Search results

This paper aims to propose a statistical method to measure the impacts of stockouts on demand, using a segmented linear regression model.

The proposed method is applied to data sets from large retail chains to measure the impacts of stockouts of an item on substitute items. The measured impacts of stockouts can be used to estimate the true demand of the sold-out item by recovering the lost demand (turned-away demand), as well as to estimate the true demand of the substitute item by reducing the extra demand.

This study found that estimated true demand by the proposed method improves sales forecasting and calculation of the annual expected revenue.

A new method to measure the impacts of stockouts on the demand of substitute items was proposed. The proposed method is practical, in that, it is conceptually simple, computationally efficient and applicable in general scenarios. Also, the proposed method is scalable for larger data sets.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.

This study investigates the relationships between the built environments of learning commons and user productivity, such as collaborative and individual work productivity and overall environmental satisfaction.

A case study was conducted in a learning commons building at a higher education campus in the USA. The data collection and analysis were conducted with the survey responses of satisfaction with indoor environments and perceived productivity as well as the objective indoor environmental quality (IEQ) measurements. Statistical analysis was performed, including descriptive analysis, principal component analysis (PCA), regression analysis and ANOVA test.

The study presents that satisfaction with noise level is positively associated with individual productivity. The results imply that the spatial properties of open-plan commons, such as visibility and accessibility, are associated with space users' interactions and collaborative productivity. Overall satisfaction is in a positive relationship with lighting satisfaction, study supporting artifacts and furniture configuration. The results of this study reveal the importance of meeting the standards in IEQ factors on individual productivity and the spatial features preferred by space users that facilitate tasks and activities.

The mixed-method approach, including subjective and objective data collection of IEQ, is rarely utilized to show the relationships with perceived productivity. This study investigates a unique building design feature such as step seats in relation to space use and perceived productivity. The findings inform library leadership about environmental characteristics related to the user experience in learning commons, a new format of academic libraries.

This study aims to understand how knowledge workers working from home during COVID-19 changed their views on physical work environments and working-from-home practices.

This study conducted a survey targeting workers in the USA recruited via Amazon Mechanical Turk. A total of 1,651 responses were collected and 648 responses were used for the analysis.

The perceived work-life balance improved during the pandemic compared to before, while the balance of physical boundaries between the workplace and home decreased. Workplace flexibility, environmental conditions of home offices and organizational supports are positively associated with productivity, satisfaction with working from home and work-life balance during the pandemic.

While the strict traditional view of “showing” up in the office from Monday through Friday is likely on the decline, the hybrid workplace with flexibility can be introduced as some activities are not significantly affected by the work location, either at home-based or corporate offices. The results of this study also highlight the importance of organizations to support productivity and satisfaction in the corporate office as well as home. With the industry collaboration, future research of relatively large sample sizes and study sites, investigating workers’ needs and adapted patterns of use in home-based and corporate offices, will help corporate real estate managers make decisions and provide some level of standardization of spatial efficiency and configurations of corporate offices as well as essential supports for home offices.

The pandemic-enforced working-from-home practices awaken the interdependence between corporate and home environments, how works are done and consequently, the role of the physical workplace. This study built a more in-depth understanding of how workers who were able to continue working from home during COVID-19 changed or not changed their views on physical work environments and working-from-home practices.

This study aims to investigate the performance of open-plan office layouts and to identify occupants’ concerns in existing open-plan office layouts.

Workplace activity questionnaire (WAQ) was administered in the form of an online survey in March 2019, as part of a design briefing process for the expansion of the office facilities located in Bangalore, India, for a Fortune 100 software technology company. A total of 4,810 questionnaires were distributed and 3,877 responses were received (80.6% response rate). After that, 849 incomplete responses were eliminated from the analysis, resulting in a final sample size of 3,028. The questionnaire included 11 key activities conducted by the office workers and established the gap between the workers’ perceived importance and support from their existing facilities using a five-point Likert scale.

The findings of this study provide strong evidence that different physical environments influence the satisfaction of occupants. An improvement of the facilities, especially by enabling areas for quiet working, should be prioritized in relation to the other activities surveyed. Also, office workers perceived significantly different support levels for quiet working depending on their department, while there was no significant difference between the workers of different buildings.

Individual demographic information was not collected because of the possibility of personal identification. There was also a lack of objective environmental measures, such as temperature and noise level. Thus, the quality of indoor environments was unknown. In this study, some respondents mentioned dissatisfaction with indoor environmental quality, including noise, temperature and air quality in their comments.

In the programming stage of a workplace design process, the WAQ survey tool has value because it renders important insight into the perception of a live workplace, which can then be used to determine priorities for a design effort. It clearly identifies the areas to focus on, ask questions about and develop improvements. Validating its reliability will enhance its credibility and confidence in its use. In addition, the large sample size provides statistical advantages in the data analysis, providing a higher likelihood to find a true positive of the findings of the study. Also, having a relatively high response rate provides an advantage of mitigating the risk of having non-response bias in the analysis.

The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters.

A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy.

The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time.

This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.