مقاله انگلیسی رایگان در مورد چگونگی مقابله با عدم تعادل مدیریت دانش – امرالد ۲۰۱۷

مقاله انگلیسی رایگان در مورد چگونگی مقابله با عدم تعادل مدیریت دانش – امرالد ۲۰۱۷

 

مشخصات مقاله
انتشار مقاله سال ۲۰۱۷
تعداد صفحات مقاله انگلیسی ۳۰ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه امرالد
نوع مقاله ISI
عنوان انگلیسی مقاله How to deal with knowledge management misalignment: a taxonomy based on a 3D fuzzy methodology
ترجمه عنوان مقاله چگونگی مقابله با عدم تعادل مدیریت دانش: یک طبقه بندی مبتنی بر یک روش فازی سه بعدی
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مدیریت
گرایش های مرتبط مدیریت دانش، مدیریت کسب و کار
مجله مجله مدیریت دانش – Journal of Knowledge Management
دانشگاه Department of Industrial Engineering – University of Naples – Italy
کلمات کلیدی سیستم های مدیریت دانش، مدیریت دانش، شرکت های کوچک و متوسط، استراتژی های تصمیم گیری، شرکت های تامین، نظریه مجموعه ای فازی سه بعدی (۳D-FST)
کلمات کلیدی انگلیسی Knowledge management systems, Knowledge management, Small to medium sized enterprises, Decision-making strategies, Supply firms, Three-dimensional fuzzy set theory (3D-FST)
کد محصول E6832
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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بخشی از متن مقاله:
۱٫ Introduction

In recent years, the literature on the subject of knowledge management (KM) has grown in comparison with studies on technology management (TM). According to the Scopus database, papers on KM made up only 29 per cent of those on TM (33 out of 114) in the period from 1971 to 1985, but between 1986 and 2000, this percentage increased to 84 per cent (1,130 out of 1,349), and in the period spanning 2001 to 2015, the percentage reached a remarkable 965 per cent (51,231 papers compared to 5,307). This enormous interest in KM has brought the issues of knowledge creation and dissemination to the fore and is reflected in the large number of studies now being published. Many papers have stressed that knowledge is a critical success factor in competitiveness for modern industrial systems (Carayannis et al., 2014; Desouza and Awazu, 2006; Lee and Wong, 2017; Lee et al., 2016; Mariano and Awazu, 2017; Wang et al., 2016), and knowledge management is becoming increasingly important for both large companies and small and medium enterprises (SMEs) (Calvo-Mora et al., 2016; Chawinga and Chipeta, 2017; Mcadam and Reid, 2001). Nevertheless, despite the extensive literature on the successful implementation of knowledge management initiatives in large companies, there has so far been little focus on SMEs. This gap is particularly relevant, as SMEs now drive economic growth in both developed and developing countries (Bagnoli and Vedovato, 2014; Cantu´ et al., 2009; Durst and Edvardsson, 2012; Massaro et al., 2016; Patrice et al., 2014; Serenko, 2013; Wee and Chua, 2013). Although various aspects of knowledge management have been explored in the literature, alignment between enterprises’ knowledge and the knowledge management systems (KMSs) used to support KM still appears to have been largely neglected. However, this is an extremely important gap, as correct alignment between an enterprise’s knowledge and its KMSs is in itself a factor that impacts positively on the processes of knowledge creation and dissemination. The relevance of this topic is justified by the fact that the literature has paid attention to the concept of knowledge as an asset but not knowledge as a liability which is created when the organisation mismanages knowledge or when the organisation adopts KMSs that are misaligned with the enterprise’s knowledge and risks to make decisions that reduce the value of assets (Caddy, 2000; Hora and Klassen, 2013; Kivits and Furneaux, 2013; Levine and Prietula, 2012; Wallace et al., 2005). This paper therefore sets out to explore the alignment between enterprise knowledge and KMSs in SMEs. In particular, it proposes a new three-dimensional (3D) fuzzy logic methodology to evaluate levels of misalignment. The proposed methodology was implemented through a field analysis based on semi-structured face-to-face interviews with representatives of a sample of 61 SMEs operating in high-tech and/or complex industries. The paper also highlights how the proposed methodology may be used as a decisionmaking tool to diagnose the state of individual enterprises and suggest appropriate changes to improve alignment. The remainder of the paper is organised as follows: after this introduction, Section 2 presents the background of the study, Section 3 illustrates the various phases of the research methodology, with details of enterprise knowledge, KMSs, and the formulation of alignment indices. Section 4 describes the field analysis and the context of investigation, and Section 5 discusses the results of the field analysis. Finally, Section 6 sets out the conclusions, offering ideas for future research and suggesting some possible implications.

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