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Homeownership for younger generations is exacerbated by the deterioration in affordability worldwide. As a result, the role of parental support in facilitating homeownership requires attention. This study aims to assess the influence of parental wealth and housing tenure as support mechanisms to facilitate homeownership for their children.

This study uses data from a representative survey of the New Zealand population.

Parents who are homeowners tend to offer more financial support to their children than those who rent. Additionally, the financial support increases when parents have investment housing as well. The results further reveal differences in financial support when considering one-child and multi-child families. The intergenerational transmission of wealth inequality appears to be more noticeable in multi-child families, where parental housing tenure plays a dominant role in determining the level of financial support provided to offspring.

The insights gained serve as a basis for refining housing policies to better account for these family transfers and promote equitable access to homeownership.

The technological disruption from artificial intelligence (AI) within the economy requires intelligent property professionals for tomorrow. This paper proposes that the direction of interaction between AI and tomorrow's property professional, the property graduate, should be AI-empowered rather than AI-directed.

The paper reflects on the growing influence of AI in property combined with literature on technological adoption in the workplace. It proposes a way forward in navigating future decision-making.

An AI-empowered paradigm promotes the importance of industry-specific knowledge to determine factual information in decision-making. In contrast, an AI-directed paradigm leads to over-dominance of the user on pre-specified knowledge available through AI tools that could lead to AI-directed output that carries significant risk for the property industry.

Navigating the future requires a paradigm that moves from a computational focus driven predominantly by technological tools to one where tomorrow's professionals have a cognitive focus that leads to AI-enabled property graduates that can apply the correct tools in the right circumstances.

This paper reflects on the increasing role that technology and AI have within the property profession and brings to light the importance of learning through experience and the transparent use of AI tools in property.

This paper aims to define intergenerational housing support and assesses and synthesizes the existing literature on intergenerational support for housing to identify trends and possible areas for future research.

The methodology employed in this paper is a systematic literature review. A total of 32 articles were chosen for assessment. Upon thorough review, summary and synthesis, general trends and three specific themes were identified.

The review of 32 papers found that intergenerational support is a crucial strategy to help younger generations achieve homeownership. However, it also highlights the potential for social inequity resulting from unequal distribution of housing resources within families, especially regarding housing. Several potential gaps in the current research are identified, including the need for explicit attention to the provider's intention, exploration into the size and form of financial support for housing, understanding how parental housing resources differ in their transfer behaviors, and examining how parental motivations influence them to provide housing support.

This paper provides recommendations for further research on the topic, while also adding perspective to understand the micro-social mechanisms behind the intergenerational reproduction of socioeconomic inequality, especially in the housing market.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

Artifacts are rarely used today to visualize thoughts, insights, and ideas in strategy work. Rather, textual and verbal communication dominates. This is despite artifacts and visual representations holding many advantages as tools to create and make sense of strategy in teamwork. To advance our understanding of the benefits of visual aids in strategy work, I synthesize insights from cognitive psychology, neuroscience, and management research. My analysis exposes distinct neurocognitive advantages concerning attention, emotion, learning, memory, intuition, and creativity from visual sense-building. These advantages increase when sense-building activities are playful and storytelling is used.

The skillsets of science, technology, engineering and mathematics (STEM) graduates are widely recognised to be important for economic prosperity. At the same time, it is broadly accepted that in England there is a need to increase the number of people studying STEM degree courses and working in STEM. However, despite decades of interventions post-16, STEM participation rates remain lower than projected requirements. Some research reports suggest a lack of positive attitudes towards these subjects and aspirations amongst some social groups. As these debates continue, official reports such as those released by the Department for Education show these patterns from the labour market and higher education (HE) extend to both attainment and participation in science and math in school.

In this paper, the authors summarise the authors' findings from the analysis of official reports, policy documents and major research reports focussing on attainment in school science and math and post-compulsory STEM participation.

The authors identify the problematic ways in which STEM subject choices are made across the student life cycle and then discuss how the leaky pipeline metaphor can be ambiguous and needs to be used with caution.

Some aspects identified here warrant further research and will be of particular interest to researchers, practitioners and policymakers.

In this new report, the authors identify the problematic ways in which STEM subject choices are made across the student life cycle in England and then discuss how the leaky pipeline metaphor can be ambiguous and needs to be used with caution.