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

  • Bulent Cekic



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


Bello-Orgaz G., Jung J.J., Camacho D. (2016), Social big data. Recent achievements and new challenges, “Information Fusion”, vol. 28, pp. 45-59.

Bollen J., Mao H., Pepe A. (2011), Modeling public mood and emotion. Twitter sentiment and socio-economic phenomena, in: Fifth International AAAI Conference on Weblogs and Social Media, July.

Bradley M.M., Lang P.J. (1999), Affective norms for English words (ANEW). Instruction manual and affective ratings, Technical report C-1, The Center for Research in Psychophysiology, University of Florida, [13.06.2020].

Danescu-Niculescu-Mizil C., Kossinets G., Kleinberg J., Lee L. (2009), How opinions are received by online communities. A case study on helpfulness votes, in: Proceedings of the 18th International Conference on World Wide Web, April, pp. 141-150.

Dave K., Lawrence S., Pennock D.M. (2003), Mining the peanut gallery. Opinion extraction and semantic classification of product reviews, in: Proceedings of the 12th International Conference on World Wide Web, May, pp. 519-528.

Go A., Bhayani R., Huang L. (2009), Twitter sentiment classification using distant supervision, CS224N Project Report, Stanford, [13.06.2020].

Healey C.G., Ramaswamy S.S. (2011), Visualizing Twitter sentiment, Sentiment viz, https://www. csc. ncsu. edu/faculty/healey/tweet_viz/tweet_app/ [06.05.2020].

Hermann M., Pentek T., Otto B. (2016), Design principles for industrie 4.0 scenarios, in: 2016 49th Hawaii International Conference on System Sciences (HICSS). Conference Proceedings, Koloa (HI), pp. 3928-3937.

Java A., Song X., Finin T., Tseng B. (2007), Why we twitter. An analysis of a microblogging community, in: International Workshop on Social Network Mining and Analysis, August, Springer, Berlin – Heidelberg, pp. 118-138.

Kagermann H., Helbig J., Hellinger A., Wahlster W. (2013), Recommendations for implementing the strategic initiative Industrie 4.0. Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group, Forschungsunion.

Lee J., Lapira E., Bagheri B., Kao H.A. (2013), Recent advances and trends in predictive manufacturing systems in big data environment, “Manufacturing Letters”, vol. 1 no. 1, pp. 38-41.

Liao Y., Deschamps F., Loures E.D.F.R., Ramos L.F.P. (2017), Past, present and future of Industry 4.0. A systematic literature review and research agenda proposal, “International Journal of Production Research”, vol. 55 no. 12, pp. 3609-3629.

Liu B. (2012), Sentiment analysis and opinion mining, “Synthesis Lectures on Human Language Technologies”, vol. 5 no. 1, pp. 1-167.

Machado C.G., Winroth M.P., Ribeiro da Silva E.H.D. (2020), Sustainable manufacturing in Industry 4.0. An emerging research agenda, “International Journal of Production Research”, vol. 58 no. 5, pp. 1462-1484.

Nasukawa T., Yi J. (2003), Sentiment analysis. Capturing favorability using natural language processing, in: Proceedings of the 2nd International Conference on Knowledge Capture, October 23-25, Sanibel Island, pp. 70-77.

Nguyen D.T., Jung J.E. (2017), Real-time event detection for online behavioral analysis of big social data, “Future Generation Computer Systems”, no. 66, pp. 137-145.

Oztemel E., Gursev S. (2020), A taxonomy of Industry 4.0 and related technologies, in: Industry 4.0. Current status and future trends, Hamilton Ortiz J. (ed.), IntechOpen, [13.06.2020].

Oztemel E., Gursev S. (2020), Literature review of Industry 4.0 and related technologies, “Journal of Intelligent Manufacturing”, vol. 31 no. 1, pp. 127-182.

Pang B., Lee L. (2008), Opinion mining and sentiment analysis, “Foundations and Trends in Information Retrieval”, vol. 2 no. 1-2, pp. 1-135.

Piccialli F., Benedusi P., Amato F. (2018), S-InTime. A social cloud analytical service oriented system, “Future Generation Computer Systems”, vol. 80, pp. 229-241.

Porter M.F. (1980), An algorithm for suffix stripping, “Program”, vol. 14 no. 3, pp. 130-137.

Ramaswamy S.S. (2011), Visualization of the sentiment of the tweets, Master’s Thesis, North Carolina State University, Raleigh, NC.

Russell J.A. (1980), A circumplex model of affect, “Journal of Personality and Social Psychology”, vol. 39 no. 6, pp. 1161-1178.

Scherer K.R. (2005), What are emotions? And how can they be measured?, “Social Science Information”, vol. 44 no. 4, pp. 695-729.

Seo Y.S., Huh J.H. (2019), Automatic emotion-based music classification for supporting intelligent IoT applications, “Electronics”, vol. 8 no. 2, p. 164.

Shannon C.E. (1948), A mathematical theory of communication, “Bell System Technical Journal”, vol. 27 no. 3, pp. 379-423.

Tang H., Tan S., Cheng X. (2009), A survey on sentiment detection of reviews, “Expert Systems with Applications”, vol. 36 no. 7, pp. 10760-10773.

Xu L.D., Xu E.L., Li L. (2018), Industry 4.0. State of the art and future trends, “International Journal of Production Research”, vol. 56 no. 8, pp. 2941-2962.

Yin Y., Stecke K.E., Li D. (2018), The evolution of production systems from Industry 2.0 through Industry 4.0, “International Journal of Production Research”, vol. 56 no. 1-2, pp. 848-861