Journal of Distribution Science (유통과학연구)

Korea Distribution Science Association (한국유통과학회)
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The Impact of Social Media Marketing Towards Purchase Decision: Interactive Flat Panel Display Technology Distribution from Indonesia's B2B Market

  • Yunita SWASTI (Communication Department, Strategic Marketing Communication, Binus Graduate Program, Bina Nusantara University) ;
  • Ricardo INDRA (Strategic Marketing Communication, Binus Graduate Program, Bina Nusantara University) ;
  • Nadia Kris SIGIT (Communication Department, Strategic Marketing Communication. Binus Graduate Program, Bina Nusantara University) ;
  • Muhammad ILHAM (Communication Department, Strategic Marketing Communication. Binus Graduate Program, Bina Nusantara University) ;
  • La MANI (Communication Department, Strategic Marketing Communication. Binus Graduate Program, Bina Nusantara University) ;
  • Muhammad ARAS (Communication Department, Strategic Marketing Communication. Binus Graduate Program, Bina Nusantara University)
  • Received : 2023.12.14
  • Accepted : 2024.09.05
  • Published : 2024.09.26
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Abstract

Purpose: This research aims to examine the impact of social media marketing on buy decisions in Indonesia's B2B market, considering the mediating roles of brand image, perceived quality, and perceived value in relation to interactive flat panel display technology. To better understand technology adoption and distribution, we utilize the innovation diffusion theory. Research Design, Data and Methodology: The Decision-Making Unit of each organization that buy interactive flat panel display technology conducted an empirical survey of 82 participants. The quantitative research design analyzed the data utilizing the PLS-SEM model. outcome: This research reveals that social media marketing significantly impacted perceived quality, brand image, perceived value, and buy decisions. The research also found that perceived quality does not significantly impact buy decisions, but perceived value and brand image significantly impacted buy decisions. This research contributes to understanding the key factorsinfluencing buy decisionsin Indonesia's B2B market. Conclusion: Thisresearch concludesthat B2B consumers in Indonesia are less concerned about product quality but prioritize the value they receive when purchasing interactive display technology. Social media marketing could impacted the distribution of interactive display technology in Indonesia's B2B market by affecting the DMU's purchasing decisions. Brandsshould leverage social media marketing to positively impact theirsuccess.

Keywords

1. Introduction

The world of technology has changed rapidly, as reveals by the industrial revolution from Industry 1.0 to Industry 4.0. Industry 4.0 originated in Germany for emerging production’s innovations (Vogel-Heuser & Hess, 2016), focutilized on the target and the Cyber-Physical System (CPS) to process and review the real-time data (Sunagar et al., 2022). The Fourth Industrial Revolution is driving automation and digitalization through the Internet of Things (IoT), cloud computing, cognitive computing, and artificial intelligence.

Industry 4.0 drives research and innovation in the technology sector worldwide. A specific brand launched the first commercial interactive flat panel display in 1991 for the B2B market. Interactive flat panel display technology integrates human touch and visual senses to facilitate communication by machines through an intuitive buyer experience. Customers can select items by touching the display, and to enhance the immersive experience, the device can recognize gestures. (Lee et al., 2020).

When delivering presentations, interactive flat panel display technology can replace conventional tools like whiteboards, projectors, flipcharts, cameras, microphones, speakers, and cables. Interactive flat panel display technology has evolved to meet market needs. Many display features are now available, such as options for infrared or P-CAP touch sensors, Android or Windows operating systems, auto-framing cameras, speakers, microphones, and more. Business presentations have transformed into interactive communication.

The transformation of interactive display technology is valuable, but it remains ineffective and insignificant until it is properly disseminated to customers. (Wani, 2015). Everett M. Rogers proposed the innovation diffusion theory (IDT) in 1962. Diffusion is the process by which an innovation is communicated through specific channels over time to members of a social system. (Rogers, 2003). According to Innovation Diffusion Theory (IDT), the characteristics of innovation that impacted varying adoption rates include relative advantage, compatibility, complexity, observability, and trialability. Messages are transmitted from one individual to another through a medium called a communication channel. Rogers categorized communication channels into mass media and interpersonal channels. While mass media can share data more quickly, interpersonal channels are more essential for the spread of new innovations or technologies (Rogers, 2003; Wani, 2015).

Innovation-decision is the process by which a decision-making unit (DMU) progresses from learning about an innovation to forming an opinion, deciding whether to adopt or reject it, implementing the new idea, and ultimately confirming the decision. (Rogers, 2003). However, if the social system decides to adopt the technology by making a buy, it will demonstrate how the technology is distributed and utilized. If the innovation is rejected by the social system, it will fade away.

The diffusion of innovation occurs only when social systems adopt and share data by others. Rogers classified people in society into five categories based on their willingness to adopt new ideas. He utilizes the time dimension to differentiate between these groups. Innovators are individuals who readily embrace new ideas and products, while laggards remain skeptical of innovations.

The innovation process inside organizations is significantly more complex than the innovation decision-making process of individuals. (Rogers, 2003). B2B organizations have decision-making units (DMU) representing various business areas that make buying decisions (Hawkins & Mothersbaugh, 2010). The complexity of factors influencing customer behavior before a buy decision makes it difficult to understand, as customers prioritize meeting their specific needs. An earlier research indicated that the buy decision process begins when customers identify a need, evaluate available options, and select a particular product and brand. (Hanaysha, 2022; Salem, 2018). B2B suppliers need to provide valuable and accurate data when promoting advanced technology products and services (Crisafilly et al., 2022; Schätzle & Jacob, 2019) so that potential customers can connect and decide which vendor and offer they want to choose (Hanaysha, 2017). Previous studies also reported that buy decisions are impacted by various variables such as social media marketing, perceived value (Hanaysha, 2018), brand image (Djatmiko & Pradana, 2016), and brand quality (Adam & Akber, 2016) .

data and communication technology (ICT) affects how consumers search for data about products and services they are interested in. Marketers can impacted purchasing behavior through targeted marketing strategies. (Hanaysha, 2022). The easiest way to reach, connect by, and interact by potential buyers is through social media, by cost-effective and unrestricted in time (Hanaysha, 2017). by the ability to facilitate two-way communication, provide reviews, offer campaigns, and share valuable content to engage, interact by, and strengthen customer correlations, social media has become one of the most important marketing tools for businesses. (Sanny et al., 2020; Tatar & Eren-Erdoğmuş, 2016). Brands can utilize social media to inform customers regarding their products and services, such as product types, features, prices, values, etc. According to Sanny et al. (2020), social media marketing significantly impacted brand image. Meanwhile, businesses find it challenging to utilize social media to accelerate strategic marketing initiatives (Tafesse & Wien, 2018).

Multiple brands of interactive flat panel displays are penetrating Indonesia’s B2B market. Some brands are already well-established, while others are newcomers. Brand image is crucial in shaping how customers perceive and characterize a brand. (Keller, 2009), particularly when a new brand enters a specific market. In previous research, Djatmiko and Pradana (2016) found that brand image impacted buying decision on technology products. The brand consumers choose, or buy depends on the insight they collect from sources, including colleagues, advertisements, and prior experiences (Chakraborty & Sheppard, 2016). According to Razy and Lajevardi (2015) analysis, customers may lower their buy risks by buying items from famous brand by positive reputation. This means a brand's image needs to leave a positive impression if it wants to be widely recognized by customers

This research examines whether social media marketing can impacted buying decisions as a distribution method through perceived quality, brand image, and perceived value. The outcomes will help us better understand how these social media marketing factors contribute to forecasting buy decisions, particularly in the distribution of interactive flat panel display technology.

2. Literature Review

2.1. Social Media Marketing

Social media is now utilized by business owners for communication, marketing, and social interaction. (Anjum et al., 2012; Constantinides & Stagno, 2011). According to previous research, many business owners are utilizing social media marketing in their communication strategies to reach their target markets. (Constantinides & Stagno, 2011). The promotional components ofsocial media include advertising, personal selling, public relations, publicity, direct marketing, and sales promotion, all a part of integrated marketing communications (Anjum et al., 2012).

Before a buyer decides to buy a product or service, they may utilize social media to collect data. Customers frequently visit a business’s social media pages to learn about various aspects, including products, contacts, pricing, and locations. They may also make simple comparisons between brands. A wide range of company platforms, including websites, e-commerce sites, applications, and other channels, are also related to social media.

Brands have begun seeking the best methods for leveraging social media to sustain their business, build stronger connections by customers, promote their products and services, and create trustworthy images. Marketers should provide tools for user self-promotion, monitor and impacted online conversations, develop content management systems, and enable real-time and personalized customer interactions (Tafesse & Wien, 2018). To optimize the effectiveness of their marketing campaigns, global brands engage various social media specialists and consultants to obtain expert advice on the content and features of their advertising before publishing it on social media. (Erdoğmuş & Çiçek, 2012; Hanaysha, 2017).

2.2. Perceived Quality

Brand quality assessment is a fundamental element inside the comprehensive research methodology for evaluating brand equity. According to Aaker (1996), customers may discern the perceived quality of a product or service by their experiential interaction by it. The present research aims to analyze the impact of consumer perception of brand quality on brand attitude and subsequent brand judgements, as discussed in the works of Liu et al. (2014) and Zeithaml (1988). According to previous research (Chomvilailuk & Butcher, 2010; Liu et al., 2014), consumer-based brand assessment is significantly impacted by the perceived quality. Various interpretations of perceived quality exist among distinct groups of consumers, including those who exhibit loyalty towards a particular brand, those who switch between brands, and those who remain loyal to alternative companies (Aaker, 1996).

Based on the literature review, some factors can be inferred to impacted product quality, including performance, features, dependability, compliance, durability, serviceability, aesthetics, and perceived quality. (Aaker, 2009; Garvin, 1984; Zeithaml, 1988). In addition to the factors described above, Cahyani et al. (Cahyani et al., 2022) reported that perceived quality was also positively impacted by social media marketing.

2.3. Brand Image

The American Marketing Association defines a brand as a name, term, sign, symbol, or design, or a combination of these that identifies and differentiates a seller’s products and services from those of other businesses (Kotler & Keller, 2012). Brand image is part of brand knowledge. According to Keller (Keller, 1993), The concept of brand image can be defined as the overall perception of a brand, shaped by the various associations that buyers have in their minds. The type, favorability, strength, and uniqueness of each brand association significantly impacted the brand image.

Product-related attributes refer to the tools or supporting materials necessary for optimizing the functionality of a product or service. Non-product-related attributes refer to the external factors associated by the purchasing or consumption of products or services (Arai et al., 2014; Keller, 1993). These attributes include price data, which is a necessary consideration in the buying process but does not directly impact the performance or functionality of the product or service. Additionally, packaging or product display data impacted the purchasing process, though it does not directly affect the product’s or service’s performance or functionality. The third kind of user imaginary is directly decided by customer experience, which is in turn dependent on demographics and psychographics. Additionally, some factors, including the duration of consumption (daily, weekly, or yearly), location, and other relevant variables, impacted usage imagery. Benefits are the advantages that individuals derive from utilizing a particular product or service (Wijaya, 2013), including functional benefits, experiential benefits, and symbolic benefits. Attitudes define a brand's comprehensive evaluation.

Favorable brand associations describe how a brand is evaluated based on the "feel" that is imprinted in consumers' minds. The "feel" in question may manifest as colors, aromas, treatments, and other sensory elements. This favorability can be invaluable in some situations but not in others. Additionally, the strength of brand associations depends on how well customers' memories store and recall product and service data. The uniqueness of brand associations is a competitive advantage that rivals may not possess. Changes in the type and strength of brand connections are likely to affect customer behavior (Romaniuk & Nenycz-Thiel, 2013).

2.4. Perceived Value

There is no consensus in the literature regarding the implications and meaning of perceived value (Sánchez-Fernández & Iniesta-Bonillo, 2007). According to Zeithaml (1988), the definition of perceived value reflects three components: “low price”, “whatever I want in a product”, “quality I get for the price I pay”, and “what I get for what I give”.

Perceived value comparesthe benefitsreceived and what is sacrificed (Woodruff & Gardial, 1996; Yang et al., 2016). According to Kuppelwieser et al. (2022), customers receive three distinct benefits: 1) Benefits of functionality: technical advantages obtained by customers, 2) Emotional benefits: psychological advantages obtained by customers, and 3) Social benefits: advantages for society as an entirety.

In marketing, perceived value is defined as the customer’s evaluation of the costs and benefits associated by purchasing a product or service (Kim & Park, 2013). The literature reviews revealed the outcome that social media marketing strategy has a positive impact on perceived value (Bazrkar et al., 2021).

2.4. buy Decision

The buy decision decides the distribution of new technology products and services. buy decisionsin B2B area are part of a succession of consumer purchasing procedures (Adam & Akber, 2016). Organizational or business requirements necessitate a more complex purchasing procedure than individual needs. Their members impacted the norms and culture of an organization. It also affects how a business handles the purchasing a product or service.

According to Hawkins and Mothersbaugh, decision-making units (DMU) are the individuals inside an organization who have impacted purchasing decisions (Hawkins & Mothersbaugh, 2010). Each member of the DMU has a specific function, such as data collecter, key impactedr, decision maker, buyer, or user. The purchasing center is the organizational decision-making element (Kotler & Keller, 2012) and comprises seven roles. Initiators are individuals who submit a request to buy something. users are consumers of goods or services and are often the originators of a need. Impactedrs are those who have sway over specifications, data, and the evaluation of alternatives. Deciders select which products, services, or suppliers to utilize. Approvers are individuals authorized to approve proposed actions by decision-makers. Buyers have the authority to select and negotiate by suppliers and arrange buys. Gatekeepers control access by prohibiting merchants or initial data from reaching the decision-making process.

When an organization decides to make a buy, it must follow a process known as the decision-making process, which consists of five phases. Problem recognition is the first step. At this stage, the need is identified, often by the department chief. During the data search phase, the organization startsseeking forsolutions and answersto meet its requirements. data can be collected through prior knowledge, internet searches, or, for complex needs, by employing datal consultants. Alternatives are evaluated and selected when the organization has multiple options from the preceding phase. The organization will first connect, compare, and shortlist vendors who meet the criteria. Then, it will discuss which vendor is best suited to fulfill its requirements from various perspectives.

At this point, the consumer decides whom to buy from, where to make the buy, which brand to choose, and whether to buy the products. buy decisions reflect that customers are willing to spend money on products and services and feel confident in their choices. Kotler and Keller identify three categories of buy decisions: product selection, brand selection, and number of buys (Kotler & Keller, 2012; Warayuanti & Suyanto, 2015). Payment policies, guarantees, delivery (Hawkins & Mothersbaugh, 2010), returns, prior experience, time pressure, sales conditions, and other variables, such as brand loyalty, personal correlation by the vendor, etc. (Adam & Akber, 2016), frequently impact the buy decision stage. The final stages of the purchasing process are utilization and post-buy evaluation, during which the consumer connectes whether they made the right choice.

Hanaysha (2022) reported that social media marketing and its dimension positively impact to buy decision. Adam (Adam & Akber, 2016) found that the brand quality impacted buy decision. Another research by Djatmiko and Pradana (2016) revealed that brand image positively affects buying decisions. Perceived value is another factor discovered to impacted buying decision (Hanaysha, 2018).

3. Research Methods and Hypothesis

This research employs a quantitative approach by explanatory research methods. The objective of explanatory research is to obtain accurate data about the phenomenon being analyzed and to enhance this data by knowledge gained from further theory development and hypothesis testing (Rustam et al., 2023; Sekaran & Bougie, 2016). The positivistic paradigm assumes that a symptom can be classified, the correlation between symptoms is causal, and a sign consists of some variables where it is impossible to observe the total number, so researchers can conduct research by focutilizing on only a few variables (Park et al., 2020). This research explores the correlation between social media marketing and purchasing decisions through perceived quality, brand image, and perceived value. The research model is illustrated in Figure 1 below:

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Figure 1: Research Model

The following hypothesis follows the discussion above:

H1: Social Media Marketing has a significant impact on Perceived Quality.

H2: Social Media Marketing has a significant impact on Brand Image.

H3: Social Media Marketing has a significant impact on Perceived Value.

H4: Social Media Marketing has a significant impact on Purchase Decision.

H5: Perceived Quality has a significant impact on buy Decision.

H6: Brand Image has a significant impact on buy Decision.

H7: Perceived Value has a significant impact on buy Decision.

The survey method is a research procedure utilized to collect data by examining a population’s attitudes, behaviors, and characteristics through sampling (Creswell, 2014). Devoid of examining individual participants, the survey aims to generalize a social phenomenon to the entire population. Thisresearch employs non-probability sampling, meaning that not all members of the population have an equal chance of being selected (Baltes & Ralph, 2020). A population of 102 companies that purchased interactive display technology brand X from an Indonesian distributor between January and October 2022 was sampled utilizing the Slovin formula. The sample of 82 participants included at least one representative from each company’s decision-making unit. The researcher employed a questionnaire survey as the sampling technique for data collection in this research (Rustam et al., 2023; Williamson, 2018). The primary data is shared via Google Forms to participants by a Likert scale 1-6. The data analysis technique utilized Structural Equation Modeling (SEM) based on Partial Least Square (PLS) by utilizing SmartPLS version 3.2.9 software.

4. Outcome and Discussion

4.1. Demographic

From the distribution of the questionnaire, 82 participants met the criteria of various companies that purchased interactive display technology. Table 1 presents the profile list of all 82 participants. The participants are male (83%) and female (17%). The decision-making unit functions of the participants include initiators (22%), buyers (21%), impactedrs (20%), users (15%), deciders (11%), and approvers (9%). There are no representatives from the gatekeepers' decision-making unit function. This participant profile aligns by the research objective of analyzing the effect of social media marketing on buy decisions.

Table 1: Demographic

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4.2. Validity and Reliability

In this research, the outer reflective model comprises a convergent validity indicator (outer loading), construct validity (Average Variance Extracted or AVE), construct reliability (Cronbach's alpha and composite reliability), and discriminant validity (cross-loading). Table 2 presents convergent validity utilizing the outer loading and AVE values. For convergent validity assessment, the decision-making criterion is that the outer loading value must be greater than 0.70 and the AVE value must be larger than 0.50. As a outcome, we can infer that the following variables are valid and have achieved convergent validity.

Table 2: Convergent Validity

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The reliability test was conducted to connect the internal consistency of the indicators in measuring specific constructs or latent variables. As revealed in Table 3, since both the Cronbach's alpha and composite reliability values are greater than 0.70, it can be concluded that the variables in this research are reliable, and the questionnaire is a consistent research instrument.

Table 3: Reliability

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The purpose of discriminant validity testing is to verify that each concept of a latent or construct variable is distinct from the concepts of other variables. Discriminant validity was connected by examining the cross-loadings of each construct. Table 4 reveals that all variables fulfilled the criterion for discriminant validity, which is variables having a cross-loading value greater than 0.70. and that the cross-loading value on the indicator is greater than the cross-loading value on the indicators from the other constructs (Hair et al., 2022).

Table 4: Discriminant Validity​​​​​​​

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4.3. Hypothesis Analysis & Discussion

Hypothesis testing examines the outcomes of t-statistics and p-values to decide whether a hypothesis is accepted or rejected. This research utilizes SmartPLS 3.2.9 software to connect the hypothesis. The criteria applied in this research are a t-statistic greater than 1.99, a p-value smaller than 0.05, and a positive path coefficient.

Based on the outcomes of the PLS-SEM analysis, social media marketing has a positive and statistically significant impact on perceived quality. Table 5 reveals a t-statistic value of 5.382 (greater than 1.99) and a p-value of 0.000 (less than 0.05). These outcomes provide empirical support for Hypothesis 1. The outcomes confirm that social media marketing is essential for establishing and enhancing customer-perceived quality. Moreover, this is consistent by previous studies (Cahyani et al., 2022). Jayasuriya et al. (2018) also discovered a positive correlation between Social Media Marketing and Perceived Quality. Brands can utilize social media marketing to enhance data about perceived quality. In the case of interactive flat panel display technology, social media can communicate details such as product performance, aesthetic design, perceived quality, and unique features not offered by competitors.

Table 5: Outcome of Hypothesis Testing​​​​​​​

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The second hypothesis tested is the impact of Social Media Marketing on Brand Image. The outcome on Table 4 is presented t-statistic 6.672 > 1.99 and p-value 0.000 < 0.05. Therefore, the second hypothesis is accepted, it means that Social Media Marketing significantly impacts on Brand Image. This outcome accordance by Sanny’s (Sanny et al., 2020) previous research said that brand image can be explained through social media marketing. According to Godey et al. (2016), social media marketing has become an essential instrument for establishing brand image. Social media marketing activities significantly impact consumers' brand perception (Bilgin, 2018). According to additional research, social media marketing activities positively affect brand image (Seo & Park, 2018). Social media marketing can also be utilized to publish content that enhances brand image (Permatasari & Aras, 2021) by providing data about the dimensions of the brand image.

In addition, Social Media Marketing has a positive correlation to Perceived Value by t-statistic 8.700 > 1.99 and p-value 0.000 < 0.05. Hence, hypothesis 3 is accepted, implying that social media marketing significantly impacts perceived value. This outcome is supported by Bazrkar et al. (2021) research that social media marketing positively affects perceived value. Marketing activities on social media can be utilized to highlight the value that customers gain when utilizing interactive display technology. What are the benefits of interactive display technology for companies? Describe the emotional experience and perceived value when customers utilize interactive display technology, as well as the additional advantages it offers compared to older technologies.

In other words, social media marketing has a positive correlation by buy decisions. The t-statistic (2.763 > 1.99) and p-value (0.007 < 0.05) indicate that social media marketing has a significant impact on buy decisions. Therefore, Hypothesis 4 is accepted. This outcome is in line by the research conducted by Hanayasha (2022) which concluded that informativeness in social media marketing have positive effect on buy decision. Further support was seen in the research of Alatawy (2021) who reported that customer buy decision impacted by social media marketing tactics. Alfian and Nilowardono (2019) also reported that social media marketing has the most significant impact on buy decisions when compared to word of mouth and brand awareness. These outcomes suggest that social media marketing can affect the distribution of interactive display technology, as indicated by buy decisions.

The fifth hypothesis examined the impact of perceived quality on buy decision. Based on the t-statistic value of 0.203 (which islessthan 1.99) and a p-value of 0.840 (which is greater than 0.05), the outcomes indicate a negative correlation. Therefore, hypothesis 5 is rejected, indicating that perceived quality has no significant impact on buy decision. The outcomes of this research are identical to those of research Lestari et al. (2019), in which no positive correlation was discovered between perceived quality and buy decision. According to Sulaiman and Chau's research (2021), perceived quality has no significant correlation by the buy decision.

Brand Image reveal a positive relation to buy Decision. T-statistic 1.937 < 1.99 and p-value 0.056 < 0.1 indicating that Brand Image have significantly buy Decision by low impacts. For some empiricals research, p-value<0.1 could be accepted by low impact (Hair et al., 2022). Thus, hypothesis 6 is accepted. According to Hammam & K.’s (2021) research, brand image significantly affects buy decision. Yusuf et al. (2022) also reported that the effect of some dimension of brand image on the buy decision is insignificant. Other studies also revealed a promising outcome on buy decision impacted by brand image (Bahari et al., 2020). Thisimplies that certain companies continue to accept all brands, regardless of their level of recognition, whether well-known or new. New brands may benefit from this situation to capitalize on more opportunities and expand their distribution.

The final objective of this research was to decide whether perceived value has a positive correlation by the buy decision. Hypothesis 7 is accepted by t-statistic 3.653 > 1.99 and p-value 0.000 < 0.05. The outcomes of this research also revealed that Perceived Value has a significant impact on buy Decision. This outcome supported by Yeo et al. (2022) that found perceived value has a positive effect on buy decision. Naseem and Yaprak (2023) also reported buy decision impacted by perceived value.

This research also demonstrates the specific indirect effect of social media marketing on buy decisions, mediated by product quality, brand image, and perceived value. In Table 6, the t-statistic (0.197 < 1.99) and p-value (0.844 > 0.05) indicate that social media, through perceived quality, has no significant impact on buy decisions. Likewise, the outcomes t-statistic 1.816 < 1.99 and p-value 0.073 < 0.1 imply that social media marketing had a weak significant impact on buy decision through brand image. Meanwhile, t-statistic 2.127 > 1.99 and p-value 0.002 < 0.05 represent that social media marketing significantly impact buy decisions mediated by perceived value.

Table 6: Specific Indirect EffectSocial media marketing activities significantly impact consumers' brand perception.​​​​​​​

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5. Conclusion

Based on the statistical outcomes and discussions, we can conclude that this research provides an academic approach to understanding the significance of social media marketing on buy decisions in Indonesia’s B2B market, by perceived quality, brand image, and perceived value mediating the distribution of interactive flat panel technology. First, this research found that social media marketing impacted product quality, brand image, perceived value, and buy decisions for interactive flat panel display technology in the B2B market. Second, it was decided that perceived quality does not affect buy decisions, but perceived value and brand image do. This suggests that the distribution of interactive flat panel display technology can be impacted by social media marketing, brand image, and perceived value.

Based on Innovation Diffusion Theory (IDT), interactive display technology is diffused when more B2B markets decide to buy and adopt it daily. Referring to these outcomes, brands can utilize social media marketing to increase distribution, represented by buy decisions, by providing data about perceived value, such as the technical functionality, emotional benefits, and social value of interactive display technology. By sharing this data through social media, more customers will become aware of the features of interactive flat panel display technology, as perceived value directly impacted distribution through customer buy decisions. Furthermore, social media can be utilized to showcase the brand's image through color elements, design aesthetics, how customers utilize interactive display technology to boost productivity, and the range of products and models offered by the brand. This can shape customer perceptions of the brand.

While perceived quality has no direct impact on buy decisions, social media marketing also has no indirect impact on buy decisions through perceived quality. The quality of the product data cannot be denied in social media marketing because some outcomes reveal perceived quality a impacted by social media marketing. Brands can also share data about product quality, the benefits of attractive designs, and other unique features, particularly those not offered by competitors. Social media marketing may significantly impact the DMU's decision to buy interactive display technology in Indonesia. This suggests that brands should leverage social media marketing to boost their revenues.

Building the brand image of a new brand in the B2B market is challenging; therefore, according to this research, brands must persuade consumers through perceived value and brand image. In this research, the quality of a product is not the primary concern of B2B customers in determining the buy. If the meaning and value they get when buying interactive flat panel display technology is convincing, they will buy it.

This research has some limitations that could guide future research directions. First, the population was limited to B2B customers of a single brand. Future studies could expand the population and include a larger sample size from multiple brands of interactive flat panel display technology. Second, this research focuses on social media marketing, perceived quality, brand image, and perceived value as variablesinfluencing buy decisions. Thus, future studies can explore additional variables such as price, B2B marketing activities, digital advertising, interpersonal communication, and trialability in relation to buy decisions, to gain deeper insights into the penetration of interactive flat panel display technology in the B2B market.

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