Qing Yang, Abdullah Al Mamun, Mohammad Masukujjaman, Zafir Khan Mohamed Makhbul, Xueyun Zhong
{"title":"中国农业企业采用物联网农业系统的情况","authors":"Qing Yang, Abdullah Al Mamun, Mohammad Masukujjaman, Zafir Khan Mohamed Makhbul, Xueyun Zhong","doi":"10.1007/s11119-024-10182-5","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>The adoption of the Internet of Things (IoT) technology in the agricultural sector has enormous potential for improving productivity, efficiency, and sustainability. Understanding the predictors affecting the acceptance of IoT-enabled agricultural systems (IAS) is crucial for policymakers, researchers, and industry practitioners.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study adopted a cross-sectional design, collected quantitative data from 458 agro-entrepreneurs through structured interviews during July 2022, and applied partial least squares structural equation modeling for data analysis.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The findings revealed that perceived need for IAS (β=0.187) and tolerance of diversity (β=0.166) positively linked with the attitude towards IAS, whereas attitude towards IAS (β=0.262), knowledge about IAS (β=0.309), industry influence (β=0.223), and IoT compatibility (β=0.274) have a positive effect on agroentrepreneurs’ intentions to adopt IAS at the 1% level of significance. Finally, the intention to adopt IAS shows a positive effect (β=0.442) on the adoption of IAS among the Chinese agro-entrepreneurs at the 1% level of significance. Using a multigroup analysis, this study also examined the associations based on the respondents’ age, gender, education level, land size, and monthly income.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This study establishes its originality by examining the relationship between original constructs derived from the theory of planned behavior and contextual factors, such as perceived need, industry influence, tolerance of diversity, innovativeness, knowledge, and compatibility, and investigating the relevant factors, thereby enhancing the comprehension of technology adoption processes in the agricultural sector. The results provide guidance to policymakers and professionals in formulating approaches to encourage the use of IoT in agriculture, supporting the objectives of the \"Agriculture 4.0 Policy\" and \"Digital Rural Development Strategy\" in China, and promoting sustainable development goals (SDG 13).</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adoption of internet of things-enabled agricultural systems among Chinese agro-entreprises\",\"authors\":\"Qing Yang, Abdullah Al Mamun, Mohammad Masukujjaman, Zafir Khan Mohamed Makhbul, Xueyun Zhong\",\"doi\":\"10.1007/s11119-024-10182-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>The adoption of the Internet of Things (IoT) technology in the agricultural sector has enormous potential for improving productivity, efficiency, and sustainability. Understanding the predictors affecting the acceptance of IoT-enabled agricultural systems (IAS) is crucial for policymakers, researchers, and industry practitioners.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>This study adopted a cross-sectional design, collected quantitative data from 458 agro-entrepreneurs through structured interviews during July 2022, and applied partial least squares structural equation modeling for data analysis.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>The findings revealed that perceived need for IAS (β=0.187) and tolerance of diversity (β=0.166) positively linked with the attitude towards IAS, whereas attitude towards IAS (β=0.262), knowledge about IAS (β=0.309), industry influence (β=0.223), and IoT compatibility (β=0.274) have a positive effect on agroentrepreneurs’ intentions to adopt IAS at the 1% level of significance. Finally, the intention to adopt IAS shows a positive effect (β=0.442) on the adoption of IAS among the Chinese agro-entrepreneurs at the 1% level of significance. Using a multigroup analysis, this study also examined the associations based on the respondents’ age, gender, education level, land size, and monthly income.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>This study establishes its originality by examining the relationship between original constructs derived from the theory of planned behavior and contextual factors, such as perceived need, industry influence, tolerance of diversity, innovativeness, knowledge, and compatibility, and investigating the relevant factors, thereby enhancing the comprehension of technology adoption processes in the agricultural sector. The results provide guidance to policymakers and professionals in formulating approaches to encourage the use of IoT in agriculture, supporting the objectives of the \\\"Agriculture 4.0 Policy\\\" and \\\"Digital Rural Development Strategy\\\" in China, and promoting sustainable development goals (SDG 13).</p>\",\"PeriodicalId\":20423,\"journal\":{\"name\":\"Precision Agriculture\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Precision Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s11119-024-10182-5\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10182-5","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Adoption of internet of things-enabled agricultural systems among Chinese agro-entreprises
Purpose
The adoption of the Internet of Things (IoT) technology in the agricultural sector has enormous potential for improving productivity, efficiency, and sustainability. Understanding the predictors affecting the acceptance of IoT-enabled agricultural systems (IAS) is crucial for policymakers, researchers, and industry practitioners.
Methods
This study adopted a cross-sectional design, collected quantitative data from 458 agro-entrepreneurs through structured interviews during July 2022, and applied partial least squares structural equation modeling for data analysis.
Results
The findings revealed that perceived need for IAS (β=0.187) and tolerance of diversity (β=0.166) positively linked with the attitude towards IAS, whereas attitude towards IAS (β=0.262), knowledge about IAS (β=0.309), industry influence (β=0.223), and IoT compatibility (β=0.274) have a positive effect on agroentrepreneurs’ intentions to adopt IAS at the 1% level of significance. Finally, the intention to adopt IAS shows a positive effect (β=0.442) on the adoption of IAS among the Chinese agro-entrepreneurs at the 1% level of significance. Using a multigroup analysis, this study also examined the associations based on the respondents’ age, gender, education level, land size, and monthly income.
Conclusion
This study establishes its originality by examining the relationship between original constructs derived from the theory of planned behavior and contextual factors, such as perceived need, industry influence, tolerance of diversity, innovativeness, knowledge, and compatibility, and investigating the relevant factors, thereby enhancing the comprehension of technology adoption processes in the agricultural sector. The results provide guidance to policymakers and professionals in formulating approaches to encourage the use of IoT in agriculture, supporting the objectives of the "Agriculture 4.0 Policy" and "Digital Rural Development Strategy" in China, and promoting sustainable development goals (SDG 13).
期刊介绍：
Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.
There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:
Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.
Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.
Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.
Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.
Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.
Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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