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The purpose of this study is to investigate the influence of light intensity and sources of carbon and nitrogen on the cultivation of Spirulina maxima.

Cultures were carried out in a modified Zarrouk medium using urea, sodium acetate and glycerol. A Taguchi experimental design was used to evaluate the effect on the production of biocompounds: productivities in biomass, carbohydrates, phycocyanin and biochar were analyzed.

Statistical data analysis revealed that light intensity and sodium acetate concentration were the most important factors, being significant in three of the four response variables studied. The highest productivities in biomass (46.94 mg.L⁻¹.d⁻¹), carbohydrates (6.11 mg.L⁻¹.d⁻¹), phycocyanin (3.62 mg.L⁻¹.d⁻¹) and biochar (22, 48 mg.L⁻¹.d⁻¹) were achieved in experiment 4 of the Taguchi matrix, highlighting as the ideal condition for the production of biomass, carbohydrates and phycocyanin.

Sodium acetate and urea can be considered, respectively, as potential sources of carbon and nitrogen to increase Spirulina maxima productivity. From the results, an optimized cultivation condition for the sustainable production of bioproducts was obtained.

This work focuses on the study of the influence of light intensity and the use of alternative sources of nitrogen and carbon on the growth of Spirulina maxima, as well as on the influence on the productivity of biomass and biocompounds. There are few studies in the literature focused on the phycocyanin production from microalgae, justifying the need to deepen the subject.

The growing prevalence of digitalisation in economies has brought attention to the significance of digital transformation and its potential to enhance the competitiveness of enterprises within the emerging market. Nevertheless, it is important to note that disruptive changes are not limited to the organisational level, as they also have broader implications for the environment, society and institutions. The incorporation of technology into the field of education, often known as educational technology (EdTech), has undergone a significant evolution in recent times, fundamentally transforming the methods and processes of teaching and learning. This chapter delves into the multifaceted landscape of digital transformation in the field of EdTech from the perspective of sustainable development, elucidating the wide range of opportunities and challenges that consumer, educators, institutions and technology providers and various stakeholders face when they embark on this journey. Further, this chapter also sheds light on how to overcome the challenges faced by the stakeholders in digital transformation of EdTech for quality education.

This paper aims to enhance early-stage cost estimation in construction projects, a critical factor in project feasibility, funding, resource allocation and scheduling. Traditional cost estimation approaches suffer from limitations such as the absence of structured methodologies, assumptions of linear cost relationships, prolonged processes and expert judgment variations. To address these challenges, this study proposes a reliable cost prediction model based on artificial neural networks (ANNs) for building construction projects in India.

To develop cost prediction model, this study collected data from 377 building construction projects in India, encompassing 17 essential cost parameters. The methodology involves data preprocessing, constructing features and fine-tuning ANN hyperparameters meticulously to achieve optimal performance.

The research showcases effectiveness of cost prediction model, evident in significantly reduced mean square error values. ANN-based prediction model excels in handling nonlinear cost dependencies and diverse project complexities, making it a valuable tool for early-stage cost estimation.

ANN-based cost prediction model is primarily designed for predicting costs associated with structural works of building projects.

The proposed solution offers stakeholders a robust data-driven decision-making tool during initial phases of construction projects. This can lead to more successful and economically viable outcomes.

This research examines the drawbacks of traditional cost estimation methods by presenting a data-driven approach leveraging machine learning. It significantly improves precision of early cost forecasts in construction projects while offering practical value to industry.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

A learning-focused culture promotes creativity, innovativeness and the acquisition of novel insights and competencies. The study aims to explore the relationship between human resource development (HRD) practice and employee competencies using organizational learning culture as a mediating variable.

Data were collected from 828 employees of 37 health care institutions comprising 24 (internationally-owned) and 13 (indigenously-owned). Construct reliability and validity was established through a confirmatory factor analysis. The proposed model and hypotheses were evaluated using structural equation modeling.

Data supported the hypothesized relationships. The results show that training and development and employee competencies were significantly related. Career development and employee competencies were significantly related. Organizational learning culture mediates the relationship between training and development and employee competencies. However, organizational learning culture did not mediate the relationship between career development and employee competencies.

The generalizability of the findings will be constrained due to the research’s health care focus and cross-sectional data.

The study’s findings will serve as valuable pointers to policy makers and stakeholders of health care institutions in developing system-level capacities that promote continuous learning and adaptive learning cultures to ensure sustainability and competitive advantage.

By evidencing empirically that organizational learning culture mediates the relationship between HRD practices and employee competencies the study extends the literature.

The study attempts to investigate the role of social media in spreading awareness regarding ayurvedic immunity boosters (AIB) and changes in diet. Further, the study examines the factors affecting the willingness to pay for ayurvedic immunity boosters (WPIB) during the pandemic and new normal situation with the moderating effect of the “fear of COVID-19 infection.”

The data were collected from millennials in two phases, i.e. the first phase (1 July–August 2021) with 300 respondents and a second phase with (June–August 2022) 257 respondents. An online questionnaire was shared with millennials using the snowball sampling technique. Descriptive statistics with SPSS and SmartPLS 4.0 software were applied to analyze the data.

The results found a variation in AIB content sharing on social media during 2021 and 2022. Results found that respondents reported significant changes in their lifestyle and diet, like consuming honey, khada, tulsi tea, etc. In 2021, health consciousness and trust significantly affected WPIB, whereas in 2022, only health consciousness was substantially affected. Fear of COVID-19 infection moderates the relationship between health consciousness, perceived fear and willingness to pay for ayurvedic products, whereas the effect on consumer preference and trust remains insignificant.

Results could help ayurvedic product manufacturing companies understand the consumers' mindset and the factors that stimulate consumers to buy these immunity boosters. Ayurvedic advertisers should design unambiguous messages that focus on health consciousness and have trustable components to encourage consumers to adopt a healthy lifestyle.

This is one of its kinds of studies that presents the contrasts of how the COVID-19 crisis has significantly changed individuals' dietary intake and affected lifestyle patterns.

In the era of Industrial Revolution (IR) 4.0, the integration of digital technologies, automation and data-driven insights has generated a broad wave of transformation across all industries, including the insurance sector. The study focuses on determining how the adoption of these technologies (InsurTech) is changing the life insurance industry, ultimately enhancing the level of customer satisfaction.

The data analysis has been performed with 304 useable responses from the policyholders of life insurance in the north-west region of India. The methodology adopted for this study is partial least squares (PLS) structural equation modeling (SEM). To investigate the predictive relevance of customer satisfaction, the PLS predict technique has been used. Also, importance performance map analysis (IPMA) has been applied to assess the important and performing dimensions of customer satisfaction.

The outcomes show that the adoption of InsurTech has a positive impact on customer satisfaction. Customer service management and policy management are among the strongest predictors of customer satisfaction, and the predictive relevance is reported to be moderate. IPMA results have suggested that improvements in online distribution of insurance services and customer service management lead to higher customer satisfaction.

The conceptual model can be tested with the moderating effect of different demographic factors (age, gender etc.), and future research can be done to analyze the mediating role of customer satisfaction between InsurTech adoption and customer loyalty.

The study offers valuable contributions to the marketing literature, shedding light on the influence of InsurTech adoption on customer satisfaction within the Indian life insurance sector. The research offers a practical approach that could help marketing professionals and policymakers comprehend the utilization of online insurance services, and this understanding can help industry experts to develop customer-oriented products and services.

This research is the first of its kind to test the association between InsurTech adoption and customer satisfaction in the life insurance sector in the Indian context. Research also provides novel insights for policymakers to enhance the satisfaction of customers towards using online insurance services in the near future in developing countries like India.

Rapid innovation and developments in personal electronic technology have encouraged users to change users' devices more frequently than ever, which has resulted in creating a massive increase in the amount of electronic waste. The study focuses on identifying the barriers to closed-loop supply chain (CLSC) in the electronic industry.

A framework for analyzing the relationships among CLSC adoption barriers is designed. The authors adopted the decision-making trial and evaluation laboratory (DEMATEL) technique to determine the critical barriers of electronic CLSC from the opinion of experts in the field.

The outcome from the analysis suggests that cost barriers, financial barrier, process barriers and supplier-side barriers are the main causal factors that prevent the adoption and implementation of e-waste CLSC. The causal relationship indicates that financial barrier is the most influential factor, while phycological barrier is the most flexible barrier to the adoption of e-waste CLSC.

This study is restricted to CLSC adoption barriers in the electronic industry by evaluating 36 sub-barriers grouped into 8 main dimensions related to different members of the supply chain.

Closed-loop adoption barriers have been proposed to understand the crucial barriers to implementation of CLSC in the electronic industry. The cause-and-effect relationship indicates the critical factors to be improved to increase adoption of e-waste CLSC, helping managers and regulatory bodies to mitigate the problem areas.

This study contributes to the literature on CLSC by adopting a multi-criteria decision-making (MCDM) technique which captures the critical barriers of e-waste CLSC adoption in Indian scenario.

This study developed a theoretical model to test the relationship between digital capability and Industry 4.0 (I4.0) and its effect on innovation performance in small and medium-sized enterprises (SMEs).

The proposed theoretical model was evaluated using partial least-squares structural equation modeling and fuzzy-set qualitative comparative analysis. The data were obtained from a sample of 536 SMEs in Chile.

The proposed model presented two dimensions of digital capability: management and information and communication technologies (ICTs). Management models composed of enterprise resource planning and customer relationship management systems are essential for optimizing organizational management. Meanwhile, ICTs facilitate the smooth flow of information within an organization, leading to improved efficiency in production processes. I4.0 is encouraged by exposing SMEs to base technologies such as data analytics. These results confirm that I4.0 influences innovation performance.

SME managers should encourage the development of digital capabilities to transition toward I4.0, as this can make SMEs more competitive and innovative in changing and dynamic scenarios.

I4.0 adoption and the development of digital capabilities can directly affect employment and national economic growth.

Most studies focus on the organizational factors affecting SMEs’ I4.0 adoption. They do not, however, address the role played by current digital capability in I4.0 technology adoption and its effect on firms’ innovation performance.

Este estudio desarrolló un modelo teórico para probar la relación entre la capacidad digital y la Industria 4.0 (I4.0) y su efecto en el desempeño de la innovación en pequeñas y medianas empresas (PYME).

El modelo teórico propuesto se evaluó mediante el uso de modelos de ecuaciones estructurales de mínimos cuadrados parciales y análisis comparativo cualitativo de conjuntos difusos. Los datos se obtuvieron de una muestra de 536 pymes de Chile.

El modelo propuesto presenta dos dimensiones de la capacidad digital: la gestión y las tecnologías de la información y la comunicación (TIC). Los modelos de gestión compuestos por sistemas de planificación de recursos empresariales y de gestión de relaciones con los clientes son esenciales para optimizar la gestión organizacional. Por su parte, las TIC facilitan el flujo fluido de información dentro de una organización, lo que conduce a una mejora de la eficiencia en los procesos de producción. La I4.0 se fomenta exponiendo a las PYME a tecnologías de base como el análisis de datos. Estos resultados confirman que la I4.0 influye en el rendimiento de la innovación.

La mayoría de los estudios se centran en los factores organizativos que afectan a la adopción de la I4.0 por parte de las pymes, pero no abordan el papel que desempeña la capacidad digital actual en la adopción de la tecnología I4.0 y su efecto en el desempeño innovador de las empresas.

Los gestores de las PYMES deben incentivar el desarrollo de capacidades digitales para realizar la transición hacia la I4.0, ya que esto puede hacer que las PYMES sean más competitivas e innovadoras en escenarios cambiantes y dinámicos.

La adopción de la I4.0 y el desarrollo de capacidades digitales pueden afectar directamente al empleo y al crecimiento económico nacional.

Several disciplines and thousands of studies have used, developed and supported technology adoption theories to guide industry and support innovation. However, within the past decade, a paradigm shift referred to as the fourth industrial revolution (4IR) has resulted in new considerations affecting how models are used to guide emerging technology integration into business strategy. The purpose of this study is to determine which technology adoption model, or models are primarily used when assessing smart technologies in the 4IR construct. It is not to investigate the rigour of existing models or their theoretical underpinnings, as this has been proven.

To achieve this, a systematic literature review based on the preferred reporting items for systematic reviews and meta-analysis methodology is used. From 3,007 publications, 125 papers between 2015 and 2021 were deemed relevant for thematic analysis.

From the literature, five perspectives were extracted. As with other information and communication technology studies, the analysis confirms that the technology acceptance model remains the predominantly used model. However, 105 of the 125 models extended their theoretical underpinnings, indicating a lack of maturity. Furthermore, the countries of study and authors’ expertise are predominantly clustered in the European and Asian regions, despite the study noting expansion into 16 different subject areas, far beyond the smaller manufacturing scope of Industry 4.0.

This study contributes theoretically by providing a baseline to develop a generalisable 4IR model grounded on existing acceptance trends identified. Practically, these insights demonstrate the current trends for strategists and policymakers to understand technology adoption within the 4IR to direct efforts that support innovation development, an increasingly crucial factor for survival in the digital age. Future research can investigate the additional constructs that were impactful while considering the level of research they were applied to.