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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.

Given the substantial challenges and disruptions that companies often encounter from within the organization and the broader market landscape – such as market turbulence, technological advancements and regulatory changes – developing robust organizational resilience and transitioning to digital business practices have become top priorities. This paper aimed to explore if digital human resource management (HRM) significantly influences the organizational resilience within the context of emerging economy.

To analyze data collected from HRM experts active in the business sector of Bosnia and Herzegovina, we utilized in this paper multiple regression analysis. This approach allowed us to explore the relationships and impacts within this specific regional context.

The study findings revealed that digital HRM significantly enhances organizational resilience, positively impacting its three key components: the ability to anticipate, the capacity to cope and the capability to adapt.

This study offers digital HRM strategies for enhancing organizational resilience, guiding HR professionals in using digital tools to boost employee adaptability, streamline crisis communication and improve flexibility and readiness for future disruptions.

This research adds to the existing literature and ensures practical implication on digital HRM and organizational resilience by empirically demonstrating how digital HRM strengthens organizational capabilities to foresee potential disruptions, respond effectively to crises and adapt to changing circumstances. These capabilities help organizations maintain stability and continue operations smoothly during unexpected events, thereby safeguarding their long-term sustainability and competitive edge.

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.