Is email marketing campaign still relevant in the age of social-media marketing?

Abstract

Objectives: Email marketing was popular among businesses during the advent of internet technology. It allowed for direct communication with customers. However, advancements in communication technologies brought about social media, and it enhanced communication between ventures and their customers. This study sought to understand whether email marketing campaigns are still relevant in the age of social media marketing.

Method: A quantitative online study was conducted with data gathered using a structured questionnaire. The questionnaire was housed in Google Forms and advertised for participation across Europe. An IP blocker was activated to block responses from outside Europe and Google Translator enabled translation to the local language of participants (as the original language of the questionnaire was English). A total of 1265 valid responses were gathered and analysed using SPSS.

Findings: It was revealed in this study that: a) email marketing is still relevant in the age of social media, and b) social media complements and enhances email marketing campaigns. This is because when companies decide to implement online marketing strategies, they tend to combine the different online marketing channels to enhance their reach.

Conclusion and Future Research: It is concluded that companies seeking to enhance the overall outcome of their online marketing campaign should adopt different channels (including email marketing campaigns) in line with the mixed-strategy Nash equilibrium model, as this would help them to reach more customers. Furthermore, future research should look at consumers’ perception of email marketing in the social media era and be more market-specific in order to validate findings from this study.

Keywords: Email Marketing, Nash Equilibrium, Social Media Marketing

1.      Introduction

Recently, email is considered the most reliable means of electronic commerce and marketing (Sabbagh, 2021). It is one of the fastest methods for exchanging messages over the internet. On the same note, it may be used to book the messages received in the users' inboxes, which they can view whenever they so desire. Jeshurun (2018) stated that email marketing campaigns can include features like creating relevant databases for the target market through internet searches, writing effective emails, creating a message for increasing response rates, and sending the created messages on a one-to-one basis, as opposed to bulk mailing format. It provides a complete database for the company to mail to and can also be used for email marketing campaigns at a specific point in time or within a certain time interval, giving marketers greater control over their email marketing campaign strategies. Email marketing, as described by Jeshurun (2018), is a form of direct marketing that makes use of electronic mail as a means of communicating fundraising or commercial messages to an audience. The scholar went further to state that an effective email marketing strategy includes frequently planning the contents, creative content development, and using spam-free deployment systems, as well as tracking, analyzing, and reporting the email campaign.

The opportunities that social business intelligence (SBI) and high-impact applications offer for sustainable business development have generated high enthusiasm in the academic setting, with more light being shed on data analytics and industry-driven research and development that are focused on analyzing big data from semi-structured content. Today, companies gather a high volume of unstructured content in contextual formats like company documents, emails, websites, and social media (SM) content (Chen et al., 2012). Due to reliance on moods, sentiments, and individual preferences, marketing communication has gained special interest. Thus, the development of SBI, as it relates to products, processes, and services in creating marketing campaigns, presents challenges for business intelligence (BI) and SM professionals alike (Rainey & Robert, 2015). Essentially, the discussion points to the fact that social media comes with advanced data features that allow its users to generate extensive (unstructured) data in real-time and analyze it to reach a definitive marketing decision. Thus, this research seeks to assess the relevance of email marketing campaigns in today’s world, considering the pros that come with social media marketing (especially in the area of data analysis).

The first section explains the purpose of the research. The second section reviews relevant literature on social media and email marketing campaigns. The third section discusses the research method. Gathered data is analyzed in the fourth section, while the last section discusses findings from the research and its contributions, as well as the conclusion and future lines of research.

2.      Literature Review

2.1. Email Marketing Campaigns

Email marketing is widely used for direct online marketing campaigns, especially to strengthen customers' loyalty in order to create leverage for cross-and up-selling potential. Although this instrument is based on the initial form of online communication (email), evidence suggests that it is still one of the instruments widely used for online marketing activities (Ahrholdt et al., 2019), making it possible to reach consumers in the most inexpensive and effective way (Hudák et al., 2017). In turn, the reaction of customers to emails can be measured and analyzed by optimizing the campaigns.

However, businesses are faced with challenges in implementing efficient email campaigns when it comes to the response gotten from the emails and segmenting customers based on loyalty. Decision tree analysis can be used to extract customer-based information from the response gathered through email campaign data. In a study aimed at predicting customer loyalty and enhancing response rates for email campaigns, specifically by adopting open and click-through rates based on decision tree analysis (Qabbaah et al., 2019), the scholars found similar issues with email marketing campaigns. Furthermore, Mouro (2016) identified the most influential factors behind the decision of ventures to open emails as promotions and campaigns. Sandage (2017) pointed out that increasing the opening rate of emails can be done by making sure that the "from" line, pre-header, and subject line are constructed in a friendly way.

To further stress the significance of email marketing, it is imperative to review the 2016-2020 and 2020-2024 reports by Radicati Group (2020), where it was stated that about 7.7 billion emails will be available across the world by the end of 2020, while the global email traffic, within the same period, is expected to reach about 257 billion emails per day. On the same note, emails remain the mainstay of daily business activities and communication between ventures and their customers. It is also highly useful when one considers that it is mandatory for any kind of online activity. Such online activities may include social networks, instant messages, or other kinds of online accounts that require registration with email. The projection was that in 2020, the daily email sent and received between ventures and their customers could be higher than 293 billion. Going further, the said figure is expected to reach 347 billion emails per day by 2023. The effectiveness and reach of email marketing could be greatly improved with the aid of social media tools and numerous analytical techniques. Based on the Litmus 2019 State of return on investment, it is projected that companies receive $42 in return for each dollar they invest in email marketing. Therefore, the question that researchers constantly ask is whether email marketing is dead. Based on the Radicati Group’s 2020 record, it is reported that there is no chance for such a thing to happen. This is because email marketing still remains an integral tool for attracting and retaining customers, with a potential return on investment that could go up to 4400%.

In recent times, email marketing campaigns have been utilized across other fields like tourism (Floriˇci´c, 2018; Fotache et al., 2016), education (Tarczydło & Miłoń, 2019; Grubor et al., 2018), health (Wadia, 2020; Bradley et al., 2020; Mathews & Buys, 2020), and politics (46), and these studies have been able to document the effectiveness of such campaigns.

2.2. Social Media Marketing, Social Data and Social Media Analytics

In today’s business environment, identifying how customers spend most of their time on social media has become the main factor in attracting future customers. This is because once the marketer is able to develop an understanding of potential customers (in areas like gender, age, race, residence, hobbies, and so on), they can use that portfolio to create market prospecting. Such data is known as social data (Păvăloaia et al., 2020).

However, social data is imperfect for a number of reasons, like its limitation when a user decides not to make personal data available for public consumption (in most cases, where users prefer to remain anonymous). Additionally, there are numerous fake accounts on social media that distribute social marketing content, which companies consider useless. Over the years, Facebook has placed emphasis on addressing such an issue by developing different artificial intelligence (AI) algorithms that can identify and delete such fake accounts. It was reported by Facebook representatives that in March 2020, they detected and removed 2 billion fake accounts in the course of the previous year (Păvăloaia et al., 2020). Thus, with the aid of a machine learning system, known as deep entity classification (DEC), they were able to reduce fake accounts to 5% of total Facebook accounts (Păvăloaia et al., 2020).

Going further, interpreting client-related data from comments on social networks is still a big challenge for companies. Often, these comments prove to be difficult to interpret as the algorithm being used might be misled by specific words or groups of words. As a result of the advancements made in the area of AI, especially in the aspect of natural language processing (NLP), many techniques have emerged for analysing user sentiments and opinions in SM interactions (Farzindar & Inkpen, 2020).

Social media analytics are computerized tools used for collecting, analyzing, monitoring, summarizing, and displaying social data. On the same note, they aid in collecting and interpreting unstructured data in order to ease interactions and extractions of the needed patterns (Zeng et al., 2010; Liere-Netheler et al., 2019). To undertake social media analytics on customer feedback, researchers normally use techniques for analysing texts and sentiments (Georgescu & Bogoslov, 2019; Georgescu & Bogoslov, 2019).

In recent times, sentiment analysis has been extensively applied within the context of social media users (Iglesias & Moreno, 2019). It has also been observed that there is an increased use of AI, such as machine and deep learning specific algorithms (Patel & Passi, 2020), random forest, support vector machine, K-nearest neighbour (KNN), etc. These could be successfully applied to data gathered through social platforms or other forms of web-based data for the purpose of analysing users’ sentiments (Sánchez-Rada & Iglesias, 2019). Studies have implemented multi-level sentiment networks that provide heatmap visualization, asterism graphics, and a map of semantic word data with emotions in order to gain a better understanding of the reactions, feelings, and sentiments of users of social networks (Ha et al., 2019). These studies offer classifications for emotions and sentiments. Other authors have developed a network (of Convolutional Neural Network type) for classifying feelings (negative, positive, and neutral) with the aid of three sets of data and a different series of algorithms specific to deep learning, like decision trees and random forests (Kim & Jeong, 2019). Feelings have also been classified with a hybrid construction between emotional word vector and conventional word embedding (Mao et al., 2019), with another designed and developed innovative system that can be used for deep learning types by applying different classifications for multiple emotion predicaments, similar to that of Twitter (Jabreel & Moreno, 2019).

Compared to emails, the enhanced role of social media in the marketing sphere as it relates to market prospecting and the attraction of potential customers is widely acknowledged, especially when combined with advanced platforms and technologies. Thus, another study considered big data (seen in the form of artefact novelty) as a feature of data processing technology used for gathering data about potential customers (Chen et al., 2010). Another study created a conceptual framework for analyzing how social media tools can be used to improve business value in corporations by discovering knowledge flows, leveraging network effects, and increasing capabilities for innovation (Garcia-Morales et al., 2018), while another study looked at the relationship between social media and business performance (Martn-Rojas et al., 2020). Their arguments centre on the importance of online social connectivity and social corporate networking in relation to their link with social media marketing. Another study was designed with the aim of assisting IT service companies in order to narrow the gaps between their social media marketing strategies and those of their customers by identifying and comparing the logic of their posts with the posts generated by social media users (Shen et al., 2020). Going further, this model could be successfully deployed by companies in different sectors, notwithstanding the nature of the business under consideration. A specific study was conducted in the insurance sector, with evidence suggesting that social channels could be the best way for gauging potential insurers (Kopanakis, 2020).

As a result, by utilizing social media, businesses can easily formulate service policies that are appropriate for assessing customer behavior, detecting fraudulent claims, and providing claim-related information to their customers, particularly in the case of natural disasters. Therefore, it does seem that social media has increased in relevance, but it is not clear whether this relevance is higher than what is obtainable in email marketing.

2.3. Hypothesis

Based on the above discussions, it is hypothesized that:

H1. Email Marketing is no longer relevant in the age of Social Media

H2. Social Media does not complement or enhance relevance of Email Marketing

2.4. Theoretical Foundation

2.4.1.      Mixed-Strategy Nash Equilibrium

The mixed-strategy Nash equilibrium is one of the game theories. Game theory is the study of mathematical models of strategic interactions among rational agents. It is widely applied in the fields of logic, social science, computer science, and system sciences. It is an umbrella term for the science of logical decision making in humans, animals, and computers. Its applicability spans a wide range of behavioral relationships (Munoz-Garcia, 2017).

In game theory, the Nash equilibrium is a decision making theorem that states that a player will be able to attain a desired outcome if they deviate from their initial strategy. The concept is that the optimal outcome of a game can be attained in cases where no player has an incentive to deviate from the chosen strategy or one where a different strategy is chosen after considering the choice of opponents (competitors) (Munoz-Garcia, 2017). Thus, assuming that their competitors remain constant with their strategy, an individual would not be able to receive an incremental benefit. The final outcome would be that a game could have no Nash equilibrium or it might have numerous Nash equilibriums (Munoz-Garcia, 2017).

The mixed strategy Nash equilibrium, which is a more general view of a steady state, permits that the choice of the player can vary or deviate on each occasion in the course of the game. The implication is that the player can decide to choose probability, distributions, or other sets of actions obtainable. This form of steady state is known as stochastic (as it involves probability). It is modelled by a mixed strategy of Nash equilibriums. In mixed-strategy, A Nash equilibrium is a mixed strategy action based on the understanding that a single player cannot achieve a higher expected payoff based on the player's preferences in probable outcomes (such as lottery tickets). The best possible outcome would be where the player decides to make varied choices in order to increase the possibility of attaining the desired outcome. This is demonstrated in the example below.

Table 1. Mixed-Strategy Nash Equilibrium

	Player Y
Player X		Heads	Tails
	Heads	1, -1	-1, 1
	Tails	-1, 1	1, -1

In this game, pure strategy Nash equilibrium does not exist. Thus, when players are playing the game, they need to expect unpredictable outcomes; they need to randomly choose strategies to prevent possible exploitation. For instance, if player X has a 75% chance of playing the Heads and a 25% chance of playing the Tails, then when player Y chooses Tails with a 100% chance, they can get an unexpected payoff of 0.75 x (1) + 0.25 x (1) = 0.5. However, this is not possible in equilibrium since player X would want to play Tails with a 100% chance to deviate from the original mixed strategy. Considering that this game is entirely symmetric, it can easily be seen that in mixed strategy Nash equilibrium, the two players would choose Heads and Tails with 50% each. Under such a condition, the expected payoff for both players would be 0.5 x (1) + 0.5 x (1) = 0, and none of the players would be able to do better by deviating to another strategy. Generally speaking, there is no guarantee that mixing would produce a 50/50 outcome at equilibrium, but it still stands a higher chance than the pure (single) strategy.

In the context of email marketing, the implication is that a mixed strategy (adopting both email and other forms of marketing activities) would produce the best possible outcome – higher research, higher customer persuasion, higher customer interests, and so on – as against adopting only an email marketing strategy. This is because there are numerous online marketing strategies that can be used to engage customers, and social media seems to be the best in today’s business world. Thus, any business seeking to truly engage customers in the internet age should look beyond "just email marketing" and adopt a mixed strategy targeting customers through different electronic media and platforms.

3.      Research Method

3.1. Data

The data used in this study was from an online survey of 1265 business owners across Europe. Responses were gathered through Google Forms (online hosted survey) from January 1st, 2022 to June 28th, 2022, based on a structured questionnaire. A primary data analysis method is used in this study, and it conforms to ethical standards. The survey contained preliminary instructions for the participants, and it also asked for their consent to participate in the study. The sampled population were business owners. The nature of their business was irrelevant in the study, as the study gathered responses from owners of different businesses. The decision to choose business owners was based on the fact that businesses are the ones who implement email marketing strategies, and they are best positioned to assess whether or not such strategies are still relevant in the age of social media. A convenience-based sampling method was used to select the participants, as they participated in the study at their convenience (with the survey available online 24/7). Google AdSense (Continent-Targeting) was used to persuade responses across Europe. The questionnaire was originally written in English but was automatically translatable to the local languages of potential participants based on their Internet Protocol (IP) address. All non-European IP addresses are automatically blocked from participation, based on Google Adwords settings, to avoid response biases. The characteristics of the participants are presented in Table 2.

Table 2. Characteristics of participants.

Characteristics	N (%)
Gender Male Female	737 (58.3) 528 (41.7)
Age Below 20 20-29 30-39 40-59 50-59 60 and above	72 (5.7) 201 (15.9) 459 (36.3) 222 (17.5) 198 (15.7) 113 (8.9)
Education No education Elementary school Middle school High school College Graduate	18 (1.4) 42 (3.3) 98 (7.7) 110 (8.7) 802 (63.4) 195 (15.4)
Annual Income of Business Less than $10,000 $10,000-$49,999 $50,000-$99,999 $100,000-$49,999 $150,000 and above	64 (5.1) 84 (6.6) 229 (18.1) 301 (23.8) 587 (46.4)
Have you ever used email marketing for your business? Yes No	1238 (97.9) 27 (2.1)

                                                    Note: N = 1,265. The unit is US$           

Respondents were comprised of 737 (58.3%) men and 528 (41.7%) women. The majority are in their thirties (459 or 36.3%), 222 (17.5%) in their forties, 201 (15.9%) in their twenties, 198 (15.7%) in their fifties, 113 (8.9%) are aged sixty years and above, while 72 (5.7%) are aged below twenty. Respondents' top three educational qualifications are: college 802 (63.4%), graduate 195 (15.4%), and high school 110 (8.7%). Among the businesses represented, 587 (46.4%) earn a revenue of $150,000 or more per annum. 1238 (97.9%) of the respondents have used email marketing to promote their business.

3.2. Measures

A structured questionnaire was used to gather data for this study, and the questionnaire was divided into two parts. The first part focused on the respondents’ demographic profiles. The second part of the questionnaire was structured using a 5-point Likert scale. Respondents had to choose from: Totally Disagree (1) to Totally Agree (5). Validity of the test materials is imperative as it determines overall quality data. For this study, exploratory factor analysis and item-to-total correlation were used to validate and measure the reliability of the multiple items used in this study, as contained in Table 3.

Table 3. Factor loadings and reliability estimates for relevance of Email marketing in social media era.

	Items	Factor Loadings	Item-To-Total Correlation	Cronbach’s α
Email	General perception on email marketing: before and now.	.854	.891***	.812
	Email marketing in the social media era	.894	.879***
	Is email marketing relevant in the social media era?	.691	.732***
SM	Importance of social media in today’s marketing world	.677	.751***	.830
	Does social media complement or enhance email marketing?	.693	.653***
	Overall adopting of email marketing and social media marketing	.782	.738***

Note: ***p <.001. SM = Social Media

In order to examine Varimax rotation and construct validity, we used exploratory principal component analysis. 0.711 was obtained as the Kaiser-Meyer-Olkin (KMO) with a total of 2006.319 at the 0.1% significance level as the approximate Chi-square of the spherical Bartlett test, indicating that it was appropriate to use factor analysis (House et al., 2004; Bartlett, 1950; Bawa & Anilakumar, 2013; Kaiser, 1974). All factors loaded highly, ranging from 0.691 to 0.894, in line with the measurement construct. A Crombach alpha result on reliability returned 0.812 and 0.830 for email and social media, respectively. Based on existing empirical views (Cortina, 1993; Costa-Font & Mossialos, 2005), the value of an alpha is influenced by the number of items on the scale; it is imperative to consider item correlation with the alpha test (Field, 2013). The item-to-total correlation reliability test ranged from 0.653 to 0.891, an indication of the internal consistency of the measures.

4.      Analysis

4.1. Descriptive Statistics

Table 4. Descriptive Statistics of Variables

	Items	Min	Max	Mean	Std. Dev
Email	General perception on email marketing: before and now.	1	5	4.5	.46934
	Email marketing in the social media era	1	5	4.3	.44037
	Is email marketing relevant in the social media era?	1	5	4.1	.52899
SM	Importance of social media in today’s marketing world	1	5	4.4	.43520
	Does social media complement or enhance email marketing?	1	5	4.5	.40347
	Overall adopting of email marketing and social media marketing	1	5	4.4	.46934

Note: SM = Social Media

Table (4) presents the descriptive statistics of the variables loaded in this study. The main focus is on the mean value, and it ranges from 4.1 to 4.5. The implication is that the responses are closer to 5 (Totally Agreed). Thus, respondents think that email marketing is still relevant in the social media era, and social media does complement and enhance email marketing. This is in line with the earlier view that integrated online marketing activities incorporate marketing across different online channels (such as email, social media, SEO, and so on). Thus, they are all relevant and complementary, as well as enhancing each other. This is further supported by the mixed-strategy Nash equilibrium; adopting different strategies for the same goal increases the likelihood of attaining that goal.

4.2. Linear regression

Table 5. Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.849a	.721	.712	.32959
a. Predictors: (Constant), Social Media

In order to test the hypothesis, linear regression analysis was adopted. The first part of the linear regression analysis is the model summary as documented in Table 5. The table provides the R and R² values. The R value represents the simple correlation and it is 0.849 (as contained in the "R" column), an indication that there is a near perfect correlation (84.9%) between the independent and dependent variables. The R² value (the "R Square" column) shows the extent of the total variable in the dependent variable (market capitalization) that can be explained by the independent variables (trading volume and price) – and it was found that 72.1% can be explained, which is very large. All these findings are significant at p<0.05. The implication is that the decision to adopt email marketing is 72.1% influenced by the adoption of social media marketing, whereby, the higher the adoption of social media marketing, the higher the adoption of email marketing. Thus, social media marketing both complements and enhances email marketing (Watson et al., 2000). This is understandable, as companies who decide to adopt online marketing activities will likely combine the two in order to increase their reach.

Table 6. ANOVA^a

Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	19.367	4	4.842	44.571	.000b
	Residual	26.615	245	.109
	Total	45.982	249
a. Dependent Variable: Email Marketing
b. Predictors: Social Media Marketing

In the ANOVA Table 6, it is shown that the regression model predicts the dependent variable significantly well with p<0.0005, which is less than 0.05, indicating that the overall regression model statistically significantly predicts the outcome variable (implying that it is a good fit for the data). Thus, the findings from the model summary are significantly supported in the ANOVA.

Table 7. Coefficients^a

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
1	(Constant)	2.103	.370		5.678	.000
	Social Media Marketing.	.236	.298	.276	.793	.000
a. Dependent Variable: Email Marketing

The coefficient Table 7 is the final part of the regression analysis, and it provides the necessary information for predicting the relevance of email marketing in the age of social media (by focusing on the "Sig" column). The value in "B" (0.236) is positively significant. This implies that for every unit increase in social media marketing, there is a 23.6% increase in email marketing (confirming the relevance of email marketing in the social media era, as well as the fact that social media complements and enhances email marketing). Thus, the null hypothesis is rejected as: 1) Email marketing is still important (relevant) in the Social Media era; and 2) Social Media marketing complements and enhances email marketing.

5.      Discussion and Conclusion

This study set out to assess if email marketing is still relevant in the age of social media. A number of studies (Păvăloaia et al., 2020; Farzindar & Inkpen, 2020; Liere-Netheler et al., 2019) point to the understanding that email marketing is still relevant in the social media era, but it is complemented and enhanced by social media. To achieve this objective, a quantitative study was set up via an online structured survey, and 1265 responses were gathered across Europe. To avert response biases, non-European IP addresses were prohibited from participating in the study. The respondents were business owners. The choice of respondents was because they are the ones that engage in email marketing activities and can utilize their experience to state whether or not it is still relevant. The collected data was analyzed using SPSS version 25.0, with a focus on descriptive statistics and linear regression.

The findings rejected the null hypothesis, implying that: a) email marketing is still relevant in the age of social media; and b) social media complements as well as enhances email marketing. Thus, it is concluded that ventures seeking to enhance their online presence and reach should also consider integrating email marketing into their online marketing activities.

6.      Limitations and Future Study

The main limitation of this study is that it focused on business owners. Although the reason for such a decision is valid, it is imperative to state that it would be good to understand how consumers perceive the relevance of email marketing in the social media era. Additionally, the study targeted the entire European countries with little (insignificant values compared to the overall population) responses from different countries. Thus, the findings might not be entirely applicable to the whole continent as the response rate cannot be considered a true representation of the European population.

Thus, it is suggested that future studies should seek to assess the relevance of email marketing in the social media era by focusing on consumers’ perspective (as they are the ones that would decide whether or not to respond to the marketing activities). Additionally, more market-specific (based on a single country) research is also suggested to test the applicability of findings in a given country (market).

References

Ahrholdt, D., Greve, G., & Hopf, G. (2019). E-Mail-Marketing. In: Online-Marketing-Intelligence. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-26562-5_5

Appendices

Bartlett, M. S. (1950). Tests of significance in factor analysis. British Journal of Statistical Psychology, 3, 77–85. https://doi.org/10.1111/j.2044-8317.1950.tb00285.x

Bawa, A. S., & Anilakumar, K. R. (2013). Genetically modified foods: safety, risks and public concerns—a review. Journal of food science and technology, 50(6), 1035-1046. https://doi.org/10.1007/s13197-012-0899-1.

Bradley, J., Gardner, G., Rowland, M. K., Fay, M., Mann, K., Holmes, R., Foster, E., Exley, C., Don Bosco, A., Hugueniot, O., & Moynihan, P. (2020). Impact of a health marketing campaign on sugars intake by children aged 5-11 years and parental views on reducing children's consumption. BMC public health, 20(1), 331. https://doi.org/10.1186/s12889-020-8422-5

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503

Chen, H., Chiang, R. H., & Storey, V. C. (2010). Business intelligence research. MIS Q, 34, 201–203.

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98. https://doi.org/10.1037/0021-9010.78.1.98.

Costa-Font, J., & Mossialos, E. (2005). Is dread of Genetically Modified food associated with the consumers’ demand for information? Applied Economics Letters, 12(14), 859-863. https://doi.org/10.1080/13504850500365830.

Farzindar, A. A., & Inkpen, D. (2020). Natural Language Processing for Social Media. Synthesis Lectures on Human Language Technologies, 13, 1–219. https://doi.org/10.2200/S00999ED3V01Y202003HLT046

Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.

Floriˇci´c, T. (2018). Digital Tourism Promotion and E-mail Marketing. In Innovation Management, Entrepreneurship and Sustainability; IMES 2018; Vysoká škola ekonomická v Praze: Prague, Czech Republic, pp. 317–331.

Fotache, M., Dumitriu, F., & Greavu-Serban, V. (2016). Differences in Information System Skills of Offshoring Source Markets and Destination Markets. Transformation of Business Economics, 15, 478–799.

Garcia-Morales, V. J., Martín-Rojas, R., & Lardón-López, M. E. (2018). Influence of social media technologies on organizational performance through knowledge and innovation. Baltic Journal of Management, 13(3), 345-367. https://doi.org/10.1108/BJM-04-2017-0123

Georgescu, M. R., & Bogoslov, I. A. (2019). Importance and Opportunities of Sentiment Analysis in Developing E-Learning Systems through Social Media. In DIEM: Dubrovnik International Economic Meeting; Sveuˇcilište u Dubrovniku: Dubrovnik, Croatia, Vol. 4, pp. 83–93.

Grubor, A., Ðoki´c, N., & Mili´cevi´c, N. (2018). The use of e-mail marketing in accordance with permission marketing approach in promotion of a study program. Strategic Management, 23, 26–31.

Ha, H., Han, H., Mun, S., Bae, S., Lee, J., & Lee, K. (2019). An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data. Applied Sciences, 9(12), 2419. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app9122419

House, L. O., Lusk, J., Jaeger, S. R., Traill, B., Moore, M., Valli, C., ... & Yee, W. (2004). Objective and subjective knowledge: Impacts on consumer demand for genetically modified foods in the United States and the European Union. Journal of Agrobiotechnology Management and Economics, 7, 113–23.

Hudák, M., Kianičková, E., & Madleňák R. (2017). The Importance of E-mail Marketing in E-commerce. Procedia Engineering, 192, 342–347. https://doi.org/10.1016/j.proeng.2017.06.059

Iglesias, C. A., & Moreno, A. (2019). Sentiment Analysis for Social Media. Applied Sciences, 9(23), 5037. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app9235037

Jabreel, M., & Moreno, A. (2019). A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets. Applied Sciences, 9(6), 1123. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app9061123

Jeshurun, S. B. (2018). A Study on the Effectiveness of Email Marketing. Shanlax International Journal of Management, 6(S1), 84-86. https://doi.org/10.5281/zenodo.1461282

Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. DOI 10.1007/BF02291575

Kim, H., & Jeong, Y.-S. (2019). Sentiment Classification Using Convolutional Neural Networks. Applied Sciences, 9(11), 2347. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app9112347

Kopanakis (2020). Significance of Social Media Analytics for Insurance Businesses. 2020. Available online: https://www.mentionlytics.com/blog/social-media-analytics-for-insurance-businesses/

Liere-Netheler, K., Gilhaus, L., Vogelsang, K., & Hoppe, U. (2019). A Literature Review on Application Areas of Social Media Analytics. In BIS 2019: Business Information Systems; Lecture Notes in Business Information Processing; Abramowicz, W., Corchuelo, R., Eds.; Springer: Berlin/Heidelberg, Germany, Vol. 354.

Mao, X., Chang, S., Shi, J., Li, F., & Shi, R. (2019). Sentiment-Aware Word Embedding for Emotion Classification. Applied Sciences, 9(7), 1334. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app9071334

Martín-Rojas, R., García-Morales, V. J., Garrido-Moreno, A., & Salmador-Sanchez, M. P. (2020). Social Media Use and the Challenge of Complexity: Evidence from the Technology Sector. Journal of Business Research, 129, 621-640. https://doi.org/10.1016/j.jbusres.2019.12.026

Mathews, R., & Buys, D. (2020). Healthy Eating Social Marketing Campaigns: A Systematic Review of the Literature. Journal of Academy of Nutrition and Dietetics, 120 (9). DOI: https://doi.org/10.1016/j.jand.2020.06.077

Mouro, C., & Alturas, B. (2016). Factors influencing the consumer to accept advertising by e-mail, 2016 11th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-6. doi:10.1109/CISTI.2016.7521365.

Munoz-Garcia, F. (2017). Mixed Strategy Nash Equilibrium. Washington State University. https://felixmunozgarcia.files.wordpress.com/2017/08/slides_61.pdf

Patel, R., & Passi, K. (2020). Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning. IoT, 1(2), 218–239. MDPI AG. Retrieved from http://dx.doi.org/10.3390/iot1020014

Păvăloaia, V-D., Anastasiei, I-D., & Fotache, D. (2020). Social Media and E-mail Marketing Campaigns: Symmetry versus Convergence. MDPI Symmetry, 12(1940), 1-23. Doi:10.3390/sym12121940.

Qabbaah, H., Sammour, G., Vanhoof, K., Voronin, A., Ziatdinov, Y., Aslanyan, L., Sahakyan, H., Rayan, Z., Alfonse, M., Salem, A. B. M., et al. (2019). Decision Tree Analysis to Improve e-mail Marketing Campaigns. International Journal of Theoretical and Applied Science, 26, 3–36.

Rainey, D. L., & Robert, J. A. (2015). The Pursuit of Sustainability: Creating Business Value through Strategic Leadership, Holistic Perspectives, and Exceptional Performance. Information Age Publishing—IAP: Charlotte, NC, USA.

Sabbagh, F. (2021). Email Marketing: The most Important Advantages and Disadvantages. Journal of Economics and Technology Research, 2(3), 14-30. http://dx.doi.org/10.22158/jetr.v2n3p14

Sánchez-Rada, J. F., & Iglesias, C. A. (2019). Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Information Fusion, 52, 344–356. https://doi.org/10.1016/j.inffus.2019.05.003

Sandage, E. (2017). Best Practices in E-Mail Marketing. The Membership Management Report, 13(9), 6. https://doi.org/10.1002/mmr.30779

Shen, C. W., Luong, T. H., Ho, J. T., & Djailani, I. (2020). Social media marketing of IT service companies: Analysis using a concept-linking mining approach. Industrial Marketing Management, 90, 593–604. https://doi.org/10.1016/j.indmarman.2019.11.014

Tarczydło, B., & Miłoń, J. (2019). E-Marketing Campaign for a University. Case Study. Marketing of Scientific and Research Organizations, 31(1), 151-172. https://doi.org/10.2478/minib-2019-0017

The Radicati Group, Inc. (2020). (Technology Market Research Firm), Email Market, 2020–2024 Executive Summary, 27 April 2020. Available online: https://www.radicati.com/?p=16885

Wadia, R. Effectiveness of health marketing campaigns. British Dental Journal, 228, 595. https://doi.org/10.1038/s41415-020-1549-z

Watson, R. T., Zinkhan, G. M., Pitt, L. (2000). Integrated Internet Marketing. Communications of the ACM, 43(6):97-102. DOI: 10.1145/336460.336483

Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25, 13–16. DOI: 10.1109/MIS.2010.151