Big data management: implications of dynamic capabilities and data incubator

2019 ◽  
Vol 57 (8) ◽  
pp. 2113-2123 ◽  
Author(s):  
S.M. Riad Shams ◽  
Ludovico Solima
Keyword(s):  
Big Data ◽  
Data Management ◽  
Dynamic Capabilities ◽  
Relationship Management ◽  
Management Research ◽  
Content Type ◽  
Data Value ◽  
Management Concept ◽  
Capabilities Theory ◽  
Stakeholder Relationship

PurposeBig data management research and practice, however, have received enormous interest from academia and industry; the extant literature demonstrates that the authors have limited understanding and challenges in this knowledge-stream to fully capitalize with its potentials. One of the contemporary challenges is to accurately verify data veracity, and developing value from the verified data for an organization and its stakeholders. Consequently, the purpose of this paper is to develop insights on how the authors could strategically deal with the contemporary challenges in strategic management of big data, related to data veracity and data value.Design/methodology/approachThe inductive–constructivist approach is followed to develop insights at the intersection of dynamic capabilities theory and stakeholder relationship management concept, in order to strategically deal with the contemporary challenges in big data management, related to data veracity and data value.FindingsAt the intersection of dynamic capabilities theory and stakeholder relationship management concept, an implication is acknowledged, which has research and practical significance to strategically verify data source, its veracity and value. Following this implication, a framework of a data incubator is proposed to proactively develop insights on veracity and value of data. Empirical insights are also presented in this study to support this initial framework.Practical implicationsFor future research in strategic management of big data, academics will have contextual understanding on the particular interconnected and interdependent attributes from dynamic capabilities and stakeholder relationship management research streams to further enhance the understanding on big data management. For practice, these insights will be useful for executives to focus on specific attributes of the proposed data incubator to confirm data veracity and develop insights on how to design, deliver and evaluate stakeholder value based on the verified data.Originality/valueFollowing a synthesis at the intersection of dynamic capabilities theory and stakeholder relationship management concept, this study introduces a data incubator to meaningfully deal with the big data management challenges, related to veracity and value of data.

scholarly journals Stakeholder Causal Scope Analysis–Centered Big Data Management for Sustainable Tourism

2020 ◽  
pp. 109634802097854
Author(s):  
S. M. Riad Shams ◽  
Demetris Vrontis ◽  
Michael Christofi
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Relationship Management ◽  
Research Field ◽  
Tourism Industry ◽  
Research And Practice ◽  
Management Structure ◽  
Sustainability Research ◽  
Stakeholder Relationship

Sustainability concerns in the tourism industry are underresearched, although both stakeholder relationship management and data analytics knowledge streams have implications to underpin sustainability research and practice. Scholars argue that we have limited knowledge of the potential for analyzing diverse stakeholder relationship management issues from different tourism-related socioeconomic and ecological settings to fully exploit stakeholders’ contribution to tourism sustainability. Big data, as a research field, could offer varied cross-disciplinary implications for sustainable development. However, data analytics research is concerned with simplifying the overall management structure of data. In this context, at the intersection of these three research streams (tourism, stakeholder relationship management, and big data), this research note offers insights into how analyzing stakeholder causal scopes would be instrumental in simplifying tourism data management structure to support sustainability research and practice in the tourism industry.

Creating multiple value streams from big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Keyword(s):  
Big Data ◽  
Value Creation ◽  
Content Type ◽  
It Alignment ◽  
Process Creation ◽  
Data Value ◽  
Value Streams ◽  
Pertinent Information ◽  
Multiple Value ◽  
Process Alignment

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This research paper determines how service supply chains can create value with big data, by building cross-departmental processes. Based on the study’s results, the critical alignment capabilities for successful big data value creation are: IT-process alignment; IT-performance alignment; performance-process alignment; human-IT alignment; and human-process alignment. Additionally, overarching and underlying strategic and organizational alignment capabilities also impacted this value creation. The human impact on employees of big data-led process creation shouldn’t be underestimated. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

A structured literature review on Big Data for customer relationship management (CRM): toward a future agenda in international marketing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pasquale Del Vecchio ◽  
Gioconda Mele ◽  
Evangelia Siachou ◽  
Gloria Schito
Keyword(s):  
Big Data ◽  
Customer Relationship Management ◽  
Research Agenda ◽  
International Marketing ◽  
Relationship Management ◽  
Customer Relationship ◽  
Future Research ◽  
Content Type ◽  
Future Research Agenda ◽  
Structured Literature Review

PurposeThis paper aims to advance the international marketing debate by presenting the results of a structured literature review (SLR) focusing on Big Data implementation in customer relationship management (CRM) strategizing. It outlines past and present literature and frames a future research agenda.Design/methodology/approachThe research analyzes papers published in journals from 2013 to 2020, deriving significant insights about Big Data applications in CRM. A sample of 48 articles indexed at Scopus was preliminarily submitted for bibliometric analysis. Finally, 46 papers were analyzed with content and a bibliometric analysis to identify areas of thematic specializations.FindingsThe paper presents a conceptual multilevel framework demonstrating areas of specialization emerging from the literature. The framework is built around four coordinated sequences of actions relevant to “why,” “what,” “who” and “how” Big Data is implemented in CRM strategies, thus supporting the conception and implementation of an internationalization marketing strategy.Research limitations/implicationsImplications for the development of the future research agenda on international marketing arise from the comprehension of Big Data in CRM strategy.Originality/valueThe paper provides a comprehensive SLR of the articles dealing with models and processes of Big Data for CRM from an international marketing perspective. Despite these issues' relevance and the increasing literature focused on them, research in this area is still fragmented and underexplored, requiring more systematic and holistic studies.

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.

scholarly journals Big data and analytics in tourism and hospitality: opportunities and risks

2020 ◽  
Vol 6 (3) ◽  
pp. 257-262 ◽  
Author(s):  
Anca Yallop ◽  
Hugues Seraphin
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Governance ◽  
Privacy And Security ◽  
Content Type ◽  
Data Ethics ◽  
Technological Advances ◽  
International Tourist ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Purpose The purpose of this paper is to examine and provide insights into one of the most influential technologies impacting the tourism and hospitality industry over the next five years, i.e. big data and analytics. It reflects on both opportunities and risks that such technological advances create for both consumers and tourism organisations, highlighting the importance of data governance and processes for effective and ethical data management in both tourism and hospitality. Design/methodology/approach This paper is based on a review of academic and industry literature and access to trends data and information from a series of academic and industry databases and reports to examine how big data and analytics shape the future of the industry and the associated risks and opportunities. Findings This paper identifies and examines key opportunities and risks posed by the rising technological trend of big data and analytics in tourism and hospitality. While big data is generally regarded as beneficial to tourism and hospitality organisations, there are extensively held ethical, privacy and security concerns about it. Therefore, the paper is making the case for more research on data governance and data ethics in tourism and hospitality and posits that to successfully use data for competitive advantage, tourism and hospitality organisations need to solely expand compliance-based data governance frameworks to frameworks that include more effective privacy and ethics data solutions. Originality/value This paper provides useful insights into the use of big data and analytics for both researchers and practitioners and offers new perspectives on the debate on data governance and ethical data management in both tourism and hospitality. Because forecasts from the UNWTO indicate a significant increase in international tourist arrivals (1.8 billion tourist arrivals by 2030), the ways tourism and hospitality organisations manage customers’ data become important.

scholarly journals Anomaly data management and big data analytics: an application on disability datasets

2018 ◽  
Vol 2 (2) ◽  
pp. 164-176
Author(s):  
Zhiwen Pan ◽  
Wen Ji ◽  
Yiqiang Chen ◽  
Lianjun Dai ◽  
Jun Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Management ◽  
Big Data Analytics ◽  
Quality Data ◽  
Content Type ◽  
Data Mining Algorithms ◽  
Management Scheme ◽  
The Disabled ◽  
Mining Scheme

Purpose The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly. Design/methodology/approach In this paper, the authors proposed a big data management and analytic approach for disability datasets. Findings By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach. Originality/value The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.

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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