Abstract
The emergence of cloud computing, IoT, and increasing number of smart phones laid the foundation for digital business. In the current scenario of digital transformation, digitization of enterprises has become a survival strategy for organizations to cope with the fast changing and uncertain business environment. Even if digital transformation is essential for the growth of traditional business in today’s transitional phase and enables countless opportunities, but still the major challenges commonly faced here are what is need to be done and how it should be done to address business related issues. Moreover, since these days volume of data is increasing exponentially, but raw data has no value until analyzed or utilized. Algorithmic business is one of the solutions to afore mentioned said challenges. It involves the use of smart algorithms in providing important business insights, defining company processes handling customer services, analyzing business data and making important decisions. This chapter presents a brief overview about algorithmic business, discusses various aspects, deployment strategies, challenges, opportunities such as algorithmic market place etc. Speed and scale are some of the primary advantages of deploying algorithmic business in enterprises.
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References
Applin SA, Fischer MD (2015) New technologies and mixed-use convergence: how humans and algorithms are adapting to each other. In: 2015 IEEE international symposium on technology and society (ISTAS), pp 1–6. IEEE
Bucher T (2017) The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Inf Commun Soc 20(1):30–44
Chaffey D (2015) Digital business and E-commerce management. Pearson Education Limited
Chen L, Mislove A, Wilson C (2015) Peeking beneath the hood of uber. In: Proceedings of the 2015 Internet Measurement Conference, pp 495–508. ACM
Cocchia A (2014) Smart and digital city: a systematic literature review. Smart city. Springer International Publishing, pp 13–43
Cotter K, Cho J, Rader E (2017) Explaining the news feed algorithm: an analysis of the News Feed FYI blog. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems, pp 1553–1560. ACM
Datta P, Vaidhehi V (2017) Influencing the PageRank using link analysis in SEO. Int J Appl Eng Res 12(24):15122–15128
DeVito MA (2017) From editors to algorithms: a values-based approach to understanding story selection in the Facebook news feed. Digit Journal. 5(6):753–773
Earley S (2015) Analytics, machine learning, and the internet of things. IT Prof 17(1):10–13
Fleisch E, Weinberger M, Wortmann F (2015) Business models and the internet of things. Interoperability and open-source solutions for the internet of things. Springer, Cham, pp 6–10
Gartner (2013) Gartner says the internet of things installed base will grow to 26 billion units by 2020, Dec 2013. www.gartner.com/newsroom/id/2636073
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gen Comput Syst 29(7):1645–1660
Gartner (2014) Gartner’s 2014 hype cycle for emerging technologies maps the journey to digital business, Aug 2014. www.gartner.com/newsroom/id/2819918
Hermann M, Pentek T, Otto B (2016) Design principles for industrie 4.0 scenarios. In: 2016 49th Hawaii international conference on system sciences (HICSS), pp 3928–3937. IEEE
Jia X, Feng Q, Fan T, Lei Q (2012) RFID technology and its applications in internet of things (IoT). In: 2012 2nd international conference on consumer electronics, communications and networks (CECNet), pp 1282–1285. IEEE
Komninos N (2016) Smart environments and smart growth: connecting innovation strategies and digital growth strategies. Int J Knowl Based Dev 7(3):240–263
Kellner T (2013) Analyze this: the industrial internet by the numbers & outcomes. Ge report. www.gereports.com/post/74545267912/analyze-this-the-industrial-internet-by-the. Accessed 30 Nov 2014
Laney DB (2017) Infonomics: how to monetize, manage, and measure information as an asset for competitive advantage. Routledge
Latzer M, Hollnbuchner K, Just N, Saurwein F (2016) The economics of algorithmic selection on the Internet. Handbook on the economics of the internet, chapter 19, p 395
Lee I, Lee K (2015) The internet of things (IoT): applications, investments, and challenges for enterprises. Bus Horiz 58(4):431–440
Levy H (2017) The arrival of algorithmic business. smarter with Gartner. https://www.gartner.com/smarterwithgartner/the-arrival-of-algorithmic-business/
Leonhardt D, Haffke I, Kranz J, Benlian A (2017) Reinventing the IT function: the role of IT agility and IT ambidexterity in supporting digital business transformation
Mashrafi M (2017) Investigate the effect of semantic search on search engine opti-mization. Doctoral dissertation, Cardiff Metropolitan University
Mishra R, Kumar P, Bhasker B (2015) A web recommendation system considering sequential information. Decis Support Syst 75:1–10
Newell S, Marabelli M (2015) Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of ‘datification’. J Strateg Inf Syst 24(1):3–14
Perera C, Liu CH, Jayawardena S, Chen M (2014) A survey on internet of things from industrial market perspective. IEEE Access 2:1660–1679
Rader E, Gray R (2015) Understanding user beliefs about algorithmic curation in the Facebook news feed. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, pp 173–182. ACM
Rogers R (2018) Aestheticizing Google critique: A 20-year retrospective. Big Data Soc 5(1):2053951718768626
Tao F, Zuo Y, Da Xu L, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10(2):1547–1557
Xu C, Wang C, Gong L, Li X, Wang A, Zhou X (2017) Acceleration for recommendation algorithms in data mining. High performance computing for big data: methodologies and applications, p 121
Xu Z, Sugumaran V, Yen NY (2018) Special issue: algorithmic and knowledge-based approaches to assessing consumer sentiment in electronic commerce. Electron. Commer Res 18(1):1–1
Zhang S, Cabage N (2017) Search engine optimization: comparison of link building and social sharing. J Comput Inf Syst 57(2):148–159
Zhou M, Ding Z, Tang J, Yin D (2018) Micro behaviors: a new perspective in e-commerce recommender systems. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 727–735. ACM
Zhuhadar L, Kruk SR, Daday J (2015) Semantically enriched massive open online courses (moocs) platform. Comput Hum Behav 51:578–593
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Nanda, P., Patnaik, S., Patnaik, S. (2019). Towards Algorithmic Business: A Paradigm Shift in Digital Business. In: Patnaik, S., Yang, XS., Tavana, M., Popentiu-Vlădicescu, F., Qiao, F. (eds) Digital Business. Lecture Notes on Data Engineering and Communications Technologies, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-93940-7_1
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