Throughout the year, the internet has become one of the critical elements of a business. A company that invests its business on the internet platform demonstrates a better business performance. As a result, internet marketing encourages whole new genres of experience, but prior studies show some scarcity in that field. So this research will try to fill that gap by studying how virtual experiential marketing (VEM) generates customer intention.Keywords: Virtual experiential marketing; online browsing intention; online purchase intentio

Luhita, Tiladela

Rosiana, Monica

English

JP Fakultas Ekonomi dan Bisnis Unsoed (Universitas Jenderal Soedirman, Journal & Proceeding)

International Sustainable Competitiveness  Advantage 2021 131  How Virtual Experiential Marketing Designs Customer Intention?  Tiladela Luhita1, Monica Rosiana2*  1National Yunlin University Science and technology, tiladela.luhita@gmail.com, Taiwan 2Universitas Jenderal Soedirman, Indonesia *corresponding author   ABSTRACT  Throughout the year, the internet has become one of the critical elements of a business. A company that invests its business on the internet platform demonstrates a better business performance. As a result, internet marketing encourages whole new genres of experience, but prior studies show some scarcity in that field. So this research will try to fill that gap by studying how virtual experiential marketing (VEM) generates customer intention. Keywords: Virtual experiential marketing; online browsing intention; online purchase intention   1. Introduction  Found in 1960s, internet has completely transformed the computing and communications industries. Nowadays, it acts as a global information distribution tool and a platform for interaction between individuals and their devices, regardless of their physical location. Thus, internet is one of the most effective long-term investment (Leiner et al., 2021). Over the past decade, the number of people using internet globally has doubled. It emphasizes how it becomes essentials for people to get their job done.  Figure 1. Global Internet Usage Growth   Internet has grown in popularity among businesses and consumers for marketing, selling, and communication (Hoffman & Novak, 1996). Companies invest a lot of time, money, and effort into building their online marketing tools in the hopes of attracting internet users (Berthon, Pitt, & Watson, 1996). Platform-based digital markets are critically altering the way companies generate and deliver value to final customers and thus the way companies compete in the market (Cennamo, International Sustainable Competitiveness  Advantage 2021 132  2021). It is different from past marketing practices where companies maximize their business by hard-selling approach. Digital internet platform nowadays allows companies to sell their product and offer some value to their customer. A study found that firms that adopted a platform-based model and had 50% or more of their revenues from the platform had 32% higher revenue growth and 27% higher profit margins (Weill & Woerner, 2013).  The maturation of internet marketing encourages whole new genres of experience, such as internet chat rooms, interactive games, interactive images, virtual community, multi-players games — role-playing games, and virtual reality (Liu & Chen, 2006). For example, companies could provide a website that allows the viewer to alter a product’s design features, background, context, viewing angle, or distance and simulate the product’s operation. This feature will be helpful for the customer before they decide to purchase. Thus, marketers have tried hard to implement experiential marketing into the virtual world — on the Internet.  Experiential marketing research in the past has mostly focused on traditional marketing without including the internet or digital platforms. For instance, research on off-road vehicles (Arora & Chatterjee, 2017), clothing (Raturi, 2020), and luxury hotels (Verma & Jain, 2015). Because there is currently a scarcity of studies on experiential marketing on the digital platform, this study will explore the benefits of using experiential marketing on a website. The phenomenon mentioned earlier is termed “virtual experiential marketing”. Virtual experiential marketing is defined by Liu and Chen (2006) as "experiential marketing in the virtual world," i.e., "experience economy activities in the internet environment or online." It is stated that virtual experiential marketing consists of five elements named sense, interaction, pleasure, flow, and community relationship.  A well-designed experience engages the customer, makes them remember the product or service (Hoch, 2002), and leads to strong beliefs and attitudes (Klein, 2003). However, due to the uniqueness of online buying, online experiences offer several problems for businesses. Therefore, the issue focuses on creating a web environment that positively impacts users and generates favorable customer reactions. (Dailey, 2004). This web environment is called virtual experiential marketing, which consists of cues that could stimulate the desired response, such as browsing and purchasing intentions (Luo, Chen, Ching, & Liu, 2011).  2. Literature Review   2.1 Virtual Experiential Marketing (VEM) and Online Browsing Intention  In the following paragraphs, we will go through the five VEM components chosen for this study. They were obtained from previous research (Luo, Chen, Ching, & Liu, 2011).  Sense. In the absence of a physical experience, sense plays a crucial role in distinguishing products and services, motivating customers, adding value to products through attractiveness or joy (Schmitt, 1999) and affecting their cognition and mood (Dailey & Heath, 1999). Thus, atmospheric cues provided by sense will drive customers browsing intention.  Interaction. Interaction regards the degree to which customers may alter the shape and content of a mediated environment in real-time. The website's interactive elements have been linked to International Sustainable Competitiveness  Advantage 2021 133  increased positive opinions toward the online store, as well as increased browsing or regular visits to the site (Campanelli, 2004).  Thus, the discovery of information and knowledge could minimize perceived product or service uncertainties and risks, contributing to the development of favorable attitudes that enhance customer trust (Luo, Chen, Ching, & Liu, 2011).  Pleasure. It is defined as "the degree to which a person feels pleasant, joyous, happy, or pleased in a circumstance" (Menon & Kahn, 2002). This feeling could be triggered by the external stimuli that are provided by the website. When customers experience this pleasure, it will increase their website evaluation and make them stay longer to browse. Thus, when a customer's experience falls short of his or her expectations, he or she may be better served by going to another website.  Flow. The term "flow" was recently used to describe how people utilize the Internet when they become absorbed in a stimulus (e.g., Hsu et al., 2012). It is described as the total experience individuals get when they are engaged in it (Gao and Bai, 2014). According to Lee et al. (2018), there is a strong link between online behavior and earlier flow experience. According to Chou and Ting (2003), customers who have experienced flow are more prone to develop expected behavior. Thus, the better flow, the higher the chance customer has a better online browse intention.  Community relationship. Community relationships could emerge around the client, facilitating social interactions and promoting information exchange. In some circumstances, the virtual community may expand to a physical community, bringing together people with similar lifestyles and interests (Zaphiris et al., 2008). The development of this community relationship is a good driver of attitudes influenced by a social identity within a group. A community allows members to share specific interests, usually consuming a product or service, and focuses on accomplishing personal and shared goals (Rothaermela & Sugiyama, 2001). Thus, a customer with a better community relationship will tend to have a better behavioral intention because they like to stay connected with their surroundings as a social being.  H1: Sense has a positive effect on online browse intention H2: Interaction has a positive effect on online browse intention H3: Pleasure has a positive effect on online browse intention H4: Flow has a positive effect on online browse intention H5: Community relationship has a positive effect on online browse intention  2.2 Online Browsing Intention and Online Purchase Intention Janiszewski (1998) distinguished between goal-directed and exploratory search intentions. Goal-oriented customers are looking for efficient information, whereas consumers who are exploring have no defined goals. Instead, they look for product information and love the act of looking for it. Thus, if search intents are a critical factor in forecasting future purchasing intentions, search attitudes could be a useful study instrument for predicting consumers' likelihood of purchasing online (Shim, Eastlick, Lotz, & Warrington, 2001). According to Klein (1998) and Shim et al. (2001), as long as there is enough resource, if customer desires to execute a specific online action (e.g., search, purchase), he or she will most likely succeed in doing so (i.e., search, purchase). H6: Online browsing intention has a positive effect on online purchase intention.   International Sustainable Competitiveness  Advantage 2021 134  3. Research Methodology The population of this research is all students of Universitas Jenderal Soedirman. The method of data collection is by distributing questionnaires with a scale of 1-5. Based on Hair, et al (2010), the minimum sample size is 100. For this reason, it is recommended that the sample size is 5-10 times the parameter. In this study there are 17 indicators and 6 path coefficients so that the total estimated parameters are 23 parameters, so the sample taken is 115 (23x5). To test the empirical model, Structural Equation Modeling (SEM) was used. Other analytical tools used are AMOS 21.0, SPSS 16.0 and Microsoft Excel 2007.  4. Result and Discussion Based on the results of the convergent validity test, it can be seen that all loading factors are statistically significant and the loading value is > 0.60. Therefore, it can be concluded that all indicators used are declared reliable so that they can be used in the next analysis stage. The results of the discriminant validity test also show that all AVE values are higher than the square of the correlation between constructs on all relationships between variables. Therefore, it can be concluded that the test results show that all constructs used have good validity and can be used in the next analysis stage.  Model Fit Test The test of the suitability of the model shows that a model is suitable or fit to the data used in the study. This can be seen from the existing criteria, namely Chi-Square, Probability, CMIN/DF, GFI, AGFI, TLI, CFI, and RMSEA. According to Hair, et al (2010) the suitability test of this model can be accepted or the model is categorized as fit if at least 5 of these criteria are met. The results of the model suitability test can be seen in table 3 below:      International Sustainable Competitiveness  Advantage 2021 135  Table 1. Goodness of Fit Test Model Fit Index Cut-off Value Result Description χ2 – Chi Square <χ2 138,811  (p 0,05; df 115) 108,964 Good Probability > 0,05 0,780 Good CMIN/DF < 2,00 1,672 Good RMSEA < 0,08 0,057 Good GFI > 0,90 0,924 Good AGFI > 0,90 0,961 Good TLI > 0,95 0,988 Good CFI > 0,95 0,967 Good Source: Primary Data Based on the statistical results of SEM analysis in the model-goodness-of-fit test, eight criteria were obtained that were included in the good category. Based on these results, the model in this study as a whole can be categorized as a very good model or a fit model category. Hypothesis test Hypothesis testing is done by looking at the critical value or t table, by comparing the t table value with the t value in the study or comparing the p value with 0.05. The hypothesis is said to be accepted if the value of ttable < tcount or p <0.05. Based on the t distribution table, the magnitude of the t table value in this study is with a sample size of 115 and a significance level of 5% (0.05) is 1,659. As for the value of tcount on the relationship between the variables of this study can be seen in table 3. Table 2. Results Hipotesis Nilai C.R. Nilai t table P Result Sense        online browse intention 4,296 1,659 0,000 Accepted Interaction         online browse intention 5,394 1,659 0,000 Accepted Pleasure        online browse intention 5,217 1,659 0,016 Accepted Flow        online browse intention 2,119 1,659 0,034 Accepted Community relationship       online browse intention  2,035 1,659 0,042 Accepted Online browsing intention     online browse intention 3.135 1,659 0,012 Accepted Source: Primary Data 5. Conclusion • Sense has a positive effect on online browse intention • Interaction has a positive effect on online browse intention • Pleasure has a positive effect on online browse intention • Flow has a positive effect on online browse intention • Community relationship has a positive effect on online browse intention • Online browsing intension has a positive effect on online browse intention   International Sustainable Competitiveness  Advantage 2021 136  References Arora, S., & Chatterjee, R. (2017). 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How Virtual Experiential Marketing Designs Customer Intention?

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How Virtual Experiential Marketing Designs Customer Intention?

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