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Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

Contextual to the recognition of the complex interplay between health and behavioral aspects, integrated behavioral health (IBH) has emerged. Although this model is becoming popular in the Western world, its presence in the global context is not promising. This paper aims to explore the need for IBH in India and address its barriers to implementation and possible solutions.

We analyzed the case of IBH and its potential implications for India using the current evidence base, authors' reflections and experience of implementing similar programs.

This paper identifies contextual factors, including increased instances of non-communicable diseases and psychosocial and cultural determinants of health, that necessitate the implementation of IBH programs in India. The key features of different IBH models and their applicability are outlined. The current status of IBH and potential challenges in implementation in India in terms of human resources and other factors are delineated. We also discuss the potential models for implementing IBH in India.

Integrating behavioral health in primary care is considered an effective and sustainable model to promote health and well-being across various target populations. Towards this end, this paper is the first to discuss the contextual factors of IBH in India. It is a significant addition to the knowledge base on IBH and its possible implementation barriers and strategies in low- and middle-income countries.

Despite the potential benefits of open government data (OGD) and its recent adoption by many governments, little is known about monitoring its use by the public. This paper aims to address the knowledge gap by empirically examining OGD usage in Tanzania.

A field survey design was used to gather quantitative data from 288 people in seven regions of Tanzania to facilitate an analysis of the determinants of users’ behavioural intention and use behaviour of OGD. Guided by the Unified Theory of Acceptance and Use of Technology model, the study analysed the tendencies of these determinants on the use of the OGD portal.

The results show that performance expectancy, effort expectancy, social influence and facilitating conditions affect users’ acceptance and use of the OGD portal. The results suggest that OGD initiatives should focus more on their potential applications than publication. It is important to monitor and evaluate OGD use and the conditions that allow it to continue. By linking OGD with other government initiatives, governments can build strategies, capacity and social awareness. During the development of OGD platforms for extensive reuse, new data technologies like the semantic web should be considered.

Hitherto, research has underlined the significance of OGD initiatives at large and that studies in developing countries are scarce, especially that probe the uptake of the current efforts. The present study seeks to plug this research gap by examining the use of OGD in Tanzania’s context.

This paper aims to further the understanding of Open Government Data (OGD) adoption by the government by invoking two quantum physics theories – percolation theory and expander graph theory.

Extant research on the barriers to adoption and rollout of OGD is reviewed to drive home the research question for the present study. Both the theories are summarized, and lessons are derived therefrom for answering the research question.

The percolation theory solves the riddle of why the OGD initiatives find it difficult to seep across the hierarchical and geographical levels of any administrative division. The expander graph theory builds the understanding of the need for having networking among and within the key government personnel for bolstering the motivation and capacity building of the operational personnel linked with the OGD initiative. The theoretical understanding also aids in the implementation and institutionalization of OGD in general.

Intersectionality of domains for conducting research on any theme is always a need. Given the fact that there are innumerable challenges regarding the adoption of OGD by the governments across the world, the application of the two theories of quantum physics might solve the quandary in a befitting way.

There are insufficient health and nutrition education interventions targeting adolescent females in rural contexts in Indonesia. There is also a paucity of research evaluating the impact of implemented programs. This paper aims to develop and test the validity of a tailored education module to improve the knowledge, attitude and skills of adolescent females on health and nutrition.

The study was conducted between 2019 and 2023 in Ban Village, Karangasem Regency. This study used an explanatory sequential mixed methods research approach consisting of three stages: formative research using mixed methods, validation and review of the module by experts using the Delphi technique and pilot testing of the module. In the formative research stage, there were 40 female adolescent respondents implicated, in the validation and module review stage, there were 14 nutrition and public health experts implicated, and in the pilot test, a new cohort of 60 female adolescents were recruited. Validity was assessed by exploring the feasibility, reliability and linguistics of the module. The Delphi score was measured by the mean score and standard deviation.

The Health and Balanced Nutrition Education Module was impactful in improving the health and nutrition of female adolescents in Ban Village. The validation score of the module shows that from the total score of 4, construct reliability obtained a score of 3.18 with a 0.35 standard deviation. The construct feasibility and language revealed better scores, which were 3.31 with 0.4 standard deviations and 3.29 with 0.46 standard deviations, respectively. After dissemination of the module, participants’ mean score of knowledge on the importance of balanced nutrition significantly improved by 68.8% (p-value = 0.0001).

The Health and Balance Nutrition Education Module has been proven to improve the awareness of adolescents on balanced nutrition. Nevertheless, this study also has limitations due to the small number of respondents attending the information sessions and the pilot testing. Further studies should consider using implementation research for scale-up in other parts of Bali.

This study provides insight for health and nutrition educators for creating modules that better align with the context and information needs of the target group particularly for adolescents in rural areas, which are seldom neglected.

This study indicates that the trialed education materials can play a role in improving female adolescents’ knowledge of nutrition throughout their life cycle and their role in preventing stunting and noncommunicable diseases in later adult life.

The health and nutrition module trialed in the study is tailored specifically to the context of rural areas of Bali and validated by public health experts, then tested with 60 adolescents.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

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.