Cloud Computing Adoption for E-Commerce in Developing Countries: Contributing Factors and Its Implication for Indonesia

This study examines literature in cloud computing adoption for e-commerce in developing countries. The goal is to investigate contributing factors affecting cloud computing adoption of e-commerce in developing countries, in particular its implication for Indonesia. Ten themes have been identified: business size and type, customer service improvement, security, economic value, infrastructure, business process improvement, cloud computing framework, regulatory framework, user acceptance, and stakeholders’ support. Among these ten themes, the infrastructure, security, stakeholders’ support, regulatory framework, user acceptance and business size/types themes are particularly relevant to Indonesia. The paper also presents efforts and projects that are currently in place, at the governmental level, that facilitates cloud computing adoption and e-commerce in Indonesia

Big Data Now, 2015 Edition

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Blockchain Value Creation Logics and Financial Returns

With its complexities and portfolio-nature, the advent of blockchain technology presents several use cases to stakeholders for business value appropriation and financial gains. This 3-essay dissertation focuses on three exemplars and research approaches to understanding the value creation logics of blockchain technology for financial gains. The first essay is a conceptual piece that explores five main affordances of blockchain technology and how these can be actualized and assimilated for business value. Based on the analysis of literature findings, an Affordance-Experimentation-Actualization-Assimilation (AEAA) model is proposed. The model suggests five affordance-to-assimilation value chains and eight value interdependencies that firms can leverage to optimize their value creation and capture during blockchain technology implementation. The second essay empirically examines the financial returns of public firms\u27 blockchain adoption investments at the level of the three main blockchain archetypes (private-permissioned, public-permissioned and permissionless. Drawing upon Fichman\u27s model of the option value of innovative IT platform investments, the study examines business value creation through firm blockchain strategy (i.e., archetype instances, decentralization, and complementarity), learning (i.e., blockchain patents and event participation), and bandwagon effects using quarterly data of firm archetype investments from 2015 to 2020. The study\u27s propensity score matching utilization and fixed-effects modeling provide objective quantification of how blockchain adoption leads to increases in firm value (performance measured by Tobin\u27s q) at the archetype level (permissionless, public permissioned, and private permissioned). Surprisingly, a more decentralized archetype and a second different archetype implementation are associated with a lower Tobin\u27s q. In addition, IT-option proxy parameters such as blockchain patent originality, participation in blockchain events, and network externality positively impact firm performance, whereas the effect of blockchain patents is negative. As the foremost and more established use case of blockchain technology whose business value is accessed in either of the five affordances and exemplifies a permissionless archetype for financial gains, bitcoin cryptocurrency behavior is studied through the lens of opinion leaders on Twitter. The third essay this relationship understands the hourly price returns and volatility shocks that sentiments from opinion leaders generate and vice-versa. With a dynamic opinion leader identification strategy, lexicon and rule-based sentiment analytics, I extract sentiments of the top ten per cent bitcoin opinion leaders\u27 tweets. Controlling for various economic indices and contextual factors, the study estimates a vector autoregression model (VAR) and finds that finds that Bitcoin return granger cause Polarity but the influence of sentiment subjectivity is marginal and only stronger on bitcoin price volatility. Several key implications for blockchain practitioners and financial stakeholders and suggestions for future research are discussed

Smart contract and web dapp for tracing sustainability indicators in the textile and clothing value chain

Mestrado em Engenharia Informática na Escola Superior de Tecnologia e Gestão do Instituto Politécnico de Viana do CasteloNa sociedade atual, o têxtil e vestuário é um dos maiores setores de mercado do mundo. O rápido crescimento desta indústria está a ter impactos sem precedentes na sustentabilidade do planeta, respondendo por consequências negativas ambientais, sociais e de saúde. As tendências da fast-fashion, juntamente com a falta de transparência na cadeia de valor têxtil global, somam-se a cenários desfavoráveis para o mundo, à medida que os níveis crescentes de poluição e consumo de recursos dentro da cadeia de valor atingem máximos históricos a cada ano que passa. O ciclo de vida de uma peça de roupa precisa de se adaptar a um modelo económico regenerativo em vez de linear, que acaba no equivalente a um caminhão de lixo de produtos têxteis sendo descartado num aterro sanitário a cada segundo [1]. Não só as indústrias precisam de reformular os seus processos para circularizar as suas cadeias de valor e promover ações sustentáveis, mas também os consumidores precisam de participar do processo de manter os produtos no círculo da cadeia de valor, pois cabe a eles decidir o destino final de um produto vestuário aquando o seu fim da vida útil. Com estas questões em mente, esta dissertação visa desenvolver duas soluções que possam mitigar os problemas a cima mencionados e promover ações sustentáveis rumo a uma economia circular na cadeia de valor do têxtil e vestuário. Uma solução business-to-business baseada em smart contracts do Hyperledger Fabric para gerir a cadeia de valor do têxtil e vestuário com funcionalidade de rastreabilidade foi desenvolvida como prova de conceito para apoiar as reivindicações de sustentabilidade dos participantes na cadeia de valor, da fibra à peça final de vestuário. A actual funcionabilidade de rastreabilidade desenvolvida no smart contract fornece aos operadores da cadeia de valor a capacidade de rastrear um lote até à sua origem, contudo, também limita a escalabilidade devido ao aumento exponencial do tamanho do bloco, especialmente se considerarmos uma cadeia de valor circular. Para os consumidores, foi proposta uma aplicação descentralizada business-to-consumer-to-consumer com elementos de eco-gamificação para promover o envolvimento e motivação do utilizador para a realização de tarefas que contribuam para a adoção de uma economia circular na cadeia de valor do têxtil e vestuário. Após testar a usabilidade da aplicação com o questionário AttrakDiff, concluiu-se que o sistema precisa de focar a sua usabilidade em prol de um produto orientado à tarefa em vez da orientação pessoal atual da aplicação a fim de promover ações que contribuam para a economia circular da cadeia de valor do têxtil e vestuário.In today’s society, Textile and Clothing (T&C) is one of the biggest market sectors world wide.The sheer size and fast growth of this industry is having unprecedented impacts on sustainability, accounting for negative environmental, social and health consequences. The fast-fashion trends along side the lack of transparency in the global T&C value chain add up to unfavorable scenarios for the world as the increas- ing levels of pollution and resource consumption within the value chain reach historic highs with every year that passes. The lifecycle of a clothing item needs to adapt to a regenerative economic model instead of a linear one that ends up in the equivalent of a garbage truck full of textiles being disposed into a landfill every second [1]. Not only do the industries need to revamp their processes to circularize their value chains and promote sustainable actions, but the consumers also need to partake in the process of keeping the products in the value chain loop as it is up to them to make the final decision upon the end-of-life of an item of clothing. With these issues in mind,this dissertation aims to develop two solutions that can mitigate the aforementioned problems and promote sustainable actions towards a circular economy in the T&C value chain. A Proof-of-Concept (PoC) Business-to-Business (B2B) T&C value chainmanagement smart contract solution builton Hyperledger Fabric with traceability features was developed to support the sustainability claims of participants in the value chain, from fiber to garment. The current traceability feature developed into the smart contract provides value chain operators the capabilities to trace a batch back to its origin, however, it also constraints scalability due to the exponential in crease in block size specially if considering a circular value chain. For the consumers, a Business-to-Consumer-to-Consumer (B2C2C) Decentralized Application (DApp) was proposed with eco-gamification elements fo rpromoting the user’s engagement and motivation to complete tasks that contribute for the adoption of a circular economy in the T&C value chain. After testing the consumer DApp’s usability with the AttrakDiff survey, it was concluded that the system needs to focus it susability towards a task-oriented product instead of the current self-oriented results in order to promote actions that contribute to the circular economy of the T&C value chain

Factors influencing business intelligence and analytics usage extent in South African organisations

Through extensive use of business intelligence and analytics, organisations are better positioned to support fact-based decision making, ultimately leading to improved organisational performance. However, while some organisations recognise and exploit the benefits of business intelligence and analytics use, others fail to capitalise on its potential. It is pertinent therefore to examine factors influencing Business Intelligence and Analytics use within organisations. The three contexts of the Technology-Organisation-Environment (TOE) framework was used as the foundational framework. It is hoped that the findings presented will contribute to a greater understanding of factors influencing business intelligence and analytics usage extent to researchers and practitioners alike. Organisations seeking to promote fact-based decision making through greater business intelligence and analytics use will apply and be better equipped to drive such endeavours

A data management and analytic model for business intelligence applications

Most organisations use several data management and business intelligence solutions which are on-premise and, or cloud-based to manage and analyse their constantly growing business data. Challenges faced by organisations nowadays include, but are not limited to growth limitations, big data, inadequate analytics, computing, and data storage capabilities. Although these organisations are able to generate reports and dashboards for decision-making in most cases, effective use of their business data and an appropriate business intelligence solution could achieve and retain informed decision-making and allow competitive reaction to the dynamic external environment. A data management and analytic model has been proposed on which organisations could rely for decisive guidance when planning to procure and implement a unified business intelligence solution. To achieve a sound model, literature was reviewed by extensively studying business intelligence in general, and exploring and developing various deployment models and architectures consisting of naïve, on-premise, and cloud-based which revealed their benefits and challenges. The outcome of the literature review was the development of a hybrid business intelligence model and the accompanying architecture as the main contribution to the study.In order to assess the state of business intelligence utilisation, and to validate and improve the proposed architecture, two case studies targeting users and experts were conducted using quantitative and qualitative approaches. The case studies found and established that a decision to procure and implement a successful business intelligence solution is based on a number of crucial elements, such as, applications, devices, tools, business intelligence services, data management and infrastructure. The findings further recognised that the proposed hybrid architecture is the solution for managing complex organisations with serious data challenges.ComputingM. Sc. (Computing