Data supply chain (DSC): Research synthesis and future directions

In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the preexisting SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies

Predicting online e-marketplace sales performances: a big data approach

To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2,939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework

What do we know about the big data researches? A systematic review from 2011 to 2017

Big data are defined as a new phenomenon that can be novel step for improving social life and business condition. Analysing the big data’s researches to extract insights by systematic literature review is the main objective of this research. For synthesis systematically, data from 123 articles are extracted and kinds of studies that were usually done on big data area are investigated. The Systematic Review showed: the most studies were published in 2014, also the main journal that published big data’s article was ‘Big Data Research’ and country with highest investigate about big data were ‘United State and China’. Beside, most researches were done with analytic background. The main research method was experimental and major research type was case study. Our study proved that the majority of researches carried out around big data focused on data management, and most of them identify ‘volume and variety’ of as significant challenges of big data. Likewise, ‘business analytics’ was described in the major benefits

Motivation to use big data and big data analytics in external auditing

Purpose This paper aims to explore organisational intentions to use Big Data and Big Data Analytics (BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior literature and the views of auditors, business clients and regulators regarding the external auditing practices and BDA. Design/methodology/approach Using the contingency theory approach, a literature review and 21 in-depth interviews with three different types of respondents, the authors explore factors motivating the use of BDA in external auditing. Findings The study presents a few key findings regarding the use of BD and BDA in external auditing. By disclosing a comprehensive view of current practices, the authors identify two groups of motivating factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies, business clients and regulators. The research indicates a trend whereby external auditors are likely to focus on the procedures not only to satisfy regulatory requirements but also to provide more value for business clients; hence, BDA may be one of the solutions. Research limitations/implications The conclusions of this study are based on interview data collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co-operation. Future studies may investigate the issues addressed in this study further by using different research sites and a broader range of data. Practical implications Current practices and outcomes of using BD and BDA by different types of respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’ sizes, structures and information systems. Originality/value The original contribution of this study lies in the empirical investigation of the comprehensive state-of-the-art of BDA usage and motivating factors in external auditing. Moreover, the study examines the phenomenon of BD as one of the most recent and praised developments in the external auditing context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also makes a methodological contribution by using the approach of constructivist grounded theory for the analysis of qualitative data

Data supply chain (DSC): research synthesis and future directions

In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the pre-existing SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies

Identification and understanding of the influence of antecedents to strategic alignment in a business intelligence context.

The effective information management in organizations is recognized as a critical factor in developing and maintaining competitive advantage, and, for this reason, companies are massively investing in business intelligence. Business Intelligence aims to improve strategic decision-making by enabling the data to be used more efficiently and to gain a better understanding of the organization and the competitive environment (Foley & Guillemette, 2010). Main pillar of this work, BI alignment process is decisive for the successful implementation of any BI project. It is considered the first step to properly set up a winner BI plan, ensuring profitable longevity through continuous improvement, control and organization. Considering the large amount of different critical success factors to the alignment process in BI, past studies converge to what is called “major antecedents”, i.e.: the most relevant of all the CSFs, i.e.: 1) BI governance; 2) Shared BI view; 3) Data-centric business culture; 4) Shared knowledge; 5) Flexible architecture in BI. Considering this fact, all other “smaller antecedents” won’t be covered in this study. Supported by qualitative methods, the present research in the form of case study was applied to a large Canadian financial institution’s list of employees, who had implemented a business intelligence strategy for at least five years. The findings of this study can contribute to both Canadian academic and business environments, by identifying and understanding the influence of antecedents to this strategic alignment process in a Business Intelligence context.La gestion efficace de l'information dans les organisations est reconnue comme un facteur critique dans le développement et le maintien des avantages concurrentiels et, pour cette raison, les entreprises investissent massivement dans des systèmes BI. La Business Intelligence vise à améliorer la prise de décision stratégique en permettant une utilisation plus efficace des données et une meilleure compréhension de l'organisation et de l'environnement concurrentiel (Foley & Guillemette, 2010). Principal pilier de ce travail, le processus d'alignement BI est décisif pour la mise en œuvre réussie de tout projet BI. Il est considéré comme la première étape pour bien mettre en place un plan BI gagnant, assurant une longévité rentable à travers de l'amélioration continue, le contrôle et l'organisation. En considérant la grande quantité de différents facteurs de succès critiques dans le processus d'alignement BI, les études antérieures convergent vers ce qu'on appelle les “antécédents majeurs”, i.e. : les plus pertinents de tous les CSF: 1) La gouvernance BI; 2) Vision BI partagée; 3) Culture d'entreprise centrée vers les données; 4) Connaissances partagées; 5) Architecture flexible en BI. En fonction de ce fait, tous les autres “petits antécédents” ne seront pas couverts dans cette étude. Appuyée par des méthodes qualitatives, la présente recherche sous forme d'étude de cas a été appliquée sur une liste des employés d'une grande institution financière Canadienne, qui avait mis en œuvre une stratégie BI depuis au moins cinq ans. Les résultats de cette étude peuvent contribuer à la fois aux milieux universitaires et commerciaux Canadiens, en identifiant et en comprenant l'influence des antécédents sur ce processus d'alignement stratégique dans un contexte de Business Intelligence

Incorporating Data Governance Frameworks in the Financial Industry

Data governance frameworks are critical to reducing operational costs and risks in the financial industry. Corporate data managers face challenges when implementing data governance frameworks. The purpose of this multiple case study was to explore the strategies that successful corporate data managers in some banks in the United States used to implement data governance frameworks to reduce operational costs and risks. The participants were 7 corporate data managers from 3 banks in North Carolina and New York. Servant leadership theory provided the conceptual framework for the study. Methodological triangulation involved assessment of nonconfidential bank documentation on the data governance framework, Basel Committee on Banking Supervision\u27s standard 239 compliance documents, and semistructured interview transcripts. Data were analyzed using Yin\u27s 5-step thematic data analysis technique. Five major themes emerged: leadership role in data governance frameworks to reduce risk and cost, data governance strategies and procedures, accuracy and security of data, establishment of a data office, and leadership commitment at the organizational level. The results of the study may lead to positive social change by supporting approaches to help banks maintain reliable and accurate data as well as reduce data breaches and misuse of consumer data. The availability of accurate data may enable corporate bank managers to make informed lending decisions to benefit consumers

Smartelo: una herramienta para el cálculo, gestión y presentación de datos en Aulas de Señalamiento Forestal

Uno de los instrumentos de actualidad en la gestión forestal sostenible son las llmadas Aulas de Señalamiento Forestal, las cuales son parcelas forestales señalizadas en los que se han caracterizado, medido, numerado y localizado espacialmente todas las especies arbóreas que las componen. Algunas de los principales aplicaciones que poseen estas Aulas se centran en la práctica del señalamiento para la mejora en la toma de decisiones, estimación de variables dendrométricas in situ y la realización de proyectos de investigación, entre otros. Directamente relacionado la introducción y desarrollo de nuevas herramientas para la gestión forestal sostenible se presenta Smartelo, una aplicación informática que facilita el cálculo, gestión y presentación de datos forestales. Además del diseño y desarrollo de Smartelo, el presente trabajo ha sido completado con el ajuste de tres ecuaciones de crecimiento diametral para las especies características del Aula de Señalamiento de Saldaña, sean estas: Pinus nigra, Pinus sylvestris y Quercus pyrenaica.Máster en Ingeniería de MontesPremio San Isidro otorgado por la Escuela Técnica Superior de Ingenierías Agrarias (Palencia