Keywords

Health Consciousness, Theory Of Planned Behavior, Subjective Norms, Purchase Intention, Nutritional Labels, Healthy Packaged Foods.

The process of urbanization is having a significant impact on consumers' lifestyles, which in turn is leading to a decrease in available time, longer work hours, and increased stress levels. As a result, many consumers are experiencing health issues, with overweight and obesity rates on the rise worldwide. To address these concerns, consumers are increasingly turning to foods that feature healthy and wellness claims. In response to this growing demand for healthier options, manufacturers are "reformulating" their existing products to create healthier versions or introducing entirely new product lines as "healthier alternatives" (Kemp & Bui, 2011). This trend reflects the increasing number of consumers who prioritize health and well-being and are motivated to seek out products that promote a healthy lifestyle.

Moreover, as healthcare costs rise, and consumer confidence in domestic health services declines, there is a growing segment of health-conscious consumers seeking out products that promote a healthy lifestyle and well-being (Chen, 2013). This trend highlights the importance of wellness in the modern consumer market and the need for manufacturers to adapt to changing consumer preferences and behaviors by providing healthier product options (Hilton, 2017). By catering to the health-conscious consumer segment, manufacturers can tap into a growing market and increase their competitive edge in the marketplace.

The COVID-19 pandemic has led to increasing health concerns among consumers, leading them to adopt healthier diets to boost their immune systems (Ogundijo et al., 2021). In India, there are approximately 90 million health-conscious individuals who are affluent, residing in urban areas, and possess an awareness and concern for lifestyle-related health issues. As a result, the market size for the preventive healthcare segment in India is projected to reach USD 100 billion by 2022, indicating significant growth from USD 55 billion in 2018. This growth in the preventive healthcare segment reflects the increasing demand for products and services that cater to the health-conscious population, such as dietary supplements, organic foods, and fitness products (Divine & Lepisto, 2005). The rise in market size is also an indication of the shift towards a more health-oriented lifestyle among Indian consumers, as they become more conscious of the impact of their lifestyle choices on their health and well-being(Kapoor & Munjal, 2017).

The Indian food and grocery market ranks sixth in the world, and 70% of the sales come from retail alone. The growth of the packaged food sector can be attributed to various factors such as the increasing population and demand for food (Ali et al., 2015). Additionally, the rise of working women and smaller families have driven the consumption of ready-to-eat and packaged foods (Kapoor & Munjal, 2017). The government's policies such as Make in India, Mega Food parks, and incentives for food processing units have also played a significant role in the development of the industry (Manaloor et al., 2015). Products that are naturally available, fortified with nutrients & those that have health claims or which do not have high sugars and trans fats are considered healthy packaged food for this study.

The healthy foods sector consists of Organic foods, Millets/Ragi /Jowar flour, Edible cold pressed oils, Beverages like green tea and malted milk drinks, Breakfast snacks like oats, muesli, wheat flakes, Digestive biscuits, Traditional Indian Snacks- Makhana, Baked snacks, Nuts, and bars, Dairy products.

Objectives of the Study

 To analyse the impact of Health consciousness on consumer purchase decision

 To understand the buying behaviour of healthy packaged food.

Literature Review and Hypotheses Development

“Food is the Best Medicine,” it is important to integrate traditional values into modern cooking techniques and develop food products that can attract potential customers of well-being foods (Lim & An, 2021). Consumers who prioritize their health and are concerned about the safety of the food they consume are more inclined to purchase healthy and organic food. As a result, they are likely to exhibit purchase intentions towards high-quality food (Iqbal et al., 2021). In urban areas of India, individuals now view gathering information as an essential component of their shopping routine (Madhvapaty & Dasgupta, n.d.).

The theory of planned behavior (TPB) was used as the theoretical foundation for this study, which posits that individual’s behavior is influenced by their attitudes towards the behavior, subjective norms, and perceived behavioral control (Ajzen, 2002). In the context of healthy packaged foods, health consciousness, nutritional label, subjective norms, and purchase intention are key constructs that can be explained through TPB.

Being health conscious involves having an elevated level of mindfulness and significance attributed to one's individual health and general state of wellness (Chen, 2013). It is expected that higher levels of health consciousness will positively influence attitudes toward healthy packaged foods and subsequently increase purchase intention (Carfora et al., 2021). Consumers who prioritize their health are conscious and vigilant about their well-being, driven by the desire to prevent illness, maintain good health, and enhance their quality of life through healthy habits and self-awareness regarding health (Gould, 1988; Plank & Gould, 1990). Health consciousness can be defined as the extent to which the consumer has integrated health concerns and lifestyle (Jayanti & Burns, 1998). Individuals’ beliefs and values regarding their own well-being play a role in the decisions they make daily. People who prioritize their health are more likely to choose foods that will bring about more positive health outcomes (Bazzani et al., 2019).

The nutritional label (NL) is another important construct that influences purchase intention. It is a cue that provides information on the nutritional content of packaged foods and is expected to positively influence attitudes towards healthy packaged foods (Petrovici et al., 2012). Nutritional labels highlight that they are effective in improving dietary behaviors and that certain types of labels, such as front-of-package labels, may be more effective than others (Soederberget al. 2012). Accurate and consistent labeling is also important for building consumer trust in the food industry (Ellison et al., 2013). Ongoing evaluation of nutrition labeling policies and practices is needed to ensure that they remain effective in promoting healthy eating behaviors, including regular updates to the content and format of nutrition labels (Ikonen et al., 2020).

Subjective norms refer to the social pressure and influence individuals perceive from significant others, such as family, friends, and peers. Subjective norms are the perceived social pressure that influences an individual to perform or not to perform the behavior (Ajzen & Fishbein, 1980). When individuals engage in any type of behavior, they are inevitably affected by their environment, including family members, friends, colleagues, and media. In other words, consumers are consistently influenced by the people and media around them while conducting any purchase behavior (Al-Swidi et al., 2014). In India, subjective norms play a significant role in determining an individual's intention to purchase healthy food (Shamal & Mohan, 2017).

Overall, the theory of planned behavior provides a comprehensive framework for understanding the relationships between health consciousness, nutritional label, subjective norms, and purchase intention toward healthy packaged foods. This theory-building assumes that individuals are rational and make decisions based on their attitudes, subjective norms, and perceived behavioral control towards a particular behavior.

Based on the conceptual framework model in Figure 1 the formulation of hypothesis proposed in the study is:

Hypotheses

H1: Subjective norms have a significant impact on consumers Health Consciousness

H2: Subjective norms have a significant impact on consumers Purchase intention.

H3: Subjective norms have a significant impact on Nutritional label of Healthy packaged food.

H4: Health Consciousness has a significant impact on purchase intention of healthy packaged food.

H5: Nutritional label has a significant impact on purchase intention of healthy packaged food.

H6: Health Consciousness significantly mediates the relationship between subjective norms and Purchase intention.

H7: Nutritional label significantly mediates the relationship between subjective norms and Purchase intention.

Research Methodology

Study Design

Structural Equation Modelling (SEM) is a statistical approach used to analyse complex relationships among variables in a model. The Partial Least Squares (PLS) method is a type of SEM that is used to estimate path coefficients and partial out variance for the model. This approach is particularly suitable for exploratory testing and predictive applications where there is little prior knowledge about the relationships among variables.

In the context of Figure 1, PLS-SEM will be used to evaluate the relationships among the variables depicted in the diagram. The technique will estimate the strength and direction of the paths between the variables and identify any mediating or moderating effects that may exist. PLS-SEM is a powerful tool that can help researchers to better understand the complex relationships among variables in a model, and to make predictions about the behaviour of the system being studied.

Sample Selection and Data Collection

In this study, data was collected using google forms in an online mode. We got 657 responses and after cleaning the data 554 usable responses were found. Based on the earlier studies, the sample size is suitable for further analysis as Hair et al., 1998 suggested that a sample size ranging from five to ten times the number of items in the scale is considered adequate.

Measures

To test the proposed model in Figure 1, primary data was collected using a questionnaire survey method. Each construct in the above model is measured using multiple items. A five-point Likert scale ranging from 1 = “strongly disagree” to 5 = “Strongly agree”. The reliability of the items is ensured by an extensive literature review, incorporating the opinions of experts and measuring Cronbach’s alpha values.

The questionnaire is divided into two sections. The initial section intends to gather information about the participant’s personal characteristics and their general state of health. The second section included a set of questions that assess specific concepts, derived from various measurement tools.

Data Analysis

Demographics

The study included a diverse group of respondents in terms of gender, age, education, marital status, employment status, and income level. The majority of the respondents were male, accounting for 57.6% of the sample, while 42.4% were female. The age range of respondents varied, with the highest proportion of respondents falling within the age range of 18-25 years. The educational level of respondents was relatively high, with over half of them having completed post-graduation, followed by 44% with a graduation degree. The majority of respondents were employed, while a significant proportion was students. Respondents' income levels were diverse, with the highest proportion having an income less than 2 lakhs, while 23.3% had an income of 10 lakhs and above Table 1.

Table 1 Demographics
		Frequency	Percentage
Gender	Male	319	57.6
	Female	235	42.4
Age	18-25	242	43.7
	26-30	59	10.6
	31-35	51	9.2
	36-40	86	15.5
	41-45	70	12.6
	46-50	25	4.5
	51-55	11	2.0
	55 and above	10	1.8
Education	12th	14	2.5
	graduation	244	44.0
	post-graduation	295	53.2
	less than 12th	1	0.2
Marital status	unmarried	286	51.6
	Married	264	47.7
	Divorced	2	0.4
	Widowed	2	0.4
Employment Status	employed	287	51.8
	unemployed	18	3.2
	Student	185	33.4
	homemaker	37	6.7
	Retired	4	0.7
	Business	23	4.2
Income	less than 2 lakhs	218	39.4
	2-5 lakhs	119	21.5
	5 - 10 lakhs	88	15.9
	10 lakhs and above	129	23.3

Common Method Bias

Addressing bias in data is crucial as it can impact the precision of findings, as noted by Podsakoff et al. (2003). In this study, the full collinearity approach was employed to distinguish Common method bias, following the methodology outlined by Kock (2015). The Variance Inflation Factor (VIF) was found to be lower than the threshold of 3.3, in accordance with the guidelines of Hair et al. (2017); Kock (2015). Consequently, we can conclude that this study is not affected by common method bias Figure 2.

Measurement Model

Validity and Reliability

As indicated in Table 2, each construct has been measured using three different metrics: Cronbach's alpha, composite reliability and average variance extracted (AVE).

Table 2 Validity and Reliability Measures
Construct Reliability and Validity	Cronbach's alpha	Composite reliability	Average variance extracted (AVE)
Health Conscious	0.864	0.898	0.595
NL	0.879	0.908	0.624
Purchase Intention	0.826	0.878	0.591
Subjective Norms	0.760	0.851	0.594

Cronbach's alpha is a measure of internal consistency reliability, indicating the extent to which the items in a construct are measuring the same underlying concept. All of the constructs have Cronbach's alpha values above the commonly accepted threshold of 0.7, indicating good internal consistency. Composite reliability is another measure of reliability that takes into account the intercorrelations among the items in a construct. Again, all of the constructs have values above the commonly accepted threshold of 0.7, indicating good reliability.

Average variance extracted (AVE) is a measure of convergent validity, indicating the extent to which the items in a construct are measuring the same underlying concept and no other unrelated concepts. All of the constructs have AVE values above the commonly accepted threshold of 0.5, indicating good convergent validity. Overall, the high values of Cronbach's alpha, composite reliability, and AVE indicate that these constructs are reliable and valid measures of the underlying concepts they represent (Hair et al., 2013).

Discriminant validity

The Fornell-Larcker criterion is a measure of discriminant validity, indicating the extent to which each construct is different from the others. The diagonal values represent the square root of the AVE for each construct. The off-diagonal values represent the correlations between the constructs.

To meet the Fornell-Larcker criterion, the square root of the AVE for each construct should be greater than the correlations between that construct and all other constructs. Looking at Table 3 below, we can see that this criterion is met, as the square roots of the AVE values are all greater than the correlations between each construct and the others (Fornell & Larcker, 1981). Therefore, we can conclude that these constructs exhibit good discriminant validity and are distinct from each other.

Table 3 Discriminant Validity
Constructs	1	2	3	4
1.Health Conscious	0.772
2.Nutition label	0.421	0.790
3.Purchase Intention	0.426	0.376	0.769
4.Subjective Norms	0.290	0.227	0.534	0.771

The Heterotrait-Monotrait (HTMT) ratio is used as a measure of discriminant validity in structural equation modeling (SEM). Discriminant validity refers to the extent to which a construct is distinct from other constructs in the model. It involves calculating the ratio between the correlation of a pair of constructs and the geometric mean of the average variance extracted (AVE) for those two constructs. If the HTMT ratio is less than 0.9, then it is considered that the two constructs are sufficiently distinct and have adequate discriminant validity. The HTMT ratio is a more conservative measure of discriminant validity, as it takes into account both the shared variance and the unique variance between constructs.

The values in Table 4 represent the HTMT ratios between each pair of constructs. For instance, the HTMT ratio between Health Conscious and NL is 0.473, while the HTMT ratio between Purchase Intention and Subjective Norms is 0.672. These ratios are used to evaluate the degree of discriminant validity among the constructs, with values below the threshold of 0.9 indicating adequate discriminant validity.

Table 4 Variable Correlations
	1	2	3	4
1.Health Conscious
2.Nutrition label	0.473
3.Purchase Intention	0.490	0.421
4.Subjective Norms	0.355	0.262	0.672

Structural Model Assessment

After assessing the measurement model, the subsequent step involves evaluating the structural path to determine the significance of the path coefficients. The path analysis shows the relationships between different constructs, as well as the strength and significance of those relationships. Each path has a beta coefficient, which indicates the strength and direction of the relationship between the two constructs in that path. The T statistics measure the significance of the beta coefficient, and the P values indicate the probability that the relationship between the two constructs in that path occurred by chance.

Looking at the path analysis, we can see that all paths have significant relationships between the constructs. Health Consciousness and NL have a positive relationship with Purchase Intention, as indicated by their positive beta coefficients. Similarly, Subjective Norms have positive relationships with all three constructs, with the strongest relationship being with Purchase Intention.

Overall, path analysis provides insight into the relationships between different constructs and can be used to test hypotheses and make predictions about the behavior of individuals or groups based on these relationships.

Mediation Analysis

Mediation analysis is a statistical technique used to examine whether the relationship between an independent variable and a dependent variable is partially or fully mediated by one or more mediator variables. In this analysis, we can see that the relationship between Subjective Norms and Purchase Intention is significantly stronger after the mediation effect of Health Consciousness and Nutrition Label is considered.

The "Before Mediation" column in the Table 5 shows the beta coefficient for the direct effect of Subjective Norms on Purchase Intention, which is 0.534. However, after the mediation effect of Health Consciousness and Nutrition Label is considered, the beta coefficient increases to 0.427, indicating a stronger relationship between Subjective Norms and Purchase Intention.

Table 5 Path Coefficients
Path	Beta Coefficient	t value	p value	Status
Health Conscious -> Purchase Intention	0.225	5.984	0.000	Supported
NL -> Purchase Intention	0.184	4.588	0.000	Supported
Subjective Norms -> Health Conscious	0.290	7.180	0.000	Supported
Subjective Norms -> Nutrition label	0.227	5.377	0.000	Supported
Subjective Norms -> Purchase Intention	0.427	12.149	0.000	Supported

The specific indirect effects in the table show the beta coefficients, T-values, and P-values for the indirect effects of Subjective Norms on Purchase Intention through each mediator variable. The specific indirect effect through Health Consciousness is 0.065 with a T-value of 4.481 and a P-value of 0.000, indicating that this mediation effect is statistically significant. Similarly, the specific indirect effect through Nutrition Label is 0.042 with a T-value of 3.370 and a P-value of 0.001, indicating that this mediation effect is also statistically significant.

The total indirect effect of the mediator variables on the relationship between Subjective Norms and Purchase Intention is the sum of the specific indirect effects, which is 0.107 in this case. This means that the relationship between Subjective Norms and Purchase Intention is partially mediated by the two mediator variables, Health Consciousness and Nutrition Label.

In conclusion, the mediation analysis provides insight into the underlying mechanisms through which Subjective Norms affect Purchase Intention. By identifying the mediator variables and their specific indirect effects, this analysis can help inform strategies aimed at promoting healthy food choices and increasing purchase intentions among consumers Table 6.

Table 6 Mediation Analysis
Path	Before Mediation			After Mediation			Specific Indirect Effect	Beta Coeeficient	t value	p values
Subjective Norms -> Purchase Intention	0.534	16.112	0.000	0.427	12.149	0.000	Subjective Norms -> Health Conscious -> Purchase Intention	0.065	4.481	0.000
							Subjective Norms -> NL -> Purchase Intention	0.042	3.370	0.001

Discussion

The above results indicate that the constructs of Health Consciousness, Nutrition label, Purchase Intention, and Subjective Norms are all reliable and valid measures of the underlying concepts they represent. The high values of Cronbach's alpha, composite reliability and AVE indicate good internal consistency and convergent validity. Furthermore, the Fornell-Larcker criterion is met, indicating good discriminant validity and distinctness of each construct.

The path analysis reveals significant relationships between the constructs. Health Consciousness and Nutrition label have a positive relationship with Purchase Intention, suggesting that individuals who are health conscious and have a need for novelty are more likely to have a higher purchase intention(Shah Alam & Mohamed Sayuti, 2011). Health consciousness and perceived health risk have been found to be positively related to healthy food consumption behaviour (Pham et al., 2019). Similarly, Subjective Norms have positive relationships with all three constructs, with the strongest relationship being with Purchase Intention(Arvola et al., 2008). This indicates that individual’s perceived social pressure to perform a behavior is an important factor in shaping their purchase intentions.

Overall, these findings have important implications for marketers and advertisers who are trying to understand consumer behavior and develop effective marketing strategies. By understanding the underlying factors that drive purchase intentions, marketers can create targeted campaigns that appeal to the needs and values of their target audience. Additionally, the high reliability and validity of the measures used in this study provide confidence in the accuracy of the results and support the use of these measures in future research.

Limitation and Future Scope

One limitation of this study is that it was conducted using a sample of urban Indian consumers and therefore may not be generalizable to other populations. Future research should seek to replicate these findings in other cultural contexts to determine the extent to which they hold across different populations. Another limitation is that the study used a cross-sectional design, which limits the ability to establish causality between the constructs examined. Future research should use longitudinal designs to explore the causal relationships between these constructs over time.

In terms of future scope, it would be interesting to explore the impact of other factors such as price, taste, and convenience on consumer purchase intention in the context of healthy packaged foods.

Finally, given the rapidly evolving landscape of nutrition labeling policies, future research could examine the impact of changes in labeling requirements on consumer behavior and purchase intention. This would be particularly relevant considering the recent changes to nutrition labeling policies in India, which have introduced new requirements for front-of-package labeling.

Conclusion

In this study, we have measured how subjective norms (society) have an influence on the purchase behavior of consumers. Consumers who prioritize their health are utilizing food product labels based on factors such as the perceived importance of healthy nutrition, frequency of consuming healthy food, extent of media exposure, positive attitudes toward labeling reform, and perceived benefits and importance of reading labels. Society has a great influence on purchase behavior as well. Especially amongst Indians, society plays a very important role in recommending the food choices they make because of various aspects like traditions, culture, geographical locations, religions, etc.

In this study we can see that subjective norms are positively influencing the purchase behavior of the consumer. The role of health consciousness and nutritional label are also significant in purchase decision making. The relationship between subjective norm and purchase intention is partially mediated by both health consciousness and nutritional label.

Theoretical Contribution

This study has contributed to the existing literature by bringing together the aspect of health consciousness and nutritional label as parallel mediators. The constructs of subjective norm, purchase intention, health consciousness, and nutrition label together have been added to the existing literature on healthy packaged food purchase behavior. Consumers are aware of their health and are concerned about it because they are making informed decisions for the purchase of healthy packaged food products. This study emphasizes the importance of nutritional information in the labeling of a product for a better understanding of the consumer. Health consciousness as a construct has added immense value to the study as it contributes significantly to the purchase decision of the consumer.

Managerial Implication

The topic is very much in line with the need of a consumer to understand healthy choices. The study can help the marketers to understand the needs of the consumers. The policy makers can develop new bodies which can help consumers better with more health-related information and polices.

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Received: 04-Apr-2023, Manuscript No. AMSJ-23-13422; Editor assigned: 05-Apr-2023, PreQC No. AMSJ-23-13422(PQ); Reviewed: 08-May-2023, QC No. AMSJ-23-13422; Revised: 20-Jun-2023, Manuscript No. AMSJ-23-13422(R); Published: 05-Jul-2023