3 research outputs found

    Communication, culture, competency, and stakeholder that contribute to requirement elicitation effectiveness

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    In the context of software development, requirement engineering is one of the crucial phases that leads to software project success or failure. According to several disruptive changes in the software engineering landscape as well as the world’s challenge of virus pandemic, the provision of practical and innovative software applications is required. Therefore, issues resolution in requirement elicitation is potentially one of the key success factors resulting in enhanced quality of system requirement. The authors have striven to create new ways of requirement elicitation according to factor effects of communication, culture, competency, and stakeholder, by incorporating tools, processes, methods, and techniques to solve the problems comprehensively, and then proposed an adaptive and applicable conceptual framework. To illustrate these effects, the authors performed a literature review from the past 8 years, and then data analysis from interviews of 27 practitioners, observations and focus groups of software development in real-life projects

    āļ›āļąāļˆāļˆāļąāļĒāļ„āļ§āļēāļĄāļŠāļģāđ€āļĢāđ‡āļˆāđƒāļ™āļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļŠāļģāļŦāļĢāļąāļšāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒKey Success Factors Affecting Technology Transfer Success for Rubber Plantation Farmers in Thailand

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    āļāļēāļĢāļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļĻāļķāļāļĐāļēāļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āļ§āļēāļĄāļŠāļģāđ€āļĢāđ‡āļˆāđƒāļ™āļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļŠāļģāļŦāļĢāļąāļšāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒ āđ‚āļ”āļĒāđ€āļāđ‡āļšāļĢāļ§āļšāļĢāļ§āļĄāļ‚āđ‰āļ­āļĄāļđāļĨāļ”āđ‰āļ§āļĒāļāļēāļĢāļŠāļļāđˆāļĄāđāļšāļšāļŦāļĨāļēāļĒāļ‚āļąāđ‰āļ™āļ•āļ­āļ™ āļˆāļēāļāļ•āļąāļ§āļ­āļĒāđˆāļēāļ‡āļ—āļĩāđˆāđ€āļ›āđ‡āļ™āđ€āļāļĐāļ•āļĢāļāļĢāļŠāļēāļ§āļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāđāļĨāļ°āđ€āļ›āđ‡āļ™āļŠāļĄāļēāļŠāļīāļāļ‚āļ­āļ‡āļŠāļŦāļāļĢāļ“āđŒāļāļēāļĢāđ€āļāļĐāļ•āļĢāļĒāļēāļ‡āļžāļēāļĢāļē āļˆāļģāļ™āļ§āļ™ 562 āļ„āļ™ āđ‚āļ”āļĒāđƒāļŠāđ‰āđāļšāļšāļŠāļ­āļšāļ–āļēāļĄāļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āļ§āļēāļĄāļŠāļģāđ€āļĢāđ‡āļˆāđƒāļ™āļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āļĄāļĩāļ„āđˆāļēāļ­āļģāļ™āļēāļˆāļˆāļģāđāļ™āļāļĄāļēāļāļāļ§āđˆāļē 0.50 āļ—āļļāļāļ‚āđ‰āļ­ āļĄāļĩāļ„āļ§āļēāļĄāđ€āļŠāļ·āđˆāļ­āļĄāļąāđˆāļ™ 0.952 āļŠāļ–āļīāļ•āļīāļ—āļĩāđˆāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ‚āđ‰āļ­āļĄāļđāļĨ āļ„āļ·āļ­ āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒ āļŠāđˆāļ§āļ™āđ€āļšāļĩāđˆāļĒāļ‡āđ€āļšāļ™āļĄāļēāļ•āļĢāļāļēāļ™ āļŠāļąāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāđŒāļŠāļŦāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒāđ€āļžāļĩāļĒāļĢāđŒāļŠāļąāļ™ āđāļĨāļ°āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļāļēāļĢāļ–āļ”āļ–āļ­āļĒāļžāļŦāļļāļ„āļđāļ“āđ€āļŠāļīāļ‡āļžāļŦāļļ (Multiple Regression Analysis) āļœāļĨāļāļēāļĢāļ§āļīāļˆāļąāļĒāļžāļšāļ§āđˆāļē āļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āļ§āļēāļĄāļŠāļģāđ€āļĢāđ‡āļˆ āļĄāļĩ 7 āļ›āļąāļˆāļˆāļąāļĒ āđ„āļ”āđ‰āđāļāđˆ āđ€āļ™āļ·āđ‰āļ­āļŦāļēāļ—āļĩāđˆāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āļ„āļ§āļēāļĄāļĢāļđāđ‰āļ„āļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļ–āļ‚āļ­āļ‡āļœāļđāđ‰āļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āļāļēāļĢāđ€āļ›āļīāļ”āđ‚āļ­āļāļēāļŠāđƒāļŦāđ‰āļœāļđāđ‰āđ€āļ‚āđ‰āļēāļĢāđˆāļ§āļĄāļĄāļĩāļŠāđˆāļ§āļ™āļĢāđˆāļ§āļĄ āļāļēāļĢāļŠāļ­āļ”āđāļ—āļĢāļāđ€āļ—āļ„āļ™āļīāļ„āļāļēāļĢāđƒāļŠāđ‰āļ‡āļēāļ™āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āļ„āļ§āļēāļĄāļ•āđˆāļ­āđ€āļ™āļ·āđˆāļ­āļ‡āđāļĨāļ°āļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļ™āļ‚āļ­āļ‡āđ€āļ™āļ·āđ‰āļ­āļŦāļē āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļŠāđˆāļ§āļĒāđāļāđ‰āđ„āļ‚āļ›āļąāļāļŦāļēāđƒāļ™āļāļēāļĢāļ—āļģāļ‡āļēāļ™ āđāļĨāļ°āļ™āđ‚āļĒāļšāļēāļĒāđāļĨāļ°āļāļēāļĢāļŠāļ™āļąāļšāļŠāļ™āļļāļ™āļāļēāļĢāđƒāļŠāđ‰āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āļ›āļąāļˆāļˆāļąāļĒāđ€āļŦāļĨāđˆāļēāļ™āļĩāđ‰āļŠāļēāļĄāļēāļĢāļ–āļžāļĒāļēāļāļĢāļ“āđŒāļ„āļ§āļēāļĄāļŠāļģāđ€āļĢāđ‡āļˆāđƒāļ™āļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāđ„āļ”āđ‰āļĢāđ‰āļ­āļĒāļĨāļ° 78.40 āđ‚āļ”āļĒāļĄāļĩāļ„āđˆāļēāļŠāļąāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāđŒāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒ (R) āļ—āļĩāđˆāļĢāļ°āļ”āļąāļš 0.901 āđ‚āļ”āļĒāđ€āļ‚āļĩāļĒāļ™āđƒāļ™āļĢāļđāļ›āļ„āļ°āđāļ™āļ™āļ”āļīāļš āļ„āļ·āļ­ Y = 0.464X2 + 0.461X15 + 0.182X12 + 0.155X14 - 1.133X3 + 0.869X18 + 0.279X7 āļœāļĨāļˆāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļ”āļąāļ‡āļāļĨāđˆāļēāļ§āļŠāļēāļĄāļēāļĢāļ–āļ™āļģāļĄāļēāđ€āļ›āđ‡āļ™āđāļ™āļ§āļ—āļēāļ‡āļāļēāļĢāļžāļąāļ’āļ™āļēāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļ—āļēāļ‡āļāļēāļĢāđ€āļāļĐāļ•āļĢāļ—āļĩāđˆāļĄāļĩāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāđāļĨāļ°āđ€āļŦāļĄāļēāļ°āļŠāļĄāļāļąāļšāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāļĢāļ§āļĄāļ—āļąāđ‰āļ‡āļ•āđˆāļ­āļĒāļ­āļ”āļžāļąāļ’āļ™āļēāđ€āļ›āđ‡āļ™āļāļĢāļ­āļšāļ™āđ‚āļĒāļšāļēāļĒāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāđ€āļžāļ·āđˆāļ­āļāđ‰āļēāļ§āļŠāļđāđˆāļāļēāļĢāđ€āļ›āđ‡āļ™āđ€āļāļĐāļ•āļĢāļāļĢ 4.0 āļ—āļĩāđˆāļĒāļąāđˆāļ‡āļĒāļ·āļ™This research aimed to study the key success factors affecting technology transfer for rubber plantation farmers in Thailand. Data were collected by multi-stage random sampling method from 562 rubber farmers and members of the rubber agricultural cooperative, using questionnaires on key success factors affecting technology transfer with a discriminant power greater than 0.50 for all items and reliability coefficient of 0.952. Data were also analyzed by mean, standard deviation, Pearson’s product moment coefficient of correlation, and stepwise multiple regression analysis. The results revealed that there were seven predictive factors of technology transfer for rubber plantation farmers in Thailand, i.e., content in technology transfer, knowledge and competence of persons who passed on the technology transfer, participatory opportunity of the participants, integration of technology application techniques, continuity and consistency of content, technology support to solve problems, and policy and support for the use of technology. Every factor could explain and predict the success of technology transfer success at 78.40% with a relative coefficient (R) at the level of 0.901. The forecasting equation of raw score is Y = 0.464X2 + 0.461X15 + 0.182X12 + 0.155X14 – 1.133X3 + 0.869X18 + 0.279X7. The result can be used as guidelines for developing efficient and appropriate agricultural technology transfer for rubber plantation farmers and then, for conducting a technology transfer policy framework for sustainable farmers 4.0

    āļāļĢāļ­āļšāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļŠāļģāļŦāļĢāļąāļšāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒA Technology Transfer Framework for Rubber Farmers in Thailand

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    āļāļēāļĢāļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļžāļąāļ’āļ™āļēāđāļĨāļ°āļ›āļĢāļ°āđ€āļĄāļīāļ™āļāļĢāļ­āļšāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāđ€āļžāļ·āđˆāļ­āļāļēāļĢāļĒāļ­āļĄāļĢāļąāļšāđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļŠāļģāļŦāļĢāļąāļšāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒ āđ€āļ›āđ‡āļ™āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāđ€āļŠāļīāļ‡āļœāļŠāļēāļ™āļ§āļīāļ˜āļĩāđ€āļŠāļīāļ‡āļ›āļĢāļīāļĄāļēāļ“āđāļĨāļ°āđ€āļŠāļīāļ‡āļ„āļļāļ“āļ āļēāļž āđƒāļŠāđ‰āđāļšāļšāļŠāļ­āļšāļ–āļēāļĄāđāļĨāļ°āļāļēāļĢāļŠāļąāļĄāļ āļēāļĐāļ“āđŒāļāļąāļšāļāļĨāļļāđˆāļĄāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāļŠāđ‰āļēāļ‡āļāļĨāļēāļ‡āđāļĨāļ°āļ™āļēāļšāļ­āļ™ āļĢāļ§āļĄāļˆāļģāļ™āļ§āļ™ 55 āļ„āļ™ āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ‚āđ‰āļ­āļĄāļđāļĨāļ”āđ‰āļ§āļĒāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ­āļ‡āļ„āđŒāļ›āļĢāļ°āļāļ­āļšāđ€āļŠāļīāļ‡āļŠāļģāļĢāļ§āļˆāđāļĨāļ°āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđ€āļ™āļ·āđ‰āļ­āļŦāļē āđ‚āļ”āļĒāļāļĢāļ­āļšāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļ™āļĩāđ‰ āļ›āļĢāļ°āļāļ­āļšāļ”āđ‰āļ§āļĒāļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļˆāļģāļ™āļ§āļ™ 6 āļ‚āļąāđ‰āļ™āļ•āļ­āļ™ āļ„āļ·āļ­ āļ—āļģāļ„āļ§āļēāļĄāļĢāļđāđ‰āļˆāļąāļāđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āđ€āļĢāļĩāļĒāļ™āļĢāļđāđ‰āļāļēāļĢāđƒāļŠāđ‰āļ‡āļēāļ™āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āļ—āļ”āļĨāļ­āļ‡āđƒāļŠāđ‰āļ‡āļēāļ™āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩ āđ€āļĢāļĩāļĒāļ™āļĢāļđāđ‰āļāļēāļĢāđāļāđ‰āđ„āļ‚āļ›āļąāļāļŦāļē āđƒāļŠāđ‰āļ‡āļēāļ™āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļˆāļĢāļīāļ‡ āđāļĨāļ°āļ™āļģāđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāđ„āļ›āđƒāļŠāđ‰āđāļĨāļ°āļ–āđˆāļēāļĒāļ—āļ­āļ” āļ‹āļķāđˆāļ‡āļœāļĨāļ›āļĢāļ°āđ€āļĄāļīāļ™āļ„āļļāļ“āļ āļēāļžāļāļēāļĢāđ€āļĢāļĩāļĒāļ™āļĢāļđāđ‰āļŦāļĨāļąāļ‡āļāļēāļĢāđƒāļŠāđ‰āļāļĢāļ­āļšāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļ‚āļ­āļ‡āļāļĨāļļāđˆāļĄāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāļŠāđ‰āļēāļ‡āļāļĨāļēāļ‡āđāļĨāļ°āļ™āļēāļšāļ­āļ™āļ—āļąāđ‰āļ‡ 2 āļāļĨāļļāđˆāļĄāļ™āļĩāđ‰ āļžāļšāļ§āđˆāļē āļŠāļēāļĄāļēāļĢāļ–āđ€āļĢāļĩāļĒāļ™āļĢāļđāđ‰āđ„āļ”āđ‰āļ­āļĒāđˆāļēāļ‡āļĢāļ§āļ”āđ€āļĢāđ‡āļ§āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļ–āļ™āļģāļ„āļ§āļēāļĄāļĢāļđāđ‰āļ—āļĩāđˆāđ„āļ”āđ‰āļĢāļąāļšāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ„āļ›āđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāđāļāđ‰āđ„āļ‚āļ›āļąāļāļŦāļēāđ„āļ”āđ‰āļ­āļĒāđˆāļēāļ‡āļ”āļĩ āđāļĨāļ°āļˆāļēāļāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ­āļ‡āļ„āđŒāļ›āļĢāļ°āļāļ­āļšāđ€āļŠāļīāļ‡āļŠāļģāļĢāļ§āļˆāļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļāļēāļĢāļĒāļ­āļĄāļĢāļąāļšāļāļĢāļ­āļšāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļ‚āļ­āļ‡āđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļē āļˆāļģāļ™āļ§āļ™ 6 āļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāļĄāļĩāļ­āļ‡āļ„āđŒāļ›āļĢāļ°āļāļ­āļšāļĒāđˆāļ­āļĒ āļˆāļģāļ™āļ§āļ™ 25 āļ•āļąāļ§āđāļ›āļĢ āļ­āļąāļ™āļ›āļĢāļ°āļāļ­āļšāļ”āđ‰āļ§āļĒ āļāļēāļĢāļĢāļąāļšāļĢāļđāđ‰āļ„āļ§āļēāļĄāļ‡āđˆāļēāļĒ āļāļēāļĢāļĢāļąāļšāļĢāļđāđ‰āļ›āļĢāļ°āđ‚āļĒāļŠāļ™āđŒ āđ€āļˆāļ•āļ™āļēāļāļēāļĢāđƒāļŠāđ‰āļ‡āļēāļ™āļžāļĪāļ•āļīāļāļĢāļĢāļĄāļāļēāļĢāđƒāļŠāđ‰āļ‡āļēāļ™ āļ­āļīāļ—āļ˜āļīāļžāļĨāļ—āļēāļ‡āļŠāļąāļ‡āļ„āļĄ āđāļĨāļ°āļŠāļīāđˆāļ‡āļ­āļģāļ™āļ§āļĒāļ„āļ§āļēāļĄāļŠāļ°āļ”āļ§āļ āļĄāļĩāļ„āļ§āļēāļĄāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒāļāļąāļ™ āđ‚āļ”āļĒāļĄāļĩāļ„āđˆāļēāđ„āļ„āļŠāđāļ„āļ§āļĢāđŒ āđ€āļ—āđˆāļēāļāļąāļš 4.433 āļ­āļ‡āļĻāļēāļ­āļīāļŠāļĢāļ° āđ€āļ—āđˆāļēāļāļąāļš 6 āļ„āđˆāļē p-value āđ€āļ—āđˆāļēāļāļąāļš 0.618 āļ„āđˆāļēāļ”āļąāļŠāļ™āļĩāļ§āļąāļ”āļ„āļ§āļēāļĄāļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļĨāļĄāļāļĨāļ·āļ™āđ€āļŠāļīāļ‡āļŠāļąāļĄāļžāļąāļ—āļ˜āđŒ āđ€āļ—āđˆāļēāļāļąāļš 1.000 āđāļĨāļ°āļ„āđˆāļēāļ”āļąāļŠāļ™āļĩāļ„āļ§āļēāļĄāļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļŠāļąāļĄāļžāļąāļ™āļ˜āđŒ āđ€āļ—āđˆāļēāļāļąāļš 0.895 āļœāļĨāļˆāļēāļāļāļēāļĢāļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļˆāļķāļ‡āļŠāļēāļĄāļēāļĢāļ–āļŠāļĢāļļāļ›āđ„āļ”āđ‰āļ§āđˆāļēāļāļĢāļ­āļšāļāļēāļĢāļ–āđˆāļēāļĒāļ—āļ­āļ”āđ€āļ—āļ„āđ‚āļ™āđ‚āļĨāļĒāļĩāļ™āļĩāđ‰āļŠāļēāļĄāļēāļĢāļ–āļ™āļģāđ„āļ›āđƒāļŠāđ‰āļāļąāļšāđ€āļāļĐāļ•āļĢāļāļĢāļŠāļ§āļ™āļĒāļēāļ‡āļžāļēāļĢāļēāđƒāļŦāđ‰āđ€āļāļīāļ”āļ›āļĢāļ°āđ‚āļĒāļŠāļ™āđŒāđāļĨāļ°āļĄāļĩāļ„āļļāļ“āļ āļēāļžāđ„āļ”āđ‰āļˆāļĢāļīāļ‡This research aims to develop and evaluate a technology transfer framework for technology adoption among rubber plantation farmers in Thailand. The study is a combination of qualitative and quantitative methods. A questionnaire and interviews were used together for data collection from 55 rubber plantation farmers in Chang Klang and Na bon districts. Exploratory factor analysis and content analysis were conducted. As results, the technology transfer framework consists of six steps, i.e., getting to know technology, learning about technology usage, technology trials, learning about problem solving, practical application along with technology implementation and transfer. In the evaluation phase, the results found that after applying the proposed framework, the sample groups of Chang Klang and Na bon rubber farmers could learn technologies rapidly and they were able to use acquired knowledge to solve their problems effectively. Besides, the exploratory factor analysis results revealed six contributing factors that affect technology transfer and technology acceptance among the subjects. Key factors constitute: perceived ease of use, perceived benefits, usage intention, usage behavior, social influence and technological facilities. The chi-square test determined a statistically significant relationship between variables: The Chi-Square statistic was 4.433, degrees of freedom (df) equaled 6, p-value equaled 0.618, the relative harmony index (CFI) equaled 1.000, and the Correlation Index (NFI) equaled 0.895. Based on the result, the framework can be further applied. It would bring significant benefits and facilitate quality improvement for the specified farmers
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