Xin Qi, Xinlei Lv, Zhigang Li, Chunbaixue Yang, Haoran Li and Angelika Ploeger
Understanding young adults’ organic food purchasing behavior in the fresh food e-commerce platforms (FFEP) is crucial for expanding the global environmental product market. The…
Abstract
Purpose
Understanding young adults’ organic food purchasing behavior in the fresh food e-commerce platforms (FFEP) is crucial for expanding the global environmental product market. The study aims to investigate how specific characteristics of platforms and organic food information impact young adults’ perceived value, leading to their subsequent purchase intention.
Design/methodology/approach
Around 535 valid responses were collected through an online survey and then analyzed applying a two-stage structural equation model (SEM) and artificial neural network (ANN) approach.
Findings
Results of this research show that platform characteristics (including system quality and evaluation system) and product information characteristics (including organic label, ingredient information and traceability information) significantly affect young adults’ perceived utilitarian and hedonic value. The platform’s service quality has a strong effect on their perceptions of hedonic value, while the delivery system strongly influences their utilitarian value. Moreover, the perceived value, as a crucial mediator, plays a significant role in moderating the influence of platform and product information characteristics on the purchase intentions of young consumers regarding organic food.
Originality/value
Previous research has overlooked the credence attributes of organic food and particularities of online purchasing, focusing instead on general platform and product characteristics. This study addresses this gap by proposing a more appropriate model that integrates the characteristics of both the platform and product information. This offers theoretical and managerial implications for effectively stimulating organic food consumption among young adults in online environments.
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Xin Qi, Lin Wu, Xiaomin Zhou and Xianghua Ma
This study aims to drive the induction machine system with a low switching frequency.
Abstract
Purpose
This study aims to drive the induction machine system with a low switching frequency.
Design/methodology/approach
An unconventional inverter control strategy – field-oriented predictive control (FOPC) – is presented. The strategy limits current distortion by setting a boundary circle. The voltage vector, which could keep current trajectories in boundary, is selected to obtain a low switching frequency.
Findings
A dual simulation step technique is developed to investigate the influence of sampling frequency on current distortion control and switching frequency. Current control distortion can be improved, i.e. reduced, by increasing the sampling frequency; however, the switching frequency will also increase. Such a law is discovered by the dual simulation step technique and finally verified by experiments.
Originality/value
A new predictive control method, FOPC, is derived from the rotor filed coordinate machine model and presented in this paper. FOPC circumvents derivative calculations, and thus avoids high-frequency noise amplification.
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Xin-Zhou Qi, Eric Ping Hung Li, Zhuangyu Wei and Zhong Ning
This study examines the impact of university science parks’ (USPs) capabilities on revenue generation and introduces regional innovation as a moderating variable. This study aims…
Abstract
Purpose
This study examines the impact of university science parks’ (USPs) capabilities on revenue generation and introduces regional innovation as a moderating variable. This study aims to provide insights into enhancing revenue generation and fully leveraging the role of USPs in promoting revenue generation.
Design/methodology/approach
This study employs system generalized method of moments (GMM) estimation for 116 universities in China from 2008 to 2020, using hierarchical regression analysis to examine the relationships between variables.
Findings
The findings suggest that USPs play a beneficial role in fostering revenue generation. Specifically, the provision of incubation funding demonstrates a positive correlation, while USPs size exhibits an inverted U-shaped pattern, with a threshold at 3.037 and a mean value of 3.712, highlighting the prevalent issue of suboptimal personnel allocation in the majority of USPs. Moreover, the analysis underscores the critical moderating influence of regional innovation, affecting the intricate interplay between USPs size, incubation funding and revenue generation.
Research limitations/implications
The single country (China) analysis relied solely on the use of secondary data. Future studies could expand the scope to include other countries and employ primary data collection. For instance, future research can further examine how regional development and USPs strategic plan impact revenue generation.
Practical implications
The study recommends that USPs managers and policymakers recognize the importance of incubation funding and determine the optimal quantity of USPs size to effectively foster revenue generation in USPs. Policymakers can use regional innovation as a moderating variable to reinforce the relationship between USPs size and incubation funding on revenue generation.
Social implications
The study’s findings can contribute to the strategic industry growth and economic development of nations by promoting revenue generation. Leveraging the role of USPs and implementing the study’s recommendations can strengthen innovation and technology capabilities, driving strategic industry growth and economic development. This can enhance global competitiveness and promote sustainable economic growth.
Originality/value
This study introduces regional innovation as a moderating variable and provides empirical evidence of its influence on the relationship between USPs size and incubation funding on revenue generation. This adds value to research to the existing literature on USPs and revenue generation by showcasing the importance of examining the regional impact in research and innovation.
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Weisheng Li and Meng Tian
This study scrutinised Shanghai junior high school teachers’ emotions and emotion management strategies in relation to teachers’ work settings and content. A mixed-methods…
Abstract
This study scrutinised Shanghai junior high school teachers’ emotions and emotion management strategies in relation to teachers’ work settings and content. A mixed-methods approach was applied to collect data via field observations, interviews, and a quantitative survey. The aim of this study was two-fold. Firstly, it aimed to identify the typical work settings in which teachers experienced work-related emotions. Secondly, it aimed to reveal teachers’ priority work in school and how it affected teachers’ choices of emotion management strategies.
The data were analysed through the lens of emotional labour theories and professional agency theories. Findings showed that classroom teaching and the professional learning community activities were two typical settings in which the teachers experienced the most intensive emotions. Most Shanghai teachers managed their momentary emotions by either genuinely expressing their emotions that matched their roles and the scenario, or by purposely suppressing emotions to meet social and organisational expectations. Furthermore, most teachers adopted the long-term mood regulation strategy by aligning their emotions with long-term goal achievement in the future. As professional agents, the Shanghai teachers did not only manage their own emotions at work using these two strategies, but also managed students’ emotions as part of the moral education.
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Xin Qi, Margaret Band, Richard Tester, John Piggott and Steve J. Hurel
The purpose of this paper is to evaluate if slow release starch (SRS) could be used to control/limit hypoglycaemia in type 1 diabetics.
Abstract
Purpose
The purpose of this paper is to evaluate if slow release starch (SRS) could be used to control/limit hypoglycaemia in type 1 diabetics.
Design/methodology/approach
Ten type‐1 diabetic volunteers were fitted with continuous glucose monitors for two periods of 3 days when undertaking their normal routine or when consuming 60 g SRS before sleep.
Findings
The average number of nocturnal hypoglycaemic episodes where no SRS was consumed over 3 days was 2.7 ± 2.0 but only 0.7 ± 1.1 after SRS consumption before sleep. The duration of these events was equivalent to 318 ± 282 and 140 ± 337 min, respectively. Average nocturnal blood glucose concentration was 7.9 ± 1.4 mmol l−1 without SRS consumption but increased to 9.7 ± 2.7 mmol l−1 when SRS was consumed. These data were highly significant when subjected to analysis of variance (ANOVA) test on a subject by subject basis. The SRS may be used as a cost effective therapy to avoid hypoglycaemia in patients with type‐1 diabetes.
Originality/value
This paper reports for the first time the use of a physically modified waxy maize starch (SRS) to prevent/limit the incidence of nocturnal hypoglycaemia in type 1 diabetics.
However, internal and external challenges to reconciling these two objectives are growing. How other jurisdictions respond to China’s regime will have great influence on prospects…
Details
DOI: 10.1108/OXAN-DB268930
ISSN: 2633-304X
Keywords
Geographic
Topical
Mahdi Ghaemi Asl, Muhammad Mahdi Rashidi and Seyed Ali Hosseini Ebrahim Abad
The purpose of this study is to investigate the correlation between the price return of leading cryptocurrencies, including Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar…
Abstract
Purpose
The purpose of this study is to investigate the correlation between the price return of leading cryptocurrencies, including Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Peercoin and Dash, and stock return of technology companies' indices that mainly operate on the blockchain platform and provide financial services, including alternative finance, democratized banking, future payments and digital communities.
Design/methodology/approach
This study employs a Bayesian asymmetric dynamic conditional correlation multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (BADCC-MGARCH) model with skewness and heavy tails on daily sample ranging from August 11, 2015, to February 10, 2020, to investigate the dynamic correlation between price return of several cryptocurrencies and stock return of the technology companies' indices that mainly operate on the blockchain platform. Data are collected from multiple sources. For parameter estimation and model comparison, the Markov chain Monte Carlo (MCMC) algorithm is employed. Besides, based on the expected Akaike information criterion (EAIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and weighted Deviance Information Criterion (wDIC), the skewed-multivariate Generalized Error Distribution (mvGED) is selected as an optimal distribution for errors. Finally, some other tests are carried out to check the robustness of the results.
Findings
The study results indicate that blockchain-based technology companies' indices' return and price return of cryptocurrencies are positively correlated for most of the sampling period. Besides, the return price of newly invented and more advanced cryptocurrencies with unique characteristics, including Monero, Ripple, Dash, Stellar and Peercoin, positively correlates with the return of stock indices of blockchain-based technology companies for more than 93% of sampling days. The results are also robust to various sensitivity analyses.
Research limitations/implications
The positive correlation between the price return of cryptocurrencies and the return of stock indices of blockchain-based technology companies can be due to the investors' sentiments toward blockchain technology as both cryptocurrencies and these companies are based on blockchain technology. It could also be due to the applicability of cryptocurrencies for these companies, as the price return of more advanced and capable cryptocurrencies with unique features has a positive correlation with the return of stock indices of blockchain-based technology companies for more days compared to the other cryptocurrencies, like Bitcoin, Litecoin and Ethereum, that may be regarded more as speculative assets.
Practical implications
The study results may show the positive role of cryptocurrencies in improving and developing technology companies that mainly operate on the blockchain platform and provide financial services and vice versa, suggesting that managers and regulators should pay more attention to the usefulness of cryptocurrencies and blockchains. This study also has important risk management and diversification implications for investors and companies investing in cryptocurrencies and these companies' stock. Besides, blockchain-based technology companies can add cryptocurrencies to their portfolio as hedgers or diversifiers based on their strategy.
Originality/value
This is the first study analyzing the connection between leading cryptocurrencies and technology companies that mainly operate on the blockchain platform and provide financial services by employing the Bayesian ssymmetric DCC-MGARCH model. The results also have important implications for investors, companies, regulators and researchers for future studies.
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Such revelations raise the commercial profile of the research companies concerned but also have political implications for government-to-government relations. These Chinese firms…