perIS‌BAفصلنامه رشد فناوری1735-54861735-56642023-061974124articleMohammadreza MohamadkhaniMohammadreza.mohammadkhani1978@gmail.com1Ruhollah TavallaeiTavallaee.r@gmail.com2Amirhossein Mohammadiniaa.mirhosein.mn@gmail.com3mahdi farmanimahdi.farmani@ut.ac.ir4Esmail laalilaali@chmail.ir5PhD candidate, Adaptive management and development department, Faculty of Management & Accounting, Allameh Tabatabaei University, Tehran, IranAssistant Professor, Faculty of Management & Economy, Imam Hossein University (AS), Tehran, IranPhD candidate, Industrial Management department, Faculty of Management & Economy, Imam Hossein University (AS), Tehran, IranMSc, Business management department, Faculty of Management & Accounting, Farabi Campus University of Tehran, IranMSc, Aerospace Engineering department, Faculty of Aerospace Engineering, K. N. Toosi University of Technology, Tehran, IranThe purpose of this study is to provide a rich insight into the factors affecting the growth of teams and startups in Academic Development Centers. In this study, the factors affecting the growth of teams and startups were investigated using the system dynamics approach. At first, after reviewing the theoretical literature and the experts’ opinions, Variables affecting the growth of teams and startups were indentified and causal relationships among these variables were depicted. Then, the mathematical relations among these variables were determined and, accordingly, the study system was simulated at growth centre of technician and knowledge based units at Imam Hussein comprehensive university during the period 2016 to 2066. After designing the cause and effect diagrams, six scenarios for the growth of teams and startups were defined, which are: The growth center's share of the profits of successful companies, The effect of interaction with successful companies in promoting the level of entrepreneurial culture, The effect of effective communication with successful companies in attracting financial support for technology units, The effect of changes in the number of clients to enter the center on the system, Admission control policy and The effect of legal protections of technology units on the system. Of the six simulated scenarios, the fifth scenario has the greatest impact on the growth of teams and startups in Academic Development Centers. Therefore, the policy of maintaining the quality of services through controlling the amount of technology units entering the growth centers can be considered an effective and important policy.http://roshdefanavari.ir/Article/31970Growth of Teams and Startups System Dynamics Causal Loop Diagram Simulation Academic Development Centers.perIS‌BAفصلنامه رشد فناوری1735-54861735-56642023-0619742534articlemaryam dehnavim.dehnavi05@gmail.com1Mahmoud , Ghorbanimghg2020@mshdiau.ac.ir2Mohammad Karimikarimi.740@gmail.com3Ahmad zendedelpajohesh@iau-neyshabur.ac.ir4Neyshabur Branch, Islamic Azad University, Neyshabur, Iran Mashhad Branch, Islamic Azad University, Mashhad, IranNeyshabur Branch, Islamic Azad University, Neyshabur, IranNeyshabur Branch, Islamic Azad University, Neyshabur, IranDue to the changes that have taken place inside and outside organizations today, the organization needs to be more innovative, flexible and dynamic. Team building, by combining different realities and perspectives to create new capabilities, leads to innovation in organizations. The present study aims to investigate and develop a team-based organization model with an organizational innovation approach. This research is exploratory In terms of purpose. Also, a combination of quantitative and qualitative research method is used. Moreover, data collection tools are questionnaires and interviews. The statistical population was all employees of incubators and science and technology parks in Razavi Khorasan, that 108 employees were selected as a sample based on Cochran's formula. Data analysis was performed by confirmatory factor analysis using Amos software to validate the model. Performing the necessary calculations in the software while confirming the relationships between the latent variables in the structural model indicates that CFI value for the first-order measurement model of innovation and team-based organization Also, the second-order factor analysis model is equal to 0.903, 0.901 and 0.908, respectively. Given that it is more than 0.9, we can say that the data is very well fitted. According to the research findings, it can be conducted that focusing on variables of team-oriented organization can increase the organizational innovation of employees.http://roshdefanavari.ir/Article/21048Team-based Organization Organizational Innovation Confirmatory Factor.perIS‌BAفصلنامه رشد فناوری1735-54861735-56642023-0619743541articleVahid Hajilo vahid_sun62@yahoo.com1zahra moghimiShadi.moghimi@yahoo.com2University Jihad, West Azerbaijan Province, Urmia, IranDepartment of Public Administration, Bandargaz Branch, Islamic Azad University, Bandargaz, Iran Considering the increasing movement of the country's organizations towards the full benefit of information systems, it is very important to identify the factors affecting the strengthening of these systems to improve the quality of service delivery. Therefore, this research has been carried out with the aim of investigating the effective factors on strengthening the infrastructure of information systems. The current research is applied in terms of purpose and descriptive-exploratory in nature. The research community is the government organizations of the country, and 10 people were selected to form the expert panel from among the experts who have the qualifications. Among the employees, 250 people were selected as a statistical sample by simple random sampling. The research method is a combination of multi-criteria decision-making methods (hierarchical analysis process), structural equations and fuzzy inference system. After extracting the dimensions and factors from the literature and the background of the research, the ranking of the effective factors in the form of five dimensions and 15 components was done using the multi-criteria decision making method. The results showed that the legal infrastructure has established the highest correlation with the infrastructure of information systems. Also, among the dimensions, the technological infrastructure is the most important. The results of fuzzy inference also showed that the most favorable state for information subsystems is 0.698 according to the available facilities, and the optimal combination for this state is achieved when structural factors are 0.724, organizational factors are 0.58, governance factors are 0.569, social factors are 0.707, and educational factors are 0.468. Therefore, in order to strengthen the infrastructure of the country's information systems, it is suggested that the officials focus on strengthening and developing structures and supporting technological innovations in the first step.http://roshdefanavari.ir/Article/33486Information Systems Culture Management Technology Law Education.perIS‌BAفصلنامه رشد فناوری1735-54861735-56642023-0619744260articleMehran Ehteshamiehteshamimehran@gmail.com1Mohammad Hasan Cheraghalimh.cheraghali@iau.ac.ir2Bita Tabriziantabrizian@riau.ac.ir3Maryam Teimourian sefidehkhanmary.teimourian@iau.ac.ir4Department of Management, Islamic Azad University, Roudhan Branch, Tehran, IranDepartment of Industrial Management, Islamic Azad University, South Tehran Branch, Tehran, IranDepartment of Management, Islamic Azad University, Roudhan Branch, Tehran, IranDepartment of Mathematical Statistics, Islamic Azad University, Roudhan Branch, Tehran, IranThe current research aims to design the framework of the digital transformation strategy of the road freight transportation industry, focusing on Internet of Things technology and data analytics, and uses the research method of design science. This research is fundamental-applied in terms of data type, mixed in exploratory type; According to the time of data collection, it was cross-sectional and according to the method of data collection or the nature and method of the research, it was a survey. Data collection in the qualitative part was done with semi-structured interviews with 20 experts from universities and the road freight transportation industry of Iran who were selected using the purposive sampling method and the principle of saturation. A researcher-made questionnaire was used to collect data in quantitative part and the opinions of 170 employees of this industry who were selected using Cochran's formula and stratified sampling method were obtained. Data analysis was done in the qualitative part with Delphi technique, literature review and coding. In the quantitative part, inferential statistics, one-sample t-test, structural equation modeling, and SPSS and smartPLS software were used. The fifth step of the design science method was done by testing the designed strategy framework in a company in the relevant industry. The results showed that the digital transformation strategy includes two managerial and operational strategies. Also, the consequences, influencing factors, mechanisms, facilitators and obstacles were presented in this framework. Based on the results, among the components of the framework, management strategies need to be further strengthened.http://roshdefanavari.ir/Article/40120Technology Digital Transformation Strategy Internet of Things Data Analytics.perIS‌BAفصلنامه رشد فناوری1735-54861735-56642023-0619746169articleTahereh TashayoieNejadtahere.t.nejad@gmail.com1Mohsen GhadamiGhadami@srbiau.ac.ir2Mohammad Taghi Ziae Bigdelimtzbigdeli@yahoo.com3hamide reshadatjooreshadatjoo@srbiau.ac.ir4Maryam Khaliliaraghim.khaliliaraghi@srbiau.ac.ir5PhD. Student, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.,Associate Professor, Department of Finance, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. Assistant Professor, Department of Public Management (Finance), Faculty of Management, Kharazmi University, Tehran, Iran. Associate Professor, Department of Finance, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. Assistant Professor, Department of Finance, Faculty of Management and Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran. The purpose of this study is to present a localized content analysis model of four pillars of knowledge management based on the continuous change approach. This study is the practical research in terms of orientation. The statistical population includes academic experts and theoretical experts. Theoretical experts include university professors who are knowledgeable in the field of knowledge management. Experimental experts also include managers with more than 15 years of experience in the Export Development Bank. Non-probabilistic and purposeful methods have been used for sampling. The sampling process continued and finally 11 experts participated in this stage. Using qualitative analysis of grounded theory, a number of concepts of extraction and coding of indicators were performed. Following the research process and its quantitative part, by performing the fuzzy Delphi technique in 3 rounds, the criteria obtained from the qualitative analysis of the research were screened and validated. Then, ISM method was used to identify the relationships of the localized model of the four pillars of knowledge management based on the continuous change approach and to reflect the internal relationships between the criteria. Based on data analysis in the structural-interpretive modeling section, knowledge management (WM) is first level or dependent. The variables of organizational leadership (OL), organizational culture (OC), organizational technology (OT), organizational learning (LO) are the second level. The variable of continuous change (CC) is the third level. Finally, the variables of personal competence (SC), mental patterns (MP), shared vision (SV), team learning (TL), systemic thinking (ST) are in the fourth level.http://roshdefanavari.ir/Article/27259Continuous Change Knowledge Management Localized Model Content Analysis Model Structural-Interpretive Modeling.perIS‌BAفصلنامه رشد فناوری1735-54861735-56642023-0619747087articleusef sepehriazadusef_sepehri@yahoo.com1morteza mosakhanipres@qiau.ac.ir2Ali Davariali_davari@ut.ac.ir3Tehran Science and Research BranchUniversity of TehranGiven that there is a significant gap in the conceptualization of e-learning business ecosystem with e-learning approach, this paper introduces a conceptual framework of e-learning business ecosystem in the digital age by combining two concepts: digital business ecosystem and e-learning ecosystem. In order to design and explain the model of digital business ecosystem, the e-learning application has been implemented. Therefore, this research is fundamental in terms of orientation and exploratory in terms of purpose, and uses a descriptive-correlation approach to analyze the data for a quantitative stage. In this study, a conceptual model was developed by summarizing theories using library review methods, expert opinions and by reviewing studies conducted in digital businesses and e-learning (virtual education), dimensions, factors and components affecting the design of digital business ecosystem model. E-learning has been selected and extracted and the relevant indicators and the proposed conceptual model have been used to design the questionnaire. In the inferential findings and hypothesis testing section, the confirmatory factor analysis technique and the structural equation modeling technique were used. In this study, data were analyzed using SmartPLS2 software. The maximum alpha error level for testing the hypotheses was considered 0.05. As a result of the framework of the digital business ecosystem with the e-learning approach in the digital age based on dimensions and concepts: technology (infrastructure), cultural and social factors, individuals (talents), Information literacy, government and regulations, networks and interactions, stakeholders (markets and customers), environmental factors, capital, financial assistance, business factors, organizational factors, educational factors, innovation, support, evaluation are introduced.http://roshdefanavari.ir/Article/33645Ecosystem E-business Digital Ecosystem Digital Business Ecosystem Learning Ecosystem Digital Learning Ecosystem.perIS‌BAفصلنامه رشد فناوری1735-54861735-56642023-0619748899articleMansour Aminiaminimansour@yahoo.fr1Sadegh Sayyadisadegh.sayyadi@gmail.com2Associate Professor, Department of Private Law, Faculty of Law, Shahid Beheshti University, Tehran, Iran.Faculty of Law, Shahid Beheshti University, Tehran, Iran.E-commerce means using electronic tools in the process of doing new business. This type of business, as a link between technologies and the business market, has created a stable market for the employment of different groups. Some modern businesses have developed new electronic tools that make them different from other businesses. Intelligent agents, which act as tools for technology-driven companies, are responsible for the dynamic pricing of their products and services. In this pricing model, which is often done completely without the intervention of human operators and automatically (intelligently), electronic tools price by analyzing various factors. The purpose of this study is to investigate the status and legal effects of this pricing model in companies providing intelligent transportation services. The present article has been done by analytical-descriptive and library methods. Dynamic pricing model has advantages and disadvantages compared to traditional model; Advantages: 1- Transparency in Costs Declaration, 2- Facilitates and Accelerates the provision of Transportation Services, 3- Reduces Costs. Disadvantages: 1- Absolute authority of the company in determining and paying costs to drivers, 2- Impossibility and unpredictability of monitoring the design and performance of applications of companies that use this pricing model, 3- Lack of appropriate and specific laws and regulations for possible violations. Based on the findings of this study, the existing regulations are not sufficient to address the challenges and possible violations in relation to the dynamic pricing model, and it is necessary to review and amend the regulations and formulate appropriate solutions in this regard.http://roshdefanavari.ir/Article/33195Dynamic Pricing Intelligent Agents Technology Law Intelligent Transportation Electronic Commerce.