This study aimed to examine and assess the impact of the logistics industry’s environment, entry-level graduates’ characteristics and the logistics and supply chain management (LSCM) program design on the transformation of knowledge and skills at Sohar port in the Sultanate of Oman. The study employed a pragmatic research philosophy involving a structured questionnaire. The sample size included 49 mid-managers from the logistics industry who were working at Sohar Port. The study found that entry-level graduates’ characteristics and LSCM program design positively and significantly influenced the transformation of knowledge and skills. However, the organisational environment had a negative and insignificant impact on the transformation. This study revealed several dimensions that may require further research. It is pertinent to broaden the research scope to other towns, ports, and other countries in the Gulf Council Countries (GCC) to broaden the scope and generalisability of the results. According to the study findings, several recommendations are proposed for the logistics and supply chain sector in Oman to enhance the transformation of knowledge and skills by entry-level graduates, as well as for higher education institutions (HEIs). To meet the sector requirements, HEIs may improve the current university-industry collaborations by increasing the inputs of the industry in designing and developing the LSCM program. The organisational environment must reconsider the knowledge and skills transformation by entry-level graduates in their strategic plan of resources management, which must be emphasised by the remuneration system and career paths incentive. While other studies have explored knowledge and skill transformation in the context of employee training, this study aims to fill a specific research gap by focusing on the transformation of knowledge and skills by entry-level graduates, an area which has not been extensively studied before. Furthermore, this study is unique as it examines the impact of the industry’s environment, entry-level graduates’ characteristics and the LSCM program on the transformation of knowledge and skills within the unique context of Oman. This novel approach provides an opportunity to understand the specific challenges and opportunities faced by entry-level graduates in Oman and suggests strategies for addressing them.

Purpose: This study investigates the mediating effect of Environmental Attachment (EA) among consumers in an emerging market, concentrating on the impact of two key factors: Green Environmental Awareness (GEA) and Sense of Responsibility (SOR) on Sustainable Product Consumption (SPC). Design/methodology/approach: A thorough online survey was carried out with Google Docs and distributed to 304 Pakistani consumers who now use or are considering purchasing sustainable or green products. Structural Equation Modeling (SEM) was used to rigorously test the suggested model utilizing a non-probability sampling technique, specifically the stratified purposive sampling approach. Findings: Green environmental awareness (GEA) and a sense of responsibility (SOR) have been shown to have a substantial impact on creating environmental attachment (EA) in both existing and potential customers of sustainable products. The findings of this study also revealed that environmental attachment (EA) plays an important role as a mediator in the links between green environmental awareness (GEA) and the consumption of sustainable goods (SPC), as well as between a sense of responsibility (SOR) and SPC. Despite this, it is crucial to note that the projected direct effect of GEA on SPC was shown to be statistically insignificant. This conclusion implies that additional factors outside the scope of this study may influence the relationship between GEA and SPC. Research limitations/implications: It is vital to highlight that the focus of this study is on an online sample of consumers near Punjab, Pakistan. Future studies should look at other parts of Pakistan to acquire a more complete picture of sustainable consumption trends. Furthermore, our findings suggest that characteristics impacting sustainable consumption, such as Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), may differ among countries. As a result, performing a comparison analysis involving two or more countries could provide valuable insights into projecting sustainable product consumption among current and potential sustainable product customers. Originality/Value: This study contributes to the literature by investigating the factors of sustainable consumption using the lens of the Norm Activation Model theory (NAM), notably Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), to predict sustainable product consumption. The findings are important for promoting long-term goals in Pakistan and provide a framework that can be applied in other emerging markets.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

Micro-mobility has the potential to address first -mile challenges, improving transit accessibility and encouraging public transit usage. However, users’ acceptability of modal integration between various micro-mobility options and public transit remains largely unexplored in the literature. Our study investigates the user behavior for first-mile options, focusing on four alternatives: walking, bicycling, motorcycling, and bus, to access urban mass rapid transit (UMRT) in Hanoi, Vietnam. Based on data collected from 1380 individuals, a Nested Logit Model (NLM) was proposed to analyze the determinants of users’ acceptability under each access mode option as well as evaluate further impacts of shifts in access mode choice on vehicle-kilometer traveled and emissions. The analysis shows that the availability of access modes might increase UMRT use by 47.83%. While this increase further generates additional vehicle-kilometer traveled due to the increase in park-and-ride users, this is offset overall by the large number of motorcycle users shifting to UMRT. Under the most optimistic scenario, modal integration for transit-access trips leads to an average reduction of 17.7% in net vehicle-kilometer traveled or 14.5% in net CO₂ emissions or 10.9% in NO_x from private vehicles. Our findings also imply that the introduction of parking fees for bicycling- or motorcycling-access trips, while impactful, does not significantly change UMRT choice. Therefore, the pricing schemes should be a focus of parking planning surrounding stations. Finally, a number of policy suggestions for parking planning and first-mile vehicles are presented.

High-quality implementation of cross-border mergers and acquisitions (cross-border M&As) is an important pathway for emerging-market multinational enterprises (EMNEs) to enhance their international competitiveness. However, in comparison to developed countries, cross-border M&As by EMNEs are often prohibited by the liability of origin caused by negative political coverage. How and why negative political coverage affect the completion of cross-border M&As by EMNEs? What are the contextual constraints that moderate the impact of negative political coverage on cross-border M&As completion? Based on the “liability of origin” theory, this paper addresses these questions using data from the Zephyr database on cross-border M&As by EMNEs in the United States from 2016 to June 2021 and employing a logit model for estimation. The research findings are as follows: (1) Negative political coverage leads to negative perceptions of emerging market countries by host country stakeholders, creating the liability of origin and stigmatizing the corporate nationality, thereby reducing the success rate of cross-border M&As by EMNEs. (2) Increasing geographical distance leads to information asymmetry, reinforcing the negative impact of negative political coverage on the completion of cross-border M&As by EMNEs. (3) Relevant mergers and acquisitions exacerbate the negative effect of negative political coverage on the success rate of cross-border M&As by EMNEs. (4) Being a publicly traded firm and having successful experience in cross-border M&As both intensify the negative impact of negative political coverage on the success rate of cross-border M&As by EMNEs.