Non-routine Tasks Research Articles

A configurational view on technology acceptance: the example of highly integrated collaboration platforms

Highly integrated software environments for various routine and non-routine tasks promise productivity gains for organizations. To fulfill this promise, users need to be willing to employ the new technology. A combined perspective of sufficient and necessary conditions in the form of fuzzy-set qualitative comparative analysis (fsQCA) and, necessary condition analysis (NCA) is used to examine the technology acceptance of workstream collaboration tools, advancing examinations from a multivariate perspective to a more holistic view. One hundred thirty participants were trained in the software application Slack for three months. Following the training period, configurational analysis using fsQCA and NCA based on a unified theory of acceptance and use of technology (UTAUT) framework was conducted based on 116 qualified questionnaires. Necessity assessment shows that all influence factors exhibit necessity properties, with facilitating conditions and effort expectancy most substantially constraining an individual’s intention to use. Sufficiency evaluation confirms UTAUT’s variable choice and identifies social influence as a key condition that enables intention to use. Segmentation according to gender further reveals that effort expectancy and facilitating conditions are necessary conditions for female users but not for males.

An investigation into the approach of mathematics undergraduates to a non-routine task

ABSTRACT Critical thinking and problem solving are widely recognised as valuable competencies in the twenty-first century, and indispensable skills in the workplace. In this context, we investigate the strategies employed by first-year university mathematics students when faced with a non-routine task, specifically the impossible triangle. The data collection process involved the administration of a questionnaire (70 questionnaire responses) and conducting interviews (six interviews). The study used the lens of framing to examine the interplay between the task cue and students' responses, and the Ref*AER framework to interpret the knowledge of the participants. A considerable number of students approached the task using an algorithmic mathematical frame. With reference to the Ref*AER framework, the participants' inadequate conceptions were a hindrance to the successful completion of the task. We believe that the implementation of tasks like the impossible triangle in the mathematics classroom can effectively foster students' noticing and uncover deficiencies in their knowledge.

Mathematical attitudes transformation when introducing GeoGebra in the secondary classroom

The mathematical-related affect research agenda demands studies on the affect-cognition relationship, as well as interventions aimed at improving affective aspects of mathematical learning. The potential of technological environments for promoting cognitive changes in students has been widely informed and there is evidence of their influence in students´ attitudes towards mathematics, such as interest and enjoyment. Research on the so-called mathematical attitudes, more related to mental habits and closely tied to cognitive processes is much scarcer. In this study, we report the transformation of three such mathematical attitudes in the students: perseverance, precision-rigor, and autonomy, when introducing GeoGebra in two secondary classes. Quantitative and qualitative analyses performed on data from various sources, with the total number of students and with a representative sample, show how it is possible to capitalize on initial positive attitudes towards mathematics with technology to bring about a significant improvement in the three above-mentioned attitudes, deemed as genuine elements of mathematical work. GeoGebra affordances constructivity, navigability and interactivity made it possible for new forms of behaviour to emerge in the classroom. Namely, flexible and fluent perseverance on problem solving; increasing inclination for accuracy and realization of its importance for proper reasoning; and willingness to collaborate with peers, helping each other, as well as sharing insights and ideas, thus, gaining independence from the teacher to face non-routine tasks.

Cost Behavior At The RBS Hotel During The COVID-19 Pandemic

This research explores into the comprehensive examination of resource management strategies employed by hotel managers, with a specific focus on committed resources and their impact on cost behavior. This study aims to determine the cost behavior of The RBS Hotel during the the COVID-19 pandemic. This study employs a qualitative descriptive research design, and utilizing interview and observation methodologies. The findings of the study indicate that managers implemented diverse tactics to facilitate cost changes, which subsequently influenced the cost behavior of hotels. The onset of the COVID-19 pandemic resulted in a shift in the behavior of hotel costs during its initial year. There has been a decrease in both employee salaries and routine maintenance costs. The non-routine maintenance tasks experienced an upward trend during the pandemic. All three exhibit characteristics of semi-fixed cost behavior. The costs associated with room facilities are subject to variation, but with a downward trend. This study makes theoretical contributions to the fields of cost behavior theory and upper echelon theory.
 Keywords: Cost; Cost behaviour; Covid-19; Hotel

The Division of Routine and Non-Routine Housework Among Migrant and Native Couples in Germany

Gender inequality in the division of household chores is a persistent issue over time and across country contexts, while differences within and between native and migrant couples remain largely unclear. Relying on the German country case, this study examines the association between partners’ migration constellations and the division of housework. We differentiate between natives and first-generation migrants, and within first-generation migrants, we differentiate by their regions of origin. For the division of housework, we analyse traditionally female routine tasks and male non-routine tasks. Following gender and resource explanations, this study expects gendered variation in the way native, migrant, and mixed (i.e., native/migrant) couples divide housework. Utilising novel data from the German Family Demography Panel Study (FReDA-GGS sample, 2021), the paper employs OLS regression models to predict the division of housework among 11,223 cohabiting different-sex couples. The study finds a gender-traditional divide in routine household tasks among migrant couples, with heterogeneity across regions of origin. Specifically, women in Asian and Eastern European couples tend to do more routine tasks than their native counterparts, while the division of routine housework in couples from Western countries is more egalitarian. In mixed couples, the division of routine tasks is highly unequal if the male partner is a migrant. Non-routine tasks in mixed couples are, however, mostly performed by the native partner, irrespective of gender. The results suggest that the division of routine housework conforms to traditional gender roles across most migrant groups, while non-routine housework, such as financial tasks that require country-specific knowledge, is influenced by the native status, which serves as a resource in itself. * This article belongs to a special issue on “Family Research and Demographic Analysis – New Insights from the German Family Demography Panel Study (FReDA)”.

Top Achievers in Mathematics in the End of Upper Secondary School

Important questions regarding mathematical giftedness are how and when it is possible to identify. To be identified as gifted, the student must have natural potential but also an appropriate mix of motivation, support, and challenges. This study is based on longitudinal data following students from 3rd grade in primary school to the end of upper secondary school between 2005 and 2015. We focus on top achievers (<2% of age cohort) of the national mathematics final exam at the end of upper secondary school. We investigate how accurately top achievers at the end of secondary school can be identified in 3rd, 6th, and 9th grades using national tests. We identify mathematical tasks that predict future top achievement and analyze how attitudes, gender, and parental background factors relate to high proficiency. Most top achievers had already been identified by 3rd grade and almost all of them by 9th grade. However, recognizing future top achievers was not very accurate, as they were indistinguishable from many students whose performance did not reach the same level over time. The best predictor for future top achievement was a student’s ability to solve non-routine and atypical tasks in early school years.

Digitalization is not gender-neutral

Using unique linked employer-employee data for Germany and a matching approach, we provide novel insights on the individual-level employment effects of digitalization. We show that the first-time introduction of digital technology in an establishment affects women more strongly than men. This holds both in terms of lower days employed and higher days unemployed. We find that employment losses are largest for individuals conducting non-routine tasks, and again it is women who suffer the most. Our insights imply that digitalization is not gender-neutral, suggesting that it is important to avoid a gender bias in technological progress.

Technology and the Task Content of Jobs across the Development Spectrum

Abstract The tasks workers perform on the job are informative about the direction and the impact of technological change. We harmonize occupational task-content measures between two worker-level surveys, which separately cover developing and developed countries. Developing countries use routine-cognitive tasks and routine-manual tasks more intensively than developed countries, but less intensively use non-routine analytical tasks and non-routine interpersonal tasks. This is partly because developing countries have more workers in occupations with high routine content and fewer workers in occupations with high non-routine content. More importantly, a given occupation has more routine content and less non-routine content in developing countries than in developed countries. Since 2006, occupations with high non-routine content gained employment relative to those with high routine content in most countries, regardless of their income level or initial task intensity, indicating the global reaches of the technological change that reduces the demand for occupations with high routine content.

Identifying Application Areas for Machine Learning in the Retail Sector

Machine learning (ML) has the potential to take on a variety of routine and non-routine tasks in brick-and-mortar retail and e-commerce. Many tasks previously executed manually are amenable to computerization using ML. Although procedure models for the introduction of ML across industries exist, the tasks for which ML can be implemented in retail need to be determined. To identify these application areas, we followed a dual approach. First, we conducted a structured literature review of 225 research papers to identify possible ML application areas in retail, as well as develop the structure of a well-established information systems architecture. Second, we triangulated these preliminary application areas with the analysis of eight expert interviews. In total, we identified 21 application areas for ML in online and offline retail; these application areas mainly address decision-oriented and economic-operative tasks. We organized the application areas in a framework for practitioners and researchers to determine appropriate ML use in retail. As our interviewees provided information at the process level, we also explored the application of ML in two exemplary retail processes. Our analysis further reveals that, while ML applications in offline retail focus on the retail articles, in e-commerce the customer is central to the application areas of ML.

Task Content and Job Losses in the Great Lockdown

The author examines the short-term labor market effects of the COVID-19 pandemic (aka the Great Lockdown recession) in the United States. Findings show that the task content is an important predictor of job losses; jobs with a high non-routine cognitive content are especially well-protected, even if they are not teleworkable. Teleworkability matters more for low-paying jobs. The importance of task content, particularly for non-routine cognitive tasks, is strong even after controlling for demographics and for differential sector and location shocks, while effects persist in the medium run. Jobs subject to higher structural turnover rates were much more likely to be terminated, suggesting that easier-to-replace employees were at a particular disadvantage, even within sectors. At the same time, labor hoarding may occur for more valuable matches. Individuals in low-skilled jobs fared comparatively better in industries with a high share of high-skilled workers, suggesting complementarities across skill types.

From “prepare for the unknown” to “train for what's coming”: A digital twin-driven and cognitive training approach for the workforce of the future in smart factories

Despite on-the-job assistance technology is getting popular in the Smart Operator 4.0 literature, non-routine work also requires highly-skilled and pre-trained workers to prevent serious errors and keep high efficiency and safety levels. The question that motivates this study is: ‘is it possible to train industrial workers for what exactly is coming instead of preparing them for a large set of scenarios - even very unlikely ones?’. This work proposes: (i) a structured On-the-Job Training strategy for non-routine tasks, called ‘training-on-the-go’ or ‘prescriptive training’, according to which a Prescriptive Analytics module schedules the training sessions not long before the actual performance, but only when and if needed; (ii) a proof-of-concept of a game-based training system where virtual scenes and context of an industrial site are faithfully recreated thanks to digital twin data and models; (iii) the use of evolutionary-based fuzzy cognitive maps (E-FCM) for the extraction of the workers’ implicit procedural knowledge and for the comparison of mental models of experienced vs. inexperienced workers to assess potential misconceptions or flaws in their decision-making process. This work contributes to the evolution of worker training paradigms and systems from the perspective of human-centric cyber-physical production systems and aims for current gaps in workforce training, i.e. poor timeliness and effectiveness, limited context and industrial information integration, and scarce focus on the experts’ implicit knowledge. An application study with a non-routine task on an offshore oil platform demonstrates how the proposed system facilitates knowledge transfer, offers situational awareness and sustains the workforce competence development process.

Inflation, technological-knowledge bias, and wages

A detailed examination of wage data points to a wage polarization trend vis-à-vis the distribution of qualifications. Theoretically terms, this points to the need for modeling focused on the relevance of the direction of technological knowledge. To this end, we branched production into routine and non-routine tasks. In this way, the results produced positive relationships between the relative supply of skilled workers and the skill premium and between automation and wage polarization.

Elementary School Students’ Ways of Thinking in Geometry Through the Lens of Geometric Habits of Mind

Geometric habits of mind include several ways of thinking that can be used in the learning and teaching of geometry as well as the reasoning ability in the process of solving the geometry problem. Based on this framework, revealing students' geometric reasoning process can shed light to researchers and teachers in the selection and design of methods and materials. This study is aimed to investigate the geometric problem-solving processes of elementary school students based on the GHoM. In this qualitative study case study design was adopted. The participants of the study consisted of six elementary school students. In the interviews the students can easily use reasoning with the relationship GHoM in all tasks. Generalizing and investigating invariants habits can be seen in some of the tasks. However balancing exploration and reflection habits were appeared rare when compared to other GHoMs. It can be suggested that to reveal the ways of reasoning of the students, non-routine and open-ended geometry tasks similar to those designed for this research can be utilized in mathematics classes.

A Proposal for Research on the Application of AI/ML in ITPM

According to the market research firm Tractica, the global artificial intelligence software market is forecast to grow to 126 billion by 2025. Additionally, the Gartner group predicts that during the same time as much as 80% of the routine work , which represents the bulk of human hours spent in today's project management (PM) activities, can be eliminated because of collaboration between humans and smart machines. Today's PM practices rely heavily on human input. However, that is not the optimum use of the human project manager's intuitive, innovative, and creative abilities. Many aspects of a project manager's work could be managed by machines that utilize AI/ML approaches to address nonroutine and predictive tasks. This paper describes IT project management (ITPM) processes and associated tasks and identifies the AI/ML approaches that can support them.

FDI and onshore job stability: Upgrades, downgrades, and separations in multinationals

We use linked employer–employee data to estimate the impact of firms’ foreign direct investment (FDI) into a low-wage country on workers’ job stability in a high-wage country. We are the first to consider internal (i.e., within-firm) job transitions. Specifically, we examine the impact of German firms’ FDI into the Czech Republic on the likelihood of onshore employees up- or downgrading to occupations that are more or less intensive in analytical and interactive tasks. To do so, we match firms with similar investing probabilities. We use this sample to estimate proportional hazards models to retrieve the dynamic effects on workers. We find that FDI increases the average likelihood of upgrades and downgrades by 17% and 19%, respectively. These effects are the strongest for jobs with low shares of nonroutine and interactive tasks, and they increase over time. They become substantial two years after the investment and reach 32%–46% and 35%–48%, respectively. FDI does not increase the hazard of worker–firm separations. Our results highlight the importance of internal firm restructuring, which enables firms to satisfy their altered domestic labor needs after FDI.

The effects of automation and lobbying in wage inequality: a directed technical change model with routine and non-routine tasks

The effects of automation and lobbying in wage inequality: a directed technical change model with routine and non-routine tasks

Technological change and labour market trends in Latin America and the Caribbean: a task content approach

The aim of this paper is to analyse employment profile trends in the Latin American and Caribbean countries according to the task content of workers’ jobs. This analysis seeks to approximate the impact of technological change on the labour market. The paper uses the definitions of an indicator that captures the relative importance of four types of tasks, namely cognitive versus manual and routine versus non-routine tasks, based on information from the Occupational Information Network (O*NET) and household surveys. The analysis finds that there has been growing demand for workers over the last two decades in occupations which are intensive in cognitive abilities, with higher remuneration than for occupations which are intensive in manual tasks. Cognitive skills are therefore a key variable for improving participation in current and future labour markets.

Using extended reality (XR) for medical training and real-time clinical support during deep space missions

Medical events can affect space crew health and compromise the success of deep space missions. To successfully manage such events, crew members must be sufficiently prepared to manage certain medical conditions for which they are not technically trained. Extended Reality (XR) can provide an immersive, realistic user experience that, when integrated with augmented clinical tools (ACT), can improve training outcomes and provide real-time guidance during non-routine tasks, diagnostic, and therapeutic procedures. The goal of this study was to develop a framework to guide XR platform development using astronaut medical training and guidance as the domain for illustration. We conducted a mixed-methods study—using video conference meetings (45 subject-matter experts), Delphi panel surveys, and a web-based card sorting application—to develop a standard taxonomy of essential XR capabilities. We augmented this by identifying additional models and taxonomies from related fields. Together, this “taxonomy of taxonomies,” and the essential XR capabilities identified, serve as an initial framework to structure the development of XR-based medical training and guidance for use during deep space exploration missions. We provide a schematic approach, illustrated with a use case, for how this framework and materials generated through this study might be employed.

Gender Similarities in the Mathematical Performance of Early School-Age Children

The role of gender in mathematical abilities has caught the interest of researchers for several decades; however, their findings are not conclusive yet. Recently the need to explore its influence on the development of some foundational mathematic skills has been highlighted. Thus, the current study examined whether gender differentially affects young children’s performance in several basic numeracy skills, using a complex developmentally appropriate assessment that included not only standard curriculum-based measures, but also a non-routine task which required abstract thinking. Further, 136 children (68 girls) aged 6 to 8 years old completed: (a) the third edition of the standardized Test of Early Mathematical Ability (TEMA-3) to measure their mathematical knowledge; (b) the Kaufman Brief Intelligence Test (K-BIT), and (c) a non-routine counting detection task where children watched several characters performing different counts, had to judge their correctness, and justify their answers. Furthermore, frequentist and Bayesian analyses were combined to quantify the evidence of the null (gender similarities) and the alternative (gender differences) hypothesis. The overall results indicated the irrelevance or non-existence of gender differences in most of the measures used, including children’s performance in the non-routine counting task. This would support the gender similarity hypothesis in the basic numerical skills assessed.

Work routines and risks of automation in the Russian labor market

The study explores routine and non-routine content of job tasks across occupations in the Russian labor market. Each occupation contains a bundle of job tasks, which can be routine or non-routine, cognitive or manual. Occupations, in which most job tasks are routine, are under the risk of automation, and therefore their future is of concern. Our empirical analysis uses three main data sources that contain information in the detailed occupational breakdown: on employment, on the extent of routinization, and on annual labor incomes. This data set allows to estimate task indices for disaggregated occupations and different socio-economic groups and also present empirical estimates of wage penalties and premiums for workers with different job content. The calculations suggest that the proportion of jobs in which routine tasks tend to prevail is not large and hardly exceeds 10%. From this follows that a massive substitution of labor by machines or AI in foreseeable future does not look like a plausible option. Content of job tasks is expectedly associated with the level of pay: workers with non-routine cognitive tasks are better rewarded, and non-routine manual tasks are the most penalized. A polarization scenario feared by some observers does not seem a likely option in Russia for years to come.