Knowledge management view of environmental sustainability in manufacturing SMEs in the Philippines

This study highlights the role of knowledge management (KM) in enabling small and medium enterprises (SMEs) in a manufacturing industry in a developing country to engage in environmentally sustainable business. Drawing on the knowledge-based view of the firm, it argues that resource-constrained SMEs rely on their relational capital to augment their capability to innovate in order to find better and environmentally sound ways of doing business. However, SMEs need to harness their KM orientation in order to leverage the knowledge-based resources emanating from their relational capital towards building their innovation capability. This capability is essential in integrating effective environmental management practices in business. The findings from our analysis of data from a survey of 241 manufacturing SMEs in the Philippines support these hypotheses and underscore the importance of developing an organisational capability to engage in KM in order to adopt sustainable business practices. The implications of the findings are also discussed.

SUMLOW: Early design-stage sketching of UML diagrams on an e-whiteboard

Most visual diagramming tools provide point-and-click construction of computer-drawn diagram elements using a conventional desktop computer and mouse. SUMLOW is a unified modelling language (UML) diagramming tool that uses an electronic whiteboard (E-whiteboard) and sketching-based user interface to support collaborative software design. SUMLOW allows designers to sketch UML constructs, mixing different UML diagram elements, diagram annotations, and hand-drawn text. A key novelty of the tool is the preservation of hand-drawn diagrams and support for manipulation of these sketches using pen-based actions. Sketched diagrams can be automatically 'formalized' into computer-recognized and -drawn UML diagrams and then exported to a third party CASE tool for further extension and use. We describe the motivation for SUMLOW, illustrate the use of the tool to sketch various UML diagram types, describe its key architecture abstractions and implementation approaches, and report on two evaluations of the toolset. We hope that our experiences will be useful for others developing sketching-based design tools or those looking to leverage pen-based interfaces in software applications.

Stock market liquidity and firm value : an empirical examination of the Australian market

We document a positive relation between stock liquidity and firm value. We examine the mechanism through which stock market liquidity enhances firm value by dividing firm value, as measured by Tobin’s Q, into three components, namely, operating income to price, leverage, and operating income to assets. Using the switch to broker anonymity as an exogenous shock to market liquidity, we show that the increase in liquidity around the shock leads to an increase in firm value. Our results suggest that higher firm value for more liquid stocks seems to stem from enhanced stock prices rather than from better operating performance.

Security and privacy concerns for Australian SMEs cloud adoption: empirical study of metropolitan vs regional SMEs

New national infrastructure initiatives such as the National Broadband Network (NBN) allow small and medium-sized enterprises (SMEs) in Australia to have greater access to cost effective Cloud computing. However, the ability of Cloud computing to store data remotely and share services in a dynamic environment brings with it security and privacy concerns. Evaluating these concerns is critical to address the Cloud computing underutilisation issue and leverage the benefits of costly NBN investment. This paper examines the influence of privacy and security factors on Cloud adoption by Australian SMEs in metropolitan and regional area. Data were collected from 150 Australian SMEs (specifically, 79 metropolitan SMEs and 71 regional SMEs) and structural equation modelling was used for the analysis. The findings reveal that privacy and security factors do not significantly influence the decision-making of Australian SMEs in the adoption of Cloud computing. Moreover, the results indicate that Cloud computing adoption is not influenced by the geographical location (i.e., metropolitan or regional location) of the SMEs. The findings extend the current understanding of Cloud computing adoption by Australian SMEs. The results will be useful to SMEs, Cloud service providers and policy makers devising Cloud security and privacy policies.

Maximizing real-time streaming services based on a multi-servers networking framework

In recent years, we have witnessed substantial exploitation of real-time streaming applications, such as video surveillance system on road crosses of a city. So far, real world applications mainly rely on the traditional well-known client-server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the processing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi-server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real-time streams delivery and storage. Based on this framework, we further introduce a (1-?) approximation algorithm to solve the NP-complete "maximum services"(MS) problem with the intention of handling large number of streaming flows originated by networks and maximizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on simulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme significantly outperforms the traditional solutions.

Informed options trading prior to takeovers - Does the regulatory environment matter?

We investigate the prevalence of informed options trading prior to takeover announcements, when the legal prohibition against insider trading is strictest. Although insider trading laws apply equally to the options and stock markets, the options market is considerably more transparent than the equity market, which makes insider trading in options more easily detectable. We find that privately informed investors trade in the options market prior to takeover announcements; however, their transactions are limited to liquid call options and options with high inherent leverage. Furthermore, we find that prior to takeover announcements, informed investors trade on their private information in the options market only when a SEC investigation of insider trading is unlikely to occur. Our results suggest that even prior to takeover announcements informed investors are attracted to the options market, which increases profit making potential due the greater leverage it affords, although they trade in a way which minimizes the likelihood of detection.

Capital structure of Chinese listed SMEs: an agency theory perspective

Prior work examining the antecedents of capital structure for small and medium-sized enterprises in emerging markets is limited. This paper sheds light on how the corporate governance mechanisms adopted by firms on the newly established Growth Enterprise Market (GEM) in China influence their use of debt. We find that the financial leverage of GEM firms is positively influenced by executives’ shareholding and their excess cash compensation. Ownership concentration appears to reduce leverage, whereas the percentage of tradable shares increases leverage. In contrast, institutional investors’ shareholding does not influence the level of debt. Traditional factors such as tax and operating cash flow are insignificant in explaining the debt levels among GEM firms.

Enabling efficient multi-keyword ranked search over encrypted mobile cloud data through blind storage

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.

Innovation practice using pervasive mobile technology solutions to improve population health management: a pilot study of gestational diabetes patient care in Australia

Healthcare service delivery is moving forward from individual care to population health management, because of the fast growth of health records. However, to improve population health performance, it is necessary to leverage relevant data and information using new technology solutions, such as pervasive diabetes mobile technology solution of Inet International Inc., which offers the potential to facilitate patient empowerment with gestational diabetic care. Hence, this article examines the pilot study outcomes of a small clinical trial focusing on pregnant patients affected by gestational diabetes mellitus, in an Australian not for profit healthcare context. The aims include establishing proof of concept and also assessing the usability, acceptability, and functionality of this mobile solution and thereby generate hypotheses to be tested in a large-scale confirmatory clinical trial.

Supporting scientists in re-engineering sequential programs to parallel using model-driven engineering

Developing complex computational-intensiveand data-intensive scientific applications requires effectiveutilization of the computational power of the availablecomputing platforms including grids, clouds, clusters, multicoreand many-core processors, and graphical processingunits (GPUs). However, scientists who need to leverage suchplatforms are usually not parallel or distributed programmingexperts. Thus, they face numerous challenges whenimplementing and porting their software-based experimentaltools to such platforms. In this paper, we introduce asequential-to-parallel engineering approach to help scientistsin engineering their scientific applications. Our approach isbased on capturing sequential program details, plannedparallelization aspects, and program deployment details usinga set of domain-specific visual languages (DSVLs). Then, usingcode generation, we generate the corresponding parallelprogram using necessary parallel and distributedprogramming models (MPI, OpenCL, or OpenMP). Wesummarize three case studies (matrix multiplication, N-Bodysimulation, and signal processing) to evaluate our approach.

R1SVM: a randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and support vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep autoen-coder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Auditee religiosity, external monitoring, and the pricing of audit services

Based on prior studies which show that firms headquartered in high religiosity counties exhibit high level of business ethics, this study examines whether these firms are associated with low audit risk, and therefore low audit fees. In investigating this relationship, we draw a distinction between intrinsic and extrinsic religiosity of auditees. Using a sample of 25,872 U.S. observations from 2003 to 2012, we find that intrinsic religiosity of the auditees is associated with low audit fees after controlling for auditee extrinsic religiosity, social capital, firm-specific characteristics, and county-specific characteristics. Furthermore, we find that external monitoring (institutional ownership and leverage) weakens the negative relationship between auditee intrinsic religiosity and audit fees. Finally, we conclude that the effect of auditor religiosity on audit fees is a regional effect that may affect the relationship between audit fees and auditee intrinsic religiosity.

101 ways to save money on your tax - legally! : 2016-2017 edition

101 Ways to Save Money on Your Tax – Legally! is the Australian taxpayer's essential guide to maximising returns. Comprehensively updated for 2016-2017, this indispensable resource explains all of the changes to the May 2016 budget to help you pay what you owe and not a penny more. You'll find answers to common questions, tax traps to avoid and plenty of tips from Mr. Taxman himself that can save you hundreds or even thousands of dollars! Leverage your business, education, family, property, medical expenses, levies, shares and superannuation to get the tax return you deserve – and are fully entitled to under Australian law. You'll also find expert advice for tax-effective investments, tax planning and how to find a great accountant, so you can position yourself for an even bigger return next year!

Target behavior and financing: how conclusive is the evidence?

The notion that firms have a debt ratio target that is a primary determinant of financing behavior is influential in finance. Yet, how definitive is the evidence? We address this issue by generating samples where financing is unrelated to a firm’s current debt ratio or a target. We find that much of the available evidence in favor of target behavior based on leverage ratio changes can be reproduced for these samples. Taken together, our findings suggest that a number of existing tests of target behavior have no power to reject alternatives.

Monte Carlo simulations and capital structure research

The evolution of the debt ratio under alternative types of managerialbehavior can generate non-standard leverage processes. This createsproblems for statistical inference in empirical capital structure research. We argue in this paper that when the data generating process is not standard, a useful way to evaluate the appropriateness of inferences and the empirical methodology is via Monte Carlo simulations that mimic the data generating process under alternative assumptions about managerial behavior. We illustrate with several examples.