Intelligent automation in hospitality: exploring the relative automatability of frontline food service tasks

PurposeAutomation poses to change how service work is organized. However, there is a lack of understanding of how automation influences specific sectors, including specific hospitality jobs. Addressing this gap, this paper looks at the relative automatability of jobs and tasks which fall within one specific hospitality context: frontline food service.Design/methodology/approachStudy 1 analyzes the UK Office for National Statistics' Standard Occupational Classification (2020) data to determine the degree to which frontline food service jobs consist of tasks requiring mechanical, analytical, intuitive or empathetic intelligence. Study 2 contrasts these findings to current state of intelligent automation technology development through interviews and a focus group with food service technology experts (n = 13).FindingsOf all the tasks listed under food service in the ONS SOC 2020, 58.8% are found to require mechanical, 26.8% analytical, 11.3% intuitive and 3.1% empathetic intelligence. Further, the automatability of these tasks is found to be driven by three streams of technology development in particular: (1) autonomous navigation, (2) object manipulation and (3) natural language processing.Originality/valueHospitality management literature has started to conceptualize a move from mechanical and analytical service tasks to tasks centered around intuition and empathy. While previous studies have adopted a general view to what this might mean for hospitality jobs, this paper develops a novel, task-centric framework for Actioning Intelligent Automation in Frontline Food Service.

Occupational Categorization of female Work Force in Kashmir Himalayas, India

Occupational classification has been a subject of many experiments. From the definition of a worker to their attribution to a particular category of occupation. Multiple attempts have been made in understanding and explaining the same. The present paper attempts to take the case of female workforce and delves into the subject of categorizing their prevalence across the Kashmir valley (geographic unit), which by and large corresponds to Kashmir Division (administrative unit) in a comprehensible manner. The researcher adopts the Standard deviation method to do so. Using the standard deviations observed for each occupational category, the researcher proposes a rank table visualizing the target area employing a standard deviation-based occupational coding. Based on the rank table it is observed that amongst the female workforce the primary sector is dominant in Ganderbal and Kulgam districts, secondary sector in Shopian, Pulwama and Kulgam, tertiary sector in Srinagar and Budgam and the Quaternary/Quinary sectors in Srinagar, Anantnag and Pulwama districts. The standard deviation method adopted in this paper has led to a satisfactory representation of the occupational distribution observed in the sample area. The occupational coding of the target districts using this method not only makes it easier to visualize the occupational trends but also gives us a clear sense of variation (positive and negative) amongst these categories across the districts.

The role of age structure and occupational choices in the Indian labour market

PurposeThis study aims to identify the role of age structure in occupational choices and the classification of the occupations based on the age structure of individuals in the Indian labour market.Design/methodology/approachThis study uses the first Periodic Labour Force Survey, 2017–18. The occupational classifications are based on the standardised scores for age groups and their occupations. Further, a multinomial logistic regression model has been used to estimate social and economic factors in determining the age-based occupational classifications.FindingsThe authors found age structure an essential factor in determining occupational choices. Hence, occupations in the Indian labour market have been grouped into seven categories, accordingly. In addition, social and economic factors of individuals and households do have a significant influence on the selection of age-based occupational classifications.Research limitations/implicationsThe study is limited to the occupational classification based on the age structure of individuals without any industry effects. The findings suggest that policymakers must adopt occupation-specific policies considering the age structure of individuals.Originality/valueEarlier studies are limited to the dynamics of age either on the basis of specific age groups (younger or older) or on the industrial classification in a disaggregated way. They also lack a rich approach in analysing the occupational classification considering age structure, especially in the Indian labour market. The study adds value when the role of age structure is identified in occupational choices in the Indian labour market, and hence, a novel classification of occupations into seven categories is proposed.

Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities

Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles.

Job-Education Match & Tenure Among Young Canadians: Evidence From The 1997 & 2014 Labour Force Survey

In response to the growing supply of postsecondary education graduates and the persistence of overqualification in the Canadian labour market, this study investigates the relationship between the levels of job-education match and tenure among young workers, 25 to 34 years of age, relative to the remaining workforce ages 35 to 64, using a job analysis (JA) approach based on skill levels defined by the National Occupational Classification (NOC) 2011 and education credentials defined by Statistics Canada. Using the 1997 and 2014 Labour Force Survey (LFS) files, a significant negative relationship is observed between length of tenure and overqualified workers, and a significant positive relationship with underqualified workers, in addition to significant differences in the effect that being over/underqualified has on tenure based on respondents’ age and survey year. Implications for individual, organizational, and societal stakeholders involved in the school-to-work transition are discussed.

Job-Education Match & Tenure Among Young Canadians: Evidence From The 1997 & 2014 Labour Force Survey

In response to the growing supply of postsecondary education graduates and the persistence of overqualification in the Canadian labour market, this study investigates the relationship between the levels of job-education match and tenure among young workers, 25 to 34 years of age, relative to the remaining workforce ages 35 to 64, using a job analysis (JA) approach based on skill levels defined by the National Occupational Classification (NOC) 2011 and education credentials defined by Statistics Canada. Using the 1997 and 2014 Labour Force Survey (LFS) files, a significant negative relationship is observed between length of tenure and overqualified workers, and a significant positive relationship with underqualified workers, in addition to significant differences in the effect that being over/underqualified has on tenure based on respondents’ age and survey year. Implications for individual, organizational, and societal stakeholders involved in the school-to-work transition are discussed.

Structural Change in Exports

Trade analysis on the basis of countries’ export baskets can be misleading when production is globally fragmented. The chapter argues for a switch to an analysis of the type of activities that are embodied in exports. The chapter discusses two steps towards this goal. It first discusses the transition in trade studies from product to vertical specialization. A country’s vertical specialization in trade is measured as the share of domestic value added in its gross exports. The chapter identifies three waves of vertical specialization in the world economy since 1970 and documents the servicification of manufacturing exports. Results from cross-country analysis show a robust association between specialization and productivity growth, but not between specialization and employment growth. Next, the chapter considers functional specialization in trade based on the measurement of distinct activities in exports such as fabrication, marketing and R&D, based on an occupational classification of workers. It documents how advanced economies continued to specialize in headquarter activities, while quickly moving out of fabrication activities. It also shows that there are many idiosyncratic determinants of a country’s specialization pattern beyond its general development level. The chapter ends with suggestions for further research, given that the measures of trade in value added and activities presented are still in a development phase.

Occupational Portrait of A Pandemic Workforce: Latin Americans in the Health and the Sales & Services Sectors of Canada

Reflecting on present COVID-19 pandemic times in Canada and using both visible and ethnic ancestry information from the 2016 census, the author produced an occupational portrait of the Latin American workforce of the Health and Sales & Services sectors of the country. The focus was on full-time, full-year workers, aged 25-64, who received employment income in 2015. The workforce in the Health and Sales & Services sectors totaled 5.5 thousand and 24.3 thousand individuals respectively. The occupational portrait, which was developed based on the Canadian 2016 NOC occupational classification system, revealed an active participation of Latino workers in activities enhancing sanitary protection and the economic survival of the Canadian population. Women, and established and recent immigrants as well as those reporting Central American ethnic origins were found among those who most participated in the economic activities of the sectors. The most typical jobs performed by Latin American workers were as nursing aides in the Health sector and janitorial (males) and light or specialized cleaners (women) in the Sales & Services sector. The nature of these jobs made them a high health-risk group and vulnerable one in pandemic times as they entail working in close proximity to other colleagues and the general public.

Occupation and risk of severe COVID-19: prospective cohort study of 120 075 UK Biobank participants

ObjectivesTo investigate severe COVID-19 risk by occupational group.MethodsBaseline UK Biobank data (2006–10) for England were linked to SARS-CoV-2 test results from Public Health England (16 March to 26 July 2020). Included participants were employed or self-employed at baseline, alive and aged <65 years in 2020. Poisson regression models were adjusted sequentially for baseline demographic, socioeconomic, work-related, health, and lifestyle-related risk factors to assess risk ratios (RRs) for testing positive in hospital or death due to COVID-19 by three occupational classification schemes (including Standard Occupation Classification (SOC) 2000).ResultsOf 120 075 participants, 271 had severe COVID-19. Relative to non-essential workers, healthcare workers (RR 7.43, 95% CI 5.52 to 10.00), social and education workers (RR 1.84, 95% CI 1.21 to 2.82) and other essential workers (RR 1.60, 95% CI 1.05 to 2.45) had a higher risk of severe COVID-19. Using more detailed groupings, medical support staff (RR 8.70, 95% CI 4.87 to 15.55), social care (RR 2.46, 95% CI 1.47 to 4.14) and transport workers (RR 2.20, 95% CI 1.21 to 4.00) had the highest risk within the broader groups. Compared with white non-essential workers, non-white non-essential workers had a higher risk (RR 3.27, 95% CI 1.90 to 5.62) and non-white essential workers had the highest risk (RR 8.34, 95% CI 5.17 to 13.47). Using SOC 2000 major groups, associate professional and technical occupations, personal service occupations and plant and machine operatives had a higher risk, compared with managers and senior officials.ConclusionsEssential workers have a higher risk of severe COVID-19. These findings underscore the need for national and organisational policies and practices that protect and support workers with an elevated risk of severe COVID-19.