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This study aims to empirically investigate solo travel behavior, comparing decision-making, destination choice, motivations, perceived experiences and constraints between the pre-pandemic and post-pandemic eras.

Using a mixed-methods approach, the study combines quantitative and qualitative data to enhance reliability and flexibility. An online survey attracted 250 respondents, providing demographic data and pandemic-related insights, complemented by eight in-depth interviews. Qualitative content analysis was used to extract key findings on motivations, constraints and destination choices.

The findings reveal that, pre-pandemic, independence and flexibility were key motivators for solo travel, with safety and budget as significant constraints. During the pandemic, half of the respondents continued solo travel, mainly within Europe, facing constraints related to destination choice and health concerns. Post-pandemic, solo travel interest resurged, emphasizing unchanged motivations but reduced concerns over companionship and planning. The study identifies a “traveling for revenge” trend post-pandemic, showcasing a desire to overcome constraints imposed during the pandemic.

The study contributes original insights into the nuanced changes in solo travel behavior due to the COVID-19 pandemic, offering a deeper understanding of solo travelers' evolving mindsets. It is one of the few studies to examine the pandemic's impact on solo traveling, providing short-term shifts in motivations and constraints with implications for the travel industry.

The expanded reuse of images as research data in the social sciences and humanities necessitates the understanding of scholars’ real-life interactions with the type of data. The aim of this study is to analyse activities constituting image data interactions in social science and humanities research and to provide a model describing the data interaction process.

The study is based on interviews with 21 scholars from various academic backgrounds utilising digital and print images collected from external sources as empirical research data. Qualitative content analyses were executed to analyse image data interactions throughout the research process in three task types: contemporary, historical and computational research.

The findings further develop the task-based information interaction model (Järvelin et al., 2015) originally created to explain the information interaction process. The enhanced model presents five main image data interaction activities: Data gathering, Forming dataset, Working with data, Synthesizing and reporting and Concluding, with various sub-activities. The findings show the variety of image data interactions in different task types.

The developed model contributes to understanding critical points in image data interactions and provides a model for future research analysing research data interactions. The model may also be used, for example, in designing better research services and infrastructures by identifying support needs throughout the research process.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.

This study aims to identify critical barriers to design thinking (DT) implementation in healthcare and to determine hierarchical relationships among the barriers.

Through an extensive literature review and healthcare experts' opinions, 13 barriers to DT implementation in healthcare were identified. Data were collected using survey questionnaires, and an interpretive structural modeling (ISM) -MICMAC analysis was employed to produce a hierarchical model of identified barriers.

Results reveal the absence of standalone barriers, highlighting “Lack of Organizational autonomy” and “Lack of innovation mindset” as crucial barriers at the bottom of the hierarchy. Overcoming these barriers requires a shift in organizational mindset, dedicated resources, interdisciplinary collaborations, and aligning DT with healthcare regulations.

The findings aid policymakers in recognizing interconnected barriers, enabling DT implementation through strategic mitigation. Healthcare leaders and stakeholders can use this insight to formulate effective strategies for addressing these barriers.

This research presents a distinct investigation of identifying the barriers to DT implementation in the healthcare sector in India. DT’s seamless implementation in hospitals encounters diverse barriers, hindering its full potential. This research contributes to the extant literature by providing the interrelationship between the barriers and a hierarchical model for a clear understanding of the levels of barriers.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.