Small business prosperity and wellbeing : an alternative measure for success among small business owners

This study investigated the influences of business prosperity on small business owners’ wellbeing with gender as a moderator. A sample of 687 Australian small business owners from the Household, Income and Labour Dynamics in Australia Survey (HILDA) from 2008 to 2010 was utilised. Findings suggest that procedural utility contributed to small business owners’ wellbeing over economic utility. Procedural utility was significantly related to small business owners’ wellbeing for males and females. However, economic utility contributed only to male small business owners’ wellbeing. In order to increase the understanding of these findings it is suggested that more theoretical work regarding gender differences in procedural and economic utility should be carried out.

How public owners communicate the sustainability requirements of green design-build projects

The design-build (DB) system is regarded as an effective means of delivering sustainable buildings. Specifying clear sustainability requirements to potential contractors is of great importance to project success. This research investigates the current state-of-the-practice for the definition of sustainability requirements within the public sectors of the U.S. construction market using a robust content analysis of 49 DB requests for proposals (RFPs). The results reveal that owners predominantly communicate their desired level of sustainability through the LEED certification system. The sustainability requirement has become an important dimension for the best-value evaluation of DB contractors with specific importance weightings of up to 25%. Additionally, owners of larger projects and who provide less design information in their RFPs generally allocate significantly higher importance weightings to sustainability requirements. The primary knowledge contribution of this study to the construction industry is the reveal of current trend in DB procurement for green projects. The findings also provide owners, architects, engineers, and constructors with an effective means of communicating sustainability objectives in solicitation documents.

Informal e-learning : the case of small business owners

Purpose - The purpose of this paper is to investigate the use of an informal online discussion forum (ODF) to encourage voluntary participation and promote double-loop learning by small business owners (SBOs). Design/methodology/approach - A qualitative methodology was used where data gathered from three sources, the ODF posts, in-depth interviews with participants and a focus group with non-participants. These were analysed to evaluate learning of SBOs in an ODF. Findings - This research provides evidence that an ODF for SBOs supports double-loop learning; however, participation could not be assumed simply by the online availability of the discussion resource. Research limitations/implications - Few SBOs participated in the ODF which is consistent with research finding SBOs are a difficult group to engage in learning. Four forms of data were analysed to strengthen results. Practical implications - Caution should be exercised when considering investment in e-learning for SBOs. Originality/value - Evidence showing e-learning through an informal voluntary ODF can promote deep learning for SBOs.

New sale and leasing requirements for owners of swimming pools and their application to strata schemes

This article considers the changes to the Swimming Pools Act 1992 (NSW)(Act) which established a State-wide online register of all private swimming pools in NSW requiring pool owners to register their pools by 19 November 2013. Amendments to the Act introduced changes to the conveyancing and residential tenancy regulations to require vendors and landlords to have a valid Compliance Certificate issued for their swimming pool before offering the property for sale or lease. This article provides a brief overview of the new sale and leasing requirements effective from 29 April 2014, focusing on its application to lot owners within strata and community title schemes and other owners of water front properties with pools on Crown Land Reserves.

Make-or-break : work and family interface and wellbeing among small business owners

This study responds to calls for research on work-family aspects in entrepreneurship research. Our study examined the role of work-family conflict and enhancement on small business owners’ (SBOs) wellbeing. We found work-family has negative direct effect on mental health, job and family satisfactions. Furthermore, we found that under high level of work-family conflict condition, SBOs who perceive a greater level of work-family enhancement would feel more satisfy with their life, job as well as family aspects. Interestingly, under high level of conflict, even SBOs perceive greater level of enhancement, it would not lessen the negative impact of the conflict on their mental health. These results suggest that once psychological health is harmed by work-family conflict, its negative consequences remain unchanged.

A new constraint handling method for wind farm layout optimization with lands owned by different owners

For wind farm optimizations with lands belonging to different owners, the traditional penalty method is highly dependent on the type of wind farm land division. The application of the traditional method can be cumbersome if the divisions are complex. To overcome this disadvantage, a new method is proposed in this paper for the first time. Unlike the penalty method which requires the addition of penalizing term when evaluating the fitness function, it is achieved through repairing the infeasible solutions before fitness evaluation. To assess the effectiveness of the proposed method on the optimization of wind farm, the optimizing results of different methods are compared for three different types of wind farm division. Different wind scenarios are also incorporated during optimization which includes (i) constant wind speed and wind direction; (ii) various wind speed and wind direction, and; (iii) the more realisticWeibull distribution. Results show that the performance of the new method varies for different land plots in the tested cases. Nevertheless, it is found that optimum or at least close to optimum results can be obtained with sequential land plot study using the new method for all cases. It is concluded that satisfactory results can be achieved using the proposed method. In addition, it has the advantage of flexibility in managing the wind farm design, which not only frees users to define the penalty parameter but without limitations on the wind farm division.

Australians may get their own stop online piracy act #SOPA: Christmas time for copyright owners

Christmas has come early for copyright owners in Australia. The film company, Roadshow, the pay television company Foxtel, and Rupert Murdoch's News Corp and News Limited--as well as copyright industries--have been clamoring for new copyright powers and remedies. In the summer break, the Coalition Government has responded to such entreaties from its industry supporters and donors, with a new package of copyright laws and policies. There has been significant debate over the proposals between the odd couple of Attorney-General George Brandis and the Minister for Communications, Malcolm Turnbull. There have been deep, philosophical differences between the two Ministers over the copyright agenda. The Attorney-General George Brandis has supported a model of copyright maximalism, with strong rights and remedies for the copyright empires in film, television, and publishing. He has shown little empathy for the information technology companies of the digital economy. The Attorney-General has been impatient to press ahead with a copyright regime. The Minister for Communications, Malcolm Turnbull, has been somewhat more circumspect, recognizing that there is a need to ensure that copyright laws do not adversely impact upon competition in the digital economy. The final proposal is a somewhat awkward compromise between the discipline-and-punish regime preferred by Brandis, and the responsive regulation model favored by Turnbull. In his new book, Information Doesn't Want to Be Free: Laws for the Internet Age, Cory Doctorow has some sage advice for copyright owners: Things that don't make money: Complaining about piracy. Calling your customers thieves. Treating your customers like thieves. In this context, the push by copyright owners and the Coalition Government to have a copyright crackdown may well be counter-productive to their interests.

Stepping into my shoes: Generativity as a mediator of the relationship between business owners' age and family succession

The authors investigated generativity – the concern in establishing and guiding the next generation – as a mediator of the relationship between family business owners' age and succession in family businesses. Data came from 155 family business owners in Germany from different industries between the ages of 26 and 83 years. Results showed that age was positively related to generativity, and that generativity, in turn, positively influenced an objective measure of family succession. Generativity fully mediated the positive relationship between age and family succession. The findings suggest that generativity is an important psycho-social construct for understanding ageing, careers and succession in family business settings.

Focus on opportunities as a mediator of the relationship between business owners' age and venture growth

Combining upper echelons and lifespan theories, we investigated the mediating effect of focus on opportunities on the negative relationship between business owners' age and venture growth. We also expected that mental health moderates the negative relationship between business owners' age and focus on opportunities. Path analytic findings based on data from 84 business owners (mean age = 44, range 24-74) supported these hypotheses. Findings suggest that focus on opportunities is a psychological mechanism that links business owners' age with venture growth. Our findings also indicate that mental health helps maintain a high level of focus on opportunities with increasing age.

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.

Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring

As a part of vital infrastructure and transportation networks, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs always make the infrastructure owners difficult to undertake. Structural health monitoring (SHM) is set to assess condition and foresee probable failures of designated bridge(s), so as to monitor the structural health of the bridges. The SHM systems proposed recently are incorporated with Vibration-Based Damage Detection (VBDD) techniques, Statistical Methods and Signal processing techniques and have been regarded as efficient and economical ways to solve the problem. The recent development in damage detection and condition assessment techniques based on VBDD and statistical methods are reviewed. The VBDD methods based on changes in natural frequencies, curvature/strain modes, modal strain energy (MSE) dynamic flexibility, artificial neural networks (ANN) before and after damage and other signal processing methods like Wavelet techniques and empirical mode decomposition (EMD) / Hilbert spectrum methods are discussed here.

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.

Evaluation of functional performance in commercial buildings

This report fully summarises a project designed to enhance commercial real estate performance within both operational and investment contexts through the development of a model aimed at supporting improved decision-making. The model is based on a risk adjusted discounted cash flow, providing a valuable toolkit for building managers, owners, and potential investors for evaluating individual building performance in terms of financial, social and environmental criteria over the complete life-cycle of the asset. The ‘triple bottom line’ approach to the evaluation of commercial property has much significance for the administrators of public property portfolios in particular. It also has applications more generally for the wider real estate industry given that the advent of ‘green’ construction requires new methods for evaluating both new and existing building stocks. The research is unique in that it focuses on the accuracy of the input variables required for the model. These key variables were largely determined by market-based research and an extensive literature review, and have been fine-tuned with extensive testing. In essence, the project has considered probability-based risk analysis techniques that required market-based assessment. The projections listed in the partner engineers’ building audit reports of the four case study buildings were fed into the property evaluation model developed by the research team. The results are strongly consistent with previously existing, less robust evaluation techniques. And importantly, this model pioneers an approach for taking full account of the triple bottom line, establishing a benchmark for related research to follow. The project’s industry partners expressed a high degree of satisfaction with the project outcomes at a recent demonstration seminar. The project in its existing form has not been geared towards commercial applications but it is anticipated that QDPW and other industry partners will benefit greatly by using this tool for the performance evaluation of property assets. The project met the objectives of the original proposal as well as all the specified milestones. The project has been completed within budget and on time. This research project has achieved the objective by establishing research foci on the model structure, the key input variable identification, the drivers of the relevant property markets, the determinants of the key variables (Research Engine no.1), the examination of risk measurement, the incorporation of risk simulation exercises (Research Engine no.2), the importance of both environmental and social factors and, finally the impact of the triple bottom line measures on the asset (Research Engine no. 3).