“Aging-and-Tech Job Vulnerability”: A proposed framework on the dual impact of aging and AI, robotics, and automation among older workers

2021 ◽  
Vol 11 (2) ◽  
pp. 175-201
Author(s):  
Carlos-María Alcover ◽  
Dina Guglielmi ◽  
Marco Depolo ◽  
Greta Mazzetti
Keyword(s):  
Artificial Intelligence ◽  
Labor Market ◽  
Older Workers ◽  
General Model ◽  
Job Insecurity ◽  
Work Settings ◽  
Emergent Phenomenon ◽  
Vulnerability Model ◽  
Practical Implications ◽  
Conceptual Advance

As the aging population and workforce constitute a worldwide concern, it is becoming necessary to predict how the dual threat of aging and technology at work increases the job vulnerability of older workers and jeopardizes their employability and permanence in the labor market. The objective of this paper is twofold: (1) to analyze perceptions of artificial intelligence, robotics, and automation in work settings and the expected impact of these technologies on older workers to contextualize this emergent phenomenon; and (2) to propose a general model related to “Aging-and-Tech Job Vulnerability” to explain and predict the combined effect of aging and AI/robotics/automation on job insecurity and additional outcomes among older workers. The propositions of the Age-and-Tech Job Vulnerability model developed in this paper seek to present a first approach for the conceptual advance and research on this emerging phenomenon and entails several theoretical and practical implications for organizational psychology.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

The perception of humanness in conversational journalism: An algorithmic information-processing perspective

2021 ◽  
pp. 146144482199380
Author(s):  
Donghee Shin
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Cognitive Model ◽  
Human Beings ◽  
User Perception ◽  
User Experiences ◽  
Step Flow ◽  
Algorithmic Information ◽  
Information Processing Perspective ◽  
Practical Implications

How much do anthropomorphisms influence the perception of users about whether they are conversing with a human or an algorithm in a chatbot environment? We develop a cognitive model using the constructs of anthropomorphism and explainability to explain user experiences with conversational journalism (CJ) in the context of chatbot news. We examine how users perceive anthropomorphic and explanatory cues, and how these stimuli influence user perception of and attitudes toward CJ. Anthropomorphic explanations of why and how certain items are recommended afford users a sense of humanness, which then affects trust and emotional assurance. Perceived humanness triggers a two-step flow of interaction by defining the baseline to make a judgment about the qualities of CJ and by affording the capacity to interact with chatbots concerning their intention to interact with chatbots. We develop practical implications relevant to chatbots and ascertain the significance of humanness as a social cue in CJ. We offer a theoretical lens through which to characterize humanness as a key mechanism of human–artificial intelligence (AI) interaction, of which the eventual goal is humans perceive AI as human beings. Our results help to better understand human–chatbot interaction in CJ by illustrating how humans interact with chatbots and explaining why humans accept the way of CJ.

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

To Report or Not to Report: Nurses' Attitudes toward Reporting Co-Workers Suspected of Substance Abuse

1995 ◽  
Vol 25 (2) ◽  
pp. 313-339 ◽  
Author(s):  
Jane C. Hood ◽  
Patsy L. Duphorne
Keyword(s):  
New Mexico ◽  
Diffusion Model ◽  
Substance Abusing ◽  
Social Forces ◽  
Occupational Culture ◽  
Diversion Program ◽  
Work Settings ◽  
Vulnerability Model ◽  
Logistic Regressions ◽  
Program Knowledge

Using survey data from a stratified, random sample of New Mexico nurses ( N=498), this study helps to explain why some nurses choose informal rather than formal reporting strategies when confronted with substance-abusing co-workers. The researchers mailed a questionnaire to male and female RNs and LPNs in 1989, two years after New Mexico established its diversion program for substance-abusing nurses. Using a combination of OLS and logistic regressions, the authors test the diffusion model predicting that program knowledge leads to program acceptance and implementation. Finding little evidence of a link between program knowledge and implementation, the authors then offer two alternatives. The vulnerability model predicts that workers in the least secure positions will be most likely to avoid making formal reports, whereas the occupational hegemony model argues that administrators will avoid formal reporting to maintain control over their own work settings. Because their results offer most support for the latter two models, the authors reject the diffusion model and conclude that occupational culture and organizational politics are the most important social forces intervening between program diffusion and implementation.

EMPLOYMENT OF OLDER PEOPLE IN THE MODERN LABOR MARKET

2021 ◽  
pp. 409-418
Author(s):  
В.Ю. Бабышев ◽  
Г. А. Барышева
Keyword(s):  
Labor Market ◽  
Older People ◽  
Older Workers ◽  
Age Groups ◽  
Labor Legislation ◽  
Demographic Aging ◽  
Age Cohort ◽  
Working Age ◽  
Medical Advances ◽  
The Impact

В статье рассматривается занятость лиц пожилого возраста в условиях сорвеменных технологических, медицинских и демографических изменений. Актуальность темы исследования обусловлена демографическим старением населения, современными достижениями медицины и изменением характера трудовых операций в результате научно-технического прогресса. В данной статье проверены следующие конкурирующие гипотезы: производительность работников старших возрастных групп находится ниже уровня рентабельности из-за неуклонного ухудшения здоровья или, наоборот, ценность работников старших возрастных групп на современном рынке труда возрастает из-за повышения роли опыта, навыков и квалификации. Дополнительно проанализирован вопрос влияния систем пенсионного обеспечения на мотивацию лиц пожилого возраста к продолжению трудовой деятельности. Для проверки данных гипотез на основе статистики Организации экономического сотрудничества и развития проведен анализ общего уровня занятости, участия в рабочей силе и безработицы, а также временной и неполной занятости по нескольким возрастным группам в диапазоне 15-65 лет и старше. Для оценки динамики проанализирована ситуация на 2000 и 2019 гг. В целом сделан вывод, что количественные и качественные параметры занятости у работников старших возрастных групп уступают среднему рабочему возрасту, однако во временной динамике использование человеческого капитала лиц пожилого возраста растет. В области геронтологии рекомендовано уделять повышенное внимание улучшению здоровья возрастной когорты 65 лет и старше и изменению трудового законодательства в плане стимулирования продолжения трудовой деятельности после официального наступления пенсионного возраста. The article examines the employment of older people in the context of disrupted technological, medical and demographic changes. The relevance of the research topic is due to the demographic aging of the population, modern medical advances and changes in the nature of labor operations as a result of scientific and technological progress. In this article, the following competing hypotheses are tested: the productivity of older workers is below the level of profitability due to a steady decline in health, or vice versa, the value of older workers in the modern labor market is increasing due to the increasing role of experience, skills and qualifications. Additionally, the question of the impact of pension systems on the motivation of older people to continue working is analyzed. To test these hypotheses, based on OECD statistics, we analyzed the overall level of employment, labor force participation and unemployment, as well as temporary and underemployment for several age groups in the range of 15-65+ years. To assess the dynamics, the situation for 2000 and 2019 was analyzed. In general, the author concludes that the quantitative and qualitative parameters of employment of older people are inferior to the average working age, but over time, the use of the human capital of older people is growing. In the field of gerontology, it is recommended to pay increased attention to improving the health of the 65+ age cohort and changing labor legislation in order to stimulate the continuation of work.

Who Gets Their First Job at a Large Firm? The Distinct Roles of Education and Skills

2021 ◽  
Vol 111 ◽  
pp. 465-469
Author(s):  
Jaime Arellano-Bover
Keyword(s):  
Labor Market ◽  
Older Workers ◽  
Cognitive Skills ◽  
Large Firm ◽  
Early Years ◽  
Young Workers ◽  
Large Firms ◽  
Formative Time ◽  
East And Southeast Asia ◽  
First Job

Young workers' early years in the labor market are a key and formative time. Using data from 31 countries, this article documents the selection of labor market entrants into large firms, which existing literature associates with propitious environments for young workers. The young and inexperienced are underrepresented at large firms compared to experienced and older workers. Entrants who do get their first job at large firms are positively selected in terms of education and cognitive skills. The patterns of large-firm selection (i.e., importance of education vs. skills) somewhat differ between Europe, East and Southeast Asia, and North America.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

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