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This interview study examines Hungarian journalists' and copywriters' expectations of generative AI’s impact on their professions and factors influencing these views during a period of hype.

While acknowledging the specialized knowledge of journalists and copywriters relative to the general public, the study employs the sociology of expectations framework to interpret their anticipations not as objective forecasts of the future, but rather as phenomena shaped by diverse influences. The research comprises 30 semi-structured interviews conducted in spring 2023 to explore these expectations and their contributing factors.

Results reveal ChatGPT’s media coverage as pivotal, encouraging the professionals interviewed to experiment with AI, reassess their roles, and cause a shift in their job expectations. At the same time, this shift was limited. Skepticism about hyperbolic media formulations, their own experiences with ChatGPT and projecting its constraints into the future, contextual factors, and optimism bias contributed to moderating their expectations. They perceived AI as an enhancer of efficiency and quality, not as a radical disruptor. Copywriters were more open to integrating AI in their work, than journalists.

The results underscore the importance of further research to explore subjective experiences associated with technological change, particularly considering their complex social, psychological, and cultural influences.

The study uniquely contributes to the sociology of expectations by highlighting how a complex interplay of factors can shape professionals' anticipation of the impact of AI on their careers, including optimism bias and media hype.

In this paper, the authors examine the causes of 2021–2023 inflation and evaluate whether raising interest rates is the right solution.

The authors evaluate both the macroeconomic (too much demand) and microeconomic (monopoly pricing and supply chains) explanations for the causes of inflation.

The authors argue that the spike in inflation is due to disrupted supply chains and corporations taking advantage of the situation to raise their prices. The aggregate demand stimulus from fiscal policy had all but played out by the time inflation arose, making it an unlikely cause of said inflation.

The authors' paper demonstrates that raising interest rates is the wrong solution to tackling the problem of inflation, especially since it's coming from the supply side.

This paper aims to review the literature extensively by analysing recent work and providing a guide for models, data sets and research findings.

This paper reviews the literature extensively by analysing recent work and providing a guide for models, data sets and research findings within the context of capital market imperfections. The authors further break down the literature into closer-in-nature categories for reader’s convenience and comprehension. Finally, the authors address gaps in the existing literature and propose government policies that can tone down the potential effect of credit rationing on employment.

This paper provides a map of the literature so as to help future researchers in the relevant literature and give a short insight of what has been explored so far.

This paper is original and is the result of a thorough review of an extensive literature.

This study aims to identify seasonal drought using standardized precipitation index (SPI). The following specific objectives are to generate result and identify seasonal drought and determine different scale of seasonal drought and its impacts on cropping season.

Seasonal SPI was calculated using long-term rainfall data for three seasons. The SPI was calculated using the formula and it is effective for the determinants. This study showed the functional relationship between drought duration, frequency and drought time scale using the SPI. SPI=X−X¯σ.

Seasonal drought occurs more frequently in Bangladesh that affects crops and the agricultural economy every year. More severe drought was recorded during the Kharif-1 and Kharif-2 seasons and most crops were affected in these two seasons. No severe or moderate drought was recorded during the Rabi season. The results showed that monsoon crops were severely affected severely by extreme and severe droughts during the Kharif-2 season. Eventually, the people remain jobless during the monsoon, and they experience food shortages like monga. Several obstacles were recorded during the season, including delayed preparation of land, sowing, transplanting and other farming activities because of monsoon droughts. This study revealed that very frequently, mild dryness occurs in winter, but crop loss is minimal. The scale and occurrence of extreme droughts are more frequent during monsoons and reduce crop yields, affecting livelihoods in the study area. Seasonal drought affects cropping patterns as well as reduce crop yields.

The outcome of this study derived from the secondary data and field data.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

The aim of this paper is to summarise the state-of-the-art debate on impact of artificial intelligence on unemployment and reporting up-to-date academic findings.

The paper is designed as a review of the labour vs capital conundrum, the differences between industrial automation and artificial intelligence, threat to employment, the difficulty of substituting, role of soft skills and whether technology leads to the deskilling of human workers or favors increasing human capabilities.

Some authors praise the bright future developments of artificial intelligence while others warn about mass unemployment. Therefore, it is paramount to present an up-to-date overview of the problem, compare and contrast its features with what happened in past innovation waves and contribute to academic discussion about the pros/cons of current trends.

The main value of this paper is presenting a balanced view of 100+ different studies, the vast majority from the last five years. Reading this paper will allow to quickly grasp the main issues around the thorny topic of artificial intelligence and unemployment.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0338

This study aims to examine the relationship between knowledge management and career development in an era characterized by high levels of youth unemployment and a demand for specialized skills. Despite the increasing transition to a knowledge-based economy, there is a significant gap between young people’s skills and career readiness, necessitating an in-depth analysis of the role of knowledge management at the individual, organizational and national levels.

The authors conducted a qualitative study using the theory-context-characteristics-methodology approach based on a systematic literature review. The authors created an ecological framework for reflecting on knowledge management and career development, arguing for a multidisciplinary approach that invites collaboration across sectors to generate innovative and reliable solutions.

This study presents a comprehensive review of the existing literature and trends, noting the need for more focus on the interplay between knowledge management and career development. It emphasizes the need for businesses to promote the acquisition, storage, diffusion and application of knowledge and its circulation and exchange to create international business human capital.

The findings may help multinational corporations develop managerial training programs and recruitment strategies, given the demand for advanced knowledge-based skills in the modern workspace. The study also discusses the influences of education, experience and job skills on business managers’ performance, guiding the future recruitment of talents.

To the best of the authors’ knowledge, this review is among the first to assess the triadic relationship between knowledge management, career development and the global unemployment crisis. The proposed multidisciplinary approach seeks to break down existing silos, thus fostering a more comprehensive understanding of how to address these ongoing global concerns.

This study aims to investigate how social entrepreneurial role models influence social entrepreneurial self-efficacy, social entrepreneurial intent and social entrepreneurial action, with moral obligation as a moderator.

A cross-sectional survey of 261 pupils in the South African province of the Eastern Cape was used in the research study. Structural equation modeling was used to test hypotheses.

The research revealed that having social entrepreneurial role models has a positive impact on both social entrepreneurial self-efficacy and social entrepreneurial intent. In addition, a connection was found between social entrepreneurial intent and entrepreneurial action. The influence of moral obligation was found to be a positive and a significant moderator. Moreover, the association between social entrepreneurial role models and social entrepreneurial intent was mediated by social entrepreneurial self-efficacy.

The findings are not generalizable to nonstudent samples because students constituted the sample for gathering data. Future study therefore requires considering nonstudents to generalize the outcomes. This research should be replicated in other South African provinces and other developing countries for comparative outcomes.

Since social entrepreneurial role models have been practically linked to social entrepreneurship intent and entrepreneurial efficacy, understanding the factors that influence student’s decision to start a social enterprise is critical in South Africa to develop targeted interventions aimed at encouraging young people to start new businesses. Policymakers, society and entrepreneurial education will all benefit from the findings.

This study contributes to bridging the knowledge gap as it investigates how social entrepreneurial role models influence social entrepreneurial self-efficacy, social entrepreneurial intent and social entrepreneurial action, with moral obligation as a moderator. Encouraging social entrepreneurship among South African youth would also help address societal issues. This is a pioneering study in the context of an emerging economy such as South Africa, where social entrepreneurship is so integral.

The purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.

This study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.

The study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.

The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.

The results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.

Artificial intelligence (AI) supports increased efficiency in different sectors. AI is among various sectors' most innovative and measurable solutions. AI has revolutionized new ideas in our daily lives, such as the Internet of things, the Internet of people and the sharing economy. Like other sectors, the tourism sector is one of the sectors affected by AI, where different intelligent systems are used in travel agencies and transport companies. In addition, technological breakthroughs are expected to increase in the tourism sector, leading to a rearrangement of the technological revolution in the tourism sector. We hope that the process of technological progress in the field of tourism is constantly advancing and cannot be stopped. Accordingly, we came to the following question: How can adaptation be made to the progress brought by AI to the tourism sector? The continuous technological advancement in the tourism sector is expected to lead to the end of human civilization, especially since technological machines have become more intelligent than humans.