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A study related to product service systems (PSS), SERVQUAL and knowledge management system (KMS) – A review
مطالعه مربوط به سیستم های خدمات محصول (PSS)، SERVQUAL و سیستم مدیریت دانش (KMS) - یک مرور-2021 PSS is a growing field of research in industry practice in today’s global economy. It is a new trend that has
an impact on both the production and consumption of resources. PSS aims at a more profitable and conservational usage of products. While in the past providing services was one of the strategies attained to
differentiate in competition, however today providing a product together with many services has become
a standard practice in the product industry. To remain competitive, manufacturers are forced not only to
produce a competitive product however rather a superior PSS.PSS are outlined as life cycle bound combinations of a product and completely different services, having sophisticated networks, that comprise a
manufacturer as a provider and also as a repair partner. To enable the applicability of a PSS in an industry,
it is essential to evaluate the system using metrics – the SERVQUAL MODEL, which defines ‘‘The quality as
the difference between the customers’ expectations & perceptions concerning the services delivered to
them” [1]. It is catered to measure quality by capturing the expectancy – confirmation paradigm which
suggests the consumer’s perceived quality of how well a given service delivery meets their expectations
of that delivery. So this SERVQUAL metric is used to determine the level of quality in the industry and the
five dimensions are such as tangibility, reliability, responsiveness, assurance & empathy are measured
using a five-point Likert scale. Since, organizations are more and more moving towards knowledgebased strategies, developing and managing knowledge is essential for improving the organizational performance as well as for enhancing decision-making process. This paper presents a review on the use of a
knowledge management practice in PSS for industries to store, share and utilize knowledge for enhancing
creativity & innovation in their service systems. An efficient review of the literature has been conducted
in the academic and scientific databases taking into account the date of publication of the articles titled
PSS, SERVQUAL and KMS from 2009 to 2020. To achieve the review process, all selected articles have been
categorized by publication year, the objectives of the research, the methodology used, the results, conclusion and future scope of their research are presented on a broader scale [16]. Therefore, this paper presents an overview of the literature on PSS and the evaluation methods using SERVQUAL MODEL and the
role of knowledge management in PSS and the appropriate ideas for conducting research in the future.
Copyright 2021 Elsevier Ltd. All rights reserved.
keywords: سیستم خدمات محصول | مدل SERVQUAL | سیستم مدیریت دانش | تصمیم گیری | Product service system | SERVQUAL MODEL | Knowledge management system | Decision-making |
مقاله انگلیسی |
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Understanding knowledge hiding in business organizations: A bibliometric analysis of research trends, 1988–2020
درک دانش مخفی در سازمان های کسب و کار: تجزیه و تحلیل کتابشناختی از روند تحقیق، 1988-2020-2021 This paper investigates how knowledge hiding (KH) contributes to individuals, groups, and the business pro-
cesses of organizations, with regards to improving employee performance, strategic performance, and the or-
ganization’s overall knowledge management system (KMS), as well as the consequences and costs of KH in
organizations. 117 English language research articles produced between 1988 and 2020 regarding KH are
analyzed, and insights provided into science mapping and performance analysis of KH studies, by drawing ev-
idence from publication activities, prominent themes, citation trends, and collaborations amongst contributors.
The findings reveal that KH research has mainly focused on KH behavior, knowledge sharing (KS), and the KMS.
Firms with KH practices are responsible for challenging employee creativity, motivation, and workplace envi-
ronment. This study will help business managers and leaders hone cooperative behavior to achieve an innovative
environment and desired goals. Knowledge hiding also has positive implications, where leaders may have to hide
confidential information from external elements or internal subordinates to protect the enterprise’s sovereignty
and integrity. keywords: شیوه های دانش | عملکرد استراتژیک | هيئت مدیره | سیستم مدیریت دانش | به اشتراک گذاری دانش | کارایی فرآیند | Knowledge practices | Strategic performance | Board of directors | Knowledge management system | Knowledge sharing | Process efficiency |
مقاله انگلیسی |
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Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review
هوش مصنوعی و مدل های تجاری در چشم انداز اهداف توسعه پایدار: یک مرور ادبیات سیستماتیک-2020 This paper investigates the literary corpus on the role of Artificial Intelligence (AI) in the construction of sustainable business models (SBMs). It provides a quantitative overview of the academic literature that constitutes the field. The paper discusses the relationships between AI and rapid developments in machine learning and sustainable development (SD). Specifically, the aim is to understand whether this branch of computer science can influence production and consumption patterns to achieve sustainable resource management according to Sustainable Development Goals (SDGs) outlined in the UN 2030 Agenda. Moreover, the paper aims to highlight the role of Knowledge Management Systems (KMS) in the cultural drift toward the spread of AI for SBMs. Despite the importance of the topic, there is no comprehensive review of the AI and SBM literature in light of SDGs. Based on a database containing 73 publications in English with publication dates from 1990 to 2019, a bibliometric analysis is conducted. The findings show that the innovation challenge involves ethical, social, economic, and legal aspects. Thus, considering that the development potential of AI is linked to the UN 2030 Agenda for SD, especially to SDG#12, our results also outline the framework of the existing literature on AI and SDGs, especially SDG#12, including AI’s association with the cultural drift (CD) in the SBMs. The paper highlights the key contributions, which are: i) a comprehensive review of the key underlying relationship between AI and SBMs, offering a holistic view as needed, ii) identifying a research gap regarding KMS through AI, and iii) the implications of AI concerning SDG#12. Academic and managerial implications are also discussed regarding KMS in the SBMs, where the AI can represent the vehicle to meet the SDGs allowing for the identification of the cultural change required by enterprises to achieve sustainable goals. Thus, business companies, academic re- search practitioners, and state policy should focus on the further development of the use of AI in SBMs. Keywords: Artificial Intelligence (AI) | Machine learning sustainability | Cultural drift | Sustainable business models | Knowledge Management System (KMS) |
مقاله انگلیسی |
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نقش مهندسی دانش در توسعه سیستم ترکیبی اطلاعات پزشکی مبتنی بر دانش برای فیبریلاسیون دهلیزی
سال انتشار: 2013 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 17 در این مقاله، نقش مهندسی دانش در توسعه سیستم ترکیبی اطلاعات پزشکی مبتنی بر دانش را توصیف می کنیم. مهندسی دانش نقش مهمی را در توسعه فن آوری های مختلف ایفا می کند مانند: سیستم های هوشمند، شبکه عصبی، هوش مصنوعی، سیستم های هوشمند ترکیبی، داده کاوی، سیستم های پشتیبانی تصمیم و سیستم های مبتنی بر دانش و غیره. سیستم اطلاعات پزشکی هیبرید عمدتا از اطلاعات نظام پزشکی و سیستم های پزشکی مبتنی بر دانش تشکیل شده است. این پلی تکنیک مهندسی دانش هنگام پیوستن به تکنیک های هیبرید سیستم های هوشمند برای طراحی، جهت رسیدگی به اطلاعات داده های پزشکی از فیبریلاسیون دهلیزی پایگاه دانش را اجرا می کند. فیبریلاسیون دهلیزی شایع ترین اختلال ریتم قلب است که خطر مرگ و میر را افزایش می دهد.
کلمات کلیدی: هوش مصنوعی (AI)، تصمیم گیری، مهندسی دانش، پایگاه دانش ، مرگ و میر، شیوع مرض، مدیریت دانش( KM)، مدیریت تجربه(EM)، سیستم مبتنی بر دانش( KBS) ، سیستم مدیریت دانش(KMS) ، فیبریلاسیون دهلیزی( AFib) ، تکنیک های کشف دانش( KDT)، سیستم اطلاعات پزشکی، تصمیم گیری مبتنی بر اطلاعات پزشکی، سیستم ترکیبی هوشمند اطلاعات پزشکی ( HIMIS) ، سیستم مبتنی بر دانش ترکیبی.
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