Big data analytics capability as a mediator in the impact of open innovation on firm performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
José Arias-Pérez ◽  
Alejandro Coronado-Medina ◽  
Geovanny Perdomo-Charry
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Financial Performance ◽  
Open Innovation ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Customer Involvement ◽  
The Relationship ◽  
Analytics Capability

PurposeBig data analytics capability (BDAC) is the ability of a firm to capture and analyze big data toward the generation of insights. The literature has mainly focused on analyzing the direct effects of BDAC on different aspects related to firm performance such as finances and innovation. However, the lack of works analyzing the intermediation role BDAC could play is noticeable, particularly in organizational situations that pose great challenges in terms of data processing. Thus, the aim of this paper is to analyze BDAC mediation in the relationship between open innovation (OI), particularly customer involvement, and firm performance (financial and non-financial).Design/methodology/approachStructural equation modeling was used to test the proposed model with survey data from a sample of 112 firms.FindingsThe results show that BDAC has a partial mediating effect on the relationship between OI and financial performance, and between OI and non-financial performance. Nevertheless, this mediation is greater in the first relationship.Originality/valueThe main contribution of the study is to offer a broader research perspective regarding the role of BDAC in the relationship between OI and firm performance. This study ultimately questions that research tradition in which this role has been reduced to that of a simple application of data analytics techniques. Instead, the results show BDAC is primarily an organizational skill that should be articulated with key processes, such as customer involvement, to maximize the financial and non-financial use of the large flow of data coming from the main OI activity of low and medium-technology companies.

Quality dominant logic in big data analytics and firm performance

2019 ◽  
Vol 25 (3) ◽  
pp. 512-532 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter ◽  
Marc de Bourmont
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Online Survey ◽  
Information Quality ◽  
Big Data Analytics ◽  
Firm Strategy ◽  
Theoretical Understanding ◽  
Content Type ◽  
The Relationship

Purpose Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance. Design/methodology/approach The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience. Findings The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance. Practical implications The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model. Originality/value The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.

Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shokouh Razaghi ◽  
Sajjad Shokouhyar
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Global Sourcing ◽  
Content Type ◽  
Mediating Role ◽  
Management Capability ◽  
Analytics Capability

Purpose This study aims to show that management with big data analytics capability can achieve more advantages of the global sourcing process. Furthermore, this study using its conceptual attitude model aims to show that big data analytics management capability leads to an increase in firm performance by the mediating role of integration. Design/methodology/approach Using an online questionnaire, 158 managers from 13 Iranian companies taking advantage of the global sourcing process were surveyed. The validity of the hypotheses was evaluated using partial least squares based on structural equation modeling (PLS method). Findings The results of the study showed that big data analytics management capability has a positive impact on global sourcing and firm performance directly, and by the mediating role of integration. Originality/value Previous studies have carefully addressed the role of big data and big data analytics in firms. However, this is among a few studies addressing the role of big data analytics capability, especially management capability, in improving firms’ performance. The results of this study shed light on the fact that how global sourcing takes the best advantage of big data analytics management capability for better accomplishment of organizations’ duties. The results of this study also disclose how big data analytics management capability helps organizations with their performance and bring benefits to their units.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

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 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.

Big data analytics adoption model for small and medium enterprises

2020 ◽  
Vol 11 (4) ◽  
pp. 483-513 ◽  
Author(s):  
Parisa Maroufkhani ◽  
Wan Khairuzzaman Wan Ismail ◽  
Morteza Ghobakhloo
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Small And Medium Enterprises ◽  
Big Data Analytics ◽  
Mediation Effect ◽  
Unified Framework ◽  
Content Type ◽  
Medium Enterprises ◽  
The Relationship

Purpose Big data analytics (BDA) is recognized as a turning point for firms to improve their performance. Although small- and medium-sized enterprises (SMEs) are crucial for every economy, they are lagging far behind in the usage of BDA. This study aims to provide a single and unified model for the adoption of BDA among SMEs with the integration of the technology–organization–environment (TOE) model and resource-based view. Design/methodology/approach A survey of 112 manufacturing SMEs in Iran was conducted, and the data were analysed using structural equation modelling to test the model of this study. Findings The results offer evidence of a BDA mediation effect in the relationship between technological, organizational and environmental contexts, and SMEs performance. The findings also demonstrated that technological and organizational elements are the more significant determinants of BDA adoption in the context of SMEs. In addition, the result of this study confirmed that BDA adoption could enhance the financial and market performance of SMEs. Practical implications Providing a single unified framework of BDA adoption for SMEs enables them to appreciate the importance of most influential elements (technology, organization and environment) in the adoption of BDA. Also, this study may encourage SMEs to be more willing to use BDA in their businesses. Originality/value Although there are studies on BDA adoption and firm performance among large companies, there is a lack of empirical research on SMEs, in particular, based on the TOE model. SMEs differ from large companies in terms of the availability of resources and size. Therefore, this study aimed to initiate a conceptual framework of BDA adoption for SMEs to assist them to be able to take advantage of the adoption of such technology.

Consequential factors of Big Data's Analytics Capability: how firms use data in the competitive scenario

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luis Hernan Contreras Pinochet ◽  
Guilherme de Camargo Belli Amorim ◽  
Durval Lucas Júnior ◽  
Cesar Alexandre de Souza
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Explanatory Power ◽  
Big Data Analytics ◽  
Strategic Decision ◽  
Analytical Models ◽  
Equation Modeling ◽  
Content Type ◽  
Analytics Capability

PurposeThe article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.Design/methodology/approachThe research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.FindingsThe results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.Practical implicationsThe proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.Originality/valueThe contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.

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