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The purpose of this study was to investigate the effectiveness of iCoaching during reading intervention. An interventionist received mentoring support to implement iCoaching. The goal of the study was to increase teacher-delivered, behavior-specific praise (BSP).

Using a single-case multiple-probe design across participants (Gast, 2010; Horner and Baer, 1978), iCoaching was implemented in a two-part package of (1) professional development (PD) and (2) live iCoaching sessions where three teachers received preemptive coaching comments to increase BSP delivery during reading intervention. Visual analysis identified changes in teacher behavior.

Findings demonstrated the iCoaching intervention package increased teacher knowledge and implementation of evidence-based practices (EBPs; i.e. BSP) during tiered reading intervention groups. Most student participants made gains in reading skills (accuracy, words per minute and composite score) across the areas measured.

Teacher absences, observation scheduling, an ongoing global pandemic, IEP meetings during intervention time, and other changes in the schedule were limitations of this study. The first set of earbuds lost the audio signal several times, and researchers lost the ability to hear the instruction occurring in the classroom; the earbuds were replaced by the first intervention phase.

Previous iCoaching literature demonstrates iCoaching provides implementation support for EBPs learned in PD. Peer coaching can have a positive impact on EBP implementation when iCoaching is non-evaluative, which supports teachers with EBP implementation with minimal disruption to teaching.

This manuscript extends iCoaching research (Randolph et al., 2020, 2021) from small group special education settings to general education intervention groups. Additionally, research shows iCoaching can be extended with mentoring.

This paper aims to accelerate the development of natural capital accounting via an early report of farm accountants responses to prototype natural capital accounts. The authors test an approach to co-development with this important group who are both preparers and users of natural capital accounts but are not presently included in the research or development of natural capital accounting.

Seven practicing farm accountants and three accountants with an interest in this area were interviewed to gather responses to prototype farm natural capital accounts and make changes to improve the clarity, relevance and usefulness of the accounts. The paper calls for more work in participatory co-development to speed up the development and implementation of natural capital accounting.

The authors found that all participants were supportive of the concept of natural capital accounting and the consideration of agricultural ecosystems as assets of a farm business. Most participants could interpret the accounts and saw them as useful and important to improve sustainability outcomes. Participants highlighted the need for 1) the development of reliable, consistent valuation methods that resist manipulation; 2) natural capital accounting to be affordable and provide value to users; and 3) farmers to be supported to apply and report the methods for different objectives and contexts.

Since agriculture is a significant source of greenhouse gas emissions and changes to natural capital in the economy, information included in natural capital accounts of farm businesses is important to inform policy as well as farm management decisions. This research reveals strategies for policy makers to accelerate the supply of this information to enable market and other incentives to address urgent issues related to sustainability. Results of this study are from a limited sample of well-informed individuals and are thus preliminary. However, they highlight the need (and opportunity) to further co-design natural capital accounts in agriculture with farm accountants.

Farm accountants are important stakeholders in the development and implementation of natural capital accounting processes and systems, yet they are currently excluded from the science and standard-setting processes underpinning natural capital accounting. Co-development represents a fundamental shift in how the science around natural capital accounting is done and is an important step towards creating a more transdisciplinary approach to working with users. The authors show how users can be involved in developing natural capital accounting methods, standards and reports.

Natural capital accounting is a promising method to help reverse sustainability problems, if it is co-developed with stakeholders to be useful and useable.

To the best of the authors’ knowledge, this research is the first to report on farm accountants’ perceptions of natural capital accounts in agriculture and to present a case study of co-developing natural capital accounts with farm accountants.

Immersive technologies fully immerse users in augmented environments for interactive experiences. The purpose of this study is to measure consumers’ intention towards experiencing immersive technologies at tourism destinations using an integrated theory of planned behaviour (TPB) and technology acceptance model (TAM) model within the stimulus-organism-response (S-O-R) framework, including motivation (MOT), trust (TR) and perceived risk (PR).

The survey data was collected through convenience sampling via an online questionnaire, with a sample size of 487 Indians. Structural equation modelling was conducted using SPSS and AMOS software for data analysis, ensuring a robust examination of the proposed model and its relationships.

Virtual interactivity and social interaction influence both attitude and perceived behavioural control. Attitude, perceived behavioural control, perceived usefulness and TR significantly influence intention. However, MOT, PR and perceived ease of use do not exhibit a significant influence on intention. These findings highlight the importance of these variables in shaping consumers’ intention towards experiencing immersive technologies at tourism destinations.

The findings hold significant implications for various stakeholders, including government agencies, travel firms, content creators and software developers. They can leverage these insights to enhance marketing strategies, develop immersive tourism experiences, innovate in the realm of Web 4.0 and personalize tourism offerings.

This study offers a distinctive contribution by integrating the S-O-R framework with TPB and TAM, while also incorporating key factors such as MOT, TR and PR. This novel approach provides a fresh perspective on consumer behaviour towards immersive technologies.

The concept of smart cities, driven by advancements in innovative information and communication technologies (ICTs), has gained significant attention in recent years. Smart cities aim to improve the quality of life for citizens by leveraging ICT to enhance the efficiency and effectiveness of urban services and infrastructure. One critical aspect of smart cities development is advanced innovations in water management, which play a vital role in achieving sustainability, prosperity of community and ensuring the availability of clean water resources. This chapter explores the relationship between advanced water management and smart cities development and highlights the synergies and benefits that arise from their integration. The chapter develops a framework for adopting innovative ICTs that support the gradual transformation toward next generation smart cities in the Gulf Cooperation Council (GCC) region. Such transformation aligns with the United Nations’ sustainable development goals (SDGs) and the maintenance of various social, economic, and environmental developments. The chapter begins by discussing the fundamental principles of smart cities and the role of advanced sensing technologies in enabling efficient and automated processes within urban environments. It then delves into the concept of water-sensitive cities, the importance of urban water mass balance analysis in designing sustainable water management strategies, and the emerging trends in water management. Furthermore, the chapter explores the integration of smart program management and the role of citizen engagement in the design and development of smart cities in the GCC countries and finally challenges and concerns facing these programs.

In recent decades, school discipline has become increasingly characterized by zero-tolerance policies that mandate predetermined punitive consequences for specific offenses. Zero-tolerance policies have not been shown to improve student behavioral outcomes or school climate. Further, these disciplinary policies are applied unevenly across schools and student populations. Despite the well-documented research base that demonstrates that these practices are ineffective, they remain commonplace in K-12 school across the United States. Transformative and culturally responsive educational leadership requires school leaders to examine the historical, societal, and institutional factors that contribute to the racial-discipline gap within their particular schools. This process requires committing to leading for racial justice, self-reflexive practice, and having the courage to boldly name and dismantle practices that do not create equitable outcomes for students on the margins. Drawing on tenets of Critical Race Theory and Culturally Responsive School Leadership to situate the history and proliferation of harmful disciplinary practices, this chapter discusses how critically reflexive school leaders can mobilize restorative practices to dismantle the systems, structures, and practices that reproduce inequities in schools. The chapter provides aspiring and practicing school leaders with the knowledge needed to reform existing school discipline policies and implement practices that support racial justice.

This study aims to present recent trends in touchscreen research through scientometric analysis. Devices with touchscreen are powerful tools for performing specialized operations. The touch screens of tablets, smartphones, laptops and television play an important role in teaching, learning and research.

The data was collected from Web of Science database from 2011 to 2021 and analysed using MS-Excel and VOSviewer software. After analysing 389 research papers, the authors identified the high impact journals, collaboration of countries, institutions, authors and growth trend of publications. Analysing the most used keywords, country-wise distribution of publications and research collaboration between institutions will help interpret the research trends in the selected time span.

The publications show an increase in number over the years from 2011 to 2021. Among the countries, USA has the highest number of 127 articles published, followed by England (61) and Canada (30). The results showed that the multiple authorship pattern in touchscreen publication is high when compared to single authors. The institutional analysis indicated that the organizations publishing more than five documents in the area were mostly from United Kingdom, Australia, USA and Korea. Timeline visualizations identified prominent keywords like touchscreen, performance, operant platform, Alzheimer’s disease, etc. in the subject. Interdisciplinary research is dominant in the subject, as seen from the most preferred journals and keywords.

The analysis does not include a comprehensive coverage of the research output, as only Web of Science database from 2011 to 2021 in a 10-year period is included.

The study would benefit stakeholders, including manufacturers and researchers alike, to know the future of touchscreen research.

This study is pertinent to socio-psychological fields because touchscreen technology encourages social connection among older persons and may help foster early literacy skills.

This paper will provide an understanding of the global developments in touchscreen research with recommendations for future research.

The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.

This study aims to propose a double-mediation effect of organizational justice and affective commitment (AC) through which responsible leadership (RL) influences to reduce turnover intention (TI).

The association between responsible leadership and TI, as well as the double-mediating effect of organizational justice and AC, was investigated using an integrated model. Structural equation modeling and Process Macro were used to validate the hypothesized correlations by analyzing the responses of 391 employees working in the Indian health-care sector.

The outcomes revealed a significant positive association between responsible leadership, organizational justice and AC, as well as a negative association between organizational justice, AC and TI. Moreover, the findings verified the association between responsible leadership and TI.

This study explored the double-mediating impact of organizational justice and AC on the association between responsible leadership and TI. It also supports the expert in guiding and performing the policy review as an outcome of this relationship.

The primary theoretical contribution of this study is to examine the relationship between RL and TI. This study examined the role of organizational justice (OJ) and AC as double mediators in the relationship between RL and TIs. Moreover, it has significant effects on the development of literature about RL, OJ, AC and TI.

Defining degradation in terms of physical deficiency-based condition descriptors, combined with Markov chain modelling, has been shown to provide improved predictions of degradation. However, unless these physical conditions are converted to lost value ratios (LVRs), maintenance managers would not be able to grasp the cost implications of degradation. Hence the purpose of this research is to convert the predicted deficiency-based condition ratings to lost value ratio bands.

Rectification costs were found using a Building Schedule of Rates to arrive at LVRs for each of the physical degradation conditions for the 12 building elements studied (ranging from concrete elements through finishes and ceilings to doors and windows). These LVRs were allocated into five bands with LVR interval limits of 0.00, 0.10, 0.25, 0.50, 0.75 and 1.00, with the five intervening ranges corresponding to LVR Bands A to E. These computations were compared with those arrived at independently by industry professionals.

Elements such as doors, widows and ceilings reached the maximum LVR Band E at the worst physical Condition 5 defined. However, Condition 5 for other elements only corresponded to LVR Bands A to D. Some 83% of the LVR bands assigned to the physical conditions were in agreement with those arrived at by the professionals, or differed by only one band.

The conversion of deficiency-based conditions to LVR bands yielded a completely new maintenance-oriented perspective on degradation. The banding was done using a novel ranking and clustering process that identified regions of high variation in LVRs as thresholds of the bands.

This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance.

In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement.

All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce.

The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts.

The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment.

Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance.

This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.