JEL: M00, M10, O10
RECEIVED: 19/11/2018
MODIFIED: 0 5 / 0 3 / 2 01 9
accepted: 16 / 0 4 / 2 019
ABSTRACT
The present paper addresses the need for a quantitative model for media entrepreneurship. In a framework of social media entrepreneurship that is presented by khajeheian (2013), 23 factors are introduced and categorized in five groups. Four groups of enterprise, resources, strategy and product are controllable and infrastructure is the uncontrollable group. In the current research, these groups are considered as variables and factors are used as measures. In a survey 305 Iranian students responded a questionnaire. SEM and MLP-ANN used for analysis. Results show that resources are on the highest rank, strategy and infrastructure are placed as 2nd and 3rd and enterprise and product placed at the lowest rank. This model can be used as a foundation for developing more quantitative researches in the subject of media entrepreneurship.
KEYWORDS
Media Entrepreneurship, Social Media Entrepreneurship, Entrepreneurial Action, SEM, MLP-ANN
RESUMEN
El presente documento aborda la necesidad de un modelo cuantitativo para el emprendimiento de medios. En el marco del emprendimiento en redes sociales presentado por Khajeheian (2013), se introducen y categorizan 23 factores en cinco grupos. Cuatro grupos de empresa, recursos, estrategia y producto son los controlables y la infraestructura es el grupo incontrolable. En la investigación actual, estos grupos se consideran como variables y los factores se utilizan como medidas. En una encuesta, 305 estudiantes iraníes respondieron un cuestionario. Las metodologías SEM (modelo de ecuaciones estructurales) y MLP-ANN (red neuronal artificial basada en el perceptrón multicapa) utilizados para el análisis. Los resultados muestran que los recursos están en la posición más alto, la estrategia y la infraestructura se ubican en el segundo y tercer lugar, y la empresa y el producto en el más bajo. Este modelo se puede utilizar como base para desarrollar más investigaciones cuantitativas en el tema del emprendimiento de medios.
PALABRAS CLAVE
Emprendimiento de medios, emprendimiento de redes sociales, acción empresarial, SEM, MLP-ANN
INTRODUCTION
Entrepreneurship is important in the national and global economies today so that, it main engine of economic growth and prosperity (Boufaden, 2013; Dutta, Roy, & Sobel, 2009). Small and medium-sized enterprises (SMEs) in the European Union account for 99 % of the total employment (European Commission, 2016) and In the United States, small entrepreneurs have created employment between 2.5 million and 4.7 million each year from 1994 to 2015 (bls, 2016). Khajeheian (2014) the effective role of entrepreneurship in developing countries has attracted attention; Iran is one of these countries. In recent years, entrepreneurship has increased in Iran. World statistics show In 2018, Iran ranked 72 with an increase of 13 in the world's 137 countries (Acs, Szerb, Lafuente, & Lloyd, 2018). Although the census points out that growth is in Iran but these show Iran's unfavorable position, part of this is due to government policy. Accordingly, one of Iran's researches identifies the gap between governmentrelated entrepreneurship programs in Iran with global indicators of increasing levels of government-led entrepreneurship activities, such as reforming government policies and laws for the success of Iranian entrepreneurs (Modaresi & Davodi, 2015). Statistics showed In 2015 Of the nascent Iranian entrepreneurs who entered the entrepreneurial world in the last three years, 7 % reached the stage of stability; also 4 % went out of their businesses this year (IRNA, 2016) Therefore, it is felt that entrepreneurship in Iran needs a model But we don't have this kind of model. This study seeks to provide a model for the success of media entrepreneurship in Iran. Media entrepreneurship is important because, in the era of knowledge economy, media industries can become the engine of economic development (Hollifield, 2003: 101). In this research, based on the framework Khajeheian (2013) the success factors of media entrepreneurs are modeled in an entrepreneurial way. We first identify the relationship between the variables by using our structural equations modeling, and then the accuracy of the proposed model is determined according to the mentioned framework, and the importance of the factors of this model, is determined. This research, on the one hand, has led to a strengthening of media entrepreneurship literature, whose weakness was emphasized by Khajehian (2017) and on the other hand, innovation is in the methodology.
LITERATURE ON MEDIA ENTREPRENEURSHIP
In 1971, the first research on entrepreneurship and media by looking at the field of music was published (Peterson & Berger, 1971) but until 2000, there are not many articles on the subject; however, the third millennium is a beginning of interest in research on entrepreneurship and media (Hang & Van Weezel, 2007). However, what is now clear is that entrepreneurship and media have a mutual impact on each other. (Hang & Van Weezel, 2007; Khajeheian, 2013; Khajeheian & Roshandel Arbatani, 2016). On one hand, the media can promote entrepreneurship and on the one hand, entrepreneurship phenomena heavily impact media industries as long as they, in their very nature, fall into the culture and creative industries (Hang & Van Weezel, 2007) however, our focus in this research is on the impact of entrepreneurship in the media industry, which is considered media entrepreneurship. Media entrepreneurship is still nascent. Achtenhagen (2008: 124) wrote: "As the area of media entrepreneurship is still a young and undeveloped field, this phenomenon is poorly understood" and now too some researchers still believe there is no significant improvement on media entrepreneurship (Khajeheian, 2017: 92). Of course media entrepreneurship is defined by some researchers. According to Hoag and Seo (2005, p. 3) media entrepreneurship is the creation and ownership of a small media company or media organization that creates at least one innovation in the business market. Khajeheian & Roshandel Arbatani (2016) the media entrepreneurship defines the use of limited resources by small media companies to take advantage of opportunities in a particular marketplace. But the most complete definition seems to be this definition: "Media entrepreneurship is taking the risk to exploit opportunities (creation/ discovery) by innovative use of (radical/incremental/imitative) resources (ownership/control) in transform of an idea into activities to offer value (creation/delivery) in a media form (content/platform/user data) that meets the need of a specific portion of market (businesses or consumers), either in an individual effort or by creation of new venture or entrepreneurial managing of an existing organizational entity and to earn benefit (money/attention/favorite behavior) from one of the sources that is willing to pay for (direct consumers, advertisers, data demanders or any customer of generated information of consumers)" (Khajeheian, 2017: 102). Companies the media seeks to explore new ways of using their current resources to create emerging revenue paths and increase company assets (Dutta, Roy, & Sobel, 2009).
Indeed, the emerging media industry is created with the presence of entrepreneurs who take the risk of introducing and presenting media products in response to available opportunities. According to Hang (2016), some of the opportunities include online business opportunities, online gaming business opportunities, digital TV business opportunities, and business opportunity and joint venture. However, an entrepreneurial act that we know about establishing a media company or production and the marketing of a new product / service is impossible only with taking advantage of opportunities. Achtenhagen (2017) also says research shows media companies have never been successful and have not really been entrepreneurs. Based on research, it seems that the main reason for this is the focus of media entrepreneurs on some aspects and the neglect of other dimensions of success (Khajeheian, 2013; Taheri & Roshandel Arbatani, 2018)
CONCEPTUAL MODEL
The framework that presented by Khajeheian (2013) provides a basis to model the social media entrepreneurship. In his framework, 23 factors are categorized in four categories of controllable, including enterprise, product resources and strategy; and one category of uncontrollable, namely infrastructures. The framework is depicted as a wheel on a ground. The wheel represents four controllable categories that ideally are balanced; the ground is the infrastructure category that facilitates (downward slope) or slow down (upward slope) rolling of this wheel. Figure 1 depicts this framework.
To model the social media entrepreneurship, the authors uses four controllable categories as independent variables, and infrastructure category as intermediate variable. Figure 2 depicts the conceptual model. The research hypotheses are developed as following:
H1: Strategy positively effects on Infrastructure.
H2: Product positively effects on Infrastructure.
H3: Resources positively effects on Infrastructure.
H4: Corporate positively effects on Infrastructure.
H5: Infrastructure positively effects on Entrepreneurial action.
METHODOLOGY
The survey is a flexible research approach used to investigate a wide range of topics (Mathers, Fox, & Hunn, 2007). The present research is a survey research. according to Glasow (2005), in survey research, independent and dependent variables are determined in the study and before the survey, a model is designed to determine the relationships between variables by the researcher.
Sampling
The research population consisted of students and graduates of entrepreneurship and media of Iranian universities. The reason for choosing this statistical community was their close relationship with the subject of research as well as their identification as potential and actual entrepreneurs. The sample number plays an important role in the estimation and interpretation of the structural equation modeling results. Although a small sample is not recommended, but even with 50 samples, it is possible to conclude, but it is recommended that the sample size be 100 (Share Pour 2001), so in this study, considering this consideration, 305 individuals as samples Research was selected. Overall, the statistics of the participants in this study were summarized in Table 1. As you can see, the greatest contributors to this study were men. Undergraduate also contributed 38.2% more than others. Meanwhile, between the ages of 18 and 22, 37.3% were the most contributing. Of the surveyed individuals, 77.7% stated their entrepreneurship intention, indicating that the right partners were selected.
Data Collection
Often employ the questionnaire as a tool for data collection in surveys (Mathers, Fox, & Hunn, 2007). In this study, the questionnaire was used to collect data.The distributed questionnaire was designed via researcher based on the model of Khajehian (2013) and the questionnaire was shared with people through online social media. Questionnaire items are rated with 5 option Likert scale (5 = Extremely and 1 = Very low). The researchers tried to make this questionnaire understandable for everyone, as a questionnaire will be ineffective if it is not designed in a manner easily understood.
Reliability and validity
In the first step, assess the measurement model was done. The reliability of questionnaire evaluated by Cronbach's alpha, Dillon-Goldstein's rho and check the first and second eigenvalue of the indicators' correlation matrix (Table 2). Some researchers suggest 0.7 and above as the favorable point for Cronbach's alpha (Ebrahimi et al., 2018a; Khajeheian and Ebrahimi, 2019) and DG rho (Sanchez, 2013; Hair et al., 2014; Ebrahimi et al., 2019). As the value of these coefficients is higher than 0.7, the reliability of research means is confirmed. The first eigenvalue should be much larger than 1 whereas the second eigenvalue should be smaller than 1 (Sanchez, 2013, p.57). Accordingly, according to Table 1, the reliability of the research can be confirmed.
Also, the convergent validity method was used to determine validity. According to Henseler et al. (2015) the measurement models, according to outer loadings and the AVE index. In order to examine the convergent validity, due reflective measurement model, the values of AVE (Block Communality) have been investigated. Some researchers have pointed out that a higher value of 0.5 indicates the validity of convergence models (Hair et al., 2014; Ebrahimi and Mirbargkar, 2017; Ebrahimi et al., 2018b). As shown in Table 1, the AVE index for the five research variables is higher than 0.5, so we can say that the convergence models is validity. The researchers did not linger on the AVE index for validation, and the convergent validity of the model was examined with respect to outer loadings. Since some researchers consider the value of over 0.4) for outer loadings to be a convergence validity (Hair et al., 2006; Hair et.al, 2014; Ebrahimi et al., 2017), according to the data in Table 1, the validity of the measurement model is confirmed.
Discriminant Validity was assessed at the construct level by HTMT, as it has been shown in Table 3 Values less than 0.9 are considered favorable for this index (Henseler et al. 2015).
ANALYSIS AND FINDINGS
After confirm the reflective measurement models in the first step, in the second step, using the SEM approach with the help of the software packages Plspm and Matrixpls in R software to evaluate the structural model and test the hypotheses. The main reason for using this approach is, firstly, the focus of the study on the prediction of the relationship between independent variables and the dependent variable, and secondly, the model presented in this study was designed exploratory with a previous study of previous research. Software output is calculated after the conceptual model of the research (fig 3).
The structural model test was performed based on the Inner model. R2 indicates the amount of variance in the endogenous latent variable explained by its independent latent variables. The value of R2 is presented only for the intrinsic variables of the model and its exogenous structures are equal to zero. The greater the R2 value of the intrinsic structures of the model, the better the fit of the model. As shown in Table 4, the coefficient of R2 for the Infrastructure variable is equal to 61.4 and is equal to 79.7 for the entrepreneurial action variable. Moreover, ?Mean_Redundancy? is the amount of variance in an endogenous construct explained by its independent latent variables. In other words, it reflects the ability of a set of independent latent variables to explain variation in the dependent latent variable. High redundancy means high ability to predict (Sanchez, 2013, p.68).
GoF is an indicator used for general fitting that controls both measurement and structural models. GoF can be used a global criterion that helps us to evaluate the performance of the model in both the inner and the outer models. The higher value of GoF is better (Sanchez, 2013, p.80) and in this research the value of GoF is 0.600 and its acceptable good.
In order to test the research hypotheses, direct effects were evaluated. According to the results of Table (5), it can be stated that all research hypotheses have been supported. Actually the bootstrap confidence interval (95%) provided by the percentiles 0.025 and 0.975. This is especially important for the path coefficients. As you can see from the table )5), bootstrap intervals for the path coefficients of hypothesis do not contain the zero. Hence we say that these coefficients are significant at a 5% confidence level.
In the final part of this study, the accuracy of the model was examined and the importance of its factors was determined. By focusing on the Multilayer Perceptron (MLP) Artificial Neural Network (ANN), design items and accuracy were 99.6% complete in model analysis. Also, in line with SEM results, relationships have been evaluated as Synaptic weight estimation, which tracks positively to the ultimate target of entrepreneurial action.
Also, with precision in the MLP-ANN output, we find that the resource variable has the highest importance in the proposed model. Also, the strategy variable is then of great importance. Of course, it is important to note that all the predictors have a positive effect and, in their own place, have an important role in explaining the entrepreneurial action (fig5).
DISCUSSION
The aim of this research was to model media entrepreneurship in social media platforms. Findings show that fewer than 10 percent of sample of this study, who are 305 students from the fields of entrepreneurship, media management and business, are not intended to do an entrepreneurial action, and 77 percent of them are willing to involve in entrepreneurial activities. Considering the age of sample, findings confirm Zaheer et al (2018) that attention to media entrepreneurship has a positive relationship with education level and their intention to earn income from social media. They also confirm Tsourvakas and Riskos (2018) that entrepreneurial orientation in the students of journalism has been increased. On the other side, findings of this article contradict with Goyanes (2015), that discussed students of communication in Spain lack entrepreneurial orientation, and therefore it is predictable that they prefer employment in traditional media over entrepreneurship and self-employment. Thus, based on findings of the current research, it can be concluded that Iranian students prefer entrepreneurial actions more than being employed in public or private media organizations.
Two major phases conducted in this research. First was assessment of model based on the research framework and test of hypotheses. Second phase was to importance of items in research model.. Five hypotheses tested and confirmed. Based on the model, infrastructure ranked as the strongest variable of research in predicting entrepreneurial action in social media by 0.892 and then accordingly, enterprise, product, resource and strategy are the most effective variables. On the other hand, accordingly, resources, strategy, infrastructure, enterprise and product are the most important variables in entrepreneurial action in social media platforms.
It shows resources are the most important factor in media entrepreneurship, emphasizing Alvarez and Busenitz (2001) theory of resource based entrepreneurship. Khajeheian (2013) argues that resources include competitive advantage; internal resources and external resources. Importance of this variable shows that a media entrepreneur needs to identify the competitive advantage and how to use internal and external resources to exploit this. One of the respondents of this research is a media entrepreneur that used telegram social platform to exchange research materials, but in spite of identification of a niche market, because of lack of sufficient staff to response the demand, failed. It confirms Girişimciliği (2018) that argues an entrepreneur needs to use external resources. In regard with internal resources, it is discussed that digital revolution and disruptive technologies have reduced the importance of ownership and replaced it with control (Khajeheian, Friedrichsen & Mödinger, 2018). Brawn et al (2001) emphasize on the control of resources and argue that outsourcing allows entrepreneurs to act flexible and agile and to act successfully. Social caporal is an important factor in success of entrepreneurs (Casson and Giusta, 2007; Tsourvakas & Riskos, 2018). All in all, resources are yet a fundamental need for entrepreneurial action.
Strategy has introduced as the second important variable in social media entrepreneurship. Strategic media entrepreneurship has been attracted new research interest (Hasenpusch and Baumann, 2017; Horst and Murschetz, 2019). Outlook (Dal Zatto and Kranenburg, 2008) and identifying the target customers (Tamaddoni et al, 2017)and revenue model (Tsourvakas & Riskos, 2018). Infrastructures ranked as the third variable. Based on the framework, legal infrastructures, internet access, payment, market information, and distribution are the main factors of infrastructure. Goyanes (2015) argues that entrepreneurial perception from the infrastructures may lead to increased intention to entrepreneurship. Hajmohammadi (2018) stress that internet is a source for Iranian entrepreneurs to offer new services, such as internet taxies, delivering services, e-payments and many different services. Many of researchers stress on the importance of policy for fostering of media entrepreneurship and discuss that government must reduce regulations to facilitate entrepreneurship (Rebouças, 2018; Safari, 2018; Khajeheian, 2014, 2016, Doyle, 2013). Turker and Sonmez (2009) emphasize on the importance of infrastructure on entrepreneurship intention. Finally, Enterprise and product placed at the last ranks, with a considerable distance with strategy and resources. Tsourvakas & Riskos (2018) argues that idea is the cornerstone of entrepreneurship and must be considered as a critical success factor. Ifedube (2013) stress on the entrepreneur's ability to recognize the idea that fits with opportunity. Some researchers emphasize that business knowledge effects on the success of business (De Clercq & Arenius, 2006, Fernandes, Ferreira, Raposo, Hernández and Diaz Casero, 2017). It can be concluded that access to social media and low-risk possibility of offering a product or service based on social media is the reason to rank these two important factor lower than former variables.
CONCLUSION AND SUGGESTIONS
This research contributes in development of media entrepreneurship by modeling of this concept for the first time (as far as a thorough search of databases shows). The authors hope that this model will be used by entrepreneurs as well as policymakers to develop media entrepreneurship (Khajeheian, 2016). It suggests that policy makers must pay enough attention to infrastructures and to invest sufficiently on internet, payments, and communication infrastructures and also to reduce the bureaucracy and regulations. It also concluded that entrepreneurs must balance their investment of all four categories of media entrepreneurship factors and to not overlook one in cost of others.
The authors suggest the future researchers to conduct new researches to develop measures for evaluating the success of media entrepreneurship in each of these classes. Also they suggest the researchers to work on development of more advance models that cover all 23 factors of the framework and to provide more detailed explanation of how these factors impact successful media entrepreneurship.
Another suggestion is to conduct a new research to re-explore the framework of khajeheian (2013) and to understand if new trends and disruptive technologies have changed some factors of the framework.
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Abstract
The present paper addresses the need for a quantitative model for media entrepreneurship. In a framework of social media entrepreneurship that is presented by khajeheian (2013), 23 factors are introduced and categorized in five groups. Four groups of enterprise, resources, strategy and product are controllable and infrastructure is the uncontrollable group. In the current research, these groups are considered as variables and factors are used as measures. In a survey 305 Iranian students responded a questionnaire. SEM and MLP-ANN used for analysis. Results show that resources are on the highest rank, strategy and infrastructure are placed as 2nd and 3rd and enterprise and product placed at the lowest rank. This model can be used as a foundation for developing more quantitative researches in the subject of media entrepreneurship.