Social network platforms as ubiquitous media in our today lives are the venue of many everyday activities including purchasing products and services. This study set out to explore key strategic factors of online consumer purchase behavior in social network platforms. ISM methodology is used for comparing the variables that are extracted from the Delphi technique and confirmed by the experts. To collect the data, a questionnaire was developed and distributed among a limited sample of experts in microeconomics, social networks marketing and consumer purchase behavior to compare twelve factors. The findings revealed that ‘Consumer engagement’, ‘Consumer’s value perception’ and ‘Perceived risk’ are placed at the level I. Thus, they would be positioned at the top of the ISM model. Meanwhile, findings show that ‘Trust’, ‘Social influence’, ‘Social support’ and ‘Value co-creation’ are the most important strategic variables of research. Thereafter, all linkage variables are strategic variables. This, in terms of MICMAC analysis, means such variables are significant and are worth the investment in the future based on the cross-impact analysis. The results of this study can be used for platform businesses to deepen the user engagement level and to lead the customers to purchase decision. Also future researchers can use the findings of this research to propose new models of user engagement in social network platforms or to investigate the relationship among the identified factors.

This study evaluated the impact of startup technology innovations and customer relationship management (CRM) performance on customer participation, value co-creation, and consumer purchase behavior (CPB). This analytical study empirically tested the proposed hypotheses using structural equation modeling (SEM) and SmartPLS 3 techniques. Moreover, we used a support vector machine (SVM) algorithm to verify the model’s accuracy. SVM algorithm uses four different kernels to check the accuracy criterion, and we checked all of them. This research used the convenience sampling approach in gathering the data. We used the conventional bias test method. A total of 466 respondents were completed. Technological innovations of startups and CRM have a positive and significant effect on customer participation. Customer participation significantly affects the value of pleasure, economic value, and relationship value. Based on the importance-performance map analysis (IPMA) matrix results, “customer participation” with a score of 0.782 had the highest importance. If customers increase their participation performance by one unit during the COVID-19 epidemic, its overall CPB increases by 0.782. In addition, our results showed that the lowest performance is related to the technological innovations of startups, which indicates an excellent opportunity for development in this area. SVM results showed that polynomial kernel, to a high degree, is the best kernel that confirms the model’s accuracy.

The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. A Gaussian mixture model (GMM) and multi-layer perceptron (MLP) are used as supervised and unsupervised machine learning algorithms with Python programming to cluster customers and predict consumer behavior. The results revealed that Wolt in Hungary and Snappfood in Iran are the most popular grocery apps. Users in Iran are divided into three groups of users of app services and the type of full covariance has higher accuracy compared to the other three types (96%). Meanwhile, we found that the five apps used in Hungary have provided 95% accuracy from the users’ point of view based on the diagonal covariance. The MSE value (overfitting and cross-validation) is less than 0.1 in the MLP algorithm, which shows an acceptable amount of error. The results of overfitting indicate the proper fit of the MLP model. The findings of this study could be important for managers of online businesses. In the clustering section, the accuracy and value of consumer demographic information have been emphasized. Additionally, in the classification and prediction section, a kind of “customization” has been performed with an emphasis on market segmentation. This research used GMM and MLP machine learning algorithms as a creative way to cluster and classify consumers.

In recent decades, green entrepreneurship has been at the center of attention as an effective strategy to maintain sustainability and create a competitive advantage for organizations in a circular economy. However, the successful implementation of this strategy requires organizations to have internal enablers. This study endeavored to identify and evaluate organizational enablers for green entrepreneurship in manufacturing Small and Medium Enterprises (SMEs) in Iran. Identifying organizational enablers can help SMEs in facilitating the conditions for adopting green entrepreneurship. To these ends, organizational enablers were extracted by reviewing the literature and then, using the viewpoints of 17 active experts in different industries in SMEs, they were classified. In the next step, the “Best Worst Method” was employed to prioritize the identified enablers (5 factors) and sub-enablers (20 factors). The contextual hierarchical relationships between these factors were identified through the “Interpretive Structural Modeling” method. Using the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis, the driving and dependence powers of organizational enablers were computed and the enablers were clustered. Based on the results, among the five enablers, three including total quality management, circular supply chain management, and corporate social responsibility were the most important from the point of view of the experts. Moreover, among the sub-enablers, strategic planning, green purchasing, and corporate social responsibility motivation were more important than other sub-enablers. The results of ISM analysis provided a seven-level hierarchical model and the relationships between them. The results of the MICMAC analysis led to the clustering of 20 organizational enablers in three main clusters: driving (nine factors), linkage (four factors), and dependent (seven factors). The results of this study provide practical suggestions for active senior managers to implement green entrepreneurship in SMEs.

The present study aimed to investigate the effect of online social networks on women’s entrepreneurship in Hungary and Iran in a comparative study. The statistical population of the present study consists of women entrepreneurs active in the field of online businesses in Iran and Hungary. The questionnaire was distributed via online links on social networks (on the online business website, Twitter, Facebook, and Instagram), and respondents were asked to answer questions if they were women entrepreneurs. ANOVA test was used to examine the variable of online social networks on different platforms in Iran and Hungary. The results indicate that Facebook media in Hungary in women entrepreneurship is significantly different from other online social networks. Based on the data collected, more than 95% of women entrepreneurs in Hungary use Facebook. Conversely, in Iran, Instagram has a significant difference in women's entrepreneurship from other online social networks and is the most popular media in women’s entrepreneurship. So that more than 92% of female respondents in Iran have a channel related to their online business in Instagram media. These important findings emphasize the importance and impact of social networks as entrepreneurial channels according to the atmosphere of each country.