User’s engagement through social media advertisements: a comparative study on micro-scale industry

User’s engagement through social media advertisements: a comparative study on micro-scale industry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report User’s engagement through social media advertisements: a comparative study on micro-scale industry Sayak Pal, N Thilaka, Nitesh Tripathi, Sharmila Kayal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1877199/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Before Digital Media, there were limited avenues for small-scale business to promote their business, thus they were unable to scale up. But with the emergence of Digital Media technologies, democratic platforms such as Social Media became popular among these micro-scale enterprises as a medium for promotion as they were free and provided them a worldwide reach through networking. One of the obvious ways of assessing the caption of particular content on an Online platform is to assess user engagement. It helps in understanding the overall effectiveness of particular promotional content. For this purpose, Facebook and Instagram posts were analyzed for a specific period of time and a datasheet was prepared after rigorous observation and coding. The study concluded that to a large extent there is a significant positive relationship between user engagement and conversion rate. Also, higher forms of engagement i.e., comments and queries were seen in posts that confirmed purchase intention. User’s engagement social media advertising post reaction micro-scale industry Figures Figure 1 Figure 2 Figure 3 Introduction 1.1. User engagement Engaging users in meaningful and fruitful ways is a mammoth task while prolonging the engagement timing is even more difficult to achieve. The level of engagement for the users over a post-reaction can be measured through user engagement which emphasizes the quality of experience and the positive aspects of interaction that helps in extending the usage of the application. Since the mid-1980s the concept of user engagement is slowly evolving, while ‘direct engagement’ refers to the interaction between the human and machine, Laurel explained user engagement as “the state of mind that we must attain in order to enjoy a presentation of an action”. There are several other explanations that help in defining user engagement like “catch and captivate user interest”, “draw people in” and “encourage interaction” which are linked with user experience (Mounia Lalmas, 214). The traditional business model suggests that an organization should produce products or services that will have an advantage over competitors in order to sustain itself in the market, however in 1999, Pine and Gilmore argued that the inclusion of user experience would help in deriving value for the product or service, especially through social media where the content often gets modified and reshaped from the original structure. Lessig in his study used this remixing of content to explain how user communicate which also emphasize the importance of new users in bringing forth unique perspectives (Paul M. Di Gangi, 2016). Since the focus shifted from product-centric to consumer-centric, the importance of user experience is being evaluated from multiple perspectives. In 1992, Webster stated, “the business will be defined by its customers, not its products or factories or offices”. Social media interfaces only increased the chances for markets to understand and evaluate consumer engagement more precisely and in detail (Andrea Geissinger, 2016). Social media over the time dramatically changed the character of consumers from being a passive observers to an active participants. According to Hallok in 2019, Consumers particularly engaged in three types of activities on social media including content creation, contribution to content creation, and consumption of content related to the brand. There are instances when researchers considered simple to higher types of consumer engagement through social media for their studies from liking a post to posting reviews on the posts shared by the brand (Hamidreza Shahbaznezhad, 2021). 1.2. Entrepreneurship in India Early evidence of entrepreneurship in different parts of India could be traced back to the eighteen-fifties by setting up factories by independent individuals like Thomas Parry procuring a tannery in the southern part of India in 1805 or in 1830, a daring attempt was made to set up an Iron plant in somewhere in south India. Although these initial attempts to startup the ventures primarily originated from foreign investors, the later part of the Indian cotton and textile industry saw a number of Indian entrepreneurs giving tuff competition to foreign counterparts. In India, the modern factory system was first found its root in textile industries, followed by tea, coal, and paper which also encouraged Indian entrepreneurs to actively invest in the sectors. Phiroze B. Medhora in his paper “Entrepreneurship in India” tapped on multiple aptitudes related to entrepreneurship like risk-taking, trading on a difference, and speculative attitude toward transactions, which were practiced in India before the modern factory system crept in. At the nascent stages of entrepreneurial development, castes played an important part in the selection of the industry for entrepreneurs along with religious imperialism. Phiroze B. Medhora also segregated the Indian entrepreneurs into multiple phases. The early entrepreneurial phase in the first half of the nineteen century provided a favorable environment for the growth of the Indian entrepreneurs, although they faced a series of challenges like setting up the proper facilities, locating skilled laborers, etc. The second wave of entrepreneurs witnessed the development of two major industries, sugar and cement, that required larger investments. The early evidence of this era includes a sugar factory founded in Kolkata in 1875 with the help of favorable irrigation facilities and a steady supply of sugarcane from Bihar and Uttar Pradesh when TATA managed to erect its cement factory in 1912 that was one of the early attempts in the sector. After the second world war, the planning era of entrepreneurship came into existence as it helps to create a market for entrepreneurs (Medhora, 1965). In 1990, the ‘Economic Census Report’ by the Central Statistical Organization indicated that almost 43.7 million people were employed in the non-farm sectors in rural India, which was catering to 19.7 percent of the entire rural workforce. The same Census report also presented the evidence of 1,27,98,245 enterprises from rural India which gradually increased over the years at an exponential rate. Surprisingly, a large chunk of these enterprises with around 83.2 percent was related to non-agricultural activities leading to annual growth of 5.4 percent in the non-firming sector (Coppard, 2001). Small and medium scale entrepreneurs are considered a major support system of the Indian economy, unlike the United States and other developed countries where large-scale industries monopolies the market. The intervention from the Government of India through initiatives like Start-up India (2015), Digital India campaign (2015), Atmanirbhar Bharat (2020) and Make in India initiative (2014) extended the support in different forms to strengthen the MSME industry (Sayak Pal, 2022). ‘Global Entrepreneurship Monitor (GEM)’ India report 216-17 shows that among adult population in India around 11 percent are involved in ‘early-stage entrepreneurship activities ’, while four percent of them are ‘nascent entrepreneurs’ who are actively involved in setting up their business activities and 7 percent entrepreneurs could manage to set up their business which is running for less than 3.5 years as well as five percent entrepreneurs could manage to continue their business for 42 months (EDI, 2018). According to the Micro, Small and Medium Enterprises Development (MSMED) Act, 2006 authorised by the Government of India, an enterprise falling under the ‘Micro-scale should not exceed the investment of Rs. one Crore on plant and machinery with an annual turnover up to Rs. five crores whereas for a ‘Small’ scale enterprise the investment on plant and machinery should be limited within Rs. 10 crore and an annual turnover up to Rs. 50 crores. This industry works as a supplement to the large industries and supports both domestic as well as international markets. Moreover, as per the annual report from the ‘Ministry of Micro, Small & Medium Scale Enterprises’, 5, 37,677 enterprises are registered as manufacturers, and 8, 65,058 enterprises are listed under the service category as of 31.12.2020 (MSME, 2021). Micro Small and Medium Enterprises continue to contribute significantly across various sectors of the Indian economy through their business innovations and endeavors by producing a varied range of products and services catering to consumers across the globe. According to the ‘Central Statistics Office’ (CSO), Ministry of Statistics and Programme Implementation, (Table 1) the share of MSMEs in the country’s GVA continues to be around one-third of the total GVA while the percentage of MSME share in the country’s GDP continues to hover around the similar cap with a slight increase in 2018-19 (MSME, 2021). The 73rd National Sample Survey by the ‘National Sample Survey Office’, Ministry of Statistics and Programme Implementation (for the period of 2015-16) along with the fourth all India Census of MSME (for the year 2006-2007) report shows the comparative distribution of top 10 contributing states in MSME. Uttar Pradesh and West Bengal claim to be the first and second place holder according to both the reports contributing a higher percentage among the nation’s GVP and GDP (BIAC, 2016). The 73rd National Sample Survey also implies that around 633.88 lakh unincorporated but non-agriculture MSMEs were involved in different economic activities including the registered MSMEs under (a) Sections 2m (i) and 2m (ii) of the Factories Act, 1948, (b) Companies Act, 1956 and (c) construction activities falling under Section F of ‘National Industrial Classification’ (NIC) 2008. Among the total registered MSMEs, the Micro sector tops the category with approximately 630.52 lakhs (99per cent) enterprises while small enterprises come with 3.31 lakh (0.52per cent) and medium enterprises with 0.05 lakh (0.01per cent). On the other hand, the distribution of MSMEs in the rural area is estimated at around 324.88 lakh (51.25per cent) while the urban distribution is 309 lakh (48.75per cent) (MSME, 2021). 1.3. Promoting through social media Small and medium-scale entrepreneurs can expect exponential growth where social media can be used as a vital instrument for them to explore its diverse parameters to enable marketers in penetrating the potential market through these interactive and multifaceted ways of communication. The favorable relationship that exists between the social media platforms and their users makes them feel comfortable enough to share their opinions online, helping advertisers in understanding the nature and preferences of the consumers through social media advertisements. However, the level of engagement depends on multiple determinants like the content, nature of the medium as well as the degree of acceptance of the advertising message among the audience (Hilde A. M. Voorveld, 2018). Digitization of communication channels makes it convenient while a common practice for advertisers to use social media as a vessel to transform their messages into interesting appeals to pull the attention of the buyers towards their products or services. Marketers started evolving and producing certain activities that are specifically designed for social media and aim to generate massive vibration among the users in favor of the brand. For some brands, it has also become the testing ground where they launch their products or services through innovative social media campaigns to collect feedback with the intention of further modifications in products or services, at the same time improving their social media advertising strategies while identifying potential consumers among the users (Asur, 2012). The introduction of Web 2.0 brought a huge change in online advertising which ignited acceptance among the consumer to an alarming rate as the mediums allowed interactive communication among the users. While marketers observed massive changes in consumer buying behavior during mapping their online consumption pattern, researchers also identified the behavior of the consumer is very much dependent on online advertising with two mediate variables, ‘lifestyle of the consumer’ and ‘disposition of visiting the store’. These two variables work as catalysts to bring changes in the behavior of the consumers. There are a few more indicators like the distance of the store, time constraints, price comparison, discounts, etc. which also acts as the driving force for the consumers to choose whether to go for online or offline purchases (Rebab Hamid, 2019). The study reveals that the changes in lifestyle have an impending effect on the consumer attitude towards consumption of the brand which includes consumer’s personal as well as social life. It has also been observed that the consumers often desire to adopt certain trends in their life to influence their social life to justify the sense of belongingness towards a certain level of society or a group of friends or family. The feasible reason behind such behavior is the attitude of the consumer which is the sum of his interactions with the environment. Thus, the consumer often gets indulged by the environment to adapt to the changes in their lifestyle for the sake of acceptance. While the behaviors of the consumers and their attitude toward a particular brand get influenced by several activities like ‘opinions’; ‘interests’; ‘attitudes’ and even the ‘demographics characteristics of the segment’ which vary according to the perception, based on the consumer behavior and attitude (S Sathish, 2012). 1.4. Rationale of the Study Existing studies on user engagement at social media platforms mostly focus on the brands from selected perspectives. Those studies also considered like, comment, and share as their unit for measuring the effectiveness, however, some studies also categorized the content shared on Facebook into various categories to estimate the level of user engagement. The is no evidence found to measure the user engagement for micro-scale entrepreneurs who are struggling to compete and establish their brands with limited resources on social media platforms. ‘Aaloksaji’ is a micro-scale business, based out of Kolkata, West Bengal which specializes in crafting hand-made jewelry. Mostly the posts contain images of beautifully crafted handmade pieces of jewelry along with details of the products allowing prospective consumers to react, comment, and share the posts at their convenience. While many of the works from the artisan are inspired and adapted from nature, the images shared over the page are branded with watermark to prevent unwanted and unauthorized usage or any form of manipulation. The page also includes the shop option for the consumers to skim through the prices of items and choose from the list. This study chose ‘Aaloksaji’, as it uses its prominence on Facebook and Instagram to sell products. The assessment of post consistency, engagement, and responses would help in identifying the potentiality of Facebook and Instagram promotion for micro-scale entrepreneurs. 1.5. Geographical location The study considered a micro-scale entrepreneur ‘Aaloksaji’ from Kolkata (latitude 22°33′36.00″ North, longitude 88°25′12.00″ East) with an estimated population of 1.49 Crores (2022), the capital of the state of West Bengal, India. Both the 73 rd National Survey and India Census (2006-2007) indicate the state of West Bengal as the second contributing state in India in terms of distribution of MSME, capable of further growth in the sector. 1.6. Conceptualization and operationalization of framework The study had multiple objectives to map the users’ engagement on Social media platforms. To begin with, ‘Aaloksaji’ was chosen as a micro-scale enterprise, located in Kolkata, West Bengal. Facebook and Instagram posts were evaluated for the period of 2019 (January 1, 2019, to December 31, 2019) and 2022 (January 1, 2021, to December 31, 2021) for the study while the post engagement was divided into three distinctive levels based on their impact on the users. Accordingly, the Facebook posts were divided into Level 1: lower level of post engagement, Level 2: moderate level of post engagement, and Level 3: higher level of post engagement. The post reactions were further categorized into two major types, ‘positive post-reaction, and negative reactions. On the other hand, the post comments were also categorized into four quadrants ‘positive feedback’; ‘negative feedback’; ‘enquiry/ request’, and ‘response to the comment’. However, for Instagram, the post reactions were divided into only two levels Level 1: lower level of post engagement, and Level 2: Higher level of post engagement. 1.7. Scope of the study The study aims to measure user engagement through the content shared on social media, with a special focus on Facebook and Instagram. The contents shared by ‘Aaloksaji’ was carefully analyzed for two years to observe and map the changes in users’ reaction for a considerable time. ‘Aaloksaji’ belongs to the micro-scale industry from the state of West Bengal and this study will help other small-scale industries to plan and design their social media campaigns. Review Of Literature Rebecca Dolan et al. in their study ‘Social media engagement behavior A framework for engaging customers through social media content’ assessed 2236 posts on Facebook from 12 Australian wine brands in two parts, the first part of the study is based on the quantitative content analysis through SMEB and the later part enabled in testing hypothesizes to measure the user’s influences (Rebecca Dolan, 2019). A similar study on social media post topography, focusing on user’s engagement on Facebook and Instagram was conducted by Ricardo Limongi França Coelho et al. in 2014. The Study considered 1849 publications from January to August, 204 from various beauty, food, fashion design, women’s shoes, and body fitness industry where 680 publications were taken from Facebook and 1169 publications were taken out of Instagram. In this particular case, scholars considered Like and comment as two of the dependent variables in comparison to five independent variables to map out the connecting relationship between various post categories and interaction types (Ricardo Limongi França Coelho, 2016). Dae-Hee Kim, Lisa Spiller and Matt Hettche from the Luter School of Business, Christopher Newport University, Newport News, Virginia, USA conducted a study on 92 global brands with 1086 Facebook posts under the title of “Analyzing media types and content orientations in Facebook for global brands”. The study analyzed all the Facebook posts shared over the official Facebook pages for the entire month of July 2013 from the five product categories to understand the consumer responses to posts falling into two different content categories (Dae-Hee Kim, 2015). A study published by Wondwesen Tafesse, on five automotive brands falling into the highest-selling category in the United States measured the audience responses on their Facebook brand pages for a duration of six weeks. The analysis includes 191 posts from those automotive brands to map the audience responses, interactivity, post novelty, post consistency, and post categorization on their official Facebook page (Tafesse, 2015). Another study by Kunal Swani, George Milne and Brian P. Brown on Facebook as a medium of promotion through online ‘Word of Mouth’ (WOM) captured the Facebook wall posts from the companies listed under ‘Fortune 500 companies’ for a week. The 1146 entries from 303 accounts were analyzed to map the relationship between post content and likes on Facebook for the chosen companies (Kunal Swani, 2013). Lisette de Vries, Sonja Gensler and Peter S.H. Leeflang in their study “Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing” investigated the fan pages across nine months between 2010 and 2011. The study considered 355 posts from 11 international brands across categories like mobile phones, alcoholic beverages, accessories, cosmetics, leisure wear, and food for mapping the impact of social media marketing (Lisette de Vries, 2012). Christy Ashley and Tracy Tuten from East Carolina University, engaged in a study considering the method of content analysis among the top 100 brands with 446 pages on Facebook, 97 names and 329 pages form Twitter, 21 photo sharing accounts, 17 forums, 49 blogs, Myspace contents, 39 video channels, and 27 games to explore the impact of branded social media content in engaging consumer on digital space (Christy Ashley, 2014). Irena Pletikosa Cvijikj and Florian Michahelles in the study “Online engagement factors on Facebook brand pages” also applied the method of content analysis to study the consumer engagement on Facebook through the ‘Graph API’. The 100 posts from the sponsored brands on Facebook were collected to measure the effectiveness of the Facebook posts based on the level of engagement (higher level of post engagement, lower level of post engagement, post vividness, posts interactivity etc.) (Irena Pletikosa Cvijikj, 2013). Table 1. Summary table of literature indicating user engagement studies on the social media platform Reference Area of research Measurement of Engagement Platform (Rebecca Dolan, 2019) Measuring user engagement on social media among Australian wine brands Like, Share, Comment Facebook (Ricardo Limongi França Coelho, 2016) Social media topography among various beauty, food, fashion design, women’s shoes, and body fitness industry Like, Comment Facebook & Instagram (Dae-Hee Kim, 2015) Facebook content orientation from global brands to understand the consumer responses Like, Share, Comment Facebook (Tafesse, 2015) Measuring the audience’s responses varied across the post characteristics on Facebook Like, Share Facebook (Kunal Swani, 2013) Mapping the online EOW for companies listed under ‘Fortune 500 companies’ through Facebook posts Like Facebook (Lisette de Vries, 2012) Analyzed social media posts from international brands to measure the level of social media marketing Like, Comment Social Media (Christy Ashley, 2014) Measuring consumer engagement through branded content across social media and digital platform Like, Comment Facebook, MySpace, Twitter, Blogs, Forums (Irena Pletikosa Cvijikj, 2013) Measuring Facebook post engagement from the sponsored brands Like, Comment, Share Facebook 2.1. Research Gap Various studies have been done and taken into consideration to map the user generated content from different parts of the world. The review of literature in this study put forward the quantitative aspects of social media user-generated content, brand management through user-engagement, marketing strategies of small-scale industries through different application and methods. This research finds a gap like no previous research (empirical and qualitative) has been done on ‘Aaloksaji’ which is a micro-scale business, based out of Kolkata, West Bengal which specializes in crafting hand-made jewelry. The various posts comprehend images of innovative and attractively crafted handmade pieces of jewelry along with particulars of the products allowing prospective consumers to react, comment, and share the posts at their convenience. Methodology This study adopts a quantitative methodology to validate the data through an empirical perspective. Users’ engagement/participation and reach have been quantified in order to correlate with variables and samples (population of the study) which have been taken into consideration. Generally, the correlation coefficient regulates the association among two variables, whose value assortments are between -1 and +1. The study also tried to understand if the correlation coefficient is towards +1, which specifies a positive relationship between the variables (user’s engagement- post reaction and post frequencies) and -1 which again means a negative association between the two variables. 3.1. Research Design This study adopts a correlational research design which is otherwise non-experimental in nature. Correlational research for this study is validated to identify the variables like post reaction and post frequencies of social media. Basically, this research is correlating the user’s engagement with social media through advertisement with respect to the micro-scale industry. 3.2. Method Samples chosen for this study are through stratified simple random sampling. N : Population size- The years 2019 and 2021 posts from the Facebook page and Instagram account of Aloksaji, especially the user’s engagement was taken into consideration, where the samples were heterogeneous in nature. k : Number of strata- Basically, there are two strata taken into account. N i : k Number of sampling units in ith strata N=Σ k 1 i = N N i Stratified random sampling has been chosen because of its heterogeneity in a population and further this can be categorized with subsidiary pieces of evidence. Regression Analysis- This analysis is basically done to know which factor substances most (independent variables), which factor needs to be ignored, and which one to be emphasized the influences. The formula is (Y = 100 + 7X + error term). In this paper, this test is done to know the factors which cause engagement and conversion. Analysis of Variance (ANOVA)- ANOVA is appropriate for experiments with only one independent variable (conversion of people) with two or more levels (social media- Facebook and Instagram engagement and post frequencies). Formula for ANOVA F = p < .05. For this present study, a dependent variable (for the year 2019 and 2021) there are more or less engagement and conversions. There are generally of two levels. A one-way ANOVA assumes: Independence: The value of the conversion for social media is independent of the value of other observations. Normalcy: The value of the conversion is normally distributed Variance: The variance is comparable in different engagements into clusters. Continuous: The dependent variable (number of posts and comments) is continuous and hence can be measured on a scale that can be subdivided. 3.3. Collection of data Data were collected for the years 2019 and 2021 for correlating and analyzing the paths of effective persuasion by advertisements through social media engagements. A total of 263 posts from ‘Aaloksaji’s’ Facebook page for the years 2019 and 2021 were considered for the study, along with 155 posts from ‘Aaloksaji’s’ Instagram page for both 2019 and 2021. 3.4. Research questions The study attempts to find out answers to the following questions: Q1- How monthly post frequency varies for ‘Aaloksaji’s’ Facebook page and Instagram account? Q2- How Facebook and Instagram users reacted on the posts shared over ‘Aaloksaji’s’ Facebook page and Instagram account? Q3- Is there any positive relationship exists between user engagement and conversions on ‘Aaloksaji’s’ Facebook page and Instagram account? Q4- Is there any positive connection present between the positive feedback and re-sharing of those posts on ‘Aaloksaji’s’ Facebook page? Q5- Are positive responses and post-conversion positively linked for ‘Aaloksaji’s’ Facebook page and Instagram account? 3.5. Objectives Research objectives are stated to bridge the research gap to establish the purpose of doing this research. To collect data on Aaloksaji’s Social Media presence in terms of content shared (posts) and engagement (discussion thread) To assess user’s reactions on Aaloksaji’s Facebook page and Instagram account Identify the relationship between user engagement and conversions for Aaloksaji 3.6. Hypothesis H1 a : Facebook posts’ engagement is positively related to conversion in 2019 H1 b : Facebook posts’ engagement is positively related to conversion in 2021 H2 a : There is a positive association between Instagram Posts’ engagement and conversion in 2019 H2 b : There is a positive association between Instagram Posts’ engagement and conversion in 2021 H3 a : A significant relationship exists between user’s positive feedback and re-sharing of those posts on Facebook for 2019 H3 b : A significant relationship exists between user’s positive feedback and re-sharing of those posts on Facebook for 2021 H4 a : A significant correlation exists between user’s positive responses and Conversion on Facebook for 2019 H4 b : A significant correlation exists between user’s positive responses and Conversion on Facebook for 2021 H5 a : A significant correlation exists between user’s responses and post conversion on Instagram for 2019 H5 b : A significant correlation exists between user’s responses and post conversion on Instagram for 2021 Result And Analysis The first objective of the study was to measure the presence of Aaloksaji on social media. On this regard, data was collected for a total of 24 months, including all the posts made on the official page of Aaloksaji on Facebook and account on Instagram for 2019 and 2021. Table 2. Monthly Post Frequency of Aaloksaji on Facebook: 2019 and 2021 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 44 20 17 17 31 23 15 16 4 14 11 9 12 7 6 4 1 0 1 7 2 2 0 0 Table 3. Monthly Post Frequency of Aaloksaji on Instagram: 2019 and 2021 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 27 0 13 5 16 21 5 8 3 7 5 8 5 0 8 3 6 0 1 14 0 0 0 0 The above-mentioned table depicts the whole picture of post frequency of ‘Aloksaji’, a small-scale enterprise for the years 2019 and 2021. For the study, the two most prominent social media Facebook and Instagram were taken into account. The above illustrated graph is comparing the ‘Aloksaji’ Facebook page for the years 2019 and 2021. Post frequency was the key parameter for comparing the two key elements. The year 2019 saw a steadier number of frequencies with a sudden hike in January and May compared to 2021 with a slight increment in January and August. The above illustrated graph is comparing the ‘Aloksaji’ Instagram page for the year 2019 and 2021. Post frequency was the key parameter for comparing the two key elements. There is a steady inclined in the month of January, March, May and June, and December for 2019 whereas for 2021 saw a good hike in August, but relatively low posts on February, September, October, November and December. 4.1. Hypothesis Testing The data was collected for the years 2019 and 2021 to find out the relationship between Facebook posts’ engagement and posts’ conversion for ‘Aaloksaji’. The Pearson Correlation (Table 6.) with 0.01 ( p -value) = 0.01 (α) for the year 2019 has a correlation of 0.830 compared to 2021 with a value of 0.978. Both the year indicates a very strong positive correlation between the variables, Therefore, both H1 a and H1 b are accepted which proves that there is a positive relationship between the Facebook posts’ engagement and posts’ conversion. The data was collected for the Instagram account of ‘Aaloksaji’ for the years 2019 and 2021 as well to check the correlation between posts’ engagement and posts’ conversion. The Pearson Correlation with a 0.01 level of significance indicates a value of 0.822 for the year 2019 and 0.243 for the year 2021. The data for both the years shows vast differences between them, where in 2019, the correlation is very strong positive but for 2021, the correlation is weak positive. Therefore, H2 a is accepted and H2 b is rejected. To map the relationship between positive feedback and resharing of the posts on Facebook, the entire years of 2019 and 2021 was taken. The correlation with a significance level of 0.01 for the year 2019 is 0.862, which is a very strong positive correlation and for 2021, it is 0.716 which is a strong positive correlation. Hence, both the H3 a and H3 b is accepted as a correlation for both the years are on the positive side. This recommends that the user’s positive feedback is positively related to re-sharing posts. The fourth hypothesis argued on the relationship between positive responses and conversion based on the Facebook post of ‘Aaloksaji’. The data collected from 2019 showed a correlation of 0.438 which is a moderate positive correlation whereas 2021 has a correlation of 0.784, with a significance level of 0.01 falling into the strong positive correlation category, indicating the nonexistence of a relationship between the variable, therefore accepting H4 a and rejecting H4 b . The research further tests the existence of a positive correlation between user’s responses and post conversion on Instagram. The same set of data has been used to measure the correlation between the variable are 0.335 for 2019 and 0.719 for 2021. Where 2019 is falling under the weak positive correlation, 2021 is showing a strong positive correlation, thus rejecting H5 a and accepting H5 b . Table 4. Correlation between variables: 2019 and 2021 Pearson Correlation Sig. (2-tailed), Sum of Squares and Cross-products, Covariance N H1 a Variables Facebook post engagement 2019 Facebook post conversion 2019 H1 b Facebook post engagement 2021 Facebook post conversion 2021 Post engagement 1 .830 ** 1 .978 ** Post conversion .830 ** 1 .978 ** 1 H2 a Instagram post engagement 2019 Instagram post conversion 2019 H2 a Instagram post engagement 2021 Instagram post conversion 2021 Post engagement 1 .822 ** 1 .243 Post conversion .822 ** 1 .243 1 H3 a Facebook positive feedback 2019 Facebook post resharing 2019 H3 b Facebook positive feedback 2021 Facebook post resharing 2021 Positive feedback 1 .862 ** 1 .716 ** Post resharing .862 ** 1 .716 ** 1 H4 a Facebook positive feedback 2019 Facebook post conversion 2019 H4 b Facebook positive feedback 2021 Facebook post conversion 2021 Positive feedback 1 .438 1 .784 ** Post conversion .438 1 .784 ** 1 H5 a Instagram positive responses 2019 Instagram post conversion 2019 H5 b Instagram positive responses 2021 Instagram post conversion 2021 Positive responses 1 .335 1 .719 ** Post conversion .335 1 .719 ** 1 *** Correlation is significant at the 0.01 level (2-tailed). The Regression analysis summary of post frequency for the year 2019 with the predictors (R=.941 a ) were found constant. Post frequencies for the year 2019 are of R square is .885 with adjusted percentage of the variance of Facebook and Instagram elucidates jointly with adjusted R square (.873). R-squared hence weighs the asset of the association between the user’s engagement and the post frequencies on a convenient 0 – 100per cent scale which is .873 and 103.775 (Std. Error of the Estimate) has been proved hence regression analysis of engagement of social media (Facebook and Instagram) leads to conversion proved significant. Table 5. ANOVA a :2019 Model Sum of Squares df Mean Square F Sig. 1 Regression 825573.083 1 825573.083 76.660 .000 b Residual 107692.584 10 10769.258 Total 933265.667 11 Furthermore, the abovementioned table intercepts that the output of the ANOVA analysis to check whether there is a substantial variance between the two group means (post reaction and post frequencies). It can be seen that the significance F = 76.6 < .05.value which is .000 b (i.e., p = .0), which is below 0.05. and, therefore, there is a statistically substantial variance in the mean length of time post reaction with post frequency. The sum of squares of regression is (df 1 with 825.0) and residual is (df 10 with 107.5) for the year 2019, Facebook and Instagram post reaction with post frequency of user’s engagement. This also illustrates the steady linear growth for post frequency observation of the abovementioned 2019 year of engagement. Table No 6. Linear Inverse/Growth, Paired Samples Test -2021 Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95per cent Confidence Interval of the Difference Lower Upper Pair 1 postfrequency19 - postfreq21 14.917 8.888 2.566 9.270 20.564 5.814 11 .000 The output of the Paired T test analysis and whether there is a substantial variance between the two group means (post frequencies, 2019 and 2021). It can be seen that the significance value is .000 (two tailed) (i.e., t = 5.814), with df 11 and, therefore, there is a statistically substantial variance in the mean length of time post frequency. The sum of mean is (14.91 and 7) and with residual confidence interval of the difference is (9.270 (lower)) (20.564 (Upper)) for the year 2019 and 2021, Facebook and Instagram post frequencies of user’s engagement. The table also illustrates the sig (2 tailed) with .000 which is significant. Table No 7: paired samplest-tTest of post frequency & post reaction-2021 Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95per cent Confidence Interval of the Difference Lower Upper Pair 1 postfreq21 - postreact21 -308.917 372.936 107.657 -545.869 -71.965 -2.869 11 .015 The abovementioned graph illustrates the output of the Paired T-test with paired difference analysis and whether there is a substantial variance between the two group means (post frequencies with post reaction for the year 2021). It can be seen that the significance value is .015 (two tailed) (i.e., t = 2.869), with df 11 and, therefore, there is a statistically substantial variance in the mean length of time post frequency. The sum of the mean is (308.9 and 17) and with a residual confidence interval of the difference is (-545.869 (lower)) (-71.965 (Upper)) for the year 2021. Facebook and Instagram post frequencies of user’s engagement. The table also illustrates the sig (2 tailed) with .015 which is not significant, hence null hypothesis is rejected. It can be assumed that for the year 2021, there is no association between post frequencies and post reaction of user’s engagement. The output of the ANOVA analysis and whether there is a substantial variance between the two group means (post reaction and post frequencies). It can be seen that the significance value is .000 b (i.e., p = .0), which is below 0.05. and, therefore, there is a statistically substantial variance in the mean length of time post reaction with post frequency. The sum of squares of regression is (df 1 with 725.0) and the residual is (df 10 with 104.5) for the year 2021, Facebook and Instagram post reaction with post frequency of user’s engagement. The graph also illustrates the steady linear growth for post frequency observation of the abovementioned 2021 year of engagement. Discussion And Conclusion Social media is proved to be an imperative tool in founding the relationships between small-scale business industry and customers. Nevertheless, it establishes and creates operative content and message for social media publicizing campaigns which is a task, as the small-scale industry may have struggle in order to understand the social media algorithms that drive user engagement. The best way to encounter the analytics on user engagement’s content which further leads to various reaction, discussion, comment, and share to understand the cohesiveness of its feature and AI (Artificial Intelligence) and ML (Machine Learning) algorithms, especially for niche small-scale industry. Post frequency was one of the indicators to understand the social media presence (Facebook and Instagram) for ‘Aaloksaji’. The continuous low presence in 2021 compared to 2019 was a sign of impact caused by COVID-19. The regression analysis of post frequency for 2019 shows a positive association between the user’s engagement and the post frequencies on a convenient 0 – 100per cent. The output from ANOVA also indicates a substantial variance in the mean length of post reaction with post frequency for the year 2019 on Facebook and Instagram. The correlation based on the DV postreaction19 comes to a t-8.756 with a p-value .000, accepting the H2. The Model summary and parameter estimation of post reactions for both 2019 and 2021 come to the R square value of 0.885 and 0.840 respectively with a significance of .000 for both the years. The pared sample correlation for 2019 and 2022 calculate the p-value of 0.044 and 0.000 accepting the H7. The promotion has always been a challenging task for SMEs as their activities often get restricted by budget while social media can be the key to rescuing them from their grief and helping them in identifying the potential consumers from the market. However, the coverage and engagement of different social media differ due to the nature of the medium and also the acceptance of the media among the users. Research shows that social media advertisements are closely dependent on the purpose of the medium as well as on their usage of them. The nature of social media also plays a crucial part in the selection of the medium as the users like to see and share their preferable content on their chosen platform, which provides leverage to the advertisers to choose the medium wisely while promoting their brands on social media. Another fact that has been found by the researcher is that the social media is positively related to the engagement of the platform while the study shows that using social media for promotion can prove to be a healthy option as it is an investment-friendly option as well as provides higher consumer accessibility across geographical locations, catering to the needs of the brand based on the target consumer preference and behavior (Hilde A. M. Voorveld, 2018). In this paper we account on a quantitative study that applies to understand how different heterogenous users conversed themselves for textual and visual contented features from Instagram and Facebook posts, along with originator (Aloksaji)- and context-related variables (discussion threads, post frequencies, and post reactions), and have statistically modeled its impact on user engagement. Results and findings from this study can further guide various specialists and professionals in marketing, advertising, and social media in generating and updating engaging content that can interconnect and connects more efficiently with their targeted audiences. Limitations and future directions for further research The present study has concentrated on the two parameters basically for ‘Aloksaji’ that is post engagement, and post frequencies which lead to conversion. The study is quantitative and stratified random sampling has been taken into consideration which is a limitation. The study proposes other statistical tests (non-parametric tests- cross-tabulation, chi-square) or simple random sampling that also can be put forward for further research. ‘Aloksaji’ is basically located in Kolkata so the geographic location is also a limitation for this study. The study also advocates those different geographical attributes can be re-located and other aspects of the small-scale industry can also be researched in further aspects. Declarations The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article. References Andrea Geissinger, C. L. (2016). User engagement in social media–an explorative study of Swedish fashion brands. Journal of Fashion Marketing and Management, 20(2), 177. doi: doi.org/10.1108/JFMM-02-2015-0010 Asur, S. (2012). The Economics of Attention: Social Media and Businesses. VIKALPA, 37(4), 77–85. BIAC. (2016, May 13). Financing Growth ; SMEs in Global Value Chains. World SME Forum. China: BIAC. Retrieved December 5, 2019, from biac.org: http://biac.org/wp- content/uploads/2016/06/Financing-Growth-SMEs-in-Global-Value-Chains.pdf Christy Ashley, T. T. (2014). Creative Strategies in Social Media Marketing: An Exploratory Study of Branded Social Content and Consumer Engagement. Psychology and Marketing, 20. doi: doi.org/10.1002/mar.20761 Coppard, D. (2001). The Rural Non-farm Economy in India: A Review of the Literature. national Resources institute. Dae-Hee Kim, L. S. (2015). Analyzing media types and content orientations in Facebook for global brands. Journal of Research in Interactive Marketing, 4–30. doi: doi.org/10.1108/JRIM-05-2014-0023 EDI. (2018, March 19). Only 5% of adult Indians establish own business. Retrieved January 10, 2020, from https://economictimes.indiatimes.com/:https://economictimes.indiatimes.com/articleshow/633 56372.cms?from = mdr&utm_source = contentofinterest&utm_medium = text&utm_campaign = c ppst Hamdi, Rabeb, and Romdhane Khemakhem (2019). "Online advertising and consumer behavior in Tunisia." Atlantic Marketing Journal, 8 (1), 4. Hamidreza Shahbaznezhad, R. D. (2021). The Role of Social Media Content Format and Platform in Users' Engagement Behavior. Journal of Interactive Marketing, 53(1), 48. doi: doi.org/10.1016/j.intmar.2020.05.001 Hilde A. M. Voorveld, G. v. (2018). Engagement with Social Media and Social Media Advertising: The Differentiating Role of Platform Type. Journal of Advertising, 38–54. Irena Pletikosa Cvijikj, F. M. (2013). Online engagement factors on Facebook brand pages. Social network analysis and mining, 3(4), 843–861. doi: doi.org/10.1007/s13278-013-0098-8 Kunal Swani, G. M. (2013). Spreading the word through likes on Facebook: Evaluating the message strategy effectiveness of Fortune 500 companies. Journal of Research in Interactive Marketing, 7(4), 276–279. doi: doi.org/10.1108/JRIM-05-2013-0026 Lisette de Vries, S. G. (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing, 26(2), 83, 87–88. doi: doi.org/10.1016/j.intmar.2012.01.003 Medhora, P. B. (1965). Entrepreneurship in India. Political Science Quarterly, 80(4), 558–580. Retrieved from https://www.jstor.org/stable/pdf/2146999.pdf?refreqid=excelsior%3A14f586cbeafc0dcfd71e6c3f4c1c 100c&ab_segments=&origin= Mounia Lalmas, H. O.-T. (214). Definitions. In H. O.-T. Mounia Lalmas, Measuring user engagement (p. 3). Chapel Hill: Morgan and Claypool Publishers. doi: doi.org/10.2200/S00605ED1V01Y201410ICR038 MSME. (2021). Annual Report 2020-21. New Delhi: Ministry of Micro, Small and Medium Enterprises, Government of India. Retrieved 2021, from https://msme.gov.in/sites/default/files/MSME-ANNUAL-REPORT-ENGLISH%202020- 21.pdf Paul M. Di Gangi, M. W. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing, 28(2), 53. doi: 10.4018/JOEUC.2016040104 Rebecca Dolan, J. C.-B. (2019). Social media engagement behavior A framework for engaging customers through social media content. European Journal of Marketing. doi: doi.org/10.1108/EJM-03-2017-0182 Ricardo Limongi França Coelho, D. S. (2016). Does social media matter for post typology? Impact of post content on Facebook and Instagram metrics. Online Information Review, 40(4), 458–471. doi: doi.org/10.1108/OIR-06-2015-0176 Sathish, Sundar, and A. Rajamohan (2012). "Consumer behaviour and lifestyle marketing." International Journal of Marketing, Financial Services & Management Research, 1 (10), 152–166. Sayak Pal, N. T. (2022). Digital Market Scenario in India: A Case Study on “Unicorn” Indian Digital Start-Ups. In S. K. Ketan Kotecha, In Industry 4.0 in Small and Medium-Sized Enterprises (SMEs) (1 ed.). CRC Press. doi: doi.org/10.1201/9781003200857 Tafesse, W. (2015). Content strategies and audience response on Facebook brand pages. Marketing Intelligence & Planning, 33(6), 927–933. doi: doi.org/10.1108/MIP-07-2014-0135 Voorveld, Hilde AM, Guda Van Noort, Daniël G. Muntinga, and Fred Bronner (2018). "Engagement with social media and social media advertising: The differentiating role of platform type." Journal of advertising, 47 (1), 38–54. Image Image 1, 2 and 3 are available in supplementary section. Additional Declarations No competing interests reported. Supplementary Files floatimage1.jpeg Image 1. Contribution of MSME sector to GVA and GDP of the nation from 2014-15 to 2018- 19, March 29, 2021) floatimage2.jpeg Image 2. Distribution of MSMEs among the top 10 States in India, March 29, 2022 floatimage3.jpeg Image 3. Distribution of MSME enterprises among urban and rural areas, March 29, 2021 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1877199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":123094795,"identity":"aa5d24ed-0769-406a-8784-e878e814592a","order_by":0,"name":"Sayak 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2","display":"","copyAsset":false,"role":"figure","size":7808,"visible":true,"origin":"","legend":"\u003cp\u003e‘Aaloksaji’s’ monthly post frequency on Facebook 2019 and 2022\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1877199/v1/7242399fc5d3ae95a01a350f.png"},{"id":24509902,"identity":"98760e35-36c2-465c-8b20-b6180d646f11","added_by":"auto","created_at":"2022-07-29 14:38:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8171,"visible":true,"origin":"","legend":"\u003cp\u003e‘Aaloksaji’s’ monthly post frequency on Instagram 2019 and 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nation from 2014-15 to 2018- 19, March 29, 2021)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1877199/v1/05e9126a57103b0253466f5c.jpeg"},{"id":24509904,"identity":"0cd81c40-6402-4b17-8248-b7885aa7c097","added_by":"auto","created_at":"2022-07-29 14:38:08","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":131275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImage 2.\u003c/strong\u003e Distribution of MSMEs among the top 10 States in India, March 29, 2022\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1877199/v1/296a778e8962b12fd9e0d7f7.jpeg"},{"id":24509903,"identity":"ec29f603-2674-4a1a-bc94-7818f4a2d84a","added_by":"auto","created_at":"2022-07-29 14:38:08","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":48352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImage 3.\u003c/strong\u003e Distribution of MSME enterprises among urban and rural areas, March 29, 2021\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1877199/v1/2be6f4a1d1d7377c0574a300.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"User’s engagement through social media advertisements: a comparative study on micro-scale industry","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003e1.1. User engagement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEngaging users in meaningful and fruitful ways is a mammoth task while prolonging the engagement timing is even more difficult to achieve. The level of engagement for the users over a post-reaction can be measured through user engagement which emphasizes the quality of experience and the positive aspects of interaction that helps in extending the usage of the application. Since the mid-1980s the concept of user engagement is slowly evolving, while \u0026lsquo;direct engagement\u0026rsquo; refers to the interaction between the human and machine, Laurel explained user engagement as \u0026ldquo;the state of mind that we must attain in order to enjoy a presentation of an action\u0026rdquo;. There are several other explanations that help in defining user engagement like \u0026ldquo;catch and captivate user interest\u0026rdquo;, \u0026ldquo;draw people in\u0026rdquo; and \u0026ldquo;encourage interaction\u0026rdquo; which are linked with user experience (Mounia Lalmas, 214). The traditional business model suggests that an organization should produce products or services that will have an advantage over competitors in order to sustain itself in the market, however in 1999, Pine and Gilmore argued that the inclusion of user experience would help in deriving value for the product or service, especially through social media where the content often gets modified and reshaped from the original structure. Lessig in his study used this remixing of content to explain how user communicate which also emphasize the importance of new users in bringing forth unique perspectives (Paul M. Di Gangi, 2016).\u003c/p\u003e\n\u003cp\u003eSince the focus shifted from product-centric to consumer-centric, the importance of user experience is being evaluated from multiple perspectives. In 1992, Webster stated, \u0026ldquo;the business will be defined by its customers, not its products or factories or offices\u0026rdquo;. Social media interfaces only increased the chances for markets to understand and evaluate consumer engagement more precisely and in detail (Andrea Geissinger, 2016). Social media over the time dramatically changed the character of consumers from being a passive observers to an active participants. According to Hallok in 2019, Consumers particularly engaged in three types of activities on social media including content creation, contribution to content creation, and consumption of content related to the brand. There are instances when researchers considered simple to higher types of consumer engagement through social media for their studies from liking a post to posting reviews on the posts shared by the brand (Hamidreza Shahbaznezhad, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2. Entrepreneurship in India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly evidence of entrepreneurship in different parts of India could be traced back to the eighteen-fifties by setting up factories by independent individuals like Thomas Parry procuring a tannery in the southern part of India in 1805 or in 1830, a daring attempt was made to set up an Iron plant in somewhere in south India. Although these initial attempts to startup the ventures primarily originated from foreign investors, the later part of the Indian cotton and textile industry saw a number of Indian entrepreneurs giving tuff competition to foreign counterparts. In India, the modern factory system was first found its root in textile industries, followed by tea, coal, and paper which also encouraged Indian entrepreneurs to actively invest in the sectors. Phiroze B. Medhora in his paper \u0026ldquo;Entrepreneurship in India\u0026rdquo; tapped on multiple aptitudes related to entrepreneurship like risk-taking, trading on a difference, and speculative attitude toward transactions, which were practiced in India before the modern factory system crept in. At the nascent stages of entrepreneurial development, castes played an important part in the selection of the industry for entrepreneurs along with religious imperialism. Phiroze B. Medhora also segregated the Indian entrepreneurs into multiple phases. The early entrepreneurial phase in the first half of the nineteen century provided a favorable environment for the growth of the Indian entrepreneurs, although they faced a series of challenges like setting up the proper facilities, locating skilled laborers, etc. The second wave of entrepreneurs witnessed the development of two major industries, sugar and cement, that required larger investments. The early evidence of this era includes a sugar factory founded in Kolkata in 1875 with the help of favorable irrigation facilities and a steady supply of sugarcane from Bihar and Uttar Pradesh when TATA managed to erect its cement factory in 1912 that was one of the early attempts in the sector. After the second world war, the planning era of entrepreneurship came into existence as it helps to create a market for entrepreneurs (Medhora, 1965). In 1990, the \u0026lsquo;Economic Census Report\u0026rsquo; by the Central Statistical Organization indicated that almost 43.7 million people were employed in the non-farm sectors in rural India, which was catering to 19.7 percent of the entire rural workforce. The same Census report also presented the evidence of 1,27,98,245 enterprises from rural India which gradually increased over the years at an exponential rate. Surprisingly, a large chunk of these enterprises with around 83.2 percent was related to non-agricultural activities leading to annual growth of 5.4 percent in the non-firming sector (Coppard, 2001). Small and medium scale entrepreneurs are considered a major support system of the Indian economy, unlike the United States and other developed countries where large-scale industries monopolies the market. The intervention from the Government of India through initiatives like Start-up India (2015), Digital India campaign (2015), Atmanirbhar Bharat (2020) and Make in India initiative (2014) extended the support in different forms to strengthen the MSME industry (Sayak Pal, 2022).\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Global Entrepreneurship Monitor (GEM)\u0026rsquo; India report 216-17 shows that among adult population in India around 11 percent are involved in \u0026lsquo;early-stage entrepreneurship activities \u0026rsquo;, while four percent of them are \u0026lsquo;nascent entrepreneurs\u0026rsquo; who are actively involved in setting up their business activities and 7 percent entrepreneurs could manage to set up their business which is running for less than 3.5 years as well as five percent entrepreneurs could manage to continue their business for 42 months (EDI, 2018). According to the Micro, Small and Medium Enterprises Development (MSMED) Act, 2006 authorised by the Government of India, an enterprise falling under the \u0026lsquo;Micro-scale should not exceed the investment of Rs. one Crore on plant and machinery with an annual turnover up to Rs. five crores whereas for a \u0026lsquo;Small\u0026rsquo; scale enterprise the investment on plant and machinery should be limited within Rs. 10 crore and an annual turnover up to Rs. 50 crores. This industry works as a supplement to the large industries and supports both domestic as well as international markets. Moreover, as per the annual report from the \u0026lsquo;Ministry of Micro, Small \u0026amp; Medium Scale Enterprises\u0026rsquo;, 5, 37,677 enterprises are registered as manufacturers, and 8, 65,058 enterprises are listed under the service category as of 31.12.2020 (MSME, 2021).\u003c/p\u003e\n\u003cp\u003eMicro Small and Medium Enterprises continue to contribute significantly across various sectors of the Indian economy through their business innovations and endeavors by producing a varied range of products and services catering to consumers across the globe. According to the \u0026lsquo;Central Statistics Office\u0026rsquo; (CSO), Ministry of Statistics and Programme Implementation, (Table 1) the share of MSMEs in the country\u0026rsquo;s GVA continues to be around one-third of the total GVA while the percentage of MSME share in the country\u0026rsquo;s GDP continues to hover around the similar cap with a slight increase in 2018-19 (MSME, 2021).\u003c/p\u003e\n\u003cp\u003eThe 73rd National Sample Survey by the \u0026lsquo;National Sample Survey Office\u0026rsquo;, Ministry of Statistics and Programme Implementation (for the period of 2015-16) along with the fourth all India Census of MSME (for the year 2006-2007) report shows the comparative distribution of top 10 contributing states in MSME. Uttar Pradesh and West Bengal claim to be the first and second place holder according to both the reports contributing a higher percentage among the nation\u0026rsquo;s GVP and GDP (BIAC, 2016).\u003c/p\u003e\n\u003cp\u003eThe 73rd National Sample Survey also implies that around 633.88 lakh unincorporated but\u0026nbsp;non-agriculture MSMEs were involved in different economic activities including the registered MSMEs under (a) Sections 2m (i) and 2m (ii) of the Factories Act, 1948, (b) Companies Act,\u0026nbsp;1956\u0026nbsp;and\u0026nbsp;(c)\u0026nbsp;construction\u0026nbsp;activities\u0026nbsp;falling\u0026nbsp;under\u0026nbsp;Section\u0026nbsp;F\u0026nbsp;of\u0026nbsp;\u0026lsquo;National\u0026nbsp;Industrial\u0026nbsp;Classification\u0026rsquo;\u0026nbsp;(NIC)\u0026nbsp;2008.\u0026nbsp;Among the\u0026nbsp;total\u0026nbsp;registered\u0026nbsp;MSMEs,\u0026nbsp;the\u0026nbsp;Micro\u0026nbsp;sector\u0026nbsp;tops\u0026nbsp;the\u0026nbsp;category\u0026nbsp;with\u0026nbsp;approximately\u0026nbsp;630.52\u0026nbsp;lakhs\u0026nbsp;(99per cent)\u0026nbsp;enterprises\u0026nbsp;while\u0026nbsp;small\u0026nbsp;enterprises\u0026nbsp;come\u0026nbsp;with\u0026nbsp;3.31\u0026nbsp;lakh\u0026nbsp;(0.52per cent)\u0026nbsp;and\u0026nbsp;medium\u0026nbsp;enterprises\u0026nbsp;with\u0026nbsp;0.05\u0026nbsp;lakh\u0026nbsp;(0.01per cent). On the other hand, the\u0026nbsp;distribution of MSMEs in the rural area is estimated at around 324.88 lakh (51.25per cent) while the urban distribution\u0026nbsp;is\u0026nbsp;309\u0026nbsp;lakh\u0026nbsp;(48.75per cent)\u0026nbsp;(MSME,\u0026nbsp;2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3. Promoting through social media\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmall and medium-scale entrepreneurs can expect exponential growth where social media can be used as a vital instrument for them to explore its diverse parameters to enable marketers in penetrating the potential market through these interactive and multifaceted ways of communication. The favorable relationship that exists between the social media platforms and their users makes them feel comfortable enough to share their opinions online, helping advertisers in understanding the nature and preferences of the consumers through social media advertisements. However, the level of engagement depends on multiple determinants like the content, nature of the medium as well as the degree of acceptance of the advertising message among the audience (Hilde A. M. Voorveld, 2018). Digitization of communication channels makes it convenient while a common practice for advertisers to use social media as a vessel to transform their messages into interesting appeals to pull the attention of the buyers towards their products or services. Marketers started evolving and producing certain activities that are specifically designed for social media and aim to generate massive vibration among the users in favor of the brand. For some brands, it has also become the testing ground where they launch their products or services through innovative social media campaigns to collect feedback with the intention of further modifications in products or services, at the same time improving their social media advertising strategies while identifying potential consumers among the users (Asur, 2012).\u003c/p\u003e\n\u003cp\u003eThe introduction of Web 2.0 brought a huge change in online advertising which ignited acceptance among the consumer to an alarming rate as the mediums allowed interactive communication among the users. While marketers observed massive changes in consumer buying behavior during mapping their online consumption pattern, researchers also identified the behavior of the consumer is very much dependent on online advertising with two mediate variables, \u0026lsquo;lifestyle of the consumer\u0026rsquo; and \u0026lsquo;disposition of visiting the store\u0026rsquo;. These two variables work as catalysts to bring changes in the behavior of the consumers. \u0026nbsp;There are a few more indicators like the distance of the store, time constraints, price comparison, discounts, etc. which also acts as the driving force for the consumers to choose whether to go for online or offline purchases (Rebab Hamid, 2019). The study reveals that the changes in lifestyle have an impending effect on the consumer attitude towards consumption of the brand which includes consumer\u0026rsquo;s personal as well as social life. It has also been observed that the consumers often desire to adopt certain trends in their life to influence their social life to justify the sense of belongingness towards a certain level of society or a group of friends or family. \u0026nbsp;The feasible reason behind such behavior is the attitude of the consumer which is the sum of his interactions with the environment. Thus, the consumer often gets indulged by the environment to adapt to the changes in their lifestyle for the sake of acceptance. While the behaviors of the consumers and their attitude toward a particular brand get influenced by several activities like \u0026lsquo;opinions\u0026rsquo;; \u0026lsquo;interests\u0026rsquo;; \u0026lsquo;attitudes\u0026rsquo; and even the \u0026lsquo;demographics characteristics of the segment\u0026rsquo; which vary according to the perception, based on the consumer behavior and attitude (S Sathish, 2012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4. Rationale\u0026nbsp;of\u0026nbsp;the\u0026nbsp;Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExisting studies on user engagement at social media platforms mostly focus on the brands from selected perspectives. Those studies also considered like, comment, and share as their unit for measuring the effectiveness, however, some studies also categorized the content shared on Facebook into various categories to estimate the level of user engagement. The is no evidence found to measure the user engagement for micro-scale entrepreneurs who are struggling to compete and establish their brands with limited resources on social media platforms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Aaloksaji\u0026rsquo; is a micro-scale business, based out of Kolkata, West Bengal which specializes in\u0026nbsp;crafting hand-made jewelry. Mostly the posts contain images of beautifully crafted\u0026nbsp;handmade\u0026nbsp;pieces of jewelry\u0026nbsp;along\u0026nbsp;with\u0026nbsp;details\u0026nbsp;of\u0026nbsp;the\u0026nbsp;products\u0026nbsp;allowing\u0026nbsp;prospective\u0026nbsp;consumers\u0026nbsp;to\u0026nbsp;react, comment, and share the posts at their convenience. While many of the works from the artisan are inspired and adapted\u0026nbsp;from\u0026nbsp;nature,\u0026nbsp;the\u0026nbsp;images\u0026nbsp;shared\u0026nbsp;over\u0026nbsp;the\u0026nbsp;page\u0026nbsp;are\u0026nbsp;branded\u0026nbsp;with\u0026nbsp;watermark\u0026nbsp;to\u0026nbsp;prevent\u0026nbsp;unwanted and unauthorized usage or any form of manipulation. The page also includes the shop option for the\u0026nbsp;consumers\u0026nbsp;to\u0026nbsp;skim\u0026nbsp;through\u0026nbsp;the\u0026nbsp;prices of\u0026nbsp;items and\u0026nbsp;choose\u0026nbsp;from\u0026nbsp;the\u0026nbsp;list. This study chose \u0026lsquo;Aaloksaji\u0026rsquo;, as it uses its prominence on Facebook and Instagram to sell products. The assessment of post consistency, engagement, and responses would help in identifying the potentiality of Facebook and Instagram promotion for micro-scale entrepreneurs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5. Geographical location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study considered a micro-scale entrepreneur \u0026lsquo;Aaloksaji\u0026rsquo; from Kolkata (latitude 22\u0026deg;33\u0026prime;36.00\u0026Prime; North, longitude 88\u0026deg;25\u0026prime;12.00\u0026Prime; East) with an estimated population of 1.49 Crores (2022), the capital of the state of West Bengal, India. Both the 73\u003csup\u003erd\u003c/sup\u003e National Survey and India Census (2006-2007) indicate the state of West Bengal as the second contributing state in India in terms of distribution of MSME, capable of further growth in the sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6. Conceptualization and operationalization of framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study had multiple objectives to map the users\u0026rsquo; engagement on Social media platforms. To begin with, \u0026lsquo;Aaloksaji\u0026rsquo; was chosen as a micro-scale enterprise, located in Kolkata, West Bengal. Facebook and Instagram posts were evaluated for the period of 2019 (January 1, 2019, to December 31, 2019) and 2022 (January 1, 2021, to December 31, 2021) for the study while the post engagement was divided into three distinctive levels based on their impact on the users. Accordingly, the Facebook posts were divided into Level 1: lower level of post engagement, Level 2: moderate level of post engagement, and Level 3: higher level of post engagement. The post reactions were further categorized into two major types, \u0026lsquo;positive post-reaction, and negative reactions. On the other hand, the post comments were also categorized into four quadrants \u0026lsquo;positive feedback\u0026rsquo;; \u0026lsquo;negative feedback\u0026rsquo;; \u0026lsquo;enquiry/ request\u0026rsquo;, and \u0026lsquo;response to the comment\u0026rsquo;. However, for Instagram, the post reactions were divided into only two levels Level 1: lower level of post engagement, and Level 2: Higher level of post engagement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7. Scope of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study aims to measure user engagement through the content shared on social media, with a special focus on Facebook and Instagram. The contents shared by \u0026lsquo;Aaloksaji\u0026rsquo; was carefully analyzed for two years to observe and map the changes in users\u0026rsquo; reaction for a considerable time. \u0026lsquo;Aaloksaji\u0026rsquo; belongs to the micro-scale industry from the state of West Bengal and this study will help other small-scale industries to plan and design their social media campaigns.\u0026nbsp;\u003c/p\u003e"},{"header":"Review Of Literature","content":"\u003cp\u003eRebecca Dolan et al. in their study \u0026lsquo;Social media engagement behavior A framework for engaging customers through social media content\u0026rsquo; assessed 2236 posts on Facebook from 12 Australian wine brands in two parts, the first part of the study is based on the quantitative content analysis through SMEB and the later part enabled in testing hypothesizes to measure the user\u0026rsquo;s influences (Rebecca Dolan, 2019). A similar study on social media post topography, focusing on user\u0026rsquo;s engagement on Facebook and Instagram was conducted by Ricardo Limongi Fran\u0026ccedil;a Coelho et al. in 2014. The Study considered 1849 publications from January to August, 204 from various beauty, food, fashion design, women\u0026rsquo;s shoes, and body fitness industry where 680 publications were taken from Facebook and 1169 publications were taken out of Instagram. In this particular case, scholars considered Like and comment as two of the dependent variables in comparison to five independent variables to map out the connecting relationship between various post categories and interaction types (Ricardo Limongi Fran\u0026ccedil;a Coelho, 2016). Dae-Hee Kim, Lisa Spiller and Matt Hettche from the Luter School of Business, Christopher Newport University, Newport News, Virginia, USA conducted a study on 92 global brands with 1086 Facebook posts under the title of \u0026ldquo;Analyzing media types and content orientations in Facebook for global brands\u0026rdquo;. The study analyzed all the Facebook posts shared over the official Facebook pages for the entire month of July 2013 from the five product categories to understand the consumer responses to posts falling into two different content categories\u0026nbsp;(Dae-Hee Kim, 2015).\u003c/p\u003e\n\u003cp\u003eA study published by Wondwesen Tafesse, on five automotive brands falling into the highest-selling category in the United States measured the audience responses on their Facebook brand pages for a duration of six weeks. The analysis includes 191 posts from those automotive brands to map the audience responses, interactivity, post novelty, post consistency, and post categorization on their official Facebook page (Tafesse, 2015). Another study by Kunal Swani, George Milne and Brian P. Brown on Facebook as a medium of promotion through online \u0026lsquo;Word of Mouth\u0026rsquo; (WOM) captured the Facebook wall posts from the companies listed under \u0026lsquo;Fortune 500 companies\u0026rsquo; for a week. The 1146 entries from 303 accounts were analyzed to map the relationship between post content and likes on Facebook for the chosen companies (Kunal Swani, 2013). Lisette de Vries, Sonja Gensler and Peter S.H. Leeflang in their study \u0026ldquo;Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing\u0026rdquo; investigated the fan pages across nine months between 2010 and 2011. The study considered 355 posts from 11 international brands across categories like mobile phones, alcoholic beverages, accessories, cosmetics, leisure wear, and food for mapping the impact of social media marketing (Lisette de Vries, 2012).\u003c/p\u003e\n\u003cp\u003eChristy Ashley and Tracy Tuten from East Carolina University, engaged in a study considering the method of content analysis among the top 100 brands with 446 pages on Facebook, 97 names and 329 pages form Twitter, 21 photo sharing accounts, 17 forums, 49 blogs, Myspace contents, 39 video channels, and 27 games to explore the impact of branded social media content in engaging consumer on digital space (Christy Ashley, 2014). Irena Pletikosa Cvijikj and Florian Michahelles in the study \u0026ldquo;Online engagement factors on Facebook brand pages\u0026rdquo; also applied the method of content analysis to study the consumer engagement on Facebook through the \u0026lsquo;Graph API\u0026rsquo;. The 100 posts from the sponsored brands on Facebook were collected to measure the effectiveness of the Facebook posts based on the level of engagement (higher level of post engagement, lower level of post engagement, post vividness, posts interactivity etc.) (Irena Pletikosa Cvijikj, 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary table of literature indicating user engagement studies on the social media platform\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eArea of research\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasurement of Engagement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlatform\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Rebecca Dolan, 2019)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eMeasuring user engagement on social media among Australian wine brands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike, Share, Comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Ricardo Limongi Fran\u0026ccedil;a Coelho, 2016)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eSocial media topography among various beauty, food, fashion design, women\u0026rsquo;s shoes, and body fitness industry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike, Comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eFacebook \u0026amp; Instagram\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Dae-Hee Kim, 2015)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eFacebook content orientation from global brands to understand the consumer responses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike, Share, Comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Tafesse, 2015)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eMeasuring the audience\u0026rsquo;s responses varied across the post characteristics on Facebook\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike, Share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Kunal Swani, 2013)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eMapping the online EOW for companies listed under \u0026lsquo;Fortune 500 companies\u0026rsquo; through Facebook posts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Lisette de Vries, 2012)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eAnalyzed social media posts from international brands to measure the level of social media marketing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike, Comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eSocial Media\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Christy Ashley, 2014)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eMeasuring consumer engagement through branded content across social media and digital platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike, Comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eFacebook, MySpace, Twitter, Blogs, Forums\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.53731343283582%\"\u003e\n \u003cp\u003e\u003cem\u003e(Irena Pletikosa Cvijikj, 2013)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.25373134328358%\"\u003e\n \u003cp\u003eMeasuring Facebook post engagement from the sponsored brands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.34328358208955%\"\u003e\n \u003cp\u003eLike, Comment, Share\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.865671641791046%\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.1. Research Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVarious studies have been done and taken into consideration to map the user generated content from different parts of the world. The review of literature in this study put forward the quantitative aspects of social media user-generated content, brand management through user-engagement, marketing strategies of small-scale industries through different application and methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research finds a gap like no previous research (empirical and qualitative) has been done on \u0026lsquo;Aaloksaji\u0026rsquo; which is a micro-scale business, based out of Kolkata, West Bengal which specializes in crafting hand-made jewelry. The various posts comprehend images of innovative and attractively crafted handmade pieces of jewelry along with particulars of the products allowing prospective consumers to react, comment, and share the posts at their convenience.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study adopts a quantitative methodology to validate the data through an empirical perspective. Users\u0026rsquo; engagement/participation and reach have been quantified in order to correlate with variables and samples (population of the study) which have been taken into consideration. Generally, the correlation coefficient regulates the association among two variables, whose value assortments are between -1 and +1. The study also tried to understand if the correlation coefficient is towards +1, which specifies a positive relationship between the variables (user\u0026rsquo;s engagement- post reaction and post frequencies) and -1 which again means a negative association between the two variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1. Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopts a correlational research design which is otherwise non-experimental in nature. Correlational research for this study is validated to identify the variables like post reaction and post frequencies of social media. Basically, this research is correlating the user\u0026rsquo;s engagement with social media through advertisement with respect to the micro-scale industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples chosen for this study are through stratified simple random sampling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e: Population size- The years 2019 and 2021 posts from the Facebook page and Instagram account of Aloksaji, especially the user\u0026rsquo;s engagement was taken into consideration, where the samples were heterogeneous in nature.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ek\u003c/em\u003e: Number of strata- Basically, there are two strata taken into account.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e: k Number of sampling units in \u003cem\u003eith\u003c/em\u003e strata\u003c/p\u003e\n\u003cp\u003eN=\u0026Sigma;\u003csub\u003ek\u003c/sub\u003e1 \u003csub\u003ei\u003c/sub\u003e\u003csub\u003e=\u003c/sub\u003e\u003csub\u003e\u0026nbsp;N\u003c/sub\u003e \u003cem\u003eN \u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStratified random sampling has been chosen because of its heterogeneity in a population and further this can be categorized with subsidiary pieces of evidence.\u003c/p\u003e\n\u003cp\u003eRegression Analysis- This analysis is basically done to know which factor substances most (independent variables), which factor needs to be ignored, and which one to be emphasized the influences. The formula is (Y = 100 + 7X + error term). In this paper, this test is done to know the factors which cause engagement and conversion.\u003c/p\u003e\n\u003cp\u003eAnalysis of Variance (ANOVA)- ANOVA is appropriate for experiments with only one independent variable (conversion of people) with two or more levels (social media- Facebook and Instagram engagement and post frequencies).\u003c/p\u003e\n\u003cp\u003eFormula for ANOVA \u003cem\u003eF\u003c/em\u003e= \u003cem\u003ep\u003c/em\u003e \u0026lt; .05.\u003c/p\u003e\n\u003cp\u003eFor this present study, a dependent variable (for the year 2019 and 2021) there are more or less engagement and conversions. There are generally of two levels. A one-way ANOVA assumes:\u003c/p\u003e\n\u003cp\u003eIndependence: The value of the conversion for social media is independent of the value of other observations.\u003c/p\u003e\n\u003cp\u003eNormalcy: The value of the conversion is normally distributed\u003c/p\u003e\n\u003cp\u003eVariance: The variance is comparable in different engagements into clusters.\u003c/p\u003e\n\u003cp\u003eContinuous: The dependent variable (number of posts and comments) is continuous and hence can be measured on a scale that can be subdivided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Collection of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected for the years 2019 and 2021 for correlating and analyzing the paths of effective persuasion by advertisements through social media engagements. A total of 263 posts from \u0026lsquo;Aaloksaji\u0026rsquo;s\u0026rsquo; Facebook page for the years 2019 and 2021 were considered for the study, along with 155 posts from \u0026lsquo;Aaloksaji\u0026rsquo;s\u0026rsquo; Instagram page for both 2019 and 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Research questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study attempts to find out answers to the following questions:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ1-\u003c/em\u003e How monthly post frequency varies for \u0026lsquo;Aaloksaji\u0026rsquo;s\u0026rsquo; Facebook page and Instagram account?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ2-\u003c/em\u003e How Facebook and Instagram users reacted on the posts shared over \u0026lsquo;Aaloksaji\u0026rsquo;s\u0026rsquo; Facebook page and Instagram account?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ3-\u003c/em\u003e Is there any positive relationship exists between user engagement and conversions on \u0026lsquo;Aaloksaji\u0026rsquo;s\u0026rsquo; Facebook page and Instagram account?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ4-\u003c/em\u003e Is there any positive connection present between the positive feedback and re-sharing of those posts on \u0026lsquo;Aaloksaji\u0026rsquo;s\u0026rsquo; Facebook page?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ5-\u003c/em\u003e Are positive responses and post-conversion positively linked for \u0026lsquo;Aaloksaji\u0026rsquo;s\u0026rsquo; Facebook page and Instagram account?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch objectives are stated to bridge the research gap to establish the purpose of doing this research.\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eTo collect data on Aaloksaji\u0026rsquo;s Social Media presence in terms of content shared (posts) and engagement (discussion thread)\u003c/li\u003e\n \u003cli\u003eTo assess user\u0026rsquo;s reactions on Aaloksaji\u0026rsquo;s Facebook page and Instagram account\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIdentify the relationship between user engagement and conversions for Aaloksaji\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. Hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH1\u003csub\u003ea\u003c/sub\u003e:\u003c/em\u003e Facebook posts\u0026rsquo; engagement is positively related to conversion in 2019\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH1\u003csub\u003eb\u003c/sub\u003e:\u003c/em\u003e Facebook posts\u0026rsquo; engagement is positively related to conversion in 2021\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH2\u003csub\u003ea\u003c/sub\u003e:\u003c/em\u003e There is a positive association between Instagram Posts\u0026rsquo; engagement and conversion in 2019\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH2\u003csub\u003eb\u003c/sub\u003e:\u003c/em\u003e There is a positive association between Instagram Posts\u0026rsquo; engagement and conversion in 2021\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH3\u003csub\u003ea\u003c/sub\u003e:\u003c/em\u003e A significant relationship exists between user\u0026rsquo;s positive feedback and re-sharing of those posts on Facebook for 2019\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH3\u003csub\u003eb\u003c/sub\u003e:\u003c/em\u003e A significant relationship exists between user\u0026rsquo;s positive feedback and re-sharing of those posts on Facebook for 2021\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH4\u003csub\u003ea\u003c/sub\u003e:\u003c/em\u003e A significant correlation exists between user\u0026rsquo;s positive responses and Conversion on Facebook for 2019\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH4\u003csub\u003eb\u003c/sub\u003e:\u003c/em\u003e A significant correlation exists between user\u0026rsquo;s positive responses and Conversion on Facebook for 2021\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH5\u003csub\u003ea\u003c/sub\u003e:\u003c/em\u003e A significant correlation exists between user\u0026rsquo;s responses and post conversion on Instagram for 2019\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH5\u003csub\u003eb\u003c/sub\u003e:\u003c/em\u003e A significant correlation exists between user\u0026rsquo;s responses and post conversion on Instagram for 2021\u003c/p\u003e"},{"header":"Result And Analysis","content":"\u003cp\u003eThe first objective of the study was to measure the presence of Aaloksaji on social media. On this regard, data was collected for a total of 24 months, including all the posts made on the official page of Aaloksaji on Facebook and account on Instagram for 2019 and 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Monthly Post Frequency of Aaloksaji on Facebook: 2019 and 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJan\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.399366085578446%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFeb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMar\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.240887480190175%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eApr\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJun\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.606973058637084%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJul\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAug\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOct\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNov\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDec\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.399366085578446%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.240887480190175%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.606973058637084%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.399366085578446%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.240887480190175%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.606973058637084%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Monthly Post Frequency of Aaloksaji on Instagram: 2019 and 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJan\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.399366085578446%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFeb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMar\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.240887480190175%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eApr\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJun\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.606973058637084%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eJul\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAug\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOct\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNov\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDec\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.399366085578446%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.240887480190175%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.606973058637084%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.399366085578446%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.240887480190175%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.874801901743265%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.606973058637084%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.082408874801901%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.55784469096672%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe above-mentioned table depicts the whole picture of post frequency of \u0026lsquo;Aloksaji\u0026rsquo;, a small-scale enterprise for the years 2019 and 2021. For the study, the two most prominent social media Facebook and Instagram were taken into account.\u003c/p\u003e\n\u003cp\u003eThe above illustrated graph is comparing the \u0026lsquo;Aloksaji\u0026rsquo; Facebook page for the years 2019 and 2021. Post frequency was the key parameter for comparing the two key elements. The year 2019 saw a steadier number of frequencies with a sudden hike in January and May compared to 2021 with a slight increment in January and August.\u003c/p\u003e\n\u003cp\u003eThe above illustrated graph is comparing the \u0026lsquo;Aloksaji\u0026rsquo; Instagram page for the year 2019 and 2021. Post frequency was the key parameter for comparing the two key elements. There is a steady inclined in the month of January, March, May and June, and December for 2019 whereas for 2021 saw a good hike in August, but relatively low posts on February, September, October, November and December.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1. Hypothesis Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data was collected for the years 2019 and 2021 to find out the relationship between Facebook posts\u0026rsquo; engagement and posts\u0026rsquo; conversion for \u0026lsquo;Aaloksaji\u0026rsquo;. The Pearson Correlation (Table 6.) with 0.01 (\u003cem\u003ep\u003c/em\u003e-value) = 0.01 (\u0026alpha;)\u0026nbsp;for the year 2019 has a correlation of 0.830 compared to 2021 with a value of 0.978. Both the year indicates a very strong positive correlation between the variables, Therefore, both H1\u003csub\u003ea\u003c/sub\u003e and H1\u003csub\u003eb\u003c/sub\u003e are accepted which proves that there is a positive relationship between the Facebook posts\u0026rsquo; engagement and posts\u0026rsquo; conversion.\u003c/p\u003e\n\u003cp\u003eThe data was collected for the Instagram account of \u0026lsquo;Aaloksaji\u0026rsquo; for the years 2019 and 2021 as well to check the correlation between posts\u0026rsquo; engagement and posts\u0026rsquo; conversion. The Pearson Correlation with a 0.01 level of significance indicates a value of 0.822 for the year 2019 and 0.243 for the year 2021. The data for both the years shows vast differences between them, where in 2019, the correlation is very strong positive but for 2021, the correlation is weak positive. Therefore, H2\u003csub\u003ea\u003c/sub\u003e is accepted and H2\u003csub\u003eb\u003c/sub\u003e is rejected.\u003c/p\u003e\n\u003cp\u003eTo map the relationship between positive feedback and resharing of the posts on Facebook, the entire years of 2019 and 2021 was taken. The correlation with a significance level of 0.01 for the year 2019 is 0.862, which is a very strong positive correlation and for 2021, it is 0.716 which is a strong positive correlation. Hence, both the H3\u003csub\u003ea\u003c/sub\u003e and H3\u003csub\u003eb\u003c/sub\u003e is accepted as a correlation for both the years are on the positive side. This recommends that the user\u0026rsquo;s positive feedback is positively related to re-sharing posts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe fourth hypothesis argued on the relationship between positive responses and conversion based on the Facebook post of \u0026lsquo;Aaloksaji\u0026rsquo;. The data collected from 2019 showed a correlation of 0.438 which is a moderate positive correlation whereas 2021 has a correlation of 0.784, with a significance level of 0.01 falling into the strong positive correlation category, indicating the nonexistence of a relationship between the variable, therefore accepting H4\u003csub\u003ea\u003c/sub\u003e and rejecting H4\u003csub\u003eb\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eThe research further tests the existence of a positive correlation between user\u0026rsquo;s responses and post conversion on Instagram. The same set of data has been used to measure the correlation between the variable are 0.335 for 2019 and 0.719 for 2021. Where 2019 is falling under the weak positive correlation, 2021 is showing a strong positive correlation, thus rejecting H5\u003csub\u003ea\u003c/sub\u003e and accepting H5\u003csub\u003eb\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Correlation between variables: 2019 and 2021\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePearson Correlation Sig. (2-tailed), Sum of Squares and Cross-products, Covariance N\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.0670640834575265%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH1\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.43666169895678%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post engagement 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post conversion 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.663189269746646%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH1\u003csub\u003eb\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.84053651266766%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post engagement 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post conversion 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePost engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.830\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.978\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePost conversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.830\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e.978\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"81.66915052160954%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.0670640834575265%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH2\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.43666169895678%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram post engagement 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram post conversion 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.663189269746646%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH2\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.84053651266766%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram post engagement 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram post conversion 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePost engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.822\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePost conversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.822\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"81.66915052160954%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.0670640834575265%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH3\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.43666169895678%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook positive feedback 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post resharing 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.663189269746646%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH3\u003csub\u003eb\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.84053651266766%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook positive feedback 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post resharing 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePositive feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.862\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.716\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePost resharing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.862\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e.716\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"81.66915052160954%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.0670640834575265%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH4\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.43666169895678%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook positive feedback 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post conversion 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.663189269746646%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH4\u003csub\u003eb\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.84053651266766%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook positive feedback 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eFacebook post conversion 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePositive feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.784\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Post conversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e.784\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"81.66915052160954%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.0670640834575265%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH5\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.43666169895678%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram positive responses 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram post conversion 2019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"5.663189269746646%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH5\u003csub\u003eb\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.84053651266766%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram positive responses 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.330849478390462%\"\u003e\n \u003cp\u003e\u003cem\u003eInstagram post conversion 2021\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePositive responses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.719\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.532554257095157%\"\u003e\n \u003cp\u003ePost conversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.864774624373958%\"\u003e\n \u003cp\u003e.719\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.534223706176963%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*** Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003cp\u003eThe Regression analysis summary of post frequency for the year 2019 with the predictors (R=.941\u003csup\u003ea\u003c/sup\u003e) were found constant. Post frequencies for the year 2019 are of R square is\u0026nbsp;.885\u0026nbsp;with adjusted percentage of the variance of Facebook and Instagram elucidates jointly with adjusted R square (.873). R-squared hence weighs the asset of the association between the user\u0026rsquo;s engagement and the post frequencies on a convenient 0 \u0026ndash; 100per cent scale which is .873 and\u0026nbsp;103.775 (Std. Error of the Estimate) has been proved hence regression analysis of engagement of social media (Facebook and Instagram) leads to conversion proved significant.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e ANOVA\u003csup\u003ea\u003c/sup\u003e:2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"25.769854132901134%\"\u003e\n \u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.80064829821718%\"\u003e\n \u003cp\u003e\u003cem\u003eSum of Squares\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.479740680713128%\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.990275526742302%\"\u003e\n \u003cp\u003e\u003cem\u003eMean Square\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.479740680713128%\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.479740680713128%\"\u003e\n \u003cp\u003e\u003cem\u003eSig.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"9.40032414910859%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.369529983792546%\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.80064829821718%\"\u003e\n \u003cp\u003e825573.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479740680713128%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.990275526742302%\"\u003e\n \u003cp\u003e825573.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479740680713128%\"\u003e\n \u003cp\u003e76.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479740680713128%\"\u003e\n \u003cp\u003e.000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.067978533094813%\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003e107692.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.774597495527727%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.856887298747765%\"\u003e\n \u003cp\u003e10769.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.774597495527727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.774597495527727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.067978533094813%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.75134168157424%\"\u003e\n \u003cp\u003e933265.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.774597495527727%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.856887298747765%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.774597495527727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.774597495527727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFurthermore, the abovementioned table intercepts that the output of the ANOVA analysis to check whether there is a substantial variance between the two group means (post reaction and post frequencies). It can be seen that the significance \u003cem\u003eF\u003c/em\u003e= 76.6 \u0026lt; .05.value which is .000\u003csup\u003eb\u003c/sup\u003e (i.e., p = .0), which is below 0.05. and, therefore, there is a statistically substantial variance in the mean length of time post reaction with post frequency. The sum of squares of regression is (df 1 with 825.0) and residual is (df 10 with 107.5) for the year 2019, Facebook and Instagram post reaction with post frequency of user\u0026rsquo;s engagement. This also illustrates the steady linear growth for post frequency observation of the abovementioned 2019 year of engagement.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable No 6.\u003c/strong\u003e Linear Inverse/Growth,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePaired Samples Test -2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"bottom\" width=\"24.03560830860534%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" width=\"50.89020771513353%\"\u003e\n \u003cp\u003ePaired Differences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" width=\"7.270029673590504%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" width=\"7.270029673590504%\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" width=\"10.534124629080118%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"14.244186046511627%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"23.837209302325583%\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"18.8953488372093%\"\u003e\n \u003cp\u003eStd. Error Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"43.02325581395349%\"\u003e\n \u003cp\u003e95per cent Confidence Interval of the Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"50%\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"50%\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.777777777777778%\"\u003e\n \u003cp\u003ePair 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.22222222222222%\"\u003e\n \u003cp\u003epostfrequency19 - postfreq21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2592592592592595%\"\u003e\n \u003cp\u003e14.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.148148148148149%\"\u003e\n \u003cp\u003e8.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.62962962962963%\"\u003e\n \u003cp\u003e2.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.962962962962964%\"\u003e\n \u003cp\u003e9.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.962962962962964%\"\u003e\n \u003cp\u003e20.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2592592592592595%\"\u003e\n \u003cp\u003e5.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2592592592592595%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.518518518518519%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe output of the Paired T test analysis and whether there is a substantial variance between the two group means (post frequencies, 2019 and 2021). It can be seen that the significance value is .000\u003csup\u003e\u0026nbsp;(two tailed)\u003c/sup\u003e (i.e., t = 5.814), with df 11 and, therefore, there is a statistically substantial variance in the mean length of time post frequency. The sum of mean is (14.91 and 7) and with residual confidence interval of the difference is (9.270 (lower)) (20.564 (Upper)) for the year 2019 and 2021, Facebook and Instagram post frequencies of user\u0026rsquo;s engagement. The table also illustrates the sig (2 tailed) with .000 which is significant.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable No 7:\u003c/strong\u003e paired samplest-tTest of post frequency \u0026amp; post reaction-2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"bottom\" width=\"23.293768545994066%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" width=\"51.632047477744806%\"\u003e\n \u003cp\u003ePaired Differences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" width=\"7.270029673590504%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" width=\"7.270029673590504%\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" width=\"10.534124629080118%\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"15.229885057471265%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"20.977011494252874%\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"21.264367816091955%\"\u003e\n \u003cp\u003eStd. Error Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"42.52873563218391%\"\u003e\n \u003cp\u003e95per cent Confidence Interval of the Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"50%\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"50%\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"5.786350148367952%\"\u003e\n \u003cp\u003ePair 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.50741839762611%\"\u003e\n \u003cp\u003epostfreq21 - postreact21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.863501483679525%\"\u003e\n \u003cp\u003e-308.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.830860534124628%\"\u003e\n \u003cp\u003e372.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.979228486646884%\"\u003e\n \u003cp\u003e107.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.979228486646884%\"\u003e\n \u003cp\u003e-545.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.979228486646884%\"\u003e\n \u003cp\u003e-71.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.270029673590504%\"\u003e\n \u003cp\u003e-2.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.270029673590504%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.534124629080118%\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe abovementioned graph illustrates the output of the Paired T-test with paired difference analysis and whether there is a substantial variance between the two group means (post frequencies with post reaction for the year 2021). It can be seen that the significance value is .015\u003csup\u003e\u0026nbsp;(two tailed)\u003c/sup\u003e (i.e., t = 2.869), with df 11 and, therefore, there is a statistically substantial variance in the mean length of time post frequency. The sum of the mean is (308.9 and 17) and with a residual confidence interval of the difference is (-545.869 (lower)) (-71.965 (Upper)) for the year 2021. Facebook and Instagram post frequencies of user\u0026rsquo;s engagement. The table also illustrates the sig (2 tailed) with .015 which is not significant, hence null hypothesis is rejected. It can be assumed that for the year 2021, there is no association between post frequencies and post reaction of user\u0026rsquo;s engagement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe output of the ANOVA analysis and whether there is a substantial variance between the two group means (post reaction and post frequencies). It can be seen that the significance value is .000\u003csup\u003eb\u003c/sup\u003e (i.e., p = .0), which is below 0.05. and, therefore, there is a statistically substantial variance in the mean length of time post reaction with post frequency. The sum of squares of regression is (df 1 with 725.0) and the residual is (df 10 with 104.5) for the year 2021, Facebook and Instagram post reaction with post frequency of user\u0026rsquo;s engagement. The graph also illustrates the steady linear growth for post frequency observation of the abovementioned 2021 year of engagement.\u003c/p\u003e"},{"header":"Discussion And Conclusion","content":"\u003cp\u003eSocial media is proved to be an imperative tool in founding the relationships between small-scale business industry and customers. Nevertheless, it establishes and creates operative content and message for social media publicizing campaigns which is a task, as the small-scale industry may have struggle in order to understand the social media algorithms that drive user engagement. The best way to encounter the analytics on user engagement\u0026rsquo;s content which further leads to various reaction, discussion, comment, and share to understand the cohesiveness of its feature and AI (Artificial Intelligence) and ML (Machine Learning) algorithms, especially for niche small-scale industry. Post frequency was one of the indicators to understand the social media presence (Facebook and Instagram) for \u0026lsquo;Aaloksaji\u0026rsquo;. The continuous low presence in 2021 compared to 2019 was a sign of impact caused by COVID-19. The regression analysis of post frequency for 2019 shows a positive association between the user\u0026rsquo;s engagement and the post frequencies on a convenient 0 \u0026ndash; 100per cent. The output from ANOVA also indicates a\u0026nbsp;substantial variance in the mean length of post reaction with post frequency for the year 2019 on Facebook and Instagram. The correlation based on the DV postreaction19 comes to a t-8.756 with a p-value .000, accepting the H2. The Model summary and parameter estimation of post reactions for both 2019 and 2021 come to the R square value of 0.885 and 0.840 respectively with a significance of .000 for both the years. The pared sample correlation for 2019 and 2022 calculate the p-value of 0.044 and 0.000 accepting the H7.\u003c/p\u003e\n\u003cp\u003eThe promotion has always been a challenging task for SMEs as their activities often get restricted by budget while social media can be the key to rescuing them from their grief and helping them in identifying the potential consumers from the market. However, the coverage and engagement of different social media differ due to the nature of the medium and also the acceptance of the media among the users. Research shows that social media advertisements are closely dependent on the purpose of the medium as well as on their usage of them. The nature of social media also plays a crucial part in the selection of the medium as the users like to see and share their preferable content on their chosen platform, which provides leverage to the advertisers to choose the medium wisely while promoting their brands on social media. Another fact that has been found by the researcher is that the social media is positively related to the engagement of the platform while the study shows that using social media for promotion can prove to be a healthy option as it is an investment-friendly option as well as provides higher consumer accessibility across geographical locations, catering to the needs of the brand based on the target consumer preference and behavior (Hilde A. M. Voorveld, 2018).\u003c/p\u003e\n\u003cp\u003eIn this paper we account on a quantitative study that applies to understand how different heterogenous users conversed themselves for textual and visual contented features from Instagram and Facebook posts, along with originator (Aloksaji)- and context-related variables (discussion threads, post frequencies, and post reactions), and have statistically modeled its impact on user engagement. Results and findings from this study can further guide various specialists and professionals in marketing, advertising, and social media in generating and updating engaging content that can interconnect and connects more efficiently with their targeted audiences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and future directions for further research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study has concentrated on the two parameters basically for \u0026lsquo;Aloksaji\u0026rsquo; that is post engagement, and post frequencies which lead to conversion. The study is quantitative and stratified random sampling has been taken into consideration which is a limitation. The study proposes other statistical tests (non-parametric tests- cross-tabulation, chi-square) or simple random sampling that also can be put forward for further research. \u0026lsquo;Aloksaji\u0026rsquo; is basically located in Kolkata so the geographic location is also a limitation for this study. The study also advocates those different geographical attributes can be re-located and other aspects of the small-scale industry can also be researched in further aspects.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/li\u003e\n \u003cli\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/li\u003e\n \u003cli\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/li\u003e\n \u003cli\u003eThe authors have no financial or proprietary interests in any material discussed in this article.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndrea Geissinger, C. L. 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Muntinga, and Fred Bronner (2018). \"Engagement with social media and social media advertising: The differentiating role of platform type.\" Journal of advertising, 47 (1), 38\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Image","content":"\u003cp\u003eImage 1, 2 and 3 are available in supplementary section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"User’s engagement, social media advertising, post reaction, micro-scale industry","lastPublishedDoi":"10.21203/rs.3.rs-1877199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1877199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBefore Digital Media, there were limited avenues for small-scale business to promote their business, thus they were unable to scale up. But with the emergence of Digital Media technologies, democratic platforms such as Social Media became popular among these micro-scale enterprises as a medium for promotion as they were free and provided them a worldwide reach through networking. One of the obvious ways of assessing the caption of particular content on an Online platform is to assess user engagement. It helps in understanding the overall effectiveness of particular promotional content. For this purpose, Facebook and Instagram posts were analyzed for a specific period of time and a datasheet was prepared after rigorous observation and coding. The study concluded that to a large extent there is a significant positive relationship between user engagement and conversion rate. Also, higher forms of engagement i.e., comments and queries were seen in posts that confirmed purchase intention.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"User’s engagement through social media advertisements: a comparative study on micro-scale industry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-29 14:38:06","doi":"10.21203/rs.3.rs-1877199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5f71a63-31ee-483a-a470-bcaf5abdf83f","owner":[],"postedDate":"July 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-20T02:29:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-29 14:38:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1877199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1877199","identity":"rs-1877199","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}