Social Media’s Role in Achieving Marketing Goals in Iran during the COVID-19 Pandemic

Abstract

This article explores the impact of social media (SM) on the marketing goals of organizations in Iran during the COVID-19 pandemic. We examine the extent to which firms utilize social media marketing to promote their products in Iran compared to the pre-COVID-19 era. The validity and reliability of the 279 survey results are confirmed using internal and external validity and Cronbach’s alpha. The results show that there is a significant positive relationship between the use of SM and the distraction level. Moreover, the gender of the marketer has an impact on the perceived usefulness and application of SM. Finally, a positive effect of working hours per day on the SM usage and the marketing performance is observed. Despite a negative distraction effect, there is no evidence of reduced marketing performance. This research could help organizations to influence the purchasing processes of customers more effectively and at a lower cost.

1. Introduction

With the discovery of the Acute Respiratory Syndrome Coronavirus 2 (COVID-19) in late 2019 and the subsequent pandemic, the global economy experienced an unprecedented shock. Control measures such as social distancing, which slow down the expansion of the outbreak, place drastic pressure on countries’ economies (Bodenstein et al. 2022). The imposed restrictions have paved the way for digital marketing as the only solution for businesses to weather the lockdowns and the resulting restrictive measures to the greatest possible extent (Thukral and Ratten 2021). The risks to employee health during COVID-19 led to a change in their presence in the workplace (Qi 2022). Internet businesses, such as digital marketing agencies, e-commerce, etc., did not need to shut down their businesses completely, compared to a wide range of businesses that shut down during the COVID-19 pandemic (Rezaeinejad 2021). In most cases, these businesses created a platform where their employees could operate remotely. People met their basic needs online, restaurants received orders using only online platforms, and businesses largely moved away from traditional methods to meet the needs of online ordering (Kashyap and Raghuvanshi 2020).

The process of attracting customers through social media (SM) is called social media marketing (SMM), which is a relatively new phenomenon in Iran and represents a significant digital transformation for business models. In the early 2010s, social media marketing (SMM) started to gain traction in Iran, with the substantial digital transformation of business models (Abzari et al. 2014). SMM focuses on the use of SM in marketing, selling, and buying products and services (Hollebeek et al. 2021).

Approximately 80% of SM users report that they use SM as a primary source to search for information about sales, deals, products, events, jobs, and personal study (Whiting and Williams 2013). During the pandemic, communication with customers through a constant and responsible presence on virtual media had a great impact on customers’ attitudes. The use of social media platforms should be considered not only as a gateway for advertising but also to answer the questions and concerns of the customers. More than 90% of SM users report using SM as the main source to obtain information about sales, deals, products, events, businesses, and self-study (Ahearne et al. 2022). Blogging, posting, sharing, tweeting, bookmarking, commenting, and networking are some of SM’s most popular functions. As shown in Figure 1, SM can be divided into a quadrilateral framework.

The social publications in Figure 1 refer to the production and sharing of content through SM (e.g., blogs and news sites). With the appropriate marketing strategy in SM, one can attract a large audience and grow one’s business. Due to the popularity of SM, a presence on and marketing via SM will be useful for almost any business; however, SMM is not a direct sales channel; rather, it is only a tool to increase sales (Salam et al. 2021; Tajvidi and Karami 2021; Rodionov et al. 2022). The pervasiveness of the internet has led to the emergence of SMM as a new platform for digital marketing activities. Meanwhile, the use of tools such as SM based on the online platform is becoming more and more prominent (Ahearne and Rapp 2010). This transformation has highlighted the importance of this research to obtain insights for emerging businesses that implement SMM.

Examining the literature on the impact of COVID-19 on the corporate marketing strategy and marketers’ use of SM reveals several gaps. Naturally, little research has been done on consumer behavior under a pandemic context. Due to the likelihood of new pandemics posing risks in the future, understanding consumers’ behavior in the presence of these pervasive conditions is important for marketers as well as business policymakers. Most studies have focused on a few specific variables (e.g., the effect of attitudes towards SM usage), with a small number of application consequences (Mason et al. 2021). In this study, by adapting the model of Guenzi and Nijssen (2020), we will respond to these challenges. This model is important due to its novelty and the implementation results of the conceptual model. The goal is to provide basic mechanisms for SM use by marketers and provide clear guidelines to managers in pandemic situations. We use three variables under a pandemic condition: an external environmental factor for the unprecedented acceptance of the online shopping market; a social factor that includes changing work practices and social relationships, and an organizational factor to reflect the need to change marketing strategies concerning broader technical support.

Instagram has become an essential element in customer relationship management for companies in Iran, where purchases are generally made with a sense of belonging to a group, following a trend, or imitating celebrities. In this research, we examine a specific pattern of the relationships between variables using collected data from Iranian marketers’ community at a certain time. The main purpose of this research is to study the impact of SM on the marketing goals of organizations during the COVID-19 pandemic. Moreover, solutions that are relatively new in Iran are to be examined, including the relationship between SM and its impact on achieving marketing goals in various organizations, especially startup companies.

Previous research has mainly focused on the positive aspects of SM use by marketers and ignores the negative effects (distraction and privacy). Hence, a more balanced view that can help managers both to reinforce the positive effects of using SM in marketing and to counteract their negative effects would be beneficial. In this study, we respond to these challenges by adapting the Guenzi and Nijssen (2020) model. This model is important due to its novelty and the implementation results of the conceptual model. The goal is to provide basic mechanisms for SM use by marketers and create clear guidelines for managers in pandemic or outbreak circumstances.

This research has three important goals. First, by applying the theory of Motivation–Opportunity–Ability (MOA) introduced by Blumberg and Pringle (1982), and the research by Guenzi and Nijssen (2020), we consider a marketing framework of environmental, organizational, and personal factors that influences SM’s application for marketers. Researchers have used the MOA theory extensively to predict organizational behaviors and job performance. In this context, the MOA logic suggests that marketing professionals should have the motivation and ability to use SM for marketing purposes and understand the opportunities created to apply these tools in light of the recent COVID-19 pandemic. In addition to identifying previous cases, the impact of SM on marketing performance in the recent pandemic is discussed in this study. The presented MOA framework measures the daily use of SM by a sample of Iranian marketers. We focus on the marketer’s ability to integrate SM into the work. As Brooks et al. (2017) noted, previous research has focused on the positive aspects of SM use by marketers and ignores the negative effects, such as distraction and privacy (Tajvidi and Karami 2021). In this research, SM’s negative impacts of distraction on marketing performance are also examined. Hence, a more balanced view that can help managers both to reinforce the positive effects of using SM in marketing and to counteract their negative effects is presented.

As a research gap, one should point out that little research has been conducted on consumer behavior under the COVID-19 pandemic. Considering the possibility of the risk of new pandemics in the future, understanding consumer behavior in the presence of these pervasive conditions is important for marketers as well as business policymakers. Most studies have focused on a few specific variables (e.g., the effect of attitudes towards SM use), with few practical implications. In this research, we will respond to these challenges by adapting Guenzi and Nijssen’s (2020) model. With the appropriate marketing strategy in SM, one can attract a large audience and grow one’s business. Due to the popularity of SM, a presence on and marketing via SM will be beneficial for almost any business.

2. Literature Review

Kashyap and Raghuvanshi (2020) identified preventive strategies with six key success factors for pandemic control with an economy-oriented approach. There are two basic strategies for using SM as a marketing tool: in the first, businesses examine the customer response and comments and use them for marketing purposes (passive approach); in the second, the new, more popular model of internet marketing offers an active approach using direct interaction with potential customers through SM.

During the pandemic, the decrease in the export of Iranian products led to a decrease in the country’s income. In the domestic sector, the demand for some goods and services (e.g., transportation, restaurants and hotels, clothing) was affected due to a decline in household incomes (Salamzadeh and Dana 2021). On the other hand, supply faced a shock due to disruption in the raw material supply network and the limited activities of trade unions. In this section, the risk factors affecting businesses during a pandemic are described at three levels. These risks are classified in Figure 2.

Declining global production due to COVID-19 carries significant risks. The pandemic crisis has severely challenged the global supply chains (international level risk) and imposed supply and demand shocks. Decreased exports to other countries impact Iranians’ declining incomes, which increases national and economic risk levels. Reducing business tax revenues also limit the government’s main sources of revenue. On the other hand, the uncertainty in the time that it may take to return to normalcy and the inactivity of some businesses reduce the overall volume of economic activities (Ikram et al. 2021). Customer attitudes and expectations are also changing, which results in increased industry and business risks. Online sales businesses, food and healthcare manufacturers, and telecommuting companies are experiencing a lower risk, while other businesses run with a medium to high risk of bankruptcy (Belas et al. 2022).

Customers have changed their shopping habits and behavior to adapt to the pandemic crisis. To adapt to the market changes and new consumer behaviors, companies must take appropriate measures (Eger et al. 2021). It is important to note that marketing strategies are directly impacting the competitive advantages of firms. Therefore, firms must intelligently use innovative marketing measures to stay competitive by maintaining customer trust (Rosário and Raimundo 2021). Some strategies that enhance the marketing capabilities of companies during a crisis are developing a product portfolio with rapid innovation based on customer needs, avoiding exploiting the crisis to make short-term gains in social marketing, communicating with customers through various channels, strengthening the distribution network of products, benchmarking the marketing efforts of the best in the industry, optimizing best practices, adopting innovative measures to deal with falling demand, paying attention to customer expectations in different market segments, and prioritizing customers and products (Wang et al. 2020; Polas and Raju 2021). SM can facilitate the acquisition, interpretation, and analysis of marketing information. This provides not only important insights for marketers regarding customers and competitors but also facilitates their communication. Schultz et al. (2012) showed that SM users outperform their non-using counterparts in achieving their marketing goals. Proper SM integration helps marketers to manage their marketing tasks efficiently and effectively by facilitating better marketing performance.

In addition to the positive effects, SM may also have detrimental effects on marketing performance. Universal access turns SM into a potential distraction mechanism. Using SM while performing job tasks creates a continuous flow of interruptions. A user may forget some of the information needed to process job tasks, and, as a result, some information is lost or no longer enters the working memory.

Marketing research has extensively applied the MOA theory to examine topics such as new product introduction, the managerial evaluation of marketing performance, and advertising. The MOA presupposes that employees must both feel motivated and have the skills and knowledge to perform a job, but that environmental factors such as market conditions or organizational support are also essential. In this study, the behavior that we aim to shed light on is the use of SM by marketers in their work. We first define each of the MOA factors concerning the marketing context during a pandemic.

Ability is conceptualized as a set of skills needed to use SM in combination with other traditional communication tools (telephone or email) to achieve marketing goals. Hence, it is referred to as the “integration ability” (Guenzi and Nijssen 2020). The interaction between motivation and ability is rarely pursued in detail in the MOA research (Siemsen et al. 2008). Especially at the individual level, the ability to integrate SM with other tools can act as the main motivator for using this type of tool. The ability to use SM can help marketers better to manage customer relationships and save time. In the MOA model, it is the motivation and ability of the marketer that the model intends to predict. Since the MOA operates at the conceptual level, some arguments for social learning theory, social information processing theory, and organizational support theory have been used to develop the hypotheses. Marketers need the motivation to cope with customer resistance to change, as well as to use new marketing tools (Schillewaert et al. 2005). Marketers will be more inclined to adopt and use a tool if they expect a positive result. The ability or skill in this research refers to the use of SM to better manage customer relationships. These skills include selecting appropriate information and communication resources and the skill of using the related tools and integrating them into daily activities. A positive understanding of the skill indicates confidence in deciding when and how to use the SM tool more effectively. As a result, perceived ability leads to the successful use of SM as a marketing tool. Opportunity refers to a set of environmental variables that affect a person’s motivation and ability to behave in a certain way. If organizations want marketers to utilize SM tools, they must ensure that they have ample opportunity to do so. This opportunity may be due to factors related to the market conditions during COVID-19, such as the unprecedented rise of online shopping or organizational resources that empower marketers.

Understanding customer expectations, as well as competitors’ use of SM technology, can serve as an important factor influencing a marketer’s evaluation regarding the usefulness of new technology and its subsequent application. During the pandemic, the number of users in a digital marketer’s business environment could increase due to the emergence of new technologies, including SM. Under these circumstances, marketers that do not adopt technology-driven innovations are likely to incur high opportunity costs. If the marketer realizes that competitors have also used the new technology, the impact will be stronger and the use of this technology will be recognized as a necessity for them (Avlonitis and Panagopoulos 2005). Sabnis et al. (2013) expressed positive mutual support in the field of marketing. Positive results will convince users of the usefulness of the tool. However, existing research shows a different type of support for the interaction between motivation and ability.

3. Research Method

In this research, we consider the willingness of customers and competitors to use SM in business during the pandemic, as well as organizational support to use SM in marketing processes. High competitive pressure and competitors’ use of SM may also contribute to SM adoption, and customers’ and marketers’ readiness to accept a new technology will result in the spread and dominance of the technology in the market. The organizational environment in this context refers to the opportunities (e.g., training) that the organization provides to the marketer to utilize SM. Unlike external factors, these internal factors are under the management of the firm. Customers prioritize trusted relationships; in other words, despite the recession, customers rely more on “trust” than “low prices” (Matzler et al. 2006). This finding represents an important opportunity for brands. Although marketing budgets have been shrinking, businesses have changed their business models to focus on digital opportunities and online customer access. The widespread closure of shopping malls and increasing online ordering and in-person delivery is the most significant change under quarantine conditions (Bakalis et al. 2020). Since the consumer is unable to attend the store in this case, the adoption of digital technology is likely to improve existing habits to facilitate work, study, and consumption more easily. Public policies also change consumption habits, especially in public places, such as airports, concerts, and public parks (Sheth 2020). Fletcher and Griffiths (2020) studied the COVID-19 pandemic from the volatility, uncertainty, complexity, and ambiguity perspective. Manthiou (2020) acknowledges that the digital and virtual shopping habits that have developed during quarantine remain, given the strength of the habits. These have provided opportunities for organizations to innovate in redesigning existing products.

Hypothesis Test

The list of explored hypothesis tests is provided below.

Hypothesis 1.

Given the unprecedented opportunity to adopt online shopping during the pandemic, the more the marketer understands the market readiness for SM, (a) the more he/she understands the usefulness of the tool (motivation) and (b) his/her ability to integrate SM improves.

Hypothesis 2.

Given the wider technical and information support during the pandemic, the greater the marketer’s perception of the organization’s support for SM, (a) the greater the understanding of the usefulness of this tool (motivation), and (b) the better the ability to integrate the use of SM into the job.

Hypothesis 3.

The greater the marketer’s understanding of SM due to changing work practices and social relationships during the pandemic, (a) the better the understanding of the usefulness of this tool (motivation) and (b) the higher the ability to integrate SM into the job.

Hypothesis 4a.

The greater the motivation (perceived usefulness) of a marketer in using SM during the pandemic, the more frequently he/she uses SM in his/her job.

Hypothesis 4b.

The more the marketer can integrate different SM into the job during the pandemic, the more the marketer uses SM in his/her job.

Hypothesis 4c.

Marketers who have a high level of skill and motivation (perceived usefulness) utilize SM more than their counterparts having a lower level of skill and motivation.

Hypothesis 5.

The more a marketer uses SM during the pandemic, the better his/her marketing performance is.

Hypothesis 6.

The more a marketer uses SM in the pandemic, the higher the level of distraction is.

Hypothesis 7.

The higher the distraction level of the marketer in the pandemic, the lower his/her marketing performance is.

4. Computational Experiments

4.1. Statistical Sample

Since the structural equation modeling method is used in this research, determining the minimum sample size is crucial. Considering that the sample size is normally large in SM, and that there is no general agreement on the sample size required for structural models, many researchers select a minimum required sample size of 200 (Sivo et al. 2006; Hoe 2008). Structural equation modeling requires around 20 samples for each factor, (i.e., a hidden variable), where the minimum sample size is 200 people. After the electronic distribution of the questionnaire, 298 responses were received, and, of these, 279 responses were accepted after removing inappropriate and incomplete responses. It should be pointed out that the probability sampling technique was used.

4.2. Data Collection

Three tools of observation, interview, and questionnaire were used to collect the needed information. To test the framework and hypotheses, an announcement was placed for participation in the electronic survey in the specialized center of Infogram. Infogram is a specialized content center with customers from private or public organizations who want their message to customers conveyed accurately and properly.

Organizational marketing managers were asked to respond. The network sampling method was used to obtain the number of questionnaires. This method is utilized in qualitative research, especially when it is not possible to identify and access the volume of exports concerning the subject of research due to specific conditions, such as the COVID-19 pandemic. In this method, after identifying or selecting the first person, he/she is asked to introduce another expert in that field until the data saturation stage is reached; this process continues.

Table 1. Characteristics of the sample.

Gender	%	Experience	%	Age	%	B2B	%	Industry	%
Male	73.6	10≤	46.4	30>	5.1	B2B	69.3	Food	12.7
Female	26.4	10–20	33.9	31–40	56.8	Mix	18.6	Telecommunication	10.3
		>20	19.7	41–50	36.9	B2C	12.1	Construction	15.6
				>50	1.2			Cosmetics	9.8
								Garments	7.9
								Distribution	15.4
								Other	28.3

provides some descriptive specifications of the sample. The data show that the majority of the participants are male (73.6%), have less than 10 years of marketing experience (46.4%), and operate in the B2B business type (69.3%).

The criteria of the questionnaire are based on the background of the existing marketing literature; some scales are also new or modified (adapted from Gonzi and Nijsen 2020).

Table 2. Division of the questions according to the research variables.

Variable	Type of Variable	Relevant Question Number
Perceived market readiness	Independent	1, 2, 3
Perceived organizational support	Independent	4, 5, 6
Peer influence	Independent	7, 8, 9
Perceived usefulness	Independent	10, 11, 12, 13, 14, 15
Ability to integrate	Independent	16, 17, 18
Social media use	Independent	19, 20, 21, 22
Sales performance	Dependent	23, 24
Distraction	Independent	25, 26, 27

shows how the questions are divided according to the research variables.

One criterion of the general scale of changing work practices and interpersonal social relationships was combined with another criterion of IT support and then expanded to a third criterion to invest in the organization providing SM training to marketing staff considering the way in which customers order under pandemic conditions. In this stage, three criteria have been used to create marketing skills in integrating SM with other marketing tools in sales tasks. Compared to the broad criteria for using SM, the criterion of interest shows the ability to use them in marketing. Respondents are asked to report the average time that they have spent using this new media collection for marketing over the past two months. Marketing time spent on traditional methods (in-person, telephone, or email) was also evaluated.

Finally, marketing performance was measured using two criteria. Business executives and the press believe that SM can be used both to attract new customers and to maintain and expand business with existing customers (Nijssen et al. 2017). In the final research questionnaire, 27 closed questions were used. The designed questionnaire has two main parts; the first part contains the demographic characteristics of the statistical sample, including the control variables, such as age, experience, gender, industry, and type (B2B or B2C) and the types of SM used. Experience, which is a measure relatively equal to age, was not considered. Employability is also added as a condition for accepting the survey, since some organizations have strict policies on using SM, which can still be effective in using it for business purposes. In the second part, questions are asked to measure the variables in the hypotheses and the operational model of the research. Next, the two marketing managers were asked to review the response templates and clarify the guidelines. Based on their feedback, the necessary changes were made to identify the use of SM for marketing purposes. Then, the survey was presented to the whole sample.

4.3. Data Collection Tools

Data collection tools are reliable when they have the two important characteristics of reliability and validity. Since the respondents to the questionnaire had significant work experience in marketing, the questionnaire was designed to obtain accurate and complete information. As a result, internal validity was confirmed. External validation was confirmed by distributing a questionnaire to different geographical areas in the form of telephone interviews or sending electronic questionnaires to obtain information on the marketers.

Convergent validity is a measure used to fit measurement models in the PLS method. The researchers introduced the AVE criterion for measuring convergent validity and found that, in the case of AVE, the critical value is 0.5. The AVE criterion shows the degree of correlation of a structure with its characteristics (Hair et al. 2019). The higher this correlation, the greater the fit, which means that AVE values above 0.5 indicate acceptable convergence validity. Discriminant validity is a measure of how different the metrics of different factors are. In a questionnaire, several questions are asked to assess different factors; thus, it is necessary to determine that these questions are different from each other and do not overlap. This criterion is sometimes referred to as divergent validity. Convergent validity refers to the correlation between the questions of one structure, whereas divergent validity refers to the lack of correlation between the questions of one structure and the questions of another structure (Hair et al. 2019). Fornell and Larcker (1981) noted that divergent validity is acceptable when the amount of AVE for each structure is greater than the common variance between that structure and other structures (i.e., the square of the value of the correlation coefficients between structures) in the model.

Accordingly, the acceptable divergent validity of a measurement model implies that a structure in the model interacts more with its characteristics than with other structures. In the partial least squares method and structural equation modeling, this is accomplished via a matrix in which cells contain the values of the correlation coefficients between the structures and the principal diameter of the root matrix of the AVE values belonging to each structure. The SmartPLS software is used to obtain partial least squares (PLS) estimates for parameters and measurements in the structural equation model. In the SmartPLS software, the Latent Variable Correlations section in the output file is used, where the main diameter shows the squared AVE.

Table 3. Fornell and Larcker criterion.

SP	SMU	PU	POS	PMR	PI	D	AI
							1.00	AI
						1.00	0.696	D
					1.00	0.560	0.683	PI
				1.00	0.549	0.442	0.634	PMR
			1.00	0.662	0.681	0.661	0.759	POS
		1.00	0.703	0.599	0.636	0.601	0.767	PU
	1.00	0.766	0.788	0.606	0.649	0.754	0.866	SMU
1.00	0.583	0.585	0.688	0.612	0.568	0.505	0.537	SP

confirms the divergent validity.

4.4. Consistency and Homogeneity

Consistency indicates the extent to which measuring instruments produce the same results under similar conditions. It also implies obtaining the same results for the same person given that the test is repeated or run by different researchers. Homogeneity is an indicator that all parts of the test are internally compatible. To determine homogeneity, different operational definitions of concepts are tested on similar individuals, where the obtained results must be highly interdependent. In this research, different formats are used to collect the desired variables and observe the time separation between the relevant questions in the questionnaire tool. The smallest correlation observed between the model variables can act as a confirmation of the bias of the method used.

One method of calculating consistency is Cronbach’s alpha (Downing 2004). If Cronbach’s alpha coefficient turns out to be more than 0.7, the consistency of the questionnaire is evaluated as desirable. According to the output of the SmartPLS software in Figure 3, the values of Cronbach’s alpha coefficient are shown in

Table 4. Cronbach’s alpha coefficient values for observed variables.

Variable	No. of Questions	Cronbach’s Alpha
Perception of market readiness (PMR)	3	0.696
Perception of organizational support (POS)	3	0.927
Impact of matched groups (PI)	3	0.830
Perceived usefulness (motivation) (PU)	6	0.934
Ability to integrate (AI)	3	0.953
Use of social media for marketing (SMU)	4	0.943
Marketing performance (SP)	2	0.877
Distraction (D)	3	0.938
Total Alpha	27	0.905

. According to the obtained values, this test has very good internal consistency. The numerical reliability coefficient ranges from 0 to 1, indicating a lack of reliability and reliability of 100%, respectively.

5. Data Analysis and Testing of Hypotheses

In this research, the structural equation model and path analysis are used to identify the effects of the variables presented in the conceptual model and to confirm/reject the hypotheses. Path analysis (structural model) is a technique that shows the relationships between research variables (independent, mediator, and dependent) simultaneously. To analyze the collected data, statistical indicators are first utilized to describe and summarize the demographic characteristics of the sample in the study, including gender, age, experience, marketing type, industry, average hours of SM use per day, and the type of media used by the individuals.

Table 5. Types of social media used by marketers.

Social Media Type	Time Allocation (h)	No. of SM	%
Messaging platforms for direct interaction with customers (e.g., WhatsApp, Skype, Facebook Messenger)	75	1–2	65.9
Social and professional networks (e.g., LinkedIn, Instagram)	53	3–4	32.4
Online conference/webinar (such as Adobe Connect)	31	≥5	1.7
Video hosting/sharing/storage (e.g., YouTube)	23
Subscription/storage space provided (e.g., Slide Share)	18

shows the types of SM used by the marketers under study.

Among the 279 people who answered this question, 74 (approximately 26.4%) were women and 205 (approximately 73.6%) were men. Moreover, there were 14 people under 30 years old (approximately 5.1%), 158 people between 31 and 40 years old (approximately 56.8%), 103 people between 41 and 50 years old (approximately 36.9%), and 4 people more than 50 years old (approximately 1.2%). The marketers reported that they spent an average of 20.9% of their working day using SM (compared to 32.6% and 24.2% for telephone and in-person, and the remaining 22.3% for activities without interpersonal interaction or communication).

5.1. Analysis of the Marketers

The most widely used SMM platforms are WhatsApp, Skype, and Facebook Messenger, which are used by 75% of the respondents; social and professional networks (LinkedIn, Instagram), with 53% use; video hosting or sharing or storage (YouTube), with 23% use; online conferences or webinars (Adobe Connect), with 31% use; and Slide Share usage, by 18%. Most marketers (65.9%) use only one or two types of SM, and the percentage of people using social networking and professional operating systems (LinkedIn, Facebook) in our sample is close to 59% (LinkedIn 2019).

The mean, standard deviation (Std.), and reliability obtained from the analysis of each of the main research variables are shown in

Table 6. Descriptive indicators of the main variables in the sample.

	Average	Standard Deviation	Reliability
PMR	3.18	1.35	0.828
POS	3.33	1.26	0.953
PI	3.49	1.02	0.898
PU	3.66	1.11	0.948
AI	3.29	1.04	0.969
SMU	3.42	1.73	0.959
SP	3.25	1.15	0.942
D	2.68	1.39	0.960

. Among these variables, the highest mean belongs to “perceived usefulness”, and the lowest mean belongs to “distraction”. Moreover, the highest variance is related to the variable “SM utilization” and the lowest variance is related to the variable “impact of matched groups”. Considering that the semantic differentiation scale is used in the questionnaire, the general concept of the spectrum spans the range of 1 to 5. Therefore, the higher the variable value is from 3 (the middle of the spectrum), the more the respondent agrees with the question and vice versa.

The opinions of the sampled Iranian marketers are analyzed from the perspective of indicators such as mean and standard deviation. According to

Table 7. The mean and standard deviation for the observed variables.

	No.	Indicators	Marker	Mean	St.D.
PMR	1	In your industry, given the unprecedented popularity of online shopping, do customers expect suppliers to use SM?	PMR1	2.32	1.18
	2	In your industry, given the unprecedented popularity of the online shopping market, is face-to-face referral still popular?	PMR2	3.40	1.09
	3	In your industry, given the unprecedented popularity of the online shopping market, do your competitors use SM to engage with customers?	PMR3	3.82	1.21
POS	1	Does your firm provide enough support to take advantage of its SM marketing potential?	POS1	3.48	1.02
	2	Is the technical equipment in your firm enough to ensure the optimal use of social media?	POS2	3.17	1.45
	3	Does your firm help you develop the skills needed to take advantage of your SMM potential?	POS3	3.33	1.68
PI	1	Do your colleagues use social media for marketing purposes?	PI1	3.64	1.29
	2	Do your supervisors use social media for marketing purposes?	PI2	3.58	1.03
	3	Do your top executives use SMM?	PI3	3.26	1.38
PU	1	Finding a new customer	PU1	3.65	1.05
	2	Establishing initial communication with potential customers	PU2	3.56	0.7
	3	Strengthening relationships with current customers	PU3	3.75	1.87
	4	Keeping in touch with existing customers	PU4	2.91	1.69
	5	Transferring information to customers	PU5	3.55	1.02
	6	Personalizing supply/customer relationships	PU6	3.50	1.07
AI	1	Mean and St.D. for the integration capability variable	AI1	3.07	1.45
	2	Integration with other available communication tools	AI2	3.12	1.08
	3	Integration with other available work tools	AI3	3.64	1.99
SMU	1	What type of SM or other methods do you use in your marketing business?	SMU1	3.26	1.55
	2	How much time has been spent using these media during the average business day in the last two months?	SMU2	3.30	1.38
	3	What type of SM or other methods do you use in your marketing business?	SMU3	3.33	1.08
	4	How much time has been spent using these media during the average business day in the last two months?	SMU4	2.79	1.09
SP	1	Above-average sales	SP1	3.29	1.03
	2	Higher than average sales of new customers	SP2	3.20	1.46
D	1	Do you usually interrupt what you do to read the news on SM?	D1	2.66	1.22
	2	Do you usually interrupt what you do to interact with your friends on SM?	D2	2.60	1.48
	3	Do you often stop working to pursue some hobbies on SM?	D3	2.79	1.07

, the average value for the market readiness perception variable is 3.18. Considering the average value for the three presented indicators, we conclude that the perception of market readiness and opportunities created during COVID-19 in utilizing SM is high. The maximum value is related to the perception of competitors’ willingness to use SM, which shows that the sampled marketers have recognized the strategy of competitors in the SM marketing environment.

The table shows that the perception of organizational support and the opportunities created during the pandemic in applying SMM is high. The maximum value is related to the index of organizations’ willingness to employ SMM, which shows that marketers have recognized the organization’s willingness to implement SM marketing. Considering the average value for the impact of matched groups implies that the impact of peer groups on utilizing SMM during the pandemic is high.

The average value for the six indicators of perceived usefulness reveals that the perceived usefulness and motivation in using SMM during a pandemic are high. The maximum value is related to the perceived usefulness index in strengthening existing relationships with current customers using SM, which implies that the sampled marketers during the pandemic have recognized the strategy of the organization’s tendency towards SM marketing. Similarly, the ability to integrate SMM for marketing purposes and improving marketing performance is high. The maximum value is related to the SM integration index with other available marketing tools, which implies that the sampled marketers during the COVID-19 pandemic have recognized the strategy of the organization’s desire for an SM marketing space.

The average value for the marketing performance shows that the use of SM in the pandemic to achieve marketing goals and improve marketing performance is high. Aligned with the average of 3.26 related to the index of SM types, it demonstrates that during the COVID-19 outbreak, the sampled marketers have identified the strategy of the organization’s inclination towards the SM marketing space. The maximum value of the SM utilization index has led to an increase in average sales, which indicates that the sampled marketers, under the recent pandemic conditions, have recognized the SM utilization strategy to improve the marketing performance. The average value for the three indicators of distraction shows that the distraction caused by utilizing SMM is low, where the maximum average value is related to the entertainment pursuit index (D3).

5.2. Hypothesis Tests

Before examining the status of hypotheses, the correlation between the research variables is tested and analyzed. As can be seen in

Table 8. Correlations between the variables.

	1	2	3	4	5	6	7	8
PMR	0.624
POS	0.66	0.872
PI	0.54	0.68	0.747
PU	0.59	0.70	0.63	0.755
AI	0.63	0.75	0.68	0.76	0.913
SMU	0.60	0.78	0.64	0.76	0.86	0.855
SP	0.61	0.68	0.56	0.58	0.53	0.58	0.981
D	0.44	0.66	0.56	0.60	0.69	0.75	0.50	0.889

, the results of the correlation analysis show that there is a significant positive correlation between all pairs of research variables. The average extracted variance (AVE) for the structures also exceeded 0.5. The concepts of path analysis are explained through its main feature, the path diagram, which reveals possible causal links between the variables. There are two very important outputs, the value of the t-statistic and the path coefficients (factor loads). If the factor load value between the questionnaire and the hidden variables is greater than 0.4, one can conclude that the question used for this construct measures the desired hidden variable well. To test the research hypotheses, a path analysis in two modes of standard estimation and significance coefficients is discussed. The null hypothesis (H0: there is no significant relationship between the two variables) and the alternative hypothesis (H1: there is a significant relationship between the two variables) are considered to refute/confirm any of the research hypotheses.

Figure 4 shows the results of a structural model for all assumed research relationships. Since PLS requires fewer assumptions on the data distribution than other covariance matrix techniques, it makes the findings less sensitive to deviations from the mean. To test the effects and statistical significance of the hypothetical paths in the structural model, the SmartPLS bootstrap option with 500 samples is used, which is generally recommended to achieve stable results. According to the output of the SmartPLS software in Figure 4, the direct effects between the research variables are calculated according to the research hypotheses.

Figure 5 shows the significant coefficients for all considered relationships. Except for the moderating effect of perceived utility multiplication on merging capabilities, all relationships have a t-score greater than 1.96 with a 95% confidence level.

According to Figure 5, the t-statistic value for the first part of hypothesis 1 (3.212) confirms that the more marketers understand the market readiness for SM, the greater the understanding of the usefulness of this tool (motivation) is. The t-value for the second part is 3.130, which confirms that the more marketers understand the market readiness for SM, the greater the ability to integrate SM into their business. Moreover, all parts of hypothesis 2 are approved, with the values of 5.413, 6.339, 3.862, and 4.355, for the first part, respectively. The first two parts of hypothesis 4 are also approved, with the values of 4.808 and 13.908, respectively. However, the last part of hypothesis 4, which is “Marketers who have a high level of ability and motivation (perceived usefulness) during COVID-19 use SM more than their counterparts with a lower level of ability and motivation”, is rejected, with a statistical value of 0.045. The fifth and sixth hypotheses are confirmed, with the statistics of 6.329 and 24.876, respectively. Finally, the seventh hypothesis, “The higher the level of marketing attention of the marketer in the pandemic, the lower his/her marketing performance”, with a value of 2.066, is rejected.

Table 9. Results of the structural equation model.

Hypothesis (H0)	Assumed Impact	Route Coefficient	Test Statistics (t)	Conclusion
Perception of market readiness → Perceived usefulness	+	0.194	3.142	Confirmed
Perception of market readiness → Ability to integrate	+	0.187	3.153	Confirmed
Perception of organizational support → Perceived usefulness	+	0.4	5.450	Confirmed
Perception of organizational support → Ability to integrate	+	0.447	6.519	Confirmed
Using social media → Distraction	+	0.754	25.126	Confirmed
Using social media → Marketing performance	+	0.469	6.44	Confirmed
Impact of matched groups → Perceived usefulness	+	0.257	3.968	Confirmed
Impact of matched groups→ Ability to integrate	+	0.276	4.543	Confirmed
Distraction → Marketing performance	−	0.152	2.168	Rejected
Perceived usefulness → Using social media	+	0.247	4.552	Confirmed
Ability to integrate→ Using social media	+	0.001	0.047	Rejected
Ability to integrate→ Using social media	+	0.677	13.211	Confirmed

shows the values of standard impact coefficients and the t-statistics for all variables. According to these values, the impact of using SM on the marketing performance is shown to be 46.9%. This model also explains the effect of using SM for marketing purposes (when traditional marketing tools are not applicable), causing 75.4% distraction during the activity. The results in Table 9 show that the unprecedented popularity of the online shopping market improves the motivation and the ability to integrate SM into marketing by 19.4% and 18.7% (β = 0.194 and β = 0.187, p < 0.05), respectively. The results of wider technical and organizational support on increasing the motivation and ability to integrate SM into marketing are also positive and significant, with an impact rate of 40% and 44.7% (β = 0.40 and β = 0.447, p < 0.05), respectively.

The effects of changing work practices and interpersonal relationships on increasing the motivation and ability to integrate SM into marketing, with an impact of 25.7% and 27.6%, are also positive. The adjustment effect of these variables on the use of SM was not confirmed. This finding confirms that the motivation, as well as the ability, to integrate SMM can improve the SM usage in the marketing environment during a pandemic. The motivation and the ability to integrate SM have a significant positive impact on the use of SM in marketing. The effect of SM integration ability is 67.7% stronger than the motivation effect, with a 24.7% impact. The adjustment effect of these variables on the use of SM was not confirmed.

The results showed that there was a significant positive relationship between SM use and the level of distraction, at 75.4% (β = 0.754, p < 0.05). However, the negative effect of predicted distraction on the marketing performance, which shows a positive effect of 15.2%, is not confirmed (β = 0.152). The control variables significantly affect the dependent variables. First, according to previous research (Schultz et al. 2012), we found that older marketers are less likely to integrate SM into their marketing careers (β = 0.04, p < 0.05). Second, the gender of the marketer has a positive effect on the perceived usefulness of SM, which means that women (approximately 26.4% of the sample) are more open to using SM. This finding is consistent with some findings from previous research that show that women are more attracted to SM due to curiosity and the opportunity for social interaction (Lim et al. 2017). Finally, a positive effect of working hours per day on SM usage and marketing performance was observed. In general, the results of the study of the control variables gave higher validity to the findings.

To examine the combined effect of the SM that a marketer uses on his/her performance, on the one hand, marketers who use a variety of SM may have a wider range of expertise, which allows them to make better use of existing benefits and thus improve their performance. To investigate the moderating effect of using different types of SM, a measure called “number of SMs used” was developed for each respondent. The results of this analysis showed a slight adjustment of the relationship between the marketing performance and the use of SM (β = −0.07) by this measurement. In addition, although the direct effect of diversity on the yield was positive, this effect was not significant (β = 0.03).

6. Discussion

The present study is applied research in the field of e-marketing, where it is beneficial to develop applied knowledge to identify the most important factors affecting marketing through SM tools. In this research, a specific pattern of relationships between variables is tested, for which data that examine the characteristics of the statistical sample have been collected during the COVID-19 pandemic. Here, the willingness of customers and businesses to use SM to build relationships during the pandemic, as well as SM’s influence and the organizational support for using SM in marketing processes, are explored. Organizational support in this context refers to the opportunities that the organization provides to the marketer to use SM. Unlike external factors, these internal factors are controlled by the firm’s management. If the firm provides specific training on using SM, marketers can quickly become familiar with the new technology and utilize it.

The purpose of this study is to investigate the impact of SMM during the COVID-19 pandemic. We aimed to gain knowledge regarding SMM and its impact on people’s purchasing decisions during the pandemic. Achieving this knowledge helps organizations to influence the purchasing processes of individuals in a more effective way and at a lower cost and, thus, to increase productivity.

Researchers use the Motivation–Opportunity–Ability (MOA) framework of consumer information processing theory to explore the key factors in performance information processing, satisfaction with marketing performance evaluation systems, and top management goals to change them. Despite the popularity of the MOA theory in the literature, there is no comprehensive method to connect its components. In the MOA model, it is the motivation and ability of the marketer that the model intends to predict. Since the MOA operates at the conceptual level, some arguments from social learning theory, social information processing theory, and organizational support theory have been used to develop the hypotheses in this research.

The framework of the MOA was developed by collecting reports on the demographic status of the respondents via a questionnaire. Indicators such as gender, age, marketing experience, membership in SM, and their main activities were analyzed for the sample. To evaluate the significance between the observable variables and the main variables, the measurement of each of the research variables and the fitness of measurement models from the confirmatory factor analysis were used. Finally, to investigate the status of each of the research hypotheses, structural equation modeling was applied using the SmartPLS software and an effective separation table was extracted. Appropriate statistical methods were utilized and the obtained research hypotheses were reported.

7. Conclusions, Managerial Insights, Limitations, and Future Suggestions

This research studied the impact of SM on the marketing goals of organizations in Iran during the COVID-19 pandemic. This was undertaken to gain knowledge about internet marketing and its impact on people’s purchasing decisions during the pandemic. This knowledge can help organizations to influence the purchasing processes of individuals in a more effective way and at a lower cost and, thus, to increase productivity. During the pandemic, the tendency of people to obtain information through SM has also increased significantly. In this regard, an attempt was made to investigate and explain the impact of internet social media on the achievement of marketing goals during a pandemic by proposing a model. Thus, all organizations benefiting from conventional SM in Iran were selected as the statistical population. In total, 279 sets of responses were accepted after review. The validity and reliability of the questionnaire were also confirmed using content validity by the supervisors, as well as internal validity, external validity, and Cronbach’s alpha.

The framework of the MOA was considered. First, a report on the demographic status of the respondents to the questionnaire was presented, and indicators such as gender, age, marketing experience, membership in SM, and their main activities were analyzed for the sample. To evaluate the significance between the observable variables and the main variables, the measurement of each of the research variables and the fitness of measurement models from the confirmatory factor analysis were used. Finally, to investigate the status of each of the research hypotheses, structural equation modeling was applied using the SmartPLS software and the effective separation table was extracted. The appropriate statistical methods were utilized and the obtained research hypotheses were reported.

The impact of wider technical and organizational support on increasing the motivation and ability to integrate SM into marketing is positive and significant, with an impact rate of 40% and 44.7%, respectively. The effects of changing work practices and interpersonal relationships on increasing the motivation and ability to integrate SM into marketing, with an impact of 25.7% and 27.6%, respectively, are also positive. The findings also confirm that motivation, as well as the ability to integrate SMM, can improve SM usage in the marketing environment during a pandemic. The motivation and the ability to integrate SM have a significant positive impact on the use of SM in marketing. The results confirm the positive effect of using SM on marketing performance. The effect of SM integration ability is 67.7% stronger than the motivation effect, with 24.7% impact. The adjustment effect of these variables on the use of SM was not confirmed.

The main contributions of this research are as follows: (1) considering a marketing framework of environmental, organizational, and personal factors that influence social media’s application for marketers in Iran; (2) identifying the impact of social media on marketing performance during the COVID-19 pandemic in Iran’s market; (3) focusing on the marketer’s ability to integrate social media into their work and measuring the daily use of social media in a sample of Iranian marketers, through the proposed MOA framework; (4) examining social media’s negative impacts of distraction on marketing performance; (5) presenting a more balanced view that can help managers both to reinforce the positive effects of using social media in marketing and to counteract their negative effects.

Some of the managerial insights are as follows. There was a significant positive relationship between SM use and the level of distraction, at 75.4%. However, the negative effect of predicted distraction on marketing performance, which shows a positive effect of 15.2%, was not confirmed. The control variables significantly affected the dependent variables. We found that older marketers are less likely to integrate SM into their marketing careers. Second, the gender of the marketer has a positive effect on the perceived usefulness and the use of SM, which means that women (approximately 26.4% of the sample) are more open to using SM. Finally, a positive effect of working hours per day on SM usage and marketing performance was observed. The results confirm that utilizing social media as a complement to traditional communication tools improves the marketers’ performance. The perceived belief in the usefulness of this tool (motivation), along with its applicability (integrating social media into their marketing work), can be quite strong. Despite a negative distraction effect, there is no evidence of reduced individual marketing performance.

There are many benefits when using SM, some of which are access to a large audience, better and more engaging communication with customers, increasing customer loyalty, and utilizing the customer perspective to gain valuable information about one’s customers. The main drawback of the SM marketing method is its high implementation costs and the time that it takes to succeed, which is problematic for many startups and senior executives at large brands. Moreover, if limited financial resources are available and a long-term SM marketing strategy cannot be supported, there is only a small likelihood of success. Another disadvantage is that different sections of society attract destructive people. For example, sometimes, negative comments on products and services might not be true and are only intended to disparage the brand and create negative feedback among other audiences. Another problem is the difficulty in accurately measuring the true rate of return on the investment of SM marketing.

This research has been conducted only among a sample of Iranian marketers and, as a result, the statistical sample is homogeneous in terms of cultural and value background, as well as the use and influence of SM in Iran. To the best of the authors’ knowledge, due to the COVID-19 pandemic conditions and the novelty of the study, little research has been carried out in this area so far, which has led to the difficulty of explaining and comparing the results of this research with previous research. This research focuses on the positive and negative effects of consumer interaction in SM, while, in many cases, these interactions include negative reviews and feedback about the brand that can drastically reduce business. In this study, the primary reasons for people to join virtual business pages have not been investigated. Recognizing and explaining these causes can provide a more comprehensive understanding of the causes and extent of people’s usage of SM.

As one of the limitations of this work, one can mention that this research was conducted only among a number of Iranian marketers and, as a result, the statistical sample was homogeneous in terms of the cultural and value background, as well as the level of use and penetration of SM in Iran. Due to the novelty of the subject under investigation and due to the pandemic conditions, little research has been done in this field so far, which has led to the difficulty of explaining and comparing the results of this research with past research. Moreover, this research has focused on the positive effects of communication between consumers in SM, while, in many cases, these communications include criticism and negative feedback. Another limitation is that, in this research, the primary causes of people joining the virtual pages of businesses have not been investigated; thus, comprehending and explaining these causes can lead to a broader understanding of the causes and the degree of influence of people regarding SM.

Although, compared to previous studies, a more comprehensive measurement model of SM adoption has been considered in this research, a future stream may be attaining more accurate measurement methods. By linking the use of different social media to specific tasks and characteristics (enhancing relationships with existing customers), researchers may present a more detailed insight into the effects of each type of SM. It is also possible to examine the power of different combinations of SM as another future research direction. Because SM can take up marketers’ time and distract them, more research is needed to better understand this matter and other negative issues (e.g., privacy infringement). One resolution for this could be a systematic simplification and focus on the goal, which could be considered as another future research theme.