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The Future of Employment: How Susceptible Are Jobs to Computerisation?
Abstract
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupations probability of computerisation, wages and educational attainment.
... The transition from STEM (Science, Technology, Engineering, and Mathematics) to STEAM (Science, Technology, Engineering, Arts, and Mathematics) represents a critical evolution in educational philosophy aimed at addressing the challenges and demands of the 22nd century. Initially, STEM education emerged to strengthen technical and scientific skills, responding to economic and industrial needs (Frey & Osborne, 2013). However, as industries and societies began prioritizing creativity, design thinking, and human-centric innovation, policymakers and educators recognized the value of integrating the arts into STEM. ...
... In South Korea, Kim (2020) reported that STEAM initiatives included robotics competitions and coding workshops, which not only enhanced technical skills but also encouraged teamwork and innovation. Meanwhile, Frey and Osborne (2013) argued that STEAM programs address the risks of job automation by equipping students with unique skill sets, including adaptability and creative problemsolving. . In the United Kingdom, the Royal Academy of Engineering (2020) noted that STEAM workshops in underprivileged schools led to a 25% increase in students pursuing higher education in STEM fields. ...
... One of the most significant vulnerabilities associated with automation and AI is the potential for massive job displacement. Automation threatens to replace up to 47% of current occupations within a few decades, as predicted by Frey and Osborne(2013). Many traditional jobs, especially those in manufacturing, retail, and transportation, are at risk of being automated. ...
This research examines the future of education, focusing on the need to adapt current transdisciplinary educational models to the demands of the automation-driven workforce. The study explores the shift from STEM to STEAM (Science, Technology, Engineering, Arts, and Mathematics), analyzing how the integration of the arts impacts student outcomes, attitudes, and creativity. It delves into the potential transformations in the job market and the vulnerabilities posed by automation in the 22nd century. Using an inductive approach, a systematic literature review was conducted, synthesizing insights from 75 relevant studies on transdisciplinary STEAM education. The research proposes innovative, transdisciplinary STEAM models aimed at cultivating resilience, innovation, and sustainable skillsets among students. The goal is to inform policymakers and educators on how to future-proof education, ensuring learners are equipped for the challenges and opportunities of an automated workforce.
... The rapid adoption of AI in IT has created a dual-edged dynamic within the job market. Roles that were traditionally labor-intensive, such as data entry, routine network maintenance, and basic coding, are being progressively replaced by AI-driven systems capable of executing these tasks with greater accuracy and efficiency (Frey & Osborne, 2017). Simultaneously, this evolution has given rise to an unprecedented demand for new roles. ...
... The findings of this study underline the transformative influence of Artificial Intelligence (AI) on the Information Technology (IT) workforce. While automation has streamlined operational processes, it has significantly altered the employment landscape by displacing roles centered on routine and repetitive tasks, such as network maintenance, basic coding, and system monitoring (Frey & Osborne, 2017;McKinsey & Company, 2023). However, this displacement is counterbalanced by a surge in demand for specialized roles, including AI engineers, data scientists, and machine learning experts (Acemoglu & Restrepo, 2022;Colombo & Grilli, 2024). ...
... Frey & Osborne, 2017;McKinsey & Company, 2023). While these disruptions pose significant challenges, they are offset by the creation of high-demand roles such as AI engineers, data scientists, and automation specialists, reflecting AI's role as a catalyst for innovation and growth(Acemoglu & Restrepo, 2022;Colombo & Grilli, 2024).This dynamic reshaping of the IT workforce underscores the urgent need for skill transformation. ...
The advent of Artificial Intelligence (AI) is profoundly reshaping the global employment landscape, particularly within the Information Technology (IT) sector. This research examines the dual-edged impact of AI on IT jobs, exploring both the displacement of routine tasks and the creation of advanced roles requiring specialized AI expertise. The study synthesizes quantitative and qualitative data from industry reports, academic studies, and case analyses to provide a comprehensive view of this transformation. Findings indicate that while AI automates repetitive tasks, such as system monitoring and basic coding, it simultaneously drives demand for roles like AI engineers, data scientists, and automation specialists. Additionally, the paper delves into the changing skill requirements, highlighting the need for upskilling in AI technologies, machine learning frameworks, and ethical considerations in AI deployment. By evaluating the challenges and opportunities AI presents, this research aims to inform strategies for workforce adaptation, emphasizing the importance of continuous learning and proactive policy development to mitigate negative impacts and maximize benefits. The findings underscore the critical need for a collaborative approach among organizations, governments, and educational institutions to prepare the IT workforce for an AI-driven future.
... These findings align with broader studies, including Frey and Osborne's (2013) predictions that nearly half of U.S. jobs face automation risks. The literature indicates that such anxieties are shaped by industry-specific vulnerabilities and demographic factors (Autor, 2015). ...
... The "Public Anxieties About AI" study highlighted that individuals from regions with robust social safety nets, such as Scandinavian countries, reported lower levels of anxiety compared to those from countries with less comprehensive welfare systems. This aligns with findings by Frey and Osborne (2013) and Nemitz (2018), suggesting that societal structures significantly influence public perceptions of AI's risks and benefits. ...
... Regions with robust social safety nets and access to continuous education tend to report lower levels of AI-related anxiety. This observation aligns with studies by Frey and Osborne (2013) and Autor (2015), who highlight the protective effects of supportive societal structures. Conversely, countries with minimal welfare provisions face heightened public apprehensions, reflecting the broader socio-economic vulnerabilities exacerbated by AI adoption. ...
The rapid proliferation of artificial intelligence (AI) across societal domains marks a pivotal transformation in how individuals interact with technology, solve problems, and perceive the world. AI’s ability to augment decision-making, automate processes, and provide tailored services is lauded as a cornerstone of progress. However, this development also triggers significant concerns regarding its cognitive, social, and ethical implications. While some embrace AI’s promise of innovation, others express anxieties rooted in fears of job displacement, privacy erosion, and diminished human cognition. This paper synthesises findings from two studies—“Public Anxieties About AI” and “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking”—to explore these challenges and their implications for critical thinking, business, and society at large. In doing so, it integrates extensive references from these works to substantiate the analysis.
... intelligent finance has transformed the role of accounting from traditional bookkeeping and reporting to data-driven analysis and decision support (Felden & Chamoni, 2007;Richins et al., 2017). The role of accountants has undergone a fundamental metamorphosis-from being backend bookkeeping operators to becoming core business partners in helping companies with strategic planning, resource optimization, efficiency enhancement, and risk reduction (Frey & Osborne, 2017;Kokina & Davenport, 2017). ...
... This transformation demands that accountants possess higher technical literacy and interdisciplinary knowledge (Frey & Osborne, 2017;Richins et al., 2017). It has also propelled financial management toward a more intelligent, real-time, and precise approach. ...
... Standardized business processes are gradually being supplanted by machines, while traditional accounting functions are attenuating. Concurrently, the role of accountants in strategic planning, business decision-making, and creating corporate value has become increasingly prominent (Richins et al., 2017;Frey & Osborne, 2017). Consequently, traditional accounting talent cultivation is confronted with the challenge of upgradation, as the market urgently demands intelligent accounting professionals proficient in digital and intelligent technologies. ...
The burgeoning digital economy and the incessant transformation and upgradation of industries have precipitated novel demands for the cultivation of accounting professionals. The exigency for intelligent accounting talent has been accentuated by the industry and is increasingly being deliberated upon within the context of the paradigm shift and evolution of accounting education. Application-oriented universities are entrusted with the responsibility of nurturing pragmatic professionals adept at adapting to regional economic exigencies. However, a plethora of contemporary application-oriented universities are confronted with challenges in accounting education, encompassing imprecise talent development objectives, an obsolete curriculum structure, inadequate practical training components, and feeble faculty competencies. In an endeavor to surmount these quandaries, our university has explored a pathway for cultivating the practical abilities of accounting professionals through a paradigm predicated on "digital empowerment, industry-education synergy, and knowledge-action synergy." This paper propounds measures such as digital empowerment, the integration of "Course-Competition-Certification," and industry-education collaboration to nurture intelligent accounting talent that is congruous with the prevailing socioeconomic development.
... Technological disruption anxiety encompasses concerns about job security, changes in job roles, and adaptability due to technological advancements [95]. It can lead to increased stress and uncertainty among employees. ...
... Frey and Osborne [95] ANX1 A situation in which income is decreasing. ...
In the dynamic field of organizational behavior, comprehending the determinants of employee engagement, burnout, and job satisfaction is pivotal. This research investigates the influence of various workplace factors, such as recognition, fairness, leadership, and workload, on these key employee outcomes. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis, the study examines data from 25,285 employees. The results indicate that recognition significantly boosts employee engagement, while fairness and involvement also positively contribute, albeit to a lesser extent. Transformational leadership plays a dual role, enhancing engagement and reducing burnout. Notably, workload overload presents a nuanced impact, affecting both engagement and burnout. The study additionally reveals the detrimental effect of technological disruption anxiety on job satisfaction. A significant finding from the Multi-Group Analysis (MGA) is the varying impact of these factors between the private and public sectors, particularly in the context of transformational leadership’s effect on burnout and the differential influence of workload on burnout. These insights are critical for formulating effective organizational strategies and policies, highlighting the need for customized recognition initiatives, equitable management approaches, and well-balanced workload allocation.
... The historical analysis of automation potential reveals varying predictions across different studies. A study by Frey and Osborne (2013) in the US showed that the rapid adoption of automation and new technologies will lead to radical changes in the labour market, as 47% of jobs could be automated by 2050. A study by Arntz et al. (2016) shows that on average, in 21 OECD countries, only 9% of jobs are fully automated. ...
... For the purposes of the study, the value of the parameter varies within the range [0.1; 0.5] (10%; 50%). A review of the literature 5 shows that the potential for automation, according to different studies, ranges from 9% (Arntz et al., 2016) to 50% (Frey and Osborne, 2013). ...
As technological progress rapidly transforms the labour market, traditional labour taxation systems face a double challenge: declining tax revenues and the growing need to finance social security systems. This study analyses the impact of labour income tax cuts on employment in the context of technological progress using a general equilibrium model calibrated for the European Union economic zone. The simulation results show that labour income tax cuts have a positive effect on employment, especially at lower levels of automation, but that this effect weakens with increasing levels of automation. The study reveals that while tax cuts stimulate economic activity and partly compensate for the loss of tax revenue through increased consumption and investment, there is a persistent negative impact on government revenue. This points to the need to find alternative sources of tax revenue to ensure the sustainability of public finances in the context of technological progress.
... Makinelerin insanların yaptığı bazı işleri yapar hale gelmesine bağlı olarak insanlar tarafından yapılabilecek işlerin sayısındaki azalma ve bu durumla ilişkili olarak ortaya çıkan teknolojik işsizlik aslında yeni bir olgu değildir. İlk endüstri devrimiyle başlayan ve bunu izleyen üç endüstri devrimiyle devam eden iki yüzyılı aşan süre boyunca hemen her endüstri kollunda hızlı bir makineleşme ve otomasyon gerçekleşmiş ve buna bağlı olarak işlerin yapılış şeklinde sürekli bir değişim meydana gelmiştir (Frey ve Osborne, 2017). Bu değişime bağlı olarak çalışanların deneyimlerini, endişelerini ve verdikleri tepkileri açıklamak amacıyla çeşitli çalışmalar yapılmıştır. ...
... pek çok iş kolunda çoğaltmak mümkündür. Kesin olan gerçek şu ki yapay zeka tabanlı sistemler giderek daha fazla bilişsel ve/veya elle yapılan, rutin ve/veya rutin olmayan işi yapar hale gelmektedir ve bazı çalışanlar işlerini kaybetme riskine bağlı olarak bu durumdan olumsuz etkilenmektedir (Frey ve Osborne, 2017). Nitekim bilgisayarlaşmanın, dijitalleşmenin ve otomasyonun bir sonucu olarak işletmelerin yüksek eğitimli çalışan talebinde son yıllarda bir daralma gözlenmektedir. ...
Bu araştırmanın amacı Türkçeye uyarlanmış olan akıllı teknoloji, yapay zekâ, robotik ve algoritmalar farkındalığı ölçeğinin ikinci aşama geçerlilik ve güvenilirlik testlerini yapmaktır. Bu doğrultuda ölçeğin Türkçeye uyarlandığı ilk çalışmanın devamı niteliğinde bir araştırma yürütülmüştür. Araştırmanın verisi İstanbul’da çeşitli iş kollarında faaliyet gösteren on dört işletmede görev yapan, kartopu ve elverişlilik örnekleme tekniklerine göre ulaşılan iki yüz elli yedi beyaz yakalı çalışandan dijital anket formu ile toplanmıştır. Geçerlilik ve güvenilirlik testleri yapılırken ölçeğin Türkçeye uyarlandığı ilk çalışmadan farklı olarak katılımcıların tekno güvensizlik, yapay zekâ farkındalığı ve psikolojik esenlik düzeyleri ölçülmüştür. Elde edilen bulgular ölçeğin yeterli düzeyde geçerli ve güvenilir olduğunu göstermekte ve Türkçeye uyarlandığı ilk çalışmanın bulgularını desteklemektedir. Bu nedenle ilk çalışmada sunulan dört maddeli ve tek boyutlu ölçeğin maddelerinde herhangi bir değişiklik yapılmamıştır.
... According to the OECD (2017) and Ansal (2018), these changes have the potential to increase women's participation in highdemand, high-paying professions. However, Frey and Osborne (2013) caution that automation may disproportionately impact women, as they are overrepresented in roles susceptible to mechanization, such as telemarketing, data entry, and clerical work. Addressing this risk requires targeted investments in digital literacy and STEM education for women, enabling them to access opportunities in emerging industries. ...
Women's employment is a critical driver of economic growth, social equity, and sustainable development. Despite significant progress over the years, global and regional labor markets continue to exhibit stark gender disparities, with women facing systemic barriers such as gender pay gaps, limited access to education, cultural norms, and caregiving burdens. This study examines the factors influencing women's labor force participation in Turkey, a country with one of the lowest female employment rates among OECD countries, while providing comparative insights from global trends. Employing the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis framework, the research identifies the economic benefits of women's employment, the structural barriers they face, and the emerging opportunities for gender-inclusive labor markets. The findings reveal that women's employment contributes significantly to household welfare, workplace innovation, and sustainable economic development. However, challenges such as cultural stereotypes, unequal pay, and inadequate childcare infrastructure hinder women's full participation. Opportunities arising from digital transformation, green economy jobs, and policy-driven solutions like microfinance programs provide pathways for enhancing women's workforce engagement. Threats such as automation and insufficient enforcement of gender equality laws further exacerbate inequalities. This study concludes that targeted interventions are essential to address systemic barriers and leverage emerging opportunities. Recommendations include investing in caregiving infrastructure, promoting STEM education for women, enforcing anti-discrimination laws, and supporting women entrepreneurs. By fostering gender-inclusive labor policies, Turkey and other countries can unlock the transformative potential of women's employment, achieving not only economic resilience but also greater social equity and sustainable development.
... The Use of Technology for the Benefit of All. The rapid pace of scientific discoveries and technological applications has several negative implications, especially in the realm of production and use of services and employment (e.g., job loss, increased inequality, and pollution; Frey & Osborne, 2017;Nota et al., 2020;Glenn et al., 2017). Unsurprisingly, this era is aptly named the "fourth industrial revolution" (Schwab, 2016) or the "third digital revolution" (Gershenfeld et al., 2017). ...
This paper reports the development and psychometric requisites of the Career Confidence for Activities in the Future (CCAF). This scale of 25 items assesses adolescents’ self-efficacy beliefs about the capacity to learn and execute professional activities necessary to handle six global challenges. These are the safeguard of the environment and life on the planet, health and well-being of all the people, fair and sustainable economic development, the use of technology for the benefit of all, the construction of right-conscious and inclusive societies, and the valorization of culture and education to cosmopolitanism. Two studies were carried out. Study 1 included 706 adolescents (M age = 17.39; SD = 0.81; 31.9% male and 67.8% female) and examined the factor structure through exploratory and confirmatory factor analyses. Additionally, this study examined the convergent and discriminant validity of the questionnaire with measures of career adaptability and the propensity to sustainability in making decisions about one’s future. Study 2 included 706 adolescents (M age = 17.31; SD = 0.89; 50% male and 50% female) and provided measurement invariance across genders. Results supported the use of the CCAF in career guidance activities to encourage adolescents to positively contribute to handling the six global challenges considered.
... The rapid development of digital technologies has consistently been identified as both a driver of innovation and a challenge to employment structures, with automation and artificial intelligence (AI) displacing jobs traditionally performed by humans (Brynjolfsson & McAfee, 2014). The socio-political implications of unemployment, including its impact on social inequality and political stability, have been explored in the works of scholars such as Frey and Osborne (2017), who highlight how technological advances disproportionately affect lower-skilled workers. ...
This article examines how Finland and Germany integrate the principles of economic democracy within their legal frameworks to address unemployment challenges in the digital era. Digitalisation and globalisation have intensified unemployment as a multidimensional issue, linked to justice, inclusiveness, and human rights. The study explores legal mechanisms employed by these nations to uphold economic democracy and mitigate unemployment. Finland’s universal welfare model prioritises inclusiveness through comprehensive social support, while Germany’s social market economy achieves a balance between market flexibility and contribution-based protections. Both countries tackle digital challenges, including automation and unequal access to technology, by strengthening labour rights, promoting workforce education and retraining, and enhancing social protections. This comparative legal analysis highlights how robust legal systems can address the socio-economic impacts of digitalisation and provide a model for inclusive economic policies.
... However, internet development may also generate potential negative effects. First, new technologies like artificial intelligence and robotics might substitute human labor, reducing labor demand [48]. Second, reduced information transmission costs might encourage farmers to return home for entrepreneurship or agricultural production [49,50]. ...
Labor force mobility plays a crucial role in achieving balanced regional development in China. This study investigates whether urban internet development can effectively attract labor force inflow using data from the China Labor-force Dynamic Survey (CLDS) and constructing a comprehensive urban internet development index through factor analysis. Employing a conditional logit model and addressing potential endogeneity through instrumental variables, we find that (1) urban internet development significantly attracts labor force inflow, with a one-unit increase in the urban internet development index significantly raising the log odds of individual city choice by 0.2, and this effect remains robust across multiple specifications and estimation methods; (2) the attraction effect shows significant heterogeneity—it is stronger among highly educated, younger, foreign language proficient, and unmarried individuals, and more pronounced in regions with lower housing costs, lower income levels, and inland locations. These findings suggest that less-developed cities should prioritize internet infrastructure development to accumulate high-quality labor resources and achieve high-quality economic development, while also enhancing support for bottom-tier workers through public-benefit online platforms.
... This movement took place both through cuts in the number of traditional manufacturing jobs and an increase in service sector jobs (Thurow, 1999). On the threshold of the fourth industrial revolution, knowledge economy workers -the backbone of the middle class -are now under threat (Coates and Morrison, 2016), as well as service sector workers (Frey and Osborne, 2013). And this transformation constitute another future research field. ...
Introduction: Sustainable development is based on three interrelated and
equally important pillars; the environmental, the economic and the social. The
social pillar involves building a framework that promotes the well-being of the
whole population with the ultimate aim of preserving social cohesion, while
reducing social discrimination. In our analysis, the concept of social sustainability
refers to the need for the creation of a society that contains all the conditions
for sustainable development in terms of equal opportunities for employment
and social well-being. Currently, significant problems and dysfunctions exist as
long as several European labor markets are fragmented with a strong insidersoutsiders
divergence, job-polarization, high labor market slack, high in-work
poverty rates especially in precarious forms of employment. In Europe as well as
globally, addressing these issues is of major importance in order to ensure social
sustainability, given that the permacrisis (multiple crises), along with the Mega-
Trends have a clear impact on the structure of economy and labor market,
industrial relations systems, and business models.
Methods: The present paper analyses the state of play of social sustainability
in Europe and aims to identify specific policy responses that could offer viable
solutions to old and emerging challenges in terms of social inclusion through
the examination of secondary quantitative data.
Results: The permacrisis era, along with the Mega-Trends that are taking place
and seem to gradually have a clear impact on the structure of economy and labor
market, substantially affecting every aspect of society, since social inequalities
have the tendency to interrelate and getting reproduced.
Discussion: There is a need for knowledge-based and evidence-informed policy
making, both in terms of policy design and implementation, for a true and actual
sustainable (as well as inclusive) development, within momentous times.
... Auch im gewerblichen Hochbau erreichen technologische Entwicklungen berufliche Arbeits-und Geschäftsprozesse und führen dort zu veränderten Arbeitsprozessen und Qualifikationen. Überlegungen zu "Substituierbarkeitspotentialen" von Berufen, wie sie spätestens seit dem Jahr 2013 (Frey & Osborne 2013) wiederholt angestellt werden, schließen sich an. Demnach können die in Berufen bzw. in Berufsbildern gefassten Kerntätigkeiten auch im Bauwesen daraufhin analysiert werden, inwieweit sie "durch den Einsatz moderner Technologien übernommen werden könnten" (Dengler & Matthes 2018, S. 1 (Rauner 2017, S. 145) zu befähigen. ...
Die Entwicklung und Fertigung klassischer Holzverbindungen mittels 3D-Druck und CNC-gesteuerter Fräsen ist für Lehrkräfte berufsbildender Schulen im Bereich Bautechnik ein völlig neuer Ansatz. Ein speziell dafür entwickeltes Konzept ermöglicht es angehenden Lehrkräften, sich diese Technologien anzueignen und die Aus- und Weiterbildung zukunftsorientiert zu gestalten. Durch die Reflexion ihres eigenen Lernprozesses entwickeln sie Kompetenzen, die sie später in ihrer Berufspraxis nutzen können, um innovative Technologien weiter zu erforschen. Gleichzeitig werden sie in die Lage versetzt, technologische Kompetenzentwicklung bei anderen zu initiieren und zu begleiten.
... We then analyse the potential impact on the US labour market, focusing on the number of at-risk jobs and their relationship to wages and education levels. 5 ...
Recently, there has been significant discussion about artificial intelligence (AI) and Large Language Models (LLMs) as they relate to teaching and learning. To date, literature exists about the role of AI in Physical Therapy treatment, but not Physical Therapy education. This review will identify the benefits of adapting AI into Physical Therapy education to better prepare the healthcare providers of tomorrow. Survey research shows that AI can improve access to information, increase productivity, and reduce errors. However, most also feel ill-informed on the topic. A majority agreed that AI concepts should be included in Physical Therapy education. While valid concerns exist about AI sources giving false information, as well as the potential to use these services to plagiarize work, tools are already in existence to mitigate these issues. Potential benefits of AI in physical therapy education include the ability to produce examples, provide different explanations, and assist in assessing student learning while providing immediate feedback on performance. With the scope of AI rapidly expanding, we believe it is imperative for physical therapy educators to have access to current information about its potential benefits, uses, and limitations. While many professionals do not currently possess much knowledge on the topic, there is a growing consensus as to the role it will play in our profession in the future. While discussions of AI can raise more questions than answers, preparation now will reduce the need for urgent adaptations in the future.
... Bunun yanı sıra; kurumlar, sosyal sorumluluk projeleri ve toplum destek programları aracılığıyla teknolojik dönüşümün yarattığı olumsuz etkileri azaltmayı hedeflemelidir. Örneğin; işten çıkarılan çalışanlar için sunulan yeniden eğitim ve iş bulma programları, toplumsal dayanışmayı güçlendirebilmekte ve işsizliğin olumsuz sonuçlarını hafifletebilmektedir [14]. Bu tür çabalar, hem ekonomik gelişimi desteklemekte hem de bireylerin geçim kaynaklarını sürdürmelerine katkıda bulunmaktadır. ...
Bu çalışma, akıllı dijital teknolojilerin kurumsal kültür üzerindeki derin etkilerini inceleyerek iş dünyasının dinamikleri ve organizasyonel iş akışlarının kapsamlı dönüşümünü vurgulamaktadır. Dijital dönüşüm, yalnızca teknolojik araçların benimsenmesini değil, aynı zamanda iş süreçlerinin, stratejilerin ve çalışan rollerinin yeniden tanımlanmasını gerektirerek verimlilik, esneklik ve rekabetçiliği artırırken yeni zorluklar ve fırsatlar sunar. COVID-19 pandemisi sırasında dijital iletişim araçlarının önemi artmış, uzaktan çalışma modeli benimsenmiş ve bu araçların iş birliği ve ekip ruhunu korumadaki önemi belirgin hale gelmiştir. Dijital platformlar, çalışanların kişiselleştirilmiş öğrenme ve gelişim fırsatlarına erişimini kolaylaştırarak iş tatmini ve motivasyonunu artırır. Ayrıca, büyük veri analitiği ve yapay zeka destekli veri tabanlı karar verme süreçleri, organizasyonların stratejik kararlar almasını ve operasyonel süreçlerini optimize etmesini sağlar, ancak veri güvenliği ve gizliliği kritik endişeler arasında yer alır.
... Based on other scientific reaserchears can make it harder to use AI because the persons might resist using AI or even slightly adding them to their work. Obvuous, because they're afraid of being replaced by AI one day (Frey et al., 2017). So raising the trust to AI and being open about what it can do can help reduce these fears (Zogaj et al., 2020). ...
The article examines the importance of innovation and innovative activity for business development
and ensuring economic growth. It is emphasized that innovative development in modern business
conditions is the foundation for the formation of sustainable competitive advantages of an individual
business entity, industry, and the national economy. Therefore, an extremely important task is
optimizing approaches to managing innovative activities based on flexibility, inclusiveness, and
adaptability.
Key theoretical and methodological approaches to managing innovative activities are studied, and the
main trends in the development of innovative management are also investigated. It is established that
further digitalization creates new opportunities for developing business processes, including through
the active implementation of artificial intelligence technologies.
A thorough statistical analysis of the global artificial intelligence market is carried out, and an
analytical assessment of the state and prospects for developing artificial intelligence technologies in
Ukraine is also carried out.
... The problem of the impact of modern technologies on the development of employment and unemployment has been repeatedly addressed in empirical research. In their widely cited work, Frey and Osborne (2017) attempted to estimate the vulnerability of employment to computerization in the United States economy. As a result, they found that 47% of all workers in the US economy work in jobs where humans could be replaced by computers within the next 10-20 years. ...
The aim of the paper is to identify changes in the labour markets resulting from the use of new technologies. The basis for the analysis are theoretical and empirical findings in economic theory and data about the development of new technologies and their employment effects. The article shows how the views of economic theory on the impact of new technologies on the labor market have developed, starting from classical economics until recent times. In particular, views were highlighted that saw the effect of displacing the labor force and the effect of creating new jobs. The analysis indicates that application of the task-based approach to study labour demand enables better identification of transition channels of modern technology on the size and structure of labour demand. In the empirical analysis of the paper, panel causality test was conducted with data for the period 2000-2023 for 11 Central and Eastern European (CEE) countries. The empirical research did not confirm the alarmist predictions about the possibility of high technological unemployment as a result of technical progress. However, changes in the structure of labour demand are evident. According to the causality test results, technological development appears to be particularly associated with total employment, youth employment and youth unemployment.
In today's world, the rapid advancement of technology causes radical changes in the business world and economy, causing significant transformations in the labor market. This article aims to discuss the advancement of technology and the effects of this advancement on unemployment from the perspective of human psychology. First of all, basic concepts and connections will be emphasized in order to understand the unemployment problem caused by the changes created by technology in the business world. It will be discussed how technology increases automation and how advanced technologies such as artificial intelligence affect labor demand. The social and economic consequences of unemployment crises will be examined, focusing on the direct effects of these changes on unemployment rates. The article will also discuss in detail the psychological experiences of individuals experiencing unemployment and the emotional difficulties they face in this process. The effects of unemployment on individuals will be examined in the context of factors such as self-esteem, stress and hopelessness, and how psychological resilience can be protected or strengthened will be emphasized. In addition, the focus will be on how people can develop coping mechanisms in times of crisis and how they can learn from these processes, and how individuals can achieve their journey of empowerment from crisis. Issues such as resilience, adaptation and retraining will be discussed as exit strategies from crises, and methods that will facilitate individuals' re-entry into the workforce will be discussed. As a result, this article aims to be an important resource for understanding the effects of technology on unemployment and discovering how individuals can emerge stronger from this difficult process. It aims to guide researchers, policy makers and social workers on how human psychology can be supported and how restructuring processes can be managed while dealing with unemployment crises.
This study aims to examine science teachers' views and concerns about artificial intelligence (AI). Phenomenology design, one of the qualitative research method designs, was used in the study. The study group consisted of five science teachers, one doctoral and four master's degree graduates. Semi-structured interview was preferred as a data collection tool. Inductive content analysis was used to analyze the data. Participants defined AI as robots with humanoid behavior and alternative learning tools. Teachers stated that AI increased academic achievement, motivation and class participation rate. It was found that the participants' concerns about AI stemmed from lack of experience and knowledge, security issues and reliability of information. It was also concluded that the participants were concerned about workload, asocialization, decrease in skills, and privacy of personal data. The participants stated that they had problems in terms of being technologically inadequate, not being able to adapt to AI and lack of knowledge, inadequate AI outputs, and difficulties in applied trainings. It is recommended that science teachers should be given practical trainings to reduce their concerns about AI.
In today's world, the rapid advancement of technology causes radical changes in the business world and economy, causing significant transformations in the labor market. This article aims to discuss the advancement of technology and the effects of this advancement on unemployment from the perspective of human psychology. First of all, basic concepts and connections will be emphasized in order to understand the unemployment problem caused by the changes created by technology in the business world. It will be discussed how technology increases automation and how advanced technologies such as artificial intelligence affect labor demand. The social and economic consequences of unemployment crises will be examined, focusing on the direct effects of these changes on unemployment rates. The article will also discuss in detail the psychological experiences of individuals experiencing unemployment and the emotional difficulties they face in this process. The effects of unemployment on individuals will be examined in the context of factors such as self-esteem, stress and hopelessness, and how psychological resilience can be protected or strengthened will be emphasized. In addition, the focus will be on how people can develop coping mechanisms in times of crisis and how they can learn from these processes, and how individuals can achieve their journey of empowerment from crisis. Issues such as resilience, adaptation and retraining will be discussed as exit strategies from crises, and methods that will facilitate individuals' re-entry into the workforce will be discussed. As a result, this article aims to be an important resource for understanding the effects of technology on unemployment and discovering how individuals can emerge stronger from this difficult process. It aims to guide researchers, policy makers and social workers on how human psychology can be supported and how restructuring processes can be managed while dealing with unemployment crises. INTRODUCTION In today's world, the rapid advancement of technology leads to profound changes in the business world and economy. These changes have the potential to alter traditional business models and transform some lines of business, while creating new demands in the labor market. The advancement of technology, the increase in automation and the spread of advanced technologies such as artificial intelligence radically affect the structure of the workforce and, accordingly, cause fluctuations in unemployment rates. Unemployment can have not only economic but also profound psychological effects on individuals. This process can be a source of great stress for individuals who have to cope with a series of challenges that come with job loss, such as financial uncertainty, decreased self-esteem, social isolation and concerns about the future. However, periods of unemployment can also be an opportunity for individuals to develop personally and professionally. In this process, resilience and adaptation skills can be developed, new skills can be learned, and career orientations can be reshaped. This article aims to examine in depth the effects of technology on business and unemployment, especially from the perspective of human psychology. First, we will focus on how technology has changed the labor market and the direct effects of this change on unemployment rates. Then, it will focus on the psychological experiences of individuals experiencing unemployment and how they can come out of this process stronger. Finally, it will be discussed how people can develop coping strategies in times of crisis and how they can learn from these processes. The aim of this article is to understand the complex effects of technology on unemployment and human psychology and to discover ways that individuals can emerge stronger from this process. Ultimately, this study aims to be a valuable resource for social workers, policy makers and crisis managers, as it can contribute to the development of effective strategies for supporting and restructuring individuals during periods of unemployment.
This article analyzes and compares the knowledge and implementation of Industry 4.0 (I4.0) technologies in maquiladoras in Tijuana and Ciudad Juárez through a descriptive quantitative approach. Online surveys were conducted with manufacturing sector workers to assess their familiarity with and practical application of 19 specific technologies. The results show that, while knowledge levels are moderate, practical adoption of these technologies remains limited, with Ciudad Juárez showing a slight advantage. The findings highlight the importance of improving I4.0 training to enable more effective implementation and support technological transformation in these border regions. Although the study's geographical scope is limited, it is recommended that future research encompass a wider diversity of industrial contexts. This research contributes to the understanding of the transition towards I4.0 and provides valuable insights for designing policies aimed at technological training in the sector.
هدفت الدراسة إلى تعرف الوعي مبفهوم الذكاء اإلصطناعي وأهميته فى مجال إدارة املوارد البشرية،
والكشف عن واقع وحتديات استخدام أدوات وتطبيقات الذكاء اإلصطناعي وحتديد أهم االحتياجات
التدريبية الالزمة للموظفني باملركز الوطني للوثائق واحملفوظات، واستشراف التأثيرات املتوقعة الستخدام
أدوات وتطبيقات الذكاء اإلصطناعي يف املركز الوطني للوثائق واحملفوظات يف حتسني أداء موظفي إدارة
املوارد البشرية، ولتحقيق أهداف الدراسة استخدمت الدراسة املنهج اخملتلط الذي يزاوج بني املنهجني
الكمي والكيفي، وذلك باستخدام استراتيجية التصميم التفسيري املتسلسل )�Sequen Explanatory
Design tial)، استخدمت الدراسة اإلستبانة واملقابلة جلمع البيانات من أفراد العينة التي بلغت )135(
فرد من قيادات وموظفي املركز الوطني للوثاق واحملفوظات واخلبراء يف مجال الذكاء اإلصطناعي.
تكونت اإلستبانة من ثالثة محاور، احملور األول أهمية استخدام تطبيقات الذكاء اإلصطناعي يف إدارة
املوارد البشرية من وجهة نظرهم واشتملت على ثالثة أبعاد هي )التوظيف والفلترة، التدريب والتطوير،
امليزة التنافسية(. واحملور الثاني وعي املوظفني مبجاالت تطبيقات الذكاء اإلصطناعي واحنوى على ثالثة
أبعاد هي )النظم اخلبيرة، اخلوارزميات اجلينية، ونظم املنطق الغامض( واحملور الثالث حتديات توظيف
ً أداة املقابلة النوعية
تطبيقات الذكاء اإلصطناعي يف بيئة العمل، كما استخدم الفريق البحثي أيضا
املتعمقة شبه املقننة. توصلت الدراسة إلى أن هناك وعي بأهمية أدوات الذكاء اإلصطناعي وتطبيقاته
املركز املستقبلية ومدى ارتباطه بتوجهات اململكة يف مجال التحول الرقمي، كما أشارت نتائج الدراسة
أنه على الرغم من أن الذكاء اإلصطناعي يعتبر تقنية جديدة مثيرة لالهتمام، إال أنه ال يزال أمامه طريق
طويل ليقطعه قبل أن يتم تنفيذه بشكل استراتيجي مثالي، ويحتاج إلى دمجه يف اخلطط اإلستراتيجية
للمركز، والتعاون مع جهات ريادية مبجال الذكاء اإلصطناعي مع مراعاة الظروف البيئية واألصالة
الثقافية السعودية، كما كشفت النتائج عن التحديات التي تواجه توظيف أدوات وتطبيقات الذكاء
اإلصطناعي يف إدارة املوارد البشرية باملركز الوطني للوثائق واحملفوظات منها: تعقيد مجاالت املوارد
البشرية، والقيود التي تفرضها البيانات الصغيرة، والثانية األسئلة املرتبطة بالعدالة وغيرها من القيود
األخالقية والقانونية، وردود الفعل السلبية احملتملة للموظفني على قرارات اإلدارة على البيانات املستندة
على اخلوارزميات، كما كشفت الدراسة عن مجموعة من أهم االحتياجات التدريبية لدى موظفي املركز
الوطني للوثائق واحملفوظات متثلت أهمها يف التدريب على أفضل املمارسات لتنفيذ الذكاء اإلصطناعي
باملوارد البشرية، التدريب علي تطبيقات الذكاء اإلصطناعي إلدارة املهام الوظيفية، تدريب القادة على
روبوتات احملادثة املتاحة واملدعومة بالذكاء اإلصطناعي. وقدمت الدراسة تصور مقترح أشتمل على
املبادئ الالزمة لتوظيف تطبيقات الذكاء اإلصطناعي يف ممارسات ادارة املوارد البشرية باملركز الوطني
للوثائق واحملفوظات والوثائق.الرئيسية )النظم اخلبيرة، اخلوارزميات اجلينية، نظم املنطق الغامض( وهو ما يعكس توجهات قيادات
Over the past 20 years, production processes have become more fragmented, with various stages offshored to different locations, impacting international and domestic labor markets. CEE countries have become key offshoring destinations due to their skilled labor and competitive costs. This study aims to assess how shifts in international production affect labor markets in the V4 countries. We estimate a system of employment-share equations derived from a translog cost function, incorporating offshoring and domestic outsourcing, using the SUR method. Our findings suggest that offshoring negatively impacts employment for less-skilled occupations, while its effect on high-skilled jobs is insignificant. Thus, the need for continuous training and upskilling of the workforce to maintain competitiveness is highlighted by our findings.
Automation is transforming labor markets, reshaping industries, and altering the nature of work at an unprecedented pace. This paper examines the impact of automation on wage inequality, focusing on its effects across different industries and demographic groups. While automation has the potential to increase productivity and economic growth, it simultaneously introduces significant challenges for wage distribution, particularly among low-skill occupations vulnerable to displacement by automated technologies. These shifts often benefit highly skilled workers, leading to a bifurcation in the labor market where technological advantages amplify existing wage disparities. Through a comprehensive analysis of labor market data, this study explores key patterns in automation adoption and their correlations with wage inequality. Using case studies from industries such as manufacturing, retail, and services, the paper investigates how the displacement of routine jobs contrasts with wage gains in tech-driven sectors. Demographic factors, including education, gender, and geographic location, are also analyzed to understand their compounding effects on inequality. In addition to highlighting the risks posed by automation, the research examines the role of education, skill levels, and policy interventions as potential mitigators. Proactive measures, such as workforce retraining and reskilling programs, are evaluated as strategies to ensure workers are equipped for an increasingly automated economy. The findings underscore the need for balanced technological integration and equitable policy frameworks to prevent automation from exacerbating existing inequalities, while ensuring that the benefits of innovation are broadly distributed. JEL classification: E24, J31.
The concepts of work found in literature distinguish themselves and approach the subject from different perspectives. Aiming to expand and update the reflection on work and its implications for workers, this article presents the main characteristics of human work throughout history from the perspective of industrial revolutions and the potential impacts that the Industry 4.0 paradigm has on it. As critical components of the social sustainability pillar, work and workers must be a focal point in the face of technological changes with potential impacts. To achieve this, impressions of professionals from academic and industrial institutions in Brazil and Germany were collected through self-administered questionnaires and subjected to content analysis. The process highlights impacts ranging from physical aspects of work environments to psychological effects on workers. Ultimately, the article concludes by offering a thoughtful reflection on future guidelines for human work in the context of the new industrial model.
Este capítulo presenta un análisis sobre la integración de las TIC e IA artificial dentro de los planes de estudio que conllevan a la obtención de la titulación de profesional en psicología, y las posibilidades de acceder con dicha titulación a un programa de educación superior en IA. Concluyendo que la inclusión de las TIC se debe en su mayoría a un requerimiento nacional acorde con la modalidad educativa, y que el ingreso a programas de educación superior en IA está mediado por el prerrequisito de conocimiento previo en programación, matemática y/o estadística, incluso limitado a esas áreas.
Lo anterior, en cuanto a la práctica profesional de la psicología dentro del contexto colombiano es correspondiente con una alta brecha digital, baja apropiación de TIC e IA en el escenario productivo, y dificultades de infraestructura tecnológica de forma generalizada.
On the verge of the AI Age—the Mechanocene—we tend to reproduce past narratives where humans are replaced by machines. This leads us to identifying skills and activities that will not be automated by AI soon, and prepare both current and future generations for those ‘safe’ occupations. However, there is the risk that the set of non-automatable tasks becomes empty sooner than expected, preparing new generations for skills that will not be needed in the end. Instead of this incremental route, in this paper we imagine a final destination where AI has all the skills humans have. We argue that in an age where humans can be intensively assisted, augmented and coupled with AI, we need to rethink enhancement and assessment under the extended mind thesis—a philosophical theory suggesting that technological tools can become integral parts of our cognitive processes. Under this perspective, we can still identify skills that are useful in this AI age, especially if humans want to understand and influence their world. Then, if these skills are set as the goals for education, we need to reliably assess they are achieved. However, a world of ubiquitous AI extenders generates enormous challenges for the assessment of individuals, when humans will be mostly operating as part of AI-human hybrids and collectives. In this context, the skills of the individual, human or machine, must be evaluated in terms of their contribution to the human-machine teams they are expected to be embedded in. This further takes education and assessment from the traditional aspiration of achieving fully autonomous skills to the reality of more integrated and interdependent human-machine scenarios of the AI age.
The objective of the chapter is to analyze the main challenges of the formation of human capital at the postgraduate level in the digital era in order to determine the main competencies to be developed by students. The postgraduate degree is the educational level oriented towards the development of professional skills of human capital that allows them to be autonomous professionals prepared to perform effectively and efficiently in a digital world. The challenges that the Fourth Industrial Revolution has established for the educational system: Incorporation of technological impact in academic training; formulation of proposals to innovate in training processes; generation of knowledge based on social innovation. Students who access the postgraduate level need to acquire skills that allow them to adapt to a work environment in constant technological evolution, in addition to acquiring both technical knowledge and relevant social skills. Digital leadership has become a strategic factor for organizational success in the era of technological transformation
Recent developments of generative artificial intelligence (GenAI) have reignited discussions about technological unemployment, yet empirical evidence remains scarce. Using a comprehensive dataset of over one million online job postings from China spanning 2018 to 2024, this study provides a timely assessment of GenAI’s impact on local and regional labor markets following the surge of GenAI tools in late 2022. Our analysis reveals a nontrivial job displacement effect of GenAI in the short and medium run, disproportionately affecting entry-level, high-wage positions and highly-educated workers. Notably, this displacement effect is more pronounced in larger, more economically developed cities, suggesting a reduction in job opportunity disparities across regions in era of GenAI. We also find that lower labor costs and higher occupation diversity increase the resilience of local labor markets to the disruptive effects of GenAI. These findings offer crucial insights for policymakers to mitigate the adverse consequences of GenAI while harnessing its potential for positive societal transformation.
Purpose
The purpose of this study was to investigate the impact of artificial intelligence (AI) adoption on knowledge workers' innovative work behaviors (IWB), as well as the mediating role of stress appraisal and the moderating role of individual learning abilities.
Design/methodology/approach
This study analyzed the questionnaire results of 313 knowledge workers, and data analysis was conducted by using SPSS 25.0, SPSS 25.0 macro-PROCESS and AMOS 28.0.
Findings
This study found that AI adoption has a double-edged sword effect on knowledge workers' IWB. Specifically, AI adoption can promote IWB by enhancing knowledge workers' challenging stress appraisal, while inhibiting IWB by fostering their hindering stress appraisal. Moreover, individual learning ability significantly moderated the relationship between AI adoption and stress appraisal, which further influenced IWB.
Originality/value
This study integrates the conflicting findings of previous studies and proposes a comprehensive theoretical model based on the theory of cognitive appraisal of stress. This study enriches the research on AI in the field of knowledge management, especially extending the understanding of the relationship between AI adoption and knowledge workers’ IWB by unraveling the psychological mechanisms and behavior outcomes of users' technology usage. Additionally, we provide new insights and suggestions for organizations to seek the cooperation and support of employees in introducing new technologies or driving intelligent transformation.
The era of AI and automation technologies has fundamentally changed the structure of jobs and skill needs in the global market, driving significant transformation in the digital business world. This research aims to analyze the impact of these technological developments on the jobs and skills required by the modern workforce. Using the literature review method, this research evaluates various academic sources and related industry reports. The findings show that there is a shift away from routine work towards jobs based on analytical, creative and interpersonal skills, with a growing demand for expertise in data management, programming and emotional intelligence. In addition, the research reveals a significant skills gap, which could exacerbate socio-economic inequality if not addressed. The implications of this study emphasize the importance of investing in retraining and workforce skills development to support adaptation to technological transformation, as well as the need for collaboration between government, educational institutions and industry in designing inclusive and sustainable policies.
Purpose
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.
Design/methodology/approach
Data was collected from hospitality employees across different generations in the USA, and 279 responses were analyzed using partial least squares structural equation modeling.
Findings
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.
Practical implications
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.
Originality/value
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.
El rider recorre el espacio urbano como intermediario entre empresa de plataformas y cliente final, realizando tareas de baja cualificación y de fácil reemplazo. Su naturaleza ambigua, con reconocimiento legal como asalariado, aunque suela establecer una relación mercantil con la empresa de plataformas, le confiere notas de vulnerabilidad y precarie-dad. En este artículo se analizan similitudes y diferencias materiales e identitarias entre repartidores que operan en un mismo territorio, atendiendo a la autopercepción y estrategias laborales diferenciadas entre autónomos y asalariados, que activan en el desempeño y negociación de su situación particular. Como resultado de un trabajo de campo exten-so en la Región de Murcia, se revelan discursos y prácticas que explican al rider como una figura híbrida que a menudo se autodefine en relación con las notas de flexibilidad y autogestión, una identificación que difiere de los presupuestos materiales que lo explican como subordinado y dependiente de la empresa de plataformas. Los resultados muestran una autopercepción de agencia y flexibilidad que se entiende como estrategia consciente de posicionamiento donde la autonomía es ilusoria. Se presenta así la flexi-vulnerabilidad como herramienta analítica y atributo que captura esta naturaleza dual, dependiente de la empresa de plataformas, al tiempo que agente de estrategias de autonomía a corto-medio plazo.------------------------------------------------------------
The rider travels the urban space as an intermediary between the platform company and the end customer, performinglow-skilled and easily replaceable tasks. Its ambiguous nature, with legal recognition as a salaried employee, althoughusually establishes a commercial relationship with the platform company, gives notes of vulnerability and precari-ousness. This article analyzes similarities and material and identity differences between delivery riders operating in thesame territory, focusing on the self-perception and labor strategies differentiated between self-employed and salariedworkers, which they activate in the performance and negotiation of their particular situation. As a result of extensivefieldwork in the Region of Murcia, discourses and practices are revealed that explain the rider as a hybrid figure whooften defines himself in relation to the notes of flexibility and self-management, an identification that differs fromthe material assumptions that explain him as subordinate and dependent on the platform company. The results showa self-perception of agency and flexibility that is understood as a conscious positioning strategy where autonomy is il-lusory. Flexi-vulnerability is thus presented as an analytical tool and attribute that captures this dual nature, dependenton the platform company, while at the same time an agent of short-medium term autonomy strategies.
I investigate how the introduction of industrial robots is shaping the demographic composition of the US labor force between 1993 and 2014. I find that robots decreased employment of men and women by 3.7 and 1.6 percentage points, contributing to the secular decline in the gender employment gap, and that they decreased employment among Whites and non-Whites by 1.8 and 4.5 percentage points, widening the race/ethnicity employment gap. These effects follow from the persistent occupational segregation in the labor market. Although robots are primarily used in manufacturing, their adverse effects spill over to local service industries, in particular for Blacks and Hispanics. (JEL E24, J15, J16, L60, O33, R23)
Purpose
With its continuous development and application in the hotel industry, artificial intelligence (AI) is gradually replacing many jobs traditionally performed by humans. This research aims to understand how this threat and opportunity of substitution affects hotel employees’ behavioral decision-making.
Design/methodology/approach
This study uses a structural equation model, ordinary least squares and bootstrapping method to analyze the data collected with a field study and a scenario experiment from star-hotels in Shanghai, Paris and Seoul.
Findings
The results discovered that employees’ AI awareness has a positive relationship with their work engagement and AI boycott through two paths. The promoting path involves recovery level, while the hindering path includes job insecurity. In addition, the estimates showed that AI awareness has a great indirect effect on work engagement or AI boycott when innovativeness as a job requirement is high.
Practical implications
The findings offer insights to help hotels optimize the relationship between AI and hotel human workers while providing valuable implications for addressing behavioral dilemmas faced by hotel employees in the era of AI.
Originality/value
By integrating the behavioral decision-making literature with the conservation of resources theory, the study focuses on the dual mechanisms – challenging and hindering – through which AI awareness influences hotel employees’ coping strategies.
This paper studies the relationship between exposure to artificial intelligence (AI) and workers’ wages across European countries. Overall, a positive relationship between exposure to AI and workers’ wages is found, however it differs considerably between workers and countries. High-skilled workers experience far higher wage premiums related to AI-related skills than middle- and low-skilled workers. Positive associations are concentrated among occupations moderately and highly exposed to AI (between the 6 th and 9 th decile of the exposure), and are weaker among the least exposed occupations. Returns of AI-related skills among high-skilled workers are even higher in Eastern European Countries compared to Western European countries. The heterogeneity likely originates from the difference in overall labour costs between country groups. The results presented in this study were obtained from the estimation of Mincerian wage regressions on the 2018 release of the EU Structure of Earning Survey.
The integration of artificial intelligence (AI) with autonomous systems represents one of the most transformative technological advancements of the modern era. This study explores the evolution, applications, and challenges of these systems across key sectors, including transportation, healthcare, manufacturing, agriculture, and defense. Autonomous systems leverage AI algorithms, such as machine learning and deep learning, to perform tasks with minimal human intervention by processing data, making decisions, and executing actions. Their applications range from self-driving vehicles and robotic surgical systems to precision farming and military operations. These innovations promise enhanced efficiency, accuracy, and scalability, but they also raise significant ethical, societal, and regulatory challenges. Issues such as algorithmic bias, accountability in case of failures, job displacement, and potential misuse in defense highlight the complexities of widespread adoption. To address these challenges, the study emphasizes the need for robust regulatory frameworks, ethical guidelines, and interdisciplinary collaboration to ensure the safe and equitable deployment of AI and autonomous systems. Through a thematic analysis of recent research, this article provides insights into the transformative potential and the multifaceted concerns of these technologies, paving the way for future advancements and responsible innovation.
L’article propose une analyse prospective d’un phénomène nouveau, l’exploitation des données du Big et de l’ Open Data afin d’accéder à plus de connaissances clés pour irriguer l’innovation collaborative. À l’ère des datas, l’objectif est de proposer une articulation nouvelle de la co-innovation au sein des clusters , permettant aux acteurs engagés de profiter pleinement des opportunités d’innovation permises par les données. Les résultats de l’étude exploratoire autorisent la conceptualisation de modèles de clusters « nomades » (Thiaw, 2022), faisant référence à des espaces d’innovation translocaux , fondés sur la mutualisation de ressources et de connaissances au profit d’acteurs hétérogènes décidés à collaborer pour innover ensemble. L’originalité du modèle réside dans la non-nécessité de la proximité géographique des parties prenantes et la prise en compte des données dans les processus de co-innovation, pour s’adapter aux réalités de la nouvelle économie.
Creating proposals and pricing strategies for B2B clients is a resource-intensive process that requires customization and precision. This paper presents a Generative AI system that automates proposal generation by incorporating client data, RFP requirements, and competitive insights. Additionally, the system suggests dynamic pricing models optimized for market conditions and customer profiles. A case study in the consulting sector demonstrated a 20% reduction in proposal development time and a 10% increase in win rates. The findings underline AI's capability to enhance efficiency and competitiveness in B2B sales processes.
Many signals of interest are corrupted by faults of an unknown type. We propose an approach that uses Gaus-sian processes and a general "fault bucket" to capture a priori uncharacterised faults, along with an approxi-mate method for marginalising the potential faultiness of all observations. This gives rise to an efficient, flex-ible algorithm for the detection and automatic correc-tion of faults. Our method is deployed in the domain of water monitoring and management, where it is able to solve several fault detection, correction, and predic-tion problems. The method works well despite the fact that the data is plagued with numerous difficulties, in-cluding missing observations, multiple discontinuities, nonlinearity and many unanticipated types of fault.
This chapter discusses services offshoring, which refers to the migration of jobs across national borders, mostly from rich countries to poor ones, with imported products and activities flowing back to the United States. A measure of tradability is described, built from common notions of job characteristics related to offshorability. A selection of tradable occupations do indeed have characteristics of offshorability, including Internet-enabled, high-information content, no-face-to-face customer contact. The calculated index of offshorability offers strong potential for understanding jobs (tasks) at risk. The two measures of tradability and offshorability offer a combined potential to do the same. The chapter concludes with the expectation that, as technology and policy allow for more trade in these activities, the United States should gain world market share in these activities, not lose it.
What explains the current low rate of employment in the US? While there has been substantial debate over this question in recent years, we believe that considerable added insight can be derived by focusing on changes in the labor market at the turn of the century. In particular, we argue that in about the year 2000, the demand for skill (or, more specifically, for cognitive tasks often associated with high educational skill) underwent a reversal. Many researchers have documented a strong, ongoing increase in the demand for skills in the decades leading up to 2000. In this paper, we document a decline in that demand in the years since 2000, even as the supply of high education workers continues to grow. We go on to show that, in response to this demand reversal, high-skilled workers have moved down the occupational ladder and have begun to perform jobs traditionally performed by lower-skilled workers. This de-skilling process, in turn, results in high-skilled workers pushing low-skilled workers even further down the occupational ladder and, to some degree, out of the labor force all together. In order to understand these patterns, we offer a simple extension to the standard skill biased technical change model that views cognitive tasks as a stock rather than a flow. We show how such a model can explain the trends in the data that we present, and offers a novel interpretation of the current employment situation in the US.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
We develop a new methodology for quantifying the tasks undertaken within occupations using 3,000 verbs from around 12,000 occupational descriptions in the Dictionary of Occupational Titles (DOTs). Using micro-data from the United States from 1880-2000, we find an increase in the employment share of interactive occupations within sectors over time that is larger in metro areas than non-metro areas. We provide evidence that this increase in the interactiveness of employment is related to the dissemination of improvements in transport and communication technologies. Our findings highlight a change in the nature of agglomeration over time towards an increased emphasis on human interaction.
This paper raises basic questions about the process of economic growth. It questions the assumption, nearly universal since Solow’s seminal contributions of the 1950s, that economic growth is a continuous process that will persist forever. There was virtually no growth before 1750, and thus there is no guarantee that growth will continue indefinitely. Rather, the paper suggests that the rapid progress made over the past 250 years could well turn out to be a unique episode in human history. The paper is only about the United States and views the future from 2007 while pretending that the financial crisis did not happen. Its point of departure is growth in per-capita real GDP in the frontier country since 1300, the U.K. until 1906 and the U.S. afterwards. Growth in this frontier gradually accelerated after 1750, reached a peak in the middle of the 20th century, and has been slowing down since. The paper is about “how much further could the frontier growth rate decline?” The analysis links periods of slow and rapid growth to the timing of the three industrial revolutions (IR’s), that is, IR #1 (steam, railroads) from 1750 to 1830; IR #2 (electricity, internal combustion engine, running water, indoor toilets, communications, entertainment, chemicals, petroleum) from 1870 to 1900; and IR #3 (computers, the web, mobile phones) from 1960 to present. It provides evidence that IR #2 was more important than the others and was largely responsible for 80 years of relatively rapid productivity growth between 1890 and 1972. Once the spin-off inventions from IR #2 (airplanes, air conditioning, interstate highways) had run their course, productivity growth during 1972-96 was much slower than before. In contrast, IR #3 created only a short-lived growth revival between 1996 and 2004. Many of the original and spin-off inventions of IR #2 could happen only once – urbanization, transportation speed, the freedom of females from the drudgery of carrying tons of water per year, and the role of central heating and air conditioning in achieving a year-round constant temperature. Even if innovation were to continue into the future at the rate of the two decades before 2007, the U.S. faces six headwinds that are in the process of dragging long-term growth to half or less of the 1.9 percent annual rate experienced between 1860 and 2007. These include demography, education, inequality, globalization, energy/environment, and the overhang of consumer and government debt. A provocative “exercise in subtraction” suggests that future growth in consumption per capita for the bottom 99 percent of the income distribution could fall below 0.5 percent per year for an extended period of decades.
The provision of TotalCare® styled service offerings by original equipment manufacture (OEM) suppliers of high-integrity assets is intended to provide improved levels of system availability to the operator. A key element of such service offerings is the ability to minimize unplanned equipment downtime, and the utilization of advanced diagnostic and prognostic monitoring tools is a significant component in achieving this. Monitoring methods, founded on novelty detection technologies, are now a well-established condition monitoring technique. This approach is particularly appropriate for monitoring high-integrity plant where fault conditions arise with extremely low levels of probability. The approach described in this article is to establish empirically based models of normality that are guided by engineering knowledge and utilize key features normally used by expert engineers. However, rather than consider generic modelling approaches, it is proposed that application of models that adapt their sensitivity to the operation of individual assets offer greater prognostic efficiency. This article demonstrates how this can be achieved by considering asset-specific models that adapt the threshold of alerting in accordance with the observed normal running of the plant.
“Circuits and Electronics” (6.002x), which began in March 2012, was the first MOOC developed by edX, the consortium led by MIT and Harvard. Over 155,000 students initially registered for 6.002x, which was composed of video lectures, interactive problems, online laboratories, and a discussion forum. As the course ended in June 2012, researchers began to analyze the rich sources of data it generated. This article describes both the first stage of this research, which examined the students’ use of resources by time spent on each, and a second stage that is producing an in-depth picture of who the 6.002x students were, how their own background and capabilities related to their achievement and persistence, and how their interactions with 6.002x’s curricular and pedagogical components contributed to their level of success in the course.
Purpose – The Japanese prefabrication industry not only has automated its processes to a high extent, but it also innovates due to the fact that it delivers buildings of outstanding quality accompanied by a multitude of services. In order to explore and specify the concepts and parameters that have driven this industry, Japan's prefabrication industry, its cultural, economic and technological surrounding, as well as the applied processes, technologies and economic strategies, have to be illustrated and analysed. The purpose of this paper is to identify, describe and analyse these concepts and their related parameters, as well as to recognise the most influential drivers for the future that provide an indication into which direction the industry could evolve. Design/methodology/approach – Being aware that literature does not provide relevant information and data, which would allow the authors to explore concepts and parameters explaining the success of the Japanese prefabrication industry, the authors performed field surveys, visited factories, R&D centres and sales points of all major Japanese prefabrication companies. In some cases the authors also interviewed general managers, researchers and developers, and academicians at Japanese universities. Based on an extensive literature review in the area of product development, production technology, modularisation, mass customisation, and innovation, the authors qualitatively and quantitatively analysed all major prefabrication companies according to a fixed scheme. Findings – The concepts and parameters identified and analysed in this paper demonstrate that the Japanese prefabrication industry, which is leading in large‐scale industrialization, nowadays focuses towards services that are related to the building's utilisation phase, rather than delivering products. By involving customers it enhances the companies' customer relations, thus creating competitive advantages. Originality/value – Overall the paper identifies that Japanese prefabrication industry acts rather like a “production industry” than a “construction industry”. Similar to many other high‐tech industries, Japan's prefabrication industry incorporates the latest product and process technologies and combines automation, products and services into complex value‐capturing systems.
The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation via Particle Filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of-life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electro-chemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.
Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into "intelligent" analysis methods that are sufficiently robust to support large-scale deployment. Existing systems are typically plagued by large false-alarm rates, and an inability to cope with sensor artifact in a principled manner. This paper has two aims: 1) proposal of a novel, patient-personalized system for analysis and inference in the presence of data uncertainty, typically caused by sensor artifact and data incompleteness; 2) demonstration of the method using a large-scale clinical study in which 200 patients have been monitored using the proposed system. This latter provides much-needed evidence that personalized e-health monitoring is feasible within an actual clinical environment, at scale, and that the method is capable of improving patient outcomes via personalized healthcare.
Assistive social robots, a particular type of assistive robotics designed for social interaction with humans, could play an important role with respect to the health and psycho-logical well-being of the elderly. Objectives Assistive social robots are believed to be useful in eldercare for two reasons, a functional one and an affective one. Such robots are developed to function as an interface for the elderly with digital technology, and to help increase the quality of life of the elderly by providing companionship, respectively. There is a growing attention for these devices in the literature. However, no comprehensive review has yet been performed to in-vestigate the effectiveness of such robots in the care of the elderly. Therefore, we systematically reviewed and analyzed existing literature on the effects of assistive social robots in health care for the elderly. We focused in particular on the com-panion function. Data Sources A systematic search of MEDLINE, CINAHL, Psy-cINFO, The Cochrane Library databases, IEEE, ACM libraries and finally Google Scholar was performed for records through December 2007 to identify articles of all studies with actual subjects aimed to assess the effects of assistive social robots on the elderly. This search was completed with information derived from personal expertise, contacts and reports. Study Selection and Data Extraction Since no randomized controlled trials (RCT)'s have been found within this field of research, all studies reporting effects of assistive robotics in elderly popula-tions were included. Information on study design, interventions, controls, and findings were extracted for each article. In medical journals only a few articles were found, whereas about 50 publications were found in literature on ICT and robotics. Data Synthesis The identified studies were all published after 2000 in-dicating the novelty of this area of research. Most of these publications contain the results of studies that report positive effects of assistive social robots on health and psychological well-being of elders. Solid evidence indicating that these ef-fects can indeed be attributed to the actual assistive social robot, its behavior and its functionality is scarce. Conclusions There is some qualitative evidence as well as limited quantitative evidence of the positive effects of assistive social robots with respect to the elderly. The research designs, however, are not robust enough to establish this. Confounding variables often cannot be excluded. This is partly due to the chosen research designs, but also because it is unclear what research methodology is adequate to investigate such effects. Therefore, more work on methods is needed as well as robust, large-scale studies to establish the effects of these devices. Assistive social robots in elderly care: a review G8(2)Review-Broekens-v4.indd 1 29-5-2009 10:52:03
A systematic review is provided on artificial agent methodologies applicable to control engineering of autonomous vehicles and robots. The paper focuses on some fundamentals that make a machine autonomous: decision making that involves modelling the environment and forming data abstractions for symbolic processing and logic based reasoning. Most relevant capabilities, that directly affect decision making, are briefly reviewed, such as navigation, autonomous path planning, path following control and communications. Although many autonomous vehicles have been engineered in the past without using the agent oriented approach, most decision making on board of vehicles is similar to or can be classified as some kind of agent architecture, even if in a naïve form. First the ANSI standard of intelligent systems is recalled then a summary of the fundamental types of possible agent architectures for autonomous vehicles are presented, starting from reactive, through layered, to advanced architectures in terms of beliefs, goals and intentions. The review identifies some missing links between computer science results on discrete agents and engineering results of continuous sensing, actuation and path planning. In this context design tools for "abstractions programming" are identified as needed to fill in the gap between logic based reasoning and sensing. Finally, research is reviewed on autonomous vehicles in water, on the ground, in the air and in space with comments on their methods of decision making. On of the main conclusion of this review is that standardisation of decision making through agent architectures is desirable for the future of intelligent vehicle developments and their legal certification.
The article studies the evolution of the constitutional arrangements in seventeenth-century England following the Glorious Revolution of 1688. It focuses on the relationship between institutions and the behavior of the government and interprets the institutional changes on the basis of the goals of the winners—secure property rights, protection of their wealth, and the elimination of confiscatory government. We argue that the new institutions allowed the government to commit credibly to upholding property rights. Their success was remarkable, as the evidence from capital markets shows.
Are judicial rulings based solely on laws and facts? Legal formalism holds that judges apply legal reasons to the facts of a case in a rational, mechanical, and deliberative manner. In contrast, legal realists argue that the rational application of legal reasons does not sufficiently explain the decisions of judges and that psychological, political, and social factors influence judicial rulings. We test the common caricature of realism that justice is "what the judge ate for breakfast" in sequential parole decisions made by experienced judges. We record the judges' two daily food breaks, which result in segmenting the deliberations of the day into three distinct "decision sessions." We find that the percentage of favorable rulings drops gradually from ≈ 65% to nearly zero within each decision session and returns abruptly to ≈ 65% after a break. Our findings suggest that judicial rulings can be swayed by extraneous variables that should have no bearing on legal decisions.
Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing
impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research
problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising
modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard
procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be
identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models
in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less
complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-model-based offline handwriting recognition system
and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough
review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic
offline handwriting recognition.
Leading economists revisit a provocative essay by John Maynard Keynes, debating Keynes's vision of growth, inequality, work, leisure, entrepreneurship, consumerism, and the search for happiness in the twenty-first century.
In 1931 distinguished economist John Maynard Keynes published a short essay, “Economic Possibilities for Our Grandchildren,” in his collection Essays in Persuasion. In the essay, he expressed optimism for the economic future despite the doldrums of the post-World War I years and the onset of the Great Depression. Keynes imagined that by 2030 the standard of living would be dramatically higher; people, liberated from want (and without the desire to consume for the sake of consumption), would work no more than fifteen hours a week, devoting the rest of their time to leisure and culture. In Revisiting Keynes, leading contemporary economists consider what Keynes got right in his essay—the rise in the standard of living, for example—and what he got wrong—such as a shortened work week and consumer satiation. In so doing, they raise challenging questions about the world economy and contemporary lifestyles in the twenty-first century.
The contributors—among them, four Nobel laureates in economics—point out that although Keynes correctly predicted economic growth, he neglected the problems of distribution and inequality. Keynes overestimated the desire of people to stop working and underestimated the pleasures and rewards of work—perhaps basing his idea of “economic bliss” on the life of the English gentleman or the ideals of his Bloomsbury group friends. In Revisiting Keynes, Keynes's short essay—usually seen as a minor divertissement compared to his other more influential works—becomes the catalyst for a lively debate among some of today's top economists about economic growth, inequality, wealth, work, leisure, culture, and consumerism.
ContributorsWilliam J. Baumol, Leonardo Becchetti, Gary S. Becker, Michele Boldrin, Jean-Paul Fitoussi, Robert H. Frank, Richard B. Freeman, Benjamin M. Friedman, Axel Leijonhufvud, David K. Levine, Lee E. Ohanian, Edmund S. Phelps, Luis Rayo, Robert Solow, Joseph E. Stiglitz, Fabrizio Zilibotti
A great deal of attention has lately been given to addressing software bugs such as errors in operating system drivers or security bugs. However, there are many other lesser known errors specific to individual applications or APIs and these violations of application-specific coding rules are responsible for a multitude of errors. In this paper we propose DynaMine, a tool that analyzes source code check-ins to find highly correlated method calls as well as common bug fixes in order to automatically discover application-specific coding patterns. Potential patterns discovered through mining are passed to a dynamic analysis tool for validation; finally, the results of dynamic analysis are presented to the user.The combination of revision history mining and dynamic analysis techniques leveraged in DynaMine proves effective for both discovering new application-specific patterns and for finding errors when applied to very large applications with many man-years of development and debugging effort behind them. We have analyzed Eclipse and jEdit, two widely-used, mature, highly extensible applications consisting of more than 3,600,000 lines of code combined. By mining revision histories, we have discovered 56 previously unknown, highly application-specific patterns. Out of these, 21 were dynamically confirmed as very likely valid patterns and a total of 263 pattern violations were found.
When five television studios became entangled in a Justice Department antitrust lawsuit against CBS, the cost was immense. As part of the obscure task of "discovery" — providing documents relevant to a lawsuit — the studios examined six million documents at a cost of more than 100,000. Some programs go beyond just finding documents with relevant terms at computer speeds. They can extract relevant concepts — like documents relevant to social protest in the Middle East — even in the absence of specific terms, and deduce patterns of behavior that would have eluded lawyers examining millions of documents. "From a legal staffing viewpoint, it means that a lot of people who used to be allocated to conduct document review are no longer able to be billed out," said Bill Herr, who as a lawyer at a major chemical company used to muster auditoriums of lawyers to read documents for weeks on end. "People get bored, people get headaches. Computers don't." Computers are getting better at mimicking human reasoning — as viewers of "Jeopardy!" found out when they saw Watson beat its human opponents — and they are claiming work once done by people in high-paying professions. The number of computer chip designers, for example, has largely stagnated because powerful software programs replace the work once done by legions of logic designers and draftsmen. Software is also making its way into tasks that were the exclusive province of human decision makers, like loan and mortgage officers and tax accountants. These new forms of automation have renewed the debate over the economic consequences of technological progress.
This paper addresses the question of how much a previously obtained map of a road environment should be trusted for vehicle localisation during autonomous driving by assessing the probability that roadworks are being traversed. We compare two formulations of a roadwork prior: one based on Gaussian Process (GP) classification and the other on a more conventional Hidden Markov Model (HMM) in order to model correlations between nearby parts of a vehicle trajectory. Importantly, our formulation allows this prior to be updated efficiently and repeatedly to gain an ever more accurate model of the environment over time. In the absence of, or in addition to, any in-situ observations, information from dedicated web resources can readily be incorporated into the framework. We evaluate our model using real data from an autonomous car and show that although the GP and HMM are roughly commensurate in terms of mapping roadworks, the GP provides a more powerful representation and lower prediction error.
We assess the extent to which manufacturing decline and housing booms contributed to changes in U.S. non-employment during the 2000s. Using a local labor market design, we estimate that manufacturing decline significantly increased non-employment during 2000-2007, while local housing booms decreased non-employment by roughly the same magnitude. The effects of manufacturing decline persist through 2011, but we find no persistent non-employment effects of local housing booms, most plausibly because housing booms were associated with subsequent busts of similar magnitude. We also find that housing booms significantly reduce the likelihood that displaced manufacturing workers remain non-employed, suggesting that housing booms "mask" non-employment growth that would have otherwise occurred earlier in the absence of the booms. Applying our estimates to the national labor market, we find that housing booms reduced non-employment growth by roughly 30 percent during 2000-2007 and that roughly 40 percent of the aggregate increase in non-employment during 2000-2011 can be attributed to manufacturing decline. Collectively, our results suggest that much of the non-employment growth during the 2000s can be attributed to manufacturing decline and these effects would have appeared in aggregate statistics earlier had it not been for the large, temporary increases in housing demand.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
This paper examines shifts over time in the relative demand for skilled labor in the United States. Although de-skilling in the conventional sense did occur overall in nineteenth century manufacturing, a more nuanced picture is that occupations “hollowed out”: the share of “middle-skill” jobs – artisans – declined while those of “high-skill” – white collar, non-production workers – and “low-skill” – operatives and laborers increased. De-skilling did not occur in the aggregate economy; rather, the aggregate shares of low skill jobs decreased, middle skill jobs remained steady, and high skill jobs expanded from 1850 to the early twentieth century. The pattern of monotonic skill upgrading continued through much of the twentieth century until the recent “polarization” of labor demand since the late 1980s. New archival evidence on wages suggests that the demand for high skill (white collar) workers grew more rapidly than the supply starting well before the Civil War.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
In the American South, post-bellum economic stagnation has been partially attributed to white landowners' access to low-wage black labor; indeed, Southern economic convergence from 1940 to 1970 was associated with substantial black out-migration. This paper examines the impact of the Great Mississippi Flood of 1927 on agricultural development. Flooded counties experienced an immediate and persistent out-migration of black population. Over time, landowners in flooded counties dramatically mechanized and modernized agricultural production relative to landowners in nearby similar non-flooded counties. Landowners resisted black out-migration, however, benefiting from the status quo system of labor-intensive agricultural production.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
This paper is about long-term navigation in environments whose appearance changes over time - suddenly or gradually. We describe, implement and validate an approach which allows us to incrementally learn a model whose complexity varies naturally in accordance with variation of scene appearance. It allows us to leverage the state of the art in pose estimation to build over many runs, a world model of sufficient richness to allow simple localisation despite a large variation in conditions. As our robot repeatedly traverses its workspace, it accumulates distinct visual experiences that in concert, implicitly represent the scene variation - each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running an independent vision based pose estimation system. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical time varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor throughout, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a three month period at different times of day, in different weather and lighting conditions. In all, we process over 136,000 frames captured from 37km of driving.
Job polarization refers to the recent disappearance of employment in occupations in the middle of the skill distribution. Jobless recoveries refers to the slow rebound in aggregate employment following recent recessions, despite recoveries in aggregate output. We show how these two phenomena are related. First, job polarization is not a gradual process; essentially all of the job loss in middle-skill occupations occurs in economic downturns. Second, jobless recoveries in the aggregate are accounted for by jobless recoveries in the middle-skill occupations that are disappearing.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
With the rapid advancement of technology, robots could now be easily equipped with the state of art computing, electronics and communications technologies, with the potential to act as intelligent companions and with important applications in the education, entertainment, games and healthcare industries. The integration of all these technologies is the first step to realizing social robots - robots which will walk amongst humans, playing out their assigned roles whilst interacting with humans in a humanistic way. Social robots can have definite roles and tasks, such as educators, cleaners or guides, with a profound impact on human daily life. This advanced class of robots, just as we humans do, is made up of a complex array of intercon- nected modules - electronics (inner physical workings), sensing, planning and cognition, and finally, intelligence, interaction and communications. This paper provides an overview of each of these individual aspects, and how advanced technology in these areas can be integrated to form a social robot that can meld seamlessly into the human society. Index Terms— Social Robots, Machine Intelligence, Human- Robot Interaction, Ubiquitous Robotics I. INTRODUCTION
This paper presents a new picture of the labor market effects of technological change in pre-WWII United States. I show that, similar to the recent computerization episode, the electrification of the manufacturing sector led to a "hollowing out" of the skill distribution whereby workers in the middle of the distribution lost out to those at the extremes. To conduct this analysis, a new dataset detailing the task composition of occupations in the United States for the period 1880-1940 was constructed using information about the task content of over 4,000 occupations from the Dictionary of Occupational Titles (1949). This unique data was used to measure the skill content of electrification in U.S. manufacturing. OLS estimates show that electrification increased the demand for clerical, numerical, planning and people skills relative to manual skills while simultaneously reducing relative demand for the dexterity-intensive jobs which comprised the middle of the skill distribution. Thus, early twentieth century technological change was unskill-biased for blue collar tasks but skill-biased on aggregate. These results are in line with the downward trend in wage differentials within U.S. manufacturing up to 1950. To overcome any threat to the exogeneity of the electricity measure, due for example to endogenous technological change, 2 instrumental variable strategies were developed. The first uses cross-state differences in the timing of adoption of state-level utility regulation while the second exploits differences in state-level geography that encouraged the development of hydro-power generation and thus made electricity cheaper. The results from these regressions support the main conclusions of the paper.
Despite its importance, choosing the structural form of the kernel in
nonparametric regression remains a black art. We define a space of kernel
structures which are built compositionally by adding and multiplying a small
number of base kernels. We present a method for searching over this space of
structures which mirrors the scientific discovery process. The learned
structures can often decompose functions into interpretable components and
enable long-range extrapolation on time-series datasets. Our structure search
method outperforms many widely used kernels and kernel combination methods on a
variety of prediction tasks.
This paper presents a new deterministic approximation technique in Bayesian
networks. This method, "Expectation Propagation", unifies two previous
techniques: assumed-density filtering, an extension of the Kalman filter, and
loopy belief propagation, an extension of belief propagation in Bayesian
networks. All three algorithms try to recover an approximate distribution which
is close in KL divergence to the true distribution. Loopy belief propagation,
because it propagates exact belief states, is useful for a limited class of
belief networks, such as those which are purely discrete. Expectation
Propagation approximates the belief states by only retaining certain
expectations, such as mean and variance, and iterates until these expectations
are consistent throughout the network. This makes it applicable to hybrid
networks with discrete and continuous nodes. Expectation Propagation also
extends belief propagation in the opposite direction - it can propagate richer
belief states that incorporate correlations between nodes. Experiments with
Gaussian mixture models show Expectation Propagation to be convincingly better
than methods with similar computational cost: Laplace's method, variational
Bayes, and Monte Carlo. Expectation Propagation also provides an efficient
algorithm for training Bayes point machine classifiers.
Purpose
The purpose of this paper is to show how mobile robots are addressing a variety of hospital logistic needs.
Design/methodology/approach
The paper includes in‐depth interviews with developers of the Aethon hospital mobile robot logistics system.
Findings
Robotics can greatly improve hospital logistic services such as moving food, lab samples, prescriptions and even add a bit of entertainment in the process.
Practical implications
Hospital administrators have new answers to the old challenges of moving items in a timely and cost‐effective manner around their facility.
Originality/value
Hospitals are first implementers of mobile robotics to look for answers to their logistic problems. They do not have to break new ground for themselves.
This is an invited introduction to the first issue of the IEEE Transactions on Affective Computing, telling personal stories and sharing viewpoints of a pioneer and visionary of the field of Affective Computing. This article is not intended to be a thorough or a historical account of the development of the field, for the author is not a historian and cannot begin to properly credit the extraordinary efforts of hundreds of people who helped to cultivate and expand the rich and fertile landscape that extends before us now.
A simple exogenous growth model gives conservative estimates of the economic implica-tions of machine intelligence. Machines complement human labor when they become more productive at the jobs they perform, but machines also substitute for human labor by taking over human jobs. At first, expensive hardware and software does only the few jobs where computers have the strongest advantage over humans. Eventually, computers do most jobs. At first, complementary effects dominate, and human wages rise with computer productiv-ity. But eventually substitution can dominate, making wages fall as fast as computer prices now do. An intelligence population explosion makes per-intelligence consumption fall this fast, while economic growth rates rise by an order of magnitude or more. These results are robust to automating incrementally, and to distinguishing hardware, software, and human capital from other forms of capital.
This paper reports on a household survey specially designed to measure what we call the “offshorability” of jobs, defined as the ability to perform the work duties from abroad. We develop multiple measures of offshorability, using both self-reporting and professional coders. All the measures find that roughly 25% of U.S. jobs are offshorable. Our three preferred measures agree between 70% and 80% of the time. Furthermore, professional coders appear to provide the most accurate assessments, which is good news because the Census Bureau could collect data on offshorability without adding a single question to the CPS. Empirically, more educated workers appear to hold somewhat more offshorable jobs, and offshorability does not have systematic effects on either wages or the probability of layoff. Perhaps most surprisingly, routine work is no more offshorable than other work.
The purpose of this chapter is not to examine the present or the near future, but to dispel short-sighted views and take a look into the future. From the earliest times of which we have record down to the beginning of the eighteenth century, there was no very great change in the standard of living of the average man living in the civilized centers of the earth. This slow rate of progress, or lack thereof, can be attributed to two things—the remarkable absence of important technical improvements and the failure of capital to accumulate. It is argued here that the modern age began with the accumulation of capital, which began in the sixteenth century. This was initially due to the rise of prices, and the profits to which that led, which resulted from the treasure of gold and silver which Spain brought from the New World into the Old.
This article argues that medieval craft guilds emerged in order to provide transferable skills through apprenticeship. They prospered for more than half a millennium because they sustained interregional specialized labor markets and contributed to technological invention by stimulating technical diffusion through migrant labor and by providing inventors with temporary monopoly rents. They played a leading role in preindustrial manufacture because their main competitor, rural putting out, was a net consumer rather than producer of technological innovation. They finally disappeared not through adaptive failure but because national states abolished them by decree.
New estimates of nominal earnings and the cost of living are presented and used to make a fresh assessment of changes in the real earnings of male and female manual workers in Britain from 1770 to 1870. Workers' average real earnings are then adjusted for factors such as unemployment, the number of their dependants, and the costs of urbanization. The main finding is that the standard of living of the average working-class family improved by less than 15 percent between the 1780s and 1850s. This long plateau is shown to be consistent with other economic, political, and demographic indicators.
The development of new machinery in nineteenth-century American canning followed two paths. Automative, labor-saving devices were developed to replace labor in unskilled tasks while deskilling, human-capital-saving machinery was designed to make craft labor more replaceable. Cannery operators appear to have focused on deskilling machinery as the key to greater managerial control over production. Craft workers through organizational power and pressing for higher wages seem to have stimulated the early and sustained search for deskilling machinery. Because human-capital-saving machinery allowed wage cuts, they could be adopted prior to their being used as labor-saving devices.
This paper shows the employment structure of 16 European countries has been polarizing in recent years with the employment shares of managers, professionals and low-paid personal services workers increasing at the expense of the employment shares of middling manufacturing and routine office workers. To explain this job polarization, the paper develops and estimates a simple model to capture the effects of technology, globalization, institutions and product demand effects on the demand for different occupations. The results suggest that the routinization hypothesis of Autor, Levy and Murnane (2003) is the single most important factor behind the observed shifts in employment structure. We find some evidence for offshoring to explain job polarization although its impact is much smaller. We also find that shifts in product demand are acting to attenuate the polarizing impact of routinization and that differences or changes in wage-setting institutions play little role in explaining job polarization in Europe.
It was 1997-eons ago, in internet years¿and the Web was only beginning to take off. People used dial-up modems to get online, and Netscape Navigator was the browser of choice. Google was still a research project of two Stanford students, and Facebook-well, Mark Zuckerberg was a 13-year-old having his Star Wars-themed bar mitzvah. Flash forward to 2011. The Web has since reinvented itself time and again: when businesses embraced it in the late 1990s, when Google dominated search in the early 2000s, when user-generated content became prominent in the mid-2000s. Today the Web is going through another reinvention, morphing into a place where our social interactions are ever more important. And the main force behind this phenomenon is, of course, Facebook, led by Zuckerberg, now a 27-year-old billionaire.
The paper reviews the macroeconomic data describing the British economy from 1760 to 1913 and shows that it passed through a two stage evolution of inequality. In the first half of the 19th century, the real wage stagnated while output per worker expanded. The profit rate doubled and the share of profits in national income expanded at the expense of labour and land. After the middle of the 19th century, real wages began to grow in line with productivity, and the profit rate and factor shares stabilized. An integrated model of growth and distribution is developed to explain these trends. The model includes an aggregate production function that explains the distribution of income, while a savings function in which savings depended on property income governs accumulation. Simulations with the model show that technical progress was the prime mover behind the industrial revolution. Capital accumulation was a necessary complement. The surge in inequality was intrinsic to the growth process: technical change increased the demand for capital and raised the profit rate and capital’s share. The rise in profits, in turn, sustained the industrial revolution by financing the necessary capital accumulation. After the middle of the 19th century, accumulation had caught up with the requirements of technology and wages rose in line with productivity.
A great deal of attention has lately been given to addressing software bugs such as errors in operating system drivers or security bugs. However, there are many other lesser known errors specific to individual applications or APIs and these violations of application-specific coding rules are responsible for a multitude of errors. In this paper we propose DynaMine, a tool that analyzes source code check-ins to find highly correlated method calls as well as common bug fixes in order to automatically discover application-specific coding patterns. Potential patterns discovered through mining are passed to a dynamic analysis tool for validation; finally, the results of dynamic analysis are presented to the user.The combination of revision history mining and dynamic analysis techniques leveraged in DynaMine proves effective for both discovering new application-specific patterns and for finding errors when applied to very large applications with many man-years of development and debugging effort behind them. We have analyzed Eclipse and jEdit, two widely-used, mature, highly extensible applications consisting of more than 3,600,000 lines of code combined. By mining revision histories, we have discovered 56 previously unknown, highly application-specific patterns. Out of these, 21 were dynamically confirmed as very likely valid patterns and a total of 263 pattern violations were found.
A new approach has been developed that encourages developers to avoid premature commitment to certain design choices and actively develop promising alternatives for parts of the design. This approach is called Programming by Optimization (PbO) where developers specify a potentially large design space of programs that accomplish a given task from which versions of the program optimized for various use contexts are generated automatically, including parallel versions derived from the same sequential sources. A simple, generic programming language is described that supports the specification of such design spaces along with ways in which specific programs that perform well in a given use context can be obtained from these specifications through relatively simple sourcecode transformations and powerful design-optimization methods.