مدیریت نوآوری

مدیریت نوآوری

ارائه چارچوبی در مدیریت منابع انسانی برای پیش‌بینی تمایل به ترک خدمت نیروهای دانشی در سازمان‌های نوآور با رویکرد فراترکیب

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشیار، گروه مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران
2 استادیار، گروه مدیریت، مجتمع مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران
3 دانشجوی کارشناسی ارشد رشته مدیریت کسب و کار، مجتمع مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران،ایران
10.22034/imj.2026.553847.2942
چکیده
در سازمان‌های نوآور، نیروهای دانشی به‌عنوان دارایی‌های راهبردی و محرک اصلی خلق، انتقال و به‌کارگیری دانش، نقشی اساسی در حفظ و توسعه مزیت رقابتی ایفا می‌کنند. ازاین‌رو، تمایل به ترک خدمت در این گروه می‌تواند پیامدهایی همچون از دست رفتن دانش ضمنی، افزایش هزینه‌های جذب و آموزش و کاهش توان نوآوری سازمان را به دنبال داشته باشد. پژوهش حاضر با هدف ارائه چارچوبی نوآورانه در مدیریت منابع انسانی برای پیش‌بینی تمایل به ترک خدمت نیروهای دانشی در سازمان‌های نوآور انجام شده است. بدین منظور، با بهره‌گیری از رویکرد فراترکیب کیفی، ابعاد و مؤلفه‌های مؤثر بر تمایل به ترک خدمت شناسایی شد. در فرایند جست‌وجوی نظام‌مند در پایگاه‌های علمی معتبر ۵۳۰ مطالعه، در نهایت ۳۱ مقاله واجد شرایط انتخاب و متون آن‌ها کدگذاری گردید. ترکیب یافته‌ها با روش تحلیل مضمون به استخراج ۱۳ بُعد کلان و ۳۶ مؤلفه فرعی انجامید که ساختار چندوجهی تمایل به ترک خدمت نیروهای دانشی را تبیین می‌کنند. از معیارهای چهارگانه لینکلن و گوبا برای اعتبارسنجی پژوهش استفاده شد. این چارچوب در پژوهش مادر، به‌عنوان مبنای مهندسی ویژگی‌ها و طراحی متغیرهای ورودی، در کنار مجموعه‌ای از الگوریتم‌های یادگیری ماشین برای پیش‌بینی تمایل به ترک خدمت نیروهای دانشی به کار گرفته شده است. بر این اساس، یافته‌های پژوهش ضمن غنی‌سازی ادبیات نظری، زمینه توسعه سامانه‌های تحلیلی و تصمیم‌یار در حوزه مدیریت منابع انسانی داده‌محور را فراهم می‌سازد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Developing a Framework in Human Resource Management for Predicting Knowledge Workers’ Turnover Intention in Innovative Organizations: A Meta-Synthesis Approach

نویسندگان English

seyed Asghar jafari 1
Afshin Alipour 2
Ali Naeimi Khondabi 3
1 Associate Professor, Department of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran
2 Assistant Professor, Department of Management, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran
3 M.A. Student in Business Administration, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran
چکیده English

In innovative organizations, knowledge workers function as strategic assets and primary drivers of knowledge creation, transfer, and application, thereby playing a vital role in sustaining and enhancing competitive advantage. Consequently, their turnover intention can lead to adverse outcomes, including the loss of tacit knowledge, increased recruitment and training costs, and a reduced capacity for organizational innovation. This study aims to develop an innovative human resource management framework for predicting the turnover intention of knowledge workers in innovative organizations. To achieve this purpose, a qualitative meta-synthesis approach was employed to identify the dimensions and components influencing turnover intention. Through a systematic search of reputable scientific databases, 530 studies were initially screened, of which 31 eligible articles were selected and coded. The thematic synthesis of these texts resulted in the identification of 13 overarching dimensions and 36 subcomponents that collectively describe the multidimensional nature of turnover intention among knowledge workers. The credibility of the findings was enhanced using Lincoln and Guba’s four trustworthiness criteria. The resulting framework was subsequently applied in the parent study as the basis for feature engineering and the development of input variables, which were integrated into machine learning algorithms for predicting turnover intention. Accordingly, the findings of this research enrich the theoretical literature while providing a foundation for developing analytical and decision-support systems within data-driven human resource management.

کلیدواژه‌ها English

Knowledge workers
Turnover intention
Innovative organizations
Meta-synthesis
Machine learning
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  • تاریخ دریافت 25 شهریور 1404
  • تاریخ بازنگری 25 آذر 1404
  • تاریخ پذیرش 03 اسفند 1404