scholarly journals Organizational performance and capabilities to analyze big data: do the ambidexterity and business value of big data analytics matter?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmad Ibrahim Aljumah ◽  
Mohammed T. Nuseir ◽  
Md. Mahmudul Alam
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Organizational Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Manufacturing Firms ◽  
Business Value ◽  
Primary Data ◽  
Content Type

PurposeThe aim of the study is to examine the impact of the big data analytics capabilities (BDAC) on the organizational performance. The study also examines the mediating role of ambidexterity and the moderating role of business value of big data (BVBD) analytics in the relationship between the big data analytics capabilities and the organizational performance.Design/methodology/approachThis study collected primary data based on a questionnaire survey among the large manufacturing firms operating in UAE. A total of 650 questionnaires were distributed among the manufacturing firms and 295 samples were used for final data analysis. The survey was conducted from September to November in 2019, and data were analyzed based on partial least squares structural equation modeling (PLS-SEM).FindingsThe big data analysis (BDA) scalability is supported by the findings on the performance of firm and its determinants such as system, value of business and quality of information. The roles of business value as a moderator and ambidexterity as mediator are found significant. The results reveal that there is a need for managers to consider the business value and quality dynamics as crucial strategic objectives to achieve high performance of the firm.Research limitations/implicationsThe study has significant policy implication for practitioners and researchers for understanding the issues related to big data analytics.Originality/valueThis is an original study based on primary data from UAE manufacturing firms.

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.

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamad Bahrami ◽  
Sajjad Shokouhyar
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Culture

PurposeBig data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approachThe study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.FindingsThe results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/valueThe present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

Big data analytics and competitive advantage: the strategic role of firm-specific knowledge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajiv Dahiya ◽  
Son Le ◽  
John Kirk Ring ◽  
Kevin Watson
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Management Strategies ◽  
Big Data Analytics ◽  
Specific Knowledge ◽  
Content Type ◽  
Absorptive Capacities ◽  
Strategic Role

PurposeWhile advances in big data analytics (BDA) provide valuable business insights and immense business value, many firms find it difficult to gain advantage from their BDA initiatives. Noting the strategic role of firm-specific knowledge, we develop a framework examining the relation between firm specificity of BDA knowledge and competitive advantage. We also examine the dynamic evolution of BDA capabilities and the associated knowledge management strategies.Design/methodology/approachWe review the resource-based view (RBV), capabilities life cycles and absorptive capacity perspectives along with the literature on BDA competitive advantage. Identifying two key BDA factors, application customization and data proprietorship, we develop a BDA competitive advantage framework. We also investigate the absorptive capacities employed by firms to advance their BDA capabilities. We use anecdotal cases to support our theoretical arguments.FindingsWe propose that BDA solutions with vendor-based applications (noncustomized) and public data will not generate firm-specific knowledge and therefore not provide competitive advantage. In contrast, BDA solutions with custom applications and proprietary data will provide high-level firm-specific knowledge and potentially result in sustained competitive advantage. We further suggest the relevant absorptive capacities and the knowledge management strategies for BDA capability development.Practical implicationsOur framework provides managers with insights into how to develop and enhance firm-specific knowledge from their BDA solutions to gain competitive advantage.Originality/valueOur study offers a new BDA firm-specific knowledge framework for competitive advantage.

scholarly journals “Big results require big ambitions”: big data, data analytics and accounting in masters courses

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Karen Mcbride ◽  
Christina Philippou
Keyword(s):  
Big Data ◽  
Critical Thinking ◽  
Data Analysis ◽  
Data Analytics ◽  
Thinking Skills ◽  
Accounting Education ◽  
Professional Qualification ◽  
Content Type ◽  
The Impact

Purpose Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same. Design/methodology/approach The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis. Findings Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes. Research limitations/implications The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research. Practical implications This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes. Social implications The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics. Originality/value While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Sunil Luthra ◽  
Sachin Kumar Mangla ◽  
Yigit Kazancoglu
Keyword(s):  
Big Data ◽  
South African ◽  
Reverse Logistics ◽  
Data Analytics ◽  
Structural Equation ◽  
Online Survey ◽  
Big Data Analytics ◽  
Primary Data ◽  
Manufacturing Companies ◽  
Content Type

PurposeThe study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance.Design/methodology/approachThe primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.FindingsThe results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.Practical implicationsThe theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability.Originality/valueThis research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors’ knowledge, it is the first study to be conducted in the South African context.

Davenport and Prusak on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 7-11 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Early Years ◽  
Content Type ◽  
Potential Risks ◽  
The Relationship

Purpose Larry Prusak and Tom Davenport have long been leading voices in the knowledge management (KM) field. This interview aims to explore their views on the relationship between KM and big data/analytics. Design/methodology/approach An interview was conducted by email with Larry Prusak and Tom Davenport in 2015 and updated in 2016. Findings Prusak and Davenport hold differing views on the role of KM today. They also see the relationship between KM and big data/analytics somewhat differently. Davenport, in particular, has much to say on how big data/analytics can be best utilized by business as well as its potential risks. Originality/value It is important to understand how two of the most serious KM thinkers since the early years of KM understand the relationship between big data/analytics, KM and organizations. Their views can help shape thinking in these fields.

scholarly journals The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vaibhav S. Narwane ◽  
Rakesh D. Raut ◽  
Vinay Surendra Yadav ◽  
Naoufel Cheikhrouhou ◽  
Balkrishna E. Narkhede ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Business Performance ◽  
Big Data Analytics ◽  
Content Type ◽  
Performance Factors ◽  
Mediating Role ◽  
Sustainable Procurement

PurposeBig data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.Design/methodology/approachA two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.FindingsStatistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.Research limitations/implicationsThis study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.Originality/valueFor the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Relationship between big data analytics and organisational performance of the Technical University of Kenya and Strathmore University in Kenya

2020 ◽  
Vol 69 (6/7) ◽  
pp. 537-556 ◽  
Author(s):  
Lucy Wachera Kibe ◽  
Tom Kwanya ◽  
Ashah Owano
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Negative Relationship ◽  
Big Data Analytics ◽  
Deep Understanding ◽  
Primary Data ◽  
Mixed Method Research ◽  
Organisational Performance ◽  
Content Type ◽  
Technical University

Purpose Big data analytics is a set of procedures and technologies that entails new forms of integration to uncover large unknown values from large data sets that are various, complex and of an immense scale. The use of big data analytics is generally considered to improve organisational performance. However, this depends on capabilities of different organisations to provide the resources required for big data analytics. This study aims to investigate the influence of big data analytics on organisational performance of Technical University of Kenya (TUK) and Strathmore University (SU). Design/methodology/approach This study was conducted as a mixed method research to enable a deep understanding of the concept. Primary data was collected through structured questionnaires and interviews with clientele and information communication technology staff from the TUK and SU, both in Nairobi, Kenya. Secondary data was collected through interviews and questionnaires. Data was analysed and presented using descriptive statistics. Findings The findings revealed that most of the variables of organisational performance such as innovativeness, creativeness, effectiveness, productiveness and efficiency are affected positively by conducting big data analytics in both institutions. The results demonstrate that the TUK showed a negative relationship between big data analytics and competiveness and profitability while SU showed a positive relationship between the two variables. In terms of regression analysis, the findings revealed that SU showed a good relationship between independent and dependant variables while the TUK had a weak influence. Originality/value This study is original in terms of its subject matter, scope and application.

Unlocking causal relations of barriers to big data analytics in manufacturing firms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rakesh Raut ◽  
Vaibhav Narwane ◽  
Sachin Kumar Mangla ◽  
Vinay Surendra Yadav ◽  
Balkrishna Eknath Narkhede ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Service Providers ◽  
Big Data Analytics ◽  
Analytic Network Process ◽  
Manufacturing Firms ◽  
Content Type ◽  
Expert Opinions ◽  
Network Process

PurposeThis study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.Design/methodology/approachA total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).FindingsThe results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.Practical implicationsThe findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.Originality/valueThe paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.

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