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The purpose of this study is to provide a detailed demonstration of how artificial intelligence (AI) can be used to potentially generate valuable insights and recommendations regarding the role of accounting in addressing key sustainability-related issues.

The study offers a novel method for leveraging AI tools to augment traditional scoping study techniques. The method was used to show how the authors can produce recommendations for potentially enhancing organisational accountability pertaining to seasonal workers.

Through the use of AI and informed by the knowledge base that the authors created, the authors have developed prescriptions that have the potential to advance the interests of seasonal workers. In doing so, the authors have focussed on developing a useful and detailed guide to assist their colleagues to apply AI to various research questions.

This study demonstrates the ability of AI to assist researchers in efficiently finding solutions to social problems. By augmenting traditional scoping study techniques with AI tools, the authors present a framework to assist future research in such areas as accounting and accountability.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

Given the effects of natural and social factors, data on both the supply and demand sides of electricity will produce obvious seasonal fluctuations. The purpose of this article is to propose a new dynamic seasonal grey model based on PSO-SVR to forecast the production and consumption of electric energy.

In the model design, firstly, the parameters of the SVR are initially optimized by the PSO algorithm for the estimation of the dynamic seasonal operator. Then, the seasonal fluctuations in the electricity demand data are eliminated using the dynamic seasonal operator. After that, the time series after eliminating of the seasonal fluctuations are used as the training set of the DSGM(1, 1) model, and the corresponding fitted, and predicted values are calculated. Finally, the seasonal reduction is performed to obtain the final prediction results.

This study found that the electricity supply and demand data have obvious seasonal and nonlinear characteristics. The dynamic seasonal grey model based on PSO-SVR performs significantly better than the comparative model for hourly and monthly data as well as for different time durations, indicating that the model is more accurate and robust in seasonal electricity forecasting.

Considering the seasonal and nonlinear fluctuation characteristics of electricity data. In this paper, a dynamic seasonal grey model based on PSO-SVR is established to predict the consumption and production of electric energy.

The influence of weather on business activities and human behaviour has been explored in several fields (e.g. finance and psychology), but little research about weather and retail sales is found in the retail or fashion literature. The purpose of the study is to analyse the relationship between temperature, one aspect of weather, and retail sales of seasonal garments.

The researchers collected sales data from a retailer of branded women's business wear in the Seoul‐Kyunggi area in South Korea. Along with the sales data for seasonal basic styles, corresponding daily and weekly average temperature data were collected and evaluated. The analysis for the study was drawn using descriptive statistics including graphical evaluations, correlation analysis and paired samples t‐test. Interviews with the retailer's merchandisers were used to supplement interpretation of the statistical data.

Results of this study provide strong evidence that fluctuations in temperature can impact sales of seasonal garments. During sales periods when drastic temperature changes occurred, more seasonal garments were sold. However, the temperature changes from day to day or week to week did not affect the number of garments sold for the whole season. Of the seasonal garments expected to sell within the same season, the selling periods of each product category differed depending on type of fabric and design. For some seasonal garments, the actual sales dates were one week to two weeks in variance from the merchandisers' forecasts.

Limitations in the sample (i.e. product category) and location of stores (i.e. geographic region) prevent the generalization of results to all seasonal garments or retailers. In spite of these limitations, this study can be a pilot study that supports the significant relationship between temperature and sales of seasonal basic products by quantifying the temperature effects on sales of particular products. Therefore, future studies are needed to establish generalized conclusions with a larger sample.

As little academic research is available about weather's effect on sales of garments, the present study contributes to the field of clothing and retail distribution by providing evidence of significant relationships between temperature and sales of seasonal clothing.

The purpose of this paper is to examine the model of seasonal employee leadership (SEL) in a service management process and to create a multi-dimensional scale to gauge this construct. This is because very recent qualitative research by Arasli and Arici (2019), which is the first stage of this scale, recommended a multi-dimensional SEL model for the hospitality industry.

Making use of data gathered from 1,343 seasonal hotel employees, the authors established a new scale to examine the SEL model. Two separate data sets were collected; the first set was used to perform an exploratory factor analysis, while the second set was processed to confirm the initial factor results using a confirmatory factor analysis.

The results show that the measurement scale developed in this research provides considerable reliability, as well as convergent and discriminant validities. In particular, the findings confirmed a four-dimensional measurement scale of the SEL: seasonal leader’s qualities, core influence, operational influence and terminal influence.

The paper is the first attempt to develop a new scale which measures the SEL approach in the hospitality literature. Therefore, this study contributes to the current literature through developing and testing the four-dimensional SEL scale and shedding light on the importance of an industry-specific leadership in managing seasonal hotel employees effectively.

Internal migration has grown intensively in India in the present decades, far greater than international migration, though the latter has received far more attention in literature and public policy. Among internal migrants, seasonal movement is another growing phenomenon in India which has received the least attention till now. The purpose of the study is to show the intensities of short-term morbidity and major morbidity among the rural and urban internal migrants and how such disease burdens have affected the health of regular/permanent and temporary/seasonal migrants.

This present paper has been developed on the basis of data of India Human Development Survey-II (IHDS-II), 2011–2012, has been availed to find out the intensities of short-term morbidity and major morbidity among the rural and urban migrants as well as the health condition of the seasonal migrants. For the analysis of regular or permanent migrants, a total of 3,288 migrants (of which 1,136 rural migrants and 2,152 urban migrants) were surveyed in IHDS-II, 2011–2012, regarding the persistence of different types of short-term morbidity among the migrant class. Two-sample (rural migrants and urban migrants) “t” test for mean difference with unequal variances with null hypothesis – H0: diff = 0, and alternate hypothesis – Ha: diff < 0; Ha: diff > 0 where diff = mean (rural) – mean (urban) has been executed. For the seasonal migrants a sample of 41,424 migrants of which 2,691 seasonal migrant workers and 38,733 non-seasonal migrant workers were surveyed in IHDS-II, 2011–2012, to find out their health condition. OLS regression on the number of medical treatments undertaken in a month on the nature of migrant workers has been conducted. Socio-economic factors (like adult literacy) and basic amenities required for a healthy living (like indoor piped drinking water, separate kitchen in the household, household having a flush toilet, household having electricity and intake of meals everyday) are taken as control variables in the regression analysis.

The results of morbidity analysis in this paper show that the morbidity patterns among the migrants vary with the geographical differences. The short-term morbidity and that of the major morbidity show different proneness to ill health for rural and urban migrants. However, seasonal migrants are more susceptible to ill health than the regular migrants and are also potential for generating health risks. Also lack of provision of basic services creates negative health impact on seasonal migrants.

The paper is based on secondary data and hence lacks numerous relevant health issues of migrants in rural and urban sectors which could have been possible through primary data survey.

Migration and migrants are a relevant issue both internationally and nationally. Economic development of a country like India depends to a greater extent on the contributions of migrant labourers as majority of the labourers in India belong to informal sector of which most of the workers are from migrant class.

Migrants contribution to economic development depend on their productive capacity and hence health of these section of people is a relevant issue. This study is based on the morbidity pattern of migrants both regular and seasonal migrants and their susceptibility in various geographical locations and provision of basic amenities.

This work is original research study by the author.

This paper aims to explore the effects generated by the Milan World Expo 2015 on both firm performance and seasonality structure. It aims to answer the following research question: Did the Milan Expo 2015 influence only hotel results without changing seasonal patterns, or was this mega event able to reconfigure seasonal periods?

The present analysis is based on Smith Travel Research (STR) data. This source offers daily data on a large sample of Milan hotels (approximately 80 per cent of the total), representing more than 30,000 rooms. The empirical data relate to a period of 12 years, 11 of which are focused on the pre-event period (2004-2014), while 2015 is centered on the Milan Expo. This data comprise 4,383 daily observations. For each day, three operating measures were analyzed: occupancy, average daily rate (ADR) and revenue per available room (RevPAR).

The empirical findings fully support the first hypothesis: the four seasonal periods built around the main market segments are relevant lenses for understanding Milan’s demand structure before Expo 2015. The findings also support the second hypothesis relating to the effects generated by the event: Expo 2015 was able to improve hotel performance during the four seasonal periods analyzed. The most fragile seasonality registered the highest rise. Finally, the last two hypotheses to be investigated are as follows: did the Milan Expo 2015 simply improve hotel performance, without changing the underlying seasonal patterns (H3), or did this event reconfigure the demand structure (H4)? The analyses carried out lend more support to the fourth hypothesis, suggesting that new seasonal patterns emerged during Expo 2015.

This paper explores the impact of a mega event on seasonal patterns of hotel performance metrics. At least three original aspects are introduced. First, to analyze the Milan demand variation, a market segment approach that proposes an innovative seasonal matrix is developed. This is based on the three main client groups attracted by the destination. Second, the effects generated by the Expo are measured with consideration given to the four seasonal periods. Third, based on graphical and statistical analysis, the paper confirms that new seasonal patterns emerged during the Expo.

The purpose of this paper is to explore the dynamics between management approach, human resource systems and practices, and responses of seasonal workers.

After reviewing literature on contingent workers focusing on seasonal workers in particular, this paper presents a case study of how seasonal work is managed in a specific organisational context.

There is a noticeable gap between the organisation's initial approach to human resource management (during recruitment and induction) and the way employees are actually managed during the course of their employment. While seasonal employees may have low levels of organisational commitment as a consequence, nevertheless their commitment to colleagues, supervisors, and in some cases, clients has side‐benefits for the organisation.

The research is based on a single case study and has illustrative value. The characteristics of seasonal work described in the case reflect a specific industry and organisational context.

The findings suggest that employers of seasonal workers should consider the influence of human resource management systems and practices on the expectations and experience employees have of work.

The paper makes an empirical contribution as seasonal work has received little attention to date. Moreover, as seasonal work potentially combines short‐term finite employment with longer‐term relational aspects, we are able to highlight the relevance of cyclical time to an understanding of how employees perceive and experience work.