Political forecast cycles

A moral hazard model is used to show why overly optimistic revenue forecasts prior to elections can be optimal: Opportunistic governments can increase spending and appear more competent; ex post deficits emerge in election years, thereby producing political forecast cycles – as also found for US states in the empirical literature. Additionally, we obtain three theoretical results which are tested with panel data for Portuguese municipalities. The extent of manipulations is reduced when (i) the winning margin is expected to widen; (ii) the incumbent is not re-running; and/or (iii) the share of informed voters (proxied by education) goes up.     

科技企业孵化器何以激活区域创新——风险投资与孵化基金的中介作用

完善创新生态系统、提升区域创新能力是我国科技企业孵化器建设的重要目标。基于2013-2018年中国（内地）30个省份面板数据，采用面板数据模型实证检验科技企业孵化器是否促进了区域创新能力提升，并基于中介效应模型探讨风险投资和孵化基金在其中的间接作用。结果发现：①科技企业孵化器建设显著提升了区域创新水平，但主要增加的是实用新型和外观设计专利申请授权数总量，对发明专利申请授权数并没有显著促进作用。上述结果在剔除直辖市样本、采用随机效应模型及空间计量模型的稳健性检验后依然成立；②通过中介效应模型检验发现，区域风险投资和孵化基金集聚效应是科技企业孵化器影响区域创新水平的主要机制；③科技企业孵化器对区域创新的影响在不同区域间差异较大，在东部地区的创新激励效应更加显著，而且政策工具强度对科技企业孵化器与区域创新水平的关系具有正向调节作用。     

Post-script—Retail forecasting: Research and practice

This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.     

Negative Spillovers Across Partnerships for Responsible Innovation: Evidence from the 2014 Ebola Outbreak

Humanity faces ongoing and contemporaneous grand challenges. Occasionally, abrupt shocks escalate a grand challenge’s salience over others. Prior research has advocated forming partnerships to address grand challenges via responsible innovation. Yet, it remains unclear how temporal changes in the salience of a grand challenge impact innovation performances of partnerships. We address this research gap by bridging the literature on issue salience, responsible innovation and interorganizational relationships. We argue that shocks either aid or harm the performance of partnerships for responsible innovation depending on whether their domains are directly or indirectly affected. The Ebola outbreak in 2014 sets the empirical context to test our theory. We find that while the innovation performance of Ebola partnerships formed after the outbreak rose eleven-fold, the performance of partnerships treating Influenza fell by 84.9 per cent. Our theory and findings have immediate implications for today’s COVID-19 outbreak, cautioning against salience shifts among concurrent grand challenges.     

The diffusion of mobile payments: Profiling the adopters and non-adopters,Roger's way

This study aims to establish various market segments based on consumers' attitudinal elements influencing the adoption of mobile payments (m-payments). It also examines the best discriminators between identified clusters of m-payment users. Three hundred and sixty m-payment users participated in the study. Cluster and discriminant analyses were employed for the analysis. The findings suggest that two segments are established, and strategies can be developed accordingly. The study provides marketers with numerous touchpoints to propagate the adoption of m-payment apps. It also comprehensively explains m-payment users in developing economies by superimposing the cohorts to Roger's diffusion of innovation model.     

Adverse effect of social media on generation Z user's behavior: Government information support as a moderating variable

Social media emerged as a primary source of information among young users, but its severe effect on mental state due to information overload, still an area of concern for the researchers. Generation Z users' addiction to the mobile phone/gadgets is increasing with a rise in social media and consequently their behavioral outcomes have transformed completely. Nowadays behavioral issues including stress, fatigue, ‘fear of missing out’ and ‘phubbing’, anxiety etc. rising each day but the relationship among these issues and information overload is less examined. The social media users are unable to filter the trustworthy information due to its enormous size and thus role of information support from government becomes essential. The current study extends the S-O-R theory exploring relationship between the stimulus of enormous information on the responses generated among the Generation Z such as social media fatigue, ‘fear of missing out’, ‘phubbing’ and anxiety. The findings from a sample of 319 users belonging to Generation Z suggest that social media platforms need to understand user's compulsive usage that is resulting into fatigue and consequently anxiety. The role of government information support on reducing fatigue and anxiety is found to be positive. The relationship between ‘fear of missing out’, ‘phubbing’ and anxiety is found significant and shows presence of partial mediation. The study offers significant theoretical and practical implications. It is significant for the service providers and social media platforms to advance interfaces with minimum fatigue for the users and offers information support to the users to reduce stress caused by information load.     

The role of network embeddedness across multiple social networks: Evidence from mobile social network games

This article examines peer influences from network relationships within a social network game (i.e., embeddedness) and across such games (i.e., multiplexity). Drawing on social influence theory, we develop a bivariate Poisson model of users’ repeated visits and latent attrition that accommodates peer interaction after controlling for homophily. We estimate the model using data from two social network games with considerable overlap among network members. We find that friends who are only multiplex across games exert greater peer influence on users’ game visits than members who are embedded within a single game. We also determined that ignoring network multiplexity across games may lead firms to mistarget users due to biased peer influences of embedded friends. This result provides an unresearched explanation—strength of peer influence—for the mixed findings in previous literature on network embeddedness. We utilized our results to conduct several scenario analyses to demonstrate how firms can effectively manage users’ engagement and target users in multiple social network games.     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

基于CiteSpace 的创新链研究综述

运用CiteSpace软件，以1995—2022年中国知网（CNKI）核心期刊和中文社会科学引文索引（CSSCI）期刊为数据源，从热点关键词、发文作者、发文机构以及突现词方面对创新链相关领域文献进行可视化分析。研究发现：有关创新链的研究角度大概可以分为产业、技术、区域、模式和价值链5个方面；从发文特征来看，发文量最多的是张杰，群体内相较于群体间合作较多；从研究机构分布来看，机构间的合作比较分散，且合作大多发生在一个国家或地区；从未来演进趋势来看，文献近期表现为国家政策主导。