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In this chapter, we explore ‘unanticipated excess’ through the lens of our own doctoral research projects, which are presented as distinct vignettes: Reade’s digital ethnography of young women’s relations with ‘fitspo’ (fitness inspiration) content on Instagram, Moran’s social media ethnography of African young people in Australia and Lyall’s show-and-tell interviews with users of digital self-tracking devices. While our projects differ in many ways, we share research practices that did not fully anticipate the challenges of digitalised research fields. In coming to terms with our unanticipated excess, we reflect on inescapable moments and uneasy feelings from our fieldwork. In so doing, we argue that excess need not be considered a ‘failure’ – to establish boundaries, to filter data or to engage in objective analysis – but should rather be seen as an important part of reflexive research practice. Excess holds possibilities and potentials to foster care and camaraderie between digital scholars and can push us and our work – empirically, methodologically and ethically – in new directions. It also presents an opportunity to continue to champion integrity over production as we move forward in our personal and collective research journeys.

Managing public ‘affect’ was a critical component of Aotearoa New Zealand's COVID-19 policy approach, which was predicated on collective emotional feelings of calmness, compassion and trust. A long history of health promotion efforts have involved co-opting children as tools to manipulate (adult) public affect towards motivating behavioural change or accepting health interventions. Little research has yet considered the consequences of objectifying children for affect management in the name of public health. The Pandemic Generation study compared the perspectives of Auckland children aged 7–11, generated through co-drawing comics about their pandemic experience, with a critical discourse analysis of children's representation in New Zealand COVID-19 public health messaging. In this chapter, I argue that by leveraging performative care for children to manipulate an adult public affect, the New Zealand government erased children's subjectivities, their care-giving roles and contributions, further disenfranchising children as members of the ‘public’ in public health.

Digital ethnographers acknowledge that online spaces are always co-produced within the social, political, material and sensory – never distinct from what we may think of as ‘offline’. However, in documenting our fieldwork (e.g. fieldnotes, screenshots and recordings) and representing our findings in research outputs, scholars tend to draw more firm boundaries around our object of study. The excess, the digital life on the margins of digital ethnography often entangled with the fieldwork site, is cut away to present a neatened case study that can be analysed. In this chapter, I examine the excess and ‘unrelated’ screenshots I took during a digital ethnography project in 2020 to explore what these ‘offcuts’ can offer in contextualising my encounters with the short-form video app TikTok. Over nine months in 2020, I observed healthcare workers using the app to share health information and analyse their content. At the same time, with the pandemic unfolding across the world, I was scrolling through the news on Twitter, watching press conferences from health authorities, sharing funny TikToks with friends and receiving information in a family group chat. This layering of everyday experiences of the pandemic forms part of how I sensed and experienced TikTok content during my digital ethnography. I examine these ‘excess’ screenshots to think through the always more-than-digital boundaries of digital ethnographic fieldwork. I reflect on the messy entanglement of digital ethnography, where my own digital practices – intensified by COVID-19 lockdown conditions – and the broader conditions they emerged from, became inevitably enmeshed with my research practice.

The use of mobile digital devices requires secure behaviour while using these devices. To influence this behaviour, one should be able to adequately measure the behaviour. The purpose of this study is to establish a model for measuring secure behaviour, and to use this model to measure the secure behaviour of individuals while using mobile digital devices such as smartphones and laptops.

Based on a wide-ranging questionnaire (N = 1000), this study investigates the degree of influence that a relatively large number of factors have on secure behaviour while using mobile digital devices. These factors include knowledge and cognitive attitude, but also affective attitude, as well as several types of bias.

This study has provided a model for measuring secure behaviour. The results of the measurements show that knowledge, bias, cognitive attitude and affective attitude all have impact on secure behaviour while using mobile digital devices. Moreover, none of these factors is of minor importance.

This study shows that it is important to also consider previously undervalued factors, such as affective attitude and various types of bias, when designing interventions to improve secure behaviour while using mobile digital devices.

Most research on secure behaviour has only looked at a small number of influencing factors, usually limited to knowledge and cognitive attitude. This study shows that one needs a more elaborate model for measuring secure behaviour, and that previously undervalued factors have a clear influence on secure behaviour.

Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation.

This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process.

The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics.

Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms.

The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.