scholarly journals Doing social big data analytics: A reflection on research question, data format, and statistical test-Convergent aspects

2016 ◽  
Vol 14 (12) ◽  
pp. 591-597
Author(s):  
Han-Woo Park ◽  
Kyoung-ho Choi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Research Question ◽  
Big Data Analytics ◽  
Statistical Test ◽  
Data Format ◽  
Social Big Data

scholarly journals A Preliminary Systematic Performanceon Critical Success Factors Categories for Big Data Analytics

2019 ◽  
Vol 9 (1) ◽  
pp. 2320-2324 ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Critical Success Factors ◽  
Success Factors ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Successful Implementation ◽  
Critical Success ◽  
Data Driven Decisions

Big Data could be used in any industry to make effective data-driven decisions. The successful implementation of Big Data projects requires a combination of innovative technological, organizational, and processing approaches. Over the last decade, the research on Critical Success Factors (CSFs) within Big Data has developed rapidly but the number of available publications is still at a low level. Developing an understandingof the Critical Success Factors (CSFs) and their categoriesare essential to support management in making effective data-driven decisions which could increase their returns on investments.There islimited research conducted on the Critical Success Factors (CSFs) of Big DataAnalytics (BDA) development and implementation.This paper aims to provide more understanding about the availableCritical Success Factors (CSFs) categoriesfor Big Data Analytics implementation and answer the research question (RQ) “What are the existing categories of Critical Success Factors for Big Data Analytics”.Based on a preliminary Systematic Literature Review (SLR) for the available publications related to Big Data CSFs and their categories in the last twelve years (2007-2019),this paper identifiesfive categoriesfor Big Data AnalyticsCritical Success Factors(CSFs), namelyOrganization, People, Technology, Data Management, and Governance categories.

scholarly journals Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 286-313
Author(s):  
Ahmed M. Shahat Osman ◽  
Ahmed Elragal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Smart Cities ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Use Case ◽  
Data Driven Decision Making ◽  
Domain Independent

Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs' decision-making. This paper's main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC's healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC.

scholarly journals Management Transition to Big Data Analytics: Exploratory Study on Airline Industry

2019 ◽  
Vol 12 (10) ◽  
pp. 48
Author(s):  
Filomena Izzo
Keyword(s):  
Big Data ◽  
Airline Industry ◽  
Exploratory Study ◽  
Data Analytics ◽  
Industrial Development ◽  
Research Question ◽  
Big Data Analytics ◽  
Management Model ◽  
Transition Management ◽  
Strategic Role

This paper is addressed to the managers in field of Management Transition to Big Data Analytics, and those who guide and teach them. The research question is following: what is the Transition Management Model to Big Data Analytics in Airline Industry? The reason to choice Airline Industry is attributed to the strategic role that this sector has for the industrial development of a Country. There are few studies on Management Transition to Big Data Analytics and more less in Airline Industry, the present study attempts to contribute to the discussion on this topic.

scholarly journals Does big data analytics influence frontline employees in services marketing?

2017 ◽  
Vol 23 (3) ◽  
pp. 623-644 ◽  
Author(s):  
Saradhi Motamarri ◽  
Shahriar Akter ◽  
Venkat Yanamandram
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Service Providers ◽  
Research Question ◽  
Big Data Analytics ◽  
Frontline Employees ◽  
Skill Levels ◽  
Content Type ◽  
Knowledge Intensive ◽  
The Impact

Purpose Big data analytics (BDA) helps service providers with customer insights and competitive information. It also empowers customers with insights about the relative merits of competing services. The purpose of this paper is to address the research question, “How does big data analytics enable frontline employees (FLEs) in effective service delivery?” Design/methodology/approach The research develops schemas to visualise service contexts that potentially benefit from BDA, based on the literature drawn from BDA and FLEs streams. Findings The business drivers for BDA and its level of maturity vary across firms. The primary thrust for BDA is to gain customer insights, resource optimisation and efficient operations. Innovative FLEs operating in knowledge intensive and customisable settings may realise greater value co-creation. Practical implications There exists a considerable knowledge gap in enabling the FLEs with BDA tools. Managers need to train, orient and empower FLEs to collaborate and create value with customer interactions. Service-dominant logic posits that skill asymmetry is the reason for service. So, providers need to enhance skill levels of FLEs continually. Providers also need to focus on market sensing and customer linking abilities of FLEs. Social implications Both firms and customers need to be aware of privacy and ethical concerns associated with BDA. Originality/value Knitting the BDA and FLEs research streams, the paper analyses the impact of BDA on service. The research by developing service typology portrays its interplay with the typologies of FLEs and BDA. The framework portrays the service contexts in which BD has major impact. Looking further into the future, the discussion raises prominent questions for the discipline.

scholarly journals A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics

2020 ◽  
Vol 2 (1) ◽  
pp. 47 ◽  
Author(s):  
Justian Knobbout ◽  
Esther Van der Stappen
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Learning Analytics ◽  
Data Analytics ◽  
Large Scale ◽  
Research Question ◽  
Big Data Analytics ◽  
Organizational Capabilities ◽  
Business Analytics ◽  
Specific Element

Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “<em>What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?”</em> Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – <em>Data, Management, People, Technology</em>, and <em>Privacy &amp; Ethics.</em> Capabilities presently absent from existing learning analytics frameworks concern <em>sourcing and integration, market, knowledge, training, automation, </em>and <em>connectivity</em>. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.

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