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This paper seeks to examine the development of a pilot program for using podcasting as a tool in the provision of information literacy in an academic library. It aims to discuss the implementation process and the issues encountered in developing a podcasting series at the Curtin University Library.

The possibilities for using podcasts to deliver library information literacy in an academic library are discussed in reference to current literature and trends. The method for creating a podcasting series, including the equipment, software, RSS feed, legal issues and cost and staffing implications, is outlined along with the parameters used by the Curtin University Library in the development of a pilot series.

The paper finds that podcasts offer libraries a new method of delivering information literacy to their clients. It is possible to create a podcasting series with minimal expense and the simple production method enables many libraries to take advantage of this new technology. The podcasting series at Curtin has proven to be popular with downloads increasing steadily over the course of the semester. There have been over 9,000 downloads of the audio files to the end of November 2006. By taking advantage of this ubiquitous technology libraries can communicate with their clientele in a new and exciting way.

The paper outlines how to create a podcasting series for information literacy in an academic library environment, and provides recommendations for other libraries wishing to create their own podcasting series.

We examined the attitudes of millennial-aged business students toward economic, social and environmental corporate responsibility (CR). Currently, these individuals are of an age that they have entered the workforce and are now ascending or have ascended into roles of leadership in which they have decision-making power that influences their company’s CR agenda and implementation. Thus, following the ecological systems perspective, we tested both the macro influence of cultural values (survival/self-expression and traditional/secular-rational values) and structural forces (income inequality, welfare socialism and environmental vulnerability) on these individuals’ attitudes toward CR.

This is a multilevel study of 3,572 millennial-aged students from 28 Asian, American, Australasian and European societies. We analyzed the data collected in 2003–2009 using hierarchical linear modeling.

In our multilevel analyses, we found that survival/self-expression values were negatively related to economic CR and positively related to social CR while traditional/secular-rational values was negatively related to social CR. We also found that welfare socialism was positively related to environmental CR but negatively related to economic CR while environmental vulnerability was not related to any CR. Lastly, income equality was positively related to social CR but not economic or environment responsibilities. In sum, we found that both culture-based and structure-based macro factors, to varying extents, shape the attitudes of millennial-aged students on CR in our sample.

Our study is grounded in the ecological systems theory framework, combined with research on culture, politico-economics and environmental studies. This provides a multidisciplinary perspective for evaluating and investigating the impact that societal (macro-level) factors have on shaping attitudes toward businesses’ engagement in economic, social and environmental responsibility activities. Additionally, our multilevel research design allows for more precise findings compared to a single-level, country-by-country assessment.

This study was inspired by research of strategists on strategic innovation (SI), aiming to provide a unique model to enhance the digitization of small and medium-sized enterprises (SMEs) in Bangladesh to fill the gap toward a digital economy.

A survey was used to collect data from 180 SMEs in the manufacturing industry for this research. The results indicate that strategic innovativeness (SI), human capital (HC), infrastructure and technology and resistance to change significantly influence the digitalization in Bangladesh SMEs.

The link between SI and SMEs' digitalization in Bangladesh is mediated by HC. The results show that HC plays a big role in the connection between SI and the digitalization of SMEs. This study may be valuable for SMEs managers, researchers and policymakers in Bangladesh and other developing nations, who want to learn more about SI in adopting digitalization.

The specialized knowledge and abilities of strategists allow them to establish parallels between the past and present, enabling them to make a sustained forecast about the digital economy. This study encourages small and medium-sized businesses to develop their SI and advance their HC, which could further deject resistance to change toward enhancing and adopting digitalization in SMEs sectors.

The assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.

An extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.

The models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).

All the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.

The accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.

To date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.