Sociodemographic Factors and Meat Alternative Purchase: A Longitudinal Study

Abstract

Background: Meat alternative products have gained attention in recent years. This study examined the factors associated with meat alternative purchase in Japan. Methods: This longitudinal study used data from nationwide surveys conducted in 2022 and 2023 with persons aged 15–79 years in Japan. In total, 11,476 participants were included in the analysis. The outcome indicators were changes in meat alternative purchase in 2022 and 2023, categorized into four groups: “no in both years,” “purchased only in 2022,” “purchased only in 2023,” and “purchased in both years.” A multinomial logistic regression model was used, with “no in both years” as the reference group. Potential factors included sex, age, household income, education, health check-up status, and population density in residential areas. Results: Factors that were significantly associated with meat alternative purchase in any year were being woman, being aged <65 years, not having a lower annual income (5–16 million yen), having a higher educational attainment, and having undergone/intended to undergo health check-up (p < 0.05). In particular, sex was significantly associated with “Purchased only in 2022,” “Purchased only in 2023,” and “Purchased in both years”; the multivariate-adjusted OR (95% CI) of “Purchased in both years” for women was 3.07 (2.16–4.34). Conclusion: This study suggests that sociodemographic characteristics may influence the consumption of meat alternatives, impacting the development of the meat alternative industry.

1. Introduction

The market for alternative proteins has experienced significant growth in recent years as a sustainable and reliable source of protein to replace traditional animal-based products [1]. Meat alternatives can be broadly categorized into five groups: plant-based meat, mycoprotein, insect-based food, cultured meat products, and microbial/fermented products. Among these, insect-based food and cultured meat, which are relatively unfamiliar to consumers, tend to face barriers in consumer acceptance owing to food neophobia [2]. These products align with the concept of “planetary health,” proposed in 2015 by The Rockefeller Foundation–Lancet Commission on planetary health [3], which advocates for sustainable diets that consider both human and environmental health. From the perspective of reducing environmental impact and promoting healthy diets, meat alternatives have recently gained attention as a new protein source. Concerns regarding meat consumption from the perspectives of environmental impact and animal welfare have led to an increase in the proportion of vegans and vegetarians [4]. To achieve international sustainability goals, the importance of transitioning from meat-based to plant-based diets has been emphasized, particularly in high-income countries, including Japan [5].

Previous studies have investigated the associations between sociodemographic characteristics and the perception or acceptance of meat alternative products. For example, a systematic review of studies conducted in Western countries reported that the acceptance of plant-based alternative products tend to be higher among women, younger people, those with higher educational attainment, and urban residents [6]. In contrast, men have been shown to be more receptive to insect-based and cultured meat products [6,7]. A study based on a survey of 350 individuals aged 18–30 years in Italy found that low health awareness was associated with reluctance to accept alternative proteins, while no significant associations were observed with gender, income, or residential area [8]. Similarly, a 2023 study involving 1536 Japanese participants aged 18–69 years found that individuals with more flexible values were more likely to accept soybean-based meat alternatives, particularly men, but no significant associations were identified with age, household income, educational level, or place of residence (urban vs. non-urban) [9]. Another study involving 2006 young adults (aged 18–25 years) in Japan and China perceived meat alternatives as viable substitutes for conventional meat and associated their consumption with potential health benefits [10]. These findings suggest that the factors associated with perceptions or acceptance of meat alternatives remain inconclusive across different populations and cultural contexts.

Regarding purchasing behavior, a study using consumer survey data from 38,966 U.S. households reported that purchasers of plant-based meat alternatives were more likely to be younger, single, female, college-educated, employed, high-income, and non-white individuals [11]. An analysis of survey data from 1000 Finnish adults aged 18–75 years showed that lower educational attainment and financial strain were associated with less frequent consumption of pulses and plant-based meat alternatives [12]. In Western countries, various factors associated with the consumption of meat alternative products have been examined, similarly to studies on consumer perceptions. However, to the best of our knowledge, no previous epidemiological study has examined the sociodemographic characteristics of consumers of meat alternative in Japan using actual purchase data.

Hence, this study examined the association between six potential factors of sociodemographic characteristics (sex, age, household income, education, population density of the residential areas, and health check-up status) and meat alternative purchase in Japan between 2022 and 2023.

2. Methods

2.1. Study Design

This longitudinal study was conducted using data from the Japan COVID-19 and Society Internet Survey (JACSIS) conducted at two time points (2022 and 2023). The JACSIS is a nationwide self-report survey based on men and women aged 15–79 years who are panel members of an Internet research company. The 2022 survey, which began on 12 September 2022, and was completed on 19 October 2022, comprised 32,000 participants, whereas the 2023 survey, which began on 25 September 2023, and was completed on 17 November 2023, comprised 33,000 participants.

2.2. Participants

Figure 1 shows a flowchart of the participants. The number of valid responses was 28,630 for the 2022 survey and 28,481 for the 2023 survey. Participants who answered, “prefer not to answer” or “don’t know” for household income, “other” or “don’t know” for educational attainment, or lacked data on meat alternative purchase or purchase intentions were excluded. In total, 11,476 participants who provided continuous responses in both 2022 and 2023 were included in the analysis.

2.3. Potential Associated Factors

Based on previous studies examining perceptions of meat alternatives [6,7,8,9,10,11,12], six variables were selected as potential associated factors: sex, age, household income, education level, population density of the residential areas, and health check-up status.

Age was categorized into four groups: “<22 years,” “22–39 years,” “40–64 years,” and “≥65 years.” The cutoff at 22 years was chosen to reflect the typical age of undergraduate students in the Japanese education system, as 83.8% of individuals aged 18 in Japan were enrolled in higher education institutions, such as universities, in 2022 [13]. Household annual income was categorized into four groups: “<5 million JPY,” “5–9.9 million JPY,” “10–15.9 million JPY,” and “≥16 million JPY.” For reference, on September 1, 2022, 1 US dollar was equivalent to approximately 140 Japanese yen; therefore, 5 million JPY corresponded to approximately 35,700 USD. Educational attainment was categorized into two groups: “Junior high school, high school, or vocational school graduate” and “Junior college, technical college, or university graduate and above.” Based on the densely inhabited districts (DID) information, “DID = 0” was classified as “non-concentration areas” and “DID > 0” was classified as “concentration areas.” Intention to undergo a health check-up was determined based on the participants’ responses to questions regarding the status of health check-up in the past year. Those who answered “had a health check-up, with no abnormal findings,” “had a health check-up, with some abnormal findings,” “had a health check-up, but the results are unknown (either not yet received or forgotten),” “did not undergo a health check-up due to reasons other than COVID-19, but plan to in the future,” or “did not undergo a health check-up due to COVID-19, but plan to in the future” were categorized as “Yes” (i.e., underwent health check-up/intend to undergo health check-up). Those who answered, “did not undergo a health check-up due to reasons other than COVID-19, and do not plan to in the future” or “did not undergo a health check-up due to COVID-19, and do not plan to in the future” were categorized as “No” (i.e., did not undergo health check-up/do not intend to undergo health check-up).

2.4. Meat Alternative Purchase/Purchase Intention

The objective variables were the binary variables of (1) meat alternative purchase in the past three months and (2) meat alternative purchase intention at each time point of the survey. Responses to the question “Have you purchased meat alternative (such as soy-based meat) in the past three months?” were categorized into four groups: “Not purchased” (i.e., not purchased in both years), “Purchased only in 2022,” “Purchased only in 2023,” and “Purchased in both years.” Similarly, meat alternative purchase intention was categorized into four groups: “Did not intend purchasing in both years” (i.e., no intention in both years), “Intended purchasing only in 2022,” “Intended purchasing only in 2023,” and “intended to purchase in both years.”

The broad definition of meat alternatives sometimes includes plant-based meat, cultured meat products, insect-based food, and so on. However, in Japan, “soy meat” is a more prevalent term than “alternative meat” and has been used for a longer period of time [9]. In addition, cultured meat is still not prevalent; according to survey data of 2023, only 18% of Japanese respondents were aware of how cultured meat is produced [10].

2.5. Other Factors

This study also examined different grocery purchasing patterns according to groups of objective variables (i.e., meat alternative purchase groups). Regarding whether “perishable foods (fresh foods/seafoods)” and “non-perishable foods” were purchased on e-commerce websites, responses options were “purchased in the past three months” and “not purchased in the past three months.” For purchase intention, responses were collected with the options “intention to purchase” and “no intention to purchase.”

Additionally, respondents were asked about their usual frequency of using high-end supermarkets and e-commerce websites (both platform-based and direct-to-consumer). The response options were as follows: “almost every day (≥4 days a week),” “1–3 times a week,” “1–3 times a month,” “1–2 times every 3 months,” “approximately once every 6 months or less,” and “do not use this store/service.” These response options were grouped as follows: “almost every day (≥4 days a week)” and “1–3 times a week” were classified as “Frequent use,” “1–3 times a month” and “1–2 times every 3 months” were classified as “Occasional use,” and “approximately once every 6 months or less” and “do not use this store/service” were classified as “Rarely use.”

2.6. Ethical Considerations

The study was reviewed and approved by the Research Ethics Committee of the Osaka International Cancer Institute (no. 20084-2).

2.7. Statistical Analysis

A multinomial logistic regression analysis was conducted using meat alternative purchase and purchase intention as objective variables, and odds ratios (ORs) and 95% confidence intervals (95% CI) were calculated. The reference category for meat alternative purchase was “Not purchased,” and for purchase intention, it was “Did not intend purchasing in both years” (no intention in both years). Two models were used for the analysis: a sex- and age-adjusted model (Model 1) and a multivariate-adjusted model (Model 2; the model with simultaneous inclusion of all potential associated factors). Statistical analyses were performed using R version 4.0.3. Statistical significance was set at p <0.05.

3. Results

3.1. Basic Characteristics

Of the 11,476 participants included in the analysis, 52.8% were men and 47.2% were women. Participants’ average age was 51.5 ± 16.1 years. Regarding meat alternative purchase, 94.1% did not report any purchase, 2.6% reported purchase only in 2022, 1.9% reported purchase only in 2023, and 1.4% reported purchase in both the years, with the largest group being the one that did not purchase meat alternatives. Regarding purchase intention, 89.2% did not show purchase intention in both years, 4.2% indicated purchase intention only in 2022, 3.2% indicated purchase intention only in 2023, and 3.4% indicated purchase intention in both the years, with the largest group showing no purchase intention in both the years. Overall, the percentage of meat alternative purchasers as well as those with the intention to purchase decreased in 2023 compared with 2022.

Table 1. Descriptive statistics of potential factors according to meat alternative purchase groups.

		No Purchase		Purchase Only in 2022		Purchase Only in 2023		Purchase in Both Years
		n	%	n	%	n	%	n	%
n		10,802		296		219		159
Sex
	Men	5813	53.8	118	39.9	83	37.9	46	28.9
	Women	4989	46.2	178	60.1	136	62.1	113	71.1
Age (years)
	<22	142	1.3	1	0.3	6	2.7	5	3.1
	22–39	2929	27.1	93	31.4	82	37.4	48	30.2
	49–64	4698	43.5	123	41.6	82	37.4	70	44.0
	≥65	3033	28.1	79	26.7	49	22.4	36	22.6
Household income (Million JPY) 1
	<5	5383	49.8	136	45.9	99	45.2	60	37.7
	5–9.9	4135	38.3	120	40.5	89	40.6	67	42.1
	10–15.9	1005	9.3	29	9.8	27	12.3	27	17.0
	≥16	279	2.6	11	3.7	4	1.8	5	3.1
Educational attainment 2
	Lower	4019	37.2	80	27.0	70	32.0	48	30.2
	Higher	6783	62.8	216	73.0	149	68.0	111	69.8
Residence place 3
	Non-concentration areas	2861	26.5	64	21.6	53	24.2	36	22.6
	Concentration areas	7941	73.5	232	78.4	166	75.8	123	77.4
Health check-up 4
	Yes	9249	85.6	256	86.5	199	90.9	145	91.2
	No	1553	14.4	40	13.5	20	9.1	14	8.8

¹ On 1 September 2022, 1 US dollar was worth 140 Japanese yen (JPY). Five million JPY was approximately 35,700 US dollars. ² Junior high school, high school, or vocational school graduate were classified as “lower,” and junior college, technical college, or university or more were classified as “higher” educational attainment. ³ Based on the densely inhabited districts (DID) information, “DID = 0” was classified as non-concentration areas and “DID > 0” was classified as concentration areas. ⁴ The respondents were categorized as “Yes” for “have had” or “intend to have” health check-up and “No” for “have not had” or “do not intend to have” health check-up.

presents the descriptive statistics of the potential factors for meat alternative purchase. The groups that indicated purchase in both 2022 and 2023 had a higher proportion of women, were aged <65 years (<22, 22–39, 49–64 years), had a higher household income (5–9.9, 10–15.9, ≥16 million JPY), had higher educational attainment, were residing in population concentration areas, and had or intended to undergo a health check-up, in comparison to those who did not purchase meat alternatives.

The descriptive statistics of the potential factors for intention to purchase meat alternatives also showed a similar trend (

Table 2. Descriptive statistics of potential factors according to meat alternative purchase intention groups.

		Meat Alternative Purchase Intention
		Did Not Intend Purchasing in Both Years		Intended Purchasing Only in 2022		Intended Purchasing Only in 2023		Intended Purchasing in Both Years
		n	%	n	%	n	%	n	%
N		10,242		486		362		386
Sex
	Men	5617	54.8	202	41.6	143	39.5	98	25.4
	Women	4625	45.2	284	58.4	219	60.5	288	74.6
Age (years)
	<22	132	1.3	11	2.3	5	1.4	6	1.6
	22–39	2744	26.8	153	31.5	140	38.7	115	29.8
	49–64	4458	43.5	196	40.3	140	38.7	179	46.4
	≥65	2908	28.4	126	25.9	77	21.3	86	22.3
Household income (Million JPY) 1
	<5	5129	50.1	214	44.0	173	47.8	162	42.0
	5–9.9	3915	38.2	196	40.3	147	40.6	153	39.6
	10–15.9	935	9.1	58	11.9	34	9.4	61	15.8
	≥16	263	2.6	18	3.7	8	2.2	10	2.6
Educational attainment 2
	Lower	3824	37.3	159	32.7	120	33.1	114	29.5
	Higher	6418	62.7	327	67.3	242	66.9	272	70.5
Residence place 3
	Non-concentration areas	2730	26.7	109	22.4	86	23.8	89	23.1
	Concentration areas	7512	73.3	377	77.6	276	76.2	297	76.9
Health check-up 4
	Yes	8751	85.4	428	88.1	313	86.5	357	92.5
	No	1491	14.6	58	11.9	49	13.5	29	7.5

¹ On 1 September 2022, 1 US dollar was worth 140 Japanese yen (JPY). Five million JPY was approximately 35,700 US dollars. ² Junior high school, high school, or vocational school graduate were classified as “lower,” and Junior college, technical college, or university or more were classified as “higher” education attainment. ³ Based on the densely inhabited districts (DID) information, “DID = 0” was classified as non-concentration areas and “DID > 0” was classified as concentration areas. ⁴ The respondents were categorized as “Yes” for “have had” or “intend to have” health check-up and “No” for “have not had” or “do not intend to have” health check-up.

).

Table 3. Descriptive statistics of food purchasing channels according to meat alternative purchase groups.

				Purchase of Meat Alternative
		Total		No Purchase		Purchase Only in 2022		Purchase Only in 2023		Purchase in Both Years
		n	%	n	%	n	%	n	%	n	%
n		11,476		10,802		296		219		159
Purchased perishable foods (fresh foods/seafoods) on e-commerce websites in the past three months
	Yes	1249	10.9	1085	10.0	59	19.9	52	23.7	53	33.3
	No	10,227	89.1	9717	90.0	237	80.1	167	76.3	106	66.7
Purchased non-perishable foods on e-commerce websites in the past three months
	Yes	2970	25.9	2673	24.7	118	39.9	82	37.4	97	61.0
	No	8506	74.1	8129	75.3	178	60.1	137	62.6	62	39.0
Intention to purchase perishable foods (fresh foods/seafoods) on e-commerce websites
	Yes	1519	13.2	1330	12.3	69	23.3	52	23.7	68	42.8
	No	9957	86.8	9472	87.7	227	76.7	167	76.3	91	57.2
Intention to purchase non-perishable foods on e-commerce websites
	Yes	3230	28.1	2900	26.8	143	48.3	91	41.6	96	60.4
	No	8246	71.9	7902	73.2	153	51.7	128	58.4	63	39.6
Frequency of use for high-end supermarkets
	Frequent use 1	260	2.3	204	1.9	22	7.4	15	6.8	19	11.9
	Occasional use 2	1966	17.1	1727	16.0	108	36.5	65	29.7	66	41.5
	Rarely use 3	9250	80.6	8871	82.1	166	56.1	139	63.5	74	46.5
e-commerce websites (platform-based)
	Frequent use 1	2179	19.0	1980	18.3	88	29.7	49	22.4	62	39.0
	Occasional use 2	7652	66.7	7237	67.0	188	63.5	139	63.5	88	55.3
	Rarely use 3	1645	14.3	1585	14.7	20	6.8	31	14.2	9	5.7
e-commerce websites (direct-to-consumer)
	Frequent use 1	328	2.9	272	2.5	23	7.8	16	7.3	17	10.7
	Occasional use 2	2296	20.0	2052	19.0	104	35.1	71	32.4	69	43.4
	Rarely use 3	8852	77.1	8478	78.5	169	57.1	132	60.3	73	45.9

¹ Includes respondents who answered “almost every day (≥4 days a week)” or “1–3 times a week”. ² Includes respondents who answered “1–3 times a month” or “1–2 times every 3 months”. ³ Includes respondents who answered “approximately once every 6 months or less” or “do not use this store/service”.

shows the descriptive statistics of food purchasing channels according to meat alternative purchase groups. Regarding the purchase of perishable foods on e-commerce websites in the past three months, 33.3% of the “Purchased in both years” group responded “yes,” compared to 10.0% of the “No purchase” group. Regarding the purchase of non-perishable foods on e-commerce websites in the past three months, 61.0% of the “Purchased in both years” group responded “yes,” compared to 24.7% of the “No purchase” group.

The proportion of frequent visitors of high-end supermarkets, e-commerce platforms (platform-based), and e-commerce websites (direct-to-consumer) were higher in the “Purchased in both years” group, compared to the “No purchase” group.

3.2. Main Results: Meat Alternative Purchase

Table 4 shows the ORs (95% CIs) for the association between each potential associated factor and meat alternative purchase. The ORs for “Purchased only in 2022” were significantly higher among women and those with higher educational attainment (both in Model 1 and 2); the multivariate-adjusted ORs (95% CIs) were 1.86 (1.47, 2.36) for women and was 1.61 (1.24, 2.11) for higher educational attainment. In the “Purchased only in 2023” group, ORs were significantly higher for women, and were significantly lower for those who did not have or intend to have a check-up and those aged ≥65 years in Model 1. Similarly, in Model 2 (the multivariate-adjusted model), the OR for women was significantly higher, while the OR for those aged ≥65 years (reference: aged <22) was significantly lower; the multivariate-adjusted ORs (95% CIs) were 1.95 (1.48, 2.58) for women and 0.42 (0.17, 0.99) for those aged ≥65 years. In the “Purchased in both years” group, ORs for women and those with a household income between 5–9.9 million JPY and 10–15.9 million JPY were significantly higher (both in Model 1 and 2); the multivariate-adjusted ORs (95% CIs) were 3.07 (2.16–4.34) for women, 1.47 (1.02, 2.11) for those with a household income of 5–9.9 million JPY, and 2.35 (1.45, 3.80) for those with a household income of 10–15.9 million JPY.

3.3. Purchase Intention

Table 5 shows the ORs (95% CIs) for the association between each potential associated factor and meat alternative purchase intention. Similar to the results for meat alternative purchase (Table 4), the ORs for women were significantly higher in all three categories of meat alternative purchase intention, “Intended purchasing only in 2022,” “Intended purchasing only in 2023,” and “intended to purchase in both years.”

Table 4. Association between potential factors and meat alternative purchase.

		No Purchase	Purchase Only in 2022		Purchase Only in 2023		Purchase in Both Years
			Model 1 1	Model 2 2	Model 1 1	Model 2 2	Model 1 1	Model 2 2
Sex
	Men	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Women		1.77 (1.40, 2.24)	1.86 (1.47, 2.36)	1.89 (1.44, 2.50)	1.95 (1.48, 2.58)	2.85 (2.02, 4.02)	3.07 (2.16, 4.34)
Age (years)
	<22	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	22–39		4.88 (0.68, 35.3)	4.59 (0.63, 33.2)	0.72 (0.31, 1.69)	0.69 (0.30, 1.62)	0.53 (0.21, 1.36)	0.51 (0.20, 1.31)
	49–64		4.06 (0.56, 29.3)	3.98 (0.55, 28.8)	0.46 (0.20, 1.07)	0.43 (0.19, 1.01)	0.49 (0.20, 1.25)	0.47 (0.18, 1.18)
	≥65		3.98 (0.55, 28.8)	4.18 (0.58, 30.4)	0.42 (0.17, 0.99)	0.42 (0.17, 0.99)	0.38 (0.15, 0.99)	0.44 (0.17, 1.14)
Household income (Million JPY)
	<5	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	5–9.9		1.19 (0.92, 1.54)	1.13 (0.87, 1.46)	1.16 (0.86, 1.56)	1.11 (0.82, 1.50)	1.54 (1.07, 2.21)	1.47 (1.02, 2.11)
	10–15.9		1.23 (0.81, 1.86)	1.08 (0.71, 1.65)	1.51 (0.97, 2.34)	1.37 (0.88, 2.14)	2.62 (1.63, 4.21)	2.35 (1.45, 3.80)
	≥16		1.70 (0.90, 3.19)	1.48 (0.78, 2.79)	0.84 (0.31, 2.31)	0.78 (0.28, 2.15)	1.81 (0.72, 4.59)	1.65 (0.65, 4.21)
Educational attainment
	Lower	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Higher		1.68 (1.29, 2.18)	1.61 (1.24, 2.11)	1.27 (0.95, 1.70)	1.21 (0.90, 1.63)	1.48 (1.05, 2.09)	1.31 (0.92, 1.87)
Place of residence
	Non-concentration areas	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Concentration areas		1.29 (0.98, 1.71)	1.23 (0.93, 1.64)	1.11 (0.81, 1.51)	1.08 (0.79, 1.48)	1.21 (0.83, 1.76)	1.15 (0.79, 1.67)
Health check-up
	Yes	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	No		0.91 (0.65, 1.27)	0.95 (0.67, 1.33)	0.55 (0.35, 0.88)	0.57 (0.36, 0.91)	0.54 (0.31, 0.94)	0.59 (0.34, 1.03)

¹ Odds ratios (95% confidence intervals) from the multinomial logistic regression model adjusted for sex and age (adjusted only for age in analyses with sex as explanatory variable; adjusted only for sex in analyses with age as explanatory variable). ² Odds ratios (95% confidence intervals) from the multinomial logistic regression model with all explanatory variables in the table simultaneously included as covariates.

Table 4. Association between potential factors and meat alternative purchase.

		No Purchase	Purchase Only in 2022		Purchase Only in 2023		Purchase in Both Years
			Model 1 1	Model 2 2	Model 1 1	Model 2 2	Model 1 1	Model 2 2
Sex
	Men	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Women		1.77 (1.40, 2.24)	1.86 (1.47, 2.36)	1.89 (1.44, 2.50)	1.95 (1.48, 2.58)	2.85 (2.02, 4.02)	3.07 (2.16, 4.34)
Age (years)
	<22	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	22–39		4.88 (0.68, 35.3)	4.59 (0.63, 33.2)	0.72 (0.31, 1.69)	0.69 (0.30, 1.62)	0.53 (0.21, 1.36)	0.51 (0.20, 1.31)
	49–64		4.06 (0.56, 29.3)	3.98 (0.55, 28.8)	0.46 (0.20, 1.07)	0.43 (0.19, 1.01)	0.49 (0.20, 1.25)	0.47 (0.18, 1.18)
	≥65		3.98 (0.55, 28.8)	4.18 (0.58, 30.4)	0.42 (0.17, 0.99)	0.42 (0.17, 0.99)	0.38 (0.15, 0.99)	0.44 (0.17, 1.14)
Household income (Million JPY)
	<5	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	5–9.9		1.19 (0.92, 1.54)	1.13 (0.87, 1.46)	1.16 (0.86, 1.56)	1.11 (0.82, 1.50)	1.54 (1.07, 2.21)	1.47 (1.02, 2.11)
	10–15.9		1.23 (0.81, 1.86)	1.08 (0.71, 1.65)	1.51 (0.97, 2.34)	1.37 (0.88, 2.14)	2.62 (1.63, 4.21)	2.35 (1.45, 3.80)
	≥16		1.70 (0.90, 3.19)	1.48 (0.78, 2.79)	0.84 (0.31, 2.31)	0.78 (0.28, 2.15)	1.81 (0.72, 4.59)	1.65 (0.65, 4.21)
Educational attainment
	Lower	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Higher		1.68 (1.29, 2.18)	1.61 (1.24, 2.11)	1.27 (0.95, 1.70)	1.21 (0.90, 1.63)	1.48 (1.05, 2.09)	1.31 (0.92, 1.87)
Place of residence
	Non-concentration areas	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Concentration areas		1.29 (0.98, 1.71)	1.23 (0.93, 1.64)	1.11 (0.81, 1.51)	1.08 (0.79, 1.48)	1.21 (0.83, 1.76)	1.15 (0.79, 1.67)
Health check-up
	Yes	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	No		0.91 (0.65, 1.27)	0.95 (0.67, 1.33)	0.55 (0.35, 0.88)	0.57 (0.36, 0.91)	0.54 (0.31, 0.94)	0.59 (0.34, 1.03)

¹ Odds ratios (95% confidence intervals) from the multinomial logistic regression model adjusted for sex and age (adjusted only for age in analyses with sex as explanatory variable; adjusted only for sex in analyses with age as explanatory variable). ² Odds ratios (95% confidence intervals) from the multinomial logistic regression model with all explanatory variables in the table simultaneously included as covariates.

Table 5. Association between potential factors and meat alternative purchase intention.

		Did Not Intend Purchasing in Both Years	Intended Purchasing Only in 2022		Intended Purchasing Only in 2023		Intended Purchasing in Both Years
			Model 1 1	Model 2 2	Model 1 1	Model 2 2	Model 1 1	Model 2 2
Sex
	Men	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Women		1.70 (1.41, 2.04)	1.77 (1.47, 2.13)	1.86 (1.50, 2.31)	1.89 (1.52, 2.35)	3.59 (2.85, 4.53)	3.85 (3.04, 4.87)
Age (years)
	<22	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	22–39		0.72 (0.38, 1.37)	0.70 (0.37, 1.33)	1.47 (0.59, 3.66)	1.43 (0.57, 3.55)	1.09 (0.47, 2.52)	1.03 (0.44, 2.40)
	49–64		0.57 (0.31, 1.08)	0.55 (0.29, 1.05)	0.92 (0.37, 2.27)	0.90 (0.36, 2.24)	1.06 (0.46, 2.45)	1.00 (0.43, 2.31)
	≥65		0.56 (0.29, 1.06)	0.59 (0.31, 1.12)	0.76 (0.30, 1.90)	0.75 (0.30, 1.90)	0.75 (0.32, 1.76)	0.81 (0.34, 1.91)
Household income (Million JPY)
	<5	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	5–9.9		1.24 (1.01, 1.52)	1.20 (0.98, 1.48)	1.07 (0.85, 1.35)	1.04 (0.83, 1.32)	1.30 (1.03, 1.64)	1.22 (0.97, 1.55)
	10–15.9		1.59 (1.17, 2.16)	1.48 (1.08, 2.02)	1.08 (0.74, 1.59)	1.02 (0.69, 1.50)	2.26 (1.65, 3.09)	1.96 (1.43, 2.70)
	≥16		1.80 (1.09, 2.97)	1.68 (1.01, 2.78)	0.94 (0.46, 1.94)	0.89 (0.43, 1.83)	1.36 (0.70, 2.62)	1.21 (0.62, 2.34)
Educational attainment
	Lower	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Higher		1.27 (1.04, 1.54)	1.18 (0.97, 1.44)	1.20 (0.96, 1.50)	1.18 (0.94, 1.48)	1.57 (1.25, 1.96)	1.42 (1.13, 1.79)
Place of residence
	Non-concentration areas	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Concentration areas		1.24 (0.998, 1.54)	1.21 (0.97, 1.50)	1.14 (0.89, 1.46)	1.13 (0.88, 1.45)	1.19 (0.94, 1.52)	1.13 (0.89, 1.45)
Health check-up
	Yes	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	No		0.76 (0.57, 1.00)	0.79 (0.60, 1.05)	0.85 (0.62, 1.15)	0.86 (0.63, 1.17)	0.45 (0.31, 0.66)	0.48 (0.33, 0.71)

¹ Odds ratios (95% confidence intervals) from the multinomial logistic regression model adjusted for sex and age (adjusted only age for sex, only sex for age). ² Odds ratios (95% confidence intervals) from the multinomial logistic regression model with all explanatory variables in the table simultaneously included as covariates.

Table 5. Association between potential factors and meat alternative purchase intention.

		Did Not Intend Purchasing in Both Years	Intended Purchasing Only in 2022		Intended Purchasing Only in 2023		Intended Purchasing in Both Years
			Model 1 1	Model 2 2	Model 1 1	Model 2 2	Model 1 1	Model 2 2
Sex
	Men	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Women		1.70 (1.41, 2.04)	1.77 (1.47, 2.13)	1.86 (1.50, 2.31)	1.89 (1.52, 2.35)	3.59 (2.85, 4.53)	3.85 (3.04, 4.87)
Age (years)
	<22	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	22–39		0.72 (0.38, 1.37)	0.70 (0.37, 1.33)	1.47 (0.59, 3.66)	1.43 (0.57, 3.55)	1.09 (0.47, 2.52)	1.03 (0.44, 2.40)
	49–64		0.57 (0.31, 1.08)	0.55 (0.29, 1.05)	0.92 (0.37, 2.27)	0.90 (0.36, 2.24)	1.06 (0.46, 2.45)	1.00 (0.43, 2.31)
	≥65		0.56 (0.29, 1.06)	0.59 (0.31, 1.12)	0.76 (0.30, 1.90)	0.75 (0.30, 1.90)	0.75 (0.32, 1.76)	0.81 (0.34, 1.91)
Household income (Million JPY)
	<5	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	5–9.9		1.24 (1.01, 1.52)	1.20 (0.98, 1.48)	1.07 (0.85, 1.35)	1.04 (0.83, 1.32)	1.30 (1.03, 1.64)	1.22 (0.97, 1.55)
	10–15.9		1.59 (1.17, 2.16)	1.48 (1.08, 2.02)	1.08 (0.74, 1.59)	1.02 (0.69, 1.50)	2.26 (1.65, 3.09)	1.96 (1.43, 2.70)
	≥16		1.80 (1.09, 2.97)	1.68 (1.01, 2.78)	0.94 (0.46, 1.94)	0.89 (0.43, 1.83)	1.36 (0.70, 2.62)	1.21 (0.62, 2.34)
Educational attainment
	Lower	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Higher		1.27 (1.04, 1.54)	1.18 (0.97, 1.44)	1.20 (0.96, 1.50)	1.18 (0.94, 1.48)	1.57 (1.25, 1.96)	1.42 (1.13, 1.79)
Place of residence
	Non-concentration areas	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	Concentration areas		1.24 (0.998, 1.54)	1.21 (0.97, 1.50)	1.14 (0.89, 1.46)	1.13 (0.88, 1.45)	1.19 (0.94, 1.52)	1.13 (0.89, 1.45)
Health check-up
	Yes	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	No		0.76 (0.57, 1.00)	0.79 (0.60, 1.05)	0.85 (0.62, 1.15)	0.86 (0.63, 1.17)	0.45 (0.31, 0.66)	0.48 (0.33, 0.71)

¹ Odds ratios (95% confidence intervals) from the multinomial logistic regression model adjusted for sex and age (adjusted only age for sex, only sex for age). ² Odds ratios (95% confidence intervals) from the multinomial logistic regression model with all explanatory variables in the table simultaneously included as covariates.

4. Discussion

This study examined the associations between six potential factors influencing meat alternative purchase and meat alternative purchase in Japan. The results indicated that factors that were significantly associated with meat alternative purchase in any year were being woman, being aged <65 years, not having a lower annual income (5–16 million yen), having a higher educational attainment, and having undergone/intended to undergo health check-up (p < 0.05). In particular, sex was significantly associated with “Purchased only in 2022,” “Purchased only in 2023,” and “Purchased in both years”. Point estimates of the ORs for women were higher for “Purchased in both years” than for “Purchased only in 2022” or “Purchased only in 2023,” which may indicate that compared to men, women are more likely to continue purchasing meat alternatives. In addition, being a woman was a common factor associated with both purchase and purchase intention. In addition, higher income was also associated with meat alternative purchase, which aligns with findings reported in studies conducted in Western countries [11,12].

The findings of this study are consistent with those of previous studies that have reported meat alternative products (particularly plant-based meat alternative) to generally have a higher acceptance among women, younger people, and those with higher educational attainment [2].

On the other hand, in the present study, no significant association was observed between population density and residential areas. Additionally, the higher proportion of e-commerce site use among the meat alternative purchase group may indicate that meat alternative may have a broad appeal and may not be limited by regional characteristics within the Japanese context.

Percentages of the purchase of perishable or non-perishable foods on e-commerce websites were higher in the “Purchased in both years” group than the “No purchase” group. To our knowledge, previous studies have not specifically examined meat alternative purchases through online platforms. Direct-to-consumer e-commerce websites can communicate product information and value directly to consumers on their websites. Since environmental considerations are emphasized as characteristics of consumer acceptance of meat alternative products [14], the ability to present environmental considerations through e-commerce websites may be more beneficial in encouraging the purchase of meat alternatives. In Japan, the retail e-commerce market grew at an average annual rate of 7.3% from 2017 to 2022. However, total sales in 2022 amounted to 122,434.1 million USD, which still falls short of the offline retail sales of 701,861.8 million USD [15]. Therefore, the expansion and increased convenience of direct-to-consumer services are expected to contribute to the future widespread adoption of meat alternatives.

However, previous studies have shown that price improvements are required to encourage consumers to consume meat alternative products [16]. As household income was also significantly associated with meat alternative purchase in the present study, meat alternative may be less accessible to those with lower incomes because of its price.

This study has some limitations. First, the definition of “meat alternative” in the survey questions was not detailed. The survey labeled it as “meat alternative (such as soy-based meat),” but clarity regarding specific products respondents may have had in mind when answering was lacking. For example, common alternative dishes such as tofu hamburgers were excluded; however, some respondents might have included them in their responses. Additionally, different types of alternative protein products, such as cultured meat and insect-based foods, are often discussed in contexts similar to meat alternative; however, the findings of this study cannot be generalized to these products. Second, this study used longitudinal data from 2022 and from 2023 to capture short-term trends. The global market size for plant-based meat was valued at 7.17 billion USD in 2023, and is expected to grow at an average annual growth rate of 19.4% from 2024 to 2030 [17]. To analyze the expansion of the meat alternative market and the changes in consumer behavior more comprehensively, a long-term longitudinal study spanning 10 years is required.

5. Conclusions

This study suggests that factors such as sex and income are associated with meat alternative purchase in Japan. Sociodemographic factors influence consumers’ meat alternative purchase in Japan, which is similar to findings in other countries. These results suggest that sociodemographic characteristics may influence the consumption of meat alternatives, impacting the development of the meat alternative industry.

In addition, this study highlights a potential association between the use of e-commerce websites and the purchase of meat alternative products. Further research considering purchasing channels, particularly online platforms, may provide valuable insights for the effective promotion and dissemination of meat alternatives and other novel food products.