Opportunities and Challenges for Locally Sourced Meat and Seafood: An Online Survey of U.S. Restaurant Procurers

Abstract

Considering the growing consumer demand for local food products in the United States, several restaurants are seeking to include locally sourced meat and seafood products on their menus. Despite this trend, limited research has examined what factors encourage or discourage restaurants from purchasing or purchasing more local proteins. This study extends the literature by investigating what traits are desired and what barriers exist when purchasing local proteins for restaurant procurers (those tasked with purchasing ingredients), utilizing data from an online survey. The results indicate that the top three desired traits for locally raised meats (beef, pork, chicken) are naturally grown, hormone-free, and organic. The most important traits for seafood (fish, crab, shrimp, and oysters) are the different certifications (i.e., Marine Stewardship Council, state/local certification, and Aquaculture Stewardship Council). The most common barriers are inconsistent quality, high cost, limited availability, and further processing needs. Results from a logistic regression suggest that restaurants characterized by fewer seats, higher entrée prices, and longer tenure are more likely to purchase local proteins. Restaurants willing to pay more for local proteins tend to be full-service types, have fewer seats, have higher entrée prices, serve other local foods, and cater to both tourists and residents.

1. Introduction

The USDA defines local foods as agricultural products sold within 400 miles of (or within the same state as) where it was produced (CRS, 2020). Approximately 8.0% of farms in the U.S. are involved with local food production, and demand for local food continues to grow (CRS, 2020; USDA ERS, 2021). Locally sourced meat and seafood (hereafter “local proteins” for convenience) became a topic of discussion in 2020 when COVID-19-related food chain disruptions in the U.S. caused a surge in protein demand (The Food Industry Association [FMI] and Foundation for Meat and Poultry Education and Research, 2020) and widespread shortages, partly due to grocery store scarcities (Guzman, 2020) and panic buying (Lusk & McCluskey, 2020). This prompted national (USDA, 2021) and U.S. state (Niche Meat Processor Assistance Network, 2022) investments in local meat processing to improve local protein supply chains.

However, when one increases the capacity in one sector of a supply chain, one must also consider how this impacts the other links in the supply chain. This is evidenced by abundant local meat processing feasibility studies in the U.S., noted elsewhere (Richards & Vassalos, 2020). Other local protein studies in the U.S. during the same period surveyed producers (Richards, 2020), processors, and consumers of local meats (Richards & Vassalos, 2021) and seafood (Cheplick et al., 2021; Richards et al., 2022). Restaurants were not surveyed during this time, as many were closed due to COVID-19-related restrictions. However, by mid-June 2021, all states had dropped their COVID-19 restrictions, allowing restaurants to reopen (The New York Times, 2021).

Food away from home (FAFH) is a large part of U.S. food consumption, comprising approximately 58.5% of all food expenditures (USDA ERS, 2024). In the most recent United States Department of Agriculture (USDA) report, FAFH sales were allocated between restaurants (73.0%), schools (7.3%), retail stores and vending machines (4.1%), hotels (4.0%), recreational places (3.5%), drinking establishments (3.2%), and other locations (4.9%) such as prisons, hospitals, nursing homes, and military bases (Lin, 2020). In addition to being the top FAFH expenditure, restaurants also represent one of the most prominent outlets for meat and seafood, where U.S. consumers eat approximately 38.9% of their beef, 26.9% of their pork, 47.0% of their chicken, 29.6% of their turkey, and 39.0% of their seafood (Love et al., 2020; USDA ERS, 2020).

Relevant previous studies focus on individual consumer motivations for eating local foods or choosing a restaurant based on its local food menu. These studies suggest that consumers may choose a restaurant offering local foods because these restaurants are considered more trustworthy and authentic and may generate a more positive image (Angelopoulos et al., 2019; Bacig & Young, 2019). A certain percentage of customers desire to buy locally both for at-home and away-from-home consumption, and part of a restaurant’s marketing plan may include “signaling” to these customers that the restaurant shares similar values (Lillywhite & Simonsen, 2014; Lin et al., 2020). In addition, some customers may also have social motivations to eat at restaurants offering local foods, such as community attachment, environmental and health concerns, and personal values (Choi et al., 2021; Lang & Lemmerer, 2019; Severt et al., 2022; Shafieizadeh & Tao, 2020).

However, there are several gaps in the research on this topic. First, to our knowledge, a national survey of restaurant procurers in the U.S. has not yet been performed. The U.S. studies mentioned above took place in individual states: Nevada (Curtis & Cowee, 2009), Colorado (Pepinski & Thilmany, 2004), Iowa (Sharma, 2007; Sharma et al., 2014), Utah (Brain et al., 2015), Ohio (Inwood et al., 2009), Alabama (Reynolds-Allieaâ’ & Fields, 2012), New York (Dragon, 2016), and Tennessee (Griffith et al., 2018). International studies were conducted in Bali (Bacig & Young, 2019), Greece (Angelopoulos et al., 2019), and New Zealand (Roy & Ballantine, 2020). The only national U.S. study we know of was conducted on the top 100 American chain restaurants and focused on these companies’ publicly available website statements (Yoon et al., 2020). Most existing research focuses either broadly on all local food products or narrowly on just one local protein, such as beef (Griffith et al., 2018; McKay et al., 2019), and only one study we know of explicitly identifies restaurant characteristics associated with purchasing local foods (Curtis & Cowee, 2009). Additionally, willingness-to-pay studies focus on restaurant customers, not procurers (Griffith et al., 2018; McKay et al., 2019; Ortiz, 2010).

To close these research gaps, we expand the literature with a national U.S. survey, focusing on multiple local protein types (beef, pork, chicken, fish, shrimp, crab, and oysters). In doing so, we aim to identify (1) restaurant characteristics related to its likelihood of purchasing and paying more for local animal proteins, (2) barriers to restaurants purchasing these local proteins, and (3) desirable local protein traits and certifications. Interviews with restaurant owners and employees such as chefs and executive chefs indicate that their own motivations to buy local ingredients include freshness, quality, taste, and uniqueness (Curtis & Cowee, 2009; Pepinski & Thilmany, 2004; Sharma et al., 2014). Small, independent, gourmet restaurants appear more likely to purchase local foods (Curtis & Cowee, 2009). In contrast, chain restaurants and franchises are less likely to buy local proteins due to supply chain complexities, higher product volume and consistency needs, and food safety and traceability concerns (Torres, 2016). Moreover, local producers prefer selling to independent restaurants, as these restaurants are more likely to source local foods (Lin et al., 2020; Sharma, 2007; Yoon et al., 2020).

Barriers to restaurants purchasing local ingredients include year-round availability issues, distribution problems, and a lack of convenient purchasing and product information (Brain et al., 2015; Curtis & Cowee, 2009; Inwood et al., 2009; Reynolds-Allieaâ’ & Fields, 2012; Roy & Ballantine, 2020). Furthermore, wait staff may not adequately communicate the locally sourced food message to customers (Dragon, 2016). However, the benefits seem to outweigh the barriers, as consumers appear to be willing to pay more for locally sourced foods, and these benefits do not appear to be reduced through increased menu prices (Griffith et al., 2018; Lin et al., 2020; McKay et al., 2019; Ortiz, 2010; Sharma et al., 2014).

In addition, this study collected data about restaurant characteristics and their potential relation to procurers’ decisions to purchase and pay more for local proteins, as these observable restaurant traits may have implications for local protein producers seeking to market their products.

2. Materials and Methods

Restaurant procurement may be delegated to a chef or executive chef or performed by the restaurant owner, with the role differing between restaurants based on how job duties are assigned. For this study, it was essential to target those individuals with procurement responsibilities, as they are the decision-makers for what food ingredients the restaurant purchases. The National Restaurant Association estimates the number of restaurants in the U.S. to be 749,000 (NRA, 2010). Approximately 349,000 of these restaurants are part of national chains, which were excluded from this study for the reasons explained in the literature review. This leaves a potential restaurant pool of approximately 400,000 independent restaurants of varying sizes and service levels. The targeted restaurant procurers for this study make up a small percentage of the U.S. population. For this reason, an online survey instrument was chosen. In addition, our literature review on local foods discovered that surveys were the most popular method for gathering data, with most papers reporting survey analysis (Cheplick et al., 2021; Richards & Vassalos, 2020, 2021; Richards et al., 2022). The survey was distributed nationally to capture the largest possible sample size while addressing a lack of national U.S. studies of restaurant procurers on this topic (as noted above).

Coastal U.S. states were targeted so that local seafood could be studied alongside local meats while staying within the general definition of “local” provided by the USDA (CRS, 2020) and keeping survey costs within a budget of USD 25,000. The respondents were sampled by Qualtrics, an online survey service provider in the U.S., through its national network of consumer panels. Because these were panels of adult U.S. consumers in the general population, the survey included screening questions to determine whether the respondent (1) worked in the restaurant industry, (2) had food purchasing authority, and (3) did not work in a chain restaurant.

From the 800 individuals allowed to start the survey (individuals responding “yes” to working in the restaurant industry), 275 were screened out for working in restaurant chains and 113 were excluded for not having food purchasing authority at the restaurant where they worked. This left a sample of n = 412 for this study. It is unknown how many individuals were excluded from the sample for not working in the restaurant industry, nor does Qualtrics specify their panel demographics other than to state that their sample is representative of the general U.S. population. Finally, for regional comparisons, a quota was placed on specific zip codes so that approximately 25% of respondents were recruited from the northeastern, southeastern, northwestern, and southwestern U.S., respectively.

Human subject approval, validation, and pretesting were performed before launching the survey. Clemson University’s Institutional Review Board (IRB) granted human subject approval for this research under the exempt provisions under U.S. 45 CFR 46.104(d), Section 2. An advisory panel of agribusiness and restaurant professionals reviewed the survey questionnaire for clarity and correct use of restaurant industry terminology. The survey was also pretested by Qualtrics, with a pilot run of 10 survey responses before launch in June 2021.

The survey included questions about restaurant characteristics, meat and seafood purchasing preferences, willingness to pay, and barriers to buying local proteins. In addition, the survey used display logic, so participants only answered questions about proteins they had knowledge of purchasing. A questionnaire sample is available in the supplemental materials; display logic is illustrated in the shaded boxes preceding the questions.

Survey analysis was performed using summary statistical analysis followed by regression analysis. Regression analysis used the R statistical computation platform (Version 4.4.2, using the stats and MASS packages).

Table 1. Description of Variables in the Regression Models.

Variable	Description	Response Categories
Type	Restaurant Type	(1) Fine Dining, (2) Premium Casual, (3) Casual Dining, (4) Fast Casual Dining, (5) Family Style, (6) Fast Food
Seats	Number of Seats	(1) Under 25, (2) 25 to 49, (3) 50 to 74, (4) 75 to 99, (5) 100 to 124,
		(6) 125 to 149, and (7) 150 or more
Entrée Cost	Average Entrée Price	(1) USD 10 to USD 14, (2) USD 15 to USD 19, (3) USD 20 to USD 24, (4) USD 25 to USD 29, (5) USD 30 to USD 34,
		and (6) USD 35 and over
Alcohol	Serves Alcohol	(1) Serves alcohol, (0) Does not serve alcohol
OtherLocal	Buys Other Local Foods	(1) Buys other local foods, (0) Does not buy other local foods
LocalRes	Percent Local Residents	(1) Mostly Tourists (>90%), (2) 75% Tourists/25% Local,
	versus Tourist Customers	(3) 50% Tourists/50% Local, (4) 25% Tourists/75% Local, and (5) >90% Local
Years	Years in Business	(1) 0 to 4 years, (2) 5 to 9 years, (3) 10 to 14 years, (4) 15 to 19 years,
		(5) 20 to 24 years, and (6) over 25 years
South	Located in Southern U.S.	(1) Located in Southern U.S., (0) Not Located in Southern U.S.
East	Located in Eastern U.S.	(1) Located in Eastern U.S., (0) Not Located in Eastern U.S.

summarizes the independent variables for the regression models. These variables are based on restaurant descriptors used by the National Restaurant Association for research and economic analysis (NRA, 2010). As part of this analysis, we treated the restaurant (Type) variable as a categorical, ordered variable from the highest level of service (fine dining) to the lowest level of service (fast food). The service levels assumed for each restaurant type are consistent with the National Restaurant Association’s definition. Summary statistical analysis, shown in

Table 2. Restaurant Characteristics (n = 412).

	Percent
Type of Restaurant
Fine Dining	26.2%
Premium Casual Dining	22.6%
Casual Dining	18.9%
Fast Casual Dining	8.7%
Family Style	9.2%
Fast Food	14.3%
Seating Capacity
Under 25 Seats	1.9%
25 to 49 Seats	23.3%
50 to 74 Seats	37.9%
75 to 99 Seats	28.2%
100 to 124 Seats	6.8%
125 to 149 Seats	1.5%
150 or More Seats	0.5%
Average Entrée Cost
USD 10 to USD 14	1.7%
USD 15 to USD 19	13.1%
USD 20 to USD 24	37.6%
USD 25 to USD 29	24.5%
USD 30 to USD 34	14.8%
USD 35 or More	8.3%
Serves Alcohol
Yes	76.7%
No	23.3%
Percent of Customers Local Versus Tourists
Mostly Local (more than 90%)	10.9%
75% Local, 25% Tourists	54.9%
50% Local, 50% Tourists	27.9%
25% Local, 75% Tourists	6.3%
Mostly Tourists (more than 90%)	0.0%
Years Restaurant Has Been in Business
0 to 4 Years	2.7%
5 to 9 Years	39.1%
10 to 14 Years	47.1%
15 to 19 Years	8.7%
20 to 24 Years	1.2%
25 Years or More	1.2%
Location of Restaurant in the United States
Northeastern U.S.	23.3%
Northwestern U.S.	24.5%
Southeastern U.S.	26.9%
Southwestern U.S.	25.2%

,

Table 3. Restaurant Characteristics: Purchase Local Protein (n = 379) vs. Do Not Purchase (n = 33).

	Purchase (Yes)	Purchase (No)	Difference (Y − N)	t-Test
Type of Restaurant
Fine Dining	25.6%	33.3%	−7.7%
Premium Casual Dining	23.2%	15.2%	8.1%
Casual Dining	19.8%	9.1%	10.7%
Fast Casual Dining	8.7%	9.1%	−0.4%	NS
Family Style	7.7%	27.3%	−19.6%
Fast Food	15.0%	6.1%	9.0%
Seating Capacity
Under 25 Seats	2.1%	0.0%	2.1%
25 to 49 Seats	24.0%	15.2%	8.9%
50 to 74 Seats	37.5%	42.4%	−5.0%
75 to 99 Seats	28.0%	30.3%	−2.3%	NS
100 to 124 Seats	6.6%	9.1%	−2.5%
125 to 149 Seats	1.3%	3.0%	−1.7%
150 or More Seats	0.5%	0.0%	0.5%
Average Entrée Cost
USD 10 to USD 14	1.6%	3.0%	−1.4%
USD 15 to USD 19	12.7%	18.2%	−5.5%
USD 20 to USD 24	36.7%	48.5%	−11.8%	**
USD 25 to USD 29	25.3%	15.2%	10.2%
USD 30 to USD 34	14.8%	15.2%	−0.4%
USD 35 or More	9.0%	0.0%	9.0%
Serves Alcohol
Yes	77.3%	69.7%	7.6%	NS
No	22.7%	30.3%	−7.6%
Percent of Customers Local Versus Tourists
Mostly Local (more than 90%)	11.3%	6.1%	5.3%
75% Local, 25% Tourists	54.4%	60.6%	−6.3%
50% Local, 50% Tourists	28.2%	24.2%	4.0%	NS
25% Local, 75% Tourists	6.1%	9.1%	−3.0%
Mostly Tourists (more than 90%)	0.0%	0.0%	0.0%
Years Restaurant Has Been in Business
0 to 4 Years	2.6%	3.0%	−0.4%
5 to 9 Years	38.0%	51.5%	−13.5%
10 to 14 Years	47.5%	42.4%	5.1%	**
15 to 19 Years	9.2%	3.0%	6.2%
20 to 24 Years	1.3%	0.0%	1.3%
25 Years or More	1.3%	0.0%	1.3%
Location of Restaurant in the United States
Northeastern U.S.	22.2%	36.4%	−14.2%
Northwestern U.S.	25.3%	15.2%	10.2%
Southeastern U.S.	26.6%	30.3%	−3.7%	**
Southwestern U.S.	25.9%	18.2%	7.7%

Significance codes: ‘**’ 5% NS: Not Significant.

,

Table 4. Local Protein and Other Local Purchases (n = 379).

	Frequency	Percent
Local Proteins Purchased
Local Meats Only	23	6.1%
Local Seafood Only	16	4.2%
Both Local Meats and Seafood	340	89.7%
Does Restaurant Buy Other Local Products?
Yes	273	72.0%
No	106	28.0%
What Other Local Products Purchased? (n = 273)
Local produce	199	52.5%
Non-alcoholic beverages	167	44.1%
Alcoholic beverages	159	42.0%
Other	1	0.3%

,

Table 5. Comparing Barriers to Purchasing or Purchasing More Local Proteins.

	Purchasers	Non-Purchasers	Difference
Barriers to those purchasing (n = 379) and not purchasing (n = 33)
Quality of the product is inconsistent	43.8%	78.8%	−35.0%
Price point is too high	42.0%	69.7%	−27.7%
Limited availability/difficulty sourcing	40.1%	48.5%	−8.4%
Quality of the product does not meet my specifications	33.5%	33.3%	0.2%
Seafood needs to be processed	27.2%	15.2%	12.0%
Meats need to be processed	23.2%	9.1%	14.1%
I do not know where to source	7.9%	6.1%	1.9%
Customers do not value local meats and seafood	5.8%	3.0%	2.8%
Other	0.0%	0.0%	0.0%

and

Table 6. What Would Make Buying Local Proteins More Convenient.

	Purchasers	Non-Purchasers	Difference
Would You Buy/Buy More Local Proteins if More Convenient?
Yes	80.8%	70.6%	10.2%
No	19.2%	29.4%	−10.2%
What Would Make Purchasing Local Proteins More Convenient?
An aggregator of local protein (i.e., protein hub)	51.7%	35.3%	16.5%
A distributor of local protein	61.5%	23.5%	38.0%
A wholesale market or buying point	51.7%	58.8%	−7.1%
Direct delivery from producers	32.2%	17.6%	14.5%
Other	0.7%	0.0%	0.7%

and Figure 1, Figure 2, Figure 3 and Figure 4, was performed using Microsoft Excel and the open-source R statistical computation platform (using the summarytools package).

Regression Models

Respondents were asked whether they purchased local proteins (Y = 1) or did not (Y = 0). Because these are dichotomous dependent variables, logistic regression is used for analysis (Wright, 1995). The general form of the logistic model is

P(Y = 1) = 1/(1 + exp[−(β₀ + β₁X₁ + β₂X₂ + β₃X₃ + … + β_pX_p)])

(1)

Pr(Y = 1) = The probability of purchasing local proteins;

X_i = Independent variables (8 restaurant and restaurant customer characteristics);

β_i = Coefficients of the model, each representing parameters of the model.

Respondents were also asked about their frequency of purchasing local proteins, their willingness to pay for local proteins, and what additional proteins they desire. Because these variables have ordered choices and multiple cut points, we used ordered logit regression (Greene, 2012) to estimate probabilities that a choice lies in one threshold or another. A general form of the ordered logit model is shown below, where Y represents an ordering of responses.

Pr(Y_i=j) = Pr (μ_j−1 <Y_i ≤ μ_j) = Pr (μ_j−1< [ β₀ + β_i X_i + ε_i] ≤ μ_j)

(2)

Y_i = Predicted ranking (purchase frequency, willingness to pay more, desire to purchase more);

μ_j = μ is the categorical threshold_, with _j representing the ranking or cut point;

X_i = Independent variables (8 restaurant and restaurant customer characteristics);

β_i = Coefficients of the model, each representing parameters of the model;

ε_i = Random error term.

3. Results

3.1. Characteristics of All Restaurant Respondents

Table 2 shows the results of all respondents concerning restaurant characteristics where they are employed. The most common are fine dining establishments, seating capacity of 50 to 74 seats, USD 20 to USD 24 average entrée cost, serving alcohol, catering primarily to residents, and being in business for 10 to 14 years. The geographic regions (East, West, North, and South) were sampled subject to a quota, making responses from each region approximately equal.

3.2. Characteristics of Restaurants Purchasing Local Proteins

Table 3 shows the differences between restaurants that purchase local protein (n = 379) and those that do not (n = 33). Welch’s t-test results show that the average entrée cost, years in business, and restaurant location significantly differed between the two restaurant groups. The means associated with the type of restaurant, seating capacity, serving alcohol, and percentage of local customers did not significantly differ.

Restaurant procurers who purchase local proteins appeared more likely to purchase multiple local proteins and local foods. Table 4 illustrates that restaurants purchasing local proteins often purchase both local meats and seafood. Most of these restaurants (72%) also bought other local products such as produce, non-alcoholic beverages, and alcoholic beverages. This may be valuable information for producers looking to market their local proteins to restaurants: target restaurants already buying local produce or beverages.

3.3. Local Proteins Purchased and Desired

Figure 1 illustrates the percentage of restaurants responding that they purchased a particular protein and the percentage of that protein sourced locally. The percent local “menu share” is estimated by multiplying the response values for both questions. Chicken, fish, and beef are the top three local proteins by menu share, followed by pork, crab, shrimp, and oysters. Those producers looking to sell locally sourced proteins to restaurants may be best served by offering some of the more commonly purchased proteins.

Participants were asked whether they desired to buy more local proteins, and 93.6% responded affirmatively (78% yes and 15.6% maybe). In order of response rate, these restaurant managers sought to buy more beef, chicken, fish, pork, shrimp, oysters, and crab. The additional amount of local protein desired averaged about 65% by protein type, with some variation between proteins. Potential local demand can be illustrated by adding the estimated local menu share (Figure 1) to the additional desired amount (percent of restaurants seeking to purchase more local protein multiplied by the percentage of additional desired local protein) to arrive at a potential demand or menu share.

Figure 2 shows that the top proteins restaurants desire are chicken, fish, and beef, which have the highest potential menu share, between 80% and 90%. Local pork, shrimp, crab, and oysters have a smaller potential menu share, between a low of 46.5% for local oysters and a high of 63.7% for local pork. Interestingly, this illustrates a need for more local proteins among restaurants and a desire to source a mix of local and non-local proteins.

3.4. Desired Traits and Barriers When Purchasing Local Proteins

From a list, respondents were asked to rank the most desired traits of local meats and seafood based on available certification programs and other traits. Participants could also write a response. The nine listed local meat traits were ranked: Certified Naturally Grown, no growth hormones, Certified Organic, Certified Grass-Fed, pastured/free range, state or locally certified, Certified Humane, knowing the farmer, and write-in responses. The six listed local seafood traits were ranked: Marine Stewardship Council (MSC)-certified, state or locally certified, Aquaculture Stewardship Council (ASC)-certified, farm-raised, knowing the producer/fisher, and write-in responses. Write-in responses were few and included nutrition, freshness, leanness, packaging type, and knowing the feedstock components.

Both buyers and non-buyers of local products were asked about barriers to purchasing local proteins. Table 5 shows the responses of both groups and the differences between responses. Both groups ranked inconsistent quality, high cost, limited availability, and difficulty sourcing as the top three barriers to purchasing. Interestingly, both groups ranked the barriers in the same order. The non-purchasers seemed more concerned about quality, price, and availability and less about processing needs.

Most purchasers (80.8%) and non-purchasers (70.6%) replied affirmatively that they would buy more local proteins if the process were more convenient. When asked what would make the process more convenient, respondents replied that local protein distributors, wholesale markets and buying points, and local protein aggregators would be more helpful than direct delivery from the producer. Wholesale markets and buying points were more important to the non-purchasers, and distributors of local proteins were more important to the purchasers (Table 6).

3.5. Willingness to Pay for Local Proteins

Figure 3 and Figure 4 illustrate respondents’ willingness to pay for local meats and seafood. The mode of both histograms is a 1% to 20% premium. Producers of local proteins may have to determine whether this range of potential premiums is sufficient for profitability. The histograms also point out that some restaurants are willing to pay a higher premium, and these results are analyzed further with regression analyses.

3.6. Logistic Regression Results

Logistic regression shows significant variables for restaurants purchasing local proteins are Seats (fewer), Entrée Cost (higher), Years in business (higher), and Location (East). Marginal effects suggest that an increase in restaurant seating decreases the probability of purchasing local proteins by 3.4%; an increase in entrée prices increases the probability of purchasing local proteins by 3.5%; an increase in the number of years a restaurant has been in business increases the probability of purchasing local proteins by 5.4%; and the restaurant being in the eastern U.S. decreases the probability of purchasing local proteins by 6.8% (

Table 7. Logistic Regression: Factors Influencing the Probability of Purchasing Local Proteins.

Coefficients:	Value	Std. Error	Marginal Effects
Type	0.001	0.008	0.000
Seats	−0.031 *	0.014	−0.034
EntréeCost	0.029 *	0.012	0.035
Alcohol	0.016	0.032	0.014
LocalRes	0.022	0.019	−0.023
Years	0.048 **	0.017	0.054
South	0.019	0.027	0.024
East	−0.063 *	0.027	−0.068
(Intercept)	0.839 ***	10.818
N	412
AIC	94.23

*** p < 0.001; ** p < 0.01; * p < 0.05.

).

3.7. Ordered Logit Regression Results

We investigated the ordered, categorical responses of restaurant buyers’ willingness to pay, frequency of purchases, and desired number of additional purchases for local meats and seafood using ordered logit regression. The significant independent variables for these three regression analyses are summarized for each local protein in

Table 8. Ordered Logit Regression for Factors Influencing Willingness to Pay, Purchasing Frequency, and Purchasing More Local Proteins.

Protein	Willingness to Pay for Local Proteins	Purchasing Frequency of Local Proteins	Purchasing More Local Proteins
Beef	Type (−) ***	Type (+) *
		Seats (−) *	Seats (−) ***
	Entrée Cost (+) *	Entrée Cost (+) **
		Alcohol (+) *	Alcohol (+) ***
	South (−) **	Local Residents (−) *	Years (+) *
		South (−) ***
Pork	Type (−) ***	Alcohol (+) **	Seats (−) **
	Seats (−) *	Local Residents (−) **	Alcohol (+) **
		Other Local Foods (+) *	Local Residents (−) **
		South (−) ***
Chicken	Type (−) **		Type (+) *
	Seats (−) ***		Seats (−) ***
		Entrée Cost (+) **	Alcohol (+) ***
		Alcohol (+) *	Local Residents (−) **
	South (−) ***	South (−) ***	South (−) **
	East (+) **		East (+) **
Oysters			Type (−) **
			Entrée Cost (+) **
	Other Local Foods (+) *	Other Local Foods (+) **	Alcohol (+) *
			Years (−) **
		South (−) *	South (−) **
Shrimp	South (−) *	Alcohol (+) ***	Alcohol (+) ***
		Years (+) * Local Residents (−) ***	South (−) *
Crab	Type (−) **
	Other Local Foods (+) **
	Local Residents (−) *	Entrée Cost (+) **	Alcohol (+) **
Fish	Type (−) **	Entrée Cost (+) **	Seats (−) **
	Seats (−) ***		Entrée Cost (+) **
	Local Residents (−) ***
	South (−) ***	South (−) ***	Alcohol (+) ***

*** p < 0.01; ** p < 0.05; * p < 0.10; NS = Not Significant; +/− = Coefficient Sign.

, Columns 2–4, with full regression results in Appendix A (

Table A1. Willingness to Pay for Local Meats (Ordered Logit).

	Local Beef		Local Pork		Local Chicken
Coefficients	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value
Type	−0.327	−4.23 ***	−0.311	−3.51 ***	−0.149	−2.19 **
	(0.077)		(0.088)		(0.068)
Seats	−0.117	−1.08	−0.208	−1.74 *	−0.331	−3.18 ***
	(0.107)		(0.101)		(0.104)
EntréeCost	0.158	1.75 *	−0.023	−0.23	0.046	0.54
	(0.090)		(0.101)		(0.086)
Alcohol	−0.096	0.37	−0.0502	−0.18	−0.0273	−0.12
	(0.259)		(0.272)		(0.231)
LocalRes	−0.085	−0.60	−0.239	−1.52	−0.208	−1.54
	(0.140)		(0.157)		(0.135)
OtherLocal	−0.016	−0.05	0.474	1.37	0.110	0.41
	(0.308)		(0.346)		(0.270)
Years	0.124	0.88	0.222	1.38	0.195	1.45
	(0.139)		(0.160)		(0.135)
South	−0.671	−3.10 **	−0.386	−1.62	−0.787	−3.81 ***
	(0.216)		(0.238)		(0.206)
East	0.308	1.43	0.317	1.31	0.469	2.32 **
	(0.214)		(0.242)		(0.202)
Intercepts:	Value	Std. Error	Value	Std. Error	Value	Std. Error
1|2	−2.553	0.7101	−2.652	0.8214	−2.4071	0.6573
2|3	−1.602	0.6962	−1.246	0.8014	−1.2592	0.6466
3|4	−0.122	0.6922	0.2760	0.7945	0.1354	0.6428
4|5	1.015	0.6983	1.5174	0.8012	0.9238	0.6468
5|6	3.326	0.7294	2.8865	0.8339	1.9099	0.6420
6|7					3.3241	0.7122
	Res. Dev.: 952.12		Res. Dev.: 773.98		Res. Dev.: 1131.29
	AIC: 980.12		AIC: 801.98		AIC: 1161.29

*** p < 0.01; ** p < 0.05; * p < 0.10. Res. Dev. = Residual Deviance.

,

Table A2. Willingness to Pay for Local Seafood (Ordered Logit).

	Local Oysters		Local Shrimp		Local Crab		Local Fish
Coefficients	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value
Type	−0.064	−0.67	0.066	−0.76	0.180	−1.93 *	−0.155	−2.26 **
	(0.096)		(0.087)		(0.093)		(0.069)
Seats	−0.062	−0.0.45	0.177	1.42	0.102	0.78	−0.305	−2.91 ***
	(0.139)		(0.124)		(0.130)		(0.105)
EntréeCost	0.095	0.88	−0.100	−1.02	−0.005	−0.05	−0.040	−0.44
	(0.108)		(0.098)		(0.102)		(0.089)
Alcohol	−0.092	−0.29	0.448	1.62	0.172	0.60	0.044	0.19
	(0.311)		(0.276)		(0.286)		(0.230)
LocalRes	−0.205	−1.15	−0.252	−1.49	−0.328	−1.93 *	−0.376	−2.64 ***
	(0.177)		(0.168)		(0.169)		(0.142)
OtherLocal	0.634	1.73 *	0.314	0.90	0.817	2.23 **	0.040	0.16
	(0.367)		(0.348)		(0.366)		(0.263)
Years	0.140	0.74	0.031	0.21	−0.011	0.06	0.169	1.24
	(0.190)		(0.147)		(0.179)		(0.136)
South	−0.360	−1.38	−0.400	−1.66 *	−0.367	−1.47	−0.756	−3.64 ***
	(0.261)		(0.240)		(0.249)		(0.208)
East	0.267	1.02	−0.184	−0.77	0.026	0.11	0.190	0.94
	(0.261)		(0.238)		(0.242)		(0.202)
Intercepts	Value	Std. Error	Value	Std. Error	Value	Std. Error	Value	Std. Error
1|2	−0.8987	0.9233	−1.4092	0.8802	−1.3682	0.9123	−2.6332	0.6600
2|3	0.3229	0.9122	0.2035	0.8722	0.1874	0.9061	−1.5423	0.6487
3|4	1.7476	0.9194	1.6390	0.8769	1.6044	0.9107	−0.2431	0.6418
4|5	3.3137	0.9453	2.7803	0.8848	2.5642	0.9139	0.6769	0.6440
5|6	4.5622	0.9989	4.9403	0.9733	3.6745	0.9298	1.6776	0.6521
6|7			6.5659	1.3252	6.7702	1.3478	4.6683	0.9418
	Res. Dev.: 640.76		Res. Dev.: 775.65		Res. Dev.: 756.44		Res. Dev.: 1091.36
	AIC: 668.8		AIC: 805.65		AIC: 86.44		AIC: 1121.36

*** p < 0.01; ** p < 0.05; * p < 0.10. Res. Dev. = Residual Deviance.

,

Table A3. Frequency of Purchasing Local Meats (Ordered Logit).

	Local Beef		Local Pork		Local Chicken
Coefficients	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value
Type	0.138	1.73 *	−0.043	−0.47	0.089	1.29
	(0.080)		(0.092)		(0.069)
Seats	−0.203	−1.80 *	0.139	1.01	−0.174	−1.51
	(0.113)		(0.137)		(0.115)
EntréeCost	0.250	2.47 **	0.168	1.53	0.190	2.00 **
	(0.102)		(0.110)		(0.095)
Alcohol	0.590	2.06 *	0.607	2.00 **	0.453	1.78 *
	(0.286)		(0.302)		(0.254)
LocalRes	−0.262	−1.66 *	−0.402	−2.11 **	−0.124	−0.84
	(0.157)		(0.190)		(0.148)
OtherLocal	0.539	1.57	0.710	1.81 *	0.222	0.82
	(0.343)		(0.392)		(0.270)
Years	−0.127	−0.08	−0.065	−0.35	−0.081	−0.55
	(0.157)		(0.185)		(0.148)
South	−0.580	−2.58 ***	−0.784	−3.00 ***	−0.912	−4.21 ***
	(0.225)		(0.261)		(0.218)
East	−0.039	−0.17	0.363	1.35	0.1570	0.24
	(0.229)		(0.268)		(0.211)
Intercepts:	Value	Std. Error	Value	Std. Error	Value	Std. Error
1|2	−1.8739	0.8182	−0.6589	0.9259	−3.3809	0.7730
2|3	0.3974	0.7625	1.1839	0.8938	−1.1106	0.6943
3|4	1.3068	0.7866	2.3225	0.9072	−0.1978	0.6936
4|5	2.9769	0.7807	3.4859	0.9230	1.4612	0.6961
	Res. Dev.: 738.09		Res. Dev.: 618.67		Res. Dev.: 832.53
	AIC: 809.09		AIC: 644.67		AIC: 858.53

*** p < 0.01; ** p < 0.05; * p < 0.10. Res. Dev. = Residual Deviance.

,

Table A4. Frequency of Purchasing Local Seafood (Ordered Logit).

	Local Oysters		Local Shrimp		Local Crab		Local Fish
Coefficients	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value
Type	0.020	−0.15	−0.106	−1.01	−0.035	−0.33	0.012	0.17
	(0.139)		(0.105)		(0.108)		(0.071)
Seats	0.080	0.38	0.016	0.11	0.005	0.03	−0.114	−1.02
	(0.209)		(0.147)		(0.152)		(0.111)
EntréeCost	0.169	1.19	0.101	0.90	0.231	2.04 **	0.221	2.28 **
	(0.142)		(0.112)		(0.114)		(0.097)
Alcohol	0.455	1.09	1.240	3.51 ***	−0.118	−0.37	0.386	1.55
	(0.416)		(0.354)		(0.316)		(0.249)
LocalRes	0.113	0.47	−0.627	−2.9 ***	0.290	1.52	0.155	1.02
	(0.244)		(0.211)		(0.190)		(0.152)
OtherLocal	1.075	2.15 **	0.532	1.31	0.099	0.25	−0.146	−0.53
	(0.500)		(0.406)		(0.395)		(0.276)
Years	−0.169	−0.72	0.289	1.75 *	0.044	0.22	−0.004	−0.03
	(0.234)		(0.165)		(0.200)		(0.140)
South	−0.701	−1.93 *	−0.213	−0.74	−0.449	−1.60	−0.785	−3.6 ***
	(0.364)		(0.288)		(0.280)		(0.220)
East	0.333	0.92	−0.213	−0.74	0.127	0.46	0.151	0.70
	(0.363)		(0.290)		(0.275)		(0.222)
Intercepts:	Value	Std. Error	Value	Std. Error	Value	Std. Error	Value	Std. Error
1|2	−1.9286	1.2013	−0.1773	1.0876	−3.6269	1.1065	−3.5999	0.7555
2|3	−0.4058	1.1670	2.1664	1.0192	−1.4695	1.0265	−1.5073	0.6932
3|4	0.4673	1.1654	3.2413	1.0289	−0.4141	1.0201	−0.4988	0.6908
4|5	2.4269	1.1785	4.7954	1.0626	1.3195	1.0235	0.8894	0.6908
	Res. Dev.: 329.16		Res. Dev.: 504.80		Res. Dev.: 521.74		Res. Dev.: 810.18
	AIC:355.2		AIC:530.80		AIC:547.74		AIC:836.18

*** p < 0.01; ** p < 0.05; * p < 0.10. Res. Dev. = Residual Deviance.

,

Table A5. Desiring to Purchase More Local Meats (Ordered Logit).

	Local Beef		Local Pork		Local Chicken
Coefficients	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value
Type	0.054	0.56	−0.013	−0.12	0.162	1.72 *
	(0.097)		(0.111)		(0.094)
Seats	−0.633	−3.99 ***	−0.411	−2.21 **	−0.622	−3.64 ***
	(0.159)		(0.186)		(0.171)
EntréeCost	0.232	1.71	0.113	0.80	0.067	0.48
	(0.135)		(0.142)		(0.141)
Alcohol	1.420	4.02 ***	0.824	2.36 **	1.234	3.72 ***
	(0.353)		(0.349)		(0.331)
LocalRes	−0.326	−1.56	−0.497	−2.30 **	−0.531	−2.43 **
	(0.209)		(0.216)		(0.218)
OtherLocal	0.271	0.61	0.634	1.42	0.019	0.05
	(0.441)		(0.446)		(0.374)
Years	0.350	1.75 *	0.238	1.00	0.038	0.18
	(0.200)		(0.238)		(0.204)
South	−0.420	−1.41	−0.421	−1.35	−0.656	−2.20 **
	(0.298)		(0.312)		(0.298)
East	0.418	1.40	0.218	0.71	0.671	2.29 **
	(0.298)		(0.309)		(0.292)
Intercepts	Value	Std. Error	Value	Std. Error	Value	Std. Error
1|2	−3.6948	1.3521	−1.9778	1.1650	−3.9954	1.1682
2|3	0.1272	0.9241	0.1387	1.0764	−1.2411	0.9523
3|4	1.5592	0.9247	1.2700	1.0775	0.2357	0.9467
4|5	3.0660	0.9415	2.7328	1.0904	1.8436	0.9554
	Res. Dev.: 485.26		Res. Dev.: 423.27		Res. Dev.: 458.27
	AIC: 511.26		AIC: 449.27		AIC: 484.27

*** p < 0.01; ** p < 0.05; * p < 0.10. Res. Dev. = Residual Deviance.

and

Table A6. Desiring to Purchase More Local Seafood (Ordered Logit).

	Local Oysters		Local Shrimp		Local Crab		Local Fish
Coefficients	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value	Value/SE	t Value
Type	−0.260	−2.08 **	−0.046	−0.38	0.068	0.55	0.146	1.56
	(0.125)		(0.121)		(0.125)		(0.094)
Seats	0.064	0.35	−0.135	−0.79	0.178	0.95	−0.348	−2.22 **
	(0.182)		(0.169)		(0.187)		(0.157)
EntréeCost	0.290	2.08 **	0.175	1.20	0.067	0.47	0.333	2.47 **
	(0.139)		(0.146)		(0.142)		(0.135)
Alcohol	0.713	1.89 *	1.389	3.80 ***	0.969	2.45 **	1.487	4.51 ***
	(0.376)		(0.365)		(0.395)		(0.330)
LocalRes	0.225	0.98	−0.040	−0.18	−0.179	−0.76	0.249	1.06
	(0.230)		(0.222)		(0.234)		(0.233)
OtherLocal	−0.144	−0.30	0.090	0.19	−0.110	−0.21	−0.191	−0.53
	(0.474)		(0.471)		(0.516)		(0.330)
Years	−0.422	−2.03 **	−0.055	−0.25	0.035	0.15	−0.043	−0.19
	(0.207)		(0.224)		(0.238)		(0.218)
South	−0.658	−2.04 **	−0.522	−1.66 *	−0.270	−0.85	−0.101	−0.35
	(0.323)		(0.314)		(0.317)		(0.284)
East	0.554	1.69	0.454	1.43	0.393	1.17	0.167	059
	(0.328)		(0.317)		(0.336)		(0.286)
Intercepts	Value	Std. Error	Value	Std. Error	Value	Std. Error	Value	Std. Error
1|2	−6.1667	1.5393	−4.4130	1.5268	−2.2887	1.3931	−3.7622	1.1009
2|3	−3.3800	1.1869	−1.2753	1.1774	0.4836	1.2384	−1.3115	0.9605
3|4	−1.5956	1.1451	0.0573	1.1678	1.7955	1.2533	0.0485	0.9567
4|5	0.6698	1.1451	2.4388	1.1844	3.4385	1.2795	1.8564	0.9646
	Res. Dev.: 346.94		Res. Dev.: 378.41		Res. Dev.: 386.92		Res. Dev.: 473.12
	AIC:372.32		AIC: 404.41		AIC:412.92		AIC:499.12

*** p < 0.01; ** p < 0.05; * p < 0.10. Res. Dev. = Residual Deviance.

).

In general, the sign of the significant variable is the indicator of directional probability. For the dummy variables (Alcohol, South, East, and OtherLocal), a positive sign indicates that the regression results suggest a higher probability that a restaurant serves alcohol, purchases other local foods, and is in the Southeastern U.S. Likewise, a negative sign suggests the opposite. For the ordered categories of Seats, Entrée Cost, and Years (in business), the signs of the significant variables suggest the direction but cannot identify which category is more probable; instead, it suggests a higher or lower categorical response based on the sign, indicating a movement toward or away from the base response. For example, a negative sign for significant variables of Seats, Entrée Cost, and Years points to fewer seats, lower entrée cost, and fewer years in business. Finally, for the variables of LocalRes (percent local residents) and Type (restaurant type description ranging from fine dining to fast food), the interpretation becomes vaguer, as these responses are proxies for how many tourists a restaurant serves and what level of service a restaurant offers to its clients, as explained earlier. A negative sign for significant variables of LocalRes and Type suggests, perhaps, fewer local residents and a higher level of service, but these are more indirect measures.

The significant variables for willingness to pay (Table 8, Column 2) for individual proteins are restaurant Type (positive for proteins beef, pork, chicken, crab, and fish), number of Seats (negative for pork, chicken, and fish), Entrée Cost (positive for beef), catering to Local Residents (negative for crab and fish), not located in the South (beef, chicken, shrimp, and fish), offering Other Local Foods (crab and oysters), and being located in the East (chicken).

The significant variables for purchasing frequency (Table 8, Column 3) are Type (negative for beef), Seats (negative for beef), Entrée Cost (positive for beef, chicken, and fish), Local Residents (negative for beef, pork, and shrimp), Years (positive for shrimp), Alcohol (positive for beef, pork, chicken, and shrimp), Other Local Foods (positive for pork and oysters), and South (negative for beef, pork, chicken, oysters, and fish).

The significant variables for desiring to purchase more local protein (Table 8, Column 4) are Type (negative for chicken, positive for oysters), Seats (negative for beef, pork, chicken, and fish), Entrée Cost (positive for oysters and fish), Alcohol (positive for all proteins), Local Residents (negative for pork and chicken), Years in business (positive for beef, negative for oysters), South (negative for chicken, oysters, and shrimp), and East (positive for chicken).

4. Discussion

The elements of the discussion that are most relevant to producers looking to market their products to restaurants are the characteristics of restaurants seeking to buy local proteins, desired attributes of local meat and seafood, barriers to purchasing or purchasing more local proteins, and restaurants’ willingness to pay for local proteins.

4.1. Characteristics of Restaurants That Buy Local Proteins

Using summary statistics and regression analysis on survey data, we identified restaurant characteristics associated with a higher probability of purchasing local proteins. Summary statistics show that restaurants that purchase local proteins (versus those that do not) have significant differences between the means for average entrée cost and number of years in business and are more likely to be in the western U.S. The logistic regression results are similar, with restaurants buying local proteins more likely to have fewer seats, have higher entrée prices, be located in the western U.S., and have been in business longer.

A restaurant’s regional location may influence whether it purchases or is willing to pay more for local proteins. The logistic regression results suggest that restaurants in the western U.S. are generally more likely to buy local proteins. This finding could be a function of the fact that the farm-to-table restaurant movement was started in 1971 at Chez Panisse in Berkeley, California (Pesci & Brinkley, 2022) and has had a long time to grow into a more significant social movement, which may also explain the survey finding that restaurants that buy local proteins have generally been in business longer. However, there may be additional opportunities for local proteins in the eastern U.S., as additional analyses show that restaurants in the eastern U.S. are more likely to pay more for local chicken and may desire to purchase more local chicken.

On the flip side, there may be challenges for local proteins in the southern U.S. Ordered logit regression results suggest restaurants in the southern U.S. are generally less likely to buy local proteins frequently, are less willing to pay for local proteins, and are less desirous of purchasing more local proteins in the future. This finding has some cultural roots, as Southerners tend to eat out for lunch more frequently than those in different parts of the country (Chamberlain, 2013), and survey results suggest that alcohol service (perhaps a proxy for dinner service) is associated with local protein purchases.

Another possible reason for this finding is regional household income. The western and northeastern United States have the highest median household income, followed by the Midwest, with the southern United States being the lowest (US Census Bureau, 2022). Southern diners, on average, may be less likely to pay higher entrée prices, as the regression results suggest. These results are supported by recent consumer studies finding that individuals with higher-than-average household incomes are more likely to consume local meats (Adu-Gyamfi et al., 2016; Curtis, 2014; Knight et al., 2006; Makweya & Oluwatayo, 2019; Sri Lestari et al., 2016; Stutzman, 2008; Tackie et al., 2018; Tait et al., 2018), which holds true for seafood consumers as well (Batzios et al., 2003; Cheng & Capps, 1988; Harper, 2015; Herrmann et al., 1994; House et al., 2003; Quagrainie, 2019; Van Houcke et al., 2018; Wessells & Anderson, 1995; Yen & Huang, 1996). So, it is likely that restaurants’ menu offerings are designed to cater to customer budgets.

4.2. Desired Attributes of Local Meat and Seafood

Restaurant protein buyers ranked all-natural, hormone-free, and organic as the top three traits desired in locally sourced meats. This finding is consistent with the literature review of consumer preference studies involving local meats: all-natural and hormone-free are usually at the top of the list (Grannis et al., 2000; Merritt et al., 2018; Picardy et al., 2020; Tait et al., 2018). Organic being ranked third is inconsistent with consumer studies; however, it suggests that restaurant procurers value organic certification more highly than individual consumers, although some consumer studies find that organic certification is not as appealing as simply being locally sourced (Loureiro & Hine, 2002; Naspetti & Bodini, 2008; Stanton et al., 2012; Yangui et al., 2019). Restaurants buying local seafood rank Marine Stewardship Council (MSC) certification and Aquaculture Stewardship Council (ASC) as first and third for desired attributes, respectively, with locally certified as the second most desired attribute. These findings suggest that obtaining certifications for meat and fishery products may be key to attaining restaurant menu placements. An explanation may be that the restaurant buyers need verification of how and where the local protein was produced, as they have limited interaction with the producer. Knowing that the local meat producer or fisherman ranked last for both protein groups suggests that restaurant procurers have little interaction with the producers, especially if they purchase local proteins through a food service distributor.

4.3. Barriers to Purchasing or Purchasing More Local Proteins

Several barriers we identified to restaurants purchasing or purchasing more local proteins were consistent with our literature review. These barriers include availability concerns, convenience issues, and distribution problems. Our survey findings also point to barriers of inconsistent quality, high local protein prices, and additional processing needs. Interestingly, “do not know where to source [local protein]” was one of the least cited barriers to purchasing local protein, inconsistent with some of the literature.

Responses citing inconsistent quality as a barrier are both surprising and concerning. Whether this perceived quality issue relates to how the animals are raised, harvested, or processed is unknown. Further research is needed to determine what quality attributes restaurant purchasers find lacking. However, if one were to speculate, this may involve aggregating proteins from multiple producers (with different methods for raising and harvesting) and the limited processing options described below.

High prices for local proteins are a barrier to restaurants, and this is mirrored in individual consumer studies. For instance, price is commonly cited as a reason consumers do not buy local foods and meats (Barska & Wojciechowska-Solis, 2020; Chambers et al., 2007; Eastwood, 1996; Gwin & Lev, 2011; Knight et al., 2006; McEachern et al., 2010; Megicks et al., 2012). Higher local protein prices translate into higher entrée costs that must be passed on to the consumer. Restaurants probably realize that their clientele has a price threshold for specific menu items, which may be why restaurants in the survey sourced a mix of local and non-local proteins (Figure 1 and Figure 2).

Respondents did mention that limited meat and seafood processing was a purchasing barrier. Further research is needed to discover what processing needs are desired. However, a reasonable assumption is that local meats and seafood must be processed to the same degree and quality as comparable non-local proteins sourced through food service distributors.

Local foods typically have few sales outlets and require a significant time commitment to source (McEachern et al., 2010; Megicks et al., 2012; Shi & Hodges, 2016). Our results suggest that local protein producers must make their products more convenient for restaurants. Respondents suggest they would buy more local proteins if the process were made more convenient through aggregators and distributors of local proteins. Other suggestions included adding local protein offerings to existing food hubs or having wholesale protein buying points in multiple locations.

4.4. Restaurants’ Willingness to Pay for Local Proteins

Restaurants likely to pay more for local proteins trend toward a more full-service model of dining, have higher entrée prices and fewer seats, purchase other local foods, cater to both tourists as well as residents, and are not located in the southern U.S. Generally, willingness to pay for local proteins is grouped around a 1% to 20% premium for both local meat and seafood. This finding is consistent with a 2010 restaurant consumer study that found consumers’ willingness to pay for local meats at restaurants was between 5% and 28% (Ortiz, 2010), and it is also consistent with individual consumer willingness to pay studies (Agabriel et al., 2014; Byrd et al., 2018; Curtis et al., 2012; Grannis et al., 2000; Makweya & Oluwatayo, 2019; Picardy et al., 2020; Sanders et al., 2007; Thilmany et al., 2003; Umberger et al., 2003).

A larger question may be whether restaurants would be willing to pay for local proteins if household income, unemployment rates, or the general U.S. economy further constrain consumer restaurant spending. For example, household spending on food away from home dropped during the last fiscal downturn (the “Great Recession” between 2007 and 2010). It took full-service restaurants eleven years to recover, the same amount of time it took unemployment to rebound to pre-recession levels (Cho et al., 2018). Perhaps even more detrimental to restaurants’ willingness to pay for local proteins may be the effects of inflation on food and labor costs, which are up 29% and 31%, respectively, since February 2020 (NRA, 2010).

5. Conclusions

To the best of our knowledge, this is the only national restaurant survey in the U.S. This study is an effort to cover a gap in the literature as well as provide local producers with a partial road map for marketing local animal proteins to restaurants. The implications of this study show that producers may want to target their marketing efforts on restaurants that have fewer seats and higher entrée prices, have been in business longer, and are in the western U.S. Additional restaurant characteristics of higher levels of service, serving of alcohol, and catering mostly to local residents may also be helpful for producers creating restaurant marketing plans.

Producers may also wish to provide some of the most requested protein types along with available certifications. Estimates of potential menu share indicate that chicken, fish, and beef are the top three proteins. Pork, shrimp, crab, and oysters were purchased between 19% and 42% less frequently in this survey (Figure 1 and Figure 2). Certification programs that highlight positive protein attributes associated with all-natural, hormone-free, organic, or sustainably sourced farm and fishery products appear to be highly valued by restaurants buying local proteins.

Restaurants are generally willing to pay a premium in the 1% to 20% range for most protein types. However, producers looking to supply restaurants with local proteins should pay attention to the needs of the restaurant procurer. Barriers to restaurants purchasing or purchasing more local proteins include inconsistent quality, high cost, limited availability, and further processing needs. Local producers marketing their products to restaurants must closely match their supply, quality, price, and format (processed form) to restaurants’ needs. This includes producers marketing their products through aggregators, distributors, and food hubs, with attention to quality and consistency.

6. Limitations and Future Research

The findings of this study are not all-inclusive for every restaurant in the United States. Although representing about two-thirds of the U.S. population (US Census Bureau, 2020), the sample only includes restaurant procurers in coastal states due to our desire to study local seafood purchases. Further research in non-coastal states on locally raised meats and inland aquacultural products would be beneficial to compare to this study. There appear to be regional opportunities for marketing additional local proteins in the U.S. Whether this is due to an abundance or lack of locally raised or harvested proteins or the willingness of restaurants to purchase and pay for these proteins is unknown.

Another limitation of this research is that desired quality characteristics and processing needs are not differentiated by protein type. In addition, since certification programs may differ depending on the protein, it is hard to predict which traits are most desired for local proteins overall. This study also focuses exclusively on non-chain restaurants and does not evaluate if any chain restaurant establishments would be likely buyers. This could also be a reason for a small number of survey respondents reporting not buying local proteins.

International application of this research may be limited, as this study focuses on the most highly consumed proteins in the U.S. and the desired traits and purchasing barriers associated with these proteins. In addition, restaurant industries in other countries may have different methods of purchasing ingredients, and many international studies often define “local” as originating in that country, regardless of the distance between production and consumption. However, much U.S. and international consumer research on local food cited in the literature review appears to share similar results, so if restaurant procurers outside the U.S. are buying food ingredients to cater to their clientele, there may be useful implications from this study. For a more in-depth literature review of U.S. and international consumer preferences for local proteins, please see the literature review section in USDA ERS (2024).

Future research could focus on the above-noted study limitations or expand this research to other away-from-home food-purchasing institutions, such as schools, hotels, entertainment venues, and military bases.