IMS Unison University

The shift towards sustainable and circular building systems has placed modular timber systems in the limelight as an alternative with lower carbon footprints. Nevertheless, the durability of the used material and the efficiency of the reuse cycle are factors that are now significantly impacted by digitalization. In the investigation described in this article, the influence of overall Supply Chain Digital Twin maturity and overall Supply Chain Responsiveness on Timber Durability and the overall Reuse Potential of Timber in the emerging modular timber buildings in Jordan was investigated. In the research design adopted for the quantitative approach, using Python software for the analysis of the results of the survey conducted using the questionnaire administered to 131 respondents in the industry. The results showed that DTM had a remarkably positive effect on TD (β = 0.43, p < 0.001) and CRP (β = 0.57, p < 0.001), accounting for 35% and 49% variance, respectively. SCR partially mediated the relation between DTM and CRP (indirect effect = 0.07, p = 0.002), suggesting that the agility of the supply chain magnifies the effectiveness of DTM. Monte Carlo analyses validated the robustness of the findings, with DTM and SCR cumulatively accounting for more than 75% variance in the sensitivity of reuse performance. This study concludes that upgrading digital maturity and responsiveness is a strategy for achieving long-lasting modular timber construction on a circular basis. This study provides novel empirical insights into the relation of digital transformation to the aims of a circular economy.

The rapid integration of artificial intelligence into education has fundamentally transformed how students learn, adapt, and develop competencies required for an increasingly complex world. This chapter presents a qualitative analysis of adaptability in AI-enhanced educational contexts, drawing on secondary data from research studies, institutional reports, longitudinal datasets, and documented case studies from leading global universities, including Stanford University, MIT, Arizona State University, and the University of Edinburgh. Using thematic analysis of educational research published between 2019 and 2024, the study identifies three key shifts in student adaptability: a move from static knowledge acquisition to dynamic capability building, from individual learning to collaborative human–AI partnerships, and from reactive problem-solving to proactive opportunity identification. The findings indicate that AI-supported learning environments are associated with enhanced critical thinking, creative problem-solving, and cross-disciplinary knowledge integration. At the same time, the analysis highlights unresolved concerns regarding AI dependency, long-term intellectual development, and the ethical implications of algorithmic guidance, underscoring the need for more robust theoretical and ethical frameworks. Overall, the chapter contributes to emerging scholarship on human–AI collaboration in education while documenting practical approaches that strengthen student adaptability and academic resilience.

Collaboration in the hybrid work environment is shifting from physical co-location to co-creation driven by trust, empathy, and the effective use of digital tools, including artificial intelligence (AI). This chapter explores how teamwork across time zones, screens, and cultural boundaries depends on psychological safety, mutual accountability, and inclusivity, while navigating tensions between autonomy and cohesion, flexibility and fragmentation, and visibility and equity. It examines how AI-enabled tools—such as collaborative chatbots, intelligent scheduling systems, and real-time analytics—are reshaping team interaction, decision-making, and creativity. Addressing key research gaps, including the decline of informal social interactions, algorithmic bias, and the absence of integrated human–AI frameworks, the chapter proposes a Human–AI Collaboration Model that combines inclusive leadership, emotional intelligence, and flexible digital practices. Ultimately, it positions AI as a co-pilot that strengthens human capabilities and supports the development of resilient, trust-driven teams in a rapidly evolving hybrid world.

Periods of global crisis significantly disrupt creative industries, particularly the visual and performing arts, where artistic production is closely linked to institutional support, regulatory frameworks, and cultural policy. This paper examines creative industry regulation in times of crisis by drawing comparative lessons from COVID-19 enforcement measures and healthcare cartel policies implemented in India, Australia, and Singapore. Through an interdisciplinary policy analysis, the study explores how emergency regulatory responses—originally designed for essential sectors such as healthcare—offer valuable insights for governing creative and design-led industries during periods of uncertainty. The research contextualizes these regulatory approaches within the creative economy, emphasizing their implications for artistic labour, cultural organizations, and design-driven enterprises. By comparing national responses, the study identifies key governance themes including regulatory flexibility, collaborative compliance, and crisis-driven innovation. The paper argues that adaptive regulatory models can support continuity in creative production while safeguarding artistic autonomy. The findings contribute to emerging scholarship on cultural governance by proposing transferable policy lessons that strengthen resilience in visual and performing arts ecosystems. This research offers strategic insights for policymakers, cultural institutions, and creative practitioners seeking sustainable regulatory frameworks for future crises.

This paper introduces TempNet, a groundbreaking multi-scale attention-based deep learning architecture that achieves unprecedented accuracy in operational renewable energy forecasting. The proposed hybrid model integrates multi-scale convolutional neural networks with different kernel sizes of {1, 3, 5, 7} for temporal pattern extraction, bidirectional LSTM layers with 128 units each, and sophisticated attention mechanisms to address the fundamental challenges of renewable energy variability. In a comprehensive evaluation on 18 months of high-resolution renewable energy data (44,166 observations), TempNet achieved exceptional performance with near-perfect forecasting metrics (RMSE of 0.44, MAE of 0.1, R2 of 0.99), showing significant improvements compared to baseline models. The proposed architecture incorporates several critical innovations, including a robust preprocessing pipeline with IQR-based outlier detection affecting 4.2% of observations, cyclical encoding for temporal features, and advanced data augmentation strategies that increased training samples by 25%. Cross-validation analysis across temporal folds demonstrates consistent performance with a coefficient of variation below 3% for all metrics, while bootstrap validation confirms statistical significance with 95% confidence intervals. Comparing TempNet with previous methods shows its superiority. The model maintains exceptional accuracy across diverse operational scenarios, including extreme weather events where maximum absolute error remained below 2.3% of mean generation capacity, significantly outperforming the 8-12% typically reported in the literature. TempNet's computational efficiency enables real-time deployment in smart grid environments, with inference times suitable for operational forecasting horizons from 1 to 24 hours. These breakthrough results can help achieve higher renewable penetration levels, reduce reserve requirements, and improve grid operations, supporting the global transition toward sustainable energy infrastructure.

The global economy is undergoing a rapid transformation driven by digital technologies that are reshaping international trade. Digitalization—defined as the integration of digital technologies into business and government processes—has emerged as a critical factor influencing export performance, competitiveness, supply chain efficiency, and market reach. This paper critically examines the role of digitalization in export trade by synthesizing empirical research from multiple regions and sectors, including manufacturing, services, and small and medium enterprises (SMEs). It assesses both quantitative outcomes (e.g., export volume growth, cost reductions) and qualitative implications (e.g., transformation of trade structures, enhanced service exports). Empirical studies indicate that digitalization reduces trade costs, enhances connectivity, and broadens access to global value chains, thereby positively affecting export participation and diversification. However, benefits depend on digital readiness, infrastructure quality, and firm-level capabilities. Digitalization also enables new forms of trade, such as e-commerce and digitally deliverable services, which are driving global export growth and contributing significantly to services trade. Despite advantages, digitalization presents challenges: skill gaps, cybersecurity risks, regulatory heterogeneity, and unequal access to digital infrastructure can limit its potential. Policy actions that strengthen digital infrastructure, promote digital adoption among exporters—particularly SMEs—and ensure regulatory harmonization can amplify the positive impact of digitalization on export trade. The paper concludes with recommendations for stakeholders to leverage digital technologies strategically to enhance export competitiveness and sustainable growth.

Purpose This study investigates how Big Data Analytics Capability (BDAC) influences firm performance in the healthcare sector and examines the mediating role of servant leadership in this relationship. Despite extensive research on leadership and digital transformation, limited studies explore how human-centric leadership styles facilitate the translation of data capabilities into organizational performance − particularly in ethically sensitive, data-rich healthcare environments. This study addresses this gap by integrating perspectives from the Resource-Based View (RBV) and sociomaterialism theory. Design/methodology/approach A quantitative, cross-sectional design was employed using data collected from 150 doctors in middle and top management roles across private and multi-specialty hospitals in the Delhi NCR region of India. Respondents were selected through a combination of convenience and snowball sampling. Structural Equation Modeling (SEM) using the Lavaan package in R was applied to test the hypothesized relationships and mediation effects. Findings Results reveal that BDAC has a significant positive impact on firm performance and that servant leadership partially mediates this relationship. The findings indicate that BDAC enhances organizational outcomes not merely through technological investment but through leadership practices that promote trust, inclusivity, and ethical decision-making. Research limitations/implications The study’s cross-sectional design limits causal inference, and the focus on a single regional healthcare ecosystem may constrain generalizability. Future research should adopt longitudinal or cross-sectoral approaches to further validate these findings and explore potential moderating effects of culture and organizational maturity. Practical implications The study offers concrete strategies for healthcare leaders, including the integration of data-literacy and servant-leadership training, ethics-driven data governance frameworks, and decision-support systems aligned with human values. These interventions can help organizations transform data-driven insights into patient-centered, ethically sound, and performance-enhancing actions. Originality/value This study advances theory by positioning servant leadership as a socio-cognitive and ethical mechanism that activates the latent value of BDAC. It extends the RBV framework by demonstrating how intangible human capabilities complement technological resources, and it enriches sociomaterialism theory by emphasizing human–technology interdependence in healthcare decision-making.

ABSTRACT Despite traditional conservatism, women are advancing in entrepreneurship, particularly in the small business sector—micro, small, and medium enterprises (MSMEs). With a shifting government perspective favoring female entrepreneurship, women are becoming significant economic contributors. This study uses a Gioia-based grounded theory approach to qualitatively examine themes from participants’ responses. Data were collected from female entrepreneurs in small businesses—MSMEs—in different cities using representative sampling to ensure diversity. Themes from 28 female MSME entrepreneurs corresponded with Ansoff’s growth matrix and transformational leadership theories. Their insights shed light on the factors, obstacles, and opportunities emerging female entrepreneurs face in smaller businesses, emphasizing their influence on those who aspire to follow in their footsteps. These findings help policymakers promote female-led small businesses and increase female entrepreneurs’ economic contributions.

Purpose The study aims to investigate how consumer consciousness (environmental and health-related) and perceptions of innovativeness jointly influence switching purchase intent to actual switching to green cosmetic products. By identifying the psychological and perceptual factors that bridge the intention-behaviour gap, the research seeks to provide a predictive understanding of green consumer behaviour, particularly within an emerging market segment. Design/methodology/approach To discern the complex psychological mechanism of switching buying behaviour towards green cosmetics, the present study aims to integrate the stimulus-organism-behaviour-consequences (SOBC) and diffusion of innovation in the green cosmetics context. PLS-SEM was employed to analyse the data from a sample comprising 404 respondents through the mall intercept technique. This study aims to make a theoretical contribution by connecting these frameworks and, more practically, by guiding strategies for long-term product positioning and consumer engagement. Findings We found that the influence of hedonism and social and product innovativeness, especially concerning environmental and health consciousness, showed a positive impact on the actual switching to green cosmetics. Originality/value This study presents a novel, integrated behavioural model that connects psychological antecedents, such as environmental and health consciousness, with actual consumer actions in the under-explored green cosmetics market. This research connects switching intentions to actual switching from traditional cosmetics to green cosmetics, in contrast to prior studies that concentrate solely on attitudes or intentions, thus offering practical insights for companies to develop sustainability-oriented marketing and product strategies grounded in empirically validated consumer motivations.