Analyzing of live marketing in driving digital workforce growth with the Structural Equation Model (SEM) Approach

 Corresponding author: Andri Purnamawati Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Analyzing of live marketing in driving digital workforce growth with the Structural Equation Model (SEM) Approach Andri Purnamawati *, Asih Endah Subandiyah and La Ode Rahdian Oktafiar Department of Management, Diploma III Program, STIE IEU Yogyakarta, Indonesia. World Journal of Advanced Research and Reviews, 2025, 27(01), 967-973 Publication history: Received on 10 May 2025; revised on 05 July 2025; accepted on 08 July 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.1.2591 Abstract This study aims to evaluate the impact of live marketing strategies on the growth of employment in the digital sector using the Structural Equation Modeling (SEM) approach. Secondary data collected between 2015 to 2024 were analyzed to examine the relationship among live marketing activities, the state of digital infrastructure and the level of digital workforce absorption. The results of the study show that live marketing has a significant positive impact on digital workforce absorption, with digital infrastructure as an important supporting factor. The findings reveal that live marketing exerts a significant positive effect on digital workforce absorption, with digital marketing strategies and workforce development initiatives within the digital economy. Keywords: Live Marketing; Workforce Absorption; Digital Economy; Structural Equation Modeling 1. Introduction The advancement of digital technology has significantly transformed marketing strategies, particularly through the rise of live marketing, a direct promotional approach utilizing digital platforms such as social media. This trend aligns with regional projections from the e-Conomy SEA 2024 report (Google, Temasek, & Bain), which anticipates substantial employment growth driven by live commerce across Southeast Asia. Live marketing enables companies to establish realtime, twoway communication with consumers, enhancing user experience and stimulating spontaneous purchasing decisions (Zhang & Wang, 2021). This phenomenon has also reshaped the structure of the labor market by creating new forms of employment, particularly within the creative economy sector. Job roles such as content creators, live broadcast hosts, digital community managers, and digital marketing analysts are increasingly in demand (Putri et al., 2022). This shift underscores the significant potential of live marketing as a key driver of digital workforce expansion. The demand for digital workers continues to rise in parallel with the growing need for new skills sets, particularly in information technology, digital marketing, and visual communication. A study by Hartono and Lestari, reported a 17% increase in the digital workforce during the COVID-19 pandemic, with live marketing emerging as one of the largest employment drivers, especially among youth and creative professionals (Hartono & Lestari, 2023) . This trend is reinforced by the Ministry of Manpower (2023), which identified live marketing and digital commerce as among the fastest-growing employment domains. However, the direct contribution of live marketing to workforce development remains underexplored within a rigorous quantitative framework. Yet, a comprehensive understanding of its impact on the labor market is essential for designing World Journal of Advanced Research and Reviews, 2025, 27(01), 967-973 968 effective labor education and training policies (Santoso & Dewi, 2024). Therefore, an analytical approach is required one that can systematically capture both the direct and indirect effects of the various interrelated variables. Structural Equation Modeling (SEM) is a robust statistical technique for analyzing causal relationships among latent and complex variables. SEM enables researchers to examine both direct and indirect relationships between constructs, including dimensions such as consumer engagement, technology adoption, social media influence, and digital workforce demand (Hair et al., 2020). This study seeks to address a gap in the existing literature by examining the contribution of live marketing to digital job creation. The primary objective is to identify key elements within live marketing that influence labor demand in the digital economy. The findings are expected to serve as a reference for industry players, policymakers and educational institutions in formulating adaptive strategies for the rapidly evolving digital labor market. Thus, this study offers not only theoretical contributions by developing a model of the relationships among key variables, but also practical policy recommendations. In light of the growing importance of digitalization in driving economic growth, understanding the impact of live marketing on workforce dynamics represents a critical first step toward building a sustainable and inclusive digital ecosystem (Nugroho & Sari, 2025). This observation aligns with macroeconomic insights Bank Indonesia (BI, 2024) which emphasize how digital financial systems and payment innovations are reshaping the employment landscape in Indonesia. 2. Literature review 2.1. Conceptual Issues 2.1.1. Live Marketing in the Digital Era Live marketing is a digital marketing strategy that facilitates direct interaction between businesses and consumers through live broadcasts on platforms such as TikTok, YouTube, Instagram, and Shopee Live (Zhang & Wang, 2021). The primary objective is to create a real-time, personalized, and interactive experience that enhances sales conversion rates (Chen et al., 2022), The effectiveness of this method lies in its key features: interactivity, time scarcity, and a high level of perceived authenticity, all of which encourage impulse purchasing behavior among consumers. 2.1.2. The Growth of Live Marketing and Implications for the Digital Workforce The rapid growth of live marketing has created demand for new job roles, such as live streaming hosts, video editors, digital marketing strategists, and content analysts (Putri et al., 2022), The study highlights that this sector offer extensive employment opportunities, particularly for the younger generation who are adept at using digital technologies. In fact, several companies have established dedicated teams to manage live commerce as an integral component of their digital marketing strategies. 2.1.3. Transformation of Workforce Absorption in the Digital Era Digital transformation has significantly reshaped the employment landscape, with digital skills such as SEO, social media management, and online communication now in high demand. This transformation is further emphasized in the digital development roadmap issued by Ministry of Communication and Information Technology (2024), which prioritizes human capital readiness across Indonesia’s online sectors. A report from the Ministry of Manpower (2023) notes that approximately 23% of the increase in labor demand originates from the digital sector, including areas such as live marketing and e-commerce. This trend signifies a shift from conventional employment toward technology driven and service oriented digital jobs. 2.1.4. Link between Live Marketing and workforce Absorption The relationship between live marketing and job creation is multifaceted. On the one hand, it generates direct employment opportunities in roles such as live stream hosts, content creators and digital production technicians (Santoso & Dewi, 2024). On the other hand, it produces indirect effects by increasing demand for supporting services, including logistics, digital training, and online customer support. This study posits that the broader implementation of live marketing will lead to a greater demand for a workforce equipped with adequate digital competencies. World Journal of Advanced Research and Reviews, 2025, 27(01), 967-973 969 2.1.5. Structural Equation Modeling (SEM) Approach Structural Equation Modeling (SEM) is a multivariate statistical technique used to examine the relationship among complex latent variables (Hair et al., 2020). This approach is particularly relevant to the present study, as it enables the analysis of constructs that cannot be measured directly, such as perceptions of digital engagement, the effectiveness of live marketing strategies, and job interest. SEM facilitates the investigation cause and effect relationships, including the role of mediating and moderating variables, while also allowing for comprehensive model evaluation. SEM consists of two core components: the Measurement Model, which links latent variables to their observed indicators, and the Structural Model, which describes the relationship between constructs. There are two primary approaches to SEM: Covariance Based SEM (CB-SEM), which is commonly used for theory testing, and Variance Based SEM (PLS-SEM), which is more suitable for exploratory research and prediction. This study employs the PLS SEM approach, utilizing Smart PLS software to estimate and evaluate the proposed model. 2.2. Theoretical Model Used This study is based on the integration of three theoretical frameworks the Technology Acceptance Model (TAM), consumer engagement theory, and digital labor demand theory. The Technology Acceptance Model (TAM) posits that perceived usefulness and ease of use of live marketing platforms influence the adoption of technology in business activities (Yu & Cho, 2021). Meanwhile, consumer engagement theory suggests that the intensity of digital interactions enhances simultaneously generating demand for human resources in digital marketing activities. 2.3. Review of Empirical Literature The study by Fadillah et al., confirms that the phenomenon of live streaming commerce functions only as a marketing channel but also as a new employment ecosystem, contributing to the rise in demand for hosts and digital support personnel. This is consistent with the findings of the present study which also confirm the significant effect of live marketing on employment (Fadillah et al., 2023). Unlike the study by Santoso, which emphasizes digital infrastructure as the dominant factor influencing digital employment, this research finds that live marketing plays a more prominent role. This highlights a paradigm shift in the current era of business digitalization, where consumer engagement strategies have become increasingly central to workforce absorption in the digital sector (Santoso, 2020) Model Development, this research develops Structural Equation Modeling (SEM) approach that integrates both marketing (live marketing) and technological (digital infrastructure) dimensions, whereas prior studies have typically focused on only one of these aspects. This provides a comprehensive picture of the factors that influence digital workforce absorption simultaneously. 3. Methodology 3.1. Research Design The analysis was carried using Smart PLS software with a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The model comprises three main constructs: live marketing as the exogenous (independent) variable, digital infrastructure as the mediating (intervening) variable, and digital workforce as the endogenous (dependent) variable. The use of SEM enables a comprehensive evaluation of both direct and indirect relationships among complex constructs in a systematic manner. 3.2. Sources of Data This study employs a quantitative method using a Structural Equation Modeling (SEM) approach to evaluate the effect of live marketing on digital employment. The analysis is based on secondary data obtained from official sources, including employment statistics and labor-connectivity indicators published by the Central Statistics Agency (BPS, 2024), which were instrumental in modeling internet penetration and platform adoption rates throughout the analysis period. Additional data were collected from digital industry reports and academic publications spanning the years 2015 to 2024. The dataset includes variables such as live streaming activity, internet penetration rate, number of social media and ecommerce users, and digital employment figures. As of 2024, Indonesia recorded over 160 million active social media users, according to We Are Social & Hootsuite (2024), a critical foundation that supports the scalability and widespread adoption of live marketing. World Journal of Advanced Research and Reviews, 2025, 27(01), 967-973 970 3.3. Validity of the model To strengthen the validity of the model, a convergent validity test was conducted by assessing the Average Variance Extracted (AVE) value and loading factor of each indicator. All indicators in the model demonstrate satisfactory validity (AVE>0.5 and loading>0.7), indicating that the research instrument meets the statistical criteria requirements for SEM model testing. 3.4. Data of Tested Variables Table 1 Data of Tested Variables Year X1.1 (Live Session) X1.2 (Live Transaction) X1.3 (Live MSME) X2.1 (% Internet) X2.2 (Active social media) X2.3 (Ecommerce Users) Y1.1 (Ecommerce TK) Y1.2 (Live Host) Y1.3 (% TK Growth) 2015 1200 150 50 25% 65 million 40 million 120 thousand 800 2% 2016 2200 230 150 32% 78 million 55 million 160 thousand 1.100 3.5% 2017 4100 340 400 39% 92 million 70 million 200 thousand 1.400 5% 2018 7300 620 1.200 48% 105 million 85 million 250 thousand 2.200 6% 2019 14.000 1.200 2.800 55% 120 million 95 million 320 thousand 3.500 8% 2020 22.000 2.500 6.000 64% 135 million 110 million 420 thousand 5.800 12% 2021 29.000 3.800 9.000 70% 145 million 125 million 550 thousand 8.500 15% 2022 36.000 5.100 13.000 77% 152 million 130 million 660 thousand 11.000 17% 2023 41.000 6.800 16.000 80% 157 million 140 million 750 thousand 14.500 18% 2024 45.000 7.500 18.000 82% 160 million 145 million 830 thousand 16.000 19% 4. Results and discussion 4.1. Convergent Validity Test Results Table 2 Results of Convergent Validity Test Results Construct Indicator Loading Factor AVE Description Live Marketing LM1, LM2, LM3 0.72-0.84 0.63 Valid Digital Infrastructure ID1, ID2, ID3 0.70-0.81 0.58 Valid Workforce Absorption AW1, AW2, AW3 0.75-0.86 0.66 Valid Source: Smart PLS Data Processing 4.1.1. Live Marketing The factor loading value for each indicator in the Live Marketing construct exceed 0.70, with an AVE value of 0.63 surpassing the minimum threshold of 0.50. These results confirm that the construct demonstrates good convergent validity, with indicators LM1, LM2, and LM3 consistently representing the underlying concept live marketing. 4.1.2. Digital Infrastructure With an AVE value of 0.58 and factor loading ranging from 0.70 to 0.81, the Digital Infrastructure construct also meets the criteria for convergent validity. This indicates that the indicators used accurately represent the condition of digital infrastructure as a mediating variable in between live marketing and digital employment. World Journal of Advanced Research and Reviews, 2025, 27(01), 967-973 971 4.1.3. workforce Absorption The workforce Absorption variable demonstrates an AVE of 0.66 and high factor loading, indicating strong internal consistency. This suggests that the indicators effectively capture and represent the underlying construct of digital employment. Overall, all constructs in this research model satisfy the requirements of convergent validity. This indicates that the instruments and indicators employed are appropriate and effective in representing the latent variables under investigation, an essential criterion for establishing a reliable and valid SEM model. 4.2. Path Analysis (Path Coefficient) and Significance Table 3 Results of Path Analysis (Path Coefficient) and Significance Path of Influence Path Coefficient tStatistics p-Value Description Live Marketing → Workforce Absorption 0.581 9.32 0.000 Significant Live Marketing → Digital Infrastructure 0.527 7.85 0.000 Significant Digital Infrastructure → Workforce Absorption 0.423 6.47 0.000 Significant Source: Smart PLS Data Processing 4.2.1. Live Marketing and Workforce Absorption The analysis reveals that live marketing significant influence the absorption of digital workers. A path coefficient of 0.581 with a p value <0.01 indicates a strong relationship between the intensity of live marketing activities and the growth of employment in the digital sector. This suggests that marketing strategies utilizing live broadcasts are not only effective in boosting transactions but also creating diverse digital job roles, such as live stream hosts, content creators, and online logistics coordinators. This finding is supported by platform reports from Shopee, TikTok Shop, and Katadata (2024), which highlight the growing demand for specialized live streaming personnel in e-commerce operations. It is also consistent with the study by Putri et al., which found that the rise of live commerce in Indonesia has generated numerous new employment opportunities, particularly for techsavvy youth (Putri et al., 2022). Furthermore, the results reinforce findings by Wijaya and Sari, who argue that live marketing accelerates the digitalization of MSMEs, thereby increasing the demand for digital labor (Wijaya & Sari, 2021) . 4.2.2. Live Marketing and Digital Infrastructure The analysis also demonstrates that live marketing encourages the strengthening of digital infrastructure, with a path coefficient of 0.527. This strategy motivates both businesses and the government to invest in supporting technologies such as bandwidth, digital payment systems, and interactive platforms. Although the contribution of digital infrastructure to employment is smaller than the direct impact of live marketing, it remains a crucial catalyst for the development of the digital ecosystem. This finding aligns with Financial Services Authority (OJK, 2024), which emphasizes that digital platforms and the integration of MSMEs into financial services generate employment opportunities through fintech and logistics. It is also consistent with Rahmawati, who argues that the success of the digital sector heavily depends on the availability of adequate technological infrastructure, including internet connectivity and social media penetration. While the influence of digital infrastructure is relatively lower than that of live marketing, the results highlight that technological presence is necessary, but it is digital marketing strategies such as live streaming that serve as the primary driving force behind employment growth in the digital era (Rahmawati, 2020). 4.2.3. Digital Infrastructure and Workforce Reliable digital infrastructure plays a critical role in enhancing digital workforce absorption. Regions with consistent internet connectivity and adequate technological support are more likely to attract investment and foster the growth of technology driven employment. The path coefficient of 0.423 (p > 0.01) indicates that digital infrastructure serves as an intermediary variable, linking live marketing initiatives to workforce expansion. World Journal of Advanced Research and Reviews, 2025, 27(01), 967-973 972 4.3. R-square Test Results Table 4 R-square Test Results Endogenous Variable R² Description Digital Infrastructure (ID) 0.278 ID variation explained by LM by 27.8% Workforce Absorption 0.781 Variation of AW explained by LM & ID 78.1% Source: Smart PLS Data Processing 4.3.1. Digital Infrastructure The findings reveal that Live Marketing explains for 27.8% of the variance in the Digital Infrastructure construct, indicating a measurable yet partial influence. The remaining 72.2% is likely attributed to external determinants beyond the scope of this model, such as public policy frameworks, private technological investment, and the national readiness for digital adoption. Although this level of explanatory power may be categorized as moderate to low, it remains acceptable within the context of complex socio-economic research involving diverse and interrelated variables. 4.3.2. Workforce Absorption The analytical results indicate that 78.1% of the variance in the workforce absorption construct is explained by the joint combined of Live Marketing and Digital Infrastructure. This proportion is considered substantially high within management and marketing research, underscoring the robustness and relevance of the proposed model in capturing the dynamics of digital workforce engagement. The remaining 21.9% is likely attributable to external factors not incorporated in the current framework such as workforce education levels, regulatory support, or international competitiveness. Overall, the R² value of 0.781 affirms the model’s strong explanatory capacity, reinforcing its applicability as a foundation for strategic planning in digital marketing and employment policy formulation. 5. Conclusion Drawing upon SEM-based analysis of secondary data spanning from 2015 to 2024, the study confirms that live marketing exerts a statistically significant and positive effect on digital workforce, with a path coefficient of 0.58 (p > 0.01). This result underscores that live stream driven commerce not only boosts transactional activity but also stimulates the emergence of new job opportunities within the digital economy. While digital infrastructure also demonstrates a meaningful contribution to employment outcomes with a smaller path coefficient of 0.42 (p > 0.01), its influence is remains secondary to that of live marketing. Collectively, the model explains 78% of the variance in digital workforce absorption, indicating strong explanatory power and underscoring the relevance of the constructs in capturing key dynamics of workforce transformation in the digital era. Live marketing, as an interactive and engagement centric digital strategy, emerges as a key catalyst for employment generation, surpassing even technological infrastructure in its impact. For future research, the integration of additional predictors such as public policy measures, workforce education levels, and consumer behavior is recommended. 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