scholarly journals Factors Influencing Intent to Adopt Big Data Analytics in Malaysian Government Agencies

2021 ◽  
Vol 22 (3) ◽  
pp. 1315-1345
Author(s):  
Mad Khir Johari Abdullah Sani ◽  
Muhamad Khairulnizam Zaini ◽  
Noor Zaidi Sahid ◽  
Norshila Shaifuddin ◽  
Tamara Adriani Salim ◽  
...  
Keyword(s):  
Big Data ◽  
Technology Acceptance ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Trust Model ◽  
Government Agencies ◽  
Public Agencies ◽  
Initial Trust ◽  
Malaysian Government

In Big Data Analytics (BDA), many government agencies directly raised their ICT expenditure in their effort to understand the attitude of the users towards new technologies. This research is intended to analyze factors affecting IT practitioners’ behavioral intentions in adopting (BDA) using a combination of multiple technology acceptance models. The synergistic three IS theory strengths: (1) Task Technology Fit (TTF), (2) Unified Technology Acceptance and Utilization Theory (UTAUT), and the (3) Initial Trust Model (ITM). The concept was validated in Malaysian government agencies, one of the highly dependent BDA promoters and initiators. 186 respondents in the Information Management departments of public agencies were recruited as part of the rigorous methodology to gather rich data. Partial least squares were analyzed by the structural models (PLS). The two key factors determine behavioral intention to adopt BDA in government agencies. Firstly, the assumption that the technology is going to produce great results raises the expectation of performance. Technological fit was the second determinant factor. Initial trust, on the other hand, was found to be adversely related to the BDA intention. Implicitly, the proposed model would be useful to IT officers in public agencies in making investment choices and designing non-adopter-friendly outreach strategies because they have more barriers to acceptance than adopters and lead adopters in the reward ladder. All public agencies will benefit from the findings of this study in gaining awareness of BDA application and fostering psychological empowerment of employees to adopt this revolutionary approach. The article outlines how dynamic TTF, UTAUT and ITM are for researchers to integrate in their emerging decision support framework for the study of new technology adoption.

Research Challenges in Big Data Analytics

2017 ◽  
pp. 83-99
Author(s):  
Sivamathi Chokkalingam ◽  
Vijayarani S.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
New Technologies ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Data Types ◽  
Customer Preferences ◽  
Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

Big Data and Big Data Analytics

2021 ◽  
pp. 1-19
Author(s):  
Sam Goundar ◽  
Akashdeep Bhardwaj ◽  
Shavindar Singh ◽  
Mandeep Singh ◽  
Gururaj H. L.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
New Technologies ◽  
Big Data Analytics ◽  
Small Island ◽  
Island State ◽  
Diverse Data ◽  
Analytical Tools ◽  
Small Island State

Big data is emerging, and the latest developments in technology have spawned enormous amounts of data. The traditional databases lack the capabilities to handle this diverse data and thus has led to the employment of new technologies, methods, and tools. This research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges. Through a research drawing on survey questionnaires, observation of the business processes, interviews and secondary research methods, the organizations, and companies in a small island state are identified to survey which of them use analytical tools to handle big data and the benefits it proposes to these businesses. Organizations and companies that do not use these tools were also surveyed and reasons were outlined as to why these organizations hesitate to utilize such tools.

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 SURVEY ON ACCURACY OF PREDICTIVE BIG DATA ANALYTICS IN HEALTHCARE

2019 ◽  
Vol 01 (02) ◽  
pp. 77-86
Author(s):  
Smys S
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Research Area ◽  
Care Industry ◽  
The Core ◽  
The Cost

The failures in the most of research area, identified that the lack of details about the actionable and the valuable data that conceived actual solutions were the core of the crisis, this was very true in case of the health care industry where even the early diagnoses of a chronic disease could not save a person’s life. This because of the impossibility in the prediction of the individual’s outcomes in the entire population. The evolving new technologies have changed this scenario leveraging the mobile devices and the internet services such as the sensor network and the smart monitors, enhancing the practical healthcare using the predictive modeling acquiring a deeper individual measures. This affords the researches to go through the huge set of data and identify the patterns along with the trends and delivering solutions improvising the medical care, minimizing the cost and he regulating the health admittance, ensuring the safety of human lives. The paper provides the survey on the predictive big data analysis and accuracy it provides in the health care system.

scholarly journals Big Data and IoT Opportunities for Small and Medium-Sized Enterprises (SMEs)

2019 ◽  
pp. 77-88
Author(s):  
Siti Aishah Mohd Selamat ◽  
Simant Prakoonwit ◽  
Reza Sahandi ◽  
Wajid Khan
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
21St Century ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Technological Advancement ◽  
New Wave ◽  
Advanced Technologies ◽  
Data Explosion

The advancement of technology and emergence of internet of things (IoT) has exponentially caused a data explosion in the 21st century era. As such, the arrival of IoT is set to revolutionize the development of the small and medium-sized enterprise (SME) organizations by shaping it into a more universal and integrated ecosystem. Despite evidential studies of the potential of advanced technologies for businesses, the SMEs are apprehensive towards new technologies adoption such as big data analytics and IoT. Therefore, the aim of this chapter is to provide a holistic study of big data and IoT opportunities, challenges, and applications within the SMEs context. The authors hope that the outcome of this study would provide foundational information on how the SMEs can partake with the new wave technological advancement and in turn, spurring more SMEs for adoption.

scholarly journals Big data analytics and sustainable textile manufacturing

2020 ◽  
Vol 58 (8) ◽  
pp. 1699-1714 ◽  
Author(s):  
Dieu Hack-Polay ◽  
Mahfuzur Rahman ◽  
Md Morsaline Billah ◽  
Hesham Z. Al-Sabbahy
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Environmental Sustainability ◽  
Textile Industry ◽  
Data Analytics ◽  
New Technologies ◽  
Developing World ◽  
Big Data Analytics ◽  
Content Type ◽  
Member Exchange

PurposeThe purpose of this article is to discuss issues associated with the application big data analytics for decision-making about the introduction of new technologies in the textile industry in the developing world.Design/methodology/approachThe leader–member exchange theoretical framework to consider the nature of the relationships between owners and followers to identify the potential issues that affect decision-making was used. However, decisions to adopt such environmentally friendly biotechnologies are hampered by the lack of awareness amongst owners, intergenerational conflict and cultural impediments.FindingsThe article found that the limited use of this valuable technological resource is linked to several factors, mainly cultural, generational and educational factors. The article exposes two key new technologies that could help the industry reduce its carbon footprint.Originality/valueThe study suggests more awareness raising amongst plant owners and greater empowerment of new generations in decision-making in the industry. This study, therefore, bears significant implications for environmental sustainability in the developing world where the textile industry is one of the major polluting industries affecting water quality and human health.

Digital Investigation of Cybercrimes Based on Big Data Analytics Using Deep Learning

2018 ◽  
pp. 79-101 ◽  
Author(s):  
Ezz El-Din Hemdan ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Digital Forensics ◽  
New Technologies ◽  
Early Stage ◽  
Big Data Analytics ◽  
Learning Techniques ◽  
Timely Fashion ◽  
Digital Investigation

Big Data Analytics has become an important paradigm that can help digital investigators to investigate cybercrimes as well as provide solutions to malware and threat prediction, detection and prevention at an early stage. Big Data Analytics techniques can use to analysis enormous amount of generated data from new technologies such as Social Networks, Cloud Computing and Internet of Things to understand the committed crimes in addition to predict the new coming severe attacks and crimes in the future. This chapter introduce principles of Digital Forensics and Big Data as well as exploring Big Data Analytics and Deep Learning benefits and advantages that can help the digital investigators to develop and propose new techniques and methods based on Big Data Analytics using Deep Learning techniques that can be adapted to the unique context of Digital Forensics as well as support performing digital investigation process in forensically sound and timely fashion manner.

scholarly journals Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Data Analytics ◽  
Business Performance ◽  
Big Data Analytics ◽  
Integrated Model ◽  
Task Technology Fit ◽  
Acceptance Model ◽  
And Task

The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of Big Data Analytics and its effect on business performance. A questionnaire was used to collect data from 280 companies in CPG & Retail, Healthcare, Banking and Telecom in India. The data were analysed using exploratory and confirmatory factor analyses. Further, structural equation modelling was used to test the proposed model. The findings show that the research model for integrating the TAM for adoption and TTF model for utility provides a more comprehensive understanding of Big Data Analytics usage. The study identified task technology fit , individual technology fit, organizational data fit, organizational process fit, and business strategy fit as Tidd important variables for affecting Big Data Analytics usage using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Competitive fit and partner support/customer fit were also found to be directly affecting Big Data Analytics usage, which in turn has significant influence on business performance. The model explained 71.4 percent of Business performance. The integrated model may be used as a guideline to ensure a positive outcome of Big Data Analytics usage in organizations. This study combined both the key ideas of TAM and TTF to show that they were necessary in predicting Big Data Analytics usage and business performance.

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