Consumers’ Behavior of Restaurant Selection

Fidan, Hafize; Teneva, Atanaska; Stankov, Stanko; Dimitrova, Eva

doi:10.1109/hitech.2018.8566405

Cited by 8 publications

(11 citation statements)

References 7 publications

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“…The analysis of the mean values for the attributes considered important in the decision-making process of restaurant selection indicated that the most important attribute for respondents is the “cleanliness of spaces”, which is a finding supported by other research [5,6,36,38,40,59,60,61,62], and is followed by food made from “fresh ingredients (not frozen)” [5,31]. Therefore, unlike other studies, which place “food quality” on the first rank when it comes to a restaurant selection (an intrinsic attribute) [10,11,12,13,14,29,30,31,32,33,34], Romanian consumers in Cluj-Napoca pay much more attention to “cleanliness of spaces” (an extrinsic attribute). Other important attributes were found to be the “ambiance” and the “variety of the menu”.…”

Section: Discussionsupporting

confidence: 58%

“…Among the internal features, food quality is considered a crucial factor of influence in the selection process of a restaurant [10,11,12,13,29,30,31,32] and a predictor of customer satisfaction [14,33,34]. It consists of two dimensions: organoleptic and presentation [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is also argued that restaurant managers should consider these factors starting with the phase of restaurant concept creation, as the wrong one is often found to be the main cause of failure [26]. Even though the selection attributes that are considered to be important for consumers when dining out represented an area of research in other countries [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43], no similar studies were identified in Romania.…”

Section: Literature Reviewmentioning

confidence: 99%

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Assessing the Importance of Health in Choosing a Restaurant: An Empirical Study from Romania

Chiciudean

Harun

Mureșan

et al. 2019

IJERPH

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In the context of a spectacular growth of the Romanian restaurant market, it has become a necessity for managers to analyze the decision-making process related to restaurant selection toward obtaining a competitive advantage, which can be achieved through better segmentation and adequate targeting. The main objectives involved the identification of the main factors that influence restaurant selection by evaluating the role that health concerns play in this process and identifying consumers’ restaurant profiles. A survey was conducted using face-to-face interviews as the contact method, in order to identify the main factors considered important for consumers in the city of Cluj-Napoca in the decision-making process related to restaurant selection. Principal component analysis (PCA) was conducted to group the attributes. The non-hierarchical cluster analysis through the use of the k-means method was used to define different groups within the sample and identify common features. Results suggest that the analyzed restaurant market is dominated by three segments of consumers, of which the largest is represented by “health seekers”: a group of young women with medium and low incomes. As such, the possibility of consuming healthy meals within a restaurant is the most important factor for them during the decision-making process for restaurant selection. The present study has important managerial implications. Restaurant managers should admit that this process represents the starting point in designing restaurant concepts, as this type of information is fundamental for management decisions. On the other hand, the study offers important information regarding consumer perception of food, which has recently changed significantly, especially in the segment of young consumers to whom health is very important.

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Section: Discussionsupporting

confidence: 58%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Importance of Health in Choosing a Restaurant: An Empirical Study from Romania

Chiciudean

Harun

Mureșan

et al. 2019

IJERPH

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“…Seeing food advertisements in the media or watching others eat increases individuals' desire to order food (Burger et al, 2011). What customers desire in the restaurant industry is typically viewed as a behavioral intention (Fidan et al, 2018). More https://jaauth.journals.ekb.eg/ accurately, the desire to order is typically equated with the desire or intent to purchase, which has been more extensively investigated in the literature (Chung et al, 2017;Etemad-Sajadi, 2014).…”

Section: Perceived Authenticity and Desire To Ordermentioning

confidence: 99%

The Impact of Ethnic Restaurant Menu Visual Appeal and Informativeness on Customers’ Desire to Order

Alotaibi

Alshiha

2021

Journal of Association of Arab Universities for Tourism and Hos

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As restaurant menus play a critical role in customers' perceptions and behavioral intentions, this study examined the effect of ethnic restaurants' menus' visual appeal and informativeness on customers' perceived authenticity, quality, and desire to order. This study also examined the mediating role of the menus' informativeness between the menus' visual appeal and customers' perceived authenticity. Data were obtained from 416 customers who had visited an ethnic restaurant one month prior to the data collection. Based on the structural equation modeling results, this study found that only the menus' informativeness increased these customers' perceived authenticity. Also, the indirect effects of visual appeal on perceived authenticity through the menus' informativeness were discovered. Lastly, perceived authenticity is an antecedent of the customers' desire to order and perceived quality. These findings suggest that ethnic restaurants should carefully encourage customers to read the information provided in menus to positively increase their perception of authenticity. Theoretical and managerial implications are also provided.

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“…They used Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning approach, to provide an understanding of the big data analytics application towards the execution of an active engine of health recommender system. While Fidan et al [30] integrated the user-based ontological AI profile with a wide-ranging scientific experimental diabetes for personalized recommendations based on geographical status, cultural, distinct socioeconomic, and predominantly to AI-based patients. This is quite different from our own AI model.…”

Section: Related Workmentioning

confidence: 99%

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

et al. 2020

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Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician's rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient's data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and F1-measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and 99% F1-measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and F1 Measure score is 80%.

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Consumers’ Behavior of Restaurant Selection

Cited by 8 publications

References 7 publications

Assessing the Importance of Health in Choosing a Restaurant: An Empirical Study from Romania

Assessing the Importance of Health in Choosing a Restaurant: An Empirical Study from Romania

The Impact of Ethnic Restaurant Menu Visual Appeal and Informativeness on Customers’ Desire to Order

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

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