scholarly journals Stakeholder Causal Scope Analysis–Centered Big Data Management for Sustainable Tourism

2020 ◽  
pp. 109634802097854
Author(s):  
S. M. Riad Shams ◽  
Demetris Vrontis ◽  
Michael Christofi

Sustainability concerns in the tourism industry are underresearched, although both stakeholder relationship management and data analytics knowledge streams have implications to underpin sustainability research and practice. Scholars argue that we have limited knowledge of the potential for analyzing diverse stakeholder relationship management issues from different tourism-related socioeconomic and ecological settings to fully exploit stakeholders’ contribution to tourism sustainability. Big data, as a research field, could offer varied cross-disciplinary implications for sustainable development. However, data analytics research is concerned with simplifying the overall management structure of data. In this context, at the intersection of these three research streams (tourism, stakeholder relationship management, and big data), this research note offers insights into how analyzing stakeholder causal scopes would be instrumental in simplifying tourism data management structure to support sustainability research and practice in the tourism industry.

2019 ◽  
Vol 57 (8) ◽  
pp. 2113-2123 ◽  
Author(s):  
S.M. Riad Shams ◽  
Ludovico Solima

PurposeBig data management research and practice, however, have received enormous interest from academia and industry; the extant literature demonstrates that the authors have limited understanding and challenges in this knowledge-stream to fully capitalize with its potentials. One of the contemporary challenges is to accurately verify data veracity, and developing value from the verified data for an organization and its stakeholders. Consequently, the purpose of this paper is to develop insights on how the authors could strategically deal with the contemporary challenges in strategic management of big data, related to data veracity and data value.Design/methodology/approachThe inductive–constructivist approach is followed to develop insights at the intersection of dynamic capabilities theory and stakeholder relationship management concept, in order to strategically deal with the contemporary challenges in big data management, related to data veracity and data value.FindingsAt the intersection of dynamic capabilities theory and stakeholder relationship management concept, an implication is acknowledged, which has research and practical significance to strategically verify data source, its veracity and value. Following this implication, a framework of a data incubator is proposed to proactively develop insights on veracity and value of data. Empirical insights are also presented in this study to support this initial framework.Practical implicationsFor future research in strategic management of big data, academics will have contextual understanding on the particular interconnected and interdependent attributes from dynamic capabilities and stakeholder relationship management research streams to further enhance the understanding on big data management. For practice, these insights will be useful for executives to focus on specific attributes of the proposed data incubator to confirm data veracity and develop insights on how to design, deliver and evaluate stakeholder value based on the verified data.Originality/valueFollowing a synthesis at the intersection of dynamic capabilities theory and stakeholder relationship management concept, this study introduces a data incubator to meaningfully deal with the big data management challenges, related to veracity and value of data.


2021 ◽  
Vol 29 (1) ◽  
pp. 177-185
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
S. Baskar ◽  
Ching-Hsien Hsu ◽  
Seifedine Nimer Kadry ◽  
...  

Author(s):  
Shweta Kumari

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.


Author(s):  
P. Venkateswara Rao ◽  
A. Ramamohan Reddy ◽  
V. Sucharita

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.


Author(s):  
Muhammad Adeel Mannan ◽  
Saboohi Mehmood ◽  
Muhammad Shafiq ◽  
Aqeel-ur-Rehman

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.


2020 ◽  
Vol 37 (4) ◽  
pp. 1-5
Author(s):  
Nove E. Variant Anna ◽  
Endang Fitriyah Mannan

Purpose The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation. Design/methodology/approach The Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019). Findings It can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well. Originality/value This paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.


2019 ◽  
Vol 8 (3) ◽  
pp. 1572-1580

Tourism is one of the most important sectors contributing towards the economic growth of India. Big data analytics in the recent times is being applied in the tourism sector for the activities like tourism demand forecasting, prediction of interests of tourists’, identification of tourist attraction elements and behavioural patterns. The major objective of this study is to demonstrate how big data analytics could be applied in predicting the travel behaviour of International and Domestic tourists. The significance of machine learning algorithms and techniques in processing the big data is also important. Thus, the combination of machine learning and big data is the state-of-art method which has been acclaimed internationally. While big data analytics and its application with respect to the tourism industry has attracted few researchers interest in the present times, there have been not much researches on this area of study particularly with respect to the scenario of India. This study intends to describe how big data analytics could be used in forecasting Indian tourists travel behaviour. To add much value to the research this study intends to categorize on what grounds the tourists chose domestic tourism and on what grounds they chose international tourism. The online datasets on places reviews from cities namely Chicago, Beijing, New York, Dubai, San Francisco, London, New Delhi and Shanghai have been gathered and an associative rule mining based algorithm has been applied on the data set in order to attain the objectives of the study


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