scholarly journals Big Data and surveillance: Hype, commercial logics and new intimate spheres

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172092585 ◽  
Author(s):  
Kirstie Ball ◽  
William Webster
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Service User ◽  
Data Analytics ◽  
Service Providers ◽  
Big Data Analytics ◽  
User Engagement ◽  
Embodied Experience ◽  
Data Practices ◽  
Private And Public

Big Data Analytics promises to help companies and public sector service providers anticipate consumer and service user behaviours so that they can be targeted in greater depth. The attempts made by these organisations to connect analytically with users raise questions about whether surveillance, and its associated ethical and rights-based concerns, are intensified. The articles in this special themed issue explore this question from both organisational and user perspectives. They highlight the hype which firms use to drive consumer, employee and service user engagement with analytics within both private and public spaces. Further, they explore extent to which, through Big Data, there is an attempt to expand surveillance into the emotional registers of domestic, embodied experience. Collectively, the papers reveal a fascinating nexus between the much-vaunted potential of analytics, the data practices themselves and the newly configured intimate spheres which have been drawn into the commercial value chain. Together, they highlight the need for conceptual and regulatory innovation so that analytics in practice may be better understood and critiqued. Whilst there is now a rich variety of scholarship on Big Data Analytics, critical perspectives on the organising practices of Big Data Analytics and its surveillance implications are thin on the ground. Combined, the articles published in this special theme begin to address this shortcoming.

scholarly journals The Smart Triad : Big Data Analytics , Cloud Computing and Internet of Things to shape the Smart Home, Smart City, Smart Business & Smart Country

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3594-3600 ◽  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Smart Home ◽  
Service Providers ◽  
Irrigation System ◽  
Electronic Devices ◽  
Big Data Analytics ◽  
User Privacy

Big data analytics, cloud computing & internet of things are a smart triad which have started shaping our future towards smart home, city, business, country. Internet of things is a convergence of intelligent networks, electronic devices, and cloud computing. The source of big data at different connected electronic devices is stored on cloud server for analytics. Cloud provides the readymade infrastructure, remote processing power to consumers of internet of things. Cloud computing also gives device manufacturers and service providers access to ―advanced analytics and monitoring‖, ―communication between services and devices‖, ―user privacy and security‖. This paper, presents an overview of internet of things, role of cloud computing & big data analytics towards IoT. In this paper IoT enabled automatic irrigation system is proposed that saves data over ―ThingSpeak‖ database an IoT analytics platform through ESP8266 wifi module. This paper also summarizes the application areas and discusses the challenges of IoT.

Architecture of modern platforms for big data analytics

2021 ◽  
pp. 67-74
Author(s):  
Liudmyla Zubyk ◽  
Yaroslav Zubyk
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Service Providers ◽  
Big Data Analytics ◽  
Business Value ◽  
Third Party ◽  
Unstructured Data ◽  
Business Decision ◽  
World Industry

Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

scholarly journals Use of Big Data to Improve Environmental Sustainability in Developing Countries

2017 ◽  
Vol 12 (11) ◽  
pp. 249 ◽  
Author(s):  
Maged Adel Abdo Mukred ◽  
Zheng Jianguo
Keyword(s):  
Big Data ◽  
Multinational Corporations ◽  
Environmental Sustainability ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Government Sector ◽  
Wide Range ◽  
Private And Public ◽  
The Government ◽  
Set Up

Big data inhibits the ability to significantly impact a wide range of fields in an economy, from the government sector to commercial sectors like retail and healthcare. Not only has it altered the way companies assess their product’s demand and supply patterns but has also phenomenally helped in making the environment healthier in recent years. It carries the ability to identify valuable data from a huge dataset with exceptional parallel processing. This study presents the general introduction of big data bringing forth its various features and advantages along with the challenges which organizations face while using with respect to environmental sustainability. Observations have also been made on the findings of various researches, and studies and surveys performed by some international organizations in the recent years on the urgent need of taking necessary measures and initiatives to prevent further depletion of natural resources thus making the environment sustainable. Making the issue the study aim, future studies must intend to explore how multinational corporations can enhance environmental sustainability through big data analytics. Lastly, recommendations have been made to organisations– private and public in hiring adequate expertise and set-up, thereby making big data analytics more efficient and reliable.

Performance-based evaluation of academic libraries in the big data era

2020 ◽  
pp. 016555152091851 ◽  
Author(s):  
A Y M Atiquil Islam ◽  
Khurshid Ahmad ◽  
Muhammad Rafi ◽  
Zheng JianMing
Keyword(s):  
Big Data ◽  
Technology Acceptance ◽  
Academic Libraries ◽  
Data Analytics ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Data Practices ◽  
Ease Of Access ◽  
Acceptance Model ◽  
Performance Based Evaluation

The concept of big data has been extensively considered as a technological modernisation in organisations and educational institutes. Thus, the purpose of this study is to determine whether the modified technology acceptance model (MTAM) is viable for evaluating the performance of librarians in the use of big data analytics in academic libraries. This study used an empirical research method for collecting data from 211 librarians working in Pakistan’s universities. On the basis of the findings of the MTAM analysis by structural equation modelling, the performances of the academic libraries were comprehended through the process of big data. The main influential components of the performance analysis in this study were the big data analytics capabilities, perceived ease of access and the usefulness of big data practices in academic libraries. Subsequently, the utilisation of big data was significantly affected by skills, perceived ease of access and the usefulness of academic libraries. The results also suggested that the various components of the academic libraries lead to effective organisational performance when linked to big data analytics.

scholarly journals Usefulness and Impact of Big Data in Libraries: An Opportunity to Implement Embedded Librarianship

2021 ◽  
Author(s):  
Subhajit Panda
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Information Overload ◽  
Big Data Analytics ◽  
Library Services ◽  
Data Practices ◽  
Data Life Cycle ◽  
Data Volume ◽  
Primary Focus

The concept of Big Data has been extensively considered as a technological modernisation in Library & Information centres. According to IDC, data volume is set to increase exponentially and envisages a data volume of over 160 zettabytes by the year 2025. Size is the first, and at times, the only dimension that leaps out at the mention of Big Data. Big Data is defined as information overload due to the volume, velocity, variety, variability & veracity of the data which must be processed to get value and better visualisation. Big Data contains the answer to several valuable questions related to patterns, trends & associations of user behaviour. It plays a major role in helping libraries to clearly understand the changing user needs, accordingly, reshape & restructure their services & procedures. The primary focus of this study was to explore the concept of Big Data in a library environment, steps to introduce Big Data in libraries and the use of Big Data in providing library services using the concept of data life cycle developed by DataONE. The main influential components to perform this study was the capabilities of Big Data analytics, the need & usefulness of Big Data practices, its significant utilisation in libraries and discuss some globally taken practical initiatives. The study highlights the important role of Big Data analytics capabilities to uncover new challenges of information utilisation, consequently helps a librarian to fulfil his role as an Embedded Librarian, both in theoretical & practical terms.

scholarly journals Industry 4.0 Technologies for the Manufacturing and Distribution of COVID-19 Vaccines

2022 ◽  
Vol 13 ◽  
pp. 215013192110686
Author(s):  
Azza Sarfraz ◽  
Zouina Sarfraz ◽  
Muzna Sarfraz ◽  
Aminah Abdul Razzack ◽  
Shehar Bano ◽  
...  
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Disease Transmission ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Large Scale ◽  
Process Development ◽  
Big Data Analytics ◽  
Middle Income ◽  
Resource Limited

Background The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent networks along the vaccine value chain. Vaccine 4.0 may enable maintenance processes, streamline logistics, and enable optimal production of COVID-19 vaccines. Vaccine 4.0 Framework The challenge in applying Vaccine 4.0 includes the requirement of large-scale technologies for digitally transforming manufacturing, producing, rolling-out, and distributing vaccines. With our framework, Vaccine 4.0 analytics will target process performance, process development, process stability, compliance, quality assessment, and optimized maintenance. The benefits of digitization during and post the COVID-19 pandemic include first, the continual assurance of process control, and second, the efficacy of big-data analytics in streamlining set parameter limits. Digitization including big data-analytics may potentially improve the quality of large-scale vaccine production, profitability, and manufacturing processes. The path to Vaccine 4.0 will enhance vaccine quality, improve efficacy, and compliance with data-regulated requirements. Discussion Fiscal and logistical barriers are prevalent across resource-limited countries worldwide. The Vaccine 4.0 framework accounts for expected barriers of manufacturing and equitably distributing COVID-19 vaccines. With amalgamating big data analytics and biometrics, we enable the identification of vulnerable populations who are at higher risk of disease transmission. Artificial intelligence powered sensors and robotics support thermostable vaccine distribution in limited capacity regions, globally. Biosensors isolate COVID-19 vaccinations with low or limited efficacy. Finally, Vaccine 4.0 blockchain systems address low- and middle-income countries with limited distribution capacities. Conclusion Vaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic.

scholarly journals Does big data analytics influence frontline employees in services marketing?

2017 ◽  
Vol 23 (3) ◽  
pp. 623-644 ◽  
Author(s):  
Saradhi Motamarri ◽  
Shahriar Akter ◽  
Venkat Yanamandram
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Service Providers ◽  
Research Question ◽  
Big Data Analytics ◽  
Frontline Employees ◽  
Skill Levels ◽  
Content Type ◽  
Knowledge Intensive ◽  
The Impact

Purpose Big data analytics (BDA) helps service providers with customer insights and competitive information. It also empowers customers with insights about the relative merits of competing services. The purpose of this paper is to address the research question, “How does big data analytics enable frontline employees (FLEs) in effective service delivery?” Design/methodology/approach The research develops schemas to visualise service contexts that potentially benefit from BDA, based on the literature drawn from BDA and FLEs streams. Findings The business drivers for BDA and its level of maturity vary across firms. The primary thrust for BDA is to gain customer insights, resource optimisation and efficient operations. Innovative FLEs operating in knowledge intensive and customisable settings may realise greater value co-creation. Practical implications There exists a considerable knowledge gap in enabling the FLEs with BDA tools. Managers need to train, orient and empower FLEs to collaborate and create value with customer interactions. Service-dominant logic posits that skill asymmetry is the reason for service. So, providers need to enhance skill levels of FLEs continually. Providers also need to focus on market sensing and customer linking abilities of FLEs. Social implications Both firms and customers need to be aware of privacy and ethical concerns associated with BDA. Originality/value Knitting the BDA and FLEs research streams, the paper analyses the impact of BDA on service. The research by developing service typology portrays its interplay with the typologies of FLEs and BDA. The framework portrays the service contexts in which BD has major impact. Looking further into the future, the discussion raises prominent questions for the discipline.

Perceived strategic value-based adoption of Big Data Analytics in emerging economy

2017 ◽  
Vol 30 (3) ◽  
pp. 354-382 ◽  
Author(s):  
Surabhi Verma ◽  
Som Sekhar Bhattacharyya
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Service Providers ◽  
Big Data Analytics ◽  
It Adoption ◽  
Content Type ◽  
Factors Affecting ◽  
Strategic Value ◽  
Toe Framework ◽  
Adoption Model

Purpose The purpose of this paper is to provide an insight about factors affecting Big Data Analytics (BDA) utilization and adoption in Indian firms. Research studies have so far focused on BDA adoption in developed economies. This study examines the factors that influence BDA usage and adoption in the context of emerging economies. Design/methodology/approach This study proposed a theoretical model of factors influencing BDA utilization and adoption. Two independent research streams – first, the top managers’ perceived strategic value (PSV) in BDA and second, the factors that influence the adoption of BDA theoretically – have been integrated with the technology-organization-environment (TOE) framework. In the BDA context, there was a theoretical necessity to identify the driver and barriers of BDA from the TOE framework on PSV and adoption of BDA. A qualitative exploratory study using face-to-face semi-structured interviews was carried out to collect data from 22 different enterprises and service providers in India. India was selected as the context as it is one of the fastest growing large economies of the world with huge potential of BDA to improve the business landscape. Findings The results showed that the major reason behind BDA non-adoption is that the organizations did not realize the strategic value (SV) of BDA, and they were not ready to make the changes because of technological, organizational and environmental difficulties. The findings corroborate previous results about significant factors affecting IT adoption and implementation and provide new and interesting insights. The main factors identified as playing a significant role in organizations’ adoption of BDA were SV of BDA, complexity, compatibility, IT assets, top management support, organization data environment, perceived costs, external pressure and industry type. Research limitations/implications The main limitation related to this study is the difficulty in generalizing the findings to a larger population of enterprises. To overcome this, a statistical survey has been planned to be conducted in the future. Practical implications The BDA adoption model in this study will have both managerial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BDA adoption in developing counties, in terms of formulating better strategies for BDA adoption. For managers, using the research model of this study could assist in increasing their understanding of why some organizations choose to adopt BDA, while similar ones facing similar conditions do not. Also, the understanding of the strategic utilization of BDA in different business processes may improve the adoption of BDA in organizations. Originality/value This paper contributes in exploring and enhancing the understanding of the factors affecting the utilization and adoption of BDA in organizations from an Indian perspective. This study is an attempt to develop and explore a BDA adoption model by the fusion of PSV and TOE framework. The effect of the three contexts of this framework (technological, organizational and environmental) on the strategic utilization of BDA has been studied for the first time.

Big data analytics in digital platforms: how do financial service providers customise supply chain finance?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hua Song ◽  
Mengyin Li ◽  
Kangkang Yu
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Service Providers ◽  
Big Data Analytics ◽  
Financial Service ◽  
Content Type ◽  
Digital Platforms ◽  
Supply Chain Finance ◽  
Potential Risks

PurposeThis study examines the role of financial service providers (FSPs) in assessing the supply chain credit of small and medium-sized enterprises (SMEs) and how they help SMEs obtain supply chain finance (SCF) through an established digital platform using big data analytics (BDA).Design/methodology/approachThis study conducted data mining analysis on the archival data of China's FSPs in the mobile production industry from 2015 to 2018, using neural networks in the first stage and multiple regression in the second stage.FindingsThe findings suggest that digital platforms sponsored by FSPs have a discriminative effect based on implicit BDA on identifying the quality and potential risks of borrowers. The results also show that tailored information utilised by FSPs has a supportive effect based on explicit BDA in helping SMEs obtain financing.Originality/valueThis study contributes to the emergent research on BDA in supply chain management by extending the contextual research on information signalling and platform theory in SCF. Furthermore, it examines the distinctive financing decision models of FSPs and provides a solution that addresses the information deficiency and overload of both lenders and borrowers and plays a certain reference role in alleviating the financing problems of SMEs.

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