Bayesian Modeling of Labor Earnings in Construction

Labor costs constitute a significant portion of construction costs. Reliable forecasts provide insight into the movements of labor costs and are critical to the success of projects. Past studies have primarily focused on forecasting construction cost indexes or material costs. Only a few studies have concentrated on forecasting construction labor costs. This study presents a multivariate Bayesian structural time series (MBSTS) model to characterize the future values of construction labor's average hourly earnings (AHE) using a set of candidate predictors. The methodologies commonly used by past studies do not adequately address the uncertainties associated with the modeling process. In contrast, MBSTS recognizes the uncertainty in its modeling process, which enables practitioners to quantify and account for future labor cost risks in decisions. Furthermore, the MBSTS method results in a transparent model, helping analysts investigate the rationality of the parameters. This article trains MBSTS models under four different data subset lengths (i.e., 150, 144, 138, and 132 months) to study the consistency of the explanatory variables and their corresponding coefficients. The analysis results indicate that the gross domestic product (GDP), housing starts (HS), number of building permits (BP), Construction Cost Index (CCI), Dow Jones Industrial Average (DJI), and Standard and Poor's 500 index (SPI) are the most frequently used predictors in the regression component of the MBSTS models. The results indicate an inverse relationship between AHE from one side and HS and BP from the other. Likewise, a direct relationship exists between AHE and GDP, CCI, DJI, and SPI. The MBSTS model performed well on the validation subset in the midrange prediction intervals (i.e., 12- and 18-month periods). However, it was outperformed by conventional time series models [i.e., seasonal autoregressive integrated moving average (SARIMA)] when used for short-term forecasting. The proposed framework can be applied to facilitate monetary resource allocation in projects.

MODELLING PROFITABILITY FOR STOCKS ON ZAGREB STOCK EXCHANGE

In a market economy, one of main goals for every company is to maximize the wealth of the shareholders, which is a result of maximizing the market value of the company. There are various profitability measures for company, butfrom ownership perspective most used measure of profitability is Return on equity (ROE) ratio. This ratio relates to the earnings left over for equity investors after debt service costs for company has been deducted from total capital invested in the asset. Numerous studies have been conducted with numerous factors examined to determine factors that have impact on business performance. In this paper, the authors intention was to explore studies so far done on profitability of companies and to find an area that hadn't been yet examined, and to give substantiation ofprofitability determinants grounded on dynamic panel data. For this purpose, this paper explored variables that have impact on profitability of companies whose shares were most traded on Zagreb stock exchange (one of criteria for share to be included in market index). Variables included in research are: Net Financial Debt (NFD)/ EBITDA ratio, yearly revenue percent change, Enterprise Value (EV)/ EBITDA ratio, dividend yield, operating margin ratio, debt to equity ratio and current liquidity ratio. Analysis was done on data of companies included in the official stock index of the Zagreb stock exchange, Crobex from 2010 to 2019 (before Covid-19 pandemic). The data was taken from the Thomson Reuters database where all data for selected companies necessary for this paper were collected.

The impacts of policy measures on Japanese SMEs during the pandemic

Using monthly firm-level survey data, this study examines the effects of the three major government support measures on labour costs of Japanese SMEs during the COVID-19 pandemic. Policy measures were useful for supporting troubled SMEs to mitigate the effects of the economic downturn. However, they may have supported not just otherwise viable corporations, but also unprofitable but still operating corporations – often referred to as ‘zombies'. Our empirical results suggest that policy measures have heterogeneous effects on corporate activities. ‘Employment adjustment subsidies' and ‘COVID19 subsidies' had persistently mitigated the decline of labour costs, suggesting that employment and wage adjustments might have been inappropriate for improving labour productivity. On the other hand, ‘funding supports' by banks, which were accompanied by the obligation to repay the principal in the future, had a negative and persistent impact on labour costs. The results suggest that, unlike the other support measures, funding supports were policies that may have improved labour productivity by reducing labour costs during the pandemic.

Customer's Adoption Intentions toward Autonomous Delivery Vehicle Services: Extending DOI Theory with Social Awkwardness and Use Experience

The high demand and acute timeliness that characterizes instant delivery entail the challenges of high labor costs and an increase in courier traffic accidents. Autonomous delivery vehicles (ADVs) may serve as a key solution, with their attendant reduced labor input and higher efficiency. Customers play a key role in the successful implementation of ADVs on a large scale. However, understanding the factors that affect customers' intentions to use ADVs is still limited. Compared to autonomous driving, ADV customers are ultimately not the real users, who only are served by ADVs during the last leg of a trip. On account of this, the Technology Acceptance Model (TAM) may not be well-fitted for explaining the dynamics involved in ADV adoption. Within the context of ADVs, our study identified influencing factors that have not been captured by prior studies. This study incorporates infection risk, use experience, and social awkwardness into the Diffusion of Innovation (DOI) theory to explore customers' intentions to use ADVs. Data from 691 survey respondents were collected to validate the research design. The results demonstrate that compatibility, social influence, infection risk, green image, social awkwardness, and use experience all have a significantly positive impact on customers' intentions to adopt ADV services, while complexity and perceived risk both exhibited a negative impact. But no effect could be found for relative advantage, which may be because of the fact that customers only need ADVs to meet their delivery demand. This study contributes to understanding customers' adoption intentions toward ADVs, informing policymakers in formulating ADV regulations and standards, and promoting the large-scale application of ADVs in instant delivery services.

Bayesian Modeling of Labor Earnings in Construction

Labor costs constitute a significant portion of construction costs. Reliable forecasts provide insight into the movements of labor costs and are critical to the success of projects. Past studies have primarily focused on forecasting construction cost indexes or material costs. Only a few studies have concentrated on forecasting construction labor costs. This study presents a multivariate Bayesian structural time series (MBSTS) model to characterize the future values of construction labor's average hourly earnings (AHE) using a set of candidate predictors. The methodologies commonly used by past studies do not adequately address the uncertainties associated with the modeling process. In contrast, MBSTS recognizes the uncertainty in its modeling process, which enables practitioners to quantify and account for future labor cost risks in decisions. Furthermore, the MBSTS method results in a transparent model, helping analysts investigate the rationality of the parameters. This article trains MBSTS models under four different data subset lengths (i.e., 150, 144, 138, and 132 months) to study the consistency of the explanatory variables and their corresponding coefficients. The analysis results indicate that the gross domestic product (GDP), housing starts (HS), number of building permits (BP), Construction Cost Index (CCI), Dow Jones Industrial Average (DJI), and Standard and Poor's 500 index (SPI) are the most frequently used predictors in the regression component of the MBSTS models. The results indicate an inverse relationship between AHE from one side and HS and BP from the other. Likewise, a direct relationship exists between AHE and GDP, CCI, DJI, and SPI. The MBSTS model performed well on the validation subset in the midrange prediction intervals (i.e., 12- and 18-month periods). However, it was outperformed by conventional time series models [i.e., seasonal autoregressive integrated moving average (SARIMA)] when used for short-term forecasting. The proposed framework can be applied to facilitate monetary resource allocation in projects.

The potential of tobacco in supporting farmers in East Lombok Regency during the Covid-19 Pandemic

Tobacco is a leading commodity that has high economic value. The derivative products from tobacco commodities have contributed greatly to state revenues through excise taxes, employment, and sources of farmers' income. This research aims to;1) explain the characteristics of tobacco farming;2) investigate the economic feasibility of tobacco farming;3) analyze the determinants of tobacco farmers' income. The research was conducted in Suralaga District, East Lombok Regency. The study was conducted from July 2021 to October 2021. The sampling technique used was purposive sampling. Primary data was collected from 40 tobacco farmers and analyzed by with R/C ratio and multiple linear regression. The results of this study indicate that tobacco farming is declared economically feasible, This result is evidenced by the R/C ratio value is 1.67 > 1. Furthermore, the results of the regression estimation show that determine the income of tobacco farmers are the tobacco production, land area, seed cost, fertilizer cost, pesticide cost, labor costs, and farming experience.

Control and Insecurity in Australian and Canadian Universities during the COVID-19 Pandemic

We studied 14 universities across Canada and Australia to examine how the COVID-19 crisis, mediated through management strategies and conflict over financial control in higher education, influenced workers’ job security and affective outcomes like stress and happiness. The countries differed in their institutional frameworks, their union density, their embeddedness in neoliberalism and their negotiation patterns. Management strategies also differed between universities. Employee outcomes were influenced by differences in union involvement. Labour cost reductions negotiated with unions could improve financial outcomes, but, even in a crisis, management might not be willing to forego absolute control over finance, and it was not the depth of the crisis that shaped management decisions. Alternate :Cette étude examine comment la pandémie de COVID-19 et les stratégies mises en oeuvre par la gestion universitaire ont influencé la sécurité d'emploi, le stress et le bonheur des travailleurs de l'enseignement supérieur. Les données quantitatives et qualitatives primaires proviennent d'une enquête menée dans quatorze universités en Australie et au Canada, complétée par des recherches secondaires. L'analyse examine les réponses des institutions et des travailleurs à la pandémie, ainsi que les conflits qui en résultent en matière de contrôle financier et ce, tant aux niveaux macro (secteur), méso (université) et micro (individu). Au niveau macro, les réponses des universités ont été façonnées par les politiques publiques de l'État aux niveaux national et infranational. Dans les deux pays l’approche avait une forme nettement " néolibérale ". Toutefois, les universités australiennes ont été davantage exposées à la pression financière en faveur des suppressions d'emplois, et la direction de ces universités a peut-être été plus encline à procéder à des mises à pied que l'ensemble des universités canadiennes. Les différences au niveau du soutien institutionnel au syndicalisme au niveau macro ont influencé la manière dont le personnel universitaire a été affecté aux niveaux méso et micro. La restructuration des universités, dans les deux pays, a eu un impact négatif sur la sécurité d'emploi et les perspectives de carrière, ce qui a entraîné une diminution de la satisfaction professionnelle et une augmentation du stress. Pour de nombreux membres du personnel professionnel, le travail à domicile était nouveau et libérateur, tandis que pour d’autres membres du personnel universitaire, le travail à domicile était une expérience négative. Notre analyse démontre que les expériences du personnel universitaire ont été influencées par d'autres facteurs que les modalités de travail mises en place par les universités pendant la pandémie de COVID-19. Les approches des universités en matière de protection de l'emploi, de restructuration et d'engagement avec le personnel par le biais des syndicats semblent influencer la satisfaction, le stress et le bonheur du personnel. Nos résultats s'inscrivent dans le prolongement de la littérature qui documente la manière dont les processus de néolibéralisation sont régulièrement contestés par le personnel universitaire dans le cadre de diverses actions individuelles et collectives, en particulier en temps de crise. Nous soutenons que la théorisation des luttes pour le contrôle du travail devrait être étendue aux luttes pour le contrôle des finances.

Scheduling Scheme Design of Hotel Service Robot: A Heuristic Algorithm to Provide Personalized Scheme

With the rapid development of machine learning and artificial intelligence, hotel service robots are widely used, but there are many problems to be solved in the scheduling scheme of hotel service robots. In this study, the Pareto optimal definition is used to model the problem, and a nondominated sorting heuristic method including genetic algorithm and differential evolution algorithm is designed to solve this problem. Experimental results show the effectiveness and stability of our algorithm. In addition, compared with the previous methods, the method proposed in this paper can provide a more personalized and reasonable service robot scheduling scheme for hotels. Finally, the hotel can optimize its management and operation and further deepen the degree of hotel intelligence.

How Do Firms Respond to Reduced Labor Costs? Evidence from the 2007 Swedish Payroll Tax Reform

One way for policymakers to reduce labor costs and stimulate the recruitment of marginalized groups of labor in a highly unionized economy is to lower payroll taxes. However, the efficiency of this policy instrument has been questioned, and previous evaluations have mostly found small employment effects for such reforms. We investigate the effects of a payroll tax cut in Sweden that decreased firms’ labor costs in relation to the number of young employees that they had employed when the reform was implemented in 2007. We find that most firms received small labor cost savings as a result of the reform, but those that received larger cost savings increased their number of employees significantly more than firms that received no, or minor, labor cost savings. Our findings also suggest that the payroll tax cut increased the total wages paid to incumbent workers, but the wage effect was too small to offset the positive extensive-margin employment effect of the reform. In total, we find that the Swedish payroll tax reform created 18,100 jobs over the period 2006–2008;most of these jobs were within the targeted group of young employees.

Development of a UVC-based disinfection robot

Purpose>The disinfection robot developed by the authors and team focuses on achieving fast and precise disinfection under a given or specific disinfection zone. This looks to solve problems with traditional robots that pay less attention to the level, efficiency and zones of disinfection. To effectively support and guarantee normal running for the whole system, a digital twin system is applied to the disinfection robot. This study aims to achieve fast, precise and thorough disinfection via the developed mobile robot.Design/methodology/approach>The designed robot is composed primarily of the following three parts: a mobile platform, a six-axis robotic arm and a ultraviolet-C (UVC) LED array. The UVC LED array is installed on the end-effector to achieve large-scale, precise manipulation. The adoption of all types of advanced sensors and the development of an intuitive and user-friendly client interface are helpful in achieving remote control, path planning, data monitoring and custom disinfection functions.Findings>Disinfection of three different locations in the laboratory was performed;the dosage distribution of the surface as radiated by the UVC robot was detected;and feasibility of development was validated.Originality/value>The developed disinfection robot achieved fast, precise and thorough disinfection for a given or specific disinfection zone.

Capacity Investment and Process Efficiency at Flexible Firms

When firms are endowed with volume flexibility, capacity investment may influence the subsequent production process via affecting the structure of production cost. Yet, the strategic interaction between capacity and production decisions has not been adequately addressed. In this paper, we consider two firms serving one market under price sensitive and uncertain demand. Firms incur costs to build capacity and produce. The firm’s capacity affects production cost through its influence on process efficiency, while the specific effects on the two firms differ. We establish a two-stage game-theoretical framework to characterize the problem and obtain two firms’ equilibrium capacity and production decisions. The results show that the firm whose process efficiency is more prone to improving as capacity expands will invest in more capacity and achieve a more efficient process, provided that production is not overly labor and material intensive. However, its competitor will spin off capacity and suffer profit reduction. Moreover, the firms are encouraged to scale up capacity investment to achieve a more efficient process in an expanding and more volatile market.

Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Construction Labor Productivity in Construction Sites during the COVID-19 Pandemic Using Relative Importance Index (RII)

The construction industry is heavily reliant on the production of laborers, and construction labor costs share a big part in the total cost of the projects. Due to the emergence of the COVID-19, the construction industry has been experiencing restrictions and limitations in their project sites, affecting labor productivity. This paper aims to determine which factors are critical in influencing Construction Labor Productivity in the National Capital Region (NCR) in the Philippines during the COVID-19 pandemic by obtaining each of the critical factors' respective relative importance index. In order to achieve the study's objective, the researchers asked experts under the category AAAA companies who have a site and managerial experience during the pandemic to participate in an online questionnaire survey. 34 factors were considered for this study and categorized into four groups: (1) Human/labor;(2) Management;(3) Technological;and (4) COVID-19. The findings of the study were able to identify the 10 significant factors affecting labor productivity during the pandemic: (1) Laborer's Experience and Skill;(2) Availability of Materials;(3) Clarity of Instructions and Daily Task Assignments;(4) Coordination among Level Design Disciplines;(5) Shortage of Laborers, Construction Method;(6) Prolonged Delivery Period, Limited Number of Work Personnel Per Zone, Leadership of Construction Management;(7) Clarity of the Drawings and Specifications;(8) Communication among Laborers, Rework;(9) Social Distancing, Laborer's Absenteeism;and (10) Availability of Personal Protective Equipment. The study can guide construction firms for efficient management of laborers during a pandemic to improve construction labor productivity and accomplish a cost-effective project. © 2021 IEEE.

Surviving and growing in the post-COVID world: the case of Indian hotels. (Special Issue: How are hospitality and tourism businesses in India responding to the Covid-19 pandemic?.)

Purpose: As the world tries to come to terms with the exact economic and human cost of the Covid-19 pandemic, businesses are trying to discern and act on the implications of the "new normal" in order to survive and grow. Amongst all the industries impacted by the Covid-19 pandemic, travel and tourism has been the hardest hit. Operators across the value chain are trying to innovate and improvise in order to mitigate the impact of the pandemic on cash flows and working capital. The primary purpose of this paper then is to take a closer look at the measures adopted by the Indian hotel industry to restore revenues, conserve resources and attain greater operational and cost efficiencies in the midst of the Covid-19 crisis. The article also brings to the fore the growth-enhancing measures adopted by the industry in anticipation of a "new normal" in the post-Covid world. Design/methodology/approach: This study is based on primary data collected via a survey, followed by a series of interviews with a select group of senior finance managers from a wide spectrum of hotels based in India. Findings: Our findings suggest that Indian hotels have adopted several measures to enhance cash flows/revenues, reduce operating costs and cash outflows and conserve resources in order to tide themselves over during the crisis. Prominent cash flow-enhancing measures include price drops on special offers and reductions on list prices. Cost-cutting measures include economizing on energy consumption, reductions in labour costs and the postponement of maintenance expenses and discretionary spending. Resource conservation measures that have been adopted focus on postponement of capital expenditure and an increasing shift to localizing supply chains. Growth-enhancing measures adopted by the industry in anticipation of the long-term "new normal" include restructuring of the workforce and measures aimed at attracting domestic tourists and increasing product diversity. Originality/value: This study attempts to understand the short-term and long-term financial management strategies adopted by Indian hotels in the face of unprecedented disruption caused by the Covid-19 pandemic. To the best of our knowledge, this paper is the first of its kind in the Indian context.

Diagnosis of labor cost variation at residential buildings during the COVID-19 pandemic

In the last year, the COVID-19 pandemic has been affecting the construction sector worldwide in all its activities, both public and private, due to an increase in the budget initially planned for each project due to the incidence of different factors. This is clearly evidenced in the situation presented in Peru, where on March 16, 2020, the government ordered a mandatory social lockout, which in turn disabled the activity of all economic sectors. This stoppage lasted about four months, therefore the resumption of activities in the projects generated an alteration of costs as a result of the effects on productivity, the implementation of -PPE-Personal Protective Equipment according to the guidelines established by the Peruvian Government through Ministerial Resolution No. 448-2020-MINSA;and the variation in the costs of resources. In this context, the present research aims to diagnose the variation of labor costs in the construction of buildings, which allows updating the cost of man hours according to the reduction of performance and the implementation of PPE according to the guidelines established by law. These results show a percentage increase in budgets due to COVID-19 considerations. © 2021 IEEE.

Technical Efficiency of Cooperative and Non-Cooperative Dairies in Poland: Toward the First Link of the Supply Chain

Several studies conducted in various countries have addressed the technical efficiency of dairies. However, there is a paucity of research on the technical efficiency of dairies in Poland, particularly in relation to their legal form (i.e., cooperatives vs. non-cooperatives). The existing literature also does not provide insights into the technical efficiency of these entities with respect to different regions’ milk production capacity. Therefore, this paper aims to: (1) evaluate and compare the technical efficiency of cooperative and non-cooperative dairies in Poland, and (2) examine dairies’ technical efficiency due to spatial disparities in milk production potential. We use data envelopment analysis (DEA) to investigate the technical efficiency of 108 dairies in Poland for the year 2019. The milk production capacity of provinces is examined by applying the zero unitarization method. The results show that when assuming constant returns to scale (CRS), dairy cooperatives are less technically efficient than non-cooperatives, whereas when assuming variable returns to scale (VRS), these differences are not statistically significant. For inefficient dairies, we observe the greatest potential for improvement in labor costs and depreciation. Both cooperatives and non-cooperatives operate mostly under decreasing returns to scale. Thus, the potential for enhancing the technical efficiency of dairies through the consolidation process seems to be exploited. Our findings reveal that the technical efficiency of dairies in Poland is not differentiated by regional milk production potential.

Methodology for Constructing and Using a Mathematical Model for Assessing Labor Costs for the Development of Distance Learning Courses

Distance learning plays a significant role in solving the problem of the sustainable development of society. Unfortunately, the development and implementation of distance courses are still carried out intuitively, which does not allow practical implementation of effective mathematics methods and slows down the introduction of breakthrough technologies. The aim of the research was to develop a new model and a methodology for assessing the labor costs of designing distance learning courses by university teachers based on a comprehensive assessment of the courses’ quality. The suggested model originally extends the constructive cost evaluation model already used in IT industry. The developed criteria make it possible to assess labor costs in relation to the quality of course development. The paper provides and analyzes such calculations on the example of courses in the theory of algorithms and programming.