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Article
Publication date: 9 January 2023

Wei Guan, Wenhong Ding, Bobo Zhang and Jerome Verny

The deployment of blockchain technology (BT) throughout the supply chain is usually led by large firms that dominate the supply chain. Leading firms can encourage other…

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Abstract

Purpose

The deployment of blockchain technology (BT) throughout the supply chain is usually led by large firms that dominate the supply chain. Leading firms can encourage other resource-constrained partners to get on board by providing technical and financial support. However, due to the uncertain consequences of relying on leading firms, these partners may still be reluctant to adopt BT. Drawing on resource dependence theory, this study aims to investigate whether and when supply chain alignment can be used as a dependency coping strategy to increase the willingness of resource-constrained partners to adopt BT. Moreover, it aims to examine the motivators for supply chain alignment.

Design/methodology/approach

This study adopted a survey research design and collected data from 364 small and medium-sized enterprises in China.

Findings

Supply chain alignment positively affects BT adoption. The effect of supply chain alignment on BT adoption is contingent on guanxi (a Chinese cultural tradition of interpersonal connections that facilitate a mutual exchange of favors). Relative advantage, technology complexity, organizational readiness and cost are motivators for supply chain alignment. Supply chain alignment mediates the effect of cost, technology complexity and relative advantage on BT adoption.

Originality/value

This research addresses the problem of resource dependency in the context of BT adoption which has been overlooked by previous research. Moreover, this paper enriches the BT literature by identifying supply chain alignment as an important channel for technology–organization–environment factors to influence BT adoption.

Details

Journal of Enterprise Information Management, vol. 36 no. 2
Type: Research Article
ISSN: 1741-0398

Keywords

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Article
Publication date: 23 August 2022

Yong He, Xiaohua Zeng, Huan Li and Wenhong Wei

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous…

101

Abstract

Purpose

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).

Design/methodology/approach

In this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.

Findings

The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.

Originality/value

(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 16 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 1 July 2020

Wenchao Duan, Siqi Yin, Wenhong Liu, Jian Yang, Qingfeng Zhu, Lei Bao, Ping Wang, Jianzhong Cui and Zhiqiang Zhang

The purpose of this paper is to investigate the effect of pulsed magnetic field (PMF) with different duty cycles on the melt flow and heat transfer behaviors during direct-chill…

320

Abstract

Purpose

The purpose of this paper is to investigate the effect of pulsed magnetic field (PMF) with different duty cycles on the melt flow and heat transfer behaviors during direct-chill (DC) casting of large-size magnesium alloy billet and find the appropriate range of duty cycle.

Design/methodology/approach

A transient two-dimensional mathematical model coupled electromagnetic field, flow field and thermal field, is conducted to study the melt flow and temperature field under PMF and compared with that under the harmonic magnetic field.

Findings

The results reveal that melt vibration and fluctuation are generated due to the instantaneous impact of repeated thrust and pull effects of Lorentz force under PMF. The peak of Lorentz force decreases greatly with the increasing duty cycle, but the melt fluctuation region is expanded with higher duty cycle, which accelerates the interior melt velocity and reduces the temperature gradient at the liquid-solid interface. However, PMF with overly high duty cycle has adverse effect on the melt convection and limited influence on the interior melt. A duty cycle of 20% to 50% is a reasonable range.

Practical implications

This paper can provide guiding significance for the setting of duty cycle parameters on DC casting under PMF.

Originality/value

There are few reports on the effect of PMF parameters during DC casting with applying PMF, especially for duty cycle, a parameter unique to PMF. The findings will be helpful for applying the external field of PMF on DC casting.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 31 no. 3
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 23 January 2025

Wei Liu, Mi Zhou and Wen hong Zhao

There has long been a belief that authentic leadership positively impacts employee outcomes, consistently proven to be effective. However, is this conclusion universally…

26

Abstract

Purpose

There has long been a belief that authentic leadership positively impacts employee outcomes, consistently proven to be effective. However, is this conclusion universally applicable in any situation? To answer this question, we investigated the interaction effect between authentic leadership and two types of team conflict (task conflict and relationship conflict) on employee creativity as well as the mediating role of employee trust in the leader (affective-based and cognitive-based), looking to open up a new milestone for authentic leadership.

Design/methodology/approach

This study involved 589 employees across 75 teams, with data collected using a dual-wave, multi-source approach.

Findings

The results demonstrated that authentic leadership can enhance employee creativity, but this effect is not absolute. Specifically, the interaction between authentic leadership and task conflict negatively affected employee creativity, with this relationship mediated by cognitive-based trust. Conversely, there was no significant impact of authentic leadership on relationship conflict affecting creativity, nor was this relationship mediated by affective-based trust.

Originality/value

This study challenges the long-held belief that authentic leadership always positively impacts employee creativity. Moreover, it provides new perspectives on the different situations in which authentic leadership has varying impacts on employee creativity. Additionally, the study contributes to a further systematic and comprehensive understanding of the impact of authentic leadership.

Details

Leadership & Organization Development Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-7739

Keywords

Available. Open Access. Open Access
Article
Publication date: 7 June 2021

Tamoor Khan, Jiangtao Qiu, Ameen Banjar, Riad Alharbey, Ahmed Omar Alzahrani and Rashid Mehmood

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.

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Abstract

Purpose

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.

Design/methodology/approach

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

Findings

The Johansen test findings have shown that the fruit crop growth, energy use, CO2 emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO2 emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO2 will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO2 emissions to agricultural areas to fruit crops.

Originality/value

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

Details

International Journal of Climate Change Strategies and Management, vol. 13 no. 2
Type: Research Article
ISSN: 1756-8692

Keywords

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