مدل‌سازی ترکیبی PLS-ANN برای بررسی نقش میانجی فناوری‌های صنعت 0/4 و رضایت مشتری در رابطه بین QMP و عملکرد سازمانی

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

چکیده
هدف: این پژوهش با هدف بررسی روابط میان QMP، فناوری‌های صنعت 4.0 و عملکرد سازمانی انجام شده است و در این میان، نقش میانجی رضایت مشتری و فناوری مورد توجه قرار گرفته است. هدف اصلی مطالعه، تبیین نحوه اثرگذاری ترکیب کیفیت و فناوری بر ارتقای عملکرد سازمانی در شرکت‌های تولیدی ایران بوده است.
روش‌شناسی پژوهش: پژوهش از رویکرد ترکیبی استفاده کرده و داده‌ها با روش مدل‌سازی معادلات ساختاری (PLS-SEM) و شبکه عصبی مصنوعی (ANN) تحلیل شده‌اند. جامعه آماری شامل کارکنان شرکت‌های تولیدی ایران بوده و داده‌ها از طریق پرسشنامه‌ای معتبر با 205 پاسخ‌دهنده گردآوری شده است. ابزار پژوهش دارای پنج متغیر اصلی، چهارده مولفه فرعی و چهل‌وپنج شاخص بوده است.
یافتهها: نتایج نشان داده‌اند که QMP تاثیر مستقیم و معناداری بر رضایت مشتری و عملکرد سازمانی دارند. همچنین، فناوری صنعت 4.0 و رضایت مشتری نقش میانجی موثری در این روابط ایفا کرده‌اند. تحلیل شبکه عصبی نیز بیان کرده است که رضایت مشتری، مدیریت فرآیند و داده‌محوری بیشترین اهمیت را در پیش‌بینی عملکرد سازمانی داشته‌اند. یافته‌ها در مجموع تایید کرده‌اند که ترکیب QMP با فناوری‌های نوین می‌تواند راهبردی کارآمد برای بهبود عملکرد سازمانی باشد.
اصالت/ارزش افزوده علمی:این تحقیق با ترکیب دو روش PLS و ANN، رویکردی نوآورانه برای تحلیل هم‌زمان روابط علی و پیش‌بینی غیرخطی ارایه داده است. همچنین با بررسی هم‌زمان دو متغیر میانجی رضایت مشتری و فناوری و اجرای پژوهش در بستر بومی شرکت‌های ایرانی، خلأ مطالعاتی موجود را پوشش داده و به توسعه ادبیات مدیریت کیفیت و تحول دیجیتال کمک کرده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Hybrid PLSANN modeling to investigate the mediating role of Industry 4.0 technologies and customer satisfaction in the relationship between quality management practices and organizational performance

نویسندگان English

Amir Mohammad Khani,
Arman Rezasoltani
Ahmad Jafarnejad Chaghoshi
Mohammad Ali Nikkhah
Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
چکیده English

Purpose: This study was conducted to investigate the relationships between Quality Management Practice (QMP), Industry 4.0 technologies, and organizational performance, and among them, the mediating role of customer satisfaction and technology was considered. The main objective of the study was to explain how the combination of quality and technology affects organizational performance improvement in Iranian manufacturing companies.
Methodology: The study used a mixed approach, and the data were analyzed using structural equation modeling (PLS-SEM) and Artificial Neural Network (ANN). The statistical population comprised employees of Iranian manufacturing companies, and the data were collected via a valid questionnaire administered to 205 respondents. The research tool had five main variables, fourteen sub-components, and forty-five indicators.
Findings: The results showed that QMP has a direct and significant effect on customer satisfaction and organizational performance. Also, Industry 4.0 technology and customer satisfaction played an effective mediating role in these relationships. Neural network analysis also indicates that customer satisfaction, process management, and data-centricity are most important for predicting organizational performance. The findings have collectively confirmed that combining QMP with new technologies can be an efficient strategy for improving organizational performance.
Originality/Value: By combining the two methods, PLS and ANN, this research has presented an innovative approach for simultaneous analysis of causal relationships and nonlinear prediction. Also, by simultaneously examining the two mediating variables of customer satisfaction and technology and conducting the research in the local context of Iranian companies, it has covered the existing research gap and contributed to the development of the literature on quality management and digital transformation.

کلیدواژه‌ها English

Quality management
Industry 4/0 technologies
Customer satisfaction
Organizational performance
PLS-SEM
Artificial neural network
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