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

Using dissolved H₂O in rhyolitic glasses to estimate palaeo-ice thickness during a subglacial eruption at Bláhnúkur(Torfajökull, Iceland)

The last decade has seen the refinement of a technique for reconstructing palaeo-ice thicknesses based on using the retained H2O and CO2 content in glassy eruptive deposits to infer quenching pressures and therefore ice thicknesses. The method is here applied to Bláhnúkur, a subglacially erupted rhyolitic edifice in Iceland. A decrease in water content from ~0.7 wt.% at the base to ~0.3 wt.% at the top of the edifice suggests that the ice was 400 m thick at the time of the eruption. As Bláhnúkur rises 350 m above the surrounding terrain, this implies that the eruption occurred entirely within ice, which corroborates evidence obtained from earlier lithofacies studies. This paper presents the largest data set (40 samples) so far obtained for the retained volatile contents of deposits from a subglacial eruption. An important consequence is that it enables subtle but significant variations in water content to become evident. In particular, there are anomalous samples which are either water-rich (up to 1 wt.%) or water-poor (~0.2 wt.%), with the former being interpreted as forming intrusively within hyaloclastite and the latter representing batches of magma that were volatile-poor prior to eruption. The large data set also provides further insights into the strengths and weaknesses of using volatiles to infer palaeo-ice thicknesses and highlights many of the uncertainties involved. By using examples from Bláhnúkur, the quantitative use of this technique is evaluated. However, the relative pressure conditions which have shed light on Bláhnúkur’s eruption mechanisms and syn-eruptive glacier response show that, despite uncertainties in absolute values, the volatile approach can provide useful insight into the mechanisms of subglacial rhyolitic eruptions, which have never been observed

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

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

Hard Substrata Community Patterns, 1-120m, North Jamaica

Tropical marine communities from shallow-water (\u3c30 m) carbonate environments are often dominated by hermatypic scleractinian corals with lesser amounts of crustose coralline algae and endolithic demosponges. Living cover is typically high (80-100%). Along the north-central coast of Jamaica and at many western Atlantic sites, communities existing below 55 m inhabit a vertical to overhanging wall of reef limestone, the deep fore reef, which extends to approximately 130 m. At 60 m the community resembles that of shallower water, although scleractinians are less abundant and encrusting and erect demosponges are much more abundant. Coralline algae and macroalgae are also important space occupants at 60 m and living cover approaches 65%. Encrusting sponges and coralline, filamentous, and macroalgae predominate in the middle region of the deep fore reef A low-diversity assemblage occupying 40% of the substratum and dominated by diminutive encrusting and endolithic? demosponges and largely endolithic filamentous algae occurs from 100-130 m, the lower limit of the deep fore reef. Community structure and zonation on the shallower reefs is controlled by a number of biotic and abiotic factors, most notably predation/grazing, light intensity, and turbulence. On the deep fore reef, grazing and turbulence are greatly reduced. While reduced in intensity, light continues to exert a strong influence on community bathymetric zonation. Sedimentation also exerts an important control on the spatial distribution of the deep fore-reef biota with the most diverse assemblages flourishing in areas protected from sediment Despite a regime of reduced disturbance in deep water, community diversity remains relatively constant to a depth of 90-100 m

Geomorphology and community composition of two adjacent reef areas, Discovery Bay, Jamaica

The north coast of Jamaica possesses a highly variable topography which reflects tectonic displacement of Pleistocene rock units. Holocene reef development is intimately related to the underlying Pleistocene rocks and reflects such variation in basement morphology. Underwater surveys have established that two adjacent reef areas, the East and West Fore Reefs at Discovery Bay, Jamaica, exhibit contrasting bottom profiles which are due to differential offset of the underlying Pleistocene shelf carbonates...

Patterns of reef community structure, North Jamaica

Reef communities were quantitatively surveyed over the range of 0.5 to 56 m in the vicinity of Discovery Bay, Jamaica, at intervals spanning 1977–1982. This study provides data on reef communities which were subsequently altered by major disturbance events (e.g., Hurricane Allen in 1980 and the mass mortality of the urchin, Diadema in 1982). Living cover by the sessile epibiota is typically high, between 82–95%, at the census sites. Corals occupy from 28–60% of the reef surface with no clear depth-related trends in cover. The most striking bathymetric trends are displayed by the algae and sponges. Cover by macro- and filamentous algae (8–32%) and fleshy sponges (2–15%) is positively correlated with increasing depth on the fore reef while cover by coralline algae (4–37%) and boring sponges (0–32%) is negatively correlated with increasing depth.Coral species diversity (H′, loge) increases from 0.13 on the seaward portion of the reef crest at 0.5 m to 2.12 at 22 m on the fore reef escarpment. This highest value at 22 m may represent an edge effect. Despite the lowered rates of physical and biotic disturbance on the fore reef slope (30–56 m), diversity values fluctuate between 1.49–1.76 and are similar to, or higher than, values from the shallower (0.5–15 m) reef crest and fore reef terrace.Q-mode cluster analyses of eight reef sites resulted in the delineation of the following five reef zones (listed from onshore to offshore): the Rear Zone (1 m depth), Reef Crest (approximately 0.5–5 m), Lower Fore Reef Terrace (approximately 10–25 m), Upper Fore Reef Slope (approximately 30–45 m) and Lower Fore Reef Slope (approximately 45–60+ m). Similar zonations were produced regardless of whether the distribution of coral species or larger, operational taxonomic units (e.g., corals, coralline algae, macro- and filamentous algae, boring sponges and fleshy or erect sponges) were employed. Both biotic and abiotic factors contribute to this zonation