Purpose: This research aims to investigate the impact of technological challenges, including techno-overload, techno-complexity, and techno-insecurity, on employee job satisfaction within the banking sector of Saudi Arabia. Additionally, the study examines the mediating roles of supervisor support and job clarity in buffering the effects of technological challenges on job satisfaction. Method: The study employs a quantitative research design, utilizing an online questionnaire to collect data from banking employees in Saudi Arabia. The sample size of 135 participants was determined using the rule of thumb technique. Random sampling was utilized to ensure representativeness. Data analysis was conducted using Statistical Package for Social Sciences (SPSS) to explore the relationships between technological challenges, supervisor support, job clarity, and employee job satisfaction. Findings: The findings of the study reveal a significant negative impact of techno-overload, techno-complexity, and techno-insecurity on employee job satisfaction within the banking sector of Saudi Arabia. Moreover, supervisor support and job clarity were found to mediate these relationships, highlighting their importance in mitigating the adverse effects of technological challenges on job satisfaction. Originality/Significance: This research contributes to the existing body of knowledge by providing empirical evidence on the relationships between technological challenges, supervisor support, job clarity, and employee job satisfaction within the specific context of Saudi Arabian banks. The findings have significant implications for organizational leaders and managers in developing evidence-based strategies to manage technological challenges and promote employee well-being in the banking sector of Saudi Arabia.

In the evolving landscape of the 21st century, universities are at the forefront of re-imagining their infrastructural identity. This conceptual paper delves into the transformative shifts witnessed within university infrastructure, focusing on the harmonisation of tangible physical assets and the expanding world of digital evolution. As brick-and-mortar structures remain pivotal, integrating digital platforms rapidly redefines the academic landscape, optimising learning and administrative experiences. The modern learning paradigm, enriched by this symbiotic relationship, offers dynamic, flexible, and comprehensive educational encounters, thereby transcending traditional spatial and temporal constraints. Therefore, this paper accentuates the broader implications of this infrastructural metamorphosis, particularly its significant role in driving economic development. The synergistic effects of physical and digital infrastructures enhance academic excellence and position universities as key players in addressing and navigating global challenges, setting forth a resilient and forward-looking educational blueprint for the future. In conclusion, integrating physical and digital infrastructures within universities heralds a transformative era, shaping a holistic, adaptable, and enriched academic environment poised to meet 21st-century challenges. This study illuminates the symbiotic relationship between tangible university assets and digital innovations, offering insights into their collective impact on modern education and broader economic trajectories.

The coastal area of Bohai Bay of China has a wide distribution of salt-accumulated soils which could pose a problem to the sustainable development of the local ecology. As a result, the land remains largely degraded and unsuitable for biophysical and agricultural purposes. In this study, we characterized the soil and native plants in the area, to properly understand and identify species with satisfactory adaptation to saline soil and of high economic or ecological value that could be further developed or domesticated, using appropriate cultivation techniques. The goal was to determine the salinity parameters of the soil, identify the inhabiting plant species and contribute to the ecosystem data base for the Bay area. A field survey involving soil and plant sampling and analyses was conducted in Yanshan and Haixing Counties of Hebei Province, China, to estimate the level of salt ions as well as plant species population and type. The mean electrical conductivity (EC) of the soils ranged from 0.47 in more remote locations to 23.8 ds/m in locations closer to the coastline and the total salt ions from 0.05 to 8.8 g/kg, respectively. Each of the salinity parameters, except HCO₃⁻ showed wide variations as judged from the coefficient of variation (CV) values. The EC, as well as chloride, sulphate, Mg and Na ions increased significantly towards the coastline but the HCO₃⁻ ion showed a relatively even distribution across sampling points. Sodium was the most abundant cation and chloride and sulphate the most abundant anions. Therefore, the most dominant salinity-inducing salt that should be properly managed for sustainable ecosystem health was sodium chloride. Based on the EC readings, the most remote location from the coastline was non-saline but otherwise, the salinity ranged from slightly to strongly-very strongly saline towards the coast. There were considerably wide variations in the number and distribution of plant species across sampling locations, but most were dominated entirely Phragmites australis, Setaria viridis and Sueda salsa. Other species identified were Aeluropus littoralis, Chloris virgata, Heteropappus altaicus, Imperata cylindrica, Puccinellia distans, Puccinellia tenuiflora and Scorzonera austriaca. On average, the sampling points furthest from the coast produced the most biomass, and the point with the highest elevation had the most diverse species composition. Among species, Digitaria sanguinalis produced the highest dry mass, followed by Lolium perenne and H. altaicus, but there were considerable variations in biomass yield across sampling locations, with the location nearest the coastline having no vegetation. The observed variations in soil and vegetation should be strongly considered by planners to allow for the sustainable development of the Bahai bay area.

In the realm of contemporary business, Business Intelligence (BI) offers significant potential for informed decision-making, particularly among executives. However, despite its global popularity, BI adoption in Malaysia’s service sector remains relatively low, even in the face of extensive data generation. This study explores the factors influencing BI adoption in this sector, employing the Technology Acceptance Model (TAM) as its conceptual framework. Drawing on relevant BI literature, the study identifies key TAM factors that impact BI adoption. Using SEM modelling, it analyses quantitative data collected from 45 individuals in managerial roles within Malaysia’s service sector, particularly in the Klang Valley. The findings highlight the crucial role of Perceived Usefulness in influencing the Behavioral Intention to adopt BI, serving as a mediating factor between Computer Self-efficacy and BI adoption. In contrast, Perceived Ease of Use does not have a direct impact on BI adoption and does not mediate the relationship between Computer Self-efficacy and Behavioral Intention. These insights demonstrate the complex nature of BI adoption, emphasizing the importance of Perceived Usefulness in shaping Behavioral Intentions. The outcomes of the study aim to guide executives in Malaysia’s service sector, outlining key considerations for successful BI adoption.

Gastronomic tourism is a form of travel that has gained relevance today, making it crucial to understand the promotion and management strategies in specific destinations. This systematic review article aims to analyze these strategies, highlighting the importance of cultural authenticity and collaboration between local actors. The methodology used is aligned with a descriptive and correlational approach, using criteria of exhaustiveness and relevance to review ethnographic research and scientific articles. The results reveal the influence of ancestral knowledge on cultural tourism, as well as the challenges of food heritage and food transculturation. In this sense, the need to design promotional strategies that promote traditionality, identity and cultural empowerment in local communities is highlighted. In conclusion, this study provides a comprehensive understanding of gastronomic tourism promotion and management strategies, underscoring the importance of preserving cultural authenticity and promoting local collaboration for the sustainable development of gastronomic tourism.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.