Strategic Orientation, Integrated Marketing Communication, and Relational Performance in E-commerce Brands: Evidence from Japanese Consumers’ Perception

In a customer-based IMC model, the importance of bilateral communication between business operators and their customers or consumers is emphasized (Bruhn & Schnebelen, 2017). The concept of IMC has evolved over the years, from the initial “one voice, one look” tactical approach to the more holistic strategic business process of managing entire companies (Kliatchko, 2008). While the former version of IMC is defined from the customers’ perspective, the latter is designed from a corporate management’s perspective, which is the key difference between the two. Viewed from the vantage point of customers, as this study intended to do, IMC is a concept that exists in a different dimension from where companies’ strategic positions or various management and organizational issues arise. The customer-based IMC model is intended to integrate various marketing communications on a strategic level, as is the focal point of the “one-voice” approach, in terms of the perceptions of the customers targeted by IMC (Finne & Grönroos, 2009).

This is, however, a rather complex process for the customers involved. Under the relationship-based communication model, additional unwanted pieces of information in the external environment often interfere with corporate messages before they reach the customers (Duncan & Moriarty, 1998). Furthermore, if inconsistent messages are transmitted through different communication channels in a marketing campaign, it could result in incoherent corporate and brand images, making it more difficult for the intended images to leave an impression on customers, possibly even leaving them with a sense of distrust (Buchanan-Oliver & Fitzgerald, 2016). Moreover, if inconsistent information is communicated, it will be difficult to alter customer awareness or behavior as intended, even if the information reaches customers across multiple communication channels. If the brand qualities and imagery being communicated are kept unified and consistent, however, the perceived brand value will be enhanced, ensuring a positive evaluation from customers (Šerić et al., 2014). Hence, the unification of messages, communication channels, and projected brand images is a sound starting point for evaluating the effects of IMC from the customers’ perspective and is also a crucial element for cultivating relationships with customers. This is the main focal point of this study.

The term “customer orientation,” as a constituent factor of market orientation, is defined as “an organization’s culture to sufficiently understand its target customers to be able to create superior value for them continuously” (Narver & Slater, 1990, p. 21). Gatignon and Xuereb (1997), however, define customer orientation as “the ability to identify, analyze, understand, and answer customer needs” (p. 4). At any rate, a customer-oriented company always gathers market information, shares it among all concerned departments and decision-makers, and swiftly adapts to the ever-changing market (Kohli & Jaworski, 1990). This corporate strategy to remain sensitive to market changes and pursue customer orientation to its fullest extent is also supported by the dynamic capabilities theory, which views IMC as an integral part of corporate strategy for gaining a competitive advantage. Furthermore, Butkouskaya et al. (2017) recently provided empirical evidence of the positive impact of customer orientation on IMC consistency.

For market-oriented businesses today, fast-paced technological innovations and other sources of pressure from the external environment are constantly in play and cannot be ignored. According to Gatignon and Xuereb (1997), the term “technology orientation” means “an organization’s ability to invest in R&D and apply new technologies to develop new products and conduct marketing communication and other marketing activities” (Gatignon & Xuereb, 1997, p. 5). For example, social networks enable marketers to communicate with their target customers and engage them in interactive dialogues, obtain feedback, collect larger amounts of customer data, and access new digital channels to meet ever-changing customer needs (Peltier, Zahay, & Lehmann, 2013; Schultz, 2016). Likewise, since customers can obtain the latest information on brands from businesses, this positively influences their perception of brand communication (Ndubisi, Malhotra, & Wah, 2009). Considering the importance of having the latest information on customers and the market for conducting IMC successfully, it is surmised that technology orientation positively affects the marketing communication activities of a business (Mulhern, 2009). As such, the following hypotheses are proposed:

In this study, a web-based survey was used, as it would enable efficient data collection on statistical populations representing an e-commerce marketplace. The author delegated several steps of the research process to Rakuten Insight, which had approximately 2.2 million qualified panelists in 2019, including the recruitment of participants, construction of a web-based questionnaire and answer form, and data collection. Questionnaires were distributed to 10,000 panelists selected by sex and age using simple random sampling.

The participants in this study were selected from a pool of individuals who registered as study candidates on the Rakuten Insight website. Anyone willing to participate could enroll in the study on a first-come, first-served basis, unless they met the exclusion criteria. The distribution of questionnaires began on June 25, 2020, and ended on July 12, 2020, when the target numbers of respondents for each sex and age were met. The final sample size was 300 respondents, who were randomly chosen among e-commerce users over 20 years of age who had online shopping experience. Their demographic characteristics are shown in Table 1.

In this study, metrics proven to be reliable and suitable in previous research were adopted, and all data were measured on a 5-point Likert scale. As shown in Table 2 below, the six metrics used by Narver and Slater (1990) were applied as IMC antecedents for customer orientation. For technology orientation, this study used the four metrics adopted by Gatignon and Xuereb (1997). For IMC, the five metrics developed by Lee and Park (2007) and used by Porcu, Del Barrio-García, and Kitchen (2017) and Šerić et al. (2014) were also applied. This study made it a particular point to ask respondents whether they felt that the selected brands were communicating consistent brand messages (i.e., visual and linguistic factors) through various communication means and channels (e.g., advertising, sales promotion, public relations [PR], social networking service [SNS], etc.) and maintaining consistent brand images. To analyze IMC performance factors, the five metrics used by Delgado-Ballester (2004) on brand trust were applied, while the three metrics used by Mattila (2006) were applied for brand commitment. Lastly, for brand loyalty, five of the metrics proposed by Kim and Kim (2004) were used to obtains measurements for this study.

This study used partial least squares structural equation modelling (PLS-SEM) technique to measure the proposed study model. PLS is a component-based analysis that has been used as an alternative to covariance-based SEM such as LISREL and AMOS. PLS is most appropriate for data analysis when examining unexplored relationships and when the constructs being studied are relatively new or changing. In addition, PLS is suitable for estimating rather complex causal models and analyzing small samples, and is widely used in marketing research (Hair, Hult, Ringle, & Sarstedt, 2017).

This study meets these criteria, as the relationships analyzed herein have been neglected in the e-commerce sector, and IMC is a continuously evolving paradigm. Furthermore, the model of this study consisted of 10 hypotheses and the sample size was relatively small (N = 300), suggesting that PLS was a suitable technique for data analysis. In addition, this sample met the minimum sample size requirements suggested by Hair et al. (2017), corresponding to 10 times the largest number of structural paths directed at a particular construct in the inner path model.

For data analysis, SPSS version 24.0 was used for simple data tallying and descriptive statistics, after which SmartPLS 3 was used to examine the reliability and validity of the applied measurement scales and to test the hypotheses. PLS algorithms and bootstrapping (5,000 iterations at significance level of 5%) were used to examine whether the measurement and structural models met the criteria previously suggested by Hair et al. (2017).

First, SmartPLS 3 was used to evaluate the reliability and validity of the measurement scale. As shown in Table 2, the factor load of each construct applied surpassed the tolerance threshold of 0.7 (Carmines & Zeller, 1979) for both products, while Cronbach’s α coefficient was in the 0.830–0.921 range, and composite reliability was in the 0.898–0.941 range (Hair et al., 2017; Nunnally, 1978), suggesting that the measurement scale was sufficiently reliable.

In addition, as average variance extracted (AVE) remained in the 0.669–0.793 range for all factors, surpassing the threshold of 0.5, convergent validity could be deduced (Anderson & Gerbing, 1988). Finally, as shown in Table 3, when discriminant validity was reviewed, the positive square root of each AVE was below the square root of each correlation coefficient for each pair of factors, and the scale was proven to possess sufficient discriminant validity (Fornell & Larcker, 1981). As such, all measurements used in the measurement models met the threshold values, thereby demonstrating the validity of the measurement scale.

In the next analysis, all the hypotheses were tested and verified. First, the values of R² (goodness of fit) and Q² (predictive ability) were estimated and applied in order to demonstrate the suitability and predictability of the proposed models. For R², Falk and Miller (1992) suggested using 0.10 as a reference value. As shown in Figure 1, the exogenous variables surpassed the recommended threshold of 0.10 with all structural models, demonstrating their suitability. Q², which is an index commonly used to verify the validity of predictions made by models, must be greater than 0 (Hair et al., 2017). As shown in Figure 1, Q² surpassed 0 for all endogenous variables, demonstrating the validity of predictions made using the proposed models.

Figure 1. Results of the partial least squares structural model analysis. IMC, R² = .393; Q² = .257; BT, R² = .240; Q² = .178; BC, R² = .415; Q² = .298; BR, R² = .560; Q² = .363. R² > .1; Q² > 0. CO, customer orientation; TO, technological orientation; IMC, integrated marketing communications; BC, brand commitment; BT, brand trust; BR, brand loyalty.

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As shown in Table 4, after all hypotheses were tested for their prediction validity, bootstrapping (5,000 times) was performed to examine the statistical significance and impact of the path coefficients. The analysis demonstrated that customer orientation (H1: β = .191, p = .045) and technology orientation (H2: β = .494, p = .000) had strong positive influences on IMC, and that IMC was more influenced by technology orientation than by customer orientation. The analysis also revealed that IMC positively affected relational performance in brand trust (H3: β = .490, p = .000), brand commitment (H4: β = .211, p < .001), and brand loyalty (H5: β = .130, p < .001). In particular, IMC had a strong influence on brand trust. In terms of the relationships between relational performance factors, a significant positive influence was observed. As for the extent of that influence, the path (H6: β = .514, p < .001) from hypothesis 6 (BT → BC) showed the largest impact, followed by the path (H7: β = .471, p < .001) from hypothesis 7 (BT → BR), and then the path (H8: β = .269, p < .001) from hypothesis 8 (BC → BR).

To understand the mediating influence of brand trust and commitment in the relationship between IMC and brand loyalty, two mediation analyses were conducted using a bootstrapping procedure (MacKinnon, Lockwood, & Williams, 2004). Table 5 shows the indirect effects and the 95% bias-corrected confidence intervals obtained after applying bootstrap estimation. The two indirect effects studied were statistically significant (i.e., different from 0 in the population), as the 95% bias-corrected confidence interval of their estimates did not contain 0. Thus, as the direct effect of IMC on brand loyalty was significant, it was concluded that brand trust and brand commitment partially mediated the impact of IMC on brand loyalty. In other words, hypothesis 9 and hypothesis 10 were partially supported.

Based on the results presented above, the present section provides a summary of this study, examines its significance, and describes remaining issues that are yet to be addressed. First of all, this study provides evidence using a positive analysis of the effects of customer orientation and technology orientation on companies’ IMC as perceived by customers. As in earlier findings from the perspective of managers (Luxton, Reid, & Mavondo, 2015, 2017; Reid, 2005), these results from the customer’s point of view confirm the direct positive effect of customer orientation on IMC. This study also confirmed that companies’ activities undertaken to meet their customers’ needs positively influence the ability of companies to integrate and recognize the value of communication messages. Hence, if a company is intent on further improving the efficacy of its IMC, it might need to pay more attention to customers’ behaviors and needs, integrate the insights derived from those observations internally, and focus more on delivering high value to customers. The findings of this study are supported by a former study that focused on other countries (Butkouskaya et al., 2017).

In addition, this study found that technology orientation, like customer orientation, affected IMC, though the impact of technology orientation on IMC was larger. This result, however, is not consistent with findings from previous research, which is likely due to differences that exist between the various countries and businesses that were surveyed (Butkouskaya et al., 2017). In particular, this finding indicates that if a business can cultivate a corporate image of itself as vigorously conducting R&D in e-commerce, actively putting new technology to use, and providing novel customer service, and successfully leaves its customers with this impression, it will positively influence a brand’s value perceived by customers with respect to communication integration. In recent years, there has been a prominent trend in the e-commerce industry to offer seamless customer experiences by utilizing artificial intelligence and other cutting-edge technologies to strengthen bonds with customers. Thus, such efforts may be positively perceived by users of online shopping services in the e-commerce space.

Second, this study demonstrated the existence of a direct causal relationship between IMC and relational performance factors. That is, the hypothesis was confirmed that a company’s ability to send consistent messages to its target customers through various communication channels results in customers correctly understanding and appreciating the value of a brand. In other words, if a company’s customers are already experiencing information overload and lack sufficient processing capacity, the company sending excessive or inconsistent messages to customers likely leads to a situation where the customers’ expectations of the company’s brand are higher pre-purchase (Reid, 2005). Thus, companies should take necessary actions to avoid disparities between customers’ expectations and experiences while taking into consideration customers’ post-purchase evaluations (i.e., customer satisfaction, recommendation, and repurchase intentions) (Oliver, 1980). These findings support the results of previous research that consistent and unified communication with customers is crucial in improving the relational performance with customers through brands (Finne & Grönroos, 2009; Šerić et al., 2014, 2020).

Third, the strong influence of IMC on brand trust was found to be remarkable. This suggests that the diversification of communication channels has made it increasingly difficult for customers to assimilate and comprehend messages sent by businesses. Furthermore, as customers are constantly subjected to information overload and rendered incapable of filtering and receiving only the brand information they need, they might misunderstand brand messages that businesses provide, incorrectly perceive brand value, and ultimately make the wrong purchase decisions (Mihart, 2012). This means that a company’s ability to engage in brand communication in a consistent and unified manner for the medium- to long-term enables strong psychological relationships with customers in terms of trust and emotional attachment toward the company’s brands. Thus, it could be deduced that brand trust might be a crucial factor for maintaining sound relationships with customers in the e-commerce industry in Japan. This finding is supported by a former study that found the same result (Sawaftah, 2020; Šerić et al., 2020).

Fourth, this study found that customer attitudes toward brand trust and commitment mediated the relationship between IMC and brand loyalty in the e-commerce sector. This suggests that IMC not only has a direct positive impact on brand loyalty, but also indirectly improves it by raising end users’ level of trust and commitment toward brands. One particularly noteworthy finding is that the indirect influence of IMC over brand loyalty is greater than the direct influence when it comes to consumers’ trust in and commitment to e-commerce brands. Therefore, marketing managers should specifically focus on these factors in order to build a long-term and mutually profitable relationship with customers and cultivate brand loyalty for a competitive advantage in the e-commerce marketplace. This is consistent with previous research showing that brand trust and commitment mediate the relationship between IMC and brand loyalty (Melewar et al., 2017; Šerić et al., 2020).

Fifth, this study showed relationships between relational performance involving brands and customers and its driving factors (i.e., trust, commitment, and loyalty). The findings from the study demonstrate strong correlations between trust and commitment, trust and loyalty, and commitment and loyalty. In other words, if a company wants to gauge how its communication activity is influencing its customers’ brand loyalty, it could effectively do so by considering brand trust and commitment together as interrelated relational performance factors. The knowledge obtained from this study suggests that businesses hoping to improve their brand management should seek to understand brand loyalty not only by their customers’ actual purchase activities, but also by understanding the relationship between brand trust and brand commitment that drives those activities. This finding supports previous research conducted by Šerić et al. (2020) and Shin et al. (2019), according to which brand trust and brand commitment influence brand loyalty.

First of all, the findings of this study suggest that businesses’ utilization of state-of-the-art technology in communicating with their customers helps improve customer perception of corporate communications and positively influences the relational performance involving customers and brands (i.e., trust, commitment, loyalty). It is especially interesting to note that, in the e-commerce marketplace in Japan, IMC strongly affects not only customer orientation toward IMC, but also brand trust, commitment, and loyalty. Hence, it is crucial for e-commerce marketing managers to accurately understand and monitor customers’ needs and priorities, and to engage in consistent and unified communication activities accordingly, in order to maintain and enhance the identity of their brands.

Secondly, in the e-commerce marketplace, where technology orientation is relatively more accepted, e-commerce marketing managers should put more effort into integrating messages. To this end, it is crucial not only to manage communication channels strategically (e.g., advertising, direct response, sales promotion, PR, and SNS), but also to evaluate the externally available information that can be used from within customers’ ecosystems. There are many instances where word-of-mouth and messages transmitted by competitors cause undesirable noise, distortion, and confusion as to how brand concepts are perceived, which can result in defection by existing customers and loss of a brand’s competitive advantage. As such, e-commerce marketing managers might need to more thoroughly analyze available customer data as to the ‘what’ (i.e., the message) and the ‘how’ (i.e., the channel) of their customer communications, and send messages that cater to customer needs with optimal timing in order to improve customers’ perceptions of integrated communications.

Thirdly, the study demonstrates that IMC, if properly conducted, positively affects customer trust, emotional attachment, and post-purchase behavior. Hence, if e-commerce marketing managers hope to improve brand loyalty among their customers through brand trust and commitment, they should manage all communication channels and messages used in their IMC campaigns in an integrated manner. In order for a company to achieve these objectives, various functional departments within the organization must work together in a well-coordinated manner to optimize sales activities, product packaging and design, advertisement, and sales promotion, while adjusting each department’s communication output.

This study also has some limitations and remaining issues to be addressed. First, it is necessary to consider customers’ personal financial situations (e.g., income level, average purchasing power, price sensitivity) and personal factors (e.g., motivations, lifestyle, personality), investigate them as moderating variables of IMC, and investigate their functions. It might also be useful to consider the estimated switching cost and other situational factors that affect customers’ decision-making process and measure the extent of their influence on brand loyalty (Hellier, Geursen, Carr, & Rickard, 2003).

Second, it might be necessary to examine the effects of IMC in other industries as well. Since this study focused on analyzing customer data related to the rapidly growing e-commerce marketplace in Japan, the degree to which its conclusions could be generalized is limited. Hence, it might be necessary to expand the analysis beyond the retail industry to include the manufacturing and service industries in the future to comprehend the structural relationships between strategic orientations and IMC, as well as the relationship between IMC and relational performance, in order to improve the external applicability of the theoretical models proposed in this study.

Third, it might be necessary to analyze the proposed theoretical models in international contexts for comparison. To further advance the findings in this study, it is crucial to investigate how different cultural and economic factors in the global environment influence consumers’ perception of IMC as well as their behavior. For example, it might be useful to evaluate how the cultural factors specific to each country function as third or moderator variables affecting the relationship between companies’ orientations, IMC, and brand performance.