Conceptualizing big data practices

2020 ◽  
Vol 28 (2) ◽  
pp. 205-222 ◽  
Author(s):  
Canchu Lin ◽  
Anand S. Kunnathur ◽  
Long Li
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Dynamic Capabilities ◽  
Empirical Studies ◽  
Qualitative Interviews ◽  
Research Literature ◽  
Theoretical Understanding ◽  
Content Type ◽  
Academic Literature ◽  
Data Practices

Purpose The purpose of this paper is to provide a conceptual understanding of Big Data practices in organizations, which will enable exploring the operational and strategic roles of Big Data in organizational performance. Design/methodology/approach Both academic and non-academic literature studies on Big Data were reviewed so as to capture what was known about Big Data practices. Qualitative interviews were conducted with firm executives about Big Data practices in their organizations. Both literature review and interview results were analyzed based on the dynamic capabilities perspective. Findings The analysis of the results suggests that Big Data capability develops when the resources parts of Big Data and the skill and competency parts are integrated and then grow into a dynamic capability. Research limitations/implications This study contributes to the literature with the concept of Big Data capability that best characterizes Big Data practices in organizations. Validity of this concept should be tested in empirical studies. Originality/value The development of the concept of Big Data capability helps to fill a gap in the research literature that theoretical understanding of big data practices is lacking or needs to be updated. It motivates practitioners to develop this capability so as to create and maintain their strategic advantage.

Towards a political theory of data justice: a public good perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chi Kwok ◽  
Ngai Keung Chan
Keyword(s):  
Big Data ◽  
Public Good ◽  
Political Theory ◽  
Market Failure ◽  
Empirical Studies ◽  
Content Type ◽  
The Public ◽  
Critical Data ◽  
Data Practices ◽  
Political Theories

Purpose This study aims to develop an interdisciplinary political theory of data justice by connecting three major political theories of the public good with empirical studies about the functions of big data and offering normative principles for restricting and guiding the state’s data practices from a public good perspective. Design/methodology/approach Drawing on three major political theories of the public good – the market failure approach, the basic rights approach and the democratic approach – and critical data studies, this study synthesizes existing studies on the promises and perils of big data for public good purposes. The outcome is a conceptual paper that maps philosophical discussions about the conditions under which the state has a legitimate right to collect and use big data for public goods purposes. Findings This study argues that market failure, basic rights protection and deepening democracy can be normative grounds for justifying the state’s right to data collection and utilization, from the perspective of political theories of the public good. The state’s data practices, however, should be guided by three political principles, namely, the principle of transparency and accountability; the principle of fairness; and the principle of democratic legitimacy. The paper draws on empirical studies and practical examples to explicate these principles. Originality/value Bringing together normative political theory and critical data studies, this study contributes to a more philosophically rigorous understanding of how and why big data should be used for public good purposes while discussing the normative boundaries of such data practices.

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

Collaborative matrix factorization mechanism for group recommendation in big data-based library systems

2018 ◽  
Vol 36 (3) ◽  
pp. 458-481 ◽  
Author(s):  
Yezheng Liu ◽  
Lu Yang ◽  
Jianshan Sun ◽  
Yuanchun Jiang ◽  
Jinkun Wang
Keyword(s):  
Big Data ◽  
Matrix Factorization ◽  
Empirical Studies ◽  
Interaction Matrix ◽  
Group Recommendation ◽  
Data Set ◽  
Content Type ◽  
Potential Value ◽  
Membership Retention ◽  
Online Library

Purpose Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups. Design/methodology/approach The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed. Findings Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment. Research limitations/implications The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts. Practical implications The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient. Social implications The proposed methods have potential value to improve scientific collaboration and research innovation. Originality/value The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.

Big data and data analytics in auditing: in search of legitimacy

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Federica De Santis ◽  
Giuseppe D’Onza
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Audit Quality ◽  
Empirical Studies ◽  
Innovation Process ◽  
Structured Interviews ◽  
Senior Managers ◽  
Content Type ◽  
Audit Strategy ◽  
Audit Standards

Purpose This study aims to analyze the utilization of big data and data analytics (BDA) in financial auditing, focusing on the process of producing legitimacy around these techniques, the factors fostering or hindering such process and the action auditors take to legitimate BDA inside and outside the audit community. Design/methodology/approach The analysis bases on semi-structured interviews with partners and senior managers of Italian audit companies. Findings The BDA’s legitimation process is more advanced in the audit professional environment than outside the audit community. The Big Four lead the BDA-driven audit innovation process and BDA is used to complement traditional audit procedures. Outside the audit community, the digital maturity of audit clients, the lack of audit standards and the audit oversight authority’s negative view prevent the full legitimation of BDA. Practical implications This research highlights factors influencing the utilization of BDA to enhance audit quality. The results can, thus, be used to enhance the audit strategy and to innovate audit practices by using BDA as a source of adequate audit evidence. Audit regulators and standards setters can also use the results to revise the current auditing standards and guidance. Originality/value This study adds to the literature on digital transformation in auditing by analyzing the legitimation process of a new audit technique. The paper answers the call for more empirical studies on the utilization of BDA in financial auditing by analyzing the application of such techniques in an unexplored operational setting in which auditees are mainly medium-sized enterprises and family-run businesses.

scholarly journals Big data and dynamic capabilities: a bibliometric analysis and systematic literature review

2019 ◽  
Vol 57 (8) ◽  
pp. 2052-2068 ◽  
Author(s):  
Riccardo Rialti ◽  
Giacomo Marzi ◽  
Cristiano Ciappei ◽  
Donatella Busso
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Bibliometric Analysis ◽  
Systematic Literature Review ◽  
Dynamic Capabilities ◽  
Process Management ◽  
Big Data Analytics ◽  
Management Decision ◽  
Content Type ◽  
Management Decision Making

Purpose Recently, several manuscripts about the effects of big data on organizations used dynamic capabilities as their main theoretical approach. However, these manuscripts still lack systematization. Consequently, the purpose of this paper is to systematize the literature on big data and dynamic capabilities. Design/methodology/approach A bibliometric analysis was performed on 170 manuscripts extracted from the Clarivate Analytics Web of Science Core Collection database. The bibliometric analysis was integrated with a literature review. Findings The bibliometric analysis revealed four clusters of papers on big data and dynamic capabilities: big data and supply chain management, knowledge management, decision making, business process management and big data analytics. The systematic literature review helped to clarify each clusters’ content. Originality/value To the authors’ best knowledge, minimal attention has been paid to systematizing the literature on big data and dynamic capabilities.

Value that matters: intellectual capital and big data to assess performance in healthcare. An empirical analysis on the European context

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ginevra Gravili ◽  
Francesco Manta ◽  
Concetta Lucia Cristofaro ◽  
Rocco Reina ◽  
Pierluigi Toma
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Healthcare System ◽  
Healthcare Sector ◽  
Healthcare Organizations ◽  
Content Type ◽  
Structural Capital ◽  
And Performance ◽  
The Relationship

PurposeThe aim of this paper is to analyze and measure the effects of intellectual capital (IC), i.e. human capital (HC), relational capital (RC) and structural capital (SC), on healthcare industry organizational performance and understanding the role of data analytics and big data (BD) in healthcare value creation (Wang et al., 2018). Through the assessment of determined variables specific for each component of IC, the paper identifies the guidelines and suggests propositions for a more efficient response in terms of services provided to citizens and, specifically, patients, as well as predicting effective strategies to improve the care management efficiency in terms of cost reduction.Design/methodology/approachThe study has a twofold approach: in the first part, the authors operated a systematic review of the academic literature aiming to enquire the relationship between IC, big data analytics (BDA) and healthcare system, which were also the descriptors employed. In the second part, the authors built an econometric model analyzed through panel data analysis, studying the relationship between IC, namely human, relational and structural capital indicators, and the performance of healthcare system in terms of performance. The study has been conducted on a sample of 28 European countries, notwithstanding the belonging to specific international or supranational bodies, between 2011 and 2016.FindingsThe paper proposes a data-driven model that presents new approach to IC assessment, extendable to other economic sectors beyond healthcare. It shows the existence of a positive impact (turning into a mathematical inverse relationship) of the human, relational and structural capital on the performance indicator, while the physical assets (i.e. the available beds in hospitals on total population) positively mediates the relationship, turning into a negative impact of non-IC related inputs on healthcare performance. The result is relevant in terms of managerial implications, enhancing the opportunity to highlight the crucial role of IC in the healthcare sector.Research limitations/implicationsThe relationship between IC indicators and performance could be employed in other sectors, disseminating new approaches in academic research. Through the establishment of a relationship between IC factors and performance, the authors implemented an approach in which healthcare organizations are active participants in their economic and social value creation. This challenges the views of knowledge sharing deeply held inside organizations by creating “new value” developed through a more collaborative and permeated approach in terms of knowledge spillovers. A limitation is given by a fragmented policymaking process which carries out different results in each country.Practical implicationsThe analysis provides interesting implications on multiple perspectives. The novelty of the study provides interesting implications for managers, practitioners and governmental bodies. A more efficient healthcare system could provide better results in terms of cost minimization and reduction of hospitalization period. Moreover, dissemination of new scientific knowledge and drivers of specialization enhances best practices sharing in the healthcare sector. On the other hand, an improvement in preventive medicine practices could help in reducing the overload of demand for curative treatments, on the perspective of sharply decreasing the avoidable deaths rate and improving societal standards.Originality/valueThe authors provide a new holistic framework on the relationship between IC, BDA and organizational performance in healthcare organizations through a systematic review approach and an empirical panel analysis at a multinational level, which is quite a novelty regarding the healthcare. There is little research focussed on healthcare industries' organizational performance, and, specifically, most of the research on IC in healthcare delivered results in terms of theoretical contribution and qualitative analyzes. The authors even contributed to analyze the healthcare industry in the light of the possible existence of synergies and networks among countries.

scholarly journals How do enterprise social media affordances affect social network ties and job performance?

2019 ◽  
Vol 33 (1) ◽  
pp. 361-388 ◽  
Author(s):  
Xiayu Chen ◽  
Shaobo Wei ◽  
Robert M. Davison ◽  
Ronald E. Rice
Keyword(s):  
Social Media ◽  
Social Network ◽  
Job Performance ◽  
Organizational Performance ◽  
Network Ties ◽  
Theoretical Understanding ◽  
Content Type ◽  
Enterprise Social Media ◽  
Social Network Ties ◽  
The Impact

Purpose The purpose of this paper is to investigate how four enterprise social media (ESM) affordances (visibility, association, editability and persistence) affect social network ties (instrumental and expressive), which, in turn, influence the in-role and innovative job performance of employees. Design/methodology/approach A survey of 251 ESM users in the workplace in China was conducted. Findings All four affordances are positively associated with instrumental ties, yet only the association and editability affordances are positively related to expressive ties. Although instrumental and expressive ties are positively related to in-role and innovative job performance, instrumental ties exert stronger effects on in-role job performance, whereas expressive ties show stronger effects on innovative job performance. Research limitations/implications First, additional relevant affordances should be included in an expanded model. Second, future research could examine how patterns of affordances use (unrelated, or hierarchically or sequentially related) affect organizational network ties. Third, there are likely (many) other exogenous factors affecting the model’s relationships. Fourth, the data collected are self-reported. Practical implications This study advances the theoretical understanding of the role of ESM affordances in the workplace, especially through their influences on network ties. The findings can guide organizations on how to emphasize ESM affordances to foster instrumental and expressive ties to improve the job performance of employees. Originality/value First, it provides novel views on affordance theory in ESM contexts by empirically testing four central affordances, thereby further providing preliminary evidence for prior theoretical propositions by confirming that social media affordances might be associated with or influence relational ties. Second, the study integrates an affordance lens and a social network perspective to investigate employees’ perceived performance behavior. Including social network ties can offer a more detailed understanding of the underlying processes of how ESM affordances can and do affect job performance. Third, it supports the validity of distinguishing instrumental and expressive ties in ESM contexts, thus offering a possible explanation for the inconsistencies in prior research on the impact of social networks on employee outcomes. Finally, it also shows how two kinds of organizational performance (in-role and innovative) are somewhat differentially influenced by affordances and network ties.

Consistency at different levels

2020 ◽  
Vol 11 (4) ◽  
pp. 537-567 ◽  
Author(s):  
Tao Scofield Su ◽  
Chunhua Chen ◽  
Xiaoyu Cui ◽  
Chunsheng Yang ◽  
Weimo Ma
Keyword(s):  
Organizational Performance ◽  
Empirical Studies ◽  
Task Type ◽  
Measurement Tool ◽  
Regional Culture ◽  
Content Type ◽  
Positive Correlation ◽  
Vertical Distance ◽  
And Performance ◽  
Different Levels

Purpose This paper aims to answer following three important but not well-answered or unanswered questions in the extant trust literatures: What is the true magnitude that trust impacts on performance? Is there any consistency among the effects of trust on performance at different levels? How does vertical distance affect the trust-performance relationship? Design/methodology/approach It captures the law between trust and performance at different levels by conducting a meta-analytic examination consisting of 238 independent empirical studies, 586 effect sizes and 110,576 independent samples. Findings It makes a periodic conclusion that trust significantly promotes performance. Specifically, trust not only has stronger positive correlation with team performance than individual and organizational performance inside organization, but also strongly facilitates organizational performance between organizations. Moreover, consistency exits in the effects of trust on performance at different levels. On one hand, trust has stronger positive correlation with performance of contextual type than performance of innovative type than performance of task type at different levels. On the other hand, promotion effect of trust on performance strengthens when the vertical distance between trustors and trustees diminishes. Additionally, three potential moderators including publication status, measurement tool and common method variance moderate the focused relation, but moderating effect is not thorough for regional culture. Moderating directions of the above four potential moderators are highly consistent. Originality/value This paper answers the three important but not well-answered or unanswered questions.

Organizational learning capability, innovation and organizational performance

2019 ◽  
Vol 24 (1) ◽  
pp. 151-172 ◽  
Author(s):  
Mahmoud M. Migdadi
Keyword(s):  
Organizational Learning ◽  
Organizational Performance ◽  
Structural Equation ◽  
Measurement Model ◽  
Research Literature ◽  
Equation Modeling ◽  
Future Research ◽  
Learning Capability ◽  
Content Type ◽  
Managerial Practices

Purpose The purpose of this paper is to introduce a comprehensive, delineated and integrated conceptual model which includes organizational learning capability, innovation and organizational performance (OP). Then, an empirical investigation is undertaken to test the relationships among the proposed study model variables. Design/methodology/approach In total, 274 questionnaires were completed and returned. Statistical techniques employed included confirmatory factor analysis to examine the validity of the measurement model, and structural equation modeling to test the hypotheses. Findings The findings of this study suggest that OLC influences innovation and innovation affects OP. Finally, the results show that OLC affects OP indirectly through innovation (mediator). Research limitations/implications Future research should pay more attention to the influence of different mixture (variables) of influences on innovation and also examine other consequences of introducing innovation in organizations. In addition, more empirical papers supporting (or rejecting) the results in different contexts would be welcomed, especially longitudinal studies. Practical implications The results of this study help managers to ascertain the managerial practices that can be employed as well as determine the level of effort and resources necessary to enhance OLC. Additionally, managers should shed additional light on the innovation’s positive implications for OP. Originality/value This study focuses on the conceptualization of OLC and effects of these capabilities on innovation. It conceptualizes innovation as a multidimensional construct and tests its relationship with OP. Finally, the relationship between learning capability and OP, although implied, needs to be addressed empirically in the research literature, an objective that this study tries to achieve.

Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hamza Saleem ◽  
Yongjun Li ◽  
Zulqurnain Ali ◽  
Muhammad Ayyoub ◽  
Yu Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Information Sharing ◽  
Technological Innovation ◽  
Dynamic Capabilities ◽  
Survey Methods ◽  
Organizational Outcomes ◽  
Production Costs ◽  
Content Type ◽  
Product And Process

PurposeThis paper aims to investigate the use of big data (BDU) in predicting technological innovation, supply chain and SMEs' performance and whether technological innovation mediates the association between BDU and firm performance. Additionally, this research also seeks to explore the moderating effect of information sharing in the association between BDU and technological innovation.Design/methodology/approachUsing survey methods and structural associations in AMOS 24.0., the proposed model was tested on SME managers recruited from the largest economic and manufacturing hub of China, Pearl River Delta.FindingsThe findings suggest that BDU is positively related to technological innovation (product and process) and organizational outcomes (e.g., supply chain and SMEs performance). Technological innovation (i.e., product and process) significantly mediates the association between BDU and organizational outcomes. Moreover, information sharing positively moderates the association between BDU and technological innovations.Practical implicationsThis research provides deeper insights into how BDU is useful for SME managers in achieving the firm’s goals. Particularly, SME managers can bring technological innovation into their business processes, overcome the challenges of forecasting, and generate dynamic capabilities for attaining the best SMEs’ performance. Additionally, BDU with information sharing enables SMEs reduce their risk and decrease production costs in their manufacturing process.Originality/valueFirms always need to adopt new ways to enhance their productivity using available resources. This is the first study that contributes to big data and performance management literature by exploring the moderating and mediation mechanism of information sharing and technological innovation respectively using RBVT. The study and research model enhances our insights on BDU, information sharing, and technological innovation as valuable resources for organizations to improve supply chain performance, which subsequently increases SME productivity. This gap was overlooked by previous researchers in the domain of big data.

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