Gulf University for Science and Technology

In the digital economy, artificial intelligence (AI) is reshaping how firms create value and pursue sustainability, yet how small and medium-sized enterprises in emerging markets convert AI capability into sustainable outcomes remains insufficiently understood. Drawing on the Resource-Based View and Institutional Theory, this study examines whether eco-innovation mediates the relationship between AI capability and the economic, environmental, and social dimensions of sustainable performance, and whether market turbulence conditions these relationships. Survey data from 350 respondents in small and medium-sized hotels in Pakistan were analyzed using partial least squares structural equation modeling. The results show that AI capability strongly fosters eco-innovation, which in turn improves all three performance dimensions and partially mediates the effect of AI capability on sustainable performance. Market turbulence strengthens the contribution of eco-innovation to economic and social performance, but not to environmental performance, and does not alter the link between AI capability and eco-innovation. The study contributes to the digital economy and sustainability literature by identifying eco-innovation as the mechanism that converts AI capability into sustainable value, and market turbulence as a boundary condition on that conversion in resource-constrained service firms. For managers and policymakers, the findings indicate that AI investments yield sustainability gains when they are embedded in eco-innovation strategies, particularly in volatile markets.

We present a numerical investigation of residual-based a posteriori error estimation for finite element discretizations of convection-diffusion equations stabilized by algebraic flux correction and related algebraic stabilization techniques. In particular, we consider Algebraic Flux Correction schemes employing the BJK and Monolithic Convex (MC) limiters and algebraically stabilized methods including the Monotone Upwind-Type Algebraically Stabilized (MUAS), the Symmetrized Monotone Upwind-Type Algebraically Stabilized (SMUAS), and the BBK approach. The performance of the estimators and limiters is studied on adaptively refined meshes for several two-dimensional test problems, including boundary layers, interior layers, and a non-linear convection problem with solution-dependent transport. The experiments assess accuracy, preservation of the discrete maximum principle, adaptive mesh behavior, and computational efficiency. The results show that the interaction between stabilization and a posteriori error estimation depends strongly on mesh alignment and on the character of the convection field. In particular, for problems with moving or curved layers, the behavior of the limiters differs significantly: strongly upwind-based limiters provide the most accurate solutions, while smoother algebraic stabilizations lead to more efficient non-linear iterations. The study also indicates that residual-based estimators remain reliable for both linear and non-linear problems but may react to changes in limiter activation during adaptive refinement. Overall, the numerical results clarify the practical behavior of several widely used stabilization techniques within an adaptive framework and highlight aspects that are not yet fully explained by the current theory, particularly for non-linear transport problems.

Adopting multiple theoretical perspectives, this study separately and jointly examines the impacts of reporting Environmental, Social, and Governance (ESG) initiatives and gender diversity in boardrooms on corporate stock liquidity. Using a sample of non-financial firms listed on the Frankfurt CDAX from 2010 to 2023, the study investigates whether ESG reporting enhances stock liquidity and how gender diversity moderates this relationship. To assess ESG reporting, we utilise Refinitiv Workspace ESG scores, while gender diversity and stock liquidity are measured through various indices. The results demonstrate that firms with higher ESG reporting experience higher stock liquidity, with governance disclosure having the most substantial impact. Likewise, firms with greater gender-diverse boards exhibit higher stock liquidity. Besides, gender diversity in the boardroom positively moderates the effect of ESG reporting and its components on stock liquidity, implying that as gender diversity increases within a company, the influence of ESG reporting on enhancing stock liquidity becomes more pronounced. The results signify the roles of ESG reporting gender diversity in enhancing stock liquidity and investor confidence, and provide theoretical and practical implications for firms, investors, and policymakers to promote sustainable corporate practices and enhance market liquidity.

Context: In recent years, advanced Artificial Intelligence (AI) tools, such as Generative Pretrained Transformers (GPTs), have shown remarkable potential across diverse application areas, including academic research. One of the promising applications of using these tools for conducting Systematic Literature Reviews (SLRs), which are effort intensive and time-consuming process in software engineering research. Objective: This study investigates the use of GPT-4o to support SLR activities in the context of software engineering. Methods: We employed a mixed research method to explore the significance of GPT-4o in conducting sub-SLR activities, such as formulating research questions, generating Boolean strings, and performing quality assessments. Four benchmark SLR studies were selected and used to replicate the SLR activities using GPT-4o, which were further assessed by six domain experts. Results: In the results, GPT-4o demonstrated consistency in generating Boolean search strings, identifying themes, and quality assessment criteria across all the benchmark studies, and showed close alignment with expert evaluations. The model effectively automated key SLR activities, including literature search string formulation, validation of primary studies, quality assessment, thematic analysis, and taxonomy development. However, GPT-4o was less accurate in identifying relevant studies across databases, occasionally missing domain-specific literature. Conclusions: The findings indicate that GPT-4o can effectively support several SLR tasks in software engineering when integrated within a domain expert-in-the-loop workflow. However, the study evaluates selected activities under controlled settings rather than a full end-to-end SLR execution. Future research should extend this evaluation to additional datasets and alternative LLMs to further assess generalizability.

Heat stress poses a significant threat to cotton, affecting crucial developmental stages from fertilization to boll development and thereby reducing both yield and quality. As global climate change advances, the likelihood of severe heat waves increases, presenting a serious challenge to agricultural production and our ability to feed a growing population. Cotton’s resilience to heat involves a suite of physiological and biochemical responses, including adjustments in water management and protective mechanisms at the cellular level, such as the synthesis of heat shock proteins (HSPs) and antioxidants. These adaptations are crucial for maintaining cellular integrity and overall plant health under thermal stress. Recent research highlighted specific proteins and enzymes within the plant that help combat stress-related damage. However, the exact molecular mechanisms facilitating these protective responses are not fully delineated. Further research is needed to identify and validate additional molecular mechanisms underlying the plant’s response to heat stress. Additionally, exploring new breeding strategies for the development of more resilient cultivars. In addition, field trials and testing under real-world conditions will be essential to assess the effectiveness of the proposed strategies for mitigating the negative effects of heat stress on crop yields.