The Impact of Financial Factors on the After-Sales Service of Agricultural Machinery: A Case Study of Chinese Agricultural Machinery in South Africa

Qin, Xiaoshun; Jiang, Dongdong; Pretorius, Leon

doi:10.18488/journal.ajard.2021.111.71.78

Cited by 4 publications

(1 citation statement)

References 28 publications

(52 reference statements)

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“…It is mainly manifested in uneven financial investment in agricultural machinery research, difficulties in advanced technology for R&D, application and promotion of new agricultural machinery products, and heavy and urgent tasks of DGI of agricultural high-end equipment. The development of AHEM enterprises in China is faced with multiple challenges of expanding fields, increasing varieties, and improving functions and levels (Qin et al, 2021). First, large-scale, multi-functional and efficient, innovative, digital, and automated agricultural equipment should be developed.…”

Section: Introductionmentioning

confidence: 99%

Developing a Conceptual Partner Matching Framework for Digital Green Innovation of Agricultural High-End Equipment Manufacturing System Toward Agriculture 5.0: A Novel Niche Field Model Combined With Fuzzy VIKOR

Yin

Wang

2022

Front. Psychol.

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Digital green innovation (DGI) is the core factor that affects the digitalization and decarbonization strategy of agricultural high-end equipment manufacturing (AHEM) system. Although AHEM enterprises actively cooperate with academic research institutes to develop agricultural high-end equipment, there are many obstacles in the process of DGI. Moreover, the integration of digital technology and green innovation from the perspective of partner matching for the AHEM system has not been fully introduced in current literature. Hence, this study aimed to (i) establish a suitable framework system for the AHEM system in general, (ii) quantify the selection of DGI by academic research institutions based on niche theory, and (iii) propose an extended niche field model combined with fuzzy VIKOR model. First, a theoretical framework consisting of three core elements of technology superposition, mutual benefit, and mutual trust, and technological complementarity was constructed based on niche intensity and niche overlap degree. DGI ability superposition of technology, mutual trust, and technical complementarity are beneficial for transferring DGI knowledge from academic research institutes to the AHEM industry. Second, triangle fuzzy number and prospect theory combined with the VIKOR method were introduced into the field theory to construct the complementary field model of DGI resources. The niche field model has been successfully applied to practical cases to illustrate how the model can be implemented to solve the problem of DGI partner selection. Third, the results of a case study show that the criteria framework and the niche field model can be applied to real-world partner selection for AHEM enterprises. This study not only puts forward the standard framework of niche fitness evaluation based on niche theory but also establishes the niche domain model of innovation partner selection management based on niche theory. The standard framework and novel niche field model can help enterprises to carry out digital green innovation in the development of high-end agricultural equipment. The study has the following theoretical and practical implications: (i) constructing a criteria framework based on niche theory; (ii) developing a novel niche field model for DGI partner selection of AHEM enterprises; and (iii) assisting AHEM enterprises to perform DGI practice.

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Section: Introductionmentioning

confidence: 99%

Developing a Conceptual Partner Matching Framework for Digital Green Innovation of Agricultural High-End Equipment Manufacturing System Toward Agriculture 5.0: A Novel Niche Field Model Combined With Fuzzy VIKOR

Yin

Wang

2022

Front. Psychol.

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RETRACTED ARTICLE: Investigation on optimising agricultural cultivator openers using quadratic orthogonal rotation regression

Gao

2021

Acta Agriculturae Scandinavica, Section B — Soil & Plant Sc

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Research on Scheduling Algorithm of Agricultural Machinery Cooperative Operation Based on Particle Swarm Neural Network

2022

Advances in Multimedia

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In order to improve the cooperative operation scheduling effect of agricultural machinery, this article uses particle swarm neural network to study the cooperative operation scheduling algorithm of agricultural machinery and improves the cooperative scheduling effect of intelligent agricultural machinery. Aiming at the mixed integer nonlinear programming problem, this article proposes a collaborative algorithm of population intelligence and linear programming. The outer layer of the algorithm uses the improved particle swarm algorithm IPSO module, the inner layer uses the simplex algorithm SIM module, and the optimal solution of the MINLP problem is obtained through the iterative update of the inner and outer modules. The experimental study shows that the cooperative operation scheduling model of agricultural machinery based on particle swarm neural network proposed in this article can play an important role in modern agricultural planting and effectively improve the efficiency of agricultural planting.

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The Impact of Financial Factors on the After-Sales Service of Agricultural Machinery: A Case Study of Chinese Agricultural Machinery in South Africa

Cited by 4 publications

References 28 publications

Developing a Conceptual Partner Matching Framework for Digital Green Innovation of Agricultural High-End Equipment Manufacturing System Toward Agriculture 5.0: A Novel Niche Field Model Combined With Fuzzy VIKOR

Developing a Conceptual Partner Matching Framework for Digital Green Innovation of Agricultural High-End Equipment Manufacturing System Toward Agriculture 5.0: A Novel Niche Field Model Combined With Fuzzy VIKOR

RETRACTED ARTICLE: Investigation on optimising agricultural cultivator openers using quadratic orthogonal rotation regression

Research on Scheduling Algorithm of Agricultural Machinery Cooperative Operation Based on Particle Swarm Neural Network

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