Optimal monitoring in family-owned companies? Evidence from Asia

Manuscript Type: Empirical Research Issue: We propose that high levels of monitoring are not always in the best interests of minority shareholders. In family-owned companies the optimal level of board monitoring required by minority shareholders is expected to be lower than that of other companies. This is because the relative benefits and costs of monitoring are different in family-owned companies. Research Findings: At moderate levels of board monitoring, we find concave relationships between board monitoring variables and firm performance for family-owned companies but not for other companies. The optimal level of board monitoring for our sample of Asian family-owned companies equates to board independence of 38%, separation of the Chairman and CEO positions and establishment of audit and remuneration committees. Additional testing shows that the optimal level of board monitoring is sensitive to the magnitude of the agency conflict between the family group and minority shareholders and the presence of substitute monitoring. Practitioner/Policy Implications: For policymakers, the results show that more monitoring is not always in the best interests of minority shareholders. Therefore, it may be inappropriate for regulators to advise all companies to follow the same set of corporate governance guidelines. However, our results also indicate that the board governance practices of family-owned companies are still well below the identified optimal levels. Keywords: Corporate Governance, Board Independence, Board of Directors, Family Firms, Monitoring.

Service staff attitudes, organisational practices and performance drivers

We provide conceptual and empirical insights elucidating how organizational practices influence service staff attitudes and behaviors and how the latter set affects organizational performance drivers. Our analyses suggest that service organizations can enhance their performance by putting in place strategies and practices that strengthen the service-oriented behaviors of their employees and reduce their intentions to leave the organization. Improved performance is accomplished through both the delivery of high quality services (enhancing organizational effectiveness) and the maintenance of frontline staff(increasing organizational efficiency). Specifically, service-oriented business strategies in the form of organizational-level service orientation and practices in the form of training directly influence the manifest service-oriented behaviors of staff. Training also indirectly affects the intention of frontline staff to leave the organization; it increases job satisfaction, which, in turn has an impact on affective commitment. Both affective and instrumental commitment were hypothesized to reduce the intentions of frontline staff to leave the organization, however only affective commitment had a significant effect.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Darjeeling, a Java compatible virtual machine for microcontrollers

The Java programming language enjoys widespread popularity on platforms ranging from servers to mobile phones. While efforts have been made to run Java on microcontroller platforms, there is currently no feature-rich, open source virtual machine available. In this paper we present Darjeeling, a system comprising offline tools and a memory efficient runtime. The offline post-compiler tool analyzes, links and consolidates Java class files into loadable modules. The runtime implements a modified Java VM that supports multithreading and is designed specifically to operate in constrained execution environments such as wireless sensor network nodes. Darjeeling improves upon existing work by supporting inheritance, threads, garbage collection, and loadable modules while keeping memory usage to a minimum. We have demonstrated Java running on AVR128 and MSP430 micro-controllers at speeds of up to 70,000 JVM instructions per second.

Design and deployment of a remote robust sensor network : experiences from an outdoor water quality monitoring network

This paper investigates a wireless sensor network deployment - monitoring water quality, e.g. salinity and the level of the underground water table - in a remote tropical area of northern Australia. Our goal is to collect real time water quality measurements together with the amount of water being pumped out in the area, and investigate the impacts of current irrigation practice on the environments, in particular underground water salination. This is a challenging task featuring wide geographic area coverage (mean transmission range between nodes is more than 800 meters), highly variable radio propagations, high end-to-end packet delivery rate requirements, and hostile deployment environments. We have designed, implemented and deployed a sensor network system, which has been collecting water quality and flow measurements, e.g., water flow rate and water flow ticks for over one month. The preliminary results show that sensor networks are a promising solution to deploying a sustainable irrigation system, e.g., maximizing the amount of water pumped out from an area with minimum impact on water quality.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Monitoring and analysis of internet traffic targeting unused address spaces

Today’s evolving networks are experiencing a large number of different attacks ranging from system break-ins, infection from automatic attack tools such as worms, viruses, trojan horses and denial of service (DoS). One important aspect of such attacks is that they are often indiscriminate and target Internet addresses without regard to whether they are bona fide allocated or not. Due to the absence of any advertised host services the traffic observed on unused IP addresses is by definition unsolicited and likely to be either opportunistic or malicious. The analysis of large repositories of such traffic can be used to extract useful information about both ongoing and new attack patterns and unearth unusual attack behaviors. However, such an analysis is difficult due to the size and nature of the collected traffic on unused address spaces. In this dissertation, we present a network traffic analysis technique which uses traffic collected from unused address spaces and relies on the statistical properties of the collected traffic, in order to accurately and quickly detect new and ongoing network anomalies. Detection of network anomalies is based on the concept that an anomalous activity usually transforms the network parameters in such a way that their statistical properties no longer remain constant, resulting in abrupt changes. In this dissertation, we use sequential analysis techniques to identify changes in the behavior of network traffic targeting unused address spaces to unveil both ongoing and new attack patterns. Specifically, we have developed a dynamic sliding window based non-parametric cumulative sum change detection techniques for identification of changes in network traffic. Furthermore we have introduced dynamic thresholds to detect changes in network traffic behavior and also detect when a particular change has ended. Experimental results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach, using both synthetically generated datasets and real network traces collected from a dedicated block of unused IP addresses.

Validating denial of service vulnerabilities in web services

The loosely-coupled and dynamic nature of web services architectures has many benefits, but also leads to an increased vulnerability to denial of service attacks. While many papers have surveyed and described these vulnerabilities, they are often theoretical and lack experimental data to validate them, and assume an obsolete state of web services technologies. This paper describes experiments involving several denial of service vulnerabilities in well-known web services platforms, including Java Metro, Apache Axis, and Microsoft .NET. The results both confirm and deny the presence of some of the most well-known vulnerabilities in web services technologies. Specifically, major web services platforms appear to cope well with attacks that target memory exhaustion. However, attacks targeting CPU-time exhaustion are still effective, regardless of the victim’s platform.

Theory and development of GPS integrity monitoring system

This thesis discusses various aspects of the integrity monitoring of GPS applied to civil aircraft navigation in different phases of flight. These flight phases include en route, terminal, non-precision approach and precision approach. The thesis includes four major topics: probability problem of GPS navigation service, risk analysis of aircraft precision approach and landing, theoretical analysis of Receiver Autonomous Integrity Monitoring (RAIM) techniques and RAIM availability, and GPS integrity monitoring at a ground reference station. Particular attention is paid to the mathematical aspects of the GPS integrity monitoring system. The research has been built upon the stringent integrity requirements defined by civil aviation community, and concentrates on the capability and performance investigation of practical integrity monitoring systems with rigorous mathematical and statistical concepts and approaches. Major contributions of this research are: • Rigorous integrity and continuity risk analysis for aircraft precision approach. Based on the joint probability density function of the affecting components, the integrity and continuity risks of aircraft precision approach with DGPS were computed. This advanced the conventional method of allocating the risk probability. • A theoretical study of RAIM test power. This is the first time a theoretical study on RAIM test power based on the probability statistical theory has been presented, resulting in a new set of RAIM criteria. • Development of a GPS integrity monitoring and DGPS quality control system based on GPS reference station. A prototype of GPS integrity monitoring and DGPS correction prediction system has been developed and tested, based on the A USN A V GPS base station on the roof of QUT ITE Building.

A distributed denial of service testbed

The Denial of Service Testing Framework (dosTF) being developed as part of the joint India-Australia research project for ‘Protecting Critical Infrastructure from Denial of Service Attacks’ allows for the construction, monitoring and management of emulated Distributed Denial of Service attacks using modest hardware resources. The purpose of the testbed is to study the effectiveness of different DDoS mitigation strategies and to allow for the testing of defense appliances. Experiments are saved and edited in XML as abstract descriptions of an attack/defense strategy that is only mapped to real resources at run-time. It also provides a web-application portal interface that can start, stop and monitor an attack remotely. Rather than monitoring a service under attack indirectly, by observing traffic and general system parameters, monitoring of the target application is performed directly in real time via a customised SNMP agent.

Urban growth monitoring techniques for sustainable development

This paper discusses the role of advance techniques for monitoring urban growth and change for sustainable development of urban environment. It also presents results of a case study involving satellite data for land use/land cover classification of Lucknow city using IRS-1C multi-spectral features. Two classification algorithms have been used in the study. Experiments were conducted to see the level of improvement in digital classification of urban environment using Artificial Neural Network (ANN) technique.

Monitoring of the vertical movements of rail sleepers with the passage of trains

Due to an ever increasing demand for more frequent and higher volume of train service, the physical conditions of tracks in modem railways are deteriorating more quickly when compared to tracks built decades ago. There are incidences in both the UK and Hong Kong indicating there are needs for a more stringent checks on the rail conditions using suitable and effective non-invasive and nondestructive condition monitoring system.

A criminological analysis : using real-time monitoring to gather data on online predators

The Internet presents a constantly evolving frontier for criminology and policing, especially in relation to online predators – paedophiles operating within the Internet for safer access to children, child pornography and networking opportunities with other online predators. The goals of this qualitative study are to undertake behavioural research – identify personality types and archetypes of online predators and compare and contrast them with behavioural profiles and other psychological research on offline paedophiles and sex offenders. It is also an endeavour to gather intelligence on the technological utilisation of online predators and conduct observational research on the social structures of online predator communities. These goals were achieved through the covert monitoring and logging of public activity within four Internet Relay Chat(rooms) (IRC) themed around child sexual abuse and which were located on the Undernet network. Five days of monitoring was conducted on these four chatrooms between Wednesday 1 to Sunday 5 April 2009; this raw data was collated and analysed. The analysis identified four personality types – the gentleman predator, the sadist, the businessman and the pretender – and eight archetypes consisting of the groomers, dealers, negotiators, roleplayers, networkers, chat requestors, posters and travellers. The characteristics and traits of these personality types and archetypes, which were extracted from the literature dealing with offline paedophiles and sex offenders, are detailed and contrasted against the online sexual predators identified within the chatrooms, revealing many similarities and interesting differences particularly with the businessman and pretender personality types. These personality types and archetypes were illustrated by selecting users who displayed the appropriate characteristics and tracking them through the four chatrooms, revealing intelligence data on the use of proxies servers – especially via the Tor software – and other security strategies such as Undernet’s host masking service. Name and age changes, which is used as a potential sexual grooming tactic was also revealed through the use of Analyst’s Notebook software and information on ISP information revealed the likelihood that many online predators were not using any safety mechanism and relying on the anonymity of the Internet. The activities of these online predators were analysed, especially in regards to child sexual grooming and the ‘posting’ of child pornography, which revealed a few of the methods in which online predators utilised new Internet technologies to sexually groom and abuse children – using technologies such as instant messengers, webcams and microphones – as well as store and disseminate illegal materials on image sharing websites and peer-to-peer software such as Gigatribe. Analysis of the social structures of the chatrooms was also carried out and the community functions and characteristics of each chatroom explored. The findings of this research have indicated several opportunities for further research. As a result of this research, recommendations are given on policy, prevention and response strategies with regards to online predators.

Moving tele-monitoring and tele-treatment from promise to practice : a business model approach for a chronic lower back pain application

The availability of new information and communication technologies creates opportunities for new, mobile tele-health services. While many promising tele-health projects deliver working R&D prototypes, they often do not result in actual deployment. We aim to identify critical issues than can increase our understanding and enhance the viability of the mobile tele-health services beyond the R&D phase by developing a business model. The present study describes the systematic development and evaluation of a service-oriented business model for tele-monitoring and -treatment of chronic lower back pain patients based on a mobile technology prototype. We address challenges of multi-sector collaboration and disruptive innovation.