JIS University, Department of Management, West Bengal, India
Corresponding Author’s Email: priyodas1996@gmail.com
Social Marketing plays an important role in bringing change in society for good if perform well and the cases of COVID-19 is still being reported in India more specifically in West Bengal. Thus, the primary aim of this study was to find out the important variables that will help companies to increase the efficiency of social marketing campaigns. For achieving this objective this study adopted empirical research and it is concerned with providing practical implication without any variable manipulation it used descriptive cross-sectional research design. For the purpose data collection convenience sampling technique was utilized and data collected using an interval scale. For analysis of collected data, first a reliability analysis of the data collection instrument was conducted with 54 respondents, and it gave the value of which is good. After that for reaching out to conclusion Structural Equation Modeling was utilized first measurement model conducted for checking the model fit and after determining the model fit based on several fit indices such as CMIN/df, GFI, CFI, and RMSEA 2.08, 9.1, 9.3, 0.62 respectively and lastly structural model was checked to see the direction of the study and to check the proposed hypotheses. The results and recommendations based on recommendation was completely based on statistics and shows very interesting practical implications.
Keywords: COVID-19; COVID-19 Preventive Measures; Social Marketing
Coronavirus (COVID-19) was first reported in Wuhan, China, in December 2019. Since then, it has turned into a huge risk to lives, economic and social wellbeing of all public of the world. Aware of this global emergency, the WHO (2020), suggests all governments of the world to take urgent and concerted steps towards arresting the spread and government of countries-initiated lockdown and at present still COVID-19 poses a great threat and for this reason Government of India (GOI) and few other companies are trying to bring a behavioral change among population by spreading awareness about preventive measures of COVID-19 by utilizing Social Marketing. We know that social marketing is very effective in bringing social change. Most fundamental form of social marketing is the application of marketing principles and procedures to initiate social change or improvement – whether it is related to public health challenges, injury prevention (Smith, 2006), environmental issues (Maibach, 1993), transportation demand management (McGovern, 2005) or other social needs. As a social change bringing methodology, social marketing must be based on some theoretical models that direct the selection of the most pertinent determinants, social groups, social marketing objectives and assessment for behavior change such as theories of diffusion of innovations, political economics, and social capital (Bloom & Gundlach, 2001). In order to increase the effectiveness of COVID-19 social marketing campaigns, social marketing theories such as the Theory of planned behavior (TPB) and Health Belief Model (HBM) can aid in understanding the perception of the people to take the vaccines (Twum et al. 2021), Thus, after utilizing the Social Cognitive Theory (SCT) the focus of this study was to identify important variables that are important for increasing the efficiency of social marketing about COVID-19.
Authors have followed a descriptive study and identified that there is an urgent need for creating a demand about vaccination of COVID-19 so that they can help those who are uninformed, vaccine hesitant or actively resistant (Evans & French 2021). They suggested that it is very crucial for vaccination promoting strategy to be clear about the insight of the target group, what level they are in, identify whether they are hesitant or unaware and further they also suggested following steps- raising awareness of vaccine availability, safety, and benefits; point of decision prompts in health care facilities; use of influencers including COVID-19 vaccination ambassadors (to build trust); educational entertainment to normalize vaccination; and a service delivery strategy. Also, social marketing campaigners must use some reward or incentive structures.
To achieve desired behavior for COVID-19 social marketing is used and should be used for developing a planned behavior by identifying and removing crucial barriers of human behavior. If companies use stake holder theory to gain support related to financial and human resource so that barriers can easily be identified. However, they also suggested that to bring more effective change company should add the idea of creating social change unto their vision and mission (Şentürk, 2021).
Authors tried to understand the factors of social marketing that influences people to get vaccinated for doing so they applied TPB and HPV and used a cross-sectional survey design. They found that Attitude, social norm, perceived behavioral control, perceived susceptibility and cues to action were the main predictors of intension of COVID-19 vaccination whereas perceived severity, perceived benefits and perceived barriers did not predict the intension of COVID-19 vaccination. They suggested that the demographic factors of people should be determined and constructs of TPB and HBM should be considered while creating a campaign (Twum et al. 2021).
They carried out formative research by conducting a social marketing campaign in June 2020. Aim of their research was to reduce the number of people going out and wearing masks. For doing it properly they utilized Health Belief Model (HBM) to understand the perception of their community and develop messages accordingly and they assess the process by checking CCTV footage and reported that their social marketing campaign increases the mask usage by 69% and decreases going out by 30%. They suggested that to bring social change related to health, community participation is very vital component of response to communicable diseases (Bayram et al. 2020).
Lee (2020) provided detailed idea about what types of social marketing strategies have been using by companies and some other new should be used to reduce the impact of COVID-19 which includes practice of social distancing, washing of hands for at least 20 seconds several times a day, cover phone with plastic bag don’t touch the screen etc. and suggested that companies should focus majorly on Focus on at risk populations and locations and featuring credible messengers and More multicultural and multilingual tailored messaging. Companies should introduce contact tracing mechanisms also giving ideas about sanitation of mails and takeout packaging’s.
Based on the results of the research by Melovic et al. (2020), the authors suggested that decision makers should pay more attention to modern forms of online communication and social marketing to use their potential for improvement of public health, as well as avoid the harmful impact that certain forms of communication may have on the formation of attitudes and loss of confidence in vaccines. Professional support must be present in all forms of application of online media and social marketing in the field of immunization as important segment of public health. They stated that positive attitude is very much essential for high level of confidence in social marketing especially in the field of public health.
Hasan & Sheikh (2018) aimed to identify and examine the constructs that influence the mind- set toward social marketing all the way through social media and to study the impact of feelings toward social marketing through social media on ‘Being affected by Social marketing through Social Media’. They revealed that those who are using social media regularly have a positive attitude towards social media advertisings because their study showed that ‘social media usage’ and ‘perception of reliability has significant impact on attitude in favor of social marketing further they also stated that usage of too much fear should be avoided because they found that perception of reliability towards social marketing through social media has a significant impact. However, ‘followings on social media’ and privacy concern of social media has no significant influence on their behavior towards social marketing but attitude toward social marketing through social media has direct significant impact on target audience.
The study by Lefebvre (2011) aimed to provide an integrative framework to show light to the marketers or social marketers who are or will work with social and health issues. They integrate perception of social marketing with core roles of audience benefit, analysis of their behavioral determinants and context as well along with the use of positioning, brand strategy in marketing also tailored offering of marketing mix. After that they communicate in an evolving media environment. They found that in developed countries they only had to use persuasive communication for behavior change. They also make suggestion from their integrative model that companies should expand social marketing to development of goods and delivering it by providing some incentives and other behavioral concepts in the form price and extending place as both an access and opportunity idea for behaviors, products, and services.
Bandura’s social learning theory (SLT),’ which recently relabeled as social cognitive theory (SCT) (Bandura 1986; Bandura 1977); holds that behavior is determined by Expectancies and Incentives:
Expectancies about environmental cues that is, how events relate to each other means about what leads to what. Environmental cues in this study will be showing consequences of COVID-19.
SLT states that interaction of a person’s knowledge and experiences, the environment (i.e., those stimuli that individuals are aware of), and the behavior of individual’s (Bandura, 1986, 1977) stimuli for SLT is Social Marketing campaigns. The core concept of SLT is that behavior is outcome of both person and situation or environment and does not result from either factor alone. In the four-step pattern as depicted above Bandura (1977), a person notices something in the environment then that individual remembers what was noticed and then the person produces a behavior, and then the surroundings deliver a consequence in the form of reward or punishment, and this changes the probability the behavior will appear again. Within the context of ethics, the SLT framework offers considerable guidance with respect to how people learn to make new decisions. Previously Within the literature of management, SLT has been providing theoretical foundation for organizational behavior (Davis & Luthans 1980), strategic management (Ginter & White, 1982), sales management (Onyemah, Swain, & Hanna, 2010), and classroom instruction (Pitt et al. 2012).
After going through a lot of literatures by keeping the limitations in mind it is understood that social marketing if applied properly with strategy, behavioral change theories and with media then it can bring behavioral change and literatures related to SCT showed us that people learn from observing other phenomena. However, it is still not identified that how the efficiency of a social marketing campaign can be increased especially for spreading and bring change in behavior among people regarding COVID-19 preventive measures by utilizing Social Cognitive Theory.
How a company can increase the efficiency of social marketing about COVID-19 preventive measure so that it can bring a social change and motivates the population to adopt it?
To find out the important variables that increases social marketing efficiency, and
To see whether social marketing about showing preventive measures of COVID-19 helps change the behavior of people.
After understanding relationship between social marketing, its impact on behavioral change and SCT from the available literatures, following hypotheses is proposed to test-
Figure 1: Proposed Research Model Based on Previous Literatures and Gap
Constructs | Variables |
Environmental Cues | It would be more impactful If I see consequences and preventive measures of COVID-19- |
a) If I see it as video. | |
b) If I see it as demonstration form. | |
c) I like to see it only in a discussion forum. | |
d) If I see it as a fictional content. | |
e) if we get some emotional values. | |
Opportunity Outcomes | If we follow preventive measures as shown in advertisements, then I will adopt the behavior- |
a) If it is demonstrated properly. | |
b) If it is properly shown by a video only. | |
c) If I can participate in a discussion forum only. | |
d) If it is shown by a fictional character. | |
e) If it is shown by an authoritarian body. | |
Self-efficacy | I believe adopting these preventive measures shown in advertisements as a changed behavior- |
a) Easy to achieve. | |
b) Budget friendly so that I will continue in long run. | |
c) I don’t have to make any monetary sacrifice. | |
d) Is not inconvenient. | |
e) It does not include any medical procedure. | |
Changed Behavior of Viewers | I will adopt the preventive measures and change my behavior towards it- |
a) If proper consequences are shown. | |
b) If I understand advertisements properly. | |
c) If it shows us accurately what to do in any media. | |
d) If it shows us accurately what to do in video. | |
e) I am always motivated to adopt this change in behavior no matter what. |
The basic purpose of this study was to find out important variables that increase the efficiency of the social marketing after statistically testing the collected data and providing conclusion based on that. Thus, this study was conclusive and empirical in nature. Therefore, empirical research with a descriptive cross-sectional research design was adopted. For the convenience of data collection, convenience sampling technique was utilized and for the purpose of data collection a questionnaire based on previous literatures related to this topic and adopted theory 20 items was developed and responses were recorded by using an Interval scale i.e., Likert’s 5 scale (1= Strongly Agree to 5= Strongly Disagree).
For data analysis, first Confirmatory Factor Analysis (CFA) was used for checking the model fit. The reason behind utilizing CFA was that, focus of this study was find important variables that increases the efficiency of social marketing and to propose and validating research model based on SCT and previous work. For testing of hypotheses and providing implication Structural model was utilized.
Before starting the analysis part, a pilot test conducted for finalizing reliability of the data collection instrument by taking 54 respondents and the value of Cronbach’s alpha is shown in table 1.
Table1: Showing The value of Cronbach’s Alpha
Case Processing Summary | |||
N | % | ||
Cases | Valid | 54 | 100.0 |
Excludeda | 0 | 0.0 | |
Total | 54 | 100.0 | |
a. Listwise deletion based on all variables in the procedure |
Reliability Statistics | |
Cronbach's Alpha | N of Items |
0.925 | 20 |
For data analysis SEM technique was used. It is usually analyzed by using two steps 1. Measurement model and 2. Structural model.
Author checked measurement model after utilizing CFA to evaluate the model fit in AMOS The Chi-squared statistic was significant Chi square = 122.73, Degrees of freedom = 59, Probability level = 0.000 However, Chi-squared statistics has a trade-off with the sample size (Byrne, 2001). Figure 1 shows the measurement model which has 4 constructs, and these 4 constructs were initially measured by 20 items. At the very beginning model’s χ 2 /df were very high close to 8 which is not good for a model. After deletion of total six items the value of chi-squared became 2.08 (refer to figure 2). And the Standardized Residual Values of those 8 items were very high. The values of the model fit statistic shown in (refer to table 2).
Figure 2: Adjusted Model
Table 2: Showing Model Fit Statistics
Model Fit indices | Estimates | Cut-off values |
CMIN/DF | 2.80 | 1.00-3.00 |
GFI | 9.1 | >9 |
CFI | 9.43 | >0.95 |
RMSEA | 0.062 | <0.060 |
RMR | 0.89 |
After determining the model fit, convergent and discriminate validity were checked by Average Variance Extracted (AVE) and Composite Reliability (CR). The value of AVE was more than 0.5 as well as the square of the correlations between the factors. Values of square root of AVE, various correlations among factors and AVE are shown in table 3, table 4 and table 5 respectively.
Table 3: Calculation of AVE for Discriminate Validity of Constructs
Estimate | Square root of standardize d estimates | Sum of Square root of standardize d estimates | number of indicators | AVE | Square root of AVE | |||
E1 | < -- - | environmental_cues | 0.964 | 0.929296 | 3 | |||
E2 | < -- - | environmental_cues | 0.916 | 0.839056 | ||||
E4 | < -- - | environmental_cues | 0.33 | 0.1089 | 1.877252 | 0.625751 | 0.791044 | |
O1 | < -- - | observed_expectati ons | 0.803 | 0.644809 | 4 | |||
O2 | < -- - | observed_expectati ons | 0.858 | 0.736164 | ||||
O3 | < -- - | observed_expectati ons | 0.835 | 0.697225 | ||||
O5 | < -- - | observed_expectati ons | 0.494 | 0.244036 | 2.322234 | 0.580559 | 0.761944 | |
S1 | < -- - | self_efficacy | 0.826 | 0.682276 | 4 | |||
S2 | < -- - | self_efficacy | 0.891 | 0.793881 | ||||
S3 | < -- - | self_efficacy | 0.837 | 0.700569 | ||||
S5 | < -- - | self_efficacy | 0.127 | 0.016129 | 2.192855 | 0.548214 | 0.740415 | |
B1 | < -- - | changed_behavior | 0.965 | 0.931225 | 3 | |||
B2 | < -- - | changed_behavior | 0.912 | 0.831744 | ||||
B3 | < -- - | changed_behavior | 0.799 | 0.638401 | 2.40137 | 0.800457 | 0.894682 |
Table 4: Showing Various Correlations among Factors
Estimate | |||
environmental_cues | <--> | observed_expectations | 0.761 |
environmental_cues | <--> | self_efficacy | 0.75 |
environmental_cues | <--> | changed_behavior | 0.741 |
observed_expectations | <--> | self_efficacy | 0.751 |
observed_expectations | <--> | changed_behavior | 0.76 |
self_efficacy | <--> | changed_behavior | 0.73 |
Table 5: Showing the AVE Value
Environmental Cues | Observed Expectations | Self- efficacy | Changed Behavior | |
Environmental cues | 0.79 | |||
Observed Expectations | 0.761 | 0.76 | ||
self-efficacy | 0.75 | 0.751 | 0.74 | |
Changed Behavior | 0.741 | 0.76 | 0.73 | 0.89 |
After that convergent validity was checked and the values are more than 0.7 which is considerably good as shown (refer to table 6) So, this theory based hypothesized model was appropriate for further testing.
Table 6: Calculation of Composite Reliability
Estimate | Square of standardi zed estimates (A) | Measure ment error (1- A) | Sum of the measurem ent error (ME) | Sum of the standardiz ed loadings | Square of the Sum of the standardized loadings © | C+ME | Composite eliability (C/C+ME) | |||
E1 | <-- - | environmental_cue s | 0.964 | 0.929296 | 0.070704 | |||||
E2 | <-- - | environmental_cue s | 0.916 | 0.839056 | 0.160944 | |||||
E4 | <-- - | environmental_cue s | 0.33 | 0.1089 | 0.8911 | 1.122748 | 1.877252 | 3.524075 | 4.646823 | 0.758384 |
O 1 | <-- - | observed_expectati ons | 0.803 | 0.644809 | 0.355191 | |||||
O 2 | <-- - | observed_expectati ons | 0.858 | 0.736164 | 0.263836 | |||||
O 3 | <-- - | observed_expectati ons | 0.835 | 0.697225 | 0.302775 | |||||
O 5 | <-- - | observed_expectati ons | 0.494 | 0.244036 | 0.755964 | 1.677766 | 2.322234 | 5.392771 | 7.070537 | 0.76271 |
S1 | <-- - | self_efficacy | 0.826 | 0.682276 | 0.317724 |
S2 | <-- - | self_efficacy | 0.891 | 0.793881 | 0.206119 | |||||
S3 | <-- - | self_efficacy | 0.837 | 0.700569 | 0.299431 | |||||
S5 | <-- - | self_efficacy | 0.127 | 0.016129 | 0.983871 | 1.807145 | 2.192855 | 4.808613 | 6.615758 | 0.726842 |
B 1 | <-- - | changed_behavior | 0.965 | 0.931225 | 0.068775 | |||||
B 2 | <-- - | changed_behavior | 0.912 | 0.831744 | 0.168256 | |||||
B 3 | <-- - | changed_behavior | 0.799 | 0.638401 | 0.361599 | 2.40137 | 2.40137 | 5.766578 | 8.167948 | 0.706001 |
Discriminate validity analyses the degree to which variables of a particular construct have a high amount variation in general (Hair et al. 2010). It is achieved when AVE of each factor with shared variances among it and others and this validity should be higher correlation value between the factors (Fornell & Larcker, 1981). AVE of the factors are greater than 0.5 and more than the value of correlation between every construct as shown in table 4 & table 5.
Table 7 shows Environmental cues- 0.75, Observed Expectancy 0.76, self-efficacy- 0.73, Changed behavior- 0.71. So, all the values of CR are more than 0.7 or equal to 0.8. So, it undoubtedly showed that the convergent validity is good. The convergent validity of this proposed model is calculated by standard regression weight. These results are reasonable and propose that it is suitable to proceed further with this model.
Table 7: Maximum Likelihood Estimates
Estimate | S.E. | C.R. | P | |||
changed_behavior | <--- | environmental_cues | 1.062 | 0.034 | 31.29 | *** |
changed_behavior | <--- | observed_expectatio ns | -0.005 | 0.044 | -0.107 | 0.915 |
changed_behavior | <--- | self_efficacy | -0.039 | 0.04 | -0.992 | 0.321 |
After conducting the above analysis, a structural path estimate was conducted to check the direction of the study and it is shown in figure 3 and table 8 showing the maximum likelihood estimates and decisions regarding Hypotheses.
Figure 3: Showing the Path Estimates
Table 8: Showing Decisions Regarding Hypotheses
Proposed Hypotheses | Estimates | Results |
There is a significant impact of social marketing about showing consequence of COVID-19 on changing human behavior. | 1.062 | Null Hypothesis Rejected. |
There is a significant impact of social marketing about showing preventive measures of COVID-19 on changing human behavior. | -0.005 | Failed to reject Null Hypothesis. |
There is a significant impact of social marketing about self-efficacy on changing human behavior. | -0.039 | Failed to reject Null hypothesis. |
Social Marketing by an established company is very much important it is like the responsibility of a company to do well for the society and bring some good changes. As found in literature review, social marketing plays a great role and still playing a good role for providing some benefit to people related to COVID-19. The primary purpose of this study was to check and find some important variables that bring change in people of a society and adopt COVID-19 Preventive measures for good. Thus, after conducting this study and analyzing the data, after checking the model fit by deleting some variables from each constructs it is found that proposed hypothesized model based on SCT is a fit model and after testing of hypothesis checking path of the study, it can be said that showing consequences of COVID-19 (environmental cues) can bring change to the behavior of the people whereas showing benefit and preventive measure (Observed expectation) only will not bring any type of change in behavior. However, Self-efficacy which is very important for changing behavior has no important role to play in bringing behavioral change.
By keeping this conclusion in mind, it can be recommended from this study:
Companies should majorly focus on showing the consequences of COVID-19 (environmental cues) to bring a good change to people and more specifically it can be said that companies should demonstrate it properly along with some video content irrespective of media because estimates of these two variables of that factor is very high and also, they can use some popular face and make some fictional content for social marketing campaign. Companies should never spend their resources in a discussion forum as social campaign because this study reveals that people has zero interest in discussion forum.
Also, some other recommendations can made related from other factor i.e., adoption of preventive measure (observed expectancy), while making social marketing campaign about COVID-19 companies should always avoid communicating via any kind of fictional content along with any kind of authoritarian body. They should create the campaign with employees or any popular face.
Last but not the least recommendation can be made from the factor of self-efficacy, companies must create a campaign which is budget friendly. In other words, for achieving that change behavior people don’t like to make any kind of monetary sacrifices. Also, they should not include any kind of medical procedures and any inconvenient processes. Again, people don’t have any specific media in mind as per shown in factor 4 and modified model i.e., changed behavior, people have no specific media consideration in mind the campaign should accurately show the consequences and preventive measures should be easy to achieve.
The author declares that he has no conflict of interests.
The author is thankful to the institutional authority for completion of the work.
Bandura, A. (1986). Social foundation of thought and action.
Bandura, A., & Walters, R. H. (1977). Social learning theory (Vol. 1). Prentice Hall: Englewood cliffs.
BAYRAM, T., CAMCIOĞLU, A. E., KESKİNER, G., & BAŞIBÜYÜK, İ. Social marketing of “mask-wearing”,“physical distancing” and “staying at home” during the COVID-19 pandemic: a study from Turkey. Turkish Journal of Public Health, 18(COVID-19 Special), 112-119.
Bloom, P. N., & Gundlach, G. T. (Eds.). (2001). Handbook of marketing and society. Sage.
Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Lawrence Erlbaum Associates Publishers.
Davis, T. R., & Luthans, F. (1980). A social learning approach to organizational behavior. Academy of Management Review, 5(2), 281-290.
Evans, W. D., & French, J. (2021). Demand Creation for COVID-19 Vaccination: Overcoming Vaccine Hesitancy through Social Marketing. Vaccines, 9(4), 319.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Ginter, P. M., & White, D. D. (1982). A social learning approach to strategic management: Toward a theoretical foundation. Academy of Management Review, 7(2), 253-261.
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective: Pearson Upper Saddle River.
Hasan, M., & Sheikh, M. R. (2018). Factors affecting attitude towards social marketing through social media. Pacific Business Review International, 10(12), 20-28.
Lee, N. R. (2020). Reducing the spread of COVID-19: A social marketing perspective. Social Marketing Quarterly, 26(3), 259-265.
Lefebvre, R. C. (2011). An integrative model for social marketing. Journal of Social Marketing.
Maibach, E. (1993). Social marketing for the environment: Using information campaigns to promote environmental awareness and behavior change. Health Promotion International, 8(3), 209-224.
McGovern, E. (2005). Social marketing applications and transportation demand management: An information instrument for the 21st century.
Melovic, B., Jaksic Stojanovic, A., Vulic, T. B., Dudic, B., & Benova, E. (2020). The Impact of Online Media on Parents’ Attitudes toward Vaccination of Children—Social Marketing and Public Health. International Journal of Environmental Research and Public Health, 17(16), 5816.
Onyemah, V., Swain, S. D., & Hanna, R. (2010). A social learning perspective on sales technology usage: preliminary evidence from an emerging economy. Journal of Personal Selling & Sales Management, 30(2), 131-142.
Pitt, L., Crittenden, V. L., Plangger, K., & Halvorson, W. (2012). Case teaching in the age of technological sophistication. Journal of the Academy of Business Education, 13.
ŞENTÜRK, T. (2021). Rethinking Social Marketing and Behavioural Change in Times of Covid-19 Pandemic. Manisa Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 19(1), 321- 338.
Smith, W. A. (2006). Social marketing: an overview of approach and effects. Injury prevention, 12(suppl 1), i38-i43.
Twum, K. K., Ofori, D., Agyapong, G. K. Q., & Yalley, A. A. (2021). Intention to Vaccinate against COVID-19: a Social Marketing perspective using the Theory of Planned Behaviour and Health Belief Model. Journal of Social Marketing.