مدلسازی پذیرش فناوری از سوی کاربران برای دستیابی به شهر هوشمند مطالعه موردی: مراکز استان

سرگلزایی, شریفه; محمدابراهیم زاده سپاسگزار, صمد

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ دانشجوی دکتری شهرسازی دانشگاه هنر اصفهان

² دکترای مدیریت پروژه و ساخت، دانشگاه تربیت مدرس

چکیده

تکوین فناوری‌های دیجیتال و در دسترس قرار گرفتن آنها، عملیاتی شدن نظریه شهر هوشمند و دولت الکترونیک را امکان‌پذیر نموده است. سنسورهای جمع‌آوری داده‌های شهری، ابزارهای ویرایش‌ آنلاین سامانه‌های اطلاعات مکانی و گوشی‌های هوشمند از جمله فناوری‌هایی‌اند که به جمع‌آوری و گردش سریع اطلاعات شهری کمک می‌نمایند. در زمینه شناخت، ارتقا و توسعه‌ میزان دقت این فناوری‌ها، تحقیق‌های فراوانی منتشر شده است. با وجود این درک دقیقی از چگونگی فرایند پذیرش و عملیاتی کردن آنها توسط کاربران اعم از اشخاص یا سازمانهای شهری وجود ندارد. هدف این پژوهش مدل‌سازی پذیرش فناوری توسط کاربران مراکز استان‌ها با به کارگیری مدل ساختار یافته می‌باشد. این مقاله به معرفی یک مدل اولیه شامل نُه سازه‌ می‌پردازد که براساس مرور ادبیات موضوع طراحی شده است. این مدل براساس داده‌های 110 پرسشنامه از زاهدان مورد آزمون و اصلاح قرار گرفت. مدل اصلاحی با داده‌های حاصل از 428 پرسشنامه از بجنورد، اصفهان، شیراز و تبریز مورد اعتبارسنجی و اصلاح نهایی قرار گرفت. مدل نهایی به پنج سازه‌ کارآمد کردن فرد، قابلیت بهره‌برداری، تسهیل در انجام امور، مزیت نسبی و سازگاری به عنوان اولویت نخست کاربران مراکز استان‌ها و به سه سازه‌ کیفیت کم خدمات، امنیت داده‌ها و ذخیره انرژی با عنوان کم اهمیت‌ترین‌ها اشاره دارد. مدل پذیرش فناوری توسط کاربران مراکز استان‌ها، ابزاری مهم برای پیش‌بینی پذیرش فناوری برای مدیران شهری است. نتایج حاصله می‌تواند در جلوگیری از تأمین و اجرای ناموفق فناوری در مقیاس کلانشهری که هزینه‌های بالایی خواهند داشت، مؤثر باشد. مدل ارائه شده در این مقاله می‌تواند در شهرهای کوچک مقیاس نیز به عنوان تحقیق آتی مورد آزمون قرار گیرد.

کلیدواژه‌ها

موضوعات

برنامه ریزی شهری

عنوان مقاله [English]

Modeling Information Technology Adoption by Users in Capital Cities

نویسندگان [English]

sh s ¹
Samad Ebrahimzadeh Sepasgozar ²

¹ Isfahan Art University

² Lecturer, Construction Management and Property

چکیده [English]

Understanding individual acceptance and technology application is one of the most mature streams of technology adoption research. There have been several theoretical models, primarily developed from theories in psychology and sociology, employed to explain technology acceptance and use. The advancement and availability of digital technologies may facilitate the implementation of smart cities and e-government systems. Many policy makers tend to enhance the smart city performance in their countries, while there is not deep understanding of key factors and barriers to adopting required technologies by users. Digital technologies such as laser sensors for collecting data from urban environments, web-based versions of Geographic Information Systems, positioning systems and smartphones may help to collect and process more accurate data. There is a considerable amount of studies focusing on the introduction and development of the above-mentioned technologies, but current literature does not provide a deep understanding of the technology adoption process in developing countries. Furthermore, the process of technology adoption has not been investigated in the field of urban planning and management. Current studies in e-government are not fully focused on the local city council e-services. The present study aims to develop the Urban Technology Adoption Model consisting of such key constructs as Low Quality Services, Cost Reduction, Energy Saving, and Time Saving. This paper intensively reviews the literature and identifies nine key constructs to use for modeling the adoption process. The constructs are identified from different domains such as technology acceptance in information systems, project management and sustainable technologies. However, the concept of technology acceptance is used in the smart city context. A survey-based method was used to test the proposed model using the Structural Equation Modeling method. The proposed model was first modified based on a sample of 110 participants in a selected major city (MC1). The modified model was validated based on the data collected from four more major cities (MC2 to MC5). The analysis shows that five constructs are critical for predicting the participants’ adoption behavior including Self-Efficacy, Operation, Work Facilitation, Relative Advantage and Compatibility. These factors were the top priorities of MCs’ users. Low priority factors as determined by the participants included such constructs as Low Quality of Services, Perceived Security and Energy Saving. This model is a valuable tool to predict the process of technology adoption at the level of local government in the field of urban e-services and management. The results of the present study are important in preventing any unsuccessful technology implementation at local level. The findings are also critical for urban planners and technology managers in developing countries since they are the main target of modern technologies. The presented model in this paper should be modified for different contexts as a future research agenda. In addition, a decision-making framework should be developed in the future based on an exploratory study recruiting participants from the management level.

کلیدواژه‌ها [English]

modeling
technology adoption
Smart Cities
Major City

مراجع

Ajzen, I., & Fishbein, M. (1969). The Prediction of Behavioral Intentions in a Choice Situation. Journal of Experimental Social Psychology, 5(4), 400-416.

Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-2805.

Azar, A., & Gholamzade, R. (2016). Structural Equation Modeling, Partial Least Squares. Tehran: Negah Danesh.

Azhar, S. (2011). Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and Management in Engineering, 11(3), 241-252.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory: Prentice-Hall, Inc.

Bandura, A. (1989). Human Agency in Social Cognitive Theory. American psychologist, 44(9), 1175.

Banerjee, U., & Hine, J. (2016). Interpreting the influence of urban form on household car travel using partial least squares structural equation modelling: some evidence from Northern Ireland. Transportation Planning and Technology, 39(1), 24-44.

Belanche, D., Casaló, L. V., & Orús, C. (2016). City attachment and use of urban services: Benefits for smart cities. Cities, 50, 75-81.

Carter, L., & Bélanger, F. (2005). The Utilization of E‐Government Services: Citizen Trust, Innovation and Acceptance Factors. Information systems journal, 15(1), 5-25.

Chiu, C.-M., Hsu, M.-H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872-1888.

Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.

Damanpour, F., & Schneider, M. (2009). Characteristics of innovation and innovation adoption in public organizations: Assessing the role of managers. Journal of public administration research and theory, 19(3), 495-522.

Davari, A., & Rezazade, A. (2014). Structural Equation Modeling by PLS Software Jahad Daneshgahi.

Davis, F. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, Vol. 13, No. 3, 319-340.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS quarterly, 319-340.

Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487.

Derpsch, R., Friedrich, T., Kassam, A., & Li, H. (2010). Current status of adoption of no-till farming in the world and some of its main benefits. International Journal of Agricultural and Biological Engineering, 3(1), 1-25.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.

Gurjar, J., Agarwal, A. K., & Gupta, V. (2015). Applications of Innovative Technologies for Development of Sustainable Transport System. Journal of Advanced Research in Automotive Technology and Transportation System, 1(1 & 2).

Juma, C. (2015). The new harvest: agricultural innovation in Africa: Oxford University Press.

Karimi, M., & Niknami, S. (2011). Self-efficacy and perceived benefits/barriers on the AIDs preventive behaviors. Journal of Kermanshah University of Medical Sciences (J Kermanshah Univ Med Sci), 15(5).

Kumar, V., Kumar, U., & Shareef, M. (2006). Implementation of Quality Management Practice in EC. Paper presented at the Proceedings of the Administrative Sciences Association of Canada Conference.

Kurniati, A. C., & Nitivattananon, V. (2016). Factors influencing urban heat island in Surabaya, Indonesia. Sustainable Cities and Society, 27, 99-105.

Laland, K. N., & Brown, G. R. (2006). Niche construction, human behavior, and the adaptive‐lag hypothesis. Evolutionary Anthropology: Issues, News, and Reviews, 15(3), 95-104.

Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.

Mitropoulos, P., & Tatum, C. (1999). Technology adoption decisions in construction organizations. Journal of Construction Engineering and Management, 125(5), 330-338.

Mitropoulos, P., & Tatum, C. B. (2000). Forces driving adoption of new information technologies. Journal of Construction Engineering and Management, 126(5), 340-348.

Mohsenin, S., & Esfidani, M. (2014). Structural equation-based approach to software Smart PLS Partial Least Squares. Tehran: Mehraban Nashr Book Institute.

Momeni, M., Dashti, M., Bayramzade, S., & Soltanmohamad, N. (2013). Structural Equation Modeling with Emphasis on Reflective and Constructive. Tehran.

Mondal, P., & Basu, M. (2009). Adoption of precision agriculture technologies in India and in some developing countries: Scope, present status and strategies. Progress in Natural Science, 19(6), 659-666.

Nunnally, J. (1978). Psychometric methods: New York: McGraw-Hill.

Parasuraman, A., & Berry, L. (1988). A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality”. Journal of Retailing, York University.

Rana, N., Williams, M., & Dwivedi, Y. (2013). Examining Factors Affecting Adoption Of Online Public Grievance Redressal System: A Case Of India.

Rana, N. P., & Dwivedi, Y. K. (2015). Citizen's Adoption of an E-Government System: Validating Extended Social Cognitive Theory (SCT). Government Information Quarterly, 32(2), 172-181.

Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2013). Analysing challenges, barriers and CSF of egov adoption. Transforming Government: People, Process and Policy, 7(2), 177-198.

Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Lal, B. (2015). Examining the Success of the Online Public Grievance Redressal Systems: An Extension of the IS Success Model. Information Systems Management, 32(1), 39-59.

Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Weerakkody, V. (2015). Investigating Success of an E-Government Initiative: Validation of an Integrated IS Success Model. Information Systems Frontiers, 17(1), 127-142.

Rogers, E. M. (1962). Bibliography on the Diffusion of Innovations.

Rogers, E. M. (1995). Diffusion of Innovations. New York: The Free Press.

Sargolzaei, S., Sepasgozar, S. M. E., & Moradi, M. (2015). Intention to Use New Technologies in Urban Management: An Application and Extension of the Technology Acceptance Model for Paper presented at the international conference on research in science and technology, kualalumpur-malaysia.

Sargolzaei, s., & Sepasgozar, S. (2015). A New Framework for Predicting Acceptance of New Technology by Urban Users based on a Comparative Study Between Iran and Bangladesh. Paper presented at the The second National Conference of Urban Management, Tehran.

Sargolzaei, s., Sepasgozar, S., & Mohamadi, M. (2015). A New Model for Evaluating Urban Users from Electronic Urban Management. Paper presented at the Second International Congress of Urban Management, Tehran.

Sepasgozar, S., Loosemore, M., Davis, S., Thomson, D., & Shen, G. (2016). Conceptualising information and equipment technology adoption in construction: a critical review of existing research. Engineering, Construction and Architectural Management, 23(2).

Shareef, M. A., Kumar, U., Kumar, V., & Dwivedi, Y. K. (2009). Identifying Critical Factors For Adoption of E-Government. Electronic Government, an International Journal, 6(1), 70-96.

Sharma, S. K. (2015). Adoption of e-government services: The role of service quality dimensions and demographic variables. Transforming Government: People, Process and Policy, 9(2), 207-222.

Slaughter, E. S. (1998). Models of construction innovation. Journal of Construction Engineering and Management, 124(3), 226-231.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS quarterly, 425-478.

Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares: Springer.

Von Winterfeldt, D., & Edwards, W. (1993). Decision analysis and behavioral research.

Wan, C., & Shen, G. Q. (2015). Encouraging the use of urban green space: The mediating role of attitude, perceived usefulness and perceived behavioural control. Habitat International, 50, 130-139.

Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195.

Williamson, O. E. (1987). Transaction cost economics. Journal of Economics Behavior and Organizations, Vol. 8, No. 4, 617-625.

Wolfinbarger, M., & Gilly, M. C. (2003). ETailQ: Dimensionalizing, Measuring and Predicting Etail Quality. Journal of retailing, 79(3), 183-198.

Xue, X., Shen, Q., & Ren, Z. (2010). Critical review of collaborative working in construction projects: Business environment and human behaviors. Journal of Management in Engineering, 26(4), 196-208.

Yoo, B., & Donthu, N. (2001). Developing a Scale to Measure the Perceived Quality of an Internet Shopping Site (SITEQUAL).

Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and electronics in agriculture, 36(2), 113-132.

فصلنامه مطالعات شهری

مدلسازی پذیرش فناوری از سوی کاربران برای دستیابی به شهر هوشمند مطالعه موردی: مراکز استان

مراجع

مراجع

دوره 6، شماره 22 - شماره پیاپی 22
اردیبهشت 1396
صفحه 27-42

مدلسازی پذیرش فناوری از سوی کاربران برای دستیابی به شهر هوشمند مطالعه موردی: مراکز استان

مراجع

مراجع

دوره 6، شماره 22 - شماره پیاپی 22اردیبهشت 1396صفحه 27-42

دوره 6، شماره 22 - شماره پیاپی 22
اردیبهشت 1396
صفحه 27-42