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Based on the perspective of technology supply chain, this study explores the effect of macroeconomic uncertainty regarding the spatiotemporal evolution of urban innovation networks to establish causality.

It collects patent trading data for 283 cities in China (2005–2017) and employs the spatial econometric model to investigate the causal relationship.

The regional transfer of advanced technology in China is rising sharply, and the innovation network based on patent trading is typically high-density, multi-direction and wide-spreading. Further, macroeconomic uncertainty has a negative effect on the scale of innovation flows and the absorptive capacity in eastern cities. However, it has no significant impact on the innovation network characteristics in developed cities. In contrast, macroeconomic uncertainty is detrimental for the absorptive capacity and node importance in inland and undeveloped cities.

As macroeconomic uncertainty increases, it is important to improve the quality of the urban innovation network with a better understanding of heterogeneity to promote further suitability innovation at the region-level.

This study highlights a clear and distinctive view that macroeconomic uncertainty not only directly affects the evolution of the urban innovation network but also indirectly affects the characteristics of other city nodes via the spatial spillover mechanism.

The present study aims to propose a framework elucidating the attributes of mobile augmented reality (AR) shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) and how they translate to downstream consumer-related outcomes (i.e., immersion, psychological ownership and stickiness to the retailer).

By conducting a questionnaire-based survey, 308 responses were collected, and the data were submitted to partial least square structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses.

A few important findings were generated from the present study. First, attributes of mobile augmented reality shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) influence stickiness to the retailer through immersion and consumer empowerment in serial. Second, immersion positively influences psychological ownership. Third, the optimum stimulation level moderates the relationship between spatial presence and immersion. Lastly, a post-hoc exploratory finding yielded by the multigroup analysis uncovered the moderating effect of gender.

This study offers a novel contribution to the smart retail literature by investigating the role of mobile AR shopping apps in predicting consumers' stickiness to the retailer. A holistic framework elucidating the serial mediating effect of immersion and consumer empowerment, and the moderating roles of optimum stimulation level and gender were validated.

This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

Managing the risks associated to world food production is an important challenge for governments. A range of factors, among them extreme weather events, has threatened food production in recent years. The purpose of this paper is to analyse the impact of extreme rainfall events on the food industry in Brazil, a prominent player in this industry.

The authors use the AR-GARCH-GPD hybrid methodology to identify whether extreme rainfall affects the stock price of food companies. To do so, the authors collected the daily closing price of the 16 food industry companies listed on the Brazilian stock exchange (B3), in January 2015.

The results indicate that these events have a significant impact on stock returns: on more than half of the days immediately following the heavy rain that fell between 28 February 2005 and 30 December 2014, returns were significantly low, leading to average daily losses of 1.97 per cent. These results point to the relevance of the need for instruments to hedge against weather risk, particularly in the food industry.

Given that extreme weather events have been occurring more and more frequently, financial literature has documented attempts at assessing the economic impacts of weather changes. There is little research, however, into assessing the impacts of these events at corporate level.

O gerenciamento de riscos associados à produção mundial de alimentos é um desafio importante para governantes. Diversos fatores, entre eles os eventos climáticos extremos, têm ameaçado a produção de alimentos nos últimos anos. Neste artigo nós analisamos o impacto de eventos de chuvas extremas na indústria de alimentos no Brasil, um dos maiores produtores mundiais.

Empregamos a metodologia híbrida AR-GARCH-GPD para verificar se chuvas extremas afetam o preço das ações das empresas de alimentos. Para isso, coletamos os preços de fechamento diário de 16 empresas do setor de alimentos listadas na Bolsa de Valores do Brasil [B]³, em janeiro de 2015.

Os resultados sugerem que esses eventos exercem impacto significante sobre o retorno das ações: em mais da metade dos dias imediatamente posteriores à chuva extrema ocorrida entre 28/02/2005 e 30/12/2014, os retornos foram significantemente baixos, levando a perdas médias diárias próximas de 1,97%. Esses resultados apontam para a relevância da necessidade de instrumentos para proteção contra riscos climáticos, particularmente na indústria de alimentos.

Tendo em vista que eventos climáticos extremos têm ocorrido com uma frequência cada vez maior, a literatura de finanças tem documentado tentativas de avaliar os impactos econômicos das mudanças climáticas. No entanto, nota-se a carência de pesquisas para avaliar os impactos desses eventos no nível das empresas.

Augmented reality (AR) is transforming the business and interactive marketing landscape. This research aims to investigate consumers' degree of involvement and if a feeling of immersion and presence influences AR shopping satisfaction, comparing high- and low-immersive AR experiences.

This paper uses a quantitative approach. Two studies were carried out: a high-immersive AR experiment with 173 participants and a low-immersive AR experience with 222 participants. Findings were analyzed using partial least square structural equation modeling with SmartPLS.

Results indicate the antecedents of immersion and presence differ when it comes to different immersive AR levels. In a high-immersive AR experience, flow, information seeking and novelty are attributes related to immersion, while enjoyment and personalization are related to presence. Contrastingly, in a low-immersive AR experience, only flow is related to immersion, while information seeking, novelty and personalization are related to presence. These results highlight the role of immersion and presence as mediators for AR shopping satisfaction experience.

This study's originality lies in the use of a rival model for analysis. Findings suggest a contingent perspective of AR experience, depending on high- or low-immersion experience, so companies must pay attention for how to measure AR experiences to increase involvement and satisfaction.

This paper sets out to investigate investors' sustainable preferences under different market conditions. Specifically, the authors examine the existence of a positive sustainable asset pricing gap, and whether it is influenced by the socioeconomic and financial sentiments. The increase of uncertainty rises investors' skepticism whether sustainable companies are under-performing the traditional counterparts, causing larger increasing gap. Conversely, if sustainable assets are overperforming, the increase of market uncertainty raises investors' sustainable preferences.

The authors examine the existence of a positive sustainable asset pricing gap, and whether it is influenced by the socioeconomic and financial sentiments. Through a quantile regression, the authors remark the variability of sustainable preferences where market participants, although recognizing the present and future value added of sustainable investing, also show skepticism (i.e. asymmetric tail behavior). However, the analysis of the total change of sustainable investments returns over time demonstrates the emergence of positive viewpoints incentivized by economic and market uncertainty.

The market-driven social responsibility exalts the positive insights regarding the future of sustainable developments. As the authors discuss along the paper, investors are gaining awareness about the environmental and social goals pursued by socially responsible companies. Hence, the authors consider how economic instability might stimulate the assessment of the social and environmental impact of the unsustainable production systems, switching investments toward virtuous sustainable companies. This could generate a series of positive externalities that might improve the welfare conditions of the whole society.

The authors conduct an original empirical exercise, combining different techniques (i.e. quantile regressions and wavelet analysis). To the best of the authors’ knowledge, this is the first paper trying to evidence a systematic connection between market uncertainty and sustainable preferences accounting for different market states (thanks to quantile regressions).

The purpose of this paper is to examine the spatial components of producer heterogeneity in crop insurance product selection among US corn producers and identifies neighborhood spillover or agent marketing effects in these decisions.

County‐level insurance and yield data are used to demonstrate that a gradual shift from yield‐based insurance to revenue‐based insurance has spatial patterns. Conventional risk variables such as yield variability, price variability, prevalence of irrigation, other crops, and yield‐price relationships play an important role in this shift and are consistently estimated only when spatial components are included. A spatial random effects model is used to also identify the impact of spatial lag effects, which include neighborhood spillover and agent marketing effects, on the share of corn acres insured with revenue‐based plans vs yield‐based plans.

Theoretically consistent variables associated with risk are found to significantly influence the choice between crop revenue and yield insurance. Non‐linear parameters identify the region‐specific effects from changes in irrigation, yield price correlation, and the prevalence of corn production on insurance decisions. In addition, spatial components such as the decisions made by nearby producers and marketing drives are also found to influence decisions. These results may demonstrate the relative influence of trusted sources, such as nearby producers and insurance agents, on insurance decisions.

Traditional risk variables are consistently estimated by controlling for spatial heterogeneity. This study also reveals the propensity of producers to rely on the opinions of other producers or agents that they know.