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From May 2024, Victoria (Australia) will mandatorily raise the minimum house energy rating standards from 6 to 7 stars. However, the latest data shows that only 5.73% of new Victorian houses were designed beyond 7-star. While previous literature indicates the issue’s link to the compliance behaviour of building practitioners in the design phase, the underlying behavioural determinants are rarely explored. This study thus preliminarily examines building practitioners’ compliance behaviour with 7-star Australian house energy ratings and beyond.

Using a widely-applied method to initially examine an under-explored phenomenon, eight expert interviews were conducted with building practitioners, a state-level industry regulator and a leading national building energy policy researcher. The study triangulated the data with government-led research reports.

The experts indicate that most building practitioners involved in mainstream volume projects do not go for 7 stars, mainly due to perceived compliance costs and reliance on standardized designs. In contrast, those who work on custom projects are more willing to go beyond 7-star mostly due to the moral norms for a low-carbon environment. The experts further agree that four behavioural determinants (attitudes towards compliance, subjective norms, perceived behavioural control and personal norms) co-shape building practitioners’ compliance behaviour. Interventions targeting these behavioural determinants are recommended for achieving 7 stars and beyond.

This study demonstrates the behavioural determinants that influence building practitioners’ compliance decisions, and offers insight regarding how far they will go to meet 7 stars. It can facilitate the transition to 7 stars by informing policymakers of customized interventions to trigger behaviour change.

This study identifies the policing pre-deployment training content for Korean experts based on needs assessments. Korean policing is at an excellent level to transfer knowledge and skills. Pre-deployment training should be designed systematically and training of trainers approaches should be implemented.

This study used T-tests, Borich needs assessments, and Locus for Focus model analyses to determine the priorities of needs for pre-deployment training in policing. A survey of 116 experienced experts was conducted, with 87 responding (75%).

The study identified 26 factors that deployed law enforcement professionals want to learn from pre-deployment training. These factors were categorized into three areas: research, training design and methods and understanding of partner countries and international development cooperation. The nine highest priorities for training needs were related to understanding the status and conditions of police training in the country to which policing experts are deployed.

This study was limited to Korean policing experts. And the study did not evaluate the validity of the training curriculum or indicators.

Technical assistance in international policing development cooperation aims to train future trainers who can train local police. This study found that limited learner information and poor communication skills can lead to ineffective technical assistance.

This study highlights the importance of knowledge transfer and effective pre-deployment training for policing. The findings can be used to improve training programs and police human resource development.

Knowledge sharing in online health communities (OHCs) disrupts consumers' health information-seeking behavior patterns such as seeking health information and consulting. Based on social exchange theory, this study explores how the two dimensions of experts' free knowledge sharing (general and specific) affect customer transactional and nontransactional engagement behavior and how the quality of experts' free knowledge sharing moderates the above relationships.

We adopted negative binomial regression models using homepage data of 2,982 experts crawled from Haodf.com using Python.

The results show that experts' free general knowledge sharing and free specific knowledge sharing positively facilitate both transactional and nontransactional engagement of consumers. The results also demonstrate that experts' efforts in knowledge-sharing quality weaken the positive effect of their knowledge-sharing quantity on customer engagement.

This study provides new insights into the importance of experts' free knowledge sharing in OHCs. This study also revealed a “trade-off” between experts' knowledge-sharing quality and quantity. These findings could help OHCs managers optimize knowledge-sharing recommendation mechanisms to encourage experts to share more health knowledge voluntarily and improve the efficiency of healthcare information dissemination to promote customer engagement.

Examines the value of expert systems in marketing organizations through a national mail survey of 117 marketing executives. All the examined respondents reported the successful use of expert systems in their organizations. The results indicate that while expert systems provide operational benefits (e.g. they assist in making decisions more quickly), they also present new problems (e.g. increased security needs) that the adopting organization must consider. Based on these results, discusses implications for managers regarding the encouragement of the adoption and use of expert systems. Also presents questions concerning expert systems which require additional investigation.

The LIS profession is beginning to place expert systems in perspective. Expert systems are no longer heralded as being the only necessary tool but rather one tool among an array of several. LIS educators are realistic, both about expert systems technology and about what can be achieved within the limitations of an LIS course. New technologies for refining and controlling information are constantly emerging; LIS schools have to keep up‐to‐date with them as they emerge, but they must also ensure that they do not overprioritize one particular development at the expense of others. They can, at best, only hope to give a taste of the possibilities and potential in different areas. Expert systems are still new enough to warrant special treatment but no doubt they will be ousted by newer technologies in the course of time. Meanwhile, LIS professionals should make the most of what is currently available. Hopefully it should pay dividends in the future.

Expert systems are frequently mentioned in business circles these days. They have the potential to assist greatly in the dissemination of scarce or complex expertise. But although they can be immensely valuable if properly understood, developed and used, they can also be a waste of resources. Aimed at managers who feel the need to know more about expert systems, but who are not themselves computing specialists, what an expert system is and is not is explained. The types of application for which it is suitable, and who is most likely to find the time, trouble and expense of creating one that is most worthwhile is discussed. Different types of expert system are explained, and the means and merits of prototyping are outlined. In order to have a successful expert system, certain essentials are required: a subject area which can be suitably defined; an expert who can provide the knowledge; users who know what they want and how they want to use it; a knowledge engineer who can translate the expertise into facts and rules for the system. A short but useful glossary of technical terms which may be encountered in the world of expert systems is included.

Expert systems are computer software packages that experts in specific fields enrich with their knowledge by distilling their expertise into a set of laws for the system. The development of expert systems and the contribution they can provide in banks, whereby financial experts can produce application programmes to help lesser experts solve problems in specialised fields by responding to program queries, eg. with regard to loan approval, cross‐selling, risk analysis, treasury operations and so on, are discussed.

Argues that expert systems are a useful tool in implementing quality customer service. Examines seven steps of customer service and illustrates how expert systems can support each step. Draws on the literature in the field to cite commercial installations of expert systems to support quality customer service.

The purpose of this paper is to compare various linear opinion pooling models for aggregating probability judgments and to determine whether Cooke's performance weighting model can sift out better calibrated experts and produce better aggregated distribution.

The leave‐one‐out cross‐validation technique is adopted to perform an out‐of‐sample comparison of Cooke's classical model, the equal weight linear pooling method, and the best expert approach.

Both aggregation models significantly outperform the best expert approach, indicating the need for inputs from multiple experts. The performance score for Cooke's classical model drops considerably in out‐of‐sample analysis, indicating that Cooke's performance weight approach might have been slightly overrated before, and the performance weight aggregation method no longer dominantly outperforms the equal weight linear opinion pool.

The results show that using seed questions to sift out better calibrated experts may still be a feasible approach. However, because the superiority of Cooke's model as discussed in previous studies can no longer be claimed, whether the cost of extra efforts used in generating and evaluating seed questions is justifiable remains a question.

Understanding the performance of various models for aggregating experts' probability judgments is critical for decision and risk analysis. Furthermore, the leave‐one‐out cross‐validation technique used in this study achieves more objective evaluations than previous studies.

Neural networks and expert systems are two major branches of artificial intelligence (AI). Their emergence has created the potential for a new generation of computer‐based applications in the area of financial decision making. Both systems are used by financial institutions and corporations for a variety of new applications from credit scoring to bond rating to detection of credit card fraud. While both systems belong to the applied field of artificial intelligence, there are many differences between them which differentiate their potential capabilities in the field of business. Presents an analysis of both neural networks and expert systems applications in terms of their capabilities and weaknesses. Uses examples of financial applications of expert systems and neural networks to provide a unified context for the comparison.