نویسندگان
1 دانشکده علوم‌انسانی و اجتماعی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 گروه ارتباطات و دانش‌شناسی، دانشکده علوم انسانی و اجتماعی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران (نویسنده مسئول)
3 دانشکده و پژوهشکده فناوری اطلاعات و ارتباطات؛ دانشگاه جامع امام حسین، تهران، ایران
4 گروه ارتباطات و دانش‌شناسی، دانشکده علوم انسانی و اجتماعی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
5 گروه علم اطلاعات و دانش‌شناسی، دانشکده روانشناسی و علوم تربیتی، دانشگاه علامه طباطبائی، تهران، ایران
چکیده
کلیدواژه‌ها
عنوان مقاله [English]
نویسندگان [English]
The aim of research is to comprehensively investigate various types of Data-Driven Business Patterns in order to help entrepreneurship and innovation actors, especially digital businesses efforts.This paper is a critical-argumentative study using a narrative review with a documentary approach. In order to collect and extract information, a comprehensive search was carried out on documents in the Iranian, international scientific journals databases and some of the most famous management consulting groups in the world. The statistical population of this study was all documents related to the keyword Data-Driven Business Model in the period 2000 to 2018.
After studying documents, in the literature section, 26 patterns of Data-Driven Business Model were indexed. After analyzing similarities, 20 patterns were counted. In the second review of these cases, 13 different patterns were identified, and in the third review period, the necessity of the need for these 13 patterns was identified by 5 complementary tools. Data-driven business model patterns can help innovators and entrepreneurs utilize the patterns in proportion to their customer groups or even think of the world's most successful businesses using these patterns. In this study, for the first time, all types of data-driven business models were collected from different perspectives. If companies want to actually use these patterns, they should also be equipped with a set of complementary tools. Major complementary tools play a role in data ethics, identification of data stakeholders, and business-data alignment.
کلیدواژه‌ها [English]
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