The implications of creative industries for regional outcomes

As the 21st century progresses, the most successful economies and societies will be creative ones. Worldwide, governments are producing strategies to encourage the development of creative industries and to strengthen the role of knowledge cities nationally and internationally. There is a significant policy discussion regarding the role of creative clusters in strengthening local economies and significant energy has been expended discussing the many positive outcomes of such developments. This article takes these issues as a starting point and considers the role of creative industries within broader concerns regarding uneven metropolitan development. By developing a typology of jobs across Australia’s metropolitan regions, the article will consider the broad social and economic impacts of uneven development of creative industry jobs between metropolitan regions and also the implications for individual metropolitan regions and policy outcomes.

From the account manager’s conscience to top-of-mind : account planning in Australia

What began as the “account manager’s conscience” has grown to be top-of-mind in Australian advertising today. Account planning is a hybrid discipline which uses research to bring the consumer voice to the campaign process during strategy generation, creative development and evaluation. In Australia, account planning is subjected to the “Vegemite Factor” where planners are spread too thinly across accounts and much of the market is dominated by freelance researchers and planners. This unique environment has shaped many different perceptions of account planning in Australia. These are compared with an international definition of account planning and the current research. While many basic tenants of the definition are shared by Australian advertising professionals, the difference appears to be in the ongoing nature, team approach and level of commitment. In Australia, account planners seem to be more facilitators of the strategic direction, than directors of it. Instead of exerting a sustained influence across the campaign, most energy appears to be expended at the start of campaign development, rather than extending through to its evaluation.

Education for all : providing curriculum for students with disabilities in a standards-based environment : implications for Queensland schools

This report investigates lessons learned by educators in the United States when providing a standards-based curriculum for all students including Students with Disabilities (SWD). Assumptions about implementation of these lessons are then made to the Queensland school system. Queensland mainstream schools currently provide a standards-based curriculum for over sixteen thousand-four hundred students with mild-moderate disabilities and appear to be challenged by this new educational reform and its implications to school and teacher practices, beliefs and attitudes. The analysis of US research, literature and educational policy for this report, has provided some implications for Queensland schools in the areas of student participation, achievement and curriculum planning to provide an “education for all”. The analysis and comparison of legislation and policy, which demonstrates some significant similarities, provides greater validity for the application of lessons learned in the United States to the Queensland context. The key findings about lessons learned provides Queensland schools with some assumptions as to why and how they need to refocus school leader and teachers’ practices, beliefs and attitudes to provide an “education for all”. These lessons infer that school leaders and teachers to explicitly focus on equity, expectation, accountability, performance, alignment and collaboration so that effective curriculum is provided for SWD, indeed all students, in the Queensland standards-based curriculum environment.

Electronic workplace surveillance and employee privacy : a comparative analysis of privacy protection in Australia and the United States

More than a century ago in their definitive work “The Right to Privacy” Samuel D. Warren and Louis D. Brandeis highlighted the challenges posed to individual privacy by advancing technology. Today’s workplace is characterised by its reliance on computer technology, particularly the use of email and the Internet to perform critical business functions. Increasingly these and other workplace activities are the focus of monitoring by employers. There is little formal regulation of electronic monitoring in Australian or United States workplaces. Without reasonable limits or controls, this has the potential to adversely affect employees’ privacy rights. Australia has a history of legislating to protect privacy rights, whereas the United States has relied on a combination of constitutional guarantees, federal and state statutes, and the common law. This thesis examines a number of existing and proposed statutory and other workplace privacy laws in Australia and the United States. The analysis demonstrates that existing measures fail to adequately regulate monitoring or provide employees with suitable remedies where unjustifiable intrusions occur. The thesis ultimately supports the view that enacting uniform legislation at the national level provides a more effective and comprehensive solution for both employers and employees. Chapter One provides a general introduction and briefly discusses issues relevant to electronic monitoring in the workplace. Chapter Two contains an overview of privacy law as it relates to electronic monitoring in Australian and United States workplaces. In Chapter Three there is an examination of the complaint process and remedies available to a hypothetical employee (Mary) who is concerned about protecting her privacy rights at work. Chapter Four provides an analysis of the major themes emerging from the research, and also discusses the draft national uniform legislation. Chapter Five details the proposed legislation in the form of the Workplace Surveillance and Monitoring Act, and Chapter Six contains the conclusion.

The central role of communication in developing trust and its effect on employee involvement

Communication plays an important role in the development of trust within an organization. While a number of researchers have studied the relationship of trust and communication, little is known about the specific linkages among quality of information, quantity of information, openness, trust, and outcomes such as employee involvement. This study tests these relationships using communication audit data from 218 employees in the oil industry. Using mediation analysis and structural equation modeling, we found that quality of information predicted trust of one's coworkers and supervisors while adequacy of information predicted one's trust of top management. Trust of coworkers, supervisors, and top management influenced perceptions of organizational openness, which in turn influenced employees' ratings of their own level of involvement in the organization's goals. This study suggests that the relationship between communication and trust is complex, and that simple strategies focusing on either quality or quantity of information may be ineffective for dealing with all members in an organization.

Asset specificity and behavioural uncertainty as moderators of the sales growth : employment growth relationship in emerging ventures

Sales growth and employment growth are the two most widely used growth indicators for new ventures; yet, sales growth and employment growth are not interchangeable measures of new venture growth. Rather, they are related, but somewhat independent constructs that respond differently to a variety of criteria. Most of the literature treats this as a methodological technicality. However, sales growth with or without accompanying employment growth has very different implications for managers and policy makers. A better understanding of what drives these different growth metrics has the potential to lead to better decision making. To improve that understanding we apply transaction cost economics reasoning to predict when sales growth will be or will not be accompanied by employment growth. Our results indicate that our predictions are borne out consistently in resource-constrained contexts but not in resource-munificent contexts.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Consumer acceptance of m-services : Marketing implcations

Consumers' evolving relationships with their mobile devices and their desire to access mobile services (m-services) present new opportunities to marketers, yet little research has been conducted in the area of m-services. Using structural equation modelling, this paper examines the effect of hedonic and utilitarian value of mobile phones on product and purchase involvement. It also investigates the effect of involvement, innovativeness, and self-efficacy on use of m-services. Data were collected from a convenience sample of 250 respondents using an online survey and a modified snowball procedure. Findings are discussed, further implications for managers are suggested and directions for future research are proposed.

Examination of the influence of personal attributes on consumer use on m-services

The rapid uptake of mobile devices has created the capacity to provide services to consumers while they are on the move, and new mobile services (m-services) are constantly emerging. In past research, personal attributes have been found to be import ant in the adoption and use of information and communication technology. However, little research has been conducted in the area of m-services. To explore factors influencing the use of these services, this paper examines personal attributes in terms of motivational, attitudinal and demographic characteristics. Specifically, it investigates the influence of innovativeness, self- efficacy, involvement and impulsiveness, as well as age and gender on m-services use . Data were collected from a convenience sample of 250 respondents using an online survey and a modified snowball procedure. Age and gender were quite well balanced in the sample. The multiple regression model was significant and the hypotheses relating to the positive relationship between impulsiveness, involvement and gender and m-services were supported. Findings are discussed, further implications for managers are suggested and directions for future research are proposed.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

Be careful what you ask for : how inquiry strategy influences readiness mode

Much has been written about affecting change in the workplace, including how to help employees prepare for the process. However, little is known about how participation influences employees' emotions and attitudes at the start of an intervention. By qualitatively analyzing conversations that were triggered by an organizational change effort, we explored how different inquiry strategies influence readiness for change. We examined four inquiry strategies by combining strength or deficit frames with individual or organizational focus. Distinctive conversational patterns emerged within each strategy, which we believe influence peoples' change readiness. In this article we present four readiness modes to describe these patterns and conclude with implications for managers who seek to shape their change efforts more effectively.

Public policy implications of construction innovation research

This report undertakes an exploratory analysis of Construction Innovation research projects in order to answer the question “What are the public policy implications of Construction Innovation research?”