Dilshodjon Alidjonovich Rakhmonov

This study investigates perceptions of poverty and its underlying determinants among a purposively sampled population of social-benefit recipients in Uzbekistan. For this purpose, a survey was conducted with 1949 respondents who receive social benefits. The collected data were analyzed using an exploratory sequential mixed-methods approach. Initially, thematic analysis was conducted to identify perceptions of poverty. The output of the thematic analysis was the emergence of nine themes that illustrate the characteristics of individualistic, structural, fatalistic, and cultural attributions of poverty perceptions. Based on the output of the thematic analysis, multinomial logistic regression was conducted. The selection of the base category was based on both the theoretical framework and survey output. The regression results indicated that age, regional poverty cluster and place of residence were the most statistically significant demographic factors influencing perceptions of poverty. Among the monetary variables, personal income demonstrated the strongest explanatory power. Overall, the findings suggest that, within this sampled population, perceptions of poverty are predominantly shaped by individualistic attributions rather than structural, fatalistic or cultural ones. However, these results should be considered with caution as they may reflect internalized poverty stigma.

Background: A substantial portion of the world’s population owns and utilizes computers and mobile devices, contributing to the rapid expansion of digital advertising. Marketers swiftly recognized the communicative benefits of social media platforms like Facebook, YouTube, Twitter, Instagram, Snapchat, Pinterest, and LinkedIn. Considering the importance of social media platforms and digital modes of marketing, it is considered especially significant for small firms to integrate these platforms into their business strategies in order to improve performance. Methods: Based on this aim, this study collected data from 363 owners/managers of SMEs in Pakistan. Structural equation modeling is used to check the hypothesized model of the study. Results: The results show that compatibility, owner/manager support, employee IT skills, financial cost, government policies, and social influence significantly affect adoption of digital marketing by SMEs in Pakistan. Conclusions: Furthermore, digital marketing also positively affects SME performance. This paper discusses the study’s findings as well as managerial and academic implications, including its limitations and future research avenues.

This article investigates the expansion of organizational learning capabilities (OLC) and its growing utilization in the organization in order to identify whether OLC affects the relationship between training and development and organizational performance. This study further explores the correlation between organizational learning capabilities and organizational performance, offering insight into the different understandings of organizational triumph related to OLC. This research was conducted via survey method and data were collected from five service industries in Khyber Pakhtunkhwa (KP), Pakistan. A data sample of 300 employees was analyzed using quantitative approaches, such as descriptive statistics and partial least squares structural equation modeling (PLS-SEM). The results of the article addressed issues that are related to human capital regarding OLC, including training and development aspects which were not discussed by the literature in the past. The main findings of this paper are the formulation of an empirically and theoretically supported model related to the effect of OLC on the relationship between training and development and organizational performance. On the other hand, this study signifies that OLC is a better instrument for enhancing organizational performance, as well as in dealing with today’s indeterminate and continuously challenging economic atmosphere. This study is the first of its nature to shed light on training and development, organizational learning capabilities, and organizational performance in the service sector of KP, Pakistan.

Sustainability includes the digitalization of enterprises as a key factor. Digitalization and automation in ensuring the financial stability of enterprises will create opportunities to reduce the receivables and payables of enterprises. Digitalization and further development of digital financial activities are important in the process of transformation of state-owned enterprises. At the same time, the possibility of delivering electricity to consumers, as well as ensuring the prompt provision of electricity metering and ensuring transparency, was mainly studied. At the same time, the analysis was carried out on the example of the activities of joint stock companies of the Regional Electric networks of the Republic of Uzbekistan. The main attention was paid to the issues of ensuring financial stability by automating the accounting activities of the enterprise and developing digital information capabilities. In addition, it was proposed to involve a state-owned commercial bank as a founder along with the Ministry, which controls the activities of an enterprise with a state share interms of management activities, in order to create an opportunity to further stimulate the financial activities of a joint-stock company.

The key purpose of the article is to analyze the effect of digital transformations, such as blockchain technology (BCT), the social internet of things (SIoT), and artificial intelligence (AI) techniques, on the supply chain (SC) for traceability and for creating transparency. The partial least squares (PSL) structural equation modeling (SEM) method was applied in combination with SmartPLS v3.3.6. The package was employed to obtain information through a survey of SC Pakistani professionals using the snowball sampling technique. Traceability plays a crucial role in enhancing transparency and ultimately the performance of SC through BCT, SIoT, and AI. Therefore, the study recommends starting the digital transformation of the SC because this is a complex process that involves a wide range of internal and external stakeholders. The study findings show the importance of technologies of traceability and transparency as an analytical multidisciplinary approach to enhance the SC sector, although with certain limitations this can be taken into account by stakeholders. This study will be useful for decision makers investing in technologies of traceability and transparency in the SC. The study raises the awareness of traceability and transparency in the SC process, and also reveals research gaps and provides opportunities for further research. Despite the prevalence of studies in supply-chain traceability (SCT) and transparency, there is a dearth of empirical proof on how the digital transformation of the SC could lead to transparency and ultimately performance.

Artificial Intelligence (AI) is generating new horizons in one of the biggest challenges in the world’s society—poverty. Our goal is to investigate utilities of AI in poverty prediction via finding answers to the following research questions: (1) How many papers on utilities of AI in poverty prediction were published up until March, 2022? (2) Which approach to poverty was applied when AI was used for poverty prediction? (3) Which AI methods were applied for predicting poverty? (4) What data were used for poverty prediction via AI? (5) What are the advantages and disadvantages of the created AI models for poverty prediction? In order to answer these questions, we selected twenty-two papers using appropriate keywords and the exclusion criteria and analyzed their content. The selection process identified that, since 2016, publications on AI applications in poverty prediction began. Results of our research illustrate that, during this relatively short period, the application of AI in predicting poverty experienced a significant progress. Overall, fifty-seven AI methods were applied during the analyzed span, among which the most popular one was random forest. It was revealed that with the adoption of AI tools, the process of poverty prediction has become, from one side, quicker and more accurate and, from another side, more advanced due to the creation and possibility of using different datasets. The originality of this work is that this is the first sophisticated survey of AI applications in poverty prediction.

The purpose of this paper is to test the role of trust in supervisors in mediating the link between paternalistic leadership styles and employee voice and to investigate the contingency role of self-efficacy on the relationship between trust in supervisors and employee voices. We designed the items using survey questionnaires that were assessed by prior studies and collected data from 485 highly skilled employees of manufacturing organizations. To assess the validity of the suggested hypotheses, we used a Baron and Kenny (1986) approach and conducted hierarchical regression analysis. The findings reveal that authoritarian leadership and moral leadership styles are significantly related to trust in supervisors, which explains the association between authoritarian leadership style, and moral leadership style and employee voice. However, the results suggest that trust in supervisors does not explain the association between benevolent leadership style and employee voice. Moreover, the findings reveal that self-efficacy moderates the link between trust in supervisors and employee voice. The originality of this work lies in the fact that this research is the first to test the mediating role of trust in supervisors in the relationship between paternalistic leadership styles and employee voice and the moderating role of self-efficacy on the association between trust in supervisors and employee voice.