Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5, 2432-2453 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 24 March 2025; Revised: 28 April 2025; Accepted: 1 April 2025; Published: 23 May 2025 * Correspondence: jonardi.ghuntesa@binus.ac.id Modernized business: Live streaming feature adoption impact on business competitive advantage of Indonesia’s Gen Z perceptions Jonardi Ghuntesa1*, Samuel Oliver Gunawan2, Mulyono3 1,2,3Management Program, Binus Business School, Bina Nusantara University, Jakarta, Indonesia: jonardi.ghuntesa@binus.ac.id (E.C.) samuel.gunawan002@binus.ac.id (E. N. T.) mulyono@binus.ac.id (A.W.) Abstract: Technological developments will continue to shape the future, specifically in modernizing the business sector. Therefore, this study explores how the use of live streaming features affects Generation Z's perception of a company's competitive advantage. This research analyzes data collected through surveys consisting of 210 Gen Z respondents who use live streaming features. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to assess the factors that influence the usage of live streaming features. The results showed that Performance Expectancy and Facilitating Conditions contribute significantly to Gen Z participation in using the live streaming features. In other conditions, Effort Expectancy and Social Influence do not actively impact the user's decision to use the feature. This result confirms that usability and ease of use encourage user engagement in live streaming features, while perceived benefits and social influence do not have a strong effect in encouraging the use of the feature. Therefore, businesses in Indonesia can optimize their competitive advantage by utilizing the live streaming feature to increase interactive experiences and ensure strong facility support, such as technology reliability and creating more engaging content to boost user participation and loyalty. Keywords: Modernized business; Live streaming feature; UTAUT Model; Gen Z perception; Business competitive advantage. 1. Introduction Increasingly rapid technological innovation has driven the alteration from conventional technology to a modern digital technology [1]. Technological progress has reached the stage where the use of technology has penetrated various aspects of life. This can be seen from how technology is also integrated to support education [2] and how technology like the Internet of Things (IoT) supports activities of daily living [3]. Social media is also one of the products of the rapid development of digital technology. Even social media, which was originally just social media, has also become a place where people, especially young people can express their creativity. Technological developments have also reached the stage where the emergence of social media trends is influencing the impulsive behavior of Generation Z in choosing a product to buy [4]. According to data released by the BPS [5] in 2020, there are 71.50 million Gen Z in Indonesia. The lifestyles of each generation are clearly different. Gen Z is known and can be said to have grown up on the internet by spending a lot of time enjoying social media content. In fact, much of the content they see can bring inspiration to them [6]. This feature will undoubtedly open up new opportunities in the e- commerce business since the platform is able to reach audiences such as Gen Z, who majority of them spend their time scrolling through social media [7]. The Indonesian micro, small and medium enterprises (MSMEs) can take advantage of this momentum, considering the large potential for them to sell their products through e-commerce for the Gen Z market. MSMEs is one of the business groups that has a high contribution to Indonesia’s 2433 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate economy. This particular group contributes as much as 60,51% to the growth portion of Indonesia's Gross Domestic Product (GDP). According to data from Kementerian Koordinator Bidang Perekonomian Republik Indonesia [8] there are 66 million MSMEs in Indonesia, making about 99% of all business entities. This means that MSMEs play a crucial role in driving Indonesia’s economy. With the rapid development of technology, MSMEs get the opportunity in transforming business models and opportunities to increase product sales value [9]. However, in Indonesia itself, businesses that utilize e-commerce platforms in 2022 to early 2023 is only 37.46% and the rest of 62.54% are non- eCommerce businesses. Of course, this shows that there are still a few businesses that have transformed into utilizing e-commerce platforms. One evidence of technology developments that continues to develop today is the live streaming feature. Live streaming is a real time video-based communication feature where viewers can ask questions and interact directly with the live streaming host [10]. Live streaming features have a significant impact in changing people's behavior in everyday life, especially in shopping [11, 12]. This can be seen from how the live streaming feature becomes a medium that is not only used for real time video content, but this feature can be utilized in supporting trade or business activities, especially in e-commerce platforms [1]. According to the Information and Communication Technology (ICT) evolution of technology usage in Indonesia, the Covid-19 epidemic has caused a notable rise in the country's rate of technology use. It is estimated that 215.6 million Indonesians would be using technology by 2023. [13]. However, the uneven distribution of technology use in Indonesia is regrettably still present [14]. Just 19.75% of business owners use a marketplace or e-commerce platform to manage their companies, making the use of e-commerce platforms by business owners remains comparatively low. Data from 2022 showed that Indonesia had 2,995,986 e-commerce companies [15]. MSMEs hold a significant role in Indonesian economic growth. The presence of MSMEs opens up many job opportunities which increase the country's GDP. This also stimulates the development of new entrepreneurship [16]. On the other hand, many are still not aware of the potential that technology adoption can encourage their business competitive advantage. The relatively low technology adoption in Indonesian MSMEs is due to the lack of demand for technology usage in the process of business. This happens due to the lack of infrastructure and facilities, lack of knowledge and technological skills, higher cost, reluctance in using more advanced technology, and lack of financial incentive provided for MSMEs [17]. The lack of technology adoption by MSMEs in Indonesia is unfortunate because technology provides many benefits in helping businesses. Live streaming features itself creates various benefits for business owners in modernizing their business and boost competitive advantage. Live Streaming is an online medium that enables more interactive communication between the streamer and audience in real-time [10]. Live streaming will feature streamers through live video and there also chat or comment feature that allow streamers and audience to communicate via chat in real-time [18]. During the live broadcast, the broadcaster will deliver information related to the topic or product being discussed supported by the audience asking questions in real-time through the chat column. This will be followed by real-time information delivery by the streamer in response to the audience's information and questions through real-time chat. Interactivity that is enabled through live streaming features can increase customer engagement thus affecting the competitive advantage of business [19, 20]. This research aims to understand Gen Z perspective regarding the perception of live streaming utilisation in impacting business competitive advantage on e-commerce. Apparently, there are many MSMEs not utilizing and maximizing the use of technology to further encourage their business competitive advantage. This is very unfortunate, considering the potential of MSMEs in Indonesia which are quite large contributors to Indonesia’ GDP. The Unified Theory of Acceptance and Use of Technology (UTAUT) serves as the framework for this study, which aims to comprehend how Gen Z views the use of live streaming in business. Each acceptance factors’ significance of using live streaming 2434 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate features for MSMEs will be determined. Therefore, we ask: Does the adoption of live streaming features' Performance Expectancy (PE) factor significantly impact Indonesian MSMEs' capability to compete in the market? Does the adoption of live streaming features’ Effort Expectancy (EE) factor significantly impact Indonesian MSME’s ability to compete in the market? Does the adoption of live streaming features’ Social Influence (SI) factor significantly impact Indonesian MSME’s ability to compete in the market? Lastly, Does the adoption of live streaming features’ Facilitating Condition (FC) factor significantly impact Indonesian MSME’s ability to compete in the market? The UTAUT model is used to measure acceptance in utilizing information technology, in this case the live streaming feature as information technology [21]. 2. Literature Review and Hypothesis Development 2.1. Business Competitive Advantage Business competition is a situation where there are two or more companies competing to achieve the same goal of increasing market share and required resources. Therefore, it is very important for business owners to have a competitive strategy to compete with business competitors in the same industry [22]. Increasingly fierce business competition inevitably encourages business owners to ensure the sustainability of their business by creating strategies to overcome the competition in the market [23]. High-quality human resources, high levels of motivation and creativity, productivity, the discovery of new ideas or innovations, effective management systems and corporate structures, a broad perspective of the product and the surrounding environment, and the capacity to rival in the worldwide market are some of the elements that affect the attainment of business competitive advantage [23]. There are several indicators in providing business competitive advantage, namely: Price / cost, Quality, delivery dependability, Product Innovation, Time to Market [24]. Price is one of the important components in competitive business, where some people will tend to buy cheaper goods. Second, quality is no less important than price and even the two of these components are related to each other. People will assume that the price they spend will be comparable to the quality of the goods they will get. Therefore, nowadays discounts and promotions have an impact on customers' purchasing decisions because they will receive high quality / value at a lower price [25]. Nowadays, quality and price are not enough to increase the value of business competitive advantage, which is instead the company or business must improve their dependability and advantage of time and place/location to support product distribution [26]. Product Innovation is an action that we can take in increasing competitive advantage by looking for differentiators or product / service differentiation that can help the company's competitiveness [27]. Innovation strategies are the most important thing for competitive businesses, especially in MSMEs companies that can improve operational reliability [28]. The last indicator that affects business competitive advantages is Time to market, or a company's capacity to launch products more quickly, is the final metric that influences corporate competitive advantages. 2.2. The Unified Theory of Acceptance and Use of Technology Model The UTAUT model is built into several factors of performance expectancy, effort expectancy, social influence, and facilitating conditions with other moderating variables such as age, gender, experience and willingness [29]. Performance expectancy is determined by the belief the person has that a system will enhance their ability to conduct business. Meanwhile, effort expectancy can be measured by how easy it is to use the system. Social influence which can be measured by how far the person perceives the expectations of others regarding the use of their system. Facilitating conditions where we can measure the extent of individual confidence. The purpose of using the UTAUT model for this study to ascertain whether technology use is accepted as a competitive advantage for organizations. UTAUT is often also widely used in understanding technology acceptance in various other sectors such as banking, health 2435 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate and so on [30]. Of the four variables that will be correlated with business competitive advantage are categorised as Structural equation Models (SEM) because there are two models, namely reflective and formative. 2.3. Live Streaming Feature on E-Commerce Live streaming is a feature that can provide a realistic atmosphere that allows real-time appearance of the streamer while providing details of each product through live video [31]. Many business owners like MSMEs use live streaming to achieve effectiveness and efficiency in communicating with customers and potential customers without having to meet face-to-face [12]. Live streaming in e-commerce can influence emotions and impulse purchases from consumers. Because of the current benefits, live streaming has become more popular in e-commerce, particularly during the global pandemic of COVID-19, of course the penetration of E-Commerce Live Streaming numbers soared [32]. Based on study, live streaming has become a feature that supports interactive communication between sellers and customers. With that, it can encourage audiences to ask questions directly and sellers can make an on-time response to the audience or buyers in real-time [33]. Thus, it will help consumers or customers make purchases quickly and precisely. Coupled with the fact that Generation Z is known to have impulsive buying behaviour. Impulsive behaviour is an act of buying unexpectedly or tends to purchase for goods that are not important [34]. This can certainly affect competitive business advantage. 2.4. Live Streaming Usage in Affecting Business Competitive Advantage Transformation has occurred in people's daily shopping system.with the COVID-19 pandemic, people's habits have drastically shifted to online shopping [25]. In offline shopping, prospective buyers can ask many questions and can see directly with the product they want to buy. But in online stores, if prospective buyers want to ask questions about the product, they have to wait for a reply from the comments column or private chat with the seller. But it is unfortunate because it certainly takes time to wait for a reply. Live streaming feature provides a solution for e-commerce by making it easier for potential buyers in finding their products that meet their needs and interacting directly with sellers [35]. The interaction between seller and audience that builds through interactive live streams is called Parasocial Relationship (PSR) [36]. Therefore, Live streaming can increase social presence for online shopping [37]. Real-time contact between the audience and the streamer leads to a greater social presence in live streaming, which can definitely foster a feeling of intimacy [38]. In addition, the live streaming feature can ensure audience trust in a product through direct interaction. This is also supported by the information conveyed directly by the streamer to the audience regarding the products offered. Trust formed through direct interaction and information delivery can drive competitive advantage in a business [39, 40]. 2436 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate 2.5. Research Model Figure 1. Research Model. This study engages the UTAUT model as proposed in Figure 1 to measure how people can accept live streaming feature adoption that can affect business competitive advantage in the business world. Here we will discuss the correlation of core variables of technology acceptance which consist of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions on the variable business competitive advantage as the dependent variable [29]. 2.6. Hypotheses Development 2.6.1. Impact of Performance Expectancy of Live Streaming Feature Adoption on Business Competitive Advantage. This theory explains that an action affecting their performance (performance expectancy) can significantly affect acceptance in the adoption of technology use. In this research, we will find out whether performance expectations in the ability to use live streaming technology can affect their perceptions of performance expectancy [29]. The research conducted shows that the ability to use technology does indirectly affect the competitive advantage. However, an organisation that is able to respond well to change and is willing to improve its capabilities or use of technology will have a direct effect on competitive advantage [41]. So, this research which states the relationship between the ability to use technology and competitive advantage can be used as a reference to hypothesise that the performance factor of using the live streaming feature can affect competitive advantage. 2437 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate H01: Performance expectancy (PE) factor of Live Streaming feature adoption does not have a significant effect on the business competitive advantage of Indonesian MSMEs. Ha1: Performance expectancy (PE) factor of Live Streaming feature adoption has a significant effect on the business competitive advantage of Indonesian MSMEs. 2.6.2. Impact of Effort Expectancy of Live Streaming Feature Adoption on Business Competitive Advantage Effort expectancy describes how a person's propensity to accept a technology can be determined by their impressions of the simplicity to use them [29]. According to several studies, a sustainable competitive advantage requires an attractive UX (user experience) design in technology to increase value for the audience. Effectiveness and efficiency, trust, satisfaction, location, emotion, and time, are some of the values that can be felt through the interactivity of the technology [42, 43].One of the interactive technologies that makes it easier for buyers and sellers to communicate is the live streaming option [35]. However, this is also supported by adequate resources and infrastructure to encourage capability in using technology, so that it will affect competitive advantage [44]. Therefore, competitive advantage may be significantly impacted by the work required to implement live streaming. H02: Effort Expectancy (EE) factor of Live streaming feature adoption does not have a significant effect on the business advantage of Indonesia MSMEs. Ha2: Effort Expectancy (EE) factor of Live Streaming feature adoption has a significant effect on the business competitive advantage of Indonesian MSMEs. 2.6.3. Impact of Social Influence of Live Streaming Feature Adoption on Business Competitive Advantage According to studies, social influence from the surrounding and closest people such as from colleagues to competitors and even friends or family will significantly influence individual behaviour [29]. Social norms have an important role in shaping behaviour and intention in adopting technology. Support from friends, teaching staff, and other close people in the use of technology will also encourage the willingness to adopt the technology [45, 46]. Thus, several studies have supported that social influence plays a role in shaping the ability to adopt technology. Therefore, social influence may have a big impact on business competitiveness when it comes to live streaming technologies. H03: Social Influence (SI) factor of Live streaming feature adoption does not have a significant effect on the business advantage of Indonesia MSMEs. Ha3: Social Influence (SI) factor of Live Streaming feature adoption has a significant effect on the business competitive advantage of Indonesian MSMEs. 2.6.4. Impact of Facilitating Conditions of Live Streaming Feature Adoption on Business Competitive Advantage Facilitating conditions are one of the factors of technology acceptance where the availability of infrastructure to support technology in an environment or organisation is one of the main drivers of technology acceptance [29]. So based on the theory, the accessibility of resources can encourage business actors to adopt technology in increasing the competitiveness of the company. In this case, we can assess that the presence of the facilities and infrastructure to promote the use of live streaming can also affect the competitive advantage of businesses. Therefore, in this hypothesis we want to find out whether there is a significant effect on technology adoption on business competitive advantages. H04: Facilitating Conditions (FC) factor of Live streaming feature adoption does not have a significant effect on the business advantage of Indonesia MSMEs. Ha4: Facilitating Conditions (FC) factor of Live Streaming feature adoption has a significant effect on the business competitive advantage of Indonesian MSMEs. 2438 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate 3. Methodology 3.1. Data and Sample This research employed quantitative methodology using a survey approach that relied on the perception of Gen Z in Indonesia with the majority of them consuming live streaming content and buying products through live streaming e-commerce. The study target population consists of Gen Z in Indonesia. This research adopted structural equation modeling (SEM) techniques for analysing the data. According to Hair, et al. [47] the ideal number of samples to be used in research study must be adjusted to 10 times the number of indicators. This makes our sample size to be targeted as much as 210 respondents. Research suggests that SEM can be meaningfully employed even with modest sample sizes, this study acknowledges the potential limitations associated with the sample size. 3.2. Measurement of Business Competitive Advantage In measuring business competitive advantage, we can measure the level of customer satisfaction with the products or services we provide to them. Therefore, it can be said that customers are prioritised in every business [48]. We have several problems that affect the level of business competitive advantages, including high product or service prices, poor quality goods, inappropriate delivery, and no innovation made. This measurement is intended to determine the extent to which the use of this live streaming function to the level of competitive advantages obtained by organizations. Table 1. Dependant Variable Indicators. Dependent Variable Indicators Business Competitive Advantages Price/Cost Quality Delivery Dependability Product Innovation Time to Market 3.3. Measurement of Live Streaming Usage Perception Using UTAUT Model In this research, we use the UTAUT model to understand public acceptance of technology adoption in the form of live streaming in e-commerce. Here are the 4 main components namely: performance expectancy, effort expectancy, social influence, and facilitating conditions. 2439 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Table 2. Independent Variable Indicators. Independent Variable Xue, et al. [29] Indicators Performance Expectancy 1. Live streaming feature helps in finding products that meet the needs of the audience. 2. Live streaming features can increase productivity in shopping activities. 3. Live streaming can simplify shopping activities. 4. Live streaming can provide more reliable product information. 5. Live streaming can shorten the time in receiving product information. Effort Expectancy 1. Overall, the use of live streaming is very easy for me. 2. The interaction of using live streaming is clear and understandable. 3. Live streaming feature helps streamline the shopping process 4. I have no difficulty in using the live streaming feature. Social Influence 1. People close to me influence my behaviour in convincing me to use the live streaming feature. 2. People who inspire me give me the idea to use the live streaming feature. 3. Companies support the use of live streaming. Facilitating Condition 1. I have facilities that support me in using the live streaming feature. 2. I have the knowledge to use the live streaming feature. 3. The e-commerce platform I use provides facilities to support the use of live streaming. 4. The work environment or the place where I live provides infrastructure that supports me to use the live streaming feature. 4. Result and Discussion 4.1. Descriptive Statistics The respondents in this study consist of 210 generation Z individuals in Indonesia. Respondent data analysis is necessary to understand their background. This study provides information of the respondents such as age, gender, domicile, use of live streaming features, and frequency of live streaming usage. Here below are the descriptive statistics on respondents’ socio-demographic that represent the population of generation Z in Indonesia. Table 3. Descriptive Statistics. Statistics Age Gender Domicile Usage Frequency of Use N Mean Valid Missing 210 0 2.07 210 0 1.49 210 0 1.54 210 0 1.00 210 0 4.43 Median 2.00 1.00 2.00 1.00 5.00 Mode 2 1 2 1 5 Std.Deviation Variance .513 .263 .501 .251 .499 .249 .000 .000 .683 .467 Based on the statistics above, Frequency of Use has the highest mean value of 4.43. The median value for Gender and Usage is 1.00. Age and Domicile have an identical median value of 2.00. While Frequency of Use has a median value of 5.00. Based on respondent data, the majority of the respondents 2440 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate are women at 51.0%, followed by male respondents at 49.0%, with the majority aged between 20-25 years, who live in the Jabodetabek area. Based on the live streaming usage data, all the 210 respondents stated that they all used the live streaming feature. For the Standard Deviation (SD) and Variance (VAR) values, each of them exhibit a good distribution as each value is below the mean value. All variables including Age, Gender, Domicile, Usage, and Frequency of Use have 100% valid data with no missing values. 4.2. Based on Age Referring to the analysis of the distributed results, the researcher obtained the following details of the respondents profiles based on age. Table 4. Age. N % <20 years 21 10.00% 20 – 25 years 154 73.30% 26 – 30 years 35 16.70% 4.3. Respondent Profile Based on Age From the table above, respondents with an age between 20 to 25 years old make up the majority, accounting for 73.3%. Next, respondents aged between 26 to 30 years old are in second place with 16.7%. Meanwhile, respondents under 20 years old rank third, representing 10.0% of the total respondents. 4.4. Based on Gender The following table displays details of profile respondents by gender: Table 5. Respondent Profile Based on Gender. Gender N % Female 107 51.00% Male 103 49.00% From the table above, it can be seen that female respondents make up the majority, accounting for 51.0% of the total respondents. Meanwhile, male respondents represent 49.0% of the total. 4.5. Based on Domicile The details of the respondents profile based on domicile in this study are as follows: Table 6. Respondent Profile Based on Domicile. The result concluded that the majority of the respondents reside in Non-Jabodetabek, with a total of 54.3% respondents. Meanwhile, the rest of 45.7% respondents are from Jabodetabek. 4.6. Based on Use of Live Streaming Features The following table display detail of respondent profile by the use of the live streaming feature: Domicile N % Jabodetabek Non-Jabodetabek 96 114 45.7% 54.3% 2441 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Table 7. Respondent Profile Based on Use of Live Streaming Features. From Table 7 above, all respondents (100%) use live streaming, while none of the respondents reported not using it. 4.7. Based on Frequency of Live Streaming Usage The details of respondent profiles based on frequency of live streaming usage in this study are as follows: Table 8. Respondent Profile Based on Frequency of Live Streaming Usage. From the result above, the majority of respondents (52.4%) use the service very often. This is followed by 40.5% of respondents who use it often. Meanwhile, 5.2% of respondents have a neutral frequency of use, and the least, 1.9%, use it not often. 4.8. Data Analysis 4.8.1. Structure Test In conducting a structure test, the outer model will measure the relationship between latent variables (variables that cannot be measured directly) and their representative indicators. This model is also often referred to as an external measurement, as it connects unobservable variables with measurable data. Testing the outer model is also useful for assessing validity of each indicator in this research. Usage N % Yes 210 100.0% Frequency of Use N % Not Often Neutral Often Very Often 4 11 85 110 1.9% 5.2% 40.5% 52.4% 2442 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Figure 2. Structure Test. The structure test involving 21 indicators shows that only 15 indicators are valid. The ideal Loading Factor value should be greater than or equal to 0.7 [49]. Thus, indicators PE3, EE4, SI2, FC2, LS1, and LS3 show that the outer loading value is below the minimum outer loading value mentioned, thus making them invalid. This analysis will be further discussed in the convergent validity measurement table which shows the results of the outer loading value before invalid data is removed and after being removed. 4.8.2. Convergent Validity Construct measurement must be correlated to test convergent validity. In SmartPLS, convergent validity is assessed through reflective indicators by examining the Loading Factor value, which will show the strength of correlation between the item scores and the construct. According to previous study, the ideal Loading Factor value should be equal or above 0.7 (⋝ 0.7) [49]. The results of the convergent validity test as followed: 2443 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Table 9. Convergent Validity. Variable Indicator Outer Loading Value before Removal Outer Loading Value after Removal Performance Expectancy (X1) PE1 PE2 PE3 PE4 PE5 0.962 0.964 -0.008 0.958 0.947 0.962 0.963 - 0.958 0.947 Effort Expectancy (X2) EE1 EE2 EE3 EE4 0.907 0.763 0.797 0.595 0.896 0.804 0.837 - Social Influence (X3) SI1 SI2 SI3 0.950 -0.149 0.962 0.951 - 0.963 Facilitating Condition (X4) FC1 FC2 FC3 FC4 0.747 0.580 0.827 0.926 0.742 - 0.866 0.927 Live Streaming Usage LS1 LS2 LS3 LS4 LS5 -0.003 0.942 -0.062 0.968 0.956 - 0.943 - 0.968 0.956 The table above presenting the result of outer loading calculations shows that most of the items' outer loading values exceed 0.7. However, each of the variables has at least one or two items with outer loading value that did not exceed the expected value or below 0.7, such as PE3, EE4, SI2, FC2, LS1, LS3. The convergent validity table above includes 21 indicators to assess and measure latent variables. A 5-point Likert scale, with 1 indicating "strongly disagree" and 5 meaning "strongly agree," is applied for measurements. The results of the analysis show that several indicators of the use of live streaming features including PE3 (Live streaming features make shopping activities easier), EE4 (There are no difficulties using live streaming features), SI2 (Inspiring people for gen z provide ideas for using live streaming features), FC2 (Have knowledge in using live streaming features), and gen z's perceptions in using live streaming features including LS1 (More affordable product prices), and LS3 (More reliable delivery services) are invalid, so these indicators are not related to the use of live streaming features. Meanwhile, other indicators or items are more likely to be related to the adoption of the live streaming feature on business competitive advantage as they exhibit values that vary between 0.74 to 0.96, which indicates validity and reliability of these indicators. Therefore, the measurement results illustrate that most of the indicators used are both valid and reliable, making them suitable for use in future research. Indicators or items that have a value below 0.7 must be removed. After removal, this leaves a final result showing that there is a harmonious relationship between the items of each variable in the construct. These results also show that the construct can be used to examine each variable. The above model is also proven to have a reliable database, so it can be used for further related research. 4.8.3. Discriminant Validity Discriminant validity ensures that each latent variable is distinct from the others in the model. A model is considered to have good discriminant validity if the highest loading for each latent variable is greater than that of other variables. 2444 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Table 10. Discriminant Validity. X1 X2 X3 X4 Performance Expectancy (X1) Effort Expectancy (X2) 0.053 Social Influence (X3) 0.486 0.121 Facilitating Condition (X4) 0.069 0.112 0.051 According to Rönkkö and Cho [50] correlation threshold should fall between 0.8 and 0.9 [50]. In other words, there is a problem in the correlation between variables when the values are above 0.9. Based on the result, all the correlation values are under 0.9 and 0.8. From the table above, the value of each variable ranged from 0.051, which is the lowest value, to 0.468 which is the highest value. Therefore, the variables used in this study have a good correlation, with no correlation problem between variables. 4.8.4. Internal Consistency Reliability Measurement The model fit results of this study are considered good because the SRMR value is below 0.08, with an acceptable threshold of up to 0.10. Additionally, the NFI value is also good as it is close to 1. Table 11. Internal Consistency Reliability Measurement. Variable (Reflective Model) Cronbach’s Alpha after Removal Composite Reliability after Removal AVE after Removal Performance Expectancy (X1) 0.970 0.810 0.716 Effort Expectancy (X2) 0.801 0.843 0.720 Social Influence (X3) 0.908 0.970 0.917 Facilitating Conditions (X4) 0.804 0.921 0.915 Cronbach's Alpha with the value transcend 0.8 is generally considered to have a good reliability [51]. As for the variables in the reflective model table, it shows Cronbach's Alpha and Composite Reliability values that have met the minimum reliability value with the value above 0.8. The acceptable range for achieving Cronbach Alpha reliability is 0.7 to 0.9, where the reliability level of 0.7 is still considered good, while for a reliability level of 0.9 is considered excellent. The minimum composite reliability value is 0.7 [52]. It can be seen in the table that composite reliability value falls in between 0.81 to 0.97, Thus the research model above has reliable data for further research. Afterward, the Average Variance Extracted (AVE) value should outstrip 0.50. Cheung, et al. [53]. The AVE value on the table shows a good result with a range of 0.71 to 0.91, thus exceeding the minimum value of 0.5. This indicates that AVE values of all variables have achieved convergent validity. Based on the result, the construct appears to have an adequate level of reliability and validity and can be used to measure the variables used in this study. 4.8.5. Multicollinearity The multicollinearity test is useful to decide whether independent and dependent variables correlate significantly. The regression model will show the Variance Inflation Factor (VIF) value which will indicate whether the model below is free from multicollinearity. 2445 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Table 12. Multicollinearity. Based on the table above, PE has a VIF of 1.271, EE has a VIF of 1.018, SI has a VIF of 1.278, and FC has a VIF value of 1.011. All the indicators have a VIF value below 5, meeting the recommended criteria [54].This implies that the model is strong and free from issues related to multicollinearity. 4.9. Hypothesis Test In conducting data analysis, multiple linear regression analysis methods are applied to evaluate predictions accuracy related to how independent variables impact the dependent variables. This analysis is used to examine the impact of effort expectancy, facilitating condition, performance expectancy, and social influence on live streaming adoption impacting business competitive advantage using the multiple linear regression formula. 4.9.1. Multiple Linear Regression Analysis Multiple linear regression analysis investigates the impact of two or more independent variables on a single dependent variable [55]. Therefore, the analysis model will help researchers to: 1) Analyze which independent variables that significantly impact the dependent variable 2) Forecasting the dependent variable value based on given independent variable values. Multiple regression analysis is conducted when there are at least two independent variables. Table 13. Multiple Linear. 5. Regression Analysis Afterward, to find out whether there is a partial effect, a parameter significance test (T-test) is enforced. The significance value in Table 13 indicates whether a partial influence exists or not of each independent variable. Based on the result shown in Table 13, showed that Performance Expectancy (X1) and Facilitating Condition (X4) Play a significant partial effect on the live streaming usage, as evidenced by their significance values being below 0.05.the significance value for X1 and X4 are less than 0.01. This means that there is a significant partial effect on the Performance Expectancy and Facilitating Conditions variable. As the result on hypotheses testing, Ho1 and Ho4 succeed to be rejected. Thus Both Ha1 and Ha4 are accepted,which means that Performance Expectancy (X1) and Facilitating Condition (X4) factors of live streaming feature adoption do have significant effect on the business advantage of Indonesia MSMEs. The significance of Performance Expectancy (X1) indicates that technology that can improve performance which makes it easier to achieve benefits in certain goals is certainly preferred. Simplicity and time savings in shopping activities when using the live streaming feature is certainly also a driver of the desire to use this feature. This can be proven that indeed the live streaming feature is very Indicators VIF PE -> LS 1.271 EE -> LS 1.018 SI -> LS FC -> LS 1.278 1.011 Model t Sig. (Constant) 5.947 <0.01 X1 27.354 <0.01 X2 X3 1.049 1.785 0.296 0.076 X4 -5.754 <0.01 2446 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate interactive thanks to the advantages of this feature that allows real-time communication that could lead to creating customer engagement [10, 19, 20, 35]. When viewed from the age range of gen z respondents, the majority of which are in the range of 20-25 years, gen z itself is the most impulsive generation in terms of shopping. Thus, any facility or technology that supports them in making shopping easier will certainly be more desirable. The study also shows that the majority of respondents are women, who are known to be more impulsive in shopping. This feature will also be very beneficial for respondents who are outside Jabodetabek, especially in areas that are far from economic and technological centers, so that this interactive feature helps shopping activities even better. After all, MSMEs can further maximize its services by considering more creative utilization of life streaming features to increase its competitive advantage [12]. Facilitating Condition (X4) also plays as a main key role of gen z acceptance on live streaming feature usage. Without proper facilities that support the use of live streaming features, such as internet, gadgets, and all other necessary tools, those particular features that require all of the mentioned facilities can not be utilized properly. Infrastructure is needed to support and encourage the desire to use the technology, especially for those who live in rural areas [29]. Ini this study, the majority of the respondents lived outside Jabodetabek area. This area, when compared to the Jabodetabek area, certainly does not have as advanced infrastructure as the Jabodetabek area in terms of supporting the use of certain technologies. Lack of infrastructure in supporting the use of technologies in non- Jabodetabek areas could lead to a knowledge gap in using certain technologies such as live stream features, in comparison for those who lived in Jabodetabek area. Fortunately, most of gen z are already fluent in using much of the technologies existing these days. They certainly have access to use certain digital technologies to support their shopping activities like e-commerce. MSMEs can take advantage of this moment to be able to develop infrastructure and provide good facilities to support the use of live streaming features in e-commerce. By paying attention to facilitating conditions, MSMEs can also excel in competition by facilitating consumer access to the company's brand. Thus, this research have stated that the simplicity of using the live streaming features, along with the available support systems, significantly impacts users' choices to participate in using the features. It is shown in Table 13 that the significance value for Effort Expectancy is 0.296 and the sig value for Social Influence is 0.076. Unfortunately, these indicators such as Effort Expectancy (X2) and Social Influence (X3) didn't show that live streaming has a significant impact on business advantage, as their significance values exceed 0.05. No significant partial effect indicated on X2 and X3. Thus, both Ho2 and Ho3 failed to be rejected resulting in the Effort Expectancy (X2) and Social Influence (X3) factors of live streaming feature adoption do not have significant effect on the business advantage of Indonesia MSMEs. Effort Expectations and Social Influence did not have a significant impact in relation to users' decisions to use the live streaming feature. This can be caused by certain factors and limitations. One of them is due to lack of supporting indicators. During the structure and convergent validity test, removal of several indicators was executed which might be possible as the main factor in influencing data sufficiency for the hypotheses testing. The fact tells that Effort Expectancy (X2) is not the primary concern for MSMEs when they adopt live streaming, but as we can see many respondents prioritize the effectiveness and business impact. Based on demographic data, the ages of our respondents are around 20-25 years old, and many younger respondents are typically tech savvy and they think using live streaming feature is very easy to use (high EE). if most of our respondents think that this live streaming feature so effortless to use, effort variable will become a non significance. We can look on Table 6, it shows our respondents are from Indonesia’s urban (Jabodetabek), so we can say that they have better internet infrastructure and make easier to use this feature (High EE). due to mostly our respondents are form Indonesia’s urban (Non Jabodetabek) it means they may have technical barriers which explain why Effort Expectancy to be insignificant. 2447 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Thereupon, the result also provides implication that Social Influence (X3) is not significantly encourage MSMEs adoption decisions. Most MSMEs may rely more on internal business considerations rather than getting influenced by others. In addition, studies have shown that women are more influenced by social suggestion than the men. Because of the near-equal gender split in of the respondents as shown in Table 5, it makes canceling out the social influence effect and making it non- significant overall. Furthermore, in Tables 4 and 8, most of our respondents are frequently early adopters influenced by social media, and everyone accepts live streaming because of trends (especially in Indonesia). This research suggest that while perceived benefits and facilitations are key drivers to archieve user adoption, maybe effort expectancy and social influence may not be as crucial in determining user engagement in live streaming. 6. Conclusion The multicollinearity test confirms that all independent variables meet the recommended Variance Inflation Factor (VIF) criteria and no multicollinearity issues found on the regression model. Furthermore, the model fit results show an acceptable value of SRMR which did not outstrip the value of 0.10, and the NFI value is close to 1, suggesting that the model has a good fit. This study reveals that variables such as Performance Expectancy and Facilitating Condition significantly impact the live streaming usage, as evidenced by their significance partial effect values being below 0.01. This suggests that users' expectations regarding the benefits of live streaming and other supporting facilities have a contribution in influencing their live streaming engagement. These findings are relevant to the study conducted by Farida and Setiawan [23] where the performance expectancy significantly affects actual usage [56]. Live streaming enhances user engagement by creating an interactive experience that encourages participation and loyalty [57]. Businesses can optimize live streaming to improve business competitive advantage by ensuring strong facilitating conditions, such as reliable technology and engaging content, to maximize user interaction and retention [58]. On the other hand, Effort Expectancy and Social Influence did not show a significant impact, as their significance values exceed 0.05. This implies that the ease of use of live streaming features and the influence of social factors may not strongly determine users' decisions to use the feature. These findings suggest that while perceived benefits and supporting infrastructure are key drivers of adoption, usability and social influence may not be as crucial in determining user engagement in live streaming. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Acknowledgments: Author contributorship and open data. Jonardi Ghuntesa: Conceptualization, Methodology, Data Curation, Investigation, Formal Analysis, Writing – Original Draft, Visualization, Validation, and Resources. Samuel Oliver Gunawan: Conceptualization, Methodology, Writing – Review and Editing, Data Curation, Formal Analysis, Visualization, and Validation. Mulyono: Supervision, Investigation, Writing – Review and Editing, Formal Analysis, Resources, Project Administration, and Validation. The dataset for research has been uploaded to repository URL: https://doi.org/10.5281/zenodo.15541304 2448 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] A. A. Vărzaru and C. G. Bocean, "Digital transformation and innovation: The influence of digital technologies on turnover from innovation activities and types of innovation," Systems, vol. 12, no. 9, pp. 2-21, 2024. https://doi.org/10.3390/systems12090359 [2] R. Celeste and N. Osias, "Challenges and Implementation of Technology Integration: Basis for Enhanced Instructional Program," American Journal of Arts and Human Science, vol. 3, no. 2, pp. 106-130, 2024. https://doi.org/10.54536/ajahs.v3i2.2656 [3] M. A. Mustafa, A. Konios, and M. Garcia-Constantino, "IoT-based activities of daily living for abnormal behavior detection: Privacy issues and potential countermeasures," IEEE Internet of Things Magazine, vol. 4, no. 3, pp. 90-95, 2021. https://doi.org/10.1109/IOTM.0001.2000169 [4] A. F. Alyaa, H. Hatta, and D. A. Widyastuti, "Factors influencing impulsive buying behavior among generation Z TikTok users in Jakarta," Journal of Entrepreneurship, Management, and Industry, vol. 7, no. 1, pp. 1-8, 2024. [5] Badan Pusat Statistik, "Population by region, generation classification, and gender, INDONESIA," Retrieved: https://sensus.bps.go.id/topik/tabular/sp2020/2, 2020. [6] E. Djafarova and T. Bowes, "‘Instagram made Me buy it’: Generation Z impulse purchases in fashion industry," Journal of retailing and consumer services, vol. 59, p. 102345, 2021. https://doi.org/10.1016/j.jretconser.2020.102345 [7] R. F. Ayuni, "The online shopping habits and e-loyalty of Gen Z as natives in the digital era," Journal of Indonesian Economy and Business, vol. 34, no. 2, p. 168, 2019. https://www.doi.org/10.22146/jieb.39848 [8] Kementerian Koordinator Bidang Perekonomian Republik Indonesia, "Coordinating minister airlangga: Government supports new forms of collaboration so that Indonesian MSMEs become part of the global industrial supply chain," Retrieved: https://www.ekon.go.id/publikasi/detail/5885/menko-airlangga-pemerintah-dukung-bentuk-kolaborasi- baru-agar-umkm-indonesia-jadi-bagian-rantai-pasok-industri- global#:~:text=Jakarta%2C%2022%20Juli%202024,total%20tenaga%20kerja%20di%20Indonesia, 2024. [9] R. Meilariza, Z. Delima, and M. Zuliyati, "Digitalization of Micro, small, and medium enterprise (MSMEs) in Indonesia," International Conference on Economic, Management, and Accounting (ICEMA), KnE Social Science, pp. 245-257, 2024. https://doi.org/10.18502/kss.v9i17.16334 [10] M. Hu and S. S. Chaudhry, "Enhancing consumer engagement in e-commerce live streaming via relational bonds," Internet Research, vol. 30, no. 3, pp. 1019-1041, 2020. http://dx.doi.org/10.1108/INTR-03-2019-0082 [11] C. Qing and S. Jin, "What drives consumer purchasing intention in live streaming e-commerce?," Frontiers in Psychology, vol. 13, p. 938726, 2022. https://doi.org/10.3389/fpsyg.2022.938726 [12] J. Chen and J. Liao, "Antecedents of viewers’ live streaming watching: a perspective of social presence theory," Frontiers in Psychology, vol. 13, p. 839629, 2022. https://doi.org/10.3389/fpsyg.2022.839629 [13] S. Wahida, M. Akbar, and N. R. Marsuki, "Kesetaraan akses digital," Jurnal Kajian dan Penelitian Umum, vol. 2, no. 1, pp. 159-168, 2024. https://doi.org/10.47861/jkpu-nalanda.v2i1.858 [14] L. D. Susanti, D. T. Indrianti, and M. I. Hilmi, "digital literacy: Women's empowerment in the dasa wisma group in banyuwangi regency," Jurnal Pendidikan Luar Sekolah, vol. 6, no. 2, pp. 115–126, 2022. https://doi.org/10.21831/diklus.v6i2.49504 [15] A. L. Kusumatrisna, K. A. L. Anggraini, and T. S. V. C. Wulandari, "Statistik e-commerce 2022/2023," Badan Pusat Statistik. https://www.bps.go.id/id, 2023. [16] F. R. P. Kiswandi, M. C. Setiawan, and M. A. Ghifari, "The role of MSMEs (micro, small and medium enterprises) in Indonesia's economic growth," Jurnal Ilmiah Ekonomi Dan Manajemen, vol. 1, no. 4, pp. 154-162, 2023. https://doi.org/10.61722/jiem.v1i4.328 [17] N. Pranata et al., "Technology adoption issues and challenges for micro, small and medium enterprises: A case study of the food and beverage sub-sector in Indonesia," The Journal of Asian Finance, Economics and Business, vol. 9, no. 3, pp. 265-274, 2022. https://doi.org/10.13106/jafeb.2022.vol9.no3.0265 [18] B. Lu and Z. Chen, "Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective," Information & Management, vol. 58, no. 7, p. 103509, 2021. https://doi.org/10.1016/j.im.2021.103509 [19] R. Yuan, Y. Chen, and T. Mandler, "It takes two to tango: The role of interactivity in enhancing customer engagement on sharing economy platforms," Journal of Business Research, vol. 178, p. 114658, 2024. https://doi.org/10.1016/j.jbusres.2024.114658 [20] J. Guan-qi, "Innovation, competitive advantage, and customer loyalty: Insights from live streaming enterprises," International Journal of Interdisciplinary Studies in Social Science, pp. 19-25, 2024. https://doi.org/10.62309/kefyg970 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.3390/systems12090359 https://doi.org/10.54536/ajahs.v3i2.2656 https://doi.org/10.1109/IOTM.0001.2000169 https://sensus.bps.go.id/topik/tabular/sp2020/2 https://doi.org/10.1016/j.jretconser.2020.102345 https://www.doi.org/10.22146/jieb.39848 https://www.ekon.go.id/publikasi/detail/5885/menko-airlangga-pemerintah-dukung-bentuk-kolaborasi-baru-agar-umkm-indonesia-jadi-bagian-rantai-pasok-industri-global#:~:text=Jakarta%2C%2022%20Juli%202024,total%20tenaga%20kerja%20di%20Indonesia https://www.ekon.go.id/publikasi/detail/5885/menko-airlangga-pemerintah-dukung-bentuk-kolaborasi-baru-agar-umkm-indonesia-jadi-bagian-rantai-pasok-industri-global#:~:text=Jakarta%2C%2022%20Juli%202024,total%20tenaga%20kerja%20di%20Indonesia https://www.ekon.go.id/publikasi/detail/5885/menko-airlangga-pemerintah-dukung-bentuk-kolaborasi-baru-agar-umkm-indonesia-jadi-bagian-rantai-pasok-industri-global#:~:text=Jakarta%2C%2022%20Juli%202024,total%20tenaga%20kerja%20di%20Indonesia https://doi.org/10.18502/kss.v9i17.16334 http://dx.doi.org/10.1108/INTR-03-2019-0082 https://doi.org/10.3389/fpsyg.2022.938726 https://doi.org/10.3389/fpsyg.2022.839629 https://doi.org/10.47861/jkpu-nalanda.v2i1.858 https://doi.org/10.21831/diklus.v6i2.49504 https://www.bps.go.id/id https://doi.org/10.61722/jiem.v1i4.328 https://doi.org/10.13106/jafeb.2022.vol9.no3.0265 https://doi.org/10.1016/j.im.2021.103509 https://doi.org/10.1016/j.jbusres.2024.114658 https://doi.org/10.62309/kefyg970 2449 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate [21] N. Puspitasari, M. B. Firdaus, C. A. Haris, and H. J. Setyadi, "An application of the UTAUT model for analysis of adoption of integrated license service information system," Procedia Computer Science, vol. 161, pp. 57-65, 2019. https://doi.org/10.1016/j.procs.2019.11.099 [22] R. Abdulwase, F. Ahmed, F. Nasr, A. Abdulwase, A. Alyousofi, and S. Yan, "The role of business strategy to create a competitive advantage in the organization," Open Access J Sci, vol. 4, no. 4, pp. 135-138, 2020. https://doi.org/10.15406/oajs.2020.04.00162 [23] I. Farida and D. Setiawan, "Business strategies and competitive advantage: the role of performance and innovation," Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 3, p. 163, 2022. https://doi.org/10.3390/joitmc8030163 [24] R. Azizi, M. Maleki, M. Moradi-Moghadam, and V. Cruz-Machado, "The impact of knowledge management practices on supply chain quality management and competitive advantages," Management and Production Engineering Review, vol. 7, no. 1, p. 7, 2016. https://doi.org/10.1515/mper-2016-0001 [25] V. Gunadi, N. I. Septyani, R. Annafi, and R. N. P. Atmojo, "The effect of live streaming methods in online sales on behavioral intention in generation z," presented at the In E3S Web of Conferences (Vol. 426, p. 02127). EDP Sciences. https://doi.org/10.1051/e3sconf/202342602127, 2023. [26] J. G. Wilbard, B. D. Mbilinyi, N. S. Maliva, and K. H. Mkwizu, "Is location a competitive advantage on retail convenience shopping," International Journal of Research & Methodology in Social Science, vol. 4, no. 2, pp. 15-26, 2018. [27] M. Haseeb, M. Lis, I. Haouas, and L. WW Mihardjo, "The mediating role of business strategies between management control systems package and firms stability: Evidence from SMEs in Malaysia," Sustainability, vol. 11, no. 17, p. 4705, 2019. http://dx.doi.org/10.3390/su11174705 [28] L. M. Hewitt and L. J. J. Van Rensburg, "The role of business incubators in creating sustainable small and medium enterprises," The Southern African Journal of Entrepreneurship and Small Business Management, vol. 12, no. 1, p. 9, 2020. https://doi.org/10.4102/sajesbm.v12i1.295 [29] L. Xue, A. M. Rashid, and S. Ouyang, "The unified theory of acceptance and use of technology (UTAUT) in higher education: a systematic review," Sage Open, vol. 14, no. 1, p. 21582440241229570, 2024. https://doi.org/10.1177/21582440241229570 [30] S. Bano, W. Cisheng, A. N. Khan, and N. A. Khan, "WhatsApp use and student's psychological well-being: Role of social capital and social integration," Children and youth services review, vol. 103, pp. 200-208, 2019. https://doi.org/10.1016/j.childyouth.2019.06.002 [31] D. Wang, "E-commerce live streaming interaction quality, immersion experience and consumer purchase intention," Applied Mathematics and Nonlinear Science, vol. 9, no. 1, pp. 3-17, 2024. https://doi.org/10.2478/amns-2024-1401 [32] Y. X. Xia, S. W. Chae, and Y. C. Xiang, "How social and media cues induce live streaming impulse buying? SOR model perspective," Frontiers in Psychology, vol. 15, p. 1379992, 2024. http://dx.doi.org/10.3389/fpsyg.2024.1379992 [33] R. Zheng, Z. Li, and S. Na, "How customer engagement in the live-streaming affects purchase intention and customer acquisition, E-tailer's perspective," journal of retailing and consumer services, vol. 68, p. 103015, 2022. https://doi.org/10.1016/j.jretconser.2022.103015 [34] Y. Lina, D. Hou, and S. Ali, "Impact of online convenience on generation Z online impulsive buying behavior: The moderating role of social media celebrity," Frontiers in psychology, vol. 13, p. 951249, 2022. https://doi.org/10.3389/fpsyg.2022.951249 [35] W. Dewobroto and S. Enrica, "The utilization of live streaming technology to improve the shopping experience that generates engagement and buyer trustworthiness in Indonesia," Journal of Modern Manufacturing Systems and Technology, vol. 5, no. 2, pp. 78-87, 2021. https://doi.org/10.15282/jmmst.v5i2.6859 [36] K. Sokolova and H. Kefi, "Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions," Journal of retailing and consumer services, vol. 53, p. 101742, 2020. https://doi.org/10.1016/j.jretconser.2019.01.011 [37] P. A. Clement, J. Fang, A. O. Asare, and N. B. Kulbo, "Customer engagement and Purchase Intention in Live- streaming digital marketing platforms," The Service Industries Journal, vol. 41, no. 2, pp. 1-20, 2021. https://doi.org/10.1080/02642069.2021.1905798 [38] H. Reinikainen, J. Munnukka, D. Maity, and V. Luoma-Aho, "‘You really are a great big sister’–parasocial relationships, credibility, and the moderating role of audience comments in influencer marketing," Journal of marketing management, vol. 36, no. 3-4, pp. 279-298, 2020. https://doi.org/10.1080/0267257X.2019.1708781 [39] R. E. Widodo and T. A. Napitupulu, "Exploring the impact of live streaming for e-commerce business: A systematic literature," Journal of Theoretical and Applied Information Technology, vol. 101, no. 16, 2023. [40] Y. Li and Y. Ng, "The impact of e-commerce livestreaming on the purchasing behaviour of Chinese nationals," Advances in Economics, Management and Political Sciences, vol. 24, no. 1, pp. 137-143, 2023. https://doi.org/10.54254/2754-1169/24/20230426 https://doi.org/10.1016/j.procs.2019.11.099 https://doi.org/10.15406/oajs.2020.04.00162 https://doi.org/10.3390/joitmc8030163 https://doi.org/10.1515/mper-2016-0001 https://doi.org/10.1051/e3sconf/202342602127 http://dx.doi.org/10.3390/su11174705 https://doi.org/10.4102/sajesbm.v12i1.295 https://doi.org/10.1177/21582440241229570 https://doi.org/10.1016/j.childyouth.2019.06.002 https://doi.org/10.2478/amns-2024-1401 http://dx.doi.org/10.3389/fpsyg.2024.1379992 https://doi.org/10.1016/j.jretconser.2022.103015 https://doi.org/10.3389/fpsyg.2022.951249 https://doi.org/10.15282/jmmst.v5i2.6859 https://doi.org/10.1016/j.jretconser.2019.01.011 https://doi.org/10.1080/02642069.2021.1905798 https://doi.org/10.1080/0267257X.2019.1708781 https://doi.org/10.54254/2754-1169/24/20230426 2450 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate [41] A. Qosasi, E. Maulina, M. Purnomo, A. Muftiadi, E. Permana, and F. Febrian, "The impact of information and communication technology capability on the competitive advantage of small businesses," International Journal of Technology, vol. 10, no. 1, pp. 172-175 2019. https://doi.org/10.14716/ijtech.v10i1.2332 [42] D. Sward, "User experience design: A strategy for competitive advantage association for information systems ais electronic library (AISeL)," Retrieved: http://aisel.aisnet.org/amcis2007?utm_source=aisel.aisnet.org%2Famcis2007%2F163&utm_medium=PDF&utm_ca mpaign=PDFCoverPages, 2007. [43] I. Dwijayanti, P. P. Santoso, and B. S. Hantono, "User experience aspect assessment method for digital wallet mobile application in indonesia: A literature review," presented at the IEEE International Conference on Information Technology and Electrical Engineering (ICITEE) https://doi.org/10.1109/ICITEE53064.2021.9611828, 2021. [44] U. A. Muazu and S. Abdulmalik, "Information technology capabilities and competitive advantage: A review," International Journal of Technology and Systems, vol. 6, no. 1, pp. 1-17, 2021. https://doi.org/10.47604/ijts.1206 [45] E. Ociepa-Kicinska, M. P. Rochon, K. Palimaka, R. CzyZycki, and R. V. Celic, "Technology support educating generation-z - a necessity or an opportunity," presented at the International Conference on Information Systems Development. http://dx.doi.org/10.62036/ISD.2024.106, 2024. [46] G. A.-M. T. Hail, S. A. M. Yusof, A. Rashid, I. El-Shekeil, and A. Lutfi, "Exploring factors influencing gen z's acceptance and adoption of ai and cloud-based applications and tools in academic attainment," Emerging Science Journal, vol. 8, no. 3, pp. 815-836, 2024. http://dx.doi.org/10.28991/ESJ-2024-08-03-02 [47] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate data analysis, 8th ed. Boston, MA: Cengage Learning, 2018. [48] S. Z. Alharthi, "Sustainable competitive advantage: Toward a generic framework," International Journal of Business Research and Development, vol. 1, no. 1, pp. 17-31, 2012. [49] R. A. S. Al-Maroof and M. Al-Emran, "Students acceptance of google classroom: An exploratory study using PLS- SEM approach," International Journal of Emerging Technologies in Learning (Online), vol. 13, no. 6, p. 112, 2018. https://doi.org/10.3991/ijet.v13i06.8275 [50] M. Rönkkö and E. Cho, "An updated guideline for assessing discriminant validity," Organizational research methods, vol. 25, no. 1, pp. 6-14, 2022. https://doi.org/10.1177/1094428120968614 [51] I. Kennedy, "Sample size determination in test-retest and Cronbach alpha reliability estimates," British Journal of Contemporary Education, vol. 2, no. 1, pp. 17-29, 2022. https://doi.org/10.52589/BJCE-FY266HK9 [52] K. Arof, S. Ismail, and A. L. Saleh, "Contractor’s performance appraisal system in the Malaysian construction industry: Current practice, perception and understanding," International Journal of Engineering & Technology, vol. 7, no. 3.9, pp. 46-51, 2018. http://dx.doi.org/10.14419/ijet.v7i3.9.15272 [53] G. W. Cheung, H. D. Cooper-Thomas, R. S. Lau, and L. C. Wang, "Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations," Asia Pacific Journal of Management, vol. 41, no. 2, pp. 745-783, 2024. https://doi.org/10.1007/s10490-023-09871-y [54] N. Shrestha, "Detecting multicollinearity in regression analysis," American journal of applied mathematics and statistics, vol. 8, no. 2, pp. 39-42, 2020. https://doi.org/10.12691/ajams-8-2-1 [55] I. D. Cristiyani, "Analysis of the influence of product, price, and service quality on purchasing decisions at the hakiki coffee shop surakarta," Agrista, vol. 8, no. 2, pp. 1-10, 2020. [56] F. Bayumi, "The influence of performance expectancy, effort expectancy, and facilitating condition on bank jambi's financial performance, with actual usage as a mediating variable (study on bank Jambi mobile application users)," Jurnal Manajemen Terapan Dan Keuangan, vol. 12, no. 01, pp. 14-27, 2023. https://doi.org/10.22437/jmk.v12i01.17956 [57] A. S. K. Sonda and T. E. Balqiah, "Tiktok live shopping's use of live streaming to increase word of mouth," Jurnal Aplikasi Manajemen, vol. 21, no. 4, pp. 1091-1108, 2023. https://doi.org/10.21776/ub.jam.2023.021.04.17 [58] Z. Kamarozaman and F. Z. A. Razak, "The role of facilitating condition in enhancing user’s continuance intention," In Journal of Physics: Conference Series vol. 1793, no. 1, p. 012022, 2021. https://doi.org/10.1088/1742- 6596/1793/1/012022 https://doi.org/10.14716/ijtech.v10i1.2332 http://aisel.aisnet.org/amcis2007?utm_source=aisel.aisnet.org%2Famcis2007%2F163&utm_medium=PDF&utm_campaign=PDFCoverPages http://aisel.aisnet.org/amcis2007?utm_source=aisel.aisnet.org%2Famcis2007%2F163&utm_medium=PDF&utm_campaign=PDFCoverPages https://doi.org/10.1109/ICITEE53064.2021.9611828 https://doi.org/10.47604/ijts.1206 http://dx.doi.org/10.62036/ISD.2024.106 http://dx.doi.org/10.28991/ESJ-2024-08-03-02 https://doi.org/10.3991/ijet.v13i06.8275 https://doi.org/10.1177/1094428120968614 https://doi.org/10.52589/BJCE-FY266HK9 http://dx.doi.org/10.14419/ijet.v7i3.9.15272 https://doi.org/10.1007/s10490-023-09871-y https://doi.org/10.12691/ajams-8-2-1 https://doi.org/10.22437/jmk.v12i01.17956 https://doi.org/10.21776/ub.jam.2023.021.04.17 https://doi.org/10.1088/1742-6596/1793/1/012022 https://doi.org/10.1088/1742-6596/1793/1/012022 2451 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Appendix Research Questionnaire on Indonesian External Auditors. Variable Indicators Option Section 1 : Socio demographic 1. Name (Nama) 2. Age (Usia) 3. Gender 4. Domicile (Domisili) - - ▪ Male ▪ Female - Section 2 : Voluntariness of Live Streaming Feature Usage 1. Have you ever used the live streaming feature on an e-commerce platform? (Apakah Kamu pernah menggunakan fitur live streaming di platform e- commerce?) 2. How often do you use the live streaming feature on e-commerce platforms? (Seberapa sering Kamu menggunakan fitur live streaming di platform e-commerce?) 3. What is your purpose for using live streaming feature? (Apa tujuan anda menggunakan fitur live streaming?) ▪ Yes ▪ No 1 = Strongly Disagree (Sangat Tidak Setuju) 2 = Disagree (Tidak Setuju) 3 = Neutral (Netral) 4 = Agree (Setuju) 5 = Strongly Agree (Sangat Setuju) ▪ To search for products to purchase (Untuk mencari produk yang akan dibeli) ▪ Just looking at the products displayed on the live stream (Hanya sekedar melihat-lihat produk yang di tampilkan di live stream) ▪ To obtain more detailed information about the products displayed (Untuk mendapatkan informasi lebih detail terkait produk yang di tampilkan) ▪ Just for entertainment purposes (Hanya sekedar untuk hiburan saja) 2452 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate Section 3 : Your thoughts on the Live Streaming feature on e-commerce platforms 1. live streaming brings products at a more affordable price (Live streaming di e-commerce menghadirkan produk dengan harga yang lebih terjangkau) 1. live streaming is able to show/display product quality in real-time (Fitur live streaming di e-commerce dapat membantu anda dalam menunjukan/menampilkan kualitas produk secara real-time) 2. live streaming is able to provide delivery reliability by meeting product delivery schedules (Fitur live streaming di-ecommerce mampu menyediakan pelayanan pengiriman yang lebih handal dengan memenuhi jadwal pengiriman produk) 3. Live streaming is able to engage consumers directly in exploring ideas and understanding their needs, thus accelerating product innovation (Fitur live streaming mampu melibatkan konsumen secara langsung dalam mengeksplorasi ide dan memahami kebutuhan mereka, sehingga mempercepat inovasi produk) 5. live streaming is able to deliver products in a short time in the market (Fitur live streaming mampu dalam menghadirkan produk dalam waktu singkat dalam pasaran) 1 = Strongly Disagree (Sangat Tidak Setuju) 2 = Disagree (Tidak Setuju) 3 = Neutral (Netral) 4 = Agree (Setuju) 5 = Strongly Agree (Sangat Setuju) Performance Expectancy 1. Live streaming feature helps in finding products that meet the needs of the audience (Fitur live streaming membantu dalam menemukan produk yang sesuai dengan kebutuhan anda) 2. Live streaming features can increase productivity in shopping activities (Fitur live streaming dapat meningkatkan produktivitas dalam kegiatan berbelanja anda) 3. Live streaming can simplify shopping activities (Fitur live streaming dapat mempermudah aktivitas berbelanja) 4. Live streaming can provide more reliable product information (Fitur live streaming dapat memberikan informasi produk yang lebih handal) 5. Live streaming can shorten the time in receiving product information (Fitur live streaming dapat mempersingkat waktu dalam menerima 1 = Strongly Disagree (Sangat Tidak Setuju) 2 = Disagree (Tidak Setuju) 3 = Neutral (Netral) 4 = Agree (Setuju) 5 = Strongly Agree (Sangat Setuju) 2453 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2432-2453, 2025 DOI: 10.55214/25768484.v9i5.7484 © 2025 by the authors; licensee Learning Gate informasi produk) Effort Expectancy 1. Overall, the use of live streaming is very easy for me (Secara keseluruhan, penggunaan live streaming sangat mudah bagi saya) 2. The interaction of using live streaming is clear and understandable (Interaksi menggunakan live streaming jelas dan mudah dipahami) 3. I don't need a lot of time in shopping using live streaming (Fitur live streaming membantu mempersingkat proses berbelanja) 4. I have no difficulty in using the live streaming feature. (Saya tidak mengalami kesulitan dalam menggunakan fitur live streaming) 1 = Strongly Disagree (Sangat Tidak Setuju) 2 = Disagree (Tidak Setuju) 3 = Neutral (Netral) 4 = Agree (Setuju) 5 = Strongly Agree (Sangat Setuju) Social Influence 1. People close to me influence my behaviour in convincing me to use the live streaming feature. (Orang terdekat saya memengaruhi perilaku saya dalam meyakinkan saya untuk menggunakan fitur live streaming) 2. People who inspire me give me the idea to use the live streaming feature. (Orang yang menginspirasi saya memberikan saya ide untuk menggunakan fitur live streaming) 3. In general, companies support the use of live streaming. (Perusahaan ditempat saya bekerja mendukung untuk menggunakan fitur live streaming) 1 = Strongly Disagree (Sangat Tidak Setuju) 2 = Disagree (Tidak Setuju) 3 = Neutral (Netral) 4 = Agree (Setuju) 5 = Strongly Agree (Sangat Setuju) Facilitating Condition 1. I have facilities that support me in using the live streaming feature. (Saya memiliki fasilitas yang mendukung saya dalam menggunakan fitur live streaming) 2. I have the knowledge to use the live streaming feature. (Saya memiliki pengetahuan untuk menggunakan fitur live streaming) 3. The e-commerce platform I use provides facilities to support the use of live streaming. (Platform e-commerce yang saya gunakan menyediakan fasilitas untuk mendukung penggunaan live streaming) 4. The work environment or where I live provides infrastructure that supports me to use the live streaming feature. (Lingkungan kerja atau tempat tinggal saya menyediakan infrastruktur yang mendukung saya untuk menggunakan fitur live streaming) 1 = Strongly Disagree (Sangat Tidak Setuju) 2 = Disagree (Tidak Setuju) 3 = Neutral (Netral) 4 = Agree (Setuju) 5 = Strongly Agree (Sangat Setuju)