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During everyday work, individuals often engage in unplanned conversations that help them develop relationships, share information and coordinate tasks. Unfortunately, the work-from-home mandates issued due to the pandemic have reduced the frequency of unplanned conversations among employees. So, as businesses reopen, organizations are considering post-pandemic workplace solutions that can facilitate unplanned conversations. To aid these efforts and move research and theory on unplanned conversations forward, this study proposes and tests a multi-factor model of the antecedents of unplanned conversations.

This study adopted a multi-methodological approach and collected data using automated sensing technology, structured observations and cross-sectional survey methods. A total of 5,297 unplanned conversations among 61 employees were recorded using the custom mobile application and structured observations. Cross-sectional survey data about these employees' work contexts were also collected.

The study results showed that the model significantly predicted the frequency of unplanned conversations. Notably, technical expertise, perceived time pressure and team psychological safety significantly predicted the frequency of unplanned conversations. The study findings have both theoretical and practical significance.

Previous research studies have primarily focused on the influence of workplace designs on unplanned conversations. However, this study demonstrates that several other factors facilitate unplanned conversations. This research theorizes and empirically tests the relationship between unplanned conversations and several individual, team and organizational factors.

Despite their centrality to organizing – acknowledged by several theorists – unplanned conversations are often marginalized in organizational theory. To remedy this oversight, we recenter attention on this understudied aspect of organizing. We draw on the affordances perspective to elaborate on the spatial and temporal factors influencing unplanned conversations.

This conceptual paper integrates multidisciplinary literature on unplanned conversations to identify a range of spatiotemporal factors influencing unplanned conversations. Our approach emphasizes how various situational factors afford or hinder opportunities for unplanned conversations among organizational members.

Unplanned conversations were precisely defined as opportunistic or spontaneous conversations, characterized by the absence of pre-planning, that can be work or non-work-related. Then, the characteristics of unplanned conversations (emergent, episodic and brief, interrelated, convenient, and improvisational) were outlined, indicating their distinct organizing and structuring capabilities. The spatial (i.e. spatial proximity, visibility, legitimacy, and psychological safety) and temporal (i.e. work time pressure, work history, work expertise, and work routineness) factors identified in the study both afford and constrain individuals’ unplanned conversations. The empirically testable propositions offered in the study have significant theoretical and practical implications.

This study enriches our understanding of unplanned conversations by offering a precise conceptual definition, outlining their essential characteristics, and underscoring their theoretical and practical significance in organizing. The study highlights the need for organizations to consider the spatiotemporal factors that influence unplanned conversations.

The advancement of technology has brought much uncertainty in the access and utilization of information resources in academic libraries. This research sought to determine the extent to which academic libraries in Kenya engage in innovation and use innovative strategies. In addition, this study aims to identify the specific innovation practices and strategies used by these libraries to ensure they effectively address users’ information needs and remain relevant within the Kenyan context.

This study adopted a qualitative research design. Data was collected using face-to-face interviews that targeted key personnel in the academic library system, including the university librarian, the circulation librarian and the digital/systems librarian. In total, 21 respondents were interviewed in this study. Additional data was gathered by examining primary documents such as academic library policies, university websites and library brochures, among other sources. The study used purposive sampling techniques to select the population sample and the theoretical saturation to determine the sample size.

The study revealed a pressing need for change in the academic library landscape in Kenya. It established that the concept of innovation and innovation strategies is still new in academic libraries in Kenya, and libraries are still confined to traditional and routine duties. There is a lack of appropriate strategies for enhancing innovation practices/strategies in academic libraries. The paper, therefore, strongly recommends the reorganization of academic libraries in Kenya.

To the best of the authors’ knowledge, this research is the first to analyze innovation strategies and practices in academic libraries in Kenya. This study sheds light on the puzzles facing most academic libraries in Kenya regarding innovation and innovation strategies. It introduces the importance of academic libraries to embrace innovation as a strategy to avoid disruptive innovation.

Due to the increasing frequency of extreme weather and densifying urban landscapes, residences are susceptible to heat-related discomfort, especially those in a naturally ventilated built environment in tropical climates. Indoor thermal comfort is thus paramount to building sustainability and improving occupants' health and well-being. However, to assess indoor thermal comfort considering the urban context, it is conventional to use questionnaire surveys and monitoring units, which are both case-centric and time-intensive. This study presents a dynamic computational thermal comfort modeling framework that can determine indoor thermal comfort at an urban scale to bridge this gap.

The framework culminates in developing a deep learning model for predicting the accurate hourly indoor temperature of urban building stock by the coupling urban scale capabilities of environment modeling with single-building dynamic thermal simulations.

Using the framework, a surrogate model is created and verified for Dharavi, India's informal urban settlement. The results indicated that the developed surrogate model could predict the building's indoor temperature in several complex new urban scenarios with different building orientations, layouts, building-to-building distances and surrounding building heights, using five different random urban representative scenarios as the training set. The prediction accuracy was reliable, as evidenced by the mean bias error (MBE) and coefficient of (CV) root mean squared error (MSE) falling between 0 and 5%. The findings also showed that if the urban context is ignored, estimates of annual discomfort hours may be inaccurate by as much as 70%.

The developed computational framework could help regulators and policymakers engage in more informed and quantitative decision-making and direct efforts to enhance the thermal comfort of low-income dwellings and informal settlements.

Up to this point, majority of literature that has been presented has concentrated on building a body of knowledge about urban-based modeling from an energy management standpoint. In contrast, this study suggests a dynamic computational thermal comfort modeling framework that takes into account the urban context of the neighborhood while examining the indoor thermal comfort of the residential building stock.

Current knowledge and research on students’ utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students’ engagement with these tools.

This research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%).

Most respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI’s ethical and appropriate use.

Literature related to generative AI use in higher education specific to ChatGPT is predominantly from educators’ viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy.

This study aims to extend the known design guidelines for the polymer-based fused filament fabrication (FFF) 3D printing process with the focus on function-integrated components, specifically optomechanical parts. The potential of this approach is demonstrated by manufacturing function-integrated optomechanics for a low-power solid-state laser system.

For the production of function-integrated additively manufactured optomechanics using the FFF process, essential components and subsystems have been identified for which no design guidelines are available. This includes guidelines for integrating elements, particularly optics, into a polymer structure as well as guidelines for printing functional threads and ball joints. Based on these results, combined with prior research, a function-integrated low-power solid-state laser optomechanic was fabricated via the FFF process, using a commercial 3D printer of the type Ultimaker 3. The laser system's performance was assessed and compared to a reference system that employed commercial optomechanics, additionally confirming the design guidelines derived from the study.

Based on the design goal of function integration, the existing design guidelines for the FFF process are systematically extended. This success is demonstrated by the fabrication of an integrated optomechanic for a solid-state laser system.

Based on these results, scientists and engineers will be able to use the FFF process more extensively and benefit from the possibilities of function-integrated manufacturing.

Extensive research has been published on additive manufacturing of optomechanics. However, this research often emphasizes only cost reduction and short-term availability of components by reprinting existing parts. This paper aims to explore the capabilities of additive manufacturing in the production of function-integrated components to reduce the number of individual parts required, thereby decreasing the workload for system assembly and leading to an innovative production process for optical systems. Consequently, where needed, it provides new design guidelines or extends existing ones and verifies them by means of test series.