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Article
Publication date: 8 May 2017

Tingting Zhang, William Yu Chung Wang and David J. Pauleen

This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for…

1508

Abstract

Purpose

This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not.

Design/methodology/approach

This study is based on an event study using data from two stock markets in China.

Findings

The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along.

Research limitations/implications

This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets.

Originality/value

Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

Details

Journal of Knowledge Management, vol. 21 no. 3
Type: Research Article
ISSN: 1367-3270

Keywords

Book part
Publication date: 19 July 2022

Shelly Verma, Manju Dahiya and Simon Grima

Introduction: All countries are interested in attracting foreign direct investment (FDI) as it provides for productivity gains and modernisation for attaining sustainable…

Abstract

Introduction: All countries are interested in attracting foreign direct investment (FDI) as it provides for productivity gains and modernisation for attaining sustainable development goals. Multinational corporations (MNCs) collect a vast volume of structured and unstructured big data when seeking international expansion by the FDI route in the insurance sector, but concluding these data may not be practically feasible. So nowadays, for finalising their FDI ventures, MNCs depend on machine-based algorithms for quick analysis of big data sets.

Purpose: This chapter explores how emerging big data analytics and predictive modelling fields can scale and speed up FDI decisions in the insurance sector.

Methodology: The author used a descriptive study based on secondary data from sources like World Bank, The Organisation for Economic Co-operation and Development (OECD), World Trade Organisation (WTO), and International Finance Corporation (IFC) data repositories to identify variables such as risks, costs, trade agreements, regulatory policies, and gross domestic product (GDP) that affect FDI movements. This chapter highlights the process flow that can be beneficial to convert big data sets using statistical tools and computer software such as Statistical Analytics Software (SAS), IBM SPSS Statistics.

Findings: The application of artificial intelligence-based statistical tools on FDI variables can help derive time-series graphs and forecast revenues. The authors found that foreign investors can narrow their prospect search for industry or product to manageable from varied investment opportunities in host countries. Advancements in big data analysis offer cost-effective methods to improve decision-making and resource management for enterprises.

Details

Big Data: A Game Changer for Insurance Industry
Type: Book
ISBN: 978-1-80262-606-3

Keywords

Article
Publication date: 30 September 2021

Narender Kumar, Girish Kumar and Rajesh Kr Singh

The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In…

Abstract

Purpose

The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.

Design/methodology/approach

The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.

Findings

The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.

Practical implications

This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.

Originality/value

The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

Details

Journal of Enterprise Information Management, vol. 35 no. 1
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 2 November 2018

Ossi Ylijoki and Jari Porras

The purpose of this paper is to present a process-theory-based model of big data value creation in a business context. The authors approach the topic from the viewpoint of…

1827

Abstract

Purpose

The purpose of this paper is to present a process-theory-based model of big data value creation in a business context. The authors approach the topic from the viewpoint of a single firm.

Design/methodology/approach

The authors reflect current big data literature in two widely used value creation frameworks and arrange the results according to a process theory perspective.

Findings

The model, consisting of four probabilistic processes, provides a “recipe” for converting big data investments into firm performance. The provided recipe helps practitioners to understand the ingredients and complexities that may promote or demote the performance impact of big data in a business context.

Practical implications

The model acts as a framework which helps to understand the necessary conditions and their relationships in the conversion process. This helps to focus on success factors which promote positive performance.

Originality/value

Using well-established frameworks and process components, the authors synthetize big data value creation-related papers into a holistic model which explains how big data investments translate into economic performance, and why the conversion sometimes fails. While the authors rely on existing theories and frameworks, the authors claim that the arrangement and application of the elements to the big data context is novel.

Details

Business Process Management Journal, vol. 25 no. 5
Type: Research Article
ISSN: 1463-7154

Keywords

Article
Publication date: 3 August 2021

Luis Hernan Contreras Pinochet, Guilherme de Camargo Belli Amorim, Durval Lucas Júnior and Cesar Alexandre de Souza

The article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.

Abstract

Purpose

The 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/approach

The 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.

Findings

The 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 implications

The 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/value

The 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”.

Details

Journal of Enterprise Information Management, vol. 34 no. 5
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 3 October 2017

Vian Ahmed, Algan Tezel, Zeeshan Aziz and Magda Sibley

This paper aims to explore the current condition of the Big Data concept with its related barriers, drivers, opportunities and perceptions in the architecture, engineering…

3644

Abstract

Purpose

This paper aims to explore the current condition of the Big Data concept with its related barriers, drivers, opportunities and perceptions in the architecture, engineering and construction (AEC) industry with an emphasis on facilities management (FM).

Design/methodology/approach

Following a comprehensive literature review, the Big Data concept was investigated through two scoping workshops with industry experts and academics.

Findings

The value in data analytics and Big Data is perceived by the industry, yet the industry needs guidance and leadership. Also, the industry recognises the imbalance between data capturing and data analytics. Large IT vendors’ developing AEC industry-focused analytics solutions and better interoperability among different vendors are needed. The general concerns for Big Data analytics mostly apply to the AEC industry as well. Additionally, however, the industry suffers from a structural fragmentation for data integration with many small-sized companies operating in its supply chains. This paper also identifies a number of drivers, challenges and way-forwards that calls for future actions for Big Data in FM in the AEC industry.

Originality/value

The nature of data in the business world has dramatically changed over the past 20 years. This phenomenon is often broadly dubbed as “Big Data” with its distinctive characteristics, opportunities and challenges. Some industries have already started to effectively exploit “Big Data” in their business operations. However, despite many perceived benefits, the AEC industry has been slow in discussing and adopting the Big Data concept. Empirical research efforts investigating Big Data for the AEC industry are also scarce. This paper aims at outlining the benefits, challenges and future directions (what to do) for Big Data in the AEC industry with an FM focus.

Details

Facilities, vol. 35 no. 13/14
Type: Research Article
ISSN: 0263-2772

Keywords

Article
Publication date: 25 January 2019

Francesco Caputo, Valentina Cillo, Elena Candelo and Yipeng Liu

The purpose of this paper is to investigate the relations among soft skill, information technologies and Big Data for building a possible bridge able to link human and…

3296

Abstract

Purpose

The purpose of this paper is to investigate the relations among soft skill, information technologies and Big Data for building a possible bridge able to link human and technology dimensions for increasing firm performance.

Design/methodology/approach

Using the Business-focused Inventory of Personality , work personality of 4,758 human resources engaged in 72 high-tech European firms has been analyzed and its relations with firms’ investment in Big Data and firms’ economic performance have been tested using the structural equation modeling (SEM).

Findings

The research shows the existence of strong relations between some elements of human resources’ personality such as the work motivation and the social competencies and the firms’ economic performance. At the same time, the research clarifies the mediated effect of firms’ investment in Big Data in the relations between human resources’ organizational behavior and the firms’ economic performance.

Originality/value

The paper extends previous managerial contributions about Big Data management and human resource management providing evidence on which build more effective managerial models in the era of digital transformation.

Details

Management Decision, vol. 57 no. 8
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 22 February 2022

Jorge A. Romero and Cristina Abad

The importance of integrating cloud-based big data analytics software with enterprise resource planning (ERP) platforms is not clearly understood. Specifically, this study…

Abstract

Purpose

The importance of integrating cloud-based big data analytics software with enterprise resource planning (ERP) platforms is not clearly understood. Specifically, this study aims to look into firms that implemented SAP during the boom of ERP implementations. Further, this study aims to look into the type of cloud-based big data analytics software that those firms installed when cloud-based packages started to be available.

Design/methodology/approach

This study specifically looks at productivity and the sources of productivity, such as technical progress and efficiency change, using a non-parametric approach that does not constrain the analysis to any production function.

Findings

This study found that by the time cloud-based big data analytics software started to be available, SAP-adopters already had a competitive advantage over the non-SAP adopters manifested through productivity and specifically through technology and not efficiency. Later, when the same firms decided to integrate their ERP platforms with cloud-based big data analytics software, the firms that had installed SAP already had an initial advantage over the non-SAP-adopters.

Research limitations/implications

In support of the theory of technology organization environment (Tornatzky and Fleisher, 1990) and Posner's theoretical framework (Posner, 1961), a cloud-based big data analytics software will not change the relative position that firms have in the industry, so a cloud-based big data analytics software by itself will not provide a competitive advantage over competitors. Still, it will ensure that the preliminary technological gap that SAP-adopters already had is not magnified.

Practical implications

Knowing the sources of productivity improvement and technological improvements will give managers greater leverage when negotiating budgets, negotiating long-term contracts in better terms and in the decision process.

Originality/value

This study fills a research gap by looking into the implementation of a cloud-based big data analytics software with ERP.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 12 April 2021

Xiaofeng Su, Weipeng Zeng, Manhua Zheng, Xiaoli Jiang, Wenhe Lin and Anxin Xu

Following the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of…

1204

Abstract

Purpose

Following 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/approach

Drawing 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.

Findings

The 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/value

The 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.

Details

European Journal of Innovation Management, vol. 25 no. 4
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 16 November 2018

Alberto Ferraris, Alberto Mazzoleni, Alain Devalle and Jerome Couturier

Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and…

8158

Abstract

Purpose

Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues.

Design/methodology/approach

Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship.

Findings

The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities.

Originality/value

BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.

Details

Management Decision, vol. 57 no. 8
Type: Research Article
ISSN: 0025-1747

Keywords

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