Impact of Generative AI on Small and Medium Enterprises' Revenue Growth: The Moderating Role of Human, Technological, and Market Factors

Background: The generative artificial intelligence (AI) technologies have become strategic tools for small businesses seeking maintain competitive advantage. The usefulness of of these technologies are well-recognized in SMEs, yet it is becoming important to explore how other factors might affect the degree to which these benefits are realized. The present study emerges from this necessity, aiming to provide a quantitative assessment of how generative AI adoption affects SME revenue growth and to what degree this effect relies on human capital, technological infrastructure, and market competition. Objectives: The aim of this research is to empirically examine not only the direct effects generative AI on revenue growth but also how this relationship is shaped by several moderating factors such as human, technological, and market factors. These factors can either amplify or diminish the potential gains from generative AI adoption.Data and Methods: To understand the relationships, data from 331 SMEs were analyzed using 3 Regularization regression methods, namely, Ridge, Lesso, and Elastic Net Regression methods.Findings: The results indicates that companies benefit from adopting generative AI technologies. The moderating effects of human capital indicates that businesses not only benefit from adopting generative AI but do so especially when they have highly educated employees. This implies that human capital can enhance or is complementary to the advantages provided by the generative AI, possibly through more effective utilization. The moderating effects of existing firm’s infrastructure also has a positive effect, suggesting that the benefits of generative AI are amplified when a business has good existing technological infrastructure. This means that businesses with modern or advanced tech facilities can leverage AI technology more effectively than those with outdated or less capable infrastructure. The moderating effects of market competition showed a negative result indicating that the advantage gained from generative AI adoption may decrease as market competition intensifies. This suggests that in highly competitive markets, the edge provided by AI is less distinct, perhaps because competitors are also likely to adopt similar technologies, negating the competitive advantage.Conclusion: The findings suggest that simply deploying generative AI will not suffice; instead, it should be part of a broader strategy that considers market dynamics, skilled human capital to operate, and improving existing technological infrastructure

Applications of AI, IoT, and Cloud Computing in Smart Transportation: A Review

Smart transportation systems have emerged as a promising solution for improving the efficiency, safety, and sustainability of transportation. The integration of emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Cloud Computing has enabled the development of intelligent transportation systems that can optimize traffic flow, enhance driver safety, and reduce transportation costs. In this study, we conducted a systematic review of the literature to explore the applications of AI, IoT, and Cloud Computing in smart transportation systems. Our findings indicate that AI can be used for autonomous vehicles, traffic management, predictive maintenance, driver assistance, and demand forecasting. IoT can enable connected vehicles, real-time fleet management, smart parking, traffic monitoring, and remote diagnostics. Cloud Computing can facilitate vehicle-to-cloud communication, scalable infrastructure, data analytics, mobility-as-a-service, and predictive maintenance. The integration of these technologies can result in a comprehensive smart transportation system that can improve the overall efficiency of transportation systems. Our study provides insights for researchers, practitioners, and policymakers on the potential applications of AI, IoT, and Cloud Computing in smart transportation systems

Supporting LGBTQ Employees in the Workplace: The Role of HR Policies and Practices

The inclusion of LGBTQ individuals in the workplace has become an increasingly important topic in recent years. While progress has been made in terms of legal protections and social acceptance, discrimination and harassment continue to be major issues faced by LGBTQ employees. This study aimed to explore the role of HR policies and practices in supporting LGBTQ employees in the workplace. Through a review of existing literature and interviews with HR professionals, five key findings were identified. Firstly, non-discrimination policies that explicitly prohibit discrimination based on sexual orientation and gender identity are essential in creating an inclusive workplace. Secondly, the use of gender-neutral language in HR policies and documents is important in creating an environment that is welcoming to all employees. Thirdly, equal benefits and leave policies for LGBTQ employees, including domestic partner benefits and parental leave for same-sex couples, are important in supporting LGBTQ families. Fourthly, training and education for employees on LGBTQ issues, including how to support LGBTQ colleagues and prevent discrimination, is crucial in creating a culture of inclusion. Finally, the formation of employee resource groups for LGBTQ employees provides a safe space for support, connection, and advocacy within the workplace. Based on these findings, this study recommends that HR professionals work to develop and implement policies and practices that support LGBTQ employees. This includes creating non-discrimination policies that explicitly prohibit discrimination based on sexual orientation and gender identity, using gender-neutral language in HR documents, providing equal benefits and leave policies for LGBTQ employees, and providing training and education on LGBTQ issues for all employees. Additionally, the formation of employee resource groups for LGBTQ employees should be encouraged and supported. By implementing these recommendations, HR professionals can help create a more inclusive and supportive workplace for LGBTQ employees, leading to improved job satisfaction, employee engagement, and overall business success

Opportunities and Challenges of Cloud Computing in Developing Countries

Cloud computing presents developing nations with a multitude of options, including enhanced access to technologies and services, higher productivity and cost savings, as well as the possibility of increased economic development and the creation of new jobs. It is possible for companies to have access to enterprise-grade software and tools if they have improved access to technology and services. This may assist the organizations increase their productivity and efficiency. The delivery of essential public services like healthcare, education, and social welfare may be improved via increased efficiency and cost reductions, which can also assist bring down overall prices. The formation of new industries and enterprises, in addition to the expansion and improvement of existing ones, may help to boost economic growth and the production of new jobs. However, cloud computing presents developing nations with a number of obstacles, including inadequate internet infrastructure, a lack of available technological skills, and concerns over the privacy and security of stored data. Because of limited internet infrastructure, accessing cloud services, transferring data, and implementing security measures might be challenging, which can restrict the usage of cloud-based services. When it comes to setting up and maintaining cloud-based systems, as well as selecting the appropriate cloud service providers and solutions, it may be challenging for businesses that lack the technical experience. Concerns over data security and privacy may be a significant obstacle to the widespread adoption of cloud computing. This is because businesses may lack confidence in the safety of their data if it is housed on servers located in other countries

Machine Learning Approaches for Automated Mental Disorder Classification based on Social Media Textual Data

The application of machine learning models to mental health-related text data offers a novel approach to discern patterns and trends, aiding in the identification of subgroups and personalized treatment options. This research explores the classification of mental disorders based on text data extracted from subreddits focused on mental health. The dataset consists of 10,000 rows of text collected from four subreddits: 'BPD', 'bipolar', 'depression', and 'Anxiety', along with a combined category 'others' encompassing 'mentalillness' and 'schizophrenia'. To enable the application of machine learning models, various text preprocessing techniques were applied, including the removal of URLs, punctuation marks, and stopwords, as well as the transformation of raw text documents into a matrix of TF-IDF features. These preprocessing steps were performed on both the titles and text contents of the posts. Three machine learning models, namely Multinomial Naive Bayes, Multi-layer Perceptron, and LightGBM, were employed for the classification task. The models were trained and evaluated separately on both the post titles and the text content. The accuracy of each model was assessed to measure their performance. The results indicate that the Multinomial Naive Bayes model achieved an accuracy of 0.706 when classifying based on titles, while the accuracy increased to 0.73 when classifying based on the text content. The Multi-layer Perceptron model yielded an accuracy of 0.68 for title classification and 0.714 for text content classification. Notably, the LightGBM model exhibited superior performance, achieving an accuracy of 0.724 when using titles for classification, and an even higher accuracy of 0.77 when employing the text content. This research demonstrates the efficacy of machine learning models in classifying mental disorders using text data extracted from social media. These findings contribute to the ongoing exploration of using social media data for mental health analysis and may aid in developing automated tools for early detection and support for individuals facing mental health challenges

Advances in Deep Learning Algorithms for Agricultural Monitoring and Management

This study examines the transformative role of deep learning algorithms in agricultural monitoring and management. Deep learning has shown remarkable progress in predicting crop yields based on historical weather, soil, and crop data, thereby enabling optimized planting and harvesting strategies. In disease and pest detection, image recognition technologies such as Convolutional Neural Networks (CNNs) can analyze high-resolution images of crops to identify early signs of diseases or pest infestations, allowing for swift and effective interventions. In the context of precision agriculture, these advanced techniques offer resource efficiency by enabling targeted treatments within specific field areas, significantly reducing waste. The paper also sheds light on the application of deep learning in analyzing vast amounts of remote sensing and satellite imagery data, aiding in real-time monitoring of crop growth, soil moisture, and other critical environmental factors. In the face of climate change, advanced algorithms provide valuable insights into its potential impact on agriculture, thereby aiding the formulation of effective adaptation strategies. Automated harvesting and sorting, facilitated by robotics powered by deep learning, are also investigated, as they promise increased efficiency and reduced labor costs. Moreover, machine learning models have shown potential in optimizing the entire agricultural supply chain, ensuring minimal waste and optimum product quality. Lastly, the study highlights the power of deep learning in integrating multi-source data, from weather stations to satellites, to form comprehensive monitoring systems that allow real-time decision-making

Localization Strategy for Global Expansion via E-commerce

The global pandemic and travel limitations caused a surge in e-commerce between the end of 2019 and the beginning of 2020. As a result, the market's growth was expedited and corporate tactics were modified. As businesses started to provide their services internationally, specialized development techniques were necessary. Localization, which entails modifying an online store's content for a particular market, has several advantages for businesses, including a rise in market share and profits. By adapting their services to the specific needs and expectations of local markets, a good localization plan may help e-commerce businesses position themselves for success in the global market. Despite the fact that there is no universally applicable strategy for success, careful planning, and careful research may greatly enhance business operations and raise revenues in untapped areas. For the knowledge and use of localization methods in e-commerce to develop, more study and access to corporate data are required

The Influence of Social Environment on Men and Women's Sexuality

This article explores the influence of social environment on men and women's sexuality. Psychological research and practice historically focused on gender dualism, but recent challenges to this assumption have led to complex and controversial terminology. Gender stereotypes are prejudices or inaccurate interpretations of various genders, and they exaggerate the differences between groups while underestimating the connections. In the 21st century, the application of social software has changed people's love styles and sexual concepts. This paper conducts a literature review on the differences and influencing factors of male and female sexual concepts, factors affecting men and women's sexual concepts in the 21st century, and new developments in research on men and women's concepts

Cybersecurity Awareness and Training Programs for Racial and Sexual Minority Populations: An Examination of Effectiveness and Best Practices

The purpose of this research study was to examine the barriers of cybersecurity awareness and training programs for racial and sexual minority populations. The findings suggest that lack of representation is a significant barrier to the effectiveness of cybersecurity awareness and training programs. Many programs are designed by and for people from dominant racial and gender groups, which makes it difficult for minority populations to relate to the information presented. As a result, it is crucial to incorporate diverse perspectives into the development of materials to improve engagement and effectiveness. The study also found that language and cultural barriers pose challenges to minority populations' participation in cybersecurity awareness and training programs. Minority populations may speak languages other than English, making it challenging to access information and training materials. Additionally, cultural differences may lead to different understandings of cybersecurity risks and how to mitigate them. To overcome these barriers, training programs should provide resources in multiple languages and build cultural competence into the curriculum. Access to resources was also identified as a barrier to participation in cybersecurity awareness and training programs. Minority populations may have limited access to technology and the internet, preventing them from participating in training programs and protecting themselves from cyber threats. This barrier can be addressed by partnering with community organizations to increase access to resources and providing training programs in a variety of formats that can be accessed through different mediums. The study also found that fear of discrimination is a significant barrier to participation in cybersecurity awareness and training programs. Minority populations may be hesitant to participate in these programs due to fear of discrimination or mistreatment based on their race or sexual orientation. Therefore, it is essential to create a safe and inclusive environment in cybersecurity training programs that promotes diversity, equity, and inclusion. The study found that lack of trust is a critical barrier to participation in cybersecurity awareness and training programs. Minority populations may have less trust in institutions and government agencies due to past experiences of discrimination, leading to lower engagement in cybersecurity awareness and training programs. Thus, building trust through transparent and inclusive communication strategies can promote increased participation and engagement in these programs

Prevalence Of Mental Health and Its Impact on Employee Productivity

Mental health is an important component of general well-being; it influences how we think, feel, and conduct. Mental health difficulties are common in the workplace and may have serious consequences for both individuals and companies. It may result in lower productivity, more absenteeism, high turnover rates, legal challenges, and, eventually, a loss of revenue and profits. Employers must acknowledge the significance of mental health in the workplace and give workers with assistance and tools to manage and enhance their mental health, resulting in a more engaged, productive, and loyal workforce that contributes to the organization's success. Employee absenteeism, presenteeism, and work performance may all be severely impacted by mental health. Encourage open communication, provide mental health resources, promote work-life balance, foster a positive work environment, address mental health in employee development, encourage physical activity, regular check-ins, form a wellness committee, review organizational policies and procedures, and lead by example are all strategies to promote mental health in the workplace