scholarly journals The Behavioral Investigation of Industry 4.0 Concept: A Research On Twitter

2020 ◽  
Vol 4 (2) ◽  
pp. 91-105
Author(s):  
Bulent Cekic
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Operations Management ◽  
Industry 4.0 ◽  
Design Research ◽  
Behavioral Operations ◽  
Behavioral Operations Management ◽  
Manufacturing Operations

Abstract: Aim: This paper is going to investigate how Industry 4.0 concept especially the components of it, behaves in social networks and the context of this innovative approach find a place in time in means of content and sentiment comprising. Design / Research methods: Expeditious progress in industrialization and information techniques has made great advancement in developing the next span of production technology. Industry 4.0 is an imperative action where the intention is the alteration of modern production through digitalization and profiteering of the capabilities of new advancements. Today, the absence of powerful appliances still feigns a significant impediment for utilizing the ample potential of Industry 4.0. Notably, behavioral approaches are essential for understanding Industry 4.0, which professes novel trials. This paper briefly surveys the area of Industry 4.0 as it relates to behavioral operations by using sentiment analysis and social network analysis methods and tools by describing features of the relationship network either through numerical and visual representation Conclusions / findings: First of all, it should be presumed that the name Industry 4.0 describes various, fundamentally internet-based developments in manufacturing operations. These advancements do not only have technological but moreover accomplished organizational engagements. Appropriately, a shift from product to service orientation is assumed. Following, the introduction of novel varieties of businesses can be envisioned which embraces new particular functions within the production process sequentially the value-creation networks. Originality / value of the article: Within the context of the current state of the art in operations management literature, this paper fulfills the gap between behavioral operations and industry 4.0 context for the researchers both in operations management and behavioral sciences will benefit from this analysis. Keywords: Behavioral Operations Management, Sentiment Analysis, Industry 4.0, Social Network Analysis. JEL: C88, D23, D24, E71, M11, O14, O33

scholarly journals Lessons from social network analysis to Industry 4.0

2018 ◽  
Vol 15 ◽  
pp. 97-100 ◽  
Author(s):  
Yamila M. Omar ◽  
Meysam Minoufekr ◽  
Peter Plapper
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Industry 4.0

scholarly journals Social network analysis in operations and supply chain management: a review and revised research agenda

2020 ◽  
Vol 40 (7/8) ◽  
pp. 1153-1176 ◽  
Author(s):  
Yujia Han ◽  
Nigel David Caldwell ◽  
Abhijeet Ghadge
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Supply Chain Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Value Creation ◽  
Operations Management ◽  
Chain Management ◽  
Content Type ◽  
Contemporary Management

PurposeSocial network analysis (SNA) seeks to manage the connections between entities through investigating and understanding behaviours and relationships. This study demonstrates the increasing relevance of social network approaches to solving contemporary and looming operations management (OM) and supply chain management (SCM) problems; including the coordination operations challenges raised by increased connectivity.Design/methodology/approachThe systematic literature review approach adopted here examines 63 papers in OM and SCM published between 2000 and 2019. To-date OM reviews on SNA have focussed on discussing archetypal supply chains, what differentiates this study is the focus on how value was created in other forms of chains and operations.FindingsThis study reveals that current SNA adoption in OM is dominated by a manufacturing style focus on linear, sequential value creation, with a resulting focus only on sequential interdependence. SNA studies on reciprocally co-ordinated value creation (e.g. many service and network operations) are shown to have been neglected and are linked to a new agenda on contemporary management issues.Research limitations/implicationsBeyond encouraging the use of SNA, this study seeks to re-orient SNA approaches towards how contemporary services and networks create value.Originality/valueThrough adopting a unique combination of approaches and frameworks, this study challenges extant work to offer a substantially revised agenda for SNA use in Operations and Supply Chain Management.

scholarly journals Using social network analysis for industrial plant layout analysis in the context of industry 4.0

2020 ◽  
Vol 34 (1) ◽  
pp. 1
Author(s):  
M.L.R. Varela ◽  
Vijay Kumar Manupati ◽  
Suraj Panigrahi ◽  
Eric Costa ◽  
Goran D. Putnik
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Industry 4.0 ◽  
Industrial Plant ◽  
Layout Analysis ◽  
Plant Layout

scholarly journals IMPLEMENTASI SOCIAL NETWORK ANALYSIS DALAM PENYEBARAN INFORMASI VIRUS CORONA (COVID-19) DI TWITTER

2021 ◽  
Vol 2020 (1) ◽  
pp. 292-299
Author(s):  
Dwi Inayah ◽  
Fredy Law Purba
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Sentiment Analysis

Virus Corona (Covid-19) merupakan virus baru yang sudah menyebar ke 215 negara, termasuk Indonesia. Penyebaran yang cepat dan ketidakpastian kapan berakhirnya pandemi Covid-19 ini telah menimbulkan kekhawatiran yang dapat memengaruhi kondisi kesehatan mental masyarakat. Respon kekhawatiran tersebut biasanya diekspresikan ke dalam media sosial, salah satunya twitter. Penelitian ini bertujuan untuk mengidentifikasi kata yang paling sering muncul; mengkategorikan setiap opini yang muncul ke dalam kategori sentimen netral, positif dan negatif; serta mengetahui akun-akun twitter berpengaruh terkait Covid-19. Metode yang digunakan adalah Wordcloud Analysis, Sentiment Analysis, dan Social Network Analysis. Hasil wordcloud analysis menunjukkan bahwa kata yang sering muncul terkait Covid-19 adalah “positif”, “pandemi” dan “Indonesia”. Hasil sentiment analysis menunjukkan bahwa cuitan bersentimen netral merupakan yang terbanyak, disusul cuitan bersentimen negatif, kemudian bersentimen positif. Hasil social network analysis menunjukkan bahwa aktor paling berpengaruh adalah akun @PratiwiHAM. Dengan hasil tersebut, pemerintah dapat melakukan kerja sama dengan akun @PratiwiHAM untuk menyebarkan informasi positif, memberikan dukungan moral, dan menangkal hoax yang diharapkan dapat meningkatkan kondisi kesehatan mental masyarakat Indonesia.

Analysis of Online Social Networks for the Identification of Sarcasm

2018 ◽  
pp. 92-105 ◽  
Author(s):  
Pulkit Mehndiratta
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Future Research ◽  
Research Directions ◽  
Textual Data ◽  
Future Research Directions

With the ever-increasing acceptance of online social networks (OSNs), a new dimension has evolved for communication amongst humans. OSNs have given us the opportunity to monitor and mine the opinions of a large number of online active populations in real time. Many diverse approaches have been proposed, various datasets have been generated, but there is a need of collective understanding of this area. Researchers are working around the globe to find a pattern to judge the mood of the user; the still serious problem of detection of irony and sarcasm in textual data poses a threat to the accuracy of the techniques evolved till date. This chapter aims to help the reader to think and learn more clearly about the aspects of sentiment analysis, social network analysis, and detection of irony or sarcasm in textual data generated via online social networks. It argues and discusses various techniques and solutions available in literature currently. In the end, the chapter provides some answers to the open-ended question and future research directions related to the analysis of textual data.

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