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The objective of this paper is to study the efficacy of the ASPECTSS Design Index's concepts as drivers of design intervention for educational environments for students on the autism spectrum. Based on the seven principles of acoustics, spatial sequencing, escape spaces, compartmentalization, transitions, sensory zoning and safety, ASPECTSS formed the basis for a preliminary post-occupancy evaluation (POE) and survey of an existing school environment.

Concepts drawn from the review of other strategies for autism spectrum disorder (ASD) friendly design were integrated with the seven ASPECTSS principles to create a design framework and consequent design retro-fit for a Pre-K-12th grade public school for students on the autism spectrum. The following design interventions were proposed: colour-coding based navigation; acoustical treatments in key circulation spaces; introduction of transition alcoves; classroom reorganisation using compartmentalization principles and the introduction of escape spaces for de-escalation. Specifically, a classroom template of modules of ASPECTSS-compliant layouts was provided to all staff. The efficacy and impact of these interventions were assessed using a whole campus online staff survey with further probing using classroom observations and subsequent interviews.

The results show alignment between the implementation of the ASPECTSS informed design interventions and responses to nine of the Likert scale items were all significantly lower than the middle response, indicating a high degree of satisfaction from survey respondents. These questions and responses related to the colour scheme facilitating ease of navigation for visitors of the school, the acoustics of the building successfully mitigating sound magnification and subsequently student distractibility, the organisation of the classrooms enhancing learning and the de-escalation zones allowing improved management of disruptive behaviours in the classroom.

This study focuses primarily on the Autism ASPECTSS Design Index as a framework for assessing classroom efficacy. Other tools and frameworks may produce different insights. A single school site was studied. Validation of these findings in other school environments is necessary before generalising these strategies at scale. The use of qualitative tools, primarily teacher and staff surveys, provides one lens into the efficacy of these design strategies. Further research using measurable biometric indicators such as heart-rate and stress levels measured through wearable technology could provide a first step towards the triangulation of these findings.

These findings could help provide more standardised best practices for designing learning environments for autism, potentially providing supportive strategies with real impact on learning quality, skill development and knowledge acquisition in school environments. This could potentially have economic implications by supporting more efficient progress for autistic students through their school curriculum.

Similar to economic impact, if validated and generalised, these findings could help with sense of accomplishment, general mental health improvement, alleviation of family stress and potential reduction of stigma in the autism community.

There is a slowly emerging field of design guidance for autism schools, but very little empirical evidence on the measurable efficacy of these strategies. This research provides one type of such evidence, as measured by the perceived impact from the point of view of staff and teachers at the school.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

Surgical services are settings where resilient performance (RP) is necessary to cope with a wide range of variabilities. Although RP can benefit from a supportive built environment (BE), prior studies have focused on the operating room, giving scant attention to support areas. This study takes a broader perspective, aiming at developing BE design knowledge supportive of RP at the surgical service as a whole.

Seven BE design prescriptions developed in a previous work in the context of internal logistics of hospitals, and thus addressing interactions between workspaces, were used as a point of departure. The prescriptions were used as a data analysis framework in a case study of the surgical service of a medium-sized private hospital. The scope of the study included surgical and support areas, in addition to workflows involving patients and family members, staff, equipment, sterile instruments and materials, supplies, and waste. Data collection included document analysis, observations, interviews, and meetings with hospital staff.

Results identified 60 examples of using the prescriptions, 77% of which were related to areas other than the operating rooms. The developed design knowledge is framed as a set of prescriptions, examples, and their association to workflows and areas, indicating where it should be applied.

The design knowledge is new in surgical services and offers guidance to both BE and logistics designers.

This paper aims to explore how critical digital and data literacies are facilitated by testing different methods in the classroom, with the ambition to find a pedagogical framework for prompting sustained critical literacies.

This contribution draws on a 10-year set of critical pedagogy experiments conducted in Denmark, USA and Italy, and engaging more than 1,500 young adults. Multi-method pedagogical design trains students to conduct self-oriented guided autoethnography, situational analysis, allegorical mapping, and critical infrastructure analysis.

The techniques of guided autoethnography for facilitating sustained data literacy rely on inviting multiple iterations of self-analysis through sequential prompts, whereby students move through stages of observation, critical thinking, critical theory-informed critique around the lived experience of hegemonic data and artificial intelligence (AI) infrastructures.

Critical digital/data literacy researchers should continue to test models for building sustained critique that not only facilitate changes in behavior over time but also facilitate citizen social science, whereby participants use these autoethnographic techniques with friends and families to build locally relevant critique of the hegemonic power of data/AI infrastructures.

The proposed literacy model adopts a critical theory stance and shows the value of using multiple modes of intervention at micro and macro levels to prompt self-analysis and meta-level reflexivity for learners. This framework places critical theory at the center of the pedagogy to spark more radical stances, which is contended to be an essential step in moving students from attitudinal change to behavioral change.

Quite often than not, a new industry can be created, thanks to the countless entrepreneurs and innovative activities across the globe. Smart city (SC) is one such industry and a living lab using the key roles of the digital platform that enable a seamless flow of information and knowledge for innovation within the SC. The purpose of this paper is to illustrate how SC can be a new regional industry engine through an “open collective innovation system” as its new concept. In particular, SC provides efficient transaction costs and knowledge flows. Eventually, SC can be an innovation hub for entrepreneurship through openness.

To frame the research goals, the authors used qualitative research methodologies based on grounded theory. In particular, the author used inductive reasoning to generate arguments and conclusions about the future of an SC as a new growth engine in the era of the fourth industrial revolution. Numerous documents and prior literature were used for the preliminary conceptualization of an SC. Interview data were then coded for reasoning in an open collective innovation system based on “openness”.

SC maximizes efficiency in practicing innovation. In the perspective of innovation costs, SC can minimize transaction costs, specifically the information processing costs, through data openness. In this context, transaction costs can be considered an economic equivalent of friction in a physical system. So, as the friction is low, some movements of an object on the surface are likely to be easy. SC is optimized for innovation activities through an “open collective innovation system”. In terms of innovation networks, an SC results in an innovation efficiency derived from both the network and the spatial agglomerations in physical and cyberspace. The efficiency-based SC itself overlaps knowledge creation, dissemination and absorption, providing an open innovation (OI) ecosystem.

This paper remarkably extends that SC can be an “open collective innovation system model” and a new conceptualization. Eventually, SC will play a crucial role in developing regional industries as a new growth engine. To operate as a new growth engine fully-fledged, the SC is needed to accumulate innovative assets such as the critical mass of residents, numerous firms, etc. However, this study has some limitations. First, difficulties in any analytic approach to SC resulted from their many interdependent facets, such as social, economic, infrastructural and spatial complex systems, which exist in similar but changing forms over a huge range of scales. Also, this research is at a quite an early stage. Thus, its theoretical stability is weak. So, this paper used the qualitative methodology with a grounded theory. Another limitation is in the research methodology. The limitation of using grounded theory adapted by this work is that the results of this study may not be generalizable beyond the context of this study. This non-generalizability occurs because ours is an inductive approach to research, meaning that the findings are based on data collected and analyzed. As such, the results of this study may not be applicable to other contexts or situations. In addition, the analysis of data in the grounded theory is based on researcher’s subjective interpretations. This means that the researcher’s own biases, preferences and assumptions may influence the results of the study. The quality of the data collected is another potential limitation. If the data is incomplete or of poor quality, it can cause researcher’s own subjective interpretations.

Findings of this study have some practical implications for enterprises, practitioners and governors. First, firms should use value networks instead of value chains. Notably, the firms that pursue new products or services or startups that try to find a new venture business should take full advantage of SC. This taking advantage is possible because SC not only adapts state-of-the-art information technology (e.g. sensor devices, open data analytics, IoT and fiber optic networks) but also facilitates knowledge flow (e.g. between universities, research centers, knowledge-based partner firms and public agencies). More importantly, with globalized market competition in recent years, sustainability for firms is a challenging issue. In this respect, managers can take the benefits of SC into consideration for strategic decisions for sustainability. Specifically, industrial practitioners who engage in innovation activities have capabilities of network-related technologies (e.g. data analysis, AI, IoT and sensor networks). By using these technologies in an SC, enterprises can keep existing customers as well as attract potential customers. Lastly, the findings of this study contribute to policy implementation in many aspects. At first, for SC to become a growth engine at regional or natural levels, strong policy implementation is crucial because SC is widely regarded as a means of entrepreneurship and an innovation plaza (Kraus et al., 2015). To facilitate entrepreneurship, maker spaces used for making the prototypes to support entrepreneurial process were setup within universities. The reason for establishing maker spaces in universities is to expand networking between entrepreneurs and experts and lead to innovation through a value network. One of the policy instruments that can be adapted is the “Data Basic Income Scheme” suggested by this research to boost the usage of data, providing content and information for doing business. Also, a governor in SC as an intermediator for the process of the knowledge flow should initiate soft configuration for SC.

This work makes two theoretical contributions to OI aspects: (1) it explores dynamic model archetypes; and (2) it articulates and highlights how SC with digital technology (i.e. in the AI, IoT and big data context) can be used to create collective knowledge flow efficiently. First, the findings of this study shed light on the OI dynamic model. It reveals important archetypes of new sub-clustering creation, namely, a system that underpins the holistic process of innovation by categorization in amongst the participating value network (Aguilar-Gallegos et al., 2015). In innovation studies, scholars have particularly paid attention to a cluster’s evolution model. In the process of innovation, the “open innovation dynamic model” suggested by this study illustrates sub-clustering that happens in value networks by taking the benefits of SC. Eventually, the evolution or development of sub-clusters can bring in a new system, namely, an OI system. Second, the findings of this study contribute to the understanding of the role of digital technologies in promoting knowledge flow. The usage and deployment of digital technologies in SC may enormously and positively influence innovative activities for participants. Furthermore, the rising of digital economy, in the so-called platform business, may occur depending on advanced technologies and OI. In doing so, the findings can further tow innovation research through juxtaposition between SC and innovation research (Mehra et al., 2021).

This paper shows that the function of an SC not only improves the quality of life but also acts as an engine of new industry through an open collective innovation setting using dynamic and ecological models.

In urban studies, understanding how individuals perceive density is a complex challenge due to the subjective nature of this perception, which is influenced by sociocultural, personal and environmental factors. This study addresses these complexities by proposing a systematic framework for comprehending how people perceive density within urban contexts.

The methodology for developing the framework involved a systematic review of existing literature on the perception of density and related concepts, followed by integrating insights from empirical investigations. The framework designed through this process overcomes the limitations identified in previous research and provides a comprehensive guide for studying perceived density in urban environments.

The successful application of the framework on case studies in Glasgow and international settings enabled the identification of 20 critical spatial factors (buildings, public realm and urban massing) influencing density perception. The research provided insights into the subjective nature of density perception and the impact that spatial characters of urban form play, demonstrating the framework's effectiveness in understanding the impact of urban form, which is the realm of design and planning professions, on individual experiences.

The paper's originality lies in its comprehensive synthesis of the existing knowledge on the perception of density, the development of a user-responsive framework adaptable to future research and its application in case studies of different natures to identify recurrent links between urban form and user-specific constructs.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

The purpose of this paper is to explore the various forms of organizational and team-level actions that were perceived to be helpful or not helpful by employees as they navigate the hybrid work arrangements and how these had an impact on their work behaviors and experiences. This research utilized Bronfenbrenner's ecological systems theory as framework.

The exploratory study used a qualitative approach in gathering data via online survey from a total of 45 Filipino employees working in a hybrid work arrangement for at least three months. The analysis utilized both inductive and deductive methodologies in examining the data. Inductive thematic analysis was used in coding the data based on the participants' responses, while the deductive approach ensured that the themes are aligned with the research questions and reflect the different systems within Bronfenbrenner's EST (1986).

Results surfaced helpful organizational (e.g. provision of work tools, financial assistance, supportive policies and engagement and wellness initiatives) and team level actions (i.e. use of technology-based communication tools, open virtual door policy, effective performance management system, employee care practices and team engagement activities). Actions that were perceived as not helpful include inadequate technological infrastructure, poor communication, insufficient training, punitive policies/practices and leadership issues at the organizational level as well as unresponsive colleagues and ineffective implementation of policies/processes at the level of teams. Employees reported being able to build on savings, becoming more productive and having greater work–life balance amid hybrid work. However, they continue to be challenged by blurred boundaries and inability to disconnect from work similar to when work was done remotely and now with sustaining momentum given the shifts on where they do their work.

The findings of this study may guide programs and initiatives of human resource management practitioners and organizational leaders as they support employees in navigating through hybrid work.

The research expands extant knowledge on practices and experiences in hybrid work (Gifford, 2022). It also contributes to studies on human resource management that are nuanced based on where work is performed (Ng and Stanton, 2023) or with emerging work arrangements.

The below-average innovative capacity of the tourism sector raises the question on the potentials of digital business ecosystems (DBEs) to overcome these shortages at a destination level – especially within a smart city environment. Using the example of the German Capital Berlin, this article aims to discuss both the possibilities and inhibitors of innovative knowledge-creation by building scenarios on one specific design option: the integration of digital deep learning (DL) functionalities and traditional organizational learning (OL) processes.

Using the qualitative GABEK-method, major characteristics of a DBE as resource-, platform- and innovation systems are analyzed toward their interactions with the construction of basic action models (as the basic building blocks of knowledge).

Against the background of the research findings, two scenarios are discussed for future evolution of the Berlin DBE, one building on cultural emulation as a trigger for optimized DL functionalities and one following the idea of cultural engineering supported by DL functionalities. Both scenarios focus specifically on the identified systemic inhibitors of innovative capabilities.

While this study highlights the potential of the GABEK method to analyze mental models, separation of explicit and latent models still remains challenging – so does the reconstruction of higher order mental models which require a combined take on interview techniques in the future.

The resulting scenarios innovatively combine concepts from OL theory with the concept of DBE, thus indicating possible pathways into a tourism future where the limitations of human learning capacities could be compensated through the targeted support of general artificial intelligence (AI).

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.