scholarly journals Evaluating the system of factors influencing the investment decision under postindustrial transformations

2020 ◽  
Vol 2020 (3) ◽  
pp. 128-143
Author(s):  
Iuliia KORNIEIEVA ◽  
Keyword(s):  
Big Data ◽  
Public Investment ◽  
Investment Decision ◽  
Big Data Analytics ◽  
State Level ◽  
Developed Countries ◽  
Investment Activity ◽  
Risk Assessment Models ◽  
Wide Range ◽  
The Impact

The article analyzes the prerequisites for systematic use of Big Data sources by government agencies as a tool for forecasting systemic financial risks during investment decision processes at the state level as well as for the formation of public investment policy. The author made a comprehensive assessment of the system of factors that influence the investment decision-making process in terms of post-industrial transformations. The author proposes to solve the problem of forming investment activity information risks based on empowerment of big data analytics. The proposed model assesses the institutional framework by analyzing a wide range of factors such as the level of shadow economy, corruption and economic freedom, including government integrity, property rights, investment freedom, that influence the process of investment decision. Based on indices analysis expands the limited capabilities of risk assessment models of financial instruments based on market data, that reflect the components of expectations, covering the behavioral factor - speculative behavior. Cluster assessment of investment inflows to Ukraine was conducted based on econometric modeling using VAR analysis revealed no significant impact of indicators that reflect the presence of institutional barriers for investors concerning FDI from offshore countries (Cyprus, British Virgin Islands), the impact was limited to an average of 5%. Along with economic factors, the determinants of FDI inflows from developed countries (Germany, Great Britain, United States of America, Austria, the Netherlands) were institutional ones influencing the formation of compliance risks, the impact of which ranged within 30%.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

Big Data and Sustainable Consumption: A Review and Research Agenda

2021 ◽  
pp. 097226292110225
Author(s):  
Shobhana Chandra ◽  
Sanjeev Verma
Keyword(s):  
Big Data ◽  
Consumer Behaviour ◽  
Business Models ◽  
Sustainable Consumption ◽  
Big Data Analytics ◽  
Consumption Behaviour ◽  
Consumer Experiences ◽  
Systematic Understanding ◽  
The Impact

Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

The EU as an “enforcement patchwork”: the impact of national enforcement for compliance with EU water law in Spain and Britain

2013 ◽  
Vol 34 (2) ◽  
pp. 331-353 ◽  
Author(s):  
Mónica García Quesada
Keyword(s):  
Waste Water Treatment ◽  
National Level ◽  
State Level ◽  
Detailed Examination ◽  
Deterrent Effect ◽  
Eu Law ◽  
Wide Range ◽  
The Uk ◽  
The Impact ◽  
The Eu

AbstractFailures of compliance with European Union (EU) directives have revealed the EU as a political system capable of enacting laws in a wide range of different policy areas, but facing difficulties to ensure their actual implementation. Although the EU relies on national enforcement agencies to ensure compliance with the EU legislation, there is scarce analysis of the differential deterrent effect of national enforcement in EU law compliance. This article examines the enforcement of an EU water directive, the Urban Waste Water Treatment Directive, in Spain and the UK. It focuses on the existing national sanctions for disciplining actors in charge of complying with EU requirements, and on the actual use of punitive sanctions. The analysis shows that a more comprehensive and active disciplinary regime at the national level contributes to explain a higher degree of compliance with EU law. The article calls for a detailed examination of the national administrative and criminal sanction system for a more comprehensive understanding of the incentives and disincentives to comply with EU law at the national state level.

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

2021 ◽  
Vol 83 (4) ◽  
pp. 100-111
Author(s):  
Ahmad Anwar Zainuddin ◽  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Physical World ◽  
Future Directions ◽  
Wide Range ◽  
Time Utilization ◽  
Relationship Of ◽  
The Internet Of Things

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

scholarly journals A survey and analysis of big data management based on computational methodologies

2020 ◽  
Vol 6 (20) ◽  
pp. 48-53
Author(s):  
Shweta Kumari
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Enterprise ◽  
Enormous Amount ◽  
Current Scenario ◽  
Data Points ◽  
The Impact ◽  
Day By Day

n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

scholarly journals IMPACT OF DISRUPTIVE TECHNOLOGY ON THE PERFORMANCE OF INSURANCE FIRMS IN KENYA

2018 ◽  
Vol 3 (1) ◽  
pp. 72
Author(s):  
Ezekiel Owuor
Keyword(s):  
Big Data ◽  
Financial Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Disruptive Technology ◽  
Regulatory Body ◽  
Disruptive Technologies ◽  
Insurance Firms ◽  
Pdf Format ◽  
The Impact

Purpose:  The purpose of this paper was to explore the impact of disruptive technology on the performance of insurance firms in Kenya.Methods: The study utilized desktop literature review and focused on previously published journals in PDF format that address technology and the performance of insurance firms.  A total of 13 journals was found relating to technology and the performance of insurance firms. The study utilized a sample of 12 journals which were randomly selected from a list of published journals in PDF format relating to disruptive technology and performance of insurance firms. The theories underpinning of the study entailed Christensen's Theory of Disruptive Technology, the Diffusion of Innovation Theory and Schumpeterian Theory of Creative Destruction.Results: The review of literature revealed that various aspects of disruptive technology have a significant impact on organizational performance. The review showed that mobile phone technology has a significant influence and explains to a large extent the growth of micro insurance in Kenya. It was also found that the increase in industrial convergence, technological innovation and social digital trends increases the financial performance of financial institutions including insurance firms. The study also established that there is a strong and positive relationship between insurance innovation strategies and a firm’s performance. In addition, it was found out that real-time business evaluation through big data analytics boosts overall performance and profitability, thus thrusting the organization further into the growth cycle.Unique Contribution to theory, practice and policy: The leadership and management of insurance companies should put greater emphasis on the adoption of disruptive technologies to improve on both financial and non-financial performance as well as their competitiveness within the industry. These include Big Data, Analytics, Artificial Intelligence Systems, Cloud Computing and Digital Currency Technologies. Processes in the organizations should be refined to ensure that they are efficient and effective as this serves to increase market share and to reduce on operational costs. Moreover, explorations in disruptive technology should continue in the insurance industry as these would play a significant role in ensuring that efficiencies and effectiveness of business processes are achieved. The Insurance Regulatory Authority (IRA) should also develop policies that encourage innovation and the adoption of technology. The authority whilst exercising due diligence in its mandate to protect consumers should ensure policies do not stifle the growth and creativity of insurers. The regulatory body should also strive to create a favourable environment for the adoption of disruptive technologies.

Sign in / Sign up

Export Citation Format

Share Document