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Article
Publication date: 7 December 2021

Lei Shen, Yuhong Zhu, Chenglong Li and Syed Hamad Hassan Shah

The paper aims to explore how perceived prosumer content quality (PPCQ) and perceived interaction quality (PIQ) improve users' co-creation experiences and subsequently influence…

696

Abstract

Purpose

The paper aims to explore how perceived prosumer content quality (PPCQ) and perceived interaction quality (PIQ) improve users' co-creation experiences and subsequently influence their co-creation intentions in the future. In addition, the paper examines users' prosumer ability into consideration.

Design/methodology/approach

The research model based on stimulus-organism-response (S-O-R) paradigm is developed to observe users' participation in value co-creation activities. In total, 318 valid responses were collected from a survey. Structural equation modeling was used to examine the model and Statistical Package for the Social Sciences (SPSS) PROCESS macro (Model 58) by Hayes was applied to investigate the moderating effect of prosumer ability in mediation paths.

Findings

It is observed that co-creation intention is determined by user-learning value, social-integrative value and hedonic value, which are influenced by PPCQ and PIQ. Besides, uses' prosumer ability moderates the indirect effects of PPCQ and PIQ on co-creation intentions through co-creation experiences.

Research limitations/implications

The paper provides a prosumption perspective to explain users' co-creation intentions in social commerce and proposes the importance of user-learning, social-integrative and hedonic values in determining co-creation intentions.

Practical implications

Social commerce platforms can encourage prosumption activities and cultivate multi-level prosumers to achieve a win–win situation.

Originality/value

Little prior research has explicitly examined how and why users participate in value co-creation activities in social commerce from prosumption perspective. The current paper seeks to fill this gap and open new avenues for other value co-creation researchers.

Details

Kybernetes, vol. 52 no. 4
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 11 September 2020

Yuhong Zhao

The purpose of this paper is to examine China's approach to circular economy (CE) and investigate how the foreign concept of CE has been turned into a national strategy for…

479

Abstract

Purpose

The purpose of this paper is to examine China's approach to circular economy (CE) and investigate how the foreign concept of CE has been turned into a national strategy for implementation in production, circulation and consumption. This study aims to highlight the Chinese characteristics in the implementation of CE from central to local levels including the “trial and test” by pilot schemes and the role of local governments in CE transformation of industrial parks and in building CE cities. Based on what has been achieved, this paper aims to identify the gaps to be filled in the next stage of CE implementation.

Design/methodology/approach

This paper engages in critical analysis of state policies, plans, laws and regulations and case studies of Suzhou New District and Shanghai city in the building CE-oriented industrial park and CE city, respectively.

Findings

China has taken a top-down approach to CE characterised by strong government involvement in both policy and plan making and implementation at local levels. The government’s financial investment and administrative assistance proved to be crucial in the early stage of CE implementation to close the loop at industrial parks and in cities. In comparison, participation by enterprises and individuals is still weak and limited, which should be the focus of the next stage of CE implementation.

Originality/value

There is an absence of legal literature that studies circular economy in China. This paper fills the gap by examining the development of CE law and policy as well as CE implementation at local levels from industrial parks to cities.

Details

Journal of Property, Planning and Environmental Law, vol. 12 no. 3
Type: Research Article
ISSN: 2514-9407

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Article
Publication date: 3 February 2021

Lixuan Zhang, Iryna Pentina and Yuhong Fan

This study aims to investigate the differences in consumers’ perceptions of trust, performance expectancy and intention to hire between human financial advisors with high/low…

7361

Abstract

Purpose

This study aims to investigate the differences in consumers’ perceptions of trust, performance expectancy and intention to hire between human financial advisors with high/low expertise and robo-advisors.

Design/methodology/approach

Three experiments were conducted. The respondents were randomly assigned to human advisors with high/low expertise or a robo-advisor. Data were analyzed using MANCOVA.

Findings

The results suggest that consumers prefer human financial advisors with high expertise to robo-advisors. There are no significant differences between robo-advisors and novice financial advisors regarding performance expectancy and intention to hire.

Originality/value

This pioneering study extends the self-service technology adoption theory to examine adoption of robo-advisors vs human financial advisors with different expertise levels. To the best of the authors’ knowledge, it is among the first studies to address multi-dimensionality of trust in the context of artificial intelligence-based self-service technologies.

Details

Journal of Services Marketing, vol. 35 no. 5
Type: Research Article
ISSN: 0887-6045

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Article
Publication date: 15 December 2023

Yuhong Peng, Jianwei Ding and Yueyan Zhang

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer…

599

Abstract

Purpose

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Design/methodology/approach

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

Findings

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

Originality/value

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

Details

Marketing Intelligence & Planning, vol. 42 no. 1
Type: Research Article
ISSN: 0263-4503

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Article
Publication date: 30 May 2024

Youyang Ren, Yuhong Wang, Lin Xia, Wei Liu and Ran Tao

Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch…

66

Abstract

Purpose

Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.

Design/methodology/approach

This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.

Findings

This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.

Originality/value

The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.

Details

Grey Systems: Theory and Application, vol. 14 no. 4
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 29 April 2021

Huan Wang, Yuhong Wang and Dongdong Wu

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results…

390

Abstract

Purpose

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

Design/methodology/approach

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

Findings

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

Originality/value

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

Details

Grey Systems: Theory and Application, vol. 12 no. 2
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 20 February 2023

Yuhong Wang, Xiaoqi Sheng and Yudie Xie

This study aims to establish a centralized decision-making game model and manufacturer-led Stackelberg game model based on factors of risk aversion of supply chain members and…

390

Abstract

Purpose

This study aims to establish a centralized decision-making game model and manufacturer-led Stackelberg game model based on factors of risk aversion of supply chain members and product greenness. The research aims to study whether the introduction of the “cost + risk sharing” contract affects coordination of this type of green supply by calculating the optimal decision of each mode.

Design/methodology/approach

This research designs a supply chain model under centralized and decentralized decision-making. This model uses the Stackelberg game to calculate the optimal decision under decentralized decision-making to evaluate the effect of a green supply chain and then analyze the “cost + risk sharing” contract and the degree of coordination of the supply chain. A sensitivity analysis is conducted on the centralized mode for the impact of variables on the supply chain.

Findings

This research finds a double marginalization effect in decentralized decision-making, and the risk aversion coefficient plays a decisive role in the utility of supply chain members. The specific range of risk- and cost-sharing factors allows supply chain members to achieve Pareto improvements and provides decision-making based on the corresponding management strategies according to each other’s risk preference degree.

Research limitations/implications

The influence of each variable on the green supply chain in the centralized mode is studied by MATLAB numerical simulation. It provides reference for green supply chain members to formulate corresponding management strategies according to each other's risk preference degree.

Originality/value

This research innovatively considers manufacturers and retailers to explore the market demand for product greenness. It introduces a novel “cost + risk sharing” contract to coordinate the green supply chain.

Details

Chinese Management Studies, vol. 18 no. 1
Type: Research Article
ISSN: 1750-614X

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Article
Publication date: 11 October 2023

Yuhong Wang and Qi Si

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

126

Abstract

Purpose

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

Design/methodology/approach

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

Findings

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

Originality/value

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 1 January 2024

Bingfeng Bai and Guohua Wu

The purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and…

505

Abstract

Purpose

The purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system.

Design/methodology/approach

Through the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory.

Findings

The model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail.

Originality/value

There are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail.

Details

Chinese Management Studies, vol. 18 no. 4
Type: Research Article
ISSN: 1750-614X

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Article
Publication date: 6 January 2022

Wuyong Qian, Hao Zhang, Aodi Sui and Yuhong Wang

The purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for…

270

Abstract

Purpose

The purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.

Design/methodology/approach

Due to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.

Findings

China's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.

Originality/value

The paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Details

Grey Systems: Theory and Application, vol. 12 no. 3
Type: Research Article
ISSN: 2043-9377

Keywords

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