{"title":"利用企业家的数字足迹分析和预测一个国家的创业活动","authors":"Z. Tekic, Andrei Parfenov, Maksim Malyy","doi":"10.1108/jeee-12-2022-0389","DOIUrl":null,"url":null,"abstract":"\nPurpose\nStarting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.\n\n\nDesign/methodology/approach\nTheoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.\n\n\nFindings\nThe results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.\n\n\nPractical implications\nThe results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.\n\n\nOriginality/value\nTo the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.\n","PeriodicalId":45682,"journal":{"name":"Journal of Entrepreneurship in Emerging Economies","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysing and predicting a country’s entrepreneurial activity using insights from entrepreneurs’ digital footprint\",\"authors\":\"Z. Tekic, Andrei Parfenov, Maksim Malyy\",\"doi\":\"10.1108/jeee-12-2022-0389\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nStarting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.\\n\\n\\nDesign/methodology/approach\\nTheoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.\\n\\n\\nFindings\\nThe results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.\\n\\n\\nPractical implications\\nThe results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.\\n\\n\\nOriginality/value\\nTo the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.\\n\",\"PeriodicalId\":45682,\"journal\":{\"name\":\"Journal of Entrepreneurship in Emerging Economies\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2023-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Entrepreneurship in Emerging Economies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/jeee-12-2022-0389\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Entrepreneurship in Emerging Economies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jeee-12-2022-0389","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
Analysing and predicting a country’s entrepreneurial activity using insights from entrepreneurs’ digital footprint
Purpose
Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.
Design/methodology/approach
Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.
Findings
The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.
Practical implications
The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.
Originality/value
To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.
期刊介绍:
JEEE acquaints the readers with the latest trends and directions of explorations in the theory and practice of entrepreneurship. For the research section, the Journal of Entrepreneurship in Emerging Economies considers high quality theoretical and empirical academic research articles in the field of entrepreneurship, as well as general reviews. The ‘Entrepreneurship in practice’ section publishes insights from industry, case studies, policy focus pieces and interviews with entrepreneurs. Coverage will focus primarily on the following topics: Government policy on entrepreneurship International entrepreneurship Small and medium-sized enterprises Family-owned businesses The innovator as an individual and as a personality type New venture creation and acquisitions of a growing company Entrepreneurial behaviour in large organizations Venture financing and entrepreneurial education Minority issues in small business and entrepreneurship Corporate and non-profit entrepreneurship Ethics, the entrepreneur and the company Entrepreneurial cooperation and networking Entrepreneurial environment and cross-cultural management Comparative studies of entrepreneurship and marketing issues Development of the service sector and Chinese economy Chinese marketing and business innovation Service marketing and service innovation Brand management and network innovation Supply chain management and customer relationship management Entrepreneurial processes Risk management and venture capital Entrepreneurship and environmental sustainability Entrepreneurial growth and business sustainability Entrepreneurship, social sustainability, and social justice Entrepreneurship, proverty alleviation, and economic development.