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Article
Publication date: 4 September 2017

Yuanxing Zhang, Zhuqi Li, Kaigui Bian, Yichong Bai, Zhi Yang and Xiaoming Li

Projecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain…

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Abstract

Purpose

Projecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain densely populated areas. Conventional studies require the collection of people’s trajectory data through offline means, which is limited in terms of cost and data availability. The wide use of online social network (OSN) apps over smartphones has provided the opportunities of devising a lightweight approach of conducting the study using the online data of smartphone apps. This paper aims to reveal the relationship between the online social networks and the offline communities, as well as to project the population distribution by modeling geo-homophily in the online social networks.

Design/methodology/approach

In this paper, the authors propose the concept of geo-homophily in OSNs to determine how much the data of an OSN can help project the population distribution in a given division of geographical regions. Specifically, the authors establish a three-layered theoretic framework that first maps the online message diffusion among friends in the OSN to the offline population distribution over a given division of regions via a Dirichlet process and then projects the floating population across the regions.

Findings

By experiments over large-scale OSN data sets, the authors show that the proposed prediction models have a high prediction accuracy in characterizing the process of how the population distribution forms and how the floating population changes over time.

Originality/value

This paper tries to project population distribution by modeling geo-homophily in OSNs.

Details

International Journal of Crowd Science, vol. 1 no. 3
Type: Research Article
ISSN: 2398-7294

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Article
Publication date: 7 February 2023

Pengyu Chen and SangKyum Kim

The relationship between industrial policy and exploratory innovation is imperfect.

411

Abstract

Purpose

The relationship between industrial policy and exploratory innovation is imperfect.

Design/methodology/approach

The authors use Chinese high-tech enterprise identification policy (HTEP) as a natural experimental group to test policy impacts, spillover effects and mechanisms of action.

Findings

First, HTEP promotes exploratory innovation. In addition, HTEP has a greater impact on non-exploratory innovation. Second, HTEP has spillover effects in two phases: HTEP (2008) and the 2016 policy reform. HTEP affects exploratory innovation in nearby non-high-tech firms, and the policy effect decreases monotonically with increasing distance from the treatment group. Third, HTEP affects innovation capacity through financing constraints, technical personnel flow and knowledge flow, which explains not only policy effects but also spillover effects. Fourth, the analysis of policy heterogeneity shows that the 2016 policy reforms reinforce the positive effect of HTEP (2008). By deducting the effects of other policies, the HTEP effect is found to be less volatile. In terms of the continuity of policy identification, continuous uninterrupted identification has a crucial impact on the improvement of firms’ innovation capacity compared to repeated certification and certification expiration. Finally, HTEP has a crowding-out effect in state-owned enterprises and large firms’ innovation.

Originality/value

This paper contributes to the existing literature in several ways. First, the authors enrich the literature on industrial policy through exploratory innovation research. While previous studies have focused on R&D investment and patents (Dai and Wang, 2019), exploratory innovation helps firms break away from the inherent knowledge mindset and achieve sustainable innovation. Second, few studies have explored the characteristics of industrial policies. In this paper, the authors subdivide the sample into repeated certification, continuous certification and certification expiration according to high-tech enterprise identification. In addition, the authors compare the differences in policy implementation effects between the 2016 policy reform and the 2008 policy to provide new directions for business managers and policy makers. Third, innovation factors guided by industrial policies may cluster in specific regions, which in turn manifest externalities. This is when the policy spillover effect is worth considering. This paper fills a gap in the industrial policy literature by examining the spillover effects. Finally, this paper also explores the mechanisms of policy effects from three perspectives: financing constraints, technician mobility and knowledge mobility, which can affect not only the innovation of beneficiary firms directly but also indirectly the innovation of neighboring non-beneficiary firms.

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