Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19

Ho, Chi-Ming

doi:10.1186/s40854-022-00403-z

Cited by 8 publications

(5 citation statements)

References 69 publications

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“…Thus, this study is in line with the contagion theory that reveals the significance of uncertainty contagion and behavioral contagion (Masson (1998), Forbes and Rigobon (2002), Tsai (2017), Ozili (2021), Yuan et al (2022), Ji et al (2022)). It is also consistent with the financial innovation theory that focuses on new technologies to support traditional financial services Trichilli and Boujlb ene (2022), Adekoya et al (2022), Ho (2023.…”

Section: Cfri 133 410supporting

confidence: 84%

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Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications

Gharbi

Trichilli

Boujelbène

2023

CFRI

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PurposeThe main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.Design/methodology/approachThe authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.FindingsThe wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.Research limitations/implicationsThis study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.Practical implicationsThe study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.Originality/valueTo the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.

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Section: Cfri 133 410supporting

confidence: 84%

“…It is also consistent with the financial innovation theory that focuses on new technologies to support traditional financial services Trichilli and Boujlbène (2022), Adekoya et al . (2022), Ho (2023).…”

Section: Introductionmentioning

confidence: 99%

Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications

Gharbi

Trichilli

Boujelbène

2023

CFRI

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“…The European insurance industry is undergoing a rapid transformation that is primarily driven by digitalization and the increasing use of advanced technologies. The term "insurtech, " which refers to disruptive innovation in the insurance industry, has attracted considerable interest in recent years from both established insurance companies and startups (Ho 2023;Awais et al 2023). The implementation of next generation technologies presents a new opportunity for improving the entire insurance value chain, ranging from claims management to distribution (Balasubramanian et al 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Insurtech in Europe: identifying the top investment priorities for driving innovation

Eti,

Dinçer,

Meral

et al. 2024

Financ Innov

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The purpose of this study is to determine the essential indicators to improve insurtech systems and select the most critical alternative to increase insurtech-based investments in European countries. A novel fuzzy decision-making model is generated by integrating entropy and additive ratio assessment (ARAS) techniques with spherical fuzzy sets. First, the indicators are weighted using spherical fuzzy entropy. Then, the alternatives are ranked using spherical fuzzy ARAS. The alternatives are also ranked with the spherical fuzzy technique for order of preference by similarity to the ideal solution methodology. The main contribution of this study is that it would help investors to take the right actions to increase the performance of insurtech investments without incurring high costs. Another important novelty is that a new fuzzy decision-making model is proposed to solve this problem. The results of the two models are quite similar, proving the validity and coherency of the findings. It is found that pricing is the most critical factor that affects the performance of insurtech investments. Insurtech companies are required to make accurate pricing by conducting risk analyses to increase their profits and minimize their risks. Additionally, according to the ranking results, big data are the most appropriate way to improve the performance of insurtech investments in Europe. Big data analytics helps companies learn more about the behavior of their customers. By analyzing data about their customers’ past transactions, companies can provide more convenient services to them. This would increase customer satisfaction and enable companies to achieve long-term customer loyalty.

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“…Facing economic slowdown, post-pandemic society has reiterated the significance of technological innovation, with the result of increased academic interests in various fields [1][2][3][4][5]. The contribution of technological innovation to the economy, however, varies in different fields and depends on how innovative activities coordinate with each other [6].…”

Section: Introductionmentioning

confidence: 99%

Technological Composition and Innovation Factors in Inventive Yangtze River Delta: Evidence from Patent Inventions

Li,

Wang,

Zhang

et al. 2024

Applied Sciences

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Patents as proxy for technological trends is well noted. The rapid increase of patents in China, however, has aroused debates on its technological progress: ‘few original innovations’ are produced in advanced areas, and true ‘breakthroughs’ are disproportionate to the quantity of the applications. As different technological fields contributions vary to technological progress, a nuanced understanding towards technological composition is in need to help reveal China’s strength in technological innovation. This research takes the Yangtze River Delta (YRD), one of China’s most inventive city-regions, as an epitome to examine the issue via three steps: (1) valid patent inventions applied from 2010 to 2018 are sorted to capture the concentration and colocation features of 35 technological fields defined by the World Intellectual Property Organization (WIPO); (2) four types (intensive, extensive, distinctive, and supportive) of technologies exemplifying technology intensity and interactivity are identified by the cross-classification method and further analyzed by spatial autocorrelation; (3) how urban factors relate to innovation of these four types of technologies are explored. This research unveils a mixed but polarized structure of technological composition in the YRD where the spatial concentration of technologies is as analogous to the nation’s but colocation is not; though quite a few technologies fall into the intensive (usually high-tech) category which assumes to be more likely to breed ‘breakthroughs’, their numbers are limited and far less than extensive (usually labor intensive) or supportive. Knowledge exchange is frequent in core inventive cities where economic performance measured by GDP is most eminently linked to patent inventions of categorized technologies, the exception is intensive technology for which the significance of university students overrides other factors.

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Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19

Cited by 8 publications

References 69 publications

Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications

Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications

Insurtech in Europe: identifying the top investment priorities for driving innovation

Technological Composition and Innovation Factors in Inventive Yangtze River Delta: Evidence from Patent Inventions

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