Persuading Older Adults to Eat Less Meat
Appraising the Potential Dietary Supplements Market for the Asian Aging Population
Borui Fang (30328934), Tain Foung (31396445)
26-OCT-2021
getwd()## [1] "C:/Users/User/Desktop/ETM5950 Groupwork/5950 Rmd Template (Student)"#install.packages("tidyverse") #After you installed, then hide it
#install.packages("readxl")
#install.packages("janitor")
#install.packages("hablar")
#install.packages("ggplot2")
#install.packages("gtsummary")
#install.packages("modelsummary")
# Run libraries (or run in the respective code chunks)library(tidyverse) # to tidy and wrangle data
library(readxl) # to read Excel data
library(janitor) # to clean variable names
library(hablar) # to convert data type
library(ggplot2) # to plot visualization
library(gtsummary)
library(modelsummary)1 Introduction
1.1 Background
Lack of intake of nutrition and micronutrient affect the health situation (Rautiainen, Manson, Lichtenstein, & Sesso, 2016). People can improve the food quality to increase daily nutrient intake (Rautiainen et al., 2016), such as consuming more meat, fruits, and vegetables to diversify their diets. Alternatively, adopting dietary supplements can be another option. However, consuming meat to get daily nutrition increases the risk of developing obesity, diabetes, heart disease, and other serious illnesses (Campbell, 2021). Compared to improving food quality, dietary supplements provide the vital substance to ensure the body gets enough support, such as vitamins, minerals, herbs, etc. (FDA, 2017), and more targeted reduction of the individuals at risk of undernutrition (Rautiainen et al., 2016). For example, the older generations above 65 years old can intentionally select the dietary supplements with calcium and vitamin because of the concern in bone health (Rautiainen et al., 2016). Another example is that pregnant women and reproductive age can adopt folic acid dietary supplements to sustain their intake more efficiently (Rautiainen et al., 2016).
The global dietary supplements market will increase its value to USD 252.1 Billion by 2025 (Dietary Supplements Market, 2021). In particular, the Asia Pacific region accounted for the majority market share, which was 31.01% (Market Analysis Report, 2020) and almost USD 353 billion in 2019 (Lordan, 2021). The promising Asian dietary supplements markets were due to increased disposable income, shifting preference into moderate healthy nutrition (Market Analysis Report, 2020). Because more and more people accept dietary supplements, the markets are competitive and tend to saturate, where around 1,000 new nutritional supplements are introduced to the public annually (Fragakis, 2008). However, these markets’ highest dietary supplements usage age group is from 35-44, around 81% (Ridley, 2019). For those older generations above 65 years old, only 38.8% of them adopt the dietary supplements (Lee, Son, & Short, 2017), which means there is still a vast potential market in the dietary supplements for older adults.
1.2 Research Motivation
The GDP per Asian has increased by 82.46% from 2000 to 2018 (GDP per Capita, 2020). The increased disposable income leads to positive consequences such as higher living standards, better education, reduced poverty, and longer life expectancy (Pettinger, 2019). However, it also increased carbon dioxide emissions (Mardani, Streimikiene, Cavallaro, Loganathan, & Khoshnoudi, 2019), which is a negative effect. The dramatic increase is mainly due to escalating consumption in burning fossil energy, deforestation, and meat production (Sanglimsuwan, 2011). From the existing literature, people are encouraged to use nuclear and renewable technologies to reduce carbon dioxide emissions (Ritcie & Roser, 2020) and take a few actions toward reducing meat consumption. Thus, this paper will demonstrate the unfriendly environment of consuming meat and persuading older adults to adopt alternative dietary supplements to match their protein and nutrition supply.
The younger and middle-aged dietary supplements markets are fierce competition (Ridley, 2019), a challenge to enter this market, yet only 38.8% of older adults above 65 years old have adopted the dietary supplements (Lee et al., 2017). Thus, there is a massive gap in the dietary supplements market for older generations. Furthermore, the statistic shows that the majority of Asian countries are facing the aging trend. The aging population across 36 Asian countries increased by 1.812% between 2000 and 2018 (Population ages 65 and above, 2019). The aging trend is more advanced in developed countries (Menon & Melendez, 2009), such as Japan increased by 10.59%, 7.23% in South Korea, and Hong Kong increased by 5.86% (Population ages 65 and above, 2019). And many developing countries in Asia are on the same demographic path (Menon & Melendez, 2009). In other words, East Asia is transitioning to an aging population country, and the rest of Asia will adjust its demographic structure at a different pace (Chomik & Piggott, 2015).
Figure 1. Aging growth rate (%) from 2000 to 2018
Additionally, Sustainable Develop Goals (SDG) four and five have actively promoted gender and educational equality to promote women’s rights in society. As a result, women obtained empowerment as a benefit of achieving these SDG goals by 2030 (“THE 17 GOALS,” 2016). Kim (2021) stated that women’s educational opportunity negatively correlates with the fertility rate because they can improve outstanding performance in the workplace to achieve their dream instead of raising children as their whole life. Therefore, the decreased fertility rate will hugely increase the percentage of older adults in the future.
The reason dietary supplements companies cannot exclude older adults as their target customers is that the purchasing power of older adults is predicted to spend $15 trillion every year by 2020 (Arensberg, 2018). Besides, the senior customers are more loyal than the young customers once they select one brand. Moreover, these older adults are also more concerning their health and desire to contribute to society from the socioemotional selectivity theory (Carstensen, 2006). Therefore, educating senior customers about the negative effect of overeating meat (Yip, Crane, & Karnon, 2013) to persuade the aging population to adopt dietary supplements as the alternative daily protein, energy, vitamin intake. Thus, this is meaningful research towards the potential dietary supplements market for the aging population in Asia.
1.3 Research Objective
The main objective is to appraise the potential dietary supplements market for the aging population in Asia by persuading older adults to eat less meat. To achieve this primary objective, it will demonstrate the effect of consuming meat on carbon dioxide emissions. In addition, it will examine the relationship between women’s empowerment and the fertility rate.
1.4 Research Questions
In this research, we come up with the following research questions.
Does Women Empowerment in the workforce decrease the fertility rate and increase the aging population?
Will meat consumption increase when consumers have higher purchasing power?
Does meat consumption positive correlation with carbon dioxide emission?
Firstly, we will examine the impact of women empowerment who live and work independently on the fertility rate and percentage of aging in Asia. Secondly, we will further evaluate the correlation between the GDP per capita and meat consumption to illustrate the increasing purchase power. Lastly, we will study the correlation on meat consumption and carbon dioxide emission in hope that its positive results will support our research objective to persuade the seniors to accept dietary supplements as an alternative nutrition intake supply.
Figure 2. The logic diagram of the report
2 Data and Methodology
2.1 Data
To achieve the main research objective and answer three research questions. The hypothsis is stated as below:
Hypothesis 1a: The women empowerment in the workforce will be negatively related to the fertility rate.
Hypothesis 1b: The women empowerment in the workforce will be positively related to the percentage of the aging population.
Hypothesis 2: The GDP per capita will be positively related to the meat consumption per capita.
Hypothesis 3: The meat consumption will be positively related to carbon dioxide emission per capita.
| Types of Variable | Terms | Explanation |
|---|---|---|
| Independent Variable | Women empowerment | To examine whether women have job will reduce the willingness to birth baby |
| Independent Variable | GDP per capita | Using GDP to represent the economic development |
| Independent/Dependent Variable | Meat Consumption | To examine whether the purchasing power increased during the recent two decades |
| Dependent Variable | Fertility rate | To test whether the low fertility rate will lead to the increased number of aging population |
| Dependent Variable | Percentage of aging population | To evaluate the potential “target customer” in our dietary supplements market |
| Dependent Variable | Carbon dioxide emission per capita | To remind the environmental concern for older adults |
The conceptual model is demonstrated as below:
Figure 3. The Conceptual Model
| Information | Description | Source | Scale | Collection Method | URL |
|---|---|---|---|---|---|
| 1. Percentage of Older Adults | The worldwide population ages 65 and above, from 1960-2020 | World Bank | Continuous | Download csv | https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS |
| 2. Gross Domestic Products | Worldwide GDP per capita, Measured in 2011 U.S dollars price, from 1961-2018 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/economic-growth |
| 3. Carbon Dioxide Emissions | CO2 per caipta worldwide, investment profile risk, from 1961-2017 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/co-emissions-per-capita?tab=chart |
| 4. Animal Products Consumption | Daily protein supply from animal and plant-based foods, from 1961-2017 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/daily-protein-supply-from-animal-and-plant-based-foods?country=USA~OWID_WRL~CHN~GBR~JPN~IND~ZAF~TCD~NGA~BRA~FRA~ESP |
| 5. Fertility Rate | Fertility rate vs. contraceptive prevalence worldwide, from 1961-2020 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/fertility-vs-contraception |
| 6. Female Labor Force | Female labor force participation worldwide, from 1961-2020 | Our World in Data | Continuous | Download csv | https://ourworldindata.org/grapher/recent-ilo-lfp |
We realized that our data set covers worldwide for several decades. In order to achieve our primary research objective, we have filtered and narrowed down our selection to Asia countries only. Subsequently, we further categorized into five different regions of Central, East, South, Southeast, and Western Asia due to its unequal resource allocation in the Asia continent. Lastly, given the data limitation, we set our data focus from 2000 to 2017.Additionally, due to the data limitation, we only involve 36 Asia countries in our study.
2.2 Methodology
First, the horizontal bar chart will demonstrate the top and last five Asia countries’ aging trends. Through the bar chart, we can gain an insight on which region would be an ideal target market to focus on. Secondly, scatter plots will be adopted to display the correlations of our data variables, such as women empowerment against the fertility rate, GDP per capita against meat consumption, meat consumption against CO2 emissions, and etc.
The analysis will be further interpreted by the relevant indexes, such as p-values and beta. If the p-value is less than 0.05, our data analysis findings will be statistically significant. In addition, beta coefficient findings will be used to interpret regression analysis. If the beta is a positive figure, the dependent variable has indicated its positive relationship with the independent variable, and vice versa. However, if the beta displays zero figure, the variables have zero relationship between each other.
In practice, we will fully utilize the benefits of Microsoft Excel to tidy and filter the data to cater to our research questions. Then, we will integrate Tableau with our Microsoft Excel file to output creative visualizations. Lastly, R Studio will conclude the final work, which must include our research writing work, rmarkdown coding work, summary data, p-value and beta calculations,as well as Tableau visualization work.
3 Visualization
In Figure 4, East and Southeast Asia countries that consist of Singapore, Thailand, Hong Kong, South Korea and Japan, have demonstrated their potential dietary supplement business for the senior market since they have over 5% and above significant growth rate on its aging population sector from 2000 to 2018.
Figure 4. Aging population growth rate from 2000 to 2018.
Figure 5 has shown a zero relationship between the variables of GDP per capita and the female labor force. Therefore, this model data set’s variables are not correlated and will not make any significant impact on each other
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| GDP | 7.4e-05 | 0.0001484 | 0.498667 | 0.620889 |
| intercept | 498.134 | 79.0933 | 6.29805 | < 0.0001 |
| P-Value | Equation |
|---|---|
| 0.620889 | Labor Fource Rate = 7.39969e-05*GDP + 498.134 |
Figure 5. The relationship between GDP per capita and the number of female labour force.
Figure 6 represents a negative relationship between the variables of the female labor force and the fertility rate. The downtrend line reflects that the higher the female labor force in the market, the lower the fertility rate. As a result, the population is moving down a downtrend line as well and thus we can foresee an increasing aging population market in the near future.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| Labor Force Rate | -0.0251451 | 0.0066134 | -3.80212 | 0.0004477 |
| intercept | 58.6996 | 4.22322 | 13.8993 | < 0.0001 |
| P-Value | Equation |
|---|---|
| 0.0004477 | Labor Fertility Rate = -0.0251451*Labor Fource Rate + 58.6996 |
Figure 6. The correlation between the number of female labor force and fertility rate.
Figure 7 showcased the correlation between the variables of the GDP per capita and meat consumption data. The uptrend line reflects the positive relationship of the variables where they are positively correlated with each other. In other words, the greater the consumers’ purchasing power, the higher the meat consumption, and vice versa if the trend moves oppositely and negatively.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| GDP | 0.0007495 | 0.0001271 | 5.89703 | < 0.0001 |
| intercept | 352.213 | 51.3739 | 6.85587 | < 0.0001 |
| P-Value | Equation |
|---|---|
| < 0.0001 | Animal Products Consumption = 0.000749535*GDP + 352.213 |
Figure 7. The correlation between GDP per capita and daily meat consumption per person.
Figure 8 also indicates a positive correlated relationship between the variables of meat consumption and CO2 emission. This relationship explains that the greater the meat consumption, the higher the CO2 emissions. In another word, the higher the demand for meat then the higher the demand of deforestation to build a pasture. Hence, the increase of carbon dioxide emissions due to deforestation.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| Animal Products Consumption | 0.183867 | 0.0504524 | 3.64436 | 0.0007629 |
| intercept | -4.10693 | 33.7537 | -0.121673 | 0.903767 |
| P-Value | Equation |
|---|---|
| 0.0007629 | CO2 = 0.183867*Animal Products Consumption + -4.10693 |
Figure 8. The correlation between daily meat consumption per capita and CO2 emission per person.
Figure 9 also indicates a positive relationship between the variables of GDP per capita and CO2 emission. The rationale is similar to the abovementioned. When the consumer has higher purchasing power, they are willing to consume more meat to improve their living standards. However, the farmer must exploit the environment, such as deforestation to graze, in order to produce enough supply to fulfill the high demand of meat.
| Term | Value | StdErr | t-value | p-value |
|---|---|---|---|---|
| GDP | 0.0003981 | 2.616e-05 | 15.2203 | < 0.0001 |
| intercept | -7.13944 | 13.6427 | -0.523316 | 0.603642 |
| P-Value | Equation |
|---|---|
| < 0.0001 | CO2 = 0.000398112*GDP + -7.13944 |
Figure 9. The correlation between GDP per capita and CO2 emission per person.
In conclusion, our assumption is correct based on the trend line and relevant index from tableau, and the face validity has been ensured.
4 Analysis Results
4.1 Descriptive Summary Table
#import data
our.data <- read.csv('data/all.csv', header= TRUE, sep =",")
tb2_desc <- our.data [complete.cases(our.data),]
#Convert data type
tb2_desc_sub <- tb2_desc %>%
convert(
fct(Country),
fct(Region)
)
#To group data
tb2_desc_agg <- tb2_desc_sub %>%
group_by(Country,Region) %>%
#summarise(number_Country= n_distinct(Country)) %>%
summarise_at(vars(CO2, GDP, Animal_Products_Consumption,Percentage_Aging,Fertility_Rate, Labor_Force_Rate), mean, na.rm=TRUE)%>%
ungroup()
#Prepare the descriptive table
tb1_desc <- tb2_desc_agg %>%
select(GDP,CO2,Region, Animal_Products_Consumption,Percentage_Aging,Fertility_Rate, Labor_Force_Rate) %>%
tbl_summary (
by= Region,
missing = "no",
type = list(c(CO2,GDP,Animal_Products_Consumption, Percentage_Aging,Fertility_Rate, Labor_Force_Rate) ~ "continuous2", Region~ "categorical"),
statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} ({p}%)")
)%>%
bold_labels %>%
add_n() %>%
add_p() %>%
bold_p(t=0.05)%>%
modify_header(label="**Variable**") %>%
bold_labels() %>%
modify_spanning_header(update = starts_with("stat_")~ "**Region**") %>%
modify_caption("Table 1: Overall summary table") %>%
as_hux_table()
tb1_descRegion | |||||||
Variable | N | Central Asia, N = 4 | East Asia, N = 5 | South Asia, N = 5 | Southeast Asia, N = 7 | Western Asia, N = 15 | p-value |
| GDP | 36 | 0.002 | |||||
| Mean (SD) | 7,158 (5,087) | 24,371 (15,864) | 2,916 (989) | 9,727 (5,870) | 24,289 (20,365) | ||
| CO2 | 36 | 0.001 | |||||
| Mean (SD) | 4.5 (5.4) | 7.5 (2.6) | 0.6 (0.4) | 2.2 (2.3) | 8.6 (8.3) | ||
| Animal_Products_Consumption | 36 | 0.007 | |||||
| Mean (SD) | 30 (15) | 51 (19) | 14 (6) | 28 (11) | 35 (16) | ||
| Percentage_Aging | 36 | 0.3 | |||||
| Mean (SD) | 5.0 (1.7) | 11.3 (6.8) | 4.2 (1.1) | 5.5 (1.7) | 6.0 (4.1) | ||
| Fertility_Rate | 36 | 0.039 | |||||
| Mean (SD) | 2.82 (0.64) | 1.56 (0.57) | 3.49 (1.29) | 2.39 (0.57) | 2.61 (1.02) | ||
| Labor_Force_Rate | 36 | 0.10 | |||||
| Mean (SD) | 53 (11) | 55 (7) | 37 (21) | 58 (12) | 35 (20) | ||
| Kruskal-Wallis rank sum test | |||||||
The table demonstrates that the GDP per capita, CO2 emission per capita, meat consumption, and fertility rate are significantly different among these five regions. This also gives us insights that Western Asia and East Asia are the most wealthy regions with higher purchasing power and the most meat consumption. East Asia has the highest fertility rate. By contrast, the percentage of aging in each region is not significantly different from other regions, which means all the Asia countries face the aging trend issue. Lastly, the women labor force rate is not significantly different among these regions, which can be explained by the SDG goals four and five giving women more “power” or “rights” to work.
4.2 Predictive Summary Table
model1 <- lm( Percentage_Aging ~Fertility_Rate+ Labor_Force_Rate+GDP, #first is Y to answer Question 1
data=our.data)
model1##
## Call:
## lm(formula = Percentage_Aging ~ Fertility_Rate + Labor_Force_Rate +
## GDP, data = our.data)
##
## Coefficients:
## (Intercept) Fertility_Rate Labor_Force_Rate GDP
## 16.3749179 -3.3577283 -0.0121387 -0.0000377model1a <-
model1 %>%
tbl_regression()
model1a| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| Fertility_Rate | -3.4 | -3.8, -2.9 | <0.001 |
| Labor_Force_Rate | -0.01 | -0.03, 0.01 | 0.3 |
| GDP | 0.00 | 0.00, 0.00 | <0.001 |
|
1
CI = Confidence Interval
|
|||
model2 <- lm( Animal_Products_Consumption~ GDP+ Fertility_Rate+ Labor_Force_Rate+ Percentage_Aging, #First is Y to answer question 2
data=our.data)
model2##
## Call:
## lm(formula = Animal_Products_Consumption ~ GDP + Fertility_Rate +
## Labor_Force_Rate + Percentage_Aging, data = our.data)
##
## Coefficients:
## (Intercept) GDP Fertility_Rate Labor_Force_Rate
## -0.5294788 0.0007617 2.2948443 0.1719925
## Percentage_Aging
## 1.1987535model2a <-
model2 %>%
tbl_regression()
model2a| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| GDP | 0.00 | 0.00, 0.00 | <0.001 |
| Fertility_Rate | 2.3 | 0.44, 4.1 | 0.015 |
| Labor_Force_Rate | 0.17 | 0.10, 0.25 | <0.001 |
| Percentage_Aging | 1.2 | 0.89, 1.5 | <0.001 |
|
1
CI = Confidence Interval
|
|||
model3 <- lm( CO2 ~ GDP+ Animal_Products_Consumption, #First is Y to answer question 3
data=our.data)
model3##
## Call:
## lm(formula = CO2 ~ GDP + Animal_Products_Consumption, data = our.data)
##
## Coefficients:
## (Intercept) GDP
## 0.4495314 0.0003639
## Animal_Products_Consumption
## -0.0151971model3a <-
model3 %>%
tbl_regression()
model3a| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| GDP | 0.00 | 0.00, 0.00 | <0.001 |
| Animal_Products_Consumption | -0.02 | -0.03, 0.00 | 0.10 |
|
1
CI = Confidence Interval
|
|||
4.3 Results presentation
For the first regression, the GDP, economic development, has no significant effect on the percentage of the aging population. The fertility rate has a significant negative effect (-3.4) on the aging population rate at a 5% significance level, which means the more babies birth, the lower the percentage of older adults. The female workforce rate has a non-significant negative effect on the aging population, which shows that the more women work, the more the aging trend might decrease non-significantly.
For the second regression, the GDP has no significant effect on the number of meat consumption. The fertility rate has non-significant (P-value = 0.015) positive effect on the meat consumption. And the female workforce and percentage of aging population has significant positive effect on the meat consumption at a 5% significance level.
For the third regression, although the GDP per capita variable is at a significance level of lower than 5%, but GDP per capita have no significant effect on the carbon dioxide emissions, which means the economic situation will not affect the environment with greenhouse gas. On the other hand, although animal products consumption has a non-significant (P-value=0.1) negative effect (-0.02 beta coefficient) on the CO2 emissions
4.3.1 Our Best model
\[\widehat{Y_i} = 16.3749179 - 3.4 fr {\_}X1_i \\ -0.01 lfr{\_}X2_i \]
notes: fr-Fertility Rate lfr-Labor Force Rata
In the model 1, the GDP has no effect on the percentage of aging population, so we exclude it in the model equation. Although the female workforce rate and the fertility rate is negatively effect the percentage of aging population, when the fertility increase and more women to work, the aging trend will decrease. However, the female workforce rate has non-significant influence. Therefore, hypothesis 1a and 1b are rejected.The women empowerment (female workforce) will not signicant affect the fertility rate and the percentage of aging population.
\[\widehat{Y_i} = -0.5294788 - 2.3 fr {\_}X1_i \\ +0.17 lfr{\_}X2_i + 1.2 pa{\_}X3_i\]
notes: fr-Fertility Rate lfr-Labor Force Rate pa- Percentage of Aging population
In the model 2, the GDP has no effect on the number of meat consumption, so we did not include it as one independent variable to build the model. Althought the fertility rate has strongly positive relationship with meat consumption, it has low siginicance (P-value = 0.015). However, the female work force (Beta=0.17) and percentage of aging population (Beta=1.2) has signicant positive effect on the meat consumption (P-Value <0.001). Thus, the hypothesis 2 is accepted.
\[\widehat{Y_i} = 0.4495314 - 0.02 apc {\_}X1_i \]
notes: apc-Animal Products Consumption
In the model 3, the GDP also has no effect on the carbon dioxide emission, so that independent variable was removed from the equation. And the maet consumption has non-significant negative effect (-0.02) on carbon dioxide, where the hypothesis 3 also rejected.
This research is essential to explore the potential dietary supplyments market of the aging population in Asia. The study examines the correlations among GDP increase, meat consumption increased, and more Carbon Dioxide emitted. It shows that when people be more prosperous than before, they will consume more meats and emit more carbon dioxide. It also shows that the more affluent countries tend to have a low birth rate with a higher percentage of the aging population. Lastly, the Sustainable Develop Goal has been influential among Asians to provide equal opportunity for females.
5 Conclusion
5.1 Implications
The results for meat consumption and CO2 emission were not expected at all. Given its insignificant effect, it is impossible to explain the severe increase of CO2 emission in the real world over the years. One of the most ideal explanations for this end result is that there must be some errors within the data set and thus it causes limitations to this research project. Nonetheless, there are many journals out there pointing out that red meat production increases the greenhouse gases (GHG) emissions and thus contributing to global warming (González, N., Marquès, M., Nadal, M., & Domingo, J. L., 2020). Therefore, we stand firm on our ground with the belief that meat consumption contributes to CO2 emission.
Our regression model shows that fertility rate has a significant negative effect on the aging population, which means a decrease in fertility rate will lead to an increase in the number of aging population. Hence, the older population is expected to grow due to the current decreasing fertility rate. Since senior customers are expected to spend about $15 trillion every year from 2020 onwards (Arensberg, 2018) and they tend to have more product or brand loyalty than younger customers, they are an attractive target market for businesses, especially in the dietary supplement industry. Socioemotional Selectivity Theory (Carstensen, 2006), states that the aging population who realized their lifespan is becoming increasingly limited will become more selective with their choices and responsible in society’s contribution to achieve meaningful emotional goals(Micu & Chowdhury, 2010). In other words, the aging population will do things that benefit society to feel good. Therefore, upon learning meat consumption causes more bad than good, the seniors are expected to be more willing to consume less meat and resort to dietary supplements for their protein intake.
In short, the seniors market poses as a lucrative business opportunity for the dietary supplement market. The entrepreneurs or existing dietary supplements marketing managers should rethink about where the future market trend is moving towards and gear up for preparation.
5.2 Limitations
As the above mentioned, we think dietary supplements would be the ideal solution to reduce CO2 emissions from the reduction of eating meat. The decrease in meat consumption will lead to a decrease in deforestation, which is greener to mother nature and slows down global warming. However, our result shows that the aforementioned rationales are far from our expectation. With the collection of data, our regression model was contradicting our hypotheses. The cause of this could be attributed to data collection errors or inadequacy of the data to accurately predict the result. For instance, the GDP per capita showcased a zero relationship with CO2 emissions, which does not make sense at all. It should be the higher the GDP per capita, the higher demand from various factories to produce the needs and wants to meet the customers’ desires and thus higher production lead to higher CO2 emissions damaging the environment.
5.3 Summary and Contributions
Despite the insignificant relationships between the variables, we manage to conclude that the fertility rate has a significant effect on the aging population. From there, we can foresee the severe issues of decreasing fertility rate and increasing aging population occurring in Asia continent. This is an insightful data for the government and corporate data analytics to understand what the upcoming market trend is moving into.
Our research project showcases the potential for the dietary supplement market with the strong support from Socioemotional Selectivity Theory. As the issues of the aging population become more noticeable in society, the spending direction would be shifted entirely to a new direction to meet their needs and so would the industries. These research findings hope to contribute some ideas to the government and the medical industry to look into the future ahead of time so that they can gain a first mover advantage in the new dietary supplement market.