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Deliberate practice, an iterative process, has garnered increasing attention of researchers and practitioners in entrepreneurship and business domains in the recent years. However, research studies are on rise that found its positive correlates with superior performance. Nevertheless, whether the relationship always holds positive is an intriguing question that wedged the authors’ attention to probe the causal mechanism that might scrutinize the association otherwise. The purpose of the study is to build on the social cognitive theory and the attribution theory to project that deliberate practice deteriorates innovation performance due to augmented creative self-efficacy (CSE). While, individuals who draw on their mindfulness can overcome the harmful effects of increased CSE on innovation performance, subsequently translating deliberate practice into enhanced innovation performance through CSE.

The authors gather data from 312 entrepreneurs in the small and medium enterprises using a time-lagged research design and analyze the results using SmartPLS (v 4.0).

The findings reveal that deliberate practice significantly affects CSE of entrepreneurs. Besides, deliberate practice negatively influences innovation performance through CSE. In addition, the study finds that mindfulness significantly moderates the relationships between CSE and innovation performance and deliberate practice and innovation performance through CSE.

This is the first study that examines a moderated mediated model of CSE as a deteriorating agent and mindfulness as a coping mechanism between deliberate practice and innovation performance of entrepreneurs. The study highlights several essential theoretical and practical implications.

The industrial revolution changed the market landscape significantly in all industrial sectors. It has a noteworthy impact on enhancing the quality of goods and services. The quality aspect is of utmost concern and determines the success or failure of any product. Therefore, the presented study analyses the key barriers and solutions of Quality 4.0.

Twenty barriers and fifteen solutions were identified using a literature review and investigated using a hybrid approach. Barrier weights were evaluated with the help of the fuzzy AHP method. Furthermore, the computed weights were used to perform computations in the next step using fuzzy-TOPSIS to prioritize the ranking of identified solutions.

The research results show that “Lack of applying advanced analytics to uncover Quality 4.0 initiatives” and “Lack of integrating data from various sources across the organization” are the topmost barriers. Furthermore, “Implement a leadership development program focused on Quality 4.0” and “Cross-departmental peer learning environment” are the topmost solutions.

Managers and industrialists can benefit from Quality 4.0 through improved decision-making, process efficiency, supply chain collaboration, agile quality management, enhanced customer experience and a culture of continuous improvement. This results in better quality, operational effectiveness and a competitive edge.

The solutions need to be mapped with barriers to adopting Quality 4.0. Furthermore, the research results involve novelty by prioritizing the solutions to overcome the anticipated barriers.

The extant literature underlines the inadequacies of legal and policy frameworks addressing the safety and health concerns of sandstone mineworkers in India. Notably, Rajasthan, a state renowned for its extractive industries, mirrors these concerns. Against this backdrop, this paper aims to critically evaluate the relevant legal and policy landscape, with an emphasis on the recent central statute: the Occupational Safety, Health and Working Conditions Code of 2020 (OSHWCC). Given that the Code subsumes the key legislation pertaining to the safety and health of mineworkers, an in-depth critical analysis is essential to forge suitable policy interventions to address continued gross violations of human rights.

The critical analysis of legal and policy frameworks on silicosis in sandstone mineworkers is based on a comprehensive reading of existing literature. The literature includes relevant laws, case law, reports of the Rajasthan State Human Rights Commission and National Human Rights Commission, publicly available data and key scholarly contributions in the field.

Although the OSHWCC has made some changes to the existing regulatory architecture of mines in India, it has failed to safeguard the safety and health of mineworkers. Notably, the vast majority of mines in India – constituting approximately 90%, which are informal, seasonal and small-scale – remain beyond the jurisdiction of this Code. In Rajasthan, there are specific policies on silicosis, but these policies are poorly implemented. There is a serious shortage of doctors to diagnose silicosis cases, leading to under-diagnosis. The compensation for silicosis victims is insufficient; the distribution mechanism is complex and often delayed.

The central and many state governments have not established the regulatory institutions envisaged under the OSHWCC 2020; therefore, the working of the regulatory institutions could not be critically examined.

The paper critically evaluates laws and policies pertaining to silicosis in sandstone mineworkers, with a special emphasis on the state of Rajasthan. It offers a comprehensive critique of the OSHWCC of 2020, which has not received much attention from previous studies.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Previous studies highlighted a shortage of scale to measure digital citizenship among Indians. Accordingly, this study examined the psychometric properties of Jones and Mitchell’s (2016) digital citizenship scale in the Indian context with the help of two independent studies.

In the first study, the factorial validity of the scale was determined using the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). In the second study, Cronbach’s alpha (CA) and composite reliability (CR) values checked the internal consistency reliability of the scale. Also, convergent, discriminant and criterion validity were examined.

EFA recommended a two-factor structure explaining 58.219% of the total variance. The item loadings varied from 0.540 to 0.793. The Indian version of the scale showed one variation. In the original digital citizenship scale, the seventh statement (“I like to present myself online as someone was making positive choices”) was part of factor 1 (online respect); however, this study suggested that the seventh statement should be included in factor 2 (online civic engagement). In the second study, the acceptable (>0.70) values of CA and CR concluded the internal consistency reliability of the scale. The convergent validity was suggested by average variance explained values (>0.50). In addition, as expected, the Indian version of the digital citizenship scale reported a statistically significant positive correlation with Internet self-efficacy and a considerable negative relationship with cyberbullying. These findings concluded the criterion validity of the scale.

The Indian version of the digital citizenship scale showed appreciable psychometric properties among Indian students.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

The mental health and well-being of employees is negatively impacted by stress, anxiety and depression. There is a need to address these issues at an organisational level to enhance workforce welfare and to decrease the number of days lost due to mental health/well-being concerns. This study aimed to evaluate a mental health and well-being toolkit designed to enhance the resilience, coping and self-talk of employees.

The intervention was derived from counselling psychology and composed of an 8-hour programme, which was delivered over four consecutive weeks. A mixed methods approach was adopted, with the quantitative element assessing an intervention group (n = 10) and control group (n = 14) at baseline and at the end of the programme on measures of mental health and well-being. The qualitative aspect of the study involved interviews with the intervention group, which were thematically analysed..

Quantitatively, the experimental group showed statistically significant improvements in elements of resilience and well-being and a reduction in stress and anxiety. Qualitatively, participants experienced a positive effect on their well-being, benefited from the learning process, applied the taught strategies widely and found the session experience positive.

This was a small pilot study, nevertheless, the mixed methods nature of this investigation indicates that a counselling derived online training programme can enhance the well-being of employees within large organisations.

A remotely delivered mental health and well-being toolkit could be a useful resource to enhance the well-being of employees in all organisations.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

This paper aims to present a mixed methods study of child criminal exploitation (CCE), particularly in the form of “county lines”, in three local authorities in southeast England. The data is analysed using a framework constructed from two relevant contextual and relational theories to understand experiences of CCE and the safeguarding responses undertaken to protect young people from harm.

Data were drawn from a survey (n = 118); interviews (n = 5) and focus groups (n = 15) with professionals; interviews with parents (n = 4); and from secondary analysis of eight interviews with young people affected by criminal exploitation. The analytic framework was applied to the data to answer two research questions: what is the role and significance of young people’s interactions with peers, parents and professionals in their CCE trajectories; and how do professionals understand and address risks within contexts in which young people are exposed to CCE? The mixed methods data collected by the authors were coded and analysed using this framework.

Although professionals were aware of contexts and relational power dynamics, the situated and temporal nature of CCE remained overlooked within safeguarding systems. Professionals required more space, time and support to fully and flexibly engage with young people and their families to increase safety.

There is limited research available that critically examines safeguarding responses to CCE in diverse geographic and cultural contexts. This paper contributes a critical account of how professionals might create connections and opportunities for change with young people, identifying structural constraints within practice systems alongside emerging examples of effective practice.

Using a mixed methodology comprising interviews, case file analysis and descriptive statistics, this study aims to examine the experiences of all 43 young people in Wales subject to secure accommodation orders between 1st April 2016 and 31st March 2018.

Children in the UK aged 10–17 years who are deemed to be at a significant level of risk to themselves or others may be subject to a secure accommodation order, leading to time spent in a secure children’s home (SCH) on welfare grounds. Following a rise in the number of children in Wales referred to SCHs for welfare reasons, this paper describes these young people’s journeys into, through and out of SCHs, giving insight into their experiences and highlighting areas for policy and practice improvements.

Findings indicate that improvements in mental health support and placement availability are key in improving the experiences of this particularly vulnerable group of young people throughout their childhood.

Other practical implications of the study’s findings, such as improvements in secure transport arrangements, are also discussed.

While the findings are limited by the reliance on self-report methods and the size of the study, namely, the small number of young people with experience of SCHs who were able to participate, the findings build on the existing knowledge base around children’s residential accommodation and provide new insights into how best to support these children.