BACKGROUND: Older adults want to preserve their health and autonomy and stay in their own home environment for as long as possible. This is also of interest to policy makers who try to cope with growing staff shortages and increasing health care expenses. Ambient assisted living (AAL) technologies can support the desire for independence and aging in place. However, the implementation of these technologies is much slower than expected. This has been attributed to the lack of focus on user acceptance and user needs.OBJECTIVE: The aim of this study is to develop a theoretically grounded understanding of the acceptance of AAL technologies among older adults and to compare the relative importance of different acceptance factors.METHODS: A conceptual model of AAL acceptance was developed using the theory of planned behavior as a theoretical starting point. A web-based survey of 1296 older adults was conducted in the Netherlands to validate the theoretical model. Structural equation modeling was used to analyze the hypothesized relationships.RESULTS: Our conceptual model showed a good fit with the observed data (root mean square error of approximation 0.04; standardized root mean square residual 0.06; comparative fit index 0.93; Tucker-Lewis index 0.92) and explained 69% of the variance in intention to use. All but 2 of the hypothesized paths were significant at the P<.001 level. Overall, older adults were relatively open to the idea of using AAL technologies in the future (mean 3.34, SD 0.73).CONCLUSIONS: This study contributes to a more user-centered and theoretically grounded discourse in AAL research. Understanding the underlying behavioral, normative, and control beliefs that contribute to the decision to use or reject AAL technologies helps developers to make informed design decisions based on users' needs and concerns. These insights on acceptance factors can be valuable for the broader field of eHealth development and implementation.
After the integration of research activities, universities of applied sciences (UASs) have formulated organisational strategies to foster connections between research and education (Daas et al., 2023). Scholars stated that the behaviour of employees within UASs influences ‘the direction and tempo in which the proposed aims are reached or resisted in the wider organisation’ (Griffioen & De Jong, 2017, p. 454). Thus, employees within UASs, such as academics and lower-level managers, play a key role in establishing research-education connections (Jenkins & Healey, 2005; Van der Rijst, 2009). A recent study has shown that academics and lower-level managers connect research and education through three types of behaviours: by involving aspects of research in education, by involving aspects of education in research, and by involving aspects of research and education equally, with the first type mentioned most often (Daas & Griffioen, in review). Similar patterns are observed in previous studies highlighting how education benefits from research rather than vice versa (Taylor, 2007). However, the beliefs underpinning this behavioural focus still remain unclear. Scholars have reported factors that could influence employees in connecting research and education, such as career stages, personal abilities and the availability of resources influencing how academics combine research and teaching tasks (Coate, Barnett & Williams, 2001; Shin, 2011), and research productivity and teaching beliefs influencing how academics integrate research in teaching (Hu et al., 2015; Magi & Beerkens, 2016). Despite the important value of these insights, these studies all focus on one (set of) behaviour(s) in connecting research and education instead of considering factors influencing behaviours in connecting research and education holistically. Moreover, most of these studies consider academics instead of managers. Therefore, the purpose of this study is to investigate the beliefs underpinning the behaviour of academics and lower-level managers in UASs in connecting research and education.To study the underpinning beliefs we applied the Theory of Planned Behaviour (TPB; Ajzen, 1991) as a theoretical lens. According to the TPB, a person’s behavioural intentions are shaped through three determinants (Ajzen, 1991). Behavioural beliefs (1) refer to a person’s conceptions about the expected positive/negative outcomes of practicing the behaviour. Normative beliefs (2) consist of a person’s conceptions about whether others approve/disapprove of practicing the behaviour. Control beliefs (3) are a person's conceptions about the presumed factors that could enable/hinder in practicing the behaviour. The research question is: Which behavioural, normative and control beliefs underpin the behaviour of academics and lower-level managers in connecting research and education?
Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.
