• Open access
  • Published: 04 June 2020

Satisfaction and revisit intentions at fast food restaurants

  • Amer Rajput 1 &
  • Raja Zohaib Gahfoor 2  

Future Business Journal volume  6 , Article number:  13 ( 2020 ) Cite this article

165k Accesses

51 Citations

10 Altmetric

Metrics details

This study is to identify the positive association of food quality, restaurant service quality, physical environment quality, and customer satisfaction with revisit intention of customers at fast food restaurants. Additionally, word of mouth is investigated as moderator on the relationship of customer satisfaction with revisit intentions of customers at fast food restaurants. Data were collected through a questionnaire survey from 433 customers of fast food restaurants through convenience sampling. Hypotheses of proposed model were tested using structural equation modeling with partial least squares SEM-PLS in SMART PLS 3. The results confirmed the positive association of food quality, restaurant service quality, physical environment quality, and customer satisfaction with revisit intentions of customers at fast food restaurants. However, word of mouth does not positively moderate the relationship of customer satisfaction with revisit intentions of customers at fast food restaurants. This study emphasizes the importance of revisit intention as a vital behavioral reaction in fast food restaurants. This study reveals revisit intention’s positive association with food quality, restaurant service quality, physical environment quality, and customer satisfaction based on stimulus-organism-response (S-O-R) theory. Furthermore, it is identified that social conformity theory does not hold its assumption when consumers experience quality and they are satisfied because word of mouth does not moderate the relationship of customer satisfaction with revisit intention of customer.

Introduction

Background of the study.

Hospitality industry is observing diversified changes in highly competitive environment for restaurants [ 1 ]. Consumers are becoming conscious of food quality (FQ), restaurant service quality (RSQ), and physical environment quality (PEQ) of the fast food restaurants. Consumers switch easily in case of just one evasive experience [ 2 , 3 ]. Fast food restaurants must attract new customers and retain the existing customers. There is a growing trend in Pakistani culture to dine out at fast food restaurants with family, friends, and colleagues [ 4 ]. Restaurants focus to provide a dining experience by combining tangible and intangible essentials [ 5 ]. Decisive objective is to achieve customer satisfaction (CS), word of mouth (WOM), and future revisit intention (RVI) at fast food restaurant.

Restaurants differ in offerings, appearance, service models, and cuisines; this classifies restaurants as downscale and upscale [ 6 , 7 ]. Revisit intention is the willingness of a consumer to revisit a place due to satisfactory experience. Customer satisfaction generates a probability to revisit in presence or absence of an affirmative attitude toward the restaurant [ 8 ]. Revisit intention is a substantial topic in hospitality research [ 8 , 9 , 10 ]. To date there has been little agreement on that word of mouth can affect revisit intention after experience of customer satisfaction. For instance, when a customer is satisfied at a fast food restaurant experience, however, the customer’s family and friends do not share the same satisfying experience. Will this word of mouth affect the customer’s revisit intention? Food quality is acknowledged as a basic component of the restaurant’s overall experience to affect consumer revisit intention. Fast food quality is substantially associated with customer satisfaction and it is an important predictor of behavioral intention [ 11 ]. Service quality is an essential factor to produce consumers’ revisit intentions [ 12 ]. Furthermore, physical environment quality affects behavior of consumers at restaurants, hotels, hospitals, retail stores, and banks [ 13 ]. Physical environment quality is a precursor of customer satisfaction [ 9 ]. This suggests that customer satisfaction is associated with fast food quality, restaurant service quality, physical environment quality, and revisit intention.

Aims of the study

This study is to investigate the association of fast food quality, restaurant service quality, physical environment quality with customer’s revisit intention through mediation of customer satisfaction using S-O-R theory and moderation of word of mouth on the relationship of customer satisfaction with revisit intention based on social conformity theory. This study empirically tests a conceptual research framework based on S-O-R and social conformity theory adding value to the knowledge. Objectives of the study are given below.

To investigate the association of fast food quality, restaurant service quality, and physical environment quality with revisit intention through customer satisfaction based on S-O-R theory in the context of Pakistani fast food restaurants.

To investigate moderation of WOM on relationship of customer satisfaction with revisit intention based on social conformity theory in the context of Pakistani fast food restaurants.

Furthermore, little empirical evidence is present about customer satisfaction with respect to fast food restaurant service quality [ 14 ]. Customer satisfaction is a post-consumption assessment in service industry. Customer satisfaction acts as the feedback mechanism to boost consumer experience [ 15 ]. Customer satisfaction brings competitive advantage to the firm and produces positive behavioral revisit intention [ 16 ]. Marketing literature emphasizes customer satisfaction in anticipation of positive word of mouth, revisit intention, and revisit behavior [ 5 ]. Behavioral intention is assessed through positive WOM, and it is important in service industry [ 15 ], whereas social influence in shape of WOM affects the behavior of individuals toward conformity leading to a driving effect based on social conformity theory [ 17 ].

  • Food quality

Food quality plays a central role in the restaurant industry. Food quality is essential to satisfy consumer needs. Food quality is a substantial condition to fulfill the needs and expectations of the consumer [ 18 ]. Food quality is acknowledged as a basic component of the restaurant’s overall experience. Food quality is a restaurant selection’s most important factor, and it is considerably related to customer satisfaction [ 11 ]. Food quality affects customer loyalty, and customer assesses the restaurant on the basis of food quality [ 19 ]. Food quality entails food taste, presentation, temperature, freshness, nutrition, and menu variety. Food quality influences customers’ decisions to revisit the restaurant [ 20 ]. Academic curiosity is increasing in the restaurant’s menus, as variety of menu items is considered the critical characteristic of food quality [ 11 ]. Taste is sensual characteristic of food. Taste is assessed after consumption. Nonetheless, customers foresee taste before consumption through price, quality, food labels, and brand name. Taste of food is important to accomplish customer satisfaction. Presentation of food enhances dining customer satisfaction [ 21 , 22 ]. Customer’s concerns of healthy food substantially affect customer’s expectations and choice of a restaurant [ 23 ]. Freshness is assessed with the aroma, juiciness, crispness, and fresh posture of the food. Food quality enhances customer satisfaction [ 24 ].

  • Restaurant service quality

Quality as a construct is projected by Juran and Deming [ 25 , 26 ]. Service quality is comparatively a contemporary concept. Service quality assesses the excellence of brands in industry of travel, retail, hotel, airline, and restaurant [ 27 ]. Restaurant service quality affects dining experiences of customers. Service quality creates first impression on consumers and affects consumers’ perception of quality [ 28 ]. Service industry provides good service quality to the customers to attain sustainable competitive advantage. Customer satisfaction depends on quality of service at the restaurant [ 29 ]. Service quality entails price, friendliness, cleanliness, care, diversity, speed of service, and food consistency according to menu. Customer satisfaction also depends on communication between restaurant’s personnel and the customers [ 30 ]. Consumer’s evaluation of service quality is affected by level of friendliness and care. Service quality leads to positive word of mouth, customer satisfaction, better corporate image, attraction for the new customers, increase revisits, and amplified business performance. Service quality increases revisits and behavioral intentions of customers in hospitality industry [ 12 ].

  • Physical environment quality

PEQ is a setting to provide products and services in a restaurant. Physical environment quality contains artifacts, decor, spatial layout, and ambient conditions in a restaurant. Customers desire dining experience to be pleasing; thus, they look for a physical environment quality [ 31 ]. Physical environment quality satisfies and attracts new customers. PEQ increases financial performance, and it creates memorable experience for the customers [ 9 ]. Consumers perceive the quality of a restaurant based on cleanliness, quirky, comfortable welcoming, physical environment quality, and other amenities that create the ambiance [ 32 ]. Effect of physical environment quality on behaviors is visible in service businesses such as restaurants, hotels, hospitals, retail stores, and banks [ 33 ]. Physical environment quality is an antecedent of customer satisfaction [ 34 ]. Thus, restaurants need to create attractive and distinctive physical environment quality.

  • Customer satisfaction

Customer satisfaction contains the feelings of pleasure and well-being. Customer satisfaction develops from gaining what customer expects from the service. Customer satisfaction is broadly investigated in consumer behavior and social psychology. Customer satisfaction is described “as the customer’s subjective assessment of the consumption experience, grounded on certain associations between the perceptions of customer and objective characteristics of the product” [ 35 ]. Customer satisfaction is the extent to which an experience of consumption brings good feelings. Customer satisfaction is stated as “a comparison of the level of product or service performance, quality, or other outcomes perceived by the consumer with an evaluative standard” [ 36 ]. Customer satisfaction constructs as a customer’s wholesome evaluation of an experience. Customer satisfaction is a reaction of fulfilling customer’s needs.

Customer satisfaction brings escalated repeat purchase behavior and intention to refer [ 37 ]. Dissatisfied consumers are uncertain to return to the place [ 38 ]. Satisfactory restaurant experience can enhance revisit intention of the consumer. Positive WOM is generated when customers are not only satisfied with the brand but they demand superior core offering and high level of service [ 15 ].

  • Word of mouth

Word of mouth is described as “person-to-person, oral communication between a communicator and receiver which is perceived as a non-commercial message” [ 39 ]. WOM is also defined as “the informal positive or negative communication by customers on the objectively existing and/or subjectively perceived characteristics of the products or services” [ 40 ]. Moreover, [ 41 ] defines it as “an informal person to person communication between a perceived non-commercial communicator and a receiver regarding a brand, a product, an organization or a service”. WOM is described as a positive or negative statement made by probable, actual or former customers about a product or a company, which is made available through offline or online channels [ 42 , 43 ]. WOM is an important and frequent sensation; it is known for long time that people habitually exchange their experiences of consumptions with others. Consumers complain about bad hotel stays, talk about new shoes, share info about the finest way of getting out tough stains, spread word about experience of products, services, companies, restaurants, and stores. Social talks made more than 3.3 billion of brand impressions per day [ 44 ].

WOM has substantial impact on consumer’s purchasing decision; therefore, a vital marketing strategy is to initiate positive WOM [ 45 ]. However, negative WOM is more informative and diagnostic where customers express their dissatisfaction [ 38 ]. Word of mouth communications are more informative than traditional marketing communications in service sector. WOM is more credible than advertisement when it is from friends and family [ 46 ]. WOM is a vital influencer in purchase intention. WOM escalates affection that enhances commitment of consumer purchase intention. WOM is generated before or after the purchase. WOM helps the consumers to acquire more knowledge for the product and to reduce the perceived risk [ 47 ]. WOM in the dining experience is very important. People tend to follow their peers’ opinions when they are to dine out.

  • Revisit intention

To predicting and to explain human behavior is the key determination of consumer behavior research. Consumer needs differ and emerge frequently with diverse outlooks. Revisit intention is to endorse “visitors being willing to revisit the similar place, for satisfactory experiences, and suggest the place to friends to develop the loyalty” [ 48 ]. Consumer forms an attitude toward the service provider based on the experience of service. This attitude can be steady dislike or like of the service. This is linked to the consumer’s intention to re-patronize the service and to start WOM. Repurchase intention is at the core of customer loyalty and commitment. Repurchase intention is a significant part of behavioral and attitudinal constructs. Revisit intention is described as optimistic probability to revisit the restaurant. Revisit intention is the willingness of a consumer to visit the restaurant again. Furthermore, the ease of visitors, transportation in destination, entertainment, hospitability, and service satisfaction influence visitor’s revisit intention.

Consumer behavior encircles the upcoming behavioral intention and post-visit evaluation. Post-visit evaluation covers perceived quality, experience, value, and the satisfaction. Restaurant managers are interested to understand the factors of consumer revisit intention, as it is cost effective to retain the existing customers in comparison with attract new customers [ 49 ]. Substantial consideration is prevailing in literature for the relationship among quality attributes, customer satisfaction, and revisit intention. There is a positive association between customer satisfaction and revisit intention. Indifferent consumer, accessibility of competitive alternatives and low switching cost can end up in a state where satisfied consumers defect to other options [ 2 ]. Consumer behavior varies for choice of place to visit, assessments, and behavioral intentions [ 50 ]. The assessments are about the significance perceived by regular customers’ satisfactions. Whereas, future behavioral intentions point to the consumer’s willingness to revisit the similar place and suggest it to the others [ 51 ].

S-O-R model is primarily established on the traditional stimulus–response theory. This theory explicates individual’s behavior as learned response to external stimuli. The theory is questioned for oversimplifying ancestries of the behaviors and ignoring one’s mental state. [ 52 ] extended the S-O-R model through integrating the notion of organism between stimulus and response. S-O-R concept is embraced to reveal individual’s affective and cognitive conditions before the response behavior [ 53 ]. S-O-R framework considers that environment comprises stimuli (S) leading changes to the individual’s internal conditions called organism (O), further leading to responses (R) [ 52 ]. In S-O-R model, the stimuli comprise of various components of physical environment quality, organism indicates to internal structures and processes bridging between stimuli and final responses or actions of a consumer [ 9 ]. Behavioral responses of an individual in a physical environment quality are directly influenced by the physical environment quality stimulus [ 54 ]. S-O-R framework is implemented in diverse service contexts to examine how physical environment quality affects customer’s emotion and behavior [ 55 ]. The effect of stimulation in an online shopping environment on impulsive purchase is investigated through S-O-R framework [ 56 ]. The effects of background music, on consumers’ affect and cognition, and psychological responses influence behavioral intentions [ 57 ]. Perceived flow and website quality toward customer satisfaction affect purchase intention in hotel website based on S-O-R framework [ 58 ]. Therefore, this study conceptualizes food quality, restaurant service quality, and physical environment quality as stimuli; customer satisfaction as organism; and revisit intention as response.

Moreover, social conformity theory (SCT) is to support the logical presence of WOM in the conceptual framework as a moderator on the relationship of customer satisfaction and revisit intention. Social conformity influences individual’s attitudes, beliefs and behaviors leading to a herding effect [ 17 , 59 ]. Thus, social influence (WOM) moderates the relationship of customer satisfaction and revisit intention. Following hypotheses are postulated, see Fig.  1 .

figure 1

Conceptual research framework

Food quality is positively associated with customer satisfaction in fast food restaurant.

Restaurant service quality is positively associated with customer satisfaction in fast food restaurant.

Physical environment quality is positively associated with customer satisfaction in fast food restaurant.

Customer satisfaction is positively associated with revisit intention of customer in fast food restaurant.

Customer satisfaction mediates between food quality and revisit intention of customer in fast food restaurant.

Customer satisfaction mediates between restaurant service quality and revisit intention of customer in fast food restaurant.

Customer satisfaction mediates between physical environment quality and revisit intention of customer in fast food restaurant.

WOM positively moderates the relationship between customer satisfaction and revisit intention of customer in fast food restaurant.

There are two research approaches such as deductive (quantitative) and inductive (qualitative). This study utilized the quantitative research approach as it aligns with the research design and philosophy. Quantitative research approach mostly relies on deductive logic. Researcher begins with hypotheses development and then collects data. Data are used to determine whether empirical evidence supports the hypotheses [ 60 ]. The questionnaires survey is used. This study chose the mono-method with cross-sectional time horizon of 6 months. Deductive approach is utilized in this study. Cross-sectional time horizon also known as “snapshot” is used when investigation is related with the study of a specific phenomenon at a particular time [ 61 ]. Questionnaire survey is mostly used technique for data collection in marketing research due to its effectiveness and low cost [ 62 ]. Data are collected through self-administered questionnaires. Following the footsteps of Lai and Chen [ 63 ] and Widianti et al. [ 64 ] convenience sampling is applied. Famous fast food restaurants in twin cities (Rawalpindi and Islamabad) of Pakistan were chosen randomly. Furthermore, 650 questionnaires (with consideration of low response rate) were distributed to the customers at famous fast food restaurants. Moreover, researchers faced difficulty in obtaining fast food restaurant’s consumers data.

It yielded a response rate of 68.92% with 448 returned questionnaires. Fifteen incomplete questionnaires are not included; thus, 433 responses are employed for data analysis from fast food restaurant customers. The obtained number of usable responses was suitable to apply structural equation modeling [ 65 , 66 , 67 , 68 ].

Sample characteristics describe that there are 39.7% females and 60.3% males. There are 31.4% respondents of age group 15–25 years, 48.3% of age group 26–35, 12.2% of age ranges between 36 and 45, 6.7% of age ranges between 46 and 55, and 1.4% of age group is above 56 years. The educational level of the respondents indicates that mostly respondents are undergraduate and graduate. Occupation of respondents reflects that 28.6% work in private organizations and 24.9% belong to student category. Monthly income of 29.3% respondents ranges between Rupees 20,000 and 30,000 and 25.6% have monthly income of Rupees 41,000–50,000. Average monthly spending in fast food restaurants is about Rupees 3000–6000, see Table  1 .

Measures of the constructs

Food quality is adopted from measures developed by [ 69 ]. Food quality contains six items such as: food presentation is visually attractive, the restaurant offers a variety of menu items, and the restaurant offers healthy options. Restaurant service quality is adopted with six items [ 70 ]. This construct contains items such as: efficient and effective process in the welcoming and ushering of the customers, efficient and effective explanation of the menu, efficient and effective process in delivery of food. Physical environment quality is adopted with four items [ 71 ], and one item is adopted from measures developed by [ 70 ]. The items are such as: the restaurant has visually striking building exteriors and parking space, the restaurant has visually eye-catching dining space that is comfortable and easy to move around and within, and the restaurant has suitable music and/or illumination in accordance with its ambience. Revisit intention is measured through four adapted items [ 8 ]; such as: I would visit again in the near future and I am interested in revisiting again. Customer satisfaction is measured by three adopted items [ 29 ]; such as: I am satisfied with the service at this restaurant, and the restaurant always comes up to my expectations. Word of mouth is measured with four adopted items such as: my family/friends mentioned positive things I had not considered about this restaurant, my family/friends provided me with positive ideas about this restaurant [ 72 ]. Each item is measured on 5-point Likert scale, where 1 = strongly disagree, 3 = uncertain, and 5 = strongly agree.

Results and discussion

Validity and reliability.

Validity taps the ability of the scale to measure the construct; in other words, it means that the representative items measure the concept adequately [ 73 ]. The content validity is executed in two steps; firstly, the items are presented to the experts for further modifications; secondly, the constructive feedback about understanding of it was acquired by few respondents who filled the questionnaires. Each set of items is a valid indicator of the construct as within-scale factor analysis is conducted.

The factor analyses allotted the items to their respective factor. Fornell and Lacker’s [ 74 ] composite reliability p is calculated for each construct using partial least squares (PLS) structural equation modeling and Cronbach’s coefficient α [ 75 ]. Cronbach’s α is used to evaluate the reliability of all items that indicates how well the items in a set are positively related to one another. Each Cronbach’s α of the instrument is higher than .7 (ranging from .74 to .91); see Table  2 .

Common method bias

Same measures are used to collect data for all respondents; thus, there can be common method bias [ 76 ]. Firstly, questionnaire is systematically constructed with consideration of study design. Secondly, respondents were assured for the responses to be kept anonymous [ 77 ]. Common method bias possibility is assessed through Harman’s single factor test [ 78 , 79 , 80 , 81 , 82 , 83 ]. Principal axis factor analysis on measurement items is exercised. The single factor did not account for most of the bias and it accounted for 43.82% variance that is less than 50%. Thus, common method bias is not an issue [ 80 , 81 ].

SEM-PLS model assessment

Survey research faces a challenge to select an appropriate statistical model to analyze data. Partial least squares grounded structural equation modeling (SEM-PLS) and covariance-based structural equation modeling (CB-SEM) are generally used multivariate data analysis methods. CB-SEM is based on factor analysis that uses maximum likelihood estimation. PLS-SEM is based on the principal component concept; it uses the partial least squares estimator [ 84 ]. PLS-SEM is considered appropriate to examine complex cause–effect relationship models. PLS-SEM is a nonparametric approach with low reservations on data distribution and sample size [ 84 ].

Measurement model assessment

To evaluate convergent validity measurement model (outer model) is assessed that includes composite reliability (CR) to evaluate internal consistency, individual indicator reliability, and average variance extracted (AVE) [ 85 ]. Indicator reliability explains the variation in the items by a variable. Outer loadings assess indicator reliability; a higher value (an item with a loading of .70) on a variable indicates that the associated measure has considerable mutual commonality [ 85 ]. Two items RSQ 14 and PEQ 24 are dropped due to lower value less than .60 [ 86 ]. Composite reliability is assessed through internal consistency reliability. CR values of all the latent variables have higher values than .80 to establish internal consistency [ 85 ]; see Table  2 .

Convergent validity is the extent to which a measure correlates positively with alternative measures of the same variable. Convergent validity is ensured through higher values than .50 of AVE [ 74 ], see Table  2 . Discriminant validity is the degree to which a variable is truly distinct from other variables. Square root of AVE is higher than the inter-construct correlations except customer satisfaction to hold discriminant validity [ 74 ]. Additional evidence for discriminant validity is that indicators’ individual loadings are found to be higher than the respective cross-loadings, see Table  3 .

Structural model assessment

Structural model is assessed after establishing the validity and reliability of the variables. Structural model assessment includes path coefficients to calculate the importance and relevance of structural model associations. Model’s predictive accuracy is calculated through R 2 value. Model’s predictive relevance is assessed with Q 2 , and value of f 2 indicates substantial impact of the exogenous variable on an endogenous variable in PLS-SEM [ 85 ]. SEM is rigueur in validating instruments and testing linkages between constructs [ 87 ]. SMART-PLS produces reports of latent constructs correlations, path coefficients with t test values. The relationships between six constructs of food quality, restaurant service quality, physical environment quality, customer satisfaction, word-of-mouth, and revisit intention are displayed in Fig.  2 after bootstrapping. Bootstrapping is a re-sampling approach that draws random samples (with replacements) from the data and uses these samples to estimate the path model multiple times under slightly changed data constellations [ 88 ]. Purpose of bootstrapping is to compute the standard error of coefficient estimates in order to examine the coefficient’s statistical significance [ 89 ].

figure 2

Bootstrapping and path coefficients

Food quality is positively associated to customer satisfaction in fast food restaurant; H 1 is supported as path coefficient = .487, T value = 8.349, P value = .000. Restaurant service quality is positively associated with customer satisfaction; H 2 is supported as path coefficient = .253, T value = 4.521, P value = .000. Physical environment quality is positively associated with customer satisfaction in fast food restaurant; H 3 is supported as path coefficient = .149, T value = 3.518, P value = .000. Customer satisfaction is positively associated with revisit intention of customer in fast food restaurant; H 4 is supported as path coefficient = .528, T value = 11.966, P value = .000. WOM positively moderates the relationship between customer satisfaction and revisit intention of customer in fast food restaurant; H 8 is not supported as path coefficient = − .060, T value = 2.972, P value = .003; see Table  4 .

Assessing R 2 and Q 2

Coefficient of determination R 2 value is used to evaluate the structural model. This coefficient estimates the predictive precision of the model and is deliberated as the squared correlation between actual and predictive values of the endogenous construct. R 2 values represent the exogenous variables’ mutual effects on the endogenous variables. This signifies the amount of variance in endogenous constructs explained by total number of exogenous constructs associated to it [ 88 ]. The endogenous variables customer satisfaction and revisit intention have R 2  = .645 and .671, respectively, that assures the predictive relevance of structural model. Further the examination of the endogenous variables’ predictive power has good R 2 values.

Blindfolding is to cross-validate the model’s predictive relevance for each of the individual endogenous variables with value of Stone–Geisser Q 2 [ 90 , 91 ]. By performing the blindfolding test with an omission distance of 7 yielded cross-validated redundancy Q 2 values of all the endogenous variables [ 88 ]. Customer satisfaction’s Q 2  = .457 and RVI’s Q 2  = .501; this indicates large effect sizes. PLS structural model has predictive relevance because values of Q 2 are greater than 0, see Table  5 .

Assessing f 2

Effect size f 2 is the measure to estimate the change in R 2 value when an exogenous variable is omitted from the model. f 2 size effect illustrates the influence of a specific predictor latent variable on an endogenous variable. Effect size f 2 varies from small to medium for all the exogenous variables in explaining CS and RVI as shown Table  6 .

Additionally, H 5 : CS mediates between food quality and RVI is supported as CS partially mediates between FQ and RVI. Variation accounted for (VAF) value indicates that 70% of the total effect of an exogenous variable FQ on RVI is explained by indirect effect. Therefore, the effect of FQ on RVI is partially mediated through CS. Similarly, the VAF value indicates that 70% of the total effect of an exogenous variable RSQ and 35% VAF of PEQ on RVI is explained by indirect effect. Therefore, the effects of RSQ and PEQ on RVI are also partially mediated through CS. H 6 is supported as the effect of CS is partially mediated between RSQ and RVI of customer in fast food restaurant. H 7 is supported as the effect of CS is partially mediated between PEQ and RVI of customer in fast food restaurant, see Table  7 . This clearly indicates that customer satisfaction mediates between all of our exogenous variables (food quality, restaurant service quality and physical environment quality) and dependent variable revisit intention of customer in fast food restaurant [ 88 , 92 ] (Additional files 1 , 2 and 3 ).

This is interesting to note that food quality, restaurant service quality, physical environment quality, and customer satisfaction are important triggers of revisit intention at fast food restaurants. However, surprisingly, word of mouth does not moderate the relationship of customer satisfaction with revisit intention of customer at fast food restaurant. The results of the study correspond with some previous findings [ 15 , 29 , 32 , 69 , 93 ]. Positive relationship between customer satisfaction and revisit intention is consistent with the findings of the previous studies [ 5 , 8 , 94 , 95 , 96 ]. Food quality is positively associated with revisit intention; this result as well corresponds to a previous study [ 24 ]. Furthermore, interior and amusing physical environment is an important antecedent of revisit intention at a fast food restaurant; this finding is congruent with previous findings [ 29 , 70 , 97 , 98 ] and contrary to some previous studies [ 9 , 15 ].

Intensified competition, industry’s volatile nature, and maturity of the business are some challenges that fast food restaurants face [ 5 ]. Amid economic crunch, competition becomes even more evident, driving fast food restaurants to look for unconventional ways to appeal the customers. In fact, these findings somehow show that significance of physical environment quality in creating revisit intention is probably lower in comparison with food quality and restaurant service quality. Nonetheless, fast food restaurant’s management should not underrate the fact that physical environment quality considerably affects the revisit intention. Due to this, the importance of physical environment quality must not be overlooked when formulating strategies for improving customer satisfaction, revisit intention and creating long-term relationships with customers.

Managerial implications

The results imply that restaurant management should pay attention to customer satisfaction because it directly affects revisit intention. Assessing customer satisfaction has become vital to successfully contest in the modern fast food restaurant business. From a managerial point of view, the results of this study will help restaurant managers to better understand the important role of food quality, restaurant service quality and physical environment quality as marketing tool to retain and satisfy customers.

Limitations

There are certain limitations with this study. This study is cross sectional, and it can be generalized to only two cities of Pakistan. Scope of research was limited as the data were collected from two cities of Pakistan (Islamabad and Rawalpindi) using convenience sampling.

Future research

A longitudinal study with probability sampling will help the researchers to comprehensively investigate the relationships among the constructs. Moreover, it would be useful for future research models to add information overload as an explanatory variable and brand image as moderating variable in the research framework. Additionally, moderation of WOM can be investigated in other relationships of conceptual model.

The study encircles the key triggers of customer satisfaction and revisit intention in fast food restaurants. It also offers a model that defines relationships between three factors of restaurant offer (food quality, restaurant service quality, and physical environment quality), customer satisfaction, word of mouth, and revisit intention at fast food restaurants. The model specially focuses the revisit intention as dependent variable of conceptual model despite behavior intentions. The findings suggest the revisit intention is positively associated with customer satisfaction, food quality, restaurant service quality, and physical environment quality in a fast food restaurant.

However, contrary to the findings of a previous study [ 99 ], WOM do not positively moderate between the relationship of customer satisfaction and revisit intention. The empirical findings confirm the significant impact of food quality, restaurant service quality, physical environment quality, and customer satisfaction which are important antecedents of revisit intention at fast food restaurant through mediation of customer satisfaction. Moreover, findings of the research support the assumptions of SOR theory strengthening our conceptual model which states the external stimuli (FQ, RSQ, PEQ) produced internal organism (CS) which led to the response (RVI). However; assumption of social conformity theory failed to influence the satisfied customer. In other words, customer satisfaction plays dominating role over social influence (i.e. WOM) in making revisit intention. Therefore, WOM was not able to influence the strength of relationship of CS and RVI.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Social conformity theory

Stimulus-organism-response

Structural equation modeling with partial least squares

Rhou Y, Singal M (2020) A review of the business case for CSR in the hospitality industry. Int J Hosp Manag 84:102330

Google Scholar  

Berezina K, Cobanoglu C, Miller BL, Kwansa FA (2012) The impact of information security breach on hotel guest perception of service quality, satisfaction, revisit intentions and word-of-mouth. Int J Hosp Manag 24(7):991–1010

Shariff SNFBA, Omar MB, Sulong SNB, Majid HABMA, Ibrahim HBM, Jaafar ZB, Ideris MSKB (2015) The influence of service quality and food quality towards customer fulfillment and revisit intention. Can Soc Sci 11(8):138–144

Rana M, Lodhi R, Butt G, Dar W (2017) How determinants of customer satisfaction are affecting the brand image and behavioral intention in fast food industry of Pakistan. J Tour Hospit 6(316):2167-0269

Marinkovic V, Senic V, Ivkov D, Dimitrovski D, Bjelic M (2014) The antecedents of satisfaction and revisit intentions for full-service restaurants. Mark Intell Plan 32(3):311–327

Harun A, Prybutok G, Prybutok VR (2018) Insights into the antecedents of fast-food purchase intention and the relative positioning of quality. Qual Manag J 25(2):83–100

Qin H, Prybutok VR (2009) Service quality, customer satisfaction, and behavioral intentions in fast-food restaurants. Int J Qual Serv Sci 1(1):78–95

Chen JV, Htaik S, Hiele TM, Chen C (2017) Investigating international tourists’ intention to revisit Myanmar based on need gratification, flow experience and perceived risk. J Qual Assur Hosp Tour 18(1):25–44

Ali F, Amin M, Ryu K (2016) The role of physical environment, price perceptions, and consumption emotions in developing customer satisfaction in Chinese resort hotels. J Qual Assur Hosp Tour 17(1):45–70

Pareigis J, Edvardsson B, Enquist B (2011) Exploring the role of the service environment in forming customer’s service experience. Int J Qual Serv Sci 3(1):110–124

Ozdemir B, Caliskan O (2015) Menu design: a review of literature. J Foodserv Bus Res 18(3):189–206

Sadeghi M, Zandieh D, Mohammadi M, Yaghoubibijarboneh B, Nasrolahi Vosta S (2017) Investigating the impact of service climate on intention to revisit a hotel: the mediating role of perceived service quality and relationship quality. Int J Manag Sci Eng Manag 12(1):12–20

Blackston M, Lebar E (2015) Constructing consumer-brand relationships to better market and build businesses. In: Fournier S, Breazeale M, Avery J (eds) Strong brands, strong relationships. Routledge, Abingdon, p 376

Malik SA, Jaswal LH, Malik SA, Awan TM (2013) Measuring service quality perceptions of the customers of restaurant in Pakistan. Int J Qual Res 7(2):187–200

Sivadas E, Jindal RP (2017) Alternative measures of satisfaction and word of mouth. J Serv Mark 31(2):119–130

Ha Y, Im H (2012) Role of web site design quality in satisfaction and word of mouth generation. J Serv Manag 23(1):79–96

Zhang W, Yang J, Ding X-Y, Zou X-M, Han H-Y, Zhao Q-C (2019) Groups make nodes powerful: Identifying influential nodes in social networks based on social conformity theory and community features. Expert Syst Appl 125:249–258

Peri C (2006) The universe of food quality. Food Qual Prefer 17(1):3–8

Susskind AM, Chan EK (2000) How restaurant features affect check averages: a study of the Toronto restaurant market. Cornell Hotel Restaurant Adm Q 41(6):56–63

Jin N, Lee S, Huffman L (2012) Impact of restaurant experience on brand image and customer loyalty: moderating role of dining motivation. J Travel Tour Mark 29(6):532–551

Carins JE, Rundle-Thiele S, Ong DL (2020) Keep them coming back: the role of variety and aesthetics in institutional food satisfaction. Food Qual Prefer 80:103832

Josiam BM, Monteiro PA (2004) Tandoori tastes: perceptions of Indian restaurants in America. Int J Contemp Hosp Manag 16(1):18–26

Choi J, Zhao J (2010) Factors influencing restaurant selection in south florida: Is health issue one of the factors influencing consumers’ behavior when selecting a restaurant? J Foodserv Bus Res 13(3):237–251

Ryu K, Han H (2010) Influence of the quality of food, service, and physical environment on customer satisfaction and behavioral intention in quick-casual restaurants: moderating role of perceived price. J Hosp Tour Res 34(3):310–329

Deming WE (1982) Quality, productivity, and competitive position. Massachusetts Institute of Technology, Center for Advanced Engineering Study, Cambridge

Juran JM, Gryna FM, Bingham RS (1974) Quality control handbook. McGraw-Hill, Michigan

Dabholkar PA (2015) How to improve perceived service quality by increasing customer participation. Paper presented at the Proceedings of the 1990 Academy of Marketing Science (AMS) annual conference

Lai IK (2015) The roles of value, satisfaction, and commitment in the effect of service quality on customer loyalty in Hong Kong–style tea restaurants. Cornell Hosp Q 56(1):118–138

Jalilvand MR, Salimipour S, Elyasi M, Mohammadi M (2017) Factors influencing word of mouth behaviour in the restaurant industry. Mark Intell Plan 35(1):81–110

Wall EA, Berry LL (2007) The combined effects of the physical environment and employee behavior on customer perception of restaurant service quality. Cornell Hotel Restaurant Adm Q 48(1):59–69

Yuksel A, Yuksel F, Bilim Y (2010) Destination attachment: effects on customer satisfaction and cognitive, affective and conative loyalty. Tour Manag 31(2):274–284

Adam I, Adongo CA, Dayour F (2015) International tourists’ satisfaction with Ghanaian upscale restaurant services and revisit intentions. J Q Assur Hosp Tour 16(2):181–201

Baek E, Choo HJ, Yoon S-Y, Jung H, Kim G, Shin H, Kim H (2015) An exploratory study on visual merchandising of an apparel store utilizing 3D technology. J Global Fashion Mark 6(1):33–46

Wu H-C, Ko YJ (2013) Assessment of service quality in the hotel industry. J Qual Assur Hosp Tour 14(3):218–244

Pizam A, Shapoval V, Ellis T (2016) Customer satisfaction and its measurement in hospitality enterprises: a revisit and update. Int J Contemp Hosp Manag 28(1):2–35

Westbrook RA, Oliver RL (1991) The dimensionality of consumption emotion patterns and consumer satisfaction. J Consum Res 18(1):84–91

Prayag G, Hosany S, Muskat B, Del Chiappa G (2017) Understanding the relationships between tourists’ emotional experiences, perceived overall image, satisfaction, and intention to recommend. J Travel Res 56(1):41–54

Alegre J, Garau J (2010) Tourist satisfaction and dissatisfaction. Ann TTour Res 37(1):52–73

Arndt J (1967) Word of mouth advertising: a review of the literature. Advertising Research Foundation, New York

Bayus BL (1985) Word of mouth-the indirect effects of marketing efforts. J Advert Res 25(3):31–39

Harrison-Walker LJ (2001) The measurement of word-of-mouth communication and an investigation of service quality and customer commitment as potential antecedents. J Serv Res 4(1):60–75

Curina I, Francioni B, Hegner SM, Cioppi M (2020) Brand hate and non-repurchase intention: a service context perspective in a cross-channel setting. J Retail Consum Serv 54:102031

Hennig-Thurau T, Gwinner KP, Walsh G, Gremler DD (2004) Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? J Interact Mark 18(1):38–52

Berger J, Schwartz EM (2011) What drives immediate and ongoing word of mouth? J Mark Res 48(5):869–880

Moliner-Velázquez B, Ruiz-Molina M-E, Fayos-Gardó T (2015) Satisfaction with service recovery: moderating effect of age in word-of-mouth. J Consum Mark 32(6):470–484

Royo-Vela M, Casamassima P (2011) The influence of belonging to virtual brand communities on consumers’ affective commitment, satisfaction and word-of-mouth advertising: the ZARA case. Online Inf Rev 35(4):517–542

Dhillon J (2013) Understanding word-of-mouth communication: a case study of banking sector in India. J Bus Manag 9(3):64–72

Chien M (2017) An empirical study on the effect of attractiveness of ecotourism destination on experiential value and revisit intention. Appl Ecol Environ Res 15(2):43–53

Abubakar AM, Ilkan M, Al-Tal RM, Eluwole KK (2017) eWOM, revisit intention, destination trust and gender. J Hosp Tour Manag 31:220–227

Chen C-F, Tsai D (2007) How destination image and evaluative factors affect behavioral intentions? Tour Manag 28(4):1115–1122

Allameh SM, Khazaei Pool J, Jaberi A, Salehzadeh R, Asadi H (2015) Factors influencing sport tourists’ revisit intentions: the role and effect of destination image, perceived quality, perceived value and satisfaction. Asia Pac J Mark Logist 27(2):191–207

Mehrabian A, Russell JA (1974) The basic emotional impact of environments. Percept Motor Skills 38(1):283–301

Zhang KZK, Benyoucef M (2016) Consumer behavior in social commerce: a literature review. Decision Support Syst 86:95–108. https://doi.org/10.1016/j.dss.2016.04.001

Article   Google Scholar  

Lee H-J, Yun Z-S (2015) Consumers’ perceptions of organic food attributes and cognitive and affective attitudes as determinants of their purchase intentions toward organic food. Food Qual Prefer 39:259–267

Yeh C-H, Wang Y-S, Li H-T, Lin S-Y (2017) The effect of information presentation modes on tourists’ responses in Internet marketing: the moderating role of emotions. J Travel Tour Mark 34(8):1018–1032

Lim SH, Lee S, Kim DJ (2017) Is online consumers’ impulsive buying beneficial for e-commerce companies? An empirical investigation of online consumers’ past impulsive buying behaviors. Inf Syst Manag 34:85–100

Wang L, Baker J, Wakefield K, Wakefield R (2017) Is background music effective on retail websites? J Promot Manag 23(1):1–23

Ali F (2016) Hotel website quality, perceived flow, customer satisfaction and purchase intention. J Hosp Tour Technol 7(2):213–228

Wang Z, Du C, Fan J, Xing Y (2017) Ranking influential nodes in social networks based on node position and neighborhood. Neurocomputing 260:466–477

Saunders MN (2011) Research methods for business students, 5/e. Pearson Education India, Bengaluru

Flick U (2015) Introducing research methodology: a beginner’s guide to doing a research project. Sage, Thousand Oaks

Zikmund WG, Babin BJ, Carr JC, Griffin M (2013) Business research methods. Cengage Learning, Boston

Lai W-T, Chen C-F (2011) Behavioral intentions of public transit passengers—the roles of service quality, perceived value, satisfaction and involvement. Transp Policy 18(2):318–325

Widianti T, Sumaedi S, Bakti IGMY, Rakhmawati T, Astrini NJ, Yarmen M (2015) Factors influencing the behavioral intention of public transport passengers. Int J Qual Reliab Manag 32(7):666–692

Hair JF Jr, Sarstedt M, Hopkins L, Kuppelwieser VG (2014) Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. Eur Bus Rev 26(2):106–121

Krejcie RV, Morgan DW (1970) Determining sample size for research activities. Educ Psychol Measur 30(3):607–610

Rahi S, Alnaser FM, Ghani MA (2019) Designing survey research: recommendation for questionnaire development, calculating sample size and selecting research paradigms. In: Economic and Social Development: Book of Proceedings, pp 1157–1169

Wahab S, bin Mohamad Shah MF, Faisalmein SN (2019) The relationship between management competencies and internal marketing knowledge towards internal marketing performance. Paper presented at the Proceedings of the Regional Conference on Science, Technology and Social Sciences (RCSTSS 2016)

Namkung Y, Jang S (2007) Does food quality really matter in restaurants? Its impact on customer satisfaction and behavioral intentions. J Hosp Tour Res 31(3):387–409

Liu C-H, Chou S-F, Gan B, Tu J-H (2015) How “quality” determines customer satisfaction: evidence from the mystery shoppers’ evaluation. TQM J 27(5):576–590

Meng JG, Elliott KM (2008) Predictors of relationship quality for luxury restaurants. J Retail Consum Serv 15(6):509–515

Cham TH, Lim YM, Aik NC, Tay AGM (2016) Antecedents of hospital brand image and the relationships with medical tourists’ behavioral intention. Int J Pharm Healthc Mark 10(4):412–431

Sekaran U (2006) Research methods for business: a skill building approach. Wiley, New York

Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50. https://doi.org/10.2307/3151312

Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334

Simonin BL (1999) Ambiguity and the process of knowledge transfer in strategic alliances. Strateg Manag J 20(7):595–623. https://doi.org/10.1002/(sici)1097-0266(199907)20:7%3c595:aid-smj47%3e3.0.co;2-5

Robson MJ, Katsikeas CS, Bello DC (2008) Drivers and performance outcomes of trust in international strategic alliances: the role of organizational complexity. Organ Sci 19(4):647–665. https://doi.org/10.1287/orsc.1070.0329

Greene CN, Organ DW (1973) An evaluation of causal models linking the received role with job satisfaction. Adm Sci Q 18(1):95–103

Konrad AM, Linnehan F (1995) Formalized HRM structures: Coordinating equal employment opportunity or concealing organizational practices? Acad Manag J 38(3):787–820

Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879–903. https://doi.org/10.1037/0021-9010.88.5.879

Podsakoff PM, Organ DW (1986) Self-reports in organizational research: problems and prospects. J Manag 12(4):531–544. https://doi.org/10.1177/014920638601200408

Scott SG, Bruce RA (1994) Determinants of innovative behavior: a path model of individual innovation in the workplace. Acad Manag J 37(3):580–607

Simonin BL (2004) An empirical investigation of the process of knowledge transfer in international strategic alliances. J Int Bus Stud 35(5):407–427

Lowry PB, Gaskin J (2014) Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Trans Prof Commun 57(2):123–146

Hair JF Jr, Hult GTM, Ringle C, Sarstedt M (2016) A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, Thousand Oaks

Nunnally JC (1978) Psychometric theory. McGraw-Hill, New York

Gefen D, Straub D, Boudreau M-C (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst 4(1):7

Hair J (2013) Using the SmartPLS software. Kennesaw State University. Powerpoint presentation/lecture

Vinzi VE, Chin WW, Henseler J, Wang H (2010) Handbook of partial least squares: concepts, methods and applications. Springer, Berlin

Geisser S (1974) A predictive approach to the random effect model. Biometrika 61(1):101–107

Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B (Methodol) 36:111–147

Carrión GC, Nitzl C, Roldán JL (2017) Mediation analyses in partial least squares structural equation modeling: guidelines and empirical examples. In: Latan H, Noonan R (eds) Partial least squares path modeling. Springer, Berlin, pp 173–195

Lockyer T (2003) Hotel cleanliness—How do guests view it? Let us get specific. A New Zealand study. Int J Hosp Manag 22(3):297–305

Ha J, Jang SS (2010) Perceived values, satisfaction, and behavioral intentions: the role of familiarity in Korean restaurants. Int J Hosp Manag 29(1):2–13

Ryu K, Han H, Jang S (2010) Relationships among hedonic and utilitarian values, satisfaction and behavioral intentions in the fast-casual restaurant industry. Int J Contemp Hosp Manag 22(3):416–432

Ryu K, Lee H-R, Gon Kim W (2012) The influence of the quality of the physical environment, food, and service on restaurant image, customer perceived value, customer satisfaction, and behavioral intentions. Int J Contemp Hosp Manag 24(2):200–223

Jang S, Liu Y, Namkung Y (2011) Effects of authentic atmospherics in ethnic restaurants: investigating Chinese restaurants. Int J Contemp Hosp Manag 23(5):662–680

Martín-Ruiz D, Barroso-Castro C, Rosa-Díaz IM (2012) Creating customer value through service experiences: an empirical study in the hotel industry. Tour Hosp Manag 18(1):37–53

Kuo Y-F, Hu T-L, Yang S-C (2013) Effects of inertia and satisfaction in female online shoppers on repeat-purchase intention: the moderating roles of word-of-mouth and alternative attraction. Manag Serv Qual Int J 23(3):168–187

Download references

Acknowledgements

The authors gratefully acknowledge the conducive research environment support provided by Department of Management Sciences at COMSATS University Islamabad, Wah Campus and Higher Education Commission Pakistan for provision of free access to digital library.

The authors declare that there was no source of funding for this research.

Author information

Authors and affiliations.

Department of Management Sciences, COMSATS University Islamabad, Wah Campus, G. T. Road, Wah Cantt., 47040, Pakistan

Amer Rajput

Management Sciences, Riphah International University, Al-Mizan IIMCT Complex, 274-Peshawar Road, Rawalpindi, Pakistan

Raja Zohaib Gahfoor

You can also search for this author in PubMed   Google Scholar

Contributions

RG conceptualized the study while corresponding author AR furnished the data analysis and finalized the manuscript for the submission. Both authors read and approved the final manuscript..

Corresponding author

Correspondence to Amer Rajput .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1..

PLS Algorithm.

Additional file 2.

Bootstrapping.

Additional file 3.

Blindfolding.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Rajput, A., Gahfoor, R.Z. Satisfaction and revisit intentions at fast food restaurants. Futur Bus J 6 , 13 (2020). https://doi.org/10.1186/s43093-020-00021-0

Download citation

Received : 18 October 2019

Accepted : 26 February 2020

Published : 04 June 2020

DOI : https://doi.org/10.1186/s43093-020-00021-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

fast food industry research paper

Advertisement

Advertisement

Big data in the food supply chain: a literature review

  • Original Article
  • Open access
  • Published: 24 January 2022
  • Volume 4 , pages 33–47, ( 2022 )

Cite this article

You have full access to this open access article

  • Abderahman Rejeb 1 ,
  • John G. Keogh 2 &
  • Karim Rejeb 3  

14k Accesses

35 Citations

5 Altmetric

Explore all metrics

The emergence of big data (BD) offers new opportunities for food businesses to address emerging risks and operational challenges. BD denotes the integration and analysis of multiple data sets, which are inherently complex, voluminous and are often of inadequate quality and structure. While BD is a well-established method in supply chain management, academic research on its application in the food ecosystem is still lagging. To fill this knowledge gap and capture the latest developments in this field, a systematic literature review was performed. Forty-one papers were selected and thoroughly examined and analysed to identify the enablers of BD in the food supply chain. The review primarily attempted to obtain an answer to the following research question: “What are the possibilities of leveraging big data in the food supply chain?“ Six significant benefits of applying BD in the food industry were identified, namely, the extraction of valuable knowledge and insights, decision-making support, improvement of food chain efficiencies, reliable forecasting, waste minimization, and food safety. Finally, some challenges and future research directions were outlined.

Similar content being viewed by others

fast food industry research paper

Big data optimisation and management in supply chain management: a systematic literature review

Idrees Alsolbi, Fahimeh Hosseinnia Shavaki, … Mukesh Prasad

fast food industry research paper

Big Data Analytics for Supply Chain Management: A Literature Review and Research Agenda

fast food industry research paper

Benefits of Big Data in Supply Chain Management

Avoid common mistakes on your manuscript.

1 Introduction

The food industry is an integral part of every economy and plays a critical role in supplying the necessities for human survival and provides consumer choice (Turi et al. 2014 ). According to estimates, US$14 trillion of foods is produced, packaged and sold worldwide every year and encompasses a multitude of transactions between suppliers, retailers and consumers (Ji et al. 2017 ). At the same time, the global food system is still encountering a series of serious challenges such as the increase of world population, rapid urbanization, ageing of countries’ populations, sustainability, and the alarming global change of the environment (Cerqueira et al. 2019 ). Similarly, the fragmented nature of global food supply chains presents an additional challenge to respond to consumers’ requirements in terms of food safety, quality, and authenticity. The food supply chain is a dynamic system encompassing food brands, primary producers, processors, regulators, third-party actors and other resources engaged in various processes and governance (Yu and Nagurney 2013 ). With the fast pace of technology developments, the conventional ways of managing and delivering food products to markets and consumers are evolving. Today, technology is viewed as a critical enabler, and Nambiar (Nambiar 2010 ) argued that food suppliers could use technology to enable continuous monitoring to preserve quality and provide cheaper food products to consumers. The use of technology results in increased operational efficiencies and savings throughout all the links of the food supply chain (Huscroft et al. 2013 ; Jayaraman et al. 2008 ; Jovanovic et al. 1994 ).

Digital technologies are constantly developed and deployed across the agro-food system, from the farmer to the consumer (Rotz et al. 2019 ). Over the past twenty years, advances in information and communication technologies (ICTs) have enabled new opportunities and innovations for improving the outcomes of agricultural activities (Xin and Zazueta 2016 ). For example, Radio Frequency IDentification (RFID) technology can be integrated into the food supply chain allowing organizations to gain enhanced granularity in supply chain traceability for compliance and business process improvement (Attaran and Attaran 2007 ). RFID also enables the real-time monitoring and visibility of re-usable assets such as pallets or totes carrying food products. It facilitates the acquisition of more accurate inventory data and tracking of food cargo at various levels of aggregation in the supply chain. The emergence of the Internet of Things (IoT) enhances the pervasive presence of ‘things’ or ‘objects’ with RFID tags, sensors and actuators interacting or participating on a network (Atzori et al. 2010 ). This can benefit the food industry and improve aspects such as the management of food loss (food loss occurs in pre-consumer phases) and food waste (Wen et al. 2018 ). The use of IoT in food chains has also intensified with billions of ubiquitous and interconnected devices ranging from mobile tools, equipment and machinery on farms to household appliances and temperature-sensing devices (Rao and Clarke 2019 ). When IoT is combined with other technologies, it helps to visualize food supply chain processes and geographic mapping of supply routes (Rejeb 2018a , b ; Rejeb et al. 2019 ). Furthermore, sophisticated tools, devices and technology also include autonomous guided vehicles (AGV), precision farming using robotics and artificial intelligence (AI), distributed ledger technology (DLT), cloud computing and BD tools that combine to reshape agriculture at an unprecedented pace (Phillips et al. 2019 ). Technology is leveraged to process and handle large data streams from multiple sources and origins in the food chain.

BD is perceived to be a critical technology in food chains, agriculture, and other sectors of the economy (Sonka 2014 ). BD is defined as “ a conglomeration of the booming volume of heterogeneous data sets, which is so huge and intricate that processing it becomes difficult, using the existing database management tools ” (Subudhi et al. 2019 , p.2). It can be understood as the processing and analysis of large data sets obtained from various sources such as online user interactions, consumer-generated content, commercial transactions, sensor devices, monitoring systems or any other consumer tracking tools (Li et al. 2019 ). BD also refers to the massive amounts of digital information about human activities, which are generated by a wide range of high-throughput tools and technologies (Marchetti 2016 ). According to Cavanillas et al. ( 2016 ), BD is an emerging field where innovative technology offers new ways of extracting value from the volumes of data and information generated. In the context of food supply chains, BD is a fast-growing area that supports decision-making processes, differentiates and identifies final products based on market demands, and aids in food safety (Armbruster and MacDonell 2014 ). Research and developments on crop improvement and sustainable agriculture have significantly benefitted from the usage of BD in crop modelling for targeting genotypes to different environments (Löffler et al. 2005 ). For instance, analyses based on consumption and crop growth data could aid farmers in determining which crop varieties to plant and which to minimize, enhancing crop yield, increasing sales, and maximizing returns on investment (Tao et al. 2021 ). Similarly, the use of big geospatial data (e.g., from wireless networks, farm machinery telemetry, and periodic remote sensing) enables better management practices in soil erosion, water pollution, and disaster risk management in agriculture (Řezník et al. 2017 ). The ability to collect and analyze data on crop variety, quantity, quality, location, weather events, market prices, and management decisions can support predictive analytics tasks and enable farmers and farming cooperatives to improve crop forecasting (Jakku et al. 2019 ). The use of BD also encourages the development of precision agriculture, which contributes to water conservation (O’Connor et al. 2016 ), soil preservation, limited carbon emissions (Ochoa et al. 2014 ), and optimal productivity (Mayer et al. 2015 ).

Furthermore, the advent of BD has the potential to improve the design of food supply chains, the relationship development among stakeholders, enhance customer service systems, and manage daily value-added operations (Waller et al. 2013 ). The application of BD can help food businesses become more profitable by increasing their operational efficiencies, improving their potential economic gains, and optimizing their resource allocation. When BD is combined with artificial intelligence (AI) tools, the risks related to the occurrences of pathogens, contaminants or adulterants used in economically motivated adulterations (EMA) in the agriculture chain can be predicted (Marvin et al. 2017 ; Spink et al. 2019 ). Although these benefits are tangible, several challenges remain.

While BD has gained remarkable attention from both scholars and practitioners, research investigating the applications of BD in food chains remains scarce (Rotz et al. 2019 ). Moreover, few studies are using BD analytics with a focus on sustainable agriculture and food supply chains (Kamble et al. 2019 ). Therefore, to fill this knowledge gap, the primary goal of this study is to explore the relevant literature and identify how BD potentially impacts food supply chains. By synthesizing the literature published in leading journals, authors strive to demonstrate how the adoption of BD in the food supply chain will improve operational efficiencies, enhance food quality and safety, and develop a sustainable food ecosystem. In dealing with this increasingly important topic, this study aims to provide a deeper understanding of the following research question (RQ):

RQ: What are the possibilities of leveraging BD in the food supply chain?

The contributions of this research to the BD literature is significant. Based on the authors’ current understanding and knowledge, this study presents the first reference to the potential of BD in food supply chains. Besides, the review is among the first to capture the dynamic nature of this topic, providing a systematic review of the recent investigations on BD in the context of food supply chains from literature appearing in leading journals. The review of previous scholarly research provides a timely summary of current evidence that can be used to increase the understanding of BD for scholars focused in the food, technology and supply chain industry. Food industry practitioners and decision-makers can derive new insights into how to design sustainable food supply chains with the emerging field of BD. Thus, this study is motivated by the limited discourse about the usefulness of BD in supply chain management (Engelseth et al. 2018 ). Hence, this gap in the literature is what authors explicitly intend to fill.

The remainder of the paper is structured as follows. Section  2 describes the methodology of the review. The subsequent section presents the statistical classification of publications. Section  4 provides a detailed discussion of the possibilities of BD in the food supply chain based on the findings of the reviewed literature. In Section 5 , some challenges of BD are discussed. The last section concludes the papers, discusses the research contributions, limitations and future research directions.

2 Methodology

2.1 research protocol development.

To answer the research question of the present study, the authors conducted a systematic review of published literature following the guidelines proposed by Denyer and Tranfield ( 2009 ). A systematic literature review (SLR) is a scientific activity that aims to evaluate and interpret all available research relevant to a particular research question or topic area or phenomenon of interest (Kitchenham and Charters 2007 ; Kitchenham 2004 ). An SLR is also a method that helps to consolidate and advance scientific research through locating, appraising and summarizing the existing literature. In order to survey the current state of scientific knowledge regarding the research question, an SLR is driven by prescribed steps to ensure the relevance of the retrieved literature, the minimization of research errors and bias, and the reliability of the quality assessment. The presentation and the process of the SLR in this study aim to establish a familiarity with what is already published about BD applications in the food supply chain. Along the process, care is taken in ensuring that the steps of the review are transparent, rigorous, reliable and repeatable. Furthermore, the authors developed and strictly followed a review protocol that is based on the iterative cycle of identifying adequate search keywords, selecting the relevant studies, and eventually carrying out the analysis. The review protocol is generated based on the central research question and the search string in order to extract the relevant studies. All the authors jointly specified and developed the necessary stages of the protocol. Table 1 describes in detail the selection of the search database, the collection of studies, and the eligibility criteria.

2.2 Data collection

Based on the surveyed Scopus research database, the initial result of the search queries was 131 publications. To further refine the results, the corresponding author undertook the removal of duplicates and the articles with missing bibliographic data points. The publications were also analyzed and filtered according to the eligibility criteria mentioned in Table  1 . The authors screened the titles and abstracts to identify the initial relevant studies, retrieving 62 publications for full-text review. After reading the full content and assessing the quality of articles, a total number of 41 articles were selected for complete review. The final selection of articles was guided by the research question of this study. In other words, out of the 62 publications, authors only considered publications that identified the possibilities of BD from the food chain perspective. As a result, all the 41 publications were relevant to the scope of the present study, and they provided discussions on BD from the perspective of food supply chains. Figure  1 shows the process of data collection.

figure 1

Schematic presentation of data collection

3 Statistical classification of publications

3.1 publications by year, country, and journal, 3.1.1 publications by year.

The search was carried out in October 2019. Figure  2 presents the number of publications published by year and extracted from the execution of the research protocol. Despite being a well-established technology, the interest in BD within the food industry has considerably increased over the recent years. Papers studying the application of BD to food supply chains were almost all published from 2013 onward. More specifically, there is an upward trend in the number of articles published on the subject from the year 2013 to 2019. The number of articles published from 2014 onward has exponentially increased, showing that the applications of BD have gained more recognition and increasing academic attention among food chain researchers. The reason is that many globalized food supply chains are currently migrating to an Industry 4.0 setting, embracing modern technological solutions that are commonly used in other industries (e.g., automotive industry). Industry 4.0 represents a milestone for the modernization and acceleration of food supply chains. As a critical technological component of this emerging paradigm, BD promises a revolutionary leap in the management of food chains among highly dispersed networks of several actors. BD contributes to the successful development of data-driven food supply chains responding to the core needs of businesses and other stakeholders. Out of the total reviewed studies, 36 papers were published in the last three years (2016-2019), reflecting that the integration of BD into food chain activities is still a nascent research area worth discussing and exploring in a much more in-depth manner.

figure 2

Publication details according to year

3.1.2 Publications by country

In order to analyse the geographical distribution of publications concerning BD in the food supply chain, the authors’ affiliations were identified at the time of publication. As shown in Fig.  2 , a significant contribution to the BD literature in the context of food supply chains came primarily from the USA and the UK, with 15 and 7 papers, respectively. This finding is predictable for both countries. For example, Armbruster and MacDonell ( 2014 ) noted that several efforts are steadily underway in the US food system to harness BD to preserve the quality and safety of food products. BD applications in weather and climate have been applied in the USA in the establishment of climate predictions and disaster response in real-time network systems using satellite image data (Lee et al. 2015 ). According to the analytical agency Mind Commerce, the market size of BD in the US in 2013 reached $20 billion, whereas, in 2014, the value was $29, achieving a growth rate of 45% (Ramzaev 2015 ). The importance of BD is also rising in the UK, where the technology has been identified as a driver for economic growth and one of the eight key government priorities (Government 2013 ). The UK government invested £ 73 million to help public and academic projects to unlock the potential of BD in diverse sectors of the economy. Agrimetrics is one of the agricultural innovation centres recently launched in the UK to engage with the food industry stakeholders and enable detailed and collective understanding of the needs of farmers, food producers, retailers, and consumers through the use of BD and analytical tools (Agrimetrics 2015 ). To a lesser extent, scholars from Canada and China were equally responsible for the publication of 4 articles. In this regard, Barrados and Mitchell ( 2017 ) pointed out that there is a proliferation of automated data systems in Canada. This finding is consistent with the assertion of Clarke and Margetts ( 2014 ) who noted that the government of Canada was later than the UK and the US in introducing an open data initiative, which was set up in 2011 by Tony Clement, President of the Treasury Board. Five countries, including India, Japan, Malaysia, South Korea, and Spain, were responsible for ten articles (two each). Only one publication was identified in every remaining country within the sample of the relevant literature.

When authors considered the analysis of publications on a continental basis, researchers from North America are the central contributors to the literature representing 37% of the total participation. To a lesser extent, relevant contributions for each of Europe and Asia represented respectively, 29% and 24% of the total studies. There was an increasing international focus on BD applications to food supply chains that are reflected in the contributions of developing countries in Africa with 8% of the total relevant studies. In comparison, Oceania represents 2% of the total studies. These findings suggest that the rise of BD is not limited to developed economies, but also the technology has extended to the food supply chains of the developing economies (Fig. 3 ).

figure 3

The distribution of publications among countries

3.1.3 Publications by journal

The reputation and credibility of the journal ranking have a significant impact on how people assess the value of the publication. The classification of journals was facilitated by the use of the BibExcel tool. The reviewed publications were from 37 journals. While ranking the journals based on the citation analysis, twenty-nine (29) articles were published in journals that had an impact factor in Journal Citation Report- JCR (2019) . Table  2 presents the journal titles, the number of publications, and the impact factors exceeding 4. The category “ Others ” includes 29 journals, of which only 18 journals have an impact factor. It should be noted that all the publications spanned across a wide range of fields that cover food sciences, manufacturing, computer sciences, supply chain management and logistics, and business. The variety of the scope of the journals reflects the multi-dimensional perspectives of BD and its versatile applications to several areas in the food supply chain.

3.2 Big data publications based on the type of research

Figure  4 presents the distribution of the selected 41 papers by the methodological approach used. Two main research approaches were identified for the classification of articles; conceptual and empirical. Conceptual papers review and discuss the applications, theories, capabilities, and challenges of BD based either on the extant literature or without the collection of primary data. However, empirical papers tend to present data collected through case studies, interviews and focus on measurable and visible BD activities and processes in the food supply chain through other methodological approaches such as algorithmic analyses, prototypes, and system designs. As shown in Figs.  4 , 17 papers provided a conceptual discussion or review on BD. The remaining 24 papers dealt with the topic using empirical research approaches that included case studies and interviews (7), algorithmic and mathematic analyses (4), prototype and system design (4), survey and multi-methods (3). Table  3 presents in detail the classification of these studies according to their methodological approaches.

figure 4

Distribution based on the type of research

Figure  5 shows the trend of how different research approaches have been used to study BD in the context of food supply chains during the period 2013-2019. The trend depicted in Fig.  5 reveals that there is a steep increase in the conceptual and review studies. The trend also shows that the concepts applied to BD research are being tested and validated through empirical techniques and methods such as case studies, interviews, algorithms, prototypes and surveys. While there is a sharp increase in theoretical studies, the increase in studies using empirical investigations is not significant. Therefore, empirical studies are necessary in order to assess the effectiveness and efficiency of BD in the food supply chain.

figure 5

Distribution of research approaches during the period 2013-2019

4 Review discussion

4.1 increased knowledge and insights.

In highly uncertain business environments, the dynamic and globalized nature of the food supply chains has created both fragmentation and complexity with a higher dependency on data and information analysis (Gereffi et al. 2012 ; Kamble et al. 2019 ). Large unstructured data sets are now generated on a real-time basis, which challenges the current approaches for decision-making and calls for a revamped focus on advanced analytical tools (Xin and Zazueta 2016 ). The proliferation of new technologies has given rise to a wave of data originating from different sources such as IoT and wireless sensor networks, the web, mobile applications, and social media. The ability to effectively process these data, manage information and extract knowledge is becoming key for achieving competitive advantage (Curry 2016 ). Advances in information technology offer new possibilities to extract new insights and knowledge from BD (Akhtar et al. 2018 ). The advantage of BD tools compared to conventional analytics and business intelligence is their ability to more effectively process the massive volume of data than others (Subudhi et al.  2019 ; Alfian et al. 2017 ).

In food supply networks, BD enables companies to discover consumers’ needs, create new values, and improve the management of their organizational processes (Ji et al. 2017 ). According to Engelset et al. ( 2019 ), BD is not a pure technology per-se; instead, it is a valuable method and tool set to manage, analyze, capture, search, share, store, transfer, visualize and query supply chain information. In the agriculture field, BD can help to efficiently extract value from the vast amounts of data such as environmental information, biological data, agricultural equipment information, monitoring data of production processes, sales and management data, food safety procedures, yield rates and soil health (Li et al. 2019 ). The high capabilities to process and handle large datasets can optimize the operational decisions and coordination in the food chain. As such, the knowledge gained from the application of BD can be useful in designing adaptive processes for the optimization of the food supply chain. In this context, companies operating in the food industry would be able to optimize process steps from procurement to production to marketing by deriving new insights that were traditionally ‘hidden’ within data patterns (Ji et al. 2017 ). In this regard, Sonka ( 2014 ) argued that BD tools are more efficient in enabling analysts to explore massive quantities of texts and identify the relevant descriptors within the information. BD allows food retailers to adapt and become consumer-centric by providing useful analytical tools necessary for extracting relevant insights into consumer sentiments and behaviours (Singh et al. 2018 ).

In the era of BD, food supply chains are heavily dependent on the use of technology to create valuable knowledge. The mining of the data generated at each echelon of the supply chain provides an effective basis for agri-food decision-making, optimization of processes, and identification of interdependencies (Li et al. 2019 ). For example, a BD platform is needed to handle a large amount of unstructured and continuously generated real-time sensor data (Alfian et al. 2017 ). The time and temperature information retrieved from the sensor network and analyzed with BD tools provide real-time insights into the product shelf-life information (Li and Wang 2017 ) and can help to reduce food waste. The intuitiveness of IoT networks and connected sensors across the food supply chain can be enhanced with BD to capture data related to time and temperature and to share it with exchange partners in order to dynamically manage the optimization of storage, packaging, delivery and selling according to the data drawn from the sensor networks (Li and Wang 2017 ). The increased data visualization capability can be applied in real-time to fresh food supply chains to improve customer value and reduce costs (Engelseth et al. 2019 ). Khanna et al. ( 2018 ) argued that the combination of BD, advanced information and computational technologies could improve knowledge of the processes and relationships in the agri-food sector. Tan et al. ( 2017 ) pointed out that the ability of BD to extract embedded knowledge from large amounts of data can help to solve several specific issues in the halal food industry, such as the contamination of halal food products. Therefore, food businesses, including small and medium enterprises can utilize BD to create actionable knowledge and insights, strengthen their oversight and management of data, and improve their competitiveness in the increasingly competitive global marketplace (O’Connor and Kelly 2017 ). Based on the previous discussion, we develop the following research proposition (RP):

RP1: BD supports food supply chains by increasing knowledge and actionable insights.

4.2 Improved decision-making

According to Malakooti ( 2012 ), decision-making is a complex, multi-dimensional process that can take place spontaneously without any prior planning, or it may emerge after exhaustive and well-contrived analysis. The complexity of supply chain management has resulted in a lengthy decision-making process due to the time required to access information that is necessary to make business decisions. In the context of global food supply chains, strategic decision-making is essential as the holistic efforts could increase the profitability of an entire chain from an efficient framework (Zhong et al. 2017 ). Despite the advances in technology and decision support systems, achieving responsive and adequate decision making is a difficult task. However, leveraging BD in food supply chains can significantly improve decision-making. Moreover, BD counteracts the conventional ways of thinking and decision-making that are based on the intuition and experience of the owner or manager (O’Connor and Kelly 2017 ). BD enables a more informed, evidence-based decision-making (Akhtar et al. 2018 ) by providing managers with access to explicit information and equipping them with new tools and capabilities (Sonka 2014 ). BD provides sophisticated tools where farmers can assess different scenarios from different farming decisions (Xin and Zazueta 2016 ). In this regard, Kamilaris et al. ( 2018 ) developed the AgriBigCAT platform that can support farmers in their decision-making processes and administration planning to meet the challenges of increasing food production at a lower environmental impact. Moreover, BD increases the visualization of information across the food network and drives enhanced transparency, higher productivity, and informed decision making (Ji et al. 2017 ). Decision making would no longer be undertaken in food supply chains with insufficient or fragmented data and information. Consumers also benefit from the outputs of BD initiatives as it can provide contextual information about the food, its origin, method of processing and other information, which aids in a more informed purchasing decision. Lin and Mahalik ( 2019 ) argued that BD improves data storage and enhances the application of agri-food scientific research by providing intelligent decision-making. Tan et al. ( 2017 ) noted that halal industry players could make better and more efficient decisions using BD. Therefore, BD enables food supply chain exchange partners to be involved in interactive and consistent decision processes. BD leads to more intelligent and smarter decision making that can improve the operational performance of food chains, reduce costs, minimize the cycle time of decisions, and mitigate potential risks. Thus, we suggest the following research proposition:

RP2: BD facilitates decision-making processes in the food supply chain.

4.3 Improved efficiencies

Managing efficiencies in food supply chains is an ongoing process that requires the better utilization of available resources, the optimization of processes, and the minimization of costs (Angkiriwang et al. 2014 ). Hence, food supply chains are pressured to enhance efficiencies at every stage, from procurement, logistics, manufacturing, marketing and sales to after-sale services. Similarly, the agri-food sector is dynamic, diverse, and requires more sophisticated tools to improve efficiencies (Duncan et al. 2019 ).

As technology is critical for improving supply chain efficiencies (Attaran 2017 ), the use of BD and its visualization capabilities allows firms to automate the process of exploring hidden patterns that can occur in the food supply chain efficiently and cost-effectively (Ji et al. 2017 ). BD allows food supply chain businesses to explore every opportunity to improve their operational efficiencies, simplify processes, and reduce transaction costs. The management, analysis and response to food-related data can be facilitated through BD and automated to predict situations in real-time (Tzounis et al. 2017 ). For example, Kshetri ( 2017 ) argued that a system based on BD could deliver information to farmers and water service providers on a real-time basis about the current and predicted water and soil moisture levels. Alfian et al. ( 2017 ) proposed a real-time monitoring system that utilizes smartphone-based sensors and BD to handle IoT-generated sensor data and helps food operators to implement critical strategies related to the perishable supply chain. Farmers can capitalize on BD to monitor the health status of animals in the food chain. To confirm this development, Sivamani et al. ( 2018 ) proposed a method based on BD to control the nutritional intake of the livestock, improve the health and diet of animals, and support the early detection of diseases.

While the applications of BD to agriculture dates back to the 1990 s (Carolan and Carolan 2017 ), the technology can play a substantial role in advancing modern precision agriculture. Precision agriculture is a technology-driven approach for the management of farming activities such as the monitoring, estimation and prediction of crop-related data. According to Bucci et al. ( 2018 ) precision agriculture is adopted by innovative farmers who rely on the capabilities of BD to enable the intelligent usage of precision farming data. Similarly, BD is a promising instrument for farmers wishing to develop smart agriculture, improve their productivity, and enhance their integration in the food supply chain. The constellation of technologies in the agri-food sector, such as remote sensing, satellite imagery and high-spatial-resolution BD from farms, has already produced a sophisticated method of farming that increases the efficiency of agricultural production and enables site-specific crop and livestock management decisions (Khanna et al. 2018 ). In this respect, Li and Mahalik ( 2019 ) posit that BD can utilize data from GPS/GIS to track crop yields, determine the optimization of crops, and increase harvesting productivity. The combination of BD with IoT data can help farmers optimize their farm operation. In research by Kamilaris et al. ( 2018 ), BD is used in an online software platform to analyze geophysical information from various sources, estimate the impact of livestock on the environment, and increase resource efficiencies. Khanna et al. ( 2018 ) noted that in 2017, Great Lakes Watershed Management System brought environmental forecasting capability to precision agriculture by allowing farmers to input GIS coordinates for their fields, run tillage and fertilizer management scenarios, and to view predicted estimates of nutrient loading and soil erosion to nearby water bodies. Therefore, the enormous potential of BD applications to enhance precision agriculture is evident in the reviewed papers. Therefore, BD aids in the efficient usage of scarce resources (e.g. water) and the optimization of crop cultivation and harvesting. Furthermore, BD helps to develop more accurate models for agriculture management and monitoring of farming activities. Consequently, we introduce the following research proposition:

RP3: BD has a positive impact on the operational efficiencies of food supply chains.

4.4 Reliable forecasting

Food supply chains are inherently complex to the extent that inputs cannot be completely controlled, managed, and safeguarded against uncertainties. Therefore, forecasting is a necessary activity that aims to evaluate the value of events in the future with uncertainty based on the observed patterns from the previous record (Ahmed 2004 ). Demand forecasting has long been a critical issue of the food industry that calls for reconsidering sophisticated technologies such as BD to aid more accurate and useful forecasting (Nita 2015 ). Hence, BD can act as a critical enabler in the food supply chain because of its power to aid forecasting accuracy and precision. The predictive capabilities of BD are beneficial to support the management of food chains, which are increasingly characterized by their short life cycles and speed of response. Moreover, the technology enables the systematization of demand forecasting, resulting in improved accuracy of consumers’ demands, reduced distribution costs and disposal losses (Nita 2015 ). Farm management and operations will dramatically change because of the high resolution of BD information, real-time forecasting, and transparent prediction models. In crop management, Badr et al. ( 2016 ) noted that BD could provide the data required to run crop models under different climate and management scenarios, and this approach is useful for mitigating some food security issues. The authors argued further that technology and BD-centric forecasting could support decision-makers, crop growers, and researchers to gain a deeper understanding, better manage supply and demand of the food chain, anticipate food-related challenges, and develop practical solutions to overcome food insecurity and price uncertainties. Testing the credibility of forecasting results, Nita ( 2015 ) found that a BD-enabled system for a food manufacturer could produce a high forecasting accuracy within 70% of the target commodities. The benefits of proper and reliable forecasting include the optimization of food chain operations, lower product perishability, better planning and utilization of resources, and the improvement of the overall supply chain performance. BD also drives more collaborative forecasting and scheduling between the food business and its supply chain exchange partners, resulting in better inter-organizational collaborations. Thus, the following research proposition emerges:

RP4: BD leads to more accurate and reliable forecasting in the food supply chain.

4.5 Waste minimization

In the context of agri-food supply chains, waste represents a catch-all term that encompasses non-value-adding activities, excess inventories, additional wait times, unnecessary processing steps, and other variabilities. According to Hicks et al. ( 2004 ), waste is a strategic issue in the supply chain that forces companies to seek ways to minimize all types of waste and thus achieve cost-savings. Research on food waste has established that one-third of the food produced is either wasted or is lost, accounting for 1.3 billion tons per year (Mishra and Singh 2018 ). (Note, food loss refers to pre-consumption stages such as pre and post-harvest loss whereas food waste occurs when the food is consumable but discarded).

Supply chain waste may stem from ineffective quality or process control, and large quantities of inventories can perish in agri-food supply chains. As the minimization of resource waste is a topic of paramount importance in the food supply chain, there is a high potential for BD tools to reduce waste in the food supply chain (Mishra and Singh 2018 ). The minimization of food waste through BD can result in increased resource utilization, better profitability and reduced risk of food insecurity. The visualization capabilities of BD can enhance the traceability of food supply chains and the visibility of key business processes. Belaud et al. ( 2019 ) pointed out that BD leads to more sustainable food supply chain designs that valorize agricultural waste. Li and Wang ( 2017 ) developed a BD-based system that aggregates time when the temperature exceeds a certain threshold at each stage of a supply chain and estimates the impact of improper quality control and perishability of food products (e.g., reduction in shelf-life, risk of spoilage). This increased control enables retailers and manufacturers to deliver satisfactory food quality and overcome the severe financial consequences of food loss and waste in the supply chain. Another benefit of BD tools is transparency, in the sense that whenever products pass through the supply chain, effective waste-related decisions can be dynamically made, such as pricing of food products based on their current shelf-life (Li and Wang 2017 ). The possibility of uncovering hidden and valuable insights with BD can also help food chain actors to reduce overall waste. For example, retailers today are utilizing BD for waste reduction by using consumer complaints made in retail stores (Mishra and Singh 2018 ). Data captured from social media (e.g., Twitter) can be analyzed using BD in order to develop effective waste minimization policies in the food chain. Therefore, BD contributes to more sustainable food chains as it can dramatically reduce the occurrence of perishability in the food chain and the immense food loss and food waste. Beyond overcoming the economic losses of waste, the technology also helps to incorporate other sustainability considerations that are relevant to food safety. For example, the aggregation of food data in a BD system empowers the trace-back and track-forward capabilities of the business. Hence, this capability enables the reduction of unnecessary food waste and the fast detection of products involved in foodborne illness outbreaks, their sources, and their current locations (if still in the supply chain). As a result, we outline the following research proposition:

RP5: BD reduces waste in the food supply chain.

4.6 Food safety

Food safety represents a growing and critically important public health issue (Aung and Chang 2014 ). It is a joint responsibility of all actors involved in the food industry to ensure that food is safe to consume. With the increasing concerns and awareness of consumers toward food safety, food supply chain partners are obligated to secure and protect food products from any sort of contamination or adulteration, whether it be unintentional or intentional. The assurance of food safety means that food is safe from causing harm (Demartini et al. 2018 ). To maintain food safety, the use of technology and information systems can provide incentives and accountability measures that are critical for identifying best manufacturing practices for food operators at various stages in the food supply chain (Ahearn et al. 2016 ). In this regard, Marvin et al. ( 2017 ) confirmed the significant role of BD in predicting the presence of pathogens or contaminants by matching the information on environmental factors with pathogen growth or hazard occurrence. Zhang et al. ( 2013 ) developed algorithms that used BD and visualized images to model contamination conditions in an IoT-based food supply chain, helping to develop consumer confidence in the food ecosystem. To assist farmers in the selection of the most eco-friendly beef cattle supplier, Singh et al. ( 2018 ) proposed a BD cloud-computing framework for carbon minimization. The captured information related to carbon footprint can be used by abattoir and processors in their supplier selection decisions while accommodating carbon footprint emissions in this process. Moreover, the deployment of BD in combination with ERP, IoT and other data sources connected to logistics providers can facilitate enhanced product tracking and risk management of food. By providing real-time information about the product, its condition (e.g., temperature), destination routes, including traffic and weather patterns, BD may prove valuable for trend detection of potential contamination during the delivery of food products (Tan et al. 2017 ). As stated earlier, the increased transparency gained from the BD application can provide thorough and real-time monitoring of the quality of perishable food products. In the highly complex global food chain, BD enables supply chain exchange partners to establish more effective and cooperative relationships in order to maintain food safety and enhance transparency. Li and Wang ( 2017 ) outlined that with BD applications, consumers would be able to obtain more information about the product shelf-life variation over time. Access to a granular level of information creates a conducive environment that not only assures food safety but it establishes more trust, confidence and commitment. Such digital transformation, is, according to Li and Wang ( 2017 ), a suitable framework for strategic innovation for marketing, quality management, and supply chain optimization. Therefore, BD can be viewed as a critical and value-adding element for food safety management that can respond to consumers’ growing concerns about food quality and safety. Based on the previous discussion, we suggest the following research proposition:

RP6: BD improves food safety management across the supply chain.

5 Further challenges of big data

The application of BD has tremendous potential in food supply chains. To achieve competitiveness, the food and restaurant industry could embrace BD to derive actionable business insights, make evidence-based decisions (Coble et al. 2018 ; Lokers et al. 2016 ), optimize operational efficiencies, produce reliable forecasts, minimize food waste, and ensure food quality and safety. In their study, Ma et al. ( 2018 ) argued that BD could enable restaurant owners to predict future visitors. For the service-oriented food industry, the implementation of BD has become a necessity given the ability of the technology to provide insights into customer spending habits and support restaurants to more accurately grasp the market trend (Tai et al. 2020 ). Although the benefits of BD for food supply chain players, including those operating in the foodservice industry, are tangible, several challenges are still hampering its wide-scale implementation.

5.1 Data complexity

According to Waldherr et al. ( 2017 ), the challenges of BD stem mainly from the growing amounts of data, the high speed of data generation, and the diversity of data formats and structures. The BD ecosystem is characterized by a great variety of data sources and the velocity of data flows for which advanced computational methods are imperative to analyze data (Zhou 2019 ). Similarly, the need for these methods and techniques is pressing as they allow to manage knowledge of chemical components of foods of importance to human health (Tao et al. 2018 ). Moreover, the increasing interconnectedness and complexity of BD result in overlaps, various links of data, and growing noise. To purify BD, food businesses are required to devise new strategies, tools and technologies that can improve data quality and analysis. In BD applications, poor data quality or so-called “dirty data” (Li et al. 2019 ) could increase concerns over the reliability and validity of BD analyses and create additional costs for food firms. For example, analysts approximate that the cost of poor data quality within a typical business is between 8% and 12% of revenues (Sethuraman 2012 ). Therefore, subtracting noise from BD is a challenging task because data keeps on varying inconsistently concerning time, thereby affecting the mechanism of effective data management (Subudhi et al.  2019 ).

5.2 Security and privacy issues

The BD-driven food supply chains bring enormous challenges for food businesses, especially during data collection, storage, visualization, and information sharing. For instance, these include issues about data security and privacy (Sharma et al. 2018 ,  2020 ). As per Duncan et al. ( 2019 ), cybersecurity threats are problematic in the BD era because of inappropriate access to BD systems, data, or analytical technologies and the nefarious use of information for fraudulent food activities. Food supply chain partners need to secure the public and private information of individuals and businesses, including physical and digital footprints, searches, transaction histories, audio and video communications, service registrations, conversations, and messages (Li et al. 2019 ). The BD ecosystem is fraught with data security risks, which necessitate being carefully evaluated before food businesses engage in the adoption of BD systems. Thus, to sharpen their competitive advantage, food businesses have to ensure a high level of data security to implement BD successfully. Furthermore, the aggregation of data from different and distant information sources has also raised several privacy concerns due to the so-called private information leakage (Guo and Wang 2019 ). As a result, BD systems might entail collecting consumers’ private information without consideration of regulations, laws, and existing standards. Therefore, consumer-privacy issues could deter food businesses from shifting towards BD-enabled food supply chains.

5.3 Organizational challenges

At the organizational level, the lack of necessary capabilities and resources might hinder the applications of BD in the food industry. In this context, Kshetri ( 2017 ) points out that organizations might be in shortage of BD engineers and scientists who can understand, interpret, and perform analytics. This critique is also highlighted in the study of Tan et al. ( 2017 ) who argue that the halal industry still encounters the lack of talented professionals who could work with BD tools and techniques. Besides the need for analytical and technical know-how, organizations might commit sizeable initial investment to implement BD systems (Sonka 2014 ). For resource-constrained food businesses, BD might not be an economically feasible solution since the incorporation of IoT-based systems, and the expansion of human resources through BD corporate training programs could be a costly and risky investment. BD applications in food services can be unaffordable and almost exclusively developed for larger food firms. Therefore, when seeking to invest in BD applications, incapacitated food industry stakeholders, including farmers and foodservice organizations, could be skeptical of the benefits of BD for their business processes and reluctant to integrate BD systems into their organizational structure. This uncertainty could be further aggravated by the lack of interoperability (Jeppesen et al. 2018 ) among the technologies leveraged in the food supply chain.

6 Conclusions

This study aims to investigate the current state of research on the applications of BD to food supply chains by conducting an SLR on all relevant studies through an appropriate review methodology. Forty-one (41) articles were thoroughly examined and analyzed for this purpose. The findings of this SLR showed that the application of BD to food supply chains is getting increasingly popular with an increase in the number of publications recently. Initially, the SLR was focused on identifying the type of methodologies that were used in the reviewed publications. The use of conceptual approaches to contextualize and extend discussions on the possibilities of BD in the food chain was frequently noticed. Empirical methodologies were employed to demonstrate and validate the effectiveness of BD in sustaining food supply chains from different aspects. A significant number of studies (n= 13) used a case study methodology and interviews to gather data. Some studies developed and proposed prototypes, applied surveys or created system designs to validate the benefits of BD to food manufacturers, retailers, and consumers. The enablers of BD in the food industry identified from the SLR contribute to the literature, concepts, and theories on the capabilities of BD in bringing effective solutions to the management of food chains. In many instances, the ability to extract useful knowledge and insights from data demonstrates the enormous potential of BD and is frequently reported in the majority of studies. However, an observed lack of research studies investigating the capabilities of BD in optimizing food processes and supporting food procurement, processing and marketing is identified. It can be a potential area for further research.

The theoretical findings reveal that previous research on the application of BD to food supply chains have focused primarily on providing the basic concepts of BD and use cases demonstrating its benefits. A paucity of studies synthesizing the advantages of BD was found in the literature review. Hence, this study fills a knowledge gap and presents a contribution to the literature in the form of a detailed SLR. The findings of the SLR revealed six key enablers of BD in the food supply chain namely;

Improved knowledge and predictive insights

Decision-making support

Enhanced efficiencies

More accurate forecasting

Process-based waste minimization

Food safety management

The findings of the review revealed that BD implementations could be impeded by the poor data quality, security and privacy concerns, lack of organizational capabilities and skills, high initial investment costs, and resistance to operate with BD systems. Thus, future research studies may investigate the solutions necessary to accelerate the uptake of BD in the food industry. Research in this direction will help to provide a more balanced understanding of what enables and hinders the development of BD-based food supply chains. Further, this study identifies that BD can be combined with other technological tools such as IoT, AI, cloud computing, and decision support systems (DSS) to substantiate the value of technology in the agri-food industry. Scholars may investigate to what extent food businesses can benefit from the integration of these technologies in the supply chain. The findings of the SLR are one of the initial attempts to contribute to the understanding of BD applications and its connection to the food research area. The utilization of BD could unlock several benefits and sustain the delivery of safer food products to consumers. Therefore, food industry practitioners and decision-makers would gain a deeper understanding of the promising role of BD in contributing to the evolution of sustainable activities in their organizations. The enablers of BD identified in this study may be considered in the formulation of guidelines necessary for BD implementations in the food chain.

Although this study provides a timely review of an increasingly emerging technological capability, we recognize several limitations. The use of Scopus as a comprehensive database does not guarantee the full coverage of the extant literature. Some articles outside of Scopus might be relevant to the scope of the study but have not been considered. Hence, we encourage the replication of review studies in the future and the use of other accessible databases such as Web of Science and Google Scholar. The findings of this study are also limited to the selected number of publications, and therefore, the theoretical inferences drawn here should be validated with other empirical research methods such as expert interviews.

Agrimetrics (2015) Agrimetrics: the first Centre for Agricultural Innovation is open for business. https://agrimetrics.co.uk/agrimetrics-the-first-centre-for-agricultural-innovation-is-open-for-business/ . Accessed 14 Nov 2019

Ahearn MC, Armbruster W, Young R (2016) Big Data’s potential to improve food supply chain environmental sustainability and food safety. Int Food Agribus Manag Rev 19:1–18. https://ideas.repec.org/a/ags/ifaamr/240704.html . Accessed 14  Nov 2019

Ahmed SR (2004) Applications of data mining in retail business. In: Int. Conf. Inf. Technol. Coding Comput. 2004 Proc. ITCC 2004, IEEE, Las Vegas, vol 2, pp 455-459. https://doi.org/10.1109/ITCC.2004.1286695

Akhtar P, Khan Z, Frynas JG, Tse YK, Rao-Nicholson R (2018) Essential micro-foundations for contemporary business operations: top management tangible competencies, relationship-based business networks and environmental sustainability. Br J Manag 29:43–62. https://doi.org/10.1111/1467-8551.12233

Article   Google Scholar  

Alfian G, Syafrudin M, Rhee J (2017) Real-time monitoring system using smartphone-based sensors and NoSQL database for perishable supply chain. Sustainability 9:2073. https://doi.org/10.3390/su9112073

Angkiriwang R, Pujawan IN, Santosa B (2014) Managing uncertainty through supply chain flexibility: reactive vs. proactive approaches. Prod Manuf Res 2:50–70. https://doi.org/10.1080/21693277.2014.882804

Armbruster WJ, MacDonell MM (2014) Informatics to support international food safety. In: Proc. 28th EnvironInfo 2014 Conf., Oldenburg, Germany, pp 127–134. http://enviroinfo.eu/sites/default/files/pdfs/vol8514/0127.pdf . Accessed 14 Nov 2019

Attaran M (2007) RFID: an enabler of supply chain operations. Supply Chain Manag Int J 12:249–257. https://doi.org/10.1108/13598540710759763

Attaran M (2017) Additive manufacturing: the most promising technology to alter the supply chain and logistics. J Serv Sci Manag 10:189–205

Google Scholar  

Atzori L, Iera A, Morabito G (2010) The internet of things: A survey. Comput Netw 54:2787–2805

Aung MM, Chang YS (2014) Traceability in a food supply chain: Safety and quality perspectives. https://doi.org/10.1016/j.foodcont.2013.11.007

Badr G, Klein LJ, Freitag M, Albrecht CM, Marianno FJ, Lu S, Shao X, Hinds N, Hoogenboom G, Hamann HF (2016) Toward large-scale crop production forecasts for global food security. IBM J Res Dev 60:5:1–5:11. https://doi.org/10.1147/JRD.2016.2591698

Banerjee S, Viswanathan V, Raman K, Ying H (2013) Assessing prime-time for geotargeting with mobile big data. J Mark Anal 1:174–183. https://doi.org/10.1057/jma.2013.16

Barrados M, Mitchell JI (2017) Getting started with big data: The promises and challenges of evaluating healthcare quality. In: Petersson GJ, Breul JD (eds) Cyber Soc. Big Data Eval. Comp. Policy Eval. Transaction Publishers, New Brunswick

Belaud J-P, Prioux N, Vialle C, Sablayrolles C (2019) Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Comput Ind 111:41–50. https://doi.org/10.1016/j.compind.2019.06.006

Blettler MCM, Abrial E, Khan FR, Sivri N, Espinola LA (2018) Freshwater plastic pollution: Recognizing research biases and identifying knowledge gaps. Water Res 143:416–424. https://doi.org/10.1016/j.watres.2018.06.015

Bronson K (2019) Looking through a responsible innovation lens at uneven engagements with digital farming, NJAS - Wagening. J Life Sci 90–91. https://doi.org/10.1016/j.njas.2019.03.001

Bronson K (2019) The digital divide and how it matters for canadian food system equity. Can J Commun 44:63–68

Bucci G, Bentivoglio D, Finco A (2018) Precision agriculture as a driver for sustainable farming systems: state of art in literature and research. Calitatea 19:114–121

Carolan M (2017) Publicising food: Big Data, precision agriculture, and co-experimental techniques of addition. Sociol Rural 57:135–154. https://doi.org/10.1111/soru.12120

Carolan M (2018) Big data and food retail: Nudging out citizens by creating dependent consumers. Geoforum 90:142–150. https://doi.org/10.1016/j.geoforum.2018.02.006

Cassavia N, Masciari E, Pulice C, Saccà D (2017) Discovering user behavioral features to enhance information search on Big Data. ACM Trans Interact Intell Syst 7:1–7. https://doi.org/10.1145/2856059

Cavanillas JM, Curry E, Wahlster W (2016) The big data value opportunity. In: New Horiz. Data-Driven Econ. Springer, Cham, pp 3–11

Cerqueira M, Pastrana LM (2019) Does the future of food pass by using nanotechnologies? Front Sustain Food Syst 3:16. https://doi.org/10.3389/fsufs.2019.00016

Clarke A, Margetts H (2014) Governments and citizens getting to know each other? Open, closed, and big data in public management reform. Policy Internet 6:393–417. https://doi.org/10.1002/1944-2866.POI377

Coble KH, Mishra AK, Ferrell S, Griffin T (2018) Big data in agriculture: A challenge for the future. Appl Econ Perspect Policy 40:79–96. https://doi.org/10.1093/aepp/ppx056

Curry E (2016) The big data value chain: definitions, concepts, and theoretical approaches. In: New Horiz. Data-Driven Econ. Springer, Cham, pp 29–37

Demartini M, Pinna C, Tonelli F, Terzi S, Sansone C, Testa C (2018) Food industry digitalization: from challenges and trends to opportunities and solutions. IFAC-Pap 51:1371–1378. https://doi.org/10.1016/j.ifacol.2018.08.337

Denyer D, Tranfield D (2009) Producing a systematic review. In: Sage Handb. Organ. Res. Methods, Sage Publications Ltd, Thousand Oaks, pp 671–689

Duncan SE, Reinhard R, Williams RC, Ramsey F, Thomason W, Lee K, Dudek N, Mostaghimi S, Colbert E, Murch R (2019) Cyberbiosecurity: a new perspective on protecting U.S. Food and Agricultural System. Front Bioeng Biotechnol 7. https://doi.org/10.3389/fbioe.2019.00063

Engelseth P, Wang H (2018) Big data and connectivity in long-linked supply chains. J Bus Ind Mark 33:1201–1208. https://doi.org/10.1108/JBIM-07-2017-0168

Engelseth P, Molka-Danielsen J, White BE (2019) On data and connectivity in complete supply chains. Bus Process Manag J 25:1145–1163. https://doi.org/10.1108/BPMJ-09-2017-0251

Fraser A (2019) Land grab/data grab: precision agriculture and its new horizons. J Peasant Stud 46:893–912. https://doi.org/10.1080/03066150.2017.1415887

Gereffi G, Lee J (2012) Why the world suddenly cares about global supply chains. J Supply Chain Manag 48:24–32. https://doi.org/10.1111/j.1745-493X.2012.03271.x

Giagnocavo C, Bienvenido F, Ming L, Yurong Z, Sanchez-Molina JA, Xinting Y (2017) Agricultural cooperatives and the role of organisational models in new intelligent traceability systems and big data analysis. Int J Agric Biol Eng 10:115–125. https://doi.org/10.25165/ijabe.v10i5.3089

Government HM (2013) Seizing the data opportunity: a strategy for UK data capability, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/254136/bis-13-1250-strategy-for-uk-data-capability-v4.pdf . Accessed 14 Nov 2019

Gružauskas V, Baskutis S, Navickas V (2018) Minimizing the trade-off between sustainability and cost effective performance by using autonomous vehicles. J Clean Prod 184:709–717. https://doi.org/10.1016/j.jclepro.2018.02.302

Guo T, Wang Y (2019) Big data application issues in the agricultural modernization of China. Ekoloji 28:3677–3688. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063972060&partnerID=40&md5=9242c206ed052dc847c93fc8cf10ce2e

Hicks C, Heidrich O, McGovern T, Donnelly T (2004) A functional model of supply chains and waste. Int J Prod Econ 89:165–174. https://doi.org/10.1016/S0925-5273(03)00045-8

Huscroft JR, Hazen BT, Hall DJ, Hanna JB (2013) Task-technology fit for reverse logistics performance. Int J Logist Manag 24:230–246. https://doi.org/10.1108/IJLM-02-2012-0011

Jakku E, Taylor B, Fleming A, Mason C, Fielke S, Sounness C, Thorburn P (2019) If they don’t tell us what they do with it, why would we trust them?” Trust, transparency and benefit-sharing in Smart Farming, NJAS - Wagening. J Life Sci: 90–91. https://doi.org/10.1016/j.njas.2018.11.002

Jayakrishnan M, Mohamad A, Azmi F, Abdullah A (2018) Adoption of business intelligence insights towards inaugurate business performance of Malaysian halal food manufacturing. Manag Sci Lett 8:725–736. http://growingscience.com/beta/msl/2824-adoption-of-business-intelligence-insights-towards-inaugurate-business-performance-of-malaysian-halal-food-manufacturing.html . Accessed 29 Nov 2019

Jayaraman V, Ross AD, Agarwal A (2008) Role of information technology and collaboration in reverse logistics supply chains. Int J Logist Res Appl 11:409–425. https://doi.org/10.1080/13675560701694499

Jeppesen JH, Ebeid E, Jacobsen RH, Toftegaard TS (2018) Open geospatial infrastructure for data management and analytics in interdisciplinary research. Comput Electron Agric 145:130–141. https://doi.org/10.1016/j.compag.2017.12.026

Ji G, Hu L, Tan KH (2017) A study on decision-making of food supply chain based on big data. J Syst Sci Syst Eng 26:183–198. https://doi.org/10.1007/s11518-016-5320-6

Jovanovic B, MacDonald GM (1994) The life cycle of a competitive industry. J Polit Econ 102:322–347. https://doi.org/10.1086/261934

Kamble SS, Gunasekaran A (2019) Big data-driven supply chain performance measurement system: a review and framework for implementation. Int J Prod Res 0:1–22. https://doi.org/10.1080/00207543.2019.1630770

Kamble SS, Gunasekaran A, Gawankar SA (2019) Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. Int J Prod Econ 219:179–194. https://doi.org/10.1016/j.ijpe.2019.05.022

Kamilaris A, Anton A, Blasi AB, Boldú FXP (2018) Assessing and mitigating the impact of livestock agriculture on the environment through geospatial and big data analysis. Int J Sustain Agric Manag Inform 4:98.  https://doi.org/10.1504/IJSAMI.2018.094809

Khanna M, Swinton SM, Messer KD (2018) Sustaining our natural resources in the face of increasing societal demands on agriculture: directions for future research. Appl Econ Perspect Policy 40:38–59. https://doi.org/10.1093/aepp/ppx055

Kitchenham B (2004) Procedures for undertaking systematic reviews: Joint technical report, Comput. Sci. Dep. Keele Univ. TRSE-0401 Natl. ICT Aust. Ltd0400011T 1

Kitchenham B, Charters S (2007) Guidelines for performing Systematic Literature Reviews in Software Engineering, Version 2.3, University of Keele (Software Engineering Group,School of Computer Science and Mathematics) and Durham (Department of Computer Science)

Kotaro O (2015) Predictive analytics solution for fresh food demand using heterogeneous mixture learning technology. NEC Tech J 10:83–86

Kshetri N (2017) The economics of the Internet of Things in the global south. Third World Q 38:311–339. https://doi.org/10.1080/01436597.2016.1191942

Lee J-P, Lee J-G, Mo E, Lee J, Lee J-K (2015) Design and implementation of disaster information alert system using python in ubiquitous environment. In: Park D-S, Chao H-C, Jeong Y-S, JJ (Jong H. Park) (eds) Adv Comput Sci Ubiquitous Comput. Springer, Singapore, pp 403–409

Li D, Wang X (2017) Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain. Int J Prod Res 55:5127–5141. https://doi.org/10.1080/00207543.2015.1047976

Li N, Mahalik NP (2019) A big data and cloud computing specification, standards and architecture: agricultural and food informatics. Int J Inf Commun Technol 14:159–174. https://doi.org/10.1504/IJICT.2019.097687

Li J, Li X, Peng Y (2019) Application of big data in agricultural internet of things. Rev Fac Agron 36:1521–1529. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073269640&partnerID=40&md5=17165555014b0b7d5e3b60a2f691cf6e

Löffler CM, Wei J, Fast T, Gogerty J, Langton S, Bergman M, Merrill B, Cooper M (2005) Classification of maize environments using crop simulation and geographic information systems. Crop Sci 45:1708–1716. https://doi.org/10.2135/cropsci2004.0370

Lokers R, Knapen R, Janssen S, van Randen Y, Jansen J (2016) Analysis of Big Data technologies for use in agro-environmental science. Environ Model Softw 84:494–504. https://doi.org/10.1016/j.envsoft.2016.07.017

Ma X, Tian Y, Luo C, Zhang Y (2018) Predicting future visitors of restaurants using Big Data. In: 2018 Int. Conf. Mach. Learn. Cybern. ICMLC, pp 269–274. https://doi.org/10.1109/ICMLC.2018.8526963

Malakooti B (2012) Decision making process: typology, intelligence, and optimization. J Intell Manuf 23:733–746. https://doi.org/10.1007/s10845-010-0424-1

Marchetti S, Giusti C, Pratesi M (2016) The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy. AStA Wirtsch.- Sozialstatistisches Arch 10:79–93. https://doi.org/10.1007/s11943-016-0190-4

Marvin HJP, Janssen EM, Bouzembrak Y, Hendriksen PJM, Staats M (2017) Big data in food safety: An overview. Crit Rev Food Sci Nutr 57:2286–2295. https://doi.org/10.1080/10408398.2016.1257481

Mayer J, Gunst L, Mäder P, Samson M-F, Carcea M, Narducci V, Thomsen IK, Dubois D (2015) Productivity, quality and sustainability of winter wheat under long-term conventional and organic management in Switzerland. Eur J Agron 65:27–39. https://doi.org/10.1016/j.eja.2015.01.002

Mishra N, Singh A, Rana NP, Dwivedi YK (2017) Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique. Prod Plan Control 28:945–963. https://doi.org/10.1080/09537287.2017.1336789

Mishra N, Singh A (2018) Use of twitter data for waste minimisation in beef supply chain. Ann Oper Res 270:337–359. https://doi.org/10.1007/s10479-016-2303-4

Nambiar AN (2010) Traceability in agri-food sector using RFID. In: 2010 Int. Symp. Inf. Technol., IEEE, Kuala Lumpur, Malaysia, pp 874–879. https://doi.org/10.1109/ITSIM.2010.5561567

Nita S (2015) Application of big data technology in support of food manufacturers commodity demand forecasting. NEC Tech J 10:90–93

Ochoa K, Carrillo S, Gutierrez L (2014) Energy efficiency procedures for agricultural machinery used in onion cultivation (Allium fistulosum) as an alternative to reduce carbon emissions under the clean development mechanism at Aquitania (Colombia). IOP Conf Ser Mater Sci Eng 59:012008. https://doi.org/10.1088/1757-899X/59/1/012008

O’Connor N, Mehta K (2016) Modes of greenhouse water savings. Procedia Eng 159:259–266. https://doi.org/10.1016/j.proeng.2016.08.172

O’Connor C, Kelly S (2017) Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector. J Knowl Manag 21:156–179

Phillips PWB, Relf-Eckstein J-A, Jobe G, Wixted B (2019) Configuring the new digital landscape in western Canadian agriculture, NJAS - Wagening. J Life Sci: 100295. https://doi.org/10.1016/j.njas.2019.04.001

Rao AR, Clarke D (2019) Perspectives on emerging directions in using IoT devices in blockchain applications. Internet Things 100079. https://doi.org/10.1016/j.iot.2019.100079

Ramzaev MV (2015) Modern aspects in development of branch applications on the basis of Big Data: possibilities, prospects and limitations. In: Proc. Inf. Technol. Nanotechnol. ITNT-2015 CEUR Workshop Proc, pp 355–363

Rejeb A (2018a) Blockchain potential in tilapia supply chain in Ghana. Acta Tech Jaurinensis 11:104–118

Rejeb A (2018b) Halal meat supply chain traceability based on HACCP, blockchain and Internet of Things. Acta Tech Jaurinensis 11:1–30. https://doi.org/10.14513/actatechjaur.v11.n1.000

Rejeb A, Keogh JG, Treiblmaier H (2019) Leveraging the Internet of Things and blockchain technology in supply chain management. Future Internet 11:161. https://doi.org/10.3390/fi11070161

Řezník T, Lukas V, Charvát K, Charvát K, Křivánek Z, Kepka M, Herman L, Řezníková H (2017) Disaster risk reduction in agriculture through Deospatial (Big) Data Processing. ISPRS Int J Geo-Inf 6:238. https://doi.org/10.3390/ijgi6080238

Rotz S, Duncan E, Small M, Botschner J, Dara R, Mosby I, Reed M, Fraser EDG (2019) The politics of digital agricultural technologies: a preliminary review. Sociol Rural 59:203–229. https://doi.org/10.1111/soru.12233

Sethuraman MS (2012) Big Data’s Impact on the Data Supply Chain, Cognizant, Cognizant, New Jersey

Sharma R, Kamble SS, Gunasekaran A (2018) Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives. Comput Electron Agric 155:103–120. https://doi.org/10.1016/j.compag.2018.10.001

Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A (2020) A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput Oper Res 119. https://doi.org/10.1016/j.cor.2020.104926

Singh A, Shukla N, Mishra N (2018) Social media data analytics to improve supply chain management in food industries. Transp Res Part E Logist Transp Rev 114:398–415. https://doi.org/10.1016/j.tre.2017.05.008

Singh A, Kumari S, Malekpoor H, Mishra N (2018) Big data cloud computing framework for low carbon supplier selection in the beef supply chain. J Clean Prod 202:139–149. https://doi.org/10.1016/j.jclepro.2018.07.236

Sivamani S, Choi J, Cho Y (2018) A service model for nutrition supplement prediction based on Fuzzy Bayes model using bigdata in livestock. Ann Oper Res 265:257–268. https://doi.org/10.1007/s10479-017-2490-7

Article   MathSciNet   MATH   Google Scholar  

Sonka S (2014) Big data and the Ag sector more than lots of numbers. Int Food Agribus Manag Rev 17:1–20. https://econpapers.repec.org/article/agsifaamr/163351.htm . Accessed 14 Nov 14 2019

Spink J, Bedard B, Keogh J, Moyer DC, Scimeca J, Vasan A (2019) International survey of food fraud and related terminology: preliminary results and discussion. J Food Sci 84:2705–2718. https://doi.org/10.1111/1750-3841.14705

Subudhi BN, Rout DK, Ghosh A (2019) Big data analytics for video surveillance. Multimed Tools Appl 78:26129–26162. https://doi.org/10.1007/s11042-019-07793-w

Tai CLP, Sou ROP, Lam CCC (2020) Chapter 21 - The role of information technology in the food industry. In: Gibson M (ed) Food Soc. Academic, London, pp 393–404. https://doi.org/10.1016/B978-0-12-811808-5.00021-0

Tan MII, Fezarudin FZ, Yusof FM, Rosman AS, Husny ZJM (2017) Review article on potentials of Big Data in the Halal Industry. Pertanika J Soc Sci Humanit 25:65–76

Tao Q, Cui X, Zhao S, Yang W, Li W, Zhang B, Yu R (2018) The food quality safety management system based on block chain technology and application in rice traceability. J Chin Cereals Oils Assoc 33:102–110

Tao Q, Ding H, Wang H, Cui X (2021) Application research: big data in food industry. Foods 10:2203. https://doi.org/10.3390/foods10092203

Tesfaye K, Sonder K, Caims J, Magorokosho C, Tarekegn A, Kassie GT, Getaneh F, Abdoulaye T, Abate T, Erenstein O (2016) Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data. Int Food Agribus Manag Rev 19:1–18

Turi A, Goncalves G, Mocan M (2014) Challenges and competitiveness indicators for the sustainable development of the supply chain in food industry. Procedia - Soc Behav Sci 124:133–141. https://doi.org/10.1016/j.sbspro.2014.02.469

Tzounis A, Katsoulas N, Bartzanas T, Kittas C (2017) Internet of things in agriculture, recent advances and future challenges. Biosyst Eng 164:31–48

Waldherr A, Maier D, Miltner P, Günther E (2017) Big Data, Big Noise: the challenge of finding issue networks on the web. Soc Sci Comput Rev 35:427–443. https://doi.org/10.1177/0894439316643050

Waller MA, Fawcett SE, Science D (2013) Predictive analytics, and Big Data: a revolution that will transform supply chain design and management. J Bus Logist 34:77–84. https://doi.org/10.1111/jbl.12010

Wen Z, Hu S, De Clercq D, Beck MB, Zhang H, Zhang H, Fei F, Liu J (2018) Design, implementation, and evaluation of an Internet of Things (IoT) network system for restaurant food waste management. Waste Manag 73:26–38. https://doi.org/10.1016/j.wasman.2017.11.054

Xin J, Zazueta F (2016) Technology trends in ICT – Towards data-driven, farmer-centered and knowledge-based hybrid cloud architectures for smart farming. Agric Eng Int CIGR J 18:275–279. https://cigrjournal.org/index.php/Ejounral/article/view/3937 . Accessed 14 Nov 2019

Yu M, Nagurney A (2013) Competitive food supply chain networks with application to fresh produce. Eur J Oper Res 224:273–282. https://doi.org/10.1016/j.ejor.2012.07.033

Zhang Q, Huang T, Zhu Y, Qiu M (2013) A case study of sensor data collection and analysis in Smart City: Provenance in smart food supply chain. Int J Distrib Sens Netw 9:382132. https://doi.org/10.1155/2013/382132

Zhong R, Xu X, Wang L (2017) Food supply chain management: systems, implementations, and future research. Ind Manag Data Syst 117:2085–2114. https://doi.org/10.1108/IMDS-09-2016-0391

Zhou T, Song Z, Sundmacher K (2019) Big Data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 5:1017–1026. https://doi.org/10.1016/j.eng.2019.02.011

Download references

Open access funding provided by Széchenyi István University (SZE).

Author information

Authors and affiliations.

Doctoral School of Regional Sciences and Business Administration, Széchenyi István University, 9026, Győr, Hungary

Abderahman Rejeb

Henley Business School, University of Reading, Greenlands, Henley-on-Thames, RG9 3AU, UK

John G. Keogh

Faculty of Sciences of Bizerte, University of Carthage, Zarzouna, Bizerte, 7021, Tunisia

Karim Rejeb

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Abderahman Rejeb .

Ethics declarations

Conflict of interests.

The authors declare that there is no conflict of interest regarding the submission of this article.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Rejeb, A., Keogh, J.G. & Rejeb, K. Big data in the food supply chain: a literature review. J. of Data, Inf. and Manag. 4 , 33–47 (2022). https://doi.org/10.1007/s42488-021-00064-0

Download citation

Received : 17 November 2020

Accepted : 29 December 2021

Published : 24 January 2022

Issue Date : March 2022

DOI : https://doi.org/10.1007/s42488-021-00064-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Supply chain
  • Food safety
  • Find a journal
  • Publish with us
  • Track your research
  • Research article
  • Open access
  • Published: 15 February 2022

Determining intention, fast food consumption and their related factors among university students by using a behavior change theory

  • Alireza Didarloo 1 ,
  • Surur Khalili 2 ,
  • Ahmad Ali Aghapour 2 ,
  • Fatemeh Moghaddam-Tabrizi 3 &
  • Seyed Mortaza Mousavi 4 , 5  

BMC Public Health volume  22 , Article number:  314 ( 2022 ) Cite this article

17k Accesses

4 Citations

Metrics details

Today, with the advancement of science, technology and industry, people’s lifestyles such as the pattern of people’s food, have changed from traditional foods to fast foods. The aim of this survey was to examine and identify factors influencing intent to use fast foods and behavior of fast food intake among students based on the theory of planned behavior (TPB).

A cross-sectional study was conducted among 229 university students. The study sample was selected and entered to the study using stratified random sampling method. Data were collected using a four-part questionnaire including Participants’ characteristics, knowledge, the TPB variables, and fast food consumption behavior. The study data were analyzed in SPSS software (version 16.0) using descriptive statistics (frequencies, Means, and Standard Deviation) and inferential statistics (t-test, Chi-square, correlation coefficient and multiple regressions).

The monthly frequency of fast food consumption among students was reported 2.7 times. The TPB explained 35, 23% variance of intent to use fast food and behavior of fast food intake, respectively. Among the TPB variables, knowledge ( r  = .340, p  < 0.001) and subjective norm ( r  = .318, p  < 0.001) were known as important predictors of intention to consume fast foods - In addition, based on regression analyses, intention ( r  = .215, p  < 0.05), perceived behavioral control ( r  = .205, p  < 0.05), and knowledge ( r  = .127, p  < 0.05) were related to fast food consumption, and these relationships were statistically significant.

Conclusions

The current study showed that the TPB is a good theory in predicting intent to use fast food and the actual behavior. It is supposed that health educators use from the present study results in designing appropriate interventions to improve nutritional status of students.

Peer Review reports

Over the past few decades, non-communicable diseases such as eczema, asthma, cancer, type 2 diabetes, obesity, etc. have increased in developed countries [ 1 , 2 ]. Also, these diseases are more prevalent with increasing urbanization in developing countries [ 3 , 4 , 5 ]. The occurrence of many non-communicable diseases is related to diet [ 6 ]. Food habits are rooted from cultural, environmental, economic, social and religious factors. An effective factor in the development of chronic diseases is lifestyle, dietary patterns and habits. Inappropriate food habits and unhealthy environments have increased the incidence of non-communicable diseases in the world [ 7 , 8 ].

Many developing countries with a tendency towards Western dietary culture go away from traditional and local diets [ 6 ]. Healthy foods with nutrients have been replaced by new foods called fast foods [ 9 ]. Fast food is the food prepared and consumed outside and often in fast food restaurants [ 10 ]. Fast food is often highly processed and prepared in an industrial fashion, i.e., with standard ingredients and methodical and standardized cooking and production methods [ 10 ]. In fast food, vitamins, minerals, fiber and amino acids are low or absent but energy is high [ 9 ]. Fast food consumption has increased dramatically in the last 30 years in European and American countries [ 11 ].

Previous studies reported patterns of inappropriate and harmful food consumption in Iranian children and adolescents [ 12 , 13 ]. Most fast food customers are adolescents and youth, as these products are quickly and easily produced and relatively inexpensive [ 14 ]. One Iranian study shows that 51% of children eat inappropriate snacks and drinks over a week [ 15 ]. It is also reported that adults today consume fast food more than previous generations [ 16 ]. Faqih and Anousheh reported that 20% of adolescents and 10% of adults consumed sandwiches 3 or more times a week [ 17 ].

According to two studies, children and adolescents who consume fast food have received more energy, saturated fat, sodium, carbohydrates and more sugar than their peers, but they have less fiber, vitamin A and C, and less fruit and vegetables [ 18 , 19 ]. Also, because of the use of oils to fry these foods at high temperatures, these types of foods may contain toxic and inappropriate substances that threaten the health of consumers [ 20 ].

In a study in the United States on young people between 13 and 17 years old, it was found that there is a significant relationship between weight gain and obesity with pre-prepared foods [ 21 ]. According to the Center for Disease Control and Prevention (2007–2008), 17% of children aged 2 to 19 years and 34% of those aged 20 years and older were obese [ 22 ]. Many Health problems were caused by human health behavior(e.g. exercising regularly, eating a balanced diet, and obtaining necessary inoculations, etc.) and studying behavior change theories/models provides a good insight into the causes and ways of preventing these problems [ 23 ]. One of these theories is the Theory of Planned Behavior (TPB), which is a developed form of the Theory of reasoned action (TRA), and describes a healthy behavior that is not fully under the control of a person [ 24 ]. This theory can successfully predict eating habits and behaviors, and recently this theory has received considerable attention from researchers in identifying norms and beliefs related to the use of fast food [ 25 ].

Based on the TPB, intention to conduct a behavior with following three concepts is controlled: 1. Attitudes (positive and negative evaluation of a behavior), 2. Subjective norms (social pressure received from peers, family, health care providers for doing or not doing a given health behavior), 3. Perceived behavior control (This refers to a person’s perception of the ease or difficulty of performing the behavior of interest.) [ 26 , 27 , 28 ].

The TPB has been tested on different behaviors such as healthy food choice [ 24 ], physical activity [ 29 ], and fast food consumption [ 30 ]. For instance, the study conducted by Seo et al. showed that fast food consumption behavior was significantly associated with behavioral intention and perceived behavioral control. In addition, their findings highlighted that behavioral intention was significantly related to subjective norm and perceived behavioral control [ 28 ].

Given that our study population has cultural diversity and nutritional behaviors different from the societies of other countries and According to the mentioned materials, the researchers decided to test the study with the aim of investigating and explaining the intention and behavior of fast food consumption and their related factors based on the TPB among Urmia University of Medical Sciences students. The results of this study will increase the awareness and knowledge about fast food and, in addition, its results can be used in research, hospitals and healthcare settings.

This cross-sectional study was performed on students of Urmia University of Medical Sciences located in northwest Iran in academic year of 2018–2019. The inclusion criteria for the study are females and males who studied at Urmia University of Medical Sciences, and students’ voluntary participation in the study and obtaining written consent from the students and University principals for the students’ participation in the study. The lack of willingness to continue participating in the study and not signing the informed consent form were considered as exclusion criteria.

According to the results of the study of Yar Mohammadi and et al. [ 31 ], with a 95% confidence interval and an error of 0.05, using the formula for estimating the proportion in society, taking into account the 10% drop rate, sample size was estimated 330students. A randomized stratified sampling method was used to select the study samples. The study sample was randomly selected from each of the strata based on the share of the total sample.

Questionnaire

The data gathering tool in this study was a self-reported questionnaire (Additional file  1 ), which was designed according to the existing measures in scientific literature [ 32 , 33 , 34 ]. The study instrument was translated from English to Persian using a standard forward-backward translation technique [ 35 ]. The original instrument was translated by a bilingual specialist. The Persian version was then retranslated into English by two independent bilingual professionals to assess retention of the original meaning in the source language. Subsequently, translators worked separately in the translation process and then prepared the final version of the Persian translation. Content validity of The Persian version of questionnaire was evaluated by a panel of experts such as 3 nutrition specialists, 3 health education specialists, and 2 instrument designers. After receiving their comments, crucial revisions were conducted in the study tool. Finally, validity of the study instrument was confirmed. The present questionnaire including four following sections:

General characteristics

The first part contains personal information such as age, gender, weight, height, field of study, student education, father’s education, mother’s education, father job, mother’s job, ethnicity, marital status, participating in nutrition educational classes, students’ monthly income, family’s monthly income, housing status, information resource for healthy nutrition.

Constructs of the TPB

The second part contains questions about the constructs of the theory of planned behavior (attitude, subjective norms, perceived behavioral control and behavioral intention). In general, attitudes, subjective norm and perceived behavioral control of students were measured using indirect items. The internal reliability of all subscales of the TPB variables was good, with a Cronbach’s alpha of 0.852.

Attitude toward fast food use

The attitude of the people was evaluated using 28 indirect items (14 items of behavioral beliefs, 14 items of expectations evaluation) based on five-point the Likert scale (from strongly agree to strongly disagree) or (from very important to not at all important), and the score of each item varied from 1 to 5. The minimum and maximum score for the attitude subscale was 14 and 350, respectively. The internal reliability of attitude subscale was good, with a Cronbach’s alpha of 0.778.

Subjective norm

Subjective norms of students were measured by 10 indirect items (5 items of normative beliefs, 5 items of motivation to comply) based on five-point the Likert scale (from strongly agree to strongly disagree) or (from very important to not at all important), and the score of each item varied from 1 to 5. The minimum and maximum score for the subjective norm subscale was 5 and 125, respectively. The internal reliability of subjective norm subscale was good, with a Cronbach’s alpha of 0.726.

Perceived behavioral control

Perceived behavioral control were measured by 18 indirect items (9 items of control beliefs, 9 items of perceive power) based on five-point the Likert scale (from strongly agree to strongly disagree) or (from extremely difficult to extremely easy), and the score of each item varied from 1 to 5. The minimum and maximum score for the perceived behavioral control subscale was 9 and 225, respectively. The internal reliability of subscale of perceived behavioral control was good, with a Cronbach’s alpha of 0.815.

Behavioral intention

Behavioral intention was evaluated by 8 items based on five-point the Likert scale (from strongly agree to strongly disagree), and the score of each item varied from 1 to 5. The minimum and maximum score for the Behavioral intention subscale was 8 and 40, respectively. The internal reliability of behavioral intention subscale was good, with a Cronbach’s alpha of 0.821.

Knowledge of participants

And the third and fourth parts are items related to food knowledge and fast food behavior. Students’ knowledge of fast food was evaluated by 14 items, and the score of each item varied from 0 to 2. The minimum and maximum score for the knowledge subscale was 0 and 28, respectively. The internal reliability of students’ knowledge was good, with a Cronbach’s alpha of 0.783.

Fast food use

Students’ fast food consumption was assessed by frequency of use in a past month. The term “Fast food” was defined as hamburgers, doughnuts, hot dog, snack, pizza, fried chicken and fried potatoes. The frequency of fast food use was analyzed for each food category.

Statistical analyses

All statistical analyzes were performed using SPSS 16.0 software. Descriptive statistics methods such as frequencies, means and standard deviations were used along with independent t and χ2 tests. Pearson correlation test was used to investigate the relationship between TPB variables with intent to use fast food and the real use of fast food. Multiple regressions were used for further analysis.

Descriptives

A total of 330 students were selected and recruited to the study, but some subjects (31 samples) were excluded from the study due to incomplete questionnaires (21cases), and no return of questionnaires (10 cases). Statistical analyses were performed on 229 students. Of these, 28.4% of the students were males and 71.6% were females. The results of the study showed that the average age for all the students was 22.10 ± 3.30 (the average age for male and female sexes were 22.66 ± 4.47 and 21.84 ± 2.50, respectively). The two sexes differed in terms of BMI, so that the mean of BMI was higher in boy students than in girls, and this difference was statistically significant. Almost more than 72% of the students had normal weight, and 28% of subjects were in other weights. Approximately 20.51, 54.50, 79.77% of the students reported the professional doctoral degree, Azeri ethnicity and single.

In addition, findings revealed that 64.90% of the participants lived in the dormitory, and 35.10% of them lived in personal or rental housing. The most common level of education for father (37.10%) and mother (44.10%) of students was diploma. Nearly, 46.50% of students gained food information (especially fast food) from health care providers, while 53.50% of them received their food information from other sources. Most students had zero monthly income, but 61.61% of the students reported their family’s monthly income more than 50 million Rials and 38.39% of their family had income lower than the mentioned amount. Table  1 provides detailed information on students’ characteristics.

Main analysis

Table 2 presents the mean score of knowledge and variables of the study-related theoretical framework. As the mean score of subjective norm, perceived behavioral control and behavioral intention in male students compared to female students was high, but those were not significant statistically( p  > 0.05).

Some variables of the TPB were significantly correlated with each other ( P  < 0.01, Table  3 ). In particular, fast food consumption behavior was highly ( r  = 0.382) correlated with behavioral intention. Multiple regression analyses were conducted to determine the relative importance of the variables of the TPB to behavioral intention and fast food consumption behavior (Tables  4 and 5 ). In these analyzes, when the attitude toward behavior, subjective norms, and perceived control was regressed to behavioral intention, the model was very significant ( P  = 0.000) and explained 0.347 of variance of behavioral intention. While attitude and perceived behavioral control were not significant, the subjective norms and students’ knowledge were significantly related to the intention to eat fast food. It seems that subjective norms and students’ knowledge to be the most important predictors of behavioral intent. Table  4 shows more information about predictors of behavioral intention.

The second model, using fast food consumption as a dependent variable, was also very significant ( P  = 0.000), and explained nearly a quarter of the variance (0.231) of fast food consumption. Both behavioral intention and perceived behavioral control were significantly associated with fast food consumption, of which behavioral intention appeared to be more important. Table  5 presents more information about predictors of fast food consumption.

This investigation was conducted on a sample of university students to assess the status of their fast-food consumption. It also examined the factors affecting behavioral intent and fast food consumption by applying the TPB. The results of the present study showed that students consumed fast food at an average of 2.7 times a month. Fast food in male students was often reported more than female students. A study on fast food consumption among students at Daejeon School reported monthly frequencies of fast food types: 2.7 for burgers, 2.1 for French fries, 1.8 for chicken [ 24 ]. Results of Kim study and other similar researches [ 31 , 36 ] approximately were in line with findings of the present study.

Given that most men do not have the time and skill to make traditional foods, and because of a lot of work, they prefer to turn to fast-foods, and so they are more likely to use fast foods. Meanwhile, the results of some studies indicate that most women are not very happy from high weight and are more likely to reduce their weight [ 37 ]. Therefore women do not have a positive attitude toward obesogenic foods compared to men [ 38 ], which can be a reason for consuming less fast food among women. Instead, the results of a study done by Seo et al. In Korea indicated that fast food consumption among high school students was 4.05 times a month and this consumption was reported among boys more than girls [ 28 ]. The results of the Korean study were contrary to the results of the study, meaning that fast food in Korean samples was more than Iranian. The reason for this difference can be traced to factors such as sample size, cultural, social, and economic characteristics of the samples.

Performing and not performing the behavior by a person is a function of several factors based on the theory of planned behavior. One of these factors is the person’s intention and desire to do the behavior. Behavioral intention itself is also affected by factors such as attitude, students’ knowledge, social pressure, and perceived behavioral control. In the present study, based on linear regression analysis, students’ knowledge and social pressure were both related to their intention and consume fast foods. That is, students who had the necessary information about nutrition, especially fast foods, had a high intent to choose and consume foods.

Several studies have examined the relationship between knowledge of foods and their contents and attitudes toward fast foods and processed foods or relationship between attitudes toward food additives and food choice behavior [ 39 , 40 , 41 , 42 ]. Aoki et al. [ 39 ] found that information about food and its contents positively or negatively affects attitudes and intentions towards food. They pointed out that food information was important for consumers in choosing food. Back and Lee [ 43 ] found that consumers had inadequate and incorrect information about foods, which could affect their attitudes or intent. These studies suggest that providing more information about foods and their compounds can help them to improve their attitude towards foods. Therefore, training on the performance, benefits and safety of foods, including positive and negative sides, should prevent misunderstandings about food supplements and reduce food safety concerns.

The findings of the present investigation showed that subjective norms of students were effective on intent to use fast foods. Friends had the most impact on the plan to eat fast foods, as expected. In addition, the normative beliefs of students were also more positive for friends than family and teachers. This conclusion suggests that most training programs should focus on their friends as a critical group that may affect intent to use fast foods.

Results of some previous studies were similar to findings of the current study. One study conducted by Mirkarimi et al. highlighted that subjective norms had the main role on students’ intent to use fast foods [ 44 ]. In the other words, they found that behavioral intention was affected by subjective norms. In addition, the study of Yarmohammadi and et al. showed that subjective norms predict intention and behavior [ 31 ].

In this study, TPB demonstrated to be a sound conceptual framework for explaining closely35% of the variance in students’ behavioral intention to consume fast-food. Among the TPB variables, subjective norm and knowledge of students were the most important predictors of intention to use fast foods. These findings are consistent with other results that identify that subjective norms have a significant effect on consuming fruits and vegetables [ 45 ]. In study of Lynn Fudge, Path analysis highlighted that TPB explained adolescent fast-food behavioral intention to consume fast food. The model identified subjective norms had the strongest relationship with adolescent behavioral intention to consume fast food [ 46 ].

The results of this study showed that the attitude toward fast food behavior did not predict intent and the behavior. However, some studies have reported contradictory findings with the study. For example, the findings of Stefanie and Chery’s study showed that attitude was a predictor for intent to use healthy nutrition [ 47 ]. Yarmohammadi and colleagues stated in their study that attitude was the most important predictor of behavioral intent [ 31 ]. In the study of determinants of fast food intake, Dunn et al. has identified attitude as a predictor of the intent of fast food consumption [ 32 ]. The results of studies by Seo et al., Ebadi et al., along with the findings of this study, showed that attitude toward fast food consumption is not significantly related to behavioral intention [ 28 , 48 ]. Based on the findings of the current study, fast-food consumption of students was also influenced by some the TPB variables. Multiple linear regression analyses revealed that the constructs of the TPB explained fast food use behaviors with R-squared (R 2 ) of 0.23. In these analyses, intention, perceived behavioral control, and knowledge were known as effective factors on fast-food consumption. Among the TPB constructs, behavioral intention was the most important predictor of fast-food consumption. The intention plays a fundamental role in the theory of planned behavior. The intentions include motivational factors that influence behavior and show how much people want to behave and how hard they try to do the behavior [ 49 ]. In study Ebadi et al., regression analysis showed the intention as a predictor of fast food consumption behavior [ 48 ]. In studies of Stefanie et al. and Seo et al., has reported intention as correlate of the behavior [ 28 , 47 ]. All these studies confirmed and supported this part of our study findings. In addition, the results indicated that perceived behavioral control directly influenced the behavior of fast-food consumption. Some investigations confirmed this portion of our results. For instance, the results of Dunn et al. showed that perceived behavioral control (PBC) and intent predicted the behavior of fast food consumption [ 32 ]. Also, in the study of Seo et al., regression analysis showed that fast food consumption behavior was correlated with perceived behavioral control [ 28 ]. Yarmohammadi et al. found that in predicting behavior, perceived behavioral control along with intention could predict 6% of behavior [ 31 ]. Although this study provides valuable knowledge regarding the relationships between behavioral intent and TPB variables, this study, like other studies, has a number of limitations. First, a cross-sectional study was used to examine the relationship between the variables. Due to the fact that in cross-sectional studies, all data are collected in a period of time, as a result, these studies do not have the necessary ability to examine the cause-and-effect relationships between variables. Second, the results of this type of study can only be generalized to populations with similar characteristics and have no generalizability beyond that. Third, since the data of this study were collected using the self-report questionnaire, the respondents may have errors and bias in completing the questionnaire and this can affect the results of the study.

In sum, this study was conducted to identify factors influencing intention and behavior of fast-food consumption among students by using the theory of planned behavior. The findings revealed that changeability of students’ intention to use fast food and their real behavior is dependent on the TPB variables. As this theoretical framework explained 35, 23% of intent to consume fast-foods and fast-food consumption, respectively. Among the TPB constructs, knowledge and subjective norm were known as the most important predictors of intention to use fast foods. In addition, the results indicated that intention and perceived behavioral control were the most important factors influencing consumption of fast foods among participants. It is imperative that health educators and promoters use these results in designing suitable educational interventions to improve people’s nutritional behavior.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are not publicly available due to confidentiality of data and subsequent research, but are available from the corresponding author on reasonable request.

Abbreviations

Theory of Planned Behavior

Theory of Reasoned Action

Statistical Package for Social Sciences

Body Mass Index

ISAAC Steering Committee. Worldwide variation in prevalence of symptoms of asthma, allergic rhino conjunctivitis, and atopic eczema: ISAAC. Lancet. 1998;351:1225–32.

Google Scholar  

Anonymous. Variations in the prevalence of respiratory symptoms, self-reported asthma attacks, and use of asthma medication in the European Community respiratory health survey (ECRHS). Eur Respir J. 1996;9:687–95.

Hijazi N, Abalkhail B, Seaton A. Diet and childhood asthma in a society in transition: a study in urban and rural Saudi Arabia. Thorax. 2000;55:775–9.

CAS   PubMed   PubMed Central   Google Scholar  

Asher MI, Montefort S, Björkstén B, et al. Worldwide time trends in the prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and eczema in childhood: ISAAC phases one and three repeat multicountry cross-sectional surveys. Lancet. 2006;368:733–43.

PubMed   Google Scholar  

Beaglehole R, Bonita R, Horton R, et al. Priority actions for the non-communicable disease crisis. Lancet. 2011;377:1438–47.

Devereux G. The increase in the prevalence of asthma and allergy: food for thought. Nat Rev Immunol. 2006;6:869–74.

CAS   PubMed   Google Scholar  

Nazari B, Asgari S, Sarrafzadegan N, et al. Evaluation and types of fatty acids in some of the most consumed foods in Iran. J Isfahan Med School. 2010;27(99):526–34.

Word Health Organization (WHO). Diet, nutrition and the prevention of chronic diseases report of a joint WHO/FAO expert consultation. Geneva: WHO.2003. Available at: http://whqlibdoc.who.int/publications/9241590416.pdf . [Accessed Jun 21, 2011].

Ashakiran S, Deepthi R. Fast foods and their impact on health. JKIMSU. 2012;1(2):7–15.

Vaida N. Prevalence of fast food intake among urban adolescent students. IJES. 2013;2(1):353–9.

Bowman SA, Vinyard BT. Fast food consumption of US adults: impact on energy and nutrient intakes and overweight status. J Am Coll Nutr. 2004;23(2):163–8.

Abdollahi M, Amini M, Kianfar H, et al. Qualitative study on nutritional knowledge of primary-school children and mothers in Tehran. EMHJ-Eastern Mediterranean Health Journal. 2008;14(1):82–9.

Shahanjarini A, Shojaezadeh D, Majdzadeh R, et al. Application of an integrative approach to identify determinants of junk food consumption among female adolescents. Iran J Nutr Sci Food Technol. 2009;4(2):61–70.

Lee JS. A comparative study on fast food consumption patterns classified by age in Busan. Korean J Commun Nutr. 2007;12(5):534–44.

Dehdari T, Mergen T. A survey of factors associated with soft drink consumption among secondary school students in Farooj city, 2010. J Jahrom Univ Med Sci. 2012;9(4):33–9.

Brownell KD. Does a" toxic" environment make obesity inevitable? Obssity Manage. 2005;1(2):52–5.

Faghih A, Anousheh M. Evaluating some of the feeding behaviors in obese patients visiting affiliating health centers. Hormozgan Med J. 2008;12(1):53–60.

Paeratakul S, Ferdinand DP, Champagne CM, et al. Fast-food consumption among US adults and children: dietary and nutrient intake profile. J Am Diet Assoc. 2003;103(10):1332–8.

Timperio AF, Ball K, Roberts R, et al. Children’s takeaway and fast-food intakes: associations with the neighbourhood food environment. Public Health Nutr. 2009;12(10):1960–4.

Pour Mahmoudi A, Akbar TabarTuri M, Pour Samad A, et al. Determination of peroxide in the oil consumed in restaurants and snack bar Yasuj. J Knowledge. 2008;13(1):116–23 [In Persian].

SadrizadehYeganeh H, AlaviNaein A, DorostiMotlagh A, et al. Obesity is associated with certain feeding behaviors in high school girls in Kerman. Payesh Quarterly Summer. 2007;6(3):193–9 [In Persian].

Greger N, Edwin CM. Obesity: a pediatric epidemic. Pediatr Ann. 2001;30(11):694–700.

Ghaffari M, Gharghani ZG, Mehrabi Y, et al. Premarital sexual intercourse-related individual factors among Iranian adolescents: a qualitative study. Iran Red Crescent Med J. 2016;18(2):e21220.

PubMed   PubMed Central   Google Scholar  

Kim KW, Ahn Y, Kim HM. Fast food consumption and related factors among university students in Daejeon. Korean J Commun Nutr. 2004;9(1):47–57.

Harris KM, Gordon-Larsen P, Chantala K, et al. Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Arch Pediatr Adolesc Med. 2006;160(1):74–81.

Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211.

Branscum P, Sharma M. Using the theory of planned behavior to predict two types of snack food consumption among Midwestern upper elementary children: implications for practice. Int Quarterly Commun Health Educ. 2011;32(1):41–55.

Seo H-s, Lee S-K, Nam S. Factors influencing fast food consumption behaviors of middle-school students in Seoul: an application of theory of planned behaviors. Nutr Res Pract. 2011;5(2):169–78.

Hewitt AM, Stephens C. Healthy eating among 10-13-year-old New Zealand children: understanding choice using the theory of planned behavior and the role of parental influence. Psychol Health Med. 2007;12:526–35.

Didarloo A, Shojaeizadeh D, EftekharArdebili H, et al. Factors influencing physical activity behavior among Iranian women with type 2 diabetes using the extended theory of reasoned action. Diabetes Metab J. 2011;35(5):513–22.

Yarmohammai P, Sharirad GH, Azadbakht L, et al. Assessing predictors of behavior of high school students in Isfahan on fast food consumption using theory of planned behavior. Journal of Health Syst Res. 2011;7(4):449–59.

Dunn K, Mohr P, Wilson C, et al. Determinants of fast food consumption: an application of the theory of planned behavior. Appetite. 2011;23(57):349–57.

Dunn KI, Mohr PB, Wilson CJ, Wittert GA. Beliefs about fast food in Australia: a qualitative analysis. Appetite. 2008;51(2):331–4.

Denney-Wilson E, Crawford D, Dobbins T, Hardy L, Okely AD. Influences on consumption of soft drinks and fast foods in adolescents. Asia Pac J Clin Nutr. 2009;18(3):447–52.

Brisling RW. The wording and translation of research instruments. In: Loner WJ, Berry JW, editors. Field Methods in Cross-cultural Research. Beverly Hills, CA: Sage; 1986. p. 134–64.

Sanaye S, Azarghashb A, Derisi M, et al. A survey on knowledge and attitude of students of ShahidBeheshti University of medical sciences toward fast food. Scientific J Med Council Islamic Republic Iran. 2016;34(1):23–30.

Driskell JA, Meckna BR, Scales NE. Differences exist in the eating habits of university men and women at fast-food restaurants. J Nutr. 2006;26(10):524–30.

CAS   Google Scholar  

Morse KL, Driskell JA. Observed sex differences in fast-food consumption and nutrition self-assessments and beliefs of college students. Sci Direct J Nutr Res. 2009;29(3):173–9.

Aoki K, Shen J, Saijo T. Consumer reaction to information on food additives: evidence from an eating experiment and a field survey. J Econ Behav Organ. 2010;73:433–8.

Stern T, Haas R, Meixner O. Consumer acceptance of wood-based food additives. Br Food J. 2009;11:179–95.

Kim H, Kim M. Consumers' awareness of the risk elements associated with foods and information search behavior regarding food safety. J East Asian Soc Diet Life. 2009;19:116–29.

Seo S, Kim OY, Shim S. Using the theory of planned behavior to determine factors influencing processed foods consumption behavior. Nutr Res Pract. 2014;8(3):327–35.

Back BS, Lee YH. Consumer's awareness and policies directions on food additives-focusing on consumer information. J Consum Stud. 2006;17:133–50.

Mirkarimi K, Mansourian M, Kabir MJ, et al. Fast food consumption behaviors in high-school students based on the theory of planned behavior (TPB). Int J Pediatr. 2016;4(7):2131–42.

Murnaghan DA, Blanchard CM, Rodgers WM, et al. Predictors of physical activity, healthy eating and being smoke-free in teens: a theory of planned behavior approach. Psychol Health. 2010;25:925–41. https://doi.org/10.1080/08870440902866894 .

Article   PubMed   Google Scholar  

Julie Lynn Fudge. Explaining adolescent behavior intention to consume fast food using the theory of planned behavior. Dissertation Submitted to the Graduate Faculty Of the North Dakota State University Of Agriculture and Applied Science. lib.ndsu.nodak.edu. 2013.

Stefanie A, Chery S. Applying the theory of planned behavior to healthy eating behaviors in urban native American youth. Int J Behav Nutr Phys Act. 2006;30(3):1–10.

Ebadi L, Rakhshanderou S, Ghaffari M. Determinants of fast food consumption among students of Tehran: application of planned behavior theory. Int J Pediatr. 2018;6(10):8307–16.

Pender NJ, Murdaugh C, Parsons MA. Health promotion in nursing practice. 4th edition. Upper Saddle River, NJ: Prentice-Hall Health Inc; 2002. p. 250–5.

Download references

Acknowledgements

The article authors hereby express their gratitude to Vice Chancellors for Research of Urmia University of Medical Sciences and Education Department for supporting this study.

This study is supported by Urmia University of Medical Science, grant number(No: 2017–2323) .

Author information

Authors and affiliations.

Social Determinants of Health Research Center, Clinical Research Institute, Urmia University of Medical Sciences, The Province of Western Azarbaijan, Urmia, 5756115198, Iran

Alireza Didarloo

Faculty of Health, Urmia University of Medical Sciences, the Province of Western Azarbaijan, Urmia, 5756115198, Iran

Surur Khalili & Ahmad Ali Aghapour

Reproductive Health Research Center, Urmia University of Medical Sciences, the Province of Western Azarbaijan, Urmia, 5756115198, Iran

Fatemeh Moghaddam-Tabrizi

Faculty of Paramedical Sciences, Urmia University of Medical Sciences, the Province of Western Azarbaijan, Urmia, 5756115198, Iran

Seyed Mortaza Mousavi

Department of Paramedical Science, School of Paramedical Sciences, Urmia University of Medical Sciences, Urmia, Iran

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contribute in conceive, design of this study. A.D, S.K, A.A,FTM and S.M contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. All authors revised the manuscript critically for important intellectual content and read and approved the final manuscript.

Corresponding author

Correspondence to Seyed Mortaza Mousavi .

Ethics declarations

Ethics approval and consent to participate.

Research has been presented in the ethics committee of Urmia University of Medical Sciences and has received the code of ethics (IR. UMSU.REC.1397.43). written informed consent was obtained from all participants in this study, and all provisions of the Helsinki Statement on Research Ethics were considered.

Consent for publication

Not applicable.

Competing interests

The authors declared no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

The questionnire used in the study to collect the data. The first part of the questionnaire included General characteristics. The second part of the questionnaire consisted of the Constructs of TPB. The third part consisted of knowledge of participants. The fourth part consisted of Fast food use.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Didarloo, A., Khalili, S., Aghapour, A.A. et al. Determining intention, fast food consumption and their related factors among university students by using a behavior change theory. BMC Public Health 22 , 314 (2022). https://doi.org/10.1186/s12889-022-12696-x

Download citation

Received : 07 December 2020

Accepted : 02 February 2022

Published : 15 February 2022

DOI : https://doi.org/10.1186/s12889-022-12696-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Theory of planned behavior

BMC Public Health

ISSN: 1471-2458

fast food industry research paper

You are using an outdated browser. Please upgrade your browser to improve your experience.

Market Research Food & Beverage Market Research Food Service & Hospitality Market Research Fast Food Market Research

Fast Food Market Research Reports & Industry Analysis

Fast food industry research & market reports, refine your search, fast food and fast casual restaurants - 2024 u.s. market research report with updated recession risk forecasts.

Mar 01, 2024  |  Published by: Kentley Insights  |  USD 295

... comprehensive and in-depth assessments of the industry in the United States with over 100+ data sets covering 2015-2028. This Kentley Insights report is full of industry insights including historical and forecasted market size, revenue and ... Read More

Street Stalls/Kiosks in Israel

Feb 19, 2024  |  Published by: Euromonitor International  |  USD 990

... wellness trend, which received a notable boost from the experiences of the COVID-19 crisis. However, the rising cost of living and the decrease in disposable incomes has impacted this market, as it has other areas ... Read More

Street Stalls/Kiosks in the Netherlands

Feb 15, 2024  |  Published by: Euromonitor International  |  USD 990

... or shops on the high street. The result was a dramatic decline in current value sales in 2020, followed by rapid and robust recovery post-pandemic in terms of transaction volume and current value sales. However, ... Read More

Street Stalls/Kiosks in Sweden

... and high costs of living many locals chose not to eat out as much, leading to slower transaction growth. Street stalls/kiosks witnessed a strong recovery from the pandemic in 2022, following a partial recovery in ... Read More

Street Stalls/Kiosks in Greece

... the action. With sales surpassing pre-pandemic levels by 2022, further growth in transactions in 2023 was largely driven by attendance at these sorts of events. The largest music festivals in Athens are held during the ... Read More

Consumer Foodservice By Location in Sweden

... in retail locations. However, value growth in 2023 was partly driven by high inflation rates and menu prices. Transactions continued to increase in retail locations in 2023, following two years of double-digit growth pushing sales ... Read More

Street Stalls/Kiosks in Peru

... categories and consumer foodservice. Chained street stalls/kiosks saw a decline in the number of outlets in 2023, which were already at a low level. In contrast, independent street stalls/kiosks, which represent by far the largest ... Read More

Street Stalls/Kiosks in France

... socialising and out-of-home activities. Sales also benefited from the resumption of events such as festivals, trade fairs, and other gatherings. For instance, attendance at music festivals increased further in 2023, compared to 2022, with over ... Read More

Street Stalls/Kiosks in the Czech Republic

... more inclined to favour cheaper foodservice options. It was also a major beneficiary of the return to normal routines in the wake of COVID-19, as these types of businesses are heavily reliant on foot traffic, ... Read More

Street Stalls/Kiosks in Bulgaria

... prices and the impact of inflation. The market has already reached its pre-pandemic levels in value terms; however, a full recovery in terms of outlet numbers is not expected until 2027. It is common for ... Read More

Street Stalls/Kiosks in Slovakia

... not have the same expenses in terms of rent etc and therefore are in a position to offer lower prices. As such, street stalls/kiosks offering traditional local street food at affordable prices, located at train ... Read More

Street Stalls/Kiosks in Norway

... self-service cafeterias. The category is struggling to compete against limited-service restaurants, which offer a wide range of food at affordable price points. In many cases, street stalls/kiosks cannot compete with these chained operations, strong branding ... Read More

Street Stalls/Kiosks in China

Feb 14, 2024  |  Published by: Euromonitor International  |  USD 990

... such as those offering braised snacks, fried chicken, and breakfast, also experienced growth, their performances were moderate, mainly characterised by an increase in chained outlets and brand turnover. The swift expansion of tea street stalls ... Read More

Consumer Foodservice in the US

Feb 13, 2024  |  Published by: Euromonitor International  |  USD 2,100

... year of a return to normalcy. High inflation and its reverberating effects throughout the economy, however, had other ideas for 2023. While high inflation had affected the industry for a few years, a reversal in ... Read More

2024 Fast Food and Fast Casual Restaurants Global Market Size & Growth Report with Updated Recession Risk Forecasts

Feb 05, 2024  |  Published by: Kentley Insights  |  USD 295

... revenue, growth, and regional share across 4 global regions, 22 subregions, and 195 countries. Historical data is from 2012 through 2023, with forecasts for 2024 and 2027. The historical data utilizes in-depth survey results from ... Read More

Fast Food Market, Size, Global Forecast 2024-2030, Industry Trends, Share, Growth, Insight, Impact of Inflation, Company Analysis

Feb 01, 2024  |  Published by: Renub Research  |  USD 2,490

... to Renub Research. Food is known to play an essential role in developing and preventing many diseases. The practice of taking food also differs from society to society. Globalization and urbanization have greatly affected people's ... Read More

Street Vendors in the US - Industry Market Research Report

Jan 31, 2024  |  Published by: IBISWorld  |  USD 1,095

... seen growth through the end of 2023 because of heightened consumer demand for unique food products and more outdoor gatherings, though revenue also benefited later in 2020 due to falling demand for indoor dining. Street ... Read More

Fast Food Restaurants in Canada - Industry Market Research Report

Jan 31, 2024  |  Published by: IBISWorld  |  USD 875

... and product innovation. However, mounting internal competition and changing consumer tastes have pressured revenue growth during the same period. Products with higher profit, such as coffee and smoothies, have become more prominent at traditional fast-food ... Read More

Fast-Food & Quick-Service Restaurants

Jan 29, 2024  |  Published by: First Research, Inc.  |  USD 129

... (all based in the US), as well as Caf&eacute; de Coral (Hong Kong), Greggs (UK), Restaurant Brands International (Canada), and Seven &amp; i Food Systems (Japan). COMPETITIVE LANDSCAPE Demand is driven by consumer tastes and ... Read More

Restaurants, Fast-Food, Pizza Delivery, Takeout and Family (U.S.): Analytics, Extensive Financial Benchmarks, Metrics and Revenue Forecasts to 2030, NAIC 722513

Jan 26, 2024  |  Published by: Plunkett Research, Ltd.  |  USD 2,495

... benchmarks, historic numbers, growth rates and forecasts that will save countless hours of research. Key Findings: Restaurants, Fast-Food, Pizza Delivery, Takeout and Family Industry (U.S.) to reach $491,420,813,815 by 2030. Restaurants, Fast-Food, Pizza Delivery, Takeout ... Read More

Full-Service, Fast-Food, Pizza Delivery, Coffee Shops and Other Eating Places (U.S.): Analytics, Extensive Financial Benchmarks, Metrics and Revenue Forecasts to 2030, NAIC 722500

... data including metrics, benchmarks, historic numbers, growth rates and forecasts that will save countless hours of research. Key Findings: Full-Service, Fast-Food, Pizza Delivery, Coffee Shops and Other Eating Places Industry (U.S.) to reach $1,835,456,494,072 by ... Read More

Restaurants (Full-Service & Fast Food) and Bars (U.S.): Analytics, Extensive Financial Benchmarks, Metrics and Revenue Forecasts to 2030, NAIC 722000

... benchmarks, historic numbers, growth rates and forecasts that will save countless hours of research. Key Findings: Restaurants (Full-Service &amp; Fast Food) and Bars Industry (U.S.) to reach $1,820,772,842,120 by 2030. Restaurants (Full-Service &amp; Fast Food) ... Read More

Fast Food and Takeaway Food Services in New Zealand - Industry Market Research Report

Jan 23, 2024  |  Published by: IBISWorld  |  USD 790

... strong consumer demand. Rising consumer health consciousness and greater awareness regarding maintaining a healthy lifestyle are boosting demand for healthier fast food options. Rising demand for healthy fast food has largely offset declines in demand ... Read More

Vegan Fast Food Market: Industry Size, Share, Competition, Trends, Growth Opportunities and Forecasts by Region - Insights and Outlook by Product, 2024 to 2031

Jan 18, 2024  |  Published by: OG Analysis  |  USD 4,450

... Competition, Growth Opportunities, and Outlook to 2031 The Global Vegan Fast Food Market Research Report is a comprehensive and insightful analysis designed to assist stakeholders, industry professionals, and decision-makers in identifying Vegan Fast Food market ... Read More

Fast Food Wrapping Paper Market by Material (Aluminum Foil, Paper, Plastic), Fast Food Type (Burgers, Chicken, Pizza), End-Use - Global Forecast 2024-2030

Jan 15, 2024  |  Published by: 360iResearch  |  USD 4,749

... billion in 2023 and expected to reach USD 3.85 billion in 2024, at a CAGR 3.70% to reach USD 4.79 billion by 2030. FPNV Positioning Matrix The FPNV Positioning Matrix is pivotal in evaluating the ... Read More

< prev 1 2 3 4 5 6 7 8 9 10 next >

Filter your search

  • Europe (28)
  • Global (80)
  • Middle East (7)
  • North America (71)
  • Oceania (10)
  • South America (7)

Research Assistance

Live help

Join Alert Me Now!

Start new browse.

  • Consumer Goods
  • Food & Beverage
  • Heavy Industry
  • Life Sciences
  • Marketing & Market Research
  • Public Sector
  • Service Industries
  • Technology & Media
  • Company Reports
  • Reports by Country
  • View all Market Areas
  • View all Publishers

The Effect of the Minimum Wage on the Fast Food Industry

Using data from a longitudinal survey of fast food restaurants in Texas, the authors examine the impact of recent changes in the federal minimum wage on a low-wage labor market The authors draw four main conclusions. First, the survey results indicate that less than 5 percent of fast food restaurants use the new youth subminimum wage even though the vast majority paid a starting wage below the new hourly minimum wage immediately before the new minimum went into effect. Second, although some restaurants increased wages by an amount exceeding that necessary to comply with higher minimum wages in both 1990 and 1991, recent increases in the federal minimum wage have greatly compressed the distribution of starting wages in the Texas fast food industry. Third, employment increased relatively in those firms likely to have been most affected by the 1991 minimum wage increase. Fourth, changes in the prices of meals appear to be unrelated to mandated wage changes. These employment and price changes do not seem consistent with conventional views of the effects of increases in a binding minimum wage.

  • Acknowledgements and Disclosures

MARC RIS BibTeΧ

Download Citation Data

Published Versions

More from nber.

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

15th Annual Feldstein Lecture, Mario Draghi, "The Next Flight of the Bumblebee: The Path to Common Fiscal Policy in the Eurozone cover slide

fast food industry research paper

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

  •  We're Hiring!
  •  Help Center

Fast Food Industry

  • Most Cited Papers
  • Most Downloaded Papers
  • Newest Papers
  • Save to Library
  • Last »
  • Person-centred Therapy Follow Following
  • Humor (Psychology) Follow Following
  • Customer experience Follow Following
  • Customer services quality Follow Following
  • Consumer-Based brand equity Follow Following
  • Mental Health nursing Follow Following
  • Place promotion and marketing Follow Following
  • Adult Nursing Follow Following
  • Fast Food Marketing Follow Following
  • Promotions Follow Following

Enter the email address you signed up with and we'll email you a reset link.

  • Academia.edu Publishing
  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Am J Lifestyle Med
  • v.12(5); Sep-Oct 2018

The Hidden Dangers of Fast and Processed Food *

The fundamental concern as we look to reform health in America is the known reality that most chronic diseases that afflict Americans are predominantly lifestyle induced; and the belief is that the vast majority of heart attacks and strokes could be prevented if people were willing to adopt healthy lifestyle behaviors. In addition, healthy lifestyles would impact a significant number of cancers which are also believed to be related to lifestyle exposures, especially to obesity, cigarettes, and other toxins.

Over the past 50 years, the health of Americans has gotten worse, and now 71% of Americans are overweight or obese—not 66%, which was reported 5 years ago. 1 That means a staggering 100 million people in America are obese. Today, eating processed foods and fast foods may kill more people prematurely than cigarette smoking. 2

Authorities determined the 71% figure by classifying people with a body mass index (BMI) over 25 kg/m 2 as overweight or obese. Yet in long-lived societies such as in the “Blue Zones” (Ikaria, Greece; Sardinia, Italy; Okinawa, Japan; the Nicoya Peninsula of Costa Rica; and Loma, Linda California) and wherever we find groups of centenarians, we observe a healthy BMI below 23 kg/m 2 , not 25 kg/m 2 . If we use above 23 kg/m 2 as the demarcation for overweight or obesity, then we find that 88% of Americans are overweight. And out of the approximately 10% that are of normal weight, the majority of those so-called “normal weight individuals” are either cigarette smokers, or suffer from alcoholism, drug addiction or dependency, autoimmune disease, occult cancers, inflammatory disorders, autoimmune conditions, digestive disorders, irritable bowel syndrome, and other illnesses that lower their body weight. Therefore, perhaps that only about 5% of the American population is at a normal weight as a result of eating healthy and living a healthy life. A recent study documented that only 2.7% of Americans adopt a relatively healthy lifestyle by combining exercise with healthy eating. 3 The Standard American Diet (SAD) is clearly not a healthy diet.

I use the term “Fast Food Genocide” because most don’t understand the depth and breadth of the harm as a large segment of our society eats a diet worse than the dangerous SAD. Many people recognize that junk food, fast food, processed food, white flour, sugar, maple syrup, honey, agave nectar, and all the junk people are eating contribute to in obesity, diabetes, heart attacks, strokes, dementia and cancer, but many don’t realize the strong causative role an unhealthy diet may have in mental illness. Currently, 1 in 5 Americans suffers from a psychiatric disorder. And many people don’t realize the harm that processed foods have on Americans living in urban areas where they don’t have easy access to whole, fresh foods.

These unfortunate folks live in what we call “food deserts,” with reduced availability to fresh fruits and vegetables. Because of the limited access to supermarkets, they eat more unhealthy fast and processed foods and end up having 7 times the risk of early-life stroke (before age 45), putting people in nursing homes in their 30s, 40s, and 50s. 4 - 7

The vulnerable poor in these areas also have double the risk of heart attack, double the risk of diabetes, and 4 times the risk of renal failure 8 - 10 ; Unfortunately, the decrease in life span due to food inequality is shocking but rarely discussed. A substantial proportion of people in these urban environments are overweight, prediabetic, or fully diabetic. Researchers determined that compared with other areas in America with easy access to supermarket food, that the YPLL (Years of Potential Life Lost) for an overweight diabetic living in a zone classified as a food desert was a shocking 45 years! 11 , 12

A link may even exist between fast food, processed food, commercial baked goods, and sweets and destruction of brain cell and a lowering of intelligence. Candy and sweetened baked goods may even stimulate the brain in an addictive fashion, which can lead to more serious illnesses.

The nutritional fundamentals accepted by the World Health Organization and most nutritional authorities today include vegetables, beans, nuts, seeds, and fruit as healthy foods; and salt, saturated fat, and excess sugar as disease causing. Excessive amounts of animal products may lead to premature aging, increased risk of chronic disease and higher all-cause mortality. Multiple studies have been published on hundreds of thousands of people, followed for decades showing that the objective endpoint of death is increased with higher amounts of animal product consumption. 13 - 17 Furthermore, refined carbohydrates may not just lead to being overweight and diabetic but also contribute to dementia, mental illness, and cancer. 18 - 21 There is considerable evidence today that heart disease is not only promoted by saturated fat and increased animal products but also by refined carbohydrates, including white rice, white bread, sugar, honey, maple syrup, and agave nectar. 22 - 25

Research has shown that excess calories shorten lifespan, whereas moderate caloric restriction slows the aging process and protects the body and brain. Americans consume more calories than any other population; and they consume foods, many of which have minimal or no nutritional value (soda and alcohol as examples). So let’s consider the individual who is consuming 50 excess calories per day. What will be the short- and long-term result? Fifty excess calories per day, over and above your basic metabolic needs, over a 10-year period, adds about 50 pounds of extra body weight. The excess weight increases the risk of multiple chronic illnesses, cancers, and also takes many years of life away from the individual simply as a result of consuming only 50 calories a day too many.

Conversely, if an individual consumed 50 calories a day less that their metabolic requirements what would happen then? Would he or she become too thin, anorexic, and unhealthy? Would their bones fall apart? Obviously not! When you moderately caloric-restrict, even a small amount such as 50 to 100 calories a day, weight remains about the same, the person is slim, not too thin, and healthy. He or she will have a lower body fat percentage, and the skeletal mass, bones, and muscle mass are strong. In this scenario, the metabolic rate would slow down accordingly. The respiratory quotient, (the number of calories lost through respiration) would decrease, the body temperature would lower, and thyroid function would decrease slightly, all lowering the metabolic rate, which overall may result in a slowing of the aging process. The secret to a long life and freedom from chronic disease may be simply to moderately reduce calories in order to slow down our metabolic rate. The only behavior proven scientifically to dramatically increase life span in every species of animals, including primates, is to lower caloric intake while maintaining an environment of micronutrient adequacy, assuring that we have exposure to every micronutrient humans need. The American diet is also deficient in antioxidants and phytochemicals that are needed for normal immune function, for maximizing brain health, protecting against dementia, chronic illness, cancer, and premature aging.

A nutritarian diet is designed to establish excellent micronutrient intake without excess calories . A nutritarian diet is designed to help prolong human life span, decrease the risk of cancer, and keep the brain functioning well for many years. This principle is represented by the equation I use: H = N /C, which means your healthy life expectancy (H) is proportional to the micronutrient (N) per calorie intake (C) over your life span. This means that we are encouraged to seek out foods that are rich in nutrients. We should try to limit or exclude empty-calorie foods and drinks. We should also limit or avoid calorically dense foods, and not eat for recreation or when we are not hungry.

A nutritarian diet is rich in phytochemicals and antioxidants. It is a vegetable-based, utilizing a wide assortment of colorful vegetables, root vegetables, green vegetables, peas, beans, mushrooms, onions, nuts, seeds, and some intact whole grains. While the standard American diet and most traditional diets are grain-based and lack sufficient exposure to the broad spectrum of antioxidants and phytochemicals (with their anticancer effects), it is important to note that not all plant-based diets are equally cancer-protective. As an example, a rice-heavy, macrobiotic diet limits phytochemical diversity, and brown rice produced in this country is contaminated with arsenic, extensively documented by Consumer Reports and white rice is refined, high glycemic food, and therefore not a healthy starch.

In comparison, the SAD is almost the opposite of a nutritarian diet. Over 55% of the SAD’s calories are processed foods, and about 33% of calories come from animal products. If we are looking at the amount of fresh produce (fruits and vegetables) consumed in America, the food consumption data reports about 10%; but in actuality, it is less than 5%, because they include French fries and ketchup in the definition of “produce!” The point here is that processed foods such as bread, pasta, salad oil, mayonnaise, doughnuts, cookies, rice cakes, breakfast bars, chips, soda, candy, and popcorn do not contain a significant micronutrient benefit. A piece of chicken is like a bagel, because they are both rich sources of macronutrients (calories), but neither one contains the necessary amounts of micronutrients, especially the antioxidants and phytochemicals only found in plants.

The high glycemic white flour products with added sweetening agents, flood the bloodstream with glucose without fiber, nutrients, or phytochemicals; and these baked goods are also high in acrylamides and advanced glycation end-products, further increasing the glycoproteins in our tissues. The resulting spike in glucose leads to abnormally high amounts of insulin, which will also promote angiogenesis, which fuels the growth of fat cells, increases cellular replication and tumor growth. The liberal amount of animal protein (including chicken which many incorrectly believe is the more healthy meat) consumed by most Americans promotes excessive insulin-like growth factor–1 (IGF-1), making a synergistic “sandwich” of insulin and IGF-1, which may accelerate aging of the brain, interfere with cellular detoxification and repair, and promote cancer. 26 The SAD has created a nutritional disaster and a significant health crisis that will not be solved by governmental “health care reform.”

Now when we think about “fast food” we’re not just referring to the food in fast food restaurants. Fast foods include chips, soda, cookies, candy, breakfast cereals, bars, French fries, burgers, pizza, white flour baked goods, and all other high-calorie, low-nutrient foods that people often eat multiple times per day. These are processed foods and for many, are the primary source of calories. These fast foods have certain characteristics: They can be accessed easily and quickly; they don’t need to be prepared; they come out of a bag or box ready to go right into your mouth. You can eat them rapidly and they’re absorbed very quickly into the bloodstream. These fast foods typically contain multiple chemicals and synthetic ingredients. They are calorically dense, highly flavored, and nutritionally barren. Fast foods typically contain extra corn syrup, sugar, artificial sweeteners, salt, coloring agents, and other potentially disease promoting chemicals.

When calories flood the bloodstream rapidly they have dramatic biological effects. Let’s compare 200 calories of white bread to 200 calories of beans. The white bread would be metabolized into simple sugars (glucose) which enters the bloodstream in 5 to 10 minutes. This requires a rapid increase in insulin; and the rapid insulin response will remain for hours. On the other hand, the carbohydrates from beans will take much more time to be digested and, as a result these calories enter the bloodstream slowly. Essentially, the calories will trickle in over hours. When eating beans, a small amount of glucose enters the blood each minute and therefore you won’t need much of an insulin response to deal with this amount of sugar. As mentioned above, the buildup of advanced glycation end products (AGEs) accelerates aging and chronic disease. 27 , 28 When a diabetic suffers from kidney failure, blindness, or a leg amputation, a major causative factor is the buildup of AGEs in the tissues. Interestingly, these same glycated end-products and glycoproteins build up in the tissues of people who are not diabetic but who continually expose themselves to excess sugar and white flour products.

Next, it is important to understand that oils are also processed foods. When consumed, oil enters the bloodstream rapidly similar to high glycemic carbohydrates. Anything cooked in oil should be considered a fast food. Beans, nuts, and seeds are whole foods whose calories are absorbed gradually over hours. In contrast, the calories from oil are absorbed rapidly, and are largely empty calories (with insignificant micronutrients and no fiber)—a combination that leads to obesity, disease, and premature aging.

If I set up a buffet dinner and I asked all the guests to form 2 lines and then gave everyone on the right side a tablespoon of olive oil, and each of those on the left side an apple to consume while they were waiting in line, those who ate the 65-calorie apple will generally eat 65 less calories from the buffet. But those who had the 120-calorie tablespoon of oil will not usually consume 120 calories less. The oil contains neither fiber, nor micronutrients and contains nothing to decrease the appestat. A matter of fact, if you put oil on food, it may actually increase one’s appetite. Not only will these individuals not eat fewer calories—they will eat even more than the 120 calories from the oil. 29 When added or mixed into food, oil drives overeating behavior.

Nutrients and fiber are needed to control the appestat, so you consume a healthy amount of calories. My experience has demonstrated with thousands of patients, the more nutrient and fiber dense your diet becomes the lower your drive to overeat. 30 This is extremely important, because even a moderate amount of extra fat on the body induces more rapid aging and increases the risk of diabetes, heart disease and cancer. A mild degree of caloric restriction becomes comfortable and achievable when the diet is high in micronutrients and fiber. When you have enough micronutrients and fiber in your diet, you don’t feel driven to overeat. But when you don’t have enough micronutrients and fiber in your diet, you become a food-craving, overeating machine.

Even worse is what happens when you eat food fried in oil because fried food may create carcinogenic and mutagenic aldehydes. 31 Food that is fried such as in a fast food restaurant is usually cooked in oil that has been heated and used multiple times. One serving of French fries or fried chicken that is cooked in a fast food restaurant has 100 times the level of aldehydes designated as safe by the World Health Organization. Even the fumes are so toxic they increase the risk of cancer. People working in restaurants that fry the food, or those working in a movie theater making popcorn, have a heighted risk of lung and other cancers, even if they don’t eat any of the fried foods. 32

The explosion of fast food restaurants has significantly increased the intake of fried foods, and people are now eating 1000 times the amount of soybean oil compared with the early 1900s. 33 Humans never ate 400 calories of oil a day the way people do in America, especially in the Southern states—which are known for the highest stroke and heart attack rates in the world. 34 When you use nuts and seeds as your source of fat as opposed to oil, we see the opposite effect.

The Physician’s Health Study, the Nurses’ Health Study, Iowa Women’s Health Study, the Adventist Health Study—any study with large numbers of people followed for decades—demonstrates the relationship between nut and seed consumption and longer life span. We always have to give more credence to clinical research studies that involve large numbers of people followed over decades using objective endpoints such as mortality. When you do that, you find that people who consume nuts and seeds regularly have lower cancer rates, lower cardiovascular death rates, lower sudden cardiac death, less irregular heartbeats, and an increase in life span.

A 2015 meta-analysis that included over 44 000 deaths demonstrated an almost 40% decrease in cardiovascular mortality for people eating nuts and seeds regularly (one serving a day). The European PreviMed study, which randomized 7216 individuals to nuts or olive oil as part of a Mediterranean diet showed a 39% decrease in all-cause mortality in the nut eaters. 35

When we look at the health implications of animal protein we should compare this type of nutrition with plant-based proteins, especially when an individual has cardiovascular disease, diabetes, obesity, or even cancer. When your protein comes from beans, nuts, seeds, and greens, the body more gradually assimilates a complete array of amino acids to make functional proteins and hormones, keeping IGF-1 production much lower. Adequate amounts of plant protein keep IGF-1 in that moderate range, between 100 and 175, which is where it should be. The average American’s IGF-1 level is around 225, which is a level which has been linked to cancer promotion. When we eat a variety of plants, we get a full balance of amino acids, which slowly enter the blood—and we also digest some of the bacteria in the digestive track and some of the cells that slough off of the villi endothelium, enabling the utilization of partially incomplete plant proteins, now made complete. Conversely, when you eat large portions of meat, eggs, or cheese, the amino acid mix enters the bloodstream faster and because it is already biologically complete, it stimulates excessive amounts of IGF-1, again increasing the risk of cancer. 36 - 43

The average American consumes 10 to 20 ounces a day of animal products, whereas the safe level of consumption is likely less than 10 ounces per week . My estimate of 10% of calories as an upper limit of safe consumption is for a person with favorable genetics and is still likely more animal products than ideal for the nonelderly adults. It may be the case that under 5% of calories from animal products would be more ideal for life span and for facilitating disease reversal. Of course, any diet designed to optimize health should include a broad array of colorful plants with phytochemicals and antioxidants, which have been shown to increase life span and prevent cancer.

The animal products served at fast food restaurants are making the health of the population much worse, creating dangerous carcinogens from the food being grilled, barbecued, and fried at high temperatures. The World Health Organization has classified processed meats (hot dogs, sausage, bacon, and lunch meats) a class 1 carcinogen. AGEs are also highest in barbecued and fried animal products which also contain cancer-causing chemicals such as heterocyclic amines, polycyclic aromatic hydrocarbons, and lipid peroxidases, which are mutagenic.

There are 2 phases of the digestive cycle: the anabolic phase, when you are eating and digesting, and the catabolic phase, when digestion has ceased. When you are eating and digesting food, the body turns those calories into stored glycogen, increasing fat storage and the storage of waste. During this phase of the digestive cycle, growth hormones and fat storage hormones are activated.

When your body is finished digesting, you enter the catabolic phase, where the stored glycogen and fat are utilized for energy. This is the phase when your body can most effectively detoxify and enhance cellular repair. It is the time when the liver and kidneys work together to remove aldehydes, AGEs, and other toxic metabolites. Repair and healing is enhanced during the catabolic phase when you are not eating food.

Most Americans have made their bodies so toxic, that when they enter the catabolic phase of the digestive cycle, they feel uncomfortable. That means they feel fatigue, headache, stomach cramping or fluttering, anxiety, or other uncomfortable symptoms when they stop digesting food and the body starts to mobilize waste and repair the damage. They typically interpret these symptoms as hunger or low blood sugar, because they feel better if they eat again—even though there is no biological need for calories at this time; and so they just get fatter and sicker. Every addiction has a “high” during the caloric rush and a “low” during withdrawal and repair from the disease-causing diet and resultant metabolic wastes and toxins that accrue from it. The American diet results in withdrawal symptoms and discomfort which promotes overeating and too-frequent eating. The lower the quality of the food consumed, the more discomfort felt when not eating and digesting, which makes it very difficult to maintain a healthy body weight.

If you’re healthy and eating nutritious food, you feel nothing when you enter the catabolic phase, with no desire to eat again until glycogen stores are nearly exhausted. True hunger is a mild sensation felt in the throat and base of the neck. True hunger heightens taste sensitivity too, making eating more pleasurable. True hunger directs when you should eat and therefore it’s more difficult to become overweight if you pay attention to the signs your body sends to your brain. Being overweight requires eating outside of the demands of true hunger, either recreationally or because of withdrawal symptoms from improper eating, stimulating the overconsumption of calories.

Enhanced detoxification—reduction of metabolic waste, aldehydes, and AGEs—occurs most effectively in the catabolic phase. That means the longer you live in the catabolic phase of the digestive cycle, the longer you live. If you finish dinner earlier or have a lighter dinner, and you have a 13-hour window between the end of dinner and the start of breakfast, you are going to live longer. A recent study had women with breast cancer followed for 10 years and found that those who finished dinner earlier and had a 13-hour window before the start of breakfast had a 26% reduction in the risk of death or recurrence from breast cancer. 44 , 45 The increased nighttime window was also linked to improved glycemic control and a lower HbA1c (glycated hemoglobin). They had no better diet, no different number of calories, no better food; they just finished dinner earlier.

The goal for excellent health is to eat as infrequently as possible. Many people believe just the opposite and eat frequent small meals that increase endothelial dysfunction leading to an increased risk for arteriosclerosis and cardiovascular disease. In addition, all the fad diets encourage people to make the wrong choices about what and when to eat. Many suggest the use of frequent high-protein meals so as not to feel the effects of normal detoxification. When the digestive track is continually busy, it results in accelerated aging.

Processed and fast foods are also high in salt. The fast and processed food manufacturers don’t just put salt on the French fries and on the meat, they also put salt in the French fry batter and inside the chopped meat. They also include high fructose corn syrup in most foods. The added fat, sugar, and salt create a taste that makes people crave these foods, a sensation that many describe as an addiction. Both sugar and salt intake increase stroke risk, especially when consumed daily for years. Additionally, what is generally not appreciated is that the regular consumption of artificially sweetened soda creates more of a stroke risk. 46 High salt does not merely raise blood pressure; it also causes microvascular hemorrhaging, which damages the interior walls of the blood vessels in the brain and increases permeability and the propensity for hemorrhagic stroke. 47 , 48

Over the past 30 years, we’ve also seen an explosion of diabetes in Japan, Korea, and China, occurring at a lower body weight than we typically see in America, likely because the cumulative effects of eating more fast food, more oil and sugar, along with all of the white rice (a refined, high glycemic food), which they already had in their diet.

We know that people have the power to change when significant effort and attention is directed to the problems at hand. With good information, emotional support, increased food availability and food preparation instruction, we have found people enthused and willing to work together for change. They don’t have to be convinced of the tragic dangers of fast food; they see the obesity, diabetes, leg amputations, strokes, and blindness all around them. But if people don’t have good information, then they don’t have a choice. If they don’t have access to healthy, affordable food, and they don’t know how to make it taste good, then they are not given a chance to change.

The goal for physicians and other health care professionals is to work to transform America’s inner cities into zones of nutritional excellence. Our nation’s pride and heritage are based on the equal opportunity to achieve the American dream of prosperity and happiness. This critical information needs to be spread and put into action by community activists, teachers, educators, celebrities, health professionals, athletes, and politicians. The more people who know the critical importance of eating healthfully, and the more they take a stand, the greater the effect will be on transforming the health of all in America. By working together, we can save millions of lives.

Acknowledgments

This work was presented at Lifestyle Medicine 2017, October 22-25; Tucson, AZ.

Authors’ Note: The opinions presented in this article are those of the author and may not represent those of the Guest Editor, Editor, or the American Journal of Lifestyle Medicine.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval: Not applicable, because this article does not contain anystudies with human or animal subjects.

Informed Consent: Not applicable, because this article does not contain anystudies with human or animal subjects.

Trial Registration: Not applicable, because this article does not contain anyclinical trials.

IMAGES

  1. Fast food industry analysis

    fast food industry research paper

  2. (PDF) What are the Challenges Faced in the Food Manufacturing

    fast food industry research paper

  3. Economic Impact of the U.S. Fast Food Industry on the Global Economy

    fast food industry research paper

  4. Kfc and Global Fast Food Industry Essay Example

    fast food industry research paper

  5. 6+ Fast Food Industry Analysis

    fast food industry research paper

  6. 😂 Research paper about fast food chains. Free fast food industry Essays

    fast food industry research paper

VIDEO

  1. Careers in the New Zealand Food Industry, Research and Development, Baking Sector

  2. The Last Food Production Practical Of Batch 2019-23 #chefsumitpant #foodslide #ihmcollege #kitchen

  3. Food Processing and Related Industries in India

  4. EXPLORING THE WONDERS OF FOOD TECHNOLOGY , POST GRADUATE DEPARTMENT OF FOOD TECHNOLOGY

  5. Food technology career #foodtechnology #july2023 #auditor #foodtechnologist #job

  6. Which FAST FOOD Spots Have The Best Of These? #shorts #food #fastfood #restaurant #pizza #burger

COMMENTS

  1. Fast Food Trend Analysis by Evaluating Factors Leading to Customer Satisfaction

    The Fast Food Market Overview Research revealed that by 2022 the Trend of Fast Food Industry is estimated to bloom from $533,24 4 M to $743,859 M at a 4.8% CAGR (Co mpound Annual Growth Rate). On the

  2. (PDF) Food Retailing: Fast Food Industry

    Michael P. Jackson. Kate Tuck. The retail industry employs approximately 2 145 000 people or 9.8 per cent of the total working population in Great Britain. Almost twothirds of those working in ...

  3. Satisfaction and revisit intentions at fast food restaurants

    Revisit intention is a substantial topic in hospitality research ... How determinants of customer satisfaction are affecting the brand image and behavioral intention in fast food industry of Pakistan. J Tour Hospit 6(316):2167-0269 ... (2015) How to improve perceived service quality by increasing customer participation. Paper presented at the ...

  4. Food service industry in the era of COVID-19: trends and research

    Abstract. Coronavirus disease 2019 (COVID-19) is a new type of respiratory disease that has been announced as a pandemic. The COVID-19 outbreak has changed the way we live. It has also changed the food service industry. This study aimed to identify trends in the food and food service industry after the COVID-19 outbreak and suggest research ...

  5. Factors Affecting Customer Satisfaction in Fast Food Restaurant ...

    Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. ... In the industry of fast-food ...

  6. Purchasing Intentions toward Fast Food: The Mediating Role of ...

    We highlight the role of fast food awareness and the features affecting the intentions of individuals buying it. The fast food industry is developing rapidly, opening new doors for various stakeholders. The objective of the study is to identify the impact of knowledge of fast food on the desire to buy fast food, study its impact on fast food purchasing intentions, and uncover the effect of ...

  7. Big data in the food supply chain: a literature review

    The food industry is an integral part of every economy and plays a critical role in supplying the necessities for human survival and provides consumer choice (Turi et al. 2014).According to estimates, US$14 trillion of foods is produced, packaged and sold worldwide every year and encompasses a multitude of transactions between suppliers, retailers and consumers (Ji et al. 2017).

  8. PDF Fast Food Industry in the Post-pandemic Era

    3 CASE STUDY OF KFC. KFC (Kentucky Fried Chicken) is a fast food company affiliated to Parkson Retail Group, and its company business mainly include providing eat-in services, and the impact of the pandemic on Yum Brands continues to expand. Following shutting down one third of global restaurants, it is reported that, Yum Brands has temporally ...

  9. Sustainability

    The fast food restaurant business is one of the fastest-growing industries in the world. International and local restaurant chains are trying to satisfy the demands of customers for a variety of products and services. Along with changing market trends, customers are now becoming more sophisticated and demanding. Customer satisfaction is an essential business issue, as entrepreneurs have ...

  10. Determining intention, fast food consumption and their related factors

    Background Today, with the advancement of science, technology and industry, people's lifestyles such as the pattern of people's food, have changed from traditional foods to fast foods. The aim of this survey was to examine and identify factors influencing intent to use fast foods and behavior of fast food intake among students based on the theory of planned behavior (TPB). Methods A cross ...

  11. Fast Food Market Research Reports & Industry Analysis

    Competition, Growth Opportunities, and Outlook to 2031 The Global Vegan Fast Food Market Research Report is a comprehensive and insightful analysis designed to assist stakeholders, industry professionals, and decision-makers in identifying Vegan Fast Food market ... Read More. Fast Food Wrapping Paper Market by Material (Aluminum Foil, Paper ...

  12. Fast food consumption and overweight/obesity prevalence in students and

    Previous research has identified a strong positive association between the availability of fast food and its consumption as well as fast food consumption and obesity outcomes [5, 8, 10, 14, 15]. However, some studies assessed the fast food consumption on the general obesity based on Body Mass Index (BMI) [ 5 , 8 , 10 , 16 ].

  13. Fast-food, everyday life and health: a qualitative study of 'chicken

    Introduction. Excess consumption of fast food has been linked with a variety of health problems including obesity and type 2 diabetes (Jeffery et al., 2006; Pereira et al., 2005; Stender et al., 2007).Fast food is energy dense and nutrient poor compared to food prepared at home (Guthrie, 2002) and portion sizes have been increasing over the past 50 years (Young & Nestle, 2003).

  14. PDF Fast Food Trend Analysis by Evaluating Factors Leading to ...

    the fast food operators started to gain benefits by providing full-service restaurants. The trend of Five years revealed growth by 3.5% in the Global Fast Food industry by generating revenue of $668bn in 2018 (IBISWORLD 2018). Since the trend of five years revealed the growth in fast food restaurant chains similarly

  15. Fast Food Consumption and its Impact on Health

    Mean percentage of calories from fast food among children and adolescents aged 2-19 years, by sex and age: United States, 2011-2012 Harmful Effects of Fast food: Social Hazards: The fast food ...

  16. The Effect of the Minimum Wage on the Fast Food Industry

    The Effect of the Minimum Wage on the Fast Food Industry. Lawrence F. Katz & Alan B. Krueger. Working Paper 3997. DOI 10.3386/w3997. Issue Date February 1992. Using data from a longitudinal survey of fast food restaurants in Texas, the authors examine the impact of recent changes in the federal minimum wage on a low-wage labor market The ...

  17. Food industry digitalization: from challenges and trends to

    The study outcome contributes on the identification and prioritization of different steps toward an Industry 4.0 implementation in the food industry context. The research methodology is based on data collection through questionnaire, interviews and focus groups provided by Siemens expertise.

  18. Fast Food and Fast Research: Life-threatening Phenomena

    Dear Editor. The surge in fast food consumption in recent years is considered a threat to human health. This change in the life habit has raised serious concerns among health policy-makers and medical nutrition researchers. Environmental stress, multitasking, low physical activity, and low academic achievement have been shown to influence the ...

  19. Customer Retention in Fast Food Industry

    This research has brought out how to gain customer retention to the services and the factors that influence the customer retention. For this research sample size of 164 consumers from different fast food restaurant has been taken randomly on the basis of convenience sampling. Multiple Regressions were used as the statistical tool.

  20. PDF Research Paper Supply Chain Integration and Firm Performance: The Food

    The fast food delivery industry has been a growing one in the world currently. Many food retailers in Turkey has extended their services to include delivery. In view of the continuous benefits integration provides to firms, this research seeks to investigate whether these fast food firms have integrated with their supply chain partners and ...

  21. Fast Food Industry Research Papers

    Following these, a hypothetic case is analyzed, whereby a new low-cost retailer enters the fast-food industry in Greece. The scenario of the case discussed in this assignment suggests, a new low-cost retailer enters the fast-food industry in Greece, which obviously affects the business of Goody's.

  22. AI In The Fast Lane: Revolutionizing Fast Food Through Technology

    Current Uses of AI in Fast Food Chains. Already, fast food chains use various applications within the industry, significantly impacting how restaurants operate and interact with customers.

  23. The Hidden Dangers of Fast and Processed Food

    Because of the limited access to supermarkets, they eat more unhealthy fast and processed foods and end up having 7 times the risk of early-life stroke (before age 45), putting people in nursing homes in their 30s, 40s, and 50s. 4 - 7. The vulnerable poor in these areas also have double the risk of heart attack, double the risk of diabetes, and ...

  24. A Study on Scenario of Fast-Food Industry in India

    The Indian food industry is projected to grow at a compounded annual growth rate of approximately 10% per year (2020-2025) 16 with multinational fast food corporations being the major segments. 17 ...