بهبود انتشار نوآوری از طریق تحلیل عملیاتی مدل‌سازی عامل‌بنیان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 استاد، گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

3 استاد، دانشگاه شهید بهشتی.

10.52547/jimp.10.1.117

چکیده

هدف این پژوهش شناخت و معرفی روشی برای بهبود انتشار نوآوری با استفاده از مدل­سازی عامل‌بنیان می باشد. و در این رهگذر تحلیلی عملیاتی برای ایجاد مدل­‌سازی عامل­‌محور ارائه و بررسی گردید. مدل عامل­‌محور این پژوهش در ترکیب با مدل انتشار باس ایجاد شد. در این مدل ورودی­‌ها شامل احتمال پذیرش به‌­واسطه رسانه‌­های جمعی و احتمال پذیرش به­‌واسطه گفت­‌وگوهای کلامی است که با بهره‌­گیری از مدل انتشار باس تخمین زده شده‌­اند. خروجی مدل نیز تعداد کل پذیرندگان نوآوری در هر گام زمانی در بازار بالقوه است که پس از اجرای مدل، انطباق این داده‌ها با داده‌­های دنیای واقعی تأیید گردید. برای ساخت مدل از داده‌­های واقعی مربوط به انتشار نوآوری (انتشار تلویزیون) در ایران و برای توسعه مدل از ساختار شبکه­‌ای ترجیحی استفاده شد. پس از برپاسازی مدل، مباحث مربوط به اعتبارسنجی و اعتباربخشی آن با انجام آزمای‌ش­های مربوطه و براساس گام‌­های ارائه‌­شده در تحلیل عملیاتی مدل­سازی عامل‌­بنیان لحاظ گردید و روشی که برای بهبود انتشار نوآوری معرفی شده است در قالب 10 سناریو موردبررسی و آزمون قرار گرفت. پس از تحلیل خروجی­‌های این سناریوها، روش پیشنهادی در بهبود انتشار نوآوری، به‌­ویژه برای غلبه بر تأخیر اولیه در پذیرش نوآوری مؤثر تشخیص داده شد.  

کلیدواژه‌ها


عنوان مقاله [English]

Enhancing Diffusion of Innovation through Operational Analysis of Agent-Based Modelling

نویسندگان [English]

  • Ehsan Abolfathi 1
  • Abbas Toloie Eshlaghy 2
  • Mohammad Reza Hamidi Zadeh 3
1 Ph.D Student, Department of industrial management, Science and Research Branch, Islamic Azad university, Tehran, Iran.
2 Professor, Department of industrial management, Science and Research Branch, Islamic Azad university, Tehran, Iran.
3 Professor, Shahid Beheshti university.
چکیده [English]

The purpose of this study is to introduce a method to improve innovation diffusion by using agent-based modeling. In this regard, an operational analysis to create agent-based modeling is investigated. agent-based model of this research is carried out in combination with the bass model. in this model, inputs include advertising effects and word-of-mouth effects estimated using the bass diffusion model. output of the model is also the total number of innovation adopters at each time step in the potential market that has been validated after the model has been correspond to real data (diffusion of television in Iran). to be more correspondent to real-world, the agent-based model is developed by preferential attachment-based network. after creating the model, the validation and verification were carried out by experiments and the operational analysis of the agent-based modeling. after model validation, we examine our method (using artificial innovators) for improving the diffusion of innovation. after validating the model, the proposed method for improving innovation diffusion through ten scenarios is investigated. in addition, a proposed criterion for analyzing the output of the innovation diffusion is presented, which is used to analyze the outputs. after the analysis, the method of artificial innovators was effective.

کلیدواژه‌ها [English]

  • Operational Analysis
  • Agent-Based Modeling
  • Diffusion of Innovation Marketing
  • Bass Model
  • Artificial Innovators
1.Abdul Majid, M. & Herawan, T. (2013). Modelling Reactive and Proactive Behaviour in Simulation: A Case Study in a University Organisation. International Journal of Multimedia and Ubiquitous Engineering, 8(6), 329-338.
2.Abolfathi, E., Toloie, A., & Hamidizadeh, M. (2017). An operating anatomy for agent-based modeling stand on the categorization of research done in the humanities: the diffusion of innovation in Iran. Modern researches in decision making, 3(2), 1-25.
3.Abolfathi, E., Toloie, A., & Hamidizadeh, M. (2017). Providing marketing agent-based model to adopt innovation in world-class. PhD thesis, Department of industrial management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4.Afshar, K.M., Abolfathi, E., Rajab Poor, M. (2017). A New strategy in dynamic systems applying matlab. Dafoos aja Publications.
5.Alam Tabriz, A., Hamidi Zadeh, M. R. & others (2017). New Product Development Model in the Iran Automotive Industry. Industrial Management Perspective, 7(2), 33–51.
6.Arthur, W. B. (1994). Inductive reasoning and bounded rationality. American Economic Review (Papers and Proceedings), 84, 406–411.
7.Baptista, M., Martinho, C., Lima, F., Santos, P., Prendinger, H. (2014). An agent-based model of consumer behavior based on the BDI architecture and neoclassical theory. Conference: Association for Business Simulation and Experiential Learning, 170-178.
8.Barabási, A., Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
9.Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 36(9), 1057–1079.
10.Batty, M. (2005). Cities and complexity: Understanding cities with cellular automata, agent-based models and fractals. Cambridge, Massachusetts: MIT Press.
11.Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. PNAS Colloquium, 99(3), 7280–7287.
12.Brown, D. G., Riolo, R., Robinson, D. T., North, M., & Rand, W. (2005). Spatial process and data models: Toward integration of agent-based models and GIS. Journal of Geographical Systems, 7(1), 25–47. Special Issue on Space–Time Information Systems.
13.Cavoski, S., Markovic A. (2016). Analysis of Customer Behaviour and Online Retailers Strategies Using the Agent-Based Simulation. Journal for theory and practice Management, 77, 13-24.
14.Delre, S., Jager, W., Bijmolt, T., Janssen, M. (2010). Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion. Journal of Product Innovation Management, 27(2), 267–282.
15.Dignum, V. Gilbert, N. Wellman. M. (2016) Introduction to the special issue on autonomous agents for agent-based modeling. Autonomous Agents and Multi-Agent Systems. 30(6), 1021–1022.
16.Dilaver, O. & Gilbert, N. (2017). research gate , Discover scientific knowledge, and make your research visible Retrieved may 7, 2017, from researchgate, website:https://www.researchgate.net/project/Agent-based-Macroeconomic-Models-An-Anatomical-Review.
17.Dodson, J.A.; & Muller, E. (1978) Models of new product diffusion through advertising and word-of-mouth. Managment. Science. 24, 1568–1578.
18.Ducasse, P., Harsaae J., Pralle, H., Tsusaka, M., Tzur, T. (2007). Go-to-Market Advantage: The New Battlefield for Consumer Companies. Boston Consulting Group.
19.Eppstein, M., Grover, D., Marshal, J., Rizzo, D. (2011). An agent-based model to study market penetration of plug-in hybrid electric vehicles. Energy Policy, 39(6), 3789– 3802.
20.Fourt, L. A., Joseph W. W. (1960). Early prediction of market success of new grocery products. Journal of Marketing, 25(2), 31–38.
21.Garcia, R. Rummel, P. & Hauser, J. (2007). Validating Agent-based Marketing Models Using Conjoint Analysis. Journal of Business Research, 60(8), 848-857.
22.Garcia, R. & Jager, W. (2011). From the Special Issue Editors: Agent-Based Modeling of Innovation Diffusion. Journal of Product Innovation Management, 28(2), 148–151.
23.Ghajavand, H., Zandieh, M., Dorry N.B. (2011) Application of meta-heuristic algorithms to the logistic integration network distribution model. Industrial Management Perspective, 1(3), 99–119.
24.Gilbert, N.,  Jager, W., Deffuant, G., Adjali, L. (2007). Complexities in markets:Introduction to the special issue.  Journal of Business Research, 60(8), 813–815.
25.Gilbert, N. (2008). Agent-based models. Sage Publications.
26.Goldenberg, J., Libai, B., & Muller, E. (2010). The chilling effect of network externalities. International Journal of Research in Marketing, 27(1), 4–15.
27.Gordon, R. (2011) Critical social marketing: definition, application and domain. Journal of Social Marketing, 1(2), 82-99.
28.Helda, P., Wilkinsonb, F., Marks, R., Young, L. (2014). Agent-Based Modelling, a New Kind of Research. Australasian Marketing Journal, 22(1), 4-14.
29. Heppenstall, A., Evans, A., & Birkin, M. (2006). Using hybrid agent-based systems to model spatially-influenced retail markets. Journal of Artificial Societies and Social Simulation, 9(3), 2.
30.Jager, W. (2015). Introduction To Agent Based Modeling. Retrieved feb 23, 2015, from creative commons, website: creativecommons.org/license/by-sa/3.0/legalcode.
31.Janssen, M. & Jager, W. (2001). Fashions, habits and changing preferences: Simulation of psychological factors affecting market dynamics. Journal of Economic Psychology, 22(6), 745-772.
32.Kangur, A., Jager, W., Verbrugge, R., Bockarjova, M. (2017). An agent-based model for diffusion of electric vehicles. Journal of Environmental Psychology, 52, 166-182.
33.Kim, D.H., Y.G. Shin, S.S. Park & D.S. Jang, (2009). Forecasting diffusion of technology by using bass model. Proceedings of the International Conference on Computational Methods in Sciences and Engineering, Sept. 25-30, Hersonissos, Crete, 149-152.
34.Linton, J.D. (2002). Forecasting the market diffusion of disruptive and discontinuous innovation. Institute of Electrical and Electronics Engineers Trans. Eng. Manag, 49, 365–374.
35.Mall, A., Michael D. C., Spivey, L., Tratz, A., Waltermann, B., Walters, J. (2013). Playing to Win in Emerging Markets. Boston Consulting Group.
36.North, M. J., & Macal, C. M. (2007). Managing business complexity: Discovering strategic solutions with agent-based modeling and simulation. Oxford University Press.
37.Oakland, J.S. (1999). Total Organizational Excellence: Achieving World-Class Performance. Oxford: Butterworth Heinemann.
38.Rabieh, M., Salari, H. & others (2017). Causal Loop Model for Problem of Traffic Accident: The System Dynamics Approach. Industrial Management Perspective, 7(1), 115–143.
39.Rand, W. (2006). Machine learning meets agent-based modeling: When not to go to a bar. Agent 2006, Chicago, IL, USA.
40.Rand, W. T.Rust, R. (2011) Agent-based modeling in marketing: Guidelines for rigor. Intern. J. of Research in Marketing, 28(3), 167-280.
41.Rangoni, R. & Jager, W. (2017). Social Dynamics of Littering and Adaptive Cleaning Strategies Explored Using Agent-Based Modelling. Journal of Artificial Societies and Social Simulation, 20(2).
42.Rauh, J., Schenk T., Schrodl, D. (2011). The simulated consumer – An agent-based approach to shopping behavior. Erdkunde, 66(1), 13–25.
43.Rogers, E. M. (2003). Diffusion of Innovations. Simon and Schuster.
44.Rook, L. (2006). An Economic Psychological Approach to Herd Behavior. Journal of Economic, 40 (1), 75–95.
45.Serrano, E. Iglesias, C. Garijo, M. (2015) “A Novel Agent-Based Rumor Spreading Model in Twitter. '15 Companion Proceedings of the 24th International Conference on World Wide Web, 811-814.
46.Tidd, J., Bessant, J. (2009). Managing Innovation: Integrating Technological, Market, and Organizational Change. Wiley Publications.
47.Vattam, S. Goel, K. & Rugaber, S. (2011). Behavior Patterns: Bridging Conceptual Models and Agent-Based Simulations in Interactive Learning Environments. 11th IEEE International Conference on Advanced Learning Technologies, 139-143.
48.Wellman, P. (2014). Putting the Agent in Agent-Based Modeling. 13th International Conference on Autonomous Agents and Multi agent Systems.
49.Wurzer, G. (2011). An Introduction to NetLogo, Vienna University of Technology. Retrieved june 5, 2017, from netlogo training website: www.iemar.tuwien.ac.at
50.Zhang, T. Gensler, S. & Garcia, R. (2011). A Study of the Diffusion of Alternative Fuel Vehicles: An Agent-Based Modeling Approach. Journal of Product Innovation Management, 28(2), 152-168.