics have sought to define them and highlight the potential and difficulties of creating intelligent urban communities using the Internet of Things. Due to a lack of comprehensive resilience due to respect for the component of a smart city, city leaders and organizers/designers must distinguish between the smart city system and its related activities, which smart city implementation representatives must enthusiastically accept. By enforcing acceptable standards and developing partnerships, smart cities that use Internet of Things (IoT) and cutting-edge innovation-based assets help ordinary citizens. As a result, this research identifies and promotes the development of a smart city framework adoption model based on a Systematic Literature Review (SLR) for the framework's adoption and Behavior Intention, Perceived Privacy, Perceived Security, and Perceived Value, as well as the self-efficacy and effort-expectancy of smart cities (Smart Economy, Smart Environment, Smart Government, Smart Living, Smart Mobility and Smart People). A review survey was also used to obtain input from respondents on their observations and acceptance of the proposed smart city framework adoption model by city professionals (city planners and organizers, engineers and academics, and important stakeholders). These findings, which influenced Balochistan Province policy, demonstrated that two cities in the province made the right option in implementing smart city technology.

References

  • Bakıcı, T., Almirall, E., & Wareham, J. A smart city initiative: the case of Barcelona. Journal of the knowledge economy, vol. 4, no. 2, pp. 135-148, 2013.
  • Caragliu, A. A., Del Bo, C., Kourtit, K., & Nijkamp, P. Comparative performance assessment of Smart Cities around the North Sea basin. 2011.
  • Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Scholl, H. J. Understanding smart cities: An integrative framework. In 2012 45th Hawaii international conference on system sciences. IEEE. pp.2289-2297, 2012.
  • Ma, H., Marti-Gutierrez, N., Park, S. W., Wu, J., Lee, Y., Suzuki, K.,& Mitalipov, S. Correction of a pathogenic gene mutation in human embryos. Nature, vol. 548, no. 7668, pp. 413-419, 2017.
  • Davenport, T. H., & Westerman, G. Why do so many high-profile digital transformations fail? Harvard Business Review, vol. 9, pp. 15, 2018.
  • Hämäläinen, M., & Tyrväinen, P. Improving smart city design: A conceptual model for governing complex smart city ecosystems. In Bled eConference. University of Maribor Press. 2018.
  • House, F. Freedom in the World vol. 2016, 2017.
  • Suzuki, L. R. Smart cities IoT: Enablers and technology road map. In Smart City Networks Springer, Cham. pp.167-190, 2017.
  • Anthopoulos, L. G. Understanding the smart city domain: A literature review. Transforming city governments for successful smart cities, 9-21. 2015.
  • Bengtsson, F., Granath, G., & Rydin, H. Photosynthesis, growth, and decay traits in Sphagnum–a multispecies comparison. Ecology and Evolution, vol. 6, no. 10, pp.3325-3341, 2016.
  • Badii, C., Bellini, P., Cenni, D., Difino, A., Nesi, P., & Paolucci, M. Analysis and assessment of a knowledgebased smart city architecture providing service APIs. Future Generation Computer Systems, vol. 75, pp. 14-29,2017.
  • Deng, L., & Yu, D. Deep learning: methods and applications. Foundations and trends in signal processing, vol.7, no. 3–4, pp.197-387, 2014.
  • Slade, M., Amering, M., Farkas, M., Hamilton, B., O'Hagan, M., Panther, G., ... & Whitley, R. Uses and abuses of recovery: implementing recovery‐oriented practices in mental health systems. World Psychiatry, vol. 13, no.1, pp. 12-20, 2014.
  • Algethmi, M. A. Mobile commerce innovation in the airline sector: an investigation of mobile services acceptance in Saudi Arabia (Doctoral dissertation, Brunel University School of Engineering and Design Ph.D.Theses), 2014.
  • Dutot, V. Factors influencing near field communication (NFC) adoption: An extended TAM approach. The Journal of High Technology Management Research, vol. 26, no. 1, pp.45-57, 2015.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. User acceptance of information technology: Toward a unified view. MIS Quarterly, pp. 425-478, 2003.
  • Benbasat, I., & Barki, H. Quo Vadis TAM?. Journal of the association for information systems, vol. 8, no. 4,pp.7, 2007.
  • Venkatesh, V., Thong, J. Y., & Xu, X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, pp.157-178, 2012.
  • Slade, M., Amering, M., Farkas, M., Hamilton, B., O'Hagan, M., Panther, G., ... & Whitley, R. Uses and abuses of recovery: implementing recovery‐oriented practices in mental health systems. World Psychiatry, vol. 13, no.1, pp. 12-20, 2014.
  • Baabdullah, A. M. Factors influencing adoption of mobile social network games (M-SNGs): The role of awareness. Information Systems Frontiers, vol. 22, no. 2, 411-427, 2020.
  • Morosan, C., & DeFranco, A. It's about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, vol. 53, pp. 17-29, 2016.
  • Rind, M. M., Shaikh, A. A., Kumar, K., Solangi, S., & Chhajro, M. A. Understanding the factors of customer satisfaction: An empirical analysis of Telecom broadband services. In 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICES). IEEE, pp. 1-4, 2018.
  • El-Masri, M., & Tarhini, A. Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, vol. 65, no. 3, pp. 743-763, 2017.
  • Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. Prevalence of childhood and adult obesity in the United States, 2011-2012. Jama, vol. 311, no. 8, pp. 806-814, 2014.
  • Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. Security-related factors in extended UTAUT model for NFC-based mobile payment in the restaurant industry. Computers in Human Behavior, Vol. 70,pp. 460-474, 2017.
  • Sekaran, U., & Bougie, R. Research for Business–A Skill Building Approach. John-Wiley and Sons, New York, NY, vol. 4, pp.401-415, 2010.
  • Arpaci, I., Kilicer, K., & Bardakci, S. Effects of security and privacy concerns on the educational use of cloud services. Computers in Human Behavior, vol. 45, pp. 93-98, 2015.
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. 2011.
  • Finn, R. D., Attwood, T. K., Babbitt, P. C., Bateman, A., Bork, P., Bridge, A. J., Mitchell, A. L. InterPro in 2017—beyond protein family and domain annotations. Nucleic acids research, vol. 45, no. 1, pp. 190-199, 2017.
  • Arpaci, I., Kilicer, K., & Bardakci, S. Effects of security and privacy concerns on the educational use of cloud services. Computers in Human Behavior, vol. 45, pp. 93-98. 2015.
  • Schumann, L., & Stock, W. G. The information service evaluation (ISE) model. arXiv preprint arXiv:, pp.1407.4831, 2014.
  • Venkatesh, V., J. Y. Thong, & X. Xu, Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, pp. 157-178, 2012.
  • Susanto, T. D., & Goodwin, R. (2011, August). User acceptance of SMS-based eGovernment services. In International Conference on Electronic Government (pp. 75-87). Springer, Berlin, Heidelberg.
  • Almuraqab, N. A. S., & Jasimuddin, S. M. Factors that Influence End‑Users’ Adoption of Smart Government Services in the UAE: A Conceptual Framework. Electronic Journal of Information Systems Evaluation, vol. 20,no.1, pp11-23, 2017.
  • Wang, Y. S., Tseng, T. H., Wang, Y. M., & Chu, C. W. Development and validation of an internet entrepreneurial self-efficacy scale. Internet Research. 2019.
  • Hwang, J. R., Ho, J. H., Ting, S. M., Chen, T. P., Hsieh, Y. S., Huang, C. C., ... & Wen, F. Performance of 70 nm strained-silicon CMOS devices. In the 2003 Symposium on VLSI Technology. Digest of Technical Papers (IEEE Cat. No. 03CH37407) IEEE, pp. 103-104 (2003).
  • Agha, R. A., Fowler, A. J., Saeta, A., Barai, I., Rajmohan, S., Orgill, D. P., ... & Rosin, D. The SCARE Statement: consensus-based surgical case report guidelines. International Journal of Surgery, vol. 34, pp. 180-186, 2016.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. User acceptance of information technology: Toward a unified view. MIS Quarterly, pp. 425-478, 2003.
  • Giffinger, R., & Gudrun, H. (2010). Smart cities ranking: an effective instrument for the positioning of the cities?. ACE: architecture, city, and environment, vol. 4, no. 12,pp. 7-26.
  • Bokolo, A. J., Majid, M. A., & Romli, A. A trivial approach for achieving smart city: A way forward towards a sustainable society. In 2018 21st Saudi Computer Society National Computer Conference (NCC) IEEE. pp. 1-6, 2018.
  • Azkuna, I. Smart Cities Study: International study on the situation of ICT, innovation, and Knowledge in cities. The Committee of Digital and Knowledge-based Cities of UCLG, Bilbao. 2012.
  • Owoc, M., & Marciniak, K. Knowledge management is the foundation of a smart university. In 2013 Federated Conference on Computer Science and Information Systems. IEEE. pp. 1267-1272, 2013.
  • Azkuna, I. Smart Cities Study: International study on the situation of ICT, innovation, and Knowledge in cities. The Committee of Digital and Knowledge-based Cities of UCLG, Bilbao. 2012.
  • Madkour, M. H., Heinrich, D., Alghamdi, M. A., Shabbaj, I. I., & Steinbüchel, A. PHA recovery from biomass. Biomacromolecules, vol.14, no. 9, pp. 2963-2972. Pp. 2013.
  • Dominguez-Valenzuela, J. A., Gherekhloo, J., Fernández-Moreno, P. T., Cruz-Hipolito, H. E., Alcántara-de la Cruz, R., Sánchez-González, E., & De Prado, R. First confirmation and characterization of target and non-target site resistance to glyphosate in Palmer amaranth (Amaranthus palmeri) from Mexico. Plant Physiology and Biochemistry, vol. 115, pp. 212-218. 2017.
  • Su, Z., Xie, E., & Li, Y. Entrepreneurial orientation and firm performance in new ventures and established firms. Journal of Small Business Management, vol. 49, no. 4, pp.558-577, 2011.
  • van Zoonen, W., Verhoeven, J. W., & Vliegenthart, R. Social media’s dark side: Inducing boundary conflicts. Journal of Managerial Psychology. 2016.
  • Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z. and Dowling, N. F. (2018). Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, MMWR Surveillance Summaries, vol. 67, no. 6, pp.1, 2014.
  • Suleman, D., & Zuniarti, I. Consumer Decisions toward Fashion Product Shopping in Indonesia: The effects of Attitude, Perception of Ease of Use, Usefulness, and Trust. Management Dynamics in the Knowledge Economy, vol. 7, no. 2, pp.133-146, 2019.
  • Bashir, A., Shah, N. N., Hazari, Y. M., Habib, M., Bashir, S., Hilal, N., and Fazili, K. M. Novel variants of SERPIN1A gene: interplay between alpha1-antitrypsin deficiency and chronic obstructive pulmonary disease.Respiratory Medicine, vol. 117, pp.139-149, 2016.