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\"The book is aimed at providing up-to-date research on the emergence of inlfuencer marketing and metaverse technology, innovative applications of metaverse in influencer marketing, role and future of metaverse in influencer marketing. This book can provide more details about impact, application and role of meaverse in influencer marketing and customer engagement. There is a need to disseminate about various aspects and emerging trends involved in the metaverse and its role and applications in influencer marketing. The aim is to bring together readers such as researchers, developers, practitioners and educators interested in advancement of the state of the metaverse and its applications in influencer marketing and digital world\"-- Provided by publisher.

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

In the pursuit of creating more sustainable and resilient structures, the exploration of construction materials and strengthening methodologies is imperative. Traditional methods of relying on steel for strengthening proved to be uneconomical and unsustainable, prompting the investigation of innovative composites. Fiber-reinforced polymers (FRPs), known for their lightweight and high-strength properties, gained prominence among structural engineers in the 1980s. This period saw the development of novel approaches, such as near-surface mounted and externally bonded reinforcement, for strengthening of concrete structures using FRPs. In recent decades, additional methods, including surface curvilinearization and external prestressing, have been discovered, demonstrating significant additional benefits. While these techniques have shown the enhanced performance, their full potential remains untapped. This article presents a comprehensive review of current approaches employed in the fortification of reinforced cement concrete structures using FRPs. It concludes by identifying key areas that warrant in-depth research to establish a sustainable methodology for structural strengthening, positioning FRPs as an effective replacement for conventional retrofitting materials. This review aims to contribute to the ongoing discourse on modern structural strengthening strategies, highlight the properties of FRPs, and propose avenues for future research in this dynamic field.

Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.

PurposeThis research aims to investigate the impact of artificial intelligence (AI) adoption on the innovation dynamics of Chinese manufacturing enterprises, with a specific focus on the intricate interplay with the labor structure.Design/methodology/approachLeveraging panel data of listed companies from 2010 to 2022, this study employs the two-way fixed effects (TWFE) model to examine the influence of AI adoption on Chinese manufacturing companies' innovativeness. Firm-level AI adoption is measured by constructing a three-dimensional attention, application and absorption index.FindingsThe results indicate that (1) AI adoption has a positive impact on both internal innovation capability and external innovation interaction, (2) AI adoption has dual effects on the education and skill structure of labor in manufacturing enterprises and (3) enterprises with a highly educated and skilled workforce exhibit a stronger influence of AI adoption on innovativeness.Originality/valueThis research contributes to the academic and practical discourse by unveiling the underlying mechanisms of AI affecting innovation and introducing a new measurement of the AI adoption index. The findings emphasize the need for a highly educated and skilled workforce to navigate the complexities of AI-driven innovation, offering valuable theoretical and practical implications for policymakers and enterprises.

The increase of social media (SM) has led to continuous deviations in how day-to-day entrepreneurial activities can be carried out. Additionally, studies devoted to SM entrepreneurship and SM are relatively limited and fragmented in their focus. However there is growing interest from scholars, practitioners and academia for upcoming studies and exploration within small and medium-sized enterprises (SMEs) context. This research explores the impact of entrepreneurial orientation (EO) on SM adoption and SME performance in developing countries. We employed the resource-based view (RBV) as the foundation for developing the conceptual framework The present study employed a closed-ended questionnaire to collect data from SMEs located in Pakistan. Partial-least-squares-structural equation-modeling (PLS-SEM) was utilized for the analysis of 423 responses. The results proved a direct positive link between EO–SMEs performance, EO–SM adoption, SM adoption–SMEs performance, innovation capabilities (IC), and SME performance. Partial mediation was found between EO and SME performance, and the significant moderation effect of IC was found between SM adoption and SME performance. This paper has implications for practitioners and researchers regarding SM adoption in the SMEs. It builds an empirical, multi-dimensional hypothesized model, including mediating and moderating roles affecting the relationships.

Small and medium enterprises (SMEs) have become a vibrant and dynamic sector of the world economy. Information technology plays a vital role in improving the productivity and competitiveness of SMEs. The dynamic business environment has brought fierce competition among SMEs and, therefore, requires the owners to interact with internal and external members actively. Hence, this study aims to investigate the impact of technology, organization, and environment as important factors in the performance of small and medium-sized enterprises. It also examines the mediating role of social media adoption. Items were developed to measure the various purposes of social media use in organizations, which contribute to increasing the measurement of social media usage. For the empirical investigation, this paper used a closed-ended questionnaire. Using a quantitative method, we observed 423 responses through structural equation modeling. The findings of the study indicate that technology, organization, and the environment play effective roles for SME performance. More importantly, social media adoption positively mediates the relationship between technology, organization, environment, and SME performance. The study also helps organizations realize the advantages of using social media and specifies the rationale behind an organization’s investment in social media.

The changing dynamics of consumer behavior in today’s digitally driven market presents opportunities and challenges for sustainable marketing development [...]

Increasing globalization and rapid digitization across industries have led to greater international competition. Furthermore, the emergence of new innovation has created both challenges and opportunities for small and medium-sized enterprises (SMEs). SMEs have recently been attracted to social media applications to reach a larger audience, improve their relationship with potential customers, and retain existing customers. However, the scant focus has been devoted to comprehensively understanding the adoption of social media in the SME context in developing countries. This study investigates the effects of technological-organizational-environmental (TOE) factors on social media adoption and its effect on SME performance. Data was collected by generating online survey link. SmartPLS 3.3 was used for the path analysis of 381 SMEs. The findings revealed a significant effect of relative advantage, cost-effectiveness, compatibility, interactivity (technological), entrepreneurial orientation (organizational), and customer pressure (environmental) factors, and an insignificant effect of top management support (organizational) and competitive pressure (environmental) determinants on social media adoption. The study found a significant influence of social media on SME performance. This paper offers several implications for decision-makers, policy-makers, and scholars interested in social media and its use. It builds an empirical, integrated framework for SMEs in developing countries.

Agriculture is an essential component of human sustenance in this world. These days, with a growing population, we must significantly increase agricultural productivity to meet demand. Agriculture moved toward technologies as a result of the demand for higher yields with less resources. Increasing awareness of the significance and influence of agricultural practices in global climate change has made the use of energy-efficient innovations a vital aspect of the agriculture sector. The use of greenhouses to provide controlled environments that encourage effective plant growth is one of the current associated approaches. If not properly maintained, the energy used to run the greenhouses’ chillers, heaters, humidifiers, carbon dioxide (CO₂) generators, and carbon emissions becomes expensive. The goal of this research is to create a sustainable greenhouse model while achieving the best plant growth requirements with minimal use of energy. In order to achieve the lowest possible amount of energy consumption, the optimization model considered temperature, humidity, CO₂ levels, and sunlight as essential parameters in the environment. The Artificial Bee Colony (ABC) optimization technique was utilized for setting the environmental parameters for plant growth, considered for the suggested system. The system’s inputs were plant-preferred factors, and plant comfort was achieved by applying ABC to boost the parameters’ efficiency. A fuzzy controller was utilized to regulate different devices, including humidifiers, heaters, chillers, and CO₂ generators, by entering the introduced values. The overall efficacy of the fuzzy controllers that switch On/Off the actuators was obtained by minimizing the error between the best estimates of environmental factors and the ABC optimized values. Additionally, the suggested method was contrasted with other effective algorithms, such as Genetic Algorithm (GA), Firefly Algorithm (FA), and Ant Colony Optimization (ACO). Based on the results of the comparison analysis between the ABC algorithm and current practices, present procedures do not minimize the fluctuations in the inaccuracy between the target and actual environmental parameters, which is a necessary step towards increasing energy efficiency. The suggested method used 162.19 kWh for temperature control, 84.65405 kWh for Humidity, 131.2013 kWh for Sunlight, and 603.55208 kWh for CO₂ management, indicating the maximum energy efficiency. ACO needed 172.2621 kWh, 88.269 kWh, 175.7127 kWh, and 713.2125 kWh, in contrast to FA 169.7983 kWh, 86.04496 kWh, 155.8442 kWh, and 743.7986 kWh. Temperature, Humidity, Sunlight, and CO₂ were measured by GA at 164.1609 kWh, 86.19566 kWh, 174.6429 kWh, and 734.9514 kWh, respectively. In terms of Plant comfort, the suggested approach also outperformed 0.986770848 ACO (0.944043), FA (0.949832), and GA (0.946076). It is important to note that the research being done has the potential to minimize operating costs and maximize the amount of energy needed for plant growth, thereby creating a model for sustainable greenhouse agriculture.