Document Type : Original Article

Authors

1 PhD student in Information Science and Knowledge, University of Tehran, Tehran, Iran

2 Associate Professor, University of Tehran, Tehran, Iran

3 Assistant Professor, University of Tehran, Tehran, Iran

Abstract

Background and Objectives: The criteria for framing a data-driven business model model based on effective information economics policy are wide-ranging due to the complexity of this type of knowledge and their differences among entrepreneurs. A business model is a set of beliefs in order to create value from the proposed business. In the past, there was a heterogeneous understanding of the terms and concepts of business model. Words such as business idea, business concept, revenue model, or economic model can be found in the business model literature. The present study provides an information economy policy framework for data-driven businesses.
Methodology: The type of research is mixed and applied from the type of data theorizing of the foundation. The statistical population of the study was the data-driven companies registered in Tehran Science and Technology Park with an approximate number of 200 companies, of which 15 for the qualitative part by snowball method and for the quantitative part using Morgan table from the whole company. Registered with data-driven focus in Tehran Science and Technology Park, 132 companies were randomly selected. The research tools included interviews and a researcher-made questionnaire. The validity of the questionnaire was confirmed using content validity method and the reliability of the questionnaire was confirmed using Cronbach's alpha test. Data analysis was performed using fuzzy Delphi method and SPSS19 software.
Findings: Findings obtained in the fuzzy Delphi section with 15 experts showed that the index of promotion of products and services using information had the highest weight. In second place was the monitoring index of the effectiveness and efficiency of improvements in the revenue stream of partners. In addition, the indicators of paying attention to customer needs, paying attention to customer needs and creating creativity and creating new and innovative fields are below average. According to other findings, all components of data matching, data providers and data providers have the ability to measure the dimension of information creation. Product and service promotion index using information with a weight of 0.05391 had the highest weight. In the second place, the monitoring index of effectiveness and efficiency of the improvements made in the revenue stream of the partners was 0.04968. The average of the indicators is equal to 0.015157 and it is observed that the indicators of paying attention to customer needs, paying attention to the needs of customers and creating creativity and creating new and innovative fields are lower than the average. Also, all dimensions of information creation, information dissemination and information application have the ability to measure the information economy policy framework and the highest factor load is related to the information application dimension with a factor load of 0.497.
Discussion: The results showed that the dissemination of information has three components of information facilitation (including indicators of providing information infrastructure, information analysis and providing advice in the field of information, dissemination of defects and breakdowns of information, helping other organizations to use and exploit information, providing services Outsourcing information analysis, providing consulting services in the field of information strategy, creating specialized departments of infrastructure and information analysis), creating an information value network (including indicators for sharing information between several different organizations that have a single group of customers with The aim is to improve the cooperation of organizations for better service, standardization of information sharing mechanisms, monitoring of information quality indicators and determination of data sharing strategy and its control policies) and partner in the delivery network (including indicators of information sharing among participants in Delivery, control and measurement of the improvement of transactions, strengthening markets and increasing advertising power), which is related to the second sub-question of the research (what are the main and sub-components of providing information dissemination framework for data-driven businesses?). Also, Business partners, information flow integration with the aim of optimizing operational results and reducing costs, monitoring the clarity of information exchanged between the business, improving effectiveness and efficiency In the company's revenue stream, standard information quality and information exchange protocols, performance The product and its function can be the product of the state of sharing), because it is related to the third sub-question of the research (what are the main and secondary components of providing an information framework for the business and the function of a particular agent?). Therefore, dimensions of information creation, dissemination and function have the ability to measure the variables of the information economy policy framework.

Keywords

Nishitani, K., & Kokubu, K. (2020). Can firms enhance economic performance by contributing to sustainable consumption and production? Analyzing the patterns of influence of environmental performance in Japanese manufacturing firms. Sustainable Production and Consumption, 21, 156-169.
Kraus, P., Stokes, P., Cooper, S. C., Liu, Y., Moore, N., Britzelmaier, B., & Tarba, S. (2020). Cultural antecedents of sustainability and regional economic development-a study of SME ‘Mittelstand’firms in Baden-Württemberg (Germany). Entrepreneurship & Regional Development, 32(7-8), 629-653.
Almaqtari, F. A., Farhan, N. H., Yahya, A. T., & Al-Homaidi, E. A. (2020). Macro and socio-economic determinants of firms' financial performance: empirical evidence from Indian states. International Journal of Business Excellence, 21(4), 488-512.
Liu, G., & Zhang, C. (2020). Economic policy uncertainty and firms' investment and financing decisions in China. China Economic Review, 63, 101279.
Goel, R. K., & Nelson, M. A. (2021). How do firms use innovations to hedge against economic and political uncertainty? Evidence from a large sample of nations. The Journal of Technology Transfer, 46(2), 407-430.
Giroud, X., & Mueller, H. M. (2019). Firms' internal networks and local economic shocks. American Economic Review, 109(10), 3617-49.
Bosio, E., Djankov, S., Jolevski, F., & Ramalho, R. (2020). Survival of Firms during Economic Crisis. World Bank Policy Research Working Paper, (9239).
Li, J., Xia, J., & Zajac, E. J. (2018). On the duality of political and economic stakeholder influence on firm innovation performance: T heory and evidence from C hinese firms. Strategic Management Journal, 39(1), 193-216.
Long, X., Chen, Y., Du, J., Oh, K., & Han, I. (2017). Environmental innovation and its impact on economic and environmental performance: evidence from Korean-owned firms in China. Energy Policy, 107, 131-137.
Pekovic, S., Grolleau, G., & Mzoughi, N. (2020). Coopetition in innovation activities and firms' economic performance: An empirical analysis. Creativity and Innovation Management, 29(1), 85-98.
WirtzB , Pistoia , A , Ullrich ,S , Gottel V (2016) . Business models : Origin , Development and Future Research Perspectives . Long Range Planing , 49(1) , 36-54.
Seiberth, G., & Gründinger, W. (2018). Data-driven Business Models in Connected Cars, Mobility Services & Beyond. In.
Hakimzadeh, F., Abdolmaleki, J. (2011), Proposal Writing in Qualitative and Combined Studies, First Edition, Tehran, Sociologists Publications. (in Persian).
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2014). Big data for big business? A taxonomy of data-driven business models used by start-up firms. A taxonomy of data-driven business models used by start-up firms.
Lim, C., Kim, K.-H., Kim, M.-J., Heo, J.-Y., Kim, K.-J., & Maglio, P. P. (2018). From data to value: A nine-factor framework for data-based value creation in information-intensive services. International journal of information management, 39, 121-135.
Mitchell K ,Busenitz . L , Lant T, McDougal P , Morse E , Smith J (2002) . Toward a Theory people side of Enterpreneurship Research . Enterpreneueship Theory and Practice , 27(2) : PP, 93-104.
Brownlow, J., Zaki, M., Neely, A., & Urmetzer, F. (2015). Data and analytics-data-driven business models: A blueprint for innovation. Cambridge Service Alliance.
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2016). Capturing value from big data–a taxonomy of data-driven business models used by start-up firms. International Journal of Operations & Production Management, 36(10), 1382-1406.
Zolnowski, A., Anke, J., & Gudat, J. (2017). Towards a Cost-Benefit-Analysis of Data-Driven Business Models.
Liu, W., Du, C., Chu, X., & Wang, Z. (2021). “Inverted quarantine” in the face of environmental change: Initiative defensive behaviors against air pollution in China. Sustainable Production and Consumption, 26, 493-503.
Arendt, F., & Scherr, S. (2020). News-stimulated public-attention dynamics and vaccination coverage during a measles outbreak: An observational study. Social Science & Medicine, 265, 113495.
Wang, D., Tian, S., Fang, L., & Xu, Y. (2020). A functional index model for dynamically evaluating China's energy security. Energy Policy, 147, 111706.
Meng, Q., Li, M., Liu, W., Li, Z., & Zhang, J. (2021). Pricing policies of dual-channel green supply chain: Considering government subsidies and consumers' dual preferences. Sustainable Production and Consumption, 26, 1021-1030.
Bartolacci, F., Paolini, A., Quaranta, A. G., & Soverchia, M. (2018). The relationship between good environmental practices and financial performance: Evidence from Italian waste management companies. Sustainable Production and Consumption, 14, 129-135.
Qiao, H., & Su, Q. (2021). Impact of government subsidy on the remanufacturing industry. Waste Management, 120, 433-447.
El Ouadghiri, I., Guesmi, K., Peillex, J., & Ziegler, A. (2021). Public attention to environmental issues and stock market returns. Ecological Economics, 180, 106836.
Yogo, U. T. (2015). Trust and the willingness to contribute to environmental goods in selected African countries. Environment and Development Economics, 20(5), 650-672.
Jin, L., Chen, C., Wang, X., Yu, J., & Long, H. (2020). Research on information disclosure strategies of electricity retailers under new electricity reform in China. Science of The Total Environment, 710, 136382.
Lynch, J., Skirvin, D., Wilson, P., & Ramsden, S. (2018). Integrating the economic and environmental performance of agricultural systems: A demonstration using Farm Business Survey data and Farmscoper. Science of the Total Environment, 628, 938-946.