Understanding market agility for new product success with big data analytics
درک چابکی بازار برای موفقیت محصول جدید با تجزیه و تحلیل داده های بزرگ-2020
The complexity that characterises the dynamic nature of the various environmental factors makes it very compelling for firms to be capable of addressing the changing customers needs. The current study examines the role of big data in new product success. We develop a qualitative research with case study approach to look at this. Specifically, we look at multiple cases to get in-depth understanding of customer agility for new product success with big data analytics. The findings of the study provide insight into the role of customer agility in new product success. This study unpacks the interconnectedness of the effective use of data aggregation tools, the effectiveness of data analysis tools and customer agility. It also explores the link between all of these factors and new product success. The study is reasonably telling in that it shows that the effective use of data aggregation and data analysis tools results in customer agility which in itself explains how an organisation senses and responds speedily to opportunities for innovation in the competitive marketing environment. The current study provides significant theoretical contributions by providing evidence for the role of big data analytics, big data aggregation tools, customer agility, organisational slack and environmental turbulence in new product success.
Keywords: Big data analytics | Customer agility | Effective use of data | New product success
Role of big data and social media analytics for business to business sustainability: A participatory web context
نقش تجزیه و تحلیل داده های بزرگ و رسانه های اجتماعی برای پایداری تجارت از مشاغل: زمینه وب مشارکتی-2020
The digital transformation is an accumulation of various digital advancements, such as the transformation of the web phenomenon. The participatory web that allows for active user engagement and gather intelligence has been widely recognised as a value add tool by organisations of all shapes and sizes to improve business productivity and efficiency. However, its ability to facilitate sustainable business-to-business (B2B) activities has lacked focus in the business and management literature to date. This qualitative research is exploratory in nature and fills this gap through findings arising from interviews of managers and by developing taxonomies that highlight the capability of participatory web over passive web to enable different firms to engage in business operations. For this purpose, two important interrelated functions of business i.e. operations and marketing have been mapped against three dimensions of sustainability. Consequently, this research demonstrates the ability of big data and social media analytics within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities. The research findings will be useful for both academics and managers who are interested in understanding and further developing the business use of participatory web tools to achieve business sustainability. Hence, this may be considered as a distinct way of attaining sustainability.
Keywords: Participatory web | Marketing and operations | Big data | Social media analytics | Business sustainability | Business-to-business (B2B)
Automatic and adaptive localization for ensemble-based history matching
محلی سازی خودکار و سازگار برای تطبیق تاریخ مبتنی بر گروه-2020
Ensemble-based history matching methods are among the state-of-the-art approaches to reservoir characterization. In practice, however, they often suffer from ensemble collapse, a phenomenon that deteriorates history matching performance. To prevent ensemble collapse, it is customary to equip an ensemble history matching algorithm with a certain localization scheme. In a previous study, the authors propose an adaptive localization scheme that exploits the correlations between model variables and simulated observations. Correlation-based adaptive localization not only overcomes some longstanding issues (e.g., the challenges in handling non-local or time-lapse observations) arising in the conventional distance-based localization, but also is more convenient and flexible to use in real field case studies. In applications, however, correlation-based localization is also found to be subject to two problems. One is that, it requires to run a relatively large ensemble in order to achieve decent performance in an automatic manner, which becomes computationally expensive in large-scale problems. As a result, certain empirical tuning factors are introduced to reduce the computational costs. The other problem is that, the way used to compute the tapering coefficients in the previous study may induce discontinuities, and neglect the information of certain still-influential observations for model updates. The main objective of this work is to improve the efficiency and accuracy of correlation-based adaptive localization, making it run in an automatic manner but without incurring substantial extra computational costs. To this end, we introduce two enhancements that aim to avoid the aforementioned two problems, namely, empirical tuning and discontinuities. We apply the resulting automatic and adaptive correlation-based localization with these two enhancements to a 2D and a 3D cases investigated in the previous study, and show that it leads to better history matching performance (in terms of efficiency and/or estimation accuracy) than that is achieved in the previous work.
Keywords: Ensemble data assimilation | Iterative ensemble smoother | Correlation-based adaptive localization | Seismic history matching | Big data assimilation
Adding value to Linked Open Data using a multidimensional model approach based on the RDF Data Cube vocabulary
افزودن ارزش به داده های باز شده پیوند شده با استفاده از روش مدل چند بعدی مبتنی بر واژگان مکعبی داده های RDF -2020
Most organisations using Open Data currently focus on data processing and analysis. However, although Open Data may be available online, these data are generally of poor quality, thus discouraging others from contributing to and reusing them. This paper describes an approach to publish statistical data from public repositories by using Semantic Web standards published by the W3C, such as RDF and SPARQL, in order to facilitate the analysis of multidimensional models. We have defined a framework based on the entire lifecycle of data publication including a novel step of Linked Open Data assessment and the use of external repositories as knowledge base for data enrichment. As a result, users are able to interact with the data generated according to the RDF Data Cube vocabulary, which makes it possible for general users to avoid the complexity of SPARQL when analysing data. The use case was applied to the Barcelona Open Data platform and revealed the benefits of the application of our approach, such as helping in the decision-making process.
Keywords: Linked Open Data | Multidimensional modelling | Conceptual modelling | RDF Data Cube vocabulary | Semantic web | Big data
Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
تجزیه و تحلیل داده های بزرگ به عنوان یک رویکرد برتری عملیاتی برای افزایش عملکرد پایدار زنجیره تأمین-2020
Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.
Keywords: Big data analytics | Operational excellence | Dynamic capability view | Supply chain sustainability | Learning performance
Is the private sector more efficient? Big data analytics of construction waste management sectoral efficiency
آیا بخش خصوصی کارآمدتر است؟ تجزیه و تحلیل داده های بزرگ از کارآیی بخش مدیریت زباله های ساختمانی-2020
Efficiency disparity between the public and private sectors is a non-trivial issue that concerns fundamental choices of socio-political-economic systems. Waste management academia and industry also wrestle with issues relating to the choice between public and private sectors. To examine the disparity exclusively caused by “sector”, in statistics language, one needs data that is sufficiently big to control many other confounders, e.g., sites, project types, and construction technologies. This paper attempts to ascertain the construction waste management (CWM) efficiency disparity between the public and private sectors by using big data in Hong Kong. The waste disposal records of 132 projects, including 70 public and 62 private projects, were extracted and analysed. By comparing the waste generation flows (WGFs) and accumulative WGFs, it is found that, by and large, there is no significant efficiency disparity in CWM between the two sectors. However, a closer investigation discovered that the private sector outperforms their public counterpart in demolition projects, while the latter performs better in foundation and new building projects. Although there are private projects with higher CWM performance, their divergence between the best and average projects are larger than public ones. Such findings thus reject casual remarks that the private sector is more efficient in CWM. The underlying reasons maybe the waste management index practice promoted in public projects while the private sector is often incentivized to perform better CWM to save waste disposal levies. Future research is recommended to delve into the causes of the efficiency disparity and introduce CWM interventions accordingly.
Keywords: Public-private disparity | Economic efficiency | Construction waste management | Big data | Hong Kong
Digital transformation and localizing the Sustainable Development Goals (SDGs)
تحول دیجیتال و بومی سازی اهداف توسعه پایدار (SDG)-2020
This paper examines how digital transformation can impact the localization and achievement of the Sustainable Development Goals (SDGs). We collect data on the progress made towards SDGs, existing e-governance and big data initiatives, as well as the state of localization in seven countries from different parts of the world. We find that localization allows governments to effectively tailor sustainable development strategies at the local level, which can be boosted with digital transformation. Localization requires local governments’ effective planning by ensuring that budgetary allocations reflect the priorities of local communities. Our main recommendations are that adequate data are necessary to identify and follow-up with decision makers, which requires a review of institutional competence in dealing with information and data and the use of digital transformation for this purpose. Appropriate funding for development programs and projects and effective application at the local level are also important. This requires policy makers to direct and encourage investments in the ‘The Digital Network Architecture’ (DNA) infrastructure and human capital. A key limitation lies in its sample of countries used with their own cultural and population features. However, our findings provide a good basis to analyse further case studies with more heterogeneous compositions as well as other practices of digital transformation.
Keywords: Sustainable Development Goals | Localization | Digital transformation | Big data | e-government | Information technology | Regional development | Virtual decentralization
Mining discriminative spatial cues for aerial image quality assessment towards big data
استخراج نشانه های مکانی تبعیض آمیز برای ارزیابی کیفیت تصویر هوایی نسبت به داده های بزرگ-2020
Evaluating massive-scale aerial/satellite images quality is useful in computer vision and intelligent applications. Traditional local features-based algorithms have achieved impressive performance. However, spatial cues, i.e., geometric property and topological structure, have not been exploited effectively and explicitly. Thus, in this paper, we propose a novel method for image quality assessment towards aerial/satellite images, where discriminative spatial cues are well encoded. More specifically, in order to mine inherent spatial structure of aerial images, each image is segmented into several basic components such as buildings, airport and playground. Afterwards, a weighted region adjacency graph (RAG) is built based on the basic components to represent the spatial feature of each aerial image. We integrate the spatial feature with other transform domain features, and train a support vector regression model to achieve image quality assessment. Experiments demonstrate that our method shows competitive or even better performance compared with several state-of-the-art algorithms.
Keywords: Big data | Artificial intelligent | Data mining | Image quality assessment
Analytics in the era of big data: The digital transformations and value creation in industrial marketing
تجزیه و تحلیل در عصر داده های بزرگ: تحولات دیجیتال و ایجاد ارزش در بازاریابی صنعتی-2020
Big data analytics has been a topical area in the past decade. Despite it is emphased as a promising tool for the B2B sectors, there is a short of academic studies about this phenomenon in the industrial markets. Existing big data analytics focuses more on the consumers marketing aspect, while in fact both the consumers data and the machine-generated transaction data can be gathered and analysed at the interorganisational level. Subsequently, there is a need to increase the attention on the B2B aspects of big data analytics and the interactions of stakeholders. This paper, therefore, investigates the digital transformation enabled by big data analytics in the industrial markets and provides a conceptual framework. It solicits research articles that provide insights into various industrial contexts of this topic and applied both qualitative and quantitative approaches to identify the big data gathering and applications for value creation.
Keywords: Big data | B2B analytics | Digital transformations | Management revolution | Value creation
Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management
ادغام تجزیه و تحلیل داده های بزرگ جاسازی شده معماری شهر هوشمند با وب سایت RESTful برای ارائه خدمات کارآمد و مدیریت انرژی-2020
Emergence of smart things has revolutionized the conventional internet into a connected network of things, maturing the concept of Internet of Things (IoT). With the evolution of IoT, many attempts were made to realize the notion of smart cities. However, demands for processing enormous amount of data and platform incompatibilities of connected smart things hindered the actual implementation of smart cities. Keeping it in view, we proposed a Big Data analytics embedded smart city architecture, which is further integrated with the web via a smart gateway. Integration with the web provides a universal communication platform to overcome the platform incompatibilities of smart things. We introduced Big Data analytics to enhance data processing speed. Further, we evaluated authentic datasets to determine the threshold values for intelligent decision-making and to present the performance improvement gained in data processing. Finally, we presented a representational state transfer (RESTful) web of things (WoT) integrated smart building architecture (smart home) to reveal the performance improvements of the proposed smart city architecture in terms of network performance and energy management of smart buildings.
Keywords: Smart city | Big Data analytics | Smart home | Web of things | RESTful architecture