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The effect of weather factors on restaurant sales

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Abstract

Weather factors have been shown to affect human behavior and mood. On the other hand, mood has a strong effect on total demand and demand for specific products. In this study, we have tested the effect of 17 different weather factors on the demand for specific restaurant menu items. We have also tested the effect of weather factors on the demand for different menu item categories, on hot and cold, and light, medium, and heavy menu items. The results indicate that different weather factors have different effects on different menu items and that the sales of some items are more affected by weather while others are not affected. These results can be used in the process of menu engineering and for the differential pricing of menu items according to the changes in weather factors.

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... Other micro factors driving restaurant demand include menu design (Wansink et al., 2001), food authenticity (Phung et al., 2019), advertising and promotion (Park and Jang, 2012), branding (Kwun and Oh, 2004), and consumer demographics such as age, income, and household size (Kim and Geistfeld, 2003). Myriad external factors can potentially affect restaurant sales: national economic conditions (e.g., gross domestic product, unemployment rate, and interest rate); national social-demographic characteristics (e.g., population and disposable income); weather; time (i.e., of a day, week, or year); events; government policies; and various crises, such as financial downturns and infectious diseases (Lee and Ha, 2012, Lee and Ha, 2014, Reynolds et al., 2013, Reynolds and Balinbin, 2003, Bujisic et al., 2017, Lasek et al., 2016. ...
... Moreover, customer demand data obtained via self-report surveys, including revealed choice surveys, may be susceptible to selfreport bias and errors in participant recall (Beshears et al., 2008). Another group of micro-level studies, which take individual restaurants as the unit of analysis, rely on historical data (e.g., restaurant sales and visits) to depict restaurant demand (Bujisic et al., 2017). These studies can be more accurate and reliable than survey-based studies but often include small sample sizes and focus on only one or a few specific restaurants. ...
... temperature2it: the square of average temperature in a given county each day. This variable helps capture the non-linear effect of temperature on restaurant demand(Bujisic et al., 2017).• rest_restrictionit: the presence of government restrictions on restaurant businesses, where rest_restriction = 1 indicates that these restrictions are in effect and rest_restriction = 0 otherwise. ...
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Purpose: This paper aims to evaluate the early effects of the pandemic of coronavirus disease 2019 (COVID-19) and accompanying stay-at-home orders on restaurant demand in U.S. counties. Design/methodology/approach: Two sets of daily restaurant demand data were collected for each U.S. county: foot traffic data and card transaction data. A two-way fixed-effects panel data model was used to estimate daily restaurant demand from February 1 to April 30, 2020. Findings: Results show that a 1% increase in daily new COVID-19 cases led to a 0.0556% decrease in daily restaurant demand, while stay-at-home orders were collectively associated with a 3.30% drop in demand. The extent of these declines varied across counties; ethnicity, political ideology, eat-in habits, and restaurant type diversity were found to moderate the effects of the COVID-19 pandemic and stay-at-home orders. Originality: This study represents a pioneering attempt to investigate the economic impact of COVID-19 on restaurant businesses. Practical implications: These results characterize the regional restaurant industry's resilience to COVID-19 and identify particularly vulnerable areas that may require supplementary assistance to recover.
... The practical value of weather forecasts and the prediction of demand due to weather variability and demand sensitivity is well acknowledged in the literature (Agnew and Thornes, 1995, Harrison, 1992, Curtis, 2003, Lazo et al., 2011, Steele, 1951. Previous research revealed clear indications that climate, seasonality and weather events can affect economic outcomes, for example, in the US (Bujisic et al., 2017, Lazo et al., 2011, Starr-McCluer, 2000, the UK Palutikof, 1999, Agnew andThornes, 1995), Germany , South Korea (Bahng and Kincade, 2012) or Japan (Yohannes and Matsuda, 2016). ...
... Weather events can influence consumers' choice of products affecting total sales (Murray et al., 2010). For example, a study of restaurant visitors demonstrates how temperature changes correlate with variations in total sales and the selection of menu items (Bujisic et al., 2017). Research suggests that climate can have very contrasting effects on different consumer goods and services industries. ...
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Purpose Little research on the influence of external factors, such as weather and holiday periods, on retail sales on alcoholic beverages is available. This study aims to investigate how weekly retail sales of different alcoholic beverages vary in association with daily maximum temperatures and annual federal holidays across selected US counties in the years 2013 to 2015. The research provides information, which can contribute to better sales forecasts. Design/methodology/approach Secondary data of weekly retail sales (volume) of alcoholic beverages from 37,346 stores in 651 counties in the USA are analysed. The data cover on average 21% of all existing US counties and 12% of the total US off-trade retail sales of alcoholic beverages in the period studied (Euromonitor, 2017). Additional data of federal holidays and meteorological data are collated for each county in the sample. Seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) are applied to develop forecasting models and to investigate possible relationships and effects. Findings The results indicate that off-trade retail sales of beer, liquor, red and white wine are temperature sensitive throughout the year, while contrary to expectations rosé, sparkling and other wines are not. Sales sensitivities to temperature also differ by geography. In the warmest regions, liquor and white wine sales do not respond to temperature changes, as opposed to the coolest regions, where they are responsive. Public holidays, particularly Easter, Thanksgiving, Christmas and New Year holidays, represent a constant influencing factor on short-term sales increases for all investigated alcoholic beverage categories. Originality/value This is the first large-scale study of weather and holiday-related sales variations over time, across geographies and different alcoholic beverage categories. Seasonal and non-seasonal short-term sales variations are important for retailers and manufacturers alike. Accounting for expected changes in demand accommodates efficiencies along the supply chain and has implications for retail management, as well as adjusting marketing efforts in competing categories.
... Sunny days cause an increase in satisfaction level of people instead of cloudy or rainy days (Schwarz and Clore, 1983), although their prosperity does not change in routine life. In fact, Human behaviour and weather variables are directly linked and large literature supports this fact (Baron and Bell, 1976;Davis et al., 1978;Cunningham, 1979;Bujisic et al., 2017;Howarth and Hoffman, 1984;Rind, 1996). In addition, Cunningham (1979), and Schneider et al. (1980) found that hot and cold temperature affects people helping behaviour. ...
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This study examined whether the impact of terrorism events on the stock market varies based on seasonal anomalies (i.e., spring, summer, autumn, and winter). For this purpose, this study selected and obtained the data of 344 terrorist events that occurred in Pakistan and daily closing index price data of KSE 100 for the period ranging from 2008 to 2017. To fulfil the study's objective, this study applies the event day analysis by using five days window (-2, -1, 0, +1, +2) by employing the Exponential Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. The findings of this study shows that there is no significant impact of terrorist events on stock returns in Pakistan on pre-event day 2 and event day. However, terrorism events have a significant positive impact on stock returns on pre-event day 1 and post-event day 1. On the contrary, stock returns on post-event day 2 showed a significant positive but in negative direction. In addition, this study also found that stock market returns vary significantly based on seasonal anomalies. However, it is also revealed that the impact of terrorism (event day) on the stock market’s returns does not significantly vary during all seasons in Pakistan except autumn and similarly, the impact of terrorism (post-event day 1) on the stock market’s returns does not significantly vary during said weather seasons in Pakistan. Furthermore, the positive impact of terrorism (during post-event day 2) upon the stock market’s returns significantly differs during weather seasons of spring, summer, and autumn in Pakistan. As per the results, this study suggests that the investor should invest on event day and resell/ withdraw his investment on post-event day 1 in order to earn higher profit.
... Sunny days cause an increase in satisfaction level of people instead of cloudy or rainy days (Schwarz and Clore, 1983), although their prosperity does not change in routine life. In fact, Human behaviour and weather variables are directly linked and large literature supports this fact (Baron and Bell, 1976;Davis et al., 1978;Cunningham, 1979;Bujisic et al., 2017;Howarth and Hoffman, 1984;Rind, 1996). In addition, Cunningham (1979), and Schneider et al. (1980) found that hot and cold temperature affects people helping behaviour. ...
Preprint
Full-text available
This study examined whether the impact of terrorism events on the stock market varies based on seasonal anomalies (i.e., spring, summer, autumn, & winter). For this purpose, this study selected and obtained the data of 344 terrorist events occurred in Pakistan and daily closing index price data of KSE 100 for the period ranging from 2008 to 2017. To fulfill the study's objective, this study applies the event day analysis by using five days window (-2,-1, 0, +1, +2) by employing the Exponential Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model. The findings of this study shows that there is no significant impact of terrorist events on stock returns in Pakistan on pre-event day 2 and event day. However, terrorism events have significant positive impact on stock returns on pre-event day 1 and post-event day 1. On the contrary, stock returns on post-event day 2 showed significant positive but in negative direction. In addition, this study also found that stock market returns vary significantly based on seasonal anomalies. However, it is also revealed that the impact of terrorism (event day) on the stock market's returns does not significantly varies during all seasons in Pakistan except autumn and similarly, the impact of terrorism (post event day 1) on the stock market's returns does not significantly varies during said weather seasons in Pakistan. Furthermore, the positive impact of terrorism (during post event day 2) upon the stock market's returns significantly differs during weather seasons of spring, summer, & autumn in Pakistan. As per the results this study suggests that the investor should invest on event day and resell/ withdraw his investment on post event day 1 in order to earn higher profit.
... Cranage and Andrew (1992) use exponential smoothing to predict the monthly sales (in USD) at a restaurant. Bujisic et al. (2017) analyze the effects of weather factors on restaurant sales. Their paper also contains a review of forecasting approaches for restaurant sales (with and without weather factors). ...
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... Weather is always a challenge for food cart business around coastal regions as the vendors only expect 50% of the average revenue during the rainy days (Bujisic et al., 2016). By introducing delivery services, the vendors may reduce the impact of the revenue scenario. ...
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... Weather has a huge impact on many aspects of consumer behavior (Bujisic et al., 2017). The vast majority of prior studies have investigated its influence on mood (Cunningham, 1979;Goldstein, 1972;Howarth & Hoffman, 1984;Persinger & Levesque, 1983;Schwarz & Clore, 1983). ...
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Despite the prevalent use of savings messages (e.g., “get $x off” and “save $x”), no previous tourism and hospitality research has examined their effect on consumer responses. To fill that void, this study investigates the joint effect of savings message type (gain-framed vs. nonloss-framed) and weather conditions (sunny vs. rainy) on consumer attitude. The results show that individuals in rainy weather respond more favorably to a gain-framed (vs. nonloss-framed) message, and this effect is attenuated among people in sunny weather. Furthermore, this study reveals a boundary condition. When the amount of savings is presented in percentage terms (e.g., “get x% off” and “save x%”), the superiority of a gain frame disappears. Theoretical and managerial implications are discussed.
... Cranage and Andrew [10] use exponential smoothing to predict the monthly sales (in USD) at a restaurant. Bujisic et al. [5] analyze the effects of weather factors onto restaurant sales. The paper also contains a review of forecasting approaches for restaurant sales (with and without weather factors). ...
Preprint
Full-text available
Accurate demand forecasting is one of the key aspects for successfully managing restaurants and staff canteens. In particular, properly predicting future sales of menu items allows a precise ordering of food stock. From an environmental point of view, this ensures maintaining a low level of pre-consumer food waste, while from the managerial point of view, this is critical to guarantee the profitability of the restaurant. Hence, we are interested in predicting future values of the daily sold quantities of given menu items. The corresponding time series show multiple strong seasonalities, trend changes, data gaps, and outliers. We propose a forecasting approach that is solely based on the data retrieved from Point of Sales systems and allows for a straightforward human interpretation. Therefore, we propose two generalized additive models for predicting the future sales. In an extensive evaluation, we consider two data sets collected at a casual restaurant and a large staff canteen consisting of multiple time series, that cover a period of 20 months, respectively. We show that the proposed models fit the features of the considered restaurant data. Moreover, we compare the predictive performance of our method against the performance of other well-established forecasting approaches.
... Climatic conditions affect the attendance of outdoor restaurants (Égerházi et al., 2009) or even the choice of meals (Bujisic et al., 2017). According to the restaurant managers in Pilsen, outdoor areas are used 6-7 months a year from April to September or October. ...
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Contents: 1 Background information. U Purpose and composition of the paper. 2 Introduction. 3 Literature review. 4 Implications for the use of revenue management. 5 Complexity of pricing in the restaurant sector. 6 Menu analysis. 7 Empirical study to be undertaken. References. Appendix. Questionnaire.
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The sensitivity of UK tourism to climate variability (on intra- and inter-annual scales) was investigated using empirical statistical models. A set of climate indices (mean monthly and annual temperature, rainfall and sunshine) describes present day variability in climate, while tourism demand is described by a dataset comprising domestic (monthly numbers of tourist nights) and international (annual numbers of trips abroad) tourist flows. An understanding of climate sensitivity based on real data then provided a basis for the examination of potential effects of climate change on the economically important tourism sector. Outbound flows of tourists are more responsive to climate variability of the preceding year, whereas domestic tourism is more responsive to variability within the year of travel. For outward tourism, wetter- and duller-than-average conditions in the year previous to travel seem to encourage more trips abroad. Drier- and warmer-than-average conditions increase same-month domestic trips, but a change in the direction of the association in subsequent months is indicative of inelasticity in the system. The anomalously warm year of 1995 in the UK was used to represent the potential impact of climate change. The results suggest that the generally warmer and drier conditions of 1995 benefited the UK domestic tourist industry by an estimated £309 million, relative to mean climate.
Article
A field study was conducted in which the helping behavior of 440 adults was measured under hot, comfortable, or cold temperatures. A subject was given an opportunity to help an experimenter by (1) answering a questionnaire, (2) picking up dropped groceries, (3) looking for a lost contact lens, or (4) picking up a dropped book while the experimenter walked on crutches. No support was found for the hypothesis that hot and cold temperature inhibit the display of helping behavior.
Article
As costs continue to escalate, menu analysis has once again become a significant topic of interest among foodservice professionals. A variety of menu analysis methods were discussed in hospitality literature over the last 30 years. Despite the fact that they offer different approaches and focus on different characteristics, they all have a common goal of finding a strategy that will help restaurant managers reveal more profitable menu items and possibly increase menu profitability. This article proposes an index method that helps to determine changes in overall menu contribution margin based on changes in contribution margin per individual menu item, proportion of sold items, and quantity of menu items sold. This method is meant to be a supplementary tool for analysis to support and enhance current menu analysis techniques.
Article
This research examines the relevance of routine activity theory to three property offences: burglary, robbery, and larceny-theft. We hypothesized that temperature would combine with time of day to predict these offences. This hypothesis was tested using a moderator-variable time-series analysis of property crime reports to police in Minneapolis over a 2-year period. The analysis indicated that time of day and day of the week were the best predictors of all three property crimes. After controlling for 281 temporal variables (e.g. holidays, school closings, and interactions with time of day and day of the week), temperature also emerged as a significant predictor of property offences. Contrary to Queletet's thermic law, more crimes were reported during summer than other months. The results are consistent with predictions derived from routine activity theory.
Article
While the term restaurant revenue management was defined in this journal in 1998, the history of publications in the Cornell Hospitality Quarterly (CHQ) related to managing restaurant profitability spans nearly fifty years. Of the 160 published articles related to restaurant profitability, more than one-quarter have appeared in the CHQ, which is more than three times that of any other journal. This article presents a new, decision-based framework for restaurant profitability, which expands on the earlier revenue-focused framework. The existing CHQ articles are categorized using the framework, and the gaps are used as the basis for identifying a large number of worthwhile, but as yet unanswered research questions related to restaurant profitability.
Article
Technology systems can support restaurant managers' efforts to improve sales and profits through revenue management. By subdividing a meal into its component sections, a manager can determine which systems to apply at a particular stage for the purpose of providing the greatest revenue benefit for a particular restaurant. In adopting technology, managers must first conduct a financial analysis to determine whether the technology's cost will be more than offset by revenue improvements. If that financial calculation is favorable, management must then consider benefits to both employees and customers and must also take into account employees' and customers' perceptions of the technology's utility and ease of use. Without those elements in place, the technology faces dim prospects no matter what its prospective financial benefit.
Article
Challenges related to operations managementconsistentlyconfrontfoodservice managers. It is becoming more important for managers to devise time-saving managerial techniques to decrease costs and increase profits. Planning is the managerial function that drives all other operations. Food production forecasting is an integral part of planning. The purpose of this research was to determine current forecasting techniques used by commercial foodservice operations. Personal inter views with six managers determined operational information and menu complexity, as well as current forecasting techniques. All managers reported forecasting food production on a daily basis. Eighty-three percent of the operations used computer systems to aid in forecasting. The amount of time needed to generate a 7-day forecast was 5 to 45 minutes. Foodservice managers relied most heavily upon historical data to generate forecasts. Results of this study indicated that commercial foodservice operations have begun to utilize technology to increase operational effectiveness.
Article
The purpose of this study was to develop, test and evaluate simple math ematical time series forecasting models to predict covers in a commercial foodservice environment. Total covers were selected as the focus of this research because they have been identified as the most common forecast numbers for food sales forecasts within hotel food and beverage operations (Schmidgall, 1989).KeyWords:Forecasting, foodservice, mathematical (time series) models, hospitality management.
Article
This study examines the relationship between daily weather and daily shopping patterns. The weather construct is operationalised using data from the National Institute of Water and Atmospheric Research, whilst the shopping data is a shopper count from a major shopping centre. Results from a multiple regression analysis suggest that the most tangible weather variables (rainfall and temperature) provide cues for shopping decisions. Possible linkages between weather, mood, and behaviour are discussed as an alternative to the physical deterrents / inducements explanation of this association.
Article
In principle, restaurant operators should be able to apply the time-based philosophy of revenue management to restaurant meals. To do so, however, requires a revision in the way most restaurateurs traditionally have viewed sales. Most restaurants track item contribution margin, sales per server, revenue per day part, or similar operating ratios. A different type of measure, revenue per available seat-hour, integrates the duration of the meal as a factor in the revenue calculation. Certain elements of current-day restaurant practice, such as differential pricing (e.g., early bird specials, AARP discounts), promoting special events (such as wine tastings on off nights), and managing table turnover carry the seeds of revenue management, but few restaurants have established the necessary strategic approach to assemble those tactics into a coherent revenue-management strategy. This article seeks only to establish a framework for such a strategy, and not to set a practical road map for its execution.
Article
The article presents a menu analysis methodology that is compared to menu engineering and the Miller matrix. Cost/margin analysis uses item food cost percentage and weighted dollar contribution margin to develop a sales mix that will both minimize the overall food cost percentage and optimize sales revenue and gross profit return. Previous methods of menu analysis have treated food cost percentage and dollar contribution margin in a mutually exclusive manner. This has resulted in biased interpretation of the data. Cost/margin analysis displays the food cost and contribution margin graphically and clearly identifies the menu items that are helping or hindering the revenue-cost-profit objectives. This information allows the menu to be designed to improve forecasting and cost control techniques.
Article
In this study, we attempted to determine whether a relationship exists between stock returns and the weather variables of temperature, humidity, and cloud cover in the Korean stock market. We delineated three key implications with regard to weather effects. First, after the 1997 financial crisis, the presence of a weather effect disappeared. Second, the inclusion of weather variables helps to model the GJR-GARCH process in the conditional variance. Third, the interaction effects of weather variables fully demonstrate the weather effect, but the interaction effects also vanished after the crisis. Overall, the findings of this study indicate that the weather effect was weakened as the result of heightened market efficiency.
Article
Explores seasonal differences in the purchase behaviour of shoppers in Cyprus. The analysis investigates situational factors and demographic/lifestyle attributes associated with consumers’ shopping behaviour in summer and winter. The situational factors include the frequency with which consumers shopped in a large Cypriot market, the usual time of day they shopped, their travel time to the market, the time they spent in it, and whether they were motivated by price/value considerations; the demographic/lifestyle elements encompassed age, gender, education, income, and the transportation mode consumers employed to reach the market. Differences were found in shopping patterns between the two seasons. For instance, in the winter, consumers purchased adult’s clothing to a greater extent than in the summer. In contrast, in the summer consumers purchased more food or beverage and spent more money than in the winter. Based on the findings, the paper includes explicit recommendations for marketing action. The results indicate that store managers can be proactive in their marketing efforts by being aware of situational influences on customers’ purchase behaviour.
Article
Purpose – In many industrial contexts, firms are encountering increasingly uncertain demand. Numerous factors are driving this phenomenon; however, a major change that is spreading among different sectors is the ever-growing attention to customers. Companies have identified that customers are critical not only because they directly influence the success of specific products or firms, but also because they play a fundamental role in many internal processes. Although the role of customers in business processes has been deeply analysed, the issue of demand forecasting and the role of customers has not been fully explored. The present study aims to examine the impact of heterogeneity of customer requests on demand forecasting approaches, based on three action research cases. Based on the analysis of customer behaviour, an appropriate methodology for each case is designed based on clustering customers according to their demand patterns. Design/methodology/approach – Objectives are achieved by means of three action research case studies, developed in cooperation with three different companies. The paper structures a general methodology based on these three experiences to help managers in better dealing with uncertain demand. Findings – By means of proper analysis of customers' heterogeneity and by using simple statistical techniques such as cluster analysis, forecasting performance can significantly improve. In these terms, this work claims that focusing on customers' heterogeneity is a relevant topic both for practitioners and researchers. Originality/value – The paper proposes some specific guidelines to forecast demand where customers' differences impact significantly on demand variability. In these terms, results are relevant for practitioners. Moreover, the paper claims that this issue should be better analysed in future researches and proposes some guidelines for future works.
Article
This study developed and evaluated mathematical (time-series) forecasting models to predict restaurant covers. The purpose of the study was to determine if model selection would differ for short-term and long-term data sets. In both the short- term and long-term studies, deseasonalized data modeled best. Therefore, daily seasonal differences account for a large portion of the demand variance, and the effect should be included in the forecasting model.
Article
Profiles the development of menu-engineering models and, in particular, the movement supporting the quantification of all costs associated with the production of a menu item. Reports the findings of a study of upscale restaurant menu planners. While all menu planners adopted elements of menu engineering when planning menus, most rejected the opportunity to factor in non-material direct costs as a major component of determining menu content and prices. In particular, individual dish labour cost was not considered an important menu-planning criterion. Dishes which attracted low sales, but which planners felt added interest to the menu, were included on the menu. This supports the view of most advocates of quantitative menu analysis that the profitability of individual dishes on the menu is only one of several important criteria when designing the menu.
Article
Propounding theories is one thing, but too often the intended beneficiary hasn't the time or tools to check their usefulness. Here's a case where researchers worked with a local restaurant to test their ideas, make recommendations for improvement, and track the results.
Managing food service operations to achieve a specific food cost percentage has long been a fundamental principle of the restaurant business. Management bonuses and other rewards are often based on achieving these predetermined goals. Available tools such as menu engineering and contribution margin, although sound in theory, are not frequently used. Demonstrates the use of menu engineering and contribution margin concepts in terms of customers served. Concludes that the goal of any restaurant should be to apply marketing techniques based on menu engineering and contribution margin concepts in order to achieve the highest possible financial results.
Article
65 male college students learned and later recalled a paired associate list (word-number pairs) in 1 of 5 air (dry bulb) temperatures (52, 62, 72, 82, or 92°F), with wet bulb temperature held constant. They learned and recalled best at 72°F, with performance declining at successively lower and higher air temperatures. In a 2nd experiment with 85 Ss, dry and wet bulb temperatures were varied from 52 to 82°F, in 5° increments, with relative humidity held constant. Other male students learned equally well in these effective temperatures. It is concluded that rote verbal learning may not be impaired even by quite low air temperatures, if relative humidity is controlled appropriately. (French & German summaries) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Conducted 2 field studies on the relationship of weather variables to helping behavior. In Study 1 (540 adult Ss), which was executed in the spring and summer and subsequently replicated in the winter, the amount of sunshine reaching the earth was found to be a strong predictor of an S's willingness to assist an interviewer. Smaller relationships were also found between helping and temperature, humidity, wind velocity, and lunar phase. Exp II was conducted indoors with 130 dining parties to control for comfort factors. Sunshine, lunar phase, and S's age and sex were found to predict the generosity of the tip left for a restaurant waitress. Sunshine and temperature were also significantly related to the 6 waitresses' self-reports of mood. (35 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
This paper examines the use of an intuitively appealing forecasting model for the food-service industry. After examining the current state of forecasting a brief introduction to the time-series method is undertaken. Finally, the simplest form of the model is applied in a case study.
Article
The impacts on the UK retailing sector of the extreme climate of 1995 are analysed in the context of monthly climate conditions in the previous two decades. Over the period from November 1994 to October 1995, the mean monthly Central England temperature (CET) was 1.6°C above the 1961–1990 normal, the highest mean 12-month temperature since the CET records began in 1659. Retail activities are geared towards average conditions and are therefore affected in the short-term by any unexpected change in supply and demand. This study focuses on those areas of retailing where the responses to climate in terms of a change in consumer demand are most likely to be clear: first, clothing and footwear and, second, food and drink. Economic time series are extracted from official government publications (1972–1995). Stepwise multiple regressions are performed to assess the amount of variance in the retail series accounted for by monthly temperature, rainfall and sunshine indices.