INFLUENCE OF CONTENT MODERATION ON SOCIAL MEDIA …

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International Journal of Accounting & Business Management

www.ftms.edu.my/journals/index.php/journals/ijabm

Vol. 9(No.1), April, 2021

ISSN: 2289-4519 DOI: 10. 24924/ijabm/2021.04/v9.iss1/17.35

This work is licensed under a Creative Commons Attribution 4.0 International License.

Research Paper

INFLUENCE OF CONTENT MODERATION ON SOCIAL MEDIA

MARKETING

Prashaant A/L Gopalkrishnan

MBA Alumni,

FTMS Global College,

[email protected]

Tusna a/p Ravishankar

Lecturer,

FTMS Global College,

[email protected]

ABSTRACT

The internet has transformed how we socialize and transact and with the ever-

increasing number of users connecting online, that trend does not seem to disappear

anytime soon. Social media has become an integral part in today’s society with

approximately 3.8 billion people currently active on social media. With that, traditional

marketing methods have also evolved with brands and companies leveraging on the

Internet and social media through online marketing, advertising, and sales, generating

millions in the process. This exponential growth has brought up the ugly side of the

Internet and social media as scammers, hackers and basically anyone with a laptop and

decent knowledge of technology seek to exploit the vulnerabilities for personal gain,

potentially costing millions in damages as well as ruining lives and reputations, in the

process. This research aims to discover the role content moderation has in influencing

social media and social media marketing. This was accomplished by formulating a series

of hypotheses, which was then tested against a convenience sampling of 248

respondents. The findings confirmed and outlined the positive impacts each dependent

had with its predictor. Most importantly, the findings also confirmed the positive impact

content moderation mediated between social media and social media marketing. This

research has important implications for online business owners and consumers alike.

Key Words: social media, social media marketing, content moderation, consumer, brands, companies, business.

1. Introduction

Prior to the social media bang, marketers felt social media marketing was just another new phase that is likely to pass or eventually die out, something along the lines of

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pyramid and networking scams. That though process shifted when Facebook started to attract attention from 2004, with the development of numerous social media marketing strategies. This ‘marketing’ tool is responsible for start-ups and established companies to gain the traction and attention without having to spend millions of dollars on advertisements. 90% of marketing executives currently plan their marketing strategies by utilizing social media while successful businesses utilize social media for brand marketing, lead generation, customer retention, research, and e-commerce. Currently, social media manages to significantly reduce marketing expenses, lead time needed to market products and services, while increasing the effectiveness of marketing and overall user experience (Yahoo!, 2013). Benefit of social media for business include brand building, growth, content creation, distribution, communication, gaining insights, advertising, and proving ROI (Newberry, 2018) (Borges Tiago & Verissimo, 2014) (Kirtis & Karahan, 2011) (Lipsman, et al., 2012). Based on this, it can be concluded that with the continuous rise in social media users, there will be an equal if not greater increase in online marketing, advertising, and purchasing. This calls for a better set of controls to be set in place to ensure users and brands are protected from online threats and anyone looking to capitalize on the vulnerabilities. The aim of this research is to understand the impact content moderation plays in social media marketing.

To understand the impact of social media on social media marketing. To understand the impact of social media with content moderation. To understand the impact of content moderation on social media marketing. To understand the impact / influence of content moderation on social media

marketing. Content moderation is classified as the independent variable while social media marketing is the dependent variable. Changes in any of these variables should potentially influence user behaviour towards social media marketing. The research questions are to study the impact of content moderation on social media marketing.

What is the impact of social media with social media marketing? What is the impact of content moderation in social media? What is the impact of social media marketing on users? What is the impact / influence of content moderation on social media marketing?

This research will highlight the role of social media marketing in social media and how content moderation can be used to further improve the overall process and experience. Through the identification of these relationships, online businesses would be able to improve on their existing process or introduce a new element that can help elevate user experience which would attract more people to the site and in turn, generate a higher revenue.

2. Literature Review

Content Moderation

Content moderation is the practice of monitoring and applying a pre-determined set of rules and guidelines to user-generated submissions to determine best if the communication is permissible or not (TaskUs, n.d.). This is accomplished through screening user-generated content (UGC) posted to Internet sites, social media, and other online outlets, to determine the appropriateness of the content for a given site, locality, or jurisdiction. The process can result in UGC being removed by a moderator, acting as an agent of the platform or site in question. Increasingly, social media platforms rely on massive quantities of UGC data to populate them and to drive user engagement; with that increase has come the need for platforms and sites to enforce their rules and

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relevant or applicable laws, as the posting of inappropriate content is considered a major source of liability (Roberts, 2017).

There are 5 common types of moderation that exist in today’s environment which are pre, post, reactive, distributed and automated (Grimes-Viort, 2010). Pre-moderation is typically the most preferred type of moderation as content needs to be approved by moderators before being published online and becoming visible to other users (Open Access BPO, 2014). Post-moderation is completely inverse as it allows contents to be posted but replicated in a queue for a moderator to pass or remove afterwards (Grimes-Viort, 2010). Reactive moderation is used as a sole moderation method that relies on users to report the content when they feel it is not appropriate for the community (Cogito, 2018). Distributed moderation is a rare type of UGC moderation method that relies on implementing a rating system where the rest of the online community can score or vote on whether submissions are either in line with community expectations or within the rules of use (Chrum, 2013). Automated moderation uses various tools to process UGC with pre-defined rules to accept or reject the content posted online (Cogito, 2018).

Social Media

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. By design, social media is internet-based and gives users quick electronic communication of content. Content includes personal information, documents, videos, and photos. Users engage with social media via computer, tablet or smartphone via web-based software or web application, often utilizing it for messaging (Dollarhide, 2019). As summarized by (Leonardi, et al., 2013), social media is increasingly being implemented in work organizations as tools for communication among employees and as such, is imperative that we understand how it enables and constrains the communicative activities through which work is accomplished because it is these very dynamics that constitute and perpetuate organizations.

According to (Kaplan & Haenlein, 2010), the concept of social media is top of the agenda of many business executives today as they try to understand and identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life and Twitter. In another study by (Indaco & Manovich, 2016), a key form of contemporary city life is social media and its contents and concludes any analysis of urban structures and cultures needs to consider social media activity.

Social Media Marketing

Marketing is the activity, set of institutions and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large. Traditionally, organizations accomplish these goals through a marketing mix that includes the Four Ps: Product, Price, Promotion and Place. Social media marketing is the utilization of social media technologies, channels and software to create, communicate, deliver and exchange offerings that have value for an organization’s stakeholders, all through by adding a Fifth P: Participation (Tuten & Solomon, 2017). Social media marketing is primarily internet-based but has similarities with non-internet-based marketing methods like word-of-mouth marketing. It is the way of promoting a website, brand or business by interacting with or attracting the interest of current or prospective customers through the channels of social media (Saravanakumar & SuganthaLakshmi, 2012).

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Social media marketing has been heralded as an integral element of 21st century business but literature on this remains fragmented and is focused on isolated issues, such as tactics for effective communication (Felix, et al., 2017). Social media marketing is one of the means in building and maintaining brand loyalty and has been a central theme of research for marketers. Results have shown that brand loyalty of customers is positively affected when the brand appears on various platforms and offers applications on social media (Erdogmus & Cicek, 2012). CRITICAL REVIEW OF KEY THEORIES AND APPROACHES Commercial Content Moderation Model Commercial content moderation (CCM) is the model or rather service that is carried out by firms specializing in content moderation. This service is engaged by businesses or companies with an online brand of which management of reputation is a key part of their business practices. As such, they require commercial content moderation to guard against digital damage to their brand that could be caused by disturbing, lewd, or even illegal content being transmitted or displayed on their sites. CCM is not an industry but rather a series of practices with shared characteristics that take place in a variety of worksites. Workers are dispersed globally with the work almost always done in secret. Moderators act as digital gatekeepers for a platform, company, brand or site, deciding what content will make it to the platform and what content will remain there (Roberts, 2016). However, this type of moderation comes at a cost. Moderators view and deal with material that is homophobic, sexist, racist and disturbing as a regular part of their daily work. This constitutes considerable psychological risks to moderators. As there is extraordinarily little information regarding moderators, there is a monumental problem in evaluating existing systems that cater to address this type of issue. Moderators are typically barred from discussing work through non-disclosure agreements. Moderators have been known to have gone into depression, experienced high levels of stress, fatigue and distress (Arsht & Etcovitch, 2018). User Moderation Model User moderation model allows any user to moderate any other user’s contributions. This type of moderation works fairly decently on a large site with a sufficiently large population since the relatively small number of instigators are weeded out by votes of the rest of the population. In the context of internet forums, Slashdot is one that adopts a user moderation model (Wikipedia, 2019). Slashdot is a popular technology-news website that publishes frequently short news posts and allows its readers to comment on them. The moderation system deployed is a community or user-based moderation model that awards either a negative or positive score to every comment and upholds the quality of discussions by discouraging spam and offensive comments. This moderation model is slightly different than others as it is only done reciprocity and is absent a complex community structure. As this model requires users to moderate other users’ contributions, not all content will receive the same traction. Number of replies of a comment depends mostly on its quality but there is faint evidence that user reputation influences the connectivity in the network. Typically, the most reactions occur when high diversity in opinions occur as users are more inclined to be linked with people who express different points of view (Gomez, et al., 2008). Supervisor Moderation

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Typically referred to as unilateral moderation, this type of moderation is usually seen on internet forums. A group of people are selected by the webmaster, usually on a long-term basis, to act as delegates, enforcing the community rules on the webmaster’s behalf. Moderators are given special privileges to delete or edit others’ contributions and / or exclude people based on their email address or IP address, and generally attempt to remove negative contributions throughout the community (Wikipedia, 2019). This type of moderation, similar to reactive moderation is beneficial if moderators / supervisors are selected properly. This method can promptly remove sensitive content and is easily scaled as the community grows. However, it is also prone to the negative effects outlined above if moderators miss offensive text, images, or video (Smith, 2019). While this type of moderations leverages intrinsic motivation and local experts are more likely to have context to make hard calls, the downside to this is that the moderators themselves might not feel they receive the recognition they deserve while some might resent the fact that the platform is making money from free labour. Another flaw with this model is that the moderators themselves may not necessarily be consistent and fair when deciding whether the content stays or has to be removed (Bernstein, n.d.). Automated Moderation An increasingly popular moderation method which involves the use of a variety of tools to filter, flag and reject user submissions. These tools can range from simple filters, which search for banned words or block certain IP addresses, to machine learning algorithms, which detect inappropriate content in images and video. At present time, many of these tools are used in addition to some kind of human moderation, but as they grow more sophisticated in their ability to analyse conversation they may become a viable standalone option in the near future (Smith, 2019). Automated moderation uses hash-matching algorithms to detect images associated with copyrighted material or child pornography. This is accomplished through identifying images by a unique code, called a hash, and compare them against the hash of known copyrighted or child pornography images. Images identical to a known copyrighted or illegal image can then be automatically flagged or filtered out (Duarte, et al., 2018). The advantage of automated moderation is that it can act quickly and thus, preventing people from being hurt by the content (Bernstein, n.d.). Also, this method can be effective at identifying content that contains a known keyword or image or matches a known hash or metadata pattern. However, it is not capable of parsing the meaning of the context of text, such as whether it contains hate speech or terrorist propaganda, is a lawful use of a copyrighted work or reveals criminal intent (Duarte, et al., 2018). Also, these systems have been known to make embarrassing errors, often ones that the creators did not intend. Errors are often interpreted as intentional platform policy. Even if a perfectly fair, transparent and accountable algorithm were possible, culture would evolve and training data would become out of date thus rendering this model ineffective (Bernstein, n.d.). CRITICAL REVIEW OF RELEVANT LITERATURES (Perrin, et al., 2015) finds nearly two-thirds of American adults (65%) use social networking sites, up from 7% when Pew Research Centre began systematically tracking social media usage in 2005. The reports from this study have documented in great detail how the rise of social media has affected such things as work, politics and political deliberation, communications patterns around the globe, as well as the way people get and share information about health, civic life, news consumption, communities, teenage life, parenting, dating and even people’s level of stress. The figures reported are for social media usage among all adults and not just among those Americans who are

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internet users. According to (Zafarani, et al., 2014), the rise of social media has seen the web become a vibrant and lively realm in which billions of individuals around the globe interact, share, post and conduct numerous daily activities. Information is collected, curated, and published by citizen journalists and simultaneously shared or consumed by thousands of individuals, who give spontaneous feedback. This study also points out that the social media world has no geographical boundaries and incessantly churns out oceans of data and as a result, people are faced with an exacerbated problem of big data. This study also notes that social media is significantly different from the traditional data that is familiar with data mining. (Sajid, 2016) outlines social media as engaging with customers online and among the “best possibilities available” for an item to get in touch with potential customers. According to the study, community social networking websites are all about social networking in a way that espouses believe in among parties and areas engaged. This study classifies social media as a website that allows users to discuss their material, views and motivates connections and group development. It also discusses the ideas of social media and social media promotion as well as other aspects such as the development and advantages, importance of social media promotion and social media promotion methods. (Fuchs, 2017) gives a critical introduction to studying social media by discussing the concepts needed for understanding the world of social media. This study introduces a theoretical framework for critically understanding social media that are used for discussing social media platforms in the context of specific topics, being social, participatory culture, communication and media power, political economy, political ethics, surveillance and privacy, democracy and the public sphere, global capitalism, the gift and sharing economy, power and collaborative work and the commons. This study concludes that social media is a complex term with multi-layered meaning and therefore studying social media would require the need of studying social theory and social philosophy. The study by (Miller, et al., 2016) theorises that social media should not be seen primarily as the platforms upon which people post, but rather as the contents that are posted on these platforms. This study offered a comparative analysis summarizing the results of the research and exploring the impact of social media on politics, gender, education, and commerce. This study argues that the only way to appreciate and understand something as intimate and ubiquitous as social media is to be immersed in the lives of the people who post. Only then can we discover how people all over the world have already transformed social media is such unexpected ways and be able to access the consequences. According to (Chang, et al., 2015), social media marketing is an influential marketing method and liking or sharing social media messages can increase the effects of popular cohesion and message diffusion. This research investigated how persuasive messages could lead internet users to click like and share messages in social media marketing activities. A hypotheses was developed on the basis of elaboration likelihood model and a 392 fans survey from a fan page in Facebook, with structural equation modelling that analysed the questionnaire data. Results show there are three types of persuasive messages that are important in order for someone to click like and / or to share posted messages. This research provided valuable recommendations for social media marketing activities. CONCEPTUAL FRAMEWORK

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Figure 1: Conceptual Framework

This conceptual framework was adopted from (Adom, et al., 2018) and adjusted for the purpose of this research. Social Media, Social Media Marketing and Content Moderation are the independent variables that determine the positive or negative impacts towards the influence of content moderation on social media marketing. Studies have been conducted on social media marketing among brands to examine the relationships among those perceived activities, value equity, brand equity and purchase intention (Kim & Ko, 2012) (Ashley & Tuten, 2015). Studies have also concluded that with careful planning, social media campaigns can positively brand loyalty of customers, and there is a relatively easy and cost-effective measurement to track customers’ online investments in company’s brands (Erdogmus & Cicek, 2012) (Hoffman & Fodor, 2010). Further studies show social media content marketing (SMCM) to play an important role in conveying effective information to consumers and its relation in keeping consumers motivated to continuously engage with the brands. Social media facilitates the social interaction of consumers which leads to increased trust and intention to purchase (Ahmad, et al., 2016) (Hajli, 2014). H1: There is a positive and significant impact on social media marketing if campaigns are carefully planned and executed. Numerous studies have been conducted on the impact of content moderation in social media platforms. Content moderation is an important aspect for companies with an online brand of which reputation management is a key part of their business practice. To ensure continuous visits and activity from users and advertisers in their platforms, companies design processes and policies that strikes a balance among attracting users and advertisers, responding to jurisdictional norms and legal demands and remaining profitable and appealing to shareholders (Roberts, 2016) (Roberts, 2018) (Gillespie, 2018) (Gerrard, 2018). Study has also shown that when users received an explanation when their posts are removed, it reduces the odds of future post removals. Findings suggest that removal explanations may be under-utilized in moderation practices and it is potentially worthwhile to invest time and resources into providing them (Jhaver, et al., 2019). H2: There is a positive and significant impact on social media with the continuous deployment of content moderation.

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Studies show that social media platforms have revolutionized the state of marketing, advertising, and promotions. Social media has transformed the Internet from a platform for information, to a platform for influence (Hanna, et al., 2011) (de Vries, et al., 2012). With every technological advancement, there comes a dark side that has short and long-term implications (Baccarella, et al., 2018). Content moderation serves a useful function in ensuring popular online platforms are not served as breeding grounds for the worst type of online behaviour thus protecting users from potential harm as well as platform owners who stand to lose millions in advertising (Arsht & Etcovitch, 2018) (Handley, 2017) (Solon, 2016) (Solon, 2017). User-generated content (UGC) is important for building brand recognition and trust but content moderation helps prevent bullies or trolls from taking advantage of the brand and helps increase traffic and search engine rankings (EBS, 2019). Social networks can help companies spread good or bad news fast. Social media is not as widely moderated or censored as mainstream media which leaves the company or brand open to anything whether positive or negative (Assaad & Gomez, 2011). H3: There is a positive and significant impact on social media marketing with content moderation in place. Social media brings the world closer together and that has changed the practices of journalism, marketing and advertising (Akar & Topcu, 2011) (Saravanakumar & SuganthaLakshmi, 2012). With the benefits of social media, there is a downside that needs to be addressed and that is achieved through content moderation (Roberts, 2016) (Baccarella, et al., 2018). Marketing researchers and practitioners show significant interests in understanding the opportunities and usage of the phenomenon of social media and its usage in marketing with literature indicating that social media marketing is increasingly vital to corporate marketing strategies (Whiting & Deshpande, 2016). For 88% of consumers, online reviews are as reliable as personal recommendations while 97% of consumers take time to read reviews about local businesses.

H4: There is a positive and significant impact on the influence of content moderation on social media marketing

3. Methodology

Research Paradigm A research paradigm is the set of common beliefs and agreements shared between scientists on how problems should be understood and addressed. There are three different approaches to educational research which are Positivism, Interpretivism and Critical Theory (Patel, 2015). Essentially, researchers need to be able to understand and articulate beliefs about the nature of reality, what can be known about it and how to go about attaining the knowledge (Rehman & Alharthi, 2016). Epistemology, which comes from the Greek word, epistêmê, is the philosophy of knowledge or how we come to know. It is intimately related to ontology and methodology as ontology involves the philosophy of reality, epistemology addresses how we come to know that reality while methodology identifies the particular practices used to attain knowledge of it (Krauss, 2005) This study will adopt the positivism research paradigm as the attempt is to understand and measure the subject through a series of quantitative methods. Empirical evidence will be gathered then analysed and formulated in the form of a theory that explains the effect of the independent variable on the dependent variable. The approach to analysing data is deductive with first proposing a hypotheses and then either confirming or rejecting it based on the results of statistical analysis. The aim of this paradigm is to

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provide explanations and make predictions based on measurable outcomes. The measurable outcomes are undergirded by four assumptions which are determinism, empiricism, parsimony, and generalizability (Rehman & Alharthi, 2016) (Kivunja & Kuyini, 2017). The adoption of a positivism research paradigm ensured objectivity of the research data collection method, analysis, and interpretation. In doing so, the research is not reliant on the judgement and opinions of the researcher. Research Design and Approach In line with the explanatory research, a deductive and quantitative approach was adopted as this research was formulated from literature review which generated a framework of critical factors, emphasizing on causality. A deductive research approach works from narrowing down the general observation to specific findings. Due to its nature, it is informally called a “top-down” approach. Conclusions follows logically from built premises. Deduction approach starts from a theory which in turn leads to formulated of hypotheses. Conclusion or confirmation is made based on the findings observed (Burney & Saleem, 2008). A quantitative approach includes positivism and post positivism world view. In its broadest sense, positivism is a rejection of metaphysics, a position that contents the goal of knowledge is simply to describe the phenomena that we experience (Trochim, 2020). Positivism is said to only be suitable for physical sciences where one deals with matter only which has no possibility of changing with time and context if physical parameters remain the same. Post positivism is an appeal to probability and variation of results in terms of difference in context, situation, and environment. Thus, post positivism challenges the notion of absolute truth and can be used for both physical sciences as well as social sciences (Grover, 2015). In conclusion, this research originated from a post-positivism quantitative nature, through a deductive approach that would allow the researcher to propose several hypotheses based on real world issues. Data would be collected, then converted into numerical values to test said hypotheses and statistically analysed to reach a viable conclusion. Data Collection Primary data was collected for this study between April 2020 and May 2020, through online questionnaires which were distributed firstly to the acquaintances of the researcher who subsequently forwarded it to their acquaintances. According to (Evans & Mathur, 2005), Internet penetration is greatest in industrialized countries and lowest in less-developed ones, thus in some regions, the potential for online surveys is greater than the current application. Respondents were given a series of questions centring around social media, social media marketing and content moderation. Prior to cascading and at the start of the questionnaire, the researcher explained the purpose of this research to the respondents and assured them their responses will be kept private and used solely for the purpose of this research. Only respondents consenting to participate in the questionnaire, completed it. The respondents were thanked for their time and valuable inputs. Population and Sampling This research was conducted as an audience market research, which is a research conducted on a specific sample. Businesses use this as a means to communicate with their audience and integrate their views and opinions into their products and services. This research found audience market research to be useful as it could help in proving the proposed hypotheses (Hales, 2019).

Sample size was determined by firstly considering estimation of the levels of precision and risk that the researcher was willing to accept. In a social research as a general rule,

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a 5% margin of error and a confidence level of 95% is acceptable. Larger sample sizes reduce sampling error but at a decreasing rate. However, the formula for determining sample size of the population has virtually no effect on how well the sample is likely to describe the population and would be most unusual for it to be an important consideration when deciding on sample size (Taherdoost, 2017). According to (Lance & Vandenberg, 2015), based on the meta-analyses that previous researchers conducted, a typical sample size was approximately 200. Recommendations of 5:1 and 10:1 are widely adopted even though they are based on relatively little empirical evidence. Therefore, factoring into account all considerations, this research will accept a sample size of between 200 to 300 surveys.

The non-probability sampling technique was adopted for this research as it is to be based on the subjective judgement of the researcher, is less stringent and time consuming. Convenience sampling was selected due to the convenience factor to the researcher in terms of speed, cost effectiveness and ease of availability of the sample (Trochim, 2020). Another advantage of convenience sampling is that it can be useful for establishing the plausibility of relationships among variables which is a desirable step for theory-building. However, the drawback is that non-probability sampling do not meet the basic assumption of inferential statistics, that an estimate can be made of the chance that members of the population have been included in the sample (Clark, 2017). Another obvious disadvantage is that it is likely to be biased and effects of outliers are potentially more devastating due to high self-selection possibility (Etikan, et al., 2016).

4. Results and Discussion

Descriptive Statistics and Normality Analysis

Normality tests was conducted to determine if sample data was drawn from a normally distributed population, within certain amounts of tolerance (Ghasemi & Zahediasl, 2012). Skewness for Social Media was -0.23 while Social Media Marketing and Content Moderation were at -0.63 and -0.83 respectively. Data appears to be fairly symmetrical for Social Media and moderately skewed for Social Media Marketing and Content Moderation. The negative values indicate a longer left-hand tail. Kurtosis characterizes the relative peakedness or flatness of a distribution compared to a normal distribution. The readings for Social Media, Social Media Marketing and Content Moderation were at -0.57, -0.03 and 0.18, respectively. The negative readings for Social Media and Social Media Marketing are called platykurtic distribution meaning flat-topped curve while the positive reading for Content Moderation is called a leptokurtic distribution meaning high peak. As a general guideline for skewness, if the number is greater than +1 or -1, it is an indication that the distribution is substantially skewed (Hair Jr., et al., 2014). As for kurtosis, if the number is greater than +1.96 or lower than -1.96, the bell-shaped distribution is significantly different from a standard normal distribution (Pett, 2015). Both skewness and kurtosis readings for this study fall within the acceptable range.

Descriptive Statistics

N Minimum Maximum Mean Std.

Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std.

Error Statistic

Std.

Error

SM1 248 3 5 4.26 .758 -.479 .155 -1.117 .308

SM2 248 1 5 3.52 .985 -.234 .155 -.464 .308

SM3 248 1 5 3.61 .941 -.301 .155 -.276 .308

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SM4 248 1 5 4.19 .877 -.887 .155 .360 .308

SM5 248 1 5 4.03 .856 -.719 .155 .405 .308

SM6 248 2 5 4.27 .749 -.537 .155 -.836 .308

SMM1 248 3 5 4.65 .563 -1.383 .155 .952 .308

SMM2 248 3 5 4.54 .602 -.941 .155 -.113 .308

SMM3 248 3 5 4.63 .562 -1.194 .155 .452 .308

SMM4 248 2 5 4.04 .869 -.480 .155 -.649 .308

SMM5 248 2 5 4.22 .776 -.512 .155 -.836 .308

SMM6 248 2 5 4.29 .729 -.573 .155 -.696 .308

CM1 248 2 5 4.41 .697 -.836 .155 -.241 .308

CM2 248 3 5 4.65 .584 -1.479 .155 1.166 .308

CM6 248 3 5 4.56 .614 -1.062 .155 .090 .308

CM4 248 3 5 4.56 .652 -1.197 .155 .237 .308

CM5 248 1 5 4.04 .901 -.724 .155 .096 .308

SM 248 2.67 5.00 3.9805 .55870 -.234 .155 -.578 .308

SMM 248 3.00 5.00 4.3945 .48010 -.637 .155 -.003 .308

CM 248 3.00 5.00 4.4452 .47876 -.835 .155 .185 .308

Valid N

(listwise) 248

Table 1: Descriptive Statistics and Normality Analysis

Reliability analysis

The Alpha coefficient method is a suitable method that can be used for Likert scale items

(Ercan, et al., 2007). Cronbach’s alpha is an index of reliability associated with the variation

accounted for by the true score of the underlying construct, with the construct being the

hypothetical variable that is being measured. Alpha coefficients range in value from 0 to 1

and may be used to describe the reliability of factors extracted from questions with two

possible answers and / or multi-point formatted questionnaires or scales. A score of 0.7 has

been indicated as an acceptable reliability coefficient (Santos, 1999).

From the table below, the overall reliability of the entire construct is good with the

individual constructs all above the acceptable value. Overall, the values demonstrate an

adequate level on consistency and as such, no changes to the research instrument were

made.

Whole Instrument:

Reliability Statistics

Cronbach's Alpha N of Items

.826 17

Social Media:



.720 6

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Social Media Marketing:



.783 6

Content Moderation:



.717 5

Table 2: Research Instrument readings

In measuring for sampling adequacy, the Kaiser Meyer Olkin (KMO) measure and Bartlett’s

Test of Sphericity was carried out with the KMO reading returned as 0.826. According to

(Field, 2009), KMOS statistics vary between 0 and 1. A value closer to 1 indicates that

patterns of correlations are relatively compact and as such, factor analysis should yield

distinct and reliable factors. As a general rule of thumb, values between 0.5 and 0.7 are

mediocre, 0.7 and 0.8 are good, 0.8 and 0.9 are great and 0.9 and 1.0 are excellent.

Bartlett’s test compares an observed correlation matrix to the identity matrix. Essentially, it

checks to see if there is a certain redundancy between the variables that can be summarized

with a few number of factors (Statology, 2019). Based on the results below, there is a strong

relationship among the variables as the significant level of the test was small enough to

reject the null hypothesis suggesting that the variables in the correlation matrix is not an

identity matrix and uncorrelated (Dansoh, et al., 2017).

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .823

Bartlett's Test of Sphericity Approx. Chi-Square 1476.247

df 136

Sig. .000

Table 3: KMO and Bartlett's tests

Regression Analysis

To determine whether any of the differences between the means are statistically significant,

the F-test determines whether the proposed relationship between the response variable

and the set of predictors is statistically reliable and can be useful when the research

objective is either prediction or explanation (Murphy, et al., 2014). In this research, model

significance was obtained by determining the F statistic with significance value or 0.05 or

lower. A p-value less than 0.05 is statistically significant. It indicates strong evidence against

the null hypothesis, as there is less than a 5% probability the null is correct. Therefore, we

are able to reject the null hypothesis and accept the alternate one (McLeod, 2019).

Based on the table below, the model displays a F statistic of 18.68 with a significance value

of 0.000. Given that it is lower than 0.05, we can conclude the relationship between Social

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Media Marketing and Social Media is extremely significant for this study and can reject the

null hypothesis.

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 4.018 1 4.018 18.680 .000b

Residual 52.915 246 .215

Total 56.934 247

a. Dependent Variable: SMM

b. Predictors: (Constant), SM

Table 4: ANOVA significance model

MEDIATION ANALYSIS

The estimated regression coefficients, or beta coefficients, represent both the type of

relationship, be it positive or negative, and the strength of the relationship between the

independent and dependent variables in the regression variate. The sign of the coefficient

denotes whether the relationship is positive or negative, whereas the value of the

coefficient indicates the change in the dependent value each time the independent variable

changes by one unit (Hair Jr., et al., 2014).

H1: Impact of Social Media on Social Media Marketing

Model below tested the impact of social media with social media marketing. The results are

accepted based on a significance value of 0.000, which indicates a positive relationship with

a 26.6% of impact Social Media has with Social Media Marketing.

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients

t Sig. Collinearity Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 3.486 .212 16.419 .000

SM .228 .053 .266 4.322 .000 1.000 1.000

a. Dependent Variable: SMM

Table 5: H1 coefficient summary

H2: Impact of Social Media with Content Moderation

The model below tested the impact of social media with content moderation. Based on the

findings, with a coefficient value of 0.4246, which indicates a 42.46% positive impact social

media has with content moderation.

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H3: Impact of Content Moderation on Social Media Marketing

The model tested the impact of content moderation on social media marketing. The results

below have been validated with a significance level of 0.000. With a coefficient value of

0.4744, there is a 47.44% positive impact content moderation has on social media

marketing.


H4: Impact of Social Media on Social Media Marketing mediated by Content Moderation

The final model tested the impact of social media on social media marketing mediated by

content moderation. By refereeing to the Lower Level Confidence Interval and Upper Level

Confidence Level, we see the values are not zero and are positive. From that, we can

conclude that the hypothesis is accepted and there exists a mediation effect (Amira &

Puteh, 2019). Effect is 0.2014 which, is a 20.14% positive impact content moderation

mediates between social media and social media marketing.

Table 8: Indirect effect summary

Hypothesis Testing

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Hypothesis Sig Value Beta Result Interpretation

H1: Impact of Social

Media with Social

Media Marketing

0.000 0.266 Accepted

Beta coefficient of 0.266 indicates a

26.6% positive impact on social media

with social media marketing. Therefore,

social media is a significant predictor of

social media marketing


Media with Content

Moderation

0.0000 0.4246 Accepted


42.46% positive impact on social media

with content moderation. Therefore,

social media is a significant predictor of

content moderation.

H3: Impact of

Content Moderation

on Social Media

Marketing

0.0000 0.4744 Accepted


47.44% positive impact content

moderation has on social media

marketing. Therefore, content

moderation is a significant predictor of

social media marketing.


Media on Social

Media Marketing

mediated by

Content Moderation

BootLLCI:

0.1291

BootULCI:

0.2776

0.2014 Accepted


20.14% positive impact content

moderation mediates between social

media and social media marketing.

Table 9: Hypotheses summary

Discussion of Findings

Based on the conducted analysis, it can be concluded that all hypotheses of this study were accepted given that all findings had a significance level lower than 0.05. Beta coefficients all returned positive at significant levels indicating positive and solid relationships between the dependent and independent variables. The results are visualized in the empirically validated model below. The purpose of this study was to

examine the influence of content moderation on social media marketing. Social media and Content Moderation, being the constants, were all validated and found to positively impact Social Media Marketing in addition to complementing each other. Content Moderation was found to mediate 20.14% of the impact between Social Media and Social Media Marketing.

5. Conclusion The objective of this research was to investigate the influence content moderation has on the social media marketing and the overall impact to users’ experience. The first objective was to understand the impact of social media on social media marketing. As hypothesized, there is a 26.6% positive impact on social media with social media marketing. Second objective was to understand the impact of social media with content moderation. Based on the findings of this research, there is a 42.6% positive impact on social media with content moderation. While previous studies have agreed that content moderation is necessary and an important aspect in preserving the sanity of social media, many are concerned with the policies surrounding censorship as they believe it to infringe on user’s freedom of speech. The third objective was to understand the

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impact of content moderation on social media marketing. The findings confirm the hypothesis as there was a 42.46% positive impact content moderation had on social media marketing. However, there has been no research yet attempted on the impact content moderation has towards social media marketing. Given the positive outcome content moderation has on social media marketing, this hypothesis can be adopted for future studies. Final hypothesis was to understand the impact of social media on social media marketing mediated by content moderation. The findings conclude a 20.14 positive impact content moderation mediates between social media and social media marketing. With both Lower Level Confidence Interval and Upper Level Confidence Level positing positive numbers above zero, with a beta coefficient of 20.14%, we can conclude there is a significant impact content moderation mediates between social media and social media marketing. As such, the findings of this research are in line with the initial objectives of this research. As there has been no empirical studies related to the specific nature of this research, content moderation as a mediator is added on to the existing body of knowledge.

Limitations and Suggestions for Further Research

There were a few limitation related to this research namely sample and research design. In the research instrument, respondents were asked to evaluate a series of questions related to content moderation. As such, the questions themselves may have caused confusion and responses may not have been entirely accurate or present an actual account of the users’ perspective. Therefore, to avoid any confusion which can lead to inaccuracies, it is recommended that future questionnaires include a brief description regarding the topics or fields being researched. Demographic questions could have included country of residence to allow further segmentation and classification. Samples consisted mainly of users with a college and university education, who are typically technologically savvy and familiar with current social media policies and protocols. To avoid generalization, replicating this research across various groups and education backgrounds would create a greater representation sample, potentially increasing the validity of the findings. Other limitations to this study are related to the measurement of the constructs as model fitness is way below the R² value as outlined by (Hair Jr, et al., 2011) but within the range proposed by (Ferguson, 2009). Impact of social media with social media marketing and impact of impact of social media on social media marketing mediated by content moderation while significant, still came off as rather low which may indicate, the determinant used in the research did not completely represent the concept. Therefore, further research into these areas is recommended and warranted. This research could have also factored in the perceived risks, fear of identity theft, consumer online shipping fears as well as the influences towards online purchase intentions (Jordan, et al., 2018). All mentioned observations call for further investigation and suggestions for future research.

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