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Chapters explore the concept of toy tourism (Toyrism) with the objective of the study of the Indian toy industry and the impact of toys on the tourism economy of the country. The first part of this chapter deals with introducing Indian toys and their growth aspect. Study conducted to identify different determinants of the toyrism, as per the previous studies, culture, co-creational values and experiential values has strong association with toyrism. Culture has strong associations with toys which reflect regional culture, elaborate culture through toy storytelling such as puppet shows in Rajasthan, internationally Barbie dolls replicate American culture. Co-creational values have considered toys as innovative characteristics, and experiential values of toys related to created experiential aspects of toys, which tourist can explain and the concept is well taken by the Walt Disney with their creation of Disneyland Toy Story. The next part of this chapter discusses some of the government schemes to create toys for all travellers to develop indigenous, innovative toys with development of clusters. The last part of this chapter included the conclusion and way forward to research.

This paper aims to examine the optimal sourcing strategies and pricing decisions of competing toy manufacturers and to discuss how manufacturers’ decisions are impacted by competition.

The authors consider a single-period model to characterise the competition between two competing toy manufacturers. Both of them are free to choose between virgin material and recycled material. The authors consider two types of consumers: sensitive consumers who are concerned about product safety and prefer the toy made of virgin material and insensitive consumers who do not care what material is used in the toy. The competing manufacturers play a Cournot competition.

The results reveal a special case of a win-win situation for both the manufacturer and the consumer. In addition, an increasing number of sensitive consumers does not always raise the price of virgin-material toys.

The authors derive the manufacturer’s equilibrium sourcing strategies, corresponding market-clearing prices and profits obtained.

The paper investigates how toy manufacturers’ optimal sourcing strategies are impacted by competition, considering market segments.

This study aims to examine how couplepreneurs foster an entrepreneurial mindset in their kids.

A qualitative research approach with semi-structured interviews was used as the data collection technique. Narrative analysis was conducted on a sample of 20 couplepreneurs in Mthatha, South Africa.

Narratives of how couplepreneurs foster an entrepreneurial mindset in their kids included purchasing toys and games for kids that encourage entrepreneurship; competition and team activities among kids that are related to entrepreneurship; the piggy bank; encouraging kids to read entrepreneurial books; and kid entrepreneur showcases.

Sample size challenges are a notable limitation, including research being conducted in only one province of South Africa. Caution is advised when attempting to generalise the results to other contexts.

Understanding the strategies used by couplepreneurs to instil an entrepreneurial mindset in children can help parents to influence and encourage their children's entrepreneurial growth, resulting in more creative and innovative people who make a positive contribution to society, economy and the community.

While there is a body of literature on couple entrepreneurship, there are shortcomings in studies examining how coupleprenuers in African countries instil an entrepreneurial mindset in their children. As a result, this study aims to complement the current corpus of African literature on entrepreneurship, particularly in the context of South Africa.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

The present chapter explicates urban and rural childhoods in India. It presents childhood as a dynamic product arising out of an intersection of children's experiences in different familial–socio-cultural contexts, and children's positions within parent–child interactions and relations. These contexts and interactions tend to colour and shape the childhoods that children inhabit. Drawing on fieldwork conducted in urban and rural India, the chapter documents (1) nature of children's engagements and (2) parent–child relations, explicitly observed in parent–child interactions, provisioning warmth and care; parental control and supervision over children and children's participation in the overall fabric of family life and so forth. Forty-eight parents (24 urban and 24 rural) of children aged 7–11 years participated in the study. Qualitative data gathered through semi-structured interviews and home observations revealed distinctions in urban and rural Indian childhoods. Urban childhood is characterised by rights and privileges, and the centrality of academic pursuits, while rural childhood is featured with subtle induction into economic and social fabric of rural life. Although the world of ‘Indian childhood’ seemed plural, childhood playfulness and learning seemed to be the unifying themes. Geared to the fact that children have to make a living with limited means in the future, both childhoods were accelerated in preparation for future. Dwelling on the complexities in children's lives, this article appreciates diversity and multiplicity in childhoods.

The purpose of this paper is to investigate the process by which the level of immersion in virtual reality-based behavioral simulation (VR-BS) impacts on the non-cognitive and cognitive outcomes. The cognitive outcome is measured using the increase in the level of Sharia financial literacy, while the noncognitive outcome is measured using the behavioral intention to use VR-BS.

The method consists of two parts: First, the development of VR-BS, in the context of sharia financial literacy, using the waterfall model. Second, testing the effectiveness of VR-BS using the theory of interactive media effects framework. The participants were 142 students from three secondary schools (two Islamic religious schools and one public school) in Yogyakarta and Central Java, Indonesia. Partial least squares structural equation modeling was used for testing the hypotheses.

VR-BS creates a perceived coolness and vividness, which in turn has an impact on increasing the participants’ engagement. Also, the use of VR has an impact on natural mapping, which increases a user’s engagement through its perceived ease of use. As predicted, the user’s engagement affects VR’s behavior, mediated by the user’s attitude toward VR media. VR’s interactivity, however, does not impact on the cognitive aspect.

The participants were not randomly selected, as the data were collected during the COVID-19 pandemic. As a result, the majority of the participants had never tried VR before this study. The participants, however, were digital natives.

It is implied from the findings that Islamic financial business actors and the relevant government agencies (e.g. the Indonesian Financial Services Authority [OJK], the Ministry of Education, Culture, Research and Technology and the Ministry of Religious Affairs) should collaborate to best prepare the future generation of ummah by using VR-BS in their joint promotion and education programs. The results of the current study reveal that the use of VR-BS may attract people to engage in Islamic financial activities. By engaging in such activities, or at least engaging in real-life simulations/classes/workshops, people may gradually acquire more knowledge about Islamic finance.

As predicted, the user’s engagement has an impact on behavior toward VR-BS, which is mediated by attitude toward VR-BS.