Search results

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.

Based on a quantitative investigation, this paper endeavors to examine Industry 4.0 (I4.0) adoption process by studying the impacts of absorptive capacity (AC) and innovative ambidexterity (exploration, exploitation), while also considering the moderating influence of learning capability (LC).

Data has been gathered through administering questionnaire to 468 managers representing 175 manufacturing firms. Subsequently, PLS-SEM technique has been employed to verify the research hypotheses.

Study findings reveal that AC is significantly associated with I4.0 adoption and innovation ambidexterity. However, innovation ambidexterity demonstrates partial (only exploration) significant association with the adoption of I4.0. Similarly, the findings indicate that LC acts as a partial moderator between innovation ambidexterity (exploration) and I4.0 adoption.

The study presents significant insights into I4.0 adoption process. The findings may support managers of manufacturing firms to understand and assess the influence of integrating contextual factors facilitating successful adoption of I4.0. The study emphasizes necessity of managers’ awareness regarding the importance of firm’s AC to transform smoothly to I4.0 technologies. In addition to, encouraging the innovation ambidexterity along with LC to enhance the adoption of I4.0.

While researchers demonstrate increasing interest in applying I4.0, concrete evidence to support the I4.0 adoption process is, still, insufficient due to ongoing challenges in digital transformation. Consequently, further research is needed, particularly in exploring how a firm’s ability to realize knowledge and foster innovation contributes to implementing I4.0. This paper seeks to tackle this lack of research by examining the connection between AC, innovation ambidexterity, and LC and the adoption of I4.0 in an emerging economy.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

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.

The purpose of this study was to investigate restaurant and foodservice workers’ perceptions of working with a service robot and the extent to which the workers’ well-being was impacted by a mandated service robot adoption.

This study used a qualitative methodology where 42 US restaurant and foodservice workers from two organizations were interviewed. The data analyzed generated 1,302 coded segments that clustered into six overarching themes.

The findings from this research revealed that restaurant and foodservice workers who regularly use service robots in the dining room experience a complex set of issues and challenges related to robot reliability, management training and support, leveraging the robot to entertain the customer, feelings of dread, anger and frustration, and indications of decreased physical exertion as a proxy for well-being.

As an initial qualitative investigation, the results of this study can be used as a starting point for quantitative investigations, as well as informing restaurant and foodservice industry stakeholders as to the best practices for a comprehensive and successful service robot adoption and integration.

This research presents an intersection between service robot technology acceptance with worker well-being using a broad range of frameworks including National Institute for Occupational Safety and Health’s Future of Work, SERVQUAL and technology acceptance models to gain a deep and rich set of service worker perspectives.

本研究旨在调查餐厅和餐饮服务工作人员对与服务机器人合作的感知, 以及服务机器人采用对工作人员福祉的影响程度。

本研究采用定性方法, 对来自两家机构的42名美国餐厅和餐饮服务工作人员进行了访谈。分析的数据生成了1,302个编码段, 分为六个总体主题。

本研究的发现显示, 餐厅和餐饮服务工作人员在餐厅使用服务机器人时经历了一系列与机器人可靠性、管理培训和支持、利用机器人娱乐顾客、恐惧、愤怒和挫折感、以及作为福祉代理的身体活动减少相关的复杂问题和挑战。

作为初步的定性调查, 本研究的结果可作为定量调查的起点, 并向餐饮服务行业的利益相关者提供有关全面和成功采用和整合服务机器人的最佳实践。

本研究将服务机器人技术接受与工作人员福祉相结合, 利用包括NIOSH的未来工作、SERVQUAL和技术接受模型在内的广泛框架, 获得了丰富多样的服务工作者观点。

This research investigates the impact of artificial intelligence (AI) on accounting jobs and universities implementing AI-focused accounting programs and courses to meet market requirements. By drawing insights from universities implementing AI-focused programs, this research offers a roadmap for educational institutions seeking to prepare accountants for future AI-driven jobs.

The study utilized the PRISMA guidelines to write this systematic review by studying the five years of relevant literature available on this topic. A thematic analysis was applied to extract data from selected studies to answer the research questions.

By fostering an environment emphasizing technical knowledge, critical thinking, communication, and innovation, universities can ensure that graduates are well-equipped to thrive in the ever-evolving accounting landscape.

This paper highlights the urgency of adapting accounting education to meet the challenges posed by AI generated job landscape. The research added valuable knowledge on the topic for universities that seek to advance their accounting courses.

Grounded in the cognitive appraisal theory, this paper aims to investigate how employees cognitively evaluate and respond to the introduction of service robots, with a particular focus on generational differences.

Data was collected from hospitality employees across different generations in the USA, and 279 responses were analyzed using partial least squares structural equation modeling.

The results indicate negative service robot awareness and perceived risk significantly contribute to increased turnover intentions, with job insecurity mediating these associations. Notably, Generation Z employees exhibit distinct attitudes toward service robots compared to older generations, indicating a varying response pattern across different generational cohorts.

Organizations operating in the hospitality industry can use these findings to tailor interventions aimed at addressing concerns related to job insecurity and turnover intentions arising from the integration of service robots. Recognizing the diverse perspectives among different generational groups, organizations can implement targeted approaches to ensure a smoother transition and enhance employee acceptance of service robot technologies.

This study contributes to the literature by shedding light on the nuanced interplay between employees’ cognitive evaluations, generational differences and the introduction of service robots in the hospitality sector. The insights generated offer valuable guidance for both academics and industry practitioners, facilitating the development of strategies to foster a mutually beneficial integration of service robots into the workforce.

本研究基于认知评估理论, 探讨员工如何对引入服务机器人进行认知评估和响应, 特别关注世代差异。

采集了来自美国不同世代的酒店员工的数据, 分析了279份回应, 采用了部分最小二乘结构方程建模（PLS-SEM）分析方法。

研究结果显示, 对服务机器人的负面认知和感知风险显著增加了员工的离职意向, 工作不安全感在这些关系中起到了中介作用。值得注意的是, 与老一辈相比, Z世代员工对服务机器人表现出明显不同的态度, 显示出不同世代群体对服务机器人的响应模式各异。

本研究通过揭示员工认知评估、世代差异和服务机器人引入在酒店业中的微妙互动, 对文献做出了贡献。所得的洞见为学术界和行业从业者提供了宝贵指导, 有助于制定策略, 促进服务机器人与工作人员的互利融合。

在酒店业运营的组织可以利用这些发现来定制干预措施, 解决由引入服务机器人引发的工作不安全感和离职意向问题。认识到不同世代群体的多样化观点, 组织可以实施有针对性的方法, 确保服务机器人技术的平稳过渡, 增强员工对其的接受度。

The current study is a review-based analysis combined with some case studies that focuses on establishing a link between artificial intelligence (AI) and the emerging trends of regenerative tourism and green destinations. Regenerative tourism and green destinations are the new hallmark, promoting sustainability in the travel industry by restoring ecosystems and encouraging friendly practices. The incorporation of AI into sustainable tourism has a potential to revolutionize how one can approach tourism by providing customer experiences and to contribute towards a sustainable future. AI has naturally found its place in industries due to the advancements in data analysis and computing power. In the context of tourism, AI’s data-driven capabilities are discussed in the current review, to showcase how they enable recommendations for intelligent automation and efficient resource management. With the implementation of AI-powered technologies, tourism operations become more efficient, providing opportunities for sustainable development and conservation in green destinations. The integration of AI in destinations encompasses applications such as energy management, waste reduction, transportation optimization and sustainable resource management. These AI-driven solutions play an important role in minimizing the impact caused by tourism activities while conserving natural resources. Additionally, AI facilitates delivering experiences that align with eco-values through recommendation systems and virtual assistants. The chapter tackles issues related to AI such as protecting data privacy, addressing biases dealing with job displacement and ensuring cultural relevance. It emphasizes the significance of inclusive implementation of AI and explores the challenges faced when implementing AI solutions in developing regions that have limited resources.

This real estate insight provides a comprehensive analysis of the current state and future potential of tokenization in the real estate industry mentioning several challenges to overcome to take advantage of this technology. We highlight potential benefits, including enhanced liquidity, increased security and improved accessibility. Additionally, the real estate insight critically discusses potential drawbacks, such as regulatory challenges and technological risks, and explores the impact of tokenization on real estate prices.

This real estate insight employs a comprehensive literature review alongside a qualitative analysis of various case studies to explore current implementations of tokenization within the real estate industry. Multiple applications of tokenization in the real estate industry are examined, including fractional ownership, property management and transaction processes. The study investigates the optimization potential of tokenization for asset liquidity in the real estate area, transaction transparency and security. It also critically discusses potential challenges, such as regulatory compliance, security vulnerabilities and market adoption.

The future of real estate tokenization, driven by blockchain technology and smart contracts, offers significant potential for growth, enhancing liquidity and accessibility through fractional ownership. Smart contracts automate and secure transactions, while evolving standards and regulatory frameworks in regions like North America, Europe and Asia support market expansion. Since its initial implementation with the St. Regis Aspen Resort STO, a stream of successful projects has highlighted the viability of tokenization. However, challenges remain, including the need for regulatory clarity, industry and customer education, displacements of market participants and jobs and environmental impacts. Integrating advanced technologies like AI and IoT can further streamline property management and investment decisions.

The real estate insight’s practical implications extend to industry professionals, policymakers and technology developers. Professionals gain insights into how tokenization can enhance liquidity and security in the real estate sector, guiding strategic decision-making. For policymakers, understanding potential challenges like regulatory compliance and technological risks informs the development of supportive regulations. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Highlighting the need for robust security measures and regulatory compliance in tokenization systems may foster better design practices. Therefore, the real estate insight’s findings could significantly shape the future development of tokenization integration in the real estate industry.

This real estate insight offers original value through a comprehensive analysis of the current and future impacts of tokenization in the real estate industry. It examines various applications of tokenization and critically discusses the potential challenges. The focus on informing strategic decisions for professionals and policymakers enhances its utility as a resource. Additionally, by addressing both the benefits and drawbacks, this study contributes to the broader discourse on the societal implications of tokenization. In the context of rapid technological advancement, such thorough studies are rare, further underscoring the real estate insight’s originality.

This paper aims to analyse the short- and long-term effects of entering the Spanish labour market under tough economic conditions on young immigrant–native earnings and employment outcomes.

The authors use cohorts, where the entry cohort into the labour market is the unit of observation. As a database, the authors use the continuous sample of working histories covering the period 2007–2021. Then, the authors estimate the model using weighted least squares.

The results show that the great recession and COVID-19 led to a blockage at the entrance of the labour market, reducing the number of workers. Additionally, the authors observe an adverse impact in terms of employment and earnings on those entering the labour market. Besides, this effect varies in intensity and persistence for natives and immigrants, as well as by country of birth, age of entry, gender and educational level.

A contribution to the literature is the analysis of the earnings and employment trajectories of young people entering the Spanish labour market for the first time during an adverse shock, such as the 2008 economic crisis or the COVID-19 crisis, and the possible differences that exist between native and immigrant workers. So, the authors analyse the labour market trajectories of workers covering the most recent years. Likewise, the authors carry out an extensive heterogeneity analysis in which they distinguish workers by educational level, gender, age of entry into the labour market and immigrants by their country of birth. This represents an additional contribution. The use of a cohort approach also contributes to the existing literature.