Research Article: 2026 Vol: 30 Issue: 2
N Meena Rani, Xavier Institute of Management and Entrepreneurship, Bangalore, India
Ganaraj Khandige, Xavier Institute of Management and Entreopreneurship, Bangalore, India
Raghib Anwer, Xavier Institute of Management and Entrepreneurship, Bangalore, India
Harshwardhan, Xavier Institute of Management and Entrepreneurship, Bangalore, India
Naveen Kumar S, Xavier Institute of Management and Entrepreneurship, Bangalore, India
Citation Information: Rani., N.M, S., Khandige.G., Anwer., R, Harshwardhan & S., N.K. (2026) Influence of social media marketing on online purchase intentions regarding wearable products: do demographics matter?.
Academy of Marketing Studies Journal, 30(2), 1-19.
This study examines the effect of social media marketing on the online purchase decision making of wearable products such as smart watches and fitness bands. The quantitative study was used to collect the data through a structured questionnaire from 410 respondents. The aspects of social media, video advertisements, product reviews, discounts, interactivity, and the quality of content, and demographic factors such as gender and age of the respondents were captured and analysed. We tested the influence of consumers’ gender and age on their SM purchase behaviour and found that there is no significant association between respondents’ demographics including gender and age and their SM purchase behaviour. It also provides practical recommendations to marketing experts on user engagement strategies to enhance the performance of online sales.
Social Media Marketing, Online Purchase Decision, Wearable Products, Consumer Behaviour, Digital Marketing.
The fast evolution of digital technology and the growth of the use of smartphones have certainly transformed the consumer behavior of information search and product acquisition. Social media (Instagram, Facebook, YouTube, and Twitter) has become a new communication medium used by brands, and the segment of wearable items, including smart watches, fitness devices, and wireless earphones, receive an increasing amount of attention in this matter (Ntumba & Budree, 2020). The acceptance of wearable technology has continued to grow in the recent industry reports due to numerous reasons, such as heightened health consciousness, shifts in lifestyles, and enhanced technology (Haroon et al., 2025). Social media marketing has a great influence in shaping consumer awareness, engagement, and trust and purchase intentions. Despite the high number of brands who are capitalizing on influencer marketing, paid ads and user-generated content, empirical studies are not conducted to demonstrate the ability of the mentioned activities to result in an increment in the online purchase decisions of wearable products.
Social Media Marketing and Online Customer Buying Behavior
Social media marketing now plays an essential role in shaping how consumers make their online purchases. Social media platforms control how consumers search for information and evaluate products and decide which products to purchase according to the research conducted by multiple scholars. Varghese & Agrawal (2021) claim that social media is a major player in affecting consumer buying behaviour as it helps to raise awareness and determine customer preferences. Their research reveals that customers mainly depend on social media for product-related information before making a purchase. On the other hand, Lee (2013) wrote that social media influences the consumer decision-making process at each stage, including problem recognition, information search, alternative evaluation, and final decision. Prasath & Yoganathen (2018) in their work established that social media marketing is the main factor driving consumers' buying decision-making process. Their study highlights that promotional effort and consumer communication through social media could be an effective means of triggering purchases. In addition to it, Alkharabsheh & Zhen (2021) stated that social media marketing positively influences the consumer buying decision process Romadhoni et al., (2023). This effect is even more significant when social media marketing is supported by content marketing that will provide value and necessary online convenience. The available body of research is highly promising the notion that social media marketing is directly and positively correlated to online consumer purchasing behaviour. Social media also contributes to developing trust, attitudes as well as the ultimate purchase decisions in addition to delivering the required information Bilro et al., (2022); Fischer et al., (2023); Giustiniani et al., (2022); Husain, Ahmad & Khan, (2022).
Social media marketing elements that influence the buyers' decisions
Different facets of social media marketing have varying impacts on buyers. Such facets are content marketing, online convenience, interaction, and promotional activities. Alkharabsheh & Zhen (2021) highlighted content marketing as one of the largest influences on consumer purchase decisions. Consumers are more likely to get interested and buy when companies give them helpful, clear, and engaging content on social media. Their research also pointed out that aspects of online convenience, like quick information access and easy purchase procedures, have a positive impact on the buying decision process. Varghese & Agrawal (2021) pointed out that social media platforms allow companies to create brand awareness and establish customer relationships. Interacting with the brand, reading the reviews, and seeing other customers' feedback can increase customers' trust in the products. In the same way, Lee (2013) mentioned that sharing information and communication over social media platforms changes the way consumers evaluate and make their final buying decision. Prasath & Yoganathen (2018), on the other hand, disclosed that social media promotional strategies and marketing activities are the main reasons why consumers change their buying decisions. Such actions attract customers to a brand and later influence their purchase decisions. Therefore, the literature indicates that more focused social media marketing components, such as content quality, online convenience, interaction, and promotional strategies, significantly contribute to influencing purchasers' decisions.
Demographic factors influence online purchase decision variations
Demographic factors are also important in understanding differences in online purchase decisions. These factors include gender and age. Waheed et al. (2014) have said that the buying behaviour of consumers depends on various factors, among them being their demographic features. According to their research, consumers can be influenced by age, income, and level of education when making a buying decision. Equally, Šadic et al. (2018); Trauntschnig & Hetz, (2020) established that demographics play a significant role in determining the decisions to buy technical products. The preferences and decision patterns of the various population groups are varied. A comparison of the Generation Y and Baby Boomers conducted by Parment (2013) revealed apparent distinctions between the two in terms of shopping behaviour and involvement of buyers. The research revealed that young people and older people interact in different ways in terms of the purchasing decision. This implies that there is a likelihood of an impact of age and generational difference on online purchasing behaviour. As Mansi & Pandey (2016) discovered, demographic features determine the decision-making in the sphere of procurement specialists. Though their work involved sustainable procurement, they contribute to the notion that demographic factors can influence the decision-making behaviour. The relevant literature shows that differences in online purchase decisions are brought about by demographic factors. Varied age groups, income levels and generations have varied reactions to advertisement activities and buying decisions differ.
Research gap
Although numerous studies have been conducted on social media marketing and consumer buying behaviour, there are a few gaps. Firstly, several studies, such as Varghese & Agrawal (2021), Lee (2013), Prasath & Yoganathen (2018), centre primarily on the overall impact of social media on consumer behaviour. Nevertheless, they don't thoroughly compare different social media marketing elements that influence online buying decisions when working in combination. Secondly, Alkharabsheh & Zhen (2021) have dissected content marketing, social media marketing, and online convenience collectively, yet their research was limited to a specific conference setting. Thirdly, demographic factors have been researched individually in various contexts. For instance, Waheed et al. (2014) concentrated on selected consumer factors, and Parment (2013) analysed generational differences in shopping behaviour. However, the research that combines demographic factors with social media marketing variables to explain online buying behaviour is lacking. It is necessary to conduct a study that brings together social media marketing elements and demographic factors to account for differences in online consumer buying behaviour. This research intends to bridge the gap by studying the combined influence of social media marketing and demographic characteristics on the decision to purchase online.
Theoretical Background
The theoretical framework of this research establishes its foundation through both past empirical investigations and the consumer decision-making process. The framework integrates social media marketing and levels of features and demographics so that it can be more valid to explain the differences in online purchase decisions. Theoretical foundation of the present study is the consumer decision-making process. The research shows that social media affects all three stages which consumers use to make purchasing decisions according to Lee 2013. Differently put, social media marketing is a driver that shapes the thinking and decision-making of consumers via the Internet. Varghese & Agrawal (2021) also remarked that social media networks impact consumer awareness and attitude, which in turn determines purchasing behaviour. Prasath & Yoganathen (2018) showed that social media marketing creates a major influence on how people make their purchasing decisions. Alkhara Bsheh and Zhen (2021) demonstrated that social media marketing together with content marketing and online convenience create positive effects on how consumers make their buying decisions. This study identified the dependent variable as online consumer buying behaviour. Lee (2013) explained buying behaviour as being influenced by the information and interaction that people get through social media. Varghese & Agrawal (2021) further indicated that decisions regarding purchases are a result of attitudes and awareness created in consumers through social media. Prasath & Yoganathen (2018) corroborated that social media marketing has a major effect on the final buying decision. Demographic factors have been taken as moderators in this research as they could impact the way consumers respond to social media marketing. Waheed et al. (2014) have mentioned that demographic factors such as age, income, and education have a significant effect on consumer buying behaviour. Adi et al. (2018) have also demonstrated that the demographic characteristics of a person influence his/her purchasing decisions. Parment (2013) has revealed that there are differences in the shopping behaviour of members of Generation Y and those of Baby Boomers. Mansi and Pandey (2016), too, were in favour of the notion that demographic characteristics influence decision, making behavior.
The study uses quantitative research. The target population for the research included internet buyers who utilize social media platforms and have experience of purchasing products through the internet. They are chosen because they are the ones most directly influence by social media marketing and online buying environments. The study used convenience sampling (Hossan, Dato’Mansor & Jaharuddin, 2023). A structured questionnaire was formulated, considering the variables that were identified in the literature review. Important items of the study included social media marketing elements (content marketing, online convenience, interaction, promotional activities), online consumer buying behaviour, demographic factors (age, gender). The questionnaire consisted of close ended questions and likert scale statements (e.g., strongly disagree to strongly agree). The survey was designed using an online survey platform (Google Forms). The social media platforms were used to distribute the survey link. The confidentiality and anonymity of the respondents were maintained.
Hypotheses
There are numerous studies supporting the idea that social media marketing is a major factor that affects how consumers make purchasing decisions. Based on the existing literature, the following hypotheses being formulated Table 1.
| Table 1 Descriptive Statistics of Social Media Marketing Factors | |||||
| Descriptive Statistics | |||||
| N | Minimum | Maximum | Mean | Std. Deviation | |
| SM Useful | 410 | 1 | 5 | 3.68 | 1.040 |
| SM Easy | 410 | 1 | 5 | 3.56 | 1.173 |
| Discount | 410 | 1 | 5 | 3.78 | 1.015 |
| Reels | 410 | 1 | 5 | 3.69 | 1.080 |
| Info posts | 410 | 1 | 5 | 3.76 | 1.064 |
| Reviews | 410 | 1 | 5 | 3.56 | 1.188 |
| Unboxing | 410 | 1 | 5 | 3.75 | 1.063 |
| Prefer Video ads | 410 | 1 | 5 | 3.66 | .996 |
| Video ads on SM persuasive | 410 | 1 | 5 | 3.92 | .973 |
| Infographics | 410 | 1 | 5 | 3.70 | .999 |
| carousels | 410 | 1 | 5 | 3.79 | .965 |
| Interactive polls | 410 | 1 | 5 | 3.35 | 1.172 |
| giveaways | 410 | 1 | 5 | 3.43 | 1.143 |
| Call to Action (CTA) | 410 | 1 | 5 | 3.34 | 1.237 |
| Q&A and Livestream | 410 | 1 | 5 | 3.27 | 1.152 |
| Response to comments | 410 | 1 | 5 | 3.76 | 1.078 |
H1: Consumers’ gender significantly influences variations in online purchase decisions
H2: Consumers’ age significantly influences variations in online purchase decisions
Descriptive statistics present a summary of how respondents view various social media marketing components which affect their decisions to buy wearable products. The study conducted its measurements by using a five-point Likert scale which assigned a value of 1 to strong disagreement and a value of 5 to strong agreement. Respondents showed a moderate positive view of social media marketing practices because their mean values between 3.27 and 3.92 (N=410) show this result. The factor "Video ads on social media are persuasive" achieved the highest average score (M=3.92, SD=0.973) which indicates that video-based promotional content represents the strongest force that leads users to engage with products and make purchases. Users developed a positive view of informative content which used visual organization to present information through content formats that received high ratings with carousels (M=3.79), discount-related posts (M=3.78), informational posts (M=3.76), response to comments (M=3.76), and unboxing content (M=3.75) as examples. The constructs of perceived usefulness (SM Useful, M = 3.68) and ease of use (SM Easy, M = 3.56) received positive evaluations because users found social media platforms useful and simple to navigate when researching wearable products. The results show that users reach moderate agreement with reels (M = 3.69), infographics (M = 3.70), and preferred video ads (M = 3.66), which indicates that short-form and visual storytelling formats help shape consumer attitudes toward products. The interactive engagement tools produced lower mean scores, which showed that they might have effectiveness problems. The lowest mean rating was recorded by Q&A and livestream sessions (M = 3.27, SD = 1.152), while call-to-action posts (M = 3.34) and interactive polls (M = 3.35) followed behind, which shows that respondents regarded these methods as having less power to attract interest and promote wearable product advertising. The results showed that giveaways (M = 3.43) and reviews (M = 3.56) achieved moderate agreement because both elements needed better ways to build credibility and audience trust Table 2.
| Table 2 Gender-Wise Group Statistics | |||||
| Group Statistics | |||||
| gen | N | Mean | Std. Deviation | Std. Error Mean | |
| Freq. of using SM | 1 | 137 | 4.86 | .488 | .042 |
| 2 | 273 | 4.77 | .606 | .037 | |
| Freq. of online shopping | 1 | 137 | 4.27 | .928 | .079 |
| 2 | 273 | 4.10 | 1.004 | .061 | |
| SM Useful | 1 | 137 | 3.85 | .992 | .085 |
| 2 | 273 | 3.60 | 1.056 | .064 | |
| SM Easy | 1 | 137 | 3.48 | 1.195 | .102 |
| 2 | 273 | 3.59 | 1.163 | .070 | |
| Discount | 1 | 137 | 3.76 | .982 | .084 |
| 2 | 273 | 3.79 | 1.033 | .063 | |
| Reels | 1 | 137 | 3.64 | 1.049 | .090 |
| 2 | 273 | 3.72 | 1.096 | .066 | |
| Info posts | 1 | 137 | 3.68 | 1.091 | .093 |
| 2 | 273 | 3.80 | 1.050 | .064 | |
| Reviews | 1 | 137 | 3.55 | 1.111 | .095 |
| 2 | 273 | 3.57 | 1.226 | .074 | |
| Unboxing | 1 | 137 | 3.89 | .968 | .083 |
| 2 | 273 | 3.68 | 1.103 | .067 | |
| Prefr Video ads | 1 | 137 | 3.59 | .967 | .083 |
| 2 | 273 | 3.70 | 1.010 | .061 | |
| Video ads on SM persuasive | 1 | 137 | 4.04 | .927 | .079 |
| 2 | 273 | 3.86 | .992 | .060 | |
| infographics | 1 | 137 | 3.77 | .970 | .083 |
| 2 | 273 | 3.66 | 1.013 | .061 | |
| carousels | 1 | 137 | 3.85 | .977 | .083 |
| 2 | 273 | 3.75 | .960 | .058 | |
| intactiv polls | 1 | 137 | 3.47 | 1.098 | .094 |
| 2 | 273 | 3.29 | 1.204 | .073 | |
| giveaways | 1 | 137 | 3.35 | 1.167 | .100 |
| 2 | 273 | 3.48 | 1.131 | .068 | |
| CTA | 1 | 137 | 3.41 | 1.222 | .104 |
| 2 | 273 | 3.30 | 1.245 | .075 | |
| Q&A and Livestream | 1 | 137 | 3.26 | 1.152 | .098 |
| 2 | 273 | 3.27 | 1.154 | .070 | |
| Response to comments | 1 | 137 | 3.75 | 1.042 | .089 |
| 2 | 273 | 3.76 | 1.098 | .066 | |
The group Females 2: n = 273). Male respondents reports slightly higher mean scores for frequency of social media use, online shopping, perceived usefulness, unboxing content, persuasive video ads, infographics, carousels, interactive polls, and CTA. In contrast, female respondents show marginally higher means for perceived ease of use, discounts, reels, information posts, preference for video ads, and giveaways. Overall, the mean differences across most variables are small, suggesting broadly similar perceptions and engagement patterns across gender groups Table 3.
| Table 3 Gender-Wise Independent Sample T Test | ||||||||||
| Independent Samples Test | ||||||||||
| Levene's Test for Equality of Variances | t-test for Equality of Means | |||||||||
| F | Sig. | t | df | Sig. (2-tailed) | Mean Difference | Std. Error Difference | 95% Confidence Interval of the Difference | |||
| Lower | Upper | |||||||||
| fre SM | Equal variances assumed | 8.412 | .004 | 1.483 | 408 | .139 | .088 | .060 | -.029 | .206 |
| Equal variances not assumed | 1.593 | 329.463 | .112 | .088 | .056 | -.021 | .198 | |||
| Freq.of online shopping | Equal variances assumed | .332 | .565 | 1.669 | 408 | .096 | .171 | .103 | -.030 | .373 |
| Equal variances not assumed | 1.714 | 292.471 | .088 | .171 | .100 | -.025 | .368 | |||
| SM Useful | Equal variances assumed | 2.073 | .151 | 2.269 | 408 | .024 | .246 | .108 | .033 | .459 |
| Equal variances not assumed | 2.317 | 288.198 | .021 | .246 | .106 | .037 | .455 | |||
| SM Easy | Equal variances assumed | .284 | .595 | -.909 | 408 | .364 | -.112 | .123 | -.353 | .130 |
| Equal variances not assumed | -.900 | 265.962 | .369 | -.112 | .124 | -.356 | .133 | |||
| Discount | Equal variances assumed | 1.003 | .317 | -.336 | 408 | .737 | -.036 | .106 | -.245 | .173 |
| Equal variances not assumed | -.342 | 285.245 | .733 | -.036 | .105 | -.242 | .170 | |||
| Reels | Equal variances assumed | .309 | .579 | -.765 | 408 | .445 | -.087 | .113 | -.309 | .136 |
| Equal variances not assumed | -.776 | 283.268 | .438 | -.087 | .112 | -.306 | .133 | |||
| Info posts | Equal variances assumed | .244 | .621 | -1.075 | 408 | .283 | -.120 | .111 | -.339 | .099 |
| Equal variances not assumed | -1.061 | 263.387 | .290 | -.120 | .113 | -.342 | .102 | |||
| Reviews | Equal variances assumed | 2.329 | .128 | -.163 | 408 | .870 | -.020 | .125 | -.265 | .224 |
| Equal variances not assumed | -.169 | 297.457 | .866 | -.020 | .121 | -.257 | .217 | |||
| Unboxing | Equal variances assumed | 8.063 | .005 | 1.852 | 408 | .065 | .206 | .111 | -.013 | .424 |
| Equal variances not assumed | 1.934 | 306.085 | .054 | .206 | .106 | -.004 | .415 | |||
| Prefer Video ads | Equal variances assumed | .211 | .646 | -1.040 | 408 | .299 | -.108 | .104 | -.313 | .097 |
| Equal variances not assumed | -1.055 | 283.243 | .292 | -.108 | .103 | -.311 | .094 | |||
| Video ads on SM persuasive | Equal variances assumed | .716 | .398 | 1.765 | 408 | .078 | .179 | .102 | -.020 | .379 |
| Equal variances not assumed | 1.805 | 289.375 | .072 | .179 | .099 | -.016 | .375 | |||
| infographics | Equal variances assumed | .640 | .424 | 1.093 | 408 | .275 | .114 | .105 | -.091 | .320 |
| Equal variances not assumed | 1.109 | 283.260 | .268 | .114 | .103 | -.089 | .317 | |||
| carousels | Equal variances assumed | .185 | .667 | .911 | 408 | .363 | .092 | .101 | -.107 | .291 |
| Equal variances not assumed | .906 | 268.207 | .366 | .092 | .102 | -.108 | .292 | |||
| interactive polls | Equal variances assumed | .737 | .391 | 1.421 | 408 | .156 | .174 | .123 | -.067 | .415 |
| Equal variances not assumed | 1.465 | 295.768 | .144 | .174 | .119 | -.060 | .408 | |||
| giveaways | Equal variances assumed | .008 | .930 | -1.051 | 408 | .294 | -.126 | .120 | -.361 | .109 |
| Equal variances not assumed | -1.040 | 265.062 | .299 | -.126 | .121 | -.364 | .112 | |||
| CTA | Equal variances assumed | .129 | .720 | .837 | 408 | .403 | .108 | .130 | -.146 | .363 |
| Equal variances not assumed | .842 | 276.922 | .401 | .108 | .129 | -.145 | .362 | |||
| Q&A and Live stream | Equal variances assumed | .033 | .856 | -.099 | 408 | .921 | -.012 | .121 | -.249 | .225 |
| Equal variances not assumed | -.099 | 272.883 | .921 | -.012 | .121 | -.250 | .226 | |||
| Response to comments | Equal variances assumed | .099 | .753 | -.057 | 408 | .955 | -.006 | .113 | -.229 | .216 |
| Equal variances not assumed | -.058 | 285.607 | .954 | -.006 | .111 | -.225 | .212 | |||
The independent samples t-test results indicate that most variables do not show statistically significant gender differences (p > 0.05), suggesting similar perceptions and behaviours across the two groups. A significant difference is observed only for SM Useful (t = 2.269, p = 0.024), where Group 1 (male) reports higher perceived usefulness than Group 2 (female). Variables such as frequency of social media use, online shopping, unboxing content, and perceived persuasiveness of video ads show marginal differences but are not statistically significant at the 5% level. Overall, except for perceived usefulness of social media, gender does not appear to significantly influence social media engagement, content preference, or online shopping behaviour in this sample Table 4.
| Table 4 Age-Wise Group Statistics | |||||
| Variable | Stat | 18–24 yrs | 25–30 yrs | 31–40 yrs | > 40 yrs |
| SM Useful | Mean | 3.76 | 3.71 | 3.62 | 3.60 |
| Std dev. | 1.084 | 1.008 | 1.051 | 1.027 | |
| SM Easy | Mean | 3.74 | 3.6 | 3.42 | 3.41 |
| Std dev. | 1.215 | 1.168 | 1.023 | 1.305 | |
| Discount | Mean | 3.78 | 3.7 | 3.94 | 4.04 |
| Std dev. | 0.962 | 1.023 | 1.054 | 1.147 | |
| Reels | Mean | 3.75 | 3.72 | 3.63 | 3.61 |
| Std dev. | 1.06 | 1.102 | 1.075 | 1.083 | |
| Info posts | Mean | 3.86 | 3.8 | 3.77 | 3.39 |
| Std dev. | 1.051 | 1.114 | 0.948 | 1.033 | |
| Reviews | Mean | 3.74 | 3.55 | 3.54 | 3.45 |
| Std dev. | 1.223 | 1.235 | 0.991 | 1.071 | |
| Unboxing | Mean | 3.95 | 3.91 | 3.74 | 3.65 |
| Std dev. | 1.081 | 1.094 | 0.807 | 1.301 | |
| Prefr Video ads | Mean | 3.79 | 3.74 | 3.65 | 3.64 |
| Std dev. | 1.053 | 0.972 | 1.01 | 0.81 | |
| Video ads persuasive | Mean | 3.92 | 3.9 | 3.94 | 4.0 |
| Std dev. | 0.927 | 0.972 | 1.084 | 1 | |
| Carousels | Mean | 3.8 | 3.93 | 3.91 | 4.13 |
| Std dev. | 0.978 | 0.964 | 0.948 | 0.869 | |
| Response to comments | Mean | 3.67 | 3.86 | 3.71 | 3.61 |
| Std dev. | 1.103 | 1.053 | 1.077 | 1.118 | |
The age-wise descriptive statistics show that different age groups show moderate differences in how they view social media marketing elements which affect their decisions to buy wearable devices. Respondents between 18-24 years of age showed higher average values on platform perception metrics which included social media usefulness (M = 3.76) and ease of use (M = 3.74) because they demonstrated more active usage of social media platforms than people from other age groups. The segment gave a mean score of 3.74 for reviews and 3.94 unboxing content because they preferred informative content which showed actual experiences to evaluate their purchasing decisions. The 40-year-old group showed stronger preference for standard content formats which include discounts (M = 4.04) and persuasive video ads (M = 4.00) and carousel posts (M = 4.13) which received the highest average scores out of all tested content types.
The older consumers exhibit better responses to the marketing messages that provide valuable information using organized visual displays. The age group of 25 to 30 years exhibited higher interaction rates for the informational content with an average score of 3.80 and for the brand response to comments with an average score of 3.86 since this age group appreciates interactive communication and brand response. The respondents aged 18 to 24 years exhibited slightly higher evaluations for most variables. The participants exhibited higher platform usability and content navigation expectations since they evaluated ease of use with a mean score of 3.42. The standard deviation values for all age groups remained close to one unit, indicating that the participants exhibited moderate response variability while displaying different preferences for each age group Table 5.
| Table 5 Anova | ||||||||
| Sum of Squares | df | Mean Square | F | Sig. | ||||
| SM Useful | Between Groups | (Combined) | 1.138 | 3 | .379 | .349 | .790 | |
| Linear Term | Unweighted | .046 | 1 | .046 | .042 | .837 | ||
| Weighted | .537 | 1 | .537 | .494 | .483 | |||
| Deviation | .601 | 2 | .301 | .276 | .759 | |||
| Within Groups | 441.642 | 406 | 1.088 | |||||
| Total | 442.780 | 409 | ||||||
| SM Easy | Between Groups | (Combined) | 5.303 | 3 | 1.768 | 1.286 | .279 | |
| Linear Term | Unweighted | 1.094 | 1 | 1.094 | .796 | .373 | ||
| Weighted | 3.925 | 1 | 3.925 | 2.857 | .092 | |||
| Deviation | 1.377 | 2 | .689 | .501 | .606 | |||
| Within Groups | 557.907 | 406 | 1.374 | |||||
| Total | 563.210 | 409 | ||||||
| Discount | Between Groups | (Combined) | 4.368 | 3 | 1.456 | 1.417 | .237 | |
| Linear Term | Unweighted | 2.169 | 1 | 2.169 | 2.111 | .147 | ||
| Weighted | 1.522 | 1 | 1.522 | 1.481 | .224 | |||
| Deviation | 2.846 | 2 | 1.423 | 1.384 | .252 | |||
| Within Groups | 417.312 | 406 | 1.028 | |||||
| Total | 421.680 | 409 | ||||||
| Reels | Between Groups | (Combined) | 1.715 | 3 | .572 | .488 | .691 | |
| Linear Term | Unweighted | 1.006 | 1 | 1.006 | .859 | .355 | ||
| Weighted | .483 | 1 | .483 | .413 | .521 | |||
| Deviation | 1.232 | 2 | .616 | .526 | .591 | |||
| Within Groups | 475.563 | 406 | 1.171 | |||||
| Total | 477.278 | 409 | ||||||
| Info posts | Between Groups | (Combined) | 3.369 | 3 | 1.123 | .992 | .397 | |
| Linear Term | Unweighted | 2.729 | 1 | 2.729 | 2.410 | .121 | ||
| Weighted | .945 | 1 | .945 | .835 | .361 | |||
| Deviation | 2.424 | 2 | 1.212 | 1.071 | .344 | |||
| Within Groups | 459.726 | 406 | 1.132 | |||||
| Total | 463.095 | 409 | ||||||
| Reviews | Between Groups | (Combined) | 3.198 | 3 | 1.066 | .754 | .520 | |
| Linear Term | Unweighted | 1.056 | 1 | 1.056 | .747 | .388 | ||
| Weighted | 2.508 | 1 | 2.508 | 1.775 | .184 | |||
| Deviation | .690 | 2 | .345 | .244 | .784 | |||
| Within Groups | 573.777 | 406 | 1.413 | |||||
| Total | 576.976 | 409 | ||||||
| Unboxing | Between Groups | (Combined) | 2.680 | 3 | .893 | .789 | .500 | |
| Linear Term | Unweighted | .009 | 1 | .009 | .008 | .929 | ||
| Weighted | .173 | 1 | .173 | .152 | .696 | |||
| Deviation | 2.507 | 2 | 1.254 | 1.108 | .331 | |||
| Within Groups | 459.440 | 406 | 1.132 | |||||
| Total | 462.120 | 409 | ||||||
| Prefer Video ads | Between Groups | (Combined) | .434 | 3 | .145 | .145 | .933 | |
| Linear Term | Unweighted | .044 | 1 | .044 | .044 | .835 | ||
| Weighted | .015 | 1 | .015 | .015 | .902 | |||
| Deviation | .418 | 2 | .209 | .210 | .811 | |||
| Within Groups | 405.118 | 406 | .998 | |||||
| Total | 405.551 | 409 | ||||||
| Video ads on SM persuasive | Between Groups | (Combined) | .229 | 3 | .076 | .080 | .971 | |
| Linear Term | Unweighted | .171 | 1 | .171 | .179 | .672 | ||
| Weighted | .085 | 1 | .085 | .089 | .765 | |||
| Deviation | .143 | 2 | .072 | .075 | .928 | |||
| Within Groups | 386.952 | 406 | .953 | |||||
| Total | 387.180 | 409 | ||||||
| infographics | Between Groups | (Combined) | .234 | 3 | .078 | .077 | .972 | |
| Linear Term | Unweighted | .130 | 1 | .130 | .129 | .720 | ||
| Weighted | .028 | 1 | .028 | .028 | .867 | |||
| Deviation | .205 | 2 | .103 | .102 | .903 | |||
| Within Groups | 408.264 | 406 | 1.006 | |||||
| Total | 408.498 | 409 | ||||||
| carousels | Between Groups | (Combined) | 5.489 | 3 | 1.830 | 1.978 | .117 | |
| Linear Term | Unweighted | 4.370 | 1 | 4.370 | 4.723 | .030 | ||
| Weighted | 4.220 | 1 | 4.220 | 4.561 | .033 | |||
| Deviation | 1.269 | 2 | .635 | .686 | .504 | |||
| Within Groups | 375.623 | 406 | .925 | |||||
| Total | 381.112 | 409 | ||||||
| Interactive polls | Between Groups | (Combined) | 3.119 | 3 | 1.040 | .756 | .519 | |
| Linear Term | Unweighted | .285 | 1 | .285 | .208 | .649 | ||
| Weighted | .150 | 1 | .150 | .109 | .742 | |||
| Deviation | 2.969 | 2 | 1.485 | 1.080 | .341 | |||
| Within Groups | 558.306 | 406 | 1.375 | |||||
| Total | 561.424 | 409 | ||||||
| giveaways | Between Groups | (Combined) | 5.606 | 3 | 1.869 | 1.434 | .232 | |
| Linear Term | Unweighted | .499 | 1 | .499 | .383 | .536 | ||
| Weighted | 2.057 | 1 | 2.057 | 1.579 | .210 | |||
| Deviation | 3.549 | 2 | 1.774 | 1.362 | .257 | |||
| Within Groups | 529.116 | 406 | 1.303 | |||||
| Total | 534.722 | 409 | ||||||
| Call to Action | Between Groups | (Combined) | 1.574 | 3 | .525 | .341 | .795 | |
| Linear Term | Unweighted | .256 | 1 | .256 | .167 | .683 | ||
| Weighted | .089 | 1 | .089 | .058 | .810 | |||
| Deviation | 1.486 | 2 | .743 | .483 | .617 | |||
| Within Groups | 623.977 | 406 | 1.537 | |||||
| Total | 625.551 | 409 | ||||||
| Q&A and Live stream | Between Groups | (Combined) | 6.086 | 3 | 2.029 | 1.534 | .205 | |
| Linear Term | Unweighted | 2.775 | 1 | 2.775 | 2.099 | .148 | ||
| Weighted | 1.532 | 1 | 1.532 | 1.159 | .282 | |||
| Deviation | 4.554 | 2 | 2.277 | 1.722 | .180 | |||
| Within Groups | 536.862 | 406 | 1.322 | |||||
| Total | 542.949 | 409 | ||||||
| Response to comments | Between Groups | (Combined) | 3.810 | 3 | 1.270 | 1.093 | .352 | |
| Linear Term | Unweighted | .224 | 1 | .224 | .192 | .661 | ||
| Weighted | .035 | 1 | .035 | .030 | .862 | |||
| Deviation | 3.775 | 2 | 1.888 | 1.624 | .198 | |||
| Within Groups | 471.799 | 406 | 1.162 | |||||
| Total | 475.610 | 409 | ||||||
A one-way ANOVA test was carried to find out if different age groups have unique perceptions of the elements of social media marketing. The findings of the study indicated that all variables failed to establish significant association at the 5% significance level though mean scores were lightly different. This could probably indicate that all age groups might have carried same perceptions of social media marketing practices that they used for wearable products. The two core platform-related constructs of social media usefulness and ease of use displayed constant usability evaluation across all age demographics according to the research results which showed F values of 0.349 and 1.286 respectively. Content-driven factors including discounts (F = 1.417, p = 0.237), reels (F = 0.488, p = 0.691), informational posts (F = 0.992, p = 0.397), reviews (F = 0.754, p = 0.520), and unboxing videos (F = 0.789, p = 0.500) also did not exhibit significant age-based differences. This indicates that informational and experiential content formats appeal relatively equally across younger and older audiences. The advertising-related variables of preference for video ads and perceived persuasiveness of video ads showed extremely non-significant results which demonstrated that video advertising effectiveness depends on age segmentation between sample groups.
The carousel posts showed the most variation between age groups because they produced the highest F-value (F = 1.978, p = 0.117) which was not statistically significant. The results indicate that different visual storytelling formats show different appeal to specific demographic groups, which researchers need to explore in future studies. The interactive polls and giveaways and CTAs and Q&A or livestream formats showed non-significant results which demonstrated that interactive features do not create different responses between age categories.
Theoretical Contributions
This study establishes its academic value through its investigation of a specific elements- gender and age of the respondents, which affect online purchasing decisions for wearable products. Unlike previous research which examined social media marketing, this study investigates specific content elements and the most important demographic characteristics- gender and age to provide deeper understanding of how digital consumers behave.
Managerial Implications
The research results indicate that marketers need to focus their efforts on two specific advertising formats which include persuasive video advertising and visually structured content that includes carousels and unboxing videos and informational posts. The identical perception of social media usefulness and platform ease of use by users shows that brands need to balance their efforts between making platforms usable and providing content that delivers value to users. The low user interaction with interactive features such as livestreams and polls and CTAs shows that the existing strategies need complete redesigning to enhance user engagement through more valuable interactive experiences. The study results show that social media platforms can reach different demographic groups through integrated strategies whereas minor content changes will boost specific audience engagement.
The findings indicate that demographic variables such as age and gender do not significantly influence most aspects of social media-driven purchase behaviour for wearable products. Therefore, marketers may adopt a relatively unified and integrated social media strategy rather than heavily segmenting campaigns based on demographics, focusing instead on content quality and value delivery to appeal to a broad audience efficiently.
With respect to social media marketing elements, persuasive video advertisements, carousel posts, informational content, and unboxing videos should be prioritised, as these formats showed stronger positive responses. At the same time, interactive features such as livestreams, polls, and generic CTAs require strategic redesign to enhance engagement, while improving ease of use and seamless purchase processes can further strengthen consumer trust and online buying intentions.
Limitations of the Study
Notwithstanding the provision of important insights, there are some limitations to the study that need to be kept in mind while interpreting the findings. Firstly, the study has used a convenient sampling approach, which might affect the generalizability of the findings to a larger population. Secondly, the findings are based on self-administered questionnaires, which might give rise to response bias or subjective interpretation by the respondents. Thirdly, the study has used a cross-sectional research approach, which captures perceptions at a point in time and does not measure changes in behavior over a period. Furthermore, the study has been conducted on wearable products, which might limit the generalizability of the findings to other product groups.
Future Research Directions
Future research could extend this study by investigating the effects of social media marketing on various product groups and cultural settings. Future research could also employ longitudinal research designs to explore the dynamics of consumer perceptions and online purchasing decisions over time. Further research could be conducted on emerging trends such as AI-powered content, influencer authenticity, and targeted advertising to gain more insights into online consumer engagement. Future research could also investigate other variables such as digital literacy or online usage patterns to gain more insights into the differences in online purchasing decisions.
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Received: 02-Mar-2026, Manuscript No. AMSJ-26-16959; Editor assigned: 03-Mar-2026, PreQC No. AMSJ-26-16959(PQ); Reviewed: 10-Mar-2026, QC No. AMSJ-26-16959; Revised: 17-Mar-2026, Manuscript No. AMSJ-26-16959(R); Published: 24-Mar-2026