Adoption of agricultural technologies in the developing world: A meta-analysis dataset of the empirical literature.
Ontology highlight
ABSTRACT:
SUBMITTER: Ruzzante S
PROVIDER: S-EPMC8479612 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Adoption of agricultural technologies in the developing world: A meta-analysis dataset of the empirical literature.
Data in brief 20210920
The meta-analysis dataset presented is a convenience sample from 218 separate studies of agricultural technology adoption in Africa, Asia, and Latin America. Each study uses survey data to estimate a form of multiple regression of adoption of a technology (dependent variable) with a diverse array of predictor variables. Fifteen predictor variable categories are included in this dataset: Age, education, gender, household size, farming experience, land size, soil fertility, land slope, distance to ...[more]
Similar Datasets
Project description:Information accessibility is a pivotal factor influencing farmers' adoption of Agricultural Green Production Technologies (AGPT). However, the widespread issue of information poverty presents a significant obstacle to this adoption process, thereby hindering the progression towards sustainable agricultural development. To address this information deficit, farmers have begun to utilize the Internet and participate in government-led onsite assembly training programs to acquire the necessary knowledge. Yet there is still a lack of research evidence on the effectiveness and comparative advantages of internet and offline training. This study explores the impact of various information access channels on farmers' adoption of green production technologies in agriculture, focusing on a sample of 731 family farms located in Sichuan Province. The issue of endogeneity was addressed using the Conditional Mixed Process Estimation Method. The sample underwent a t-test and heterogeneity analysis. The findings revealed that both internet-based information access and participation in training significantly bolstered farmers' adoption of AGPT, with the former proving to be more effective. Notably, heterogeneity was observed among farmers, differentiated by age and the number of village cadres within their family units.
| S-EPMC11309494 | biostudies-literature
Project description:BackgroundThe potential benefit of mobile health (M-Health) in developing countries for improving the efficiency of health care service delivery, health care quality, and patient safety, as well as reducing cost, has been increasingly recognized and emphasized in the last few years.ObjectiveLimited research has investigated the facilitators and barriers for the adoption of M-Health in developing countries to secure successful implementation of the technology. To fill this knowledge gap, we propose an integrative model that explains the patient's adoption behavior of M-Health in developing countries grounded on the unified theory of acceptance and use of technology, dual-factor model, and health belief model.MethodWe empirically tested and evaluated the model based on data collected using a survey method from 280 patients living in a developing country. Partial least squares (PLS-SEM) technique was used for data analysis.ResultsThe results showed that performance expectancy, effort expectancy, social influence, perceived health threat, M-Health app quality, and life quality expectancy have a direct positive effect on patients' intention to use M-Health. The results also showed that security and privacy risks have a direct negative effect on the patient's intention to use M-Health. However, resistance to change was found to have an indirect negative effect on patients' intention to use M-Health through the performance expectancy.ConclusionThe research contributes to the existing literature of health information systems and M-Health by better understanding how technological, social, and functional factors are associated with digital health applications and services use and success in the context of developing countries. With the widespread availability of mobile technologies and services and the growing demand for M-Health apps, this research can help guide the development of the next generation of M-Health apps with a focus on the needs of patients in developing countries. The research has several theoretical and practical implications for the health care industry, government, policy makers, and technology developers and designers.
| S-EPMC6821538 | biostudies-literature
Project description:IntroductionHealthcare workers' adoption of mHealth is critical to the success or failure of clinician based mHealth services in the developing world. mHealth adoption is affected or promoted by certain factors, some of which are peculiar to the developing world. Identifying these factors and evaluating them will help develop a valid and reliable measuring instrument for more successful prediction of mHealth adoption in the future. The aim of this study was to design and develop such an instrument.MethodA Healthcare workers' mHealth Adoption Questionnaire (HmAQ) was developed based on five constructs identified through a prior literature review: multi-sectorial engagement and ownership; staffing and technical support; reliable infrastructure; usefulness and stewardship; and intention to adopt. After testing face and content validity, the questionnaire was administered to 104 nurses and midwives in the Ewutu-Senya district of the Central Region of Ghana who used a maternal mHealth intervention. After data collection confirmatory factor analysis and structural equation modelling were applied and the Healthcare Worker mHealth Adoption Impact Model (HmAIM) developed.ResultsExploratory factor analysis showed the eigenvalue of all five components to be significant (cumulative total greater than 1.0). Bartlett's Test of Sphericity was significant, the Kaiser-Meyer-Olkin value was 0.777, and the mean Cronbach's α value was 0.82 (range 0.81-0.83). Confirmatory factor analysis showed that constructs for the HmAQ were within acceptable limits and valid. Structural equation modelling showed the causal relationships between components. This resulted in development of the HmAIM. A modified model was then developed using the averages of individual construct items. This model showed strong correlation among the constructs. Further research will be required to understand new dimensions of mHealth adoption as a result of emerging technology needs, new complexities in the healthcare work environment, and how different cadres of healthcare workers respond to it.ConclusionThe study presents a valid and reliable instrument, the HmAIM, to serve as a tool for assessment of healthcare workers' mHealth adoption in the developing world. Use of the instrument will enhance the likelihood of successful adoption of mHealth implementations.
| S-EPMC9526526 | biostudies-literature
Project description:Adoption of new agricultural technologies is always at the center of policy interest in developing countries. In reality, despite the visible benefits of many of the new agricultural technologies, including machinery and management practices, farmers either do not adopt them or it takes a long time to begin the adoption process and scaling up. To enhance the provision of irrigation using surface water and to enhance irrigation efficiency, Bangladesh has been trying to introduce the axial-flow-pump (AFP) appropriate for surface water irrigation, which can lift up to 55% more water, conditional on the water head, than a conventional centrifugal pump. Despite the visible benefits of the AFP, the uptake of the AFP for irrigation is low in the targeted zone of Bangladesh. The present study demonstrates that the new technology must be modified to adapt to local demand and specifications. Most importantly, the price of the new technology must be competitive with the prices of the existing available substitute technologies to ensure a rapid uptake and scaling up of this new agricultural technology.
| S-EPMC6289226 | biostudies-literature
Project description:The railway industry has witnessed increasing adoption of digital technologies, known as Railway 4.0, that is revolutionizing operations, infrastructure, and transportation systems. However, developing countries face challenges in keeping pace with these technological advancements. With limited research on Railway 4.0 adoption in developing countries, this study was motivated to investigate the awareness, readiness, and challenges faced by railway professionals towards implementing Railway 4.0 technologies. The aim was to assess the level of awareness and preparedness and identify the key challenges influencing Railway 4.0 adoption in Nigeria's railway construction industry. A questionnaire survey (was distributed to professionals in the railway construction sector to gather their perspectives on awareness of, preparation for, and challenges associated with the use of Railway 4.0 technologies. The results revealed that awareness of Railway 4.0 technologies was moderate, while readiness was low among the professionals. Using exploratory factor analysis, 10 underlying challenge constructs were identified including lack of technical know-how, resistance to change, infrastructure limitations, and uncertainty about benefits, amongst others. Partial Least Square Structural Equation Modelling (PLS-SEM) confirmed these constructs, with reliability and availability, lack of technical know-how, lack of training and resources, and uncertainties in benefit and gains having significant influence on awareness and readiness. The study concludes that focused efforts in training, infrastructure improvement, supportive policies, and communicating the advantages of Railway 4.0 are critical to drive adoption in Nigeria and other developing economies. The findings provide insights into tailoring Railway 4.0 implementation strategies for developing contexts.
| S-EPMC10878949 | biostudies-literature
Project description:The recent advancements in ICT have made it possible for teaching and learning to be conducted outside the four walls of a University. Furthermore, the recent COVID-19 pandemic that has crippled educational activities in all nations of the world has further revealed the urgent need for academic institutions to embrace and integrate alternative modes of teaching and learning via social media platforms and emerging technologies into existing teaching tools. This article contains data collected from 850 face to face University students during the COVID-19 pandemic lockdown. An online google form was used to elicit information from the students about their awareness and intention to use these alternative modes of teaching and learning. The questions were structured using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This data article includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file, the descriptive statistics, and reliability analysis computed for all the UTAUT variables. The dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies. The challenges that could inhibit the adoption of these technologies were also revealed.
| S-EPMC7316039 | biostudies-literature
Project description:Low energy houses are forms of housing that use less energy from the design, technologies and building products from any source than a traditional or average contemporary house. The survey dataset examines architect׳s awareness and adoption of building envelope technologies (BET) for energy efficient housing in Lagos State, Nigeria. The dataset was based on seventy-four (74) returned questionnaires of both registered and non-registered Architects. A multistage sampling that involved cluster sampling and random sampling of architects in Lagos State was adopted. Descriptive statistical tools were used to present the dataset. The dataset contains the intent of promoting energy sustainability by architect while designing their building envelopes, the awareness of the building envelope strategies to adopt, factors influencing their adoption of these strategies, strategies that can be adopted to improve adoption of building envelope technologies for energy efficiency in housing units. The dataset can be used for evolving housing energy policy by decision makers.
| S-EPMC6141384 | biostudies-literature
Project description:As a comprehensive technology with social, economic, and ecological benefits, integrated pest management (IPM) is crucial in fundamentally alleviating the environmental pollution caused by traditional high-input agriculture. Based on the random-sampled data of 981 farmer households in major Indica-rice-producing areas in Anhui Province, this study analyzes the impact of agricultural production knowledge on farmers' willingness to adopt IPM technology through logit models, considering integrated knowledge and categorized knowledge. The results indicate that integrated agricultural production knowledge significantly increases farmers' willingness to adopt IPM technology. However, pest-management knowledge was the only one out of four specific disciplines that significantly individually affect farmers' adoption intention. The more knowledge farmers acquire about pest management, the higher intention they have to adopt IPM. Some demographic and household characteristics also significantly influence their willingness. Based on these results, we suggest that increasing farmers' agricultural production knowledge, especially knowledge about pest management, is essential in promoting IPM technology. Besides this, IPM technology should be promoted purposely and consciously, combined with farmers' individual and family characteristics.
| S-EPMC9657904 | biostudies-literature
Project description:The present dataset ("dataset 3") is a subset of a large metastudy on AML classfication. It contains normalized gene expression values of 1181 samples. In total, three datasets were generated, each containing data of a different platforms: dataset 1 (Affymetrix HG-U133 A microarrays), dataset 2 (Affymetrix HG-U133 2.0 microarrays) and dataset 3 (RNA-seq). Dataset 3 was generated using the following strategy: All data sets published in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) on 20 September 2017 were reviewed for inclusion in the present study. Basic criteria for inclusion were the cell type under study (human peripheral blood mononuclear cells (PMBCs) and/or bone marrow samples) as well as the species (Homo sapiens). Furthermore, GEO SuperSeries were excluded to avoid duplicated samples. We filtered the datasets for data generated with high-throughput RNA sequencing (RNA-seq) and excluded studies with very small sample sizes (< 10 samples). We then applied a disease-specific search, in which we filtered for acute myeloid leukemia, other leukemia and healthy or non-leukemia-related samples. The results of this search strategy were then internally reviewed and data were excluded based on the following criteria: (i) exclusion of duplicated samples, (ii) exclusion of studies that sorted single cell types (e.g. T cells or B cells) prior to gene expression profiling, (iii) exclusion of studies with inaccessible data. Other than that, no studies were excluded from our analysis. In total, the datasets contained samples from the following GSE Series: GSE63085, GSE32874, GSE58335, GSE86884, GSE63703, GSE63646, GSE63816, GSE72790, GSE81259, GSE85712, GSE45735, GSE64655, GSE87186, GSE49642, GSE52656, GSE62190, GSE66917, GSE67039, GSE61162, GSE67184, GSE49601, GSE78785, GSE79970. All raw data files were downloaded from GEO. Transcript abundances were calculated using kallisto version 0.43.0 and all data was normalized with the R package DESeq2 (R version R-3.2.4, DESeq2 version 1.12.4) with standard parameters. Genome build hg38 was used for read alignment. No filtering of low-expressed genes was performed.
2019-12-18 | GSE122515 | GEO
Project description:BackgroundMeta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature.ResultsWe present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability.ConclusionsAn empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies.
| S-EPMC3087509 | biostudies-literature