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Mobile Networks and Applications

, Volume 24, Issue 1, pp 47–68 | Cite as

Innovative Citizen’s Services through Public Cloud in Pakistan: User’s Privacy Concerns and Impacts on Adoption

  • Umar Ali
  • Amjad MehmoodEmail author
  • Muhammad Faran Majeed
  • Siraj Muhammad
  • Muhammad Kamal Khan
  • Houbing Song
  • Khalid Mahmood Malik
Article

Abstract

The world is going to be more and more digital with effective utilization of information and communication technologies in government services to provide services to their citizens. In developing countries, public cloud is now considered a powerful platform, for providing scalable and cost effective public services to the citizens, due to their limited resources and budget. Public cloud has been adopted by both developed and developing countries for providing e-government services. User’s adoption is equally essential just like the government’s adoption of new services. Government needs to assess the user’s behavior intention and use behavior before choosing public cloud as platform for their innovative citizen’s services also known as government to citizen’s services (G2C). As citizen’s information is stored on public cloud, which is provided by a third party, so user’s concerns about privacy of information may affect the adoption of these services. The aim of this study is to find out the privacy factors that influence the adoption of e-government services by choosing and recommend suitable technology adoption model. As a methodology, the Unified Theory of Acceptance and Use of Technology (UTAUT) Model was amended to add two additional privacy variables from e-commerce domain i.e., Perceived Internal Privacy Risk (PIPR) and Cloud Information Privacy Concern (CIPC). Thus the new model included in addition to PIPR and CIPC, its own four elements of performance expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC). The data was collected from respondents who had used both public cloud and e-government services. Structure Equation Modeling (SEM) was used to investigate the effect of all variables on Behavior Intention (BI) and Use Behavior (UB). The findings show that Performance Expectancy (PE), Effort Expectancy (EE) and Social Influence (SI) had positive effects on user’s Behavior Intention (BI) while Cloud Information Privacy Concerns (CIPC) and Perceived Internet Privacy Risks (PIPR) had negative effects on Behavior Intention (BI). The Facilitating Conditions (FC) and Behavior Intention (BI) had a strong positive effect on User Behavior (UB).

Keywords

Electronic government Cloud computing Technology adoption model Information privacy Government-to-citizen 

References

  1. 1.
    Mukerji M (2013) Introduction. In: ICTs and development, Springer, pp. 1–11Google Scholar
  2. 2.
    Basu S (2004) E-government and developing countries: an overview. International Review of Law, Computers, & Technology 18(1):109–132MathSciNetGoogle Scholar
  3. 3.
    Harris JG, Alter AE (2010) Cloudrise: rewards and risks at the dawn of cloud computing. 20Google Scholar
  4. 4.
    Umar MM, Mehmood A, Song H, Choo K-KR (2017) I-marks: an iris code embedding system for ownership identification of multimedia content. Comput Electr Eng 63:209–219Google Scholar
  5. 5.
    Alshawi S, Alalwany H (2009) E-government evaluation: Citizen’s perspective in developing countries. Inf Technol Dev 15(3):193–208Google Scholar
  6. 6.
    Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 425–478Google Scholar
  7. 7.
    Alshehri M, Drew S, Alfarraj O (2012) A comprehensive analysis of E-government services adoption in Saudi Arabia: obstacles and challenges. High Educ 6:8–2Google Scholar
  8. 8.
    Suna D, Changb G, Suna L, Wanga X (2011) Surveying and analyzing security, privacy and trust issues in cloud computing environments. Procedia Engineering 15:2852–2856Google Scholar
  9. 9.
    Khan R, Mehmood A (2012) Realization of interoperability & portability among open clouds by using agents mobility & intelligence. International Journal of Multidisciplinary Sciences and Engineering 3(7):7–11Google Scholar
  10. 10.
    Heeks R, Bailur S (2007) Analyzing E-government research: perspectives, philosophies, theories, methods, and practice. Gov Inf Q 24(2):243–265Google Scholar
  11. 11.
    Mehmood A, Roman M, Umar MM, Song H (2015) Cloud computing security: a survey. International Journal of Computer Science and Information Security 13(7):20Google Scholar
  12. 12.
    Mehmood A, Alrajeh N, Mukherjee M, Abdullah S, Song H (2018) A survey on proactive, active and passive fault diagnosis protocols for wsns: network operation perspective. Sensors (Basel, Switzerland) 18(6):1787Google Scholar
  13. 13.
    Kurfal M, Arifolu A, Tokdemir G, Pain Y (2017) Adoption of E-government Services in Turkey. Comput Hum Behav 66:168–178.  https://doi.org/10.1016/j.chb.2016.09.041 Google Scholar
  14. 14.
    Zhang Z, Mehmood A, Shu L, Huo Z, Zhang Y, Mukherjee M (2018) A survey on fault diagnosis in wireless sensor networks. IEEE Access 6:11349–11364Google Scholar
  15. 15.
    Sang S, Lee J, Lee J (2010) E-government adoption in Cambodia: a partial least squares approach, trans- forming government: people. Process and Policy 4(2):138–157Google Scholar
  16. 16.
    Mehmood A, Ahmed SH, Sarkar M (2017) Cyber-physical systems in vehicular communications, in: Handbook of Research on Advanced Trends in Microwave and Communication Engineering, IGI Global, pp. 477– 497Google Scholar
  17. 17.
    Arshad S, Shah MA, Wahid A, Mehmood A, Song H, Yu H (2018) Samadroid: a novel 3-level hybrid malware detection model for android operating system. IEEE Access 6:4321–4339Google Scholar
  18. 18.
    Sharma MK, Thapliyal MP (2011) G-cloud: (E-governance in cloud). International Journal Engg TechSci 2(2):134–137Google Scholar
  19. 19.
    Huang M, Zhang Y, Jing W, Mehmood A (2017) Wireless Internet: 9th International Conference, WICON 2016, Haikou, China, December 19-20, 2016, Proceedings, Vol. 214, SpringerGoogle Scholar
  20. 20.
    Susanto TD, Goodwin R (2013) User acceptance of SMS-based e-government services: differences between adopters and non-adopters. Gov Inf Q 30(4):486–497Google Scholar
  21. 21.
    Majeed MF, Esichaikul V, No ME (2013) Use of Multi-agent Based Platform for Providing Document-Centric Interoperability in the Realm of E-government, in: International Conference on Advances in Information Technology, Springer, pp. 141–149Google Scholar
  22. 22.
    Mehmood A, Mauri JL, Noman M, Song H (2015) Improvement of the wireless sensor network lifetime using leach with vice-cluster head. Ad Hoc & Sensor Wireless Networks 28(1-2):1–17Google Scholar
  23. 23.
    Moon MJ, Norris DF (2005) Does managerial orientation matter? The adoption of reinventing government and E-government at the municipal level. Inf Syst J 15(1):43–60Google Scholar
  24. 24.
    Yildiz M (2007) E-government research: reviewing the literature, limitations, and ways forward. Gov Inf Q 24(3):646–665Google Scholar
  25. 25.
    Lee J, Kim HJ, Ahn MJ (2011) The willingness of e-government service adoption by business users: the role of offline service quality and Trust in Technology. Gov Inf Q 28(2):222–230Google Scholar
  26. 26.
    Qaiser N, Khan HGA (2010) E-government challenges in public sector. Int J Comput Sci 7(5):310–317Google Scholar
  27. 27.
    Mehmood A, Khanan A, Mohamed AHH, Mahfooz S, Song H, Abdullah S (2018) Antsc: an intelligent naive bayesian probabilistic estimation practice for traffic flow to form stable clustering in vanet. IEEE Access 6:4452–4461Google Scholar
  28. 28.
    Mehmood A, Mukherjee M, Ahmed SH, Song H, Malik KM (2018) Nbc-maids: Na¨ıve bayesian classifi- cation technique in multi-agent system-enriched ids for securing iot against ddos attacks, J Supercomput 1–15Google Scholar
  29. 29.
    Ren L, Zhang L, Wang L, Tao F, Chai X (2017) Cloud manufacturing: key characteristics and applications. Int J Comput Integr Manuf 30(6):501–515Google Scholar
  30. 30.
    Zhou J, Yang J, Song H, Ahmed SH, Mehmood A, Lv H (2016) An online marking system conducive to learning. J Intell Fuzzy Syst 31(5):2463–2471Google Scholar
  31. 31.
    Aldegheishem A, Yasmeen H, Maryam H, Shah MA, Mehmood A, Alrajeh N, Song H (2018) Smart road traffic accidents reduction strategy based on intelligent transportation systems (tars). Sensors 18(7):1983Google Scholar
  32. 32.
    Mell P, Grance T, et al (2011) The NIST Definition of Cloud Computing, Tech. rep., National Institute of Standards & TechnologyGoogle Scholar
  33. 33.
    Wyld DC (2009) The utility of cloud computing as a new pricing and consumption model for information technology. International Journal of Database Management Systems (IJDMS) 1(1):1–20Google Scholar
  34. 34.
    Khan F, Zhang B, Khan S, Chen S (2011) Technological Leap Frogging E-government through Cloud Com- puting. In: 4th IEEE International Conference on Broadband Network and Multimedia Technology (IC- BNMT), pp. 201–206Google Scholar
  35. 35.
    Botta A, De Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and internet of things: a survey. Futur Gener Comput Syst 56:684–700Google Scholar
  36. 36.
    Attaran M (2017) Cloud computing technology: leveraging the power of the internet to improve business performance. Journal of International Technology and Information Management 26(1):112–137Google Scholar
  37. 37.
    Orakwue E (2010) Private clouds: secure managed services. Information Security Journal: A Global Perspective 19(6):295–298Google Scholar
  38. 38.
    Hofmann P, Woods D (2010) Cloud computing: the limits of public clouds for business applications. IEEE Internet Comput 14(6):90–93Google Scholar
  39. 39.
    P. Géczy, N. Izumi, K. Hasida, Cloudsourcing: Managing Cloud Adoption, Tech. rep. (2011)Google Scholar
  40. 40.
    Krutz RL, Vines RD (2010) Cloud security: a comprehensive guide to secure cloud computing. Wiley Publishing, HobokenGoogle Scholar
  41. 41.
    Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure Management in Private and Hybrid Clouds. IEEE Internet Comput 13(5):14–22Google Scholar
  42. 42.
    Montano DE, Kasprzyk D (2015) Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health behavior: Theory, Research and Practice 95–124Google Scholar
  43. 43.
    Francescato G, Weidemann S, Anderson JR (2018) Evaluating the built environment from the users Perspective: implications of attitudinal models of satisfaction. In: Building performance evaluation, Springer, pp. 87–97Google Scholar
  44. 44.
    de Camargo Fiorini P, Seles BMRP, Jabbour CJC, Mariano EB, de Sousa Jabbour ABL (2018) Management theory and big data literature: from a review to a research agenda. Int J Inf Manag 43:112–129Google Scholar
  45. 45.
    Hariguna T, Lai M, Hung C, Chen S (2017) Understanding information system quality on public E-government service intention: an empirical study. International Journal of Innovation and Sustainable Development 11(2-3):271–290Google Scholar
  46. 46.
    Carter L, Belanger F (2004) Citizen adoption of electronic government initiatives. In: proceedings of the 37th annual Hawaii international conference on system sciences, IEEEGoogle Scholar
  47. 47.
    Alkhater N, Walters R, Wills G (2018) An empirical study of factors influencing cloud adoption among private sector Organisations. Telematics Inform 35(1):38–54Google Scholar
  48. 48.
    Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36:157–178Google Scholar
  49. 49.
    Reddick CG (2005) Citizen interaction with E-government: from the streets to servers? Government Information Quarterly 22(1):38–57Google Scholar
  50. 50.
    Maruping LM, Bala H, Venkatesh V, Brown SA (2017) Going beyond intention: integrating behavioral expectation into the unified theory of acceptance and use of technology. J Assoc Inf Sci Technol 68(3):623–637Google Scholar
  51. 51.
    Harby FA, Qahwaji R, Kamala M (2012) End-users acceptance of biometrics authentication to secure E- commerce within the context of Saudi culture: applying the UTAUT model, globalization, Technology Diffusion and Gender Disparity: Social Impacts of ICTs 225–246Google Scholar
  52. 52.
    Van SC, Shim JT, Johnson R, Jiang JJ (2006) Concern for Information Privacy and Online Consumer Purchasing, Tech. rep.Google Scholar
  53. 53.
    Tsohou A, Lee H, Irani Z (2014) Innovative public governance through cloud computing: information privacy, business models and performance measurement challenges. Transforming Government: People, Process and Policy 8(2):251–282Google Scholar
  54. 54.
    Moqbel MA, Bartelt VL (2015) Consumer acceptance of personal cloud: integrating trust and risk with the technology acceptance model. AIS Transactions on Replication Research 1(1):1–5Google Scholar
  55. 55.
    AlAwadhi S, Morris A (2008) The use of the UTAUT model in the adoption of E-government Services in Kuwait. In: proceedings of the 41st annual Hawaii international conference on system sciences, IEEE, pp. 219–219Google Scholar
  56. 56.
    Foster SP (2000) The digital divide: some reflections. The International Information & Library Review 32(3-4):437–451Google Scholar
  57. 57.
    McGarr O, Gavaldon G (2018) Exploring Spanish pre-service teachers talk in relation to ICT: balancing different expectations between the university and practicum school. Technol Pedagog Educ 27(2):199–209Google Scholar
  58. 58.
    Angelopoulos K, Diamantopoulou V, Mouratidis H, Pavlidis M, Salnitri M, Giorgini P, Ruiz JF (2017) A holistic approach for privacy protection in E-government. In: proceedings of the 12th international conference on availability, Reliability and Security, ACM, p. 17Google Scholar
  59. 59.
    Sekaran U, Bougie R (2006) Research methods for business: a skill building approach. John Wiley & Sons, HobokenGoogle Scholar
  60. 60.
    Rehman M, Kamal MM, Esichaikul V (2016) Adoption of E-government Services in Pakistan: a comparative study between online and offline users. Inf Syst Manag 33(3):248–267Google Scholar
  61. 61.
    Cohen P, West SG, Aiken LS (2014) Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press, HoveGoogle Scholar
  62. 62.
    Churchill GA, Iacobucci D (2006) Marketing research: methodological foundations. Dryden Press, New YorkGoogle Scholar
  63. 63.
    Schmitt A, Reimer A, Hermanns N, Kulzer B, Ehrmann D, Krichbaum M, Huber J, Haak T (2017) Depres- Sion is linked to Hyperglycaemia via suboptimal diabetes self-management: a cross-sectional mediation analysis. Journal of Pychosomatic Research 94:17–23Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of SwatMingoraPakistan
  2. 2.Institute of Information TechnologyKohat University of Science & TechnologyKohatPakistan
  3. 3.Department of Computer ScienceShaheed Benazir Bhutto University SheringalSheringalPakistan
  4. 4.Department of Information & Communication TechnologyAsian Institute of TechnologyKhlong NuengThailand
  5. 5.Department of English & Applied LinguisticsAllama Iqbal Open UniversityIslamabadPakistan
  6. 6.Department of Electrical, Computer, Software, and Systems EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachUSA
  7. 7.Department of Computer Science and EngineeringOakland UniversityRochesterUSA

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