Analyzing Midair Object Pointing Mappings for Smart Display Input

One common task when controlling smart displays is the manipulation of menu items. Given current examples of smart displays that support distant bare hand control, in this paper we explore menu item selection tasks with three different mappings of barehand movement to target selection. Through a series of experiments, we demonstrate that Positional mapping is faster than other mappings when the target is visible but requires many clutches in large targeting spaces. Rate-based mapping is, in contrast, preferred by participants due to its perceived lower effort, despite being slightly harder to learn initially. Tradeoffs in the design of target selection in smart tv displays are discussed.

Fuzzy based intelligent decision support model for restaurant menu management

Restaurants operate in a dynamic highly competitive business environment with slim profit margin, changing customers, and costs incurring from materials and labour. Despite a solid demand for restaurant meals, the restaurant industry has a high failure rate, especially within the first three years of commencement. As the most important marketing, sales, and operational tool, a menu list must be well designed and well planned for profitability and competitive advantage. Menu Analysis (MA) or Menu Engineering (ME) refer to the broad range of techniques and procedures applied for effective marketing and operational decisions on menus. Choice of dishes is the food and beverage manager or the decision maker’s (DM) challenge which is a critical strategy decision. Quantitative or precise measures required for accurate evaluation of the effectiveness of a menu item is difficult to obtain. This pioneer study presents a Menu Management Decision Support Model (MMDSM) that applies Fuzzy Multicriteria Decision Making (FMCDM) approach to deal with the inherent imprecision in input using qualitative linguistic input values and obtain reliable outputs with increased decision options. Improving the widely used ME model with two inputs, the MMDSM includes four input parameters of Profit Factor, Popularity, Contribution Margin, and Sales Price related to each Menu Item. The MMDSM is successfully tested with a case study having one hundred and sixty-one Menu Item details from a U.S. restaurant. The results are verified by conducting a sensitivity analysis and the model can be developed into a commercial application.

Consumer’s preferences among low-calorie food alternatives in casual dining restaurants

Purpose This study aims to examine consumers’ preferences among four calorie-reducing approaches – resizing, reformulation, substitution and elimination – and to understand what motivates consumers to order low-calorie food using the theory of planned behavior (TPB) as a model. Design/methodology/approach A conceptual model was developed based on the TPB with an additional construct of food choice motives. A total of 467 responses were collected. Descriptive statistics, multiple regression and ANOVA were used in the data analysis. Findings Consumers have a clear preference for substitution and elimination. The pattern that ingredients with higher calories were modified at a higher priority indicated that consumers might improve food decisions based on calorie information. Practical implications Restaurants should allow substitution or elimination of certain ingredients from menu items. Including more low-calorie sides will cost restaurants less than changing the main dish. A menu item on an entrée form is more amenable to modification for decreased calorie content as consumers have high taste expectations for popular traditional foods (e.g. burgers and pizza). Originality/value This is the first study to investigate consumers’ preferences among the four popular calorie-reducing approaches. A novel “Build Your Own Meal” approach was used in the questionnaire to allow the participants to choose from more than 150 ingredients, which compensated for personal preferences, thus mitigating possible limitations associated with studies of this kind, and was a good indicator of the participants’ actual ordering behavior.

Carbon labels on restaurant menus: do people pay attention to them?

This empirical study explores visual attention to carbon labels on restaurant menus. Carbon labels can support consumers in making deliberate low-carbon menu choices if, and only if, consumers pay attention to such labels. Using eye-tracking, we investigate how much attention consumers pay to carbon labels. We also compare how much attention they pay to carbon labels compared to labels indicating the benefit of each menu item to the local community. Results indicate that both carbon labels and labels communicating local community benefits attract little attention, suggesting that they are insufficient to direct patrons towards ordering low emissions menu items. Directing patrons toward more environmentally friendly menu items could significantly reduce the negative environmental impact of tourism and hospitality.

IMPLEMENTASI ALGORITMA APRIORI UNTUK ANALISIS PENJUALAN KONTER BERBASIS WEB

Promosi yang efektif dapat meningkatkan angka penjualan. Untuk menentukan dan mengembangkan promosi agar lebih terarah dan tepat sasaran, Konter perlu mengidentifikasi pasar sasaran dan motif membeli. Salah satu cara untuk mengenali kondisi pasar adalah mengetahui minat beli konsumen, yang dapat diamati melalui data-data transaksi pembelian. Algoritma apriori dapat dimanfaatkan dalam proses penjualan, dengan memberikan hubungan antar data penjualan, dalam hal ini adalah kuota atau aksesoris handphone yang di beli sehingga akan didapat pola pembelian konsumen. Pihak konter dapat memanfaatkan informasi tersebut untuk mengambil tindakan bisnis yang sesuai, dalam hal ini informasi dapat menjadi bahan pertimbangan untuk menentukan strategi penjualan selanjutnya. Dengan menggunakan metode data mining yaitu market basket analysis dan algoritma apriori, dihasilkan aturan asosiasi yang menunjukkan pola beli konsumen dan seberapa kuat suatu item mempengaruhi item lain. Dari hasil analisa dan pengujian telah dilakukan ujicoba sistem sebanyak tiga kali menggunakan data transaksi Konter G Cell selama periode bulan Desember 2019 sampai November 2020 (1 Tahun) dengan merubah parameter minimum support dan minimum confidence maka didapat hasil kombinasi menu item yang dapat di buat untuk proses pengembangan promosi.

The Improvement Of Menu Based On Pare As A Diet Menu Specially In Diabetes Mellitus Patients In Hospitals

Background: Diabetes Mellitus (DM) is a disease whose prevalence has increased every year. The increase in prevalence is thought to be due to lifestyle changes and urbanization that affect the pattern and selection of food menus. One of the easiest management is to consume drugs, but the use of repentance for a long time will cause side effects and quite expensive costs.Pare is one type of vegetable that can be used as an alternative to the main food menu for diabetic mellitus, but because the taste is bitter, Pare is rarely used as a daily menu. Even at the Hospital a Pare-based menu is rarely applied to patients, especially those with Diabetes. Object: This study aims to examine the development of Pare-based menus so that it can be used as an alternative menu for inpatients in hospitals, especially for people with Diabetes Mellitus. Methods:There are two methods used in this study, experimental research methods and statistical descriptions. Result: The results of the stir-fried bitter melon from the experiment were bitter melon vegetables whose color was fresh green, the distinctive aroma of bitter melon still smelled, the texture was soft and the bitter taste was somewhat reduced. Panelist's hedonic test, who stated that they rather like 6 (18.8%), and like 21 (65.6%) and really like 4 (12.5%). Conclsion: Based on the results of the study, it can be concluded that bitter melon can be a basic menu item for type 2 diabetes mellitus patients hospitalized at the hospital.

From the Total-Factor Framework to Food Cost Performance Disaggregation—Developing an Innovative Model to Enhance Menu Performance

The menu serves as a form of advertisement and has also been recognized as an important marketing communication tool between restaurant and consumer. Extant literature have assessed the menu item performance by using the multi-factor efficiency index. However, there is a paucity of literature focusing on the efficiency disaggregation in order to improve the performance of individual food, labor, and other resources. The aim of this study was to determine the input targets for each menu item by comparing the efficiency frontier established by all menu items in two types of restaurant chains. In order to achieve this goal, the study sought to establish the food and labor cost performance assessment model utilizing the total-factor framework combined with a slack-based measure (SBM) of efficiency in data envelopment analysis (DEA) (SBM-DEA). Resource-saving target ratios (RSTR) for panel data with 35 menu items in these two different cultural type of restaurant chains including Chinese-style and Japanese-style restaurants for twelve months were assessed in a total-factor framework. The four-quadrant analysis based on efficiency and unit profit offered different strategies to the restaurateur being studied. The empirical findings indicated that the average total-factor food cost efficiency (TFFCE) was better than the total-factor labor cost efficiency (TFLCE) in these two types of restaurants. The TFFCE (80%) and TFLCE (61%) of the Chinese-style restaurant were better than those (TFFCE (76%) and TFLCE (50%)) of the Japanese-style restaurant in the observant periods. Even though the Chinese-style restaurant had a better resource efficiency, the restaurateur was still able to improve at least 20% in terms of food cost efficiency and 39% of labor cost efficiency, respectively. Managerial discussion and future study are also discussed.