Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity

© 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. The world's rivers deliver 19 billion tonnes of sediment to the coastal zone annually, with a considerable fraction being sequestered in large deltas, home to over 500 million people. Most (more than 70 per cent) large deltas are under threat from a combination of rising sea levels, ground surface subsidence and anthropogenic sediment trapping, and a sustainable supply of fluvial sediment is therefore critical to prevent deltas being 'drowned' by rising relative sea levels. Here we combine suspended sediment load data from the Mekong River with hydrological model simulations to isolate the role of tropical cyclones in transmitting suspended sediment to one of the world's great deltas. We demonstrate that spatial variations in the Mekong's suspended sediment load are correlated (r = 0.765, P < 0.1) with observed variations in tropical-cyclone climatology, and that a substantial portion (32 per cent) of the suspended sediment load reaching the delta is delivered by runoff generated by rainfall associated with tropical cyclones. Furthermore, we estimate that the suspended load to the delta has declined by 52.6 ± 10.2 megatonnes over recent years (1981-2005), of which 33.0 ± 7.1 megatonnes is due to a shift in tropical-cyclone climatology. Consequently, tropical cyclones have a key role in controlling the magnitude of, and variability in, transmission of suspended sediment to the coast. It is likely that anthropogenic sediment trapping in upstream reservoirs is a dominant factor in explaining past, and anticipating future, declines in suspended sediment loads reaching the world's major deltas. However, our study shows that changes in tropical-cyclone climatology affect trends in fluvial suspended sediment loads and thus are also key to fully assessing the risk posed to vulnerable coastal systems

Practices of innovation in mobile computing alliances

Global technology alliances (GTAs) and innovation capabilities are the two main themes in this chapter. Drawing on innovation concepts and the resource-based view, this chapter explains how firms gain access to complementary resources, dispersed in the international realm, and incorporate these within their organizations through GTAs. This emergent form of international resource-seeking and network collaboration across borders can be seen as a new phenomenon in international business (IB) (Kogut and Zander, 1992; Rugman and D’Cruz, 2000; Yamin, 2011). Innovation capabilities can be attained by combining a firm’s technology resources with those of overseas alliance partners, offering progress towards enhancement of competitive advantage. From a theoretical aspect, we combine the resource-based view with innovation concepts, and comprehensively examine the dynamics of resources and innovation at both a firm and a network level

Modelling the supply chain perception gaps

This study applies the research of perception gap analysis to supply chain integration and develops a generic model, the 3-Level Gaps Model, with the goal of contributing to harmonization and integration in the supply chain. The model suggests that significant perception gaps may exist among supply chain members with regards to the importance of different performance criteria. The concept of the model is conceived through an empirical and inductive approach, combining the research discipline of supply chain relationship and perception gap analysis. First hand data has been collected through a survey across a key buyer in the motor insurance industry and its eight suppliers. Rigorous statistical analysis testified the research hypotheses, which in turn verified the validity and relevance of the developed 3-Level Gaps Model. The research reveals the significant existence of supply chain perception gaps at all three levels as defined, which could be the root-causes to underperformed supply chain

Big data analytics: does organizational factor matters impact technology acceptance?

Abstract Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. In this regard, this paper tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. These models are applied on the construct, intended usage of big data and also the mediation effect of the OLC constructs is assessed. The data for the study is collected from the students pertaining to information technology disciplines at University of Liverpool, online programme. Though, invitation to participate e-mails are sent to 1035 students, only 359 members responded back with filled questionnaires. This study uses structural equation modelling and multivariate regression using ordinary least squares estimation to test the proposed hypotheses using the latest statistical software R. It is proved from the analysis that compared to other models, model 4 (which is constructed by using the constructs of OLC and TAM frameworks) is able to explain 44% variation in the usage pattern of big data. In addition to this, the mediation test performed revealed that the interaction between OLC dimensions and TAM dimensions on intended usage of big data has no mediation effect. Thus, this work provided inputs to the research community to look into the relation between the constructs of OLC framework and the selection of big data technology