scholarly journals GrandBase: generating actionable knowledge from Big Data

2017 ◽  
Vol 1 (2) ◽  
pp. 105-126 ◽  
Author(s):  
Xiu Susie Fang ◽  
Quan Z. Sheng ◽  
Xianzhi Wang ◽  
Anne H.H. Ngu ◽  
Yihong Zhang
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Empirical Studies ◽  
Knowledge Extraction ◽  
Data Sources ◽  
Future Research ◽  
Content Type ◽  
Actionable Knowledge ◽  
True Value ◽  
Truth Discovery

Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Big data analytics: transforming data to action

2017 ◽  
Vol 23 (3) ◽  
pp. 703-720 ◽  
Author(s):  
Daniel Bumblauskas ◽  
Herb Nold ◽  
Paul Bumblauskas ◽  
Amy Igou
Keyword(s):  
Big Data ◽  
Business Process ◽  
Data Analytics ◽  
Business Process Management ◽  
Large Scale ◽  
Process Management ◽  
Big Data Analytics ◽  
Model Framework ◽  
Content Type ◽  
Actionable Knowledge

Purpose The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the organization. A case utilizing a dashboard provides a practical application for analysis of big data. Design/methodology/approach The model can be used both by scholars and practitioners in business process management. This paper builds and extends theories in the discipline, specifically related to taking action using big data analytics with tools such as dashboards. Findings The authors’ model made use of industry experience and network resources to gain valuable insights into effective business process management related to big data analytics. Cases have been provided to highlight the use of dashboards as a visual tool within the conceptual framework. Practical implications The literature review cites articles that have used big data analytics in practice. The transitions required to reach the actionable knowledge state and dashboard visualization tools can all be deployed by practitioners. A specific case example from ESP International is provided to illustrate the applicability of the model. Social implications Information assurance, security, and the risk of large-scale data breaches are a contemporary problem in society today. These topics have been considered and addressed within the model framework. Originality/value The paper presents a unique and novel approach for parsing data into actionable knowledge items, identification of viruses, an application of visual dashboards for identification of problems, and a formal discussion of risk inherent with big data.

Big data analytics with swarm intelligence

2016 ◽  
Vol 116 (4) ◽  
pp. 646-666 ◽  
Author(s):  
Shi Cheng ◽  
Qingyu Zhang ◽  
Quande Qin
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Swarm Intelligence ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Underlying Mechanism ◽  
Research Area ◽  
Future Research ◽  
Content Type

Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

The fall and rise of intellectual capital accounting: new prospects from the Big Data revolution

2018 ◽  
Vol 26 (3) ◽  
pp. 381-399 ◽  
Author(s):  
Matteo La Torre ◽  
Vida L. Botes ◽  
John Dumay ◽  
Michele Antonio Rea ◽  
Elza Odendaal
Keyword(s):  
Big Data ◽  
Intellectual Capital ◽  
Empirical Studies ◽  
User Interaction ◽  
Security And Privacy ◽  
Future Research ◽  
Content Type ◽  
Accounting Research ◽  
Science Theory ◽  
Practical Factors

Purpose As Big Data is creating new underpinnings for organisations’ intellectual capital (IC) and knowledge management, this paper aims to analyse the implications of Big Data for IC accounting to provide new conceptual and practical insights about the future of IC accounting. Design/methodology/approach Based on a conceptual framework informed by decision science theory, the authors explain the factors supporting Big Data’s value and review the academic literature and practical evidence to analyse the implications of Big Data for IC accounting. Findings In reflecting on Big Data’s ability to supply a new value for IC and its implications for IC accounting, the authors conclude that Big Data represents a new IC asset, and this represents a rationale for a renewed wave of interest in IC accounting. IC accounting can contribute to understand the determinants of Big Data’s value, such as data quality, security and privacy issues, data visualisation and users’ interaction. In doing so, IC measurement, reporting and auditing need to keep focusing on how human capital and organisational and technical processes (structural capital) can unlock or even obstruct Big Data’s value for IC. Research limitations/implications The topic of Big Data in IC and accounting research is in its infancy; therefore, this paper acts at a normative level. While this represents a research limitation of the study, it is also a call for future empirical studies. Practical implications Once again, practitioners and researchers need to face the challenge of avoiding the trap of IC accountingisation to make IC accounting relevant for the Big Data revolution. Within the euphoric and utopian views of the Big Data revolution, this paper contributes to enriching awareness about the practical factors underpinning Big Data’s value for IC and foster the cognitive and behavioural dynamic between data, IC information and user interaction. Social implications The paper is relevant to prepares, users and auditors of financial statements. Originality/value This paper aims to instill a novel debate on Big Data into IC accounting research by providing new avenues for future research.

scholarly journals The job demands-resources model and the international work context – a systematic review

2014 ◽  
Vol 2 (3) ◽  
pp. 260-279 ◽  
Author(s):  
Lucy T.B. Rattrie ◽  
Markus G. Kittler
Keyword(s):  
Systematic Review ◽  
Empirical Studies ◽  
Domestic Work ◽  
Job Demands ◽  
Empirical Literature ◽  
Future Research ◽  
Content Type ◽  
International Work ◽  
Work Related ◽  
Work Contexts

Purpose – The purpose of this paper is to provide a synthesis and evaluation of literature surrounding the job demands-resources (JD-R) model (Demerouti et al., 2001) in the first decade since its inception, with particular emphasis on establishing an evidence-based universal application towards different national and international work contexts. Design/methodology/approach – The study uses a systematic review approach following the stages suggested by Tranfield et al. (2003). Based on empirical data from 62 studies, the authors systematically analyse the application of the JD-R model and queries whether it is applicable outside merely domestic work contexts. Findings – The authors find convincing support for the JD-R model in different national contexts. However, the authors also found an absence of studies employing the JD-R model in cross-national settings. None of the empirical studies in the sample had explicitly considered the international context of today’s work environment or had clearly associated JD-R research with the IHRM literature. Research limitations/implications – Based on the wide acceptance of the JD-R model in domestic work contexts and the increased interest in work-related outcomes such as burnout and engagement in the IHRM literature, the study identifies a gap and suggests future research applying the JD-R model to international work and global mobility contexts. Originality/value – This study is the first to systematically assess the application of the JD-R model in domestic and international work contexts based on a systematic review of empirical literature in the first decade since the inception of the model. The study identifies a lack of internationally focussed JD-R studies and invites further empirical research and theoretical extensions.

Consumers’ self-congruence with a “Liked” brand

2017 ◽  
Vol 51 (2) ◽  
pp. 367-390 ◽  
Author(s):  
Elaine Wallace ◽  
Isabel Buil ◽  
Leslie de Chernatony
Keyword(s):  
Social Relations ◽  
Brand Loyalty ◽  
Structural Equation ◽  
Large Scale ◽  
Equation Modeling ◽  
Future Research ◽  
Tie Strength ◽  
Brand Love ◽  
Content Type ◽  
Self Image

Purpose Brand “Likes” on Facebook facilitate self-expression, forming part of consumers’ virtual selves. Yet, consumers’ brand “Likes” may bear little resemblance to their material realities. This paper aims to test similarities of brand image with self-image for Facebook “Likes” to determine whether self-congruence with a “Liked” brand leads to positive offline brand outcomes. It also investigates whether consumers’ perceptions about their Facebook social relations influence self-congruent brand “Likes”. Design/methodology/approach A large-scale survey was conducted of regular Facebook users who “Liked” brands. Data from 438 respondents was analysed and hypotheses tested using structural equation modeling. Findings Empirical results show that the perceived self-congruence with a “Liked” brand increases with social tie strength. Perceived social tie strength is informed by perceived attitude homophily. When the perceived self-congruence with a “Liked” brand is higher, brand love and word of mouth (WOM) are enhanced. Consumers also have greater brand loyalty and offer more WOM when brands are loved. Research limitations/implications Findings demonstrate the influence of consumers’ cognitive network on “Likes” and brand outcomes. Further replication would enhance generalisability. Future research should use a wider sample and investigate other variables. Practical implications Findings support managers seeking to grow and analyse Facebook “Likes” by providing insights into brand loyalty, brand love and WOM for “Liked” brands. Originality/value The paper addresses the dearth of research exploring how consumers’ perceptions of their Facebook network influence their online brand behaviour and how perceived self-congruence with a “Liked” brand relates to brand outcomes.

Potential outcomes of work integration social enterprises for people who are homeless, at risk of homelessness, or transitioning out of homelessness

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sarah Barton ◽  
Hayley Porter ◽  
Susanne Murphy ◽  
Rosemary Lysaght
Keyword(s):  
At Risk ◽  
Financial Stability ◽  
Social Enterprise ◽  
Empirical Studies ◽  
Target Population ◽  
Social Enterprises ◽  
Potential Outcomes ◽  
Future Research ◽  
Content Type ◽  
Work Integration

Purpose Social enterprise has the potential to serve as a mechanism of social and economic opportunity for persons experiencing homelessness. This paper aims to identify potential outcomes of work integration social enterprises (WISEs) for people who are homeless, at risk of homelessness, or transitioning out of homelessness. Design/methodology/approach Searches of 14 databases were completed using keywords and subject headings pertaining to homelessness, social enterprise and employment, respectively. These searches were then combined to identify literature concerning WISEs with homeless populations. The initial search yielded 784 unique articles. Through screening, 29 articles were selected and independently coded to establish themes. Findings The analysis identified the potential for WISEs to contribute positively to the lives of the target population in the areas of connection to the community, employment skill building, mental health, personal agency and empowerment, relationship-building, structure and time use, financial stability and housing. There were less positive and mixed findings regarding substance use, crime/delinquency, physical health and transition to mainstream employment. Future research should further explore causal relationships between WISE approaches and strategies and their potential implications for persons emerging from homelessness. Originality/value Prior to this research, there have not been any recent publications that synthesize the existing body of literature to evaluate the potential outcomes of WISE participation for homeless populations. This paper lays the groundwork for future empirical studies.

Big data analysis of trends in the second-hand apparel market: a comparison between 2014 and 2019

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanghee Kim ◽  
Hongjoo Woo
Keyword(s):  
Big Data ◽  
Design Methodology ◽  
Semantic Network ◽  
Distribution Channels ◽  
Subject Area ◽  
Future Research ◽  
Semantic Network Analysis ◽  
Content Type ◽  
The Past ◽  
Network Patterns

Purpose According to the perspective of evolutionary economic theory, the marketplace continuously evolves over time, following the changing needs of both customers and firms. In accordance with the theory, the second-hand apparel market has been rapidly expanding by meeting consumers’ diverse preferences and promoting sustainability since 2014. To understand what changes in consumers’ consumption behaviors regarding used apparel have driven this growth, the purpose of this study is to examine how the second-hand apparel market product types, distribution channels and consumers’ motives have changed over the past five years. Design/methodology/approach This study collected big data from Google through Textom software by extracting all Web-exposed text in 2014, and again in 2019, that contained the keyword “second-hand apparel,” and used the Node XL program to visualize the network patterns of these words through the semantic network analysis. Findings The results indicate that the second-hand apparel market has evolved with various changes over the past five years in terms of consumer motives, product types and distribution channels. Originality/value This study provides a comprehensive understanding of the changing demands of consumers toward used apparel over the past five years, providing insights for retailers as well as future research in this subject area.

Determinants of the firm performance of returnee entrepreneurs in Somalia: the effects of external environmental conditions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamed-Abdullahi Mohamed ◽  
Asmat-Nizam Abdul-Talib ◽  
AfifahAlwani Ramlee
Keyword(s):  
Firm Performance ◽  
Conceptual Framework ◽  
Empirical Studies ◽  
Theoretical Background ◽  
Future Research ◽  
African Countries ◽  
Content Type ◽  
Somali Diaspora ◽  
Post Conflict ◽  
Perceived Environmental Uncertainty

Purpose This study aims to examine the role of returning Somali diaspora entrepreneurs on firm performance and their perceived environmental obstacles. Design/methodology/approach The paper draws on a broad literature review and covers a theoretical background to develop a research framework. It presents several propositions to be empirically tested to determine the influence of returnee entrepreneurs’ success and the challenges they face in the process. Findings The paper offers an overview of how Somali diaspora returnee entrepreneurs can use their resources to succeed in their business and the possible environmental uncertainties that could hinder them. The study highlights some under-researched areas and provides future research directions. Research limitations/implications A research investigation is needed to test the proposed conceptual framework empirically. Further research is also recommended to use other predictors when investigating the perceived environmental uncertainty faced by returnee entrepreneurs. Practical implications In the diaspora entrepreneurship literature, returnee entrepreneurs in post-conflict African countries did not get enough attention. Hence, the study will contribute theoretically to the literature. Originality/value The paper provides a conceptual framework that will help understand returnee entrepreneurs in post-conflict states in Africa, paving the way for empirical studies on the topic.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Bibliometric analysis and critical review of the research on big data in the construction industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yusheng Lu ◽  
Jiantong Zhang
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Bibliometric Analysis ◽  
Critical Review ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
The Body ◽  
Future Research ◽  
Temporal And Spatial Distribution ◽  
Content Type

PurposeThe digital revolution and the use of big data (BD) in particular has important applications in the construction industry. In construction, massive amounts of heterogeneous data need to be analyzed to improve onsite efficiency. This article presents a systematic review and identifies future research directions, presenting valuable conclusions derived from rigorous bibliometric tools. The results of this study may provide guidelines for construction engineering and global policymaking to change the current low-efficiency of construction sites.Design/methodology/approachThis study identifies research trends from 1,253 peer-reviewed papers, using general statistics, keyword co-occurrence analysis, critical review, and qualitative-bibliometric techniques in two rounds of search.FindingsThe number of studies in this area rapidly increased from 2012 to 2020. A significant number of publications originated in the UK, China, the US, and Australia, and the smallest number from one of these countries is more than twice the largest number in the remaining countries. Keyword co-occurrence is divided into three clusters: BD application scenarios, emerging technology in BD, and BD management. Currently developing approaches in BD analytics include machine learning, data mining, and heuristic-optimization algorithms such as graph convolutional, recurrent neural networks and natural language processes (NLP). Studies have focused on safety management, energy reduction, and cost prediction. Blockchain integrated with BD is a promising means of managing construction contracts.Research limitations/implicationsThe study of BD is in a stage of rapid development, and this bibliometric analysis is only a part of the necessary practical analysis.Practical implicationsNational policies, temporal and spatial distribution, BD flow are interpreted, and the results of this may provide guidelines for policymakers. Overall, this work may develop the body of knowledge, producing a reference point and identifying future development.Originality/valueTo our knowledge, this is the first bibliometric review of BD in the construction industry. This study can also benefit construction practitioners by providing them a focused perspective of BD for emerging practices in the construction industry.

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