MARKETING 5.0: AN EMPIRICAL INVESTIGATION OF ITS PERCEIVED EFFECT ON MARKETING PERFORMANCE

: The study aims to explore the effect of marketing 5.0 on marketing performance. Marketing 5.0 was conceptualized using three dimensions: predictive marketing, contextual marketing, and augmented reality marketing. This study uses a questionnaire to collect data from a sample of employees working in marketing departments in 25 furniture stores. Eight employees were selected based on their managers' recommendations regarding employee knowledge of digital marketing. The total number of the sample is 200 participants. Data were collected using a questionnaire designed as a five-point Likert scale. A total of 190 questionnaires were returned valid for data analysis. The results revealed that both contextual and augmented marketing significantly affect marketing performance. In contrast, predictive marketing exerts no significant effect on marketing performance. The above results concluded that enhancing marketing performance requires advanced human-oriented technologies. These technologies transform customers from traditional marketing environments into real-world environments by collecting and analyzing real-time customer data during the shopping process at sale points to affect customer behavioral intention and purchasing decisions. Theoretically, this study enriches the literature on marketing 5.0 by investigating the effects of three related kinds of marketing (predictive marketing, contextual marketing, and augmented marketing) on marketing performance. It provides researchers with a theory based upon which they could develop new models to examine the effects of marketing 5.0 on marketing outcomes. Empirically, the study shows that augmented marketing in the marketing 5.0 era is the most significant. It could be used to enhance the customer shopping experience and to achieve marketing objectives. Consequently, marketing managers are asked to use current customer data to provide them with real-time contextual offers.

Introduction.Marketing journeys include five milestones: Marketing 1.0 or product-driven marketing; Marketing 2.0 or customer-oriented marketing; Marketing 3.0 or human-centric marketing; Marketing 4.0 or digital-based marketing; and Marketing 5.0 or human-technology collaboration marketing.Marketing 1.0 focused on the products and their distribution, while Marketing 2.0 shifted to the customers.Therefore, companies were differentiated based on their customers.Since the third milestone (Marketing 3.0), marketing has emphasized customer management instead of market management.Marketing 4.0 was introduced in line with Industry 4.0 technologies.The last milestone (Marketing 5.0) is digital trust marketing (Lies, 2019).
predictive analytics has numerous applications such as direct marketing, product recommendation, and advertising based on predicting customer responses, customer likes, and customer add clicks.In terms of the effect of predictive marketing on business performance, some prior studies (e.g., Brynjolfsson et al., 2021) found that using predictive analytics has a causal relationship with business performance.Dung et al. (2018) added that applying predictive marketing results in numerous outcomes, such as increasing customer satisfaction, reducing marketing costs, and improving business performance.Therefore, it was expected that predictive marketing has a significant effect on marketing performance, as proposed in the following hypothesis: H1: Predictive marketing enhances marketing performance.
Contextual marketing has been defined as providing customized and contextual information to customers at the point of need in real-time (Luo, 2003).According to Kotler et al. (2021), contextual marketing refers to making personalized sense and respond experiences.Therefore, it provides customers with personalized interactions by which a marketer can perform real-time marketing (Hermina et al., 2022).As two basic components of the marketing process, content refers to the information delivered to customers, while context describes how such information is delivered (Luo, 2003).Hence, contextual marketing has been labeled as conducting the marketing process in a contextualized manner in particular situations (Vanessa and Japutra, 2021).That is, collecting customer contextual data every place a customer is found, such as shopping online or in a mall, to understand customer needs (Tang et al., 2013).Contextual marketing results in numerous effects on other customer-related outcomes, such as customer behavioral intention, particularly those who use mobile commerce (Lee andJun, 2007), customer satisfaction (Luo, 2003), and customer trust (Lee, 2005).Therefore, it was expected that contextual marketing is positively related to marketing performance, as suggested in the hypothesis as follows: H2: Contextual marketing enhances marketing performance.
Augmented reality «is one of the most recent developments in human-computer interaction technology» (Billinghurst et al.,2015).It refers to viewing real-world environments using computer-generated virtual information (Carmigniani et al., 2011).It may be applied in numerous domains, such as marketing.Therefore, augmented reality marketing was described as an integration of augmented reality and marketing (Chylinski et al., 2020).Two common examples of such integration are virtual mirrors by which a customer may wear virtual clothes (Rauschnabel et al., 2022) and mobile shopping applications that allow customers to experiment with products such as furniture in their houses (Scholz and Duffy, 2018).Hence, a key benefit of augmented reality is positioning customers in real and virtual environments concurrently (Ng and Ramasamy, 2018).Regarding its effects, it was emphasized that augmented reality marketing brings various effects, such as enhancing customer attitudes toward brands (Rauschnabel et al., 2022) and making online shopping easier (Kannaiahand Shanthi, 2015).Other advantages of augmented reality marketing comprise allowing marketers to collect real-time information to satisfy customer needs, improve firms' ability to conduct innovative marketing and therefore attain competitive advantage, advance the promotion of products and services using interactive experiences, and provide firms with feedback on their products and services, increase customer motivation to search for products and services (Ng and Ramasamy, 2018), and increase customer engagement (Jessen et al., 2020).Based on these studies, it was expected that augmented reality marketing enhances marketing performance, as presumed in the hypothesis as follows: H3: Augmented reality marketing enhances marketing performance.
Methodology and research methods.The population of the study consists of marketing employees working at 25 furniture stores.Eight employees were selected based on their managers' recommendations based on employee knowledge in digital marketing.That is, the total number of the sample is 200 participants.Data were collected using a questionnaire designed as a five-point Likert scale ranging from 1 «strongly disagree» to 5 «strongly agree».A total of 190 questionnaires were returned valid for data analysis.
Sources: developed by the author.
The theory of the study (Figure 1) assumes that the three dimensions of Marketing 5.0 (predictive marketing (PM), contextual marketing (CM), and augmented marketing (AM) have significant effects on marketing performance (MP).Therefore, the model is designed to test three hypotheses: H1 (PM → MP), H2 (CM → MP), and H3 (AM → MP).Results.Table 2 presents the outer loadings of research variables.The table shows that the outer loadings of the items PM3, CM4, and AM1 were less than 0.7, i.e., 0.645, 0.376, and 0.612, respectively.According to Chang et al. ( 2016), all outer loadings in PLS-SEM should be higher than 0.70.Therefore, the items mentioned above were excluded.It means that the research variables are measured using 13 items; 3 items to assess each independent variable and 4 items to gauge the dependent one.It should be noted that construct reliability and validity, collinearity statistics, and model fit were tested based on these 13 items.Sources: developed by the author based on SmartPLS software outputs.
Reliability was measured using Cronbach's alpha coefficients and composite reliability (CR).In turn, validity was measured by the average variance extracted (AVE) and variance extracted factor (VIF) to assess discriminant validity.The thresholds of construct reliability, validity, and collinearity (Table 3) are met.Values of Cronbach's alpha coefficients are higher than 0.70 (Fitzpatrick et al., 2021), values of Dijkstra-Henseler'srhoA are higher than 0.70 (Kim, 2022), composite reliabilities (CR) are more than 0.70 and less than 0.95, and the values of the average variance extracted (AVE) are higher than 0.50 (Purwanto and Sudargini, 2021).Finally, values of VIF are less than 3 (Hair et al., 2019).Sources: developed by the author based on SmartPLS software outputs.
The coefficient of determination, i.e., R 2 value, was used to evaluate the predictive power of the structural model.A value of R 2 from 0.50 to 0.75 indicates a moderate predictive power (Jony and Serradell-López, 2021).For the current study, R-squared equals 0.573.It means a moderate predictive accuracy of Marketing 5.0 dimensions of marketing performance.Moreover, the predictive relevance of the structural model was measured using Stone-Geisser's Q 2 value, which should be higher than zero (Hair et al., 2016;Aichouche, 2021).For the present structural model, it was found that the model shows an acceptable predictive relevance of the endogenous latent variable (Q 2 = 0.404).In terms of model fit, it was revealed that the current model fits data as the Standardized Root Mean Square Residual (SRMR) equals 0.067, which is less than 0.08, and the normed fit index (NFI) equals 0.913, which is higher than 0.90 (Ramayah et al., 2017).
The structural model (Figure 2) indicates a weak effect of predictive marketing on marketing performance, a moderate effect of contextual marketing on marketing performance, and a high effect of augmented marketing on marketing performance.Table 4 reports the detailed results to assess the significance level of these effects.
Table 4 shows that there is a significant effect of augmented marketing on marketing performance (β = 0.688, t = 19.427,P = 0.000).Besides, there is a significant effect of contextual marketing on marketing performance (β = 0.139, t = 2.135, P = 0.033), while there is no significant effect of predictive marketing on marketing performance (β = 0.002, t = 0.041, P = 0.967).Sources: systematized by the author based on SmartPLS software outputs.
Specifically, findings showed that H2 and H3 are accepted, while H1 is rejected.Regarding the significant effect of augmented marketing on marketing performance, as pointed out in the current study, previous studies confirm that such a type of marketing results in higher levels of marketing performance based on the characteristics of augmented marketing itself, such as using virtual information to create a real-world environment (Ng and Ramasamy, 2018;Carmigniani et al., 2011;Chylinski et al., 2020).That improves customer attitudes toward brands (Rauschnabel et al., 2022), makes online shopping easier (Kannaiahand Shanthi, 2015), and enhances customer engagement (Jessen et al., 2020).For the current study, it was found digital simulations of products, positioning customers in real and virtual environments, using virtual information, and digital experiments of products have a significant role in lifting customer attitudes, customer satisfaction, and customer engagement.
Moreover, the result concerning the significant effect of contextual marketing on marketing performance is in line with many previous studies.Contextual marketing means making a personalized marketing experience (Kotler et al., 2021) in which customers receive real-time information during shopping (Hermina et al., 2022;Vanessa and Japutra, 2021).Hence, previous results showed a significant linkage between contextual marketing and customer behavior intention, customer satisfaction and trust (Lee and Jun, 2007;Luo, 2003;Lee, 2005).The current result reveals that ads via electronic banners, using contextual information to understand customer needs by their location, improve customer outcomes.In terms of the non-significant effect of predictive marketing on marketing performance as established in this study, it was noted that this result is inconsistent with previous studies.Simply, predictive marketing is expecting marketing demand using proactive actions (Kotler et al., 2021), such as conducting predictive analytics after collecting customer data by technological means to estimate current patterns to develop future events (Lies, 2019;Brynjolfsson et al., 2021).Such results could be explained based on the nature of the predictive marketing process, which is carried out in a non-real-time context.For example, marketers must collect and analyze customer present and historical data to make future decisions.
Conclusions.This study explores the effects of Marketing 5.0 as a concept measured by three dimensions (predictive marketing, contextual marketing, and augmented marketing) on marketing performance.Three hypotheses related to the effect of each dimension on marketing performance were tested.The results revealed that both contextual and augmented marketing has significant effects on marketing performance, while predictive marketing exerts no significant effect.Based on these results, it was concluded that enhancing marketing performance requires using advanced human-oriented technologies.It transforms customers from traditional marketing environments into real-world environments by collecting and analyzing real-time customer data during the shopping process at sale points to affect customer behavioral intention and, therefore, customer purchasing decisions.Theoretically, this study enriches the literature on Marketing 5.0 by investigating the effects of three related kinds of marketing (predictive marketing, contextual marketing, and augmented marketing) on marketing performance.It provides researchers with a theory based upon which they could develop new models to examine the effects of Marketing 5.0 on marketing outcomes.Empirically, the study shows that augmented marketing in the Marketing 5.0 era is the most significant one that could be used to enhance the customer shopping experience and to achieve marketing objectives.Consequently, marketing managers are asked to use current customer data to provide them with real-time contextual offers.
This study is limited to a sample of marketing participants working at furniture stores.Therefore, further studies are required to investigate the impact of Marketing 5.0 dimensions on marketing performance using samples from different industries.Moreover, Marketing 5.0 was regarded as a construct of three dimensions: predictive marketing, contextual marketing, and augmented marketing.Therefore, future studies are required to adopt other dimensions of marketing.

Figure 1 .
Figure 1.The theory of the study Sources: developed by the author using SmartPLS software.

Figure 2 .
Figure 2. Study structural model Sources: systematized by the author based on SmartPLS software outputs.

Table 1 . Measure of variables Variables Code Items References Marketing
performance MP1 Our sales increase every year.Hidayatullah et al. (2019) MP2 Our customers increase every year.MP3 Our profits are increased.MP4Our firm exceeds the existing profit target Predictive marketing PM1 Using predictive analytics helps firms estimate customer demand (*) Brynjolfsson et al. (2021); Moraru and Vincenti (2016)PM2 Firms can predict customer responses using predictive marketing.

Table 2 . Outer loadings of research variables
Note: PM -Predictive Marketing, CM -Contextual Marketing, AM -Augmented Real Marketing, MP -Marketing performance.

Table 4 . Results of structural model test
Note: PM -Predictive Marketing, CM -Contextual Marketing, AM -Augmented Real Marketing, MP -Marketing performance, β -Standardized Impact Coefficient, P value -Probability Value.