A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

Man and the Last Great Wilderness: Human Impact on the Deep Sea

The deep sea, the largest ecosystem on Earth and one of the least studied, harbours high biodiversity and provides a wealth of resources. Although humans have used the oceans for millennia, technological developments now allow exploitation of fisheries resources, hydrocarbons and minerals below 2000 m depth. The remoteness of the deep seafloor has promoted the disposal of residues and litter. Ocean acidification and climate change now bring a new dimension of global effects. Thus the challenges facing the deep sea are large and accelerating, providing a new imperative for the science community, industry and national and international organizations to work together to develop successful exploitation management and conservation of the deep-sea ecosystem. This paper provides scientific expert judgement and a semi-quantitative analysis of past, present and future impacts of human-related activities on global deep-sea habitats within three categories: disposal, exploitation and climate change. The analysis is the result of a Census of Marine Life – SYNDEEP workshop (September 2008). A detailed review of known impacts and their effects is provided. The analysis shows how, in recent decades, the most significant anthropogenic activities that affect the deep sea have evolved from mainly disposal (past) to exploitation (present). We predict that from now and into the future, increases in atmospheric CO2 and facets and consequences of climate change will have the most impact on deep-sea habitats and their fauna. Synergies between different anthropogenic pressures and associated effects are discussed, indicating that most synergies are related to increased atmospheric CO2 and climate change effects. We identify deep-sea ecosystems we believe are at higher risk from human impacts in the near future: benthic communities on sedimentary upper slopes, cold-water corals, canyon benthic communities and seamount pelagic and benthic communities. We finalise this review with a short discussion on protection and management methods

Big Data for the Greater Good: An Introduction

Big Data, perceived as one of the breakthrough technological developments of our times, has the potential to revolutionize essentially any area of knowledge and impact on any aspect of our life. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, analysts, researchers, and business users can analyze previously inaccessible or unusable data to gain new insights resulting in better and faster decisions, and producing both economic and social value; it can have an impact on employment growth, productivity, the development of new products and services, traffic management, spread of viral outbreaks, and so on. But great opportunities also bring great challenges, such as the loss of individual privacy. In this chapter, we aim to provide an introduction into what Big Data is and an overview of the social value that can be extracted from it; to this aim, we explore some of the key literature on the subject. We also call attention to the potential ‘dark’ side of Big Data, but argue that more studies are needed to fully understand the downside of it. We conclude this chapter with some final reflections

The effects of silica and water on the viscosity of hydrous quartzofeldspathic melts

The viscosities of hydrous melts (0.65 to 2.8 wt% H2O) with quartzofeldspathic compositions corresponding to Ab, Ab74Qz26, and Ab48Qz52 (mole proportions calculated on the basis of eight oxygen atoms; Ab 5 NaAlSi3O8, Qz 5 Si4O8) have been determined between 980 and 1375 8C at pressures between 190 and 360 MPa using the falling sphere technique. The use of large bubble-free hydrous glass cylinders (placed in internally heated pressure vessels) previously prepared and already containing markers and platinum spheres allows falling distances up to several centimeters to be measured with a precision of 650 to 200 mm. This results in a precision of 615% relative or less for most viscosity data (610% relative or less if the temperature is known within 65 8C). For a water content of 2.8 wt% H2O, viscosity increases with increasing Qz content. In the investigated viscosity range, no significant deviation from Arrhenian behavior is observed and the activation energy of viscous flow increases slightly with decreasing water content of the melt (for Ab). Combining the experimental data obtained in this study with data for a haplogranitic composition investigated previously by Schulze et al. (1996) shows that the viscosities, and hence, the activation energies of viscous flow are similar for compositions with the same atom ratio (Si 1 Al)/(H 1 Na 1 K) (SA/HNK). Thus, melt viscosity is constant if Al, charge balanced by Na or K, is exchanged with Si 1 H (H incorporated as OH or H2O). The viscosities (in dPa·s) of all investigated hydrous haplogranite compositions with water contents ranging between 0.7 and 8.2 wt% H2O can be calculated to better than 60.15 log units using the expression: logh 5 21.8 1 [940 1 5598·(SA/HNK)0.3774]·1/T where T is expressed in Kelvin and varies from 1073 to 1650 K

Big data analytics in healthcare: promise and potential

Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome