The impact of omnichannel integrated marketing communications (IMC) on product and retail service satisfaction

Vera Butkouskaya (Marketing Department, HSE Graduate School of Business, Moscow, Russia)
Olga Oyner (Marketing Department, HSE Graduate School of Business, Moscow, Russia)
Sergey Kazakov (Marketing Department, HSE Graduate School of Business, Moscow, Russia)

Journal of Economics, Finance and Administrative Science

ISSN: 2218-0648

Article publication date: 17 May 2023

Issue publication date: 11 December 2023

9061

Abstract

Purpose

This study reviewed three customer-perceived components of integrated marketing communications (IMCs): consistency, interactivity and connectivity, as predictors of positive customer evaluation (product and retail service satisfaction).

Design/methodology/approach

The customer data from 260 surveys were analysed using structural equation modelling (SEM). The data were collected from the emerging economy in the Moscow region (Russia).

Findings

The results reported that IMC consistency positively impacts product and service satisfaction. However, the effect of IMC interactivity was only significant in the case of service satisfaction. Meanwhile, IMC connectivity positively influenced only product satisfaction.

Research limitations/implications

The study contributes to the marketing communications theory by defining three components of omnichannel IMC. It also adds to the customer behaviour theory by confirming the diverse nature of product and service evaluation. This study focuses on the retail industry.

Practical implications

This research suggests that three components of IMC should be applied together towards enhancing the customer's positive post-purchase evaluation. Meanwhile, consistency enhances product and service satisfaction, interactive impacts satisfaction with the organization and connectivity with the retail service.

Originality/value

The shift toward omnichannel marketing requires a broader perspective on communication integration. This research reports a novelty result of estimating the separate effect of each component of omnichannel IMC (consistency, interactivity and connectivity) on product and service satisfaction.

Keywords

Citation

Butkouskaya, V., Oyner, O. and Kazakov, S. (2023), "The impact of omnichannel integrated marketing communications (IMC) on product and retail service satisfaction", Journal of Economics, Finance and Administrative Science, Vol. 28 No. 56, pp. 319-334. https://doi.org/10.1108/JEFAS-09-2022-0237

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Vera Butkouskaya, Olga Oyner and Sergey Kazakov

License

Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

In the digital era, omnichannel marketing practices bring advantages to the company (Cui et al., 2021; Payne et al., 2017; Shi et al., 2020). The presence of online and offline channels can alter how customers collect information about products, communicate with the company and buy. As a result, it may increase the customer base and bring more purchases. However, the shift towards omnichannel marketing causes a range of challenges to the organization, including communication integration (Cui et al., 2021). One of the main is the information channel caused by increased information received by customers through various channels and digital devices. Also, recent research calls for studies on enhancing service quality in omnichannel retailing (Beck and Rygl, 2015; Bolton et al., 2018; Shi et al., 2020). The company can solve the omnichannel challenge by implementing integrated marketing communications (IMCs) as a dynamic capability (Butkouskaya et al., 2020).

However, little research focuses on understanding the customer perception of IMC (Butkouskaya et al., 2020). It is an important issue considering that the IMC is accepted to be a customer-centric concept (Kitchen and Schultz, 2009). Understanding the customer perception of IMC practices is essential if the company wants to apply communication integration successfully (Butkouskaya et al., 2020). Some existing studies on marketing communications define the IMC's roles in company-customer communications as oriented toward creating consistent, interactive and omnichannel communications, reducing the information noise effect (Butkouskaya et al., 2020; Porcu et al., 2017; Lee and Park, 2007; Shi et al., 2020). All these characteristics, individually and simultaneously, can affect customer satisfaction. Specifically, previous research (Butkouskaya et al., 2020) demonstrated the positive impact of message and channel consistency on customer satisfaction. Nevertheless, no previous research considers other customer-perceived IMC characteristics, such as interactivity or omnichannel communications. Furthermore, the extant research is virtually silent on how information consistency (and inconsistency) across customer touchpoints in an omnichannel environment affects customer satisfaction, engagement and loyalty (Verhoef et al., 2015; Fulgoni, 2016; Swoboda et al., 2016; Payne et al., 2017).

Most omnichannel experience researchers think about either the critical characteristics of seamless (Shi et al., 2020; Yrjölä et al., 2018) or the quality of delivery and logistics (Bouzaabia et al., 2013; Sorkun et al., 2020). At the same time, the characteristics of the main point of contact, the product itself, are not considered. Earlier studies noted the need for a holistic approach. Additionally, in the omnichannel market environment, not only product satisfaction but also satisfaction with the retailer's service influences the customer's behavioural choice (Sorkun et al., 2020). For example, Lemke et al. (2011) empirically proved that the quality of consumer experience is assessed holistically by the consumer and includes both the product itself and the service relationships.

Following the above-mentioned, this study aims to analyse the impact of three customer-perceived components of IMC, named message and channel consistency, interactivity and omnichannel communications, on customer satisfaction with the product and retailer service. The article applies partial least-squares structural equation modelling (PLS-SEM) to analyse the data from a customer survey. The final sample consists of 260 respondents (customers) from the retail industry in an emerging economy (Moscow region, Russia).

The results of the research provide both theoretical and practical contributions. The study adds to the theoretical concept of IMC research from the customer perspective. The different view of IMC as three components (channel and message consistency, interactivity and connectivity) enriches the knowledge about the IMC implementation in omnichannel marketing strategy. This study adds to the customer behaviour theory by demonstrating the variations in the product and retailer service satisfaction nature. From the practical perspective, the research gives a broader view of the IMC implementation as the company's dynamic capability in the omnichannel environment. Understanding the IMC implementation from the customer perspective contributes to marketing as an administrative science (Salcedo, 2021a). The article underlines the vital role of three IMC components as an integrative company's strategy addressing the information integration challenge of omnichannel marketing. Implementing IMC practices may help companies impact customer post-purchase evaluation. However, the research explicitly underlines the diverse nature of product and service satisfaction and aims to explain the role of each IMC component separately. Moreover, this research analyses the data from emerging markets contributing towards generalizing marketing practices in the context of emerging economies such as Russia, China, Latin America and India, among others (Aguinis et al., 2020).

This manuscript follows the typical structure of social science journals (Salcedo, 2021b). The article starts with a literature review. Then it explains the data collection and analysis process. In the discussion section, the study explains the results obtained. Finally, it concludes with the main contribution and ideas for future research lines.

2. Literature review

2.1 Omnichannel marketing and IMC

Omnichannel marketing has become a new norm in the industry for the past few years. It is a fundamental transformation in business and customer experience (Cui et al., 2021). An omnichannel approach to retailing is a response to the changing nature of how customers shop in alternation between online and offline channels and the increasing use of digital devices (e.g. smartphones and tablets). Combining online and offline channels and multiple devices create an information channel related to the customer perception of the company's communication (Cui et al., 2021). Compared to the multichannel approach focussing on the message and channel integration, in an omnichannel environment, retailers must focus additionally on establishing a seamless experience (Hickman et al., 2020). Verhoef et al. (2015, р. 4) have defined it as “the synergetic management of the numerous available channels and customer touchpoints in such a way that the customer experience across channels and the performance over channels are organized”. In seamless omnichannel experience, the research underlines the importance of channel integration, information consistency regarding product descriptions, prices, purchase and delivery conditions, communication interactivity and cross-touchpoint connectivity (Porcu et al., 2017; Shi et al., 2020). Implementing IMC practices in the company's strategy may address the information challenges of omnichannel marketing (Payne et al., 2017).

In one of the earlier approaches, the authors defined IMC as a simple message and channel consistency of companies' communications (Schultz and Schultz, 1998; Butkouskaya et al., 2020). (Lazaris and Vrechopoulos 2014, pp. 2, 3) underline that multi-channel integration is oriented toward a superior customer experience with the same style of information consistent and seamless across channels. Following Shi et al. (2020, p. 329), omnichannel consistency is concerned with providing customers with the same product information and service across channels. It refers to the degree customers experience consistent content and process interactions across channels. Šerić et al. (2020) demonstrated the positive impact of customer-perceived consistency of marketing communications on customer-brand relationship outcomes.

Ha and James (1998, p. 462) argue that interactivity is ‘the extent to which the communicator and the audience respond to or are willing to facilitate each other's communication needs. With the growth of digital technologies, the definition of interactivity evolved to the need for synchronous and asynchronous communications exchange (one-to-one, one-to-many and many-to-many) (Kiousis, 2002). Moriarty and Schultz (2012) recognized the importance of the shift from one-way to two-way communications in defining the IMC concept. “As a result of the pandemic, there is much more obvious use of interactivity in other, aligned functions such as sales and customer services. The effectiveness of interactive marketing will only be increased by focusing on the fundamental roles of the marketing discipline, strategy, and segmentation, and understanding customers at the individual level” (McDonald, 2022, p. 17). Peltier et al. (2003) underlined the need for interactive communications in the online market environment. Thus, IMC interactivity is essential for effective communications in an omnichannel environment (Porcu et al., 2017).

The shift from offline or online to omnichannel online and offline communications presented a need for a new way of communications connectivity (Mulhern, 2013; Shi et al., 2020). Based on Shi et al. (2020), the connectivity is the extent to which the cross-channel service content and information are linked and interconnected. Thus, IMC connectivity is an essential component of omnichannel marketing communications. Following the requirements of omnichannel experience characteristics, this research defines omnichannel IMC as channel and message consistency (Butkouskaya et al., 2020; Šerić and Vernuccio, 2020); communication interactivity (Šerić and Vernuccio, 2020; Porcu et al., 2017) and channel connectivity (Shi et al., 2020).

2.2 Omnichannel IMC and customer satisfaction

The customer behaviour literature defines customer satisfaction as a judgement toward a product or service feature or the product or service itself. It responds to evaluating the pleasurable level of consumption-related fulfilment (Oliver, 1997). Recently, research also claims that customer satisfaction in an omnichannel context is a holistic concept of product and service satisfaction. In the omnichannel market environment, not only product satisfaction but also satisfaction with the retailer's service influences the customer's behavioural choice (Sorkun et al., 2020). Marketing researchers measure product satisfaction as a customer evaluation of the purchase decision reasonability, customer feeling about the purchase and brand, and brand positive response (Butkouskaya et al., 2020; Hellier et al., 2003; Oliver, 1997). Retail research mainly measures service satisfaction concerning feelings about the retailer, evaluation of its service quality and perceived outcomes of the retail service (Dagger et al., 2007; Klaus and Maklan, 2013; Sorkun et al., 2020).

Product satisfaction shows the degree of compliance between expectations about product performance with perceptions of product performance (Barber and Venkatraman, 1986; Cardozo, 1965). Satisfaction occurs when performance exceeds expectations. In opposite situations, when performance falls short to reach expectations, it appears a negative disconfirmation that leads to dissatisfaction. Service satisfaction is the consumer's judgement of the intangible elements during buying (Liljander and Strandvik, 1997). Like product satisfaction, it reveals the compliance between service expectations and perceived performance. However, service characteristics underline that it consists of various dimensions. Thus, to be judged positively, all the service dimensions must perform well in service satisfaction. While for negative judgment, it is sufficient to have one or few poor performance dimensions (Ofir and Simonson, 2001).

Considering that most often, product satisfaction is a complete evaluation, and service satisfaction can vary depending on its separate dimensions' quality, this research studies the separate effect of IMC on product and retail service satisfaction.

Previous literature suggests the possible relationships between IMC and customer behaviour (Butkouskaya et al., 2020; Lee, 2020; Šerić and Vernuccio, 2020; Shi et al., 2020). The IMC implementation may help the company to build quality communications with the customers in an omnichannel environment. Message and channel consistency may help to understand the communication better, thus reducing cognitive dissonance that causes dissatisfaction. In other words, when the message is consistent, the receiver understands its meaning more clearly, which helps build more realistic expectations. For example, Butkouskaya et al. (2021) empirically confirmed the positive impact of IMC channel and message consistency on customer satisfaction. Thus, the study suggested the following hypothesis:

H1a.

IMC consistency positively impacts product satisfaction.

H1b.

IMC consistency positively impacts retail service satisfaction.

Interactive communications provide customers’ feedback, which the company can use to improve the decision-making towards better satisfaction of customer needs. Lee (2020) demonstrated the significant positive impact of omnichannel characteristics (such as integrated promotion and interactive information access) on customer satisfaction in retailing. Also, interactive communications provide more information about customer wants and needs to a company helping to improve customer knowledge and providing higher value offers (Peltier et al., 2006). Šerić and Vernuccio (2020) confirm the impact of IMC consistency and interactivity on reputation and brand engagement. Thus, we suggest the hypothesis of the separate positive effect of IMC interactivity on product and service satisfaction as follows:

H2a.

IMC interactivity positively impacts product satisfaction.

H2b.

IMC interactivity positively impacts retail service satisfaction.

Connectivity may simplify and facilitate the customers' decision-making process in an omnichannel environment. Shi et al. (2020) conceptualized omnichannel experience, including IMC connectivity and demonstrated its positive impact on shopping intention. The connectivity of various media channels used by the company simplifies the purchase process for the customer as they can select the channel that is more suitable for them even though they have started their online customer journey in another one. Hamouda (2019) showed a positive relationship between omnichannel integration quality and customer satisfaction in banking. Thus, we can suggest the following:

H3a.

IMC connectivity positively impacts product satisfaction.

H3b.

IMC connectivity positively impacts retail service satisfaction.

3. Method

3.1 Research design

This study applies a quantitative approach to the research design (Creswell and Creswell, 2017) to investigate and gauge the IMC effects on customer satisfaction with the product and retail services. The present research design is grounded in the relevant theoretical background. It is non-experimental and collects quantitative data through an online survey. A developed questionnaire comprises scales grounded in the prior literature (Lee, 2020; Šerić and Vernuccio, 2020; Shi et al., 2020). The acquired research data spans the retail industry solely. The study does not pertain to a longitudinal type and collects data at a specific time frame. Finally, this study pertains to causal multivariate data research. In contrast, the applied structural equation modelling approach (Hair et al., 2019) validates the linkage between the exogenous and endogenous latent construct variables in the theoretical model developed under this study.

3.2 Data collection

The present research applied retail customer surveying and a convenience sampling method (Winton and Sabol, 2021) for data collection. This study collected data during January 2022 in the emerging market of the Russian Federation from retail customers of both genders, all active age groups, education and income levels. Moreover, the sample comprised five types of dwellings to ensure a balanced geographical data distribution to the possible extent (Table 1).

The study employed an online customer panel from a third-party market research company to build a research sample. Initially, the service provider had recruited participants from a database of 500,000 individuals by ensuring their willingness to participate. Then, the organization qualified relevant survey participants by asking a dichotomous filtering question about their customer experience in combined online-offline purchasing (Please, specify if you had a recent omnichannel retail experience, for example, searching for a product offline and purchasing it online, Yes/No). After the filtration questions, the final sample consisted mainly of respondents from the Moscow region. It is expected results, as in Megapolis, lifestyle is intense. Therefore, it motivates customers to go for the omnichannel experience, such as searching and buying online. The delineated procedures' implementation facilitated the generation of a sample containing n = 261 participants.

Survey participants received and returned the completed questionnaire forms in January 2022. Table 1 depicts a descriptive analysis of the received sample utilized for data collection in this study. A total number of 600 surveys were sent. The final sample comprises 260 respondents from the Moscow region (Russia). It represents a response rate of 43.3%. To avoid bias in the sample, the respondent's profile was controlled on gender with a proportional number of male (53%) and female (47%) respondents. Out of the age ranges (18–24, 25–34, 35–44, 45–54, and above 55), the primary number of samples is concentrated in the 25–54 age group (72%) to focus the investigation on the most active buyers' groups. Out of all income groups (low, medium, mid-to-high, high), this study accents its attention on the customer with medium-to-high income to investigate the perception of IMC among active buyers with the disposal of income to spend for retail services. As most of the respondents are from the Moscow region, it is expected that the educational level of most of the respondents is higher and postgraduate (68%), and the urbanization size (number of inhabitancy) is more than 100K (64%). The sample is representative and proportional to the region's population statistics.

3.3 Measurement scales

A questionnaire developed to verify the hypotheses posed in this research contained 48 questions. Six questions were according to the sample descriptive statistics. The rest of the questionnaire comprises the questions relevant to the measurement items that form the conceptualized measurement model (Figure 1).

The model's measurement items denoted five-point Likert ordinal scales that could help survey participants express their attitudes towards statements from 1 “totally disagree” to 5 “totally agree”. In the questionnaire development, measurement scales relevant to customer satisfaction and repurchase intention were adapted from Butkouskaya et al. (2020) and Sorkun et al. (2020). The model's IMC measurement items complied with the prior literature, e.g. IMC message and channel consistency were in line with Butkouskaya et al. (2020), IMC interactivity was obtained from Porcu et al. (2017) and IMC omnichannel communications were extracted from Shi et al. (2020). The Appendix depicts the full details and description of the measurement scales applied in the present study.

3.4 Analytical procedure

The study applies a two-step procedure for the model validations. First, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) validate the measurement scales. The criteria of the measurement model are checked based on the evaluation of reliability, convergent validity and discriminant validity (Hair et al., 2022). Second, this research applies the PLS-SEM to check the theoretical model. Some reasons explain the selection of the PLS-SEM approach: the few restrictions on the data normality and the accepted technique for analysing the novelty areas of the research (Hair et al., 2019). SmartPLS v.4 software application is credited by researchers for its versatility, convenience, and usability in running the PLS-SEM procedure in the domain of social sciences (Shoaib et al., 2021). The model run was set to percentile bootstrap, and performed the bootstrapping procedure on 5000 subsamples, according to the suggestion by Hair et al. (2022).

4. Results

4.1 Model validation

Following the recommendation of Kautish et al. (2021), analytical data procedures comprised EFA and CFA to confirm the consistency of the measurement scales. Low factor loading values in the domain of two scales, IMC8 and IMC16, confirm their redundancy, which justified their discarding from the original model. Furthermore, removing the redundant variables substantially improved Cronbach's ɑ, CR, and AVE values, which suffice model scales' reliability and convergent validity (Table 2). The items in the measurement model fulfil the required criteria: Cronbach's alphas are above 0.7; composite reliability (CR) values are greater than 0.7; average extracted variance (AVE) values are above 0.5; the outer loadings are higher than 0.7. Implementing the heterotrait-monotrait ratio of correlations (HTMT) sequential analytical procedure verified the discriminant validity of the measurement model (Table 3). The HTML values are below 0.90. Thus, the discriminate validity is established between reflective constructs (Hair et al., 2022).

This research employed the PLS-SEM approach to empirically validate the developed theoretical model and hypotheses. Table 4 depicts the outcomes of the PLS-SEM technique application. It also indicates model fit parameters that are situated in the acceptable value thresholds (Table 4), including such criteria as the standardized root means squared residual (SRMR), the unweighted least squares discrepancy (duls) and the geodesic discrepancy (dg) (Dijkstra and Henseler, 2015).

The difference between the saturated and estimated model is that the saturated model evaluates covariation between model constructs, amid the estimated model relies on the total effects between latent model variables. According to Hair et al. (2022), estimated model fit values are more rigorous, and researchers can utilize these parameters to justify the model fit in presenting the results of their studies.

4.2 Hypotheses testing

Figure 1 visualizes the results of SEM path analysis applied to the structural model. This procedure is essential to verify the hypotheses posed in this research. The completed data analysis procedures have enough information required to test the hypotheses posed under this study. In this line, the PLS-SEM indicated a positive effect of IMC consistency on customer satisfaction with product and retail services (β = 0.398 and β = 0.401, p < 0.01, respectively). Hence, this result supports H1a and H1b.

Next, whereas path analysis has not returned an adequate p-value (0.072) for measuring the effect of IMC interactivity (IMC_i) on product satisfaction (PS) (β = 0.161), such an output justifies the rejection of H2a. Conversely, the IMC interactivity effect on satisfaction with the retailer (SR) is statistically significant (β = 0.275, p = 0.003). Thereby, this analytical result confirms H2b.

Concerning the IMC connectivity (IMC_cn) set of H3 hypotheses, the model estimation indicated its statistically significant (β = 0.183, p = 0.020) effect on product satisfaction, making it plausible to support H3a. On the contrary, IMC connectivity does not influence customer satisfaction with a retailer (β = 0.105, p = 0.172). Hence, H3b must be rejected. Table 5 depicts the results of path analysis applied to the developed theoretical model to validate a set of the posed hypotheses.

The outcomes of the hypotheses' validation confirmed that IMC message and channel consistency positively sways customer satisfaction with the product and retail service. Interestingly, IMC interactivity is a concept with a main effect on product satisfaction. Meanwhile, omnichannel IMC is mainly a concept encouraging retail service satisfaction. Nevertheless, all three IMC components simultaneously have a positive indirect effect on repurchase intention through satisfaction and need to be considered in omnichannel marketing strategies.

5. Discussion

5.1 Theoretical implications

This research has several theoretical contributions. First, the study adds to the communication theory by defining omnichannel IMC as three dimensions: consistency, interactivity and connectivity. The analysis zooms in on customer behaviour in the omnichannel market to reveal more relevant in-depth knowledge by separating customer satisfaction into product and retailer service evaluation.

Specifically, in line with previous research (Butkouskaya et al., 2020; Lazaris and Vrechopoulos, 2014), this study confirmed the significance of IMC consistency for customer satisfaction with a product and with retail service. A message, packaging, image and aesthetics repeated and communicated consistently to customers through IMC channels build stronger product associations and more realistic expectations (Shi et al., 2020). IMC consistency can predispose customer satisfaction with a product and service.

Also, as suggested, the results confirm that IMC interactivity sways customer satisfaction with the retail outlet (Lee, 2020). However, opposite to expectations, the completed study demonstrates that IMC interactivity does not influence customer satisfaction with the product. The nature of interactivity, requiring two-way communication, can be the reason for these results (Mihart, 2012). Customers interact with the retailer by default and tend to associate satisfaction with a trading organization rather than the product, even if such communications address product-related issues (Peltier et al., 2006). Meanwhile, the product satisfaction process does not imply interaction with the retailer. It happens only on the customer side as a comparison between expectations and actual performance.

According to findings, IMC connectivity can also ensure customer satisfaction in the product domain. However, different from the claims of omnichannel researchers (Shi et al., 2020), IMC connectivity does not affect retail service evaluation. The argument is that satisfaction with the retailer is not significant in IMC connectivity settings because customers are more prone to have contact with the product, including descriptions, pricing and availability on the varied platforms and devices where they search for the product, not the retailer. Also, it may be that the IMC connectivity value is more recognizable in the information search stage, which impacts product expectations and can be further compared to the actual performance rather than retail service evaluation is mainly based on the post–purchase stage. In other words, meanwhile, IMC interactivity is a part of the experience with the retail service; the IMC connectivity is an instrument of product-related decision-making.

5.2 Practical implications

From the practical perspective, the research gives a broader view of the IMC implementation as the company's dynamic capability in a new omnichannel environment. It demonstrates that implementing message and channel consistency, interactivity and connectivity as a company's strategy can tackle the challenge of information excess and variety pertinent to omnichannel marketing. The completed research has several important implications for managerial practices in planning and implementing effective IMC strategies to improve customer satisfaction.

Managers should ensure IMC consistency by using a uniform brand and product visuals, slogans, descriptions and all other relevant information in all omnichannel communications. It implies employing logos, colour schemes and typography that all have a great visual quality in online and offline communication. Consistency in communications helps to build more realistic expectations. As a result, the perceived performance will be more consistent with the expectations, and the satisfaction level will increase. It is valid for both product and service satisfaction.

Also, the firm can benefit from IMC interactivity by activating customer engagement with the retailer. The techniques to engage customers in IMC interactivity may comprise online lead generation forms, chatbots, gamification of promotions and customer contests, among others. Even though IMC interactivity does not directly affect product satisfaction, closer communications with customers can bring additional information about their needs and wants to the company. The firm can further use this information to provide better value to the customer. Meanwhile, the customers may perceive the possibility of the interactive dialogue as additional attention and extra service from the retailer. When customers feel heard by the company, their service quality perception increases.

Moreover, channel connectivity is another significant facet that ensures customer satisfaction with the product. Customer prefers seamless, user-friendly and quick contemporary marketing channels. In operating the IMC channel effectively, a customer expects not to feel a difference in interacting with the brand, product or service regardless of the utilized channel. To highlight the unison of the IMC channel processes, managers may employ, for instance, channel cross-promotions or cross-sell that will increase the efficiency of IMC channels' functioning. Also, the ability to check product information among various channels (both online and offline) makes customer decision-making faster and easier. In other words, the less time and effort needed to acquire a product positively influences product satisfaction.

Finally, retail managers in developing markets may advance their businesses by implementing multichannel IMC. In this line, however, managers should carefully evaluate several factors predisposing to the successful deployment of multichannel IMC. These factors may pertain to shopper buying habits, conventional and online media consumption levels, Internet penetration level in a particular country, local specifics of Internet usage and overall Technology Readiness Index (TRI) (Hao and Chon, 2022). Managers in developing markets must consider these facets in planning their multichannel IMC strategies. It is recommended that retail managers in emerging markets should track the execution of their multichannel IMC campaigns. Such tracking will help determine the most effective and best multichannel IMC practices that work well in specific market settings and conditions.

5.3 Limitations and future research lines

The completed study has several limitations that may provide future research opportunities. Primary, the existence of some rejected hypotheses motivates the additional research on the omnichannel IMC dimensions' impact on customer satisfaction. For example, future studies can measure the mediating role of retail service satisfaction on product satisfaction or the indirect role of IMC dimensions for repurchase intention improvements. Also, the research context comprised of data from one country (specifically the Moscow region), and only the retail industry limits the generalization of the results. Future studies may consider alternative sectors, for example, the IMC contexts of B2C manufacturers' direct marketing or B2B and apply inter-country analysis. Potential data points can include BRIC emerging markets, including Brazil, Russia, India and China. In that sense, the LATAM region also has the potential to be researched due to several factors, such as an upward trend in productivity, international research collaborations of Latin American research, and social, cultural and economic characteristics (Aguinis et al., 2020).

6. Conclusions

The fast technology development and the growth of competition increase the number of communications customers receive daily from companies. The omnichannel market environment creates a new informational challenge for information and channel integration. Thus, this research addressed the issue by investigating the role of omnichannel IMC on customer satisfaction. The study provides both valuable theoretical and practical results.

This research presents a novelty study of three interrelated dimensions of IMC: consistency, interactivity and connectivity. Following the diverse nature of product and service satisfaction, the article separately analyses the IMC impact on these two concepts of customer evaluation. The results support that IMC consistency positively affects product and service satisfaction. Meanwhile, IMC interactivity's positive impact is only significant in the case of service satisfaction, and IMC connectivity impacts only product satisfaction. Thus, the company should approach customer satisfaction management holistically in the omnichannel market. Thus, managers should implement all three dimensions of IMC simultaneously. IMC interactivity and consistency contribute to the traditional IMC role of the message and channel consistency instrument. The IMC connectivity effect on product satisfaction mirrors the result pertinent to IMC interactivity that exhibited no link with retail service satisfaction.

The study focuses on the retail industry customers in the developing economy of the Russian Federation. However, the study results can be interesting for the researchers and managers of the emerging BRIC markets, including the rapidly emerging LATAM region (Aguinis et al., 2020).

Figures

The results of path analysis applied to the structural model

Figure 1

The results of path analysis applied to the structural model

Sample descriptive statistics

VariableValue labelsCountShare in the sample
GenderMale12353%
Female13747%
Total 260100%
Age group18–243815%
25–346927%
35–446525%
45–545220%
Above 553613%
Total 260100%
Disposable incomeLow3212%
Medium12448%
Mid-to-high9838%
High62%
Total 260100%
EducationSecondary school4216%
Incomplete higher4216%
Higher-undergraduate15259%
Higher-postgraduate249%
Total 260100%
Urbanization size (number of inhabitancy)<10k35%
11k–100k7830%
101k–500k5320%
501k–1000k7429%
>1001k4216%
Total 260100%

Source(s): Own elaboration

Measurement model reliability and convergent validity

EFACFA
Constructs Factor loadingsCronbach's alphaComposite reliability (CR)Average variance extracted (AVE)
IMC consistency (IMC_co)IMC10.8050.9140.9310.660
IMC20.790
IMC30.850
IMC40.831
IMC50.834
IMC60.769
IMC70.805
IMC interactivity (IMC_i)IMC90.8240.8360.9310.672
IMC100.845
IMC110.742
IMC120.862
IMC connectivity (IMC_cn)IMC130.9060.8540.9110.774
IMC140.857
IMC150.876
Product Satisfaction (PS)PS10.8540.9060.9340.781
PS20.883
PS30.920
PS40.877
Satisfaction with Retail Service (SR)SR10.8900.9310.9480.783
SR20.890
SR30.879
SR40.892
SR50.873

Source(s): Own elaboration

Measurement model discriminant validity

IMC_cnIMC_coIMC_iPSSR
IMC_cn0.880
IMC_co0.7010.812
IMC_i0.6770.7590.820
PS0.5710.6480.5870.884
SR0.5720.6820.6500.8110.885

Note(s): IMC consistency (IMC_co); IMC interactivity (IMC_i); IMC connectivity (IMC_cn); Product Satisfaction (PS); Satisfaction with Retailer (SR)

Source(s): Own elaboration

Model goodness of fit

Saturated modelEstimated model
SRMR0.0490.085
d_ULS0.6641.999
d_G0.4270.556
Chi-square639.290760.872
NFI0.8680.842

Source(s): Own elaboration

Results of testing research hypotheses

SEM pathβ-valuep-valueResult
H1aIMC_co → PS0.398**0.000Supported
H1bIMC_co → SR0.400**0.000Supported
H2aIMC_i → PS0.1610.072Rejected
H2bIMC_i → SR0.275**0.003Supported
H3aIMC_cn → PS0.183*0.020Supported
H3bIMC_cn → SR0.1050.172Rejected

Note(s): IMC consistency (IMC_co); IMC interactivity (IMC_i); IMC connectivity (IMC_cn); Product satisfaction (PS); Satisfaction with retailer (SR). * = p < 0.05, ** = p < 0.01

Source(s): Own elaboration

Measurement scales

OCEDo you have experience making a purchase decision using a combination of online and offline communication channels (e.g. searching online and then buying from an offline store or searching from an offline store and then buying online)?
Answer: Yes/No
Omnichannel IMC
IMC consistency (Butkouskaya et al., 2020; Shi et al., 2020)
IMC1Company's communication message keeps consistent in all the visual components (e.g. logo/trademark, advertisement design, web page style, etc.) to maintain how receivers perceive them
IMC2Company's communication message maintains consistency in all the linguistic components (e.g. slogan, promo texts, brochures, etc.) to maintain how receivers perceive them
IMC3Company's communication messages through all media channels (both online and online) are coordinated carefully to maintain how receivers perceive them
IMC4Descriptions of products are integrated across different channels
IMC5The information about products is integrated across different channels (both online and offline)
IMC6The launch of new products is synchronous across different channels (both online and offline)
IMC7The information about services is integrated across different channels (both online and offline)
IMC interactivity (Porcu et al., 2017)
IMC9The company promotes the creation of special programs to facilitate my inquiries and complaints about the company's products and service
IMC10High availability of fast and customer-friendly responses to questions and complaints to clarify the confusion with the communication message perceived by receivers
IMC11Company's communication messages keep consistent between product and service messages, deriving from the experience of dealing with the organization, its staff, agents and products
IMC12I perceive that the communications with the company are reciprocal and facilitate establishing a trust-based and ongoing dialogue
IMC connectivity (Shi et al., 2020)
IMC13I can check inventory status across different channels
IMC14I can query commodities information across different channels
IMC15I can check offline inventory through different online channels
Product satisfaction (Butkouskaya et al., 2020)
PS1My decision to purchase a product/service from (Brand) was a wise one
PS2I feel good about my decision to purchase (Brand’s) product
PS3I am pleased that I purchased the product from the (Brand)
PS4If someone asks me, I will positively respond about the (Brand)
Retail service satisfaction (Sorkun et al., 2020)
SR1My feelings towards the retailer are very positive
SR2I feel good about choosing this retailer for the offerings I am looking for
SR3Overall, I am satisfied with this retailer and the service it provides
SR4I feel satisfied that this retailer produces the best results that can be achieved for me

Disclosure statement: The authors reported no potential conflict of interest.

Appendix

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Acknowledgements

This research has been conducted within the fundamental research project “Transformation of Marketing Strategies for Customer-Oriented Business in the Global Digital Economy” as a part of the HSE Graduate School of Business Research Program in 2021-2023 (Protocol No.23 dd 22.06.2021 of the HSE GSB Research Committee).

Corresponding author

Vera Butkouskaya can be contacted at: vera.butkouskaya@gmail.com

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