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کاربرد هوش مصنوعی برای تولید تلنگرهای رفتاری هوشمند | ||
| مدیریت دولتی | ||
| دوره 17، شماره 4، 1404، صفحه 938-962 اصل مقاله (613.52 K) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jipa.2025.395752.3700 | ||
| نویسندگان | ||
| سید کمال واعظی* 1؛ فرانک پاشایی2 | ||
| 1دانشیار، گروه رهبری و سرمایۀ انسانی، دانشکدۀ مدیریت دولتی و علوم سازمانی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران. | ||
| 2دانشجوی دکتری، گروه خطمشیگذاری عمومی، پردیس بینالملل کیش، دانشگاه تهران، کیش، ایران. | ||
| چکیده | ||
| هدف: هدف این پژوهش بررسی ظرفیتهای هوش مصنوعی در تولید تلنگرهای رفتاری هوشمند، در راستای بهبود فرایندهای خطمشیگذاری رفتاری است. در این راستا تلاش شد تا با استفاده از چارچوب نظری معماری سامانه تلنگر هوشمند، الگویی طراحی شود که بتواند با تکیه بر ابزارهای فناورانه و شخصیسازی تصمیمها، اثربخشی مداخلات رفتاری را ارتقا بخشد. روش: این پژوهش از نوع کیفی و مبتنی بر تحلیل مضمون دادههاست که با استفاده از مصاحبههای نیمهساختاریافته با ۱۲ نفر از خبرگان حوزههای خطمشیگذاری، اقتصاد رفتاری و هوش مصنوعی اجرا شده است. نمونهگیری بهروش گلولۀ برفی انجام شد و گردآوری دادهها تا رسیدن به اشباع نظری ادامه یافت. دادههای حاصل از مصاحبهها با بهرهگیری از روش کدگذاری باز و محوری تحلیل و در قالب مدل مفهومی ارائه شدند. همچنین از یافتههای نظری و تحلیلهای کتابخانهای نیز در طراحی مدل استفاده شد. یافتهها: یافتههای نظری پژوهش نشان داد که از ابزارهای مداخلهای مانند کلانداده، یادگیری ماشین، رویکرد الگوریتمی، عامل نرمافزار هوشمند، اینترنت اشیا و فناوریهای شناختی، بهعنوان اجزای اصلی سامانۀ تلنگر هوشمند میتوان بهره برد. نتایج حاصل از مصاحبهها حاکی است که ۹ ابزار جدید سیستمهای توصیهگر، یادگیری تقویتی، شبکههای عصبی، تحلیل پیشبینیکننده، نوتیفیکیشن، منطق فازی، پردازش زبان طبیعی، پلتفرمهای یادگیری سفارشی و سیستمهای پشتیبانی تصمیمگیری، میتوانند قابلیت شخصیسازی تلنگرها را تقویت کنند. در مدل مفهومی طراحیشده، پردازندۀ مرکزی بهعنوان یادگیرنده نمایهای تعریف شد که با تحلیل دادههای مرتبط با علایق، رفتارها و توانمندیهای کاربران، امکان تولید تلنگرهای متناسب با موقعیتهای خاص را فراهم میسازد. نتیجهگیری: تلنگرهای رفتاری مبتنی بر هوش مصنوعی، برای ارتقای اثربخشی مداخلات در خطمشیگذاری رفتاری ظرفیت چشمگیری دارند و بهجای اتکا به راهکارهای کلی، قادرند با پردازش دادههای فردی، مداخلاتی شخصیسازیشده و بهموقع ارائه دهند. استفاده از معماری سامانههای هوشمند و یادگیری نمایهای، امکان تلفیق دادههای چندمنبعی و تحلیل آنها را برای طراحی تلنگر فراهم میسازد. یافتههای این پژوهش میتواند به خطمشیگذاران، طراحان سیستمهای هوشمند و محققان حوزۀ علوم رفتاری در مسیر طراحی مداخلات اثربخشتر کمک کند و گامی در جهت توسعه خطمشیگذاری دادهمحور و رفتارمحور باشد. | ||
| کلیدواژهها | ||
| تلنگر رفتاری؛ خطمشیگذاری رفتاری؛ شخصیسازی؛ علوم رفتاری؛ هوش مصنوعی | ||
| مراجع | ||
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