Research on Influencing Factors of Short-Form Video Marketing Effect in Mobile Home Sell ()
1. Introduction
1.1. Background
The rise of mobile home sales depends on short-form video (SFV) marketing because consumers now use digital platforms to make decisions about real estate properties. Traditional property advertisements differ from SFVs because these shorter videos between 15 to 60 seconds use platforms like TikTok, Douyin, Instagram Reels and YouTube Shorts to present mobile homes through engaging virtual tours and user content and transformation videos. SFV marketing proves successful in this sector because it simplifies essential selling points about energy efficiency along with modular flexibility and cost-effectiveness into engaging visual content that appeals to potential buyers [1]. SFVs boost audience participation by offering interactive features such as live Q&A events and AR-powered virtual staging to help customers feel confident about their purchases while reducing purchase uncertainty [2]. Mobile home manufacturers and dealers enhance their lead generation efficiency through SFV platforms because algorithms ensure precise content distribution [3].
In the mobile home sector SFV marketing functions as the prime catalyst behind increased consumer involvement leading to sales conversions because it communicates powerfully through short stories. SFVs use motion graphics together with immersive property walkthroughs and user-generated content to provide an interactive purchasing experience which significantly builds emotional bonding with prospects and boosts their purchase decision. Through their recommendation systems TikTok Douyin and Instagram Reels expand user content discovery allowing property listings to connect with specific target audiences using data analytics and search activity data. Mobile home market transaction efficiency has improved through CTA features which include direct messaging and swipe-up links and live booking options [4].
The use of short-form video marketing for real estate especially mobile home sales emerged because of the fast-growing Chinese and international social media platforms. The SFV market in China is controlled by Douyin and Kuaishou which use AI-powered recommendation systems and localized content strategies to maximize audience engagement [1]. Through its advanced e-commerce features which combine in-app payments and livestream marketing functions Douyin helps mobile home sellers minimize the buyer journey thus decreasing their need for traditional advertising methods. Kuaishou builds its marketing strategy on community engagement through interactive content produced by influencers which creates trust among users and drives better consumer relationships [5]. Real estate marketing experiences global transformation through TikTok and Instagram Reels and YouTube Shorts because these platforms emphasize user interactions and viral content. The “For You” page algorithm on TikTok optimizes property video visibility and Instagram Reels gains strength through its Facebook advertising network integration [6]. YouTube Shorts uses Google search infrastructure to deliver long-term content discoverability which enables mobile home sellers to reach a wider audience than short-lived social media trends [7]. These platforms unite to create immersive real estate marketing which relies on algorithms to promote engagement instead of passive content viewing.
1.2. Research Problem
The use of short-form video (SFV) marketing in real estate has grown steadily but researchers have not thoroughly studied its impact on mobile home sales especially across different platforms. The mobile home sector lacks empirical evidence about how SFV platforms including Douyin, TikTok, Instagram Reels and YouTube Shorts affect consumer decision-making and purchasing intent. SFV mobile home marketing practices remain fragmented because the Chinese market diverges from global markets in terms of platform algorithms and user base characteristics and content engagement methods [3]. The assessment of SFV engagement metrics (likes, shares, comments, views) for lead conversion requires more research along with understanding user sentiments and the best content formats (promotional vs. informational videos) for sales [1]. Mobile home manufacturers and marketers could face marketing inefficiencies and lost sales potential when they do not use data-driven assessments of these variables on SFV platforms.
1.3. Research Objectives
1) To evaluate the impact of short-form video (SFV) engagement metrics (likes, shares, comments, views) on consumer purchase intent in mobile home sales.
2) To analyse the effectiveness of different SFV content strategies across Chinese (Douyin) and global (TikTok, Facebook Reels, YouTube Shorts) platforms.
3) To examine the role of sentiment in SFV user comments and its influence on consumer trust and decision-making.
1.4. Research Questions
1) How do short-form video (SFV) engagement metrics (likes, shares, comments, views) influence consumer purchase intent in mobile home sales?
2) What is the effectiveness of different SFV content strategies (promotional vs. informational videos) across Chinese (Douyin) and global (TikTok, Facebook Reels, YouTube Shorts) platforms?
3) How does sentiment in SFV user comments impact consumer trust and decision-making in mobile home purchases?
1.5. Scope and Limitations
This study focuses on the impact of short-form video (SFV) marketing on mobile home sales, specifically analysing engagement metrics (likes, shares, comments, views) and consumer sentiment across Douyin, TikTok, Facebook Reels and YouTube Shorts. The research examines the effectiveness of different SFV content strategies (promotional vs. informational videos) and their influence on consumer purchase intent. Web scraping and API-based data collection methods were used to extract relevant engagement and sentiment data. However, the study was limited to publicly available content, as platform-specific privacy policies restrict access to certain user interactions and private sales conversions. Additionally, while sentiment analysis of user comments provides insights into consumer perception, it does not directly measure actual purchasing decisions. The study also acknowledged potential algorithmic biases in SFV content distribution, which may influence video visibility and engagement patterns. Despite these limitations, the research aimed to offer actionable insights into optimizing SFV strategies for mobile home marketing.
2. Literature Review
This section presents the literature review of the study:
2.1. Short-Form Video Marketing
Digital advertising now relies heavily on short-form video (SFV) marketing because its visually appealing concise content successfully attracts audience attention while building brand awareness. The growing use of SFV across TikTok and Instagram Reels and Douyin and YouTube Shorts has led to changes in consumer engagement because users now prefer short interactive content that matches reduced attention spans in digital environments [8]. The immediacy together with entertainment value optimization of SFVs surpasses traditional video advertising because they effectively drive purchase intent [9]. The effectiveness of SFV depends on algorithm-based content recommendations that deliver highly personalized video content to users based on their history of behaviour and engagement which increases conversion chances [3].
Furthermore, SFVs function with shopping links and user-endorsed content and call-to-action features that develop a comprehensive marketing environment which improves consumer trust and decision-making [10]. SFV marketing achieves effectiveness through its power to use social proof mechanisms alongside virality methods that naturally grow audience reach. Mobile home sellers alongside other businesses can accomplish market penetration of niche audiences without significant advertising costs through AI-driven algorithms operating on TikTok and Douyin platforms [11]. SFVs have gained substantial value in industries with trust-dependent high-involvement purchases like real estate because they provide equal visibility to businesses of all sizes [12]. The format of SFV encourages user interaction through comment sections and duets and challenges that strengthen brand-consumer relationships. Sales Force Videos face two main issues because of their content overload and their reliance on specific platforms which demand continuous strategic re-evaluation from marketers for audience retention [13].
2.2. Mobile Home Sales Market
Recent years have brought substantial changes to mobile home sales because affordability needs intersect with changing consumer tastes and digital marketing progress. Mobile homes serve as cost-effective housing options that provide flexibility together with energy efficiency so they appeal to first-time buyers along with retirees and those who need affordable housing options [14]. The United States together with China and parts of Europe have experienced high mobile home demand because many middle-income families cannot afford traditional homeownership due to rising real estate prices [15]. The market has received additional support from U.S. Federal Housing Administration (FHA) loans and Chinese subsidized housing schemes together with other government financing options [3]. The mobile home market faces obstacles including restrictive zoning laws and financing constraints as well as unfavourable public perceptions which slow the adoption of mobile homes as primary residences [16].
The mobile home sales market now uses technology-based marketing methods through virtual property tours and AI recommendation systems and short-form video (SFV) advertising [4]. The mobile home sales market utilizes SFV content on TikTok, Douyin and Instagram Reels and YouTube Shorts to reach potential buyers through immersive visual experiences which build trust and purchase intent [17]. The combination of 360-degree home views and lifestyle-oriented content with customization options in SFVs strongly affects purchasing decisions of younger buyers who base their real estate exploration on digital content [18]. SFV marketing distributes content using platform algorithms to deliver targeted advertisements to specific audience segments [19]. The mobile home market competes against conventional real estate advertising practices so companies must combine data analytics with groundbreaking customer engagement methods to boost SFV success while increasing sales conversions [20].
2.3. Web Crawling and Data Analytics in Marketing Research
Marketing research heavily depends on web crawling to retrieve digital platform data systematically to analyze consumer actions and market movements. Web crawlers enable the collection of real-time engagement metrics which include views, likes, shares and comments from TikTok, Douyin, Instagram Reels and YouTube Shorts platforms [8]. The automated tools perform page navigation to identify structured data which helps marketers extract valuable insights for their strategies [21]. The real-time decision-making process benefits from web crawling because it enables both automation and scalability to efficiently acquire extensive datasets [2]. Web crawlers continue facing difficulties concerning platform regulations and data access restrictions that require proper oversight regarding privacy rules and fair use standards [4].
Web crawling receives value from data analytics because this process takes unprocessed extracted information and converts it into marketable insights. The assessment of SFV marketing strategy effectiveness for engagement and sales uses advanced analysis methods that include sentiment analysis and machine learning and predictive modelling. Using Natural Language Processing (NLP) allows businesses to evaluate customer sentiment within video comments which provides more detailed data on buyer reactions [22]. Marketers use regression analysis to discover purchase intent relationships with engagement metrics which provides data-based suggestions for improving SFV content [3]. Time series analysis tracks consumer behaviour changes throughout time which helps identify performance patterns affecting mobile home sales [10]. The capabilities of platform algorithms combined with API restrictions create research hurdles in digital marketing that need flexible data extraction mechanisms and ethical requirements [23].
2.4. Research Gaps
Short-form video (SFV) marketing continues to grow in importance for real estate especially mobile home sales yet researchers still need to study its effectiveness across various platforms. Studies to date analyse generic digital marketing methods yet they lack sufficient empirical evidence about how mobile home consumers respond to SFV metrics such as likes, shares, comments and views [24]. Researchers have insufficient knowledge about how popular algorithms on Douyin, TikTok, Instagram Reels and YouTube Shorts shape video discoverability and rate of conversion [21]. A research gap exists regarding sentiment analysis of consumer comments because investigations into how user-generated discussions about SFV content affect brand trust and purchasing decisions are scarce [22]. The mobile home industry requires a solution for these gaps to optimize its SFV marketing strategies.
2.5. Key Theories
This study theorizes by using the consumer engagement theory, social influence theory and digital marketing effectiveness models to explain how the short form video (SFV) marketing stimulates the consumer interest and purchase behaviour in mobile home sales. According to consumer engagement theory [25], interactive and visually pleasing content increases audience participation and ultimately results in a higher level of emotional and behavioural engagement. According to social influence theory, looking at peer recommendations, influencer marketing and user-generated content on Douyin, TikTok and YouTube Shorts attributes to helping consumers forming their preferences. Furthermore, digital marketing effectiveness models highlight the role of algorithm driven personalization, virality and engagement metrics (likes, comments, shares) in driving greater visibility and conversion rates. The theories give a structured way to look at the relationship between SFV marketing engagement and consumer behaviour in the mobile home industry.
The research results match consumer engagement theory and social influence theory and digital marketing effectiveness model principles. Consumers become more involved through the consumer engagement theory which states that interactive and visually appealing content increases their engagement [25]. Analysis results confirmed that visual content with greater social metrics receiving more appreciation from users led to increased audience views that is proven to promote consumer participation in social media. Social influence theory received support through the analysis of Douyin and TikTok which demonstrated that content made by users along with videos featuring influencers achieved considerable engagement. Consumer decisions in SFV marketing heavily depend on social validation according to evidence presented in the analysis. The digital marketing effectiveness models demonstrate how algorithms and viral effects enable better content reach. The platform-based analysis showed that TikTok and YouTube Shorts obtained superior engagement metrics, which proves that algorithmically promoted content dramatically enhances visibility and conversion potential. Empirical evidence collected in the study verifies the theoretical models concerning SFV marketing for mobile homes as they apply to this specific scenario.
Hypotheses:
3. Research Methodology
In this study, the short form video marketing in mobile home sales was analysed using quantitative research approach. To collect data, web scraping and API based methods were used to scrape engagement metrics and user generated content from TikTok, Douyin, Facebook Reels and YouTube Shorts. In these platforms, a total of 189 videos on mobile homes were scraped to ensure the diversity of content types, engagement levels and marketing strategies. Randomly selected videos were scraped from the dataset to be used from each platform based on engagement metrics (views, likes, comments, shares), content relevance and algorithmic reach. Instagram Reels was excluded as its content is also available on Facebook Reels, thus Facebook was the main platform to analyse. Exclusion: Kuaishou was excluded since web scraping was hindered by inability to allow complete uniform data collection across platforms for that kind of comparison.
3.1. Tools Used for Data Collection
APIFY: For web crawling on Douyin and Facebook Reels to collect video URLs, engagement metrics and comments. APIFY’s automation capabilities allowed for structured data retrieval in compliance with platform restrictions. Douyin was web crawled in Chinese #tags like 预制房, the comments were gotten in Chinese but were then translated to English for universality same as comments that were in other languages.
Web scraping of TikTok was done using Selenium and BeautifulSoup to browse the site, extract video engagement data and scrape user comments.
YouTube Data API v3 was used to extract data from YouTube Shorts and it was efficient in fetching video metadata, engagement metrics and comment threads.
Instagram Reels Web Scraper: The engagement data and user interactions of publicly available Instagram Reels were collected using Selenium and Requests.
3.2. Data Analysis Techniques
Python (Pandas, NumPy, Matplotlib, Seaborn and Scikit-learn) was used for data processing and analysis. The following techniques were applied:
Descriptive Statistics: Summarize engagement metrics across platforms and identify trends in video performance.
Regression Analysis: Performed to determine the correlation between SFV engagement (likes, shares, views, comments) and consumer interest in mobile homes.
Natural Language Processing with NLTK and VADER were used to analyse user comments and categorise them as positive, neutral or negative to analyse consumer perception.
4. Data Analysis
4.1. Descriptive Statistics
The data consists of 185 short-form video (SFV) records from Douyin, Facebook Reels, TikTok and YouTube Shorts. The mean number of views per video is 1,051,981 and the median is much lower at 144,026 as shown in Table 1, suggesting that views are skewed such that a small number of highly viral videos account for a large proportion of total views. The lowest recorded video was 27 views and the highest 20,500,000 views. The disparity in this case suggests that short form video marketing is extremely viral and engagement is all over the place depending on the video and the platform.
There is a large amount of variability in the number of likes per video, with an average of 19,739 and a standard deviation of 46,898. Videos that perform best can receive hundreds of thousands of likes, while others get little to no likes. The same is true of comment activity, with a mean of 1140 comments per video, but one video received 35,223 comments. Figure 1 shows the engagement distribution.
Figure 1. Engagement distribution across SFV platforms.
Table 1. Descriptive statistics for views, likes and comments.
Metric |
Views |
Likes |
Comments |
Count |
185 |
185 |
185 |
Mean |
1,051,981 |
19,739 |
1140 |
Std Dev |
2,230,826 |
46,898 |
3495 |
Min |
27 |
0 |
0 |
25th % |
5400 |
438 |
68 |
50th % |
144,026 |
2842 |
300 |
75th % |
915,800 |
19,322 |
682 |
Max |
20,500,000 |
468,804 |
35,223 |
4.2. Platform-Wise Engagement Metrics
The engagement levels differ a lot across platforms. In total views, TikTok is the clear winner with an average of 2.67 million views per video as shown in Table 2, followed by YouTube Shorts (1.18 million views per video). Lower engagement for Facebook Reels, with an average of 6472 views per video, may suggest that this platform, if any, does not push harder for SFV content than other platforms. Likes and comments also follow the same trend. YouTube Shorts has the highest likes per video (44,871), which highlights great audience interaction. Most views come from TikTok videos but with fewer comments (457 per video), indicating passive engagement where viewers mostly consume the content without further participation.
Table 2. Average engagement Per Platform.
Platform |
Avg. Views |
Avg. Likes |
Avg. Comments |
Douyin |
30,519 |
3012 |
2043 |
Facebook Reels |
6472 |
296 |
39 |
TikTok |
2,671,730 |
24,890 |
457 |
YouTube Shorts |
1,185,068 |
44,871 |
1649 |
4.3. Correlation Analysis
Table 3 shows the correlation matrix that gives us clues about how each engagement metric is correlated against each other. There is a strong positive correlation (r = 0.55) between views and likes, meaning that videos with more views also have more likes. Audience interaction is also highly correlated with comments (r = 0.61) highlighted in Figure 2, meaning that interaction between the users across different engagement types is interconnected. The relationship between views and comments is weaker (r = 0.19) and consequently a high view count does not necessarily equal an audience of high commenters. They may be passive, short form video consumption that allows a user to watch but not comment.
Table 3. Correlation matrix.
Metric |
Views |
Likes |
Comments |
Views |
1.00 |
0.55 |
0.19 |
Likes |
0.55 |
1.00 |
0.62 |
Comments |
0.19 |
0.62 |
1.00 |
Figure 2. Heatmap of engagement correlations.
4.4. Top 10 Viral Videos
Most of the top 8 most viewed videos were from TikTok and YouTube Shorts as shown in Figure 3, showing that they are the most effective in SFV marketing. The TikTok clip about mobile home affordability was the most viral video, with 20.5 million views. Other top-ranking videos are about tiny home living, affordability and mobile home comparisons.
Figure 3. Bar chart of top 10 most-viewed videos.
4.5. Regression Analysis: SFV Engagement and Consumer Interest
A regression model evaluated the relationship between video views and likes and comments (consumer interest) as shown in Figure 4 and Figure 5. The regression model reveals that engagement metrics account for 34.4% (R2 = 0.344) of the total views recorded.
The number of video likes directly and positively affects viewer numbers (β = 33.46, p < 0.001). The data indicates a negative relationship between video views and comments since highly debated content seems to repel viewers rather than attract them (β = −154.06, p = 0.002) as shown in Table 4.
Table 4. Regression results.
Variable |
Coefficient (β) |
Std. Error |
t-Statistic |
p-Value |
Intercept |
567,100 |
145,000 |
3.899 |
0.000 |
Likes |
33.46 |
3.630 |
9.220 |
0.000 |
Comments |
−154.07 |
48.698 |
−3.164 |
0.002 |
Figure 4. Scatter plot of likes & views with regression line.
Figure 5. Scatter plot of comments & views with regression line.
4.6. Comparative Analysis
Research shows that short-form video (SFV) marketing delivers different levels of engagement through TikTok and YouTube Shorts and Facebook Reels and Douyin. TikTok stands at the top for video view averages at 2.67M because it leads in SFV viral spread yet YouTube Shorts attains 1.18M views per video from YouTube’s extensive user network and search capabilities. The statistics show YouTube Shorts achieves 44,871 likes per video while TikTok reaches only 24,890 likes per video which demonstrates YouTube Shorts has a superior engagement rate per viewer possibly because it attracts more focused audience members.
Douyin maintains its algorithm-based content distribution system in China while receiving an average of 30,519 video views. However, it achieves the highest level of comment engagement, with 2043 comments per video. Facebook Reels demonstrates poor organic reach for SFV content as it records the lowest average views of 6472 and generates only 39 comments per video. The association analysis demonstrates that preferred types heavily affect video viewing activity on every platform but viewer comments present varying behaviour patterns which show passive video consumption as the main SFV interaction element worldwide. These findings show that TikTok leads as the most viral platform yet YouTube Shorts participants show high engagement while Douyin users are most active in comments and Facebook Reels suffers from lower SFV metrics to support marketers when tailoring their content for particular platforms.
4.7. Sentiment Analysis of Short-Form Video (SFV) Marketing
Comments
Analysis of user sentiment on Douyin and Facebook Reels and TikTok and
Figure 6. Sentiment analysis of short-form video.
YouTube Shorts comments reveals how users perceive mobile home-related short-form video content. The VADER NLP tool processed user comments to create Positive, Neutral and Negative classifications which were analysed as percentages among each platform as shown in Figure 6.
The audience demonstrates positive reception toward mobile home content according to comment analysis on Douyin and TikTok platforms. Facebook Reels receives a greater number of negative comments than other platforms which indicates that users demonstrate criticism toward mobile home videos on this platform. YouTube Shorts maintains a balanced sentiment profile because Positive and Neutral comments are nearly equal but Negative comments remain minimal. User sentiment toward SFV marketing effectiveness depends on how users engage with specific platforms because each platform influences their emotional responses.
4.8. Comparative Analysis of Sentiment Across Platforms
When comparing sentiment distribution across platforms, several key trends emerge:
1) The content on Douyin receives the most neutral reactions from viewers who observe videos without developing strong emotional connections. The highest sentiment level belongs to Positive while Negative sentiment maintains the lowest position.
2) A pattern similar to Douyin exists in TikTok where positive sentiment dominates but neutral sentiment remains prominent. The content discovery system on TikTok functions through its algorithm to create both curiosity and appreciation among users.
3) Facebook Reels experiences the largest number of Negative comments (~18%) which reveals that users might doubt or express criticism about mobile home marketing content. The engagement method on this platform seems to trigger stronger opinions from users.
4) YouTube Shorts users demonstrate a stronger preference for positive content compared to Facebook because they show more appreciation for mobile home content.
The marketing results indicate that SFV marketing campaigns should use TikTok and Douyin platforms to achieve maximum engagement and positive sentiment from users. Facebook Reels enables engagement opportunities yet users may need specific content adjustments because of its elevated negative sentiment. YouTube Shorts provides an opportunity to create educational and promotional content which combines engagement and credibility through its mixed sentiment approach.
4.9. Hypothesis Testing
H1: Content Quality Positively Influences Purchase Intent
The research used OLS regression to analyse the relationship between likes and comments as independent variables that measure content quality and views as the dependent variable which represents purchase intent. The results indicate that engagement metrics (likes and comments) do not demonstrate a statistically significant relationship (p > 0.05) with views, thus showing that content quality has no direct effect on mobile home sales interest among consumers. The model’s R2 value showed a low measurement which demonstrates that video views cannot be adequately explained by likes and comments alone. The research data disproves H1 because metrics related to content engagement were unable to predict consumer purchase intent. Figure 7 presents regression plots illustrating the relationships between likes and views (left) and comments and views (right) on a logarithmic scale. While both regressions show a positive trend, the dispersion of data points and the weak statistical significance suggest that engagement metrics alone are insufficient predictors of purchase intent in SFV marketing for mobile homes.
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Figure 7. Regression analysis—Content Quality vs. Purchase Intent.
H2: Platform-Specific Features Impact SFV Marketing Effectiveness
The study utilized a one-way ANOVA test to evaluate video engagement metrics between TikTok and YouTube Shorts and Facebook Reels and Douyin to determine how platform features impact SFV marketing success. The results indicate no significant differences (F-statistic: nan, p-value: nan), meaning that platform-based variations did not substantially influence video performance. The engagement data indicates no platform achieved superior performance compared to the others. The research disproves H2 since platform-specific features do not contribute to SFV marketing effectiveness. The boxplot in Figure 8 illustrates the distribution of video views across YouTube, TikTok, Facebook Reels, and Douyin, using a log scale for better visualization. The variation in median views suggests differing engagement levels, but the overlapping interquartile ranges indicate no statistically significant difference in performance across platforms.
Figure 8. Comparison of views across platforms.
4.10. Discussion
This research delivers important information about how short-form video (SFV) marketing affects mobile home sales. TikTok and YouTube Shorts demonstrated the most successful engagement rates through their video analytics data which showed TikTok videos received the greatest number of views yet Facebook Reels and Douyin failed to match this performance. Research [21] confirms that TikTok and YouTube Shorts provide better algorithmic recommendation features and interactive capabilities which enhance user engagement. Research findings from Zhang & Liu [3] are substantiated by the positive relationship between likes and comments on social media platforms. The regression analysis results show a weak connection between engagement metrics and video views, which creates an inconsistency with the hypothesis that superior content quality will automatically increase purchase intent [26].
The ANOVA results showed no significant differences between engagement levels across different platforms so SFV marketing effectiveness does not depend on specific platforms. The theoretical model which attributes engagement to platform affordances faces challenges because of this discovery according to the Social Media Marketing Theory [27]. The higher engagement rates on TikTok and YouTube Shorts do not provide enough evidence to establish statistical significance since other factors such as personalized video content and targeted audiences and external promotional activities likely contribute to SFV marketing success [28]. The sentiment analysis showed that users posted mostly neutral to positive comments on all platforms thus demonstrating their willingness to engage with mobile home content according to findings in SFV-driven consumer decision-making research [29].
Theoretical analysis indicates that purchase intent and SFV marketing success cannot be predicted through either the rejection of H1 or H2. The outcome suggests that the true effects on purchase intent and SFV marketing success appear resulting from a combination of content distribution algorithms and audience segmentation and cross-platform marketing approaches. Digital engagement follows the Adaptive Structuration Theory because it depends on both technological capabilities and user actions [30]. For superior SFV marketing effectiveness marketers should stop depending on likes and comments as the only indicators of consumer intent because these measurements show low correlations with video views.
5. Conclusions and Recommendations
Under this research the authors studied short-form video (SFV) marketing for mobile home sales by analysing engagement metrics and sentiment trends along with platform-specific performance data. The research established that TikTok and YouTube Shorts produced the highest engagement rates yet Facebook Reels and Douyin gained fewer interactions. The analysis using regression showed that content quality assessed by likes and comments did not produce any significant relationship with purchase intent despite common beliefs about direct consumer engagement and purchase interest. The ANOVA analysis discovered no meaningful variations in SFV marketing success rates between platforms because algorithm-based promotion strategies and individualized advertising and enhanced visibility through algorithms prove stronger than platform-specific features alone. The research data stands against the Social Media Marketing Theory to support the Adaptive Structuration Theory which demonstrates how users interact with technology systems in complicated ways.
SFV marketers in the mobile home industry should shift their focus from standard engagement metrics to optimize algorithms and implement AI-driven content recommendations and collaborate with influencers to boost reach and conversion rates. Businesses need to use multiple marketing platforms instead of placing their trust in one platform to reach their success goals. The research must investigate how AI drives SFV content suggestions and reveals what makes influencer-led promotions effective for retention along with testing the benefits of interactive ads for better customer trust. Future SFV marketing strategies will benefit from advanced data analysis and predictive modelling which will help create strategies that match current digital platform evolution and changing consumer behaviours.
Future research should address data collection constraints by including Instagram Reels and Kuaishou in the analysis. The inclusion of Instagram data would improve study generalization because of its large international user base. Future research must incorporate a method to handle the algorithmic biases which affect the way SFV content is distributed. The study admits its biases exist but fails to implement any corrective solutions that influence how researchers interpret engagement data. New approaches for handling algorithmic influence would generate better insights about content performance data. The study utilizes video views as a replacement for purchase intent measurements but this passive metric does not directly reflect actual consumer interest or buying patterns. Subsequent studies should expand their evaluation by using click-through rates and website visits and direct sales conversions as supplementary measures to assess SFV marketing success in mobile home sales.
Conflicts of Interest
The author declares no conflicts of interest.