Impact of Organizational Factors on Big Data Analytics Adoptions in U.S. Public Sector Organizations

2021 ◽  
Author(s):  
Gregory Smith ◽  
Sondria Miller
Keyword(s):  
Big Data ◽  
Public Sector ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Organizational Factors ◽  
Public Sector Organizations

scholarly journals Big Data and Algorithms in the Public Sector and Their Impact on the Transparency of Decision-Making

2018 ◽  
Vol 331 ◽  
pp. 301-311
Author(s):  
Gergely László Szὄke
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Public Sector ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Legal Framework ◽  
New Wave ◽  
Short Introduction ◽  
The Public ◽  
Potential Use

Big Data is clearly one of the most used buzzwords nowadays, but it really seems that the phenomenon of Big Data will have a huge effect on many different fields, and may be regarded as the new wave of the information revolution started in the 60s of the last century. The potential of exploiting Big Data promises significant benefits (and also new challenges) both in the private and the public sector – this essay will focus on this latter. After a short introduction about Big Data, this paper will first sum up the potential use of Big Data analytics in the public sector. Then I will focus on a specific issue within this scope, namely, how the use of Big Data and algorithm-based decision-making may affect transparency and access to these data. I will focus on the question why the transparency of the algorithms is raised at all, and what the current legal framework for the potential accessibility to them is.

scholarly journals Big data analytics and international market selection: An exploratory study

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Jonathan Calof ◽  
Wilma Viviers
Keyword(s):  
Big Data ◽  
Decision Maker ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Organizational Factors ◽  
International Market ◽  
Decision Makers ◽  
Market Selection ◽  
Selection Decisions ◽  
The Impact

A great deal of information is available on international trade flows and potentialmarkets. Yet many exporters do not know how to identify, with adequate precision, thosemarkets that hold the greatest potential. Even if they have access to relevant information, thesheer volume of information often makes the analytical process complex, time-consuming andcostly. An additional challenge is that many exporters lack an appropriate decision-makingmethodology, which would enable them to adopt a systematic approach to choosing foreignmarkets. In this regard, big-data analytics can play a valuable role. This paper reports on thefirst two phases of a study aimed at exploring the impact of big-data analytics on internationalmarket selection decisions. The specific big-data analytics system used in the study was theTRADE-DSM (Decision Support Model) which, by screening large quantities of marketinformation obtained from a range of sources identifies optimal product‒market combinationsfor a country, industry sector or company. Interviews conducted with TRADE-DSM users aswell as decision-makers found that big-data analytics (using the TRADE-DSM model) didimpact international market-decision. A case study reported on in this paper noted thatTRADE-DSM was a very important information source used for making the company’sinternational market selection decision. Other interviewees reported that TRADE-DSMidentified countries (that were eventually selected) that the decision-makers had not previouslyconsidered. The degree of acceptance of the TRADE-DSM results appeared to be influenced byTRADE-DSM user factors (for example their relationship with the decision-maker andknowledge of the organization), decision-maker factors (for example their experience andknowledge making international market selection decisions) and organizational factors (forexample senior managements’ commitment to big data and analytics). Drawing on the insightsgained in the study, we developed a multi-phase, big-data analytics model for internationalmarket selection.

ESTABLISHMENT OF BIG DATA ANALYTICS APPLICATION MODEL FOR MALAYSIAN PUBLIC SECTOR: AN EXPERT VALIDATION

EDPACS ◽  
2021 ◽  
pp. 1-20
Author(s):  
Zam Zarina Abdul Jabar ◽  
Muslihah Wook ◽  
Omar Zakaria ◽  
Suzaimah Ramli ◽  
Noor Afiza Mat Razali
Keyword(s):  
Big Data ◽  
Public Sector ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Application Model ◽  
Expert Validation

The influence of the practices of big data analytics applications on bank performance: filed study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hani Al-Dmour ◽  
Nour Saad ◽  
Eatedal Basheer Amin ◽  
Rand Al-Dmour ◽  
Ahmed Al-Dmour
Keyword(s):  
Big Data ◽  
Commercial Banks ◽  
Data Analytics ◽  
Middle Managers ◽  
Big Data Analytics ◽  
Organizational Factors ◽  
Positive Influence ◽  
Bank Performance ◽  
Content Type ◽  
The Impact

Purpose This paper aims to examine factors influencing the practices of big data analytics applications by commercial banks operating in Jordan and their bank performance. Design/methodology/approach A conceptual framework was developed in this regard based on a comprehensive literature review and the Technology–Environment–Organization (TOE) model. A quantitative approach was used, and the data was collected from 235 commercial banks’ senior and middle managers (IT, financial and marketers) using both online and paper-based questionnaires. Findings The results showed that the extent of the practices of big data analytics applications by commercial banks operating in Jordan is considered to be moderate (i.e. 60%). The results indicated that 61% of the variation on the practices of big data analytics applications by commercial banks could be predicated by TOE model. The organizational factors were found the most important predictors. The results also provide empirical evidence that the extent of practices of big data analytics applications has a positive influence on the bank performance. In the final section, research implications and future directions are presented. Originality/value This paper contributes to theory by filling a gap in the literature regarding the extent of the practices of big data analytics applications by commercial banks operating in developing countries, such as Jordan. It empirically examines the impact of the practices of big data analytics applications on bank performance.

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