مشخصات مقاله | |
ترجمه عنوان مقاله | نقش استفاده از اطلاعات در یک زنجیره تامین خرده فروشی: یک داده کاوی علمی سببی و رویکرد مدل سازی تحلیلی |
عنوان انگلیسی مقاله | The role of information usage in a retail supply chain: A causal data mining and analytical modeling approach |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 18 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
5.352 در سال 2018 |
شاخص H_index | 158 در سال 2019 |
شاخص SJR | 2.203 در سال 2018 |
شناسه ISSN | 0148-2963 |
شاخص Quartile (چارک) | Q1 در سال 2018 |
مدل مفهومی | دارد |
پرسشنامه | دارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی صنایع، مدیریت |
گرایش های مرتبط | لجستیک و زنجیره تامین، بازاریابی |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | مجله تحقیقات کسب و کار-Journal of Business Research |
دانشگاه | Palumbo Donahue School of Business, Duquesne University, United States of America |
کلمات کلیدی | به اشتراک گذاری اطلاعات، استفاده از اطلاعات، عملکرد عملیاتی، یادگیری سازمانی، داده کاوی، تحلیل های سببی |
کلمات کلیدی انگلیسی | Information sharing، Information usage، Operational performance، Organizational learning، Data mining، Causal analytics |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.jbusres.2019.01.070 |
کد محصول | E12247 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract 1. Introduction 2. Literature review and hypotheses development 3. Research methodology 4. Results and discussion 5. Conclusion, limits, and future research References |
بخشی از متن مقاله: |
Abstract
This study utilizes both a resource-based view and organizational learning theory to present the need to distinguish information sharing from information usage. Our main research aim was to investigate the mediating role of information usage between information sharing, and operational efficiency and effectiveness. We tested the hypotheses in our relational model using empirical data obtained from food retailers in Turkey. The analysis of results from structural equation modeling reveal that the separation of information sharing from information usage is valid, and the mediating role of usage is significant in improving operational effectiveness and efficiency. We further utilized a Bayesian neural networks-based causal analytic model, i.e., universal structure modeling methodology to reveal non-trivial, implicit, previously unknown, and potentially useful relationships among the constructs. Introduction Supply chain management (SCM) is the effective coordination of the flow of materials, products, and information, not only within a company but also among other members of a supply chain in order to improve the performance of the company (Mentzer et al., 2001) and received the full interest of researchers (Autry, Rose, & Bell, 2014; Frohlich & Westbrook, 2001; Prajogo & Olhager, 2012; Stevens, 1989; Tan, Kannan, & Handfield, 1998). Globalization and amplified competition among supply chains have magnified the importance of effective SCM for which “integration” is offered as an inevitable prescription for successful performance (Cooper, Lambert, & Pagh, 1997; Mentzer et al., 2001; Seggie, Kim, & Cavusgil, 2006). Schoemaker (1993) indicates how difficult it is to achieve such coordinative integration efficiency given the asymmetry and complexity of information. Information asymmetry due to strong reluctance of demand and supply related information sharing increases the uncertainty in planning and thus in meeting customer demand. The size of supply and customer base, the length of the supply chain tiers, information technology invested by supply chain members, and communication styles among members of supply chain defines the complexity across different industries such as food, aircraft, healthcare, automotive, etc., and defines the magnitude of uncertainty. |