Exploring the role of service quality, atmosphere and food for revisits in restaurants by using a e-mystery guest approach
Journal of Hospitality and Tourism Insights
ISSN : 2514-9792
Article publication date: 26 August 2020
Issue publication date: 19 July 2021
Quality in foodservices has become essential, and new methodological ways of determining service quality enable a better representation of service processes and help to increase revisits. This paper focuses on the foodservice context and explores the relationship between staff-related service dimensions, atmosphere, food quality and revisit in a full-service setting.
Design/methodology/approach
This study combines an often neglected mystery guest approach with partial least square–structural equation modeling (PLS-SEM) to shed more light on customers' service perceptions. The mystery guest approach has been updated with a digitally supported smartphone questionnaire (e-mystery) that provides more reliable results since previous measurements experienced difficulties of feasibility in time-limited settings ( N = 247).
The findings of this study confirm the direct effects of the service quality dimensions reliability, attentiveness and atmosphere on revisit intention and highlight the mediating role of food quality. In detail, the findings showed significant results for service employees' reliability and attentiveness and underlined the role of atmosphere for revisit intention.
Originality/value
The contribution of this paper supplements that mystery guest approaches represent a reliable alternative to convenience sampling, especially in combination with a digitally supported questionnaire (e-mystery). Thereby, this paper suggests the further application of e-mystery for the hospitality and tourism industry. In terms of implications, this study highlights the importance of securing food quality by fostering specialized schools and training programs for career starters. Since the findings stress the importance of service quality and atmosphere, managers need to ensure that employees are trained in culturally sensitive communication and services to excel in service-related dimensions.
- Foodservice
- Service quality
- Food quality
- Restaurants
- Mystery guest
Bichler, B.F. , Pikkemaat, B. and Peters, M. (2021), "Exploring the role of service quality, atmosphere and food for revisits in restaurants by using a e-mystery guest approach", Journal of Hospitality and Tourism Insights , Vol. 4 No. 3, pp. 351-369. https://doi.org/10.1108/JHTI-04-2020-0048
Emerald Publishing Limited
Copyright © 2020, Bernhard Fabian Bichler, Birgit Pikkemaat and Mike Peters
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode .
1. Introduction
Food experiences represent a crucial component of tourism, contributing to tourist expenditures and overall satisfaction of traveling ( McKercher et al. , 2008 ). In this context, quality dimensions are accepted as a key to achieving competitive advantages in foodservices. Crick and Spencer (2011 , p. 466) highlighted that “organisations (…) need to understand with as much precision as possible what the guests want from the service experience.” Particularly in the foodservice context, customers have various choices between different restaurants, which could result in restaurants switching if expectations are not met ( Stevens et al. , 1995 ; Park and Jang, 2014b ). Choice and quality of food, service, price, as well as atmosphere, are often seen as the main focus of restaurants. Still, foodservices do not solely concentrate on these attributes, but instead offer holistic dining experiences ( Yuksel et al. , 2010 ). An essential element of these experiences is service quality (SQ), which is intangible, individualized and subjective by nature ( Chow et al. , 2007 ). Therefore, restaurants try to optimize customer experiences by managing specific factors of total quality management ( Psomas and Jaca, 2016 ).
There exist several schools of thought, which have defined quality dimensions differently ( Parasuraman et al. , 1988 ; Grönroos, 1984 ). The bottom line is that SQ consists of multiple dimensions, which can be classified as functional and technical ( Grönroos, 1984 ) or interactional quality ( Brady and Cronin, 2001 ). Crick and Spencer (2011) synthesized that satisfaction with the (service) product and the way the front-line staff delivered it are the minimum requirement. SQ is recognized to be a significant determinant of a company's success and therefore represents a major research stream of hospitality research ( Bouranta et al. , 2009 ; Bujisic et al. , 2014 ).
Previous literature summarized the role of service, food and environment for customers' satisfaction and behavioral intentions ( Shahzadi et al. , 2018 ; Ryu et al. , 2012 ). Crick and Spencer (2011) called for a better recognition of each sector's nuances in determining SQ, supported by the call of Shahzadi et al. (2018) for more comparative studies. Therefore, this paper sheds more light on foodservices in the small but highly touristic city of Innsbruck, Austria. Due to the long tradition in the foodservice and hospitality industry and the legal requirements ( WKO, 2020 ), this study uses an adjusted set of measures and applies e-mystery to avoid convenience sampling. The e-mystery also accounts for the importance of assessing quality continuously throughout the service process ( Crick and Spencer, 2011 ). E-mystery allows us to mirror and observe customers' service perceptions throughout the service delivery process: customers can evaluate services immediately, in real time during the service experience without introducing bias by evaluations after the service delivery process. The objective of this paper is to explore the relationship between staff-related service dimensions, atmosphere, food quality and revisit in a full-service setting. In this context, e-mystery enables to benchmark foodservice performance, which is difficult due to the intangible, perishable and inseparable nature of services ( Ladhari, 2009 ).
2. Theoretical perspectives
2.1 service dimensions and revisit intention.
Numerous studies show that behavioral intentions refer to positive word of mouth resulting in recommendations, remaining loyal and revisits ( Shahzadi et al. , 2018 ; Jani and Han, 2011 ). Following the early work of Berry et al. (2002) on how to manage service experience with food quality (functional clue), SQ (humanic clue) and atmosphere (mechanic clue) as key attributes, revisits have been discussed extensively in the foodservice context ( Karamustafa and Ülker, 2020 ; Nguyen et al. , 2018 ; Bujisic et al. , 2014 ; Ryu et al. , 2012 ). Satisfaction and behavioral intentions are often used as dependent variables and in this context, previous research underlined the mediating role of satisfaction for customers' intentions ( Barber et al. , 2011 ; Lee and Whaley, 2019 ). Measuring revisit intention is important since behavioral intentions represent the likelihood to engage in a particular behavior ( Oliver, 2014 ). Therefore, SQ is directly related to customer satisfaction and affects customers' intentions and thereby company's success ( Gupta et al. , 2007 ).
2.2 Customers’ perceptions of quality dimensions in the food industry
A plethora of studies highlighted the role of SQ, food quality and atmospheric/environment quality for the foodservice industry ( Shahzadi et al. , 2018 ; Park and Jang, 2014a ; Bujisic et al. , 2014 ; Ryu et al. , 2012 ). Grönroos (1984) separates quality dimensions into technical (e.g. food quality, meal) and functional quality, where the latter is more concerned about the service delivery process, personal contact and the atmosphere. Due to the intangible nature of the service process, the evaluation of the functional quality is highly subjective compared to food quality where a more objective assessment is possible. Later, the three-factor model by Brady and Cronin (2001) conceptualized quality dimensions as interaction quality, physical environment quality and outcome quality, which have proven to be positive predictors of SQ.
Several scholars focused on the crucial role of food quality for customers' satisfaction and intentions ( Shahzadi et al. , 2018 ; Njite et al. , 2015 ), while others emphasized the importance of SQ ( Nguyen et al. , 2018 ). In this context, previous research stressed the intangible, perishable and inseparable nature of services ( Ladhari, 2009 ). These characteristics make it difficult for service providers to assess their performance, especially given the facts that service performance can only be assessed after the service has been received and because of its heterogeneous nature, quality can also vary in terms of day, place and customer ( Parasuraman et al. , 1988 ).
Customer's perception of SQ is defined as the customer's judgment of the superiority of the service ( Zeithaml, 1988 ), which results from the comparison of the expected service and the actual perceived service performance ( Ladhari, 2009 ; Oliver, 2014 ). In this context, SERVQUAL-related approaches, based on disconfirmation theory, made a significant contribution to consumer research in the service industry ( Parasuraman et al. , 1988 ). It consists of five dimensions to measure SQ: reliability, responsiveness, empathy, tangibles and assurance. Currently, these five dimensions still play an essential role in explaining SQ ( Karamustafa and Ülker, 2020 ; Bilgihan et al. , 2018 ; Liu and Tse, 2018 ).
2.3 Service quality in restaurants
A number of studies highlighted the applicability of SERVQUAL instruments such as DINESERV ( Stevens et al. , 1995 ), DINESCAPE ( Ryu and Jang, 2008 ) or TANGSERV ( Raajpoot, 2002 ) for foodservices. All these instruments capture different dimensions of quality and differ according to whether they are full service ( Park and Jang, 2014a ; Ryu et al. , 2012 ; Jani and Han, 2011 ) or quick service ( Nguyen et al. , 2018 ; Etemad-Sajadi and Rizzuto, 2013 ; Richardson et al. , 2019 ). Depending on the research focus, research highlighted either the importance of staff-related SQ, food quality or environmental factors such as ambiance. Previous studies showed that food quality is the most important aspect for customers' total quality perceptions of full-service restaurants ( Shahzadi et al. , 2018 ) but SQ is experiencing a revival in times of increased emphasis on customer experiences permeating marketing, economics, hospitality and psychology literature ( Adhikari and Bhattacharya, 2016 ). In this context, recent literature highlighted the importance of customer experiences for the service industry ( Teixeira et al. , 2012 ; Dong and Siu, 2013 ; Brunner-Sperdin et al. , 2012 ; Kim et al. , 2017 ; Alhelalat et al. , 2017 ). Additionally, it is noted that customer experience management represents an opportunity to achieve a competitive advantage in service organizations ( Teixeira et al. , 2012 ; Pikkemaat and Zehrer, 2016 ).
Tucker (1991) understood the speed of service delivery, convenience, value-adding, lifestyle connotations as well as the technology as influencing factors on customers' perceptions of the service experience. These aspects are closely related to staff-related SQ dimensions focusing on employees' reliability, responsiveness, empathy and assurance. In this context, Luo et al. (2019 , p. 469) emphasized the role of “professionalism, the ability to respond to customers' emotions and hidden needs and build bonds with them, and the ability to deliver one-stop service” to achieve delightful service.
(a) Reliability, (b) attentiveness, (c) responsiveness and (d) atmosphere are positively related to customers’ revisit intention.
The higher the customers' perception of (a) reliability, (b) attentiveness, (c) responsiveness and (d) atmosphere, the higher the customers' perception of food quality.
2.4 Food and atmosphere in restaurants
Food quality is positively related to customers’ revisit intention.
The relationship between (a) reliability, (b) attentiveness, (c) responsiveness and (d) atmosphere and revisit intention is mediated by food quality.
Mystery guests' characteristics such as (a) age, (b) gender, (c) accompany and (d) self-reported expertise and (e) self-reported stress levels correlate with perceived food quality.
Mystery guests' characteristics such as (a) age, (b) gender, (c) accompany and (d) self-reported expertise and (e) self-reported stress levels correlate with revisit intention.
3. Methodology
Mystery guest approaches have been used in former studies in the travel and tourism industry ( Liu et al. , 2014 ; Anderson et al. , 2001 ). They represent a special form of participant observation and require potential customers to evaluate service processes ( Wiele et al. , 2005 ). In a review on mystery shopping, Wilson (1998 , p. 161) distills three possible applications: first, to act as a diagnostic tool identifying failings and weak points in an organization's service delivery; second, to encourage, develop and motivate service personnel by linking with appraisal, training and reward mechanisms; third, to assess the competitiveness of an organization's service provision by benchmarking it against the offerings of others in an industry. Wiele et al. (2005) add that mystery approaches can also be used to measure the effectiveness of (training) programs and to test if customers experience equal treatments. Despite the benefits of mystery shopping approaches such as less external pressure compared to traditional questionnaires, mystery shopping is a sensitive topic as it includes a high degree of knowledge asymmetry between customers and staff ( Wiele et al. , 2005 ). Additionally, training and briefing of mystery guests are essential to establish the reliability of mystery approaches ( Wilson, 1998 ). While Morrison et al. (1997) highlighted issues, which occur from encoding, memorizing and retrieving information for service evaluations, these issues were counteracted with the e-mystery approach. Mysterious guests were able to fill out their ratings in real time via an online questionnaire on their mobile phones.
3.1 Sampling
As previous research has shown several tensions arising from mystery guest approaches, such as ethics of participant observation and the reliability of mystery shopping approaches ( Wilson, 1998 ), particular attention was paid to the selection and preparation/training of mystery guests. Table 1 provides several key characteristics of the 66 mystery guests who were selected based on demographics and foodservice expertise. These mystery guests were identified by using a snowball sampling approach ( Gobo, 2005 ), starting with research assistants and extending it to colleagues and other contacts willing to participate. Importantly, they were trained to make sure they understood the procedures and to evaluate the SQ immediately after the termination of each service phase. In order to keep the task manageable, mystery guests were instructed to test alone, in a group of two or a larger group.
Additionally, the study controlled for mystery guests' age, gender, self-reported stress level, accompany and previous experiences. For the initial identification of the businesses, a list of all gastronomic enterprises provided by the Austrian Federal Economic Chamber (WK Tirol) was used. The research team identified a set of well-known foodservice businesses by using purposive sampling. Mystery guests were randomly assigned ( Gobo, 2005 ) to the selected enterprises and instructed to visit at different times of the day. There were no restrictions on the orders and the WK Tirol reimbursed the expenditures. The e-mystery questionnaires were filled out from November 2017 to December 2017 in the city of Innsbruck, Austria. Each of the mystery guests tested between two (minimum or 3.2% of visits) and eight (maximum or 6.4% of visits) foodservice businesses. On average, the businesses were visited four times, with a minimum of three and a maximum of six visits. Table 1 provides an overview of the mystery guests' characteristics.
3.2 Measurements
The measurements aimed to assess quality dimensions in the foodservice context. Literature acknowledged several issues concerning the feasibility (e.g. takes too long to fill out) of previous measurement scales ( Sulek and Hensley, 2004 ). In combination with the e-mystery approach, which enables a real-time assessment of the service experiences during the service encounter, measurements were tailored to the specific requirements of the mystery guest approach and the nuances of the sector ( Crick and Spencer, 2011 ). A systematic assessment of previously used constructs and items helped to synthesize the measures for the mystery guest approach (list of measures see Table A1 ).
After discussions within the research team, we decided to exclude assurance ( Parasuraman et al. , 1988 ) as a quality dimension. The paper is based on the full-service foodservice sector in Innsbruck (Austria), where commercial law and other requirements such as operating licenses are incredibly challenging ( WKO, 2020 ) and the assurance dimension is more suitable for the banking and retailing industry ( Parasuraman et al. , 1988 ). Generally speaking, in the full-service foodservice context, orders are served directly to the table and the offer ranges from casual family restaurants to fine dining. Additionally, the selected full-service companies were similar regarding employees' knowledge and the degree of professionalization due to location and size. In addition, previous studies recognized time and cost efficiency as a central aspects in collecting mystery guest data ( Sulek and Hensley, 2004 ).
The final instrument included 21 items to assess quality dimensions. These items were measured on a Likert Scale, ranging from “strongly disagree” (1) to “strongly agree” (5). Additionally, data was collected on the characteristics of the mystery guests, such as age, gender, accompany, expertise and stress levels. Single-item self-reported measures were used to ask respondents whether they consider themselves (1) “occasional”, (2) “experienced customers” or (3) “expert customers” and to rate their self-reported stress levels on a scale from (1) “relaxed” to (3) “stressed” for each service setting. Based on previous literature supporting the role of revisit and recommend intention as a proxy for loyalty ( Jani and Han, 2011 ), a combination of revisit and recommend intention was used due to time constraints connected with the mystery guest approach as a dependent variable ( Kivela et al. , 1999 ; Getty and Thompson, 1995 ).
3.3 Data analysis
First, exploratory factor analysis (EFA) was used to identify the underlying factors. Both the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (0.886) and the Bartlett test of sphericity (1504.826***) indicated the suitability of EFA. Factors exceeding 0.60 were retained ( Hair et al. , 2012 ). Moreover, Cronbach's alpha was used to assess internal validity and ranged between 0.583 and 0.878. Second, partial least square–structural equation modeling (PLS-SEM) in SmartPLS TM (v. 3.2.8) was used to analyze the data ( Ringle et al. , 2015 ). This “soft modeling approach” ( Hair et al. , 2012 , p. 416) has several advantages, such as that it can be used with less rigid theoretical backgrounds and for prediction-oriented research aimed at maximizing the explained variance of dependent variables ( Hair et al. , 2012 ; Henseler et al. , 2014 ). This approach does not require normally distributed data and is well suited for smaller sample sizes ( Henseler et al. , 2014 ). In combination with the e-mystery approach, it represents a straightforward approach to explore the relationship in greater depth.
4.1 Reliability, validity and common method bias analyses
PLS–SEM was used ( Hair et al. , 2012 ) to assess the relationships among the constructs. First, validity and reliability were assessed by using composite reliability (CR) and average variance extracted (AVE). One item was excluded since the factor loading did not exceed 0.60. The Fornell–Larcker ratio ( Fornell and Larcker, 1981 ) showed that the square roots of the AVEs are greater than the construct correlations. Additionally, cross-loadings were not a significant concern for the data and all items loaded the highest on the proposed factor. Thus, the data indicated discriminant validity for the constructs. To test for common method variance, a common method factor ( Podsakoff et al. , 2003 ) following the procedure by Liang et al. (2007) was included. The constructs of the proposed model ( Figure 1 ) explained on average 0.65 of indicator variance and showed high and significant loadings. In contrast, the common method factor only accounted for 0.03 of indicator variance on average and showed significant results only in six cases and smaller loadings (see Table A3 ). Since the ratio between substantive variance and method variance is 22:1, it was concluded that common method variance is not a serious concern for the data. Table 2 shows the identified factors, factor loadings, Cronbach's alpha, CR and AVE.
4.2 Results and hypothesis testing
The findings show that reliability ( M = 4.37, SD = 0.71), food quality ( M = 4.04, SD = 0.74) and responsiveness of staff ( M = 4.01, SD = 1.02) scored high on the Likert scale ( Table 2 ). Lower but still high values were observed for attentiveness ( M = 3.45, SD = 1.05) and atmosphere ( M = 3.78, SD = 0.79).
Figure 1 highlights the path coefficients, significance levels and R 2 values. Reliability ( β = 0.119, p < 0.05), attentiveness ( β = 0.213, p < 0.000), responsiveness ( β = 0.114, p < 0.05) and atmosphere ( β = 0.144, p < 0.001) contributed significantly to revisit. Hence, the analysis fully confirms hypotheses H1a to H1d . Reliability ( β = 0.359, p < 0.000), attentiveness ( β = 0.193, p < 0.05) and atmosphere ( β = 0.271, p < 0.000) were found to positively contribute to food quality, thereby fully supporting H2a , H2b and H2d . However, no effects on responsiveness were found and H2c was therefore rejected. This is surprising but could be explained with potential confounding variables affecting the responsiveness construct. For example, in less formal foodservice settings in Austria, it is common to self-select a table and thus, future studies should consider this heterogeneity. H3 indicated a positive relationship between food quality and revisit, which was fully supported ( β = 0.504, p < 0.000).
To assess the mediation hypotheses, estimates and T -statistics for total, direct and indirect effects were calculated following the Preacher and Hayes (2008) procedure. To check for the significance of the mediation, the 95% bias-corrected confidence intervals were calculated, using 5,000 bootstrap samples ( Table 2 ). The findings of the mediation analysis show that atmosphere ( β = 0.139, p < 0.000), attentiveness ( β = 0.102, p < 0.01) and reliability ( β = 0.177, p < 0.000) are partially mediated by food quality. This confirms hypothesis H4a , H4b and H4d , but H4c is rejected since no effects were found. Following Hair et al. (2017) , the findings show partial mediation since indirect and direct effects are both significant and in the same direction. Lastly, the influence of mystery guests' characteristics on food quality ( H5 ) and revisit ( H6 ) was assessed, but no significant effects for age, gender, stress level, accompany and expertise were found. Thus, H5a–H5e and H6a–H6e were rejected.
5. Discussion and conclusions
5.1 conclusions.
This paper highlights the importance of quality factors in the full-service foodservice industry. While staff-related SQ emerged as an important factor for revisit intention, the findings also highlighted the role of atmosphere and the mediating effects of food quality for revisits. These findings are essential since securing positive experiences leading to satisfaction and revisit is crucial for the success of foodservices. This study thus complements existing literature, which highlights the direct impact of functional quality on revisit intention ( Luo et al. , 2019 ), but also confirms studies that have shown the role of food quality in revisiting and satisfaction ( Sulek and Hensley, 2004 ). In detail, the findings underline that food quality partially mediates the relationship between attentiveness, reliability and atmosphere ( Table 3 ). Additionally, this paper also offers an important methodological contribution by emphasizing the potential of e-mystery guest approaches for future quality evaluations. Combining a traditional mystery guest approach ( Wiele et al. , 2005 ) with widely available mobile technology resulted in an e-mystery approach with real-time assessments, fixed time issues and showed an alternative to convenience sampling ( Ryu et al. , 2012 ; Sulek and Hensley, 2004 ).
5.2 Theoretical implications
The findings of this paper highlight five critical quality dimensions for foodservices. Consistent with previous studies, the findings show that SQ is a key requirement to ensure revisits ( Gupta et al. , 2007 ; Stevens et al. , 1995 ). In particular, the findings highlight the importance of functional and staff-related factors such as attentiveness and reliability ( Table 3 ). These findings correspond with Muskat et al. (2019) , who demonstrated the importance of employee interactions for dining experiences. This also supports the early work of Grönroos (1984) and Brady and Cronin (2001) , discussing the importance of functional and interactional quality.
In light of established theories, the findings provide several insights. Parasuraman et al. (2005) synthesize that “consumers retain product information in memory at multiple levels of abstraction” ( 2005 , p. 217). From a means-end chain perspective, the findings allow a process-oriented exploration of the importance of attributes (e.g. atmosphere), functional consequences (e.g. responsiveness and reliability) and psychological consequences (e.g. attentiveness) for value creation, which results in increased revisit intention. Second, in light of the theory of reasoned action ( Ajzen and Fishbein, 1980 ), which aims to explore individuals' behavior in the purchase process, the findings highlight five factors (reliability, attentiveness, responsiveness, atmosphere and food quality) that can be used to explain this process in foodservices. Following these theoretical considerations, quality assessments result from the evaluation of upstream factors, which emphasize the role of intangible experiences such as attention and reliability for service experiences in foodservices.
Additionally, it is also shown that customer's intention to revisit is affected by atmosphere ( Figure 1 ), which consists of factors such as pleasant atmosphere and clean facilities ( Table 2 ). In line with previous research, the importance of gastronomic environment and food sanitation as a basic requirement for customer satisfaction is confirmed ( Liu and Jang, 2009 ; Han and Hyun, 2017 ). In the structural model, food quality was found to partially mediate the SQ–revisit relationship ( Table 3 ). This also supplements previous studies ( Luo et al. , 2019 ; Erkmen; Hancer, 2019 ) on the importance of delightful service but also underlines the importance of food quality to achieve revisit. In summary, while much attention is given to service experiences and the service encounter, the findings highlight that the art of preparing excellent and tasty food should not be underestimated, as Bujisic et al. (2014) also reported for different types of restaurants. Also, Liu and Jang (2009) confirmed the importance of technical quality, service reliability and environmental cleanliness to secure satisfaction and achieve positive behavioral intentions. The results of this study underline that the quality of service encounters, which is in this study partially mediated by the food quality, significantly affects customers' behavioral intentions. This is also supported by Sulek and Hensley (2004) , who showed that food quality was most important for return intention and satisfaction.
Regarding early research on SERVQUAL ( Parasuraman et al. , 1988 ) and DINESERV ( Stevens et al. , 1995 ), the results indicate that nowadays SQ has become a fundamental factor for foodservices due to increasing specialization and professionalization. Recently customers are more experienced in food quality and they search for atmosphere ( Liu and Tse, 2018 ; Ryu et al. , 2012 ). In the context of ethnic restaurants, Muskat et al. (2019) found proof that an authentic atmosphere plays an important role for satisfaction in Austrian ethnic restaurants.
Integrating e-mystery guest approaches for data acquisition allows collecting real-time data over a more extended period, which provides direct assessments of customers' experiences ( Wilson, 1998 ). This provides an advantage in the evaluation of foodservices, where services and products are known to be heterogeneous, perishable and inseparable from the consumption process ( Ladhari, 2009 ). The e-mystery approach results in direct evaluations of quality dimensions and no bias is introduced by filling out the questionnaire after finishing the visit. Nevertheless, the findings underline that research designs using mystery guest approaches need to pay special attention to the training and selection of mystery guests that often have varying degrees of expertise ( Wiele et al. , 2005 ).
5.3 Managerial implications
Even though customers represent highly heterogeneous subgroups with different traits and characteristics ( Ihtiyar et al. , 2018 ), the findings of this study have important implications for the configuration of quality dimensions for foodservices. Improvements in the full-service food industry need to address staff-related factors, such as attention and reliability, but also factors that result as process outcomes (e.g. food quality). Since employees' interactions with guests contribute positively to enhance dining experiences ( Muskat et al. , 2019 ), it is necessary to train employees to stay connected with their guests ( Luo et al. , 2019 ). On the one hand, communication and emotional skills of employees seem to be of utmost importance to interact with guests and provide successful service processes ( Lloyd and Luk, 2011 ; Mattila and Enz, 2002 ). On the other hand, an increasing number of cross-cultural service encounters occur, leading to the need to train employees for culturally sensitive communication and services in restaurants ( Lee, 2015 ; Ihtiyar et al. , 2019 ). Besides, environmental factors such as atmosphere have shown to positively affect customers' intentions but appear more challenging to manage ( Liu and Tse, 2018 ). Restaurant managers need to be aware of dealing with and arranging the restaurant's environment, including factors such as atmosphere and target groups ( Bilgihan et al. , 2018 ). Recently, Karamustafa and Ülker (2020) report that restaurant attributes related to cleanliness were found to be the most important attributes when evaluated from foreigners in a tourism context. The ideal composition of ambiance, space and function as well as artifacts, signs or symbols forms a prerequisite for positive customer and employee experiences during the service process ( Karamustafa and Ülker, 2020 ; Bujisic et al. , 2014 ; Muskat et al. , 2019 ; Nguyen et al. , 2018 ).
Consequently, managers of restaurants should be able to deliver an appealing atmosphere for their target group, including light and sound solutions, an appropriate location with parking spaces and authentic menus of high quality ( Muskat et al. , 2019 ; Bilgihan et al. , 2018 ). External knowledge from light and interior designers can deliver successful inputs as well as from restaurant consulters. Since the findings also highlight the mediating role of food quality, this also points out the importance of providing high-quality education for future chefs. This requires both specialized schools for career starters and further training opportunities for employees, who choose to engage in the foodservice industry on the second educational path.
5.4 Limitations and future research
This research includes several limitations. Although the mystery guest approach has shown to be valid and promising ( Wilson, 1998 ), training and preparation remain essential. Since partial mediation was found, this indicates that there exist other potential mediators for the quality–revisit relationship ( Hair et al. , 2017 ). Thus, future research will be necessary to examine possibly omitted mediators and moderators, also in other settings and locations. It will be necessary to extend and evaluate the findings of this paper in other studies using, for example, experimental approaches or a qualitative open critical incident technique to gather deeper insights and thick descriptions of service encounters in the foodservice sector. Additionally, the e-mystery guest approach provides vital insights into the performance of SQ and can be used to gather consumer-driven knowledge for future SQ studies. Lastly, future research needs to explore the role of service innovations ( Pikkemaat et al. , 2019 ; Pikkemaat; Zehrer, 2016 ) to improve quality perceptions and if conditions relating to experiences influence customers' behavior, for example, perceived authenticity of the employees or spoken languages.
Service quality, atmosphere, food quality, revisit intention and mystery guests' characteristics
Mystery guests' characteristics
| Characteristics | No. (#) | Percentages (%) |
|---|
| = |
| Female | 38 | 57% |
| Male | 28 | 43% |
| |
| Under 20 | 10 | 15.5% |
| 21–30 | 11 | 16.5% |
| 31–40 | 10 | 15.5% |
| 41–50 | 10 | 15.5% |
| 51–60 | 12 | 17.0% |
| 61 and older | 13 | 19.0% |
| |
| Occasional customer | 16 | 24% |
| Experienced customer | 39 | 58.5% |
| Expert | 11 | 17.5% |
| |
| Single | 39 | 15.6% |
| Group of two | 167 | 67.6% |
| More than two | 41 | 16.7% |
| | 29.91 | |
| Spent more than planned | 134 | 54.2% |
| Spent less than planned | 113 | 45.8% |
| Average visits per restaurant | 4 | Accounting for 73% of all visits |
Factor analysis of SQ constructs
| | EFA loadings | PLS-SEM loadings (CR) | Mean (SD) |
|---|
| | = 0.764 | CR = 0.852 | 4.37 (0.71) |
| All ordered drinks were served quickly and perfectly | 0.759 | 0.779 | 4.43 (0.79) |
| Delivery of all ordered drinks and food left nothing to be desired | 0.707 | 0.767 | 4.42 (0.80) |
| The entire order was placed quickly and easily | 0.821 | 0.795 | 4.62 (0.66) |
| I was able to order immediately after receiving the drinks/menu | 0.791 | 0.731 | 4.49 (0.76) |
| | = 0.878 | CR = 0.895 | 3.45 (1.05) |
| The attentive nature of the staff stimulated increased consumption | 0.835 | 0.748 | 2.79 (1.31) |
| The staff literally read the wishes from my eyes | 0.845 | 0.785 | 3.17 (1.17) |
| The staff asked if everything was for the best | 0.780 | 0.747 | 3.84 (1.34) |
| I felt warmly and professionally looked after during the whole visit | 0.798 | 0.832 | 4.08 (0.94) |
| My waitress/waiter was especially attentive during the whole visit | 0.865 | 0.857 | 3.58 (1.15) |
| | = 0.701 | CR = 0.856 | 4.01 (1.02) |
| I was immediately noticed | 0.844 | 0.845 | 4.45 (0.89) |
| The welcome was very friendly | 0.786 | 0.740 | 3.81 (1.48) |
| I was immediately offered a suitable place/table | 0.817 | 0.856 | 4.14 (0.95) |
| | = 0.583 | CR = 0.774 | 3.78 (0.79) |
| The atmosphere is pleasant | 0.648 | 0.730 | 4.33 (0.76) |
| The areas are thoroughly clean | 0.745 | 0.673 | 3.10 (1.16) |
| The other guests contributed to my well-being | 0.824 | 0.816 | 3.80 (1.09) |
| | = 0.625 | CR = 0.804 | 4.04 (0.74) |
| For this type of restaurant, the range of drinks and food leaves nothing to be desired | 0.706 | 0.757 | 4.11 (0.96) |
| The sensory quality of food and beverages was excellent | 0.830 | 0.825 | 4.23 (0.88) |
| The price/performance ratio for the food/drinks offered is excellent | 0.714 | 0.694 | 3.92 (0.89) |
| | = 0.852 | CR = 0.912 | 3.88 (1.06) |
| I would recommend the restaurant because of the service experience | 0.926 | 0.923 | 3.96 (1.06) |
| I would recommend this place because of the quality of the food/drinks | 0.815 | 0.823 | 4.18 (1.03) |
| Based on all my experiences I would visit the restaurant again | 0.897 | 0.893 | 3.85 (1.27) |
Structural relationships and hypothesis decisions
| | Estimate | SE | -value | Bias corrected 95% C.I. | -value | Decision |
|---|
| : Reliability → Revisit | 0.119 | 0.052 | 2.278 | | | 0.023 | Supported |
| : Attentiveness → Revisit | 0.213 | 0.053 | 4.037 | | | 0.000 | Supported |
| : Responsiveness → Revisit | 0.114 | 0.056 | 2.027 | | | 0.043 | Supported |
| : Atmosphere → Revisit | 0.144 | 0.046 | 3.134 | | | 0.002 | Supported |
| : Reliability → Food quality | 0.359 | 0.066 | 5.437 | | | 0.000 | Supported |
| : Attentiveness → Food quality | 0.193 | 0.077 | 2.506 | | | 0.012 | Supported |
| : Responsiveness → Food quality | −0.05 | 0.068 | 0.740 | | | 0.459 | Not supported |
| : Atmosphere → Food quality | 0.271 | 0.066 | 4.110 | | | 0.000 | Supported |
| : Food quality → Revisit | 0.504 | 0.049 | 10.267 | | | 0.000 | Supported |
| : Reliability → FQ → Revisit | 0.177 | 0.034 | 4.544 | 0.108 | 0.265 | 0.000 | Supported |
| : Attentiveness → FQ → Revisit | 0.102 | 0.04 | 2.437 | 0.023 | 0.18 | 0.008 | Supported |
| : Responsiveness → FQ → Revisit | −0.025 | 0.034 | 0.744 | −0.093 | 0.038 | 0.472 | Not supported |
| : Atmosphere → FQ → Revisit | 0.139 | 0.033 | 4.205 | 0.073 | 0.202 | 0.000 | Supported |
| : Age → Food quality | 0.025 | 0.05 | 0.503 | | | 0.615 | Not supported |
| : Gender → Food quality | 0.048 | 0.056 | 0.860 | | | 0.390 | Not supported |
| : Accompany → Food quality | 0.049 | 0.048 | 1.008 | | | 0.314 | Not supported |
| : Stress level → Food quality | −0.043 | 0.056 | 0.769 | | | 0.442 | Not supported |
| : Expertise → Food quality | 0.08 | 0.055 | 1.463 | | | 0.144 | Not supported |
| : Age → Revisit | 0.017 | 0.032 | 0.542 | | | 0.588 | Not supported |
| : Gender → Revisit | 0.059 | 0.037 | 1.600 | | | 0.110 | Not supported |
| : Accompany → Revisit | 0.002 | 0.031 | 0.076 | | | 0.939 | Not supported |
| : Expertise → Revisit | 0.04 | 0.031 | 1.275 | | | 0.202 | Not supported |
| : Stress level → Revisit | 0.042 | 0.036 | 1.139 | | | 0.255 | Not supported |
SQ factors and items; all items were evaluated on a Likert scale from 1 = “strongly disagree” to 5 = “strongly agree”
| Dimensions and adapted sources | Items |
|---|
Physical facilities, equipment and appearance of people | The atmosphere is pleasant |
| The areas are thoroughly clean |
| The other guests contributed to my well-being |
Ability to perform the promised service dependably and accurately | All ordered drinks were served quickly and perfectly |
| Delivery of all ordered drinks and food left nothing to be desired |
| The entire order was placed quickly and easily |
| I was able to order immediately after receiving the drinks/menu |
Willingness to help customers and provide prompt service | I was immediately noticed |
| The welcome was very friendly |
| I was immediately offered a suitable place/table |
Individualized attention toward customers (2007) | The attentive nature of the staff stimulated increased consumption |
| The staff literally read the wishes from my eyes |
| The staff asked if everything was for the best |
| I felt warmly and professionally looked after during the whole visit |
| My waiter/waitress was especially attentive during the whole visit |
Offered variety and tasty food | For this type of restaurant, the range of drinks and food leaves nothing to be desired |
| The sensory quality of food and beverages was excellent |
| The price/performance ratio for the food/drinks offered is excellent |
Degree of intent to revisit (1999), | Based on all my experiences I would visit the restaurant again |
| I would recommend the restaurant because of the service experience |
| I would recommend this place because of the quality of the food/drinks |
Descriptives and correlations
| | | SD | Reliability | Attentiveness | Responsiveness | Atmosphere | Food quality | Revisit | Expertise | Gender | Accompany | Age | Stress |
|---|
| Reliability | 4.37 | 0.71 | 1 | | | | | | | | | | |
| Attentiveness | 3.45 | 1.05 | 0.545** | 1 | | | | | | | | | |
| Responsiveness | 4.01 | 1.02 | 0.453** | 0.624** | 1 | | | | | | | | |
| Atmosphere | 3.78 | 0.79 | 0.351** | 0.450** | 0.350** | 1 | | | | | | | |
| Food quality | 4.04 | 0.74 | 0.482** | 0.482** | 0.310** | 0.445** | 1 | | | | | | |
| Revisit | 3.88 | 1.06 | 0.590** | 0.676** | 0.527** | 0.527** | 0.766** | 1 | | | | | |
| Expertise | 2.07 | 0.64 | 0.032 | 0.031 | 0.032 | 0.036 | 0.051 | 0.120* | 1 | | | | |
| Gender | 1.58 | 0.49 | −0.109 | –0.179** | −0.024 | −0.098 | −0.027 | 0.014 | 0.342** | 1 | | | |
| Accompany | 2.01 | 0.57 | −0.097 | 0.003 | 0.027 | −0.051 | 0.031 | 0.017 | 0.118 | 0.171** | 1 | | |
| Age | 3.56 | 1.88 | 0.035 | 0.137* | 0.100 | −0.024 | 0.031 | 0.027 | –0.173** | –0.241** | −0.081 | 1 | |
| Stress | 2.15 | 0.43 | −0.008 | –0.137* | −0.019 | −0.092 | −0.090 | −0.073 | −0.102 | −0.033 | −0.007 | −0.007 | 1 |
: = 247; * < 0.05, ** < 0.01| | Substantive FL | 1 | Method FL | 2 |
|---|
| |
| All ordered drinks were served quickly and perfectly | 0.833*** | 0.694 | –0.066 | 0.004 |
| Delivery of all ordered drinks and food left nothing to be desired | 0.622*** | 0.387 | 0.144 | 0.021 |
| The entire order was placed quickly and easily | 0.889*** | 0.790 | –0.107 | 0.011 |
| I was able to order immediately after receiving the drinks/menu | 0.722*** | 0.521 | 0.042 | 0.002 |
| |
| The attentive nature of the staff stimulated increased consumption | 0.989*** | 0.978 | –0.258*** | 0.005 |
| The staff literally read the wishes from my eyes | 0.855*** | 0.731 | –0.072 | 0.050 |
| The staff asked if everything was for the best | 0.954*** | 0.910 | –0.224 | 0.174 |
| I felt warmly and professionally looked after during the whole visit | 0.447*** | 0.200 | 0.417* | 0.009 |
| My waitress/waiter was especially attentive during the whole visit | 0.771*** | 0.594 | 0.096*** | 0.005 |
| |
| I was immediately noticed | 0.937*** | 0.878 | –0.121 | 0.015 |
| The welcome was very friendly | 0.788*** | 0.621 | –0.049 | 0.002 |
| I was immediately offered a suitable place/table | 0.722*** | 0.521 | 0.166*** | 0.028 |
| |
| The atmosphere is pleasant | 0.659*** | 0.434 | 0.071 | 0.005 |
| The areas are thoroughly clean | 0.644*** | 0.415 | 0.017 | 0.000 |
| The other guests contributed to my well–being | 0.873*** | 0.762 | –0.075 | 0.006 |
| |
| For this type of restaurant. the range of drinks and food leaves nothing to be desired | 0.842*** | 0.709 | –0.087 | 0.008 |
| The sensory quality of food and beverages was excellent | 0.748*** | 0.560 | 0.076 | 0.006 |
| The price/performance ratio for the food/drinks offered is excellent | 0.695*** | 0.483 | 0.005 | 0.000 |
| |
| I would definitely recommend the restaurant because of the service experience | 1.048*** | 1.098 | –0.141*** | 0.154 |
| I would definitely recommend this place because of the quality of the food/drinks | 0.476*** | 0.227 | 0.393*** | 0.047 |
| Based on all my experiences I would visit the restaurant again | 1.083*** | 1.173 | –0.216** | 0.020 |
| | 0.79 | 0.65 | 0.001 | 0.03 |
Note(s) : * p < 0.05, ** p < 0.01, *** p < 0.001
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Acknowledgements
The authors would like to thank the Austrian Federal Economic Chamber (WK Tirol) for supporting this project, as well as Ass.-Prof. Dr. Günther Botschen who provided insight and expertise that greatly assisted the research.
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- 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
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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 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 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 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.
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 .
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 ].
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
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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.
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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
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Perspective: Soy-based Meat and Dairy Alternatives, Despite Classification as Ultra-processed Foods, Deliver High-quality Nutrition on Par with Unprocessed or Minimally Processed Animal-based Counterparts
Mark messina.
Soy Nutrition Institute Global, Washington, DC, USA
John L Sievenpiper
Departments of Nutritional Sciences and Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
Division of Endocrinology and Metabolism, Department of Medicine, St. Michael's Hospital, Toronto, Ontario, Canada
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
Patricia Williamson
Scientific and Regulatory Affairs, Research and Development, Cargill, Wayzata, MN, USA
Jessica Kiel
Scientific and Clinical Affairs, Medifast, Inc., Baltimore, MD, USA
John W Erdman, Jr
Department of Food Science and Human Nutrition, Division of Nutritional Sciences and Beckman Institute, University of Illinois at Urbana/Champaign, Urbana, IL, USA
In many non-Asian countries, soy is consumed via soy-based meat and dairy alternatives, in addition to the traditional Asian soyfoods, such as tofu and miso. Meat alternatives are typically made using concentrated sources of soy protein, such as soy protein isolate (SPI) and soy protein concentrate (SPC). Therefore, these products are classified as ultra-processed foods (UPFs; group 4) according to NOVA, an increasingly widely used food-classification system that classifies all foods into 1 of 4 groups according to the processing they undergo. Furthermore, most soymilks, even those made from whole soybeans, are also classified as UPFs because of the addition of sugars and emulsifiers. Increasingly, recommendations are being made to restrict the consumption of UPFs because their intake is associated with a variety of adverse health outcomes. Critics of UPFs argue these foods are unhealthful for a wide assortment of reasons. Explanations for the proposed adverse effects of UPFs include their high energy density, high glycemic index (GI), hyper-palatability, and low satiety potential. Claims have also been made that UPFs are not sustainably produced. However, this perspective argues that none of the criticisms of UPFs apply to soy-based meat and dairy alternatives when compared with their animal-based counterparts, beef and cow milk, which are classified as unprocessed or minimally processed foods (group 1). Classifying soy-based meat and dairy alternatives as UPFs may hinder their public acceptance, which could detrimentally affect personal and planetary health. In conclusion, the NOVA classification system is simplistic and does not adequately evaluate the nutritional attributes of meat and dairy alternatives based on soy.
Statement of Significance : NOVA classifies soymilk and soy-based meat alternatives as ultra-processed foods (UPFs). However, criticisms of UPFs are not applicable to these foods when they are compared with their animal-based counterparts, which are classified as unprocessed or minimally processed foods. Admonitions against soymilk and soy-based meat alternatives based on their NOVA classification may dissuade consumers from consuming foods that offer health and environmental benefits.
Introduction
Over the past decade, plant-based meats and plant-based milks have markedly increased in popularity ( 1 ) because of their health and environmental attributes, and concerns over animal welfare ( 2 ). With regard to the environment, Goldstein et al. ( 3 ) concluded that plant-based beef substitutes could substantially reduce US greenhouse gas emissions, water consumption, and agricultural land occupation. Although plant-based patties made from different combinations of grains and beans have long been traditional vegetarian fare, the newest generation of plant-based meats is specifically designed to approximate the aesthetic qualities (primarily texture, flavor, and appearance) and nutritional attributes of specific types of meat in order to appeal to a broader range of consumers ( 4 ).
Despite their increased popularity, and potential environmental advantages, plant-based meat alternatives and plant-based milks have been criticized for being “highly processed.” In fact, according to the NOVA food-classification system, most plant-based meat alternatives ( 5 , 6 ) and plant-based milks ( 7 ) are classified as ultra-processed foods (UPFs; group 4) (for a detailed description, see Text Box 1 ) ( 5 ). This system categorizes all foods and food products into 4 groups according to the extent and purpose of the industrial processing they undergo ( 5 , 8 ). In contrast to plant-based meat alternatives and plant-based milks, their animal-based counterparts (beef and cow milk) are classified as unprocessed or minimally processed foods (group 1). UPFs are industrial food and drink formulations made of food-derived substances and additives, often containing little or no whole foods ( 9 ). In their recent editorial, Meyer and Taillie ( 10 ) noted with alarm the increase in and overall high intake of UPFs among US youth.
The NOVA food-classification system
• Group 1: Unprocessed/minimally processed
○ No added ingredients (fruit, vegetables, nuts,grains, meat, milk)
• Group 2: Processed culinary ingredients
○ Oils, fats, butter, vinegars, sugar, and salt, eatenwith group 1
• Group 3: Processed
○ Mix of groups 1 and 2 (chiefly for preservation)
○ Smoked and cured meats, cheeses, fresh bread,bacon, salted/sugared nuts, tinned fruit, beerand wine
• Group 4: Ultra-processed
○ Made with non-home ingredients
○ Chemicals, colorings, sweeteners, and preserva-tives
○ Industrial breads, cereals, sausage, dressings,snacks
○ High fat, sugar, and salt content is common
Classifying plant-based meat alternatives and plant-based milks as UPFs may slow their acceptance among consumers because, in most studies, UPFs are associated with an array of adverse health effects, including obesity, cardiovascular disease, and overall mortality ( 11 ). In fact, Wickramasinghe et al. ( 12 ) recently recommended restricting the marketing of plant-based meat and dairy substitutes because of their degree of processing. However, the American Society for Nutrition (ASN) maintains that “processed foods are nutritionally important to American diets because they contribute to food security, ensuring that sufficient food is available, and nutrition security, ensuring that food quality meets human nutrient needs” ( 13 ). The ASN also noted that food-processing techniques such as enrichment and fortification can add essential nutrients that might otherwise be in short supply and can alter food profiles to decrease components that may be overconsumed ( 13 ). Processing can also limit microbial contamination and reduce foodborne illness ( 14 ). In other words, processing can make foods more healthful.
The conflicting viewpoints on processed foods, and specifically plant-based meats and plant-based milks, present a confusing picture to consumers, especially health and environmentally conscious individuals who are concerned about animal welfare. This Perspective argues that maligning plant-based meats and plant-based milks because of the processing they undergo is nutritionally unjustified and counterproductive to achieving the health and environmental goals of the WHO, as well as those of other health authorities and organizations ( 15–18 ). Note that several authors have provided detailed overall critiques of the NOVA food-classification system ( 19–24 ). Therefore, the intent of this Perspective is not to critique the NOVA system in general. Nor is it to argue for reclassifying plant-based meat alternatives or plant-based milks. Rather, it is to show that, despite their classification as UPFs, these foods compare well with their animal-based counterparts, which are classified as unprocessed or minimally processed foods.
Although this Perspective discusses plant-based meat alternatives and plant-based milks in general, for 2 reasons, emphasis is placed on soymilk and soy-based meat substitutes. One, because of the large acreage devoted to growing soybeans, this legume has the greatest potential for meeting the caloric and protein needs of a growing global population. Approximately 350 million metric tons of soybeans are produced annually, and although most of that is used for animal feed (∼95%), its use is dictated by consumer demands ( 25 ).
Two, soy protein has traditionally been viewed by researchers as the reference plant protein, in part because of its high quality, and for this reason, is often compared with animal proteins, such as casein. Consequently, compared with other concentrated plant proteins, extensive clinical research has been conducted on concentrated sources of soy protein, which are the primary protein sources used in the manufacture of plant-based meat alternatives ( 26 ). For example, the ability of soy protein to lower blood cholesterol concentrations has been studied clinically for more than 50 y ( 27 ). Meta-analyses ( 28–35 ) published over the past nearly 20 y indicate a reduction in LDL cholesterol, ranging from 3.2% ( 35 ) to 6% ( 32 ). The impact of soy protein on muscle protein synthesis ( 36–38 ) and gains in muscle mass and strength ( 39 ) have also been widely studied. To this point, the results of a recent meta-analysis of longer-term studies (6–36 wk in duration) found that soy protein supplementation performed as well as whey and animal protein supplementation in individuals engaged in resistance exercise training ( 39 ).
Overview of Plant-based Meat Alternatives and Plant-based Milks
Role in meal planning.
Many authors have recommended a shift toward a plant-based diet ( 15 , 40–43 ), although the emphasis is typically on the consumption of whole foods or minimally processed foods, including whole grains, fruits, vegetables, nuts, legumes, and healthy oils ( 12 ). However, while these foods are nutritionally desirable, they are unlikely to fully address the orosensory preferences and practical needs of most consumers.
Legumes are an inexpensive, nutrient-rich source of protein ( 44 ), the consumption of which is recommended by health authorities throughout the world ( 45–48 ). Even so, legumes play a small role in the diets of developed countries and their intake is not expected to increase in the coming years in any region in the world ( 49 ). Furthermore, because pulses (grain legumes) are not an important part of Western diets, they require some education about how to cook and prepare them and how to incorporate them into recipes ( 50 ). As noted by van der Weele et al. ( 51 ), pulses are not novel from either a societal or technological point of view, and they have an unfavorable reputation as being old-fashioned.
In contrast to legumes, meat intake is expected to markedly increase over the next 30 y in many developing regions ( 52 , 53 ). Therefore, plant-based meat alternatives that imitate many of the properties of meat are more likely to impact consumption trends, and thus address environmental concerns, than is the direct consumption of legumes and beans. Research indicates that, while vegetarian and vegan consumers will accept plant-based meat alternatives that lack meat-like sensory properties, omnivorous and flexitarian consumers prefer alternatives that resemble animal-based protein as much as possible ( 54–57 ). In contrast, a recent UK survey found that most meat-eaters agree with the ethical and environmental arguments for vegetarianism/veganism but do not follow these diets because of practical reasons relating to taste, price, and convenience ( 58 ).
Detzel et al. ( 59 ) noted that, despite being highly processed, high-quality, plant-based, protein-rich foods can help reduce the environmental impact of food consumption while appealing to potential user groups beyond dedicated vegetarians and vegans. Furthermore, according to Lonkila and Kaljonen ( 60 ), consumers want convenient products that are easy to use and cook, attributes that are associated with meat and milk. Plant-based meat alternatives and plant-based milks are designed to meet these consumer preferences and can easily substitute for animal protein without requiring modification of meal patterns or food habits ( 61 , 62 ).
Also, because animal products, and especially meat, play an important role in structuring meals ( 62 , 63 ), plant-based substitutes that have the same functional properties allow an easy transition from animal-based to plant-based diets ( 64 ). Other alternative protein sources such as cultured meat, algae, and insects require more technological change than plant proteins, as well as requiring more social-institutional change for their acceptance ( 51 ). According to Hoek et al. ( 65 ), replacement of meat is most likely to be achieved by significantly improving the sensory quality of meat substitutes, but decreasing the cost and increasing the availability of these products are also important for greater consumer acceptance ( 66 ).
Finally, evidence suggests that the food environment is an important determinant of food consumption ( 67 , 68 ) and that certain eating context patterns, such as eating alone or eating while watching television, may promote the consumption of UPFs ( 69 , 70 ). Since plant-based meats and plant-based milks are designed to be used in the same way as their animal-based counterparts, the food environment does not favor 1 type (animal or plant) of milk or meat over the other.
Nutritional implications
Recent research has addressed calls to gain a better understanding of the nutritional and health implications of plant-based substitutes, especially when replacing meat and dairy products ( 12 ). For example, Salomé et al. ( 61 ) assessed the effects of plant-based substitutes on the nutritional quality of the French diet by simulating separately the replacement of meat, milk, and dairy desserts with 96 plant-based substitutes. These authors found that overall plant-based substitutes had small and heterogeneous effects on diet quality and nutrient security, although plant-based substitutes that include legumes, such as soy, were shown to be more nutritionally adequate substitutes for animal products than other plant-based substitutes ( 61 ).
These overall findings align with the conclusion of Bohrer ( 71 ), that modern meat analogues can offer roughly the same composition of nutrients as traditional meat products. Similarly, Farsi et al. ( 72 ) concluded that plant-based meat alternatives can be a healthful replacement for meat, but also emphasized the need to choose options that are low in sodium and sugar, and high in fiber, protein, and nutrient density. From a protein perspective, these authors recommended choosing soy-based and mycoprotein-based (protein derived from fungi for human consumption) meat alternatives, but also noted the high sodium content of soy-based alternatives.
More in-depth analysis comes from van Vliet et al. ( 73 ), who found that, despite similarities based on front-of-package nutrition information, metabolomic profile abundances between a soy-based meat alternative (18 samples of the same product) and grass-fed ground beef (18 samples) differed by 90% (171 out of 190 profiled compounds; P < 0.05). However, the impact, if any, of these differences on the health status of the individuals consuming these products was not determined. Furthermore, all foods have vastly different metabolic profiles, including even those within the same botanical group ( 74 , 75 ).
Direct experimental insight about health outcomes comes from Crimarco et al. ( 76 ), who compared the effect on nutrient intake and cardiovascular disease (CVD) markers of consuming ∼2.5 servings/d of plant-based meat (pea- and soy protein-based) with meat-based counterparts over an 8-wk period. In response to the plant-based meats, concentrations of LDL cholesterol ( 77 ) and trimethylamine-N-oxide ( 78 ), a proposed but not established CVD risk factor ( 79 ), were statistically significantly reduced. In terms of nutrient intake, there were no differences in sodium or protein intake, whereas in response to the consumption of plant-based products, saturated fat was lower and fiber intake higher, although the fiber difference was not statistically significant. More recently, the replacement of ∼5 servings/wk of meat with plant-based meat alternatives led to favorable changes (e.g., an increase in butyrate-metabolizing potential and a decrease in the Tenericutes phylum) in the gut microbiome over a 4-wk period ( 80 ).
Soy protein quality
Until recently, most of the research aimed at determining the quality of soy protein focused on the soy protein ingredients rather than traditional Asian soyfoods. The soy protein ingredients, soy protein isolate (SPI), soy protein concentrate (SPC), and soy flour, are composed of ≥90%, 65–90%, and 50–65% protein, respectively ( 26 ). An advantage of these concentrated sources of soy protein is that they more easily allow greater amounts of protein to be incorporated into experimental diets, especially into products such as beverages or baked goods (e.g., muffins) that can be made indistinguishable from products containing the control protein. This enables better participant blinding and enhanced compliance.
The high quality of soy protein was firmly established by a series of nitrogen balance studies by Young and colleagues conducted in the early 1980s ( 81–86 ). In the early 1990s, the protein digestibility corrected amino acid score (PDCAAS) was adopted by the US FDA and FAO as the method of choice for determining protein quality. Utilizing 2 different laboratories, Hughes et al. ( 87 ) determined that the untruncated PDCAAS of 3 different SPIs ranged from 0.95 to 1.02 and the scores for the single SPC were 1.02 and 1.05. These values are similar to those determined by Rutherfurd et al. ( 88 ) for SPI and by Mathai et al. ( 89 ) for SPI and soy flour. According to the USDA, to qualify as a high-quality protein requires a score of at least 0.8.
In 2011, an FAO consultation recommended transitioning from the PDCAAS to the digestible indispensable amino acid score (DIAAS) ( 90 ). Given that some methodological issues remain to be resolved ( 91 ), it will likely be several years before the DIAAS is accepted by regulatory bodies. Preliminary data using the DIAAS also support the high quality of soy protein ( 88 , 89 ), although, in general, the quality of plant protein is rated slightly lower using this method compared with the PDCAAS ( 88 ). Very recently, Fanelli et al. ( 92 ) determined that the DIAAS for the Impossible Burger [(Impossible Foods) primary protein source is soy] was similar to the DIAAS for 80% ground beef when calculated using the indispensable amino acid (IAA) pattern for the older child, adolescent, and adult.
Applicability of criticisms of processed foods to soy-based meats and soymilk
As previously noted, the consumption of UPFs has been associated with a range of adverse health outcomes ( 11 ). Diets high in UPFs are associated with poor diet quality ( 93 ), but there is debate about the extent to which diet quality accounts for the associations between UPF intake and adverse health outcomes ( 19 , 94 ). Many of the effects of processing will be identified by existing food-classification systems (nutritional rating systems) that are based exclusively on nutrient (and fiber) content. This is true for several of the major criticisms of UPFs, such as their high energy density ( 95 , 96 ), high glycemic index (GI) ( 97 ) or high glycemic glucose equivalent ( 98 ), hyper-palatability ( 95 ), and low satiety potential ( 97 ). However, as noted by others, processing can lead to textural and structural changes to the food matrix not identified by nutritional rating systems that can speed up the rate at which UPFs are consumed ( 96 , 99 , 100 ). Reducing the orosensory exposure time of a food can delay the onset of satiation ( 101 ). UPFs have been shown to be less satiating than minimally processed foods ( 97 , 102 ), which can promote increased energy intake ( 103 ).
Energy intake rate may be an especially important contributor to the links between UPF intake and obesity. Forde et al. ( 100 ) recently showed, after pooling data from 5 studies that measured energy intake rates across a total sample of 327 foods, that when going from unprocessed, to processed, to ultra-processed, the average energy intake rate increased from 35.5 ± 4.4 to 53.7 ± 4.3 to 69.4 ± 3.1 kcal/min ( P < 0.05), respectively. Additional explanations for the harmful effects of UPFs include the presence of artificial food additives ( 104–106 ) and artificial sweeteners, which have been linked to alterations to the gut microbiota ( 106–108 ), although not reliably in humans ( 109 , 110 ). Also, food processing, and particularly heat treatment, may produce contaminants (e.g., acrylamide) in UPFs that may increase cancer risk ( 111 ). Bisphenol A, a contaminant suspected of migrating from plastic packaging of UPFs, has been shown to possess endocrine-disruptive properties ( 112 ).
Finally, although not related to personal health, claims have also been made that UPFs are not sustainably produced ( 9 , 113 ), which is likely to become an increasingly important consideration in the formulation of dietary guidelines. According to the Society for Nutrition Education “environmental sustainability should be inherent in dietary guidance, whether working with individuals or groups about their dietary choices or in setting national dietary guidance” ( 114 ).
There are a variety of soy-based meat alternatives and soymilks on the market. For the examination of the applicability of the criticisms of UPFs to soy-based meat alternatives and soymilk, 5 soy protein–based burgers were compared with 80% lean beef ( Table 1 ) and 2 soymilks were compared with whole and 2% cow milk, the 2 most commonly consumed milks in the United States ( Table 2 ). Silk Original Soymilk and Silk Organic Unsweetened Soymilk were chosen for comparison because these products are the top 2 selling stock-keeping units in the US refrigerated soy plant-based beverage category. Silk is the leading brand based on US national sales data (Kristie Leigh, Danone North American, personal communication September 10, 2021).
Nutrient, caloric, and fiber content of lean beef and selected soy-based burgers 1
| Soy-based burgers | |
|---|
| Nutrient | Incogmeato (Morningstar Farms) ( ) | Impossible (Impossible Foods) ( ) | Boca vegan (Boca Foods Company) ( ) | Gardein (Conagra Brands Pinnacle Foods) ( ) | Morningstar Vegan (Morningstar Farms) ( ) | Beef 80% lean, raw ( ) |
|---|
| Serving size, g | 120 | 113 | 71 | 85 | 113 | 113 |
| kcal | 280 | 240 | 70 | 130 | 270 | 287 |
| kcal/g | 2.33 | 2.12 | 0.99 | 1.50 | 2.39 | 2.50 |
| Protein, g | 21 | 19 | 13 | 14 | 27 | 19 |
| Protein, % kcal | 33.6 | 31.7 | 74.3 | 43.1 | 38.6 | 27.0 |
| Fat, g | 18 | 14 | 1 | 5.0 | 18 | 23 |
| Fat, % kcal | 64.8 | 52.5 | 12.9 | 52.9 | 57.9 | 70.9 |
| Saturated fat, g | 5.0 | 8.0 | 0 | 0 | 2.5 | 8.6 |
| Saturated fat, % kcal | 18 | 17 | 0 | 0 | 8 | 27 |
| Carbohydrate, g | 12 | 9 | 6 | 8 | 8 | 0 |
| Carbohydrate, % kcal | 19.2 | 15.0 | 34.3 | 24.6 | 11.4 | 0 |
| Fiber, g | 8 | 3 | 4 | 2 | 4 | 0 |
| Vitamins, μg |
| B-6 | NI | 0.34 | NI | NI | NI | 365 |
| B-12 | 2.4 | 3.1 | NI | NI | NI | 2.4 |
| Minerals |
| Iron, mg | 4.0 | 4.2 | 1.8 | 1.6 | 1.9 | 2.2 |
| Zinc, mg | NI | 5.5 | NI | NI | NI | 4.7 |
| Selenium, μg | NI | NI | NI | NI | NI | 17 |
| Potassium, mg | 620 | 610 | NI | 240 | 180 | 305 |
| Sodium, mg | 370 | 370 | 450 | 340 | 580 | 66 |
Nutrient, caloric, and fiber content of cow milk and soy milk 1
| Cow milk | Silk |
|---|
| Nutrient | Whole ( ) | Reduced-fat ( ) | Original ( ) | Organic unsweetened ( ) |
|---|
| Serving size, mL | 240 | 240 | 240 | 240 |
| Total energy, kcal/serving | 149 | 122 | 110 | 80 |
| kcal/mL | 0.62 | 0.51 | 0.46 | 0.33 |
| Protein, g | 7.7 | 8.1 | 8.0 | 7.0 |
| Protein, % kcal | 20.6 | 26.4 | 29.0 | 35.0 |
| Fat, g | 7.9 | 4.8 | 4.5 | 4.0 |
| Fat, % kcal | 47.9 | 35.6 | 36.4 | 45.0 |
| Saturated fat, g | 4.63 | 3.07 | 0.50 | 0.50 |
| Saturated fat, % kcal | 28.0 | 22.6 | 4.1 | 5.6 |
| Carbohydrate, g | 11.7 | 11.7 | 9.0 | 3.0 |
| Carbohydrate, % kcal | 31.4 | 38.4 | 32.7 | 15.0 |
| Sugars | 12.3 | 12.2 | 6.0 | 1.0 |
| Fiber | 0 | 0 | 2 | 2 |
| Vitamins |
| Riboflavin, μg | 412 | 451 | 400 | 400 |
| Folate, μg | 12.2 | 12.2 | 40.0 | 50.0 |
| Thiamin, μg | 112 | 95 | NI | NI |
| Niacin, μg | 217 | 224 | NI | NI |
| Vitamin B-6, μg | 88 | 93 | NI | NI |
| Vitamin B-12, μg | 1.3 | 1.3 | 3.0 | 3.0 |
| Vitamin A, RAE | 112 | 134 | 150 | 150 |
| Vitamin D, μg | 3.2 | 2.9 | 3.0 | 3.0 |
| Minerals |
| Calcium, mg | 276 | 293 | 450 | 300 |
| Potassium, mg | 322 | 342 | 380 | 350 |
| Magnesium, mg | 24.4 | 26.8 | 50.0 | 40.0 |
| Phosphorus, mg | 205 | 224 | 220 | 80 |
| Iron, mg | 0.07 | 0 | 1.30 | 1.00 |
| Zinc, mg | 0.90 | 1.17 | NI | NI |
| Iodine, μg | NI | NI | NI | NI |
| Sodium, mg | 105 | 115 | 90 | 75 |
Energy density
The connection between energy density, UPF intake, and weight gain was highlighted by a recent 2-wk crossover study involving 20 overweight adults ( 96 ). When consuming the diet composed primarily of UPFs, participants gained weight, whereas weight was lost during the unprocessed diet phase. The much higher nonbeverage energy density (2.147 vs. 1.151 kcal/g) of the UPF diet was suggested as being a key factor contributing to the weight gain. The energy density (kilocalories/gram) of the soy burgers in Table 1 is similar to or lower than that of beef. On a percentage calorie basis, the soy-based burgers contain similar or higher amounts of protein, but similar or lower amounts of fat and, unlike the beef, contain dietary fiber. It is reasonable to speculate that the fiber content of soy-based burgers could promote satiety relative to beef ( 115 ). Therefore, there is little reason to suggest the eating rate (grams/minute) or, more importantly, the energy intake rate (kilocalories/minute) of the soy burgers would be greater than beef. The soy-based burgers do contain carbohydrate, although much of that is fiber. As somewhat of an aside, although only one of the soy-based burgers qualifies as a high-sodium food (≥460 mg/serving), 2 others come close to doing so. Therefore, manufacturers of soy-based meat alternatives should be encouraged to keep sodium content in mind when producing new, or reformulating, products.
Table 2 shows that the soymilks have a lower energy density than both whole and 2% cow milk and contain similar amounts of protein. The major difference between milk types is with respect to carbohydrate content: the soymilks contain fiber (2 g/serving) and sucrose, whereas cow milk has no fiber and contains lactose. However, the soymilks contain a lower percentage of calories from carbohydrate and are lower in sugar. Neither the energy density nor macronutrient content suggests that soymilk would result in a faster eating rate or greater energy intake rate than cow milk. Although not necessarily related to satiety, it is notable from an overall health perspective that, as a percentage of calories, the soymilks and soy burgers are lower in saturated fat than their animal-based counterparts.
Glycemic response
There is convincing evidence that reducing postprandial glycemia is a desirable physiological goal ( 116 , 117 ) and that doing so reduces the risk of developing diabetes ( 118 , 119 ) and coronary artery disease ( 120 ). As noted, the impact of processing on the GI has been highlighted as a factor possibly contributing to the adverse health outcomes associated with UPF intake ( 97 ). Processing can affect the GI of foods ( 121–123 ) even independently of fiber content ( 124 ).
The American Diabetes Association recommends consumption of low (<55) and medium ( 56–69 ) GI foods for people with diabetes and other individuals looking to control blood sugar concentrations. Both soymilk and cow milk are acceptable foods according to these criteria ( 125 ). The GI and the glycemic load (GL; a measure that combines the GI with the amount of carbohydrate in a food) of soymilks depend upon the amount of added sugar ( 126 ).
Serrano et al. ( 127 ) concluded that soymilk was a low-GI food based on the results of a crossover study in which 29 young adults ingested 500 mL water, 500 mL glucose solution (20.5 g/500 mL), or 500 mL of soymilk on 3 separate occasions. Sun et al. ( 128 ) found that, in Chinese participants, coingestion of cow milk or soymilk with bread lowered the postprandial blood glucose response relative to bread alone. Also, Law et al. ( 129 ) found no difference between the effect of cow milk and soymilk on blood glucose or insulin concentrations at 180 min after consuming a meal that, in addition to each milk, contained bread and jam (cow milk was 2% fat and the soymilk was made using SPI). Finally, Atkinson et al. ( 121 ) reported that the GIs of cow milk (full-fat) and soymilk were 39 and 34, respectively, although more recent work from this group reported an average GI of only 25 for 13 different cow milks of variable fat content ( 130 ). The evidence overall suggests that there is nothing inherent to soymilk that would cause it to have a higher GI or GL than cow milk.
Hyper-palatability/satiety
Preliminary research indicates that many UPFs that are often high in fat and have a high GL are hyper-palatable and linked to addictive-like eating behaviors ( 131 , 132 ). However, recent research shows that UPFs are not in and of themselves hyper-palatable ( 133 ). Furthermore, and more importantly, research shows that soymilk is not viewed as hyper-palatable in comparison to cow milk ( 134–138 ). With regard to meat, from a sensory perspective, it is the gold standard that the new generation of plant-based meat alternatives is trying to emulate (as opposed to a black bean burger, which is not designed to mimic the taste of meat) ( 4 ). While this standard may be matched, it is not clear how it could be exceeded, a conclusion that aligns with recent survey results ( 139 ).
As noted previously, one concern about UPFs is that their physical and structural characteristics may result in lower satiety potential and higher glycemic response ( 97 ) and may, because of their higher energy density, be consumed at a faster energy intake rate than less-processed foods ( 96 ). These attributes could lead to an increased energy intake, which, in turn, could lead to obesity and associated adverse health outcomes. However, evidence indicates that these concerns do not apply to soy-based meats or soymilk.
No clinical studies were identified that compared the effects of a soy-based burgers with meat, or soymilk with cow milk, on weight loss. However, in the Study With Appetizing Plantfood-Meat Eating Alternative Trial (SWAP-MEAT), weight loss occurred in the group consuming plant-based meat alternatives, some of which were based on pea protein and some on soy protein ( 76 ). Therefore, at the very least, the results indicate that plant-based meats are not inherently obesogenic. Also, meal replacements containing isolated proteins led to greater weight loss than traditional weight-loss diets ( 140–142 ), which suggests that, at the least, concentrated sources of proteins such as SPI and SPC do not promote weight gain.
Two studies compared beef and products made with soy protein ingredients on metabolic parameters related to weight loss. In one, obese participants consumed either a vegetarian (soy) high-protein, weight-loss (HPWL) diet or a meat-based HPWL for 2 wk and then crossed over to the opposite diet ( 143 ). Assessments of appetite control, weight loss, and gut hormone profile (glucagon like peptide 1, ghrelin, and peptide YY) did not differ between the diets. The soy-HPWL and meat-HPWL diets were each composed of 30% protein, 30% fat, and 40% carbohydrate. The meat-HPWL diet was based on chicken and beef; the soy-HPWL diet was based on soy protein ingredients. In the other study, meals (400 kcal) containing beef or SPC were matched for macronutrients and fiber or serving size (2 different arms) and consumed by 21 young, healthy adults ( 144 ). The type of protein consumed within a mixed meal had little effect on appetite, satiety, or food intake.
Finally, a study in 96 healthy adults found no difference between the mean (±SD) chewing time associated with 5 g chicken (16.9 ± 5.6 s) and 5 g vegetarian (soy-based) chicken (17.9 ± 6.2 s), although the former resulted in a bolus of chicken that had significantly more ( P < 0.001) and smaller ( P < 0.001) particles than vegetarian chicken ( 145 ). The similar chewing time suggests that energy intake rate is not likely to differ between meat and soy-based meat alternatives.
Sustainability
As noted earlier, claims have been made that UPFs are not sustainably produced ( 9 , 113 ), which is likely to become an increasingly important consideration in the formulation of dietary guidelines ( 114 ). As discussed below, evidence indicates that soy-based meat and dairy products have environmental advantages. However, it is important to acknowledge that, as is the case for the impact of diet on health, there are widely differing opinions about the effects of diet on climate and its potential to affect global warming ( 146 , 147 ). Establishing the global warming potential (GWP) of a dietary pattern or food is a complex process that involves a scientific understanding that continues to evolve. The environmental impact of any food, whether it be soymilk or soy-based meat, will depend, in part, upon the specific composition of the product in question.
Legumes have been shown to have an extremely low GWP, in comparison to nearly all other protein sources ( 148–151 ), although this depends in part upon the management of the agro-ecosystem used (e.g., mono-cropping vs. conservation agriculture) ( 152 ). In 2011, González et al. ( 153 ) determined that, of the 22 plant and animal protein sources evaluated, soybeans were the most efficiently produced and provided the most protein (grams) per greenhouse gas emissions [GHGE; kilogram carbon dioxide (kg CO 2 ) equivalents]. Tessari et al. ( 154 ) emphasized that, when considering the environmental impact of foods, it is important to consider nutritional value and, in particular, IAA content. When this metric was used, there was little difference between animal and plant protein sources, except for soybeans, which exhibited the smallest environmental footprint.
Soybeans, like all legumes, can fix nitrogen because of the bacterial symbionts (rhizobia) that inhabit nodules on their roots. The amount of ammonia produced by rhizobial fixation of nitrogen by legumes rivals that of the world's entire fertilizer industry ( 155 ). The fact that legumes do not require nitrogen fertilizer for growth represents an important environmental advantage because half the nitrogen applied to fields for crop fertilization is thought to be lost into the environment, creating environmental concerns due to entry in surface and groundwater ( 156 , 157 ).
While the environmental impact of soybean production is an important consideration, it is only 1 factor affecting the environmental impact of soy protein ingredients and the products made using them. Therefore, the conclusion by van Mierlo et al. ( 158 ) that soy protein ingredients are keys to mimicking the nutrient profile of meat, while minimizing environmental impact with regard to climate change, land use, water use, and fossil fuel depletion, is notable. This conclusion agrees with work by Thrane et al. ( 159 ). Reducing water and land use is particularly notable. Several groups have determined that the GWP of meat alternatives is lower than that of meat ( 3 , 160–164 ). For example, the GWP of an Impossible Burger was determined to be lower than that of a beef burger and to require less land and water for its production ( 165 ).
With respect to soymilk, research has shown that its production requires considerably less water than to produce cow milk ( 166 , 167 ). Also, shelf-stable soymilk was found to produce far fewer GHGE than shelf-stable cow milk ( 168 ). In agreement, Poore and Nemecek ( 148 ) found that, for each of the 5 criteria considered (GHGE, land use, acidification, eutrophication, water scarcity), and when expressed on a per-protein basis, soymilk production always resulted in a lower environmental impact than cow milk. Very recently, Coluccia et al. ( 169 ) also concluded that soymilk has a lower carbon footprint than cow milk.
Summary and Conclusions
The increased role of plant-based meat alternatives and plant-based milks in the diets of consumers around the world necessitates that scientists and health professionals have a detailed understanding of their nutritional, health, and environmental attributes, and considerable progress in this regard has been made. Nevertheless, plant-based products have been criticized for being overly processed ( 12 ). While it is undoubtedly true that many UPFs are not nutrient dense ( 170 , 171 ), it is important not to assume that “ultra-processed” equals poor nutritional quality, since quality does not depend solely on the intensity or complexity of processing but on the final composition of the food itself ( 172 ).
As discussed, soy-based meats and soymilk compare favorably with their animal-based counterparts nutritionally. Further, there is no evidence that the major criticisms of UPFs [including high energy density ( 95 , 96 ), high GI ( 97 ), hyper-palatability ( 95 ), and low satiety potential ( 97 )] apply to these soy-based products. Certainly, within each category of plant-based meat alternatives and plant-based milks there will be variations in nutrient content because of differences in the protein source, fat source, and the extent of fortification. Therefore, consumers will need to compare Nutrition Facts panels. Consumers are best advised to choose soymilks that are protein-rich (6–8 g/cup), low in sugar, and that are fortified with calcium and vitamin D, and to keep sodium content in mind when choosing plant-based meats. However, admonitions against the consumption of products simply because they are classified as UPFs are unwarranted and may impair society's acceptance of plant-based diets—thus preventing the related health and environmental advantages from being realized.
While it may be true that the consumption of many UPFs should be discouraged based on nutrient content, this generalization does not apply to all such foods. Rather, the nutritional composition of the final product and its impact on health and sustainability should serve as the ultimate guide concerning the merits of a specific food, not the extent to which that food is considered processed. In summary, in the case of soy-based meat alternatives and soymilks, the NOVA classification system is overly simplistic and of little utility for evaluating the true nutritional attributes of these foods.
Acknowledgments
The authors’ responsibilities were as follows—MM: wrote the initial draft of the manuscript with contributions from JWE and JLS; and all authors: reviewed and commented on subsequent drafts of the manuscript and read and approved the final manuscript.
Author disclosures: MM is employed by the Soy Nutrition Institute Global, an organization that receives funding from the United Soybean Board and industry members who are involved in the manufacture and/or sale of soyfoods and/or soybean components. JLS has received research support from the Canadian Foundation for Innovation, Ontario Research Fund, Province of Ontario Ministry of Research and Innovation and Science, Canadian Institutes of health Research (CIHR), Diabetes Canada, PSI Foundation, Banting and Best Diabetes Centre (BBDC), American Society for Nutrition (ASN), INC International Nut and Dried Fruit Council Foundation, National Dried Fruit Trade Association, National Honey Board (the USDA honey “Checkoff” program), International Life Sciences Institute (ILSI), Pulse Canada, Quaker Oats Center of Excellence, The United Soybean Board (the USDA soy “Checkoff” program), The Tate and Lyle Nutritional Research Fund at the University of Toronto, The Glycemic Control and Cardiovascular Disease in Type 2 Diabetes Fund at the University of Toronto (a fund established by the Alberta Pulse Growers), and The Nutrition Trialists Fund at the University of Toronto (a fund established by an inaugural donation from the Calorie Control Council). He has received food donations to support randomized controlled trials from the Almond Board of California, California Walnut Commission, Peanut Institute, Barilla, Unilever/Upfield, Unico/Primo, Loblaw Companies, Quaker, Kellogg Canada, WhiteWave Foods/Danone, Nutrartis, and Dairy Farmers of Canada. He has received travel support, speaker fees, and/or honoraria from Diabetes Canada, Dairy Farmers of Canada, FoodMinds LLC, International Sweeteners Association, Nestlé, Pulse Canada, Canadian Society for Endocrinology and Metabolism (CSEM), GI Foundation, Abbott, General Mills, Biofortis, ASN, Northern Ontario School of Medicine, INC Nutrition Research and Education Foundation, European Food Safety Authority (EFSA), Comité Européen des Fabricants de Sucre (CEFS), Nutrition Communications, International Food Information Council (IFIC), Calorie Control Council, International Glutamate Technical Committee, and Physicians Committee for Responsible Medicine. He has or has had ad hoc consulting arrangements with Perkins Coie LLP, Tate & Lyle, Wirtschaftliche Vereinigung Zucker eV, Danone, and Inquis Clinical Research. He is a member of the European Fruit Juice Association Scientific Expert Panel and former member of the Soy Nutrition Institute (SNI) Scientific Advisory Committee. He is on the Clinical Practice Guidelines Expert Committees of Diabetes Canada, European Association for the study of Diabetes (EASD), Canadian Cardiovascular Society (CCS), and Obesity Canada/Canadian Association of Bariatric Physicians and Surgeons. He serves or has served as an unpaid scientific advisor for the Food, Nutrition, and Safety Program (FNSP) and the Technical Committee on Carbohydrates of ILSI North America. He is a member of the International Carbohydrate Quality Consortium (ICQC), Executive Board Member of the Diabetes and Nutrition Study Group (DNSG) of the EASD, and Director of the Toronto 3D Knowledge Synthesis and Clinical Trials foundation. His wife is an employee of AB InBev. PW is employed by Cargill, Inc, a global food company headquartered in Wayzata, MN. Cargill produces soy-based food and industrial products. JK is employed by Medifast Inc., a nutrition and weight-management company based in Baltimore, Maryland, that uses soy protein in many of its products. JWE is a scientific advisory to the Soy Nutrition Institute Global.
Perspective articles allow authors to take a position on a topic of current major importance or controversy in the field of nutrition. As such, these articles could include statements based on author opinions or point of view. Opinions expressed in Perspective articles are those of the author and are not attributable to the funder(s) or the sponsor(s) or the publisher, Editor, or Editorial Board of Advances in Nutrition . Individuals with different positions on the topic of a Perspective are invited to submit their comments in the form of a Perspectives article or in a Letter to the Editor.
Abbreviations used: CVD, cardiovascular disease; DIAAS, digestible indispensable amino acid score; GHGE, greenhouse gas emissions; GI, glycemic index; GL, glycemic load; GWP, global warming potential; HPWL, high-protein, weight-loss; IAA, indispensable amino acid; PDCAAS, protein digestibility corrected amino acid score; SPC, soy protein concentrate; SPI, soy protein isolate; UPF, ultra-processed food.
Contributor Information
Mark Messina, Soy Nutrition Institute Global, Washington, DC, USA.
John L Sievenpiper, Departments of Nutritional Sciences and Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. Division of Endocrinology and Metabolism, Department of Medicine, St. Michael's Hospital, Toronto, Ontario, Canada. Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.
Patricia Williamson, Scientific and Regulatory Affairs, Research and Development, Cargill, Wayzata, MN, USA.
Jessica Kiel, Scientific and Clinical Affairs, Medifast, Inc., Baltimore, MD, USA.
John W Erdman, Jr, Department of Food Science and Human Nutrition, Division of Nutritional Sciences and Beckman Institute, University of Illinois at Urbana/Champaign, Urbana, IL, USA.
Atomic absorption spectroscopy for food quality evaluation
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1. Introduction. The demand for high quality food has constantly increased during recent decades, as has the interest in the food quality issue both in response to market pressure (such as requests from increasingly demanding and knowledgeable consumers) and also as a reaction to other factors, for example health and environmental concerns [].The consumer of the 21st Century is a highly ...
The research results show that food quality (X1) has a positive and significant effect on customer satisfaction (Z). ... This paper attempts to examine existing guidelines and regulations in ...
Journal of Food Quality publishes original research on issues of food quality, including the handling of food from a quality and sensory perspective and covers both medical and functional foods. Articles Most Recent; Most Cited; Research Article. Open access.
A journal devoted to sensory, consumer and behavioural research in food and non-food products. An official journal of the Sensometric Society and the official journal of the European Sensory Science Society Food Quality and Preference is a journal devoted to sensory, consumer and behavioural …. View full aims & scope.
This questionnaire can serve as the base for future research on food quality evaluation and the role of environmental-social cues, bringing together both extracted food quality cues from extant studies and new ones. ... Therefore, many papers heeded calls on food "packaging" (Ares et al., 2010; Milošević et al., 2012; Venter et al., 2011).
The aim of this paper was to review literature on the definition and components of food quality. Food quality refers to those quality attributes of food which makes the food acceptable to consumers.
Food Quality and Safety is an international, peer-reviewed journal highlighting emerging and innovative science and technology in the agro-food field, ... Extensive research has focused on high concentrations of arsenic species in marine organisms. ... Submit your paper . Accepting high quality, up-to-date research in the areas of food quality ...
Explore the latest full-text research PDFs, articles, conference papers, preprints and more on FOOD QUALITY MANAGEMENT. Find methods information, sources, references or conduct a literature review ...
Previous research discussed SQ, food quality and atmosphere as main drivers of behavioral intentions in the restaurant context (Namkung and Jang, 2008; Shahzadi et al., 2018). Specifically, research revealed that food quality predicts both patronage and willingness to pay in the restaurant context (Njite et al., 2015; Sulek and Hensley, 2004).
and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, in-cluding food and beverage products such as grapes and wine [1], chocolate [2], honey [3], whiskey [4], avocado pulp [5], and a variety of other food products [6].
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.
Section Information. This section embraces all aspects of food that lead to positive or negative impacts on its quality and safety. Food characterization, processing, preservation and analysis involving chemical, physical, sensorial, microbiological and toxicological approaches are welcomed, from field studies to market surveys, from local to ...
Food quality is a central issue in today's food economics [3], and the last few decades testify that ... the use frequency of quality, health, and environmental cues by these consumers. The paper of Januszewska et al. [26] was the only one that reported the findings of a comparative analysis between ... consumer behavior research, both for ...
Aims and scope. Journal of Food Quality is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles related to all aspects of food quality ...
INTRODUCTION This research paper is produced to find out the relationship between food quality and customer's satisfaction and mediating effect of food safety upon them. ... Gender Value of cronbach alpha for food quality variable is .772 that is greater than 7 and proves research questions of food quality are reliable. Valid Valid Cumulative ...
The universe of food quality. Food Quality and Preference 17 (2006) 3-8. • van Boekel M. A.J.S. Kinetic Modeling of Food Quality: A Critical Review. Comprehensive Reviews in Food Science and Food Safety, 7 (2008), 144-158. Module: Sustainable processing for organic food products
The goal was to obtain an overview of consumers' relative food preferences and especially herring's position, meal patterns and food choice motives, focusing particularly on the relationship between food and health. A working paper based on existing literature on Russian consumers written earlier this year (Honkanen & Voldnes, 2006) pointed ...
PDF | On Mar 1, 2016, Wilna H. Oldewage-Theron and others published Food Quality and Food Safety | Find, read and cite all the research you need on ResearchGate
Until recently, most of the research aimed at determining the quality of soy protein focused on the soy protein ingredients rather than traditional Asian soyfoods. The soy protein ingredients, soy protein isolate (SPI), soy protein concentrate (SPC), and soy flour, are composed of ≥90%, 65-90%, and 50-65% protein, respectively ( 26 ).
Research on food quality perception and its impact on consumer food choice has employed a variety of different approaches, most notably the means - end approach, expectancy value approaches ...
This paper explores prioritisation of requirements for informational short food supply chains. ... and can increase coordination in the distribution of local food products. Current research on smart short food supply chain technologies includes informational platforms [26], smart farming applications [27], sensor embedded systems [28], and ...
The data collected (283 valid questionnaires) were analysed using SPSS 20.0. The findings showed that service quality and food quality have a positive influence on customer satisfaction. In ...
Food scientists and technologists determine the chemical composition and physical characteristics of foods routinely as part of their quality management, product development, or research activities.