Business Strategy

Given the emergence of digital transformation from Industry 4 and the rapid dissemination of technological innovations as well as their impact as a strong driving force in new banking businesses, efforts should be made to identify the dimensions of this core factor as rapidly as possible. Providing a comprehensive overview of all aspects of the model. The purpose of this article is to provide insights into the state of the art of digital transformation in the banking industry and suggest ways for future research. Existing literature, especially research for 2019 and 2020, has enhanced our understanding of the specific aspects of digital transformation in future banking, and we are now grasping a clearer picture of nature, how, and the consequences of these in the years ahead. However, all its dimensions are not yet clear. In this article, by review of 218 articles in the WOS database, and the analysis has utilized the data-driven tools,165 questionnaire and 90 specialized interviews with experts, extracted factors of the research, and using SMART PLS statistical software, ANFIS and MATLAB software for analysis. The paper, therefore, tends to understand Open Banking Innovation based on the digital transformation area and its dimensions in its previously known domains.

Stock price crash risk has a significant impact on investors, creditors, managers, and shareholders, so the prediction of this phenomenon is a very important issue in investment and risk management decisions. This research investigates the effect of business strategy and stock price synchronicity on stock price crash risk. Following Bentley et al.[2], composite strategy score has been used to proxy for an organization’s business strategy, expanded market model regression following Chen et al. [3] to measure the firm-specific crash risk, and R2 method of Johnstone [14] to calculate the stock price synchronicity. In order to achieve this point, financial information of 171 companies that are listed on Tehran stock exchange have been selected during the time period of 2013 to 2018, and data was analysed using regression model. According to the results, companies with defender (analyser and prospector) business strategy are less (more) prone to future crash risk. Moreover, results show that stock price syn-chronicity has positive effect on stock price crash risk, while in companies with analyser business strategy it can reduce the stock price crash risk. The interactive effect of business strategy and stock price synchronicity on stock price crash risk in companies with prospector and defender business strategy is not significant. Other findings suggest that Institutional ownership has positive, and company’s age has negative effect on stock price crash risk.

One of the challenging issues for investors and professionals is appropriate models to evaluate financial situation of the firms. In this regard, many models have been extracted by researchers using different financial ratios to resolve these issues. However, choosing a model based on the conditions and users’ needs is complex. The main objective of this study is to identify the effect of contingency variables on the firms’ financial performance forecasting models. The statistical population of the research includes all firms listed in Tehran Stock Exchange during the period 2011-2018, among which 154 firms were selected. The research data were collected from firm's financial statements and other source. Multiple Discriminant Analysis and Logit Regression model were used to test the research hypotheses. According to the results of discriminant analysis, environmental uncertainty and firm size positively improve the predictive power of the firm's financial performance, and business strategy and business competition don’t improve the predictive power of the firm's financial performance. Also, the results of logit regression indicated that environmental uncertainty, business strategy, and firm size improve predictive power of the firm's financial performance; but, business competition don’t improve predictive power of the model. The results of comparing the two methods showed that the Discriminant analysis method outperformed the logistic regression method.