درخت حوزه‌های تخصصی

The objective of the present study is to provide a strategic entrepreneurial marketing model in the construction industry in the construction companies of the north of the country using the Grounded theory. The present study is a qualitative research which uses Grounded theory by focusing on deep and semi-structured interview and providing an entrepreneurial strategic marketing strategy. In this study, 30 interviews were conducted on university and construction industry experts in construction companies of the north of the country to reach a consensus that was carried out using snowball sampling. Data were analyzed by open, axial and selective coding and the case studies were presented. In the current study, instead of testing the theory and confirming or rejecting it, it was presented limited to reality. The collected data were analyzed using Nvivo software and the components were identified. The results indicated that the causal conditions affecting strategic entrepreneurial marketing include communication capabilities, organizational capability, organizational strategy, management support and organization and organizational systems. The intervening condition include relationship orientation, environmental capability, and corporate governance, and ground conditions include system thinking, application of technology and organizational culture. Furthermore, the proposed strategies include intellectual capital management, strategic alignment and knowledge elevation to improve strategic and tactical performance of the company as a result of strategic entrepreneurial marketing.

Today, news texts can be created automatically and presented to readers without human participation through technologies and methods such as big data, deep learning, and natural language generation. With this research, we have developed an application that can contribute to the literature regarding The Studies on Robot Journalism Applications with a technology-reductionist perspective. Robot journalism application named Robottan Al Haberi (the English equivalent of the application name is “get the news from the robot”) produces news text by placing weather, exchange rates, and earthquake data in certain templates. The news texts, which are produced by placing the data in appropriate spaces on the template and with a maximum length of 280 characters, are automatically shared via the Twitter account @robottanalhaber. The weather information is shared once a day, the exchange rate information is shared three times a day, and the earthquake information is shared instantly. Here, we aim to produce automatic and short news by using the available structured data by placing them in specific news templates suggesting different options or a combination of them for different situations.