Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability

Chen, Yi Ting; Sun, Edward W.; Lin, Yi‐Bing

doi:10.1016/j.ejor.2019.02.046

Cited by 12 publications

(5 citation statements)

References 62 publications

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“…are increasingly used in SCM to integrate and organize every aspect of the chain; thus, not surprisingly, supply chains have been transformed by big data analytics (Wang et al, 2016;Nguyen et al, 2018). Big data analytics may enable supply chains to make informed decisions, identify and mitigate risks, enhance operational processes, design new products to the market, and engage in market analyses for particular products, and so on (Silvestre, 2015;Govindan et al, 2019;Moktadir et al, 2019;Chen et al, 2020;Duan et al, 2020;Jabbour et al, 2020 Zhan andTan, 2020). Big data analytics is particularly important to the area of SCM as it supplies the techniques to support decision making in growing global, volatile, and dynamic value networks (RoBann et al, 2018).…”

Section: Big Data Analytics In Supply Chainsmentioning

confidence: 99%

Risks associated with the implementation of big data analytics in sustainable supply chains

Kusi‐Sarpong

Orji

Gupta

et al. 2021

Omega

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Section: Big Data Analytics In Supply Chainsmentioning

confidence: 99%

Risks associated with the implementation of big data analytics in sustainable supply chains

Kusi‐Sarpong

Orji

Gupta

et al. 2021

Omega

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“…Mobile communication is a general-purpose technology to increase innovation across all industries and sectors [1]. For instance, the fourth generation (4G) of Long-Term Evolution (LTE) mobile telecommunications technology standardizes high-speed wireless communications for mobile phones and data terminals [2]. In that regard, the fifth generation (5G) of wireless network technologies is recognized as the key enabler for realizing the full economic and social benefits of digital transformation [3].…”

Section: Introductionmentioning

confidence: 99%

“…(r; γ, σ, r, p) ∂r2 < 0Then given thatG(r; γ, σ, r, p) = −σγ 2 r 2 + 2σγ 2 rr − rp ∂G(r; γ, σ, r, p) ∂r = −2σγ 2 r + 2σγ 2 r − p ∂ 2 G(r; γ, σ, r, p) ∂r 2…”

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A Subsidization Scheme for Maximizing Social Welfare in Mobile Communications Markets

Agualimpia-Arriaga,

Vuelvas,

Páez-Rueda

et al. 2024

Systems

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In contemporary mobile communications markets, various agents or players interact to pursue welfare. Regulatory policies enacted by governments in certain markets aim to maximize social welfare. However, some countries, both least developed and developing, often adopt successful models from developed nations without local market validation. Therefore, network economics serves as a pertinent framework for analyzing such policies. This paper introduces a novel scheme based on constrained optimization problems, where the constraints represent multilevel economic game equilibria within a system model involving three agents: the central planner, the mobile network operator, and the mobile data users. These agents strategically optimize their payoff functions by considering benefit factors and decision variables such as the subsidization factor, pricing, and data consumption. To this end, a three-stage dynamic game is proposed to model the players’ interactions, employing the backward induction method to ascertain the subgame perfect equilibrium from the Nash equilibrium. A case study is presented, demonstrating a 31.16% increase in social welfare between scenarios involving no adoption of the subsidization factor and its adoption at the optimal value when the central planner enacts it to other players in the game, even if they do not necessarily attain maximum payoff values. In countries aligning with this proposed model, social welfare is maximized through a subsidization scheme.

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“…It provides decision-makers with actionable insights, enabling them to forecast future outcomes and anticipate potential opportunities or risks. Meanwhile, prescriptive analytics aims to improve prediction accuracy and prescribe better decision options that generate new value for an organization, such as future expected revenues, profits, intangible assets such as intellectual capital, and opportunities for future growth [8].…”

Section: Introductionmentioning

confidence: 99%

“…Some organizations, for instance, fail to properly plan for BA in terms of acquiring talented personnel with sufficient experience, managing the change related to business processes and organizational decision-making culture, and allocating the necessary financial resources for such investments [15]. In addition, Shi et al [8] recently drew attention to a concerning statistic revealing that 95% of projects related to analytics-driven innovation encounter failure due to a range of technical and cultural challenges. In the same vein, Conboy et al [17] indicate that there is still a lack of understanding from a practical standpoint about how to use analytics to improve business, as well as managerial and cultural issues emerge as the primary barriers to the widespread adoption of BA solutions.…”

Section: Introductionmentioning

confidence: 99%

Determining the Factors Influencing Business Analytics Adoption at Organizational Level: A Systematic Literature Review

Horani

Khatibi

Alsoud

et al. 2023

BDCC

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The adoption of business analytics (BA) has become increasingly important for organizations seeking to gain a competitive edge in today’s data-driven business landscape. Hence, understanding the key factors influencing the adoption of BA at the organizational level is decisive for the successful implementation of these technologies. This paper presents a systematic literature review that utilizes the PRISMA technique to investigate the organizational, technological, and environmental factors that affect the adoption of BA. By conducting a thorough examination of pertinent research, this review consolidates the current understanding and pinpoints essential elements that shape the process of adoption. Out of a total of 614 articles published between 2012 and 2022, 29 final articles were carefully chosen. The findings highlight the significance of organizational factors, technological factors, and environmental factors in shaping the adoption of the BA process. By consolidating and analyzing the current body of research, this paper offers valuable insights for organizations aiming to adopt BA successfully and maximize their benefits at the organizational level. The synthesized findings also contribute to the existing literature and provide a foundation for future research in this field.

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Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability

Cited by 12 publications

References 62 publications

Risks associated with the implementation of big data analytics in sustainable supply chains

Risks associated with the implementation of big data analytics in sustainable supply chains

A Subsidization Scheme for Maximizing Social Welfare in Mobile Communications Markets

Determining the Factors Influencing Business Analytics Adoption at Organizational Level: A Systematic Literature Review

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