scholarly journals The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance

2021 ◽  
Vol 165 ◽  
pp. 120557
Author(s):  
Smail Benzidia ◽  
Naouel Makaoui ◽  
Omar Bentahar
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Environmental Performance ◽  
Data Analytics ◽  
Process Integration ◽  
Big Data Analytics ◽  
Green Supply Chain ◽  
Chain Process ◽  
Supply Chain Process ◽  
The Impact

scholarly journals A comparative study to measure the impact of big data analytics on service supply chain process

2018 ◽  
Vol 8 (4) ◽  
pp. 356-365
Author(s):  
Bhawna Singh ◽  
Rushina i Singh ◽  
Suman Shokeen
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Null Hypothesis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Strategic Decision ◽  
Chain Process ◽  
Service Supply Chain ◽  
Supply Chain Process ◽  
The Impact

Big data analytics is becoming a key to success for many organization as it extracts the productive value from a huge amount of raw data. This data helps in strategic decision making for continuous process improvements and advancement. This study also focusses on the impact of big data analytics on one of the most important process of an organization which is service supply chain process. ERP (Enterprise Resource Planning) is the tool which is used for big data analytics and based on that study was initiated for pre- and post-implementation of ERP for the years 2015 and 2017 respectively. Null hypothesis and H1 hypothesis are formed. Then, ten major factors have been identified which act as performance indicators for service supply chain process. Based on these factors data has been collected and analyzed. After obtaining the values one-way ANNOVA technique has been applied for testing of hypothesis. It has been observed that F calculated value comes out to be very high in comparison to the F table value which rejects the null hypothesis and proves that Big data analytics has an impact on service supply process.

The Impact of Innovative Technologies on the Insurance Market Operation

2019 ◽  
Vol 2 (99) ◽  
pp. 6-22 ◽  
Author(s):  
Jerzy Łańcucki
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Present Article ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insurance Market ◽  
Innovative Technologies ◽  
Market Operation ◽  
Innovative Products ◽  
The Impact

Innovative technologies are being increasingly used on the insurance market, as well. However, while analyzing and assessing their impact on the market what must be taken into account are not only distributors of insurance services, but also institutions performing regulatory and supervisory tasks, and perhaps above all, customers and consumers. The present article seeks to analyze the conditions, benefits and barriers associated with the application of innovative technologies with special regard to big data analytics, artificial intelligence and the possibility of absorption of innovative products and services by their customers.It has been emphasized that while evaluating the suitability of innovative technologies for the insurance market it is vital to confront product offers provided by insurance undertakings and intermediaries with the expectations, needs and skills of the purchasers of these products.

The Impact of Smart Technologies on theManagement and Strategic Control: A Structured Literature Review

2021 ◽  
pp. 11-30
Author(s):  
Rosa Lombardi ◽  
Raffaele Trequattrini ◽  
Federico Schimperna ◽  
Myriam Cano-Rubio
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Literature Review ◽  
Business Intelligence ◽  
Data Analytics ◽  
Business Management ◽  
Big Data Analytics ◽  
Strategic Control ◽  
Smart Technologies ◽  
The Impact

This research proposes a systematic literature review (SLR) of the application of big data, analytics, business intelligence, and artificial intelligence to company management and strategic control. Thus, this paper attempts to answer the following research questions: 1) How is the literature on the application of big data, analytics, business intelligence, and artificial intelligence to management and strategic control developed in the business, management and accounting fields? 2) On which aspects of this application does the literature focus? 3) What are the implications that arise for companies? In this paper, we used a longitudinal study of research documents in the form of last-decade literature collected from Scopus database as the leading source for the international scenario. After, we selected business, management, and accounting areas, and screened the titles and abstracts of the research documents, we based the final result on 60 scientific documents as sources relevant to the aim of this SLR. The findings highlight four main topic clusters. We specifically explain smart technologies' usefulness for each analyzed business function, and, while adopting a critical perspective, we point out the interesting current streams of research resulting from the application of new sources of technology. We conclude by proposing valuable insights gleaned from the study. Thus, our results are useful for both the academic and the professional community.

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.

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.

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.

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