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The purpose of this paper is to portray the future of tourism in New Zealand based upon a philosophy of sustainability and cultural identity as a response to the present 2025 Tourism Strategy.

The research deployed a scenario planning methodology resulting in four portraits of the future.

Environmental issues and global migration are the key issues that will shape the future of New Zealand tourism. In order to address these issues four scenarios were constructed. New Zealand Wonderland portrays a future based upon a grounded international reputation for environmentalism driven by good governance, climate change targets and ecotourism. Indiana Jones and the Search for Cultural Identity position a future driven by rapid growth and unregulated air travel resulting in environmental degradation. A Peaceful Mixture is a balance of socio‐cultural and environmental dimensions of sustainability at the centre of a tourism product shaped upon Maori culture and economic prosperity. The final scenario, New Zealand in Depression, is the worst possible outcome for New Zealand's tourism industry as the three dimensions of economy, community, and environment are not at equilibrium. New Zealand would be over‐polluted with an uncontrolled number of migrants.

The research was a social construction of ten experts’ views on the future of sustainable tourism.

New Zealand's present approach to the future of tourism is shaped by the 2025 Tourism Framework (http://tourism2025.org.nz/). This is derived from a business perspective and a neoliberal political philosophy and it is void of the words ecotourism and sustainability. This paper argues that the present strategy will fail because of community disengagement that proposes a range of alternative directions based upon a political discourse of sustainability and shaped by environmental credentials and cultural identity.

The use of “homework”, activities outside of the classroom or session, is widely applied in a range of disciplines including teaching, therapy and training. The argument advanced by advocates is that it provides an opportunity to consolidate knowledge learnt in the classroom and develop mastery in an applied environment. However, the use of homework has not been widely discussed or researched within business coaching, which is a form of personal development. This exploratory study aims to examine whether homework, as a coaching intervention, may enhance the clients' learning experience.

Data were collected from eight early career coaches and eight coaching clients. Not all clients were related to the coaches. Each client had experienced a minimum of three coaching sessions. Interviews were recorded and analysed using thematic analysis. The study explored the use of (1) client-led, (2) coach-led and (3) collaboratively developed homework during the engagements.

The findings indicated that homework is widely used and was perceived to have mixed effects. The positioning of the homework by the coach, including the terminology used to describe the activity, and the type of work can affect the level of engagement and thus the perceived value generated.

This is the first study to explore the nature of “homework” in coaching. More work is needed to better inform the use of “homework” in coaching practice, including the type of work and how this is agreed with different types of clients, for example, should homework be coach, collaborative or client led?

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.

Mobile dating apps are widely used in the queer community. Whether for sexual exploration or dating, mobile and geosocial dating apps facilitate connection. But they also bring attendant privacy risks. This chapter is based on original research about the ways gay and bisexual men navigate their privacy on geosocial dating apps geared toward the LGBTQI community. It argues that, contrary to the conventional wisdom that people who share semi-nude or nude photos do not care about their privacy, gay and bisexual users of geosocial dating apps care very much about their privacy and engage in complex, overlapping privacy navigation techniques when sharing photos. They share semi-nude and nude photos for a variety of reasons, but generally do so only after building organic trust with another person. Because trust can easily break down without supportive institutions, this chapter argues that law and design must help individuals protect their privacy on geosocial dating apps.

Benford's Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford's Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford's Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford's Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford's Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.