This study aims at predicting the interrelationship between among Chat GPT with its six dimensions, tourist’s satisfaction and Chat GPT usage intention as perceived by tourist, and as well as to examine the moderating effect of traditional tour operator services on the relationships between all the variables. Data were collected from 624 tourists. The study hypotheses were tested and the direct and indirect effects between variables were examined using the PLS-SEM. The SEM results showed that Chat GPT’s six dimensions have a positive and significant direct impact on tourist’s satisfaction, and emphasis the moderating role of Traditional Tour Operator Services “TTOS” on the relationship between GPT’s six dimensions and “TS”, and on the relationship between ‘TS” and Chat GPT usage intention. These findings yield valuable insights for everyone interested in the use of IT in the tourism industry, and provide effective strategies for optimizing the use of technological applications by traditional tour operators.

This research study was undertaken to complete a comparative study of the seminal work conducted by Anderson and Ruderman on procedural and distributive justice systems versus unionization. This research was conducted in 2023. The main focus of this research effort was to determine if current U.S. organizations were utilizing any form of justice system in protecting employees’ rights and providing processes that would prevent employees from having a desire to join a union for its protections. Parts of the original survey used by Anderson and Ruderman were used in this study to address the research questions and hypotheses posed for this study. A statistical analysis of the data was conducted, and the results indicated employees have a need for protection in their employment relationship. It is suggested that procedural and distributive justice systems be implemented as an alternative to unionization of employees to meet these employee protections.

This research implements sustainable environmental practices by repurposing post-industrial plastic waste as an alternative material for non-conventional construction systems. Focusing on the development of a recycled polymer matrix, the study produces panels suitable for masonry applications based on tensile and compressive stress performance. The project, conducted in Portoviejo and Medellín, comprises three phases combining bibliographic and experimental research. Low-density polyethylene (LDPE), high-density polyethylene (HDPE), and polypropylene (PP) were processed under controlled temperatures to form a composite matrix. This material demonstrates versatile applications upon cooling—including planks, blocks, caps, signage, and furniture (e.g., chairs). Key findings indicate optimal performance of the recycled thermoplastic polymer matrix at a 1:1:1 ratio of LDPE, HDPE, and PP, exhibiting 15% deformation. The proposed implementation features 50 × 10 × 7 cm panels designed with tongue-and-groove joints. When assembled into larger plates, these panels function effectively as masonry for housing construction, wall cladding, or lightweight fill material for slab relieving.

The major objective of this research paper is to assess the management effectiveness of Sheikh Badin National Park District Dera Ismail Khan Khyber Pakhtunkhwa, Pakistan with respect to tourist’s satisfaction. A sample size of 389 respondents (local community, wildlife staff, tourists) were selected through simple random sampling to conclude respondents’ attitude towards phenomenon investigated through three-level Likert scale as a measurement tool. Association between a dependent variable (management effectiveness) was assessed on the independent variables (tourist satisfaction) through a chi-square test. Association of management effectiveness was highly significant with tourists satisfaction from promos of park (p = 0.000), access to information (p = 0.000), roads network (p = 0.000), residential facilities (p = 0.000), trained guides (p = 0.000), safety from crimes and criminals (p = 0.000), provision of health and security services (p = 0.000), overall satisfaction of tourists (p = 0.000), recommendation of SBNP to other tourists (p = 0.000) and revisit intentions of tourists (p = 0.000). Improvement in security measures, better advertisement and improvement in park infrastructure were major recommendations considering the study.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

In a time of a growingly age-diverse workforce, modern organizations are facing the challenge of simultaneously maintaining job satisfaction for both younger and older workers. In that regard, this study aims to analyse and further explore the difference in job expectations of employees from the IT industry who belong to different age groups. Based on the extant literature, an appropriate research model was designed, which was subsequently tested using the data gathered through the surveys conducted over the past fourteen years. The research results show that the main difference between younger and older employees within the IT industry is related to professional and personal growth. Specifically, younger employees primarily look for personal development and rapid professional advancement, which are of minor importance to their older counterparts. Intriguingly, the obtained results showed no difference between the younger and older employees regarding the work environment, including its competitiveness.