Enhancing Nogi Livin’s Brand Awareness: A Customer Decision Journey Perspective through Social Media

: This study investigates the effectiveness of digital marketing strategies employed by Nogi Livin, an emerging Indonesian furniture company, in enhancing brand awareness and influencing customer decision journeys. The global furniture market's expansion is leveraged by digitalization and changing consumer preferences, presenting challenges and opportunities for companies like Nogi Livin. Using the McKinsey Consumer Decision Journey Model, this research delineates the impact of social media strategies on customer engagement and purchasing decisions. A mixed-method approach, including quantitative data from online surveys and qualitative insights, was employed. Structural Equation Modeling (SEM) was used to measure the relationship between online presence and brand awareness, and subsequently, the influence of brand awareness on customer engagement. Findings indicate a significant positive correlation between strategic online engagements and enhanced brand awareness, which strongly links to increased customer engagement and positively affects the consumer decision journey. This research contributes to theoretical and practical understandings of digital marketing's impact in the furniture industry.


I. INTRODUCTION
As the global furniture market is anticipated to grow substantially over the next decade, digitalization and shifting consumer preferences continue to redefine competitive landscapes.The furniture industry, valued at approximately USD 630.55 billion in 2022, is expected to reach around USD 1,051.77 billion by 2032, expanding at a compound annual growth rate (CAGR) of 5.3% from 2023 to 2032 (Precedence Research, 2022).This growth is driven by technological advancements that have not only streamlined manufacturing processes but also transformed consumer interactions through digital platforms (Datta, 2019).As digital marketing becomes increasingly integral to business strategies, its impact on customer engagement and purchasing decisions warrants thorough investigation.The proliferation of digital channels offers unprecedented opportunities for brands to engage with consumers, yet also poses challenges in terms of competition and customer expectations (Holliman & Rowley, 2014).This digitalization is experienced by many players in the furniture industry, including Nogi Livin, an emerging furniture brand in Indonesia.Making use of digital technology, Nogi Livin leverages social media to enhance brand awareness and influence the consumer decision journey.Understanding the impact of digital marketing on consumer behavior is pivotal for companies like Nogi Livin, which aim to carve out a niche in the increasingly digital-first marketplace.
Despite the recognized potential of digital marketing, Nogi Livin faces several challenges that could impede its ability to effectively capitalize on these opportunities.The primary research problem stems from the need to understand how Nogi Livin's online presence, particularly on social media, can be optimized to improve brand awareness and foster customer engagement.There is a lack of comprehensive insight into how different dimensions of online presence-such as content quality, engagement strategies, and platform choice-impact the stages of the consumer decision journey.This gap in understanding could hinder Nogi Livin's ability to effectively leverage digital marketing to enhance its competitive advantage and achieve sustainable growth in the evolving furniture market.

II. LITERATURE REVIEW A. Online Presence
In the digital age, online presence encompasses more than just a company's visibility on the internet; it involves a multifaceted digital footprint across various platforms, crucial for engaging with a brand's audience.As Kaplan and Haenlein (2010) define, it includes the availability and visibility on websites, social media, and other online channels.The evolution from merely having a website to requiring active engagement across diverse digital platforms highlights the growing complexity of online presence, which now demands strategies tailored to specific audience preferences and behaviors (Berthon et al., 2012;Winer, 2009).This shift underscores the necessity for brands to continually adapt to the rapidly changing digital landscape to maintain and enhance their market position.

B. Brand Awareness
The impact of a strong online presence is profoundly influential in enhancing business performance through increased visibility where most customers are active-online.This visibility is essential for building brand awareness, which refers to the extent to which consumers are familiar with and recognize a brand (Keller, 1993) 2018), highlight the necessity for brands to continuously evolve their online strategies to sustain and enhance brand awareness, particularly among digitally native demographics.For Nogi Livin, leveraging these insights into an optimized online presence is more than a marketing tool; it's a strategic asset essential for thriving in the digital marketplace.

C. Customer Engagement
The synergy between online presence, brand awareness, and customer engagement is pivotal in modern marketing dynamics.Robust online visibility and brand familiarity, key outcomes of effective brand awareness, are essential precursors to deep customer engagement (Holliman and Rowley, 2014).Brand awareness significantly affects customer engagement by fostering familiarity and trust, which are crucial for encouraging customers to interact with the brand (Keller, 1993).Customer engagement transcends transactional interactions, encompassing emotional, cognitive, and behavioral investments that customers make in a brand.This multidimensional concept involves customers' thoughts, feelings, and actions towards a brand, ranging from mental engagement and emotional connections to actions such as purchases and brand promotion (Brodie et al., 2011;Hollebeek et al., 2014).The rise of social media has transformed customer engagement, facilitating direct and real-time interactions that shift communication from brand-centric to customer-centric, thus deepening engagement and building community around the brand (Tafesse & Wien, 2018;Laroche et al., 2013).Effective content that educates, entertains, or solves problems plays a crucial role in attracting and retaining customer attention, thereby enhancing engagement and strengthening relational ties within brand communities.

D. Customer Journey
The customer journey is a critical concept in marketing that reflects the sum of experiences a customer has with a brand, from initial awareness through to loyalty (Richardson, 2010).The relationship between customer engagement and the customer journey is dynamic, where engagement at each stage-awareness, consideration, decision-making, and post-purchase-directly influences the path and outcomes of the journey (Lemon & Verhoef, 2016).Engaged customers are likely to move smoothly through these stages, showing higher levels of satisfaction and loyalty due to emotional connections formed with the brand at each touchpoint.The McKinsey Consumer Decision Journey Model offers a modern interpretation of this process, portraying it as non-linear and circular, where customers might enter at any stage and loop back through various stages multiple times, especially in digital contexts where information is always accessible (Edelman & Singer, 2015).This model breaks the journey into four phases: consider, evaluate, buy, and enjoy, advocate, bond, suggesting a continuous interaction cycle rather than a linear path.Each phase has distinct challenges and opportunities for marketers, from ensuring visibility in the consideration phase to maintaining a seamless buying experience and providing excellent post-purchase support to foster loyalty.The circular nature of the McKinsey Model highlights the importance of continuous engagement and adaptation to changing consumer expectations and behaviors.Brands need to leverage digital technologies to enhance interactions at every stage, using tools like social media for direct communication and feedback, and data analytics to refine marketing strategies and personalize customer experiences.This holistic approach to the customer journey not only enhances customer satisfaction and loyalty but also sets the foundation for sustained business success in a digitally-driven market environment.
The literature review illustrates a clear linkage among online presence, brand awareness, customer engagement, and the consumer decision journey.It indicates that a strong online presence positively influences brand awareness, which in turn enhances customer engagement and impacts the consumer decision journey.
Consequently, the hypotheses for this research are formulated as follows: H1: Online presence has a positive effect on brand awareness.H2: Brand awareness has a positive effect on customer engagement.H3: Customer engagement positively affects all stages of the consumer decision journey.

III. METHODOLOGY
This study employs a mixed-method approach, utilizing both qualitative interviews and quantitative surveys.The quantitative data was gathered through online surveys designed to measure the impact of Nogi Livin's online presence on brand awareness, customer engagement, and their influences on the consumer decision journey.
Quantitative data was collected through structured online surveys, which included questions on various aspects of customer interaction with Nogi Livin's digital presence.The dissemination strategy aimed for broad reach via social media, encouraging ample participation to exceed the "10 times rule" for sample size as advised by Hair et al. (2017).This rule ensures sufficient statistical power and accurate model estimation in PLS-SEM analysis.
The collected data were analyzed using Structural Equation Modeling -Partial Least Squares (SEM-PLS) to understand the relationships between the constructs of online presence, brand awareness, customer engagement, and their impacts on the consumer decision journey.SEM-PLS is particularly effective in exploratory research where model paths are hypothesized but not directly observable.It handles complex models well and is robust against deviations from normality, making it ideal for analyzing behavioral data from marketing research.The analysis focused on measuring path coefficients, assessing construct reliabilities, and ensuring the model's predictive validity through R-square values and Q-squared values for predictive relevance.

IV. RESULTS
The analysis of the survey data using Structural Equation Modeling (SEM-PLS) revealed significant relationships between the constructs as hypothesized.The findings affirm the substantial role of digital marketing strategies in influencing consumer behavior in the furniture industry, specifically for Nogi Livin.From the survey disseminated across Nogi Livin's social media platforms, a total of 104 responses were collected from Nogi Livin customers.

A. Descriptive Statistic
Descriptive statistics play a crucial role in survey data analysis by enabling researchers to ascertain the central tendencies of respondents' answers.In this study, where responses were rated using a Likert scale ranging from 1 to 5, the mean scores offer insights into the levels of agreement or endorsement by the respondents, categorized as follows (Healey, 2014): This categorization helps in understanding how different elements related to Nogi Livin's online presence, brand awareness, and customer engagement are perceived by the audience, ultimately informing strategic decisions to enhance brand awareness leading to consumer interaction and satisfaction.

B. Measurement Model 1. Validity Test a. Convergent Validity
The convergent validity of the measurement model was verified by examining the outer loadings and Average Variance Extracted (AVE) for each construct.As per the data, each indicator's loading exceeded the acceptable threshold of 0.5, with many well above 0.7, indicating a strong relationship between the indicators and their respective constructs.Tables 2  and 3 below present the outcomes of the Outer Loadings and Average Variance Extracted (AVE) analyses, respectively.

b. Discriminant validity
Discriminant validity was verified using the Fornell-Larcker criterion and cross-loadings, ensuring that the constructs were distinct and shared more variance with their indicators than with other constructs.The Fornell-Larcker criterion and the examination of cross-loadings serve as the primary methods for assessing discriminant validity, thereby underscoring the uniqueness of each construct within the model (Fornell & Larcker, 1981).Table 4 displays the results from the Fornell-Larcker criterion analysis, while Table 5 outlines the findings from the Cross-loadings analysis.These reliability tests are crucial for ensuring the measurement model's robustness and validity, which in turn lends credibility to the findings derived from the structural model analysis.The high levels of both composite reliability and Cronbach's Alpha across most constructs support the conclusion that the survey items are appropriately capturing the intended constructs and that the constructs themselves are consistently represented within the measurement model.

Structural Model a. R-square values
R-square values are of particular interest, as they measure the proportion of variance in the dependent variables explained by the model.Table 8 and Figure 2 below present the result of R square values and SEM-PLS model in this study.

b. Path Coefficients
The path coefficients in the structural model provide an estimation of the strength and significance of the relationships between the constructs.In the context of Nogi Livin's brand dynamics, the model revealed several noteworthy path coefficients.Table 9 below presents the results of Path Coefficients.The path from Online Presence to Brand Awareness registered a coefficient of 0.646, indicating a strong positive relationship.This suggests that as customers perceive the online presence of Nogi Livin to be more robust, their awareness of the brand increases substantially.This relationship is one of the most potent in the model, highlighting the critical role that online presence plays in shaping brand awareness.
Moving to the relationship between Brand Awareness and the various stages of the Consumer Journey, Brand Awareness has a notable impact on Customer Engagement, with a coefficient of 0.810.This high coefficient indicates that the more aware customers are of the Nogi Livin brand, the more likely they are to engage with it.This finding emphasizes the importance of building brand awareness as a precursor to fostering deep customer engagement.
Within the Consumer Journey itself, Customer Engagement has several significant paths leading to different stages.The coefficients indicate that Customer Engagement influences the first stage of the Consumer Journey (CJ 1) with a coefficient of 0.453, the second stage (CJ 2) with 0.390, and the third stage (CJ 3) with 0.391.This progression illustrates a consistent and positive influence of customer engagement on these initial stages.Furthermore, Customer Engagement shows a strong positive influence on the fifth stage (CJ 5) with a coefficient of 0.781, suggesting that engagement is a critical factor in cultivating customer loyalty and encouraging repurchase behaviors.The path from Online Presence to Brand Awareness yielded a T-statistic of 8.678, far exceeding the critical value of 1.96, which corresponds to a 95% confidence level, hence denoting a highly significant relationship.This suggests very strong evidence supporting the hypothesis that online presence significantly contributes to brand awareness.
Furthermore, Customer Engagement's impact on the Consumer Journey's various stages was evaluated.Each path coefficient associated with Customer Engagement was subjected to this robust significance testing.For the path leading to CJ 5, the T-statistic was 18.036, and to CJ 1, it was 5.323, both indicating extremely significant effects.The other stages, CJ 2, CJ 3, and CJ 4, also displayed significant T-statistics of 3.896, 3.824, and 6.191, respectively, each surpassing the threshold for significance.
The strongest relationship within the model was between Brand Awareness and Customer Engagement, as indicated by an exceptional T-statistic of 18.309.This emphasizes the substantial influence of brand awareness on how customers engage with the brand, highlighting its centrality to the marketing strategy.
These T-statistics are pivotal for supporting the validity of the structural model, demonstrating that the observed relationships are not due to random chance but are statistically significant.With all p-values reported as 0.000, the results provide compelling evidence that the paths tested are indeed influential and should be considered critical components of Nogi Livin's strategy to enhance customer experiences and outcomes.

d. Predictive Relevance (Q2) Evaluation
In evaluating the predictive relevance of the structural model, the Q2 values provide an indication of the model's ability to predict endogenous constructs.In this study, Online Presence is an exogenous variable serving as a predictor rather than being predicted, thus a Q2 value is not applicable.The SRMR is a goodness-of-fit index representing the average discrepancy between the observed correlations and the model's predicted correlations.An SRMR value less than 0.08 is generally considered to indicate good fit.In the case of the Nogi Livin model, the SRMR value is 0.069, which falls below this threshold, indicating a good fit between the model and the observed data.This low SRMR value suggests that the residual correlations (the differences between the observed and predicted correlations) are small, which is indicative of a model that captures the underlying data structure well.
The NFI, also known as the Bentler-Bonett Index, is another measure of fit that compares the specified model to a baseline model, typically a null model where all variables are uncorrelated.NFI values range between 0 and 1, with values closer to 1 indicating a better fit.For this model, the NFI value is 0.774.Although values above 0.9 are typically desired for an excellent fit, an NFI value above 0.75 can be considered as indicating an acceptable fit, especially in exploratory research or complex models.

V. DISCUSSION
The results from the structural model analysis strongly support Hypothesis 1, demonstrating a significant positive relationship between online presence and brand awareness.The path coefficient for online presence to brand awareness was found to be 0.646 with a t-value significantly exceeding the critical threshold, indicating a robust influence.This finding underscores the critical role that a well-managed and dynamic online presence plays in increasing consumer awareness of a brand.For Nogi Livin, this means that strategic investments in enhancing their digital footprint across various platforms-such as optimizing their website, maintaining active social media profiles, and engaging in online marketing-can significantly elevate their brand visibility and recognition in the competitive furniture market.Hypothesis 2 is also supported, as evidenced by a substantial path coefficient of 0.81 from brand awareness to customer engagement.This relationship suggests that when customers are aware of a brand, they are more likely to engage with it through various digital interactions, such as following the brand on social media, participating in discussions, and sharing brand content.The strong linkage between brand awareness and customer engagement highlights the importance of creating memorable and impactful brand messages that resonate with the target audience, thus fostering a deeper, more emotional connection with the brand.
The analysis supported Hypothesis 3, demonstrating that customer engagement has a significant positive effect across all stages of the consumer decision journey, with varying degrees of impact.Notably, the stages associated with loyalty (CJ5) and post-purchase experiences (CJ4) showed the highest R-square values, suggesting that engagement at earlier stages has a profound influence on fostering customer loyalty and encouraging repeat purchases.This finding is critical for Nogi Livin as it indicates that increasing customer engagement can lead to higher customer retention rates and more robust customer relationships.

V. CONCLUSION AND RECOMMENDATION
In conclusion, this research provides robust evidence supporting the significant roles of online presence influences brand awareness, which then influences customer engagement and finally subsequently influences the consumer decision journey.As digital marketing landscapes continue to evolve, understanding these dynamics becomes crucial for brands aiming to maintain competitive advantage and foster sustainable growth.
Future research might explore the long-term effects of sustained customer engagement on brand loyalty and the potential of emerging technologies to enhance online customer experiences.Additionally, comparative studies across different markets or industries could offer broader insights into the generalizability of these findings.The findings of this study contribute to the theoretical and practical understanding of how digital marketing strategies can be optimized to enhance customer interactions and brand perception in the furniture industry.Specifically, the strong relationships identified between online presence, brand awareness, and customer engagement highlight the interconnectedness of these elements and their collective impact on the consumer decision journey.
On a practical level, the findings of this study suggest players in the furniture industry strengthen the importance of online presence.Online presence is not merely about increasing visibility but also about strategically engaging customers to foster long-term relationships.This involves not only maintaining an attractive and functional website but also engaging actively with customers on social media, personalizing marketing communications, and ensuring consistent and appealing content across digital channels.Additionally, the significant impact of customer engagement on the consumer decision journey stages suggests that brands should focus on strategies that not only attract customers but also keep them engaged through personalized and interactive experiences.

Figure 2 :Brand
Figure 2: R Square Value and SEM-PLS Model non-parametric statistical technique, was employed to ascertain the significance of the path coefficients within Nogi Livin's structural model.The T-statistics obtained from this method indicate the robustness of the hypothesized relationships between the constructs.Table10below presents the T-statistics via Bootstrapping Result.

Table 1 : Descriptive statistics
1.00 -2.33: Indicates a Low level of agreement or positive response.2.34 -3.66: Suggests a Medium level of agreement.3.67 -5.00: Reflects a High level of agreement.The table 1 below provides a summary of the descriptive statistics for Nogi Livin, showcasing the distribution of responses across various aspects measured in the survey.

Table 5 : Cross-loadings Result
All constructs in the study met the discriminant validity criteria set, allowing this research to conclude that the measurement model is valid.With discriminant validity established, the model is well-positioned to provide reliable insights into how different aspects of Nogi Livin's customer experiences relate to each other, making it suitable for the subsequent structural model analysis.2.Reliability Test a. Composite ReliabilityGenerally, a Composite Reliability score of 0.7 or above is considered acceptable, indicating good internal consistency, although scores above 0.6 may also be considered satisfactory, especially in the early stages of research (Hair, Black, Babin, Anderson, & Tatham, 2006).Table6below presents the result of the composite reliability test.

Table 6 : Composite Reliability Result Composite Reliability
phases of scale development, a lower threshold may be considered tolerable.Literature suggests that a Cronbach's Alpha value of 0.6 can be deemed acceptable under such circumstances, recognizing the developmental nature of the instrument and the potential for refinement(Hair, Black, Babin, & Anderson, 2014; DeVellis, 2016).Table 7 presents the result of Cronbach's Alpha.3885 * Corresponding Author: Febrihana Meganingsih Volume 07 Issue 06 June 2024 Available at: www.ijcsrr.orgPage No. 3879-3890

2581-8341 Volume 07 Issue 06 June 2024 DOI: 10.47191/ijcsrr/V7-i6-38, Impact Factor: 7.943 IJCSRR @ 2024 www.ijcsrr.org 3888 * Corresponding Author: Febrihana Meganingsih Volume 07 Issue 06 June 2024 Available at: www.ijcsrr.org Page No. 3879-3890
Table 11 below presents the Predictive Relevance (Q2) Result.The Q2 values for the Consumer Journey stages-CJ 1 (Consideration Stage) through CJ 5 (Loyalty Stage)-and Customer Engagement reflect the model's varying predictive capabilities.The Q2 of 0.553 for Customer Engagement indicates a strong ability of the model to predict customer engagement behaviors based on the preceding constructs, notably Online Presence and Brand Awareness.For Brand Awareness, the model demonstrates a moderate level of predictive relevance with a Q2 of 0.275, indicating that Online Presence has a substantial impact on predicting Brand Awareness.This value, coupled with the path coefficient for Online Presence to Brand Awareness, emphasizes the influence of a strong digital footprint on how customers perceive the brand.Further down the Consumer Journey, the Q2 values vary, with the Loyalty Stage (CJ 5) demonstrating a particularly high Q2 of 0.533, which shows the model's effectiveness at predicting loyalty intentions and behaviors, a critical aspect of the long-term customer-brand relationship.These Q2 values validate the structural model's efficacy in not only understanding but also forecasting the influence of marketing strategies on customer perceptions and behaviors across different touchpoints of the Consumer Journey.The results underscore the importance of leveraging online presence and brand awareness as foundational elements that significantly contribute to shaping and predicting the subsequent stages of customer engagement and loyalty within the Nogi Livin brand experience.e.Model FitEvaluating the overall fit of the structural model is a vital step in PLS-SEM analysis, as it provides an indication of how well the proposed model represents the observed data.Two indices commonly used to assess model fit are the Standardized Root Mean Square Residual (SRMR) and the Normed Fit Index (NFI).Table12below presents the Standardized Root Mean Square Residual (SRMR) and the Normed Fit Index (NFI) result. ISSN: