An extended Fuzzy-AHP approach to rank the influences of socialization-externalization-combination-internalization modes on the development phase

Tyagi, Satish; Agrawal, Shubham; Yang, Kai; Ying, Hao

doi:10.1016/j.asoc.2016.10.017

Cited by 26 publications

(19 citation statements)

References 51 publications

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“…There are various works which have been conducted in the domain of disaster management (Beigi et al 2016), such as studies on dispatching resources in a natural disaster by monitoring real-time tweets (Chen et al, 2016) and on exploring the application of social media by non-profit organisations and media firms during natural disasters (Muralidharan et al, 2011). Analysis of Twitter data has also been conducted by researchers in the domain of operation management; such analyses include capturing big data in the form of tweets to improve the supply-chain innovation capabilities (Tan et al, 2015), investigating the state of logistics-related customer service which is provided by e-retailers on Twitter (Bhattacharjya et al, 2016), examining the process of service recovery in the context of operations management (Fan et al, 2016), developing a framework for assimilating social media into the supply chain management (Sianipar and Yudoko, 2014;Chae, 2015), determining the ranking of knowledge-creation modes by using extended fuzzy analytic hierarchy process (Tyagi et al, 2016), exploring the amalgamation of conventional knowledge management and the insights derived from social media (O'leary, 2011), improving the efficiency of the knowledge-creation process by developing a set of lean thinking tools (Tyagi et al, 2015a), and optimising the configuration of a platform via the coupling of product generations (Tyagi et al, 2015b).…”

Section: Related Workmentioning

confidence: 99%

Social media data analytics to improve supply chain management in food industries

Singh

Shukla

Mishra

2018

Transportation Research Part E: Logistics and Transportation Re

222

115

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This paper proposes a big-data analytics-based approach that considers social media (Twitter) data for the identification of supply chain management issues in food industries. In particular, the proposed approach includes (i) the capturing of relevant tweets based on keywords, (ii) the pre-processing of the raw tweets, and (iii) text analysis using a support vector machine (SVM) and hierarchical clustering with multiscale bootstrap resampling. The result of this approach included a cluster of words which could inform supply-chain (SC) decision makers about customer feedback and issues in the flow/quality of food products. A case study in the beef supply chain was analysed using the proposed approach, where three weeks of data from Twitter were used. The results indicated that the proposed text analytics approach can be helpful to effectively identify and summarise crucial customer feedback for the supply chain management. This study proposes a holistic approach, in which social media data are utilised in the domain of the food supply chain. The findings of the analysis have been linked to all the segments of the supply chain.

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Section: Related Workmentioning

confidence: 99%

Social media data analytics to improve supply chain management in food industries

Singh

Shukla

Mishra

2018

Transportation Research Part E: Logistics and Transportation Re

222

115

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“…As a whole, it offers an environment wherein all team member exercise leadership behavior and collaborative decision making which consequently leads to improved organizational knowledge outcomes [12]. Therefore, we may hypothesize within Motivationenhancing HPWPs that: [62]. The more employees interact and collaborate with each other, the more they share their inner thoughts [1].…”

Section: 22mentioning

confidence: 99%

Developing Intellectual Capital in Professional Service Firms Using High Performance Work Practices as Toolkit

Rehman

Hawryszkiewycz

Sohaib

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The role of Professional Service Firms (PSFs) has always been crucial in the development of knowledge economies. The effectiveness of these firms is highly attributed to the knowledge capabilities and skills embedded in its human resources and how effectively these resources are utilized in the optimal benefit of the firm. Owing to the ever-increasing growth of the services sector globally, it's critical for the PSFs to gain in-depth awareness on the application of High-Performance-Work-Practices (HPWPs) so as to continually maintain quality of their services to the clients. However, the mechanism for systematically designing and implementing these practices in intellectual capital context is still not fully developed. This research, therefore, theoretically investigates and suggests a linkage mechanism on how Strategic HRM Practices (HPWPs) via (Ability, Motivation and Opportunity)-enhancing bundles stimulate intellectual capital development in professional service firms. By presenting a conceptual framework, this study offers practically meaningful insights to the managers in the service firms on how to implement these practices for effectively meeting client needs and sustaining a competitive advantage.

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“…In this work, the index weights of the four MCDM methods can be calculated by AHP. e AHP method, proposed by Saaty [111], is a widely used tool for modeling unstructured problems by synthesizing subjective and objective information in many disciplines, such as politics, economics, biology, sociology, management science, and life sciences [112][113][114]. It can elicit a corresponding priority vector according to pair-by-pair comparison values [115] obtained from the scores of experts on an appropriate scale [116].…”

Section: Index Weightsmentioning

confidence: 99%

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

Zhang

Kou

et al. 2020

Complexity

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In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.

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An extended Fuzzy-AHP approach to rank the influences of socialization-externalization-combination-internalization modes on the development phase

Cited by 26 publications

References 51 publications

Social media data analytics to improve supply chain management in food industries

Social media data analytics to improve supply chain management in food industries

Developing Intellectual Capital in Professional Service Firms Using High Performance Work Practices as Toolkit

Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

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