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  <titleInfo>
    <title>Assessing farmers' adoption and continuance intention to use a mobile app for agrometeorological forecasts and advisories</title>
    <subTitle>insights from Thailand and Vietnam</subTitle>
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  <name type="personal">
    <namePart>Auxtero, Kathleen Mae Anino</namePart>
    <role>
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  <name type="personal">
    <namePart>Himanshu, Sushil Kumar</namePart>
    <role>
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  <name type="personal">
    <namePart>Yaseen, Muhammad</namePart>
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  <name type="personal">
    <namePart>Pramanik, Malay</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
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  <name type="corporate">
    <namePart>Her Majesty the Queen{u2019}s Scholarship (Thailand)</namePart>
    <role>
      <roleTerm type="text">Scholarship Donor</roleTerm>
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  <genre authority="marc">technical report</genre>
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      <placeTerm type="code" authority="marccountry">th</placeTerm>
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    <place>
      <placeTerm type="text">Pathum Thani, Thailand</placeTerm>
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    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>2025</dateIssued>
    <issuance>continuing</issuance>
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    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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    <extent>118 leaves : ill. + 1online resource</extent>
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  <abstract>The agriculture industry in the Greater Mekong Subregion (GMS) is highly vulnerable  to climate variability, making timely and localized weather information critical for  informed farm decision-making. In response to this need, a mobile application called  ClimaFarm, a digital tool that provides agrometeorological information and agronomic  advisories to farmers, was developed. This study then explores smallholder farmers'  adoption, usage behavior, and continued use of the ClimaFarm app in Nakhon Phanom,  Thailand, and Can Tho, Vietnam. Additionally, the experienced benefits, challenges,  and opportunities linked with the app use were also identified. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Binary Logistic Regression (BLR) were  used to analyze adoption behavior, while continued use was assessed through an  adapted Technology Continuance Theory (TCT) model using PLS-SEM analysis.  Findings from 168 respondents revealed that most farmers found the app useful,  convenient, and supportive in weather-based decision-making, particularly for planting,  irrigation, and fertilizer and pest management. Perceived usefulness (Ý=0.261, p&lt;0.01),  trust (Ý=0.280, p&lt;0.1), satisfaction (Ý=0.251, p&lt;0.05), and attitude (Ý=0.247, p&lt;0.05)  were significant behavior-specific predictors of continued use, while social influence  (Ý=-0.581, p&lt;0.01), and perceived usefulness (Ý=0.440, p&lt;0.1) influenced adoption.  Furthermore, socio-demographic factors such as farming experience (Ý=-0.037,  p&lt;0.05) and household income (Ý=-0.161, p&lt;0.05) also influenced adoption and  continued use, respectively. Key challenges reported included app interface and  language difficulties, limited forecast accuracy, and internet connectivity issues.  Despite these barriers, the majority of farmers reported positive early impacts, with  85% willing to recommend the app to others.  The study offers theoretical contributions to understanding the adoption of digital tools  in agriculture and delivers practical advice for app developers, policymakers, and  extension providers. It emphasizes the significance of a user-centered design, reliable  content, and inclusive implementation strategies in scaling mobile advisory tools for  climate-smart agriculture in the GMS. </abstract>
  <note>A Thesis submitted in partial fulfillment of the requirements for the degree of  Master of Agribusiness Management  </note>
  <note>Thesis (M. Am.) - Asian Institute of Technology, 2025</note>
  <subject authority="lcsh">
    <topic>Agricultural applications</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Mobile apps</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Crops and climate</topic>
    <geographic>Mekong River Region</geographic>
  </subject>
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    <titleInfo>
      <title>Thesis ; no. AB-25-03</title>
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      <namePart>Asian Institute of Technology.</namePart>
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    </name>
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  <identifier type="uri">http://203.159.5.9/ait-thesis/detail.php?q=B22764</identifier>
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